From 8b0a6b4cc300a03f7302d1820597806f6f9f920e Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Thu, 2 May 2019 20:46:47 +0200 Subject: [PATCH 001/242] Initial structure. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 269 +++++++++++++++++++ 1 file changed, 269 insertions(+) create mode 100644 HARK/ConsumptionSaving/ConsPortfolioModel.py diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py new file mode 100644 index 000000000..f870d69c4 --- /dev/null +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -0,0 +1,269 @@ +import HARK.ConsIndShockModel: IndShockConsumerType + +class PortfolioConsumerSolution(ConsumerSolution): + def __init__(self): + self.share = 0 + +class PortfolioChoiceConsumerType(IndShockConsumerType): + def __init(self): + self.solveOnePeriod = solveConsPortfolioChoice + return self + + def updateRxsDstn(self): + # Should be lognormal + self.Rfree + self.RriskAvg + self.RriskStd + self.RxsCount + self.RxsDstn = False + return self.RxsDstn + + def updateShockDstn(self): + self.ShockDstn = combineIncAndRxs(self.IncomeDstn, self.RxsDstn) + #add ShockDstn to time_vary if not already + return + + def makeRshareGrid(self): + given RshareCount return RshareGrid and add to time_inv + + def update(self): + updateRxsDstn + updateShockdstn + makeRshareGrid + + def solveTerminal(self): + do same as IndShockConsumerTyppe.solveTerminal and add + RshareFunc = 0 to ssolution_terminal + + def makeEulerErrorFunc(self) + as in KinkedRConsumertpye + + def calcBoundingValues(self) + as above + + + +def solveConsPortfolioChoice(solution_next): + return solution_next + +def ConsIndShockPortfolioSolver(ConsIndShockSolver): + def __init__(self): + return self + +# def setAndUpdateValues(self): + # great but wait + # calc sUnderbar -> mertonsammuelson + # calc MPC kappaUnderbar + # calc human wealth + + def calcdvdsEndOfPrd(self): + tensorGRid = aXtraGrid x RshareGrid + dvdsEndOfPrd = dvds(tensorGrid) + + def prepRshareNow(self): + self.RshareNow = np.zeros(len(aXtraGrid)) + + def solveShares(self): + return self + + def calcEndofPrdvP(self): + return self + + def calcCrmNowandmNrmNow(self): # this is get points??? + return + + + def solve(self): + ''' + Solves a one period consumption saving problem with risky income. + + Parameters + ---------- + None + + Returns + ------- + solution : ConsumerSolution + The solution to the one period problem. + ''' + aNrm = self.prepareToCalcEndOfPrdvP() + EndOfPrdvP = self.calcEndOfPrdvP() + solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) + solution = self.addMPCandHumanWealth(solution) + return solution + + def makeBasicSolution(self,EndOfPrdvP,aNrm,interpolator): + ''' + Given end of period assets and end of period marginal value, construct + the basic solution for this period. + + Parameters + ---------- + EndOfPrdvP : np.array + Array of end-of-period marginal values. + aNrm : np.array + Array of end-of-period asset values that yield the marginal values + in EndOfPrdvP. + + interpolator : function + A function that constructs and returns a consumption function. + + Returns + ------- + solution_now : ConsumerSolution + The solution to this period's consumption-saving problem, with a + consumption function, marginal value function, and minimum m. + ''' + solveShares() + cNrm,mNrm = self.getPointsForInterpolation(EndOfPrdvP,aNrm) + solution_now = self.usePointsForInterpolation(cNrm,mNrm,interpolator) + return solution_now + + + def usePointsForInterpolation(self,cNrm,mNrm,interpolator): + ''' + Constructs a basic solution for this period, including the consumption + function and marginal value function. + Parameters + ---------- + cNrm : np.array + (Normalized) consumption points for interpolation. + mNrm : np.array + (Normalized) corresponding market resource points for interpolation. + interpolator : function + A function that constructs and returns a consumption function. + Returns + ------- + solution_now : ConsumerSolution + The solution to this period's consumption-saving problem, with a + consumption function, marginal value function, and minimum m. + ''' + # Construct the unconstrained consumption function + cFuncNowUnc = interpolator(mNrm,cNrm) + + # Combine the constrained and unconstrained functions into the true consumption function + cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) + + # Make the marginal value function and the marginal marginal value function + vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) + + # Pack up the solution and return it + solution_now = PortfolioConsumerSolution(cFunc=cFuncNow, + vPfunc=vPfuncNow, + mNrmMin=self.mNrmMinNow, + RshareFunc=self.RshareFunc, + vhatPfunc=self.vhatPfunc) +return solution_now + + + + + + + + + + + + + + def updateRiskyPremium(self): + self.RiskyPremium = self.IncomeDstn[3] - self.Rfree + + def Rbold(self, StockShare): + self.updateRiskyPremium() # this is not optimal, but let's do it everywhere for now + return self.Rfree + self.RiskyPremium*StockShare + + def PortfolioObjective(self, StockShare): + # The portfolio objective is the expected value of interering tomorrow + # with the market resources implied by the investment strategy and the + # particular realizations of the stock price evolution. + mNrm = self.mNrmNextAta(StockShare) + VLvlNext = self.DiscFacEff*self.IncomeDstn[1]**(1.0-self.CRRA)*self.PermGroFac**(1.0-self.CRRA)*self.vFuncNext(mNrm) + + return -np.sum(VLvlNext*self.IncomeDstn[0],axis=0) + + def vOptFuncNextFromPortfolioSubproblem(self): + riskyShare = np.array([]) + vOpt = np.array([]) + vOptPa = np.array([]) + for a in self.aNrmNow: + # set aPortfolio for use in PortfolioObjective + self.aPortfolio = a + + # Set the ratio between 0 and 1 + optRes = minimize_scalar(self.PortfolioObjective, bounds=(0, 1), method='bounded') + riskyShare = np.append(riskyShare, optRes.x) + vOpt = np.append(vOpt, -self.PortfolioObjective(optRes.x)) + mNrmOpt = self.mNrmNextAta(optRes.x) + + # This is v^a(a, share(a)) + vPNext = self.uP(self.solution_next.cFunc(mNrmOpt)) + Rbold = self.Rbold(optRes.x) + vOptPa_single = self.DiscFacEff*Rbold*self.PermGroFac**(-self.CRRA)*sum( + self.IncomeDstn[1]**(-self.CRRA)* + vPNext*self.IncomeDstn[0]) + vOptPa = np.append(vOptPa, np.sum(vOptPa_single)) + # grab best policy and value and append it + self.riskyShareFunc = LinearInterp(self.aNrmNow, riskyShare) + + vOptNvrs = self.uinv(vOpt) # value transformed through inverse utility + vOptNvrs = np.insert(vOptNvrs,0,0.0) + aNrm_temp = np.insert(self.aNrmNow,0,self.BoroCnstNat) + vOptNvrsFuncNext = LinearInterp(self.aNrmNow, vOptNvrs) + self.vOptFuncNext = ValueFunc(vOptNvrsFuncNext,self.CRRA) + + self.vOptPaFuncNext = LinearInterp(self.aNrmNow, vOptPa) + + return self.vOptFuncNext, self.riskyShareFunc + + def mNrmNextAta(self, StockShare): + # Get cash on hand next period + mNrmNext = self.Rbold(StockShare)/(self.PermGroFac*self.IncomeDstn[1])*self.aPortfolio + self.IncomeDstn[2] + return mNrmNext + + def makeEndOfPrdvFunc(self,EndOfPrdvP): + ''' + Construct the end-of-period value function for this period, storing it + as an attribute of self for use by other methods. + Parameters + ---------- + EndOfPrdvP : np.array + Array of end-of-period marginal value of assets corresponding to the + asset values in self.aNrmNow. + Returns + ------- + none + ''' + VLvlNext = self.vOptFuncNext(self.aNrmNow) + EndOfPrdv = VLvlNext + + EndOfPrdvNvrs = self.uinv(EndOfPrdv) # value transformed through inverse utility + EndOfPrdvNvrs = np.insert(EndOfPrdvNvrs,0,0.0) + aNrm_temp = np.insert(self.aNrmNow,0,self.BoroCnstNat) + EndOfPrdvNvrsFunc = LinearInterp(aNrm_temp,EndOfPrdvNvrs) + self.EndOfPrdvFunc = ValueFunc(EndOfPrdvNvrsFunc,self.CRRA) + + # This could be an extension to the basic solver, but it seems that there's + # going to be a complete overhaul. Keeping it here for now. + def calcEndOfPrdvP(self): + ''' + Calculate end-of-period marginal value of assets at each point in aNrmNow. + Does so by taking a weighted sum of next period marginal values across + income shocks (in a preconstructed grid self.mNrmNext). + Parameters + ---------- + none + Returns + ------- + EndOfPrdvP : np.array + A 1D array of end-of-period marginal value of assets + ''' + + # Solve portfolio problem in a stage of its own. + self.vOptFunc, self.riskyShareFunc = self.vOptFuncNextFromPortfolioSubproblem() + + # use them to construct a vPNext + EndOfPrdvP = self.vOptPaFuncNext(self.aNrmNow) + + return EndOfPrdvP From 5a192e62ad109cf67e0554b46cb094881e1cd0be Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Mon, 6 May 2019 15:03:51 -0400 Subject: [PATCH 002/242] fix grid dimension --- .travis.yml | 1 + HARK/ConsumptionSaving/ConsIndShockModel.py | 6 + HARK/__init__.py | 2 + HARK/interpolation.py | 63 ++- README.md | 406 ++++++++++---------- requirements.txt | 1 + setup.cfg | 4 + 7 files changed, 276 insertions(+), 207 deletions(-) diff --git a/.travis.yml b/.travis.yml index 8355d4505..71237b781 100644 --- a/.travis.yml +++ b/.travis.yml @@ -7,3 +7,4 @@ install: - pip install -r requirements.txt script: - pytest HARK/tests + # - flake8 HARK diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index c353e65df..5b557d464 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -793,6 +793,12 @@ def prepareToCalcEndOfPrdvP(self): aNrmNow : np.array A 1D array of end-of-period assets; also stored as attribute of self. ''' + + # We define aNrmNow all the way from BoroCnstNat up to max(self.aXtraGrid) + # even if BoroCnstNat < BoroCnstArt, so we can construct the consumption + # function as the lower envelope of the (by the artificial borrowing con- + # straint) uconstrained consumption function, and the artificially con- + # strained consumption function. aNrmNow = np.asarray(self.aXtraGrid) + self.BoroCnstNat ShkCount = self.TranShkValsNext.size aNrm_temp = np.tile(aNrmNow,(ShkCount,1)) diff --git a/HARK/__init__.py b/HARK/__init__.py index c4d09cb22..34aeb9a30 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,2 +1,4 @@ from __future__ import absolute_import from .core import * + +__version__ = '0.10.0.dev2' diff --git a/HARK/interpolation.py b/HARK/interpolation.py index 7913a3986..08adf9872 100644 --- a/HARK/interpolation.py +++ b/HARK/interpolation.py @@ -12,6 +12,7 @@ import numpy as np from .core import HARKobject from copy import deepcopy +import warnings def _isscalar(x): ''' @@ -30,6 +31,33 @@ def _isscalar(x): return np.isscalar(x) or hasattr(x, 'shape') and x.shape == () +def _check_grid_dimensions(dimension, *args): + if dimension == 1: + if len(args[0]) != len(args[1]): + raise ValueError("Grid dimensions of x and f(x) do not match") + elif dimension == 2: + if args[0].shape != (args[1].size, args[2].size): + raise ValueError("Grid dimensions of x, y and f(x, y) do not match") + elif dimension == 3: + if args[0].shape != (args[1].size, args[2].size, args[3].size): + raise ValueError("Grid dimensions of x, y, z and f(x, y, z) do not match") + elif dimension == 4: + if args[0].shape != (args[1].size, args[2].size, args[3].size, args[4].size): + raise ValueError("Grid dimensions of x, y, z and f(x, y, z) do not match") + else: + raise ValueError("Dimension should be between 1 and 4 inclusive.") + + +def _check_flatten(dimension, *args): + if dimension == 1: + if args[0].shape != args[0].flatten().shape: + warnings.warn("input not of the size (n, ), attempting to flatten") + return False + else: + return True + + + class HARKinterpolator1D(HARKobject): ''' A wrapper class for 1D interpolation methods in HARK. @@ -729,8 +757,9 @@ def __init__(self,x_list,y_list,intercept_limit=None,slope_limit=None,lower_extr extrapolation is used above the highest gridpoint. ''' # Make the basic linear spline interpolation - self.x_list = np.array(x_list) - self.y_list = np.array(y_list) + self.x_list = np.array(x_list) if _check_flatten(1, x_list) else np.array(x_list).flatten() + self.y_list = np.array(y_list) if _check_flatten(1, y_list) else np.array(y_list).flatten() + _check_grid_dimensions(1, self.y_list, self.x_list) self.lower_extrap = lower_extrap self.x_n = self.x_list.size @@ -870,9 +899,12 @@ def __init__(self,x_list,y_list,dydx_list,intercept_limit=None,slope_limit=None, NOTE: When no input is given for the limiting linear function, linear extrapolation is used above the highest gridpoint. ''' - self.x_list = np.asarray(x_list) - self.y_list = np.asarray(y_list) - self.dydx_list = np.asarray(dydx_list) + self.x_list = np.asarray(x_list) if _check_flatten(1, x_list) else np.array(x_list).flatten() + self.y_list = np.asarray(y_list) if _check_flatten(1, y_list) else np.array(y_list).flatten() + self.dydx_list = np.asarray(dydx_list) if _check_flatten(1, dydx_list) else np.array(dydx_list).flatten() + _check_grid_dimensions(1, self.y_list, self.x_list) + _check_grid_dimensions(1, self.dydx_list, self.x_list) + self.n = len(x_list) # Define lower extrapolation as linear function (or just NaN) @@ -1057,8 +1089,9 @@ def __init__(self,f_values,x_list,y_list,xSearchFunc=None,ySearchFunc=None): new instance of BilinearInterp ''' self.f_values = f_values - self.x_list = x_list - self.y_list = y_list + self.x_list = np.array(x_list) if _check_flatten(1, x_list) else np.array(x_list).flatten() + self.y_list = np.array(y_list) if _check_flatten(1, y_list) else np.array(y_list).flatten() + _check_grid_dimensions(2, self.f_values, self.x_list, self.y_list) self.x_n = x_list.size self.y_n = y_list.size if xSearchFunc is None: @@ -1175,9 +1208,10 @@ def __init__(self,f_values,x_list,y_list,z_list,xSearchFunc=None,ySearchFunc=Non new instance of TrilinearInterp ''' self.f_values = f_values - self.x_list = x_list - self.y_list = y_list - self.z_list = z_list + self.x_list = np.array(x_list) if _check_flatten(1, x_list) else np.array(x_list).flatten() + self.y_list = np.array(y_list) if _check_flatten(1, y_list) else np.array(y_list).flatten() + self.z_list = np.array(z_list) if _check_flatten(1, z_list) else np.array(z_list).flatten() + _check_grid_dimensions(3, self.f_values, self.x_list, self.y_list, self.z_list) self.x_n = x_list.size self.y_n = y_list.size self.z_n = z_list.size @@ -1362,10 +1396,11 @@ def __init__(self,f_values,w_list,x_list,y_list,z_list,wSearchFunc=None,xSearchF new instance of QuadlinearInterp ''' self.f_values = f_values - self.w_list = w_list - self.x_list = x_list - self.y_list = y_list - self.z_list = z_list + self.w_list = np.array(w_list) if _check_flatten(1, w_list) else np.array(w_list).flatten() + self.x_list = np.array(x_list) if _check_flatten(1, x_list) else np.array(x_list).flatten() + self.y_list = np.array(y_list) if _check_flatten(1, y_list) else np.array(y_list).flatten() + self.z_list = np.array(z_list) if _check_flatten(1, z_list) else np.array(z_list).flatten() + _check_grid_dimensions(4, self.f_values, self.w_list, self.x_list, self.y_list, self.z_list) self.w_n = w_list.size self.x_n = x_list.size self.y_n = y_list.size diff --git a/README.md b/README.md index 877dfacab..cd2423b6a 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Heterogeneous Agents Resources and toolKit (HARK) -pre-release 0.9.1 - 13 July, 2018 +pre-release 0.10.0.dev2 Click the Badge for Citation Info. [![DOI](https://zenodo.org/badge/50448254.svg)](https://zenodo.org/badge/latestdoi/50448254) @@ -7,35 +7,30 @@ Click the Badge for Citation Info. Table of Contents: -* [I. Introduction](#i-introduction) -* [II. Quick start guide](#ii-quick-start-guide) -* [III. List of files in repository](#iii-list-of-files-in-repository) -* [IV. Warnings and disclaimers](#iv-warnings-and-disclaimers) -* [V. License Information](#v-license) +* [1. Introduction](#i-introduction) +* [2. Quick start guide](#ii-quick-start-guide) +* [3. List of files in repository](#iii-list-of-files-in-repository) +* [4. Warnings and disclaimers](#iv-warnings-and-disclaimers) +* [5. License Information](#v-license) ## I. INTRODUCTION -Welcome to HARK! We are tremendously excited you're here. HARK is -very much a work in progress, but we hope you find it valuable. We -*really* hope you find it so valuable that you decide to contribute -to it yourself. This document will tell you how to get HARK up and +Welcome to HARK! This document will tell you how to get HARK up and running on your machine, and what you will find in HARK once you do. -If you have any comments on the code or documentation, we'd love to -hear from you! Our email addresses are: +If you have any comments on the code or documentation, or (even better) if you want to +contribute new content to HARK, we'd love to hear from you! +Our email addresses are: * Chris Carroll: ccarroll@llorracc.org * Matthew White: mnwhite@gmail.com -* Nathan Palmer: Nathan.Palmer@ofr.treasury.gov -* David Low: David.Low@cfpb.gov -* Alexander Kaufman: akaufman10@gmail.com GitHub repository: https://github.com/econ-ark/HARK Online documentation: https://econ-ark.github.io/HARK -User guide: /Documentation/HARKmanual.pdf (in the repository) +User guide: [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf) (in the [HARK repository](https://github.com/econ-ark/HARK)) Demonstrations of HARK functionality: [DemARK](https://github.com/econ-ark/DemARK/) @@ -44,118 +39,152 @@ Replications and Explorations Made using the ARK : [REMARK](https://github.com/e ## II. QUICK START GUIDE -This is going to be easy, friend. HARK is written in Python, specifically the -Anaconda distribution of Python. Follow these easy steps to get HARK going: - -1) Go to https://www.continuum.io/downloads and download Anaconda for your -operating system - -2) Install Anaconda, using the instructions provided on that page. Now you have -installed everything you need to run most of HARK. But you still need to get HARK -on your machine. - -3) To get HARK on your machine, you should know that HARK is managed with version -control software called "Git". HARK is hosted on a website called "GitHub" devoted -to hosting projects managed with Git. - - If you don't want to know more than that, you don't have to. Go to HARK's page -on GitHub (https://github.com/econ-ark/HARK), click the "Clone or download" button -in the upper right hand corner of the page, then click "Download ZIP". Unzip it -into an empty directory. Maybe call that directory /HARK ? The choice is yours. - - You can also clone HARK off GitHub using Git. This is slightly more difficult, -because it involves installing Git on your machine and learning a little about -how to use Git. We believe this is an investment worth making, but it is up to you. -To learn more about Git, read the documentation at https://git-scm.com/documentation -or visit many other great Git resources on the internet. - -4) Open Spyder, an interactive development environment (IDE) for Python -(specifically, iPython). On Windows, open a command prompt and type "spyder". -On Linux, open the command line and type "spyder". On Mac, open the command -line and type "spyder". - -5) Navigate to the directory where you put the HARK files. This can be done -within Spyder by doing "import os" and then using os.chdir() to change directories. -chdir works just like cd at a command prompt on most operating systems, except that -it takes a string as input: os.chdir('Music') moves to the Music subdirectory -of the current working directory. - -6) Run one of HARK's modules. You can either type "run MODULENAME" after navigating -to the correct directory (see step 5), or click the green arrow "run" button in -Spyder's toolbar after opening the module in the editor. Every module should -do *something* when run, but that something might not be very interesting in -some cases. For starters, check out /ConsumptionSavingModel/ConsIndShockModel.py -See section III below for a full list of modules that produce non-trivial output. - -7) The Python environment can be cleared or reset with ctrl+. Note that -this will also change the current working directory back to its default. -To change the default directory (the "global working directory"), see -Tools-->Preferences-->Global working directory; you might need to restart -Spyder for the change to take effect. - -8) Read the more complete documentation in [HARKmanual.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.pdf). - -9) OPTIONAL: If you want to use HARK's multithreading capabilities, you will -need two Python packages that do not come automatically with Anaconda: joblib -and dill. Assuming you have the necessary permissions on your machine, the -easiest way to do this is through Anaconda. Go to the command line, and type -"conda install joblib" and then "conda install dill" (accept defaults if prompted). -If this doesn't work, but you have Git, you can just clone the packages directly -off GitHub. Go to the command line and navigate to the directory you want to put -these packages in. Then type "git clone https://github.com/joblib/joblib.git" -and then "git clone https://github.com/uqfoundation/dill". Joblib should work -after this, but there is one more step to get dill working. Navigate to dill's -directory in the command line, and then type "python setup.py build". Then you -should have joblib and dill working on your machine. - -Note: If you did not put joblib and dill in one of the paths in sys.path, you will -need to add the joblib and dill directories to sys.path. The easiest way to do this -is to open up Anaconda, and type: - -```python -import sys -sys.path.append('path_to_joblib_directory') -sys.path.append('path_to_dill_directory') +HARK is an open source project written in Python. It's compatible with both Python +2 and 3, and with the Anaconda distributions of python 2 and 3. But we recommend +using python 3; eventually support for python 2 will end. + +### Installing HARK with pip + +The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/). + +To install HARK with pip, at a command line type `pip install econ-ark`. + +If you are installing via pip, we recommend using a virtual environment such as [virtualenv](https://virtualenv.pypa.io/en/latest/). Creation of a virtual environment isolates the installation of `econ-ark` from the installations of any other python tools and packages. + +To install `virtualenv`, then to create an environment named `econ-ark`, and finally to activate that environment: + +``` +cd [directory where you want to store the econ-ark virtual environment] +pip install virtualenv +virtualenv econ-ark +source activate econ-ark +``` + +---- +### Using HARK with Anaconda + +Installing HARK with pip does not give you full access to HARK's many graphical capabilities. One way to access these capabilities is by using [Anaconda](https://anaconda.com/why-anaconda), which is a distribution of python along with many packages that are frequently used in scientific computing.. + +1. Download Anaconda for your operating system and follow the installation instructions [at Anaconda.com](https://www.anaconda.com/distribution/#download-section). + +1. Anaconda includes its own virtual environment system called `conda` which stores environments in a preset location (so you don't have to choose). So in order to create and activate an econ-ark virtual environment: +``` +conda create -n econ-ark anaconda +source activate econ-ark +``` +1. Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`. + +1. To verify that spyder has access to HARK try typing `pip install econ-ark` into the iPython shell within Spyder. If you have successfully installed HARK as above, you should see a lot of messages saying 'Requirement satisfied'. + + * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. + + * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)). + + * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone + + * If you don't want to clone HARK, but just to download it, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory. + + Once you've got a copy of HARK in a directory, return to Spyder and navigate to that directory where you put HARK. This can be done within Spyder by doing `import os` and then using `os.chdir()` to change directories. `chdir` works just like cd at a command prompt on most operating systems, except that it takes a string as input: `os.chdir('Music')` moves to the Music subdirectory of the current working directory. + +6) Most of the modules in HARK are just collections of tools. There are a few demonstration +applications that use the tools that you automatically get when you install HARK -- they are listed below in [Application Modules](#application-modules). A much larger set of uses of HARK can be found at two repositories: + * [DemARK](https://github.com/econ-ark/DemARK): Demonstrations of the use of HARK + * [REMARK](https://github.com/econ-ark/REMARK): Replications of existing papers made using HARK + +You will want to obtain your own local copy of these repos using: +``` +git clone https://github.com/econ-ark/DemARK.git +``` +and similarly for the REMARK repo. Once you have downloaded them, you will find that each repo contains a `notebooks` directory that contains a number of [jupyter notebooks](https://jupyter.org/). If you have the jupyter notebook tool installed (it is installed as part of Anaconda), you should be able to launch the +jupyter notebook app from the command line with the command: + +``` +jupyter notebook +``` +and from there you can open the notebooks and execute them. + +### Making changes to HARK + +If you want to make changes or contributions (yay!) to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files (and it's a bad idea to mess with the original code anyway because you'll likely forget what changes you made). If you are adept at GitHub, you can [fork](https://help.github.com/en/articles/fork-a-repo) the repo. If you are less experienced, you should download a personal copy of HARK again using `git clone` (see above) or the GitHub Desktop app. + +1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). If you get a permission denied error, you may need to setup SSH for GitHub, or you can clone using HTTPS: 'git clone https://github.com/econ-ark/HARK.git'. + +2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))). + +For Mac or Linux: + +Install virtualenv if you need to and then type: + +``` +virtualenv econ-ark +source econ-ark/bin/activate +``` +For Windows: ``` +virtualenv econ-ark +econ-ark\\Scripts\\activate.bat +``` + +3. Once the virtualenv is activated, you may see `(econ-ark)` in your command prompt (depending on how your machine is configured) + +3. Make sure to change to HARK directory, and install HARK's requirements into the virtual environment with `pip install -r requirements.txt`. + +4. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`. + +### Trouble with installation? + +We've done our best to give correct, thorough instructions on how to install HARK but we know this information may be inaccurate or incomplete. Please let us know if you run into trouble so we can update this guide! Here's a list of platforms and versions this guide has been verified for: + +| Installation Type | Platform | Python Version | Date Tested | Tested By | +| ------------- |:-------------:| -----:| -----:|-----:| +| basic pip install | Linux (16.04) | 3 | 2019-04-24 | @shaunagm | +| anaconda | Linux (16.04) | 3 | 2019-04-24 | @shaunagm | +| basic pip install | MacOS 10.13.2 "High Sierra" | 2.7| 2019-04-26 | @llorracc | + +### Next steps + +To learn more about how to use HARK, check out our [user manual](Documentation/HARKmanual.pdf). + +For help making changes to HARK, check out our [contributing guide](CONTRIBUTING.md). + ## III. LIST OF FILES IN REPOSITORY -This section contains descriptions of every file included in the HARK -repository at the time of the beta release, categorized for convenience. +This section contains descriptions of the main files in the repo. Documentation files: -* [README.md](https://github.com/econ-ark/HARK/blob/master/README.md): The file you are currently reading. -* [Documentation/HARKdoc.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKdoc.pdf): A mini-user guide produced for a December 2015 workshop on HARK, unofficially representing the alpha version. Somewhat out of date. -* [Documentation/HARKmanual.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.pdf): A user guide for HARK, written for the beta release at CEF 2016 in Bordeaux. Should contain 90% fewer lies relative to HARKdoc.pdf. - * [Documentation/HARKmanual.tex](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.tex): LaTeX source for the user guide. Open source code probably requires an open source manual as well. -* [Documentation/ConsumptionSavingModels.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/ConsumptionSavingModels.pdf): Mathematical descriptions of the various consumption-saving models in HARK and how they map into the code. - * [Documentation/ConsumptionSavingModels.tex](https://github.com/econ-ark/HARK/blob/master/Documentation/ConsumptionSavingModels.tex): LaTeX source for the "models" writeup. -* [Documentation/NARK.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/NARK.pdf): Variable naming conventions for HARK, plus concordance with LaTeX variable definitions. Still in development. +* [README.md](README.md): The file you are currently reading. +* [Documentation/HARKdoc.pdf](Documentation/HARKdoc.pdf): A mini-user guide produced for a December 2015 workshop on HARK, unofficially representing the alpha version. (Substantially out of date). +* [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf): A user guide for HARK, written for the beta release at CEF 2016 in Bordeaux. Should contain 90% fewer lies relative to HARKdoc.pdf. + * [Documentation/HARKmanual.tex](Documentation/HARKmanual.tex): LaTeX source for the user guide. Open source code probably requires an open source manual as well. +* [Documentation/ConsumptionSavingModels.pdf](Documentation/ConsumptionSavingModels.pdf): Mathematical descriptions of the various consumption-saving models in HARK and how they map into the code. + * [Documentation/ConsumptionSavingModels.tex](Documentation/ConsumptionSavingModels.tex): LaTeX source for the "models" writeup. +* [Documentation/NARK.pdf](Documentation/NARK.pdf): Variable naming conventions for HARK, plus concordance with LaTeX variable definitions. Still in development. Tool modules: -* HARKcore.py: +* [HARK/core.py](HARK/core.py): Frameworks for "microeconomic" and "macroeconomic" models in HARK. We somewhat abuse those terms as shorthand; see the user guide for a description of what we mean. Every model in HARK extends the classes AgentType and Market in this module. Does nothing when run. -* HARKutilities.py: +* [HARK/utilities.py](HARK/utilities.py): General purpose tools and utilities. Contains literal utility functions (in the economic sense), functions for making discrete approximations to continuous distributions, basic plotting functions for convenience, and a few unclassifiable things. Does nothing when run. -* HARKestimation.py: +* [HARK/estimation.py](HARK/estimation.py): Functions for estimating models. As is, it only has a few wrapper functions for scipy.optimize optimization routines. Will be expanded in the future with more interesting things. Does nothing when run. -* HARKsimulation.py: +* [HARK/simulation.py](HARK/simulation.py): Functions for generating simulated data. Functions in this module have names like drawUniform, generating (lists of) arrays of draws from various distributions. Does nothing when run. -* HARKinterpolation.py: +* [HARK/interpolation.py](HARK/interpolation.py): Classes for representing interpolated function approximations. Has 1D-4D interpolation methods, mostly based on linear or cubic spline interpolation. Will have ND methods in the future. Does nothing when run. -* HARKparallel.py: +* [HARK/parallel.py](HARK/parallel.py): Early version of parallel processing in HARK. Works with instances of the AgentType class (or subclasses of it), distributing commands (as methods) to be run on a list of AgentTypes. Only works with local CPU. @@ -164,31 +193,31 @@ Tool modules: when run. Model modules: -* ConsumptionSavingModel/TractableBufferStockModel.py: - A "tractable" model of consumption and saving in which agents face one +* [ConsumptionSaving/TractableBufferStockModel.py](HARK/ConsumptionSaving/TractableBufferStockModel.py): + * A "tractable" model of consumption and saving in which agents face one simple risk with constant probability: that they will become permanently unemployed and receive no further income. Unlike other models in HARK, this one is not solved by iterating on a sequence of one period problems. Instead, it uses a "backshooting" routine that has been shoehorned into the AgentType.solve framework. Solves an example of the model when run, then solves the same model again using MarkovConsumerType. -* ConsumptionSavingModel/ConsIndShockModel.py: - Consumption-saving models with idiosyncratic shocks to income. Shocks +* [ConsumptionSaving/ConsIndShockModel.py](HARK/ConsumptionSaving/ConsIndShockModel.py): + * Consumption-saving models with idiosyncratic shocks to income. Shocks are fully transitory or fully permanent. Solves perfect foresight model, a model with idiosyncratic income shocks, and a model with idiosyncratic income shocks and a different interest rate on borrowing vs saving. When run, solves several examples of these models, including a standard infinite horizon problem, a ten period lifecycle model, a four period "cyclical" model, and versions with perfect foresight and "kinked R". -* ConsumptionSavingModel/ConsPrefShockModel.py: - Consumption-saving models with idiosyncratic shocks to income and multi- +* [ConsumptionSaving/ConsPrefShockModel.py](HARK/ConsumptionSaving/ConsPrefShockModel.py): + * Consumption-saving models with idiosyncratic shocks to income and multi- plicative shocks to utility. Currently has two models: one that extends the idiosyncratic shocks model, and another that extends the "kinked R" model. The second model has very little new code, and is created merely by merging the two "parent models" via multiple inheritance. When run, solves examples of the preference shock models. -* ConsumptionSavingModel/ConsMarkovModel.py: - Consumption-saving models with a discrete state that evolves according to +* [ConsumptionSaving/ConsMarkovModel.py](HARK/ConsumptionSaving/ConsMarkovModel.py): + * Consumption-saving models with a discrete state that evolves according to a Markov rule. Discrete states can vary by their income distribution, interest factor, and/or expected permanent income growth rate. When run, solves four example models: (1) A serially correlated unemployment model @@ -197,16 +226,16 @@ Model modules: shocks for the next N periods. (3) A model with a time-varying permanent income growth rate that is serially correlated. (4) A model with a time- varying interest factor that is serially correlated. -* ConsumptionSavingModel/ConsAggShockModel.py: - Consumption-saving models with idiosyncratic and aggregate income shocks. +* [ConsumptionSaving/ConsAggShockModel.py](HARK/ConsumptionSaving/ConsAggShockModel.py): + * Consumption-saving models with idiosyncratic and aggregate income shocks. Currently has a micro model with a basic solver (linear spline consumption function only, no value function), and a Cobb-Douglas economy for the agents to "live" in (as a "macroeconomy"). When run, solves an example of the micro model in partial equilibrium, then solves the general equilibrium problem to find an evolution rule for the capital-to-labor ratio that is justified by consumers' collective actions. -* FashionVictim/FashionVictimModel.py: - A very serious model about choosing to dress as a jock or a punk. Used to +* [FashionVictim/FashionVictimModel.py](HARK/FashionVictim/FashionVictimModel.py): + * A very serious model about choosing to dress as a jock or a punk. Used to demonstrate micro and macro framework concepts from HARKcore. It might be the simplest model possible for this purpose, or close to it. When run, the module solves the microeconomic problem of a "fashion victim" for an @@ -215,9 +244,10 @@ Model modules: for the evolution of the style distribution in the population that is justi- fied by fashion victims' collective actions. -Application modules: -* SolvingMicroDSOPs/StructEstimation.py: - Conducts a very simple structural estimation using the idiosyncratic shocks +Application modules: + +* [SolvingMicroDSOPs/Code/StructEstimation.py](HARK/SolvingMicroDSOPs/Code/StructEstimation.py): + * Conducts a very simple structural estimation using the idiosyncratic shocks model in ConsIndShocksModel. Estimates an adjustment factor to an age-varying sequence of discount factors (taken from Cagetti (2003)) and a coefficient of relative risk aversion that makes simulated agents' wealth profiles best @@ -225,51 +255,49 @@ Application modules: the calculation of standard errors by bootstrap and can construct a contour map of the objective function. Based on section 9 of Chris Carroll's lecture notes "Solving Microeconomic Dynamic Stochastic Optimization Problems". -* cstwMPC/cstwMPC.py: - Conducts the estimations for the paper "The Distribution of Wealth and the +* [cstwMPC/cstwMPC.py](HARK/cstwMPC/cstwMPC.py): + * Conducts the estimations for the paper "The Distribution of Wealth and the Marginal Propensity to Consume" by Carroll, Slacalek, Tokuoka, and White (2016). Runtime options are set in SetupParamsCSTW.py, specifying choices such as: perpetual youth vs lifecycle, beta-dist vs beta-point, liquid assets vs net worth, aggregate vs idiosyncratic shocks, etc. Uses ConsIndShockModel and ConsAggShockModel; can demonststrate HARK's "macro" framework on a real model. -* cstwMPC/MakeCSTWfigs.py: - Makes various figures for the text of the cstwMPC paper. Requires many output +* [cstwMPC/MakeCSTWfigs.py](HARK/cstwMPC/MakeCSTWfigs.py): + * Makes various figures for the text of the [cstwMPC](http://econ.jhu.edu/people/ccarroll/papers/cstwMPC) paper. Requires many output files produced by cstwMPC.py, from various specifications, which are not distributed with HARK. Has not been tested in quite some time. -* cstwMPC/MakeCSTWfigsForSlides.py: - Makes various figures for the slides for the cstwMPC paper. Requires many +* [cstwMPC/MakeCSTWfigsForSlides.py](HARK/cstwMPC/MakeCSTWfigsForSlides.py): + * Makes various figures for the slides for the cstwMPC paper. Requires many output files produced by cstwMPC.py, from various specifications, which are not distributed with HARK. Has not been tested in quite some time. Parameter and data modules: -* ConsumptionSaving/ConsumerParameters.py: - Defines dictionaries with the minimal set of parameters needed to solve the +* [ConsumptionSaving/ConsumerParameters.py](HARK/ConsumptionSaving/ConsumerParameters.py): + * Defines dictionaries with the minimal set of parameters needed to solve the models in ConsIndShockModel, ConsAggShockModel, ConsPrefShockModel, and ConsMarkovModel. These dictionaries are used to make examples when those modules are run. Does nothing when run itself. -* SolvingMicroDSOPs/SetupSCFdata.py: - Imports 2004 SCF data for use by SolvingMicroDSOPs/StructEstimation.py. -* cstwMPC/SetupParamsCSTW.py: - Loads calibrated model parameters for cstwMPC.py, chooses specification. -* FashionVictim/FashionVictimParams.py: - Example parameters for FashionVictimModel.py, loaded when that module is run. +* [SolvingMicroDSOPs/SetupSCFdata.py](HARK/SolvingMicroDSOPs/Calibration/SetupSCFdata.py): + * Imports 2004 SCF data for use by SolvingMicroDSOPs/StructEstimation.py. +* [cstwMPC/SetupParamsCSTW.py](HARK/cstwMPC/SetupParamsCSTW.py): + * Loads calibrated model parameters for cstwMPC.py, chooses specification. +* [FashionVictim/FashionVictimParams.py](HARK/FashionVictim/FashionVictimParams.py): + * Example parameters for FashionVictimModel.py, loaded when that module is run. Test modules: -* Testing/ComparisonTests.py: - Early version of unit testing for HARK, still in development. Compares +* [Testing/Comparison_UnitTests.py](Testing/Comparison_UnitTests.py): + * Early version of unit testing for HARK, still in development. Compares the perfect foresight model solution to the idiosyncratic shocks model solution with shocks turned off; also compares the tractable buffer stock model solution to the same model solved using a "Markov" description. -* Testing/ModelTesting.py: - Early version of unit testing for HARK, still in development. Defines a +* [Testing/ModelTesting.py](Testing/ModelTesting.py): + * Early version of unit testing for HARK, still in development. Defines a few wrapper classes to run unit tests on subclasses of AgentType. -* Testing/ModelTestingExample.py - An example of ModelTesting.py in action, using TractableBufferStockModel. -* Testing/TBSunitTests.py: - Early version of unit testing for HARK, still in development. Runs a test +* [Testing/TractableBufferStockModel_UnitTests.py](Testing/TractableBufferStockModel_UnitTests.py) + * Early version of unit testing for HARK, still in development. Runs a test on TractableBufferStockModel. -* Testing/MultithreadDemo.py: - Demonstrates the multithreading functionality in HARKparallel.py. When +* [Testing/MultithreadDemo.py](Testing/MultithreadDemo.py): + * Demonstrates the multithreading functionality in HARKparallel.py. When run, it solves oneexample consumption-saving model with idiosyncratic shocks to income, then solves *many* such models serially, varying the coefficient of relative risk aversion between rho=1 and rho=8, displaying @@ -278,76 +306,68 @@ Test modules: the results graphically along with the timing. Data files: -* SolvingMicroDSOPs/SCFdata.csv: - SCF 2004 data for use in SolvingMicroDSOPs/StructEstimation.py, loaded by +* [SolvingMicroDSOPs/Calibration/SCFdata.csv](HARK/SolvingMicroDSOPs/Calibration/SCFdata.csv): + * SCF 2004 data for use in SolvingMicroDSOPs/StructEstimation.py, loaded by SolvingMicroDSOPs/EstimationParameters.py. -* cstwMPC/SCFwealthDataReduced.txt: - SCF 2004 data with just net worth and data weights, for use by cstwMPC.py -* cstwMPC/USactuarial.txt: - U.S. mortality data from the Social Security Administration, for use by +* [cstwMPC/SCFwealthDataReduced.txt](HARK/cstwMPC/SCFwealthDataReduced.txt): + * SCF 2004 data with just net worth and data weights, for use by cstwMPC.py +* [cstwMPC/USactuarial.txt](HARK/cstwMPC/USactuarial.txt): + * U.S. mortality data from the Social Security Administration, for use by cstwMPC.py when running a lifecycle specification. -* cstwMPC/EducMortAdj.txt: - Mortality adjusters by education and age (columns by sex and race), for use +* [cstwMPC/EducMortAdj.txt](HARK/cstwMPC/EducMortAdj.txt): + * Mortality adjusters by education and age (columns by sex and race), for use by cstwMPC.py when running a lifecycle specification. Taken from an appendix of PAPER. Other files that you don't need to worry about: -* */index.py: - A file used by Sphinx when generating html documentation for HARK. Users +* /index.py: + * A file used by Sphinx when generating html documentation for HARK. Users don't need to worry about it. Several copies are found throughout HARK. -* .gitignore: - A file that tells git which files (or types of files) might be found in +* [.gitignore](.gitignore): + * A file that tells git which files (or types of files) might be found in the repository directory tree, but should be ignored (not tracked) for the repo. Currently ignores compiled Python code, LaTex auxiliary files, etc. -* LICENSE: - License text for HARK, Apache 2.0. Read it if you're a lawyer! -* SolvingMicroDSOPs/SMMcontour.png: - Contour plot of the objective function for SolvingMicroDSOPs/StructEstimation.py. +* [LICENSE](LICENSE): + * License text for HARK, Apache 2.0. Read it if you're a lawyer! +* [SolvingMicroDSOPs/Figures/SMMcontour.png](HARK/SolvingMicroDSOPs/Figures/SMMcontour.png): + * Contour plot of the objective function for SolvingMicroDSOPs/StructEstimation.py. Generated when that module is run, along with a PDF version. -* cstwMPC/Figures/placeholder.txt: - A placeholder file because git doesn't like empty folders, but cstwMPC.py +* [cstwMPC/Figures/placeholder.txt](HARK/cstwMPC/Figures/placeholder.txt): + * A placeholder file because git doesn't like empty folders, but cstwMPC.py needs the /Figures directory to exist when it runs. -* cstwMPC/Results/placeholder.txt: - A placeholder file because git doesn't like empty folders, but cstwMPC.py - needs the /Results directory to exist when it runs. -* Documentation/conf.py: - A configuration file for producing html documentation with Sphinx, generated +* [Documentation/conf.py](Documentation/conf.py): + * A configuration file for producing html documentation with Sphinx, generated by sphinx-quickstart. -* Documentation/includeme.rst: - A very small file used by Sphinx to produce documentation. -* Documentation/index.rst: - A list of modules to be included in HARK's Sphinx documentation. This should +* [Documentation/includeme.rst](Documentation/includeme.rst): + * A very small file used by Sphinx to produce documentation. +* [Documentation/index.rst](Documentation/index.rst): + * A list of modules to be included in HARK's Sphinx documentation. This should be edited if a new tool or model module is added to HARK. -* Documentation/instructions.md: - A markdown file with instructions for how to set up and run Sphinx. You +* [Documentation/instructions.md](Documentation/instructions.md): + * A markdown file with instructions for how to set up and run Sphinx. You don't need to read it. -* Documentation/simple-steps-getting-sphinx-working.md: - Another markdown file with instructions for how to set up and run Sphinx. -* Documentation/make.bat: - A batch file for producing Sphinx documentation, generated by sphinx-quickstart. -* Documentation/Makefile: - Another Sphinx auxiliary file generated by sphinx-quickstart. -* Documentation/econtex.sty: - LaTeX style file with notation definitions. -* Documentation/econtex.cls: - LaTeX class file with document layout for the user manual. -* Documentation/econtexSetup.sty: - LaTeX style file with notation definitions. -* Documentation/econtexShortcuts.sty: - LaTeX style file with notation definitions. -* Documentation/UserGuidePic.pdf: - Image for the front cover of the user guide, showing the consumption +* [Documentation/simple-steps-getting-sphinx-working.md](Documentation/simple-steps-getting-sphinx-working.md): + * Another markdown file with instructions for how to set up and run Sphinx. +* [Documentation/make.bat](Documentation/make.bat): + * A batch file for producing Sphinx documentation, generated by sphinx-quickstart. +* [Documentation/Makefile](Documentation/Makefile): + * Another Sphinx auxiliary file generated by sphinx-quickstart. +* [Documentation/econtex.sty](Documentation/econtex.sty): + * LaTeX style file with notation definitions. +* [Documentation/econtex.cls](Documentation/econtex.cls): + * LaTeX class file with document layout for the user manual. +* [Documentation/econtexSetup.sty](Documentation/econtexSetup.sty): + * LaTeX style file with notation definitions. +* [Documentation/econtexShortcuts.sty](Documentation/econtexShortcuts.sty): + * LaTeX style file with notation definitions. +* [Documentation/UserGuidePic.pdf](Documentation/UserGuidePic.pdf): + * Image for the front cover of the user guide, showing the consumption function for the KinkyPref model. ## IV. WARNINGS AND DISCLAIMERS -This is an early beta version of HARK. The code has not been -extensively tested as it should be. We hope it is useful, but -there are absolutely no guarantees (expressed or implied) that -it works or will do what you want. Use at your own risk. And -please, let us know if you find bugs by posting an issue to the -GitHub page! +This is a beta version of HARK. The code has not been extensively tested as it should be. We hope it is useful, but there are absolutely no guarantees (expressed or implied) that it works or will do what you want. Use at your own risk. And please, let us know if you find bugs by posting an issue to [the GitHub page](https://github.com/econ-ark/HARK)! ## V. License diff --git a/requirements.txt b/requirements.txt index ea07a6bbb..f03da14e6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,3 +6,4 @@ future joblib dill scipy +flake8 diff --git a/setup.cfg b/setup.cfg index a1bb0d6cb..e20572e11 100644 --- a/setup.cfg +++ b/setup.cfg @@ -12,4 +12,8 @@ license_file = LICENSE # bdist_wheel from trying to make a universal wheel. For more see: # https://packaging.python.org/tutorials/distributing-packages/#wheels +[flake8] +exclude = .git +max-line-length = 119 + universal=1 From f67add37d69e720f3557f1fa60fad518f84dae02 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 10 May 2019 16:59:39 +0200 Subject: [PATCH 003/242] Infrastructure. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 6 +- HARK/ConsumptionSaving/ConsPortfolioModel.py | 605 ++++++++++++------- 2 files changed, 373 insertions(+), 238 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 5b557d464..44b0f03e4 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1708,10 +1708,10 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False None ''' if self.cycles!=0 or self.T_cycle > 1: - if verbose == True: + if verbose == True: print('This method only checks for the conditions for infinite horizon models with a 1 period cycle') return - + violated = False #Evaluate and report on the return impatience condition @@ -2020,7 +2020,7 @@ def checkConditions(self,verbose=False,public_call=True): impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate solutions under infinite horizon with a 1 period cycle. Depending on the model at hand, a - different combination of these conditions must be satisfied. (For an exposition of the + different combination of these conditions must be satisfied. (For an exposition of the conditions, see http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) Parameters diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index f870d69c4..04b1fe71e 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -1,81 +1,87 @@ -import HARK.ConsIndShockModel: IndShockConsumerType - -class PortfolioConsumerSolution(ConsumerSolution): - def __init__(self): - self.share = 0 +import math +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType,solveConsIndShock, ConsIndShockSolver +from HARK.utilities import approxLognormal, combineIndepDstns +from copy import deepcopy +import numpy as np +# +# class PortfolioConsumerSolution(ConsumerSolution): +# def __init__(self): +# self.share = 0 class PortfolioChoiceConsumerType(IndShockConsumerType): - def __init(self): - self.solveOnePeriod = solveConsPortfolioChoice - return self - - def updateRxsDstn(self): - # Should be lognormal - self.Rfree - self.RriskAvg - self.RriskStd - self.RxsCount - self.RxsDstn = False - return self.RxsDstn - def updateShockDstn(self): - self.ShockDstn = combineIncAndRxs(self.IncomeDstn, self.RxsDstn) - #add ShockDstn to time_vary if not already - return + time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyAvg', 'RshareCount'] - def makeRshareGrid(self): - given RshareCount return RshareGrid and add to time_inv + def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): - def update(self): - updateRxsDstn - updateShockdstn - makeRshareGrid + IndShockConsumerType.__init__(self,cycles=cycles, time_flow=time_flow, + verbose=verbose, quiet=quiet, **kwds) - def solveTerminal(self): - do same as IndShockConsumerTyppe.solveTerminal and add - RshareFunc = 0 to ssolution_terminal + self.solveOnePeriod = solveConsPortfolioChoice + # Update this *once* as it's time invariant + self.updateRiskyDstn() - def makeEulerErrorFunc(self) - as in KinkedRConsumertpye + def updateRiskyDstn(self): + self.RiskyDstn = self.approxRiskyDstn(self.RiskyCount) + self.RiskyAvg = np.mean(self.RiskyDstn[1]) - def calcBoundingValues(self) - as above +class ConsIndShockPortfolioSolver(ConsIndShockSolver): + def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyAvg, RshareCount): -def solveConsPortfolioChoice(solution_next): - return solution_next + ConsIndShockSolver.__init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool) + self.RiskyDstn = RiskyDstn + self.RiskyAvg = RiskyAvg + self.RshareCount = RshareCount + self.vPfuncNext = solution_next.vPfunc + self.updateShockDstn() + self.makeRshareGrid() -def ConsIndShockPortfolioSolver(ConsIndShockSolver): - def __init__(self): - return self + def updateShockDstn(self): + self.ShockDstn = combineIndepDstns(self.IncomeDstn, self.RiskyDstn) -# def setAndUpdateValues(self): - # great but wait - # calc sUnderbar -> mertonsammuelson - # calc MPC kappaUnderbar - # calc human wealth + def makeRshareGrid(self): + self.RshareGrid = np.linspace(0, 1, self.RshareCount) + return self.RshareGrid - def calcdvdsEndOfPrd(self): - tensorGRid = aXtraGrid x RshareGrid - dvdsEndOfPrd = dvds(tensorGrid) + def prepareToCalcRshare(self): + # Hard restriction on a + aXtraGridPos = self.aXtraGrid[self.aXtraGrid >= 0] - def prepRshareNow(self): - self.RshareNow = np.zeros(len(aXtraGrid)) + RshareGrid = self.makeRshareGrid() + self.RshareNow = np.array([]) + vHatP = np.zeros((len(aXtraGridPos), len(RshareGrid))) - def solveShares(self): - return self + Probs = self.ShockDstn[0] + PermVal = self.ShockDstn[1] + TransVal = self.ShockDstn[2] + RiskyVal = self.ShockDstn[3] - def calcEndofPrdvP(self): - return self - def calcCrmNowandmNrmNow(self): # this is get points??? - return + # Evaluate the non-constant part of the first order conditions wrt the + # portfolio share. This requires the implied resources tomorrow given + # todays shocks to be evaluated. + i_a = 0 + for a in aXtraGridPos: + i_s = 0 + for s in RshareGrid: + Rtilde = RiskyVal - self.Rfree + mNext = (self.Rfree*(1-s) + RiskyVal*s)/(self.PermGroFac*PermVal) + TransVal + vHatP_a_s = Rtilde*PermVal**(-self.CRRA)*self.vPfuncNext(mNext) + vHatP[i_a, i_s] = np.dot(vHatP_a_s, Probs) + i_s += 1 + i_a += 1 + + return vHatP def solve(self): ''' - Solves a one period consumption saving problem with risky income. + Solves a one period consumption saving problem with risky income and + a portfolio choice over a riskless and a risky asset. Parameters ---------- @@ -86,184 +92,313 @@ def solve(self): solution : ConsumerSolution The solution to the one period problem. ''' - aNrm = self.prepareToCalcEndOfPrdvP() - EndOfPrdvP = self.calcEndOfPrdvP() - solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) - solution = self.addMPCandHumanWealth(solution) + self.vHatP = self.prepareToCalcRshare() + # aNrm = self.prepareToCalcEndOfPrdvP() + # EndOfPrdvP = self.calcEndOfPrdvP() + # solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) + # solution = self.addMPCandHumanWealth(solution) + solution = [] return solution - def makeBasicSolution(self,EndOfPrdvP,aNrm,interpolator): - ''' - Given end of period assets and end of period marginal value, construct - the basic solution for this period. - - Parameters - ---------- - EndOfPrdvP : np.array - Array of end-of-period marginal values. - aNrm : np.array - Array of end-of-period asset values that yield the marginal values - in EndOfPrdvP. - - interpolator : function - A function that constructs and returns a consumption function. - - Returns - ------- - solution_now : ConsumerSolution - The solution to this period's consumption-saving problem, with a - consumption function, marginal value function, and minimum m. - ''' - solveShares() - cNrm,mNrm = self.getPointsForInterpolation(EndOfPrdvP,aNrm) - solution_now = self.usePointsForInterpolation(cNrm,mNrm,interpolator) - return solution_now - - - def usePointsForInterpolation(self,cNrm,mNrm,interpolator): - ''' - Constructs a basic solution for this period, including the consumption - function and marginal value function. - Parameters - ---------- - cNrm : np.array - (Normalized) consumption points for interpolation. - mNrm : np.array - (Normalized) corresponding market resource points for interpolation. - interpolator : function - A function that constructs and returns a consumption function. - Returns - ------- - solution_now : ConsumerSolution - The solution to this period's consumption-saving problem, with a - consumption function, marginal value function, and minimum m. - ''' - # Construct the unconstrained consumption function - cFuncNowUnc = interpolator(mNrm,cNrm) - - # Combine the constrained and unconstrained functions into the true consumption function - cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) - - # Make the marginal value function and the marginal marginal value function - vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) - - # Pack up the solution and return it - solution_now = PortfolioConsumerSolution(cFunc=cFuncNow, - vPfunc=vPfuncNow, - mNrmMin=self.mNrmMinNow, - RshareFunc=self.RshareFunc, - vhatPfunc=self.vhatPfunc) -return solution_now - - - - - - - - - - - - - - def updateRiskyPremium(self): - self.RiskyPremium = self.IncomeDstn[3] - self.Rfree - - def Rbold(self, StockShare): - self.updateRiskyPremium() # this is not optimal, but let's do it everywhere for now - return self.Rfree + self.RiskyPremium*StockShare - - def PortfolioObjective(self, StockShare): - # The portfolio objective is the expected value of interering tomorrow - # with the market resources implied by the investment strategy and the - # particular realizations of the stock price evolution. - mNrm = self.mNrmNextAta(StockShare) - VLvlNext = self.DiscFacEff*self.IncomeDstn[1]**(1.0-self.CRRA)*self.PermGroFac**(1.0-self.CRRA)*self.vFuncNext(mNrm) - - return -np.sum(VLvlNext*self.IncomeDstn[0],axis=0) - - def vOptFuncNextFromPortfolioSubproblem(self): - riskyShare = np.array([]) - vOpt = np.array([]) - vOptPa = np.array([]) - for a in self.aNrmNow: - # set aPortfolio for use in PortfolioObjective - self.aPortfolio = a - - # Set the ratio between 0 and 1 - optRes = minimize_scalar(self.PortfolioObjective, bounds=(0, 1), method='bounded') - riskyShare = np.append(riskyShare, optRes.x) - vOpt = np.append(vOpt, -self.PortfolioObjective(optRes.x)) - mNrmOpt = self.mNrmNextAta(optRes.x) - - # This is v^a(a, share(a)) - vPNext = self.uP(self.solution_next.cFunc(mNrmOpt)) - Rbold = self.Rbold(optRes.x) - vOptPa_single = self.DiscFacEff*Rbold*self.PermGroFac**(-self.CRRA)*sum( - self.IncomeDstn[1]**(-self.CRRA)* - vPNext*self.IncomeDstn[0]) - vOptPa = np.append(vOptPa, np.sum(vOptPa_single)) - # grab best policy and value and append it - self.riskyShareFunc = LinearInterp(self.aNrmNow, riskyShare) - - vOptNvrs = self.uinv(vOpt) # value transformed through inverse utility - vOptNvrs = np.insert(vOptNvrs,0,0.0) - aNrm_temp = np.insert(self.aNrmNow,0,self.BoroCnstNat) - vOptNvrsFuncNext = LinearInterp(self.aNrmNow, vOptNvrs) - self.vOptFuncNext = ValueFunc(vOptNvrsFuncNext,self.CRRA) - - self.vOptPaFuncNext = LinearInterp(self.aNrmNow, vOptPa) - - return self.vOptFuncNext, self.riskyShareFunc - - def mNrmNextAta(self, StockShare): - # Get cash on hand next period - mNrmNext = self.Rbold(StockShare)/(self.PermGroFac*self.IncomeDstn[1])*self.aPortfolio + self.IncomeDstn[2] - return mNrmNext - - def makeEndOfPrdvFunc(self,EndOfPrdvP): - ''' - Construct the end-of-period value function for this period, storing it - as an attribute of self for use by other methods. - Parameters - ---------- - EndOfPrdvP : np.array - Array of end-of-period marginal value of assets corresponding to the - asset values in self.aNrmNow. - Returns - ------- - none - ''' - VLvlNext = self.vOptFuncNext(self.aNrmNow) - EndOfPrdv = VLvlNext - - EndOfPrdvNvrs = self.uinv(EndOfPrdv) # value transformed through inverse utility - EndOfPrdvNvrs = np.insert(EndOfPrdvNvrs,0,0.0) - aNrm_temp = np.insert(self.aNrmNow,0,self.BoroCnstNat) - EndOfPrdvNvrsFunc = LinearInterp(aNrm_temp,EndOfPrdvNvrs) - self.EndOfPrdvFunc = ValueFunc(EndOfPrdvNvrsFunc,self.CRRA) - - # This could be an extension to the basic solver, but it seems that there's - # going to be a complete overhaul. Keeping it here for now. - def calcEndOfPrdvP(self): - ''' - Calculate end-of-period marginal value of assets at each point in aNrmNow. - Does so by taking a weighted sum of next period marginal values across - income shocks (in a preconstructed grid self.mNrmNext). - Parameters - ---------- - none - Returns - ------- - EndOfPrdvP : np.array - A 1D array of end-of-period marginal value of assets - ''' - - # Solve portfolio problem in a stage of its own. - self.vOptFunc, self.riskyShareFunc = self.vOptFuncNextFromPortfolioSubproblem() - - # use them to construct a vPNext - EndOfPrdvP = self.vOptPaFuncNext(self.aNrmNow) - - return EndOfPrdvP +def solveConsPortfolioChoice(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, + BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyAvg, RshareCount): + + solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyAvg, RshareCount) + solver.solve() + print(solver.vHatP) + return solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, + BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + + + + +def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): + RiskyAvgSqrd = RiskyAvg**2 + RiskyVar = RiskyStd**2 + + mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) + sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) + + return lambda RiskyCount: approxLognormal(RiskyCount, mu=mu, sigma=sigma) + + + + + +# +# def solveTerminal(self): +# do same as IndShockConsumerTyppe.solveTerminal and add +# RshareFunc = 0 to ssolution_terminal +# +# +# +# +# def solveConsPortfolioChoice(solution_next, IncomeDstn, +# LivPrb,DiscFac, CRRA, +# Rfree, PermGroFac, +# BoroCnstArt, aXtraGrid, +# vFuncBool, CubicBool, +# ShockDstn): +# return solution_next +# +# def ConsIndShockPortfolioSolver(ConsIndShockSolver): +# +# # def setAndUpdateValues(self): +# # great but wait +# # calc sUnderbar -> mertonsammuelson +# # calc MPC kappaUnderbar +# # calc human wealth +# +# def calcdvdsEndOfPrd(self): +# tensorGRid = aXtraGrid x RshareGrid +# dvdsEndOfPrd = dvds(tensorGrid) +## +# def solveShares(self): +# for i in range(len(self.aNrmNowPos)): +# result = solve_for_share(i) +# self.RshareNow[i] = result.zero +# +# return self +# +# def calcEndofPrdvP(self): +# return self +# +# def calcCrmNowandmNrmNow(self): # this is get points??? +# return +# +# +# def solve(self): +# ''' +# Solves a one period consumption saving problem with risky income. +# +# Parameters +# ---------- +# None +# +# Returns +# ------- +# solution : ConsumerSolution +# The solution to the one period problem. +# ''' +# aNrm = self.prepareToCalcEndOfPrdvP() +# EndOfPrdvP = self.calcEndOfPrdvP() +# solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) +# solution = self.addMPCandHumanWealth(solution) +# return solution +# +# def prepareToCalcEndOfPrdvP(self): +# ''' +# Prepare to calculate end-of-period marginal value by creating an array +# of market resources that the agent could have next period, considering +# the grid of end-of-period assets and the distribution of shocks he might +# experience next period. This method adds extra steps because it first +# solves the portfolio problem given the end-of-period assets to be able +# to get next period resources. +# +# Parameters +# ---------- +# none +# +# Returns +# ------- +# aNrmNow : np.array +# A 1D array of end-of-period assets; also stored as attribute of self. +# ''' +# +# # We define aNrmNow all the way from BoroCnstNat up to max(self.aXtraGrid) +# # even if BoroCnstNat < BoroCnstArt, so we can construct the consumption +# # function as the lower envelope of the (by the artificial borrowing con- +# # straint) uconstrained consumption function, and the artificially con- +# # strained consumption function. +# aNrmNow = np.asarray(self.aXtraGrid) + self.BoroCnstNat +# ShkCount = self.TranShkValsNext.size +# aNrm_temp = np.tile(aNrmNow,(ShkCount,1)) +# +# # Tile arrays of the income shocks and put them into useful shapes +# aNrmCount = aNrmNow.shape[0] +# PermShkVals_temp = (np.tile(self.PermShkValsNext,(aNrmCount,1))).transpose() +# TranShkVals_temp = (np.tile(self.TranShkValsNext,(aNrmCount,1))).transpose() +# ShkPrbs_temp = (np.tile(self.ShkPrbsNext,(aNrmCount,1))).transpose() +# +# # Get cash on hand next period +# mNrmNext = self.Rfree/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + TranShkVals_temp +# +# # Store and report the results +# self.PermShkVals_temp = PermShkVals_temp +# self.ShkPrbs_temp = ShkPrbs_temp +# self.mNrmNext = mNrmNext +# self.aNrmNow = aNrmNow +# return aNrmNow +# +# def makeBasicSolution(self,EndOfPrdvP,aNrm,interpolator): +# ''' +# Given end of period assets and end of period marginal value, construct +# the basic solution for this period. +# +# Parameters +# ---------- +# EndOfPrdvP : np.array +# Array of end-of-period marginal values. +# aNrm : np.array +# Array of end-of-period asset values that yield the marginal values +# in EndOfPrdvP. +# +# interpolator : function +# A function that constructs and returns a consumption function. +# +# Returns +# ------- +# solution_now : ConsumerSolution +# The solution to this period's consumption-saving problem, with a +# consumption function, marginal value function, and minimum m. +# ''' +# +# cNrm,mNrm = self.getPointsForInterpolation(EndOfPrdvP,aNrm) +# solution_now = self.usePointsForInterpolation(cNrm,mNrm,interpolator) +# return solution_now +# +# +# def usePointsForInterpolation(self,cNrm,mNrm,interpolator): +# ''' +# Constructs a basic solution for this period, including the consumption +# function and marginal value function. +# Parameters +# ---------- +# cNrm : np.array +# (Normalized) consumption points for interpolation. +# mNrm : np.array +# (Normalized) corresponding market resource points for interpolation. +# interpolator : function +# A function that constructs and returns a consumption function. +# Returns +# ------- +# solution_now : ConsumerSolution +# The solution to this period's consumption-saving problem, with a +# consumption function, marginal value function, and minimum m. +# ''' +# # Construct the unconstrained consumption function +# cFuncNowUnc = interpolator(mNrm,cNrm) +# +# # Combine the constrained and unconstrained functions into the true consumption function +# cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) +# +# # Make the marginal value function and the marginal marginal value function +# vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) +# +# # Pack up the solution and return it +# solution_now = PortfolioConsumerSolution(cFunc=cFuncNow, +# vPfunc=vPfuncNow, +# mNrmMin=self.mNrmMinNow, +# RshareFunc=self.RshareFunc, +# vhatPfunc=self.vhatPfunc) +# return solution_now +# + + + + # + # + # + # + # + # def updateRiskyPremium(self): + # self.RiskyPremium = self.IncomeDstn[3] - self.Rfree + # + # def Rbold(self, StockShare): + # self.updateRiskyPremium() # this is not optimal, but let's do it everywhere for now + # return self.Rfree + self.RiskyPremium*StockShare + # + # def PortfolioObjective(self, StockShare): + # # The portfolio objective is the expected value of interering tomorrow + # # with the market resources implied by the investment strategy and the + # # particular realizations of the stock price evolution. + # mNrm = self.mNrmNextAta(StockShare) + # VLvlNext = self.DiscFacEff*self.IncomeDstn[1]**(1.0-self.CRRA)*self.PermGroFac**(1.0-self.CRRA)*self.vFuncNext(mNrm) + # + # return -np.sum(VLvlNext*self.IncomeDstn[0],axis=0) + # + # def vOptFuncNextFromPortfolioSubproblem(self): + # riskyShare = np.array([]) + # vOpt = np.array([]) + # vOptPa = np.array([]) + # for a in self.aNrmNow: + # # set aPortfolio for use in PortfolioObjective + # self.aPortfolio = a + # + # # Set the ratio between 0 and 1 + # optRes = minimize_scalar(self.PortfolioObjective, bounds=(0, 1), method='bounded') + # riskyShare = np.append(riskyShare, optRes.x) + # vOpt = np.append(vOpt, -self.PortfolioObjective(optRes.x)) + # mNrmOpt = self.mNrmNextAta(optRes.x) + # + # # This is v^a(a, share(a)) + # vPNext = self.uP(self.solution_next.cFunc(mNrmOpt)) + # Rbold = self.Rbold(optRes.x) + # vOptPa_single = self.DiscFacEff*Rbold*self.PermGroFac**(-self.CRRA)*sum( + # self.IncomeDstn[1]**(-self.CRRA)* + # vPNext*self.IncomeDstn[0]) + # vOptPa = np.append(vOptPa, np.sum(vOptPa_single)) + # # grab best policy and value and append it + # self.riskyShareFunc = LinearInterp(self.aNrmNow, riskyShare) + # + # vOptNvrs = self.uinv(vOpt) # value transformed through inverse utility + # vOptNvrs = np.insert(vOptNvrs,0,0.0) + # aNrm_temp = np.insert(self.aNrmNow,0,self.BoroCnstNat) + # vOptNvrsFuncNext = LinearInterp(self.aNrmNow, vOptNvrs) + # self.vOptFuncNext = ValueFunc(vOptNvrsFuncNext,self.CRRA) + # + # self.vOptPaFuncNext = LinearInterp(self.aNrmNow, vOptPa) + # + # return self.vOptFuncNext, self.riskyShareFunc + # + # def mNrmNextAta(self, StockShare): + # # Get cash on hand next period + # mNrmNext = self.Rbold(StockShare)/(self.PermGroFac*self.IncomeDstn[1])*self.aPortfolio + self.IncomeDstn[2] + # return mNrmNext + # + # def makeEndOfPrdvFunc(self,EndOfPrdvP): + # ''' + # Construct the end-of-period value function for this period, storing it + # as an attribute of self for use by other methods. + # Parameters + # ---------- + # EndOfPrdvP : np.array + # Array of end-of-period marginal value of assets corresponding to the + # asset values in self.aNrmNow. + # Returns + # ------- + # none + # ''' + # VLvlNext = self.vOptFuncNext(self.aNrmNow) + # EndOfPrdv = VLvlNext + # + # EndOfPrdvNvrs = self.uinv(EndOfPrdv) # value transformed through inverse utility + # EndOfPrdvNvrs = np.insert(EndOfPrdvNvrs,0,0.0) + # aNrm_temp = np.insert(self.aNrmNow,0,self.BoroCnstNat) + # EndOfPrdvNvrsFunc = LinearInterp(aNrm_temp,EndOfPrdvNvrs) + # self.EndOfPrdvFunc = ValueFunc(EndOfPrdvNvrsFunc,self.CRRA) + # + # # This could be an extension to the basic solver, but it seems that there's + # # going to be a complete overhaul. Keeping it here for now. + # def calcEndOfPrdvP(self): + # ''' + # Calculate end-of-period marginal value of assets at each point in aNrmNow. + # Does so by taking a weighted sum of next period marginal values across + # income shocks (in a preconstructed grid self.mNrmNext). + # Parameters + # ---------- + # none + # Returns + # ------- + # EndOfPrdvP : np.array + # A 1D array of end-of-period marginal value of assets + # ''' + # + # # Solve portfolio problem in a stage of its own. + # self.vOptFunc, self.riskyShareFunc = self.vOptFuncNextFromPortfolioSubproblem() + # + # # use them to construct a vPNext + # EndOfPrdvP = self.vOptPaFuncNext(self.aNrmNow) + # + # return EndOfPrdvP From 640a099f473ae33832854a533e7e7aef029db477 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Mon, 13 May 2019 10:15:05 +0200 Subject: [PATCH 004/242] Solves it, still need to save. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 30 ++++++++++++++++++-- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 04b1fe71e..5a882e085 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -1,6 +1,9 @@ import math +import scipy.optimize as sciopt from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType,solveConsIndShock, ConsIndShockSolver from HARK.utilities import approxLognormal, combineIndepDstns +from HARK.interpolation import LinearInterp + from copy import deepcopy import numpy as np # @@ -50,7 +53,7 @@ def makeRshareGrid(self): def prepareToCalcRshare(self): # Hard restriction on a aXtraGridPos = self.aXtraGrid[self.aXtraGrid >= 0] - + self.aXtraGridPos = aXtraGridPos RshareGrid = self.makeRshareGrid() self.RshareNow = np.array([]) vHatP = np.zeros((len(aXtraGridPos), len(RshareGrid))) @@ -70,7 +73,8 @@ def prepareToCalcRshare(self): i_s = 0 for s in RshareGrid: Rtilde = RiskyVal - self.Rfree - mNext = (self.Rfree*(1-s) + RiskyVal*s)/(self.PermGroFac*PermVal) + TransVal + Rbold = self.Rfree + Rtilde*s + mNext = a*Rbold/(self.PermGroFac*PermVal) + TransVal vHatP_a_s = Rtilde*PermVal**(-self.CRRA)*self.vPfuncNext(mNext) vHatP[i_a, i_s] = np.dot(vHatP_a_s, Probs) i_s += 1 @@ -78,6 +82,26 @@ def prepareToCalcRshare(self): return vHatP + def calcRshare(self): + aGrid = np.array([0.0,]) + Rshare = np.array([1.0,]) + + i_a = 0 + for a in self.aXtraGridPos: + aGrid = np.append(aGrid, a) + if self.vHatP[i_a, -1] >= 0.0: + Rshare = np.append(Rshare, 1.0) + else: + residual = LinearInterp(self.RshareGrid, self.vHatP[i_a, :]) + zero = sciopt.fsolve(residual, 1.0) + Rshare = np.append(Rshare, zero) + i_a += 1 + + print(aGrid) + print(Rshare) + + return Rshare + def solve(self): ''' Solves a one period consumption saving problem with risky income and @@ -93,6 +117,7 @@ def solve(self): The solution to the one period problem. ''' self.vHatP = self.prepareToCalcRshare() + self.Rshare = self.calcRshare() # aNrm = self.prepareToCalcEndOfPrdvP() # EndOfPrdvP = self.calcEndOfPrdvP() # solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) @@ -106,7 +131,6 @@ def solveConsPortfolioChoice(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyAvg, RshareCount) solver.solve() - print(solver.vHatP) return solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) From 53bad823ccc57b55db908ada8f2edf586f67148e Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Mon, 13 May 2019 20:39:20 +0200 Subject: [PATCH 005/242] Works. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 2 +- HARK/ConsumptionSaving/ConsPortfolioModel.py | 342 +++++++++++++++---- 2 files changed, 279 insertions(+), 65 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 44b0f03e4..cf95a7926 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -811,7 +811,7 @@ def prepareToCalcEndOfPrdvP(self): # Get cash on hand next period mNrmNext = self.Rfree/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + TranShkVals_temp - + print(mNrmNext.shape) # Store and report the results self.PermShkVals_temp = PermShkVals_temp self.ShkPrbs_temp = ShkPrbs_temp diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 5a882e085..33781859f 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -1,15 +1,40 @@ +# NOTE Need to decide on Rshare vs RiskyShare import math import scipy.optimize as sciopt -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType,solveConsIndShock, ConsIndShockSolver +from HARK import Solution, NullFunc +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType,solveConsIndShock, ConsIndShockSolver, MargValueFunc, ConsumerSolution from HARK.utilities import approxLognormal, combineIndepDstns -from HARK.interpolation import LinearInterp +from HARK.interpolation import LinearInterp, LowerEnvelope from copy import deepcopy import numpy as np -# -# class PortfolioConsumerSolution(ConsumerSolution): -# def __init__(self): -# self.share = 0 + +class PortfolioConsumerSolution(Solution): + distance_criteria = ['vPfunc'] + + def __init__(self, cFunc=None, vFunc=None, + vPfunc=None, RiskyShareFunc=None, vPPfunc=None, + mNrmMin=None, hNrm=None, MPCmin=None, MPCmax=None): + # Change any missing function inputs to NullFunc + if cFunc is None: + cFunc = NullFunc() + if vFunc is None: + vFunc = NullFunc() + if RiskyShareFunc is None: + RiskyShareFunc = NullFunc() + if vPfunc is None: + vPfunc = NullFunc() + if vPPfunc is None: + vPPfunc = NullFunc() + self.cFunc = cFunc + self.vFunc = vFunc + self.vPfunc = vPfunc + self.RiskyShareFunc = RiskyShareFunc + # self.vPPfunc = vPPfunc + # self.mNrmMin = mNrmMin + # self.hNrm = hNrm + # self.MPCmin = MPCmin + # self.MPCmax = MPCmax class PortfolioChoiceConsumerType(IndShockConsumerType): @@ -58,7 +83,7 @@ def prepareToCalcRshare(self): self.RshareNow = np.array([]) vHatP = np.zeros((len(aXtraGridPos), len(RshareGrid))) - Probs = self.ShockDstn[0] + Probs = self.ShockDstn[0] # use the prepareotsolve vars inm self _tmp PermVal = self.ShockDstn[1] TransVal = self.ShockDstn[2] RiskyVal = self.ShockDstn[3] @@ -91,16 +116,240 @@ def calcRshare(self): aGrid = np.append(aGrid, a) if self.vHatP[i_a, -1] >= 0.0: Rshare = np.append(Rshare, 1.0) + elif self.vHatP[i_a, 0] < 0.0: + Rshare = np.append(Rshare, 0.0) + print("The first is neg") else: residual = LinearInterp(self.RshareGrid, self.vHatP[i_a, :]) - zero = sciopt.fsolve(residual, 1.0) + zero = sciopt.fsolve(residual, 0.5) Rshare = np.append(Rshare, zero) i_a += 1 + RshareFunc = LinearInterp(aGrid, Rshare) + return RshareFunc + + def prepareToCalcEndOfPrdvP(self): + ''' + Prepare to calculate end-of-period marginal value by creating an array + of market resources that the agent could have next period, considering + the grid of end-of-period assets and the distribution of shocks he might + experience next period. This method adds extra steps because it first + solves the portfolio problem given the end-of-period assets to be able + to get next period resources. + + Parameters + ---------- + none + + Returns + ------- + aNrmNow : np.array + A 1D array of end-of-period assets; also stored as attribute of self. + ''' + + # We define aNrmNow all the way from BoroCnstNat up to max(self.aXtraGrid) + # even if BoroCnstNat < BoroCnstArt, so we can construct the consumption + # function as the lower envelope of the (by the artificial borrowing con- + # straint) uconstrained consumption function, and the artificially con- + # strained consumption function. + aNrmNow = np.asarray(self.aXtraGrid) + ShkCount = self.TranShkValsNext.size + aNrm_temp = np.tile(aNrmNow,(ShkCount,1)) + + # Tile arrays of the income shocks and put them into useful shapes + aNrmCount = aNrmNow.shape[0] + PermShkVals_temp = (np.tile(self.PermShkValsNext,(aNrmCount,1))).transpose() + TranShkVals_temp = (np.tile(self.TranShkValsNext,(aNrmCount,1))).transpose() + RiskyShkVals_temp = (np.tile(self.RiskyShkValsNext,(aNrmCount,1))).transpose() + ShkPrbs_temp = (np.tile(self.ShkPrbsNext,(aNrmCount,1))).transpose() + + + # sAtA + sAt_aNrm = self.RshareFunc(aNrmNow) + # Get cash on hand next period + + Rtilde = RiskyShkVals_temp - self.Rfree + self.Rbold = (self.Rfree + Rtilde*sAt_aNrm) + mNrmPreTran = self.Rbold/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + mNrmNext = mNrmPreTran + TranShkVals_temp + + # Store and report the results + self.PermShkVals_temp = PermShkVals_temp + self.ShkPrbs_temp = ShkPrbs_temp + self.mNrmNext = mNrmNext + self.aNrmNow = aNrmNow + return aNrmNow + + def calcEndOfPrdvP(self): + ''' + Calculate end-of-period marginal value of assets at each point in aNrmNow. + Does so by taking a weighted sum of next period marginal values across + income shocks (in a preconstructed grid self.mNrmNext). + + Parameters + ---------- + none + + Returns + ------- + EndOfPrdvP : np.array + A 1D array of end-of-period marginal value of assets + ''' + + EndOfPrdvP = np.sum(self.DiscFacEff*self.Rbold*self.PermGroFac**(-self.CRRA)* + self.PermShkVals_temp**(-self.CRRA)* + self.vPfuncNext(self.mNrmNext)*self.ShkPrbs_temp,axis=0) + return EndOfPrdvP + + + + def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): + ''' + Unpacks some of the inputs (and calculates simple objects based on them), + storing the results in self for use by other methods. These include: + income shocks and probabilities, next period's marginal value function + (etc), the probability of getting the worst income shock next period, + the patience factor, human wealth, and the bounding MPCs. + + Parameters + ---------- + solution_next : ConsumerSolution + The solution to next period's one period problem. + IncomeDstn : [np.array] + A list containing three arrays of floats, representing a discrete + approximation to the income process between the period being solved + and the one immediately following (in solution_next). Order: event + probabilities, permanent shocks, transitory shocks. + LivPrb : float + Survival probability; likelihood of being alive at the beginning of + the succeeding period. + DiscFac : float + Intertemporal discount factor for future utility. + + Returns + ------- + None + ''' + self.DiscFacEff = DiscFac*LivPrb # "effective" discount factor + self.ShkPrbsNext = self.ShockDstn[0] # but ConsumtionSolver doesn't store the risky shocks + self.PermShkValsNext = self.ShockDstn[1] # but ConsumtionSolver doesn't store the risky shocks + self.TranShkValsNext = self.ShockDstn[2] # but ConsumtionSolver doesn't store the risky shocks + self.RiskyShkValsNext = self.ShockDstn[3] # but ConsumtionSolver doesn't store the risky shocks + self.PermShkMinNext = np.min(self.PermShkValsNext) + self.TranShkMinNext = np.min(self.TranShkValsNext) + self.vPfuncNext = solution_next.vPfunc + self.WorstIncPrb = np.sum(self.ShkPrbsNext[ + (self.PermShkValsNext*self.TranShkValsNext)== + (self.PermShkMinNext*self.TranShkMinNext)]) + + if self.CubicBool: + self.vPPfuncNext = solution_next.vPPfunc + + if self.vFuncBool: + self.vFuncNext = solution_next.vFunc + + # Update the bounding MPCs and PDV of human wealth: + # self.PatFac = ((self.Rfree*self.DiscFacEff)**(1.0/self.CRRA))/self.Rfree + # self.MPCminNow = 1.0/(1.0 + self.PatFac/solution_next.MPCmin) + # self.ExIncNext = np.dot(self.ShkPrbsNext,self.TranShkValsNext*self.PermShkValsNext) + # self.hNrmNow = self.PermGroFac/self.Rfree*(self.ExIncNext + solution_next.hNrm) + # self.MPCmaxNow = 1.0/(1.0 + (self.WorstIncPrb**(1.0/self.CRRA))* + # self.PatFac/solution_next.MPCmax) + + + + + def defBoroCnst(self,BoroCnstArt): + ''' + Defines the constrained portion of the consumption function as cFuncNowCnst, + an attribute of self. Uses the artificial and natural borrowing constraints. - print(aGrid) - print(Rshare) + Parameters + ---------- + BoroCnstArt : float or None + Borrowing constraint for the minimum allowable assets to end the + period with. If it is less than the natural borrowing constraint, + then it is irrelevant; BoroCnstArt=None indicates no artificial bor- + rowing constraint. - return Rshare + Returns + ------- + none + ''' + # Calculate the minimum allowable value of money resources in this period + self.BoroCnstNat = 0.0 #(self.solution_next.mNrmMin - self.TranShkMinNext)*\ + #(self.PermGroFac*self.PermShkMinNext)/self.Rfree + + # Note: need to be sure to handle BoroCnstArt==None appropriately. + # In Py2, this would evaluate to 5.0: np.max([None, 5.0]). + # However in Py3, this raises a TypeError. Thus here we need to directly + # address the situation in which BoroCnstArt == None: + # if BoroCnstArt is None: + # self.mNrmMinNow = self.BoroCnstNat + # else: + # self.mNrmMinNow = np.max([self.BoroCnstNat,BoroCnstArt]) + # if self.BoroCnstNat < self.mNrmMinNow: + # self.MPCmaxEff = 1.0 # If actually constrained, MPC near limit is 1 + # else: + # self.MPCmaxEff = self.MPCmaxNow + + # Define the borrowing constraint (limiting consumption function) + self.cFuncNowCnst = LinearInterp(np.array([0.0, 1.0]), + np.array([0.0, 1.0])) + + + def usePointsForInterpolation(self,cNrm,mNrm,interpolator): + ''' + Constructs a basic solution for this period, including the consumption + function and marginal value function. + + Parameters + ---------- + cNrm : np.array + (Normalized) consumption points for interpolation. + mNrm : np.array + (Normalized) corresponding market resource points for interpolation. + interpolator : function + A function that constructs and returns a consumption function. + + Returns + ------- + solution_now : ConsumerSolution + The solution to this period's consumption-saving problem, with a + consumption function, marginal value function, and minimum m. + ''' + # Construct the unconstrained consumption function + print(mNrm[0].shape) + print(cNrm[0].shape) + cFuncNowUnc = interpolator(mNrm,cNrm) + # Combine the constrained and unconstrained functions into the true consumption function + cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) + + # Make the marginal value function and the marginal marginal value function + vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) + + # Pack up the solution and return it + solution_now = ConsumerSolution(cFunc=cFuncNow, vPfunc=vPfuncNow) + return solution_now + + + def makeLinearcFunc(self,mNrm,cNrm): + ''' + Makes a linear interpolation to represent the (unconstrained) consumption function. + + Parameters + ---------- + mNrm : np.array + Corresponding market resource points for interpolation. + cNrm : np.array + Consumption points for interpolation. + + Returns + ------- + cFuncUnc : LinearInterp + The unconstrained consumption function for this period. + ''' + cFuncUnc = LinearInterp(mNrm,cNrm) + return cFuncUnc def solve(self): ''' @@ -116,24 +365,33 @@ def solve(self): solution : ConsumerSolution The solution to the one period problem. ''' + print("solveing") self.vHatP = self.prepareToCalcRshare() - self.Rshare = self.calcRshare() - # aNrm = self.prepareToCalcEndOfPrdvP() - # EndOfPrdvP = self.calcEndOfPrdvP() - # solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) + self.RshareFunc = self.calcRshare() + aNrm = self.prepareToCalcEndOfPrdvP() + EndOfPrdvP = self.calcEndOfPrdvP() + cs_solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) + cs_solution.vPfunc(0.3) # solution = self.addMPCandHumanWealth(solution) - solution = [] + solution = PortfolioConsumerSolution(cFunc=cs_solution.cFunc, + vPfunc=cs_solution.vPfunc, + RiskyShareFunc=self.RshareFunc) return solution def solveConsPortfolioChoice(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyAvg, RshareCount): +# solution = solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, +# BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyAvg, RshareCount) - solver.solve() - return solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, - BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyAvg, RshareCount) + solver.prepareToSolve() # Do some preparatory work + + portsolution = solver.solve() + + return portsolution @@ -211,50 +469,6 @@ def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): # solution = self.addMPCandHumanWealth(solution) # return solution # -# def prepareToCalcEndOfPrdvP(self): -# ''' -# Prepare to calculate end-of-period marginal value by creating an array -# of market resources that the agent could have next period, considering -# the grid of end-of-period assets and the distribution of shocks he might -# experience next period. This method adds extra steps because it first -# solves the portfolio problem given the end-of-period assets to be able -# to get next period resources. -# -# Parameters -# ---------- -# none -# -# Returns -# ------- -# aNrmNow : np.array -# A 1D array of end-of-period assets; also stored as attribute of self. -# ''' -# -# # We define aNrmNow all the way from BoroCnstNat up to max(self.aXtraGrid) -# # even if BoroCnstNat < BoroCnstArt, so we can construct the consumption -# # function as the lower envelope of the (by the artificial borrowing con- -# # straint) uconstrained consumption function, and the artificially con- -# # strained consumption function. -# aNrmNow = np.asarray(self.aXtraGrid) + self.BoroCnstNat -# ShkCount = self.TranShkValsNext.size -# aNrm_temp = np.tile(aNrmNow,(ShkCount,1)) -# -# # Tile arrays of the income shocks and put them into useful shapes -# aNrmCount = aNrmNow.shape[0] -# PermShkVals_temp = (np.tile(self.PermShkValsNext,(aNrmCount,1))).transpose() -# TranShkVals_temp = (np.tile(self.TranShkValsNext,(aNrmCount,1))).transpose() -# ShkPrbs_temp = (np.tile(self.ShkPrbsNext,(aNrmCount,1))).transpose() -# -# # Get cash on hand next period -# mNrmNext = self.Rfree/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + TranShkVals_temp -# -# # Store and report the results -# self.PermShkVals_temp = PermShkVals_temp -# self.ShkPrbs_temp = ShkPrbs_temp -# self.mNrmNext = mNrmNext -# self.aNrmNow = aNrmNow -# return aNrmNow -# # def makeBasicSolution(self,EndOfPrdvP,aNrm,interpolator): # ''' # Given end of period assets and end of period marginal value, construct From b53efd770c217246284b73a4a0888844bb9ff639 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Mon, 13 May 2019 22:36:24 +0200 Subject: [PATCH 006/242] Cleanup --- HARK/ConsumptionSaving/ConsIndShockModel.py | 7 +- HARK/ConsumptionSaving/ConsPortfolioModel.py | 369 +++---------------- 2 files changed, 58 insertions(+), 318 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index cf95a7926..da62dd947 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -706,6 +706,9 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): self.MPCmaxNow = 1.0/(1.0 + (self.WorstIncPrb**(1.0/self.CRRA))* self.PatFac/solution_next.MPCmax) + self.cFuncLimitIntercept = self.MPCminNow*self.hNrmNow + self.cFuncLimitSlope = self.MPCminNow + def defBoroCnst(self,BoroCnstArt): ''' @@ -811,7 +814,7 @@ def prepareToCalcEndOfPrdvP(self): # Get cash on hand next period mNrmNext = self.Rfree/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + TranShkVals_temp - print(mNrmNext.shape) + # Store and report the results self.PermShkVals_temp = PermShkVals_temp self.ShkPrbs_temp = ShkPrbs_temp @@ -971,7 +974,7 @@ def makeLinearcFunc(self,mNrm,cNrm): cFuncUnc : LinearInterp The unconstrained consumption function for this period. ''' - cFuncUnc = LinearInterp(mNrm,cNrm,self.MPCminNow*self.hNrmNow,self.MPCminNow) + cFuncUnc = LinearInterp(mNrm, cNrm, self.cFuncLimitIntercept, self.cFuncLimitSlope) return cFuncUnc def solve(self): diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 33781859f..c274f03db 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -38,32 +38,48 @@ def __init__(self, cFunc=None, vFunc=None, class PortfolioChoiceConsumerType(IndShockConsumerType): - time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyAvg', 'RshareCount'] + time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyShareCount'] + cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period + vFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # This is overwritten + RiskyShareFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # c=m in terminal period + solution_terminal_ = PortfolioConsumerSolution(cFunc = cFunc_terminal_, RiskyShareFunc = RiskyShareFunc_terminal_, + vFunc = vFunc_terminal_, mNrmMin=0.0, hNrm=None, + MPCmin=None, MPCmax=None) def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): IndShockConsumerType.__init__(self,cycles=cycles, time_flow=time_flow, verbose=verbose, quiet=quiet, **kwds) + if self.BoroCnstArt is not 0.0: + print("Setting BoroCnstArt to 0.0 as this is required by PortfolioChoiceConsumerType.") + self.BoroCnstArt = 0.0 + + # Chose specialized solver for Portfolio choice model self.solveOnePeriod = solveConsPortfolioChoice + # Update this *once* as it's time invariant self.updateRiskyDstn() def updateRiskyDstn(self): + # Note, we expect the thing in self.approxRiskyDstn to be a funtion of + # the number of nodes only! self.RiskyDstn = self.approxRiskyDstn(self.RiskyCount) - self.RiskyAvg = np.mean(self.RiskyDstn[1]) class ConsIndShockPortfolioSolver(ConsIndShockSolver): def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyAvg, RshareCount): + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyShareCount): ConsIndShockSolver.__init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool) + + # Store the Risky asset shock distribution self.RiskyDstn = RiskyDstn - self.RiskyAvg = RiskyAvg - self.RshareCount = RshareCount + # Store the number of grid points used approximate the FOC in the port- + # folio sub-problem. + self.RiskyShareCount = RiskyShareCount self.vPfuncNext = solution_next.vPfunc self.updateShockDstn() self.makeRshareGrid() @@ -72,53 +88,50 @@ def updateShockDstn(self): self.ShockDstn = combineIndepDstns(self.IncomeDstn, self.RiskyDstn) def makeRshareGrid(self): - self.RshareGrid = np.linspace(0, 1, self.RshareCount) + self.RshareGrid = np.linspace(0, 1, self.RiskyShareCount) return self.RshareGrid - def prepareToCalcRshare(self): - # Hard restriction on a - aXtraGridPos = self.aXtraGrid[self.aXtraGrid >= 0] - self.aXtraGridPos = aXtraGridPos + def prepareToCalcRiskyShare(self): + # Hard restriction on aNrm. We'd need to define more elaborate model + # specifics if a could become negative (or a positive return shock + # would make you worse off!) + aNrmPort = self.aXtraGrid[self.aXtraGrid >= 0] + + self.aNrmPort = aNrmPort RshareGrid = self.makeRshareGrid() self.RshareNow = np.array([]) - vHatP = np.zeros((len(aXtraGridPos), len(RshareGrid))) - - Probs = self.ShockDstn[0] # use the prepareotsolve vars inm self _tmp - PermVal = self.ShockDstn[1] - TransVal = self.ShockDstn[2] - RiskyVal = self.ShockDstn[3] - + vHatP = np.zeros((len(aNrmPort), len(RshareGrid))) # Evaluate the non-constant part of the first order conditions wrt the # portfolio share. This requires the implied resources tomorrow given # todays shocks to be evaluated. i_a = 0 - for a in aXtraGridPos: + for a in aNrmPort: + # for all possible a's today i_s = 0 for s in RshareGrid: - Rtilde = RiskyVal - self.Rfree + Rtilde = self.RiskyShkValsNext - self.Rfree Rbold = self.Rfree + Rtilde*s - mNext = a*Rbold/(self.PermGroFac*PermVal) + TransVal - vHatP_a_s = Rtilde*PermVal**(-self.CRRA)*self.vPfuncNext(mNext) - vHatP[i_a, i_s] = np.dot(vHatP_a_s, Probs) + mNext = a*Rbold/(self.PermGroFac*self.PermShkValsNext) + self.TranShkValsNext + vHatP_a_s = Rtilde*self.PermShkValsNext**(-self.CRRA)*self.vPfuncNext(mNext) + vHatP[i_a, i_s] = np.dot(vHatP_a_s, self.ShkPrbsNext) i_s += 1 i_a += 1 return vHatP - def calcRshare(self): + def calcRiskyShare(self): aGrid = np.array([0.0,]) Rshare = np.array([1.0,]) i_a = 0 - for a in self.aXtraGridPos: + for a in self.aNrmPort: aGrid = np.append(aGrid, a) if self.vHatP[i_a, -1] >= 0.0: Rshare = np.append(Rshare, 1.0) elif self.vHatP[i_a, 0] < 0.0: Rshare = np.append(Rshare, 0.0) - print("The first is neg") else: residual = LinearInterp(self.RshareGrid, self.vHatP[i_a, :]) zero = sciopt.fsolve(residual, 0.5) @@ -279,14 +292,12 @@ def defBoroCnst(self,BoroCnstArt): self.BoroCnstNat = 0.0 #(self.solution_next.mNrmMin - self.TranShkMinNext)*\ #(self.PermGroFac*self.PermShkMinNext)/self.Rfree - # Note: need to be sure to handle BoroCnstArt==None appropriately. - # In Py2, this would evaluate to 5.0: np.max([None, 5.0]). - # However in Py3, this raises a TypeError. Thus here we need to directly - # address the situation in which BoroCnstArt == None: - # if BoroCnstArt is None: - # self.mNrmMinNow = self.BoroCnstNat - # else: - # self.mNrmMinNow = np.max([self.BoroCnstNat,BoroCnstArt]) + if BoroCnstArt is None: + self.mNrmMinNow = self.BoroCnstNat + else: + self.mNrmMinNow = np.max([self.BoroCnstNat,BoroCnstArt]) + + # Can we just put in *some value* for MPCmaxNow here? # if self.BoroCnstNat < self.mNrmMinNow: # self.MPCmaxEff = 1.0 # If actually constrained, MPC near limit is 1 # else: @@ -297,60 +308,6 @@ def defBoroCnst(self,BoroCnstArt): np.array([0.0, 1.0])) - def usePointsForInterpolation(self,cNrm,mNrm,interpolator): - ''' - Constructs a basic solution for this period, including the consumption - function and marginal value function. - - Parameters - ---------- - cNrm : np.array - (Normalized) consumption points for interpolation. - mNrm : np.array - (Normalized) corresponding market resource points for interpolation. - interpolator : function - A function that constructs and returns a consumption function. - - Returns - ------- - solution_now : ConsumerSolution - The solution to this period's consumption-saving problem, with a - consumption function, marginal value function, and minimum m. - ''' - # Construct the unconstrained consumption function - print(mNrm[0].shape) - print(cNrm[0].shape) - cFuncNowUnc = interpolator(mNrm,cNrm) - # Combine the constrained and unconstrained functions into the true consumption function - cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) - - # Make the marginal value function and the marginal marginal value function - vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) - - # Pack up the solution and return it - solution_now = ConsumerSolution(cFunc=cFuncNow, vPfunc=vPfuncNow) - return solution_now - - - def makeLinearcFunc(self,mNrm,cNrm): - ''' - Makes a linear interpolation to represent the (unconstrained) consumption function. - - Parameters - ---------- - mNrm : np.array - Corresponding market resource points for interpolation. - cNrm : np.array - Consumption points for interpolation. - - Returns - ------- - cFuncUnc : LinearInterp - The unconstrained consumption function for this period. - ''' - cFuncUnc = LinearInterp(mNrm,cNrm) - return cFuncUnc - def solve(self): ''' Solves a one period consumption saving problem with risky income and @@ -365,11 +322,15 @@ def solve(self): solution : ConsumerSolution The solution to the one period problem. ''' - print("solveing") - self.vHatP = self.prepareToCalcRshare() - self.RshareFunc = self.calcRshare() + self.vHatP = self.prepareToCalcRiskyShare() + self.RshareFunc = self.calcRiskyShare() aNrm = self.prepareToCalcEndOfPrdvP() EndOfPrdvP = self.calcEndOfPrdvP() + + # Todo! + self.cFuncLimitIntercept = None + self.cFuncLimitSlope = None + cs_solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) cs_solution.vPfunc(0.3) # solution = self.addMPCandHumanWealth(solution) @@ -379,13 +340,13 @@ def solve(self): return solution def solveConsPortfolioChoice(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, - BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyAvg, RshareCount): + BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyShareCount): # solution = solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, # BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyAvg, RshareCount) + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyShareCount) solver.prepareToSolve() # Do some preparatory work @@ -408,22 +369,6 @@ def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): -# -# def solveTerminal(self): -# do same as IndShockConsumerTyppe.solveTerminal and add -# RshareFunc = 0 to ssolution_terminal -# -# -# -# -# def solveConsPortfolioChoice(solution_next, IncomeDstn, -# LivPrb,DiscFac, CRRA, -# Rfree, PermGroFac, -# BoroCnstArt, aXtraGrid, -# vFuncBool, CubicBool, -# ShockDstn): -# return solution_next -# # def ConsIndShockPortfolioSolver(ConsIndShockSolver): # # # def setAndUpdateValues(self): @@ -432,211 +377,3 @@ def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): # # calc MPC kappaUnderbar # # calc human wealth # -# def calcdvdsEndOfPrd(self): -# tensorGRid = aXtraGrid x RshareGrid -# dvdsEndOfPrd = dvds(tensorGrid) -## -# def solveShares(self): -# for i in range(len(self.aNrmNowPos)): -# result = solve_for_share(i) -# self.RshareNow[i] = result.zero -# -# return self -# -# def calcEndofPrdvP(self): -# return self -# -# def calcCrmNowandmNrmNow(self): # this is get points??? -# return -# -# -# def solve(self): -# ''' -# Solves a one period consumption saving problem with risky income. -# -# Parameters -# ---------- -# None -# -# Returns -# ------- -# solution : ConsumerSolution -# The solution to the one period problem. -# ''' -# aNrm = self.prepareToCalcEndOfPrdvP() -# EndOfPrdvP = self.calcEndOfPrdvP() -# solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) -# solution = self.addMPCandHumanWealth(solution) -# return solution -# -# def makeBasicSolution(self,EndOfPrdvP,aNrm,interpolator): -# ''' -# Given end of period assets and end of period marginal value, construct -# the basic solution for this period. -# -# Parameters -# ---------- -# EndOfPrdvP : np.array -# Array of end-of-period marginal values. -# aNrm : np.array -# Array of end-of-period asset values that yield the marginal values -# in EndOfPrdvP. -# -# interpolator : function -# A function that constructs and returns a consumption function. -# -# Returns -# ------- -# solution_now : ConsumerSolution -# The solution to this period's consumption-saving problem, with a -# consumption function, marginal value function, and minimum m. -# ''' -# -# cNrm,mNrm = self.getPointsForInterpolation(EndOfPrdvP,aNrm) -# solution_now = self.usePointsForInterpolation(cNrm,mNrm,interpolator) -# return solution_now -# -# -# def usePointsForInterpolation(self,cNrm,mNrm,interpolator): -# ''' -# Constructs a basic solution for this period, including the consumption -# function and marginal value function. -# Parameters -# ---------- -# cNrm : np.array -# (Normalized) consumption points for interpolation. -# mNrm : np.array -# (Normalized) corresponding market resource points for interpolation. -# interpolator : function -# A function that constructs and returns a consumption function. -# Returns -# ------- -# solution_now : ConsumerSolution -# The solution to this period's consumption-saving problem, with a -# consumption function, marginal value function, and minimum m. -# ''' -# # Construct the unconstrained consumption function -# cFuncNowUnc = interpolator(mNrm,cNrm) -# -# # Combine the constrained and unconstrained functions into the true consumption function -# cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) -# -# # Make the marginal value function and the marginal marginal value function -# vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) -# -# # Pack up the solution and return it -# solution_now = PortfolioConsumerSolution(cFunc=cFuncNow, -# vPfunc=vPfuncNow, -# mNrmMin=self.mNrmMinNow, -# RshareFunc=self.RshareFunc, -# vhatPfunc=self.vhatPfunc) -# return solution_now -# - - - - # - # - # - # - # - # def updateRiskyPremium(self): - # self.RiskyPremium = self.IncomeDstn[3] - self.Rfree - # - # def Rbold(self, StockShare): - # self.updateRiskyPremium() # this is not optimal, but let's do it everywhere for now - # return self.Rfree + self.RiskyPremium*StockShare - # - # def PortfolioObjective(self, StockShare): - # # The portfolio objective is the expected value of interering tomorrow - # # with the market resources implied by the investment strategy and the - # # particular realizations of the stock price evolution. - # mNrm = self.mNrmNextAta(StockShare) - # VLvlNext = self.DiscFacEff*self.IncomeDstn[1]**(1.0-self.CRRA)*self.PermGroFac**(1.0-self.CRRA)*self.vFuncNext(mNrm) - # - # return -np.sum(VLvlNext*self.IncomeDstn[0],axis=0) - # - # def vOptFuncNextFromPortfolioSubproblem(self): - # riskyShare = np.array([]) - # vOpt = np.array([]) - # vOptPa = np.array([]) - # for a in self.aNrmNow: - # # set aPortfolio for use in PortfolioObjective - # self.aPortfolio = a - # - # # Set the ratio between 0 and 1 - # optRes = minimize_scalar(self.PortfolioObjective, bounds=(0, 1), method='bounded') - # riskyShare = np.append(riskyShare, optRes.x) - # vOpt = np.append(vOpt, -self.PortfolioObjective(optRes.x)) - # mNrmOpt = self.mNrmNextAta(optRes.x) - # - # # This is v^a(a, share(a)) - # vPNext = self.uP(self.solution_next.cFunc(mNrmOpt)) - # Rbold = self.Rbold(optRes.x) - # vOptPa_single = self.DiscFacEff*Rbold*self.PermGroFac**(-self.CRRA)*sum( - # self.IncomeDstn[1]**(-self.CRRA)* - # vPNext*self.IncomeDstn[0]) - # vOptPa = np.append(vOptPa, np.sum(vOptPa_single)) - # # grab best policy and value and append it - # self.riskyShareFunc = LinearInterp(self.aNrmNow, riskyShare) - # - # vOptNvrs = self.uinv(vOpt) # value transformed through inverse utility - # vOptNvrs = np.insert(vOptNvrs,0,0.0) - # aNrm_temp = np.insert(self.aNrmNow,0,self.BoroCnstNat) - # vOptNvrsFuncNext = LinearInterp(self.aNrmNow, vOptNvrs) - # self.vOptFuncNext = ValueFunc(vOptNvrsFuncNext,self.CRRA) - # - # self.vOptPaFuncNext = LinearInterp(self.aNrmNow, vOptPa) - # - # return self.vOptFuncNext, self.riskyShareFunc - # - # def mNrmNextAta(self, StockShare): - # # Get cash on hand next period - # mNrmNext = self.Rbold(StockShare)/(self.PermGroFac*self.IncomeDstn[1])*self.aPortfolio + self.IncomeDstn[2] - # return mNrmNext - # - # def makeEndOfPrdvFunc(self,EndOfPrdvP): - # ''' - # Construct the end-of-period value function for this period, storing it - # as an attribute of self for use by other methods. - # Parameters - # ---------- - # EndOfPrdvP : np.array - # Array of end-of-period marginal value of assets corresponding to the - # asset values in self.aNrmNow. - # Returns - # ------- - # none - # ''' - # VLvlNext = self.vOptFuncNext(self.aNrmNow) - # EndOfPrdv = VLvlNext - # - # EndOfPrdvNvrs = self.uinv(EndOfPrdv) # value transformed through inverse utility - # EndOfPrdvNvrs = np.insert(EndOfPrdvNvrs,0,0.0) - # aNrm_temp = np.insert(self.aNrmNow,0,self.BoroCnstNat) - # EndOfPrdvNvrsFunc = LinearInterp(aNrm_temp,EndOfPrdvNvrs) - # self.EndOfPrdvFunc = ValueFunc(EndOfPrdvNvrsFunc,self.CRRA) - # - # # This could be an extension to the basic solver, but it seems that there's - # # going to be a complete overhaul. Keeping it here for now. - # def calcEndOfPrdvP(self): - # ''' - # Calculate end-of-period marginal value of assets at each point in aNrmNow. - # Does so by taking a weighted sum of next period marginal values across - # income shocks (in a preconstructed grid self.mNrmNext). - # Parameters - # ---------- - # none - # Returns - # ------- - # EndOfPrdvP : np.array - # A 1D array of end-of-period marginal value of assets - # ''' - # - # # Solve portfolio problem in a stage of its own. - # self.vOptFunc, self.riskyShareFunc = self.vOptFuncNextFromPortfolioSubproblem() - # - # # use them to construct a vPNext - # EndOfPrdvP = self.vOptPaFuncNext(self.aNrmNow) - # - # return EndOfPrdvP From 4ec847a32d5d34dc7dcf7fb5c94c0f7b2c1acee8 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Mon, 13 May 2019 22:37:10 +0200 Subject: [PATCH 007/242] Rbold -> Reff --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index c274f03db..7ca455bcd 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -112,8 +112,8 @@ def prepareToCalcRiskyShare(self): i_s = 0 for s in RshareGrid: Rtilde = self.RiskyShkValsNext - self.Rfree - Rbold = self.Rfree + Rtilde*s - mNext = a*Rbold/(self.PermGroFac*self.PermShkValsNext) + self.TranShkValsNext + Reff = self.Rfree + Rtilde*s + mNext = a*Reff/(self.PermGroFac*self.PermShkValsNext) + self.TranShkValsNext vHatP_a_s = Rtilde*self.PermShkValsNext**(-self.CRRA)*self.vPfuncNext(mNext) vHatP[i_a, i_s] = np.dot(vHatP_a_s, self.ShkPrbsNext) i_s += 1 @@ -181,8 +181,8 @@ def prepareToCalcEndOfPrdvP(self): # Get cash on hand next period Rtilde = RiskyShkVals_temp - self.Rfree - self.Rbold = (self.Rfree + Rtilde*sAt_aNrm) - mNrmPreTran = self.Rbold/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + self.Reff = (self.Rfree + Rtilde*sAt_aNrm) + mNrmPreTran = self.Reff/(self.PermGroFac*PermShkVals_temp)*aNrm_temp mNrmNext = mNrmPreTran + TranShkVals_temp # Store and report the results @@ -208,7 +208,7 @@ def calcEndOfPrdvP(self): A 1D array of end-of-period marginal value of assets ''' - EndOfPrdvP = np.sum(self.DiscFacEff*self.Rbold*self.PermGroFac**(-self.CRRA)* + EndOfPrdvP = np.sum(self.DiscFacEff*self.Reff*self.PermGroFac**(-self.CRRA)* self.PermShkVals_temp**(-self.CRRA)* self.vPfuncNext(self.mNrmNext)*self.ShkPrbs_temp,axis=0) return EndOfPrdvP From 5c68966370ebbeaaf855edc0ea684018d1450c73 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Mon, 13 May 2019 22:45:02 +0200 Subject: [PATCH 008/242] More changes --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 7ca455bcd..90a80a616 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -137,8 +137,8 @@ def calcRiskyShare(self): zero = sciopt.fsolve(residual, 0.5) Rshare = np.append(Rshare, zero) i_a += 1 - RshareFunc = LinearInterp(aGrid, Rshare) - return RshareFunc + RiskyShareFunc = LinearInterp(aGrid, Rshare) + return RiskyShareFunc def prepareToCalcEndOfPrdvP(self): ''' @@ -177,7 +177,7 @@ def prepareToCalcEndOfPrdvP(self): # sAtA - sAt_aNrm = self.RshareFunc(aNrmNow) + sAt_aNrm = self.RiskyShareFunc(aNrmNow) # Get cash on hand next period Rtilde = RiskyShkVals_temp - self.Rfree @@ -322,8 +322,12 @@ def solve(self): solution : ConsumerSolution The solution to the one period problem. ''' + + # First, solve the first sub-problem: the portfolio choice self.vHatP = self.prepareToCalcRiskyShare() - self.RshareFunc = self.calcRiskyShare() + self.RiskyShareFunc = self.calcRiskyShare() + + # Then solve the consumption choice given optimal portfolio choice aNrm = self.prepareToCalcEndOfPrdvP() EndOfPrdvP = self.calcEndOfPrdvP() @@ -336,7 +340,7 @@ def solve(self): # solution = self.addMPCandHumanWealth(solution) solution = PortfolioConsumerSolution(cFunc=cs_solution.cFunc, vPfunc=cs_solution.vPfunc, - RiskyShareFunc=self.RshareFunc) + RiskyShareFunc=self.RiskyShareFunc) return solution def solveConsPortfolioChoice(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, From 2f0ec5f62a1f1e0b1e0ae5211ba422f9092f34cb Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Tue, 14 May 2019 15:09:54 +0200 Subject: [PATCH 009/242] Remove dead code. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 90a80a616..410124737 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -346,9 +346,6 @@ def solve(self): def solveConsPortfolioChoice(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyShareCount): -# solution = solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, -# BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) - solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyShareCount) From e8a9a9650ba90076d101b9420cc882c17f56d0c3 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Thu, 16 May 2019 14:41:58 +0200 Subject: [PATCH 010/242] Drop Choice in names. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 410124737..5fbfcac15 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -9,7 +9,7 @@ from copy import deepcopy import numpy as np -class PortfolioConsumerSolution(Solution): +class PortfolioSolution(Solution): distance_criteria = ['vPfunc'] def __init__(self, cFunc=None, vFunc=None, @@ -36,13 +36,13 @@ def __init__(self, cFunc=None, vFunc=None, # self.MPCmin = MPCmin # self.MPCmax = MPCmax -class PortfolioChoiceConsumerType(IndShockConsumerType): +class PortfolioConsumerType(IndShockConsumerType): time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyShareCount'] cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period vFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # This is overwritten RiskyShareFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # c=m in terminal period - solution_terminal_ = PortfolioConsumerSolution(cFunc = cFunc_terminal_, RiskyShareFunc = RiskyShareFunc_terminal_, + solution_terminal_ = PortfolioSolution(cFunc = cFunc_terminal_, RiskyShareFunc = RiskyShareFunc_terminal_, vFunc = vFunc_terminal_, mNrmMin=0.0, hNrm=None, MPCmin=None, MPCmax=None) @@ -52,11 +52,11 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): verbose=verbose, quiet=quiet, **kwds) if self.BoroCnstArt is not 0.0: - print("Setting BoroCnstArt to 0.0 as this is required by PortfolioChoiceConsumerType.") + print("Setting BoroCnstArt to 0.0 as this is required by PortfolioConsumerType.") self.BoroCnstArt = 0.0 # Chose specialized solver for Portfolio choice model - self.solveOnePeriod = solveConsPortfolioChoice + self.solveOnePeriod = solveConsPortfolio # Update this *once* as it's time invariant self.updateRiskyDstn() @@ -338,12 +338,12 @@ def solve(self): cs_solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) cs_solution.vPfunc(0.3) # solution = self.addMPCandHumanWealth(solution) - solution = PortfolioConsumerSolution(cFunc=cs_solution.cFunc, + solution = PortfolioSolution(cFunc=cs_solution.cFunc, vPfunc=cs_solution.vPfunc, RiskyShareFunc=self.RiskyShareFunc) return solution -def solveConsPortfolioChoice(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, +def solveConsPortfolio(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyShareCount): solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, From 0d78d732b82883373bccde134a1f42e985731573 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Wed, 22 May 2019 15:11:02 +0200 Subject: [PATCH 011/242] Initial version of portfolio shares. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 29 ++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 5fbfcac15..bbb89965b 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -1,6 +1,8 @@ # NOTE Need to decide on Rshare vs RiskyShare import math import scipy.optimize as sciopt +import scipy.integrate +import scipy.stats from HARK import Solution, NullFunc from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType,solveConsIndShock, ConsIndShockSolver, MargValueFunc, ConsumerSolution from HARK.utilities import approxLognormal, combineIndepDstns @@ -9,6 +11,33 @@ from copy import deepcopy import numpy as np +def PerfForesightPortfolioShare(self): + PortfolioObjective = lambda share: PerfForesightPortfolioObjective(share, + self.Rfree, + self.RiskyAvg, + rho=self.CRRA, + sigma=self.RiskyStd) + return sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, np.inf), method='bounded') + + +def PerfForesightPortfolioIntegrand(share, R0, RiskyAvg, rho=4.0, sigma=0.15): + r0 = np.log(R0) + phi = np.log(RiskyAvg)-r0 + mu = r0+phi-(sigma**2)/2 + + sharedobjective = lambda r: (R0+share*(r-R0))**(1-rho) + pdf = lambda r: scipy.stats.lognorm.pdf(r, s=sigma, scale=np.exp(mu)) + + integrand = lambda r: sharedobjective(r)*pdf(r) + return integrand + +def PerfForesightPortfolioObjective(share, R0, RiskyAvg, rho=4.0, sigma=0.15): + integrand = PerfForesightPortfolioIntegrand(share, R0, RiskyAvg, rho, sigma) + a = 0.0 + b = np.inf + return ((1-rho)**-1)*scipy.integrate.quad(integrand, a, b)[0] + + class PortfolioSolution(Solution): distance_criteria = ['vPfunc'] From 62085a820e8cf424a82c64c86173483ffef82e06 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Wed, 22 May 2019 19:38:14 +0200 Subject: [PATCH 012/242] More work on limit. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 104 +++++++++++++++---- 1 file changed, 84 insertions(+), 20 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index bbb89965b..5cfaf4430 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -10,17 +10,42 @@ from copy import deepcopy import numpy as np - -def PerfForesightPortfolioShare(self): - PortfolioObjective = lambda share: PerfForesightPortfolioObjective(share, - self.Rfree, - self.RiskyAvg, - rho=self.CRRA, - sigma=self.RiskyStd) - return sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, np.inf), method='bounded') - - -def PerfForesightPortfolioIntegrand(share, R0, RiskyAvg, rho=4.0, sigma=0.15): +# +# def CambellVicApprox() +# +# # We assume fixed distribution of risky shocks throughout, so we can +# # calculate the limiting solution once and for all. +# phi = math.log(self.RiskyAvg/self.Rfree) +# RiskyAvgSqrd = self.RiskyAvg**2 +# RiskyVar = self.RiskyStd**2 +# # +# # mu = math.log(self.RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) +# # sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) +# # +# # RiskyShareLimit = phi/(self.CRRA*sigma**2) + + +def PerfForesightLogNormalPortfolioShare(obj): + PortfolioObjective = lambda share: PerfForesightLogNormalPortfolioObjective(share, + obj.Rfree, + obj.RiskyAvg, + obj.CRRA, + obj.RiskyStd) + return sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x + +def PerfForesightDiscretePortfolioShare(obj): + PortfolioObjective = lambda share: PerfForesightDiscretePortfolioObjective(share, + obj.Rfree, + obj.RiskyDstn, + obj.CRRA) + return sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x + + +# Switch here based on knowledge about risky. +# It can either be "discrete" in which case it is only the number of draws that +# are used, or it can be continuous in which case bounds and a pdf has to be supplied. + +def PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, rho, sigma): r0 = np.log(R0) phi = np.log(RiskyAvg)-r0 mu = r0+phi-(sigma**2)/2 @@ -31,11 +56,19 @@ def PerfForesightPortfolioIntegrand(share, R0, RiskyAvg, rho=4.0, sigma=0.15): integrand = lambda r: sharedobjective(r)*pdf(r) return integrand -def PerfForesightPortfolioObjective(share, R0, RiskyAvg, rho=4.0, sigma=0.15): - integrand = PerfForesightPortfolioIntegrand(share, R0, RiskyAvg, rho, sigma) +def PerfForesightLogNormalPortfolioObjective(share, R0, RiskyAvg, rho, sigma): + integrand = PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, rho, sigma) a = 0.0 - b = np.inf - return ((1-rho)**-1)*scipy.integrate.quad(integrand, a, b)[0] + b = 5.0 + return -((1-rho)**-1)*scipy.integrate.quad(integrand, a, b)[0] + + +def PerfForesightDiscretePortfolioObjective(share, R0, RiskyDstn, rho): + + vals = (R0+share*(RiskyDstn[1]-R0))**(1-rho) + weights = RiskyDstn[0] + + return -((1-rho)**-1)*np.dot(vals, weights) class PortfolioSolution(Solution): @@ -67,7 +100,7 @@ def __init__(self, cFunc=None, vFunc=None, class PortfolioConsumerType(IndShockConsumerType): - time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyShareCount'] + time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyShareCount', 'RiskyShareLimit'] cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period vFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # This is overwritten RiskyShareFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # c=m in terminal period @@ -84,12 +117,15 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): print("Setting BoroCnstArt to 0.0 as this is required by PortfolioConsumerType.") self.BoroCnstArt = 0.0 + # Chose specialized solver for Portfolio choice model self.solveOnePeriod = solveConsPortfolio # Update this *once* as it's time invariant self.updateRiskyDstn() + self.RiskyShareLimit = PerfForesightDiscretePortfolioShare(self) + def updateRiskyDstn(self): # Note, we expect the thing in self.approxRiskyDstn to be a funtion of # the number of nodes only! @@ -98,7 +134,7 @@ def updateRiskyDstn(self): class ConsIndShockPortfolioSolver(ConsIndShockSolver): def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyShareCount): + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyShareCount, RiskyShareLimit): ConsIndShockSolver.__init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, @@ -106,6 +142,7 @@ def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, # Store the Risky asset shock distribution self.RiskyDstn = RiskyDstn + self.RiskyShareLimit = RiskyShareLimit # Store the number of grid points used approximate the FOC in the port- # folio sub-problem. self.RiskyShareCount = RiskyShareCount @@ -166,7 +203,7 @@ def calcRiskyShare(self): zero = sciopt.fsolve(residual, 0.5) Rshare = np.append(Rshare, zero) i_a += 1 - RiskyShareFunc = LinearInterp(aGrid, Rshare) + RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit, slope_limit=0.0) return RiskyShareFunc def prepareToCalcEndOfPrdvP(self): @@ -373,10 +410,10 @@ def solve(self): return solution def solveConsPortfolio(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, - BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyShareCount): + BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyShareCount, RiskyShareLimit): solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyShareCount) + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyShareCount, RiskyShareLimit) solver.prepareToSolve() # Do some preparatory work @@ -395,6 +432,33 @@ def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): return lambda RiskyCount: approxLognormal(RiskyCount, mu=mu, sigma=sigma) +class LogNormalPortfolioConsumerType(PortfolioConsumerType): + + time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyShareCount', 'RiskyShareLimit'] + cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period + vFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # This is overwritten + RiskyShareFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # c=m in terminal period + solution_terminal_ = PortfolioSolution(cFunc = cFunc_terminal_, RiskyShareFunc = RiskyShareFunc_terminal_, + vFunc = vFunc_terminal_, mNrmMin=0.0, hNrm=None, + MPCmin=None, MPCmax=None) + + def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): + + PortfolioConsumerType.__init__(self,cycles=cycles, time_flow=time_flow, + verbose=verbose, quiet=quiet, **kwds) + + self.approxRiskyDstn = RiskyDstnFactory(RiskyAvg=self.RiskyAvg, RiskyStd=self.RiskyStd) + + # Update this *once* as it's time invariant + self.updateRiskyDstn() + + + self.RiskyShareLimit = PerfForesightLogNormalPortfolioShare(self) + + def updateRiskyDstn(self): + # Note, we expect the thing in self.approxRiskyDstn to be a funtion of + # the number of nodes only! + self.RiskyDstn = self.approxRiskyDstn(self.RiskyCount) From 4fd04be81e610475211cc236d24b082dc1be2fa3 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Wed, 22 May 2019 21:33:08 +0200 Subject: [PATCH 013/242] Limiting share. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 5cfaf4430..dc92ba27d 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -46,12 +46,13 @@ def PerfForesightDiscretePortfolioShare(obj): # are used, or it can be continuous in which case bounds and a pdf has to be supplied. def PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, rho, sigma): - r0 = np.log(R0) - phi = np.log(RiskyAvg)-r0 - mu = r0+phi-(sigma**2)/2 - + #r0 = np.log(R0) + #phi = np.log(RiskyAvg)-r0 + #mu = r0+phi-(sigma**2)/2 + muNorm = np.log(RiskyAvg/np.sqrt(1+sigma**2/RiskyAvg**2)) + sigmaNorm = np.sqrt(np.log(1+sigma**2/RiskyAvg**2)) sharedobjective = lambda r: (R0+share*(r-R0))**(1-rho) - pdf = lambda r: scipy.stats.lognorm.pdf(r, s=sigma, scale=np.exp(mu)) + pdf = lambda r: scipy.stats.lognorm.pdf(r, s=sigmaNorm, scale=np.exp(muNorm)) integrand = lambda r: sharedobjective(r)*pdf(r) return integrand @@ -71,6 +72,7 @@ def PerfForesightDiscretePortfolioObjective(share, R0, RiskyDstn, rho): return -((1-rho)**-1)*np.dot(vals, weights) + class PortfolioSolution(Solution): distance_criteria = ['vPfunc'] @@ -203,7 +205,7 @@ def calcRiskyShare(self): zero = sciopt.fsolve(residual, 0.5) Rshare = np.append(Rshare, zero) i_a += 1 - RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit, slope_limit=0.0) + RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit) return RiskyShareFunc def prepareToCalcEndOfPrdvP(self): From 9017b54e61d0028a9197fb9ea717d92a0ad40ecf Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 30 May 2019 23:34:37 -0400 Subject: [PATCH 014/242] Add liquidity constraints to perfect foresight model PerfForesightConsumerType can now handle an artificial borrowing constraint, like in all our other models. Currently doesn't calculate value function if BoroCnstArt is not None, as this requires additional work to compute. When this is merged, it will be a breaking change for all existing instances of PerfForesightConsumerType, as they will now require BoroCnstArt and aXtraCount to be attributes. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 171 ++++++++++++++----- HARK/ConsumptionSaving/ConsumerParameters.py | 4 + 2 files changed, 133 insertions(+), 42 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index b8b7f17ef..deab8d8a7 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -24,7 +24,7 @@ from HARK import AgentType, Solution, NullFunc, HARKobject from HARK.utilities import warnings # Because of "patch" to warnings modules from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp -from HARK.simulation import drawDiscrete, drawBernoulli, drawLognormal, drawUniform +from HARK.simulation import drawDiscrete, drawLognormal, drawUniform from HARK.utilities import approxMeanOneLognormal, addDiscreteOutcomeConstantMean,\ combineIndepDstns, makeGridExpMult, CRRAutility, CRRAutilityP, \ CRRAutilityPP, CRRAutilityP_inv, CRRAutility_invP, CRRAutility_inv, \ @@ -316,7 +316,7 @@ class ConsPerfForesightSolver(object): A class for solving a one period perfect foresight consumption-saving problem. An instance of this class is created by the function solvePerfForesight in each period. ''' - def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac): + def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount): ''' Constructor for a new ConsPerfForesightSolver. @@ -335,20 +335,25 @@ def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac): Risk free interest factor on end-of-period assets. PermGroFac : float Expected permanent income growth factor at the end of this period. + BoroCnstArt : float or None + Artificial borrowing constraint, as a multiple of permanent income. + Can be None, indicating no artificial constraint. + aXtraCount : int + Maximum number of kink points to allow in the consumption function; + additional points will be thrown out. Returns: ---------- None ''' # We ask that HARK users define single-letter variables they use in a dictionary - # attribute called notation. - # Do that first. + # attribute called notation. Do that first. self.notation = {'a': 'assets after all actions', 'm': 'market resources at decision time', 'c': 'consumption'} - self.assignParameters(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac) + self.assignParameters(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount) - def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac): + def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount): ''' Saves necessary parameters as attributes of self for use by other methods. @@ -367,10 +372,16 @@ def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac): Risk free interest factor on end-of-period assets. PermGroFac : float Expected permanent income growth factor at the end of this period. + BoroCnstArt : float or None + Artificial borrowing constraint, as a multiple of permanent income. + Can be None, indicating no artificial constraint. + aXtraCount : int + Maximum number of kink points to allow in the consumption function; + additional points will be thrown out. Returns ------- - none + None ''' self.solution_next = solution_next self.DiscFac = DiscFac @@ -378,6 +389,8 @@ def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac): self.CRRA = CRRA self.Rfree = Rfree self.PermGroFac = PermGroFac + self.aXtraCount = aXtraCount + self.BoroCnstArt = BoroCnstArt def defUtilityFuncs(self): ''' @@ -386,11 +399,11 @@ def defUtilityFuncs(self): Parameters ---------- - none + None Returns ------- - none + None ''' self.u = lambda c : utility(c,gam=self.CRRA) # utility function self.uP = lambda c : utilityP(c,gam=self.CRRA) # marginal utility function @@ -402,15 +415,18 @@ def defValueFuncs(self): Parameters ---------- - none + None Returns ------- - none + None ''' - MPCnvrs = self.MPC**(-self.CRRA/(1.0-self.CRRA)) - vFuncNvrs = LinearInterp(np.array([self.mNrmMin, self.mNrmMin+1.0]),np.array([0.0, MPCnvrs])) - self.vFunc = ValueFunc(vFuncNvrs,self.CRRA) + if self.BoroCnstArt is None: + MPCnvrs = self.MPCmin**(-self.CRRA/(1.0-self.CRRA)) + vFuncNvrs = LinearInterp(np.array([self.mNrmMin, self.mNrmMin+1.0]),np.array([0.0, MPCnvrs])) + self.vFunc = ValueFunc(vFuncNvrs,self.CRRA) + else: # TODO: Add value function for constrained version; it's not trivial. + self.vFunc = NullFunc() self.vPfunc = MargValueFunc(self.cFunc,self.CRRA) def makePFcFunc(self): @@ -419,24 +435,86 @@ def makePFcFunc(self): Parameters ---------- - none + None Returns ------- - none + None ''' - # Calculate human wealth this period (and lower bound of m) + # Calculate human wealth this period self.hNrmNow = (self.PermGroFac/self.Rfree)*(self.solution_next.hNrm + 1.0) - self.mNrmMin = -self.hNrmNow - # Calculate the (constant) marginal propensity to consume + + # Calculate the lower bound of the marginal propensity to consume PatFac = ((self.Rfree*self.DiscFacEff)**(1.0/self.CRRA))/self.Rfree - self.MPC = 1.0/(1.0 + PatFac/self.solution_next.MPCmin) - # Construct the consumption function - self.cFunc = LinearInterp([self.mNrmMin, self.mNrmMin+1.0],[0.0, self.MPC]) - # Add two attributes to enable calculation of steady state market resources + self.MPCmin = 1.0/(1.0 + PatFac/self.solution_next.MPCmin) + + # Extract the discrete kink points in next period's consumption function; + # don't take the last one, as it only defines the extrapolation and is not a kink. + mNrmNext = self.solution_next.cFunc.x_list[:-1] + cNrmNext = self.solution_next.cFunc.y_list[:-1] + + # Calculate the end-of-period asset values that would reach those kink points + # next period, then invert the first order condition to get consumption. Then + # find the endogenous gridpoint (kink point) today that corresponds to each kink + aNrmNow = (self.PermGroFac/self.Rfree)*(mNrmNext-1.0) + cNrmNow = (self.DiscFacEff*self.Rfree)**(-1./self.CRRA)*(self.PermGroFac*cNrmNext) + mNrmNow = aNrmNow + cNrmNow + + # Add an additional point to the list of gridpoints for the extrapolation, + # using the new value of the lower bound of the MPC. + mNrmNow = np.append(mNrmNow, mNrmNow[-1] + 1.0) + cNrmNow = np.append(cNrmNow, cNrmNow[-1] + self.MPCmin) + + # If the artificial borrowing constraint binds, combine the constrained and + # unconstrained consumption functions. + if self.BoroCnstArt > mNrmNow[0]: + # Find the highest index where constraint binds + cNrmCnst = mNrmNow - self.BoroCnstArt + CnstBinds = cNrmCnst < cNrmNow + idx = np.where(CnstBinds)[0][-1] + + if idx < (mNrmNow.size-1): + # If it is not the *very last* index, find the the critical level + # of mNrm where the artificial borrowing contraint begins to bind. + d0 = cNrmNow[idx] - cNrmCnst[idx] + d1 = cNrmCnst[idx+1] - cNrmNow[idx+1] + m0 = mNrmNow[idx] + m1 = mNrmNow[idx+1] + alpha = d0/(d0 + d1) + mCrit = m0 + alpha*(m1 - m0) + + # Adjust the grids of mNrm and cNrm to account for the borrowing constraint. + cCrit = mCrit - self.BoroCnstArt + mNrmNow = np.concatenate(([self.BoroCnstArt, mCrit], mNrmNow[(idx+1):])) + cNrmNow = np.concatenate(([0., cCrit], cNrmNow[(idx+1):])) + + else: + # If it *is* the very last index, then there are only three points + # that characterize the consumption function: the artificial borrowing + # constraint, the constraint kink, and the extrapolation point. + mXtra = cNrmNow[-1] - cNrmCnst[-1]/(1.0 - self.MPCmin) + mCrit = mNrmNow[-1] + mXtra + cCrit = mCrit - self.BoroCnstArt + mNrmNow = np.array([self.BoroCnstArt, mCrit, mCrit + 1.0]) + cNrmNow = np.array([0., cCrit, cCrit + self.MPCmin]) + + # If the mNrm and cNrm grids have become too large, throw out the last + # kink point, being sure to adjust the extrapolation. + if mNrmNow.size > self.aXtraCount: + mNrmNow = np.concatenate((mNrmNow[:-2], [mNrmNow[-3] + 1.0])) + cNrmNow = np.concatenate((cNrmNow[:-2], [cNrmNow[-3] + self.MPCmin])) + + # Construct the consumption function as a linear interpolation. + self.cFunc = LinearInterp(mNrmNow, cNrmNow) + + # Calculate the upper bound of the MPC as the slope of the bottom segment. + self.MPCmax = (cNrmNow[1] - cNrmNow[0]) / (mNrmNow[1] - mNrmNow[0]) + + # Add two attributes to enable calculation of steady state market resources. self.ExIncNext = 1.0 # Perfect foresight income of 1 - self.mNrmMinNow = self.mNrmMin # Relabeling for compatibility with addSSmNrm - + self.mNrmMinNow = mNrmNow[0] # Relabeling for compatibility with addSSmNrm + + def addSSmNrm(self,solution): ''' Finds steady state (normalized) market resources and adds it to the @@ -474,7 +552,7 @@ def solve(self): Parameters ---------- - none + None Returns ------- @@ -486,13 +564,13 @@ def solve(self): self.makePFcFunc() self.defValueFuncs() solution = ConsumerSolution(cFunc=self.cFunc, vFunc=self.vFunc, vPfunc=self.vPfunc, - mNrmMin=self.mNrmMin, hNrm=self.hNrmNow, - MPCmin=self.MPC, MPCmax=self.MPC) - #solution = self.addSSmNrm(solution) + mNrmMin=self.mNrmMinNow, hNrm=self.hNrmNow, + MPCmin=self.MPCmin, MPCmax=self.MPCmax) + solution = self.addSSmNrm(solution) return solution -def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac): +def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount): ''' Solves a single period consumption-saving problem for a consumer with perfect foresight. @@ -511,15 +589,22 @@ def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac): Risk free interest factor on end-of-period assets. PermGroFac : float Expected permanent income growth factor at the end of this period. + BoroCnstArt : float or None + Artificial borrowing constraint, as a multiple of permanent income. + Can be None, indicating no artificial constraint. + aXtraCount : int + Maximum number of kink points to allow in the consumption function; + additional points will be thrown out. Returns ------- - solution : ConsumerSolution - The solution to this period's problem. + solution_now : ConsumerSolution + The solution to this period's problem. ''' - solver = ConsPerfForesightSolver(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac) - solution = solver.solve() - return solution + + solver = ConsPerfForesightSolver(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount) + solution_now = solver.solve() + return solution_now ############################################################################### @@ -624,10 +709,9 @@ def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, none ''' ConsPerfForesightSolver.assignParameters(self,solution_next,DiscFac,LivPrb, - CRRA,Rfree,PermGroFac) - self.BoroCnstArt = BoroCnstArt - self.IncomeDstn = IncomeDstn + CRRA,Rfree,PermGroFac,BoroCnstArt,None) self.aXtraGrid = aXtraGrid + self.IncomeDstn = IncomeDstn self.vFuncBool = vFuncBool self.CubicBool = CubicBool @@ -1453,8 +1537,8 @@ class PerfForesightConsumerType(AgentType): ''' A perfect foresight consumer type who has no uncertainty other than mortality. His problem is defined by a coefficient of relative risk aversion, intertemporal - discount factor, interest factor, and time sequences of the permanent income - growth rate and survival probability. + discount factor, interest factor, an artificial borrowing constraint (maybe) + and time sequences of the permanent income growth rate and survival probability. ''' # Define some universal values for all consumer types cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period @@ -1463,7 +1547,7 @@ class PerfForesightConsumerType(AgentType): vFunc = vFunc_terminal_, mNrmMin=0.0, hNrm=0.0, MPCmin=1.0, MPCmax=1.0) time_vary_ = ['LivPrb','PermGroFac'] - time_inv_ = ['CRRA','Rfree','DiscFac'] + time_inv_ = ['CRRA','Rfree','DiscFac','aXtraCount','BoroCnstArt'] poststate_vars_ = ['aNrmNow','pLvlNow'] shock_vars_ = [] @@ -2581,6 +2665,9 @@ def main(): KinkyExample.initializeSim() KinkyExample.simulate() + return PFexample if __name__ == '__main__': - main() + PFexample = main() + + diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index dbeca20e0..601712efe 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -16,6 +16,8 @@ DiscFac = 0.96 # Intertemporal discount factor LivPrb = [0.98] # Survival probability PermGroFac = [1.01] # Permanent income growth factor +BoroCnstArt = 0.0 # Artificial borrowing constraint +aXtraCount = 200 # Maximum number of grid points to allow in cFunc AgentCount = 10000 # Number of agents of this type (only matters for simulation) aNrmInitMean = 0.0 # Mean of log initial assets (only matters for simulation) aNrmInitStd = 1.0 # Standard deviation of log initial assets (only for simulation) @@ -31,6 +33,8 @@ 'DiscFac': DiscFac, 'LivPrb': LivPrb, 'PermGroFac': PermGroFac, + 'BoroCnstArt': BoroCnstArt, + 'aXtraCount': aXtraCount, 'AgentCount': AgentCount, 'aNrmInitMean' : aNrmInitMean, 'aNrmInitStd' : aNrmInitStd, From 14b682d517a6b3d137bc463f397a8d41ad27d125 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 30 May 2019 23:36:17 -0400 Subject: [PATCH 015/242] Fix one line in distance metric The "universal distance metric" had a bug in one line that had never come up. If the two objects being compared have a shape, but the shapes are not the same, then it tried to take the supnorm between them; this fails because arithmetic is not defined on tuples. Fix simply converts the shape tuples into arrays. --- HARK/core.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/core.py b/HARK/core.py index 55b7136d3..4b92ebcd9 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -62,7 +62,7 @@ def distanceMetric(thing_A, thing_B): if thing_A.shape == thing_B.shape: distance = np.max(abs(thing_A - thing_B)) else: - distance = np.max(abs(thing_A.shape - thing_B.shape)) + distance = np.max(abs(np.array(thing_A.shape) - np.array(thing_B.shape))) # If none of the above cases, but the objects are of the same class, call # the distance method of one on the other elif thing_A.__class__.__name__ == thing_B.__class__.__name__: From c0b557c67261bcd19216f835b4fd1a7499553d99 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 30 May 2019 23:46:04 -0400 Subject: [PATCH 016/242] Fix typo in PerfForesight vFunc Forgot that I eliminated the redundant mNrmMin (only use mNrmMinNow). Now fixed. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index deab8d8a7..d781f94ce 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -423,7 +423,7 @@ def defValueFuncs(self): ''' if self.BoroCnstArt is None: MPCnvrs = self.MPCmin**(-self.CRRA/(1.0-self.CRRA)) - vFuncNvrs = LinearInterp(np.array([self.mNrmMin, self.mNrmMin+1.0]),np.array([0.0, MPCnvrs])) + vFuncNvrs = LinearInterp(np.array([self.mNrmMinNow, self.mNrmMinNow+1.0]),np.array([0.0, MPCnvrs])) self.vFunc = ValueFunc(vFuncNvrs,self.CRRA) else: # TODO: Add value function for constrained version; it's not trivial. self.vFunc = NullFunc() From 31c14b12017baf37b19d3b12267197c8ff52474f Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 30 May 2019 23:54:17 -0400 Subject: [PATCH 017/242] Fix comparison of float and None Apparently Py3 doesn't like to compare float and None, but Py2 is fine with this (and is correct: None is neither greater than, equal to, or less than any real number). Now fixed. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index d781f94ce..4730cc1b5 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -441,6 +441,12 @@ def makePFcFunc(self): ------- None ''' + # Use a local value of BoroCnstArt to prevent comparing None and float below. + if self.BoroCnstArt is None: + BoroCnstArt = -np.inf + else: + BoroCnstArt = self.BoroCnstArt + # Calculate human wealth this period self.hNrmNow = (self.PermGroFac/self.Rfree)*(self.solution_next.hNrm + 1.0) @@ -467,9 +473,9 @@ def makePFcFunc(self): # If the artificial borrowing constraint binds, combine the constrained and # unconstrained consumption functions. - if self.BoroCnstArt > mNrmNow[0]: + if BoroCnstArt > mNrmNow[0]: # Find the highest index where constraint binds - cNrmCnst = mNrmNow - self.BoroCnstArt + cNrmCnst = mNrmNow - BoroCnstArt CnstBinds = cNrmCnst < cNrmNow idx = np.where(CnstBinds)[0][-1] @@ -484,8 +490,8 @@ def makePFcFunc(self): mCrit = m0 + alpha*(m1 - m0) # Adjust the grids of mNrm and cNrm to account for the borrowing constraint. - cCrit = mCrit - self.BoroCnstArt - mNrmNow = np.concatenate(([self.BoroCnstArt, mCrit], mNrmNow[(idx+1):])) + cCrit = mCrit - BoroCnstArt + mNrmNow = np.concatenate(([BoroCnstArt, mCrit], mNrmNow[(idx+1):])) cNrmNow = np.concatenate(([0., cCrit], cNrmNow[(idx+1):])) else: @@ -494,8 +500,8 @@ def makePFcFunc(self): # constraint, the constraint kink, and the extrapolation point. mXtra = cNrmNow[-1] - cNrmCnst[-1]/(1.0 - self.MPCmin) mCrit = mNrmNow[-1] + mXtra - cCrit = mCrit - self.BoroCnstArt - mNrmNow = np.array([self.BoroCnstArt, mCrit, mCrit + 1.0]) + cCrit = mCrit - BoroCnstArt + mNrmNow = np.array([BoroCnstArt, mCrit, mCrit + 1.0]) cNrmNow = np.array([0., cCrit, cCrit + self.MPCmin]) # If the mNrm and cNrm grids have become too large, throw out the last From f75083aee5b203295df8bb74949ea853fb454f84 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 30 May 2019 23:57:28 -0400 Subject: [PATCH 018/242] Restore default PFexample to no borrowing constraint Now everything will run like before. --- HARK/ConsumptionSaving/ConsumerParameters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 601712efe..e38cd2091 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -16,7 +16,7 @@ DiscFac = 0.96 # Intertemporal discount factor LivPrb = [0.98] # Survival probability PermGroFac = [1.01] # Permanent income growth factor -BoroCnstArt = 0.0 # Artificial borrowing constraint +BoroCnstArt = None # Artificial borrowing constraint aXtraCount = 200 # Maximum number of grid points to allow in cFunc AgentCount = 10000 # Number of agents of this type (only matters for simulation) aNrmInitMean = 0.0 # Mean of log initial assets (only matters for simulation) From d4af60e3be402028fa4b136dc85dfdb214eed362 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Wed, 12 Jun 2019 23:01:56 -0400 Subject: [PATCH 019/242] Rename aXtraCount to MaxKinks in PerfForesight Renamed model input to be more direct, rather than use name for similar (but not same) thing in other models. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 29 +++++++++++--------- HARK/ConsumptionSaving/ConsumerParameters.py | 6 ++-- 2 files changed, 19 insertions(+), 16 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 4730cc1b5..489ac3a71 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -316,7 +316,7 @@ class ConsPerfForesightSolver(object): A class for solving a one period perfect foresight consumption-saving problem. An instance of this class is created by the function solvePerfForesight in each period. ''' - def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount): + def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks): ''' Constructor for a new ConsPerfForesightSolver. @@ -338,9 +338,10 @@ def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt BoroCnstArt : float or None Artificial borrowing constraint, as a multiple of permanent income. Can be None, indicating no artificial constraint. - aXtraCount : int + MaxKinks : int Maximum number of kink points to allow in the consumption function; - additional points will be thrown out. + additional points will be thrown out. Only relevant in infinite + horizon model with artificial borrowing constraint. Returns: ---------- @@ -351,9 +352,9 @@ def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt self.notation = {'a': 'assets after all actions', 'm': 'market resources at decision time', 'c': 'consumption'} - self.assignParameters(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount) + self.assignParameters(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks) - def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount): + def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks): ''' Saves necessary parameters as attributes of self for use by other methods. @@ -375,7 +376,7 @@ def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,Bor BoroCnstArt : float or None Artificial borrowing constraint, as a multiple of permanent income. Can be None, indicating no artificial constraint. - aXtraCount : int + MaxKinks : int Maximum number of kink points to allow in the consumption function; additional points will be thrown out. @@ -389,7 +390,7 @@ def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,Bor self.CRRA = CRRA self.Rfree = Rfree self.PermGroFac = PermGroFac - self.aXtraCount = aXtraCount + self.MaxKinks = MaxKinks self.BoroCnstArt = BoroCnstArt def defUtilityFuncs(self): @@ -506,7 +507,7 @@ def makePFcFunc(self): # If the mNrm and cNrm grids have become too large, throw out the last # kink point, being sure to adjust the extrapolation. - if mNrmNow.size > self.aXtraCount: + if mNrmNow.size > self.MaxKinks: mNrmNow = np.concatenate((mNrmNow[:-2], [mNrmNow[-3] + 1.0])) cNrmNow = np.concatenate((cNrmNow[:-2], [cNrmNow[-3] + self.MPCmin])) @@ -576,7 +577,7 @@ def solve(self): return solution -def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount): +def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks): ''' Solves a single period consumption-saving problem for a consumer with perfect foresight. @@ -598,9 +599,10 @@ def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCn BoroCnstArt : float or None Artificial borrowing constraint, as a multiple of permanent income. Can be None, indicating no artificial constraint. - aXtraCount : int + MaxKinks : int Maximum number of kink points to allow in the consumption function; - additional points will be thrown out. + additional points will be thrown out. Only relevant in infinite horizon + models with artificial borrowing constraint. Returns ------- @@ -608,7 +610,7 @@ def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCn The solution to this period's problem. ''' - solver = ConsPerfForesightSolver(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraCount) + solver = ConsPerfForesightSolver(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks) solution_now = solver.solve() return solution_now @@ -1553,7 +1555,7 @@ class PerfForesightConsumerType(AgentType): vFunc = vFunc_terminal_, mNrmMin=0.0, hNrm=0.0, MPCmin=1.0, MPCmax=1.0) time_vary_ = ['LivPrb','PermGroFac'] - time_inv_ = ['CRRA','Rfree','DiscFac','aXtraCount','BoroCnstArt'] + time_inv_ = ['CRRA','Rfree','DiscFac','MaxKinks','BoroCnstArt'] poststate_vars_ = ['aNrmNow','pLvlNow'] shock_vars_ = [] @@ -1851,6 +1853,7 @@ class IndShockConsumerType(PerfForesightConsumerType): period assets, and an artificial borrowing constraint. ''' time_inv_ = PerfForesightConsumerType.time_inv_ + ['BoroCnstArt','vFuncBool','CubicBool'] + time_inv_.remove('MaxKinks') # This is in the PerfForesight model but not ConsIndShock shock_vars_ = ['PermShkNow','TranShkNow'] def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index e38cd2091..134e6a6f9 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -16,8 +16,8 @@ DiscFac = 0.96 # Intertemporal discount factor LivPrb = [0.98] # Survival probability PermGroFac = [1.01] # Permanent income growth factor -BoroCnstArt = None # Artificial borrowing constraint -aXtraCount = 200 # Maximum number of grid points to allow in cFunc +BoroCnstArt = False # Artificial borrowing constraint +MaxKinks = 400 # Maximum number of grid points to allow in cFunc (should be large) AgentCount = 10000 # Number of agents of this type (only matters for simulation) aNrmInitMean = 0.0 # Mean of log initial assets (only matters for simulation) aNrmInitStd = 1.0 # Standard deviation of log initial assets (only for simulation) @@ -34,7 +34,7 @@ 'LivPrb': LivPrb, 'PermGroFac': PermGroFac, 'BoroCnstArt': BoroCnstArt, - 'aXtraCount': aXtraCount, + 'MaxKinks': MaxKinks, 'AgentCount': AgentCount, 'aNrmInitMean' : aNrmInitMean, 'aNrmInitStd' : aNrmInitStd, From be8b5fea84fa7e36717c979b3475cf7900dcf2de Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Wed, 12 Jun 2019 23:19:57 -0400 Subject: [PATCH 020/242] Add default values for BoroCnstArt and MaxKinks If BoroCnstArt is not specified in PerfForesight model, preSolve assumes user meant no artificial borrowing constraint (as the model has always been until now). If BoroCnstArt is specified (not None) *and* it's an infinite horizon model *and* MaxKinks is not specified, then an AttributeError is raised to notify the user. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 39 +++++++++++++------- HARK/ConsumptionSaving/ConsumerParameters.py | 4 +- 2 files changed, 28 insertions(+), 15 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 489ac3a71..3e8dbb1a3 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1591,7 +1591,20 @@ def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds): def preSolve(self): - self.updateSolutionTerminal() + self.updateSolutionTerminal() # Solve the terminal period problem + + # Fill in BoroCnstArt and MaxKinks if they're not specified or are irrelevant. + if not hasattr(self,'BoroCnstArt'): # If no borrowing constraint specified... + self.BoroCnstArt = None # ...assume the user wanted none + if not hasattr(self,'MaxKinks'): + if self.cycles > 0: # If it's not an infinite horizon model... + self.MaxKinks = np.inf # ...there's no need to set MaxKinks + elif self.BoroCnstArt is None: # If there's no borrowing constraint... + self.MaxKinks = np.inf # ...there's no need to set MaxKinks + else: + raise(AttributeError('PerfForesightConsumerType requires the attribute MaxKinks to be specified when BoroCnstArt is not None and cycles == 0.')) + + def updateSolutionTerminal(self): ''' @@ -2532,7 +2545,7 @@ def constructAssetsGrid(parameters): def main(): import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import plotFuncsDer, plotFuncs - from time import clock + from time import time mystr = lambda number : "{:.4f}".format(number) do_simulation = True @@ -2541,15 +2554,15 @@ def main(): PFexample = PerfForesightConsumerType(**Params.init_perfect_foresight) PFexample.cycles = 0 # Make this type have an infinite horizon - start_time = clock() + start_time = time() PFexample.solve() - end_time = clock() + end_time = time() print('Solving a perfect foresight consumer took ' + mystr(end_time-start_time) + ' seconds.') PFexample.unpackcFunc() PFexample.timeFwd() # Plot the perfect foresight consumption function - print('Linear consumption function:') + print('Perfect foresight consumption function:') mMin = PFexample.solution[0].mNrmMin plotFuncs(PFexample.cFunc[0],mMin,mMin+10) @@ -2565,9 +2578,9 @@ def main(): IndShockExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks) IndShockExample.cycles = 0 # Make this type have an infinite horizon - start_time = clock() + start_time = time() IndShockExample.solve() - end_time = clock() + end_time = time() print('Solving a consumer with idiosyncratic shocks took ' + mystr(end_time-start_time) + ' seconds.') IndShockExample.unpackcFunc() IndShockExample.timeFwd() @@ -2603,9 +2616,9 @@ def main(): LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once - start_time = clock() + start_time = time() LifecycleExample.solve() - end_time = clock() + end_time = time() print('Solving a lifecycle consumer took ' + mystr(end_time-start_time) + ' seconds.') LifecycleExample.unpackcFunc() LifecycleExample.timeFwd() @@ -2634,9 +2647,9 @@ def main(): CyclicalExample = IndShockConsumerType(**Params.init_cyclical) CyclicalExample.cycles = 0 - start_time = clock() + start_time = time() CyclicalExample.solve() - end_time = clock() + end_time = time() print('Solving a cyclical consumer took ' + mystr(end_time-start_time) + ' seconds.') CyclicalExample.unpackcFunc() CyclicalExample.timeFwd() @@ -2659,9 +2672,9 @@ def main(): KinkyExample = KinkedRconsumerType(**Params.init_kinked_R) KinkyExample.cycles = 0 # Make the Example infinite horizon - start_time = clock() + start_time = time() KinkyExample.solve() - end_time = clock() + end_time = time() print('Solving a kinky consumer took ' + mystr(end_time-start_time) + ' seconds.') KinkyExample.unpackcFunc() print('Kinky consumption function:') diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 134e6a6f9..ba190fa34 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -16,7 +16,7 @@ DiscFac = 0.96 # Intertemporal discount factor LivPrb = [0.98] # Survival probability PermGroFac = [1.01] # Permanent income growth factor -BoroCnstArt = False # Artificial borrowing constraint +BoroCnstArt = None # Artificial borrowing constraint MaxKinks = 400 # Maximum number of grid points to allow in cFunc (should be large) AgentCount = 10000 # Number of agents of this type (only matters for simulation) aNrmInitMean = 0.0 # Mean of log initial assets (only matters for simulation) @@ -34,7 +34,7 @@ 'LivPrb': LivPrb, 'PermGroFac': PermGroFac, 'BoroCnstArt': BoroCnstArt, - 'MaxKinks': MaxKinks, + #'MaxKinks': MaxKinks, 'AgentCount': AgentCount, 'aNrmInitMean' : aNrmInitMean, 'aNrmInitStd' : aNrmInitStd, From ec3c37245910c9ec657905ced83bbd73669df729 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Wed, 12 Jun 2019 23:38:04 -0400 Subject: [PATCH 021/242] Fix broken comparison test Changes to PerfForesightModel required changes to test comparing PF and IndShock solutions when they should be the same. Test now specifies a dictionary rather than using deepcopy and swapping the solution method. --- HARK/tests/test_modelcomparisons.py | 32 +++++++++++++++++------------ 1 file changed, 19 insertions(+), 13 deletions(-) diff --git a/HARK/tests/test_modelcomparisons.py b/HARK/tests/test_modelcomparisons.py index d8b3f6ee7..5177b4cd3 100644 --- a/HARK/tests/test_modelcomparisons.py +++ b/HARK/tests/test_modelcomparisons.py @@ -11,7 +11,7 @@ import numpy as np # Bring in the HARK models we want to test -from HARK.ConsumptionSaving.ConsIndShockModel import solvePerfForesight, IndShockConsumerType +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType @@ -30,16 +30,22 @@ def setUp(self): """ # Set up and solve infinite type import HARK.ConsumptionSaving.ConsumerParameters as Params - - InfiniteType = IndShockConsumerType(**Params.init_idiosyncratic_shocks) - InfiniteType.assignParameters(LivPrb=[1.], - DiscFac=0.955, - PermGroFac=[1.], - PermShkStd=[0.], - TempShkStd=[0.], - T_total=1, T_retire=0, BoroCnstArt=None, UnempPrb=0., - cycles=0 - ) # This is what makes the type infinite horizon + + # Define a test dictionary that should have the same solution in the + # perfect foresight and idiosyncratic shocks models. + test_dictionary = deepcopy(Params.init_idiosyncratic_shocks) + test_dictionary['LivPrb'] = [1.] + test_dictionary['DiscFac'] = 0.955 + test_dictionary['PermGroFac'] = [1.] + test_dictionary['PermShkStd'] = [0.] + test_dictionary['TranShkStd'] = [0.] + test_dictionary['UnempPrb'] = 0. + test_dictionary['T_cycle'] = 1 + test_dictionary['T_retire'] = 0 + test_dictionary['BoroCnstArt'] = None + + InfiniteType = IndShockConsumerType(**test_dictionary) + InfiniteType.cycles = 0 InfiniteType.updateIncomeProcess() InfiniteType.solve() @@ -47,8 +53,8 @@ def setUp(self): InfiniteType.unpackcFunc() # Make and solve a perfect foresight consumer type with the same parameters - PerfectForesightType = deepcopy(InfiniteType) - PerfectForesightType.solveOnePeriod = solvePerfForesight + PerfectForesightType = PerfForesightConsumerType(**test_dictionary) + PerfectForesightType.cycles = 0 PerfectForesightType.solve() PerfectForesightType.unpackcFunc() From 3125e1ba386fc9c2c8b494d9b36a7c3b36489016 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Thu, 20 Jun 2019 00:31:47 +0200 Subject: [PATCH 022/242] Add errors to initializeSim and simulate calls that does honor the required workflow. --- HARK/core.py | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/HARK/core.py b/HARK/core.py index eeb105c09..cdd88cc22 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -453,6 +453,14 @@ def initializeSim(self): ------- None ''' + if not hasattr(self, 'T_sim'): + raise Exception('To initialize simulation variables it is necessary to first ' + + 'set the attribute T_sim to the largest number of observations ' + + 'you plan to simulate for each agent including re-births.') + elif self.T_sim <= 0: + raise Exception('T_sim represents the largest number of observations ' + + 'that can be simulated for an agent, and must be a positive number.') + self.resetRNG() self.t_sim = 0 all_agents = np.ones(self.AgentCount, dtype=bool) @@ -679,7 +687,7 @@ def getPostStates(self): def simulate(self, sim_periods=None): ''' - Simulates this agent type for a given number of periods (defaults to self.T_sim if no input). + Simulates this agent type for a given number of periods. Defaults to self.T_sim if no input. Records histories of attributes named in self.track_vars in attributes named varname_hist. Parameters @@ -690,6 +698,21 @@ def simulate(self, sim_periods=None): ------- None ''' + if not hasattr(self, 't_sim'): + raise Exception('It seems that the simulation variables were not initialize before calling ' + + 'simulate(). Call initializeSim() to initialize the variables before calling simulate() again.') + + if not hasattr(self, 'T_sim'): + raise Exception('This agent type instance must have the attribute T_sim set to a positive integer.' + + 'Set T_sim to match the largest dataset you might simulate, and run this agent\'s' + + 'initalizeSim() method before running simulate() again.') + + if sim_periods is not None and self.T_sim < sim_periods: + raise Exception('To simulate, sim_periods has to be larger than the maximum data set size ' + + 'T_sim. Either increase the attribute T_sim of this agent type instance ' + + 'and call the initializeSim() method again, or set sim_periods <= T_sim.') + + # Ignore floating point "errors". Numpy calls it "errors", but really it's excep- # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is # -np.inf, np.inf/np.inf is np.nan and so on. From 0b1aca636afcd2d87f4fa240cab90698571b8aa1 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 21 Jun 2019 00:46:59 +0200 Subject: [PATCH 023/242] Implement simulate for portfolio model. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 122 ++++++++++++++++++- 1 file changed, 119 insertions(+), 3 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index dc92ba27d..9371de998 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -7,6 +7,7 @@ from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType,solveConsIndShock, ConsIndShockSolver, MargValueFunc, ConsumerSolution from HARK.utilities import approxLognormal, combineIndepDstns from HARK.interpolation import LinearInterp, LowerEnvelope +from HARK.simulation import drawLognormal from copy import deepcopy import numpy as np @@ -102,6 +103,8 @@ def __init__(self, cFunc=None, vFunc=None, class PortfolioConsumerType(IndShockConsumerType): + + poststate_vars_ = ['aNrmNow','pLvlNow','RiskyShareNow'] time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyShareCount', 'RiskyShareLimit'] cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period vFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # This is overwritten @@ -116,7 +119,8 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): verbose=verbose, quiet=quiet, **kwds) if self.BoroCnstArt is not 0.0: - print("Setting BoroCnstArt to 0.0 as this is required by PortfolioConsumerType.") + if self.verbose: + print("Setting BoroCnstArt to 0.0 as this is required by PortfolioConsumerType.") self.BoroCnstArt = 0.0 @@ -205,7 +209,7 @@ def calcRiskyShare(self): zero = sciopt.fsolve(residual, 0.5) Rshare = np.append(Rshare, zero) i_a += 1 - RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit) + RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit, slope_limit=0) # HAVE to specify the slope limit return RiskyShareFunc def prepareToCalcEndOfPrdvP(self): @@ -411,6 +415,9 @@ def solve(self): RiskyShareFunc=self.RiskyShareFunc) return solution + + + def solveConsPortfolio(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyShareCount, RiskyShareLimit): @@ -434,6 +441,16 @@ def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): return lambda RiskyCount: approxLognormal(RiskyCount, mu=mu, sigma=sigma) + +def RiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): + RiskyAvgSqrd = RiskyAvg**2 + RiskyVar = RiskyStd**2 + + mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) + sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) + + return drawLognormal(1, mu=mu, sigma=sigma) + class LogNormalPortfolioConsumerType(PortfolioConsumerType): time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyShareCount', 'RiskyShareLimit'] @@ -446,6 +463,8 @@ class LogNormalPortfolioConsumerType(PortfolioConsumerType): def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): + self.approxRiskyDstn = RiskyDstnFactory() + self.RiskyCount=0 PortfolioConsumerType.__init__(self,cycles=cycles, time_flow=time_flow, verbose=verbose, quiet=quiet, **kwds) @@ -454,7 +473,6 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): # Update this *once* as it's time invariant self.updateRiskyDstn() - self.RiskyShareLimit = PerfForesightLogNormalPortfolioShare(self) def updateRiskyDstn(self): @@ -462,7 +480,105 @@ def updateRiskyDstn(self): # the number of nodes only! self.RiskyDstn = self.approxRiskyDstn(self.RiskyCount) + def getRisky(self): + return RiskyDstnDraw(RiskyAvg=self.RiskyAvg, RiskyStd=self.RiskyStd) + + ## Simulation methods + def getStates(self): + ''' + Calculates updated values of normalized market resources and permanent income level for each + agent. Uses pLvlNow, aNrmNow, PermShkNow, TranShkNow. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + RiskySharePrev = self.RiskyShareNow + pLvlPrev = self.pLvlNow + aNrmPrev = self.aNrmNow + # Get also risky! + RfreeNow = self.getRfree() + RiskyNow = self.getRisky() # it's quite important that this is not individual + RportNow = RfreeNow + RiskySharePrev*(RiskyNow-RfreeNow) + # Calculate new states: normalized market resources and permanent income level + self.pLvlNow = pLvlPrev*self.PermShkNow # Updated permanent income level + self.PlvlAggNow = self.PlvlAggNow*self.PermShkAggNow # Updated aggregate permanent productivity level + ReffNow = RportNow/self.PermShkNow # "Effective" interest factor on normalized assets + self.bNrmNow = ReffNow*aNrmPrev # Bank balances before labor income + self.mNrmNow = self.bNrmNow + self.TranShkNow # Market resources after income + return None + + def simBirth(self,which_agents): + ''' + Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as + well as time variables t_age and t_cycle. Normalized assets and permanent income levels + are drawn from lognormal distributions given by aNrmInitMean and aNrmInitStd (etc). + + Parameters + ---------- + which_agents : np.array(Bool) + Boolean array of size self.AgentCount indicating which agents should be "born". + + Returns + ------- + None + ''' + # Get and store states for newly born agents + N = np.sum(which_agents) # Number of new consumers to make + self.aNrmNow[which_agents] = drawLognormal(N,mu=self.aNrmInitMean,sigma=self.aNrmInitStd,seed=self.RNG.randint(0,2**31-1)) + pLvlInitMeanNow = self.pLvlInitMean + np.log(self.PlvlAggNow) # Account for newer cohorts having higher permanent income + self.pLvlNow[which_agents] = drawLognormal(N,mu=pLvlInitMeanNow,sigma=self.pLvlInitStd,seed=self.RNG.randint(0,2**31-1)) + + self.t_age[which_agents] = 0 # How many periods since each agent was born + self.t_cycle[which_agents] = 0 # Which period of the cycle each agent is currently in + return None + def getControls(self): + ''' + Calculates consumption for each consumer of this type using the consumption functions. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + cNrmNow = np.zeros(self.AgentCount) + np.nan + MPCnow = np.zeros(self.AgentCount) + np.nan + for t in range(self.T_cycle): + these = t == self.t_cycle + cNrmNow[these], MPCnow[these] = self.solution[t].cFunc.eval_with_derivative(self.mNrmNow[these]) + self.cNrmNow = cNrmNow + self.MPCnow = MPCnow + return None + + def getPostStates(self): + ''' + Calculates end-of-period assets for each consumer of this type. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + self.aNrmNow = self.mNrmNow - self.cNrmNow + self.aLvlNow = self.aNrmNow*self.pLvlNow # Useful in some cases to precalculate asset level + + RiskyShareNow = np.zeros(self.AgentCount) + np.nan + for t in range(self.T_cycle): + these = t == self.t_cycle + RiskyShareNow[these] = self.solution[t].RiskyShareFunc(self.aNrmNow[these]) # should be redefined on mNrm in solve and calculated in getControls + self.RiskyShareNow = RiskyShareNow + return None # def ConsIndShockPortfolioSolver(ConsIndShockSolver): From 77f935cc37731ed5c8484b946ab24d469444ac80 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Sat, 29 Jun 2019 00:15:54 +0200 Subject: [PATCH 024/242] Improve some error handling, start allowing for stochastic ability to re-optimize, move code up the inheritance chain by allowing for sampling. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 383 +++++++++++-------- 1 file changed, 230 insertions(+), 153 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 9371de998..9435b56c9 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -1,16 +1,21 @@ -# NOTE Need to decide on Rshare vs RiskyShare +# FIXME RiskyShareLimitFunc currently doesn't work for time varying CRRA, +# Rfree and Risky-parameters. This should be possible by creating a list of +# functions instead. + import math import scipy.optimize as sciopt import scipy.integrate -import scipy.stats +import scipy.stats as stats + from HARK import Solution, NullFunc -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType,solveConsIndShock, ConsIndShockSolver, MargValueFunc, ConsumerSolution +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, solveConsIndShock, ConsIndShockSolver, MargValueFunc, ConsumerSolution from HARK.utilities import approxLognormal, combineIndepDstns from HARK.interpolation import LinearInterp, LowerEnvelope from HARK.simulation import drawLognormal from copy import deepcopy import numpy as np + # # def CambellVicApprox() # @@ -26,19 +31,19 @@ # # RiskyShareLimit = phi/(self.CRRA*sigma**2) -def PerfForesightLogNormalPortfolioShare(obj): +def PerfForesightLogNormalPortfolioShare(Rfree, RiskyAvg, RiskyStd, CRRA): PortfolioObjective = lambda share: PerfForesightLogNormalPortfolioObjective(share, - obj.Rfree, - obj.RiskyAvg, - obj.CRRA, - obj.RiskyStd) + Rfree, + RiskyAvg, + RiskyStd, + CRRA) return sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x -def PerfForesightDiscretePortfolioShare(obj): +def PerfForesightDiscretePortfolioShare(Rfree, RiskyDstn, CRRA): PortfolioObjective = lambda share: PerfForesightDiscretePortfolioObjective(share, - obj.Rfree, - obj.RiskyDstn, - obj.CRRA) + Rfree, + RiskyDstn, + CRRA) return sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x @@ -46,19 +51,19 @@ def PerfForesightDiscretePortfolioShare(obj): # It can either be "discrete" in which case it is only the number of draws that # are used, or it can be continuous in which case bounds and a pdf has to be supplied. -def PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, rho, sigma): +def PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, sigma, rho): #r0 = np.log(R0) #phi = np.log(RiskyAvg)-r0 #mu = r0+phi-(sigma**2)/2 muNorm = np.log(RiskyAvg/np.sqrt(1+sigma**2/RiskyAvg**2)) sigmaNorm = np.sqrt(np.log(1+sigma**2/RiskyAvg**2)) sharedobjective = lambda r: (R0+share*(r-R0))**(1-rho) - pdf = lambda r: scipy.stats.lognorm.pdf(r, s=sigmaNorm, scale=np.exp(muNorm)) + pdf = lambda r: stats.lognorm.pdf(r, s=sigmaNorm, scale=np.exp(muNorm)) integrand = lambda r: sharedobjective(r)*pdf(r) return integrand -def PerfForesightLogNormalPortfolioObjective(share, R0, RiskyAvg, rho, sigma): +def PerfForesightLogNormalPortfolioObjective(share, R0, RiskyAvg, sigma, rho): integrand = PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, rho, sigma) a = 0.0 b = 5.0 @@ -72,10 +77,8 @@ def PerfForesightDiscretePortfolioObjective(share, R0, RiskyDstn, rho): return -((1-rho)**-1)*np.dot(vals, weights) - - class PortfolioSolution(Solution): - distance_criteria = ['vPfunc'] + distance_criteria = ['cFunc'] def __init__(self, cFunc=None, vFunc=None, vPfunc=None, RiskyShareFunc=None, vPPfunc=None, @@ -101,22 +104,53 @@ def __init__(self, cFunc=None, vFunc=None, # self.MPCmin = MPCmin # self.MPCmax = MPCmax +class ContinuousDomain(object): + def __init__(self, lower, upper): + if lower > upper: + raise Exception('lower bounds is larger than upper bound') + else: + self.lower = lower + self.upper = upper +class DiscreteDomain(object): + def __init__(self, values): + self.values = values + self.lower = np.amin(values) + self.upper = np.amax(values) + class PortfolioConsumerType(IndShockConsumerType): + # We add CantAdjust to the standard set of poststate_vars_ here. We call it + # CantAdjust over CanAdjust, because this allows us to index into the + # "CanAdjust = 1- CantAdjust" at all times (it's the 0th offset). + poststate_vars_ = ['aNrmNow','pLvlNow','RiskyShareNow','CantAdjust'] + time_inv_ = IndShockConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'PortfolioPrb'] - poststate_vars_ = ['aNrmNow','pLvlNow','RiskyShareNow'] - time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyShareCount', 'RiskyShareLimit'] + + # Construct the terminal solution object. cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period vFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # This is overwritten RiskyShareFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # c=m in terminal period - solution_terminal_ = PortfolioSolution(cFunc = cFunc_terminal_, RiskyShareFunc = RiskyShareFunc_terminal_, - vFunc = vFunc_terminal_, mNrmMin=0.0, hNrm=None, - MPCmin=None, MPCmax=None) + + solution_terminal_ = PortfolioSolution(cFunc = cFunc_terminal_, + RiskyShareFunc = RiskyShareFunc_terminal_, + vFunc = vFunc_terminal_, + mNrmMin=0.0, + hNrm=None, + MPCmin=None, + MPCmax=None) def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): IndShockConsumerType.__init__(self,cycles=cycles, time_flow=time_flow, verbose=verbose, quiet=quiet, **kwds) + if not hasattr(self, 'PortfolioPrb'): + self.PortfolioPrb = 1.0 + + if not hasattr(self, 'PortfolioDomain'): + if self.PortfolioPrb < 1.0: + raise Exception("Please supply a PortfolioDomain when setting PortfolioPrb < 1.0.") + else: + self.PortfolioDomain = ContinuousDomain(0,1) if self.BoroCnstArt is not 0.0: if self.verbose: @@ -127,28 +161,145 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): # Chose specialized solver for Portfolio choice model self.solveOnePeriod = solveConsPortfolio - # Update this *once* as it's time invariant - self.updateRiskyDstn() + self.RiskyShareLimitFunc = lambda RiskyDstn: PerfForesightDiscretePortfolioShare(self.Rfree, RiskyDstn, self.CRRA) + + + def getPostStates(self): + ''' + Calculates end-of-period assets for each consumer of this type. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + # Calculate post decision ressources + self.aNrmNow = self.mNrmNow - self.cNrmNow + self.aLvlNow = self.aNrmNow*self.pLvlNow # Useful in some cases to precalculate asset level + + # We calculate the risky share given post decision assets aNrmNow. We + # do this for all agents that have self.CantAdjust == 0 and save the + # non-adjusters for the next section. + RiskyShareNow = np.zeros(self.AgentCount) + np.nan + for t in range(self.T_cycle): + # We need to take into account whether they have drawn a portfolio + # adjust shock or not. + these = np.logical_and(t == self.t_cycle, self.CantAdjust == 0) + RiskyShareNow[these] = self.solution[t].RiskyShareFunc(self.aNrmNow[these]) # should be redefined on mNrm in solve and calculated in getControls + + # We already updated all the adjusters in their respective time periods. + # Now we update all the non-adjusters by repeating their current share. + nonadjusters = self.CantAdjust==1 + if any(nonadjusters): + RiskyShareNow[nonadjusters] = self.RiskySharePrev[nonadjusters] + # Store the result in self + self.RiskyShareNow = RiskyShareNow + return None + + + # Simulation methods + def getStates(self): + ''' + Calculates updated values of normalized market resources and permanent income level for each + agent. Uses pLvlNow, aNrmNow, PermShkNow, TranShkNow. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + RiskySharePrev = self.RiskyShareNow + self.RiskySharePrev = RiskySharePrev # Save this for the non-adjusters! + pLvlPrev = self.pLvlNow + aNrmPrev = self.aNrmNow + # Get also risky! + RfreeNow = self.getRfree() + RiskyNow = self.getRisky() # it's quite important that this is not individual + RportNow = RfreeNow + RiskySharePrev*(RiskyNow-RfreeNow) + + # Calculate new states: normalized market resources and permanent income level + self.pLvlNow = pLvlPrev*self.PermShkNow # Updated permanent income level + self.PlvlAggNow = self.PlvlAggNow*self.PermShkAggNow # Updated aggregate permanent productivity level + ReffNow = RportNow/self.PermShkNow # "Effective" interest factor on normalized assets + self.bNrmNow = ReffNow*aNrmPrev # Bank balances before labor income + self.mNrmNow = self.bNrmNow + self.TranShkNow # Market resources after income + + # Figure out who can adjust their portfolio this period. + self.CantAdjust = stats.bernoulli.rvs(1-self.PortfolioPrb, size=self.AgentCount) + # New agents are always allowed to optimize their portfolio, because they + # have no past portfolio to "keep". + self.CantAdjust[self.new_agents] = 0.0 + return None + + def simBirth(self,which_agents): + ''' + Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as + well as time variables t_age and t_cycle. Normalized assets and permanent income levels + are drawn from lognormal distributions given by aNrmInitMean and aNrmInitStd (etc). + + Parameters + ---------- + which_agents : np.array(Bool) + Boolean array of size self.AgentCount indicating which agents should be "born". + + Returns + ------- + None + ''' + # Get and store states for newly born agents + N = np.sum(which_agents) # Number of new consumers to make + self.aNrmNow[which_agents] = drawLognormal(N,mu=self.aNrmInitMean,sigma=self.aNrmInitStd,seed=self.RNG.randint(0,2**31-1)) + pLvlInitMeanNow = self.pLvlInitMean + np.log(self.PlvlAggNow) # Account for newer cohorts having higher permanent income + self.pLvlNow[which_agents] = drawLognormal(N,mu=pLvlInitMeanNow,sigma=self.pLvlInitStd,seed=self.RNG.randint(0,2**31-1)) + + self.t_age[which_agents] = 0 # How many periods since each agent was born + self.t_cycle[which_agents] = 0 # Which period of the cycle each agent is currently in + self.new_agents = which_agents # store for portfolio choice forced to be allowed in first period + return None - self.RiskyShareLimit = PerfForesightDiscretePortfolioShare(self) + def getControls(self): + ''' + Calculates consumption for each consumer of this type using the consumption functions. - def updateRiskyDstn(self): - # Note, we expect the thing in self.approxRiskyDstn to be a funtion of - # the number of nodes only! - self.RiskyDstn = self.approxRiskyDstn(self.RiskyCount) + Parameters + ---------- + None + Returns + ------- + None + ''' + cNrmNow = np.zeros(self.AgentCount) + np.nan + MPCnow = np.zeros(self.AgentCount) + np.nan + for t in range(self.T_cycle): + these = t == self.t_cycle + cNrmNow[these], MPCnow[these] = self.solution[t].cFunc.eval_with_derivative(self.mNrmNow[these]) + self.cNrmNow = cNrmNow + self.MPCnow = MPCnow + return None + + def getRisky(self): + return self.drawRiskyFunc() class ConsIndShockPortfolioSolver(ConsIndShockSolver): def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyShareCount, RiskyShareLimit): + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, + approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc): ConsIndShockSolver.__init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool) # Store the Risky asset shock distribution - self.RiskyDstn = RiskyDstn - self.RiskyShareLimit = RiskyShareLimit + self.RiskyDstn = approxRiskyDstn(RiskyCount) + self.RiskyShareLimit = RiskyShareLimitFunc(self.RiskyDstn) + # Store the number of grid points used approximate the FOC in the port- # folio sub-problem. self.RiskyShareCount = RiskyShareCount @@ -408,7 +559,7 @@ def solve(self): self.cFuncLimitSlope = None cs_solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) - cs_solution.vPfunc(0.3) + # solution = self.addMPCandHumanWealth(solution) solution = PortfolioSolution(cFunc=cs_solution.cFunc, vPfunc=cs_solution.vPfunc, @@ -416,22 +567,27 @@ def solve(self): return solution +# The solveOnePeriod function! +def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, + PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, + approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc): -def solveConsPortfolio(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, - BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, RiskyDstn, RiskyShareCount, RiskyShareLimit): - - solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, RiskyDstn, RiskyShareCount, RiskyShareLimit) + # construct solver instance + solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, + DiscFac, CRRA, Rfree, PermGroFac, + BoroCnstArt, aXtraGrid, vFuncBool, + CubicBool, approxRiskyDstn, RiskyCount, + RiskyShareCount, RiskyShareLimitFunc) - solver.prepareToSolve() # Do some preparatory work + # Do some preparatory work + solver.prepareToSolve() + # Solve and return solution portsolution = solver.solve() return portsolution - - def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): RiskyAvgSqrd = RiskyAvg**2 RiskyVar = RiskyStd**2 @@ -441,8 +597,16 @@ def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): return lambda RiskyCount: approxLognormal(RiskyCount, mu=mu, sigma=sigma) +def RiskyDrawFactory(RiskyAvg=1.0, RiskyStd=0.0): + RiskyAvgSqrd = RiskyAvg**2 + RiskyVar = RiskyStd**2 + + mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) + sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) -def RiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): + return lambda: drawLognormal(1, mu=mu, sigma=sigma) + +def LogNormalRiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): RiskyAvgSqrd = RiskyAvg**2 RiskyVar = RiskyStd**2 @@ -453,7 +617,7 @@ def RiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): class LogNormalPortfolioConsumerType(PortfolioConsumerType): - time_inv_ = IndShockConsumerType.time_inv_ + ['RiskyDstn', 'RiskyShareCount', 'RiskyShareLimit'] + time_inv_ = IndShockConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc'] cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period vFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # This is overwritten RiskyShareFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # c=m in terminal period @@ -463,122 +627,28 @@ class LogNormalPortfolioConsumerType(PortfolioConsumerType): def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): - self.approxRiskyDstn = RiskyDstnFactory() - self.RiskyCount=0 PortfolioConsumerType.__init__(self,cycles=cycles, time_flow=time_flow, verbose=verbose, quiet=quiet, **kwds) - self.approxRiskyDstn = RiskyDstnFactory(RiskyAvg=self.RiskyAvg, RiskyStd=self.RiskyStd) - - # Update this *once* as it's time invariant - self.updateRiskyDstn() - - self.RiskyShareLimit = PerfForesightLogNormalPortfolioShare(self) + self.approxRiskyDstn = RiskyDstnFactory(RiskyAvg=self.RiskyAvg, + RiskyStd=self.RiskyStd) + # Needed to simulate. Is a function that given 0 inputs it returns a draw + # from the risky asset distribution. Only one is needed, because everyone + # draws the same shock. + self.drawRiskyFunc = RiskyDrawFactory(RiskyAvg=self.RiskyAvg, + RiskyStd=self.RiskyStd) - def updateRiskyDstn(self): - # Note, we expect the thing in self.approxRiskyDstn to be a funtion of - # the number of nodes only! - self.RiskyDstn = self.approxRiskyDstn(self.RiskyCount) + self.RiskyShareLimitFunc = lambda _: PerfForesightLogNormalPortfolioShare(self.Rfree, self.RiskyAvg, self.RiskyStd, self.CRRA) - def getRisky(self): - return RiskyDstnDraw(RiskyAvg=self.RiskyAvg, RiskyStd=self.RiskyStd) - - ## Simulation methods - def getStates(self): - ''' - Calculates updated values of normalized market resources and permanent income level for each - agent. Uses pLvlNow, aNrmNow, PermShkNow, TranShkNow. - - Parameters - ---------- - None +def matchPortfolio(portType, moments): + """ + Match a portfolio model to a set of moments. Requires that the simulations + produce `RiskyShareNow`. + """ + return portType +# optimize... - Returns - ------- - None - ''' - RiskySharePrev = self.RiskyShareNow - pLvlPrev = self.pLvlNow - aNrmPrev = self.aNrmNow - # Get also risky! - RfreeNow = self.getRfree() - RiskyNow = self.getRisky() # it's quite important that this is not individual - RportNow = RfreeNow + RiskySharePrev*(RiskyNow-RfreeNow) - # Calculate new states: normalized market resources and permanent income level - self.pLvlNow = pLvlPrev*self.PermShkNow # Updated permanent income level - self.PlvlAggNow = self.PlvlAggNow*self.PermShkAggNow # Updated aggregate permanent productivity level - ReffNow = RportNow/self.PermShkNow # "Effective" interest factor on normalized assets - self.bNrmNow = ReffNow*aNrmPrev # Bank balances before labor income - self.mNrmNow = self.bNrmNow + self.TranShkNow # Market resources after income - return None - def simBirth(self,which_agents): - ''' - Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as - well as time variables t_age and t_cycle. Normalized assets and permanent income levels - are drawn from lognormal distributions given by aNrmInitMean and aNrmInitStd (etc). - - Parameters - ---------- - which_agents : np.array(Bool) - Boolean array of size self.AgentCount indicating which agents should be "born". - - Returns - ------- - None - ''' - # Get and store states for newly born agents - N = np.sum(which_agents) # Number of new consumers to make - self.aNrmNow[which_agents] = drawLognormal(N,mu=self.aNrmInitMean,sigma=self.aNrmInitStd,seed=self.RNG.randint(0,2**31-1)) - pLvlInitMeanNow = self.pLvlInitMean + np.log(self.PlvlAggNow) # Account for newer cohorts having higher permanent income - self.pLvlNow[which_agents] = drawLognormal(N,mu=pLvlInitMeanNow,sigma=self.pLvlInitStd,seed=self.RNG.randint(0,2**31-1)) - - self.t_age[which_agents] = 0 # How many periods since each agent was born - self.t_cycle[which_agents] = 0 # Which period of the cycle each agent is currently in - return None - - def getControls(self): - ''' - Calculates consumption for each consumer of this type using the consumption functions. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - cNrmNow = np.zeros(self.AgentCount) + np.nan - MPCnow = np.zeros(self.AgentCount) + np.nan - for t in range(self.T_cycle): - these = t == self.t_cycle - cNrmNow[these], MPCnow[these] = self.solution[t].cFunc.eval_with_derivative(self.mNrmNow[these]) - self.cNrmNow = cNrmNow - self.MPCnow = MPCnow - return None - - def getPostStates(self): - ''' - Calculates end-of-period assets for each consumer of this type. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - self.aNrmNow = self.mNrmNow - self.cNrmNow - self.aLvlNow = self.aNrmNow*self.pLvlNow # Useful in some cases to precalculate asset level - - RiskyShareNow = np.zeros(self.AgentCount) + np.nan - for t in range(self.T_cycle): - these = t == self.t_cycle - RiskyShareNow[these] = self.solution[t].RiskyShareFunc(self.aNrmNow[these]) # should be redefined on mNrm in solve and calculated in getControls - self.RiskyShareNow = RiskyShareNow - return None # def ConsIndShockPortfolioSolver(ConsIndShockSolver): @@ -589,3 +659,10 @@ def getPostStates(self): # # calc MPC kappaUnderbar # # calc human wealth # + +#################### +#################### +#################### +#################### + +### From fc4cf404319c5ed19bf9bf393b7a21fd975b4d50 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Tue, 2 Jul 2019 12:18:43 +0200 Subject: [PATCH 025/242] Many changes; now there's a separation between nonadjuster and adjusters. Need to work on the discrete domain. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 266 ++++++++++++++----- HARK/core.py | 2 +- 2 files changed, 203 insertions(+), 65 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 9435b56c9..07242d6ee 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -7,8 +7,9 @@ import scipy.integrate import scipy.stats as stats -from HARK import Solution, NullFunc -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, solveConsIndShock, ConsIndShockSolver, MargValueFunc, ConsumerSolution +from HARK import Solution, NullFunc, AgentType + +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType, solveConsIndShock, ConsIndShockSolver, ValueFunc, MargValueFunc, ConsumerSolution from HARK.utilities import approxLognormal, combineIndepDstns from HARK.interpolation import LinearInterp, LowerEnvelope from HARK.simulation import drawLognormal @@ -105,46 +106,56 @@ def __init__(self, cFunc=None, vFunc=None, # self.MPCmax = MPCmax class ContinuousDomain(object): - def __init__(self, lower, upper): + def __init__(self, lower, upper, points = [np.nan]): if lower > upper: raise Exception('lower bounds is larger than upper bound') else: self.lower = lower self.upper = upper + self.points = points + + def getPoints(self): + return self.points + + def len(self): + return len(self.points) + class DiscreteDomain(object): - def __init__(self, values): - self.values = values - self.lower = np.amin(values) - self.upper = np.amax(values) + def __init__(self, points): + self.points = points + self.lower = np.amin(points) + self.upper = np.amax(points) + + + def len(self): + return len(self.points) + + def getPoints(self): + return self.points class PortfolioConsumerType(IndShockConsumerType): # We add CantAdjust to the standard set of poststate_vars_ here. We call it # CantAdjust over CanAdjust, because this allows us to index into the # "CanAdjust = 1- CantAdjust" at all times (it's the 0th offset). - poststate_vars_ = ['aNrmNow','pLvlNow','RiskyShareNow','CantAdjust'] - time_inv_ = IndShockConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'PortfolioPrb'] - - - # Construct the terminal solution object. - cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period - vFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # This is overwritten - RiskyShareFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # c=m in terminal period - - solution_terminal_ = PortfolioSolution(cFunc = cFunc_terminal_, - RiskyShareFunc = RiskyShareFunc_terminal_, - vFunc = vFunc_terminal_, - mNrmMin=0.0, - hNrm=None, - MPCmin=None, - MPCmax=None) + poststate_vars_ = ['aNrmNow', 'pLvlNow', 'RiskyShareNow', 'CantAdjust'] + time_inv_ = IndShockConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'PortfolioPrb', 'PortfolioGrid', 'PortfolioActionCount'] def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): - IndShockConsumerType.__init__(self,cycles=cycles, time_flow=time_flow, - verbose=verbose, quiet=quiet, **kwds) + # Initialize a basic AgentType + PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow, + verbose=verbose,quiet=quiet, **kwds) + if not hasattr(self, 'PortfolioPrb'): self.PortfolioPrb = 1.0 + self.PortfolioActionCount = 1 + elif self.PortfolioPrb == 1.0: + self.PortfolioActionCount = 1 + else: + # If PortfolioPrb was set and was below 1.0, there's a chance that + # the consumer cannot adjust ina given period. + self.PortfolioActionCount = 2 if not hasattr(self, 'PortfolioDomain'): if self.PortfolioPrb < 1.0: @@ -152,17 +163,73 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): else: self.PortfolioDomain = ContinuousDomain(0,1) + # Now we can set up the PortfolioGrid! This is the portfolio values + # you can enter the period with. It's exact for discrete , for continuous + # domain it's the interpolation points. + + self.PortfolioGrid = self.PortfolioDomain.getPoints() + if self.BoroCnstArt is not 0.0: if self.verbose: print("Setting BoroCnstArt to 0.0 as this is required by PortfolioConsumerType.") self.BoroCnstArt = 0.0 + # Chose specialized solver for Portfolio choice model self.solveOnePeriod = solveConsPortfolio + self.update() + self.RiskyShareLimitFunc = lambda RiskyDstn: PerfForesightDiscretePortfolioShare(self.Rfree, RiskyDstn, self.CRRA) + def preSolve(self): + AgentType.preSolve(self) + self.updateSolutionTerminal() + + def updateSolutionTerminal(self): + ''' + Updates the terminal period solution for a portfolio shock consumer. + Only fills in the consumption function and marginal value function. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + cFunc_terminal = LinearInterp([0.0, 1.0], [0.0,1.0]) # c=m in terminal period + vFunc_terminal = LinearInterp([0.0, 1.0], [0.0,0.0]) # This is overwritten + RiskyShareFunc_terminal = LinearInterp([0.0, 1.0], [0.0,0.0]) # c=m in terminal period + + if isinstance(self.PortfolioDomain, DiscreteDomain): + PortfolioGridCount = len(self.PortfolioDomain.points) + else: + # This should be "PortfolioGridCount" that was set earlier, + PortfolioGridCount = 1 + + cFunc_terminal = PortfolioGridCount*[cFunc_terminal] + RiskyShareFunc_terminal = PortfolioGridCount*[RiskyShareFunc_terminal] + + vFunc_terminal = PortfolioGridCount*[ValueFunc(self.cFunc_terminal_,self.CRRA)] + vPfunc_terminal = PortfolioGridCount*[MargValueFunc(self.cFunc_terminal_,self.CRRA)] + + # repeat according to number of portfolio adjustment situations + cFunc_terminal = self.PortfolioActionCount*[cFunc_terminal] + RiskyShareFunc_terminal = self.PortfolioActionCount*[RiskyShareFunc_terminal] + + vFunc_terminal = self.PortfolioActionCount*[vFunc_terminal] + vPfunc_terminal = self.PortfolioActionCount*[vPfunc_terminal] + + self.solution_terminal = PortfolioSolution(cFunc = cFunc_terminal, + RiskyShareFunc = RiskyShareFunc_terminal, + vFunc = vFunc_terminal, + vPfunc = vPfunc_terminal, + mNrmMin=0.0, hNrm=None, + MPCmin=None, MPCmax=None) + def getPostStates(self): ''' @@ -184,11 +251,29 @@ def getPostStates(self): # do this for all agents that have self.CantAdjust == 0 and save the # non-adjusters for the next section. RiskyShareNow = np.zeros(self.AgentCount) + np.nan + + + PortfolioGridCount = len(self.PortfolioGrid) + for t in range(self.T_cycle): # We need to take into account whether they have drawn a portfolio # adjust shock or not. - these = np.logical_and(t == self.t_cycle, self.CantAdjust == 0) - RiskyShareNow[these] = self.solution[t].RiskyShareFunc(self.aNrmNow[these]) # should be redefined on mNrm in solve and calculated in getControls + these_adjust = self.CantAdjust == 0 + these_t = t == self.t_cycle + these_pre_portfolio = np.logical_and(these_adjust, these_t) + for portfolio_index, portfolio_value in enumerate(self.PortfolioGrid): + if self.PortfolioPrb == 1.0: + # We're in the PortfolioPrb = 1, case, so there is just one + # of each solution function + these_portfolio = 0*these_t + these = these_t + else: + these_portfolio = np.equal(portfolio_value, self.RiskySharePrev) + these = np.logical_and(these_t, these_portfolio) + + # Now that we have the "these" indeces and the portfolio indeces + # we can update the appropriate RiskyShare's. + RiskyShareNow[these] = self.solution[t].RiskyShareFunc[0][portfolio_index](self.aNrmNow[these]) # should be redefined on mNrm in solve and calculated in getControls # We already updated all the adjusters in their respective time periods. # Now we update all the non-adjusters by repeating their current share. @@ -218,9 +303,14 @@ def getStates(self): self.RiskySharePrev = RiskySharePrev # Save this for the non-adjusters! pLvlPrev = self.pLvlNow aNrmPrev = self.aNrmNow - # Get also risky! + RfreeNow = self.getRfree() - RiskyNow = self.getRisky() # it's quite important that this is not individual + # In the current interpretation, everyone gets the same random return. + # This is because they all invest in the same stock/market index. + # As a result, we simply draw *one* realization from RiskyDstn. + RiskyNow = self.getRisky() + + # Calculate the portfolio return from last period to current period. RportNow = RfreeNow + RiskySharePrev*(RiskyNow-RfreeNow) # Calculate new states: normalized market resources and permanent income level @@ -232,6 +322,7 @@ def getStates(self): # Figure out who can adjust their portfolio this period. self.CantAdjust = stats.bernoulli.rvs(1-self.PortfolioPrb, size=self.AgentCount) + # New agents are always allowed to optimize their portfolio, because they # have no past portfolio to "keep". self.CantAdjust[self.new_agents] = 0.0 @@ -277,9 +368,24 @@ def getControls(self): ''' cNrmNow = np.zeros(self.AgentCount) + np.nan MPCnow = np.zeros(self.AgentCount) + np.nan + + PortfolioGridCount = len(self.PortfolioGrid) + these_cant_adjust = self.CantAdjust == 1 + for t in range(self.T_cycle): - these = t == self.t_cycle - cNrmNow[these], MPCnow[these] = self.solution[t].cFunc.eval_with_derivative(self.mNrmNow[these]) + these_t = t == self.t_cycle + for portfolio_index, portfolio_value in enumerate(self.PortfolioGrid): + if self.PortfolioPrb == 1.0: + # We're in the PortfolioPrb = 1, case, so there is just one + # of each solution function + + these_portfolio = these_t*0 + these = these_t + else: + these_portfolio = np.equal(portfolio_value, self.RiskySharePrev) + these = np.logical_and(these_t, these_portfolio) + + cNrmNow[these], MPCnow[these] = self.solution[t].cFunc[0][portfolio_index].eval_with_derivative(self.mNrmNow[these]) self.cNrmNow = cNrmNow self.MPCnow = MPCnow return None @@ -290,12 +396,16 @@ def getRisky(self): class ConsIndShockPortfolioSolver(ConsIndShockSolver): def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, - approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc): - + approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc, + PortfolioPrb, PortfolioGrid, PortfolioActionCount): ConsIndShockSolver.__init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool) + self.PortfolioPrb = PortfolioPrb + self.PortfolioGrid = PortfolioGrid + self.PortfolioActionCount = PortfolioActionCount + # Store the Risky asset shock distribution self.RiskyDstn = approxRiskyDstn(RiskyCount) self.RiskyShareLimit = RiskyShareLimitFunc(self.RiskyDstn) @@ -303,7 +413,7 @@ def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, # Store the number of grid points used approximate the FOC in the port- # folio sub-problem. self.RiskyShareCount = RiskyShareCount - self.vPfuncNext = solution_next.vPfunc + self.vPfuncsNext = solution_next.vPfunc self.updateShockDstn() self.makeRshareGrid() @@ -328,7 +438,6 @@ def prepareToCalcRiskyShare(self): # Evaluate the non-constant part of the first order conditions wrt the # portfolio share. This requires the implied resources tomorrow given # todays shocks to be evaluated. - i_a = 0 for a in aNrmPort: # for all possible a's today @@ -363,7 +472,7 @@ def calcRiskyShare(self): RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit, slope_limit=0) # HAVE to specify the slope limit return RiskyShareFunc - def prepareToCalcEndOfPrdvP(self): + def prepareToCalcEndOfPrdvP(self, adjuster=True): ''' Prepare to calculate end-of-period marginal value by creating an array of market resources that the agent could have next period, considering @@ -398,14 +507,21 @@ def prepareToCalcEndOfPrdvP(self): RiskyShkVals_temp = (np.tile(self.RiskyShkValsNext,(aNrmCount,1))).transpose() ShkPrbs_temp = (np.tile(self.ShkPrbsNext,(aNrmCount,1))).transpose() + # Calculate share at current aNrm. If non-adjusting, it's just + # self.RiskySharePrev, else we use the recently calculated RiskyShareFunc + if adjuster: + sAt_aNrm = self.RiskyShareFunc(aNrmNow) + else: + sAt_aNrm = self.RiskySharePrev - # sAtA - sAt_aNrm = self.RiskyShareFunc(aNrmNow) - # Get cash on hand next period - + # Get cash on hand next period. + # Compose possible return factors Rtilde = RiskyShkVals_temp - self.Rfree + # Combine into effective returns factors, taking into account the share self.Reff = (self.Rfree + Rtilde*sAt_aNrm) + # Apply the permanent growth factor and possible permanent shocks mNrmPreTran = self.Reff/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + # Add transitory income mNrmNext = mNrmPreTran + TranShkVals_temp # Store and report the results @@ -546,39 +662,67 @@ def solve(self): The solution to the one period problem. ''' - # First, solve the first sub-problem: the portfolio choice - self.vHatP = self.prepareToCalcRiskyShare() - self.RiskyShareFunc = self.calcRiskyShare() + PortfolioGridCount = len(self.PortfolioGrid) - # Then solve the consumption choice given optimal portfolio choice - aNrm = self.prepareToCalcEndOfPrdvP() - EndOfPrdvP = self.calcEndOfPrdvP() + cFuncs = self.PortfolioActionCount*[[]] + vPfuncs = self.PortfolioActionCount*[[]] + RiskyShareFuncs = self.PortfolioActionCount*[[]] - # Todo! - self.cFuncLimitIntercept = None - self.cFuncLimitSlope = None + for adjusterState in range(self.PortfolioActionCount): # stupid name, should be related to adjusting + if adjusterState == 1: + adjuster = False + else: + adjuster = True + + for PortfolioGridIdx in range(PortfolioGridCount): + self.vPfuncNext = self.vPfuncsNext[adjusterState][PortfolioGridIdx] + self.PortfolioGridIdx = PortfolioGridIdx + + # If it can adjust, solve the first sub-problem: the portfolio choice + if adjuster: + self.vHatP = self.prepareToCalcRiskyShare() + self.RiskyShareFunc = self.calcRiskyShare() + else: + self.RiskyShareFunc = lambda a: PortfolioGrid[PortfolioGridIdx] + + # Then solve the consumption choice given optimal portfolio choice + aNrm = self.prepareToCalcEndOfPrdvP(adjuster = adjuster) + EndOfPrdvP = self.calcEndOfPrdvP() + + # Todo! + self.cFuncLimitIntercept = None + self.cFuncLimitSlope = None + + cs_solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) + cFuncs[adjusterState].append(cs_solution.cFunc) + vPfuncs[adjusterState].append(cs_solution.vPfunc) + # This is a good place to make it defined at m!!! + RiskyShareFuncs[adjusterState].append(self.RiskyShareFunc) - cs_solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) # solution = self.addMPCandHumanWealth(solution) - solution = PortfolioSolution(cFunc=cs_solution.cFunc, - vPfunc=cs_solution.vPfunc, - RiskyShareFunc=self.RiskyShareFunc) + solution = PortfolioSolution(cFunc=cFuncs, + vPfunc=vPfuncs, + RiskyShareFunc=RiskyShareFuncs) return solution # The solveOnePeriod function! -def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, - approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc): +def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, + CRRA, Rfree, PermGroFac, BoroCnstArt, + aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, + RiskyCount, RiskyShareCount, RiskyShareLimitFunc, + PortfolioPrb, PortfolioGrid, PortfolioActionCount): + # construct solver instance solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, RiskyCount, - RiskyShareCount, RiskyShareLimitFunc) + RiskyShareCount, RiskyShareLimitFunc, + PortfolioPrb, PortfolioGrid, PortfolioActionCount) # Do some preparatory work solver.prepareToSolve() @@ -617,13 +761,7 @@ def LogNormalRiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): class LogNormalPortfolioConsumerType(PortfolioConsumerType): - time_inv_ = IndShockConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc'] - cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period - vFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # This is overwritten - RiskyShareFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,0.0]) # c=m in terminal period - solution_terminal_ = PortfolioSolution(cFunc = cFunc_terminal_, RiskyShareFunc = RiskyShareFunc_terminal_, - vFunc = vFunc_terminal_, mNrmMin=0.0, hNrm=None, - MPCmin=None, MPCmax=None) +# time_inv_ = PortfolioConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'PortfolioPrb', 'PortfolioGrid'] def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): diff --git a/HARK/core.py b/HARK/core.py index 55b7136d3..0392e058f 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -748,12 +748,12 @@ def solveAgent(agent, verbose): # Check to see whether this is an (in)finite horizon problem cycles_left = agent.cycles # NOQA infinite_horizon = cycles_left == 0 # NOQA - # Initialize the solution, which includes the terminal solution if it's not a pseudo-terminal period solution = [] if not agent.pseudo_terminal: solution.append(deepcopy(agent.solution_terminal)) + # Initialize the process, then loop over cycles solution_last = agent.solution_terminal # NOQA go = True # NOQA From 0c014aadaa61f2ff7c75ab7aaef1858a48410b7a Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Tue, 2 Jul 2019 14:07:09 +0200 Subject: [PATCH 026/242] Cleanup. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 102 ++++++++++--------- 1 file changed, 52 insertions(+), 50 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 07242d6ee..28b67b807 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -51,11 +51,7 @@ def PerfForesightDiscretePortfolioShare(Rfree, RiskyDstn, CRRA): # Switch here based on knowledge about risky. # It can either be "discrete" in which case it is only the number of draws that # are used, or it can be continuous in which case bounds and a pdf has to be supplied. - def PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, sigma, rho): - #r0 = np.log(R0) - #phi = np.log(RiskyAvg)-r0 - #mu = r0+phi-(sigma**2)/2 muNorm = np.log(RiskyAvg/np.sqrt(1+sigma**2/RiskyAvg**2)) sigmaNorm = np.sqrt(np.log(1+sigma**2/RiskyAvg**2)) sharedobjective = lambda r: (R0+share*(r-R0))**(1-rho) @@ -66,8 +62,8 @@ def PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, sigma, rho): def PerfForesightLogNormalPortfolioObjective(share, R0, RiskyAvg, sigma, rho): integrand = PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, rho, sigma) - a = 0.0 - b = 5.0 + a = 0.0 # Cannot be negative + b = 5.0 # This is just an upper bound. pdf should be 0 here. return -((1-rho)**-1)*scipy.integrate.quad(integrand, a, b)[0] @@ -105,6 +101,9 @@ def __init__(self, cFunc=None, vFunc=None, # self.MPCmin = MPCmin # self.MPCmax = MPCmax + +# These domains are convenient for switching to relavent code paths internally. +# It might be simpler to just pass in vectors instead of DiscreteDomain. class ContinuousDomain(object): def __init__(self, lower, upper, points = [np.nan]): if lower > upper: @@ -126,7 +125,6 @@ def __init__(self, points): self.lower = np.amin(points) self.upper = np.amax(points) - def len(self): return len(self.points) @@ -139,7 +137,10 @@ class PortfolioConsumerType(IndShockConsumerType): # CantAdjust over CanAdjust, because this allows us to index into the # "CanAdjust = 1- CantAdjust" at all times (it's the 0th offset). poststate_vars_ = ['aNrmNow', 'pLvlNow', 'RiskyShareNow', 'CantAdjust'] - time_inv_ = IndShockConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'PortfolioPrb', 'PortfolioGrid', 'PortfolioActionCount'] + time_inv_ = deepcopy(IndShockConsumerType.time_inv_) + time_inv_ = time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount'] + time_inv_ = time_inv_ + ['RiskyShareLimitFunc'] + time_inv_ = time_inv_ + ['AdjustPrb', 'PortfolioGrid', 'AdjustActionCount'] def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): @@ -147,26 +148,27 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow, verbose=verbose,quiet=quiet, **kwds) - if not hasattr(self, 'PortfolioPrb'): - self.PortfolioPrb = 1.0 - self.PortfolioActionCount = 1 - elif self.PortfolioPrb == 1.0: - self.PortfolioActionCount = 1 + # Check that an adjustment probability is set. If not, default to always. + if not hasattr(self, 'AdjustPrb'): + self.AdjustPrb = 1.0 + self.AdjustActionCount = 1 + elif self.AdjustPrb == 1.0: + # Always adjust, so there's just one possibility + self.AdjustActionCount = 1 else: - # If PortfolioPrb was set and was below 1.0, there's a chance that - # the consumer cannot adjust ina given period. - self.PortfolioActionCount = 2 + # If AdjustPrb was set and was below 1.0, there's a chance that + # the consumer cannot adjust in a given period. + self.AdjustActionCount = 2 if not hasattr(self, 'PortfolioDomain'): - if self.PortfolioPrb < 1.0: - raise Exception("Please supply a PortfolioDomain when setting PortfolioPrb < 1.0.") + if self.AdjustPrb < 1.0: + raise Exception("Please supply a PortfolioDomain when setting AdjustPrb < 1.0.") else: self.PortfolioDomain = ContinuousDomain(0,1) # Now we can set up the PortfolioGrid! This is the portfolio values # you can enter the period with. It's exact for discrete , for continuous # domain it's the interpolation points. - self.PortfolioGrid = self.PortfolioDomain.getPoints() if self.BoroCnstArt is not 0.0: @@ -205,23 +207,23 @@ def updateSolutionTerminal(self): RiskyShareFunc_terminal = LinearInterp([0.0, 1.0], [0.0,0.0]) # c=m in terminal period if isinstance(self.PortfolioDomain, DiscreteDomain): - PortfolioGridCount = len(self.PortfolioDomain.points) + PortfolioDomainCount = len(self.PortfolioDomain.points) else: - # This should be "PortfolioGridCount" that was set earlier, - PortfolioGridCount = 1 + # This should be "PortfolioDomainCount" that was set earlier, + PortfolioDomainCount = 1 - cFunc_terminal = PortfolioGridCount*[cFunc_terminal] - RiskyShareFunc_terminal = PortfolioGridCount*[RiskyShareFunc_terminal] + cFunc_terminal = PortfolioDomainCount*[cFunc_terminal] + RiskyShareFunc_terminal = PortfolioDomainCount*[RiskyShareFunc_terminal] - vFunc_terminal = PortfolioGridCount*[ValueFunc(self.cFunc_terminal_,self.CRRA)] - vPfunc_terminal = PortfolioGridCount*[MargValueFunc(self.cFunc_terminal_,self.CRRA)] + vFunc_terminal = PortfolioDomainCount*[ValueFunc(self.cFunc_terminal_,self.CRRA)] + vPfunc_terminal = PortfolioDomainCount*[MargValueFunc(self.cFunc_terminal_,self.CRRA)] # repeat according to number of portfolio adjustment situations - cFunc_terminal = self.PortfolioActionCount*[cFunc_terminal] - RiskyShareFunc_terminal = self.PortfolioActionCount*[RiskyShareFunc_terminal] + cFunc_terminal = self.AdjustActionCount*[cFunc_terminal] + RiskyShareFunc_terminal = self.AdjustActionCount*[RiskyShareFunc_terminal] - vFunc_terminal = self.PortfolioActionCount*[vFunc_terminal] - vPfunc_terminal = self.PortfolioActionCount*[vPfunc_terminal] + vFunc_terminal = self.AdjustActionCount*[vFunc_terminal] + vPfunc_terminal = self.AdjustActionCount*[vPfunc_terminal] self.solution_terminal = PortfolioSolution(cFunc = cFunc_terminal, RiskyShareFunc = RiskyShareFunc_terminal, @@ -253,7 +255,7 @@ def getPostStates(self): RiskyShareNow = np.zeros(self.AgentCount) + np.nan - PortfolioGridCount = len(self.PortfolioGrid) + PortfolioDomainCount = len(self.PortfolioGrid) for t in range(self.T_cycle): # We need to take into account whether they have drawn a portfolio @@ -262,9 +264,9 @@ def getPostStates(self): these_t = t == self.t_cycle these_pre_portfolio = np.logical_and(these_adjust, these_t) for portfolio_index, portfolio_value in enumerate(self.PortfolioGrid): - if self.PortfolioPrb == 1.0: - # We're in the PortfolioPrb = 1, case, so there is just one - # of each solution function + if self.AdjustPrb == 1.0: + # We're in the AdjustPrb = 1, case, so there is just one + # of each solution function, set all these_portfolio to 0 these_portfolio = 0*these_t these = these_t else: @@ -321,7 +323,7 @@ def getStates(self): self.mNrmNow = self.bNrmNow + self.TranShkNow # Market resources after income # Figure out who can adjust their portfolio this period. - self.CantAdjust = stats.bernoulli.rvs(1-self.PortfolioPrb, size=self.AgentCount) + self.CantAdjust = stats.bernoulli.rvs(1-self.AdjustPrb, size=self.AgentCount) # New agents are always allowed to optimize their portfolio, because they # have no past portfolio to "keep". @@ -369,14 +371,14 @@ def getControls(self): cNrmNow = np.zeros(self.AgentCount) + np.nan MPCnow = np.zeros(self.AgentCount) + np.nan - PortfolioGridCount = len(self.PortfolioGrid) + PortfolioDomainCount = len(self.PortfolioGrid) these_cant_adjust = self.CantAdjust == 1 for t in range(self.T_cycle): these_t = t == self.t_cycle for portfolio_index, portfolio_value in enumerate(self.PortfolioGrid): - if self.PortfolioPrb == 1.0: - # We're in the PortfolioPrb = 1, case, so there is just one + if self.AdjustPrb == 1.0: + # We're in the AdjustPrb = 1, case, so there is just one # of each solution function these_portfolio = these_t*0 @@ -397,14 +399,14 @@ class ConsIndShockPortfolioSolver(ConsIndShockSolver): def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc, - PortfolioPrb, PortfolioGrid, PortfolioActionCount): + AdjustPrb, PortfolioGrid, AdjustActionCount): ConsIndShockSolver.__init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool) - self.PortfolioPrb = PortfolioPrb + self.AdjustPrb = AdjustPrb self.PortfolioGrid = PortfolioGrid - self.PortfolioActionCount = PortfolioActionCount + self.AdjustActionCount = AdjustActionCount # Store the Risky asset shock distribution self.RiskyDstn = approxRiskyDstn(RiskyCount) @@ -662,19 +664,19 @@ def solve(self): The solution to the one period problem. ''' - PortfolioGridCount = len(self.PortfolioGrid) + PortfolioDomainCount = len(self.PortfolioGrid) - cFuncs = self.PortfolioActionCount*[[]] - vPfuncs = self.PortfolioActionCount*[[]] - RiskyShareFuncs = self.PortfolioActionCount*[[]] + cFuncs = self.AdjustActionCount*[[]] + vPfuncs = self.AdjustActionCount*[[]] + RiskyShareFuncs = self.AdjustActionCount*[[]] - for adjusterState in range(self.PortfolioActionCount): # stupid name, should be related to adjusting + for adjusterState in range(self.AdjustActionCount): # stupid name, should be related to adjusting if adjusterState == 1: adjuster = False else: adjuster = True - for PortfolioGridIdx in range(PortfolioGridCount): + for PortfolioGridIdx in range(PortfolioDomainCount): self.vPfuncNext = self.vPfuncsNext[adjusterState][PortfolioGridIdx] self.PortfolioGridIdx = PortfolioGridIdx @@ -713,7 +715,7 @@ def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc, - PortfolioPrb, PortfolioGrid, PortfolioActionCount): + AdjustPrb, PortfolioGrid, AdjustActionCount): # construct solver instance @@ -722,7 +724,7 @@ def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc, - PortfolioPrb, PortfolioGrid, PortfolioActionCount) + AdjustPrb, PortfolioGrid, AdjustActionCount) # Do some preparatory work solver.prepareToSolve() @@ -761,7 +763,7 @@ def LogNormalRiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): class LogNormalPortfolioConsumerType(PortfolioConsumerType): -# time_inv_ = PortfolioConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'PortfolioPrb', 'PortfolioGrid'] +# time_inv_ = PortfolioConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'AdjustPrb', 'PortfolioGrid'] def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): From b419c1db84da6e717da20d40df3b1285ef59fedc Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Tue, 2 Jul 2019 15:02:16 +0200 Subject: [PATCH 027/242] One step closer to discrete optimization (still needs value function at possible grids. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 84 +++++++++++++++----- 1 file changed, 65 insertions(+), 19 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 28b67b807..41af0ef4e 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -139,8 +139,8 @@ class PortfolioConsumerType(IndShockConsumerType): poststate_vars_ = ['aNrmNow', 'pLvlNow', 'RiskyShareNow', 'CantAdjust'] time_inv_ = deepcopy(IndShockConsumerType.time_inv_) time_inv_ = time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount'] - time_inv_ = time_inv_ + ['RiskyShareLimitFunc'] - time_inv_ = time_inv_ + ['AdjustPrb', 'PortfolioGrid', 'AdjustActionCount'] + time_inv_ = time_inv_ + ['RiskyShareLimitFunc', 'PortfolioDomain'] + time_inv_ = time_inv_ + ['AdjustPrb', 'PortfolioGrid', 'AdjustCount'] def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): @@ -151,14 +151,14 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): # Check that an adjustment probability is set. If not, default to always. if not hasattr(self, 'AdjustPrb'): self.AdjustPrb = 1.0 - self.AdjustActionCount = 1 + self.AdjustCount = 1 elif self.AdjustPrb == 1.0: # Always adjust, so there's just one possibility - self.AdjustActionCount = 1 + self.AdjustCount = 1 else: # If AdjustPrb was set and was below 1.0, there's a chance that # the consumer cannot adjust in a given period. - self.AdjustActionCount = 2 + self.AdjustCount = 2 if not hasattr(self, 'PortfolioDomain'): if self.AdjustPrb < 1.0: @@ -166,6 +166,11 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): else: self.PortfolioDomain = ContinuousDomain(0,1) + if isinstance(self.PortfolioDomain, DiscreteDomain): + if self.vFuncBool == False and self.verbose: + print("Setting vFuncBool to True to accomodate dicrete portfolio optimization.") + self.vFuncBool = True + # Now we can set up the PortfolioGrid! This is the portfolio values # you can enter the period with. It's exact for discrete , for continuous # domain it's the interpolation points. @@ -219,11 +224,11 @@ def updateSolutionTerminal(self): vPfunc_terminal = PortfolioDomainCount*[MargValueFunc(self.cFunc_terminal_,self.CRRA)] # repeat according to number of portfolio adjustment situations - cFunc_terminal = self.AdjustActionCount*[cFunc_terminal] - RiskyShareFunc_terminal = self.AdjustActionCount*[RiskyShareFunc_terminal] + cFunc_terminal = self.AdjustCount*[cFunc_terminal] + RiskyShareFunc_terminal = self.AdjustCount*[RiskyShareFunc_terminal] - vFunc_terminal = self.AdjustActionCount*[vFunc_terminal] - vPfunc_terminal = self.AdjustActionCount*[vPfunc_terminal] + vFunc_terminal = self.AdjustCount*[vFunc_terminal] + vPfunc_terminal = self.AdjustCount*[vPfunc_terminal] self.solution_terminal = PortfolioSolution(cFunc = cFunc_terminal, RiskyShareFunc = RiskyShareFunc_terminal, @@ -399,14 +404,15 @@ class ConsIndShockPortfolioSolver(ConsIndShockSolver): def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc, - AdjustPrb, PortfolioGrid, AdjustActionCount): + AdjustPrb, PortfolioGrid, AdjustCount, PortfolioDomain): ConsIndShockSolver.__init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool) self.AdjustPrb = AdjustPrb self.PortfolioGrid = PortfolioGrid - self.AdjustActionCount = AdjustActionCount + self.PortfolioDomain = PortfolioDomain + self.AdjustCount = AdjustCount # Store the Risky asset shock distribution self.RiskyDstn = approxRiskyDstn(RiskyCount) @@ -416,6 +422,12 @@ def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, # folio sub-problem. self.RiskyShareCount = RiskyShareCount self.vPfuncsNext = solution_next.vPfunc + + if isinstance(self.PortfolioDomain, DiscreteDomain): + self.DiscreteCase = True + else: + self.DiscreteCase = False + self.updateShockDstn() self.makeRshareGrid() @@ -423,8 +435,12 @@ def updateShockDstn(self): self.ShockDstn = combineIndepDstns(self.IncomeDstn, self.RiskyDstn) def makeRshareGrid(self): - self.RshareGrid = np.linspace(0, 1, self.RiskyShareCount) - return self.RshareGrid + # We set this up such that attempts to use RshareGrid will fail hard + # if we're in the discrete case + if not self.DiscreteCase: + self.RshareGrid = np.linspace(0, 1, self.RiskyShareCount) + return self.RshareGrid + return [] def prepareToCalcRiskyShare(self): # Hard restriction on aNrm. We'd need to define more elaborate model @@ -456,10 +472,39 @@ def prepareToCalcRiskyShare(self): return vHatP def calcRiskyShare(self): + if self.DiscreteCase: + RiskyShareFunc = self.calcRiskyShareContinuous() + else: + RiskyShareFunc = self.calcRiskyShareDiscrete() + + return RiskyShareFunc + + + def calcRiskyShareContinuous(self): + aGrid = np.array([0.0,]) + Rshare = np.array([1.0,]) + + i_a = 0 + for a in self.aNrmPort: + aGrid = np.append(aGrid, a) + if self.vHatP[i_a, -1] >= 0.0: + Rshare = np.append(Rshare, 1.0) + elif self.vHatP[i_a, 0] < 0.0: + Rshare = np.append(Rshare, 0.0) + else: + residual = LinearInterp(self.RshareGrid, self.vHatP[i_a, :]) + zero = sciopt.fsolve(residual, 0.5) + Rshare = np.append(Rshare, zero) + i_a += 1 + RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit, slope_limit=0) # HAVE to specify the slope limit + return RiskyShareFunc + + def calcRiskyShareDiscrete(self): aGrid = np.array([0.0,]) Rshare = np.array([1.0,]) i_a = 0 + # For all positive aNrms for a in self.aNrmPort: aGrid = np.append(aGrid, a) if self.vHatP[i_a, -1] >= 0.0: @@ -666,11 +711,11 @@ def solve(self): PortfolioDomainCount = len(self.PortfolioGrid) - cFuncs = self.AdjustActionCount*[[]] - vPfuncs = self.AdjustActionCount*[[]] - RiskyShareFuncs = self.AdjustActionCount*[[]] + cFuncs = self.AdjustCount*[[]] + vPfuncs = self.AdjustCount*[[]] + RiskyShareFuncs = self.AdjustCount*[[]] - for adjusterState in range(self.AdjustActionCount): # stupid name, should be related to adjusting + for adjusterState in range(self.AdjustCount): # stupid name, should be related to adjusting if adjusterState == 1: adjuster = False else: @@ -715,7 +760,7 @@ def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc, - AdjustPrb, PortfolioGrid, AdjustActionCount): + AdjustPrb, PortfolioGrid, AdjustCount, PortfolioDomain): # construct solver instance @@ -724,7 +769,8 @@ def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc, - AdjustPrb, PortfolioGrid, AdjustActionCount) + AdjustPrb, PortfolioGrid, AdjustCount, + PortfolioDomain) # Do some preparatory work solver.prepareToSolve() From 4032ff844010764e62a3a03cc018973ccf485014 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 4 Jul 2019 11:34:48 +0200 Subject: [PATCH 028/242] fix check to accomodate lists --- HARK/interpolation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/interpolation.py b/HARK/interpolation.py index 08adf9872..144101c2b 100644 --- a/HARK/interpolation.py +++ b/HARK/interpolation.py @@ -50,7 +50,7 @@ def _check_grid_dimensions(dimension, *args): def _check_flatten(dimension, *args): if dimension == 1: - if args[0].shape != args[0].flatten().shape: + if isinstance(args[0], np.ndarray) and args[0].shape != args[0].flatten().shape: warnings.warn("input not of the size (n, ), attempting to flatten") return False else: From 1ca570aaf089a1c06a56ca0e2655efeb9e133b65 Mon Sep 17 00:00:00 2001 From: Jan_Rosa Date: Thu, 4 Jul 2019 13:31:27 +0200 Subject: [PATCH 029/242] new installation instruction --- Installation_instruction.md | 171 ++++++++++++++++++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 Installation_instruction.md diff --git a/Installation_instruction.md b/Installation_instruction.md new file mode 100644 index 000000000..2ebda10a8 --- /dev/null +++ b/Installation_instruction.md @@ -0,0 +1,171 @@ +# Installing HARK +HARK is an open source project written in Python. It's compatible with both Python +2 and 3, and with the Anaconda distributions of python 2 and 3. + +## Instructions for a new user +In order to use HARK, you firstly need to download Python and add Python to the PATH. We recommend to install python 3, as eventually support for python 2 will end. If you are not confident about the installation process you can use this step-by-step guide https://realpython.com/installing-python/ . + +Next, install text editor for Python. If you do not use Anaconda Python distribution (see below), we recommend [Atom](https://atom.io/). To use Atom for Python, firstly install it (you can use this [manual](https://flight-manual.atom.io/getting-started/sections/installing-atom/)). Next, install the [packages](https://flight-manual.atom.io/using-atom/sections/atom-packages/) for Python, we recommend to install at least [autocomplete-python](https://atom.io/packages/autocomplete-python) and [atom-ide-debugger-python](https://flight-manual.atom.io/using-atom/sections/atom-packages/). The last enables debugging the Python scripts, that is to set breakpoints and call variables in certain spot of your code (with increase in your codes' sophistication you will find this tools very helpful in finding bugs). + +After installing Python and the text editor, you can install HARK package. The simplest way is to use [pip](https://pip.pypa.io/en/stable/installing/). + +To install HARK with pip, at a command line type `pip install econ-ark`. + +If you prefer to isolate the installation of `econ-ark` from the installations of any other python tools and packages, use a virtual environment such as [virtualenv](https://virtualenv.pypa.io/en/latest/). + + + +To install `virtualenv`, then to create an environment named `econ-ark`, and finally to activate that environment, at a command line type: + +``` +cd [directory where you want to store the econ-ark virtual environment] +pip install virtualenv +virtualenv econ-ark +``` +Then for Windows, type: +``` +econ-ark\Scripts\activate.bat + +``` +While for Mac or Linux: +``` +source econ-ark/bin/activate +``` +In the both cases, you may see (econ-ark) in your command prompt. + +Next, install `econ-ark` into your new virtual environment via pip: + +``` +pip install econ-ark +``` + + +--- +**!NOTE** + +If you install econ-ark into the virtual environment, your HARK scripts will not compile unless it is activated. + +To do so, type for Windows: +``` +cd [directory where you want to store the econ-ark virtual environment] +econ-ark\Scripts\activate.bat +``` +While for Mac or Linux: +``` +cd [directory where you want to store the econ-ark virtual environment] +source econ-ark/bin/activate +``` +Next, run your script +``` +cd [directory where you located your script] +python YourScript.py +``` +For using the text editor, you also need to configure the environment. If you use [Atom](https://atom.io/), simply type `atom` at a command prompt after activating the environment. Atom text editor will open and you will be able to compile your codes which use HARK. + + --- + + + + +### Using HARK with Anaconda + +Installing HARK with pip does not give you full access to HARK's many graphical capabilities. One way to access these capabilities is by using [Anaconda](https://anaconda.com/why-anaconda), which is a distribution of python along with many packages that are frequently used in scientific computing. + +1. Download Anaconda for your operating system and follow the installation instructions [at Anaconda.com](https://www.anaconda.com/distribution/#download-section). + +--- +**!NOTE** + +You can have the default python distribution from python.org and from anaconda, as they do not interfere. However, be careful with setting the PATH. To avoid problems you can eg. set the environment variables path to the default distribution and access anaconda distribution via anaconda prompt. + +--- + +2. Anaconda includes its own virtual environment system called `conda` which stores environments in a preset location (so you don't have to choose). So in order to create and activate an econ-ark virtual environment: +``` +conda create -n econ-ark anaconda +conda activate econ-ark +``` +If you want to install `econ-ark` only into this environment, you need to firstly activate the environment and then install econ-ark via pip or conda. In the second case type: + +``` +conda install econ-ark::econ-ark +``` +About the differences between conda and pip check https://www.anaconda.com/understanding-conda-and-pip/ . + +3. Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`. If `econ-ark` is installed into the particular environment, you firstly need activate it and then type `spyder`. + +`spyder` enables debugging the Python scripts, that is to set breakpoints and call variables in certain spot of your code, in order to find bugs (with increase in your codes' sophistication you will find this tools very helpful). + +4. To verify that `spyder` has access to HARK try typing `pip install econ-ark` into the iPython shell within Spyder. If you have successfully installed HARK as above, you should see a lot of messages saying 'Requirement satisfied'. + + * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. + + * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)). + + * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone + + * If you don't want to clone HARK, but just to download it, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory. + + Once you've got a copy of HARK in a directory, return to Spyder and navigate to that directory where you put HARK. This can be done within Spyder by doing `import os` and then using `os.chdir()` to change directories. `chdir` works just like cd at a command prompt on most operating systems, except that it takes a string as input: `os.chdir('Music')` moves to the Music subdirectory of the current working directory. Alternatively, you can install `econ-ark` from the local repository by navigating to that directory where you put HARK and then typing at the command line: + ``` + pip install -e . + ``` + +### HARK demonstrations + +Most of the modules in HARK are just collections of tools. To look at a demonstrations, check repository: + * [DemARK](https://github.com/econ-ark/DemARK): Demonstrations of the use of HARK + +You will want to obtain your own local copy of these repos using: +``` +git clone https://github.com/econ-ark/DemARK.git +``` +Once you have downloaded them, you will find that the repo contains a `notebooks` directory that contains a number of [jupyter notebooks](https://jupyter.org/). If you have the jupyter notebook tool installed (it is installed as part of Anaconda), you should be able to launch the jupyter notebook app from the command line with the command: + +``` +jupyter notebook +``` +If you installed the econ-ark into the particular environment, activate it first and then type `jupyter notebook`. + +## Instructions for an advanced user/developer +If you want to make changes or contributions (yay!) to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files (and it's a bad idea to mess with the original code anyway because you'll likely forget what changes you made). If you are adept at GitHub, you can [fork](https://help.github.com/en/articles/fork-a-repo) the repo. If you are less experienced, you should download a personal copy of HARK again using `git clone` (see part for a new user) or the GitHub Desktop app. + +1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). If you get a permission denied error, you may need to setup SSH for GitHub, or you can clone using HTTPS: 'git clone https://github.com/econ-ark/HARK.git'. + +2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))). + +Install virtualenv if you need to and then type: + +``` +virtualenv econ-ark +source econ-ark/bin/activate +``` +For Windows: +``` +virtualenv econ-ark +econ-ark\Scripts\activate.bat +``` + +4. Make sure to change to HARK directory, and install HARK's requirements into the virtual environment with `pip install -r requirements.txt`. + +5. Install locally the econ-ark: navigate to your local repository and then type: + +``` +pip install -e . +``` + + +5. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`. +--- +**!NOTE** + +To check the package performance with your local changes, use the same command from the command line (after navigating to your HARK directory): +``` +pip install -e . +``` +If for some reason you want to switch back to the PyPI version: +``` +pip uninstall econ-ark +pip install econ-ark (options) +``` +--- From 074dcd25acff740871e69c72d0d8c79324f0d28d Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 5 Jul 2019 10:51:34 +0200 Subject: [PATCH 030/242] Fix discrete action set portfolio optimization. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 6 +- HARK/ConsumptionSaving/ConsPortfolioModel.py | 367 +++++++++++++------ 2 files changed, 266 insertions(+), 107 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index c9a57a7a8..c4aa89b70 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1499,8 +1499,8 @@ def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds): self.verbose = verbose self.quiet = quiet self.solveOnePeriod = solvePerfForesight # solver for perfect foresight model - - + + def preSolve(self): self.updateSolutionTerminal() @@ -2119,7 +2119,7 @@ def __init__(self,cycles=1,time_flow=True,**kwds): # Add consumer-type specific objects, copying to create independent versions self.solveOnePeriod = solveConsKinkedR # kinked R solver self.update() # Make assets grid, income process, terminal solution - + def preSolve(self): self.updateSolutionTerminal() diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 41af0ef4e..395666a9f 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -162,14 +162,18 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): if not hasattr(self, 'PortfolioDomain'): if self.AdjustPrb < 1.0: - raise Exception("Please supply a PortfolioDomain when setting AdjustPrb < 1.0.") + raise Exception('Please supply a PortfolioDomain when setting AdjustPrb < 1.0.') else: self.PortfolioDomain = ContinuousDomain(0,1) - if isinstance(self.PortfolioDomain, DiscreteDomain): - if self.vFuncBool == False and self.verbose: - print("Setting vFuncBool to True to accomodate dicrete portfolio optimization.") - self.vFuncBool = True + if self.vFuncBool == False: + if self.verbose: + print('Setting vFuncBool to True to accomodate dicrete portfolio optimization.') + + self.vFuncBool = True + else: + if self.AdjustPrb < 1.0: + raise Exception('Occational inability to re-optimize portfolio (AdjustPrb < 1.0) is currently not possible with continuous choice of the portfolio share.') # Now we can set up the PortfolioGrid! This is the portfolio values # you can enter the period with. It's exact for discrete , for continuous @@ -212,18 +216,19 @@ def updateSolutionTerminal(self): RiskyShareFunc_terminal = LinearInterp([0.0, 1.0], [0.0,0.0]) # c=m in terminal period if isinstance(self.PortfolioDomain, DiscreteDomain): - PortfolioDomainCount = len(self.PortfolioDomain.points) + PortfolioGridCount = len(self.PortfolioDomain.points) else: - # This should be "PortfolioDomainCount" that was set earlier, - PortfolioDomainCount = 1 + # This should be "PortfolioGridCount" that was set earlier, + PortfolioGridCount = 1 - cFunc_terminal = PortfolioDomainCount*[cFunc_terminal] - RiskyShareFunc_terminal = PortfolioDomainCount*[RiskyShareFunc_terminal] + cFunc_terminal = PortfolioGridCount*[cFunc_terminal] + RiskyShareFunc_terminal = PortfolioGridCount*[RiskyShareFunc_terminal] - vFunc_terminal = PortfolioDomainCount*[ValueFunc(self.cFunc_terminal_,self.CRRA)] - vPfunc_terminal = PortfolioDomainCount*[MargValueFunc(self.cFunc_terminal_,self.CRRA)] + vFunc_terminal = PortfolioGridCount*[ValueFunc(self.cFunc_terminal_,self.CRRA)] + vPfunc_terminal = PortfolioGridCount*[MargValueFunc(self.cFunc_terminal_,self.CRRA)] # repeat according to number of portfolio adjustment situations + # TODO FIXME this is technically incorrect, too many in [0] cFunc_terminal = self.AdjustCount*[cFunc_terminal] RiskyShareFunc_terminal = self.AdjustCount*[RiskyShareFunc_terminal] @@ -259,34 +264,21 @@ def getPostStates(self): # non-adjusters for the next section. RiskyShareNow = np.zeros(self.AgentCount) + np.nan - - PortfolioDomainCount = len(self.PortfolioGrid) - for t in range(self.T_cycle): # We need to take into account whether they have drawn a portfolio # adjust shock or not. these_adjust = self.CantAdjust == 0 these_t = t == self.t_cycle - these_pre_portfolio = np.logical_and(these_adjust, these_t) - for portfolio_index, portfolio_value in enumerate(self.PortfolioGrid): - if self.AdjustPrb == 1.0: - # We're in the AdjustPrb = 1, case, so there is just one - # of each solution function, set all these_portfolio to 0 - these_portfolio = 0*these_t - these = these_t - else: - these_portfolio = np.equal(portfolio_value, self.RiskySharePrev) - these = np.logical_and(these_t, these_portfolio) - # Now that we have the "these" indeces and the portfolio indeces - # we can update the appropriate RiskyShare's. - RiskyShareNow[these] = self.solution[t].RiskyShareFunc[0][portfolio_index](self.aNrmNow[these]) # should be redefined on mNrm in solve and calculated in getControls + # First take adjusters + these = np.logical_and(these_adjust, these_t) + RiskyShareNow[these] = self.solution[t].RiskyShareFunc[0][0](self.aNrmNow[these]) # should be redefined on mNrm in solve and calculated in getControls + + these_cant_adjust = self.CantAdjust == 1 + these = np.logical_and(these_cant_adjust, these_t) + RiskyShareNow[these] = self.RiskySharePrev[these] # should be redefined on mNrm in solve and calculated in getControls + - # We already updated all the adjusters in their respective time periods. - # Now we update all the non-adjusters by repeating their current share. - nonadjusters = self.CantAdjust==1 - if any(nonadjusters): - RiskyShareNow[nonadjusters] = self.RiskySharePrev[nonadjusters] # Store the result in self self.RiskyShareNow = RiskyShareNow return None @@ -376,23 +368,21 @@ def getControls(self): cNrmNow = np.zeros(self.AgentCount) + np.nan MPCnow = np.zeros(self.AgentCount) + np.nan - PortfolioDomainCount = len(self.PortfolioGrid) these_cant_adjust = self.CantAdjust == 1 + these_can_adjust = self.CantAdjust == 0 for t in range(self.T_cycle): these_t = t == self.t_cycle - for portfolio_index, portfolio_value in enumerate(self.PortfolioGrid): - if self.AdjustPrb == 1.0: - # We're in the AdjustPrb = 1, case, so there is just one - # of each solution function + these = np.logical_and(these_t, these_can_adjust) + cNrmNow[these], MPCnow[these] = self.solution[t].cFunc[0][0].eval_with_derivative(self.mNrmNow[these]) - these_portfolio = these_t*0 - these = these_t - else: + if any(these_cant_adjust): + for portfolio_index, portfolio_value in enumerate(self.ShareNow[AdjustIndex]): these_portfolio = np.equal(portfolio_value, self.RiskySharePrev) these = np.logical_and(these_t, these_portfolio) - cNrmNow[these], MPCnow[these] = self.solution[t].cFunc[0][portfolio_index].eval_with_derivative(self.mNrmNow[these]) + cNrmNow[these], MPCnow[these] = self.solution[t].cFunc[1][portfolio_index].eval_with_derivative(self.mNrmNow[these]) + self.cNrmNow = cNrmNow self.MPCnow = MPCnow return None @@ -409,10 +399,20 @@ def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, ConsIndShockSolver.__init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool) + + self.PortfolioDomain = PortfolioDomain + if isinstance(self.PortfolioDomain, DiscreteDomain): + self.DiscreteCase = True + else: + self.DiscreteCase = False + self.AdjustPrb = AdjustPrb self.PortfolioGrid = PortfolioGrid - self.PortfolioDomain = PortfolioDomain self.AdjustCount = AdjustCount + self.ShareNowCount = [1] + if self.DiscreteCase: + self.ShareNow = self.PortfolioDomain.getPoints() + self.ShareNowCount.append(len(self.PortfolioDomain.getPoints())) # Store the Risky asset shock distribution self.RiskyDstn = approxRiskyDstn(RiskyCount) @@ -421,16 +421,110 @@ def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, # Store the number of grid points used approximate the FOC in the port- # folio sub-problem. self.RiskyShareCount = RiskyShareCount - self.vPfuncsNext = solution_next.vPfunc - if isinstance(self.PortfolioDomain, DiscreteDomain): - self.DiscreteCase = True - else: - self.DiscreteCase = False + self.vFuncsNext = solution_next.vFunc + self.vPfuncsNext = solution_next.vPfunc self.updateShockDstn() self.makeRshareGrid() + + def makeEndOfPrdvFunc(self, AdjustIndex, ShareIndex): + ''' + Construct the end-of-period value function for this period, storing it + as an attribute of self for use by other methods. + + Parameters + ---------- + none + + Returns + ------- + none + ''' + if not self.DiscreteCase: + raise Exception("vFuncBool == True is not supported for continuous portfolio choice.") + + # We will need to index vFuncNext wrt the state next period given choices + # today. + VLvlNext = (self.PermShkVals_temp**(1.0-self.CRRA)*\ + self.PermGroFac**(1.0-self.CRRA))*self.vFuncsNext[AdjustIndex][ShareIndex](self.mNrmNext[AdjustIndex][ShareIndex]) + EndOfPrdv = self.DiscFacEff*np.sum(VLvlNext*self.ShkPrbs_temp,axis=0) + EndOfPrdvNvrs = self.uinv(EndOfPrdv) # value transformed through inverse utility + # Manually input (0,0) pair + EndOfPrdvNvrs = np.insert(EndOfPrdvNvrs,0,0.0) + aNrm_temp = np.insert(self.aNrmNow,0,0.0) + + EndOfPrdvNvrsFunc = LinearInterp(aNrm_temp,EndOfPrdvNvrs) + self.EndOfPrdvFunc = ValueFunc(EndOfPrdvNvrsFunc,self.CRRA) + + + def makevFunc(self,solution, AdjustIndex, ShareIndex): + ''' + Creates the value function for this period, defined over market resources m. + self must have the attribute EndOfPrdvFunc in order to execute. + + Parameters + ---------- + solution : ConsumerSolution + The solution to this single period problem, which must include the + consumption function. + + Returns + ------- + vFuncNow : ValueFunc + A representation of the value function for this period, defined over + normalized market resources m: v = vFuncNow(m). + ''' + # Compute expected value and marginal value on a grid of market resources + mNrm_temp = self.mNrmMinNow + self.aXtraGrid + cNrmNow = solution.cFunc[AdjustIndex][ShareIndex](mNrm_temp) + aNrmNow = mNrm_temp - cNrmNow + vNrmNow = self.u(cNrmNow) + self.EndOfPrdvFunc(aNrmNow) + + # Construct the beginning-of-period value function + vNvrs = self.uinv(vNrmNow) # value transformed through inverse utility + # Manually insert (0,0) pair. + mNrm_temp = np.insert(mNrm_temp,0,0.0) # np.insert(mNrm_temp,0,self.mNrmMinNow) + vNvrs = np.insert(vNvrs,0,0.0) + vNvrsFuncNow = LinearInterp(mNrm_temp,vNvrs) + vFuncNow = ValueFunc(vNvrsFuncNow,self.CRRA) + return vFuncNow + + def addvFunc(self,solution): + ''' + Creates the value function for this period and adds it to the solution. + + Parameters + ---------- + solution : ConsumerSolution + The solution to this single period problem, likely including the + consumption function, marginal value function, etc. + + Returns + ------- + solution : ConsumerSolution + The single period solution passed as an input, but now with the + value function (defined over market resources m) as an attribute. + ''' + if not self.DiscreteCase: + raise Exception('You\'re not supposed to be here. Continuous choice portfolio domain does not support vFuncBool == True or AdjustPrb < 1.0.') + + vFunc = self.AdjustCount*[[]] + + for AdjustIndex in range(self.AdjustCount): # nonadjuster possible! + # this is where we add to vFunc based on non-adjustment. + # Basically repeat the above with the share updated to be the "prev" + # share an. We need to keep mNrmNext at two major indeces: adjust and + # non-adjust. Adjust will just have one element, but non-adjust will need + # one for each of the possible current ("prev") values. + for ShareIndex in range(self.ShareNowCount[AdjustIndex]): # for all share level indeces in the adjuster (1) case + self.makeEndOfPrdvFunc(AdjustIndex, ShareIndex) + vFunc[AdjustIndex].append(self.makevFunc(solution, AdjustIndex, ShareIndex)) + + solution.vFunc = vFunc + return solution + def updateShockDstn(self): self.ShockDstn = combineIndepDstns(self.IncomeDstn, self.RiskyDstn) @@ -443,6 +537,12 @@ def makeRshareGrid(self): return [] def prepareToCalcRiskyShare(self): + if self.DiscreteCase: + self.prepareToCalcRiskyShareDiscrete() + else: + self.prepareToCalcRiskyShareContinuous() + + def prepareToCalcRiskyShareContinuous(self): # Hard restriction on aNrm. We'd need to define more elaborate model # specifics if a could become negative (or a positive return shock # would make you worse off!) @@ -469,18 +569,53 @@ def prepareToCalcRiskyShare(self): i_s += 1 i_a += 1 - return vHatP + self.vHatP = vHatP + + def prepareToCalcRiskyShareDiscrete(self): + # Hard restriction on aNrm. We'd need to define more elaborate model + # specifics if a could become negative (or a positive return shock + # would make you worse off!) + aNrmPort = self.aXtraGrid[self.aXtraGrid >= 0] + + self.aNrmPort = aNrmPort + RshareGrid = self.ShareNow + self.RshareNow = np.array([]) + vHat = np.zeros((len(aNrmPort), len(RshareGrid))) + + # Evaluate the non-constant part of the first order conditions wrt the + # portfolio share. This requires the implied resources tomorrow given + # todays shocks to be evaluated. + i_a = 0 + for a in aNrmPort: + # for all possible a's today + i_s = 0 + for s in RshareGrid: + Rtilde = self.RiskyShkValsNext - self.Rfree + Reff = self.Rfree + Rtilde*s + mNrmNext = a*Reff/(self.PermGroFac*self.PermShkValsNext) + self.TranShkValsNext + + + VLvlNext = (self.PermShkValsNext**(1.0-self.CRRA)*\ + self.PermGroFac**(1.0-self.CRRA))*self.vFuncNext(mNrmNext) + vHat_a_s = self.DiscFacEff*np.sum(VLvlNext*self.ShkPrbsNext,axis=0) + + vHat[i_a, i_s] = vHat_a_s + i_s += 1 + i_a += 1 + + self.vHat = vHat def calcRiskyShare(self): if self.DiscreteCase: - RiskyShareFunc = self.calcRiskyShareContinuous() - else: RiskyShareFunc = self.calcRiskyShareDiscrete() + else: + RiskyShareFunc = self.calcRiskyShareContinuous() return RiskyShareFunc def calcRiskyShareContinuous(self): + # This should be fixed by an insert 0 aGrid = np.array([0.0,]) Rshare = np.array([1.0,]) @@ -493,33 +628,34 @@ def calcRiskyShareContinuous(self): Rshare = np.append(Rshare, 0.0) else: residual = LinearInterp(self.RshareGrid, self.vHatP[i_a, :]) - zero = sciopt.fsolve(residual, 0.5) + zero = sciopt.fsolve(residual, Rshare[-1]) Rshare = np.append(Rshare, zero) i_a += 1 RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit, slope_limit=0) # HAVE to specify the slope limit return RiskyShareFunc def calcRiskyShareDiscrete(self): + # Based on the end-of-period value function, we calculate the best + # choice today for a range of a values (those given in aNrmPort). + + # Should just use insert below ( at 0) aGrid = np.array([0.0,]) - Rshare = np.array([1.0,]) + Rshare = np.array([1.0,]) # is it true for AdjustPrb < 1? i_a = 0 # For all positive aNrms for a in self.aNrmPort: - aGrid = np.append(aGrid, a) - if self.vHatP[i_a, -1] >= 0.0: - Rshare = np.append(Rshare, 1.0) - elif self.vHatP[i_a, 0] < 0.0: - Rshare = np.append(Rshare, 0.0) - else: - residual = LinearInterp(self.RshareGrid, self.vHatP[i_a, :]) - zero = sciopt.fsolve(residual, 0.5) - Rshare = np.append(Rshare, zero) + # all values at portfolio shares should be calculated + # argmax gives optimal portfolio + share_argmax = np.argmax(self.vHat[i_a, :]) + Rshare = np.append(Rshare, self.ShareNow[share_argmax]) i_a += 1 - RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit, slope_limit=0) # HAVE to specify the slope limit + + # TODO FIXME find limiting share for perf foresight + RiskyShareFunc = scipy.interpolate.interp1d(np.insert(self.aNrmPort, 0, 0.0), Rshare, kind='zero',bounds_error=False, fill_value=Rshare[-1]) return RiskyShareFunc - def prepareToCalcEndOfPrdvP(self, adjuster=True): + def prepareToCalcEndOfPrdvP(self): ''' Prepare to calculate end-of-period marginal value by creating an array of market resources that the agent could have next period, considering @@ -554,22 +690,27 @@ def prepareToCalcEndOfPrdvP(self, adjuster=True): RiskyShkVals_temp = (np.tile(self.RiskyShkValsNext,(aNrmCount,1))).transpose() ShkPrbs_temp = (np.tile(self.ShkPrbsNext,(aNrmCount,1))).transpose() - # Calculate share at current aNrm. If non-adjusting, it's just - # self.RiskySharePrev, else we use the recently calculated RiskyShareFunc - if adjuster: - sAt_aNrm = self.RiskyShareFunc(aNrmNow) - else: - sAt_aNrm = self.RiskySharePrev - - # Get cash on hand next period. - # Compose possible return factors - Rtilde = RiskyShkVals_temp - self.Rfree - # Combine into effective returns factors, taking into account the share - self.Reff = (self.Rfree + Rtilde*sAt_aNrm) - # Apply the permanent growth factor and possible permanent shocks - mNrmPreTran = self.Reff/(self.PermGroFac*PermShkVals_temp)*aNrm_temp - # Add transitory income - mNrmNext = mNrmPreTran + TranShkVals_temp + mNrmNext = self.AdjustCount*[[]] + for AdjustIndex in range(self.AdjustCount): + for ShareIndex in range(self.ShareNowCount[AdjustIndex]): + # Calculate share at current aNrm. If non-adjusting, it's just + # self.RiskySharePrev, else we use the recently calculated RiskyShareFunc + # First generate mNrmNext for adjusters + if AdjustIndex == 0: # adjust + sAt_aNrm = self.RiskyShareFunc(aNrmNow) + # Then generate for non-adjusters + else: # non-adjuster + sAt_aNrm = self.ShareNow[ShareIndex] + + # Get cash on hand next period. + # Compose possible return factors + self.Rtilde = RiskyShkVals_temp - self.Rfree + # Combine into effective returns factors, taking into account the share + self.Reff = (self.Rfree + self.Rtilde*sAt_aNrm) + # Apply the permanent growth factor and possible permanent shocks + mNrmPreTran = self.Reff/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + # Add transitory income + mNrmNext[AdjustIndex].append(mNrmPreTran + TranShkVals_temp) # Store and report the results self.PermShkVals_temp = PermShkVals_temp @@ -594,9 +735,14 @@ def calcEndOfPrdvP(self): A 1D array of end-of-period marginal value of assets ''' - EndOfPrdvP = np.sum(self.DiscFacEff*self.Reff*self.PermGroFac**(-self.CRRA)* - self.PermShkVals_temp**(-self.CRRA)* - self.vPfuncNext(self.mNrmNext)*self.ShkPrbs_temp,axis=0) + EndOfPrdvP = self.AdjustCount*[[]] + for AdjustIndex in range(self.AdjustCount): + for ShareIndex in range(self.ShareNowCount[AdjustIndex]): + mNrmNext = self.mNrmNext[AdjustIndex][ShareIndex] + EndOfPrdvP[AdjustIndex].append(np.sum(self.DiscFacEff*self.Reff*self.PermGroFac**(-self.CRRA)* + self.PermShkVals_temp**(-self.CRRA)* + self.vPfuncNext(mNrmNext)*self.ShkPrbs_temp,axis=0)) + return EndOfPrdvP @@ -709,48 +855,61 @@ def solve(self): The solution to the one period problem. ''' - PortfolioDomainCount = len(self.PortfolioGrid) + # TODO FIXME + # This code is a mix of looping over states ( for the Risky Share funcs) + # and implicit looping such as in prepareToCalcEndOfPrdvP. I think it would + # be best to simply have a major loop here, and keep the methods atomic. cFuncs = self.AdjustCount*[[]] vPfuncs = self.AdjustCount*[[]] RiskyShareFuncs = self.AdjustCount*[[]] - for adjusterState in range(self.AdjustCount): # stupid name, should be related to adjusting - if adjusterState == 1: - adjuster = False - else: - adjuster = True + for AdjustIndex in range(self.AdjustCount): + for PortfolioGridIdx in range(self.ShareNowCount[AdjustIndex]): + if self.DiscreteCase: + self.vFuncNext = self.vFuncsNext[AdjustIndex][PortfolioGridIdx] + + self.vPfuncNext = self.vPfuncsNext[AdjustIndex][PortfolioGridIdx] - for PortfolioGridIdx in range(PortfolioDomainCount): - self.vPfuncNext = self.vPfuncsNext[adjusterState][PortfolioGridIdx] self.PortfolioGridIdx = PortfolioGridIdx # If it can adjust, solve the first sub-problem: the portfolio choice - if adjuster: - self.vHatP = self.prepareToCalcRiskyShare() + if AdjustIndex == 0: + self.prepareToCalcRiskyShare() self.RiskyShareFunc = self.calcRiskyShare() else: self.RiskyShareFunc = lambda a: PortfolioGrid[PortfolioGridIdx] - # Then solve the consumption choice given optimal portfolio choice - aNrm = self.prepareToCalcEndOfPrdvP(adjuster = adjuster) - EndOfPrdvP = self.calcEndOfPrdvP() + RiskyShareFuncs[AdjustIndex].append(self.RiskyShareFunc) - # Todo! - self.cFuncLimitIntercept = None - self.cFuncLimitSlope = None - cs_solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc) - cFuncs[adjusterState].append(cs_solution.cFunc) - vPfuncs[adjusterState].append(cs_solution.vPfunc) - # This is a good place to make it defined at m!!! - RiskyShareFuncs[adjusterState].append(self.RiskyShareFunc) + # Then solve the consumption choice given optimal portfolio choice + aNrm = self.prepareToCalcEndOfPrdvP() + + # Seems like this could already have been done above? TODO:Calculate + # and save both and compare. + EndOfPrdvP = self.calcEndOfPrdvP() + # Todo! + self.cFuncLimitIntercept = None + self.cFuncLimitSlope = None + + # Generate all the solutions + for AdjustIndex in range(self.AdjustCount): # stupid name, should be related to adjusting + for PortfolioGridIdx in range(self.ShareNowCount[AdjustIndex]): + cs_solution = self.makeBasicSolution(EndOfPrdvP[AdjustIndex][PortfolioGridIdx],aNrm,self.makeLinearcFunc) + cFuncs[AdjustIndex].append(cs_solution.cFunc) + vPfuncs[AdjustIndex].append(cs_solution.vPfunc) + # This is a good place to make it defined at m!!! # solution = self.addMPCandHumanWealth(solution) solution = PortfolioSolution(cFunc=cFuncs, vPfunc=vPfuncs, RiskyShareFunc=RiskyShareFuncs) + + if self.vFuncBool: + solution = self.addvFunc(solution) + return solution From 5e6beb06193fcb943c863c8d974362c031d724fe Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 5 Jul 2019 14:10:18 +0200 Subject: [PATCH 031/242] Fix occational adjusters. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 85 +++++++++++++++----- 1 file changed, 67 insertions(+), 18 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 395666a9f..4242904e1 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -6,10 +6,11 @@ import scipy.optimize as sciopt import scipy.integrate import scipy.stats as stats +import copy from HARK import Solution, NullFunc, AgentType -from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType, solveConsIndShock, ConsIndShockSolver, ValueFunc, MargValueFunc, ConsumerSolution +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType, solveConsIndShock, ConsIndShockSolver, ValueFunc, MargValueFunc, ConsumerSolution, utility_inv from HARK.utilities import approxLognormal, combineIndepDstns from HARK.interpolation import LinearInterp, LowerEnvelope from HARK.simulation import drawLognormal @@ -77,7 +78,7 @@ def PerfForesightDiscretePortfolioObjective(share, R0, RiskyDstn, rho): class PortfolioSolution(Solution): distance_criteria = ['cFunc'] - def __init__(self, cFunc=None, vFunc=None, + def __init__(self, cFunc=None, vFunc=None, wFunc=None, vPfunc=None, RiskyShareFunc=None, vPPfunc=None, mNrmMin=None, hNrm=None, MPCmin=None, MPCmax=None): # Change any missing function inputs to NullFunc @@ -95,6 +96,7 @@ def __init__(self, cFunc=None, vFunc=None, self.vFunc = vFunc self.vPfunc = vPfunc self.RiskyShareFunc = RiskyShareFunc + self.wFunc = wFunc # self.vPPfunc = vPPfunc # self.mNrmMin = mNrmMin # self.hNrm = hNrm @@ -131,6 +133,29 @@ def len(self): def getPoints(self): return self.points + +def calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc, CRRA): + # AdjustCount cna be inferred from AdjustPrb... + wFunc = [] + if AdjustCount == 1: + # Just take the adjuster + for ShareIndex in range(ShareNowCount[0]): + wFunc.append(vFunc[0][ShareIndex]) + else: + # Calculate stuff + for ShareIndex in range(ShareNowCount[1]): + # TODO FIXME better grid + evalgrid = np.linspace(0, 100, 200) + evVals = AdjustPrb*vFunc[0][ShareIndex](evalgrid) + (1-AdjustPrb)*vFunc[1][ShareIndex](evalgrid) + with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): + evValsNvrs = utility_inv(evVals,gam=CRRA) + evVals = np.insert(evVals, 0, 0.0) + evValsNvrs = np.insert(evVals, 0, 0.0) + + wFunc.append(ValueFunc(LinearInterp(evVals, evValsNvrs), CRRA)) + + return wFunc + class PortfolioConsumerType(IndShockConsumerType): # We add CantAdjust to the standard set of poststate_vars_ here. We call it @@ -165,13 +190,17 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): raise Exception('Please supply a PortfolioDomain when setting AdjustPrb < 1.0.') else: self.PortfolioDomain = ContinuousDomain(0,1) + + if isinstance(self.PortfolioDomain, DiscreteDomain): + self.DiscreteCase = True if self.vFuncBool == False: if self.verbose: print('Setting vFuncBool to True to accomodate dicrete portfolio optimization.') self.vFuncBool = True else: + self.DiscreteCase = False if self.AdjustPrb < 1.0: raise Exception('Occational inability to re-optimize portfolio (AdjustPrb < 1.0) is currently not possible with continuous choice of the portfolio share.') @@ -185,7 +214,10 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): print("Setting BoroCnstArt to 0.0 as this is required by PortfolioConsumerType.") self.BoroCnstArt = 0.0 - + self.ShareNowCount = [1] + if self.DiscreteCase and self.AdjustCount > 1: + self.ShareNow = self.PortfolioDomain.getPoints() + self.ShareNowCount.append(len(self.PortfolioDomain.getPoints())) # Chose specialized solver for Portfolio choice model self.solveOnePeriod = solveConsPortfolio @@ -211,6 +243,9 @@ def updateSolutionTerminal(self): ------- None ''' + + # repeat according to number of portfolio adjustment situations + # TODO FIXME this is technically incorrect, too many in [0] cFunc_terminal = LinearInterp([0.0, 1.0], [0.0,1.0]) # c=m in terminal period vFunc_terminal = LinearInterp([0.0, 1.0], [0.0,0.0]) # This is overwritten RiskyShareFunc_terminal = LinearInterp([0.0, 1.0], [0.0,0.0]) # c=m in terminal period @@ -221,23 +256,24 @@ def updateSolutionTerminal(self): # This should be "PortfolioGridCount" that was set earlier, PortfolioGridCount = 1 - cFunc_terminal = PortfolioGridCount*[cFunc_terminal] - RiskyShareFunc_terminal = PortfolioGridCount*[RiskyShareFunc_terminal] + vFunc_terminal = PortfolioGridCount*[ValueFunc(cFunc_terminal, self.CRRA)] + vFunc_terminal = self.AdjustCount*[vFunc_terminal] - vFunc_terminal = PortfolioGridCount*[ValueFunc(self.cFunc_terminal_,self.CRRA)] - vPfunc_terminal = PortfolioGridCount*[MargValueFunc(self.cFunc_terminal_,self.CRRA)] + vPfunc_terminal = PortfolioGridCount*[MargValueFunc(cFunc_terminal, self.CRRA)] + vPfunc_terminal = self.AdjustCount*[vPfunc_terminal] - # repeat according to number of portfolio adjustment situations - # TODO FIXME this is technically incorrect, too many in [0] + cFunc_terminal = PortfolioGridCount*[cFunc_terminal] cFunc_terminal = self.AdjustCount*[cFunc_terminal] + + RiskyShareFunc_terminal = PortfolioGridCount*[RiskyShareFunc_terminal] RiskyShareFunc_terminal = self.AdjustCount*[RiskyShareFunc_terminal] - vFunc_terminal = self.AdjustCount*[vFunc_terminal] - vPfunc_terminal = self.AdjustCount*[vPfunc_terminal] + wFunc_terminal = calcwFunc(self.AdjustPrb, self.AdjustCount, self.ShareNowCount, vFunc_terminal, self.CRRA) self.solution_terminal = PortfolioSolution(cFunc = cFunc_terminal, RiskyShareFunc = RiskyShareFunc_terminal, vFunc = vFunc_terminal, + wFunc = wFunc_terminal, vPfunc = vPfunc_terminal, mNrmMin=0.0, hNrm=None, MPCmin=None, MPCmax=None) @@ -377,7 +413,7 @@ def getControls(self): cNrmNow[these], MPCnow[these] = self.solution[t].cFunc[0][0].eval_with_derivative(self.mNrmNow[these]) if any(these_cant_adjust): - for portfolio_index, portfolio_value in enumerate(self.ShareNow[AdjustIndex]): + for portfolio_index, portfolio_value in enumerate(self.ShareNow): these_portfolio = np.equal(portfolio_value, self.RiskySharePrev) these = np.logical_and(these_t, these_portfolio) @@ -690,7 +726,11 @@ def prepareToCalcEndOfPrdvP(self): RiskyShkVals_temp = (np.tile(self.RiskyShkValsNext,(aNrmCount,1))).transpose() ShkPrbs_temp = (np.tile(self.ShkPrbsNext,(aNrmCount,1))).transpose() - mNrmNext = self.AdjustCount*[[]] + if self.AdjustCount == 1: + mNrmNext = [[]] + else: + mNrmNext = [[], []] + for AdjustIndex in range(self.AdjustCount): for ShareIndex in range(self.ShareNowCount[AdjustIndex]): # Calculate share at current aNrm. If non-adjusting, it's just @@ -790,7 +830,7 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): self.vPPfuncNext = solution_next.vPPfunc if self.vFuncBool: - self.vFuncNext = solution_next.vFunc + self.vFuncNext = solution_next.wFunc # Update the bounding MPCs and PDV of human wealth: # self.PatFac = ((self.Rfree*self.DiscFacEff)**(1.0/self.CRRA))/self.Rfree @@ -860,9 +900,15 @@ def solve(self): # and implicit looping such as in prepareToCalcEndOfPrdvP. I think it would # be best to simply have a major loop here, and keep the methods atomic. - cFuncs = self.AdjustCount*[[]] - vPfuncs = self.AdjustCount*[[]] - RiskyShareFuncs = self.AdjustCount*[[]] + if self.AdjustCount == 1: + cFuncs = [[]] + vPfuncs = [[]] + RiskyShareFuncs = [[]] + else: + cFuncs = [[], []] + vPfuncs = [[], []] + RiskyShareFuncs = [[], []] + for AdjustIndex in range(self.AdjustCount): for PortfolioGridIdx in range(self.ShareNowCount[AdjustIndex]): @@ -878,7 +924,8 @@ def solve(self): self.prepareToCalcRiskyShare() self.RiskyShareFunc = self.calcRiskyShare() else: - self.RiskyShareFunc = lambda a: PortfolioGrid[PortfolioGridIdx] + val = self.PortfolioGrid[PortfolioGridIdx] + self.RiskyShareFunc = scipy.interpolate.interp1d(np.array([0.0,1.0]), np.repeat(val, 2), kind='zero',bounds_error=False, fill_value=val) RiskyShareFuncs[AdjustIndex].append(self.RiskyShareFunc) @@ -909,6 +956,8 @@ def solve(self): if self.vFuncBool: solution = self.addvFunc(solution) + solution.wFunc = calcwFunc(self.AdjustPrb, self.AdjustCount, self.ShareNowCount, solution.vFunc, self.CRRA) + return solution From df5f6aa39ffedc22e7ec2ac30aceed1d4dcefaf1 Mon Sep 17 00:00:00 2001 From: Jan_Rosa Date: Sun, 7 Jul 2019 22:23:48 +0200 Subject: [PATCH 032/242] Additional comment on git clone --- Installation_instruction.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Installation_instruction.md b/Installation_instruction.md index 2ebda10a8..dfa5b053a 100644 --- a/Installation_instruction.md +++ b/Installation_instruction.md @@ -100,9 +100,9 @@ About the differences between conda and pip check https://www.anaconda.com/under * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. - * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)). + * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)). If you get a permission denied error, you may need to setup SSH for GitHub, or you can clone using HTTPS: 'git clone https://github.com/econ-ark/HARK.git'. - * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone + * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone. * If you don't want to clone HARK, but just to download it, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory. From 54b2c648fac70a1b1e11bd8a45a80688b20a1105 Mon Sep 17 00:00:00 2001 From: Jacob Dice Date: Sat, 13 Jul 2019 11:33:22 -0500 Subject: [PATCH 033/242] Conformed to PEP8 on lines 492 and 1336 --- HARK/core.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 80c3cfff4..6008d4cae 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -489,7 +489,8 @@ def simOnePeriod(self): None ''' if not hasattr(self, 'solution'): - raise Exception('Model instance does not have a solution stored. To simulate, it is necessary to run the `solve()` method of the class first.') + raise Exception('Model instance does not have a solution stored. To simulate, it is necessary' + ' to run the `solve()` method of the class first.') self.getMortality() # Replace some agents with "newborns" if self.read_shocks: # If shock histories have been pre-specified, use those @@ -1332,7 +1333,6 @@ def copy_module_to_local(full_module_name): 'y' to accept the default home directory: """+home_directory_with_module+""" 'n' to specify your own pathname\n\n""") - if target_path == 'n' or target_path == 'N': target_path = input("""Please enter the full pathname to your target directory location: """) From e0b446fc4deda41e9c1085f3036f2c41a7632107 Mon Sep 17 00:00:00 2001 From: Jacob Dice Date: Sat, 13 Jul 2019 11:40:26 -0500 Subject: [PATCH 034/242] Added documentation on where to find dcegm example --- HARK/dcegm.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/HARK/dcegm.py b/HARK/dcegm.py index 773d95f13..68b486d86 100644 --- a/HARK/dcegm.py +++ b/HARK/dcegm.py @@ -3,6 +3,8 @@ described in "The endogenous grid method for discrete-continuous dynamic choice models with (or without) taste shocks" by Iskhakov et al. (2016) [https://doi.org/10.3982/QE643 and ijrsDCEGM2017 in our Zotero] + +Example can be found in https://github.com/econ-ark/DemARK/blob/master/notebooks/DCEGM-Upper-Envelope.ipynb """ import numpy as np from HARK.interpolation import LinearInterp From ce6a260a9637094dcebbe6c7917df19fda104f4f Mon Sep 17 00:00:00 2001 From: Jacob Dice Date: Sat, 13 Jul 2019 14:29:34 -0500 Subject: [PATCH 035/242] Updated syntax to remove error in unittests --- HARK/tests/test_validators.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/HARK/tests/test_validators.py b/HARK/tests/test_validators.py index 375cd0740..0dd21e396 100644 --- a/HARK/tests/test_validators.py +++ b/HARK/tests/test_validators.py @@ -16,7 +16,7 @@ def foo(list_a, list_b): foo([1], []) except Exception: self.fail() - with self.assertRaisesRegexp( + with self.assertRaisesRegex( TypeError, 'Expected non-empty argument for parameter list_a', ): @@ -26,12 +26,12 @@ def foo(list_a, list_b): def foo(list_a, list_b): pass - with self.assertRaisesRegexp( + with self.assertRaisesRegex( TypeError, 'Expected non-empty argument for parameter list_b', ): foo([1], []) - with self.assertRaisesRegexp( + with self.assertRaisesRegex( TypeError, 'Expected non-empty argument for parameter list_a', ): From ca53f49c80f60586de34cf6f1a16d3735e48c28b Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Wed, 17 Jul 2019 12:36:43 +0200 Subject: [PATCH 036/242] add initial tests for interpolation --- HARK/tests/test_interpolation.py | 204 +++++++++++++++++++++++++++++++ 1 file changed, 204 insertions(+) create mode 100644 HARK/tests/test_interpolation.py diff --git a/HARK/tests/test_interpolation.py b/HARK/tests/test_interpolation.py new file mode 100644 index 000000000..a0118234b --- /dev/null +++ b/HARK/tests/test_interpolation.py @@ -0,0 +1,204 @@ +""" +This file implements unit tests for interpolation methods +""" +from HARK.interpolation import ( + LinearInterp, + CubicInterp, + BilinearInterp, + TrilinearInterp, + QuadlinearInterp, +) + +import numpy as np +import unittest + + +class testsLinearInterp(unittest.TestCase): + """ tests for LinearInterp, currently tests for uneven length of + x and y with user input as lists, arrays, arrays with column orientation + """ + + def setUp(self): + self.x_list = [1, 2, 3] + self.y_list = [3, 4] + self.z_list = [3, 4, 5] + self.x_array = np.array(self.x_list) + self.y_array = np.array(self.y_list) + self.z_array = np.array(self.z_list) + self.x_array_t = self.x_array.reshape(len(self.x_array), 1) + self.y_array_t = self.y_array.reshape(len(self.y_array), 1) + self.z_array_t = self.z_array.reshape(len(self.z_array), 1) + + def test_uneven_length(self): + self.assertRaises(ValueError, LinearInterp, self.x_list, self.y_list) + self.assertRaises(ValueError, LinearInterp, self.x_array, self.y_array) + self.assertRaises(ValueError, LinearInterp, self.x_array_t, self.y_array_t) + + def test_same_length(self): + linear = LinearInterp(self.x_list, self.z_list) + self.assertEqual(linear(1.5), 3.5) + linear = LinearInterp(self.x_array, self.z_array) + self.assertEqual(linear(1.5), 3.5) + linear = LinearInterp(self.x_array_t, self.z_array_t) + self.assertEqual(linear(1.5), 3.5) + + +class testsCubicInterp(unittest.TestCase): + """ tests for CubicInterp, currently tests for uneven length of + x, y and derivative with user input as lists, arrays, arrays with column orientation + """ + + def setUp(self): + self.x_list = [1, 2, 3] + self.y_list = [1, 4] + self.dydx_list = [2, 4, 6] + self.z_list = [1, 4, 9] + self.x_array = np.array(self.x_list) + self.y_array = np.array(self.y_list) + self.dydx_array = np.array(self.dydx_list) + self.z_array = np.array(self.z_list) + self.x_array_t = self.x_array.reshape(len(self.x_array), 1) + self.y_array_t = self.y_array.reshape(len(self.y_array), 1) + self.dydx_array_t = self.dydx_array.reshape(len(self.dydx_array), 1) + self.z_array_t = self.z_array.reshape(len(self.z_array), 1) + + def test_uneven_length(self): + self.assertRaises( + ValueError, CubicInterp, self.x_list, self.y_list, self.dydx_list + ) + self.assertRaises( + ValueError, CubicInterp, self.x_array, self.y_array, self.dydx_array + ) + self.assertRaises( + ValueError, CubicInterp, self.x_array_t, self.y_array_t, self.dydx_array_t + ) + + def test_same_length(self): + cube = CubicInterp(self.x_list, self.z_list, self.dydx_list) + self.assertEqual(cube(1.5), 2.25) + cube = CubicInterp(self.x_array, self.z_array, self.dydx_array) + self.assertEqual(cube(1.5), 2.25) + cube = CubicInterp(self.x_array_t, self.z_array_t, self.dydx_array_t) + self.assertEqual(cube(1.5), 2.25) + + +class testsBilinearInterp(unittest.TestCase): + """ tests for BilinearInterp, currently tests for uneven length of + x, y, f(x,y) with user input as arrays, arrays with column orientation + """ + + def setUp(self): + self.f_array = np.array([[2, 4], [4, 6]]) + self.x_array = np.array([1, 3]) + self.y_array = np.array([1, 3]) + self.z_array = np.array([1, 2, 3]) + self.z_array_t = self.z_array.reshape(len(self.z_array), 1) + self.y_array_t = self.y_array.reshape(len(self.y_array), 1) + + def test_uneven_length(self): + self.assertRaises( + ValueError, BilinearInterp, self.f_array, self.x_array, self.z_array + ) + self.assertRaises( + ValueError, BilinearInterp, self.f_array, self.x_array, self.z_array_t + ) + + def test_same_length(self): + bilinear = BilinearInterp(self.f_array, self.x_array, self.y_array) + self.assertEqual(bilinear(2, 2), 4.0) + bilinear = BilinearInterp(self.f_array, self.x_array, self.y_array_t) + self.assertEqual(bilinear(2, 2), 4.0) + + +class testsTrilinearInterp(unittest.TestCase): + """ tests for TrilinearInterp, currently tests for uneven length of + x, y, z, f(x, y, z) with user input as arrays, arrays with column orientation + """ + + def setUp(self): + self.f_array = np.array([[[3, 5], [5, 7]], [[5, 7], [7, 10]]]) + self.x_array = np.array([1, 3]) + self.y_array = np.array([1, 3]) + self.z_array = np.array([1, 3]) + self.fail_array = np.array([1, 2, 3]) + self.fail_array_t = self.z_array.reshape(len(self.z_array), 1) + self.y_array_t = self.y_array.reshape(len(self.y_array), 1) + + def test_uneven_length(self): + self.assertRaises( + ValueError, + TrilinearInterp, + self.f_array, + self.x_array, + self.y_array, + self.fail_array, + ) + self.assertRaises( + ValueError, + TrilinearInterp, + self.f_array, + self.x_array, + self.fail_array, + self.fail_array_t, + ) + + def test_same_length(self): + bilinear = TrilinearInterp( + self.f_array, self.x_array, self.y_array, self.z_array + ) + self.assertEqual(bilinear(1, 2, 2), 5.0) + bilinear = TrilinearInterp( + self.f_array, self.x_array, self.y_array_t, self.z_array + ) + self.assertEqual(bilinear(1, 2, 2), 5.0) + + +class testsQuadlinearInterp(unittest.TestCase): + """ tests for TrilinearInterp, currently tests for uneven length of + w, x, y, z, f(w, x, y, z) with user input as arrays, arrays with column orientation + """ + + def setUp(self): + self.f_array = np.array( + [ + [[[4, 6], [6, 8]], [[6, 8], [8, 11]]], + [[[6, 8], [8, 10]], [[8, 10], [10, 13]]], + ] + ) + self.x_array = np.array([1, 3]) + self.y_array = np.array([1, 3]) + self.z_array = np.array([1, 3]) + self.w_array = np.array([1, 3]) + self.fail_array = np.array([1, 2, 3]) + self.fail_array_t = self.z_array.reshape(len(self.z_array), 1) + self.y_array_t = self.y_array.reshape(len(self.y_array), 1) + + def test_uneven_length(self): + self.assertRaises( + ValueError, + QuadlinearInterp, + self.f_array, + self.x_array, + self.y_array, + self.fail_array, + self.w_array, + ) + self.assertRaises( + ValueError, + QuadlinearInterp, + self.f_array, + self.x_array, + self.fail_array, + self.fail_array_t, + self.w_array, + ) + + def test_same_length(self): + bilinear = QuadlinearInterp( + self.f_array, self.w_array, self.x_array, self.y_array, self.z_array + ) + self.assertEqual(bilinear(1, 2, 1, 2), 6.0) + bilinear = QuadlinearInterp( + self.f_array, self.w_array, self.x_array, self.y_array_t, self.z_array + ) + self.assertEqual(bilinear(1, 2, 1, 2), 6.0) From 6860d8a1fabdf7a8b18ad7cd6c687128443f85b5 Mon Sep 17 00:00:00 2001 From: Shauna Date: Thu, 25 Jul 2019 11:30:15 -0400 Subject: [PATCH 037/242] Use different assert for Python 2 v 3 --- HARK/tests/test_validators.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/HARK/tests/test_validators.py b/HARK/tests/test_validators.py index 0dd21e396..3681d9849 100644 --- a/HARK/tests/test_validators.py +++ b/HARK/tests/test_validators.py @@ -16,11 +16,19 @@ def foo(list_a, list_b): foo([1], []) except Exception: self.fail() - with self.assertRaisesRegex( - TypeError, - 'Expected non-empty argument for parameter list_a', - ): - foo([], [1]) + + if sys.version[0] == 2: + with self.assertRaisesRegexp( + TypeError, + 'Expected non-empty argument for parameter list_a', + ): + foo([], [1]) + else: + with self.assertRaisesRegex( + TypeError, + 'Expected non-empty argument for parameter list_a', + ): + foo([], [1]) @non_empty('list_a', 'list_b') def foo(list_a, list_b): From 0bd2b9a02aca29d38200ae26e1a447cf82d02f92 Mon Sep 17 00:00:00 2001 From: Shauna Date: Thu, 25 Jul 2019 11:48:02 -0400 Subject: [PATCH 038/242] Fix import error --- HARK/tests/test_validators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/tests/test_validators.py b/HARK/tests/test_validators.py index 3681d9849..6c576ae83 100644 --- a/HARK/tests/test_validators.py +++ b/HARK/tests/test_validators.py @@ -1,4 +1,4 @@ -import unittest +import unittest, sys from HARK.validators import non_empty From 9d7ebf36dae23b1d672bff1d40f52e7693490f79 Mon Sep 17 00:00:00 2001 From: Shauna Date: Thu, 25 Jul 2019 11:50:47 -0400 Subject: [PATCH 039/242] Fix type error --- HARK/tests/test_validators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/tests/test_validators.py b/HARK/tests/test_validators.py index 6c576ae83..c2446f869 100644 --- a/HARK/tests/test_validators.py +++ b/HARK/tests/test_validators.py @@ -17,7 +17,7 @@ def foo(list_a, list_b): except Exception: self.fail() - if sys.version[0] == 2: + if sys.version[0] == '2': with self.assertRaisesRegexp( TypeError, 'Expected non-empty argument for parameter list_a', From ed542ea8979882e7cc245aee7e3c4a6cb6235a5f Mon Sep 17 00:00:00 2001 From: Shauna Date: Thu, 25 Jul 2019 11:57:33 -0400 Subject: [PATCH 040/242] Fix other tests with same regexp issue --- HARK/tests/test_validators.py | 32 ++++++++++++++++++++++---------- 1 file changed, 22 insertions(+), 10 deletions(-) diff --git a/HARK/tests/test_validators.py b/HARK/tests/test_validators.py index c2446f869..a9e54e826 100644 --- a/HARK/tests/test_validators.py +++ b/HARK/tests/test_validators.py @@ -34,13 +34,25 @@ def foo(list_a, list_b): def foo(list_a, list_b): pass - with self.assertRaisesRegex( - TypeError, - 'Expected non-empty argument for parameter list_b', - ): - foo([1], []) - with self.assertRaisesRegex( - TypeError, - 'Expected non-empty argument for parameter list_a', - ): - foo([], [1]) + if sys.version[0] == '2': + with self.assertRaisesRegexp( + TypeError, + 'Expected non-empty argument for parameter list_b', + ): + foo([1], []) + with self.assertRaisesRegexp( + TypeError, + 'Expected non-empty argument for parameter list_a', + ): + foo([], [1]) + else: + with self.assertRaisesRegex( + TypeError, + 'Expected non-empty argument for parameter list_b', + ): + foo([1], []) + with self.assertRaisesRegex( + TypeError, + 'Expected non-empty argument for parameter list_a', + ): + foo([], [1]) From f29834a2822125916170ed7baca1b1985124b47d Mon Sep 17 00:00:00 2001 From: llorracc Date: Sat, 27 Jul 2019 09:55:55 +0200 Subject: [PATCH 041/242] Edits during final Bud class --- .../notebooks/DCT-Copula-Illustration.py | 8 +- .../BayerLuetticke/notebooks/OneAsset-HANK.py | 126 ++++++ HARK/BayerLuetticke/notebooks/TwoAsset.py | 391 ++++++++++++++++++ 3 files changed, 521 insertions(+), 4 deletions(-) create mode 100644 HARK/BayerLuetticke/notebooks/OneAsset-HANK.py create mode 100644 HARK/BayerLuetticke/notebooks/TwoAsset.py diff --git a/HARK/BayerLuetticke/notebooks/DCT-Copula-Illustration.py b/HARK/BayerLuetticke/notebooks/DCT-Copula-Illustration.py index 262f94a0e..72d09a72e 100644 --- a/HARK/BayerLuetticke/notebooks/DCT-Copula-Illustration.py +++ b/HARK/BayerLuetticke/notebooks/DCT-Copula-Illustration.py @@ -38,7 +38,7 @@ # $[\frac{d v}{d m} = \frac{d u}{d c}]$. # In practice, the authors solve their problem using the marginal value of money $\texttt{Vm} = dv/dm$, but because the marginal utility function is invertible it is trivial to recover $\texttt{c}$ from $(u^{\prime})^{-1}(\texttt{Vm} )$. The consumption function is therefore computed from the $\texttt{Vm}$ function -# %% {"code_folding": []} +# %% {"code_folding": [0]} # Setup stuff # This is a jupytext paired notebook that autogenerates a corresponding .py file @@ -215,14 +215,14 @@ def in_ipynb(): -# %% {"code_folding": [0]} +# %% {"code_folding": []} ## Choose an accuracy of approximation with DCT ### Determines number of basis functions chosen -- enough to match this accuracy ### EX3SS is precomputed steady-state pulled in above EX3SS['par']['accuracy'] = 0.99999 -# %% {"code_folding": [0]} +# %% {"code_folding": []} ## Implement state reduction and DCT ### Do state reduction on steady state EX3SR=FluctuationsTwoAsset(**EX3SS) # Takes StE result as input and get ready to invoke state reduction operation @@ -315,7 +315,7 @@ def DCTApprox(fullgrids,dct_index): # %% [markdown] # Depending on the accuracy level, the DCT operation choses the necessary number of basis functions used to approximate consumption function at the full grids. This is illustrated in the p31-p34 in this [slides](https://www.dropbox.com/s/46fdxh0aphazm71/presentation_method.pdf?dl=0). We show this for both 1-dimensional (m or k) or 2-dimenstional grids (m and k) in the following. -# %% {"code_folding": [0]} +# %% {"code_folding": []} ## 2D graph of consumption function: c(m) fixing k and h diff --git a/HARK/BayerLuetticke/notebooks/OneAsset-HANK.py b/HARK/BayerLuetticke/notebooks/OneAsset-HANK.py new file mode 100644 index 000000000..9b0ec72d9 --- /dev/null +++ b/HARK/BayerLuetticke/notebooks/OneAsset-HANK.py @@ -0,0 +1,126 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.2' +# jupytext_version: 1.1.3 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# +# # A One Asset HANK Model +# +# This notebook solves a New Keynesian model in which there is only a single liquid asset. This is the second model described in . For a detailed description of their solution method, see the companion two-asset HANK model notebook. + +# %% {"code_folding": [0]} +# Setup +from __future__ import print_function + +# This is a jupytext paired notebook that autogenerates a corresponding .py file +# which can be executed from a terminal command line via "ipython [name].py" +# But a terminal does not permit inline figures, so we need to test jupyter vs terminal +# Google "how can I check if code is executed in the ipython notebook" + +def in_ipynb(): + try: + if str(type(get_ipython())) == "": + return True + else: + return False + except NameError: + return False + +# Determine whether to make the figures inline (for spyder or jupyter) +# vs whatever is the automatic setting that will apply if run from the terminal +if in_ipynb(): + # %matplotlib inline generates a syntax error when run from the shell + # so do this instead + get_ipython().run_line_magic('matplotlib', 'inline') +else: + get_ipython().run_line_magic('matplotlib', 'auto') + +# The tools for navigating the filesystem +import sys +import os + +# Find pathname to this file: +my_file_path = os.path.dirname(os.path.abspath("OneAsset-HANK.ipynb")) + +# Relative directory for pickled code +code_dir = os.path.join(my_file_path, "../Assets/One/") + +sys.path.insert(0, code_dir) +sys.path.insert(0, my_file_path) + +# %% {"code_folding": [0]} +# Ignore system warnings while running the notebook +import warnings +warnings.filterwarnings('ignore') + +# Load Stationary equilibrium (StE) object EX2SS + +import pickle +os.chdir(code_dir) # Go to the directory with pickled code + +## EX2SS.p is the information in the stationary equilibrium (20: the number of illiquid and liquid weath grids ) +EX2SS=pickle.load(open("EX2SS.p", "rb")) + +# %% +from HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond import FluctuationsOneAssetIOUs, SGU_solver, plot_IRF + +# %% {"code_folding": []} +# Uncertainty Shock + +EX2SS['par']['aggrshock'] = 'Uncertainty' +EX2SS['par']['rhoS'] = 0.84 # Persistence of variance +EX2SS['par']['sigmaS'] = 0.54 # STD of variance shocks + +EX2SR=FluctuationsOneAssetIOUs(**EX2SS) +SR=EX2SR.StateReduc() + +SGUresult=SGU_solver(SR['Xss'],SR['Yss'],SR['Gamma_state'],SR['Gamma_control'],SR['InvGamma'],SR['Copula'], + SR['par'],SR['mpar'],SR['grid'],SR['targets'],SR['P_H'],SR['aggrshock'],SR['oc']) + +plot_IRF(SR['mpar'],SR['par'],SGUresult['gx'],SGUresult['hx'],SR['joint_distr'], + SR['Gamma_state'],SR['grid'],SR['targets'],SR['os'],SR['oc'],SR['Output']) + +# %% {"code_folding": [0]} +# Monetary Policy Shock + +EX2SS['par']['aggrshock'] = 'MP' +EX2SS['par']['rhoS'] = 0.0 # Persistence of variance +EX2SS['par']['sigmaS'] = 0.001 # STD of variance shocks + +EX2SR=FluctuationsOneAssetIOUs(**EX2SS) +SR=EX2SR.StateReduc() + +SGUresult=SGU_solver(SR['Xss'],SR['Yss'],SR['Gamma_state'],SR['Gamma_control'],SR['InvGamma'],SR['Copula'], + SR['par'],SR['mpar'],SR['grid'],SR['targets'],SR['P_H'],SR['aggrshock'],SR['oc']) + +plot_IRF(SR['mpar'],SR['par'],SGUresult['gx'],SGUresult['hx'],SR['joint_distr'], + SR['Gamma_state'],SR['grid'],SR['targets'],SR['os'],SR['oc'],SR['Output']) + +# %% {"code_folding": [0]} +# Productivity Shock + +EX2SS['par']['aggrshock'] = 'TFP' +EX2SS['par']['rhoS'] = 0.95 +EX2SS['par']['sigmaS'] = 0.0075 + +EX2SR=FluctuationsOneAssetIOUs(**EX2SS) +SR=EX2SR.StateReduc() + +SGUresult = SGU_solver(SR['Xss'],SR['Yss'],SR['Gamma_state'],SR['Gamma_control'],SR['InvGamma'],SR['Copula'], + SR['par'],SR['mpar'],SR['grid'],SR['targets'],SR['P_H'],SR['aggrshock'],SR['oc']) + +plot_IRF(SR['mpar'],SR['par'],SGUresult['gx'],SGUresult['hx'],SR['joint_distr'], + SR['Gamma_state'],SR['grid'],SR['targets'],SR['os'],SR['oc'],SR['Output']) + +# %% diff --git a/HARK/BayerLuetticke/notebooks/TwoAsset.py b/HARK/BayerLuetticke/notebooks/TwoAsset.py new file mode 100644 index 000000000..0589e20ec --- /dev/null +++ b/HARK/BayerLuetticke/notebooks/TwoAsset.py @@ -0,0 +1,391 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:light +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.4' +# jupytext_version: 1.1.3 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# # Two Asset HANK Model [](https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13071) +# +# [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/HARK/BayerLuetticke/notebooks?filepath=HARK%2FBayerLuetticke%2FTwoAsset.ipynb) +# +# - Adapted from original slides by Christian Bayer and Ralph Luetticke (Henceforth, 'BL') +# - Jupyter notebook originally by Seungcheol Lee +# - Further edits by Chris Carroll, Tao Wang, Edmund Crawley + +# ### Overview +# +# BL propose a method for solving Heterogeneous Agent DSGE models that uses fast tools originally employed for image and video compression to speed up a variant of the solution methods proposed by Michael Reiter. +# +# The Bayer-Luetticke method has the following broad features: +# * The model is formulated and solved in discrete time (in contrast with some other recent approaches ) like that in "When Inequality Matters for Macro and Macro Matters for Inequality" -- henceforth, the '5guys' paper +# * Solution begins by calculation of the steady-state equilibrium (StE) with no aggregate shocks +# * Both the representation of the consumer's problem and the desciption of the distribution are subjected to a form of "dimensionality reduction" +# * This means finding a way to represent them efficiently using fewer points +# * In contrast with 5guys, "Dimensionality reduction" of the consumer's decision problem is performed before any further analysis is done +# * This involves finding a representation of the policy functions using some class of basis functions +# * Dimensionality reduction of the joint distribution is accomplished using a "copula" +# * See the companion notebook for description of the copula +# * The method approximates the business-cycle-induced _deviations_ of the individual policy functions and distributions from those that characterize the riskless StE +# * This is done using the same basis functions originally optimized to match the StE individual policy function +# * The method of capturing dynamic deviations from a reference frame is akin to video compression + +# ### Setup +# +# #### The Recursive Dynamic Planning Problem +# +# BL describe their problem in a generic way; here, we will illustrate the meaning of their derivations and notation using the familiar example of the Krusell-Smith model, henceforth KS. +# +# Consider a household problem in presence of aggregate and idiosyncratic risk +# * $S_t$ is an (exogenous) aggregate state (e.g., levels of productivity and unemployment) +# * $s_{it}$ is a partly endogenous idiosyncratic state (e.g., wealth) +# * $\mu_t$ is the distribution over $s$ at date $t$ (e.g., the wealth distribution) +# * $P_{t}$ is the pricing kernel +# * It captures the info about the aggregate state that the consumer needs to know in order to behave optimally +# * e.g., KS showed that for their problem, a good _approximation_ to $P_{t}$ could be constructed using only $S_{t}$ and the aggregate capital stock $K_{t}$ +# * $\Gamma$ defines the budget set +# * This delimits the set of feasible choices $x$ that the agent can make +# +# The Bellman equation is: +# +# \begin{equation} +# v(s_{it},S_t,\mu_t) = \max\limits_{x \in \Gamma(s_{it},P_t)} u(s_{it},x) + \beta \mathbb{E}_{t} v(s_{it+1}(x,s_{it}),S_{t+1},\mu_{t+1}) +# \end{equation} +# +# which, for many types of problems, implies an Euler equation: +# \begin{equation} +# u^{\prime}\left(x(s_{it},S_t,\mu_t)\right) = \beta R(S_t,\mu_t) \mathbb{E}_{t} u^{\prime}\left(x(s_{it+1},S_{t+1},\mu_{t+1})\right) +# \end{equation} +# + +# #### Solving for the StE +# +# The steady-state equilibrium is the one that will come about if there are no aggregate risks (and consumers know this) +# +# The first step is to solve for the steady-state: +# * Discretize the state space +# * Representing the nodes of the discretization in a set of vectors +# * Such vectors will be represented by an overbar +# * e.g. $\bar{m}$ is the nodes of cash-on-hand $m$ +# * The optimal policy $\newcommand{\policy}{c}\newcommand{\Policy}{C}\policy(s_{i};P)$ induces flow utility $u_{\policy}$ whose discretization is a vector $\bar{u}_{\bar{\policy}}$ +# * Idiosyncratic dynamics are captured by a transition probability matrix $\Pi_{\bar{\policy}}$ +# * $\Pi$ is like an expectations operator +# * It depends on the vectorization of the policy function $\bar{u}_{\bar{\policy}}$ +# * $P$ is constant because in StE aggregate prices are constant +# * e.g., in the KS problem, $P$ would contain the (constant) wage and interest rates +# * In StE, the discretized Bellman equation implies +# \begin{equation} +# \bar{v} = \bar{u}_{\bar{\policy}} + \beta \Pi_{\bar{\policy}}\bar{v} +# \end{equation} +# holds for the optimal policy +# * A linear interpolator is used to represent the value function +# * For the distribution, which (by the definition of steady state) is constant: +# +# \begin{eqnarray} +# d\bar{\mu} & = & d\bar{\mu} \Pi_{\bar{\policy}} +# \end{eqnarray} +# where we differentiate in the second line because we will be representing the distribution as a histogram, which counts the _extra_ population obtained by moving up +# +# We will define an approximate equilibrium in which: +# * $\bar{\policy}$ is the vector that defines a linear interpolating policy function $\policy$ at the state nodes +# * given $P$ and $v$ +# * $v$ is a linear interpolation of $\bar{v}$ +# * $\bar{v}$ is value at the discretized nodes +# * $\bar{v}$ and $d\bar{\mu}$ solve the approximated Bellman equation +# * subject to the steady-state constraint +# * Markets clear ($\exists$ joint requirement on $\bar{\policy}$, $\mu$, and $P$; denoted as $\Phi(\bar{\policy}, \mu, P) = 0$) +# +# This can be solved by: +# 1. Given $P$, +# 1. Finding $d\bar{\mu}$ as the unit-eigenvalue of $\Pi_{\bar{\policy}}$ +# 2. Using standard solution techniques to solve the micro decision problem +# * For the constant $P$ (e.g., wage and interest rate) +# 2. Using a root-finder to solve for $P$ +# * This basically iterates the other two steps until it finds values where they are consistent + +# #### Introducing aggregate risk +# +# With aggregate risk +# * Prices $P$ and the distribution $\mu$ change over time +# +# Yet, for the household: +# * Only prices and continuation values matter +# * The distribution does not influence decisions directly + +# #### Redefining equilibrium (Reiter, 2002) +# A sequential equilibrium with recursive individual planning is: +# * A sequence of discretized Bellman equations, such that +# \begin{equation} +# v_t = \bar{u}_{P_t} + \beta \Pi_{\policy_t} v_{t+1} +# \end{equation} +# holds for policy $\policy_t$ which optimizes with respect to $v_{t+1}$ and $P_t$ +# * and a sequence of "histograms" (discretized distributions), such that +# \begin{equation} +# d\mu_{t+1} = d\mu_t \Pi_{\policy_t} +# \end{equation} +# holds given the policy $h_{t}$, that is optimal given $P_t$, $v_{t+1}$ +# * Prices, distribution, and policies lead to market clearing + +# + {"code_folding": []} +# Do some generic setup stuff +from __future__ import print_function + +# This is a jupytext paired notebook that autogenerates a corresponding .py file +# which can be executed from a terminal command line via "ipython [name].py" +# But a terminal does not permit inline figures, so we need to test jupyter vs terminal +# Google "how can I check if code is executed in the ipython notebook" + +def in_ipynb(): + try: + if str(type(get_ipython())) == "": + return True + else: + return False + except NameError: + return False + +# Determine whether to make the figures inline (for spyder or jupyter) +# vs whatever is the automatic setting that will apply if run from the terminal +if in_ipynb(): + # %matplotlib inline generates a syntax error when run from the shell + # so do this instead + get_ipython().run_line_magic('matplotlib', 'inline') +else: + get_ipython().run_line_magic('matplotlib', 'auto') + +# The tools for navigating the filesystem +import sys +import os + +# Find pathname to this file: +my_file_path = os.path.dirname(os.path.abspath("TwoAsset.ipynb")) + +# Relative directory for pickled code +code_dir = os.path.join(my_file_path, "../Assets/Two") + +sys.path.insert(0, code_dir) +sys.path.insert(0, my_file_path) + +import time + +# + {"code_folding": [0]} +## Load Stationary equilibrium (StE) object EX3SS_20 + +import pickle +os.chdir(code_dir) # Go to the directory with pickled code + +## EX3SS_20.p is the information in the stationary equilibrium (20: the number of illiquid and liquid weath grids ) +EX3SS=pickle.load(open("EX3SS_20.p", "rb")) + +## WangTao: Find the code that generates this + + +# - + +# #### Compact notation +# +# It will be convenient to rewrite the problem using a compact notation proposed by Schmidt-Grohe and Uribe (2004) +# +# The equilibrium conditions can be represented as a non-linear difference equation +# * Controls: $Y_t = [v_t \ P_t \ Z_t^Y]$ and States: $X_t=[\mu_t \ S_t \ Z_t^X]$ +# * where $Z_t$ are purely aggregate states/controls +# * Define +# \begin{align} +# F(d\mu_t, S_t, d\mu_{t+1}, S_{t+1}, v_t, P_t, v_{t+1}, P_{t+1}, \epsilon_{t+1}) +# &= \begin{bmatrix} +# d\mu_{t+1} - d\mu_t\Pi_{\policy_t} \\ +# v_t - (\bar{u}_{\policy_t} + \beta \Pi_{\policy_t}v_{t+1}) \\ +# S_{t+1} - \Policy(S_t,d\mu_t,\epsilon_{t+1}) \\ +# \Phi(\policy_t,d\mu_t,P_t,S_t) \\ +# \epsilon_{t+1} +# \end{bmatrix} +# \end{align} +# s.t. +# \begin{equation} +# \policy_t(s_{t}) = \arg \max\limits_{x \in \Gamma(s,P_t)} u(s,x) + \beta \mathop{\mathbb{E}_{t}} v_{t+1}(s_{t+1}) +# \end{equation} +# where $C$ is the transition process for $S$ +# * The solution is a function-valued difference equation: +# \begin{equation} +# \mathop{\mathbb{E}_{t}}F(X_t,X_{t+1},Y_t,Y_{t+1},\epsilon_{t+1}) = 0 +# \end{equation} +# where $\mathop{\mathbb{E}}$ is the expectation over aggregate states +# * It becomes real-valued when we replace the functions by their discretized counterparts +# * Standard techniques can solve the discretized version + +# #### So, is all solved? +# The dimensionality of the system F is a big problem +# * With high dimensional idiosyncratic states, discretized value functions and distributions become large objects +# * For example: +# * 4 income states $\times$ 100 illiquid capital states $\times$ 100 liquid asset states $\rightarrow$ $\geq$ 40,000 values in $F$ + +# ### Bayer-Luetticke method +# #### Idea: +# 1. Use compression techniques as in video encoding +# * Apply a discrete cosine transformation (DCT) to all value/policy functions +# * DCT is used because it is the default in the video encoding literature +# * Choice of cosine is unimportant; linear basis functions might work just as well +# * Represent fluctuations as differences from this reference frame +# * Assume all coefficients of the DCT from the StE that are close to zero do not change when there is an aggregate shock (small things stay small) +# +# 2. Assume no changes in the rank correlation structure of $\mu$ +# * Calculate the Copula, $\bar{C}$ of $\mu$ in the StE +# * Perturb only the marginal distributions +# * This assumes that the rank correlations remain the same +# * See the companion notebook for more discussion of this +# * Use fixed Copula to calculate an approximate joint distribution from marginals +# +# +# The approach follows the insight of KS in that it uses the fact that some moments of the distribution do not matter for aggregate dynamics + +# #### Details +# 1) Compression techniques from video encoding +# * Let $\bar{\Theta} = dct(\bar{v})$ be the coefficients obtained from the DCT of the value function in StE +# * Define an index set $\mathop{I}$ that contains the x percent largest (i.e. most important) elements from $\bar{\Theta}$ +# * Let $\theta$ be a sparse vector with non-zero entries only for elements $i \in \mathop{I}$ +# * Define +# \begin{equation} +# \tilde{\Theta}(\theta_t)=\left\{ +# \begin{array}{@{}ll@{}} +# \bar{\Theta}(i)+\theta_t(i), & i \in \mathop{I} \\ +# \bar{\Theta}(i), & \text{else} +# \end{array}\right. +# \end{equation} +# * This assumes that the basis functions with least contribution to representation of the StE policy function in levels, make no contribution at all to its changes over time + +# 2) Decoding +# * Now we reconstruct $\tilde{v}(\theta_t)=dct^{-1}(\tilde{\Theta}(\theta_{t}))$ +# * idct=$dct^{-1}$ is the inverse dct that goes from the $\theta$ vector to the corresponding values +# * This means that in the StE the reduction step adds no addtional approximation error: +# * Remember that $\tilde{v}(0)=\bar{v}$ by construction +# * But it allows us to reduce the number of derivatives that need to be calculated from the outset. +# * We only calculate derivatives for those basis functions that make an important contribution to the representation of the function +# +# 3) The histogram is recovered as follows +# * $\mu_t$ is approximated as $\bar{C}(\bar{\mu_t}^1,...,\bar{\mu_t}^n)$, where $n$ is the dimensionality of the idiosyncratic states +# * $\mu_t^{i}$ are the marginal distributions +# * The StE distribution is obtained when $\mu = \bar{C}(\bar{\mu}^1,...,\bar{\mu}^n)$ +# * Typically prices are only influenced through the marginal distributions +# * The approach ensures that changes in the mass of one state (say, wealth) are distributed in a sensible way across the other dimensions +# * Where "sensible" means "like in StE" +# * The implied distributions look "similar" to the StE one (different in (Reiter, 2009)) +# +# 4) The large system above is now transformed into a much smaller system: +# \begin{align} +# F(\{d\mu_t^1,...,d\mu_t^n\}, S_t, \{d\mu_{t+1}^1,...,d\mu_{t+1}^n\}, S_{t+1}, \theta_t, P_t, \theta_{t+1}, P_{t+1}) &= \phantom{~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} \notag +# \end{align} +# \begin{align} +# \phantom{F~~~~~~~~~} & \begin{bmatrix} +# d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n) - d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n)\Pi_{\policy_t} \\ +# dct\left[idct\left(\tilde{\Theta}(\theta_t) - (\bar{u}_{\policy_t} + \beta \Pi_{\policy_t}idct(\tilde{\Theta}(\theta_{t+1}))\right)\right] \\ +# S_{t+1} - \Policy(S_t,d\mu_t) \\ +# \Phi(\policy_t,d\mu_t,P_t,S_t) \\ +# \end{bmatrix} +# \end{align} +# 5) NOW we use standard techniques to solve this (much reduced system) + +# ### The two-asset HANK model +# +# We illustrate the algorithm in a two-asset HANK model described as below +# +# +# #### Households +# - Maximizing discounted felicity +# - Consumption $c$ +# - CRRA coefficent: $\xi$ +# - Elasticity of substitution in CES consumption bundle: $\eta$ +# - Disutility from work in GHH form: +# - Frisch elasticity $\gamma$ +# - Two assets: +# - Liquid nominal bonds $b$, greater than lower bound $\underline b$ +# - Borrowing constraint due to a wedge between borrowing and saving rate: $R^b(b<0)=R^B(b>0)+\bar R$ +# - Illiquid assets capital $k$ nonnegative +# - Trading of illiquid assets is subject to a friction governed by $v$, the fraction of agents who can trade +# - If nontrading, receive dividend $r$ and depreciates by $\tau$ +# - Idiosyncratic labor productivity $h$: +# - $h = 0$ for entreprener, only receive profits $\Pi$ +# - $h = 1$ for labor, evolves according to an autoregressive process, +# - $\rho_h$ persistence parameter +# - $\epsilon^h$: idiosyncratic risk +# +# #### Production +# - Intermediate good producer +# - CRS production with TFP $Z$ +# - Wage $W$ +# - Cost of capital $r+\delta$ +# - Reseller +# - Rotemberg price setting: quadratic adjustment cost scalled by $\frac{\eta}{2\kappa}$ +# - Constant discount factor $\beta$ +# - Investment subject to Tobin's q adjustment cost $\phi$ +# - Aggregate risks $\Omega$ include +# - TFP $Z$, AR(1) process with persistence of $\rho^Z$ and shock $\epsilon^Z$ +# - Uncertainty +# * The $\sigma^{2}$ of the idiosyncratic policy shocks follows an AR(1) +# * A "shock" is a temporary (mean-reverting) increase in the magnitude of the idiosyncratic shocks +# - Monetary policy +# - Central bank +# - Taylor rule on nominal saving rate $R^B$: reacts to deviation of inflation from target by $\theta_R$ +# - $\rho_R$: policy inertia +# - $\epsilon^R$: monetary policy shocks +# - Government (fiscal rule) +# - Government spending $G$ +# - Tax $T$ +# - $\rho_G$: intensity of repaying government debt: $\rho_G=1$ implies roll-over +# +# #### Taking stock +# +# - Individual state variables: $\newcommand{\liquid}{m}\liquid$, $k$ and $h$, the joint distribution of individual states $\Theta$ +# - Individual control variables: $c$, $n$, $\liquid'$, $k'$ +# - Optimal policy for adjusters and nonadjusters are $c^*_a$, $n^*_a$ $k^*_a$ and $\liquid^*_a$ and $c^*_n$, $n^*_n$ and $\liquid^*_n$, respectively +# + +# + +from HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset import FluctuationsTwoAsset, SGU_solver, plot_IRF + +start_time = time.perf_counter() + +## Choose an aggregate shock to perturb(one of three shocks: MP, TFP, Uncertainty) + +# EX3SS['par']['aggrshock'] = 'MP' +# EX3SS['par']['rhoS'] = 0.0 # Persistence of variance +# EX3SS['par']['sigmaS'] = 0.001 # STD of variance shocks + +#EX3SS['par']['aggrshock'] = 'TFP' +#EX3SS['par']['rhoS'] = 0.95 +#EX3SS['par']['sigmaS'] = 0.0075 + +EX3SS['par']['aggrshock'] = 'Uncertainty' +EX3SS['par']['rhoS'] = 0.84 # Persistence of variance +EX3SS['par']['sigmaS'] = 0.54 # STD of variance shocks + + +## Choose an accuracy of approximation with DCT +### Determines number of basis functions chosen -- enough to match this accuracy +### EX3SS is precomputed steady-state pulled in above +EX3SS['par']['accuracy'] = 0.9999999 + +## Implement state reduction and DCT +### Do state reduction on steady state + +EX3SR = FluctuationsTwoAsset(**EX3SS) +SR = EX3SR.StateReduc() + +print('SGU_solver') +SGUresult = SGU_solver(SR['Xss'],SR['Yss'],SR['Gamma_state'],SR['indexMUdct'],SR['indexVKdct'],SR['par'],SR['mpar'],SR['grid'],SR['targets'],SR['Copula'],SR['P_H'],SR['aggrshock']) +print('plot_IRF') +plot_IRF(SR['mpar'],SR['par'],SGUresult['gx'],SGUresult['hx'],SR['joint_distr'], + SR['Gamma_state'],SR['grid'],SR['targets'],SR['Output']) + +end_time = time.perf_counter() +print('Elapsed time is ', (end_time-start_time), ' seconds.') +# - + +#
From 019f5e75531aca9c0564bca370d907e0deb9fbe2 Mon Sep 17 00:00:00 2001 From: Eric Holscher Date: Thu, 8 Aug 2019 15:53:13 -0700 Subject: [PATCH 042/242] This adds all the Python classes to the Sphinx docs I imagine some of these should not be added, but I'm not sure which ones :) --- Documentation/index.rst | 50 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 48 insertions(+), 2 deletions(-) diff --git a/Documentation/index.rst b/Documentation/index.rst index 19c2a4b14..d0442ca0e 100644 --- a/Documentation/index.rst +++ b/Documentation/index.rst @@ -23,7 +23,55 @@ Welcome to HARK's documentation! .. autosummary:: :toctree: generated + HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUs + HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond + HARK.BayerLuetticke.Assets.One.Luetticke_wrapper + HARK.BayerLuetticke.Assets.One.SharedFunc + HARK.BayerLuetticke.Assets.One.SharedFunc2 + HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUs + HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUsBond + HARK.BayerLuetticke.Assets.One.defineSSParameters + HARK.BayerLuetticke.Assets.One.defineSSParametersIOUsBond + HARK.BayerLuetticke.Assets.Two.EX3SS_20_python2.p + HARK.BayerLuetticke.Assets.Two.EX3SS_20_python3.p + HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset + HARK.BayerLuetticke.Assets.Two.SharedFunc3 + HARK.BayerLuetticke.Assets.Two.SteadyStateTwoAsset + HARK.BayerLuetticke.Assets.Two.defineSSParametersTwoAsset + HARK.BayerLuetticke.BayerLuetticke_wrapper + HARK.BayerLuetticke.ConsIndShockModel_extension + HARK.BayerLuetticke.notebooks.DCT-Copula-Illustration.ipynb + HARK.BayerLuetticke.notebooks.DCT-Copula-Illustration + HARK.BayerLuetticke.notebooks.DCT.ipynb + HARK.BayerLuetticke.notebooks.OneAsset-HANK.ipynb + HARK.BayerLuetticke.notebooks.TwoAsset.ipynb + HARK.ConsumptionSaving.ConsAggShockModel + HARK.ConsumptionSaving.ConsGenIncProcessModel + HARK.ConsumptionSaving.ConsIndShockModel + HARK.ConsumptionSaving.ConsMarkovModel + HARK.ConsumptionSaving.ConsMedModel + HARK.ConsumptionSaving.ConsPrefShockModel + HARK.ConsumptionSaving.ConsRepAgentModel + HARK.ConsumptionSaving.ConsumerParameters + HARK.ConsumptionSaving.RepAgentModel + HARK.ConsumptionSaving.TractableBufferStockModel + HARK.FashionVictim.FashionVictimModel + HARK.FashionVictim.FashionVictimParams + HARK.SolvingMicroDSOPs.Calibration.EstimationParameters + HARK.SolvingMicroDSOPs.Calibration.SetupSCFdata + HARK.SolvingMicroDSOPs.Code.StructEstimation + HARK.SolvingMicroDSOPs.do_all + HARK.cAndCwithStickyE.StickyE_MAIN + HARK.cAndCwithStickyE.StickyE_NO_MARKOV + HARK.cAndCwithStickyE.StickyEmodel + HARK.cAndCwithStickyE.StickyEparams + HARK.cAndCwithStickyE.StickyEtools HARK.core + HARK.cstwMPC.MakeCSTWfigs + HARK.cstwMPC.MakeCSTWfigsForSlides + HARK.cstwMPC.Results.BetaDistPYResults_py3.txt + HARK.cstwMPC.SetupParamsCSTW + HARK.cstwMPC.cstwMPC HARK.dcegm HARK.estimation HARK.interpolation @@ -31,8 +79,6 @@ Welcome to HARK's documentation! HARK.simulation HARK.utilities HARK.validators - HARK.ConsumptionSaving.ConsAggShockModel - HARK.FashionVictim.FashionVictimModel Indices and tables ================== From d9136e925185d68b524fd48e39cdcc88fc7dcde0 Mon Sep 17 00:00:00 2001 From: Eric Holscher Date: Thu, 8 Aug 2019 16:05:34 -0700 Subject: [PATCH 043/242] Add a submodule of DemARK and include a notebook in the doc build for it --- .gitmodules | 3 +++ Documentation/index.rst | 2 +- Documentation/notebooks/README.rst | 12 ++++++++++++ Documentation/submodules/DemARK | 1 + 4 files changed, 17 insertions(+), 1 deletion(-) create mode 100644 .gitmodules create mode 100644 Documentation/notebooks/README.rst create mode 160000 Documentation/submodules/DemARK diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 000000000..b2bc1c87d --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "Documentation/submodules/DemARK"] + path = Documentation/submodules/DemARK + url = https://github.com/econ-ark/DemARK diff --git a/Documentation/index.rst b/Documentation/index.rst index 19c2a4b14..a5c0650f1 100644 --- a/Documentation/index.rst +++ b/Documentation/index.rst @@ -12,7 +12,7 @@ Welcome to HARK's documentation! :caption: Notebooks notebooks/Gentle-Intro-To-HARK - notebooks/DCT-Copula-Illustration + submodules/DemARK/notebooks/KinkedRconsumerType .. toctree:: :maxdepth: 2 diff --git a/Documentation/notebooks/README.rst b/Documentation/notebooks/README.rst new file mode 100644 index 000000000..b41375a6e --- /dev/null +++ b/Documentation/notebooks/README.rst @@ -0,0 +1,12 @@ +Adding a notebook +----------------- + +You can add a notebook here to be included in the docs by creating a symlink to an existing notebook. +This lets you keep them in the original place, +but also include them in your docs here. + +This would be done with a command like this:: + + ln -s ../../Examples/Gentle-Intro-To-HARK.ipynb . + +That would create a symlink here to the ``Examples/Gentle-Intro-To-HARK.ipynb`` notebook. diff --git a/Documentation/submodules/DemARK b/Documentation/submodules/DemARK new file mode 160000 index 000000000..ad40b284e --- /dev/null +++ b/Documentation/submodules/DemARK @@ -0,0 +1 @@ +Subproject commit ad40b284e3b9320daac0e4b5d75a3335072b4f12 From 1413ee5c2f9159fbdda589f4e07d5ab6c8686f9f Mon Sep 17 00:00:00 2001 From: Eric Holscher Date: Thu, 8 Aug 2019 16:07:17 -0700 Subject: [PATCH 044/242] Add RTD submodule setting --- .readthedocs.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.readthedocs.yml b/.readthedocs.yml index 80787b838..24e6d9f94 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -18,3 +18,7 @@ python: version: 3.7 install: - requirements: requirements.txt + +submodules: + include: all + recursive: true From 86a93eec37c0091df3c448d1569ce35608da4900 Mon Sep 17 00:00:00 2001 From: Eric Holscher Date: Thu, 8 Aug 2019 16:12:49 -0700 Subject: [PATCH 045/242] Don't build PDF --- .readthedocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 24e6d9f94..97f39b55d 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -11,7 +11,7 @@ sphinx: # Optionally build your docs in additional formats such as PDF and ePub formats: - - pdf + - [] # Optionally set the version of Python and requirements required to build your docs python: From 5cebbbf437d531bc79ffc2e9f6d2ce0da92d364a Mon Sep 17 00:00:00 2001 From: Eric Holscher Date: Thu, 8 Aug 2019 16:13:47 -0700 Subject: [PATCH 046/242] Try again :) --- .readthedocs.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 97f39b55d..f172a6411 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -10,8 +10,7 @@ sphinx: configuration: Documentation/conf.py # Optionally build your docs in additional formats such as PDF and ePub -formats: - - [] +formats: [] # Optionally set the version of Python and requirements required to build your docs python: From a55c0d5861b70e66bafb43513d3b3f21389490d1 Mon Sep 17 00:00:00 2001 From: Eric Holscher Date: Thu, 8 Aug 2019 16:18:39 -0700 Subject: [PATCH 047/242] Add Readme with a bit of instruction --- Documentation/submodules/README.rst | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 Documentation/submodules/README.rst diff --git a/Documentation/submodules/README.rst b/Documentation/submodules/README.rst new file mode 100644 index 000000000..ef4e4f86d --- /dev/null +++ b/Documentation/submodules/README.rst @@ -0,0 +1,11 @@ +Adding a submodule +------------------ + +This directory contains submodules that are used for generating docs. + +You can add a new one like this:: + + git submodule add https://github.com/econ-ark/DemARK + +Then when building on Read the Docs, +it should automatically be cloned and included in the build. From 115816d5d6b7eb3bc7eeb08c884dfb5dc89d6ee7 Mon Sep 17 00:00:00 2001 From: llorracc Date: Tue, 20 Aug 2019 16:44:41 -0400 Subject: [PATCH 048/242] Fix MPCnvrs issue (change var name, add docs) --- HARK/ConsumptionSaving/ConsIndShockModel.py | 182 ++++++++++++++------ 1 file changed, 134 insertions(+), 48 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index a9d45f26f..3a40326f4 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -398,7 +398,17 @@ def defUtilityFuncs(self): def defValueFuncs(self): ''' - Defines the value and marginal value function for this period. + Defines the value and marginal value functions for this period. + Uses the fact that for a perfect foresight CRRA utility problem, + if the MPC in period t is :math:`\kappa_{t}`, and relative risk + aversion :math:`\rho`, then the inverse value vFuncNvrs has a + constant slope of :math:`\kappa_{t}^{-\rho/(1-\rho)}` and + vFuncNvrs has value of zero at the lower bound of market resources + mNrmMin. See PerfForesightConsumerType.ipynb documentation notebook + for a brief explanation and the links below for a fuller treatment. + + https://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/#vFuncAnalytical + https://econ.jhu.edu/people/ccarroll/SolvingMicroDSOPs/#vFuncPF Parameters ---------- @@ -408,10 +418,11 @@ def defValueFuncs(self): ------- none ''' - MPCnvrs = self.MPC**(-self.CRRA/(1.0-self.CRRA)) - vFuncNvrs = LinearInterp(np.array([self.mNrmMin, self.mNrmMin+1.0]),np.array([0.0, MPCnvrs])) - self.vFunc = ValueFunc(vFuncNvrs,self.CRRA) - self.vPfunc = MargValueFunc(self.cFunc,self.CRRA) + # See the PerfForesightConsumerType.ipynb documentation notebook for the derivations + vFuncNvrsSlope = self.MPC**(-self.CRRA/(1.0-self.CRRA)) + vFuncNvrs = LinearInterp(np.array([self.mNrmMin, self.mNrmMin+1.0]),np.array([0.0, vFuncNvrsSlope])) + self.vFunc = ValueFunc(vFuncNvrs,self.CRRA) + self.vPfunc = MargValueFunc(self.cFunc,self.CRRA) def makePFcFunc(self): ''' @@ -1697,10 +1708,10 @@ def getPostStates(self): def checkConditions(self,verbose=False,verbose_reference=False,public_call=False): ''' - This method checks whether the instance's type satisfies the growth impatience condition - (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return - impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of - autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate + This method checks whether the instance's type satisfies the Growth Impatience Condition + (GIC), Return Impatience Condition (RIC), Absolute Impatience Condition (AIC), Weak Return + Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of + Autarky Condition (FVAC). These are the conditions that are sufficient for nondegenerate solutions under infinite horizon with a 1 period cycle. Depending on the model at hand, a different combination of these conditions must be satisfied. To check which conditions are relevant to the model at hand, a reference to the relevant theoretical literature is made. @@ -1722,36 +1733,90 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False return violated = False + + Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) + AIF = Thorn + + #Evaluate and report on the Absolute Impatience Condition + + self.Thorn = Thorn + self.AIF = AIF + if AIF<1: + self.AIC = True + if public_call: + print('The value of the absolute impatience factor for the supplied parameter values satisfies the Absolute Impatience Condition.', end = " ") + if verbose: + violated = False + print('Therefore, the absolute amount of consumption is expected to fall over time.') + print() + else: + self.AIC = False + print('The given type violates the Absolute Impatience Condition with the supplied parameter values; the AIF is %1.5f ' % (AIF), end=" ") + if verbose: + violated = True + print('Therefore, the absolute amount of consumption is expected to grow over time') + print() + + #Evaluate and report on the Growth Impatience Condition + GIF = Thorn/self.PermGroFac[0] + self.GIF = GIF - #Evaluate and report on the return impatience condition - - RIF = (self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree - if RIF<1: + if GIF<1: + self.GIC = True if public_call: - print('The return impatience factor value for the supplied parameter values satisfies the return impatience condition.') + print('The value of the growth impatience factor for the supplied parameter values satisfies the Growth Impatience Condition.', end = " ") + if verbose: + print(' Therefore, the ratio of individual wealth to permanent income will fall indefinitely.') + print() else: + self.GIC = False violated = True - print('The given type violates the Return Impatience Condition with the supplied parameter values; the factor is %1.5f ' % (RIF)) + print('The given parameter values violate the Growth Impatience Condition for this consumer type; the GIF is: %2.4f' % (GIF), end = " ") + if verbose: + print(' Therefore, the ratio of individual wealth to permanent income grow toward infinity.') + print() + - #Evaluate and report on the absolute impatience condition - AIF = self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA) - if AIF<1: + #Evaluate and report on the Return Impatience Condition + + RIF = Thorn/self.Rfree + self.RIF = RIF + if RIF<1: + self.RIC = True if public_call: - print('The absolute impatience factor value for the supplied parameter values satisfies the absolute impatience condition.') + print('The return impatience factor value for the supplied parameter values satisfies the Return Impatience Condition.', end = " ") + if verbose: + print('Therefore, the limiting consumption function is not c(m)=0') + print() else: - print('The given type violates the absolute impatience condition with the supplied parameter values; the AIF is %1.5f ' % (AIF)) + self.RIC = False + violated = True + print('The given type violates the Return Impatience Condition with the supplied parameter values; the factor is %1.5f ' % (RIF), end = " ") if verbose: - violated = True - print(' Therefore, the absolute amount of consumption is expected to grow over time') + print('Therefore, the limiting consumption function is c(m)=0') + print() - #Evaluate and report on the finite human wealth condition + #Evaluate and report on the Finite Human Wealth Condition FHWF = self.PermGroFac[0]/self.Rfree + self.FHWF = FHWF if FHWF<1: + self.hNrm = 1.0/(1.0-self.PermGroFac[0]/self.Rfree) + self.FHWC = True if public_call: - print('The finite human wealth factor value for the supplied parameter values satisfies the finite human wealth condition.') + print('The Finite Human wealth factor value for the supplied parameter values satisfies the Finite Human Wealth Condition.', end = " ") + if verbose: + print('Therefore, the limiting consumption function is not c(m)=Infinity') + print('and human wealth normalized by permanent income is %2.5f' % (self.hNrm)) + self.cNrmPDV = 1.0/(1.0-self.Thorn/self.Rfree) + print('and the PDV of future consumption growth is %2.5f' % (self.cNrmPDV) ) + print() else: - print('The given type violates the finite human wealth condition; the finite human wealth factor value %2.5f ' % (FHWF)) + self.FHWC = False + print('The given type violates the Finite Human Wealth Condition; the Finite Human wealth factor value %2.5f ' % (FHWF), end = " ") violated = True + if verbose: + print('Therefore, the limiting consumption function is c(m)=Infinity for all m') + priont() if verbose and violated and verbose_reference: print('[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') @@ -2027,11 +2092,11 @@ def preSolve(self): def checkConditions(self,verbose=False,public_call=True): ''' - This method checks whether the instance's type satisfies the growth impatience condition - (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return - impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of - autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate - solutions under infinite horizon with a 1 period cycle. Depending on the model at hand, a + This method checks whether the instance's type satisfies the Growth Impatience Condition + (GIC), Return Impatience Condition (RIC), Absolute Impatience Condition (AIC), Weak Return + Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of + Autarky Condition (FVAC). These are the conditions that are sufficient for nondegenerate + infinite horizon solutions when there is a 1 period cycle. Depending on the model at hand, a different combination of these conditions must be satisfied. (For an exposition of the conditions, see http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) @@ -2053,40 +2118,61 @@ def checkConditions(self,verbose=False,public_call=True): EPermShkInv=np.dot(self.PermShkDstn[0][0],1/self.PermShkDstn[0][1]) PermGroFacAdj=self.PermGroFac[0]*EPermShkInv - Thorn=self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA) - GIF=Thorn/PermGroFacAdj - #Evaluate and report on the growth impatience condition + Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) + GIF = Thorn/PermGroFacAdj + self.GIF = GIF + self.Thorn = Thorn + self.PermGroFacAdj = PermGroFacAdj + self.EPermShkInv = EPermShkInv + + + #Evaluate and report on the Growth Impatience Condition if GIF<1: + self.GIC = True if public_call: - print('The growth impatience factor value for the supplied parameter values satisfies the growth impatience condition.') + print('The value of the growth impatience factor for the supplied parameter values satisfies the Growth Impatience Condition.', end = " ") + if verbose: + print('Therefore, a target level of wealth exists.') + print() else: + self.GIC = False violated = True - print('The given parameter values violate the growth impatience condition for this consumer type; the GIF is: %2.4f' % (GIF)) + print('The given parameter values violate the Growth Impatience Condition for this consumer type; the GIF is: %2.4f' % (GIF), end = " ") if verbose: - print(' Therefore, a target level of wealth does not exist.') + print('Therefore, a target level of wealth does not exist.') + print() - #Evaluate and report on the weak return impatience condition + #Evaluate and report on the Weak Return Impatience Condition WRIF=(self.LivPrb[0]*(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree + self.WRIF = WRIF if WRIF<1: + self.WRIC = True if public_call: - print('The weak return impatience factor value for the supplied parameter values satisfies the weak return impatience condition.') + print('The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition.') else: + self.WRIC = False violated = True - print('The given type violates the weak return impatience condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF)) + print('The given type violates the Weak Return Impatience Condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF), end = " ") if verbose: - print(' Therefore, a nondegenerate solution is not available.') + print('Therefore, a nondegenerate solution is not available.') + print() - #Evaluate and report on the finite value of autarky condition + #Evaluate and report on the Finite Value of Autarky Condition EPermShkValFunc=np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA)) + self.EPermShkValFunc = EPermShkValFunc FVAF=self.LivPrb[0]*self.DiscFac*EPermShkValFunc*(self.PermGroFac[0]**(1-self.CRRA)) + self.FVAF = FVAF if FVAF<1: + self.FVAC = True if public_call: - print('The finite value of autarky factor value for the supplied parameter values satisfies the finite value of autarky condition.') + print('The Finite Value of autarky factor value for the supplied parameter values satisfies the Finite Value of Autarky Condition.') else: - print('The given type violates the finite value of autarky condition with the supplied parameter values. The FVAF is %2.4f' %(FVAF)) + self.FVAC = False + print('The given type violates the Finite Value of Autarky Condition with the supplied parameter values. The FVAF is %2.4f' %(FVAF), end = " ") violated = True if verbose: - print(' Therefore, a nondegenerate solution is not available.') + print('Therefore, a nondegenerate solution is not available.') + print() if verbose and violated: print('\n[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') @@ -2223,10 +2309,10 @@ def getRfree(self): def checkConditions(self,verbose=False): ''' - This method checks whether the instance's type satisfies the growth impatience condition - (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return - impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of - autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate + This method checks whether the instance's type satisfies the Growth Impatience Condition + (GIC), Return Impatience Condition (RIC), Absolute Impatience Condition (AIC), Weak Return + Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of + Autarky Condition (FVAC). These are the conditions that are sufficient for nondegenerate infinite horizon solutions with a 1 period cycle. Depending on the model at hand, a different combination of these conditions must be satisfied. To check which conditions are relevant to the model at hand, a reference to the relevant theoretical literature is made. From 997e45431a97eb8da8787add917aee24ba96e1ab Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 29 Aug 2019 15:29:11 -0400 Subject: [PATCH 049/242] Add working version of labor supply on intensive margin model This commit is mostly a copy-paste of files that had been sitting in my student submission folder for literally two years. Most of the programming here was done by Tiphanie Magne as a course project in spring/summer 2017, with some assistance from me. (TODO: Make sure she gets tagged/credited on the PR for this branch.) I dropped in the files as they were on my computer, changed the HARK package names (the work predated that), and it ran successfully. Still to do: Clean up some comments and documentation, add life cycle example, make sure simulation exists (and works), move example parameter dictionary into ConsumerParameters.py (from blahblahTM.py). --- HARK/ConsumptionSaving/ConsLaborModel.py | 559 ++++++++++++++++++ .../ConsumptionSaving/ConsumerParametersTM.py | 325 ++++++++++ 2 files changed, 884 insertions(+) create mode 100644 HARK/ConsumptionSaving/ConsLaborModel.py create mode 100644 HARK/ConsumptionSaving/ConsumerParametersTM.py diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py new file mode 100644 index 000000000..54e7d9d30 --- /dev/null +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -0,0 +1,559 @@ +#!/usr/bin/env python2 +# -*- coding: utf-8 -*- +""" +Consumption-Saving model with Endogenous Labor Supply Model - Intensive Margin +using the endogenous grid method to invert the first order condition. + +To solve this agent's problem, we normalized the problem by p_t, the permanent +productivity level, which helps us to eliminate a state variable (p_t). +We can then transform the agent's problem into a cost minimization problem for +which we solve the effective consumption purchased, x_t= z_t^alpha * c_t +It allows us to solve only one FOC with respect to x_t, instead of 2. + +We can then solve the FOC using the Endogenous Gridpoint Method, EGM. +Faster solution method than the classic rootfinding method but we should keep +in mind that the EGM solution is not well behaved outside the range of +gridpoints selected. + +@author: Tiphanie Magne +University of Delaware +""" +import sys +import os +sys.path.insert(0, os.path.abspath('../')) +sys.path.insert(0, os.path.abspath('./')) + +from copy import copy +import numpy as np +from HARK.core import Solution +from HARK.utilities import CRRAutilityP, CRRAutilityP_inv +from HARK.interpolation import LinearInterp, LinearInterpOnInterp1D, VariableLowerBoundFunc2D, BilinearInterp, ConstantFunction +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, MargValueFunc +from HARK.ConsumptionSaving.ConsGenIncProcessModel import ValueFunc2D, MargValueFunc2D + +class ConsumerLaborSolution(Solution): + ''' + A class for representing one period of the solution to a Consumer Labor problem. + ''' + distance_criteria = ['cFunc','LbrFunc'] + + def __init__(self,cFunc=None,LbrFunc=None,vFunc=None,vPfunc=None,bNrmMin = None): + ''' + The constructor for a new ConsumerSolution object. + + Parameters + ---------- + cFunc : function + The consumption function for this period, defined over bank balances: c = cFunc(b). + LbrFunc : function + The labor function for this period, defined over bank balances: l = LbrFunc(b). + vFunc : function + The beginning-of-period marginal value function for this period, defined over + bank balances: v = vPfunc(b). + vPfunc : function + The beginning-of-period marginal value function for this period, defined over + bank balances: v = vPfunc(b). + bNrmMin: float + The minimum allowable bank balances for this period. Consumption function, + labor function etc. are undefined for b < bNrmMin. + + ''' + if cFunc is not None: + setattr(self,'cFunc',cFunc) + if LbrFunc is not None: + setattr(self,'LbrFunc',LbrFunc) + if vFunc is not None: + setattr(self,'vFunc',vFunc) + if vPfunc is not None: + setattr(self,'vPfunc',vPfunc) + if bNrmMin is not None: + setattr(self,'bNrmMin',bNrmMin) + + +def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,CRRA, + Rfree,PermGroFac,BoroCnstArt,aXtraGrid,TranShkGrid,vFuncBool, + CubicBool,WageRte,LbrCost): + ''' + Solves one period of the consumption-saving model with endogenous labor supply on the intensive margin + by using the endogenous grid method to invert the first order condition, obviating any search. + + Parameters + ---------- + solution_next : ConsumerLaborSolution + The solution to the next period's problem; should have the attributes + vPfunc, cFunc and LbrFunc representing the marginal value, consumption + and labor functions. + + PermShkDstn: [np.array] + Discrete distribution of permanent productivity shocks. + TranShkDstn: [np.array] + Discrete distribution of transitory productivity shocks. + LivPrb : float + Survival probability; likelihood of being alive at the beginning of + the succeeding period. + DiscFac : float + Intertemporal discount factor. + CRRA : float + Coefficient of relative risk aversion. + Rfree : float + Risk free interest rate on assets retained at the end of the period. + PermGroFac : float + Expected permanent income growth factor for next period. + BoroCnstArt: float or None + Borrowing constraint for the minimum allowable assets to end the + period with. If it is less than the natural borrowing constraint, + then it is irrelevant; BoroCnstArt=None indicates no artificial bor- + rowing constraint. + aXtraGrid: [np.array] + Array of "extra" end-of-period asset values-- assets above the + absolute minimum acceptable level. + TranShkGrid: [np.array] + Array of transitory shock values. + vFuncBool: boolean + An indicator for whether the value function should be computed and + included in the reported solution. + CubicBool: boolean + An indicator for whether the solver should use cubic or linear inter- + polation. + WageRte: float + Wage rate. To be specified by the user. + LbrCost: float + alpha parameter indicating labor cost. + + Returns + ------- + solution_now : ConsumerLaborSolution + The solution to this period's problem, including a consumption + function cFunc (defined over the bank balances and the transitory + productivity shock), a labor function LbrFunc and a marginal value + function vPfunc. + ''' + frac = 1./(1.+LbrCost) + + if CRRA <= frac*LbrCost: + print ' Error: make sure CRRA coefficient is strictly greater than alpha/(1+alpha).' +# return None + sys.exit() + if BoroCnstArt is not None: + print ' Error: Model that cannot handle artificial borrowing constraint yet. ' + return None + if vFuncBool or CubicBool is True: + print ' Error: Model that cannot handle Cubic interpolation yet.' + return None + + # Unpack next period's solution and the productivity shock distribution, and define the inverse (marginal) utilty function + vPfunc_next = solution_next.vPfunc + TranShkPrbs = TranShkDstn[0] + TranShkVals = TranShkDstn[1] + PermShkPrbs = PermShkDstn[0] + PermShkVals = PermShkDstn[1] + TranShkCount = TranShkPrbs.size + PermShkCount = PermShkPrbs.size + uPinv = lambda X : CRRAutilityP_inv(X,gam=CRRA) + + # Make tiled versions of the grid of a_t values and the components of the shock distribution + aXtraCount = aXtraGrid.size # = 200 + bNrmGrid_rep = np.tile(np.reshape(aXtraGrid,(aXtraCount,1)),(1,TranShkCount)) # Replicated axtraGrid of b_t values (bNowGrid) for each transitory (productivity) shock + TranShkVals_rep = np.tile(np.reshape(TranShkVals,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock values for each b_t state + TranShkPrbs_rep = np.tile(np.reshape(TranShkPrbs,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock probabilities for each b_t state + + # Calculate marginal value next period for each gridpoint and each transitory shock + bNext = Rfree * bNrmGrid_rep # Next period's bank balances. (200,16) + vPNext = vPfunc_next(bNext, TranShkVals_rep) # Derive the Next period's marginal value at every transitory shock and every bank balances gridpoint + vPbarNext = np.sum(vPNext*TranShkPrbs_rep, axis = 1) # Integrate out the transitory shocks (in TranShkVals direction) + vPbarNvrsNext = uPinv(vPbarNext) # Marginal value transformed through the inverse marginal utility function + vPbarNvrsFuncNext = LinearInterp(np.insert(bNext[:,0],0,0.0),np.insert(vPbarNvrsNext,0,0.0)) # Linear interpolation over the b_t. Add a point at b_t = 0. + vPbarFuncNext = MargValueFunc(vPbarNvrsFuncNext,CRRA) # Take the marginal utility function to inverse back and get the optimal values for consumption + + # Get the next period's bank balances at each permanent shock + aNrmGrid_rep = np.tile(np.reshape(aXtraGrid,(aXtraCount,1)),(1,PermShkCount)) # Replicated axtraGrid of b_t values (bNowGrid) for each permanent (productivity) shock. (200,16) + PermShkVals_rep = np.tile(np.reshape(PermShkVals,(1,PermShkCount)),(aXtraCount,1)) # Replicated permanent shock values for each b_t state + PermShkPrbs_rep = np.tile(np.reshape(PermShkPrbs,(1,PermShkCount)),(aXtraCount,1)) # Replicated permanent shock probabilities for each b_t state + bNextPerm = (Rfree/(PermGroFac*PermShkVals_rep))*aNrmGrid_rep # (200,16) + + ''' 3/ ''' + # Construct the marginal value of end-of-period assets + EndOfPrdvP_temp = DiscFac*Rfree*LivPrb*np.sum((PermGroFac*PermShkVals_rep)**(-CRRA)*vPbarFuncNext(bNextPerm)*PermShkPrbs_rep,axis=1) # sum in the TranShkVals ~ TranShkGrid direction + EndOfPrdvP = EndOfPrdvP_temp.reshape((aXtraCount,1)) # ---- Convert it to a (row? Notes) column vector of size (200,1) + TranShkScaleFac_temp = frac*(WageRte*TranShkGrid)**(LbrCost*frac)*(LbrCost**(-LbrCost*frac)+LbrCost**(frac)) + TranShkScaleFac = TranShkScaleFac_temp.reshape((1,TranShkGrid.size)) # --- Convert it to a row (column?) vector of size (1,16) + + ''' 4/ ''' + xNowArray = (np.dot(EndOfPrdvP,TranShkScaleFac))**(-1./(CRRA-LbrCost*frac)) #(200,16) Get an array of x_t values corresponding to (a_t,theta_t) values + + ''' 5/: Error raising xNowArray to a non-integer power, frac, 1/ (1+0.36) Or NAN array''' + TranShkGrid_rep = np.tile(np.reshape(TranShkGrid,(1,TranShkGrid.size)),(aXtraCount,1)) + xNowPowered = xNowArray**frac + cNrmNow = (((WageRte*TranShkGrid_rep)/LbrCost)**(LbrCost*frac))*xNowPowered # Find optimal consumption using the solution to the effective consumption pb + LsrNow = (LbrCost/(WageRte*TranShkGrid_rep))**frac*xNowPowered # Find optimal leisure amount using the solution to the effective consumption pb + cNrmNow[:,0] = uPinv(EndOfPrdvP_temp) + LsrNow[:,0] = 1.0 + + # Check the Labor Constraint using violatesLbrCnst: boolean array. An indicator for whether + # the labor constraint is violated or not. The Agent cannot choose to work a negative amount of time. Labor = 1 - Leisure + violates_labor_constraint = LsrNow > 1. + EndOfPrdvP_temp = np.tile(np.reshape(EndOfPrdvP,(aXtraCount,1)),(1,TranShkCount)) + cNrmNow[violates_labor_constraint] = uPinv(EndOfPrdvP_temp[violates_labor_constraint]) + LsrNow[violates_labor_constraint] = 1. # Set up z =1, upper limit + + '''6/''' + aNrmNow_rep = np.tile(np.reshape(aXtraGrid,(aXtraCount,1)),(1,TranShkGrid.size)) + bNrmNow = aNrmNow_rep - WageRte*TranShkGrid_rep + cNrmNow + WageRte*TranShkGrid_rep*LsrNow + bNowExtra = np.reshape(-WageRte*TranShkGrid,(1,TranShkGrid.size)) # bank balances when c_t = 0 and z_t=0, column vector of (16,) + bNowArray = np.concatenate((bNowExtra, bNrmNow),axis=0) # ***-- Combine the two pieces of the b_t grid (when c_t=0, z_t=0 and following grid) + + cNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)),cNrmNow),axis=0) # ---- + LsrNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)),LsrNow),axis=0) + LsrNowArray[0,0] = 1.0 # Don't work at all if TranShk=0, even if bNrm=0 + LbrNowArray = 1. - LsrNowArray # Labor is the complement of leisure + + '''7''' + # Get the (pseudo-inverse) marginal value using end of period marginal value + vPnvrsNow = uPinv(EndOfPrdvP_temp) # a column vector (200,) or (200,1) with EndOfPrdvP + vPnvrsNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)), vPnvrsNow)) # Concatenate a column vector of zeros on the left edge + + # Construct consumption and marginal value functions for this period + '''8/ ''' + bNrmMinNow = LinearInterp(TranShkGrid,bNowArray[0,:]) + + ''' 9/ ''' + # Loop over each transitory shock and make a linear interpolation to get lists of optimal consumption, labor and (pseudo-inverse) marginal value + cFuncNow_list = [] # Initialize the empty list of 1D function + LbrFuncNow_list = [] + vPnvrsFuncNow_list = [] + for j in range(TranShkGrid.size): + # Adjust bNrmNow for this transitory shock + bNrmNow_temp = bNowArray[:,j] - bNowArray[0,j] + cFuncNow_list.append(LinearInterp(bNrmNow_temp,cNowArray[:,j])) # Make consumption function for this transitory shock + LbrFuncNow_list.append(LinearInterp(bNrmNow_temp,LbrNowArray[:,j])) # Make labor function for this transitory shock + vPnvrsFuncNow_list.append(LinearInterp(bNrmNow_temp,vPnvrsNowArray[:,j])) # Make pseudo-inverse marginal value function for this transitory shock + + '''10''' + # Make linear interpolation by combining the lists of consumption, labor and marginal value functions + cFuncNowBase = LinearInterpOnInterp1D(cFuncNow_list,TranShkGrid) + LbrFuncNowBase = LinearInterpOnInterp1D(LbrFuncNow_list,TranShkGrid) + vPnvrsFuncNowBase = LinearInterpOnInterp1D(vPnvrsFuncNow_list,TranShkGrid) + + '''11''' + # Construct consumption, labor, marginal value functions with bNrmMinNow as the lower bound + cFuncNow = VariableLowerBoundFunc2D(cFuncNowBase,bNrmMinNow) + LbrFuncNow = VariableLowerBoundFunc2D(LbrFuncNowBase,bNrmMinNow) + vPnvrsFuncNow = VariableLowerBoundFunc2D(vPnvrsFuncNowBase,bNrmMinNow) + + '''12''' + # Construct the marginal value function using the envelope condition + vPfuncNow = MargValueFunc2D(vPnvrsFuncNow,CRRA) + + '''13''' + # Make a solution object for this period and return it + solution = ConsumerLaborSolution(cFunc=cFuncNow,LbrFunc=LbrFuncNow,vPfunc=vPfuncNow,bNrmMin=bNrmMinNow) + return solution + + +class LaborIntMargConsumerType(IndShockConsumerType): + + ''' + A class for representing an ex ante homogeneous type of consumer in the + consumption-saving model. These consumers have CRRA utility over current + consumption and discount future utility exponentially. Their future income + is subject to transitory and permanent shocks, and they can earn gross interest + on retained assets at a risk free interest factor. + + The solution is represented in a normalized way, with all variables divided + by permanent income (raised to the appropriate power). + + This model is homothetic in permanent income. + + IndShockConsumerType: A consumer type with idiosyncratic shocks to permanent and transitory income. + His problem is defined by a sequence of income distributions, survival probabilities, + and permanent income growth rates, as well as time invariant values for risk aversion, + discount factor, the interest rate, the grid of end-of-period assets, and an artificial borrowing constraint. + ''' + time_vary_ = copy(IndShockConsumerType.time_vary_) + time_vary_ += ['LbrCost','WageRte'] + time_inv_ = copy(IndShockConsumerType.time_inv_) + + def __init__(self,cycles=1,time_flow=True,**kwds): + ''' + Instantiate a new consumer type with given data. + See ConsumerParameters.init_labor_intensive for a dictionary of + the keywords that should be passed to the constructor. + + Parameters + ---------- + cycles : int + Number of times the sequence of periods should be solved. + time_flow : boolean + Whether time is currently "flowing" forward for this instance. + + Returns + ------- + None + ''' + IndShockConsumerType.__init__(self,cycles = cycles,time_flow=time_flow,**kwds) + self.pseudo_terminal = False + self.solveOnePeriod = solveConsLaborIntMarg + self.update() + + + def update(self): + ''' + Update the income process, the assets grid, and the terminal solution. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + self.updateIncomeProcess() + self.updateAssetsGrid() + self.updateTranShkGrid() + + + def calcBoundingValues(self): + ''' + Calculate human wealth plus minimum and maximum MPC in an infinite + horizon model with only one period repeated indefinitely. Store results + as attributes of self. Human wealth is the present discounted value of + expected future income after receiving income this period, ignoring mort- + ality. The maximum MPC is the limit of the MPC as m --> mNrmMin. The + minimum MPC is the limit of the MPC as m --> infty. + + NOT YET IMPLEMENTED FOR THIS CLASS + + Parameters + ---------- + None + + Returns + ------- + None + ''' + raise NotImplementedError() + + def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): + ''' + Creates a "normalized Euler error" function for this instance, mapping + from market resources to "consumption error per dollar of consumption." + Stores result in attribute eulerErrorFunc as an interpolated function. + Has option to use approximate income distribution stored in self.IncomeDstn + or to use a (temporary) very dense approximation. + + NOT YET IMPLEMENTED FOR THIS CLASS + + Parameters + ---------- + mMax : float + Maximum normalized market resources for the Euler error function. + approx_inc_dstn : Boolean + Indicator for whether to use the approximate discrete income distri- + bution stored in self.IncomeDstn[0], or to use a very accurate + discrete approximation instead. When True, uses approximation in + IncomeDstn; when False, makes and uses a very dense approximation. + + Returns + ------- + None + ''' + raise NotImplementedError() + + + def updateTranShkGrid(self): + ''' Create a list of values for TranShkGrid using TranShkVals, index 1 of TranShkDstn + + Parameters + ---------- + none + + Returns + ------- + none + ''' + time_orig=self.time_flow + self.timeFwd() + + TranShkGrid = [] # Create an empty list for TranShkGrid that will be updated + for t in range(self.T_cycle): + TranShkGrid.append(self.TranShkDstn[t][1]) # Update/ Extend the list of TranShkGrid with the TranShkVals for each TranShkPrbs + self.TranShkGrid = TranShkGrid # Save that list in self (time-varying) + self.addToTimeVary('TranShkGrid') # Run the method addToTimeVary from AgentType to add TranShkGrid as one parameter of time_vary list + + if not time_orig: + self.timeRev() + + + def updateSolutionTerminal(self): + ''' + Updates the terminal period solution and solves for optimal consumption and labor when there is no future. + + + Parameters + ---------- + None + + Returns + ------- + None + ''' + if self.time_flow: # To make sure we pick the last element of the list, depending on the direction time is flowing + t=-1 + else: + t=0 + TranShkGrid = self.TranShkGrid[t] + LbrCost = self.LbrCost[t] + WageRte = self.WageRte[t] + + bNrmGrid = np.insert(self.aXtraGrid,0,0.0) # Add a point at b_t = 0 to make sure that bNrmGrid goes down to 0 + bNrmCount = bNrmGrid.size # 201 + TranShkCount = TranShkGrid.size # = (7,) + bNrmGridTerm = np.tile(np.reshape(bNrmGrid,(bNrmCount,1)),(1,TranShkCount)) # Replicated bNrmGrid for each transitory shock theta_t + TranShkGridTerm = np.tile(TranShkGrid,(bNrmCount,1)) # Tile the grid of transitory shocks for the terminal solution. (201,7) + + # Array of labor (leisure) values for terminal solution + LsrTerm = np.minimum((LbrCost/(1.+LbrCost))*(bNrmGridTerm/(WageRte*TranShkGridTerm)+1.),1.0) + LsrTerm[0,0] = 1.0 + LbrTerm = 1.0 - LsrTerm + + # Calculate market resources in terminal period, which is consumption + mNrmTerm = bNrmGridTerm + LbrTerm*WageRte*TranShkGridTerm + cNrmTerm = mNrmTerm # Consume everything we have + + # Make a bilinear interpolation to represent the labor and consumption functions + LbrFunc_terminal = BilinearInterp(LbrTerm,bNrmGrid,TranShkGrid) + cFunc_terminal = BilinearInterp(cNrmTerm,bNrmGrid,TranShkGrid) + + # Compute the effective consumption value using consumption value and labor value at the terminal solution + xEffTerm = LsrTerm**LbrCost*cNrmTerm + vNvrsFunc_terminal = BilinearInterp(xEffTerm,bNrmGrid,TranShkGrid) + vFunc_terminal = ValueFunc2D(vNvrsFunc_terminal, self.CRRA) + + # Using the envelope condition at the terminal solution to estimate the marginal value function + vPterm = LsrTerm**LbrCost*CRRAutilityP(xEffTerm,gam=self.CRRA) + vPnvrsTerm = CRRAutilityP_inv(vPterm,gam=self.CRRA) # Evaluate the inverse of the CRRA marginal utility function at a given marginal value, vP + + vPnvrsFunc_terminal = BilinearInterp(vPnvrsTerm,bNrmGrid,TranShkGrid) + vPfunc_terminal = MargValueFunc2D(vPnvrsFunc_terminal,self.CRRA) # Get the Marginal Value function + + bNrmMin_terminal = ConstantFunction(0.) # Trivial function that return the same real output for any input + + self.solution_terminal = ConsumerLaborSolution(cFunc=cFunc_terminal,LbrFunc=LbrFunc_terminal,\ + vFunc=vFunc_terminal,vPfunc=vPfunc_terminal,bNrmMin=bNrmMin_terminal) + + + def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): + ''' + Plot the consumption function by bank balances at a given set of transitory shocks. + + Parameters + ---------- + t : int + Time index of the solution for which to plot the consumption function. + bMin : float or None + Minimum value of bNrm at which to begin the plot. If None, defaults + to the minimum allowable value of bNrm for each transitory shock. + bMax : float or None + Maximum value of bNrm at which to end the plot. If None, defaults + to bMin + 20. + ShkSet : [float] or None + Array or list of transitory shocks at which to plot the consumption + function. If None, defaults to the TranShkGrid for this time period. + + Returns + ------- + None + ''' + if ShkSet is None: + ShkSet = self.TranShkGrid[t] + if bMin is None: + bMinSet = self.solution[0].bNrmMin(TranShkSet) + else: + bMinSet = bMin*np.ones_like(TranShkSet) + if bMax is None: + bMaxSet = bMinSet + 20. + else: + bMaxSet = bMax*np.ones_like(TranShkSet) + + for j in range(len(ShkSet)): + Shk = ShkSet[j] + B = np.linspace(bMinSet[j],bMaxSet[j],300) + C = LaborIntMargExample.solution[t].cFunc(B,Shk*np.ones_like(B)) + plt.plot(B,C) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Normalized consumption level') + plt.show() + + +############################################################################### + +if __name__ == '__main__': + import ConsumerParametersTM as Params # Parameters for a consumer type + import matplotlib.pyplot as plt + from time import clock + mystr = lambda number : "{:.4f}".format(number) # Format numbers as strings + + # Make and solve a labor intensive margin consumer i.e. a consumer with utility for leisure + LaborIntMargExample = LaborIntMargConsumerType(**Params.init_labor_intensive) + LaborIntMargExample.cycles = 0 + + t_start = clock() + LaborIntMargExample.solve() + t_end = clock() + print('Solving a labor intensive margin consumer took ' + str(t_end-t_start) + ' seconds.') + + t = 0 + bMax = 100. + + # Plot the consumption function at various transitory productivity shocks + TranShkSet = LaborIntMargExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) + C = LaborIntMargExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,C) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Normalized consumption level') + plt.show() + + # Plot the marginal consumption function at various transitory productivity shocks + TranShkSet = LaborIntMargExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) + C = LaborIntMargExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,C) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Marginal propensity to consume') + plt.show() + + # Plot the labor function at various transitory productivity shocks + TranShkSet = LaborIntMargExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) + Lbr = LaborIntMargExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,Lbr) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Labor supply') + plt.show() + + # Plot the marginal value function at various transitory productivity shocks + pseudo_inverse = True + TranShkSet = LaborIntMargExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) + if pseudo_inverse: + vP = LaborIntMargExample.solution[t].vPfunc.cFunc(B_temp,Shk*np.ones_like(B_temp)) + else: + vP = LaborIntMargExample.solution[t].vPfunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,vP) + plt.xlabel('Beginning of period bank balances') + if pseudo_inverse: + plt.ylabel('Pseudo inverse marginal value') + else: + plt.ylabel('Marginal value') + plt.show() + \ No newline at end of file diff --git a/HARK/ConsumptionSaving/ConsumerParametersTM.py b/HARK/ConsumptionSaving/ConsumerParametersTM.py new file mode 100644 index 000000000..27b7e9543 --- /dev/null +++ b/HARK/ConsumptionSaving/ConsumerParametersTM.py @@ -0,0 +1,325 @@ +''' +Specifies examples of the full set of parameters required to solve various +consumption-saving models. These models can be found in ConsIndShockModel, +ConsAggShockModel, ConsPrefShockModel, ConsLaborModel, and ConsMarkovModel. +''' +from copy import copy +import numpy as np + +# ----------------------------------------------------------------------------- +# --- Define all of the parameters for the perfect foresight model ------------ +# ----------------------------------------------------------------------------- + +CRRA = 2.0 # Coefficient of relative risk aversion +Rfree = 1.03 # Interest factor on assets +DiscFac = 0.96 # Intertemporal discount factor +LivPrb = [0.98] # Survival probability +PermGroFac = [1.01] # Permanent income growth factor +AgentCount = 10000 # Number of agents of this type (only matters for simulation) +aNrmInitMean = 0.0 # Mean of log initial assets (only matters for simulation) +aNrmInitStd = 1.0 # Standard deviation of log initial assets (only for simulation) +pLvlInitMean = 0.0 # Mean of log initial permanent income (only matters for simulation) +pLvlInitStd = 0.0 # Standard deviation of log initial permanent income (only matters for simulation) +PermGroFacAgg = 1.0 # Aggregate permanent income growth factor (only matters for simulation) +T_age = None # Age after which simulated agents are automatically killed +T_cycle = 1 # Number of periods in the cycle for this agent type + +# Make a dictionary to specify a perfect foresight consumer type +init_perfect_foresight = { 'CRRA': CRRA, + 'Rfree': Rfree, + 'DiscFac': DiscFac, + 'LivPrb': LivPrb, + 'PermGroFac': PermGroFac, + 'AgentCount': AgentCount, + 'aNrmInitMean' : aNrmInitMean, + 'aNrmInitStd' : aNrmInitStd, + 'pLvlInitMean' : pLvlInitMean, + 'pLvlInitStd' : pLvlInitStd, + 'PermGroFacAgg' : PermGroFacAgg, + 'T_age' : T_age, + 'T_cycle' : T_cycle + } + +# ----------------------------------------------------------------------------- +# --- Define additional parameters for the idiosyncratic shocks model --------- +# ----------------------------------------------------------------------------- + +# Parameters for constructing the "assets above minimum" grid +aXtraMin = 0.001 # Minimum end-of-period "assets above minimum" value +aXtraMax = 20 # Maximum end-of-period "assets above minimum" value +aXtraExtra = None # Some other value of "assets above minimum" to add to the grid, not used +aXtraNestFac = 3 # Exponential nesting factor when constructing "assets above minimum" grid +aXtraCount = 48 # Number of points in the grid of "assets above minimum" + +# Parameters describing the income process +PermShkCount = 7 # Number of points in discrete approximation to permanent income shocks +TranShkCount = 7 # Number of points in discrete approximation to transitory income shocks +PermShkStd = [0.1] # Standard deviation of log permanent income shocks +TranShkStd = [0.1] # Standard deviation of log transitory income shocks +UnempPrb = 0.05 # Probability of unemployment while working +UnempPrbRet = 0.005 # Probability of "unemployment" while retired +IncUnemp = 0.3 # Unemployment benefits replacement rate +IncUnempRet = 0.0 # "Unemployment" benefits when retired +tax_rate = 0.0 # Flat income tax rate +T_retire = 0 # Period of retirement (0 --> no retirement) + +# A few other parameters +BoroCnstArt = 0.0 # Artificial borrowing constraint; imposed minimum level of end-of period assets +CubicBool = False # Use cubic spline interpolation when True, linear interpolation when False +vFuncBool = True # Whether to calculate the value function during solution + +# Make a dictionary to specify an idiosyncratic income shocks consumer +init_idiosyncratic_shocks = { 'CRRA': CRRA, + 'Rfree': Rfree, + 'DiscFac': DiscFac, + 'LivPrb': LivPrb, + 'PermGroFac': PermGroFac, + 'AgentCount': AgentCount, + 'aXtraMin': aXtraMin, + 'aXtraMax': aXtraMax, + 'aXtraNestFac':aXtraNestFac, + 'aXtraCount': aXtraCount, + 'aXtraExtra': [aXtraExtra], + 'PermShkStd': PermShkStd, + 'PermShkCount': PermShkCount, + 'TranShkStd': TranShkStd, + 'TranShkCount': TranShkCount, + 'UnempPrb': UnempPrb, + 'UnempPrbRet': UnempPrbRet, + 'IncUnemp': IncUnemp, + 'IncUnempRet': IncUnempRet, + 'BoroCnstArt': BoroCnstArt, + 'tax_rate':0.0, + 'vFuncBool':vFuncBool, + 'CubicBool':CubicBool, + 'T_retire':T_retire, + 'aNrmInitMean' : aNrmInitMean, + 'aNrmInitStd' : aNrmInitStd, + 'pLvlInitMean' : pLvlInitMean, + 'pLvlInitStd' : pLvlInitStd, + 'PermGroFacAgg' : PermGroFacAgg, + 'T_age' : T_age, + 'T_cycle' : T_cycle + } + +# Make a dictionary to specify a lifecycle consumer with a finite horizon +init_lifecycle = copy(init_idiosyncratic_shocks) +init_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02] +init_lifecycle['PermShkStd'] = [0.1,0.2,0.1,0.2,0.1,0.2,0.1,0,0,0] +init_lifecycle['TranShkStd'] = [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0,0,0] +init_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] +init_lifecycle['T_cycle'] = 10 +init_lifecycle['T_retire'] = 7 +init_lifecycle['T_age'] = 11 # Make sure that old people die at terminal age and don't turn into newborns! + +# Make a dictionary to specify an infinite consumer with a four period cycle +init_cyclical = copy(init_idiosyncratic_shocks) +init_cyclical['PermGroFac'] = [1.082251, 2.8, 0.3, 1.1] +init_cyclical['PermShkStd'] = [0.1,0.1,0.1,0.1] +init_cyclical['TranShkStd'] = [0.1,0.1,0.1,0.1] +init_cyclical['LivPrb'] = 4*[0.98] +init_cyclical['T_cycle'] = 4 + + +# ----------------------------------------------------------------------------- +# -------- Define additional parameters for the "kinked R" model -------------- +# ----------------------------------------------------------------------------- + +Rboro = 1.20 # Interest factor on assets when borrowing, a < 0 +Rsave = 1.02 # Interest factor on assets when saving, a > 0 + +# Make a dictionary to specify a "kinked R" idiosyncratic shock consumer +init_kinked_R = copy(init_idiosyncratic_shocks) +del init_kinked_R['Rfree'] # get rid of constant interest factor +init_kinked_R['Rboro'] = Rboro +init_kinked_R['Rsave'] = Rsave +init_kinked_R['BoroCnstArt'] = None # kinked R is a bit silly if borrowing not allowed +init_kinked_R['CubicBool'] = False # kinked R currently only compatible with linear cFunc +init_kinked_R['aXtraCount'] = 48 # ...so need lots of extra gridpoints to make up for it + + +# ----------------------------------------------------------------------------- +# ----- Define additional parameters for the preference shock model ----------- +# ----------------------------------------------------------------------------- + +PrefShkCount = 12 # Number of points in discrete approximation to preference shock dist +PrefShk_tail_N = 4 # Number of "tail points" on each end of pref shock dist +PrefShkStd = [0.30] # Standard deviation of utility shocks + +# Make a dictionary to specify a preference shock consumer +init_preference_shocks = copy(init_idiosyncratic_shocks) +init_preference_shocks['PrefShkCount'] = PrefShkCount +init_preference_shocks['PrefShk_tail_N'] = PrefShk_tail_N +init_preference_shocks['PrefShkStd'] = PrefShkStd +init_preference_shocks['aXtraCount'] = 48 +init_preference_shocks['CubicBool'] = False # pref shocks currently only compatible with linear cFunc + +# Make a dictionary to specify a "kinky preference" consumer, who has both shocks +# to utility and a different interest rate on borrowing vs saving +init_kinky_pref = copy(init_kinked_R) +init_kinky_pref['PrefShkCount'] = PrefShkCount +init_kinky_pref['PrefShk_tail_N'] = PrefShk_tail_N +init_kinky_pref['PrefShkStd'] = PrefShkStd + +# ----------------------------------------------------------------------------- +# ----- Define additional parameters for the aggregate shocks model ----------- +# ----------------------------------------------------------------------------- +MgridBase = np.array([0.1,0.3,0.6,0.8,0.9,0.98,1.0,1.02,1.1,1.2,1.6,2.0,3.0]) # Grid of capital-to-labor-ratios (factors) + +# Parameters for a Cobb-Douglas economy +PermShkAggCount = 3 # Number of points in discrete approximation to aggregate permanent shock dist +TranShkAggCount = 3 # Number of points in discrete approximation to aggregate transitory shock dist +PermShkAggStd = 0.0063 # Standard deviation of log aggregate permanent shocks +TranShkAggStd = 0.0031 # Standard deviation of log aggregate transitory shocks +DeprFac = 0.025 # Capital depreciation rate +CapShare = 0.36 # Capital's share of income +DiscFacPF = DiscFac # Discount factor of perfect foresight calibration +#intercept_prev = -0.305568464142 # Intercept of AFunc function +#slope_prev = 1.06154769008 # Slope of AFunc function +intercept_prev = 0.0 # Intercept of aggregate savings function +slope_prev = 1.0 # Slope of aggregate savings function + +# Make a dictionary to specify an aggregate shocks consumer +init_agg_shocks = copy(init_idiosyncratic_shocks) +del init_agg_shocks['Rfree'] # Interest factor is endogenous in agg shocks model +del init_agg_shocks['CubicBool'] # Not supported yet for agg shocks model +del init_agg_shocks['vFuncBool'] # Not supported yet for agg shocks model +init_agg_shocks['PermGroFac'] = [1.0] # Not yet correctly handled for agg shocks model, set to 1 +init_agg_shocks['MgridBase'] = MgridBase +init_agg_shocks['aXtraCount'] = 24 +#init_agg_shocks['aXtraMax'] = 80.0 +#init_agg_shocks['aXtraExtra'] = [1000.0] +init_agg_shocks['aNrmInitStd'] = 0.0 +init_agg_shocks['LivPrb'] = LivPrb + + +# Make a dictionary to specify a Cobb-Douglas economy +init_cobb_douglas = {'PermShkAggCount': PermShkAggCount, + 'TranShkAggCount': TranShkAggCount, + 'PermShkAggStd': PermShkAggStd, + 'TranShkAggStd': TranShkAggStd, + 'DeprFac': DeprFac, + 'CapShare': CapShare, + 'DiscFac': DiscFacPF, + 'AggregateL':1.0, + 'slope_prev': slope_prev, + 'intercept_prev': intercept_prev, + 'act_T':1200 + } + +# ----------------------------------------------------------------------------- +# ----- Define additional parameters for the persistent shocks model ---------- +# ----------------------------------------------------------------------------- + +PermIncCount = 12 # Number of permanent income gridpoints in "body" +PermInc_tail_N = 4 # Number of permanent income gridpoints in each "tail" +PermIncStdInit = 0.4 # Initial standard deviation of (log) permanent income (not used in example) +PermIncAvgInit = 1.0 # Initial average of permanent income (not used in example) +PermIncCorr = 0.98 # Serial correlation coefficient for permanent income +cycles = 0 + +# Make a dictionary for the "explicit permanent income" idiosyncratic shocks model +init_explicit_perm_inc = copy(init_idiosyncratic_shocks) +init_explicit_perm_inc['PermIncCount'] = PermIncCount +init_explicit_perm_inc['PermInc_tail_N'] = PermInc_tail_N +init_explicit_perm_inc['PermIncAvgInit'] = PermIncAvgInit +init_explicit_perm_inc['PermIncStdInit'] = PermIncStdInit +init_explicit_perm_inc['PermGroFac'] = [1.0] # long run permanent income growth doesn't work yet +init_explicit_perm_inc['cycles'] = cycles +init_explicit_perm_inc['aXtraMax'] = 30 +init_explicit_perm_inc['aXtraExtra'] = [0.005,0.01] + +# Make a dictionary for the "persistent idiosyncratic shocks" model +init_persistent_shocks = copy(init_explicit_perm_inc) +init_persistent_shocks['PermIncCorr'] = PermIncCorr + +# ----------------------------------------------------------------------------- +# ----- Define additional parameters for the medical shocks model ------------- +# ----------------------------------------------------------------------------- + +CRRAmed = 1.5*CRRA # Coefficient of relative risk aversion for medical care +MedShkAvg = [0.001] # Average of medical need shocks +MedShkStd = [5.0] # Standard deviation of (log) medical need shocks +MedShkCount = 5 # Number of medical shock points in "body" +MedShkCountTail = 15 # Number of medical shock points in "tail" (upper only) +MedPrice = [1.5] # Relative price of a unit of medical care + +# Make a dictionary for the "medical shocks" model +init_medical_shocks = copy(init_persistent_shocks) +init_medical_shocks['CRRAmed'] = CRRAmed +init_medical_shocks['MedShkAvg'] = MedShkAvg +init_medical_shocks['MedShkStd'] = MedShkStd +init_medical_shocks['MedShkCount'] = MedShkCount +init_medical_shocks['MedShkCountTail'] = MedShkCountTail +init_medical_shocks['MedPrice'] = MedPrice +init_medical_shocks['aXtraCount'] = 32 + + +# ----------------------------------------------------------------------------- +# -------- Define additional parameters for the baby labor model -------------- +# ----------------------------------------------------------------------------- + +LbrDisutilCoeffs = [-1.5,0.1,0.05] # Constant, linear, and quadratic coefficients + # on transformed labor disutility factor + +# Make a dictionary for "baby labor" model +init_baby_labor = copy(init_lifecycle) +init_baby_labor['LbrDisutilCoeffs'] = LbrDisutilCoeffs +init_baby_labor['TranShkCount'] = 1 # No transitory shocks in baby labor model +init_baby_labor['TranShkStd'] = [0.0]*init_lifecycle['T_cycle'] +init_baby_labor['T_retire'] = 0 # turn off retirement +init_baby_labor['PermShkStd'] = [0.1]*init_lifecycle['T_cycle'] +init_baby_labor['UnempPrb'] = 0.0 # turn off unemployment +init_baby_labor['UnempPrbRet'] = 0.0 +init_baby_labor['PermShkCount'] = 25 # Crank up permanent shock count +init_baby_labor['aXtraCount'] = 200 # Might be important to have many gridpoints + + +# ----------------------------------------------------------------------------- +# ----Define additional parameters for the Endogenous Labor Supply model------- +# ----------------------------------------------------------------------------- + +#TranShkVals = [1] +#TranShkPrbs = [0.2] + +LivPrb = [0.99] # Survival probability +PermGroFac = [1.02] # Permanent income growth factor +LbrCost = [0.36] # Labor cost list of one element +WageRte = [1.0] # Wage Rate + +BoroCnstArt = None +CubicBool = False # Use cubic spline interpolation when True, linear interpolation when False +vFuncBool = False # Whether to calculate the value function during solution + +StateMin = 0.001 +StateMax = 20.0 +StateCount = 15 +ExponentialGrid = True + +# Make a dictionary for Endogenous Labor supply model - intensive margin +init_labor_intensive = copy(init_lifecycle) +init_labor_intensive['TranShkStd'] = [0.1]*init_lifecycle['T_cycle'] +init_labor_intensive['PermShkStd'] = [0.1]*init_lifecycle['T_cycle'] +init_labor_intensive['IncUnemp'] = 0.0 +init_labor_intensive['UnempPrb'] = 0.05 # Turn off unemployment +init_labor_intensive['UnempPrbRet'] = 0.0 +init_labor_intensive['TranShkCount'] = 15 # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks +init_labor_intensive['PermShkCount'] = 16 # Crank up permanent shock count +init_labor_intensive['BoroCnstArt'] = BoroCnstArt +init_labor_intensive['CubicBool'] = CubicBool +init_labor_intensive['vFuncBool'] = vFuncBool +init_labor_intensive ['T_retire'] = 0 # Turn off retirement +init_labor_intensive ['aXtraCount'] = 200 # May be important to have a larger number of gridpoints (than 48 initially) +init_labor_intensive ['aXtraMax'] = 20. +init_labor_intensive ['LbrCost'] = LbrCost +init_labor_intensive ['WageRte'] = WageRte +init_labor_intensive ['LivPrb'] = LivPrb +init_labor_intensive ['PermGroFac'] = PermGroFac +init_labor_intensive ['StateMin'] = StateMin +init_labor_intensive ['StateMax'] = StateMax +init_labor_intensive ['StateCount'] = StateCount +init_labor_intensive ['ExponentialGrid'] = ExponentialGrid + +init_labor_intensive ['T_cycle'] = 1 + + From f08130f64639de378c4f7a4b8b7fae3722c24317 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 29 Aug 2019 17:19:49 -0400 Subject: [PATCH 050/242] Cleaned up solver comments and variable names Added/edited various solver comments and changed a few variable names. Also changed aXtraMax in parameters, which reveals that the "negative labor" problem is handled correctly in the solver... but it can still *extrapolate* to negative labor. --- HARK/ConsumptionSaving/ConsLaborModel.py | 123 +++++++++--------- .../ConsumptionSaving/ConsumerParametersTM.py | 6 +- 2 files changed, 64 insertions(+), 65 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 54e7d9d30..865bb56c2 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -128,18 +128,18 @@ def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,C productivity shock), a labor function LbrFunc and a marginal value function vPfunc. ''' + # Make sure the inputs for this period are valid: CRRA > LbrCost/(1+LbrCost) + # and CubicBool = False. CRRA condition is met automatically when CRRA >= 1. frac = 1./(1.+LbrCost) - if CRRA <= frac*LbrCost: - print ' Error: make sure CRRA coefficient is strictly greater than alpha/(1+alpha).' -# return None + print('Error: make sure CRRA coefficient is strictly greater than alpha/(1+alpha).') sys.exit() if BoroCnstArt is not None: - print ' Error: Model that cannot handle artificial borrowing constraint yet. ' - return None + print('Error: Model cannot handle artificial borrowing constraint yet. ') + sys.exit() if vFuncBool or CubicBool is True: - print ' Error: Model that cannot handle Cubic interpolation yet.' - return None + print('Error: Model cannot handle cubic interpolation yet.') + sys.exit() # Unpack next period's solution and the productivity shock distribution, and define the inverse (marginal) utilty function vPfunc_next = solution_next.vPfunc @@ -152,99 +152,98 @@ def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,C uPinv = lambda X : CRRAutilityP_inv(X,gam=CRRA) # Make tiled versions of the grid of a_t values and the components of the shock distribution - aXtraCount = aXtraGrid.size # = 200 - bNrmGrid_rep = np.tile(np.reshape(aXtraGrid,(aXtraCount,1)),(1,TranShkCount)) # Replicated axtraGrid of b_t values (bNowGrid) for each transitory (productivity) shock - TranShkVals_rep = np.tile(np.reshape(TranShkVals,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock values for each b_t state - TranShkPrbs_rep = np.tile(np.reshape(TranShkPrbs,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock probabilities for each b_t state + aXtraCount = aXtraGrid.size + bNrmGrid = aXtraGrid # Next period's bank balances before labor income + bNrmGrid_rep = np.tile(np.reshape(bNrmGrid,(aXtraCount,1)),(1,TranShkCount)) # Replicated axtraGrid of b_t values (bNowGrid) for each transitory (productivity) shock + TranShkVals_rep = np.tile(np.reshape(TranShkVals,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock values for each a_t state + TranShkPrbs_rep = np.tile(np.reshape(TranShkPrbs,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock probabilities for each a_t state - # Calculate marginal value next period for each gridpoint and each transitory shock - bNext = Rfree * bNrmGrid_rep # Next period's bank balances. (200,16) - vPNext = vPfunc_next(bNext, TranShkVals_rep) # Derive the Next period's marginal value at every transitory shock and every bank balances gridpoint - vPbarNext = np.sum(vPNext*TranShkPrbs_rep, axis = 1) # Integrate out the transitory shocks (in TranShkVals direction) - vPbarNvrsNext = uPinv(vPbarNext) # Marginal value transformed through the inverse marginal utility function - vPbarNvrsFuncNext = LinearInterp(np.insert(bNext[:,0],0,0.0),np.insert(vPbarNvrsNext,0,0.0)) # Linear interpolation over the b_t. Add a point at b_t = 0. - vPbarFuncNext = MargValueFunc(vPbarNvrsFuncNext,CRRA) # Take the marginal utility function to inverse back and get the optimal values for consumption + # Construct a function that gives marginal value of next period's bank balances *just before* the transitory shock arrives + vPNext = vPfunc_next(bNrmGrid_rep, TranShkVals_rep) # Next period's marginal value at every transitory shock and every bank balances gridpoint + vPbarNext = np.sum(vPNext*TranShkPrbs_rep, axis = 1) # Integrate out the transitory shocks (in TranShkVals direction) to get expected vP just before the transitory shock + vPbarNvrsNext = uPinv(vPbarNext) # Transformed marginal value through the inverse marginal utility function to "decurve" it + vPbarNvrsFuncNext = LinearInterp(np.insert(bNrmGrid,0,0.0),np.insert(vPbarNvrsNext,0,0.0)) # Linear interpolation over b_{t+1}, adding a point at minimal value of b = 0. + vPbarFuncNext = MargValueFunc(vPbarNvrsFuncNext,CRRA) # "Recurve" the intermediate marginal value function through the marginal utility function - # Get the next period's bank balances at each permanent shock - aNrmGrid_rep = np.tile(np.reshape(aXtraGrid,(aXtraCount,1)),(1,PermShkCount)) # Replicated axtraGrid of b_t values (bNowGrid) for each permanent (productivity) shock. (200,16) - PermShkVals_rep = np.tile(np.reshape(PermShkVals,(1,PermShkCount)),(aXtraCount,1)) # Replicated permanent shock values for each b_t state - PermShkPrbs_rep = np.tile(np.reshape(PermShkPrbs,(1,PermShkCount)),(aXtraCount,1)) # Replicated permanent shock probabilities for each b_t state - bNextPerm = (Rfree/(PermGroFac*PermShkVals_rep))*aNrmGrid_rep # (200,16) + # Get next period's bank balances at each permanent shock from each end-of-period asset values + aNrmGrid_rep = np.tile(np.reshape(aXtraGrid,(aXtraCount,1)),(1,PermShkCount)) # Replicated grid of a_t values for each permanent (productivity) shock + PermShkVals_rep = np.tile(np.reshape(PermShkVals,(1,PermShkCount)),(aXtraCount,1)) # Replicated permanent shock values for each a_t value + PermShkPrbs_rep = np.tile(np.reshape(PermShkPrbs,(1,PermShkCount)),(aXtraCount,1)) # Replicated permanent shock probabilities for each a_t value + bNrmNext = (Rfree/(PermGroFac*PermShkVals_rep))*aNrmGrid_rep - ''' 3/ ''' - # Construct the marginal value of end-of-period assets - EndOfPrdvP_temp = DiscFac*Rfree*LivPrb*np.sum((PermGroFac*PermShkVals_rep)**(-CRRA)*vPbarFuncNext(bNextPerm)*PermShkPrbs_rep,axis=1) # sum in the TranShkVals ~ TranShkGrid direction - EndOfPrdvP = EndOfPrdvP_temp.reshape((aXtraCount,1)) # ---- Convert it to a (row? Notes) column vector of size (200,1) - TranShkScaleFac_temp = frac*(WageRte*TranShkGrid)**(LbrCost*frac)*(LbrCost**(-LbrCost*frac)+LbrCost**(frac)) - TranShkScaleFac = TranShkScaleFac_temp.reshape((1,TranShkGrid.size)) # --- Convert it to a row (column?) vector of size (1,16) + # Calculate marginal value of end-of-period assets at each a_t gridpoint + vPbarNext = (PermGroFac*PermShkVals_rep)**(-CRRA)*vPbarFuncNext(bNrmNext) # Get marginal value of bank balances next period at each shock + EndOfPrdvP = DiscFac*Rfree*LivPrb*np.sum(vPbarNext*PermShkPrbs_rep, axis=1, keepdims=True) # Take expectation across permanent income shocks + + # Compute scaling factor for each transitory shock + TranShkScaleFac_temp = frac*(WageRte*TranShkGrid)**(LbrCost*frac)*(LbrCost**(-LbrCost*frac)+LbrCost**(frac)) + TranShkScaleFac = np.reshape(TranShkScaleFac_temp,(1,TranShkGrid.size)) # Flip it to be a row vector - ''' 4/ ''' - xNowArray = (np.dot(EndOfPrdvP,TranShkScaleFac))**(-1./(CRRA-LbrCost*frac)) #(200,16) Get an array of x_t values corresponding to (a_t,theta_t) values + # Use the first order condition to compute an array of "composite good" x_t values corresponding to (a_t,theta_t) values + xNow = (np.dot(EndOfPrdvP,TranShkScaleFac))**(-1./(CRRA-LbrCost*frac)) - ''' 5/: Error raising xNowArray to a non-integer power, frac, 1/ (1+0.36) Or NAN array''' + # Transform the composite good x_t values into consumption c_t and leisure z_t values TranShkGrid_rep = np.tile(np.reshape(TranShkGrid,(1,TranShkGrid.size)),(aXtraCount,1)) - xNowPowered = xNowArray**frac - cNrmNow = (((WageRte*TranShkGrid_rep)/LbrCost)**(LbrCost*frac))*xNowPowered # Find optimal consumption using the solution to the effective consumption pb - LsrNow = (LbrCost/(WageRte*TranShkGrid_rep))**frac*xNowPowered # Find optimal leisure amount using the solution to the effective consumption pb - cNrmNow[:,0] = uPinv(EndOfPrdvP_temp) + xNowPow = xNow**frac # Will use this object multiple times in math below + cNrmNow = (((WageRte*TranShkGrid_rep)/LbrCost)**(LbrCost*frac))*xNowPow # Find optimal consumption from optimal composite good + LsrNow = (LbrCost/(WageRte*TranShkGrid_rep))**frac*xNowPow # Find optimal leisure from optimal composite good + + # The zero-th transitory shock is TranShk=0, and the solution is to not work: Lsr = 1, Lbr = 0. + cNrmNow[:,0] = uPinv(EndOfPrdvP.flatten()) LsrNow[:,0] = 1.0 - # Check the Labor Constraint using violatesLbrCnst: boolean array. An indicator for whether - # the labor constraint is violated or not. The Agent cannot choose to work a negative amount of time. Labor = 1 - Leisure - violates_labor_constraint = LsrNow > 1. + # Agent cannot choose to work a negative amount of time. When this occurs, set + # leisure to one and recompute consumption using simplified first order condition. + violates_labor_constraint = LsrNow > 1. # Find where labor would be negative if unconstrained EndOfPrdvP_temp = np.tile(np.reshape(EndOfPrdvP,(aXtraCount,1)),(1,TranShkCount)) cNrmNow[violates_labor_constraint] = uPinv(EndOfPrdvP_temp[violates_labor_constraint]) - LsrNow[violates_labor_constraint] = 1. # Set up z =1, upper limit + LsrNow[violates_labor_constraint] = 1. # Set up z =1, upper limit - '''6/''' + # Calculate the endogenous bNrm states by inverting the within-period transition aNrmNow_rep = np.tile(np.reshape(aXtraGrid,(aXtraCount,1)),(1,TranShkGrid.size)) bNrmNow = aNrmNow_rep - WageRte*TranShkGrid_rep + cNrmNow + WageRte*TranShkGrid_rep*LsrNow - bNowExtra = np.reshape(-WageRte*TranShkGrid,(1,TranShkGrid.size)) # bank balances when c_t = 0 and z_t=0, column vector of (16,) - bNowArray = np.concatenate((bNowExtra, bNrmNow),axis=0) # ***-- Combine the two pieces of the b_t grid (when c_t=0, z_t=0 and following grid) - cNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)),cNrmNow),axis=0) # ---- - LsrNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)),LsrNow),axis=0) + # Add an extra gridpoint at the absolute minimal valid value for b_t for each TranShk; + # this corresponds to working 100% of the time and consuming nothing. + bNowArray = np.concatenate((np.reshape(-WageRte*TranShkGrid,(1,TranShkGrid.size)), bNrmNow),axis=0) + cNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)),cNrmNow),axis=0) # Consume nothing + LsrNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)),LsrNow),axis=0) # And no leisure! LsrNowArray[0,0] = 1.0 # Don't work at all if TranShk=0, even if bNrm=0 - LbrNowArray = 1. - LsrNowArray # Labor is the complement of leisure + LbrNowArray = 1.0 - LsrNowArray # Labor is the complement of leisure - '''7''' - # Get the (pseudo-inverse) marginal value using end of period marginal value - vPnvrsNow = uPinv(EndOfPrdvP_temp) # a column vector (200,) or (200,1) with EndOfPrdvP - vPnvrsNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)), vPnvrsNow)) # Concatenate a column vector of zeros on the left edge + # Get (pseudo-inverse) marginal value of bank balances using end of period + # marginal value of assets (envelope condition), adding a column of zeros + # zeros on the left edge, representing the limit at the minimum value of b_t. + vPnvrsNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)), uPinv(EndOfPrdvP_temp))) # Concatenate # Construct consumption and marginal value functions for this period - '''8/ ''' bNrmMinNow = LinearInterp(TranShkGrid,bNowArray[0,:]) - ''' 9/ ''' - # Loop over each transitory shock and make a linear interpolation to get lists of optimal consumption, labor and (pseudo-inverse) marginal value - cFuncNow_list = [] # Initialize the empty list of 1D function + # Loop over each transitory shock and make a linear interpolation to get lists + # of optimal consumption, labor and (pseudo-inverse) marginal value by TranShk + cFuncNow_list = [] LbrFuncNow_list = [] vPnvrsFuncNow_list = [] for j in range(TranShkGrid.size): - # Adjust bNrmNow for this transitory shock - bNrmNow_temp = bNowArray[:,j] - bNowArray[0,j] + bNrmNow_temp = bNowArray[:,j] - bNowArray[0,j] # Adjust bNrmNow for this transitory shock, so bNrmNow_temp[0] = 0 cFuncNow_list.append(LinearInterp(bNrmNow_temp,cNowArray[:,j])) # Make consumption function for this transitory shock LbrFuncNow_list.append(LinearInterp(bNrmNow_temp,LbrNowArray[:,j])) # Make labor function for this transitory shock vPnvrsFuncNow_list.append(LinearInterp(bNrmNow_temp,vPnvrsNowArray[:,j])) # Make pseudo-inverse marginal value function for this transitory shock - '''10''' # Make linear interpolation by combining the lists of consumption, labor and marginal value functions cFuncNowBase = LinearInterpOnInterp1D(cFuncNow_list,TranShkGrid) LbrFuncNowBase = LinearInterpOnInterp1D(LbrFuncNow_list,TranShkGrid) vPnvrsFuncNowBase = LinearInterpOnInterp1D(vPnvrsFuncNow_list,TranShkGrid) - '''11''' - # Construct consumption, labor, marginal value functions with bNrmMinNow as the lower bound + # Construct consumption, labor, pseudo-inverse marginal value functions with + # bNrmMinNow as the lower bound. This removes the adjustment in the loop above. cFuncNow = VariableLowerBoundFunc2D(cFuncNowBase,bNrmMinNow) LbrFuncNow = VariableLowerBoundFunc2D(LbrFuncNowBase,bNrmMinNow) vPnvrsFuncNow = VariableLowerBoundFunc2D(vPnvrsFuncNowBase,bNrmMinNow) - '''12''' - # Construct the marginal value function using the envelope condition + # Construct the marginal value function by "recurving" its pseudo-inverse vPfuncNow = MargValueFunc2D(vPnvrsFuncNow,CRRA) - '''13''' # Make a solution object for this period and return it solution = ConsumerLaborSolution(cFunc=cFuncNow,LbrFunc=LbrFuncNow,vPfunc=vPfuncNow,bNrmMin=bNrmMinNow) return solution diff --git a/HARK/ConsumptionSaving/ConsumerParametersTM.py b/HARK/ConsumptionSaving/ConsumerParametersTM.py index 27b7e9543..7bc1750d9 100644 --- a/HARK/ConsumptionSaving/ConsumerParametersTM.py +++ b/HARK/ConsumptionSaving/ConsumerParametersTM.py @@ -260,7 +260,7 @@ # ----------------------------------------------------------------------------- LbrDisutilCoeffs = [-1.5,0.1,0.05] # Constant, linear, and quadratic coefficients - # on transformed labor disutility factor + # on transformed labor disutility factor by age # Make a dictionary for "baby labor" model init_baby_labor = copy(init_lifecycle) @@ -301,7 +301,7 @@ init_labor_intensive['TranShkStd'] = [0.1]*init_lifecycle['T_cycle'] init_labor_intensive['PermShkStd'] = [0.1]*init_lifecycle['T_cycle'] init_labor_intensive['IncUnemp'] = 0.0 -init_labor_intensive['UnempPrb'] = 0.05 # Turn off unemployment +init_labor_intensive['UnempPrb'] = 0.05 init_labor_intensive['UnempPrbRet'] = 0.0 init_labor_intensive['TranShkCount'] = 15 # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks init_labor_intensive['PermShkCount'] = 16 # Crank up permanent shock count @@ -310,7 +310,7 @@ init_labor_intensive['vFuncBool'] = vFuncBool init_labor_intensive ['T_retire'] = 0 # Turn off retirement init_labor_intensive ['aXtraCount'] = 200 # May be important to have a larger number of gridpoints (than 48 initially) -init_labor_intensive ['aXtraMax'] = 20. +init_labor_intensive ['aXtraMax'] = 80. init_labor_intensive ['LbrCost'] = LbrCost init_labor_intensive ['WageRte'] = WageRte init_labor_intensive ['LivPrb'] = LivPrb From 724c8e1a51bb763385295d3e7d8cbbca2cd7691c Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Wed, 4 Sep 2019 21:34:21 -0400 Subject: [PATCH 051/242] In progress work on cleaning LaborIntMarg docstrings Did this work several days ago, forgot to commit. Rest of comments and docstrings in file still need to be gone over. --- HARK/ConsumptionSaving/ConsLaborModel.py | 146 ++++++++++------------- 1 file changed, 65 insertions(+), 81 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 865bb56c2..9a78af141 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -1,27 +1,14 @@ -#!/usr/bin/env python2 -# -*- coding: utf-8 -*- """ -Consumption-Saving model with Endogenous Labor Supply Model - Intensive Margin -using the endogenous grid method to invert the first order condition. +Subclasses of AgentType representing consumers who make decisions about how much +labor to supply, as well as a consumption-saving decision. -To solve this agent's problem, we normalized the problem by p_t, the permanent -productivity level, which helps us to eliminate a state variable (p_t). -We can then transform the agent's problem into a cost minimization problem for -which we solve the effective consumption purchased, x_t= z_t^alpha * c_t -It allows us to solve only one FOC with respect to x_t, instead of 2. - -We can then solve the FOC using the Endogenous Gridpoint Method, EGM. -Faster solution method than the classic rootfinding method but we should keep -in mind that the EGM solution is not well behaved outside the range of -gridpoints selected. - -@author: Tiphanie Magne -University of Delaware +It currently only has +one model: labor supply on the intensive margin (unit interval) with CRRA utility +from a composite good (of consumption and leisure), with transitory and permanent +productivity shocks. Agents choose their quantities of labor and consumption after +observing both of these shocks, so the transitory shock is a state variable. """ import sys -import os -sys.path.insert(0, os.path.abspath('../')) -sys.path.insert(0, os.path.abspath('./')) from copy import copy import numpy as np @@ -37,53 +24,59 @@ class ConsumerLaborSolution(Solution): ''' distance_criteria = ['cFunc','LbrFunc'] - def __init__(self,cFunc=None,LbrFunc=None,vFunc=None,vPfunc=None,bNrmMin = None): + def __init__(self, cFunc=None, LbrFunc=None, vFunc=None, vPfunc=None, bNrmMin=None): ''' The constructor for a new ConsumerSolution object. Parameters ---------- cFunc : function - The consumption function for this period, defined over bank balances: c = cFunc(b). + The consumption function for this period, defined over normalized + bank balances and the transitory productivity shock: cNrm = cFunc(bNrm,TranShk). LbrFunc : function - The labor function for this period, defined over bank balances: l = LbrFunc(b). + The labor supply function for this period, defined over normalized + bank balances 0.751784276198: Lbr = LbrFunc(bNrm,TranShk). vFunc : function - The beginning-of-period marginal value function for this period, defined over - bank balances: v = vPfunc(b). + The beginning-of-period value function for this period, defined over + normalized bank balances 0.751784276198: v = vFunc(bNrm,TranShk). vPfunc : function - The beginning-of-period marginal value function for this period, defined over - bank balances: v = vPfunc(b). + The beginning-of-period marginal value (of bank balances) function for + this period, defined over normalized bank balances 0.751784276198: vP = vPfunc(bNrm,TranShk). bNrmMin: float - The minimum allowable bank balances for this period. Consumption function, - labor function etc. are undefined for b < bNrmMin. - + The minimum allowable bank balances for this period, as a function of + the transitory shock. cFunc, LbrFunc, etc are undefined for bNrm < bNrmMin(TranShk). + + Returns + ------- + None ''' if cFunc is not None: - setattr(self,'cFunc',cFunc) + self.cFunc = cFunc if LbrFunc is not None: - setattr(self,'LbrFunc',LbrFunc) + self.LbrFunc = LbrFunc if vFunc is not None: - setattr(self,'vFunc',vFunc) + self.vFunc = vFunc if vPfunc is not None: - setattr(self,'vPfunc',vPfunc) + self.vPfunc = vPfunc if bNrmMin is not None: - setattr(self,'bNrmMin',bNrmMin) + self.bNrmMin = bNrmMin def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,CRRA, Rfree,PermGroFac,BoroCnstArt,aXtraGrid,TranShkGrid,vFuncBool, CubicBool,WageRte,LbrCost): ''' - Solves one period of the consumption-saving model with endogenous labor supply on the intensive margin - by using the endogenous grid method to invert the first order condition, obviating any search. + Solves one period of the consumption-saving model with endogenous labor supply + on the intensive margin by using the endogenous grid method to invert the first + order conditions for optimal composite consumption and between consumption and + leisure, obviating any search for optimal controls. Parameters ---------- solution_next : ConsumerLaborSolution - The solution to the next period's problem; should have the attributes - vPfunc, cFunc and LbrFunc representing the marginal value, consumption - and labor functions. - + The solution to the next period's problem; must have the attributes + vPfunc and bNrmMinFunc representing marginal value of bank balances and + minimum (normalized) bank balances as a function of the transitory shock. PermShkDstn: [np.array] Discrete distribution of permanent productivity shocks. TranShkDstn: [np.array] @@ -94,39 +87,39 @@ def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,C DiscFac : float Intertemporal discount factor. CRRA : float - Coefficient of relative risk aversion. + Coefficient of relative risk aversion over the composite good. Rfree : float Risk free interest rate on assets retained at the end of the period. PermGroFac : float Expected permanent income growth factor for next period. BoroCnstArt: float or None Borrowing constraint for the minimum allowable assets to end the - period with. If it is less than the natural borrowing constraint, - then it is irrelevant; BoroCnstArt=None indicates no artificial bor- - rowing constraint. - aXtraGrid: [np.array] + period with. Currently not handled, must be None. + aXtraGrid: np.array Array of "extra" end-of-period asset values-- assets above the absolute minimum acceptable level. - TranShkGrid: [np.array] - Array of transitory shock values. + TranShkGrid: np.array + Grid of transitory shock values to use as a state grid for interpolation. vFuncBool: boolean An indicator for whether the value function should be computed and - included in the reported solution. + included in the reported solution. Not yet handled, must be False. CubicBool: boolean - An indicator for whether the solver should use cubic or linear inter- - polation. + An indicator for whether the solver should use cubic or linear interpolation. + Cubic interpolation is not yet handled, must be False. WageRte: float - Wage rate. To be specified by the user. + Wage rate per unit of labor supplied. LbrCost: float - alpha parameter indicating labor cost. + Cost parameter for supplying labor: u_t = U(x_t), x_t = c_t*z_t^LbrCost, + where z_t is leisure = 1 - Lbr_t. Returns ------- solution_now : ConsumerLaborSolution - The solution to this period's problem, including a consumption - function cFunc (defined over the bank balances and the transitory - productivity shock), a labor function LbrFunc and a marginal value - function vPfunc. + The solution to this period's problem, including a consumption function + cFunc, a labor supply function LbrFunc, and a marginal value function vPfunc; + each are defined over normalized bank balances and transitory prod shock. + Also includes bNrmMinNow, the minimum permissible bank balances as a function + of the transitory productivity shock. ''' # Make sure the inputs for this period are valid: CRRA > LbrCost/(1+LbrCost) # and CubicBool = False. CRRA condition is met automatically when CRRA >= 1. @@ -252,21 +245,10 @@ def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,C class LaborIntMargConsumerType(IndShockConsumerType): ''' - A class for representing an ex ante homogeneous type of consumer in the - consumption-saving model. These consumers have CRRA utility over current - consumption and discount future utility exponentially. Their future income - is subject to transitory and permanent shocks, and they can earn gross interest - on retained assets at a risk free interest factor. - - The solution is represented in a normalized way, with all variables divided - by permanent income (raised to the appropriate power). - - This model is homothetic in permanent income. - - IndShockConsumerType: A consumer type with idiosyncratic shocks to permanent and transitory income. - His problem is defined by a sequence of income distributions, survival probabilities, - and permanent income growth rates, as well as time invariant values for risk aversion, - discount factor, the interest rate, the grid of end-of-period assets, and an artificial borrowing constraint. + A class representing agents who make a decision each period about how much + to consume vs save and how much labor to supply (as a fraction of their time). + They get CRRA utility from a composite good x_t = c_t*z_t^alpha, and discount + future utility flows at a constant factor. ''' time_vary_ = copy(IndShockConsumerType.time_vary_) time_vary_ += ['LbrCost','WageRte'] @@ -294,7 +276,6 @@ def __init__(self,cycles=1,time_flow=True,**kwds): self.solveOnePeriod = solveConsLaborIntMarg self.update() - def update(self): ''' Update the income process, the assets grid, and the terminal solution. @@ -310,8 +291,7 @@ def update(self): self.updateIncomeProcess() self.updateAssetsGrid() self.updateTranShkGrid() - - + def calcBoundingValues(self): ''' Calculate human wealth plus minimum and maximum MPC in an infinite @@ -361,15 +341,19 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): def updateTranShkGrid(self): - ''' Create a list of values for TranShkGrid using TranShkVals, index 1 of TranShkDstn + ''' + Create a time-varying list of arrays for TranShkGrid using TranShkDstn, + which is created by the method updateIncomeProcess(). Simply takes the + transitory shock values and uses them as a state grid; can be improved. + Creates the attribute TranShkGrid. Parameters ---------- - none + None Returns ------- - none + None ''' time_orig=self.time_flow self.timeFwd() @@ -386,8 +370,8 @@ def updateTranShkGrid(self): def updateSolutionTerminal(self): ''' - Updates the terminal period solution and solves for optimal consumption and labor when there is no future. - + Updates the terminal period solution and solves for optimal consumption + and labor when there is no future. Parameters ---------- @@ -397,7 +381,7 @@ def updateSolutionTerminal(self): ------- None ''' - if self.time_flow: # To make sure we pick the last element of the list, depending on the direction time is flowing + if self.time_flow: # To make sure we pick the last element of the list, depending on the direction time is flowing t=-1 else: t=0 From e31fb827fd97a404a11a2a45587afbb6ffe3a438 Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Fri, 6 Sep 2019 18:32:58 +1000 Subject: [PATCH 052/242] Some attempts on issue #103. Problem: When the solver runs recursively, the linear segment steps into the recursive process and eventually dominates. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 42 +++++++++++++++++++- HARK/ConsumptionSaving/ConsumerParameters.py | 2 +- 2 files changed, 41 insertions(+), 3 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index a9d45f26f..db767ba80 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1305,7 +1305,7 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, ------- None ''' - assert CubicBool==False,'KinkedR will only work with linear interpolation (for now)' +# assert CubicBool==False,'KinkedR will only work with linear interpolation (for now)' assert Rboro>=Rsave, 'Interest factor on debt less than interest factor on savings!' # Initialize the solver. Most of the steps are exactly the same as in @@ -1316,7 +1316,44 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, # Assign the interest rates as class attributes, to use them later. self.Rboro = Rboro self.Rsave = Rsave + + def makeCubiccFunc(self,mNrm,cNrm): + ''' + Makes a cubic spline interpolation which contains the kink of the unconstrained + consumption function for this period. + + Parameters + ---------- + mNrm : np.array + Corresponding market resource points for interpolation. + cNrm : np.array + Consumption points for interpolation. + Returns + ------- + cFuncUnc : CubicInterp + The unconstrained consumption function for this period. + ''' + # call the makeCubiccFunc from ConsIndShockSolver + cFuncNowUncKink = super().makeCubiccFunc(mNrm, cNrm) + + # identify the kinks + aKinkIndex = [] + for i in range(len(self.aNrmNow)): + if abs(self.aNrmNow[i] - 0) < 1e-6: + aKinkIndex.append(i) + aKinkIndex = np.array(aKinkIndex) + print(aKinkIndex) + print(mNrm[aKinkIndex], cNrm[aKinkIndex]) + + print(cFuncNowUncKink.coeffs[21]) + + # change the coeffients at the kinked points + for i in aKinkIndex: + cFuncNowUncKink.coeffs[i+1] = [cNrm[i+1] - mNrm[i+1], mNrm[i+2] - mNrm[i+1], 0, 0] + + return cFuncNowUncKink + def prepareToCalcEndOfPrdvP(self): ''' Prepare to calculate end-of-period marginal value by creating an array @@ -2584,7 +2621,8 @@ def main(): KinkyExample.unpackcFunc() print('Kinky consumption function:') KinkyExample.timeFwd() - plotFuncs(KinkyExample.cFunc[0],KinkyExample.solution[0].mNrmMin,5) + plotFuncs(KinkyExample.cFunc[0],0.8,1) +# plotFuncs(KinkyExample.cFunc[0],KinkyExample.solution[0].mNrmMin,5) if do_simulation: KinkyExample.T_sim = 120 diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index dbeca20e0..f639d71ed 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -135,7 +135,7 @@ init_kinked_R['Rboro'] = Rboro init_kinked_R['Rsave'] = Rsave init_kinked_R['BoroCnstArt'] = None # kinked R is a bit silly if borrowing not allowed -init_kinked_R['CubicBool'] = False # kinked R currently only compatible with linear cFunc +init_kinked_R['CubicBool'] = True # kinked R currently only compatible with linear cFunc init_kinked_R['aXtraCount'] = 48 # ...so need lots of extra gridpoints to make up for it From c8b4553e2a2d738ea57a98277ca697f75a96b025 Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Sat, 7 Sep 2019 20:37:04 +1000 Subject: [PATCH 053/242] Problem solved with help from Matthew. Now the code is fully functional. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 19 ++++--------------- 1 file changed, 4 insertions(+), 15 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index db767ba80..6275b7f64 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1337,21 +1337,9 @@ def makeCubiccFunc(self,mNrm,cNrm): # call the makeCubiccFunc from ConsIndShockSolver cFuncNowUncKink = super().makeCubiccFunc(mNrm, cNrm) - # identify the kinks - aKinkIndex = [] - for i in range(len(self.aNrmNow)): - if abs(self.aNrmNow[i] - 0) < 1e-6: - aKinkIndex.append(i) - aKinkIndex = np.array(aKinkIndex) - print(aKinkIndex) - print(mNrm[aKinkIndex], cNrm[aKinkIndex]) - - print(cFuncNowUncKink.coeffs[21]) - # change the coeffients at the kinked points - for i in aKinkIndex: - cFuncNowUncKink.coeffs[i+1] = [cNrm[i+1] - mNrm[i+1], mNrm[i+2] - mNrm[i+1], 0, 0] - + cFuncNowUncKink.coeffs[self.i_kink + 1] = [cNrm[self.i_kink], mNrm[self.i_kink + 1] - mNrm[self.i_kink], 0, 0] + return cFuncNowUncKink def prepareToCalcEndOfPrdvP(self): @@ -1393,7 +1381,8 @@ def prepareToCalcEndOfPrdvP(self): # Make a 1D array of the interest factor at each asset gridpoint Rfree_vec = self.Rsave*np.ones(aXtraCount) if KinkBool: - Rfree_vec[0:(np.sum(aNrmNow<=0)-1)] = self.Rboro + self.i_kink = np.sum(aNrmNow<=0)-1 # save the index of the kink point as an attribute + Rfree_vec[0:self.i_kink] = self.Rboro self.Rfree = Rfree_vec Rfree_temp = np.tile(Rfree_vec,(ShkCount,1)) From b22408aeb04151f90801e8961e8b567465ff709c Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Mon, 9 Sep 2019 11:18:48 +1000 Subject: [PATCH 054/242] Some final updates and final version of econ-ark issue #103 --- HARK/ConsumptionSaving/ConsIndShockModel.py | 6 ++---- HARK/ConsumptionSaving/ConsumerParameters.py | 2 +- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 6275b7f64..3713c371b 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1305,7 +1305,6 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, ------- None ''' -# assert CubicBool==False,'KinkedR will only work with linear interpolation (for now)' assert Rboro>=Rsave, 'Interest factor on debt less than interest factor on savings!' # Initialize the solver. Most of the steps are exactly the same as in @@ -1381,7 +1380,7 @@ def prepareToCalcEndOfPrdvP(self): # Make a 1D array of the interest factor at each asset gridpoint Rfree_vec = self.Rsave*np.ones(aXtraCount) if KinkBool: - self.i_kink = np.sum(aNrmNow<=0)-1 # save the index of the kink point as an attribute + self.i_kink = np.sum(aNrmNow<=0)-1 # save the index of the kink point as an attribute Rfree_vec[0:self.i_kink] = self.Rboro self.Rfree = Rfree_vec Rfree_temp = np.tile(Rfree_vec,(ShkCount,1)) @@ -2610,8 +2609,7 @@ def main(): KinkyExample.unpackcFunc() print('Kinky consumption function:') KinkyExample.timeFwd() - plotFuncs(KinkyExample.cFunc[0],0.8,1) -# plotFuncs(KinkyExample.cFunc[0],KinkyExample.solution[0].mNrmMin,5) + plotFuncs(KinkyExample.cFunc[0],KinkyExample.solution[0].mNrmMin,5) if do_simulation: KinkyExample.T_sim = 120 diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index f639d71ed..348993d58 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -135,7 +135,7 @@ init_kinked_R['Rboro'] = Rboro init_kinked_R['Rsave'] = Rsave init_kinked_R['BoroCnstArt'] = None # kinked R is a bit silly if borrowing not allowed -init_kinked_R['CubicBool'] = True # kinked R currently only compatible with linear cFunc +init_kinked_R['CubicBool'] = True # kinked R is now compatible with linear cFunc and cubic cFunc init_kinked_R['aXtraCount'] = 48 # ...so need lots of extra gridpoints to make up for it From 63d759428d16ecd57d8ca6e9bd19b6919cc62daa Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Mon, 16 Sep 2019 12:48:47 +1000 Subject: [PATCH 055/242] Attempt on HARK issue #113. Finished moving example parameters from ConsumerParametersTM.py into ConsumerParameters.py, and deleting ConsumerParametersTM.py. Finished adding lifecycle example into main block (Just took the lifecycle example from ConsIndShockModel, as what I have interpreted). Some attempts on fixing the simulation for the labor model, it works as it does simulate some results, but I do not know how to test whether it is correct or not. --- HARK/ConsumptionSaving/ConsLaborModel.py | 79 ++++- HARK/ConsumptionSaving/ConsumerParameters.py | 64 ++++ .../ConsumptionSaving/ConsumerParametersTM.py | 325 ------------------ 3 files changed, 142 insertions(+), 326 deletions(-) delete mode 100644 HARK/ConsumptionSaving/ConsumerParametersTM.py diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 9a78af141..0e1263bf3 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -338,6 +338,43 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): None ''' raise NotImplementedError() + + def getControls(self): + ''' + Calculates consumption for each consumer of this type using the consumption functions. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + cNrmNow = np.zeros(self.AgentCount) + np.nan + MPCnow = np.zeros(self.AgentCount) + np.nan + for t in range(self.T_cycle): + these = t == self.t_cycle + cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) + MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) + self.cNrmNow = cNrmNow + self.MPCnow = MPCnow + return None + + def getPostStates(self): + ''' + Calculates end-of-period assets for each consumer of this type. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + self.mNrmNow = self.bNrmNow + LaborIntMargExample.solution[t].LbrFunc(self.bNrmNow, self.TranShkNow)*self.TranShkNow + return None def updateTranShkGrid(self): @@ -472,11 +509,45 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): ############################################################################### if __name__ == '__main__': - import ConsumerParametersTM as Params # Parameters for a consumer type + import HARK.ConsumptionSaving.ConsumerParameters as Params # Parameters for a consumer type + from HARK.utilities import plotFuncsDer, plotFuncs import matplotlib.pyplot as plt from time import clock mystr = lambda number : "{:.4f}".format(number) # Format numbers as strings + do_simulation = True + +############################################################################### + + # Make and solve an idiosyncratic shocks consumer with a finite lifecycle + LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) + LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once + + start_time = clock() + LifecycleExample.solve() + end_time = clock() + print('Solving a lifecycle consumer took ' + mystr(end_time-start_time) + ' seconds.') + LifecycleExample.unpackcFunc() + LifecycleExample.timeFwd() + + # Plot the consumption functions during working life + print('Consumption functions while working:') + mMin = min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]) + plotFuncs(LifecycleExample.cFunc[:LifecycleExample.T_retire],mMin,5) + + # Plot the consumption functions during retirement + print('Consumption functions while retired:') + plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire:],0,5) + LifecycleExample.timeRev() + + if do_simulation: + LifecycleExample.T_sim = 120 + LifecycleExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_age'] + LifecycleExample.initializeSim() + LifecycleExample.simulate() + +############################################################################### + # Make and solve a labor intensive margin consumer i.e. a consumer with utility for leisure LaborIntMargExample = LaborIntMargConsumerType(**Params.init_labor_intensive) LaborIntMargExample.cycles = 0 @@ -539,4 +610,10 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): else: plt.ylabel('Marginal value') plt.show() + + if do_simulation: + LaborIntMargExample.T_sim = 120 # Set number of simulation periods + LaborIntMargExample.track_vars = ['bNrmNow', 'cNrmNow'] + LaborIntMargExample.initializeSim() + LaborIntMargExample.simulate() \ No newline at end of file diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index dbeca20e0..13128c9bc 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -280,3 +280,67 @@ init_medical_shocks['MedShkCountTail'] = MedShkCountTail init_medical_shocks['MedPrice'] = MedPrice init_medical_shocks['aXtraCount'] = 32 + +# ----------------------------------------------------------------------------- +# -------- Define additional parameters for the baby labor model -------------- +# ----------------------------------------------------------------------------- + +LbrDisutilCoeffs = [-1.5,0.1,0.05] # Constant, linear, and quadratic coefficients + # on transformed labor disutility factor by age + +# Make a dictionary for "baby labor" model +init_baby_labor = copy(init_lifecycle) +init_baby_labor['LbrDisutilCoeffs'] = LbrDisutilCoeffs +init_baby_labor['TranShkCount'] = 1 # No transitory shocks in baby labor model +init_baby_labor['TranShkStd'] = [0.0]*init_lifecycle['T_cycle'] +init_baby_labor['T_retire'] = 0 # turn off retirement +init_baby_labor['PermShkStd'] = [0.1]*init_lifecycle['T_cycle'] +init_baby_labor['UnempPrb'] = 0.0 # turn off unemployment +init_baby_labor['UnempPrbRet'] = 0.0 +init_baby_labor['PermShkCount'] = 25 # Crank up permanent shock count +init_baby_labor['aXtraCount'] = 200 # Might be important to have many gridpoints + + +# ----------------------------------------------------------------------------- +# ----Define additional parameters for the Endogenous Labor Supply model------- +# ----------------------------------------------------------------------------- + +LivPrb = [0.99] # Survival probability +PermGroFac = [1.02] # Permanent income growth factor +LbrCost = [0.36] # Labor cost list of one element +WageRte = [1.0] # Wage Rate + +BoroCnstArt = None +CubicBool = False # Use cubic spline interpolation when True, linear interpolation when False +vFuncBool = False # Whether to calculate the value function during solution + +StateMin = 0.001 +StateMax = 20.0 +StateCount = 15 +ExponentialGrid = True + +# Make a dictionary for Endogenous Labor supply model - intensive margin +init_labor_intensive = copy(init_lifecycle) +init_labor_intensive['TranShkStd'] = [0.1]*init_lifecycle['T_cycle'] +init_labor_intensive['PermShkStd'] = [0.1]*init_lifecycle['T_cycle'] +init_labor_intensive['IncUnemp'] = 0.0 +init_labor_intensive['UnempPrb'] = 0.05 +init_labor_intensive['UnempPrbRet'] = 0.0 +init_labor_intensive['TranShkCount'] = 15 # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks +init_labor_intensive['PermShkCount'] = 16 # Crank up permanent shock count +init_labor_intensive['BoroCnstArt'] = BoroCnstArt +init_labor_intensive['CubicBool'] = CubicBool +init_labor_intensive['vFuncBool'] = vFuncBool +init_labor_intensive ['T_retire'] = 0 # Turn off retirement +init_labor_intensive ['aXtraCount'] = 200 # May be important to have a larger number of gridpoints (than 48 initially) +init_labor_intensive ['aXtraMax'] = 80. +init_labor_intensive ['LbrCost'] = LbrCost +init_labor_intensive ['WageRte'] = WageRte +init_labor_intensive ['LivPrb'] = LivPrb +init_labor_intensive ['PermGroFac'] = PermGroFac +init_labor_intensive ['StateMin'] = StateMin +init_labor_intensive ['StateMax'] = StateMax +init_labor_intensive ['StateCount'] = StateCount +init_labor_intensive ['ExponentialGrid'] = ExponentialGrid + +init_labor_intensive ['T_cycle'] = 1 \ No newline at end of file diff --git a/HARK/ConsumptionSaving/ConsumerParametersTM.py b/HARK/ConsumptionSaving/ConsumerParametersTM.py deleted file mode 100644 index 7bc1750d9..000000000 --- a/HARK/ConsumptionSaving/ConsumerParametersTM.py +++ /dev/null @@ -1,325 +0,0 @@ -''' -Specifies examples of the full set of parameters required to solve various -consumption-saving models. These models can be found in ConsIndShockModel, -ConsAggShockModel, ConsPrefShockModel, ConsLaborModel, and ConsMarkovModel. -''' -from copy import copy -import numpy as np - -# ----------------------------------------------------------------------------- -# --- Define all of the parameters for the perfect foresight model ------------ -# ----------------------------------------------------------------------------- - -CRRA = 2.0 # Coefficient of relative risk aversion -Rfree = 1.03 # Interest factor on assets -DiscFac = 0.96 # Intertemporal discount factor -LivPrb = [0.98] # Survival probability -PermGroFac = [1.01] # Permanent income growth factor -AgentCount = 10000 # Number of agents of this type (only matters for simulation) -aNrmInitMean = 0.0 # Mean of log initial assets (only matters for simulation) -aNrmInitStd = 1.0 # Standard deviation of log initial assets (only for simulation) -pLvlInitMean = 0.0 # Mean of log initial permanent income (only matters for simulation) -pLvlInitStd = 0.0 # Standard deviation of log initial permanent income (only matters for simulation) -PermGroFacAgg = 1.0 # Aggregate permanent income growth factor (only matters for simulation) -T_age = None # Age after which simulated agents are automatically killed -T_cycle = 1 # Number of periods in the cycle for this agent type - -# Make a dictionary to specify a perfect foresight consumer type -init_perfect_foresight = { 'CRRA': CRRA, - 'Rfree': Rfree, - 'DiscFac': DiscFac, - 'LivPrb': LivPrb, - 'PermGroFac': PermGroFac, - 'AgentCount': AgentCount, - 'aNrmInitMean' : aNrmInitMean, - 'aNrmInitStd' : aNrmInitStd, - 'pLvlInitMean' : pLvlInitMean, - 'pLvlInitStd' : pLvlInitStd, - 'PermGroFacAgg' : PermGroFacAgg, - 'T_age' : T_age, - 'T_cycle' : T_cycle - } - -# ----------------------------------------------------------------------------- -# --- Define additional parameters for the idiosyncratic shocks model --------- -# ----------------------------------------------------------------------------- - -# Parameters for constructing the "assets above minimum" grid -aXtraMin = 0.001 # Minimum end-of-period "assets above minimum" value -aXtraMax = 20 # Maximum end-of-period "assets above minimum" value -aXtraExtra = None # Some other value of "assets above minimum" to add to the grid, not used -aXtraNestFac = 3 # Exponential nesting factor when constructing "assets above minimum" grid -aXtraCount = 48 # Number of points in the grid of "assets above minimum" - -# Parameters describing the income process -PermShkCount = 7 # Number of points in discrete approximation to permanent income shocks -TranShkCount = 7 # Number of points in discrete approximation to transitory income shocks -PermShkStd = [0.1] # Standard deviation of log permanent income shocks -TranShkStd = [0.1] # Standard deviation of log transitory income shocks -UnempPrb = 0.05 # Probability of unemployment while working -UnempPrbRet = 0.005 # Probability of "unemployment" while retired -IncUnemp = 0.3 # Unemployment benefits replacement rate -IncUnempRet = 0.0 # "Unemployment" benefits when retired -tax_rate = 0.0 # Flat income tax rate -T_retire = 0 # Period of retirement (0 --> no retirement) - -# A few other parameters -BoroCnstArt = 0.0 # Artificial borrowing constraint; imposed minimum level of end-of period assets -CubicBool = False # Use cubic spline interpolation when True, linear interpolation when False -vFuncBool = True # Whether to calculate the value function during solution - -# Make a dictionary to specify an idiosyncratic income shocks consumer -init_idiosyncratic_shocks = { 'CRRA': CRRA, - 'Rfree': Rfree, - 'DiscFac': DiscFac, - 'LivPrb': LivPrb, - 'PermGroFac': PermGroFac, - 'AgentCount': AgentCount, - 'aXtraMin': aXtraMin, - 'aXtraMax': aXtraMax, - 'aXtraNestFac':aXtraNestFac, - 'aXtraCount': aXtraCount, - 'aXtraExtra': [aXtraExtra], - 'PermShkStd': PermShkStd, - 'PermShkCount': PermShkCount, - 'TranShkStd': TranShkStd, - 'TranShkCount': TranShkCount, - 'UnempPrb': UnempPrb, - 'UnempPrbRet': UnempPrbRet, - 'IncUnemp': IncUnemp, - 'IncUnempRet': IncUnempRet, - 'BoroCnstArt': BoroCnstArt, - 'tax_rate':0.0, - 'vFuncBool':vFuncBool, - 'CubicBool':CubicBool, - 'T_retire':T_retire, - 'aNrmInitMean' : aNrmInitMean, - 'aNrmInitStd' : aNrmInitStd, - 'pLvlInitMean' : pLvlInitMean, - 'pLvlInitStd' : pLvlInitStd, - 'PermGroFacAgg' : PermGroFacAgg, - 'T_age' : T_age, - 'T_cycle' : T_cycle - } - -# Make a dictionary to specify a lifecycle consumer with a finite horizon -init_lifecycle = copy(init_idiosyncratic_shocks) -init_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02] -init_lifecycle['PermShkStd'] = [0.1,0.2,0.1,0.2,0.1,0.2,0.1,0,0,0] -init_lifecycle['TranShkStd'] = [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0,0,0] -init_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] -init_lifecycle['T_cycle'] = 10 -init_lifecycle['T_retire'] = 7 -init_lifecycle['T_age'] = 11 # Make sure that old people die at terminal age and don't turn into newborns! - -# Make a dictionary to specify an infinite consumer with a four period cycle -init_cyclical = copy(init_idiosyncratic_shocks) -init_cyclical['PermGroFac'] = [1.082251, 2.8, 0.3, 1.1] -init_cyclical['PermShkStd'] = [0.1,0.1,0.1,0.1] -init_cyclical['TranShkStd'] = [0.1,0.1,0.1,0.1] -init_cyclical['LivPrb'] = 4*[0.98] -init_cyclical['T_cycle'] = 4 - - -# ----------------------------------------------------------------------------- -# -------- Define additional parameters for the "kinked R" model -------------- -# ----------------------------------------------------------------------------- - -Rboro = 1.20 # Interest factor on assets when borrowing, a < 0 -Rsave = 1.02 # Interest factor on assets when saving, a > 0 - -# Make a dictionary to specify a "kinked R" idiosyncratic shock consumer -init_kinked_R = copy(init_idiosyncratic_shocks) -del init_kinked_R['Rfree'] # get rid of constant interest factor -init_kinked_R['Rboro'] = Rboro -init_kinked_R['Rsave'] = Rsave -init_kinked_R['BoroCnstArt'] = None # kinked R is a bit silly if borrowing not allowed -init_kinked_R['CubicBool'] = False # kinked R currently only compatible with linear cFunc -init_kinked_R['aXtraCount'] = 48 # ...so need lots of extra gridpoints to make up for it - - -# ----------------------------------------------------------------------------- -# ----- Define additional parameters for the preference shock model ----------- -# ----------------------------------------------------------------------------- - -PrefShkCount = 12 # Number of points in discrete approximation to preference shock dist -PrefShk_tail_N = 4 # Number of "tail points" on each end of pref shock dist -PrefShkStd = [0.30] # Standard deviation of utility shocks - -# Make a dictionary to specify a preference shock consumer -init_preference_shocks = copy(init_idiosyncratic_shocks) -init_preference_shocks['PrefShkCount'] = PrefShkCount -init_preference_shocks['PrefShk_tail_N'] = PrefShk_tail_N -init_preference_shocks['PrefShkStd'] = PrefShkStd -init_preference_shocks['aXtraCount'] = 48 -init_preference_shocks['CubicBool'] = False # pref shocks currently only compatible with linear cFunc - -# Make a dictionary to specify a "kinky preference" consumer, who has both shocks -# to utility and a different interest rate on borrowing vs saving -init_kinky_pref = copy(init_kinked_R) -init_kinky_pref['PrefShkCount'] = PrefShkCount -init_kinky_pref['PrefShk_tail_N'] = PrefShk_tail_N -init_kinky_pref['PrefShkStd'] = PrefShkStd - -# ----------------------------------------------------------------------------- -# ----- Define additional parameters for the aggregate shocks model ----------- -# ----------------------------------------------------------------------------- -MgridBase = np.array([0.1,0.3,0.6,0.8,0.9,0.98,1.0,1.02,1.1,1.2,1.6,2.0,3.0]) # Grid of capital-to-labor-ratios (factors) - -# Parameters for a Cobb-Douglas economy -PermShkAggCount = 3 # Number of points in discrete approximation to aggregate permanent shock dist -TranShkAggCount = 3 # Number of points in discrete approximation to aggregate transitory shock dist -PermShkAggStd = 0.0063 # Standard deviation of log aggregate permanent shocks -TranShkAggStd = 0.0031 # Standard deviation of log aggregate transitory shocks -DeprFac = 0.025 # Capital depreciation rate -CapShare = 0.36 # Capital's share of income -DiscFacPF = DiscFac # Discount factor of perfect foresight calibration -#intercept_prev = -0.305568464142 # Intercept of AFunc function -#slope_prev = 1.06154769008 # Slope of AFunc function -intercept_prev = 0.0 # Intercept of aggregate savings function -slope_prev = 1.0 # Slope of aggregate savings function - -# Make a dictionary to specify an aggregate shocks consumer -init_agg_shocks = copy(init_idiosyncratic_shocks) -del init_agg_shocks['Rfree'] # Interest factor is endogenous in agg shocks model -del init_agg_shocks['CubicBool'] # Not supported yet for agg shocks model -del init_agg_shocks['vFuncBool'] # Not supported yet for agg shocks model -init_agg_shocks['PermGroFac'] = [1.0] # Not yet correctly handled for agg shocks model, set to 1 -init_agg_shocks['MgridBase'] = MgridBase -init_agg_shocks['aXtraCount'] = 24 -#init_agg_shocks['aXtraMax'] = 80.0 -#init_agg_shocks['aXtraExtra'] = [1000.0] -init_agg_shocks['aNrmInitStd'] = 0.0 -init_agg_shocks['LivPrb'] = LivPrb - - -# Make a dictionary to specify a Cobb-Douglas economy -init_cobb_douglas = {'PermShkAggCount': PermShkAggCount, - 'TranShkAggCount': TranShkAggCount, - 'PermShkAggStd': PermShkAggStd, - 'TranShkAggStd': TranShkAggStd, - 'DeprFac': DeprFac, - 'CapShare': CapShare, - 'DiscFac': DiscFacPF, - 'AggregateL':1.0, - 'slope_prev': slope_prev, - 'intercept_prev': intercept_prev, - 'act_T':1200 - } - -# ----------------------------------------------------------------------------- -# ----- Define additional parameters for the persistent shocks model ---------- -# ----------------------------------------------------------------------------- - -PermIncCount = 12 # Number of permanent income gridpoints in "body" -PermInc_tail_N = 4 # Number of permanent income gridpoints in each "tail" -PermIncStdInit = 0.4 # Initial standard deviation of (log) permanent income (not used in example) -PermIncAvgInit = 1.0 # Initial average of permanent income (not used in example) -PermIncCorr = 0.98 # Serial correlation coefficient for permanent income -cycles = 0 - -# Make a dictionary for the "explicit permanent income" idiosyncratic shocks model -init_explicit_perm_inc = copy(init_idiosyncratic_shocks) -init_explicit_perm_inc['PermIncCount'] = PermIncCount -init_explicit_perm_inc['PermInc_tail_N'] = PermInc_tail_N -init_explicit_perm_inc['PermIncAvgInit'] = PermIncAvgInit -init_explicit_perm_inc['PermIncStdInit'] = PermIncStdInit -init_explicit_perm_inc['PermGroFac'] = [1.0] # long run permanent income growth doesn't work yet -init_explicit_perm_inc['cycles'] = cycles -init_explicit_perm_inc['aXtraMax'] = 30 -init_explicit_perm_inc['aXtraExtra'] = [0.005,0.01] - -# Make a dictionary for the "persistent idiosyncratic shocks" model -init_persistent_shocks = copy(init_explicit_perm_inc) -init_persistent_shocks['PermIncCorr'] = PermIncCorr - -# ----------------------------------------------------------------------------- -# ----- Define additional parameters for the medical shocks model ------------- -# ----------------------------------------------------------------------------- - -CRRAmed = 1.5*CRRA # Coefficient of relative risk aversion for medical care -MedShkAvg = [0.001] # Average of medical need shocks -MedShkStd = [5.0] # Standard deviation of (log) medical need shocks -MedShkCount = 5 # Number of medical shock points in "body" -MedShkCountTail = 15 # Number of medical shock points in "tail" (upper only) -MedPrice = [1.5] # Relative price of a unit of medical care - -# Make a dictionary for the "medical shocks" model -init_medical_shocks = copy(init_persistent_shocks) -init_medical_shocks['CRRAmed'] = CRRAmed -init_medical_shocks['MedShkAvg'] = MedShkAvg -init_medical_shocks['MedShkStd'] = MedShkStd -init_medical_shocks['MedShkCount'] = MedShkCount -init_medical_shocks['MedShkCountTail'] = MedShkCountTail -init_medical_shocks['MedPrice'] = MedPrice -init_medical_shocks['aXtraCount'] = 32 - - -# ----------------------------------------------------------------------------- -# -------- Define additional parameters for the baby labor model -------------- -# ----------------------------------------------------------------------------- - -LbrDisutilCoeffs = [-1.5,0.1,0.05] # Constant, linear, and quadratic coefficients - # on transformed labor disutility factor by age - -# Make a dictionary for "baby labor" model -init_baby_labor = copy(init_lifecycle) -init_baby_labor['LbrDisutilCoeffs'] = LbrDisutilCoeffs -init_baby_labor['TranShkCount'] = 1 # No transitory shocks in baby labor model -init_baby_labor['TranShkStd'] = [0.0]*init_lifecycle['T_cycle'] -init_baby_labor['T_retire'] = 0 # turn off retirement -init_baby_labor['PermShkStd'] = [0.1]*init_lifecycle['T_cycle'] -init_baby_labor['UnempPrb'] = 0.0 # turn off unemployment -init_baby_labor['UnempPrbRet'] = 0.0 -init_baby_labor['PermShkCount'] = 25 # Crank up permanent shock count -init_baby_labor['aXtraCount'] = 200 # Might be important to have many gridpoints - - -# ----------------------------------------------------------------------------- -# ----Define additional parameters for the Endogenous Labor Supply model------- -# ----------------------------------------------------------------------------- - -#TranShkVals = [1] -#TranShkPrbs = [0.2] - -LivPrb = [0.99] # Survival probability -PermGroFac = [1.02] # Permanent income growth factor -LbrCost = [0.36] # Labor cost list of one element -WageRte = [1.0] # Wage Rate - -BoroCnstArt = None -CubicBool = False # Use cubic spline interpolation when True, linear interpolation when False -vFuncBool = False # Whether to calculate the value function during solution - -StateMin = 0.001 -StateMax = 20.0 -StateCount = 15 -ExponentialGrid = True - -# Make a dictionary for Endogenous Labor supply model - intensive margin -init_labor_intensive = copy(init_lifecycle) -init_labor_intensive['TranShkStd'] = [0.1]*init_lifecycle['T_cycle'] -init_labor_intensive['PermShkStd'] = [0.1]*init_lifecycle['T_cycle'] -init_labor_intensive['IncUnemp'] = 0.0 -init_labor_intensive['UnempPrb'] = 0.05 -init_labor_intensive['UnempPrbRet'] = 0.0 -init_labor_intensive['TranShkCount'] = 15 # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks -init_labor_intensive['PermShkCount'] = 16 # Crank up permanent shock count -init_labor_intensive['BoroCnstArt'] = BoroCnstArt -init_labor_intensive['CubicBool'] = CubicBool -init_labor_intensive['vFuncBool'] = vFuncBool -init_labor_intensive ['T_retire'] = 0 # Turn off retirement -init_labor_intensive ['aXtraCount'] = 200 # May be important to have a larger number of gridpoints (than 48 initially) -init_labor_intensive ['aXtraMax'] = 80. -init_labor_intensive ['LbrCost'] = LbrCost -init_labor_intensive ['WageRte'] = WageRte -init_labor_intensive ['LivPrb'] = LivPrb -init_labor_intensive ['PermGroFac'] = PermGroFac -init_labor_intensive ['StateMin'] = StateMin -init_labor_intensive ['StateMax'] = StateMax -init_labor_intensive ['StateCount'] = StateCount -init_labor_intensive ['ExponentialGrid'] = ExponentialGrid - -init_labor_intensive ['T_cycle'] = 1 - - From 8d8771c22c7f9419ab613faeccce372a1dd321f6 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Mon, 16 Sep 2019 14:33:41 +0200 Subject: [PATCH 056/242] Greatly refactor code and add some _ for internal functions (and reorder them) as well as add some docs. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 394 +++++++++++++------ 1 file changed, 271 insertions(+), 123 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 4242904e1..32206a493 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -2,23 +2,42 @@ # Rfree and Risky-parameters. This should be possible by creating a list of # functions instead. -import math -import scipy.optimize as sciopt -import scipy.integrate -import scipy.stats as stats -import copy - +import math # we're using math for log and exp, might want to just use numpy? +import scipy.optimize as sciopt # we're using scipy optimize to optimize and fsolve +import scipy.integrate # used to calculate the expectation over returns +import scipy.stats as stats # for densities related to the returns distributions +from copy import deepcopy # it's convenient to copy things some times instead of re-creating them + +# Solution is inherited from in the PortfolioSolution class, NullFunc is used +# throughout HARK when no input is given and AgentType is used for .preSolve from HARK import Solution, NullFunc, AgentType -from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType, solveConsIndShock, ConsIndShockSolver, ValueFunc, MargValueFunc, ConsumerSolution, utility_inv -from HARK.utilities import approxLognormal, combineIndepDstns -from HARK.interpolation import LinearInterp, LowerEnvelope -from HARK.simulation import drawLognormal +from HARK.ConsumptionSaving.ConsIndShockModel import ( + PerfForesightConsumerType, # for .__init__ + IndShockConsumerType, # PortfolioConsumerType inherits from it + ConsIndShockSolver, # ConsIndShockPortfolioSolver inherits from it + ValueFunc, # to do the re-curving of value functions for interpolation + MargValueFunc, # same as above, but for marginal value functions + utility_inv, # inverse CRRA + ) -from copy import deepcopy -import numpy as np +from HARK.utilities import ( + approxLognormal, # for approximating the lognormal returns factor + combineIndepDstns, # for combining the existing + ) -# +from HARK.simulation import drawLognormal # random draws for simulating agents + +from HARK.interpolation import ( + LinearInterp, # piece-wise linear interpolation + LowerEnvelope, # lower envelope for consumption function around borrowing constraint + ) + + +import numpy as np # for array operations + +# REMARK: The Campbell and Viceira (2002) approximation can be calculated from +# the code below. TODO clean up # def CambellVicApprox() # # # We assume fixed distribution of risky shocks throughout, so we can @@ -33,47 +52,233 @@ # # RiskyShareLimit = phi/(self.CRRA*sigma**2) -def PerfForesightLogNormalPortfolioShare(Rfree, RiskyAvg, RiskyStd, CRRA): - PortfolioObjective = lambda share: PerfForesightLogNormalPortfolioObjective(share, - Rfree, - RiskyAvg, - RiskyStd, - CRRA) - return sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x - -def PerfForesightDiscretePortfolioShare(Rfree, RiskyDstn, CRRA): - PortfolioObjective = lambda share: PerfForesightDiscretePortfolioObjective(share, +def _PerfForesightLogNormalPortfolioShare(Rfree, RiskyAvg, RiskyStd, CRRA): + ''' + Calculate the optimal portfolio share in the perfect foresight model. This + does not depend on resources today or the time period. + + Parameters + ---------- + Rfree : Number + The risk free interest factor + RiskyAvg : Number + The average risk factor + RiskyStd : Number + The standard deviation of the risk factor + CRRA : Number + The CRRA utility parameter + + Returns + ------- + optShare : Number + The optimal portfolio share in the perfect foresight portofolio model. + ''' + PortfolioObjective = lambda share: _PerfForesightLogNormalPortfolioObjective(share, + Rfree, + RiskyAvg, + RiskyStd, + CRRA) + optShare = sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x + return optShare + +def _PerfForesightDiscretePortfolioShare(Rfree, RiskyDstn, CRRA): + ''' + Calculate the optimal portfolio share in the perfect foresight model. This + Does not depend on resources today or the time period. This version assumes + that the return factor distribution is characterized by a discrete distribution + or can be approximated using the input values. + + Parameters + ---------- + Rfree : Number + The risk free interest factor + RiskyDstn : numpy.array + A numpy array with first element being a probability vector and the + second element being the values of the returns factor associated with + the probabilities + CRRA : Number + The CRRA utility parameter + + Returns + ------- + optShare : Number + The optimal portfolio share in the perfect foresight portofolio model. + ''' + PortfolioObjective = lambda share: _PerfForesightDiscretePortfolioObjective(share, Rfree, RiskyDstn, CRRA) - return sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x + optShare = sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x + return optShare # Switch here based on knowledge about risky. # It can either be "discrete" in which case it is only the number of draws that # are used, or it can be continuous in which case bounds and a pdf has to be supplied. -def PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, sigma, rho): - muNorm = np.log(RiskyAvg/np.sqrt(1+sigma**2/RiskyAvg**2)) - sigmaNorm = np.sqrt(np.log(1+sigma**2/RiskyAvg**2)) - sharedobjective = lambda r: (R0+share*(r-R0))**(1-rho) - pdf = lambda r: stats.lognorm.pdf(r, s=sigmaNorm, scale=np.exp(muNorm)) +def _PerfForesightLogNormalPortfolioIntegrand(share, Rfree, RiskyAvg, RiskyStd, CRRA): + ''' + Returns a function to evaluate the integrand for calculating the expectation + in the perfect foresight porfolio problem with lognormal return factors. + + Parameters + ---------- + Rfree : Number + The risk free interest factor + RiskyAvg : Number + The average risk factor + RiskyStd : Number + The standard deviation of the risk factor + CRRA : Number + The CRRA utility parameter + + Returns + ------- + integrand : function (lambda) + Can be used to evaluate the integrand and the sent to a quadrature procedure. + ''' + muNorm = np.log(RiskyAvg/np.sqrt(1+RiskyStd**2/RiskyAvg**2)) + sigmaNorm = np.sqrt(np.log(1+RiskyStd**2/RiskyAvg**2)) + sharedobjective = lambda r: (Rfree+share*(r-Rfree))**(1-CRRA) + pdf = lambda r: stats.lognorm.pdf(r, s=sigmaNorm, scale=np.exp(muNorm)) + + integrand = lambda r: sharedobjective(r)*pdf(r) + return integrand + +def _PerfForesightLogNormalPortfolioObjective(share, Rfree, RiskyAvg, RiskyStd, CRRA): + ''' + Returns the integral used in the perfect foresight portoflio choice problem + with lognormal return factors evaluated at share. + + Parameters + ---------- + Rfree : Number + The risk free interest factor + RiskyAvg : Number + The average risk factor + RiskyStd : Number + The standard deviation of the risk factor + CRRA : Number + The CRRA utility parameter + + Returns + ------- + integrand : function (lambda) + Can be used to evaluate the integrand and the sent to a quadrature procedure. + ''' + integrand = _PerfForesightLogNormalPortfolioIntegrand(share, Rfree, RiskyAvg, RiskyStd, CRRA) + a = 0.0 # Cannot be negative + b = 5.0 # This is just an upper bound. pdf should be 0 here. + return -((1-CRRA)**-1)*scipy.integrate.quad(integrand, a, b)[0] + + +def _PerfForesightDiscretePortfolioObjective(share, Rfree, RiskyDstn, CRRA): + ''' + Returns the integral used in the perfect foresight portoflio choice problem + with discretely distributed return factors evaluated at share. + + Parameters + ---------- + Rfree : Number + The risk free interest factor + RiskyDstn : numpy.array + A numpy array with first element being a probability vector and the + second element being the values of the returns factor associated with + the probabilities + CRRA : Number + The CRRA utility parameter + + Returns + ------- + integrand : function (lambda) + Can be used to evaluate the integrand and the sent to a quadrature procedure. + ''' + vals = (Rfree+share*(RiskyDstn[1]-Rfree))**(1-CRRA) + weights = RiskyDstn[0] + + return -((1-CRRA)**-1)*np.dot(vals, weights) + +def _calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc_adj, CRRA): + ''' + Set up the value function at the point of consumption, but before portofolio + choice. Depends on the probability of getting the porfolio choice this period, + the possible shares today (if AdjustPrb is not 1) and the value function + given portfolio choice (so at next sub-period). + + Parameters + ---------- + AdjustPrb : number + The probability of adjusting the portfolio this period + AdjustCount : integer + The number of adjustment states the agent can be in from the set of + possible states = (adjuster, nonadjuster) + ShareNowCount : integer + The number of current portfolio share. Only relevant if nonadjustment is + possible. + vFunc_adj : list of lists + The values of being in first index state (0 is adjuster, 1 is non-adjuster) + and possibly second index state (indicating the current portfolio share). + The latter is relevant for non-adjusters only. + CRRA : Number + The CRRA utility parameter + + Returns + ------- + integrand : function (lambda) + Can be used to evaluate the integrand and the sent to a quadrature procedure. + ''' + # AdjustCount could in principle just be inferred from AdjustPrb instead of + # cartying it arround FIXME / TODO + wFunc = [] + if AdjustCount == 1: + # Just take the adjuster + for ShareIndex in range(ShareNowCount[0]): + wFunc.append(vFunc_adj[0][ShareIndex]) + else: + # Calculate stuff + for ShareIndex in range(ShareNowCount[1]): + # TODO FIXME better grid + evalgrid = np.linspace(0, 100, 200) + evVals = AdjustPrb*vFunc_adj[0][ShareIndex](evalgrid) + (1-AdjustPrb)*vFunc_adj[1][ShareIndex](evalgrid) + with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): + evValsNvrs = utility_inv(evVals,gam=CRRA) + evVals = np.insert(evVals, 0, 0.0) + evValsNvrs = np.insert(evVals, 0, 0.0) - integrand = lambda r: sharedobjective(r)*pdf(r) - return integrand + wFunc.append(ValueFunc(LinearInterp(evVals, evValsNvrs), CRRA)) -def PerfForesightLogNormalPortfolioObjective(share, R0, RiskyAvg, sigma, rho): - integrand = PerfForesightLogNormalPortfolioIntegrand(share, R0, RiskyAvg, rho, sigma) - a = 0.0 # Cannot be negative - b = 5.0 # This is just an upper bound. pdf should be 0 here. - return -((1-rho)**-1)*scipy.integrate.quad(integrand, a, b)[0] + return wFunc -def PerfForesightDiscretePortfolioObjective(share, R0, RiskyDstn, rho): +def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): + """ + A class for generating functions that generate nodes and weights for a log- + normal distribution as parameterized by the input `RiskyAvg` and `RiskyStd` + values. The returned function takes a number of points to request and returns + a list of lists where the first list contains the weights (probabilities) and + the second list contains the values. + """ + RiskyAvgSqrd = RiskyAvg**2 + RiskyVar = RiskyStd**2 - vals = (R0+share*(RiskyDstn[1]-R0))**(1-rho) - weights = RiskyDstn[0] + mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) + sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) + + return lambda RiskyCount: approxLognormal(RiskyCount, mu=mu, sigma=sigma) + +def LogNormalRiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): + """ + A class for generating functions that draw random values from a log-normal + distribution as parameterized by the input `RiskyAvg` and `RiskyStd` + values. The returned function takes no argument and returns a value. + """ + RiskyAvgSqrd = RiskyAvg**2 + RiskyVar = RiskyStd**2 + + mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) + sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) + + return lambda: drawLognormal(1, mu=mu, sigma=sigma) - return -((1-rho)**-1)*np.dot(vals, weights) class PortfolioSolution(Solution): distance_criteria = ['cFunc'] @@ -133,29 +338,6 @@ def len(self): def getPoints(self): return self.points - -def calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc, CRRA): - # AdjustCount cna be inferred from AdjustPrb... - wFunc = [] - if AdjustCount == 1: - # Just take the adjuster - for ShareIndex in range(ShareNowCount[0]): - wFunc.append(vFunc[0][ShareIndex]) - else: - # Calculate stuff - for ShareIndex in range(ShareNowCount[1]): - # TODO FIXME better grid - evalgrid = np.linspace(0, 100, 200) - evVals = AdjustPrb*vFunc[0][ShareIndex](evalgrid) + (1-AdjustPrb)*vFunc[1][ShareIndex](evalgrid) - with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): - evValsNvrs = utility_inv(evVals,gam=CRRA) - evVals = np.insert(evVals, 0, 0.0) - evValsNvrs = np.insert(evVals, 0, 0.0) - - wFunc.append(ValueFunc(LinearInterp(evVals, evValsNvrs), CRRA)) - - return wFunc - class PortfolioConsumerType(IndShockConsumerType): # We add CantAdjust to the standard set of poststate_vars_ here. We call it @@ -224,7 +406,7 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): self.update() - self.RiskyShareLimitFunc = lambda RiskyDstn: PerfForesightDiscretePortfolioShare(self.Rfree, RiskyDstn, self.CRRA) + self.RiskyShareLimitFunc = lambda RiskyDstn: _PerfForesightDiscretePortfolioShare(self.Rfree, RiskyDstn, self.CRRA) def preSolve(self): AgentType.preSolve(self) @@ -268,7 +450,7 @@ def updateSolutionTerminal(self): RiskyShareFunc_terminal = PortfolioGridCount*[RiskyShareFunc_terminal] RiskyShareFunc_terminal = self.AdjustCount*[RiskyShareFunc_terminal] - wFunc_terminal = calcwFunc(self.AdjustPrb, self.AdjustCount, self.ShareNowCount, vFunc_terminal, self.CRRA) + wFunc_terminal = _calcwFunc(self.AdjustPrb, self.AdjustCount, self.ShareNowCount, vFunc_terminal, self.CRRA) self.solution_terminal = PortfolioSolution(cFunc = cFunc_terminal, RiskyShareFunc = RiskyShareFunc_terminal, @@ -427,6 +609,10 @@ def getRisky(self): return self.drawRiskyFunc() class ConsIndShockPortfolioSolver(ConsIndShockSolver): + ''' + A class for solving a one period consumption-saving problem with portfolio choice. + An instance of this class is created by the function solveConsPortfolio in each period. + ''' def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc, @@ -573,6 +759,10 @@ def makeRshareGrid(self): return [] def prepareToCalcRiskyShare(self): + """ + Prepare variables used to find optimal portfolio shares. Branches to either + the discrete or continuous portfolio choice set. + """ if self.DiscreteCase: self.prepareToCalcRiskyShareDiscrete() else: @@ -814,6 +1004,12 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): ------- None ''' + + # TODO: this does not yet + # calc sUnderbar -> mertonsammuelson + # calc MPC kappaUnderbar + # calc human wealth + self.DiscFacEff = DiscFac*LivPrb # "effective" discount factor self.ShkPrbsNext = self.ShockDstn[0] # but ConsumtionSolver doesn't store the risky shocks self.PermShkValsNext = self.ShockDstn[1] # but ConsumtionSolver doesn't store the risky shocks @@ -956,7 +1152,7 @@ def solve(self): if self.vFuncBool: solution = self.addvFunc(solution) - solution.wFunc = calcwFunc(self.AdjustPrb, self.AdjustCount, self.ShareNowCount, solution.vFunc, self.CRRA) + solution.wFunc = _calcwFunc(self.AdjustPrb, self.AdjustCount, self.ShareNowCount, solution.vFunc, self.CRRA) return solution @@ -988,35 +1184,14 @@ def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, return portsolution -def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): - RiskyAvgSqrd = RiskyAvg**2 - RiskyVar = RiskyStd**2 - - mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) - sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) - - return lambda RiskyCount: approxLognormal(RiskyCount, mu=mu, sigma=sigma) - -def RiskyDrawFactory(RiskyAvg=1.0, RiskyStd=0.0): - RiskyAvgSqrd = RiskyAvg**2 - RiskyVar = RiskyStd**2 - - mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) - sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) - - return lambda: drawLognormal(1, mu=mu, sigma=sigma) - -def LogNormalRiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): - RiskyAvgSqrd = RiskyAvg**2 - RiskyVar = RiskyStd**2 - - mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) - sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) - - return drawLognormal(1, mu=mu, sigma=sigma) - class LogNormalPortfolioConsumerType(PortfolioConsumerType): - + ''' + A consumer type with a portfolio choice. This agent type has log-normal return + factors. Their problem is defined by a coefficient of relative risk aversion, + intertemporal discount factor, interest factor, and time sequences of the + permanent income growth rate, survival probability, and return factor averages + and standard deviations. + ''' # time_inv_ = PortfolioConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'AdjustPrb', 'PortfolioGrid'] def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): @@ -1029,34 +1204,7 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): # Needed to simulate. Is a function that given 0 inputs it returns a draw # from the risky asset distribution. Only one is needed, because everyone # draws the same shock. - self.drawRiskyFunc = RiskyDrawFactory(RiskyAvg=self.RiskyAvg, + self.drawRiskyFunc = LogNormalRiskyDstnDraw(RiskyAvg=self.RiskyAvg, RiskyStd=self.RiskyStd) - self.RiskyShareLimitFunc = lambda _: PerfForesightLogNormalPortfolioShare(self.Rfree, self.RiskyAvg, self.RiskyStd, self.CRRA) - -def matchPortfolio(portType, moments): - """ - Match a portfolio model to a set of moments. Requires that the simulations - produce `RiskyShareNow`. - """ - return portType -# optimize... - - - - -# def ConsIndShockPortfolioSolver(ConsIndShockSolver): -# -# # def setAndUpdateValues(self): -# # great but wait -# # calc sUnderbar -> mertonsammuelson -# # calc MPC kappaUnderbar -# # calc human wealth -# - -#################### -#################### -#################### -#################### - -### + self.RiskyShareLimitFunc = lambda _: _PerfForesightLogNormalPortfolioShare(self.Rfree, self.RiskyAvg, self.RiskyStd, self.CRRA) From ad4a27ad2b27d05708217e5b34f4daddedf4fa7f Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Tue, 17 Sep 2019 11:01:01 +1000 Subject: [PATCH 057/242] Spend some time on the simulation again but still not able to fully understand the getControls() method and the consumption function in the Labor model, but the plot seems to make sense, please review on this. I am pretty certain that the ConsumerParameters.py file is fine. --- HARK/ConsumptionSaving/ConsLaborModel.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 0e1263bf3..086dc48a4 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -545,6 +545,8 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): LifecycleExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_age'] LifecycleExample.initializeSim() LifecycleExample.simulate() +# plt.plot(np.linspace(0, 5, 10000), LifecycleExample.cNrmNow_hist[30]) +# plt.show() ############################################################################### @@ -616,4 +618,6 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): LaborIntMargExample.track_vars = ['bNrmNow', 'cNrmNow'] LaborIntMargExample.initializeSim() LaborIntMargExample.simulate() +# plt.plot(np.linspace(0,100,10000), LaborIntMargExample.cNrmNow_hist[30]) +# plt.show() \ No newline at end of file From 515476034510565cf481d9b207600a4f07277969 Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Tue, 17 Sep 2019 11:18:37 +1000 Subject: [PATCH 058/242] Some comments --- HARK/ConsumptionSaving/ConsLaborModel.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 086dc48a4..313394290 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -355,8 +355,8 @@ def getControls(self): MPCnow = np.zeros(self.AgentCount) + np.nan for t in range(self.T_cycle): these = t == self.t_cycle - cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) - MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) + cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # assign consumtion values + MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # assign Marginal propensity to consume values (derivative) self.cNrmNow = cNrmNow self.MPCnow = MPCnow return None From b517d4f9f216c9d26d46723f342bf4fffccd56d5 Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Thu, 19 Sep 2019 14:07:19 +1000 Subject: [PATCH 059/242] Clean comments and docstring according to suggestions form Matthew. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 3713c371b..17b3be5e0 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1318,7 +1318,7 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, def makeCubiccFunc(self,mNrm,cNrm): ''' - Makes a cubic spline interpolation which contains the kink of the unconstrained + Makes a cubic spline interpolation that contains the kink of the unconstrained consumption function for this period. Parameters @@ -1333,10 +1333,10 @@ def makeCubiccFunc(self,mNrm,cNrm): cFuncUnc : CubicInterp The unconstrained consumption function for this period. ''' - # call the makeCubiccFunc from ConsIndShockSolver + # Call the makeCubiccFunc from ConsIndShockSolver. cFuncNowUncKink = super().makeCubiccFunc(mNrm, cNrm) - # change the coeffients at the kinked points + # Change the coeffients at the kinked points. cFuncNowUncKink.coeffs[self.i_kink + 1] = [cNrm[self.i_kink], mNrm[self.i_kink + 1] - mNrm[self.i_kink], 0, 0] return cFuncNowUncKink @@ -1380,7 +1380,7 @@ def prepareToCalcEndOfPrdvP(self): # Make a 1D array of the interest factor at each asset gridpoint Rfree_vec = self.Rsave*np.ones(aXtraCount) if KinkBool: - self.i_kink = np.sum(aNrmNow<=0)-1 # save the index of the kink point as an attribute + self.i_kink = np.sum(aNrmNow<=0)-1 # Save the index of the kink point as an attribute Rfree_vec[0:self.i_kink] = self.Rboro self.Rfree = Rfree_vec Rfree_temp = np.tile(Rfree_vec,(ShkCount,1)) From a2f34df198800a7cf06d1e147af5b3004407bbf1 Mon Sep 17 00:00:00 2001 From: llorracc Date: Thu, 19 Sep 2019 12:57:53 -0400 Subject: [PATCH 060/242] Improved doc; raise exception when AdjustProb < 1 --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 25 ++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 32206a493..ef3e80d54 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -226,6 +226,9 @@ def _calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc_adj, CRRA): integrand : function (lambda) Can be used to evaluate the integrand and the sent to a quadrature procedure. ''' + if AdjustPrb < 1: + raise Exception('The code allowing probabilities of adjustment less than 1 is not working yet; release notes will indicate when we have a release where it work') + # AdjustCount could in principle just be inferred from AdjustPrb instead of # cartying it arround FIXME / TODO wFunc = [] @@ -286,6 +289,28 @@ class PortfolioSolution(Solution): def __init__(self, cFunc=None, vFunc=None, wFunc=None, vPfunc=None, RiskyShareFunc=None, vPPfunc=None, mNrmMin=None, hNrm=None, MPCmin=None, MPCmax=None): + """We implement three different ways to allow portfolio choice. + The agent can choose + * any portfolio share ('continuous choice') + * only a specified set of portfolio shares ('discrete choice') + * With probability 1 (agent always gets to choose) + * With probability 0 < p < 1 (stochastic chance to choose) + + We allow two choices for the description of the + distribution of the stochastic variable: + 1. A generic discrete probability distribution + * Nodes and their probabilities are specified + 2. A true lognormal distribution + * The mean return and the standard deviation are specified + + In the discrete portfolio shares case, the user also must + input a function that *draws* from the distribution in drawRiskyFunc + + Other assumptions: + * distributions are time constant + * probability of being allowed to reoptimize is time constant + * If p < 1, you must specify the PortfolioSet discretely + """ # Change any missing function inputs to NullFunc if cFunc is None: cFunc = NullFunc() From 72ccaf68924321c29c3a9800712bc20890017c1e Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Thu, 19 Sep 2019 15:51:19 -0400 Subject: [PATCH 061/242] Prepare release 0.10.1.dev4 --- HARK/__init__.py | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/HARK/__init__.py b/HARK/__init__.py index bf0a93b4c..ed9538460 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,6 +1,6 @@ from __future__ import absolute_import from .core import * -__version__ = '0.10.1.dev3' +__version__ = '0.10.1.dev4' from .BayerLuetticke import * diff --git a/setup.py b/setup.py index 50619a584..62c3cd11f 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ # For a discussion on single-sourcing the version across setup.py and the # project code, see # https://packaging.python.org/en/latest/single_source_version.html - version='0.10.1.dev3', # Required + version='0.10.1.dev4', # Required # This is a one-line description or tagline of what your project does. This # corresponds to the "Summary" metadata field: From 72b60af85de7563fe8b97a235076ca577cbd2aba Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 20 Sep 2019 01:09:24 +0200 Subject: [PATCH 062/242] Fix AdjustPrb for discrete portfolio choice. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index ef3e80d54..13b21d636 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -226,8 +226,6 @@ def _calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc_adj, CRRA): integrand : function (lambda) Can be used to evaluate the integrand and the sent to a quadrature procedure. ''' - if AdjustPrb < 1: - raise Exception('The code allowing probabilities of adjustment less than 1 is not working yet; release notes will indicate when we have a release where it work') # AdjustCount could in principle just be inferred from AdjustPrb instead of # cartying it arround FIXME / TODO @@ -244,9 +242,6 @@ def _calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc_adj, CRRA): evVals = AdjustPrb*vFunc_adj[0][ShareIndex](evalgrid) + (1-AdjustPrb)*vFunc_adj[1][ShareIndex](evalgrid) with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): evValsNvrs = utility_inv(evVals,gam=CRRA) - evVals = np.insert(evVals, 0, 0.0) - evValsNvrs = np.insert(evVals, 0, 0.0) - wFunc.append(ValueFunc(LinearInterp(evVals, evValsNvrs), CRRA)) return wFunc @@ -290,23 +285,23 @@ def __init__(self, cFunc=None, vFunc=None, wFunc=None, vPfunc=None, RiskyShareFunc=None, vPPfunc=None, mNrmMin=None, hNrm=None, MPCmin=None, MPCmax=None): """We implement three different ways to allow portfolio choice. - The agent can choose + The agent can choose * any portfolio share ('continuous choice') * only a specified set of portfolio shares ('discrete choice') * With probability 1 (agent always gets to choose) * With probability 0 < p < 1 (stochastic chance to choose) - - We allow two choices for the description of the + + We allow two choices for the description of the distribution of the stochastic variable: 1. A generic discrete probability distribution * Nodes and their probabilities are specified 2. A true lognormal distribution * The mean return and the standard deviation are specified - + In the discrete portfolio shares case, the user also must input a function that *draws* from the distribution in drawRiskyFunc - Other assumptions: + Other assumptions: * distributions are time constant * probability of being allowed to reoptimize is time constant * If p < 1, you must specify the PortfolioSet discretely From 58209583f215a6a49cb3193a86b5846fbf401bc7 Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Fri, 20 Sep 2019 13:55:01 +1000 Subject: [PATCH 063/242] Corrected the lifecycle model, now it is a lifecycle model for a labor intensive margin consumer. But for some unknown reason, it does not allow me to set T_retire above 0. If T_retire is set, then there is an IndexError at line 774 in interpolation.py. After tracking of the error, I suspect that something is messed up in the process of passing bNrmGrid_rep into Interpolation.LinearInterp._eval0rder. If I set T_retire = 0, the print of length of bNrmGrid_rep and x_list in _eval0rder is 200, 201, 200, 16. If I set T_retire > 0, the print of length of bNrmGrid_rep and x_list in _eval0rder is 200, 201, 200, 1. However, I cannot identify why T_retire > 0 gives such a result. Will continue working on this. --- HARK/ConsumptionSaving/ConsLaborModel.py | 103 +++++++++++++------ HARK/ConsumptionSaving/ConsumerParameters.py | 15 ++- HARK/interpolation.py | 1 + 3 files changed, 84 insertions(+), 35 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 313394290..a06958d02 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -152,6 +152,7 @@ def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,C TranShkPrbs_rep = np.tile(np.reshape(TranShkPrbs,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock probabilities for each a_t state # Construct a function that gives marginal value of next period's bank balances *just before* the transitory shock arrives +# print(len(bNrmGrid_rep)) # to check bNrmGrid_rep that is passed into _eval0rder as x_list vPNext = vPfunc_next(bNrmGrid_rep, TranShkVals_rep) # Next period's marginal value at every transitory shock and every bank balances gridpoint vPbarNext = np.sum(vPNext*TranShkPrbs_rep, axis = 1) # Integrate out the transitory shocks (in TranShkVals direction) to get expected vP just before the transitory shock vPbarNvrsNext = uPinv(vPbarNext) # Transformed marginal value through the inverse marginal utility function to "decurve" it @@ -355,8 +356,8 @@ def getControls(self): MPCnow = np.zeros(self.AgentCount) + np.nan for t in range(self.T_cycle): these = t == self.t_cycle - cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # assign consumtion values - MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # assign Marginal propensity to consume values (derivative) + cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # Assign consumtion values + MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # Assign Marginal propensity to consume values (derivative) self.cNrmNow = cNrmNow self.MPCnow = MPCnow return None @@ -517,37 +518,7 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): do_simulation = True -############################################################################### - - # Make and solve an idiosyncratic shocks consumer with a finite lifecycle - LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) - LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once - - start_time = clock() - LifecycleExample.solve() - end_time = clock() - print('Solving a lifecycle consumer took ' + mystr(end_time-start_time) + ' seconds.') - LifecycleExample.unpackcFunc() - LifecycleExample.timeFwd() - - # Plot the consumption functions during working life - print('Consumption functions while working:') - mMin = min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]) - plotFuncs(LifecycleExample.cFunc[:LifecycleExample.T_retire],mMin,5) - # Plot the consumption functions during retirement - print('Consumption functions while retired:') - plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire:],0,5) - LifecycleExample.timeRev() - - if do_simulation: - LifecycleExample.T_sim = 120 - LifecycleExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_age'] - LifecycleExample.initializeSim() - LifecycleExample.simulate() -# plt.plot(np.linspace(0, 5, 10000), LifecycleExample.cNrmNow_hist[30]) -# plt.show() - ############################################################################### # Make and solve a labor intensive margin consumer i.e. a consumer with utility for leisure @@ -620,4 +591,70 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): LaborIntMargExample.simulate() # plt.plot(np.linspace(0,100,10000), LaborIntMargExample.cNrmNow_hist[30]) # plt.show() - \ No newline at end of file + +############################################################################### + + # Make and solve a labor intensive margin consumer with a finite lifecycle + LifecycleExample = LaborIntMargConsumerType(**Params.init_labor_lifecycle) + LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once + + start_time = clock() + LifecycleExample.solve() + end_time = clock() + print('Solving a lifecycle labor intensive margin consumer took ' + str(end_time-start_time) + ' seconds.') + LifecycleExample.unpackcFunc() + LifecycleExample.timeFwd() + + t = 0 + bMax = 100. + + # Plot the consumption function at various transitory productivity shocks + TranShkSet = LifecycleExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + C = LifecycleExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,C) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Normalized consumption level') + plt.show() + + # Plot the marginal consumption function at various transitory productivity shocks + TranShkSet = LifecycleExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + C = LifecycleExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,C) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Marginal propensity to consume') + plt.show() + + # Plot the labor function at various transitory productivity shocks + TranShkSet = LifecycleExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + Lbr = LifecycleExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,Lbr) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Labor supply') + plt.show() + + # Plot the marginal value function at various transitory productivity shocks + pseudo_inverse = True + TranShkSet = LifecycleExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + if pseudo_inverse: + vP = LifecycleExample.solution[t].vPfunc.cFunc(B_temp,Shk*np.ones_like(B_temp)) + else: + vP = LifecycleExample.solution[t].vPfunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,vP) + plt.xlabel('Beginning of period bank balances') + if pseudo_inverse: + plt.ylabel('Pseudo inverse marginal value') + else: + plt.ylabel('Marginal value') + plt.show() \ No newline at end of file diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 13128c9bc..d4940af42 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -342,5 +342,16 @@ init_labor_intensive ['StateMax'] = StateMax init_labor_intensive ['StateCount'] = StateCount init_labor_intensive ['ExponentialGrid'] = ExponentialGrid - -init_labor_intensive ['T_cycle'] = 1 \ No newline at end of file +init_labor_intensive ['T_cycle'] = 1 + +# Make a dictionary for Endogenous Labor supply model with finite lifecycle +init_labor_lifecycle = copy(init_labor_intensive) +init_labor_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02] +init_labor_lifecycle['PermShkStd'] = [0.1,0.2,0.1,0.2,0.1,0.2,0.1,0.2,0.1,0.2] # Have to set the last three element non-zero as T_retire does not work. (different from lifecycle example in ConsIndShockModel) +init_labor_lifecycle['TranShkStd'] = [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0.1,0.2,0.3] # Have to set the last three element non-zero as T_retire does not work. +init_labor_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] +init_labor_lifecycle['WageRte'] = [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9] # Wage rate in a lifecycle +init_labor_lifecycle['LbrCost'] = LbrCost * 10 # Assume labor cost is constant during the lifecycle +init_labor_lifecycle['T_cycle'] = 10 +#init_labor_lifecycle['T_retire'] = 7 # IndexError at line 774 in interpolation.py. +init_labor_lifecycle['T_age'] = 11 # Make sure that old people die at terminal age and don't turn into newborns! \ No newline at end of file diff --git a/HARK/interpolation.py b/HARK/interpolation.py index 7913a3986..d02972ad0 100644 --- a/HARK/interpolation.py +++ b/HARK/interpolation.py @@ -771,6 +771,7 @@ def _evalOrDer(self,x,_eval,_Der): i = np.maximum(np.searchsorted(self.x_list[:-1],x),1) +# print(len(self.x_list)) # To check x_list alpha = (x-self.x_list[i-1])/(self.x_list[i]-self.x_list[i-1]) if _eval: From ca2393994c7618f8e4c06e9e991f6fe869f404f0 Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Fri, 20 Sep 2019 18:22:38 +1000 Subject: [PATCH 064/242] Revert "Corrected the lifecycle model, now it is a lifecycle model for a labor intensive margin consumer." This reverts commit 58209583f215a6a49cb3193a86b5846fbf401bc7. --- HARK/ConsumptionSaving/ConsLaborModel.py | 103 ++++++------------- HARK/ConsumptionSaving/ConsumerParameters.py | 15 +-- HARK/interpolation.py | 1 - 3 files changed, 35 insertions(+), 84 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index a06958d02..313394290 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -152,7 +152,6 @@ def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,C TranShkPrbs_rep = np.tile(np.reshape(TranShkPrbs,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock probabilities for each a_t state # Construct a function that gives marginal value of next period's bank balances *just before* the transitory shock arrives -# print(len(bNrmGrid_rep)) # to check bNrmGrid_rep that is passed into _eval0rder as x_list vPNext = vPfunc_next(bNrmGrid_rep, TranShkVals_rep) # Next period's marginal value at every transitory shock and every bank balances gridpoint vPbarNext = np.sum(vPNext*TranShkPrbs_rep, axis = 1) # Integrate out the transitory shocks (in TranShkVals direction) to get expected vP just before the transitory shock vPbarNvrsNext = uPinv(vPbarNext) # Transformed marginal value through the inverse marginal utility function to "decurve" it @@ -356,8 +355,8 @@ def getControls(self): MPCnow = np.zeros(self.AgentCount) + np.nan for t in range(self.T_cycle): these = t == self.t_cycle - cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # Assign consumtion values - MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # Assign Marginal propensity to consume values (derivative) + cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # assign consumtion values + MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # assign Marginal propensity to consume values (derivative) self.cNrmNow = cNrmNow self.MPCnow = MPCnow return None @@ -518,7 +517,37 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): do_simulation = True +############################################################################### + + # Make and solve an idiosyncratic shocks consumer with a finite lifecycle + LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) + LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once + + start_time = clock() + LifecycleExample.solve() + end_time = clock() + print('Solving a lifecycle consumer took ' + mystr(end_time-start_time) + ' seconds.') + LifecycleExample.unpackcFunc() + LifecycleExample.timeFwd() + + # Plot the consumption functions during working life + print('Consumption functions while working:') + mMin = min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]) + plotFuncs(LifecycleExample.cFunc[:LifecycleExample.T_retire],mMin,5) + # Plot the consumption functions during retirement + print('Consumption functions while retired:') + plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire:],0,5) + LifecycleExample.timeRev() + + if do_simulation: + LifecycleExample.T_sim = 120 + LifecycleExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_age'] + LifecycleExample.initializeSim() + LifecycleExample.simulate() +# plt.plot(np.linspace(0, 5, 10000), LifecycleExample.cNrmNow_hist[30]) +# plt.show() + ############################################################################### # Make and solve a labor intensive margin consumer i.e. a consumer with utility for leisure @@ -591,70 +620,4 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): LaborIntMargExample.simulate() # plt.plot(np.linspace(0,100,10000), LaborIntMargExample.cNrmNow_hist[30]) # plt.show() - -############################################################################### - - # Make and solve a labor intensive margin consumer with a finite lifecycle - LifecycleExample = LaborIntMargConsumerType(**Params.init_labor_lifecycle) - LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once - - start_time = clock() - LifecycleExample.solve() - end_time = clock() - print('Solving a lifecycle labor intensive margin consumer took ' + str(end_time-start_time) + ' seconds.') - LifecycleExample.unpackcFunc() - LifecycleExample.timeFwd() - - t = 0 - bMax = 100. - - # Plot the consumption function at various transitory productivity shocks - TranShkSet = LifecycleExample.TranShkGrid[t] - B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: - B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - C = LifecycleExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,C) - plt.xlabel('Beginning of period bank balances') - plt.ylabel('Normalized consumption level') - plt.show() - - # Plot the marginal consumption function at various transitory productivity shocks - TranShkSet = LifecycleExample.TranShkGrid[t] - B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: - B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - C = LifecycleExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,C) - plt.xlabel('Beginning of period bank balances') - plt.ylabel('Marginal propensity to consume') - plt.show() - - # Plot the labor function at various transitory productivity shocks - TranShkSet = LifecycleExample.TranShkGrid[t] - B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: - B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - Lbr = LifecycleExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,Lbr) - plt.xlabel('Beginning of period bank balances') - plt.ylabel('Labor supply') - plt.show() - - # Plot the marginal value function at various transitory productivity shocks - pseudo_inverse = True - TranShkSet = LifecycleExample.TranShkGrid[t] - B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: - B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - if pseudo_inverse: - vP = LifecycleExample.solution[t].vPfunc.cFunc(B_temp,Shk*np.ones_like(B_temp)) - else: - vP = LifecycleExample.solution[t].vPfunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,vP) - plt.xlabel('Beginning of period bank balances') - if pseudo_inverse: - plt.ylabel('Pseudo inverse marginal value') - else: - plt.ylabel('Marginal value') - plt.show() \ No newline at end of file + \ No newline at end of file diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index d4940af42..13128c9bc 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -342,16 +342,5 @@ init_labor_intensive ['StateMax'] = StateMax init_labor_intensive ['StateCount'] = StateCount init_labor_intensive ['ExponentialGrid'] = ExponentialGrid -init_labor_intensive ['T_cycle'] = 1 - -# Make a dictionary for Endogenous Labor supply model with finite lifecycle -init_labor_lifecycle = copy(init_labor_intensive) -init_labor_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02] -init_labor_lifecycle['PermShkStd'] = [0.1,0.2,0.1,0.2,0.1,0.2,0.1,0.2,0.1,0.2] # Have to set the last three element non-zero as T_retire does not work. (different from lifecycle example in ConsIndShockModel) -init_labor_lifecycle['TranShkStd'] = [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0.1,0.2,0.3] # Have to set the last three element non-zero as T_retire does not work. -init_labor_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] -init_labor_lifecycle['WageRte'] = [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9] # Wage rate in a lifecycle -init_labor_lifecycle['LbrCost'] = LbrCost * 10 # Assume labor cost is constant during the lifecycle -init_labor_lifecycle['T_cycle'] = 10 -#init_labor_lifecycle['T_retire'] = 7 # IndexError at line 774 in interpolation.py. -init_labor_lifecycle['T_age'] = 11 # Make sure that old people die at terminal age and don't turn into newborns! \ No newline at end of file + +init_labor_intensive ['T_cycle'] = 1 \ No newline at end of file diff --git a/HARK/interpolation.py b/HARK/interpolation.py index d02972ad0..7913a3986 100644 --- a/HARK/interpolation.py +++ b/HARK/interpolation.py @@ -771,7 +771,6 @@ def _evalOrDer(self,x,_eval,_Der): i = np.maximum(np.searchsorted(self.x_list[:-1],x),1) -# print(len(self.x_list)) # To check x_list alpha = (x-self.x_list[i-1])/(self.x_list[i]-self.x_list[i-1]) if _eval: From 1832c4795bcd7bb0c475f9120c23d028f4817954 Mon Sep 17 00:00:00 2001 From: JackShiqiLi Date: Fri, 20 Sep 2019 18:50:23 +1000 Subject: [PATCH 065/242] Completed the lifecycle example for endogenous labor supply model. One problem: according to the model name 'endogenous labor', I assume that we should not set a specific retire period for the consumer, everything should be decide by the agent. Furthermore, if I do set a retirement period T_retire for the agent, then lifecycle example gives an IndexError. After some investigation, I found that the problem reside in line 2328-2337 in ConsIndShockModel, where we handle the IncomeDstnRet if T_retire > 0. I do not want to touch on that without instruction as it may have big influence. --- HARK/ConsumptionSaving/ConsLaborModel.py | 102 +++++++++++++------ HARK/ConsumptionSaving/ConsumerParameters.py | 15 ++- 2 files changed, 82 insertions(+), 35 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 313394290..0a8bad130 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -355,8 +355,8 @@ def getControls(self): MPCnow = np.zeros(self.AgentCount) + np.nan for t in range(self.T_cycle): these = t == self.t_cycle - cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # assign consumtion values - MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # assign Marginal propensity to consume values (derivative) + cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # Assign consumtion values + MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # Assign Marginal propensity to consume values (derivative) self.cNrmNow = cNrmNow self.MPCnow = MPCnow return None @@ -517,37 +517,7 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): do_simulation = True -############################################################################### - - # Make and solve an idiosyncratic shocks consumer with a finite lifecycle - LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) - LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once - - start_time = clock() - LifecycleExample.solve() - end_time = clock() - print('Solving a lifecycle consumer took ' + mystr(end_time-start_time) + ' seconds.') - LifecycleExample.unpackcFunc() - LifecycleExample.timeFwd() - - # Plot the consumption functions during working life - print('Consumption functions while working:') - mMin = min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]) - plotFuncs(LifecycleExample.cFunc[:LifecycleExample.T_retire],mMin,5) - # Plot the consumption functions during retirement - print('Consumption functions while retired:') - plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire:],0,5) - LifecycleExample.timeRev() - - if do_simulation: - LifecycleExample.T_sim = 120 - LifecycleExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_age'] - LifecycleExample.initializeSim() - LifecycleExample.simulate() -# plt.plot(np.linspace(0, 5, 10000), LifecycleExample.cNrmNow_hist[30]) -# plt.show() - ############################################################################### # Make and solve a labor intensive margin consumer i.e. a consumer with utility for leisure @@ -620,4 +590,70 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): LaborIntMargExample.simulate() # plt.plot(np.linspace(0,100,10000), LaborIntMargExample.cNrmNow_hist[30]) # plt.show() - \ No newline at end of file + +############################################################################### + + # Make and solve a labor intensive margin consumer with a finite lifecycle + LifecycleExample = LaborIntMargConsumerType(**Params.init_labor_lifecycle) + LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once + + start_time = clock() + LifecycleExample.solve() + end_time = clock() + print('Solving a lifecycle labor intensive margin consumer took ' + str(end_time-start_time) + ' seconds.') + LifecycleExample.unpackcFunc() + LifecycleExample.timeFwd() + + t = 0 + bMax = 100. + + # Plot the consumption function at various transitory productivity shocks + TranShkSet = LifecycleExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + C = LifecycleExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,C) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Normalized consumption level') + plt.show() + + # Plot the marginal consumption function at various transitory productivity shocks + TranShkSet = LifecycleExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + C = LifecycleExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,C) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Marginal propensity to consume') + plt.show() + + # Plot the labor function at various transitory productivity shocks + TranShkSet = LifecycleExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + Lbr = LifecycleExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,Lbr) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Labor supply') + plt.show() + + # Plot the marginal value function at various transitory productivity shocks + pseudo_inverse = True + TranShkSet = LifecycleExample.TranShkGrid[t] + B = np.linspace(0.,bMax,300) + for Shk in TranShkSet: + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + if pseudo_inverse: + vP = LifecycleExample.solution[t].vPfunc.cFunc(B_temp,Shk*np.ones_like(B_temp)) + else: + vP = LifecycleExample.solution[t].vPfunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,vP) + plt.xlabel('Beginning of period bank balances') + if pseudo_inverse: + plt.ylabel('Pseudo inverse marginal value') + else: + plt.ylabel('Marginal value') + plt.show() \ No newline at end of file diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 13128c9bc..427b16dba 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -342,5 +342,16 @@ init_labor_intensive ['StateMax'] = StateMax init_labor_intensive ['StateCount'] = StateCount init_labor_intensive ['ExponentialGrid'] = ExponentialGrid - -init_labor_intensive ['T_cycle'] = 1 \ No newline at end of file +init_labor_intensive ['T_cycle'] = 1 + +# Make a dictionary for Endogenous Labor supply model with finite lifecycle +init_labor_lifecycle = copy(init_labor_intensive) +init_labor_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02] +init_labor_lifecycle['PermShkStd'] = [0.1,0.2,0.1,0.2,0.1,0.2,0.1,0.2,0.1,0.2] +init_labor_lifecycle['TranShkStd'] = [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0.1,0.2,0.3] +init_labor_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] # Living probability decreases as time moves forward. +init_labor_lifecycle['WageRte'] = [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9] # Wage rate in a lifecycle +init_labor_lifecycle['LbrCost'] = LbrCost * 10 # Assume labor cost is constant during the lifecycle +init_labor_lifecycle['T_cycle'] = 10 +#init_labor_lifecycle['T_retire'] = 7 # IndexError at line 774 in interpolation.py. +init_labor_lifecycle['T_age'] = 11 # Make sure that old people die at terminal age and don't turn into newborns! \ No newline at end of file From 35b1fc5e43f553e95ad4c8a42c37ca66639d9120 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Mon, 23 Sep 2019 18:55:32 -0400 Subject: [PATCH 066/242] add test for core.py --- HARK/tests/test_core.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 HARK/tests/test_core.py diff --git a/HARK/tests/test_core.py b/HARK/tests/test_core.py new file mode 100644 index 000000000..34199294d --- /dev/null +++ b/HARK/tests/test_core.py @@ -0,0 +1,15 @@ +""" +This file implements unit tests for interpolation methods +""" +from HARK.core import HARKobject + +import numpy as np +import unittest + +class testHARKobject(unittest.TestCase): + def setUp(self): + self.obj_a = HARKobject() + self.obj_b = HARKobject() + + def test_distance(self): + self.assertRaises(AttributeError, self.obj_a.distance(self.obj_b)) From e38a40320435527509b4f674a4d9263e77fdab08 Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Wed, 25 Sep 2019 11:38:17 -0400 Subject: [PATCH 067/242] Prepare release 0.10.1.dev5 --- CHANGELOG.md | 18 +++++++++++++++++- HARK/__init__.py | 2 +- setup.py | 2 +- 3 files changed, 19 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 60436e37f..b1201c44b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,5 @@ HARK -Version 0.10.1.dev3 +Version 0.10.1.dev5 Release Notes # Introduction @@ -10,6 +10,22 @@ For more information on HARK, see [our Github organization](https://github.com/e ## Changes +### 0.10.1.dev5 + +Release Date: 09-25-2019 + +#### Minor Changes +- Added portfolio choice between risky and safe assets (ConsPortfolioModel). ([#241](https://github.com/econ-ark/HARK/pull/241)) + +### 0.10.1.dev4 + +Release Date: 09-19-2019 + +#### Minor Changes +- Fixes cubic interpolation in KinkedRSolver. ([#386](https://github.com/econ-ark/HARK/pull/386)) +- Documentes the procedure for constructing value function inverses and fixes bug in which survival rate was not included in absolute patience factor. ([#383](https://github.com/econ-ark/HARK/pull/383)) +- Fixes problems that sometimes prevented multiprocessing from working. ([#377](https://github.com/econ-ark/HARK/pull/377)) + ### 0.10.1.dev3 Release Date: 07-23-2019 diff --git a/HARK/__init__.py b/HARK/__init__.py index ed9538460..c7ba5e751 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,6 +1,6 @@ from __future__ import absolute_import from .core import * -__version__ = '0.10.1.dev4' +__version__ = '0.10.1.dev5' from .BayerLuetticke import * diff --git a/setup.py b/setup.py index 62c3cd11f..b0b88820e 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ # For a discussion on single-sourcing the version across setup.py and the # project code, see # https://packaging.python.org/en/latest/single_source_version.html - version='0.10.1.dev4', # Required + version='0.10.1.dev5', # Required # This is a one-line description or tagline of what your project does. This # corresponds to the "Summary" metadata field: From 9563bcf546cb8ff6fea152b381c68a957b7566d9 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Wed, 25 Sep 2019 14:41:55 -0400 Subject: [PATCH 068/242] fix logic bug while comparing types --- HARK/core.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/core.py b/HARK/core.py index 4ca8331ff..119db0143 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -53,7 +53,7 @@ def distanceMetric(thing_A, thing_B): else: distance = float(abs(lenA - lenB)) # If both inputs are numbers, return their difference - elif (typeA is int or typeB is float) and (typeB is int or typeB is float): + elif isinstance(thing_A, (int, float)) and isinstance(thing_B, (int, float)): distance = float(abs(thing_A - thing_B)) # If both inputs are array-like, return the maximum absolute difference b/w # corresponding elements (if same shape); return largest difference in dimensions From 1610bea50288015f92e4415ff63d778aab53d1af Mon Sep 17 00:00:00 2001 From: Sebastian Benthall Date: Thu, 26 Sep 2019 13:38:06 -0400 Subject: [PATCH 069/242] fixing typo in README: hNm -> hNrm --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e289b5384..1aea02781 100644 --- a/README.md +++ b/README.md @@ -130,11 +130,11 @@ You can retrieve the solution's consumption function from the `.cFunc` attribute PFexample.solution[0].cFunc ``` -Or you can retrieve the solved value for human wealth normalized by permanent income from the solution's `.hNm` attribute: +Or you can retrieve the solved value for human wealth normalized by permanent income from the solution's `.hNrm` attribute: ```python # Retrieve the solved value for human wealth normalized by permanent income -PFexample.solution[0].hNm +PFexample.solution[0].hNrm ``` For a detailed explanation of the above example please see the demo notebook [*A Gentle Introduction to HARK*](https://mybinder.org/v2/gh/econ-ark/demos/master?filepath=notebooks%2FA+Gentle+Introduction+to+HARK.ipynb). From 24dea3eccd022663de221724a6d4f031244d64a5 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 26 Sep 2019 16:24:57 -0500 Subject: [PATCH 070/242] flatten dimensions when calculating distance b/w 2 --- HARK/core.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/HARK/core.py b/HARK/core.py index 119db0143..941ead370 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -62,7 +62,8 @@ def distanceMetric(thing_A, thing_B): if thing_A.shape == thing_B.shape: distance = np.max(abs(thing_A - thing_B)) else: - distance = np.max(abs(thing_A.shape - thing_B.shape)) + # Flatten arrays so there are in the same dimensions + distance = np.max(abs(thing_A.flatten().shape[0] - thing_B.flatten().shape[0])) # If none of the above cases, but the objects are of the same class, call # the distance method of one on the other elif thing_A.__class__.__name__ == thing_B.__class__.__name__: From 3258fe1c1771ba521d1fd5e9317ad8e6b1c7ef36 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 26 Sep 2019 16:27:56 -0500 Subject: [PATCH 071/242] add base tests for core.distanceMetric --- HARK/tests/test_core.py | 75 ++++++++++++++++++++++++++++++++++++++--- 1 file changed, 71 insertions(+), 4 deletions(-) diff --git a/HARK/tests/test_core.py b/HARK/tests/test_core.py index 34199294d..246499ea7 100644 --- a/HARK/tests/test_core.py +++ b/HARK/tests/test_core.py @@ -1,15 +1,82 @@ """ This file implements unit tests for interpolation methods """ -from HARK.core import HARKobject +from HARK.core import HARKobject, distanceMetric, AgentType import numpy as np import unittest -class testHARKobject(unittest.TestCase): + +class testdistanceMetric(unittest.TestCase): def setUp(self): + self.list_a = [1.0, 2.1, 3] + self.list_b = [3.1, 4, -1.4] + self.list_c = [8.6, 9] self.obj_a = HARKobject() self.obj_b = HARKobject() + self.obj_c = HARKobject() + + def test_list(self): + # same length + self.assertEqual(distanceMetric(self.list_a, self.list_b), 4.4) + # different length + self.assertEqual(distanceMetric(self.list_b, self.list_c), 1.0) + # sanity check, same objects + self.assertEqual(distanceMetric(self.list_b, self.list_b), 0.0) + + def test_array(self): + # same length + self.assertEqual( + distanceMetric(np.array(self.list_a), np.array(self.list_b)), 4.4 + ) + # different length + self.assertEqual( + distanceMetric(np.array(self.list_b).reshape(1, 3), np.array(self.list_c)), + 1.0, + ) + # sanity check, same objects + self.assertEqual( + distanceMetric(np.array(self.list_b), np.array(self.list_b)), 0.0 + ) + + def test_hark_object_distance(self): + self.obj_a.distance_criteria = ["var_1", "var_2", "var_3"] + self.obj_b.distance_criteria = ["var_1", "var_2", "var_3"] + self.obj_c.distance_criteria = ["var_5"] + # if attributes don't exist or don't match + self.assertEqual(distanceMetric(self.obj_a, self.obj_b), 1000.0) + self.assertEqual(distanceMetric(self.obj_a, self.obj_c), 1000.0) + # add single numbers to attributes + self.obj_a.var_1, self.obj_a.var_2, self.obj_a.var_3 = 0.1, 1, 2.1 + self.obj_b.var_1, self.obj_b.var_2, self.obj_b.var_3 = 1.8, -1, 0.1 + self.assertEqual(distanceMetric(self.obj_a, self.obj_b), 2.0) + + # sanity check - same objects + self.assertEqual(distanceMetric(self.obj_a, self.obj_a), 0.0) + + +# class testHARKobject(unittest.TestCase): +# def setUp(self): +# self.obj_a = HARKobject() +# self.obj_b = HARKobject() +# self.obj_c = HARKobject() + +# def test_distance(self): +# self.assertRaises(AttributeError, self.obj_a.distance(self.obj_b)) + +# class testAgentType(unittest.TestCase): +# def setUp(self): +# self.agent = AgentType() - def test_distance(self): - self.assertRaises(AttributeError, self.obj_a.distance(self.obj_b)) +# def test_time(self): +# self.agent.time_vary = ['var_1', 'var_2'] +# self.agent.var_1 = [4.3, 2, 1] +# self.agent.var_2 = [1, 2, 3, 4, 5] +# self.agent.timeFlip() +# self.assertEqual(self.agent.var_1, [1, 2, 4.3]) +# self.assertEqual(self.agent.var_2, [5, 4, 3, 2, 1]) +# self.assertEqual(self.agent.time_flow, True) +# self.agent.timeFlip() +# self.assertEqual(self.agent.var_1, [4.3, 2, 1]) +# self.assertEqual(self.agent.var_2, [1, 2, 3, 4, 5]) +# self.assertEqual(self.agent.time_flow, False) From b85cb8ac50d2af5050b63a81b8763b4ffde4457b Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 26 Sep 2019 16:48:39 -0500 Subject: [PATCH 072/242] add base tests for core.HARKobject --- HARK/tests/test_core.py | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/HARK/tests/test_core.py b/HARK/tests/test_core.py index 246499ea7..2334736d8 100644 --- a/HARK/tests/test_core.py +++ b/HARK/tests/test_core.py @@ -55,14 +55,26 @@ def test_hark_object_distance(self): self.assertEqual(distanceMetric(self.obj_a, self.obj_a), 0.0) -# class testHARKobject(unittest.TestCase): -# def setUp(self): -# self.obj_a = HARKobject() -# self.obj_b = HARKobject() -# self.obj_c = HARKobject() +class testHARKobject(unittest.TestCase): + def setUp(self): + # similar test to distanceMetric + self.obj_a = HARKobject() + self.obj_b = HARKobject() + self.obj_c = HARKobject() + + def test_distance(self): + self.obj_a.distance_criteria = ["var_1", "var_2", "var_3"] + self.obj_b.distance_criteria = ["var_1", "var_2", "var_3"] + self.obj_c.distance_criteria = ["var_5"] + self.obj_a.var_1, self.obj_a.var_2, self.obj_a.var_3 = [0.1], [1, 2], [2.1] + self.obj_b.var_1, self.obj_b.var_2, self.obj_b.var_3 = [1.8], [0, 0.1], [1.1] + self.assertEqual(self.obj_a.distance(self.obj_b), 1.9) + # change the length of a attribute list + self.obj_b.var_1, self.obj_b.var_2, self.obj_b.var_3 = [1.8], [0, 0, 0.1], [1.1] + self.assertEqual(self.obj_a.distance(self.obj_b), 1.7) + # sanity check + self.assertEqual(self.obj_b.distance(self.obj_a), 0.0) -# def test_distance(self): -# self.assertRaises(AttributeError, self.obj_a.distance(self.obj_b)) # class testAgentType(unittest.TestCase): # def setUp(self): From aed142e53eb1f536dfc41cd5a5722b49231f29ae Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 26 Sep 2019 17:12:01 -0500 Subject: [PATCH 073/242] change default behaviour of AgentType time flow to forward --- HARK/core.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 941ead370..8e84690f0 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -175,7 +175,7 @@ class AgentType(HARKobject): 'solveOnePeriod' should appear in exactly one of these lists, depending on whether the same solution method is used in all periods of the model. ''' - def __init__(self, solution_terminal=None, cycles=1, time_flow=False, pseudo_terminal=True, + def __init__(self, solution_terminal=None, cycles=1, time_flow=True, pseudo_terminal=True, tolerance=0.000001, seed=0, **kwds): ''' Initialize an instance of AgentType by setting attributes. @@ -193,7 +193,7 @@ def __init__(self, solution_terminal=None, cycles=1, time_flow=False, pseudo_ter once before terminating. cycles=0 corresponds to an infinite horizon model, with a sequence of one period problems repeating indefinitely. time_flow : boolean - Whether time is currently "flowing" forward or backward for this + Whether time is currently "flowing" forward(True) or backward(False) for this instance. Used to flip between solving (using backward iteration) and simulating (etc). pseudo_terminal : boolean From 0984247dc5b98c66015b77db0fd9b40f3cda88ce Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 26 Sep 2019 18:02:01 -0500 Subject: [PATCH 074/242] lets not use exec in code unless absolutely required --- HARK/core.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/core.py b/HARK/core.py index 8e84690f0..99cdbaf4a 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -260,7 +260,7 @@ def timeFlip(self): none ''' for name in self.time_vary: - exec('self.' + name + '.reverse()') + self.__dict__[name].reverse() self.time_flow = not self.time_flow def timeFwd(self): From e3f224e100027be19cddb07d3157bdf6414a0e5c Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 26 Sep 2019 18:28:10 -0500 Subject: [PATCH 075/242] add base tests for AgentType --- HARK/core.py | 5 +++-- HARK/tests/test_core.py | 32 ++++++++++++++++---------------- 2 files changed, 19 insertions(+), 18 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 99cdbaf4a..1ede73d88 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -839,8 +839,9 @@ def solveOneCycle(agent, solution_last): ''' # Calculate number of periods per cycle, defaults to 1 if all variables are time invariant if len(agent.time_vary) > 0: - name = agent.time_vary[0] - T = len(eval('agent.' + name)) + # name = agent.time_vary[0] + # T = len(eval('agent.' + name)) + T = len(agent.__dict__[agent.time_vary[0]]) else: T = 1 diff --git a/HARK/tests/test_core.py b/HARK/tests/test_core.py index 2334736d8..6b931a8e2 100644 --- a/HARK/tests/test_core.py +++ b/HARK/tests/test_core.py @@ -73,22 +73,22 @@ def test_distance(self): self.obj_b.var_1, self.obj_b.var_2, self.obj_b.var_3 = [1.8], [0, 0, 0.1], [1.1] self.assertEqual(self.obj_a.distance(self.obj_b), 1.7) # sanity check - self.assertEqual(self.obj_b.distance(self.obj_a), 0.0) + self.assertEqual(self.obj_b.distance(self.obj_b), 0.0) -# class testAgentType(unittest.TestCase): -# def setUp(self): -# self.agent = AgentType() +class testAgentType(unittest.TestCase): + def setUp(self): + self.agent = AgentType() -# def test_time(self): -# self.agent.time_vary = ['var_1', 'var_2'] -# self.agent.var_1 = [4.3, 2, 1] -# self.agent.var_2 = [1, 2, 3, 4, 5] -# self.agent.timeFlip() -# self.assertEqual(self.agent.var_1, [1, 2, 4.3]) -# self.assertEqual(self.agent.var_2, [5, 4, 3, 2, 1]) -# self.assertEqual(self.agent.time_flow, True) -# self.agent.timeFlip() -# self.assertEqual(self.agent.var_1, [4.3, 2, 1]) -# self.assertEqual(self.agent.var_2, [1, 2, 3, 4, 5]) -# self.assertEqual(self.agent.time_flow, False) + def test_time(self): + self.agent.time_vary = ['var_1', 'var_2'] + self.agent.var_1 = [4.3, 2, 1] + self.agent.var_2 = [1, 2, 3, 4, 5] + self.agent.timeFlip() + self.assertEqual(self.agent.var_1, [1, 2, 4.3]) + self.assertEqual(self.agent.var_2, [5, 4, 3, 2, 1]) + self.assertEqual(self.agent.time_flow, False) + self.agent.timeFlip() + self.assertEqual(self.agent.var_1, [4.3, 2, 1]) + self.assertEqual(self.agent.var_2, [1, 2, 3, 4, 5]) + self.assertEqual(self.agent.time_flow, True) From 90562104182e9d91b0d013534d26f1024a730ae6 Mon Sep 17 00:00:00 2001 From: Samuel Brice Date: Fri, 27 Sep 2019 00:52:50 -0400 Subject: [PATCH 076/242] readme: update notebook refs to econ-ark/DemARK --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 1aea02781..1599aecd9 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ [![PyPi](https://img.shields.io/pypi/v/econ-ark.png?style=flat)](https://pypi.org/project/econ-ark/) [![Documentation Status](https://readthedocs.org/projects/hark/badge/?style=flat&version=latest)](https://hark.readthedocs.io/en/latest/?badge=latest) [![GitHub Good First Issues](https://img.shields.io/github/issues/econ-ark/HARK/good%20first%20issue.svg)](https://github.com/econ-ark/HARK/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) -[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/econ-ark/demos/master) +[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/econ-ark/DemARK/master) [![DOI](https://zenodo.org/badge/50448254.svg)](https://zenodo.org/badge/latestdoi/50448254) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org/) @@ -136,9 +136,9 @@ Or you can retrieve the solved value for human wealth normalized by permanent in # Retrieve the solved value for human wealth normalized by permanent income PFexample.solution[0].hNrm ``` -For a detailed explanation of the above example please see the demo notebook [*A Gentle Introduction to HARK*](https://mybinder.org/v2/gh/econ-ark/demos/master?filepath=notebooks%2FA+Gentle+Introduction+to+HARK.ipynb). +For a detailed explanation of the above example please see the demo notebook [*A Gentle Introduction to HARK*](https://mybinder.org/v2/gh/econ-ark/DemARK/master?filepath=notebooks/Gentle-Intro-To-HARK.ipynb). -For more examples please visit the [econ-ark/demos](https://github.com/econ-ark/demos) repository. +For more examples please visit the [econ-ark/DemARK](https://github.com/econ-ark/DemARK) repository. ## Citation @@ -190,7 +190,7 @@ Most of what economists have done so far with 'big data' has been like what Kepl An alternative approach called 'structural modeling' aims to do, for economic data, what Newton did for astronomical data: Provide a deep and rigorous mathematical (or computational) framework that distills the essence of the underlying behavior that produces the 'big data.' -The notebook [*A Gentle Introduction to HARK*](https://mybinder.org/v2/gh/econ-ark/demos/master?filepath=notebooks%2FA+Gentle+Introduction+to+HARK.ipynb) details how you can easily utilize our toolkit for structural modeling. Starting with a simple [Perfect Foresight Model](https://en.wikipedia.org/wiki/Rational_expectations) we solve an agent problem, then experiment with adding [income shocks](https://en.wikipedia.org/wiki/Shock_(economics)) and changing constructed attributes. +The notebook [*A Gentle Introduction to HARK*](https://mybinder.org/v2/gh/econ-ark/DemARK/master?filepath=notebooks/Gentle-Intro-To-HARK.ipynb) details how you can easily utilize our toolkit for structural modeling. Starting with a simple [Perfect Foresight Model](https://en.wikipedia.org/wiki/Rational_expectations) we solve an agent problem, then experiment with adding [income shocks](https://en.wikipedia.org/wiki/Shock_(economics)) and changing constructed attributes. ### For Economists: Structural Modeling with HARK @@ -200,9 +200,9 @@ The same kinds of modeling techniques are also gaining popularity among microeco HARK provides a robust, well-designed, open-source toolkit for building such models much more efficiently than has been possible in the past. -Our [*DCEGM Upper Envelope*](https://mybinder.org/v2/gh/econ-ark/demos/master?filepath=notebooks%2FDCEGM+Upper+Envelope.ipynb) notebook illustrates using HARK to replicate the [Iskhakov, Jørgensen, Rust, and Schjerning paper](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE643) for solving the discrete-continuous retirement saving problem. +Our [*DCEGM Upper Envelope*](https://mybinder.org/v2/gh/econ-ark/DemARK/master?filepath=notebooks%2FDCEGM-Upper-Envelope.ipynb) notebook illustrates using HARK to replicate the [Iskhakov, Jørgensen, Rust, and Schjerning paper](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE643) for solving the discrete-continuous retirement saving problem. -The notebook [*Making Structural Estimates From Empirical Results*](https://mybinder.org/v2/gh/econ-ark/demos/master?filepath=notebooks%2FMaking+Structural+Estimates+From+Empirical+Results.ipynb) is another demonstration of using HARK to conduct a quick structural estimation based on Table 9 of [*MPC Heterogeneity and Household Balance Sheets* by Fagereng, Holm, and Natvik](https://www.ssb.no/en/forskning/discussion-papers/_attachment/286054?_ts=158af859c98). +The notebook [*Making Structural Estimates From Empirical Results*](https://mybinder.org/v2/gh/econ-ark/DemARK/master?filepath=notebooks%2FStructural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb) is another demonstration of using HARK to conduct a quick structural estimation based on Table 9 of [*MPC Heterogeneity and Household Balance Sheets* by Fagereng, Holm, and Natvik](https://www.ssb.no/en/forskning/discussion-papers/_attachment/286054?_ts=158af859c98). ### For Computational Economics Developers From 0e77e795c637e65bf66928122b211485e5d26156 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Sat, 28 Sep 2019 13:10:35 -0500 Subject: [PATCH 077/242] use eval() only when necessary --- HARK/core.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 1ede73d88..390c3e8b8 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -852,13 +852,16 @@ def solveOneCycle(agent, solution_last): these_args = getArgNames(solveOnePeriod) # Construct a dictionary to be passed to the solver - time_inv_string = '' - for name in agent.time_inv: - time_inv_string += ' \'' + name + '\' : agent.' + name + ',' - time_vary_string = '' - for name in agent.time_vary: - time_vary_string += ' \'' + name + '\' : None,' - solve_dict = eval('{' + time_inv_string + time_vary_string + '}') + # time_inv_string = '' + # for name in agent.time_inv: + # time_inv_string += ' \'' + name + '\' : agent.' + name + ',' + # time_vary_string = '' + # for name in agent.time_vary: + # time_vary_string += ' \'' + name + '\' : None,' + # solve_dict = eval('{' + time_inv_string + time_vary_string + '}') + time_inv_dict = {parameter: agent.__dict__[parameter] for parameter in agent.time_inv} + time_vary_dict = {parameter: None for parameter in agent.time_vary} + solve_dict = {**time_inv_dict, **time_vary_dict} # Initialize the solution for this cycle, then iterate on periods solution_cycle = [] From 2aebd7f56795f4d7901527fd08dc57150aedc13e Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Sat, 28 Sep 2019 13:58:20 -0500 Subject: [PATCH 078/242] add base tests for core.AgentType --- HARK/tests/test_core.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/HARK/tests/test_core.py b/HARK/tests/test_core.py index 6b931a8e2..e32fc0517 100644 --- a/HARK/tests/test_core.py +++ b/HARK/tests/test_core.py @@ -92,3 +92,19 @@ def test_time(self): self.assertEqual(self.agent.var_1, [4.3, 2, 1]) self.assertEqual(self.agent.var_2, [1, 2, 3, 4, 5]) self.assertEqual(self.agent.time_flow, True) + self.agent.timeRev() + self.assertEqual(self.agent.time_flow, False) + self.agent.timeFwd() + self.assertEqual(self.agent.time_flow, True) + + def test_solve(self): + self.agent.time_vary = ['vary_1'] + self.agent.time_inv = ['inv_1'] + self.agent.vary_1 = [1.1, 1.2, 1.3, 1.4] + self.agent.inv_1 = 1.05 + # to test the superclass we create a dummy solveOnePeriod function + # for our agent, which doesn't do anything, instead of using a NullFunc + self.agent.solveOnePeriod = lambda vary_1: HARKobject() + self.agent.solve() + self.assertEqual(len(self.agent.solution), 4) + self.assertTrue(isinstance(self.agent.solution[0], HARKobject)) \ No newline at end of file From 409ee0cc15313694abae9b7c7157ee4f2d934da3 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Sat, 28 Sep 2019 13:59:04 -0500 Subject: [PATCH 079/242] move from eval to __dict__ to access attributes --- HARK/core.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/HARK/core.py b/HARK/core.py index 390c3e8b8..08e7edcca 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -875,7 +875,8 @@ def solveOneCycle(agent, solution_last): # Update time-varying single period inputs for name in agent.time_vary: if name in these_args: - solve_dict[name] = eval('agent.' + name + '[t]') + # solve_dict[name] = eval('agent.' + name + '[t]') + solve_dict[name] = agent.__dict__[name][t] solve_dict['solution_next'] = solution_next # Make a temporary dictionary for this period From 79f41d1e274e81dae30d7515032839ec01283b65 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Sun, 29 Sep 2019 15:52:25 -0500 Subject: [PATCH 080/242] add python version agnostic logic --- HARK/core.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 08e7edcca..328fa3868 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -859,9 +859,8 @@ def solveOneCycle(agent, solution_last): # for name in agent.time_vary: # time_vary_string += ' \'' + name + '\' : None,' # solve_dict = eval('{' + time_inv_string + time_vary_string + '}') - time_inv_dict = {parameter: agent.__dict__[parameter] for parameter in agent.time_inv} - time_vary_dict = {parameter: None for parameter in agent.time_vary} - solve_dict = {**time_inv_dict, **time_vary_dict} + solve_dict = {parameter: agent.__dict__[parameter] for parameter in agent.time_inv} + solve_dict.update({parameter: None for parameter in agent.time_vary}) # Initialize the solution for this cycle, then iterate on periods solution_cycle = [] From 502e62897a40d69a0c5d34dbfb6f79a2034602e4 Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Thu, 3 Oct 2019 18:13:07 -0400 Subject: [PATCH 081/242] Prepare release 0.10.2 --- CHANGELOG.md | 10 +++++++++- HARK/__init__.py | 2 +- setup.py | 2 +- 3 files changed, 11 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index b1201c44b..4af200686 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,5 @@ HARK -Version 0.10.1.dev5 +Version 0.10.2 Release Notes # Introduction @@ -10,6 +10,14 @@ For more information on HARK, see [our Github organization](https://github.com/e ## Changes +### 0.10.2 + +Release Date: 10-03-2019 + +#### Minor Changes +- Add some bugfixes and unit tests to HARK.core. ([#401](https://github.com/econ-ark/HARK/pull/401)) +- Fix error in discrete portfolio choice's AdjustPrb. ([#391](https://github.com/econ-ark/HARK/pull/391)) + ### 0.10.1.dev5 Release Date: 09-25-2019 diff --git a/HARK/__init__.py b/HARK/__init__.py index c7ba5e751..14522d5e7 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,6 +1,6 @@ from __future__ import absolute_import from .core import * -__version__ = '0.10.1.dev5' +__version__ = '0.10.2' from .BayerLuetticke import * diff --git a/setup.py b/setup.py index b0b88820e..45c35334c 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ # For a discussion on single-sourcing the version across setup.py and the # project code, see # https://packaging.python.org/en/latest/single_source_version.html - version='0.10.1.dev5', # Required + version='0.10.2', # Required # This is a one-line description or tagline of what your project does. This # corresponds to the "Summary" metadata field: From 78e1c3ab051b7590991a6d1d8ac0ffd318c58eb4 Mon Sep 17 00:00:00 2001 From: llorracc Date: Wed, 16 Oct 2019 23:22:29 -0400 Subject: [PATCH 082/242] priont -> print --- HARK/ConsumptionSaving/ConsIndShockModel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 030a7fc4d..b4cf55503 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1847,7 +1847,7 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False violated = True if verbose: print('Therefore, the limiting consumption function is c(m)=Infinity for all m') - priont() + print() if verbose and violated and verbose_reference: print('[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') From 629d9f5a043778d63920a42f40d1be59e0273f82 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 17 Oct 2019 08:04:08 -0500 Subject: [PATCH 083/242] Inherit all attributes from Market Class in cstwMPC (#393) * Call super in cstwMPC to get Market class attributes * remove unnecessary lines --- HARK/cstwMPC/cstwMPC.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/HARK/cstwMPC/cstwMPC.py b/HARK/cstwMPC/cstwMPC.py index d163759e6..89fb19731 100644 --- a/HARK/cstwMPC/cstwMPC.py +++ b/HARK/cstwMPC/cstwMPC.py @@ -21,7 +21,6 @@ from HARK.ConsumptionSaving.ConsAggShockModel import CobbDouglasEconomy, AggShockConsumerType from scipy.optimize import golden, brentq import matplotlib.pyplot as plt -#import csv mystr = lambda number : "{:.3f}".format(number) @@ -88,6 +87,9 @@ def __init__(self,**kwds): ''' Make a new instance of cstwMPCmarket. ''' + super().__init__(sow_vars=self.sow_vars, reap_vars=self.reap_vars, + const_vars=self.const_vars, track_vars=self.track_vars, + dyn_vars=self.dyn_vars) self.assignParameters(**kwds) if self.AggShockBool: self.sow_vars=['MaggNow','AaggNow','RfreeNow','wRteNow','PermShkAggNow','TranShkAggNow','KtoLnow'] @@ -380,7 +382,6 @@ def showManyStats(self,spec_name=None): if spec_name is not None: with open(self.my_file_path + '/Results/' + spec_name + 'Results.txt','w') as f: f.write(results_string) - f.close() def getKYratioDifference(Economy,param_name,param_count,center,spread,dist_type): From 961ee234e8f81025c99be1eddffc5cb7098a6a9f Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 17 Oct 2019 13:16:15 -0400 Subject: [PATCH 084/242] Change variable names to match other code changes Some variable names were updated in the solver while this PR was outstanding; now fixed. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 98701a17e..01e0e2f7a 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -434,8 +434,8 @@ def defValueFuncs(self): ''' # See the PerfForesightConsumerType.ipynb documentation notebook for the derivations - vFuncNvrsSlope = self.MPC**(-self.CRRA/(1.0-self.CRRA)) - vFuncNvrs = LinearInterp(np.array([self.mNrmMin, self.mNrmMin+1.0]),np.array([0.0, vFuncNvrsSlope])) + vFuncNvrsSlope = self.MPCmin**(-self.CRRA/(1.0-self.CRRA)) + vFuncNvrs = LinearInterp(np.array([self.mNrmMinNow, self.mNrmMinNow+1.0]),np.array([0.0, vFuncNvrsSlope])) self.vFunc = ValueFunc(vFuncNvrs,self.CRRA) self.vPfunc = MargValueFunc(self.cFunc,self.CRRA) From 1bb1ee57bc9e3267840f4d64ee6838ebb804a7f5 Mon Sep 17 00:00:00 2001 From: sb Date: Sun, 20 Oct 2019 18:00:09 -0400 Subject: [PATCH 085/242] adding passing (simple) tests for all methods in simulation.py --- HARK/tests/test_simulation.py | 45 +++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 HARK/tests/test_simulation.py diff --git a/HARK/tests/test_simulation.py b/HARK/tests/test_simulation.py new file mode 100644 index 000000000..47d255768 --- /dev/null +++ b/HARK/tests/test_simulation.py @@ -0,0 +1,45 @@ +import unittest, sys + +import HARK.simulation as simulation + +class SimulationTests(unittest.TestCase): + ''' + Tests for .... + ''' + + def test_drawMeanOneLognormal(self): + self.assertEqual( + simulation.drawMeanOneLognormal(1)[0], + 3.5397367004222002) + + def test_drawLognormal(self): + self.assertEqual( + simulation.drawLognormal(1)[0], + 5.836039190663969) + + def test_drawNormal(self): + self.assertEqual( + simulation.drawNormal(1)[0], + 1.764052345967664) + + def test_drawWeibull(self): + self.assertEqual( + simulation.drawWeibull(1)[0], + 0.79587450816311) + + def test_drawUniform(self): + self.assertEqual( + simulation.drawUniform(1)[0], + 0.5488135039273248) + + def test_drawBernoulli(self): + self.assertEqual( + simulation.drawBernoulli(1)[0], + False) + + + def test_drawDiscrete(self): + self.assertEqual( + simulation.drawDiscrete(1)[0], + 0) + From e8b317152b6ec296a6e75e9ea5ea8aa66c00d4ec Mon Sep 17 00:00:00 2001 From: sb Date: Sun, 20 Oct 2019 18:05:55 -0400 Subject: [PATCH 086/242] drawMeanOneLognormal calls drawLognormal with corrected mu --- HARK/simulation.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/HARK/simulation.py b/HARK/simulation.py index 5f7d2cbf2..b3cfb43fe 100644 --- a/HARK/simulation.py +++ b/HARK/simulation.py @@ -33,15 +33,9 @@ def drawMeanOneLognormal(N, sigma=1.0, seed=0): # Set up the RNG RNG = np.random.RandomState(seed) - if isinstance(sigma,float): # Return a single array of length N - mu = -0.5*sigma**2 - draws = RNG.lognormal(mean=mu, sigma=sigma, size=N) - else: # Set up empty list to populate, then loop and populate list with draws - draws=[] - for sig in sigma: - mu = -0.5*(sig**2) - draws.append(RNG.lognormal(mean=mu, sigma=sig, size=N)) - return draws + mu = -0.5*sigma**2 + + return drawLognormal(N,mu=mu,sigma=sigma,seed=seed) def drawLognormal(N,mu=0.0,sigma=1.0,seed=0): ''' From 4c9f57963f2289d9dac5bfbc8904e8681ec20f4a Mon Sep 17 00:00:00 2001 From: sb Date: Sun, 20 Oct 2019 18:11:57 -0400 Subject: [PATCH 087/242] fix comment on drawLognormal --- HARK/simulation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/simulation.py b/HARK/simulation.py index b3cfb43fe..98fb451fc 100644 --- a/HARK/simulation.py +++ b/HARK/simulation.py @@ -39,7 +39,7 @@ def drawMeanOneLognormal(N, sigma=1.0, seed=0): def drawLognormal(N,mu=0.0,sigma=1.0,seed=0): ''' - Generate arrays of mean one lognormal draws. The sigma input can be a number + Generate arrays of lognormal draws. The sigma input can be a number or list-like. If a number, output is a length N array of draws from the lognormal distribution with standard deviation sigma. If a list, output is a length T list whose t-th entry is a length N array of draws from the From 42b8e7e3e216db74fda138d608d1466dd482bf6c Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 21 Oct 2019 16:22:39 -0400 Subject: [PATCH 088/242] removing unneeded seed setting line from drawMeanOneLognormal --- HARK/simulation.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/HARK/simulation.py b/HARK/simulation.py index 98fb451fc..834fc991b 100644 --- a/HARK/simulation.py +++ b/HARK/simulation.py @@ -30,9 +30,6 @@ def drawMeanOneLognormal(N, sigma=1.0, seed=0): T-length list of arrays of mean one lognormal draws each of size N, or a single array of size N (if sigma is a scalar). ''' - # Set up the RNG - RNG = np.random.RandomState(seed) - mu = -0.5*sigma**2 return drawLognormal(N,mu=mu,sigma=sigma,seed=seed) From 9911326a7eb520cf004a092e469cf8a63a00e232 Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 21 Oct 2019 16:24:44 -0400 Subject: [PATCH 089/242] cleanup test_simulation.py --- HARK/tests/test_simulation.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/HARK/tests/test_simulation.py b/HARK/tests/test_simulation.py index 47d255768..ecb5293df 100644 --- a/HARK/tests/test_simulation.py +++ b/HARK/tests/test_simulation.py @@ -1,10 +1,11 @@ -import unittest, sys +import unittest import HARK.simulation as simulation class SimulationTests(unittest.TestCase): ''' - Tests for .... + Tests for simulation.py sampling distributions + with default seed. ''' def test_drawMeanOneLognormal(self): From 0d5240068bd6332088224b94eba07715ab80cd13 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 22 Oct 2019 11:07:43 -0400 Subject: [PATCH 090/242] fixes to requirements.txt to fix sphinx dependency errors. Fixes #406 --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 15583b720..af16adb0e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ numpy matplotlib -Sphinx +sphinx==1.6.7 numpydoc future joblib @@ -12,3 +12,4 @@ funcsigs jupyter sphinx-rtd-theme nbsphinx +recommonmark From 74358352c49ecdb550145ac725d42eb82108d1e3 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 22 Oct 2019 14:38:48 -0400 Subject: [PATCH 091/242] remove notebooks from sphinx autosummary list. see #412 --- Documentation/index.rst | 5 ----- 1 file changed, 5 deletions(-) diff --git a/Documentation/index.rst b/Documentation/index.rst index d89b96cd7..dae39abed 100644 --- a/Documentation/index.rst +++ b/Documentation/index.rst @@ -40,11 +40,6 @@ Welcome to HARK's documentation! HARK.BayerLuetticke.Assets.Two.defineSSParametersTwoAsset HARK.BayerLuetticke.BayerLuetticke_wrapper HARK.BayerLuetticke.ConsIndShockModel_extension - HARK.BayerLuetticke.notebooks.DCT-Copula-Illustration.ipynb - HARK.BayerLuetticke.notebooks.DCT-Copula-Illustration - HARK.BayerLuetticke.notebooks.DCT.ipynb - HARK.BayerLuetticke.notebooks.OneAsset-HANK.ipynb - HARK.BayerLuetticke.notebooks.TwoAsset.ipynb HARK.ConsumptionSaving.ConsAggShockModel HARK.ConsumptionSaving.ConsGenIncProcessModel HARK.ConsumptionSaving.ConsIndShockModel From c0f890eb82db406b9085f36a84d4a8e319c15a21 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 22 Oct 2019 14:58:19 -0400 Subject: [PATCH 092/242] removing symlink to missing notebook. see #412 --- Documentation/notebooks/DCT-Copula-Illustration.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 120000 Documentation/notebooks/DCT-Copula-Illustration.ipynb diff --git a/Documentation/notebooks/DCT-Copula-Illustration.ipynb b/Documentation/notebooks/DCT-Copula-Illustration.ipynb deleted file mode 120000 index 413f61774..000000000 --- a/Documentation/notebooks/DCT-Copula-Illustration.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../DCT-Copula-Illustration.ipynb \ No newline at end of file From c5bb0705b494ecfad994ac02c355aa89d86f4008 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 22 Oct 2019 14:58:54 -0400 Subject: [PATCH 093/242] remove references to non-library project files for cstwMPC. See #412 --- Documentation/index.rst | 4 ---- 1 file changed, 4 deletions(-) diff --git a/Documentation/index.rst b/Documentation/index.rst index dae39abed..851951c6a 100644 --- a/Documentation/index.rst +++ b/Documentation/index.rst @@ -62,10 +62,6 @@ Welcome to HARK's documentation! HARK.cAndCwithStickyE.StickyEparams HARK.cAndCwithStickyE.StickyEtools HARK.core - HARK.cstwMPC.MakeCSTWfigs - HARK.cstwMPC.MakeCSTWfigsForSlides - HARK.cstwMPC.Results.BetaDistPYResults_py3.txt - HARK.cstwMPC.SetupParamsCSTW HARK.cstwMPC.cstwMPC HARK.dcegm HARK.estimation From f6b984b5069a07357e0ce85ba4ab53c43ea108c1 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 22 Oct 2019 15:07:34 -0400 Subject: [PATCH 094/242] removing more non-modules from sphinx autosummary list. #412 --- Documentation/index.rst | 5 ----- 1 file changed, 5 deletions(-) diff --git a/Documentation/index.rst b/Documentation/index.rst index 851951c6a..71ae56fc0 100644 --- a/Documentation/index.rst +++ b/Documentation/index.rst @@ -25,15 +25,12 @@ Welcome to HARK's documentation! HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUs HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond - HARK.BayerLuetticke.Assets.One.Luetticke_wrapper HARK.BayerLuetticke.Assets.One.SharedFunc HARK.BayerLuetticke.Assets.One.SharedFunc2 HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUs HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUsBond HARK.BayerLuetticke.Assets.One.defineSSParameters HARK.BayerLuetticke.Assets.One.defineSSParametersIOUsBond - HARK.BayerLuetticke.Assets.Two.EX3SS_20_python2.p - HARK.BayerLuetticke.Assets.Two.EX3SS_20_python3.p HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset HARK.BayerLuetticke.Assets.Two.SharedFunc3 HARK.BayerLuetticke.Assets.Two.SteadyStateTwoAsset @@ -56,8 +53,6 @@ Welcome to HARK's documentation! HARK.SolvingMicroDSOPs.Calibration.SetupSCFdata HARK.SolvingMicroDSOPs.Code.StructEstimation HARK.SolvingMicroDSOPs.do_all - HARK.cAndCwithStickyE.StickyE_MAIN - HARK.cAndCwithStickyE.StickyE_NO_MARKOV HARK.cAndCwithStickyE.StickyEmodel HARK.cAndCwithStickyE.StickyEparams HARK.cAndCwithStickyE.StickyEtools From db5c893b99eead13e06faae4cdcebb9b0ab87222 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 22 Oct 2019 15:41:46 -0400 Subject: [PATCH 095/242] recommonmark is causing an error in parsing the HARKManual with sphinx. #412 --- Documentation/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Documentation/conf.py b/Documentation/conf.py index d026f4f7a..ba1c53723 100644 --- a/Documentation/conf.py +++ b/Documentation/conf.py @@ -41,7 +41,7 @@ 'sphinx.ext.autosummary', 'numpydoc', 'nbsphinx', - 'recommonmark', + #'recommonmark', ] # This is currently not working From e59f8087d74427ba2a098f71ee989d1505524a29 Mon Sep 17 00:00:00 2001 From: Zixuan Huang Date: Wed, 23 Oct 2019 15:29:29 -0400 Subject: [PATCH 096/242] Updated links in HARK manual Links are upated. (Links in the previous version directed to the old website. ) --- Documentation/HARKmanual.tex | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/Documentation/HARKmanual.tex b/Documentation/HARKmanual.tex index c7c02df20..ca1181ba6 100644 --- a/Documentation/HARKmanual.tex +++ b/Documentation/HARKmanual.tex @@ -168,13 +168,13 @@ \subsection{HARKcore}\label{sec:HARKcore} Macroeconomic models in HARK use the \texttt{Market} class to represent a market (or other aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual agents in the model into aggregate outcomes that are ``passed back'' to the agents. For example, the market in a consumption-saving model might combine the individual asset holdings of all agents in the market to generate aggregate capital in the economy, yielding the interest rate on assets (as the marginal product of capital); the individual agents then learn the aggregate capital level and interest rate, conditioning their next action on this information. Objects that microeconomic agents treat as exogenous when solving (or simulating) their model are thus endogenous at the macroeconomic level. Like \texttt{AgentType}, the \texttt{Market} class also has a \texttt{solve} method, which seeks out a dynamic general equilibrium: a ``rule'' governing the dynamic evolution of macroeconomic objects such that if agents believe this rule and act accordingly, then their collective actions generate a sequence of macroeconomic outcomes that justify the belief in that rule. For a more complete description, see section \ref{sec:Macroeconomics}. -Beyond the model frameworks, \texttt{HARK.core} also defines a ``supersuperclass'' called \texttt{HARKobject}. When solving a dynamic microeconomic model with an infinite horizon (or searching for a dynamic general equilibrium), it is often required to consider whether two solutions are sufficiently close to each other to warrant stopping the process (i.e.\ approximate convergence). It is thus necessary to calculate the ``distance'' between two solutions, so HARK specifies that classes should have a \texttt{distance} method that takes a single input and returns a non-negative value representing the (generally dimensionless) distance between the object in question and the input to the method. As a convenient default, \texttt{HARKobject} provides a ``universal distance metric'' that should be useful in many contexts.\footnote{Roughly speaking, the universal distance metric is a recursive supnorm, returning the largest distance between two instances, among attributes named in \texttt{distance\_criteria}. Those attributes might be complex objects themselves rather than real numbers, generating a recursive call to the universal distance metric.} When defining a new subclass of \texttt{HARKobject}, the user simply defines the attribute \texttt{distance\_criteria} as a list of strings naming the attributes of the class that should be compared when calculating the distance between two instances of that class. For example, the class \texttt{ConsumerSolution} has \texttt{distance\_criteria = ['cFunc']}, indicating that only the consumption function attribute of the solution matters when comparing the distance between two instances of \texttt{ConsumerSolution}. See \href{https://econ-ark.github.io/HARK/generated/HARK.core.html}{here} for online documentation. +Beyond the model frameworks, \texttt{HARK.core} also defines a ``supersuperclass'' called \texttt{HARKobject}. When solving a dynamic microeconomic model with an infinite horizon (or searching for a dynamic general equilibrium), it is often required to consider whether two solutions are sufficiently close to each other to warrant stopping the process (i.e.\ approximate convergence). It is thus necessary to calculate the ``distance'' between two solutions, so HARK specifies that classes should have a \texttt{distance} method that takes a single input and returns a non-negative value representing the (generally dimensionless) distance between the object in question and the input to the method. As a convenient default, \texttt{HARKobject} provides a ``universal distance metric'' that should be useful in many contexts.\footnote{Roughly speaking, the universal distance metric is a recursive supnorm, returning the largest distance between two instances, among attributes named in \texttt{distance\_criteria}. Those attributes might be complex objects themselves rather than real numbers, generating a recursive call to the universal distance metric.} When defining a new subclass of \texttt{HARKobject}, the user simply defines the attribute \texttt{distance\_criteria} as a list of strings naming the attributes of the class that should be compared when calculating the distance between two instances of that class. For example, the class \texttt{ConsumerSolution} has \texttt{distance\_criteria = ['cFunc']}, indicating that only the consumption function attribute of the solution matters when comparing the distance between two instances of \texttt{ConsumerSolution}. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.core.html}{here} for online documentation. \subsection{HARK.utilities}\label{sec:HARKutilities} The \texttt{HARK.utilities} module carries a double meaning in its name, as it contains both utility functions (and their derivatives, inverses, and combinations thereof) in the economic modeling sense as well as utilities in the sense of general tools. Utility functions included at this time are constant relative risk aversion and constant absolute risk aversion. Other functions in \texttt{HARK.utilities} include some data manipulation tools (e.g.\ for calculating an average of data conditional on being within a percentile range of different data), functions for constructing discrete state space grids, convenience functions for retrieving information about functions, and basic plotting tools using \texttt{matplotlib.pyplot}. -The module also includes functions for constructing discrete approximations to continuous distributions (e.g.\ \texttt{approxLognormal()} to approximate a log-normal distribution) as well as manipulating these representations (e.g.\ appending one outcome to an existing distribution, or combining independent univariate distributions into one multivariate distribution). As a convention in HARK, continuous distributions are approximated as finite discrete distributions when solving models; an $N$-dimensional random variable is formatted as a length $N+1$ list of 1D arrays, with the first element representing event probabilities and all other elements are realizations of the $N$ component RVs. This both simplifies solution methods (reducing numeric integrals to simple dot products) and allows users to easily test whether their chosen degree of discretization yields a sufficient approximation to the full distribution. See \href{https://econ-ark.github.io/HARK/generated/HARK.utilities.html}{here} for online documentation. +The module also includes functions for constructing discrete approximations to continuous distributions (e.g.\ \texttt{approxLognormal()} to approximate a log-normal distribution) as well as manipulating these representations (e.g.\ appending one outcome to an existing distribution, or combining independent univariate distributions into one multivariate distribution). As a convention in HARK, continuous distributions are approximated as finite discrete distributions when solving models; an $N$-dimensional random variable is formatted as a length $N+1$ list of 1D arrays, with the first element representing event probabilities and all other elements are realizations of the $N$ component RVs. This both simplifies solution methods (reducing numeric integrals to simple dot products) and allows users to easily test whether their chosen degree of discretization yields a sufficient approximation to the full distribution. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.utilities.html}{here} for online documentation. \subsection{HARK.interpolation}\label{sec:HARKinterpolation} @@ -182,19 +182,19 @@ \subsection{HARK.interpolation}\label{sec:HARKinterpolation} When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to know the ``distance'' between functions generated by successive iterations of a solution procedure. To this end, each interpolator class in HARK must define a \texttt{distance} method that takes as an input another instance of the same class and returns a non-negative real number representing the ``distance'' between the two. As each of the \texttt{HARKinterpolatorXD} classes inherits from \texttt{HARKobject}, all interpolator classes have the default ``universal'' distance method; the user must simply list the names of the relevant attributes in the attribute \texttt{distance\_criteria} of the class. -Interpolation methods currently implemented in HARK include (multi)linear interpolation up to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D ``lower envelope'' interpolator. See \href{https://econ-ark.github.io/HARK/generated/HARKinterpolation.html}{here} for online documentation. +Interpolation methods currently implemented in HARK include (multi)linear interpolation up to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D ``lower envelope'' interpolator. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.interpolation.html}{here} for online documentation. \subsection{HARK.simulation}\label{sec:HARKsimulation} -The \texttt{HARK.simulation} module provides tools for generating simulated data or shocks for post-solution use of models. Currently implemented distributions include normal, lognormal, Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use, these tools are used in the consumption-saving models of \texttt{ConsIndShockModel.py} to simulate permanent and transitory income shocks as well as unemployment events. See \href{https://econ-ark.github.io/HARK/generated/HARKsimulation.html}{here} for online documentation. +The \texttt{HARK.simulation} module provides tools for generating simulated data or shocks for post-solution use of models. Currently implemented distributions include normal, lognormal, Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use, these tools are used in the consumption-saving models of \texttt{ConsIndShockModel.py} to simulate permanent and transitory income shocks as well as unemployment events. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.simulation.html}{here} for online documentation. \subsection{HARK.estimation}\label{sec:HARKestimation} -Methods for optimizing an objective function for the purposes of estimating a model can be found in \texttt{HARK.estimation}. As of this writing, the implementation includes only minimization by the Nelder-Mead simplex method, minimization by a derivative-free Powell method variant, and two small tools for resampling data (i.e.\ for a bootstrap); the minimizers are merely convenience wrappers (with result reporting) for optimizers included in \texttt{scipy.optimize}. Future functionality will include more robust global search methods, including genetic algorithms, simulated annealing, and differential evolution. See \href{https://econ-ark.github.io/HARK/generated/HARKestimation.html}{here} for full documentation. +Methods for optimizing an objective function for the purposes of estimating a model can be found in \texttt{HARK.estimation}. As of this writing, the implementation includes only minimization by the Nelder-Mead simplex method, minimization by a derivative-free Powell method variant, and two small tools for resampling data (i.e.\ for a bootstrap); the minimizers are merely convenience wrappers (with result reporting) for optimizers included in \texttt{scipy.optimize}. Future functionality will include more robust global search methods, including genetic algorithms, simulated annealing, and differential evolution. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.estimation.html}{here} for full documentation. \subsection{HARK.parallel}\label{sec:HARKparallel} -By default, processes in Python are single-threaded, using only a single CPU core. The \texttt{HARK.parallel} module provides basic tools for using multiple CPU cores simultaneously, with minimal effort.\footnote{\texttt{HARK.parallel} uses two packages that aren't included in the default distribution of Anaconda: \texttt{joblib} and \texttt{dill}; see step 3 of the instructions in section \ref{sec:GettingStarted} for how to install them.} In particular, it provides the function \texttt{multiThreadCommands}, which takes two arguments: a list of \texttt{AgentType}s and a list of commands as strings; each command should be a method of the \texttt{AgentType}s. The function simply distributes the \texttt{AgentType}s across threads on different cores and executes each command in order, returning no output (the \texttt{AgentType}s themselves are changed by running the commands). Equivalent results would be achieved by simply looping over each type and running each method in the list. Indeed, \texttt{HARK.parallel} also has a function called \texttt{multiThreadCommandsFake} that does just that, with identical syntax to \texttt{multiThreadCommands}; multithreading in HARK can thus be easily turned on and off.\footnote{In the future, \texttt{HARK.parallel} might be absorbed into \texttt{HARK.core} and \texttt{HARK.estimation}, particularly if \texttt{joblib} and \texttt{dill} become part of the standard Anaconda distribution.} The module also has functions for a parallel implementation of the Nelder-Mead simplex algorithm, as described in Wiswall and Lee (2011). See \href{https://econ-ark.github.io/HARK/generated/HARK.parallel.html}{here} for full documentation. +By default, processes in Python are single-threaded, using only a single CPU core. The \texttt{HARK.parallel} module provides basic tools for using multiple CPU cores simultaneously, with minimal effort.\footnote{\texttt{HARK.parallel} uses two packages that aren't included in the default distribution of Anaconda: \texttt{joblib} and \texttt{dill}; see step 3 of the instructions in section \ref{sec:GettingStarted} for how to install them.} In particular, it provides the function \texttt{multiThreadCommands}, which takes two arguments: a list of \texttt{AgentType}s and a list of commands as strings; each command should be a method of the \texttt{AgentType}s. The function simply distributes the \texttt{AgentType}s across threads on different cores and executes each command in order, returning no output (the \texttt{AgentType}s themselves are changed by running the commands). Equivalent results would be achieved by simply looping over each type and running each method in the list. Indeed, \texttt{HARK.parallel} also has a function called \texttt{multiThreadCommandsFake} that does just that, with identical syntax to \texttt{multiThreadCommands}; multithreading in HARK can thus be easily turned on and off.\footnote{In the future, \texttt{HARK.parallel} might be absorbed into \texttt{HARK.core} and \texttt{HARK.estimation}, particularly if \texttt{joblib} and \texttt{dill} become part of the standard Anaconda distribution.} The module also has functions for a parallel implementation of the Nelder-Mead simplex algorithm, as described in Wiswall and Lee (2011). See \href{https://hark.readthedocs.io/en/latest/generated/HARK.parallel.html}{here} for full documentation. \section{Microeconomics: the AgentType Class}\label{sec:Microeconomics} From f418ccf3a8930d9cb2c91523f6f5ca3f18a34c17 Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 24 Oct 2019 09:54:58 -0400 Subject: [PATCH 097/242] removing recommonmark at last. see #406 #412 --- Documentation/conf.py | 1 - requirements.txt | 1 - 2 files changed, 2 deletions(-) diff --git a/Documentation/conf.py b/Documentation/conf.py index ba1c53723..bb13d48a3 100644 --- a/Documentation/conf.py +++ b/Documentation/conf.py @@ -41,7 +41,6 @@ 'sphinx.ext.autosummary', 'numpydoc', 'nbsphinx', - #'recommonmark', ] # This is currently not working diff --git a/requirements.txt b/requirements.txt index af16adb0e..c58716718 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,3 @@ funcsigs jupyter sphinx-rtd-theme nbsphinx -recommonmark From 956c4484893140b1427fb7b6522b05d26702f208 Mon Sep 17 00:00:00 2001 From: Sebastian Benthall Date: Tue, 29 Oct 2019 13:28:41 -0400 Subject: [PATCH 098/242] remove Examples/ directory, fixes #417 (#419) --- Examples/Gentle-Intro-To-HARK.ipynb | 540 -------------------- Examples/__init__.py | 0 Examples/tests/test_gentle_intro_to_hark.py | 43 -- Examples/util.py | 55 -- 4 files changed, 638 deletions(-) delete mode 100644 Examples/Gentle-Intro-To-HARK.ipynb delete mode 100644 Examples/__init__.py delete mode 100644 Examples/tests/test_gentle_intro_to_hark.py delete mode 100644 Examples/util.py diff --git a/Examples/Gentle-Intro-To-HARK.ipynb b/Examples/Gentle-Intro-To-HARK.ipynb deleted file mode 100644 index 2a56d9fc3..000000000 --- a/Examples/Gentle-Intro-To-HARK.ipynb +++ /dev/null @@ -1,540 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A Gentle Introduction to HARK\n", - "\n", - "This notebook provides a simple, hands-on tutorial for first time HARK users -- and potentially first time Python users. It does not go \"into the weeds\" - we have hidden some code cells that do boring things that you don't need to digest on your first experience with HARK. Our aim is to convey a feel for how the toolkit works.\n", - "\n", - "For readers for whom this is your very first experience with Python, we have put important Python concepts in $\\textbf{boldface}$. For those for whom this is the first time they have used a Jupyter notebook, we have put Jupyter instructions in $\\textit{italics}.$ Only cursory definitions (if any) are provided here. If you want to learn more, there are many online Python and Jupyter tutorials." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [ - 0 - ] - }, - "outputs": [], - "source": [ - "# This cell has a bit of initial setup. You can click the triangle to the left to expand it.\n", - "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n", - "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n", - "# The most common problem beginners have is to execute a cell before all its predecessors\n", - "# If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# The first step is to be able to bring things in from different directories\n", - "import os\n", - "import sys\n", - "module_path = os.path.abspath(os.path.join('..'))\n", - "if module_path not in sys.path:\n", - " sys.path.append(module_path)\n", - "\n", - "from copy import deepcopy\n", - "from time import clock\n", - "\n", - "import numpy as np\n", - "\n", - "import HARK\n", - "from Examples.util import log_progress\n", - "from HARK.utilities import plotFuncs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Your First HARK Model: Perfect Foresight\n", - "\n", - "$$\\newcommand{\\CRRA}{\\rho}\\newcommand{\\DiscFac}{\\beta}$$\n", - "We start with almost the simplest possible consumption model: A consumer with CRRA utility \n", - "\n", - "\\begin{equation}\n", - "U(C) = \\frac{C^{1-\\CRRA}}{1-\\rho}\n", - "\\end{equation}\n", - "\n", - "has perfect foresight about everything except the (stochastic) date of death, which occurs with constant probability implying a \"survival probability\" $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb < 1$. Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and currrent income) and must choose how much of those resources to consume $C_t$ and how much to retain in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$. Between periods, the agent dies with probability $\\mathsf{D}_t$, ending his problem.\n", - "\n", - "The agent's problem can be written in Bellman form as:\n", - "\n", - "\\begin{eqnarray*}\n", - "V_t(M_t,P_t) &=& \\max_{C_t}~U(C_t) + \\beta \\aleph V_{t+1}(M_{t+1},P_{t+1}), \\\\\n", - "& s.t. & \\\\\n", - "%A_t &=& M_t - C_t, \\\\\n", - "M_{t+1} &=& R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n", - "P_{t+1} &=& \\Gamma_{t+1} P_t, \\\\\n", - "\\end{eqnarray*}\n", - "\n", - "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\mathsf{\\aleph}_t\\}$. To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an $\\textit{infinite horizon}$ consumer with constant income growth and survival probability.\n", - "\n", - "## Representing Agents in HARK\n", - "\n", - "HARK represents agents solving this type of problem as $\\textbf{instances}$ of the $\\textbf{class}$ $\\texttt{PerfForesightConsumerType}$, a $\\textbf{subclass}$ of $\\texttt{AgentType}$. To make agents of this class, we must import the class itself into our workspace. (Run the cell below in order to do this)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The $\\texttt{PerfForesightConsumerType}$ class contains within itself the python code that constructs the solution for the perfect foresight model we are studying here, as specifically articulated in [these lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/). \n", - "\n", - "To create an instance of $\\texttt{PerfForesightConsumerType}$, we simply call the class as if it were a function, passing as arguments the specific parameter values we want it to have. In the hidden cell below, we define a $\\textbf{dictionary}$ named $\\texttt{PF_dictionary}$ with these parameter values:\n", - "\n", - "| Param | Description | Code | Value |\n", - "| :---: | --- | --- | :---: |\n", - "| $\\rho$ | Relative risk aversion | $\\texttt{CRRA}$ | 2.5 |\n", - "| $\\beta$ | Discount factor | $\\texttt{DiscFac}$ | 0.96 |\n", - "| $R$ | Risk free interest factor | $\\texttt{Rfree}$ | 1.03 |\n", - "| $\\newcommand{\\LivFac}{\\aleph}\\LivFac$ | Survival probability | $\\texttt{LivPrb}$ | 0.98 |\n", - "| $\\Gamma$ | Income growth factor | $\\texttt{PermGroFac}$ | 1.01 |\n", - "\n", - "\n", - "For now, don't worry about the specifics of dictionaries. All you need to know is that a dictionary lets us pass many arguments wrapped up in one simple data structure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [ - 0 - ] - }, - "outputs": [], - "source": [ - "# This cell defines a parameter dictionary. You can expand it if you want to see what that looks like.\n", - "PF_dictionary = {\n", - " 'CRRA' : 2.5,\n", - " 'DiscFac' : 0.96,\n", - " 'Rfree' : 1.03,\n", - " 'LivPrb' : [0.98],\n", - " 'PermGroFac' : [1.01],\n", - " 'T_cycle' : 1,\n", - " 'cycles' : 0,\n", - " 'AgentCount' : 10000\n", - "}\n", - "\n", - "# To those curious enough to open this hidden cell, you might notice that we defined\n", - "# a few extra parameters in that dictionary: T_cycle, cycles, and AgentCount. Don't\n", - "# worry about these for now." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's make an $\\textbf{object}$ named $\\texttt{PFexample}$ which is an $\\textbf{instance}$ of the $\\texttt{PerfForesightConsumerType}$ class. The object $\\texttt{PFexample}$ will bundle together the abstract mathematical description of the solution embodied in $\\texttt{PerfForesightConsumerType}$, and the specific set of parameter values defined in $\\texttt{PF_dictionary}$. Such a bundle is created passing $\\texttt{PF_dictionary}$ to the class $\\texttt{PerfForesightConsumerType}$:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PFexample = PerfForesightConsumerType(**PF_dictionary) \n", - "# the asterisks ** basically say \"here come some arguments\" to PerfForesightConsumerType" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In $\\texttt{PFexample}$, we now have _defined_ the problem of a particular infinite horizon perfect foresight consumer who knows how to solve this problem. \n", - "\n", - "## Solving an Agent's Problem\n", - "\n", - "To tell the agent actually to solve the problem, we call the agent's $\\texttt{solve}$ $\\textbf{method}$. (A $\\textbf{method}$ is essentially a function that an object runs that affects the object's own internal characteristics -- in this case, the method adds the consumption function to the contents of $\\texttt{PFexample}$.)\n", - "\n", - "The cell below calls the $\\texttt{solve}$ method for $\\texttt{PFexample}$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PFexample.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Running the $\\texttt{solve}$ method creates the $\\textbf{attribute}$ of $\\texttt{PFexample}$ named $\\texttt{solution}$. In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (paramterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem. \n", - "\n", - "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem. The consumption function stored as the first element (element 0) of the solution list can be retrieved by:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PFexample.solution[0].cFunc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the results proven in the associated [the lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$. \n", - "\n", - "This is why $\\texttt{cFunc}$ can be represented by a linear interpolation. It can be plotted between an $m$ ratio of 0 and 10 using the command below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mPlotTop=10\n", - "plotFuncs(PFexample.solution[0].cFunc,0.,mPlotTop)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The figure illustrates one of the surprising features of the perfect foresight model: A person with zero money should be spending at a rate more than double their income (that is, $\\texttt{cFunc}(0.) \\approx 2.08$ - the intersection on the vertical axis). How can this be?\n", - "\n", - "The answer is that we have not incorporated any constraint that would prevent the agent from borrowing against the entire PDV of future earnings-- human wealth. How much is that? What's the minimum value of $m_t$ where the consumption function is defined? We can check by retrieving the $\\texttt{hNrm}$ **attribute** of the solution, which calculates the value of human wealth normalized by permanent income:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "humanWealth = PFexample.solution[0].hNrm\n", - "mMinimum = PFexample.solution[0].mNrmMin\n", - "print(\"This agent's human wealth is \" + str(humanWealth) + ' times his current income level.')\n", - "print(\"This agent's consumption function is defined (consumption is positive) down to m_t = \" + str(mMinimum))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Yikes! Let's take a look at the bottom of the consumption function. In the cell below, set the bounds of the $\\texttt{plotFuncs}$ function to display down to the lowest defined value of the consumption function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# YOUR FIRST HANDS-ON EXERCISE!\n", - "# Fill in the value for \"mPlotBottom\" to plot the consumption function from the point where it is zero.\n", - "# plotFuncs(PFexample.solution[0].cFunc,mPlotBottom,mPlotTop)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Changing Agent Parameters\n", - "\n", - "Suppose you wanted to change one (or more) of the parameters of the agent's problem and see what that does. We want to compare consumption functions before and after we change parameters, so let's make a new instance of $\\texttt{PerfForesightConsumerType}$ by copying $\\texttt{PFexample}$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NewExample = deepcopy(PFexample)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In Python, you can set an $\\textbf{attribute}$ of an object just like any other variable. For example, we could make the new agent less patient:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NewExample.DiscFac = 0.90\n", - "NewExample.solve()\n", - "mPlotBottom = mMinimum\n", - "plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],mPlotBottom,mPlotTop)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(Note that you can pass a **list** of functions to $\\texttt{plotFuncs}$ as the first argument rather than just a single function. Lists are written inside of [square brackets].)\n", - "\n", - "Let's try to deal with the \"problem\" of massive human wealth by making another consumer who has essentially no future income. We can virtually eliminate human wealth by making the permanent income growth factor $\\textit{very}$ small.\n", - "\n", - "In $\\texttt{PFexample}$, the agent's income grew by 1 percent per period -- his $\\texttt{PermGroFac}$ took the value 1.01. What if our new agent had a growth factor of 0.01 -- his income $\\textit{shrinks}$ by 99 percent each period? In the cell below, set $\\texttt{NewExample}$'s discount factor back to its original value, then set its $\\texttt{PermGroFac}$ attribute so that the growth factor is 0.01 each period.\n", - "\n", - "Important: Recall that the model at the top of this document said that an agent's problem is characterized by a sequence of income growth factors, but we tabled that concept. Because $\\texttt{PerfForesightConsumerType}$ treats $\\texttt{PermGroFac}$ as a $\\textit{time-varying}$ attribute, it must be specified as a $\\textbf{list}$ (with a single element in this case)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Revert NewExample's discount factor and make his future income minuscule\n", - "# print(\"your lines here\")\n", - "\n", - "# Compare the old and new consumption functions\n", - "plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],0.,10.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now $\\texttt{NewExample}$'s consumption function has the same slope (MPC) as $\\texttt{PFexample}$, but it emanates from (almost) zero-- he has basically no future income to borrow against!\n", - "\n", - "If you'd like, use the cell above to alter $\\texttt{NewExample}$'s other attributes (relative risk aversion, etc) and see how the consumption function changes. However, keep in mind that \\textit{no solution exists} for some combinations of parameters. HARK should let you know if this is the case if you try to solve such a model.\n", - "\n", - "\n", - "## Your Second HARK Model: Adding Income Shocks\n", - "\n", - "Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines. Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints.\n", - "\n", - "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$. Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$. As with the perfect foresight problem, this model can be framed in terms of $\\textit{normalized}$ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory).\n", - "\n", - "\\begin{eqnarray*}\n", - "v_t(m_t) &=& \\max_{c_t} ~ U(c_t) ~ + \\phantom{\\LivFac} \\beta \\mathbb{E} [(\\Gamma_{t+1}\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ], \\\\\n", - "a_t &=& m_t - c_t, \\\\\n", - "a_t &\\geq& \\underline{a}, \\\\\n", - "m_{t+1} &=& R/(\\Gamma_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", - "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &=& 1, \\\\\n", - "u(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n", - "\\end{eqnarray*}\n", - "\n", - "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$. To create an $\\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shock joint. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks? Can't the joint distribution of permanent and transitory shocks be just about anything?\n", - "\n", - "$\\textit{Yes}$, and HARK can handle whatever correlation structure a user might care to specify. However, the default behavior of $\\texttt{IndShockConsumerType}$ is that the distribution of permanent income shocks is mean one lognormal, and the distribution of transitory shocks is mean one lognormal augmented with a point mass representing unemployment. The distributions are independent of each other by default, and are approximated with $N$ point equiprobable distributions.\n", - "\n", - "Let's make an infinite horizon instance of $\\texttt{IndShockConsumerType}$ with the same parameters as our original perfect foresight agent, plus the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary:\n", - "\n", - "\n", - "| Param | Description | Code | Value |\n", - "| :---: | --- | --- | :---: |\n", - "| $\\underline{a}$ | Artificial borrowing constraint | $\\texttt{BoroCnstArt}$ | 0.0 |\n", - "| $\\sigma_\\psi$ | Underlying stdev of permanent income shocks | $\\texttt{PermShkStd}$ | 0.1 |\n", - "| $\\sigma_\\theta$ | Underlying stdev of transitory income shocks | $\\texttt{TranShkStd}$ | 0.1 |\n", - "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | 7 |\n", - "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | 7 |\n", - "| $\\mho$ | Unemployment probability | $\\texttt{UnempPrb}$ | 0.05 |\n", - "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | 0.3 |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [ - 2 - ] - }, - "outputs": [], - "source": [ - "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n", - "\n", - "IndShockDictionary = {\n", - " 'CRRA': 2.5, # The dictionary includes our original parameters...\n", - " 'Rfree': 1.03,\n", - " 'DiscFac': 0.96,\n", - " 'LivPrb': [0.98],\n", - " 'PermGroFac': [1.01],\n", - " 'PermShkStd': [0.1], # ... and the new parameters for constructing the income process. \n", - " 'PermShkCount': 7,\n", - " 'TranShkStd': [0.1],\n", - " 'TranShkCount': 7,\n", - " 'UnempPrb': 0.05,\n", - " 'IncUnemp': 0.3,\n", - " 'BoroCnstArt': 0.0,\n", - " 'aXtraMin': 0.001, # aXtra parameters specify how to construct the grid of assets.\n", - " 'aXtraMax': 50., # Don't worry about these for now\n", - " 'aXtraNestFac': 3,\n", - " 'aXtraCount': 48,\n", - " 'aXtraExtra': [None],\n", - " 'vFuncBool': False, # These booleans indicate whether the value function should be calculated\n", - " 'CubicBool': False, # and whether to use cubic spline interpolation. You can ignore them.\n", - " 'aNrmInitMean' : -10.,\n", - " 'aNrmInitStd' : 0.0, # These parameters specify the (log) distribution of normalized assets\n", - " 'pLvlInitMean' : 0.0, # and permanent income for agents at \"birth\". They are only relevant in\n", - " 'pLvlInitStd' : 0.0, # simulation and you don't need to worry about them.\n", - " 'PermGroFacAgg' : 1.0,\n", - " 'T_retire': 0, # What's this about retirement? ConsIndShock is set up to be able to\n", - " 'UnempPrbRet': 0.0, # handle lifecycle models as well as infinite horizon problems. Swapping\n", - " 'IncUnempRet': 0.0, # out the structure of the income process is easy, but ignore for now.\n", - " 'T_age' : None,\n", - " 'T_cycle' : 1,\n", - " 'cycles' : 0,\n", - " 'AgentCount': 10000,\n", - " 'tax_rate':0.0,\n", - "}\n", - " \n", - "# Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to\n", - "# think about them for now." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As before, we need to import the relevant subclass of $\\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", - "IndShockExample = IndShockConsumerType(**IndShockDictionary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can solve our new agent's problem just like before, using the $\\texttt{solve}$ method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "IndShockExample.solve()\n", - "plotFuncs(IndShockExample.solution[0].cFunc,0.,10.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Changing Constructed Attributes\n", - "\n", - "In the parameter dictionary above, we chose values for HARK to use when constructing its numeric representation of $F_t$, the joint distribution of permanent and transitory income shocks. When $\\texttt{IndShockExample}$ was created, those parameters ($\\texttt{TranShkStd}$, etc) were used by the $\\textbf{constructor}$ or $\\textbf{initialization}$ method of $\\texttt{IndShockConsumerType}$ to construct an attribute called $\\texttt{IncomeDstn}$.\n", - "\n", - "Suppose you were interested in changing (say) the amount of permanent income risk. From the section above, you might think that you could simply change the attribute $\\texttt{TranShkStd}$, solve the model again, and it would work.\n", - "\n", - "That's $\\textit{almost}$ true-- there's one extra step. $\\texttt{TranShkStd}$ is a primitive input, but it's not the thing you $\\textit{actually}$ want to change. Changing $\\texttt{TranShkStd}$ doesn't actually update the income distribution... unless you tell it to (just like changing an agent's preferences does not change the consumption function that was stored for the old set of parameters -- until you invoke the $\\texttt{solve}$ method again). In the cell below, we invoke the method $\\texttt{updateIncomeProcess}$ so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "OtherExample = deepcopy(IndShockExample) # Make a copy so we can compare consumption functions\n", - "OtherExample.PermShkStd = [0.2] # Double permanent income risk (note that it's a one element list)\n", - "OtherExample.updateIncomeProcess() # Call the method to reconstruct the representation of F_t\n", - "OtherExample.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the cell below, use your blossoming HARK skills to plot the consumption function for $\\texttt{IndShockExample}$ and $\\texttt{OtherExample}$ on the same figure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use the line(s) below to plot the consumptions functions against each other\n" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py", - "metadata_filter": { - "cells": "collapsed" - } - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Examples/__init__.py b/Examples/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/Examples/tests/test_gentle_intro_to_hark.py b/Examples/tests/test_gentle_intro_to_hark.py deleted file mode 100644 index 3a2d6c574..000000000 --- a/Examples/tests/test_gentle_intro_to_hark.py +++ /dev/null @@ -1,43 +0,0 @@ -''' -Tests that the gentle intro to HARK notebook runs correctly -''' -from __future__ import print_function, division -from __future__ import absolute_import - -from builtins import str -from builtins import zip -from builtins import range -from builtins import object - -import os -import sys - -import nbformat -import unittest -from nbconvert.preprocessors import ExecutePreprocessor - -class TestGentleIntroToHark(unittest.TestCase): - - def test_notebook_runs(self): - # we only test that the notebook works in python3 - if sys.version_info[0] < 3: - return - - test_path = os.path.dirname(os.path.realpath(__file__)) - nb_path = os.path.join(test_path, '..', 'Gentle-Intro-To-HARK.ipynb') - with open(nb_path) as nb_f: - nb = nbformat.read(nb_f, as_version=nbformat.NO_CONVERT) - - ep = ExecutePreprocessor(timeout=60, kernel_name='python3') - ep.allow_errors = True - # this actually runs the notebook - ep.preprocess(nb, {}) - - errors = [] - for cell in nb.cells: - if 'outputs' in cell: - for output in cell['outputs']: - if output.output_type == 'error': - errors.append(output) - - self.assertFalse(errors) diff --git a/Examples/util.py b/Examples/util.py deleted file mode 100644 index 876172940..000000000 --- a/Examples/util.py +++ /dev/null @@ -1,55 +0,0 @@ -from ipywidgets import IntProgress, HTML, VBox -from IPython.display import display - -def log_progress(sequence, every=None, size=None, name='Items'): - is_iterator = False - if size is None: - try: - size = len(sequence) - except TypeError: - is_iterator = True - if size is not None: - if every is None: - if size <= 200: - every = 1 - else: - every = int(size / 200) # every 0.5% - else: - assert every is not None, 'sequence is iterator, set every' - - if is_iterator: - progress = IntProgress(min=0, max=1, value=1) - progress.bar_style = 'info' - else: - progress = IntProgress(min=0, max=size, value=0) - label = HTML() - box = VBox(children=[label, progress]) - display(box) - - index = 0 - try: - for index, record in enumerate(sequence, 1): - if index == 1 or index % every == 0: - if is_iterator: - label.value = '{name}: {index} / ?'.format( - name=name, - index=index - ) - else: - progress.value = index - label.value = u'{name}: {index} / {size}'.format( - name=name, - index=index, - size=size - ) - yield record - except: - progress.bar_style = 'danger' - raise - else: - progress.bar_style = 'success' - progress.value = index - label.value = "{name}: {index}".format( - name=name, - index=str(index or '?') - ) From 178164925832dea45bd175dd2f1f1c599e086872 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 29 Oct 2019 15:43:43 -0400 Subject: [PATCH 099/242] removing HARKManual markdown; adding recommonmark back in. fixes #420 --- .../ContributingToHARK/ContributingToHARK.md | 256 ---- Documentation/HARKmanual/HARKmanual.md | 1263 ----------------- Documentation/conf.py | 1 + 3 files changed, 1 insertion(+), 1519 deletions(-) delete mode 100644 Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.md delete mode 100644 Documentation/HARKmanual/HARKmanual.md diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.md b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.md deleted file mode 100644 index fa86bcec2..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.md +++ /dev/null @@ -1,256 +0,0 @@ -(This is an excerpt of the full manual, available at - -### 0.1 Contributing to HARK - -This section provides an overview of how users can contribute new code to -HARK, what sort of contributions are most desired, and style conventions for -the toolKit. Before attempting to extend HARK, we strongly recommend that you -familiarize yourself with its features and models by looking at the source -code. - -#### 0.1.1 What Does HARK Want? - -The easiest and most straightforward way to contribute to HARK is by writing -new general purpose tools for inclusion in one of the top-level modules. This -might be a commonly known data analysis technique (e.g. a kernel density -estimator) for HARK.utilities, an optimization method (local or global) for -HARK.estimation, an interpolation method for HARKinterpolation, etc. If you’ve -found a technique useful in your own research and believe it could be of use -to others on entirely different projects, then it probably belongs in HARK. -Likewise, if you read through a HARK module expecting to find a certain tool -or function (because of similar functions that are present, or merely because -it’s very useful) but don’t, this is a good sign that HARK has a hole that you -can fill.[1](ContributingToHARK2.html#fn1x0) - -Secondly, the development team wants to expand HARK to include more canonical -models. Most of the models available in the toolKit at the time of its public -beta release concern agents with (geometrically discounted) CRRA utility over -a unitary consumption good, who receive permanent and transitory shocks to -income and have access to a single risk free asset. This is an important class -of models, but that description has an awful lot of qualifiers. In short, -there are many more common features of dynamic household models that are not -yet in HARK, but would be very valuable for the toolKit. New model -contributions should “play nicely” with existing models and tools, following -HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent -possible. For ideas of potential model extensions for HARK, see section -??.[2](ContributingToHARK3.html#fn2x0) - -Finally, contributions to HARK are not restricted to entirely new -functionality. While we have done our best to provide flexible frameworks and -useful tools, we have almost certainly overlooked an unexpected case that -would not be (properly) handled by the toolKit as is. Moreover, there might -even be flaws in our solution or simulation methods. If you find a mistake or -error, we strongly encourage you to contribute a fix for the problem, or at -least to flag it as an Issue on GitHub so that someone else might. The -“mistake” you find might not even be in the code itself, but a deficiency in -the documentation: a misnamed (or incorrectly typed) parameter or return -variable in the docstring, an incorrect (or missing) comment, even a -grammatical or spelling error. No matter how niggling, we want your fix. - -For those who want to contribute to the HARK project without programming a new -model or digging through the code for errors and omissions, consider reading -through the Issues page on GitHub to see what other users have flagged for -development. You might find several pieces of “low hanging fruit” for you to -quickly fix. - -#### 0.1.2 Git and GitHub - -Git is a distributed version control system that has gained increasing -popularity in the decade since its creation; it is designed to enable -collaboration and “non-linear workflow” among a (potentially disperse) -programming team. As HARK is to be developed by volunteer researchers all -around the world, git is an excellent fit for the toolKit. Originally designed -by Linus Torvalds as a command line tool, others have since extended git with -further functionality and a user interface more friendly to those more -familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK -project is hosted. This section provides a brief overview of how to use GitHub -to interact with (and potentially contribute to) HARK. - -To prepare to contribute to HARK, follow these simple steps: - - 1. Make a GitHub account: Go to [github.com](https://www.github.com); the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.[3](ContributingToHARK4.html#fn3x0) - 2. Install GitHub Desktop: Go to [desktop.github.com](https://desktop.github.com/) and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account. - 3. Fork the HARK repository: Go to the [HARK repository page](https://github.com/econ-ark/HARK), click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead. - 4. Add the repository to Desktop: Open GitHub Desktop, click on the ![+ ](ContributingToHARK0x.png) icon in the upper left, and select clone. Find the repository named yourusername/HARK, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine. - -You now control a “fork” of the main HARK repository, hosted by GitHub, and -have a clone of this fork on your local machine. You are free to edit your -fork of HARK in any way you’d like; other GitHub users can view your fork (and -fork it themselves), but they cannot make changes to your fork unless you give -them permission by adding them as a contributor. Changes made to the local -copy of your fork do not get automatically sent to the remote copy on GitHub -for public viewing. To make changes to your fork on GitHub, follow these -steps: - - 1. Make local changes: Edit your local copy of the repository in any way you’d like: add or delete lines from existing files; add, delete, or rename entire files or directories. - 2. Commit local changes: Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button. - 3. Push the commit: Click on the Sync button near the upper right to “push” your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced. - -The left hand panel of the Changes tab has a summary of the files that have -been changed, added, or removed since your last commit; the right hand panel -shows the specific changes made to each changed file. After step 2, a record -of your changes has been stored locally by the git system, but is not yet -reflected in the public remote copy on -GitHub.[4](ContributingToHARK5.html#fn4x0) The “timeline” summary of the -repository at the top of the Desktop window shows the history of commits for -the repository: each dot represents a commit that was pushed to the remote -server, and each open ring represents a local commit that has not been pushed -to the remote server; the large broken ring on the far right represents -changes that have not been locally -committed.[5](ContributingToHARK6.html#fn5x0) The History tab lists all -previous commits, including the timestamp, contributor, and a record of -changes made. - -Suppose you have mucked about in your fork for a while and have made changes -to HARK that you think should be included in the main repository hosted by -Econ-Ark. To make your dream a reality, follow these steps: - - 1. Isolate the changes: If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should isolate the desired changes in a new branch of your fork. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,[6](ContributingToHARK7.html#fn6x0) then edit this branch to include only the changes you want to push to the main repository. - 2. Issue a pull request: Go to [HARK’s GitHub page](https://github.com/econ-ark/HARK) and click the “New pull request” button. Click the text that says “compare across forks”, and select econ-ark/HARK as the base fork, master as the base, your fork as the head fork, and the branch to be merged as the “compare”. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you’ve done. Click the “Create pull request” button. - 3. Be patient: Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section 0.1.3 for procedures followed by the HARK team when evaluating outside contributions. - -#### 0.1.3 Submission Approval Process - -Before submitting to HARK, the development team would like you to make sure -that your intended contribution is listed as an Issue on the GitHub page. If -you are going to fix or address an Issue that is already posted, reply to that -post with a note indicating that you are about to work on it and an estimate -of when you will make a pull request for it. If you want to do something that -no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly) -describing what you intend to do.[7](ContributingToHARK8.html#fn7x0) This will -both help prevent two people from working on the same thing (and one of them -potentially wasting time on it), and lets the development team know what’s -coming. Moreover, if you propose a contribution that is (a) already in HARK, -(b) not in the spirit of HARK, or (c) a fix for something that’s not actually -broken, this gives the team a chance to let you know not to spend effort on -it. - -After creating a pull request to submit your contribution, it will be -evaluated by one or more members of the HARK team. Criteria that the HARK team -will consider when choosing whether to accept a submission include: - - 1. Does the contribution break previous code? If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility. - 2. Is the code well documented? Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section 0.1.5) and enough comments for a reader to follow the algorithm. - 3. Is the contribution relevant? The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn’t belong in HARK. - 4. Is the code demonstrated with an example? If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you’ve contributed a new function, consider providing a snippet of code demonstrating its use.[8](ContributingToHARK9.html#fn8x0) - 5. Is the code correct? Your code should actually do what it claims to do. - 6. Does the code use HARK conventions? To the best of your ability, contributions to HARK should follow the style guidelines in section 0.1.4. We don’t demand perfection, as these are guidelines rather than rules. - 7. Is the code well integrated in HARK? In the beta release of HARK, we have tried to present a “taxonomy” of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy. - 8. Is this already in HARK? If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK. - -The HARK team intends to release more specific guidelines in the near future, -so that contributors are better able to judge their submissions before issuing -a pull request. We are in the process of recruiting “czars” to oversee -particular aspects of HARK, providing their knowledge and -expertise.[9](ContributingToHARK10.html#fn9x0) While czars might directly -evaluate submissions as a HARK team member, their more important role is to -help establish these guidelines for their domain, more specifically nailing -down what we mean by “correct” or “well integrated”. - -#### 0.1.4 Naming Conventions - -Object naming conventions in HARK are fairly different than existing -standards, and differ somewhat between tool modules vs model or application -modules. The following conventions apply throughout HARK: - - * Functions and methods are always in “camel case”: no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. approxLognormal. - * Function and method names should accurately and concisely describe what the function does; the first word in the name must be a verb.[10](ContributingToHARK11.html#fn10x0) - * Variable and class names should not have a verb as their first word. - * Class names should use no underscores and capitalize the first letter of each word; moreover, a class name must include a noun. E.g. ConsPerfForesightSolver. - -When naming variables in model modules, the HARK team strongly discourages -using single letter names, like c for consumption. Instead, we encourage -contributors to use longer, more descriptive variable names using additional -words and common abbreviations to specify the variable more precisely. In -/Documentation/NARK.pdf, we list standard single letter variable “bases” and -an array of prefixes and suffixes to adjust them. Economic variables in model -modules should (usually) not use underscores, instead using camel case to the -greatest extent possible.[11](ContributingToHARK12.html#fn11x0) The -development team prefers this standard so that users can translate between -Python code and LaTeX script with minimal work. - -Conventions for naming variables in HARK’s tool modules are significantly -closer to more commonly used standards. Variable names should be in all lower -case, with underscores between words, e.g. data_to_match. The functions and -classes in these modules are more general and almost surely do not have any -inherent “economic content”; they are numerical or algorithmic objects, not -variables that might appear in an equation in an article for a (non- -computational) economics journal. Variable names in application modules (e.g. -the files that execute the cstwMPC estimations) are a mix of the conventions -for tool and model files, as appropriate for each variable. That is, variables -that are directly related to “economic variables” in model modules should -follow those conventions, while objects created solely for data manipulation -or reporting should use the style of tool modules. - -#### 0.1.5 Documentation Conventions - -The HARK team wants the toolKit to be as accessible to users as possible; our -greatest fear[12](ContributingToHARK13.html#fn12x0) is that a new user will -open up our code, get hopelessly confused trying to read it, and then never -look at HARK again. To prevent this tragic outcome, we have tried hard to -provide comprehensive, accurate documentation and comments within the code -describing our methods.[13](ContributingToHARK14.html#fn13x0) Moreover, HARK -uses the Sphinx utility to generate a website with [online -documentation](https://econ-ark.github.io/HARK/) for all of our tool and model -modules, so that users can learn about what’s available in HARK without -digging through the source code. When making contributions to HARK, the -development team asks users to format their inline documentation to work with -Sphinx by following a few simple rules. - - * The top of every module should begin with a “docstring” providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:[14](ContributingToHARK15.html#fn14x0) - -""" - -Specifies an economic model and provides methods for solving it. More specific -description of the key features of the model and variations of it in this -module. Maybe some comments about the solution method or limitations of the -model. Your bank account routing number. - -""" - - * The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as: - -def functionName(input1,input2): - -""" - -Concise description of the function. More details about what - -the function does, options or modes available, and maybe mathematical - -methods used. Credit to a source if you poached their algorithm. - -Parameters - -–––––––––– - -input1: type - -Description of what input1 represents. - -input2: type - -Description of what input2 represents. - -Returns - -––––––– - -output_name: type - -Description of the output(s) of the function. Might have - -multiple entries. If no output, this is just "None". - -""" - - * Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it. - -Finally, if you write a new model module, the HARK team asks that you also -provide a short mathematical writeup of the model as a PDF. This document does -not need to go into great detail about the solution method for the model or -the functions and classes included in the module, merely specify the economic -model and provide a summary of how it is solved. See -/Documentation/ConsumptionSavingModels.pdf for an example of this. - diff --git a/Documentation/HARKmanual/HARKmanual.md b/Documentation/HARKmanual/HARKmanual.md deleted file mode 100644 index ee01bb1b2..000000000 --- a/Documentation/HARKmanual/HARKmanual.md +++ /dev/null @@ -1,1263 +0,0 @@ -## A User’s Guide for HARK: -Heterogeneous Agents Resources and toolKit - -May 21, 2018 - -Christopher D Carroll1 -Johns Hopkins University -Alexander M Kaufman2 -Consumer Financial Protection Bureau -David C Low3 -Consumer Financial Protection Bureau -Nathan M Palmer4 -U.S. Treasury Office of Financial Research -Matthew N White5 -University of Delaware - - - -![PIC](HARKmanual0x.png) - -1 Carroll: , email: -[ccarroll@jhu.edu](mailto:ccarroll@jhu.edu) 2 Kaufman: -[alexander.kaufman@cfpb.gov](mailto:alexander.kaufman@cfpb.gov) 3 Low: -[david.c.low@cfpb.gov](mailto:david.c.low@cfpb.gov) 4 Palmer: -[nathan.m.palmer@ofr.treasury.gov](mailto:nathan.m.palmer@ofr.treasury.gov) 5 -White: [mnwecon@udel.edu](mailto:mnwecon@udel.edu) - -## Contents - -0.1 Introduction -0.1.1 Getting Started -0.1.2 Structure of HARK -0.1.3 Other Resources -0.2 General Purpose Tools -0.2.1 HARKcore -0.2.2 HARK.utilities -0.2.3 HARK.interpolation -0.2.4 HARK.simulation -0.2.5 HARK.estimation -0.2.6 HARK.parallel -0.3 Microeconomics: the AgentType Class -0.3.1 Attributes of an AgentType -0.3.2 A Universal Solver -0.3.3 The Flow of Time and Other Methods -0.3.4 Sample Model: Perfect Foresight Consumption-Saving -0.4 Macroeconomics: the Market Class -0.4.1 Down on the Farm -0.4.2 Attributes of a Market -0.4.3 Sample Model: FashionVictim -0.5 Contributing to HARK -0.5.1 What Does HARK Want? -0.5.2 Git and GitHub -0.5.3 Submission Approval Process -0.5.4 Naming Conventions -0.5.5 Documentation Conventions -0.6 Future of HARK -0.6.1 Future Tools -0.6.2 Future Models -0.6.3 Bounty Hunting -0.6.4 All Aboard the Ark - -### 0.1 Introduction - -If you are willing to risk some mild psychological trauma, conjure to mind -your first experience of hand-coding a structural economic model. Your clunky -effort probably built on legacy code provided by an adviser or colleague – -which itself came from who-knows-what apocryphal sources. Efforts to combine -elements from one model with those from another were likely frustrated by the -“Tower of Babel” problem: Code from one source could not “speak” to code from -another without your own intermediation as a translator, possibly between two -unfamiliar languages and aided only by oracular comments that, at best, made -sense only in the context of other (now missing) code. - -After months of effort, you may have had the character-improving experience of -proudly explaining to your adviser that not only had you grafted two ideas -together, you also found a trick that speeded the solution by an order of -magnitude, only to be told that your breathtaking insight had been understood -for many years, as reflected in an appendix to a 2008 paper; or, worse, your -discovery was something that “everybody knows” but did not exist at all in -published form! - -Learning by doing has value, but only within limits. We do not require young -drivers to design an internal combustion engine before driving a car, nor must -graduate students write their own matrix inversion algorithms before running -an OLS regression. - -In recent years, considerable progress has been made in addressing these kinds -of problems in many areas of economic modeling. Macroeconomists using -representative agent models can send Dynare model files to each other; reduced -form econometricians can choose from a host of econometric packages. But -modelers whose questions require explicit structural modeling involving -nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply -be aggregated away) are mostly still stuck in the bad old days. - -The ultimate goal of the HARK project is to fix these problems. Specifically, -our aim is to produce an open source repository of highly modular, easily -interoperable code for solving, simulating, and estimating dynamic economic -models with heterogeneous agents.[2](HARKmanual2.html#fn2x0) Further, we seek -to establish (with input from the community) standards for the description and -specification of objects like discrete approximations to continuous -distributions and interpolated function approximations, so that numeric -methods can be quickly swapped without ugly “patching.” - -We hope that HARK will make it much easier and faster for researchers to -develop solution and estimation methods for new models. The open source nature -of HARK will make it easier for other researchers to audit and verify new -models and methods, and to collaborate on correcting deficiencies when found. -As HARK expands to include more canonical models and more tools and utilities, -we can all spend less time managing numerical minutiae and more time fretting -about identification arguments and data accuracy. - -#### 0.1.1 Getting Started - -If you want to get started using HARK right away, this section provides a very -easy quickstart guide for getting HARK up and running on your computer in just -a few minutes. More information can be found in the README.md file in the HARK -repository (step 4), but here is a quick version for those who want to jump -right in: - - 1. Download Anaconda: Go to and download Anaconda for your operating system; be sure to get the version for Python 2.7. - 2. Install Anaconda: Follow the easy instructions on that page to install Anaconda. - 3. Add extra packages: (optional) If you want to use HARK’s multithreading feature, you need to add two packages that aren’t part of the default Anaconda distribution. Simply open a command prompt and do conda install joblib and conda install dill, accepting defaults to install. - 4. Download HARK: Go to , the home of the HARK repository. Click on the lime green button labeled “Clone or download” toward the upper right of the main panel, and select “Download ZIP”.[3](HARKmanual3.html#fn3x0) - 5. Unzip HARK: Unpack the HARK.zip file using any archive utility, like [Peazip](http://www.peazip.org/). Put the files anywhere you’d like, maintaining the internal directory structure. - 6. Run Spyder: Open a command prompt and do spyder. Spyder is an interactive development environment (IDE) for iPython, a slightly prettier, more interactive flavor of Python. - 7. Open a HARK module: Go to the directory where you put HARK and open any file with the .py extension; we recommend /Demos/ConsumptionSaving/ConsIndShockModel.py. - 8. Run the module: Click on the green arrow “play” button toward the right side of the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue box pops up). Congratulations! HARK is now up and running on your computer. - -If you followed our recommendation to try -/Demos/ConsumptionSaving/ConsIndShockModel.py, you should see graphical -representations of the solution to a few consumption-saving models with -idiosyncratic shocks to permanent and transitory income (see -/Documentation/ConsumptionSavingModels.pdf for details of these models). If -you chose a different module, you might get some examples of some other model, -results of a structural estimation, a polite notification that this particular -module doesn’t do much on its own, or no output at all. See the README.md for -a full list of modules from which you can expect non-trivial output. - -#### 0.1.2 Structure of HARK - -HARK is written in Python, an object-oriented programming (OOP) language that -has experienced increasing popularity in the scientific community in the past -several years. A significant reason for the adoption of Python is the numpy -and scipy packages, which offer a wide array of mathematical and statistical -functions and tools; HARK makes liberal use of these libraries. Python’s -object-oriented nature allows models in HARK to be easily extended: more -complex models can inherit functions and methods from more fundamental -“parent” models, eliminating the need to reproduce or repurpose code. - -As implied in the previous section, we strongly encourage HARK users to use -the Anaconda distribution of Python, which includes all commonly used -mathematical and scientific packages, an interactive development environment -for iPython (Spyder), and a package manager that allows users to quickly -install or update packages not included in the default distribution (conda). - -For users unfamiliar with OOP, we strongly encourage you to review the -background material on OOP provided by the good people at -[QuantEcon](http://quant-econ.net/) (for more on them, see below) at this -link: [Object Oriented Programming](http://quant-econ.net/py/python_oop.html). -Unlike non-OOP languages, OOP bundles together data and functions into -objects. These can be accessed via: object_name.data and -object_name.method_name(), respectively. For organizational purposes, -definitions of multiple objects are stored in modules, which are simply files -with a .py extension. Modules can be accessed in Python via: - -import module_name as import_name - -This imports the module and gives it a local name of import_name. We can -access a function within this module by simply typing: -import_name.function_name(). The following example will illustrate the usage -of these commands. CRRAutility is the function object for calculating CRRA -utility supplied by HARK.utilities module. CRRAutility is called attributes of -the module HARK.utilities. In order to calculate CRRA utility with a -consumption of 1 and a coefficient of risk aversion of 2 we run: - -import HARK.utilities as Hutil - -Hutil.CRRAutility(1,2) - -Python modules in HARK can generally be categorized into three types: tools, -models, and applications. Tool modules contain functions and classes with -general purpose tools that have no inherent “economic content”, but that can -be used in many economic models as building blocks or utilities; they could -plausibly be useful in non-economic settings. Tools might include functions -for data analysis (e.g. calculating Lorenz shares from data, or constructing a -non-parametric kernel regression), functions to create and manipulate discrete -approximations to continuous distributions, or classes for constructing -interpolated approximations to non-parametric functions. Tool modules -generally reside in HARK’s root directory and have names like HARK.simulation -and HARK.interpolation; they do not necessarily do anything when run. - -Model modules specify particular economic models, including classes to -represent agents in the model (and the “market structure” in which they -interact) and functions for solving the “one period problem” of those models. -For example, ConsIndShockModel.py concerns consumption-saving models in which -agents have CRRA utility over consumption and face idiosyncratic shocks to -permanent and transitory income. The module includes classes for representing -“types” of consumers, along with functions for solving (several flavors of) -the one period consumption-saving problem. When run, model modules might -demonstrate example specifications of their models, filling in the model -parameters with arbitrary values. When ConsIndShockModel.py is run, it -specifies an infinite horizon consumer with a particular discount factor, -permanent income growth rate, coefficient of relative risk aversion (etc), who -faces lognormal shocks to permanent and transitory income each period with a -particular standard deviation; it then solves this consumer’s problem and -graphically displays the results.[4](HARKmanual4.html#fn4x0) Model modules -generally have Model in their name. - -Application modules use tool and model modules to solve, simulate, and/or -estimate economic models for a particular purpose. While tool modules have no -particular economic content and model modules describe entire classes of -economic models, applications are uses of a model for some research purpose. -For example, /Demos/SolvingMicroDSOPs/StructEstimation.py uses a consumption- -saving model from ConsIndShockModel.py, calibrating it with age-dependent -sequences of permanent income growth, survival probabilities, and the standard -deviation of income shocks (etc); it then estimates the coefficient of -relative risk aversion and shifter for an age-varying sequence of discount -factors that best fits simulated wealth profiles to empirical data from the -Survey of Consumer Finance. A particular application might have multiple -modules associated with it, all of which generally reside in one directory. - -#### 0.1.3 Other Resources - -In the incredibly unlikely scenario in which this document does not fill all -of the gaps in your knowledge and answer any questions you might have while -reading it, here is a collection of potentially helpful other resources. - - * [Online documentation](https://econ-ark.github.io/HARK/) for HARK tool and model modules, produced using Sphinx. - * A [tutorial on Python 2.7](https://docs.python.org/2.7/tutorial/), straight from the source. - * [Wikipedia article on object-oriented programming](https://en.wikipedia.org/wiki/Object-oriented_programming), not the subject of an edit war. - * [QuantEcon](http://quant-econ.net/), a collection of lectures on quantitative economic modeling from a couple of economists, which definitely in no way influenced HARK whatsoever. - * A [tutorial on git](https://www.atlassian.com/git/tutorials/), the repository management and tracking system used by the HARK project, created by Linus Torvalds. - * A [tutorial on GitHub](https://guides.github.com/activities/hello-world/), a website that provides a useful framework for git that makes it significantly more user-friendly, despised by Linus Torvalds. - * [Wikipedia article on Linus Torvalds](https://en.wikipedia.org/wiki/Linus_Torvalds), just in case. - * The [homepage for NumPy](http://www.numpy.org/), a numerical package extensively used by HARK. - * The [homepage for Sphinx](http://www.sphinx-doc.org/), a tool for producing HTML documentation. - * A [collection of lecture notes](http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/) by Chris Carroll, to catch you up on the basics of consumption-saving models. - * A [particular set of lecture notes](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/) by Chris Carroll, explaining many of the numeric techniques used in HARK and offering an example of a simple structural model that, in a stunning coincidence, is implemented in HARK as an example. - * A [deep dive](http://www.econ2.jhu.edu/people/ccarroll/BufferStockTheory.pdf) into the theoretical foundations of buffer stock saving. Warning: dense. - -### 0.2 General Purpose Tools - -HARK’s root directory contains six tool modules,[5](HARKmanual5.html#fn5x0) -each containing a variety of functions and classes that can be used in many -economic models– or even for mathematical purposes that have nothing to do -with economics. Some of the tool modules are very sparely populated at this -time, while others are quite large. We expect that all of these modules will -grow considerably in the near future, as new tools are “low hanging fruit” for -contribution to the project.[6](HARKmanual6.html#fn6x0) - -#### 0.2.1 HARKcore - -A key goal of the project is to create modularity and interoperability between -models, making them easy to combine, adapt, and extend. To this end, the -HARK.core module specifies a framework for economic models in HARK, creating a -common structure for them on two levels that can be called “microeconomic” and -“macroeconomic”. - -Microeconomic models in HARK use the AgentType class to represent agents with -an intertemporal optimization problem. Each of these models specifies a -subclass of AgentType; an instance of the subclass represents agents who are -ex-ante homogeneous– they have common values for all parameters that describe -the problem. For example, ConsIndShockModel specifies the IndShockConsumerType -class, which has methods specific to consumption-saving models with -idiosyncratic shocks to income; an instance of the class might represent all -consumers who have a CRRA of 3, discount factor of 0.98, etc. The AgentType -class has a solve method that acts as a “universal microeconomic solver” for -any properly formatted model, making it easier to set up a new model and to -combine elements from different models; the solver is intended to encompass -any model that can be framed as a sequence of one period problems. For a -complete description, see section 0.3. - -Macroeconomic models in HARK use the Market class to represent a market (or -other aggregator) that combines the actions, states, and/or shocks (generally, -outcomes) of individual agents in the model into aggregate outcomes that are -“passed back” to the agents. For example, the market in a consumption-saving -model might combine the individual asset holdings of all agents in the market -to generate aggregate capital in the economy, yielding the interest rate on -assets (as the marginal product of capital); the individual agents then learn -the aggregate capital level and interest rate, conditioning their next action -on this information. Objects that microeconomic agents treat as exogenous when -solving (or simulating) their model are thus endogenous at the macroeconomic -level. Like AgentType, the Market class also has a solve method, which seeks -out a dynamic general equilibrium: a “rule” governing the dynamic evolution of -macroeconomic objects such that if agents believe this rule and act -accordingly, then their collective actions generate a sequence of -macroeconomic outcomes that justify the belief in that rule. For a more -complete description, see section 0.4. - -Beyond the model frameworks, HARK.core also defines a “supersuperclass” called -HARKobject. When solving a dynamic microeconomic model with an infinite -horizon (or searching for a dynamic general equilibrium), it is often required -to consider whether two solutions are sufficiently close to each other to -warrant stopping the process (i.e. approximate convergence). It is thus -necessary to calculate the “distance” between two solutions, so HARK specifies -that classes should have a distance method that takes a single input and -returns a non-negative value representing the (generally dimensionless) -distance between the object in question and the input to the method. As a -convenient default, HARKobject provides a “universal distance metric” that -should be useful in many contexts.[7](HARKmanual7.html#fn7x0) When defining a -new subclass of HARKobject, the user simply defines the attribute -distance_criteria as a list of strings naming the attributes of the class that -should be compared when calculating the distance between two instances of that -class. For example, the class ConsumerSolution has distance_criteria = -[’cFunc’], indicating that only the consumption function attribute of the -solution matters when comparing the distance between two instances of -ConsumerSolution. See [here](https://econ- -ark.github.io/HARK/generated/HARK.core.html) for online documentation. - -#### 0.2.2 HARK.utilities - -The HARK.utilities module carries a double meaning in its name, as it contains -both utility functions (and their derivatives, inverses, and combinations -thereof) in the economic modeling sense as well as utilities in the sense of -general tools. Utility functions included at this time are constant relative -risk aversion and constant absolute risk aversion. Other functions in -HARK.utilities include some data manipulation tools (e.g. for calculating an -average of data conditional on being within a percentile range of different -data), functions for constructing discrete state space grids, convenience -functions for retrieving information about functions, and basic plotting tools -using matplotlib.pyplot. - -The module also includes functions for constructing discrete approximations to -continuous distributions (e.g. approxLognormal() to approximate a log-normal -distribution) as well as manipulating these representations (e.g. appending -one outcome to an existing distribution, or combining independent univariate -distributions into one multivariate distribution). As a convention in HARK, -continuous distributions are approximated as finite discrete distributions -when solving models; an ![N ](HARKmanual1x.png)-dimensional random variable -is formatted as a length ![N + 1 ](HARKmanual2x.png) list of 1D arrays, with -the first element representing event probabilities and all other elements are -realizations of the ![N ](HARKmanual3x.png) component RVs. This both -simplifies solution methods (reducing numeric integrals to simple dot -products) and allows users to easily test whether their chosen degree of -discretization yields a sufficient approximation to the full distribution. See -[here](https://econ-ark.github.io/HARK/generated/HARK.utilities.html) for -online documentation. - -#### 0.2.3 HARK.interpolation - -The HARK.interpolation module defines classes for representing interpolated -function approximations. Interpolation methods in HARK all inherit from a -superclass such as HARKinterpolator1D or HARKinterpolator2D, wrapper classes -that ensures interoperability across interpolation methods. For example, -HARKinterpolator1D specifies the methods __call__ and derivative to accept an -arbitrary array as an input and return an identically shaped array with the -interpolated function evaluated at the values in the array or its first -derivative, respectively. However, these methods do little on their own, -merely reshaping arrays and referring to the _evaluate and _der methods, which -are not actually defined in HARKinterpolator1D. Each subclass of -HARKinterpolator1D specifies their own implementation of _evaluate and _der -particular to that interpolation method, accepting and returning only 1D -arrays. In this way, subclasses of HARKinterpolator1D are easily -interchangeable with each other, as all methods that the user interacts with -are identical, varying only by “internal” methods. - -When evaluating a stopping criterion for an infinite horizon problem, it is -often necessary to know the “distance” between functions generated by -successive iterations of a solution procedure. To this end, each interpolator -class in HARK must define a distance method that takes as an input another -instance of the same class and returns a non-negative real number representing -the “distance” between the two. As each of the HARKinterpolatorXD classes -inherits from HARKobject, all interpolator classes have the default -“universal” distance method; the user must simply list the names of the -relevant attributes in the attribute distance_criteria of the class. - -Interpolation methods currently implemented in HARK include (multi)linear -interpolation up to 4D, 1D cubic spline interpolation, (multi)linear -interpolation over 1D interpolations (up to 4D total), (multi)linear -interpolation over 2D interpolations (up to 4D total), linear interpolation -over 3D interpolations, 2D curvilinear interpolation over irregular grids, and -a 1D “lower envelope” interpolator. See [here](https://econ- -ark.github.io/HARK/generated/HARKinterpolation.html) for online documentation. - -#### 0.2.4 HARK.simulation - -The HARK.simulation module provides tools for generating simulated data or -shocks for post-solution use of models. Currently implemented distributions -include normal, lognormal, Weibull (including exponential), uniform, -Bernoulli, and discrete. As an example of their use, these tools are used in -the consumption-saving models of ConsIndShockModel.py to simulate permanent -and transitory income shocks as well as unemployment events. See -[here](https://econ-ark.github.io/HARK/generated/HARKsimulation.html) for -online documentation. - -#### 0.2.5 HARK.estimation - -Methods for optimizing an objective function for the purposes of estimating a -model can be found in HARK.estimation. As of this writing, the implementation -includes only minimization by the Nelder-Mead simplex method, minimization by -a derivative-free Powell method variant, and two small tools for resampling -data (i.e. for a bootstrap); the minimizers are merely convenience wrappers -(with result reporting) for optimizers included in scipy.optimize. Future -functionality will include more robust global search methods, including -genetic algorithms, simulated annealing, and differential evolution. See -[here](https://econ-ark.github.io/HARK/generated/HARKestimation.html) for full -documentation. - -#### 0.2.6 HARK.parallel - -By default, processes in Python are single-threaded, using only a single CPU -core. The HARK.parallel module provides basic tools for using multiple CPU -cores simultaneously, with minimal effort.[8](HARKmanual8.html#fn8x0) In -particular, it provides the function multiThreadCommands, which takes two -arguments: a list of AgentTypes and a list of commands as strings; each -command should be a method of the AgentTypes. The function simply distributes -the AgentTypes across threads on different cores and executes each command in -order, returning no output (the AgentTypes themselves are changed by running -the commands). Equivalent results would be achieved by simply looping over -each type and running each method in the list. Indeed, HARK.parallel also has -a function called multiThreadCommandsFake that does just that, with identical -syntax to multiThreadCommands; multithreading in HARK can thus be easily -turned on and off.[9](HARKmanual9.html#fn9x0) The module also has functions -for a parallel implementation of the Nelder-Mead simplex algorithm, as -described in Wiswall and Lee (2011). See [here](https://econ- -ark.github.io/HARK/generated/HARK.parallel.html) for full documentation. - -### 0.3 Microeconomics: the AgentType Class - -The core of our microeconomic dynamic optimization framework is a flexible -object-oriented representation of economic agents. The HARK.core module -defines a superclass called AgentType; each model defines a subclass of -AgentType, specifying additional model-specific features and methods while -inheriting the methods of the superclass. Most importantly, the method solve -acts as a “universal solver” applicable to any (properly formatted) discrete -time model. This section describes the format of an instance of AgentType as -it defines a dynamic microeconomic problem;[10](HARKmanual10.html#fn10x0) - -#### 0.3.1 Attributes of an AgentType - -A discrete time model in our framework is characterized by a sequence of -“periods” that the agent will experience. A well-formed instance of AgentType -includes the following attributes: - - * solveOnePeriod: A function, or a list of functions, representing the solution method for a single period of the agent’s problem. The inputs passed to a solveOnePeriod function include all data that characterize the agent’s problem in that period, including the solution to the subsequent period’s problem (designated as solution_next). The output of these functions is a single Solution object, which can be passed to the solver for the previous period. - * time_inv: A list of strings containing all of the variable names that are passed to at least one function in solveOnePeriod but do not vary across periods. Each of these variables resides in a correspondingly named attribute of the AgentType instance. - * time_vary: A list of strings naming the attributes of this instance that vary across periods. Each of these attributes is a list of period-specific values, which should be of the same length. If the solution method varies across periods, then ’solveOnePeriod’ is an element of time_vary.[11](HARKmanual11.html#fn11x0) - * solution_terminal: An object representing the solution to the “terminal” period of the model. This might represent a known trivial solution that does not require numeric methods, the solution to some previously solved “next phase” of the model, a scrap value function, or an initial guess of the solution to an infinite horizon model. - * pseudo_terminal: A Boolean flag indicating that solution_terminal is not a proper terminal period solution (rather an initial guess, “next phase” solution, or scrap value) and should not be reported as part of the model’s solution. - * cycles: A non-negative integer indicating the number of times the agent will experience the sequence of periods in the problem. For example, cycles = 1 means that the sequence of periods is analogous to a lifecycle model, experienced once from beginning to end; cycles = 2 means that the agent experiences the sequence twice, with the first period in the sequence following the last. An infinite horizon problem in which the sequence of periods repeats indefinitely is indicated with cycles = 0. - * tolerance: A positive real number indicating convergence tolerance, representing the maximum acceptable “distance” between successive cycle solutions in an infinite horizon model; it is irrelevant when cycles > 0. As the distance metric on the space of solutions is model-specific, the value of tolerance is generally dimensionless. - * time_flow: A Boolean flag indicating the direction that time is “flowing.” When True, the variables listed in time_vary are listed in ordinary chronological order, with index 0 being the first period; when False, these lists are in reverse chronological order, with index 0 holding the last period. - -An instance of AgentType also has the attributes named in time_vary and -time_inv, and may have other attributes that are not included in either (e.g. -values not used in the model solution, but instead to construct objects used -in the solution). - -#### 0.3.2 A Universal Solver - -When an instance of AgentType invokes its solve method, the solution to the -agent’s problem is stored in the attribute solution. The solution is computed -by recursively solving the sequence of periods defined by the variables listed -in time_vary and time_inv using the functions in solveOnePeriod. The time- -varying inputs are updated each period, including the successive period’s -solution as solution_next; the same values of time invariant inputs in -time_inv are passed to the solver in every period. The first call to -solveOnePeriod uses solution_terminal as solution_next. In a finite horizon -problem, the sequence of periods is solved cycles times over; in an infinite -horizon problem, the sequence of periods is solved until the solutions of -successive cycles have a “distance” of less than tolerance. - -The output from a function in solveOnePeriod is an instance of a model- -specific solution class. The attributes of a solution to one period of a -problem might include behavioral functions, (marginal) value functions, and -other variables characterizing the result. Each solution class must have a -method called distance(), which returns the “distance” between itself and -another instance of the same solution class, so as to define convergence as a -stopping criterion; for many models, this will be the “distance” between a -policy or value function in the solutions. If the solution class is defined as -a subclass of HARKobject, it automatically inherits the default distance -method, so that the user must only list the relevant object attributes in -distance_criteria. - -The AgentType also has methods named preSolve and postSolve, both of which -take no arguments and do absolutely nothing. A subclass of AgentType can -overwrite these blank methods with its own model specific methods. preSolve is -automatically called near the beginning of the solve method, before solving -the sequence of periods. It is used for specifying tasks that should be done -before solving the sequence of periods, such as pre-constructing some objects -repeatedly used by the solution method or finding an analytical terminal -period solution. For example, the IndShockConsumerType class in -ConsIndShockModel has a preSolve method that calls its updateSolutionTerminal -method to ensure that solution_terminal is consistent with the model -parameters. The postSolve method is called shortly after the sequence of -periods is fully solved; it can be used for “post-processing” of the solution -or performing a step that is only useful after solution convergence. For -example, the TractableConsumerType in TractableBufferStockModel has a -postSolve method that constructs an interpolated consumption function from the -list of stable arm points found during solution. - -Our universal solver is written in a very general way that should be -applicable to any discrete time optimization problem– because Python is so -flexible in defining objects, the time-varying inputs for each period can take -any form. Indeed, the solver does no “real work” itself, but merely provides a -structure for describing models in the HARK framework, allowing -interoperability among current and future modules. - -#### 0.3.3 The Flow of Time and Other Methods - -Because dynamic optimization problems are solved recursively in our framework, -it is natural to list time-varying values in reverse chronological order– the -solve() method loops over the values in each time-varying list in the same -direction that a human would read them. When simulating agents after the -solution has been obtained, however, it is much more convenient for time- -varying parameters to be listed in ordinary chronological order– the direction -in which they will be experienced by simulated agents. To allow the user to -set the order in which “time is flowing” for an instance of AgentType, the -HARK framework includes functionality to easily change ordering of time- -varying values. - -The attribute time_flow is True if variables are listed in ordinary -chronological order and False otherwise. AgentType has the following methods -for manipulating time: - - * timeReport(): Prints to screen a description of the direction that time is flowing, for interactive convenience and as a reminder of the functionality. - * timeFlip(): Flips the direction of time. Each attribute listed in time_vary is reversed in place, and the value of time_flow is toggled. - * timeFwd(): Sets the direction of time to ordinary chronological order. - * timeRev(): Sets the direction of time to reverse chronological order. - -These methods are invoked to more conveniently access time-varying objects. -When a new time-varying attribute is added, its name should be appended to -time_vary, particularly if its values are used in the solution of the model -(or is part of the solution itself). For example, the solve() method -automatically adds the string ’solution’ to time_vary if it is not already -present. Note that attributes listed in time_vary must be lists if solve() or -timeFlip() are used. Some values that could be considered “time varying” but -are never used to solve the model are more conveniently represented as a -numpy.array object (e.g. the history of a state or control variable from a -simulation); because the numpy.array class does not have a reverse() method, -these attributes should not be listed in time_vary. - -The base AgentType is sparsely defined, as most “real” methods will be -application-specific. Two final methods bear mentioning. First, the __call__ -method points to assignParameters(), a convenience method for adding or -adjusting attributes (inherited from HARKobject). This method takes any number -of keyword arguments, so that code can be parsimoniously written as, for -example, AgentInstance(attribute1 = value1, attribute2 = value2). Using -Python’s dictionary capabilities, many attributes can be conveniently set with -minimal code. Second, the resetRNG method simply resets the AgentType’s random -number generator (as the attribute RNG) using the value in the attribute -seed.[12](HARKmanual12.html#fn12x0) This method is useful for (inter alia) -ensuring that the same underlying sequence of shocks is used for every -simulation run when a model is solved or estimated. - -#### 0.3.4 Sample Model: Perfect Foresight Consumption-Saving - -To provide a concrete example of how the AgentType class works, consider the -very simple case of a perfect foresight consumption-saving model. The agent -has time-separable, additive CRRA preferences over consumption, discounting -future utility at a constant rate; he receives a particular stream of labor -income each period, and knows the interest rate on assets that he holds from -one period to the next. His decision about how much to consume in a particular -period can be expressed in Bellman form as: - -![Vt\(Mt \) = max u\(Ct\) + β//Dt E\[Vt+1\(Mt+1 \)\], - - Ct - At = Mt - Ct, - - Mt+1 = RAt + Yt+1, - - Yt+1 = Γ t+1Yt, - - C1- ρ - u\(C \) = -----. - - 1 - ρ ](HARKmanual4x.png) - -An agent’s problem is thus characterized by values of ![ρ -](HARKmanual5x.png), ![R ](HARKmanual6x.png), and ![β ](HARKmanual7x.png) -plus sequences of survival probabilities ![//Dt ](HARKmanual8x.png) and -income growth factors ![Γ t ](HARKmanual9x.png) for ![t = 0,⋅⋅⋅,T -](HARKmanual10x.png). This problem has an analytical solution for both the -value function and the consumption function. - -The ConsIndShockModel module defines the class PerfForesightConsumerType as a -subclass of AgentType and provides solver functions for several variations of -a consumption-saving model, including the perfect foresight -problem.[13](HARKmanual13.html#fn13x0) A HARK user could specify and solve a -ten period perfect foresight model with the following commands: - -MyConsumer = PerfForesightConsumerType(time_flow=True, cycles=1, - -CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98, - -LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90], - -PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02], - -Nagents=1000) - -MyConsumer.solve() - -The first line makes a new instance of ConsumerType, specifying that time is -currently “flowing” forward[14](HARKmanual14.html#fn14x0) and that the -sequence of periods happens exactly once; the next three lines (all part of -the same command) set the time invariant (CRRA, Rfree, DiscFac) and time -varying parameters (LivPrb, PermGroFac). Note that dir(MyConsumer) shows all -the attributes of MyConsumer. After running the solve method, MyConsumer will -have an attribute called solution, which will be a list with eleven -ConsumerSolution objects, representing the period-by-period solution to the -model.[15](HARKmanual15.html#fn15x0) Each ConsumerSolution has several -attributes: - - * cFunc: Optimal consumption function for this period as a function of (normalized) market resources ![m = M ∕Y - t t t ](HARKmanual11x.png). Can be called like any other function: -MyConsumer.solution[0].cFunc(5) returns consumption at ![mt = 5 -](HARKmanual12x.png). - - * vFunc: Value function (over market resources ![mt ](HARKmanual13x.png)); can be evaluated like cFunc. - * mNrmMin: Minimum value of normalized market resources ![mt ](HARKmanual14x.png) such that cFunc and vFunc are defined. - * hNrm: Normalized human wealth– the PDV of future income (ignoring mortality) divided by current period income ![Yt ](HARKmanual15x.png). - * MPC: The constant marginal propensity to consume (linear consumption function). - -The following command generates the plot for the perfect foresight consumption -function: - -mMin = MyConsumer.solution[0].mNrmMin - -plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10) - -To solve a version of this problem in which the sequence of ten periods -happens three times (yielding a solution attribute with thirty-one elements), -the user can do: - -MyConsumer(cycles = 3) - -MyConsumer.solve() - -To solve an infinite horizon problem in which the agent experiences the same -one period problem indefinitely, the user can do: - -OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02, - -DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000) - -OtherConsumer.solve() - -This instance is specified as being infinite horizon by setting cycles=0. Note -that the time-varying inputs are still specified as (one element) lists, even -though they take on the same value in every period; this is because an -infinite horizon model might consist of a multi-period sequence repeated -indefinitely, rather than just one repeated period. The solution attribute of -OtherConsumer will be a list containing one instance of ConsumerSolution, -representing the solution to every period of the model. - -### 0.4 Macroeconomics: the Market Class - -The modeling framework of AgentType is deemed “microeconomic” because it -pertains only to the dynamic optimization problem of agents, treating all -inputs of the problem as exogenously fixed. In what we label as -“macroeconomic” models, some of the inputs for the microeconomic models are -endogenously determined by the collective states and controls of agents in the -model. In a dynamic general equilibrium, there must be consistency between -agents’ beliefs about these macroeconomic objects, their individual behavior, -and the realizations of the macroeconomic objects that result from individual -choices. - -The Market class in HARK.core provides a framework for such macroeconomic -models, with a solve method that searches for a dynamic general equilibrium. -An instance of Market includes a list of AgentTypes that compose the economy, -a method for transforming microeconomic outcomes (states, controls, and/or -shocks) into macroeconomic outcomes, and a method for interpreting a history -or sequence of macroeconomic outcomes into a new “dynamic rule” for agents to -believe. Agents treat the dynamic rule as an input to their microeconomic -problem, conditioning their optimal policy functions on it. A dynamic general -equilibrium is a fixed point dynamic rule: when agents act optimally while -believing the equilibrium rule, their individual actions generate a -macroeconomic history consistent with the equilibrium rule. - -#### 0.4.1 Down on the Farm - -The Market class uses a farming metaphor to conceptualize the process for -generating a history of macroeconomic outcomes in a model. Suppose all -AgentTypes in the economy believe in some dynamic rule (i.e. the rule is -stored as attributes of each AgentType, which directly or indirectly enters -their dynamic optimization problem), and that they have each found the -solution to their microeconomic model using their solve method. Further, the -macroeconomic and microeconomic states have been reset to some initial -orientation. - -To generate a history of macroeconomic outcomes, the Market repeatedly loops -over the following steps a set number of times: - - 1. sow: Distribute the macroeconomic state variables to all AgentTypes in the market. - 2. cultivate: Each AgentType executes their marketAction method, likely corresponding to simulating one period of the microeconomic model. - 3. reap: Microeconomic outcomes are gathered from each AgentType in the market. - 4. mill: Data gathered by reap is processed into new macroeconomic states according to some “aggregate market process”. - 5. store: Relevant macroeconomic states are added to a running history of outcomes. - -This procedure is conducted by the makeHistory method of Market as a -subroutine of its solve method. After making histories of the relevant -macroeconomic variables, the market then executes its calcDynamics function -with the macroeconomic history as inputs, generating a new dynamic rule to -distribute to the AgentTypes in the market. The process then begins again, -with the agents solving their updated microeconomic models given the new -dynamic rule; the solve loop continues until the “distance” between successive -dynamic rules is sufficiently small. - -#### 0.4.2 Attributes of a Market - -To specify a complete instance of Market, the user should give it the -following attributes:[16](HARKmanual16.html#fn16x0) - - * agents: A list of AgentTypes, representing the agents in the market. Each element in agents represents an ex-ante heterogeneous type; each type could have many ex-post heterogeneous agents. - * sow_vars: A list of strings naming variables that are output from the aggregate market process, representing the macroeconomic outcomes. These variables will be distributed to the agents in the sow step. - * reap_vars: A list of strings naming variables to be collected from the agents in the reap step, to be used as inputs for the aggregate market process. - * const_vars: A list of strings naming variables used by the aggregate market process that do not come from agents; they are constant or come from the Market itself. - * track_vars: A list of strings naming variables generated by the aggregate market process that should be tracked as a history, to be used when calculating a new dynamic rule. Usually a subset of sow_vars. - * dyn_vars: A list of strings naming the variables that constitute a dynamic rule. These will be stored as attributes of the agents whenever a new rule is calculated. - * millRule: A function for the “aggregate market process”, transforming microeconomic outcomes into macroeconomic outcomes. Its inputs are named in reap_vars and const_vars, and it returns a single object with attributes named in sow_vars and/or track_vars. Can be defined as a method of a subclass of Market. - * calcDynamics: A function that generates a new dynamic rule from a history of macroeconomic outcomes. Its inputs are named in track_vars, and it returns a single object with attributes named in dyn_vars. - * act_T: The number of times that the makeHistory method should execute the “farming loop” when generating a new macroeconomic history. - * tolerance: The minimum acceptable “distance” between successive dynamic rules produced by calcDynamics to constitute a sufficiently converged solution. - -Further, each AgentType in agents must have two methods not necessary for -microeconomic models; neither takes any input (except self): - - * marketAction: The microeconomic process to be run in the cultivate step. Likely uses the new macroeconomic outcomes named in sow_vars; should store new values of relevant microeconomic outcomes in the attributes (of self) named in reap_vars. - * reset: Reset, initialize, or prepare for a new “farming loop” to generate a macroeconomic history. Might reset its internal random number generator, set initial state varibles, clear personal histories, etc. - -When solving macroeconomic models in HARK, the user should also define classes -to represent the output from the aggregate market process in millRule and for -the model-specific dynamic rule. The latter should have a distance method to -test for solution convergence; if the class inherits from HARKobject, the user -need only list relevant attributes in distance_criteria. - -#### 0.4.3 Sample Model: FashionVictim - -To illustrate the Market class, consider a simple example in the emerging -economic subfield of aesthemetrics, the -FashionVictimModel.[17](HARKmanual17.html#fn17x0) -[18](HARKmanual18.html#fn18x0) This module defines a subclass of AgentType -called FashionVictimType. Each period, fashion victims make a binary choice of -style ![s ](HARKmanual16x.png): to dress as a jock (0) or punk (1). They -receive utility directly from the outfit they wear and as a function of the -proportion of the population who just wore the same style; they also pay -switching costs (![cpj ](HARKmanual17x.png),![cjp ](HARKmanual18x.png)) if -they change styles rather than keep the same as the previous period. Moreover, -they receive an idiosyncratic type 1 extreme value (T1EV) preference shock to -each style in each period. Defining the population punk proportion as ![p -](HARKmanual19x.png) and the conformity utility function as ![f : \[0,1\] → ℝ -](HARKmanual20x.png), the current period utility function is thus: - -![u\(s ;s ,p\) = s f\(p \)+ \(1- s \)f\(1- p \)+s U + \(1- s \)U - c s -\(1- s \)- c \(1 - s \)s . - - t t- 1 t t t t t t p t j pj t-1 t jp -t-1 t - -](HARKmanual21x.png) - -Fashion victims are forward looking and discount future utility at a constant -rate of ![β ](HARKmanual22x.png) per period. To simplify the analysis, we -assume they believe that the population punk proportion in the next period is -a linear function of the punk proportion in the current period, subject to a -uniformly distributed shock. No restrictions are put on the function ![f -](HARKmanual23x.png); fashion victims might be conformists who like to dress -the same as others (![f′\(p\) > 0 ](HARKmanual24x.png)) or hipsters who like -to style themselves in the minority (![f′\(p\) < 0 -](HARKmanual25x.png)).[19](HARKmanual19.html#fn19x0) A fashion victim’s -problem can be written in Bellman form as: - -![ \[ \] - -V\(st-1,pt\) = E smt∈a{x0,1}u \(st;st-1,pt\) + ηst + βE \[V \(st,pt+1\)\] , - -](HARKmanual27x.png) - -![pt+1 = apt + b + πt+1, πt+1 ~ U\[- w,w \], η0,η1 ~ T 1EV. - -](HARKmanual28x.png) - -An instance of FashionVictimType is thus characterized by values of ![Up -](HARKmanual29x.png), ![Uj ](HARKmanual30x.png), ![cpj ](HARKmanual31x.png), -![cjp ](HARKmanual32x.png) and a function ![f ](HARKmanual33x.png), as well -as beliefs about ![pt+1 ](HARKmanual34x.png) as a function of ![pt -](HARKmanual35x.png) (summarized by slope ![a ](HARKmanual36x.png), intercept -![b ](HARKmanual37x.png), and uniform shock width ![w ](HARKmanual38x.png)). -Given this information, a FashionVictimType’s infinite horizon microeconomic -model can be solved by backward induction in a few lines; the “one period -solver” is given by solveFashion. However, while individual agents treat the -dynamics of ![pt ](HARKmanual39x.png) as exogenous, they are in fact -endogenously determined by the actions of all the fashion victims in the -market.[20](HARKmanual20.html#fn20x0) A dynamic general equilibrium of the -“macroeconomic fashion model” is thus characterized by a triple of ![\(a,b,w\) -](HARKmanual40x.png) such that when fashion victims believe in this “punk -evolution rule” and act optimally, their collective fashion choices exhibit -this same rule when the model is simulated. - -The search for a dynamic general equilibrium is implemented in HARK’s Market -class with the following definitions: - -sow_vars = [’pNow’] (macroeconomic outcome is ![pt ](HARKmanual41x.png)) - -reap_vars = [’sNow’] (microeconomic outcomes are ![st ](HARKmanual42x.png) -for many agents) - -track_vars = [’pNow’] (must track history of ![pt ](HARKmanual43x.png)) - -dyn_vars = [’pNextSlope’,’pNextIntercept’,’pNextWidth’] (dynamic rule -![\(a,b,w \) ](HARKmanual44x.png)) - -millRule = calcPunkProp (aggregate process: average the style choices of all -agents) - -calcDynamics = calcFashionEvoFunc (calculate new ![\(a,b,w\) -](HARKmanual45x.png) with autoregression of ![pt ](HARKmanual46x.png)) - -act_T = 1000 (simulate 1000 periods of the fashion market) - -tolerance = 0.01 (terminate solution when ![\(a,b,w \) ](HARKmanual47x.png) -changes by less than 0.01) - -The agents attribute has a list of 22 FashionVictimTypes, which vary in their -values of ![Up ](HARKmanual48x.png) and ![U - - j ](HARKmanual49x.png), and their ![f ](HARKmanual50x.png) functions. The -marketAction method of FashionVictimType simulates one period of the -microeconomic model: each agent receives style preference shocks ![η0 -](HARKmanual51x.png) and ![η1 ](HARKmanual52x.png), sees the current -proportion of punks ![pt ](HARKmanual53x.png) (sown to them as pNow), and -chooses which style to wear, storing it in the binary array sNow, an attribute -of self. - -The millRule for this market is extremely simple: it flattens the list of -binary arrays of individual style choices (gathered in the reap step) and -averages them into a new value of ![pt ](HARKmanual54x.png), to be tracked as -a history and sown back to the agents to begin the cycle again. Once a history -of 1000 values of ![pt ](HARKmanual55x.png) has been generated with the -makeHistory method, we can calculate a new dynamic fashion rule with -calcFashionEvoFunc by regressing ![p - - t ](HARKmanual56x.png) on ![p - - t- 1 ](HARKmanual57x.png), approximating ![w ](HARKmanual58x.png) as twice -the standard deviation of prediction errors.[21](HARKmanual21.html#fn21x0) The -new fashion rule is an instance of the simple FashionEvoFunc class, whose only -methods are inherited from HARKobject. - -When the solve method is run, the solver successively solves each agent’s -microeconomic problem, runs the makeHistory method to generate a 1000 period -history of ![pt ](HARKmanual60x.png), and calculates a new punk evolution -rule based on this history; the solver terminates when consecutive rules -differ by less than 0.01 in any dimension. - -### 0.5 Contributing to HARK - -This section provides an overview of how users can contribute new code to -HARK, what sort of contributions are most desired, and style conventions for -the toolKit. Before attempting to extend HARK, we strongly recommend that you -familiarize yourself with its features and models by looking at the source -code. - -#### 0.5.1 What Does HARK Want? - -The easiest and most straightforward way to contribute to HARK is by writing -new general purpose tools for inclusion in one of the top-level modules. This -might be a commonly known data analysis technique (e.g. a kernel density -estimator) for HARK.utilities, an optimization method (local or global) for -HARK.estimation, an interpolation method for HARKinterpolation, etc. If you’ve -found a technique useful in your own research and believe it could be of use -to others on entirely different projects, then it probably belongs in HARK. -Likewise, if you read through a HARK module expecting to find a certain tool -or function (because of similar functions that are present, or merely because -it’s very useful) but don’t, this is a good sign that HARK has a hole that you -can fill.[22](HARKmanual22.html#fn22x0) - -Secondly, the development team wants to expand HARK to include more canonical -models. Most of the models available in the toolKit at the time of its public -beta release concern agents with (geometrically discounted) CRRA utility over -a unitary consumption good, who receive permanent and transitory shocks to -income and have access to a single risk free asset. This is an important class -of models, but that description has an awful lot of qualifiers. In short, -there are many more common features of dynamic household models that are not -yet in HARK, but would be very valuable for the toolKit. New model -contributions should “play nicely” with existing models and tools, following -HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent -possible. For ideas of potential model extensions for HARK, see section -0.6.2.[23](HARKmanual23.html#fn23x0) - -Finally, contributions to HARK are not restricted to entirely new -functionality. While we have done our best to provide flexible frameworks and -useful tools, we have almost certainly overlooked an unexpected case that -would not be (properly) handled by the toolKit as is. Moreover, there might -even be flaws in our solution or simulation methods. If you find a mistake or -error, we strongly encourage you to contribute a fix for the problem, or at -least to flag it as an Issue on GitHub so that someone else might. The -“mistake” you find might not even be in the code itself, but a deficiency in -the documentation: a misnamed (or incorrectly typed) parameter or return -variable in the docstring, an incorrect (or missing) comment, even a -grammatical or spelling error. No matter how niggling, we want your fix. - -For those who want to contribute to the HARK project without programming a new -model or digging through the code for errors and omissions, consider reading -through the Issues page on GitHub to see what other users have flagged for -development. You might find several pieces of “low hanging fruit” for you to -quickly fix. - -#### 0.5.2 Git and GitHub - -Git is a distributed version control system that has gained increasing -popularity in the decade since its creation; it is designed to enable -collaboration and “non-linear workflow” among a (potentially disperse) -programming team. As HARK is to be developed by volunteer researchers all -around the world, git is an excellent fit for the toolKit. Originally designed -by Linus Torvalds as a command line tool, others have since extended git with -further functionality and a user interface more friendly to those more -familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK -project is hosted. This section provides a brief overview of how to use GitHub -to interact with (and potentially contribute to) HARK. - -To prepare to contribute to HARK, follow these simple steps: - - 1. Make a GitHub account: Go to [github.com](https://www.github.com); the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.[24](HARKmanual24.html#fn24x0) - 2. Install GitHub Desktop: Go to [desktop.github.com](https://desktop.github.com/) and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account. - 3. Fork the HARK repository: Go to the [HARK repository page](https://github.com/econ-ark/HARK), click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead. - 4. Add the repository to Desktop: Open GitHub Desktop, click on the ![+ ](HARKmanual61x.png) icon in the upper left, and select clone. Find the repository named yourusername/HARK, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine. - -You now control a “fork” of the main HARK repository, hosted by GitHub, and -have a clone of this fork on your local machine. You are free to edit your -fork of HARK in any way you’d like; other GitHub users can view your fork (and -fork it themselves), but they cannot make changes to your fork unless you give -them permission by adding them as a contributor. Changes made to the local -copy of your fork do not get automatically sent to the remote copy on GitHub -for public viewing. To make changes to your fork on GitHub, follow these -steps: - - 1. Make local changes: Edit your local copy of the repository in any way you’d like: add or delete lines from existing files; add, delete, or rename entire files or directories. - 2. Commit local changes: Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button. - 3. Push the commit: Click on the Sync button near the upper right to “push” your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced. - -The left hand panel of the Changes tab has a summary of the files that have -been changed, added, or removed since your last commit; the right hand panel -shows the specific changes made to each changed file. After step 2, a record -of your changes has been stored locally by the git system, but is not yet -reflected in the public remote copy on GitHub.[25](HARKmanual25.html#fn25x0) -The “timeline” summary of the repository at the top of the Desktop window -shows the history of commits for the repository: each dot represents a commit -that was pushed to the remote server, and each open ring represents a local -commit that has not been pushed to the remote server; the large broken ring on -the far right represents changes that have not been locally -committed.[26](HARKmanual26.html#fn26x0) The History tab lists all previous -commits, including the timestamp, contributor, and a record of changes made. - -Suppose you have mucked about in your fork for a while and have made changes -to HARK that you think should be included in the main repository hosted by -Econ-Ark. To make your dream a reality, follow these steps: - - 1. Isolate the changes: If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should isolate the desired changes in a new branch of your fork. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,[27](HARKmanual27.html#fn27x0) then edit this branch to include only the changes you want to push to the main repository. - 2. Issue a pull request: Go to [HARK’s GitHub page](https://github.com/econ-ark/HARK) and click the “New pull request” button. Click the text that says “compare across forks”, and select econ-ark/HARK as the base fork, master as the base, your fork as the head fork, and the branch to be merged as the “compare”. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you’ve done. Click the “Create pull request” button. - 3. Be patient: Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section 0.5.3 for procedures followed by the HARK team when evaluating outside contributions. - -#### 0.5.3 Submission Approval Process - -Before submitting to HARK, the development team would like you to make sure -that your intended contribution is listed as an Issue on the GitHub page. If -you are going to fix or address an Issue that is already posted, reply to that -post with a note indicating that you are about to work on it and an estimate -of when you will make a pull request for it. If you want to do something that -no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly) -describing what you intend to do.[28](HARKmanual28.html#fn28x0) This will both -help prevent two people from working on the same thing (and one of them -potentially wasting time on it), and lets the development team know what’s -coming. Moreover, if you propose a contribution that is (a) already in HARK, -(b) not in the spirit of HARK, or (c) a fix for something that’s not actually -broken, this gives the team a chance to let you know not to spend effort on -it. - -After creating a pull request to submit your contribution, it will be -evaluated by one or more members of the HARK team. Criteria that the HARK team -will consider when choosing whether to accept a submission include: - - 1. Does the contribution break previous code? If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility. - 2. Is the code well documented? Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section 0.5.5) and enough comments for a reader to follow the algorithm. - 3. Is the contribution relevant? The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn’t belong in HARK. - 4. Is the code demonstrated with an example? If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you’ve contributed a new function, consider providing a snippet of code demonstrating its use.[29](HARKmanual29.html#fn29x0) - 5. Is the code correct? Your code should actually do what it claims to do. - 6. Does the code use HARK conventions? To the best of your ability, contributions to HARK should follow the style guidelines in section 0.5.4. We don’t demand perfection, as these are guidelines rather than rules. - 7. Is the code well integrated in HARK? In the beta release of HARK, we have tried to present a “taxonomy” of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy. - 8. Is this already in HARK? If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK. - -The HARK team intends to release more specific guidelines in the near future, -so that contributors are better able to judge their submissions before issuing -a pull request. We are in the process of recruiting “czars” to oversee -particular aspects of HARK, providing their knowledge and -expertise.[30](HARKmanual30.html#fn30x0) While czars might directly evaluate -submissions as a HARK team member, their more important role is to help -establish these guidelines for their domain, more specifically nailing down -what we mean by “correct” or “well integrated”. - -#### 0.5.4 Naming Conventions - -Object naming conventions in HARK are fairly different than existing -standards, and differ somewhat between tool modules vs model or application -modules. The following conventions apply throughout HARK: - - * Functions and methods are always in “camel case”: no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. approxLognormal. - * Function and method names should accurately and concisely describe what the function does; the first word in the name must be a verb.[31](HARKmanual31.html#fn31x0) - * Variable and class names should not have a verb as their first word. - * Class names should use no underscores and capitalize the first letter of each word; moreover, a class name must include a noun. E.g. ConsPerfForesightSolver. - -When naming variables in model modules, the HARK team strongly discourages -using single letter names, like c for consumption. Instead, we encourage -contributors to use longer, more descriptive variable names using additional -words and common abbreviations to specify the variable more precisely. In -/Documentation/NARK.pdf, we list standard single letter variable “bases” and -an array of prefixes and suffixes to adjust them. Economic variables in model -modules should (usually) not use underscores, instead using camel case to the -greatest extent possible.[32](HARKmanual32.html#fn32x0) The development team -prefers this standard so that users can translate between Python code and -LaTeX script with minimal work. - -Conventions for naming variables in HARK’s tool modules are significantly -closer to more commonly used standards. Variable names should be in all lower -case, with underscores between words, e.g. data_to_match. The functions and -classes in these modules are more general and almost surely do not have any -inherent “economic content”; they are numerical or algorithmic objects, not -variables that might appear in an equation in an article for a (non- -computational) economics journal. Variable names in application modules (e.g. -the files that execute the cstwMPC estimations) are a mix of the conventions -for tool and model files, as appropriate for each variable. That is, variables -that are directly related to “economic variables” in model modules should -follow those conventions, while objects created solely for data manipulation -or reporting should use the style of tool modules. - -#### 0.5.5 Documentation Conventions - -The HARK team wants the toolKit to be as accessible to users as possible; our -greatest fear[33](HARKmanual33.html#fn33x0) is that a new user will open up -our code, get hopelessly confused trying to read it, and then never look at -HARK again. To prevent this tragic outcome, we have tried hard to provide -comprehensive, accurate documentation and comments within the code describing -our methods.[34](HARKmanual34.html#fn34x0) Moreover, HARK uses the Sphinx -utility to generate a website with [online documentation](https://econ- -ark.github.io/HARK/) for all of our tool and model modules, so that users can -learn about what’s available in HARK without digging through the source code. -When making contributions to HARK, the development team asks users to format -their inline documentation to work with Sphinx by following a few simple -rules. - - * The top of every module should begin with a “docstring” providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:[35](HARKmanual35.html#fn35x0) - -""" - -Specifies an economic model and provides methods for solving it. More specific -description of the key features of the model and variations of it in this -module. Maybe some comments about the solution method or limitations of the -model. Your bank account routing number. - -""" - - * The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as: - -def functionName(input1,input2): - -""" - -Concise description of the function. More details about what - -the function does, options or modes available, and maybe mathematical - -methods used. Credit to a source if you poached their algorithm. - -Parameters - -–––––––––– - -input1: type - -Description of what input1 represents. - -input2: type - -Description of what input2 represents. - -Returns - -––––––– - -output_name: type - -Description of the output(s) of the function. Might have - -multiple entries. If no output, this is just "None". - -""" - - * Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it. - -Finally, if you write a new model module, the HARK team asks that you also -provide a short mathematical writeup of the model as a PDF. This document does -not need to go into great detail about the solution method for the model or -the functions and classes included in the module, merely specify the economic -model and provide a summary of how it is solved. See -/Documentation/ConsumptionSavingModels.pdf for an example of this. - -### 0.6 Future of HARK - -HARK is in a state of perpetual incompleteness, always expanding to include -more tools and more models. As an open source project in the hands of many -contributors around the world, the HARK team cannot fully predict future -features of the toolKit. This section provides an overview of what we would -like to see in the near future, as well as potential mechanisms to encourage -researchers to contribute their code to the project. - -#### 0.6.1 Future Tools - -The current frameworks in HARK.core concern agents who have “isolated” dynamic -problems or interact with each other only through aggregate outcomes to which -they contribute atomically. We hope to eventually provide a framework for -models in which agents directly interact with each other; this may include a -system for specifying the state of networks and how connections are formed and -broken. HARK.core might also develop a framework more suited to industrial -organization models and dynamic games.[36](HARKmanual36.html#fn36x0) - -The HARK.estimation module in particular is very sparsely populated, with its -functions mostly serving as wrappers for “off the shelf” optimization methods -from scipy.optimize. There are plenty of non-standard or more complex -optimization techniques available, including global and/or stochastic search -methods. We believe that additional optimizers are “low hanging fruit” as -straightforward contributions to HARK and thus will be added in the near -future. Moreover, there are plenty of distributions that have no approximator -in HARK.utilities (or data generator in HARK.simulation), and these functions -are among the easiest for new contributors to write. The data analysis tools -in HARK.utilities only include functions that we happened to use for our -current research projects, and we expect that this will be the mode of growth -for future tools. There are some obvious holes that we believe will be filled -quite quickly; for example, we provide a rudimentary non-parametric kernel -regression function, but have not written a kernel density estimator to -generate PDFs from data. - -The classes currently in HARK.interpolation are all variations on linear (or -cubic) spline interpolation, and each is specifically written for a fixed -number of dimensions. As these methods use full tensor grids, they are highly -impractical for representing functions in higher dimensions, suffering greatly -from the curse of dimensionality.[37](HARKmanual37.html#fn37x0) We are quite -confident that HARK.interpolation will soon include techniques more applicable -to high dimensional state spaces, including Smolnyak interpolation, other -(adaptive) sparse grid methods, and regression-based interpolation. For lower- -dimensional spaces, HARK will also soon provide Delaunay interpolation -classes. - -The HARK.parallel module currently provides a simple interface for handling -ex-ante heterogeneity among agents. While useful for speeding up moderately- -sized problems, the libraries used are only able to access the CPU of the -local machine running the code. In the very near future, the HARK team will -provide an interface (and example) for using Apache Spark, which bills itself -as a “fast and general engine for large-scale data processing”. Specifically, -users will be able to easily scale up their computing resources and run HARK -code on an Amazon EC2 session or other cluster. - -#### 0.6.2 Future Models - -The economics models currently included in HARK mostly concern consumption- -saving problems with constant relative risk aversion consumers who experience -fully permanent and fully transitory shocks to income. This is obviously a -very small subset of all consumption-saving models (which are themselves a -small subset of all dynamic models economists might be interested in). -Consider some examples of variants, features, and extensions that are not -included in HARK, but could be in the future: - - * Consumers with other utility functions, such as CARA or expo-power. - * Consumers with time-inconsistent preferences, e.g. hyperbolic discounting. - * Income shocks that are neither fully transitory nor fully permanent. - * Consumers with a bequest motive or terminal utility function. - * Utility from leisure and endogenous labor supply. - * Investment in a durable consumption good such as housing. - * Exogenous shocks to expenses. - * Multiple consumption goods, as with endogenous medical care. - * Portfolio allocation between a risky and riskless asset. - * Job search intensity as a control variable. - * Endogenous health evolution and health investment. - -The HARK team is currently developing a module for mixing discrete and -continuous choice models, in the style of Jorgensen, Rust, Iskhakov, and -Schjerning (2015). Among many other models, this framework will enable HARK -users to easily develop features like: - - * Labor supply on the extensive margin: the timing of retirement. - * Selection of job type or sector (by riskiness, specific human capital, etc). - * Choice of medical insurance contract. - * Decision to rent or own housing. - * Endogenous default on debt (with “macroeconomic” pricing of debt). - * Household formation, dissolution, and reproduction. - -Finally, we hope to provide a generic solver for any discrete choice model -with T1EV shocks. The scope of HARK is limited only by what users choose to -put in it. We hope that within a year of its debut, HARK will include a -significant number of canonical models, providing more building blocks for -future researchers to create novel combinations and new extensions. - -#### 0.6.3 Bounty Hunting - -While HARK users are free to write and contribute any tools and models that -they believe could be helpful, building the toolKit in a “bottom up” style, -the HARK team hopes to provide mechanisms for targeted “top down” development. -The team is working to secure grant funding for HARK so as to offer “bounties” -for particular models, tools, applications, or frameworks. Suppose the team -decides that the model in Important Paper X should be included as part of -HARK; we will issue a call for contributions to fill this hole, offering a -modest prize of, say, $20,000 to the programmer who submits the best -implementation of the model, as judged by clearly stated -criteria.[38](HARKmanual38.html#fn38x0) The bounty would be announced as an -Issue on HARK’s GitHub page, as well as in a potential electronic digest of -HARK news. - -Bounties might be placed on items as small as a single data analysis tool or -as large as a complex estimation of a large general equilibrium model, with -prizes scaling accordingly. We hasten to emphasize that we cannot guarantee -funding for HARK, so the bounties (like everything else in this section) are -purely speculative. As economists, the HARK team understands the incentive -compatibility problems associated with asking researchers to contribute -significant amounts of their time, effort, and unique skills in service of a -tenuous concept like the Greater Good in exchange for even more tenuous Glory. -We acknowledge that economic researchers are also optimizing agents with their -own preferences over leisure and consumption, and agree that Greed is a valid -mechanism to expand HARK. - -#### 0.6.4 All Aboard the Ark - -As observant readers may have noticed, the HARK repository is hosted by a -GitHub account named Econ-Ark, with no explanation as to what this might mean. -Looking a few years into the future, the development team believes that HARK -might be the first piece of a broader collection of computational economics -resources. HARK is open source but somewhat akin to an exclusive club: -contributions are required to adhere to standards and norms so as to foster -modularity and interoperability among models and tools. Consistent with its -namesake ship, the Econ-Ark has a significantly lower bar for admission, with -a place on board for everything. - -On one level, the Ark might serve as a warehouse for any and all quantitative -models and tools, for any economic purpose, in any subfield, and written in -any language. Through nearly unanimous feedback, the HARK team understands -that many economists are willing to share code that they have already written, -but are reluctant to expend the considerable effort to translate and format it -for HARK. In the future, we will set up a mechanism for code like this to be -hosted on the Ark, with minimal requirements imposed on its authors (and -correspondingly minimal warranty for potential users). Relevant projects on -the Ark might become bounty targets for HARK, with users incentivized to port -the original code into Python and to meet HARK standards; direct access to the -original source code will significantly expedite the translation process, and -provide a suitable benchmark for testing and verifying the contribution to -HARK. - -On a different level, the Ark might also include cohesive toolkits like HARK, -but geared toward purposes other than heterogeneous agents macroeconomics -models. For example, the previously mentioned modules for networks among -agents (and other agent-based modeling), industrial organization and dynamic -games, and traditional discrete choice models might become their own toolkits, -with some interoperability with HARK. In support of this, some of the tool -modules currently in HARK might be “promoted” and become universally shared -across all Econ-Ark toolkits. - -The dark secret of HARK is that it is named for heterogeneous agents -macroeconomics, but designed and written by structural microeconomists. The -development team believes these subfields to have considerable overlap in -terms of methods and techniques, and that HARK might not be noticed by half of -its potential audience due to its name. In a general sense, the Econ-Ark gives -us cover to expand the scope of our efforts and/or rebrand the toolKit for an -alternative audience. - diff --git a/Documentation/conf.py b/Documentation/conf.py index bb13d48a3..11361d71b 100644 --- a/Documentation/conf.py +++ b/Documentation/conf.py @@ -41,6 +41,7 @@ 'sphinx.ext.autosummary', 'numpydoc', 'nbsphinx', + 'recommonmark' ] # This is currently not working From 45c0f896f0cdc44bf35bc3a4c00f353398400e3b Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 29 Oct 2019 15:59:26 -0400 Subject: [PATCH 100/242] removing notebooks directory with symlinks to (deleted) Examples/ dir. See #409 $417 --- Documentation/notebooks/Gentle-Intro-To-HARK.ipynb | 1 - Documentation/notebooks/README.rst | 12 ------------ 2 files changed, 13 deletions(-) delete mode 120000 Documentation/notebooks/Gentle-Intro-To-HARK.ipynb delete mode 100644 Documentation/notebooks/README.rst diff --git a/Documentation/notebooks/Gentle-Intro-To-HARK.ipynb b/Documentation/notebooks/Gentle-Intro-To-HARK.ipynb deleted file mode 120000 index f27c3cca7..000000000 --- a/Documentation/notebooks/Gentle-Intro-To-HARK.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Examples/Gentle-Intro-To-HARK.ipynb \ No newline at end of file diff --git a/Documentation/notebooks/README.rst b/Documentation/notebooks/README.rst deleted file mode 100644 index b41375a6e..000000000 --- a/Documentation/notebooks/README.rst +++ /dev/null @@ -1,12 +0,0 @@ -Adding a notebook ------------------ - -You can add a notebook here to be included in the docs by creating a symlink to an existing notebook. -This lets you keep them in the original place, -but also include them in your docs here. - -This would be done with a command like this:: - - ln -s ../../Examples/Gentle-Intro-To-HARK.ipynb . - -That would create a symlink here to the ``Examples/Gentle-Intro-To-HARK.ipynb`` notebook. From 2e765e6a98727411de059fee7ba4f24a6feb9aa8 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 29 Oct 2019 16:19:54 -0400 Subject: [PATCH 101/242] no, really, remove the HARKmanual markdown --- Documentation/HARKmanual.md | 1263 ----------------------------------- 1 file changed, 1263 deletions(-) delete mode 100644 Documentation/HARKmanual.md diff --git a/Documentation/HARKmanual.md b/Documentation/HARKmanual.md deleted file mode 100644 index ee01bb1b2..000000000 --- a/Documentation/HARKmanual.md +++ /dev/null @@ -1,1263 +0,0 @@ -## A User’s Guide for HARK: -Heterogeneous Agents Resources and toolKit - -May 21, 2018 - -Christopher D Carroll1 -Johns Hopkins University -Alexander M Kaufman2 -Consumer Financial Protection Bureau -David C Low3 -Consumer Financial Protection Bureau -Nathan M Palmer4 -U.S. Treasury Office of Financial Research -Matthew N White5 -University of Delaware - - - -![PIC](HARKmanual0x.png) - -1 Carroll: , email: -[ccarroll@jhu.edu](mailto:ccarroll@jhu.edu) 2 Kaufman: -[alexander.kaufman@cfpb.gov](mailto:alexander.kaufman@cfpb.gov) 3 Low: -[david.c.low@cfpb.gov](mailto:david.c.low@cfpb.gov) 4 Palmer: -[nathan.m.palmer@ofr.treasury.gov](mailto:nathan.m.palmer@ofr.treasury.gov) 5 -White: [mnwecon@udel.edu](mailto:mnwecon@udel.edu) - -## Contents - -0.1 Introduction -0.1.1 Getting Started -0.1.2 Structure of HARK -0.1.3 Other Resources -0.2 General Purpose Tools -0.2.1 HARKcore -0.2.2 HARK.utilities -0.2.3 HARK.interpolation -0.2.4 HARK.simulation -0.2.5 HARK.estimation -0.2.6 HARK.parallel -0.3 Microeconomics: the AgentType Class -0.3.1 Attributes of an AgentType -0.3.2 A Universal Solver -0.3.3 The Flow of Time and Other Methods -0.3.4 Sample Model: Perfect Foresight Consumption-Saving -0.4 Macroeconomics: the Market Class -0.4.1 Down on the Farm -0.4.2 Attributes of a Market -0.4.3 Sample Model: FashionVictim -0.5 Contributing to HARK -0.5.1 What Does HARK Want? -0.5.2 Git and GitHub -0.5.3 Submission Approval Process -0.5.4 Naming Conventions -0.5.5 Documentation Conventions -0.6 Future of HARK -0.6.1 Future Tools -0.6.2 Future Models -0.6.3 Bounty Hunting -0.6.4 All Aboard the Ark - -### 0.1 Introduction - -If you are willing to risk some mild psychological trauma, conjure to mind -your first experience of hand-coding a structural economic model. Your clunky -effort probably built on legacy code provided by an adviser or colleague – -which itself came from who-knows-what apocryphal sources. Efforts to combine -elements from one model with those from another were likely frustrated by the -“Tower of Babel” problem: Code from one source could not “speak” to code from -another without your own intermediation as a translator, possibly between two -unfamiliar languages and aided only by oracular comments that, at best, made -sense only in the context of other (now missing) code. - -After months of effort, you may have had the character-improving experience of -proudly explaining to your adviser that not only had you grafted two ideas -together, you also found a trick that speeded the solution by an order of -magnitude, only to be told that your breathtaking insight had been understood -for many years, as reflected in an appendix to a 2008 paper; or, worse, your -discovery was something that “everybody knows” but did not exist at all in -published form! - -Learning by doing has value, but only within limits. We do not require young -drivers to design an internal combustion engine before driving a car, nor must -graduate students write their own matrix inversion algorithms before running -an OLS regression. - -In recent years, considerable progress has been made in addressing these kinds -of problems in many areas of economic modeling. Macroeconomists using -representative agent models can send Dynare model files to each other; reduced -form econometricians can choose from a host of econometric packages. But -modelers whose questions require explicit structural modeling involving -nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply -be aggregated away) are mostly still stuck in the bad old days. - -The ultimate goal of the HARK project is to fix these problems. Specifically, -our aim is to produce an open source repository of highly modular, easily -interoperable code for solving, simulating, and estimating dynamic economic -models with heterogeneous agents.[2](HARKmanual2.html#fn2x0) Further, we seek -to establish (with input from the community) standards for the description and -specification of objects like discrete approximations to continuous -distributions and interpolated function approximations, so that numeric -methods can be quickly swapped without ugly “patching.” - -We hope that HARK will make it much easier and faster for researchers to -develop solution and estimation methods for new models. The open source nature -of HARK will make it easier for other researchers to audit and verify new -models and methods, and to collaborate on correcting deficiencies when found. -As HARK expands to include more canonical models and more tools and utilities, -we can all spend less time managing numerical minutiae and more time fretting -about identification arguments and data accuracy. - -#### 0.1.1 Getting Started - -If you want to get started using HARK right away, this section provides a very -easy quickstart guide for getting HARK up and running on your computer in just -a few minutes. More information can be found in the README.md file in the HARK -repository (step 4), but here is a quick version for those who want to jump -right in: - - 1. Download Anaconda: Go to and download Anaconda for your operating system; be sure to get the version for Python 2.7. - 2. Install Anaconda: Follow the easy instructions on that page to install Anaconda. - 3. Add extra packages: (optional) If you want to use HARK’s multithreading feature, you need to add two packages that aren’t part of the default Anaconda distribution. Simply open a command prompt and do conda install joblib and conda install dill, accepting defaults to install. - 4. Download HARK: Go to , the home of the HARK repository. Click on the lime green button labeled “Clone or download” toward the upper right of the main panel, and select “Download ZIP”.[3](HARKmanual3.html#fn3x0) - 5. Unzip HARK: Unpack the HARK.zip file using any archive utility, like [Peazip](http://www.peazip.org/). Put the files anywhere you’d like, maintaining the internal directory structure. - 6. Run Spyder: Open a command prompt and do spyder. Spyder is an interactive development environment (IDE) for iPython, a slightly prettier, more interactive flavor of Python. - 7. Open a HARK module: Go to the directory where you put HARK and open any file with the .py extension; we recommend /Demos/ConsumptionSaving/ConsIndShockModel.py. - 8. Run the module: Click on the green arrow “play” button toward the right side of the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue box pops up). Congratulations! HARK is now up and running on your computer. - -If you followed our recommendation to try -/Demos/ConsumptionSaving/ConsIndShockModel.py, you should see graphical -representations of the solution to a few consumption-saving models with -idiosyncratic shocks to permanent and transitory income (see -/Documentation/ConsumptionSavingModels.pdf for details of these models). If -you chose a different module, you might get some examples of some other model, -results of a structural estimation, a polite notification that this particular -module doesn’t do much on its own, or no output at all. See the README.md for -a full list of modules from which you can expect non-trivial output. - -#### 0.1.2 Structure of HARK - -HARK is written in Python, an object-oriented programming (OOP) language that -has experienced increasing popularity in the scientific community in the past -several years. A significant reason for the adoption of Python is the numpy -and scipy packages, which offer a wide array of mathematical and statistical -functions and tools; HARK makes liberal use of these libraries. Python’s -object-oriented nature allows models in HARK to be easily extended: more -complex models can inherit functions and methods from more fundamental -“parent” models, eliminating the need to reproduce or repurpose code. - -As implied in the previous section, we strongly encourage HARK users to use -the Anaconda distribution of Python, which includes all commonly used -mathematical and scientific packages, an interactive development environment -for iPython (Spyder), and a package manager that allows users to quickly -install or update packages not included in the default distribution (conda). - -For users unfamiliar with OOP, we strongly encourage you to review the -background material on OOP provided by the good people at -[QuantEcon](http://quant-econ.net/) (for more on them, see below) at this -link: [Object Oriented Programming](http://quant-econ.net/py/python_oop.html). -Unlike non-OOP languages, OOP bundles together data and functions into -objects. These can be accessed via: object_name.data and -object_name.method_name(), respectively. For organizational purposes, -definitions of multiple objects are stored in modules, which are simply files -with a .py extension. Modules can be accessed in Python via: - -import module_name as import_name - -This imports the module and gives it a local name of import_name. We can -access a function within this module by simply typing: -import_name.function_name(). The following example will illustrate the usage -of these commands. CRRAutility is the function object for calculating CRRA -utility supplied by HARK.utilities module. CRRAutility is called attributes of -the module HARK.utilities. In order to calculate CRRA utility with a -consumption of 1 and a coefficient of risk aversion of 2 we run: - -import HARK.utilities as Hutil - -Hutil.CRRAutility(1,2) - -Python modules in HARK can generally be categorized into three types: tools, -models, and applications. Tool modules contain functions and classes with -general purpose tools that have no inherent “economic content”, but that can -be used in many economic models as building blocks or utilities; they could -plausibly be useful in non-economic settings. Tools might include functions -for data analysis (e.g. calculating Lorenz shares from data, or constructing a -non-parametric kernel regression), functions to create and manipulate discrete -approximations to continuous distributions, or classes for constructing -interpolated approximations to non-parametric functions. Tool modules -generally reside in HARK’s root directory and have names like HARK.simulation -and HARK.interpolation; they do not necessarily do anything when run. - -Model modules specify particular economic models, including classes to -represent agents in the model (and the “market structure” in which they -interact) and functions for solving the “one period problem” of those models. -For example, ConsIndShockModel.py concerns consumption-saving models in which -agents have CRRA utility over consumption and face idiosyncratic shocks to -permanent and transitory income. The module includes classes for representing -“types” of consumers, along with functions for solving (several flavors of) -the one period consumption-saving problem. When run, model modules might -demonstrate example specifications of their models, filling in the model -parameters with arbitrary values. When ConsIndShockModel.py is run, it -specifies an infinite horizon consumer with a particular discount factor, -permanent income growth rate, coefficient of relative risk aversion (etc), who -faces lognormal shocks to permanent and transitory income each period with a -particular standard deviation; it then solves this consumer’s problem and -graphically displays the results.[4](HARKmanual4.html#fn4x0) Model modules -generally have Model in their name. - -Application modules use tool and model modules to solve, simulate, and/or -estimate economic models for a particular purpose. While tool modules have no -particular economic content and model modules describe entire classes of -economic models, applications are uses of a model for some research purpose. -For example, /Demos/SolvingMicroDSOPs/StructEstimation.py uses a consumption- -saving model from ConsIndShockModel.py, calibrating it with age-dependent -sequences of permanent income growth, survival probabilities, and the standard -deviation of income shocks (etc); it then estimates the coefficient of -relative risk aversion and shifter for an age-varying sequence of discount -factors that best fits simulated wealth profiles to empirical data from the -Survey of Consumer Finance. A particular application might have multiple -modules associated with it, all of which generally reside in one directory. - -#### 0.1.3 Other Resources - -In the incredibly unlikely scenario in which this document does not fill all -of the gaps in your knowledge and answer any questions you might have while -reading it, here is a collection of potentially helpful other resources. - - * [Online documentation](https://econ-ark.github.io/HARK/) for HARK tool and model modules, produced using Sphinx. - * A [tutorial on Python 2.7](https://docs.python.org/2.7/tutorial/), straight from the source. - * [Wikipedia article on object-oriented programming](https://en.wikipedia.org/wiki/Object-oriented_programming), not the subject of an edit war. - * [QuantEcon](http://quant-econ.net/), a collection of lectures on quantitative economic modeling from a couple of economists, which definitely in no way influenced HARK whatsoever. - * A [tutorial on git](https://www.atlassian.com/git/tutorials/), the repository management and tracking system used by the HARK project, created by Linus Torvalds. - * A [tutorial on GitHub](https://guides.github.com/activities/hello-world/), a website that provides a useful framework for git that makes it significantly more user-friendly, despised by Linus Torvalds. - * [Wikipedia article on Linus Torvalds](https://en.wikipedia.org/wiki/Linus_Torvalds), just in case. - * The [homepage for NumPy](http://www.numpy.org/), a numerical package extensively used by HARK. - * The [homepage for Sphinx](http://www.sphinx-doc.org/), a tool for producing HTML documentation. - * A [collection of lecture notes](http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/) by Chris Carroll, to catch you up on the basics of consumption-saving models. - * A [particular set of lecture notes](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/) by Chris Carroll, explaining many of the numeric techniques used in HARK and offering an example of a simple structural model that, in a stunning coincidence, is implemented in HARK as an example. - * A [deep dive](http://www.econ2.jhu.edu/people/ccarroll/BufferStockTheory.pdf) into the theoretical foundations of buffer stock saving. Warning: dense. - -### 0.2 General Purpose Tools - -HARK’s root directory contains six tool modules,[5](HARKmanual5.html#fn5x0) -each containing a variety of functions and classes that can be used in many -economic models– or even for mathematical purposes that have nothing to do -with economics. Some of the tool modules are very sparely populated at this -time, while others are quite large. We expect that all of these modules will -grow considerably in the near future, as new tools are “low hanging fruit” for -contribution to the project.[6](HARKmanual6.html#fn6x0) - -#### 0.2.1 HARKcore - -A key goal of the project is to create modularity and interoperability between -models, making them easy to combine, adapt, and extend. To this end, the -HARK.core module specifies a framework for economic models in HARK, creating a -common structure for them on two levels that can be called “microeconomic” and -“macroeconomic”. - -Microeconomic models in HARK use the AgentType class to represent agents with -an intertemporal optimization problem. Each of these models specifies a -subclass of AgentType; an instance of the subclass represents agents who are -ex-ante homogeneous– they have common values for all parameters that describe -the problem. For example, ConsIndShockModel specifies the IndShockConsumerType -class, which has methods specific to consumption-saving models with -idiosyncratic shocks to income; an instance of the class might represent all -consumers who have a CRRA of 3, discount factor of 0.98, etc. The AgentType -class has a solve method that acts as a “universal microeconomic solver” for -any properly formatted model, making it easier to set up a new model and to -combine elements from different models; the solver is intended to encompass -any model that can be framed as a sequence of one period problems. For a -complete description, see section 0.3. - -Macroeconomic models in HARK use the Market class to represent a market (or -other aggregator) that combines the actions, states, and/or shocks (generally, -outcomes) of individual agents in the model into aggregate outcomes that are -“passed back” to the agents. For example, the market in a consumption-saving -model might combine the individual asset holdings of all agents in the market -to generate aggregate capital in the economy, yielding the interest rate on -assets (as the marginal product of capital); the individual agents then learn -the aggregate capital level and interest rate, conditioning their next action -on this information. Objects that microeconomic agents treat as exogenous when -solving (or simulating) their model are thus endogenous at the macroeconomic -level. Like AgentType, the Market class also has a solve method, which seeks -out a dynamic general equilibrium: a “rule” governing the dynamic evolution of -macroeconomic objects such that if agents believe this rule and act -accordingly, then their collective actions generate a sequence of -macroeconomic outcomes that justify the belief in that rule. For a more -complete description, see section 0.4. - -Beyond the model frameworks, HARK.core also defines a “supersuperclass” called -HARKobject. When solving a dynamic microeconomic model with an infinite -horizon (or searching for a dynamic general equilibrium), it is often required -to consider whether two solutions are sufficiently close to each other to -warrant stopping the process (i.e. approximate convergence). It is thus -necessary to calculate the “distance” between two solutions, so HARK specifies -that classes should have a distance method that takes a single input and -returns a non-negative value representing the (generally dimensionless) -distance between the object in question and the input to the method. As a -convenient default, HARKobject provides a “universal distance metric” that -should be useful in many contexts.[7](HARKmanual7.html#fn7x0) When defining a -new subclass of HARKobject, the user simply defines the attribute -distance_criteria as a list of strings naming the attributes of the class that -should be compared when calculating the distance between two instances of that -class. For example, the class ConsumerSolution has distance_criteria = -[’cFunc’], indicating that only the consumption function attribute of the -solution matters when comparing the distance between two instances of -ConsumerSolution. See [here](https://econ- -ark.github.io/HARK/generated/HARK.core.html) for online documentation. - -#### 0.2.2 HARK.utilities - -The HARK.utilities module carries a double meaning in its name, as it contains -both utility functions (and their derivatives, inverses, and combinations -thereof) in the economic modeling sense as well as utilities in the sense of -general tools. Utility functions included at this time are constant relative -risk aversion and constant absolute risk aversion. Other functions in -HARK.utilities include some data manipulation tools (e.g. for calculating an -average of data conditional on being within a percentile range of different -data), functions for constructing discrete state space grids, convenience -functions for retrieving information about functions, and basic plotting tools -using matplotlib.pyplot. - -The module also includes functions for constructing discrete approximations to -continuous distributions (e.g. approxLognormal() to approximate a log-normal -distribution) as well as manipulating these representations (e.g. appending -one outcome to an existing distribution, or combining independent univariate -distributions into one multivariate distribution). As a convention in HARK, -continuous distributions are approximated as finite discrete distributions -when solving models; an ![N ](HARKmanual1x.png)-dimensional random variable -is formatted as a length ![N + 1 ](HARKmanual2x.png) list of 1D arrays, with -the first element representing event probabilities and all other elements are -realizations of the ![N ](HARKmanual3x.png) component RVs. This both -simplifies solution methods (reducing numeric integrals to simple dot -products) and allows users to easily test whether their chosen degree of -discretization yields a sufficient approximation to the full distribution. See -[here](https://econ-ark.github.io/HARK/generated/HARK.utilities.html) for -online documentation. - -#### 0.2.3 HARK.interpolation - -The HARK.interpolation module defines classes for representing interpolated -function approximations. Interpolation methods in HARK all inherit from a -superclass such as HARKinterpolator1D or HARKinterpolator2D, wrapper classes -that ensures interoperability across interpolation methods. For example, -HARKinterpolator1D specifies the methods __call__ and derivative to accept an -arbitrary array as an input and return an identically shaped array with the -interpolated function evaluated at the values in the array or its first -derivative, respectively. However, these methods do little on their own, -merely reshaping arrays and referring to the _evaluate and _der methods, which -are not actually defined in HARKinterpolator1D. Each subclass of -HARKinterpolator1D specifies their own implementation of _evaluate and _der -particular to that interpolation method, accepting and returning only 1D -arrays. In this way, subclasses of HARKinterpolator1D are easily -interchangeable with each other, as all methods that the user interacts with -are identical, varying only by “internal” methods. - -When evaluating a stopping criterion for an infinite horizon problem, it is -often necessary to know the “distance” between functions generated by -successive iterations of a solution procedure. To this end, each interpolator -class in HARK must define a distance method that takes as an input another -instance of the same class and returns a non-negative real number representing -the “distance” between the two. As each of the HARKinterpolatorXD classes -inherits from HARKobject, all interpolator classes have the default -“universal” distance method; the user must simply list the names of the -relevant attributes in the attribute distance_criteria of the class. - -Interpolation methods currently implemented in HARK include (multi)linear -interpolation up to 4D, 1D cubic spline interpolation, (multi)linear -interpolation over 1D interpolations (up to 4D total), (multi)linear -interpolation over 2D interpolations (up to 4D total), linear interpolation -over 3D interpolations, 2D curvilinear interpolation over irregular grids, and -a 1D “lower envelope” interpolator. See [here](https://econ- -ark.github.io/HARK/generated/HARKinterpolation.html) for online documentation. - -#### 0.2.4 HARK.simulation - -The HARK.simulation module provides tools for generating simulated data or -shocks for post-solution use of models. Currently implemented distributions -include normal, lognormal, Weibull (including exponential), uniform, -Bernoulli, and discrete. As an example of their use, these tools are used in -the consumption-saving models of ConsIndShockModel.py to simulate permanent -and transitory income shocks as well as unemployment events. See -[here](https://econ-ark.github.io/HARK/generated/HARKsimulation.html) for -online documentation. - -#### 0.2.5 HARK.estimation - -Methods for optimizing an objective function for the purposes of estimating a -model can be found in HARK.estimation. As of this writing, the implementation -includes only minimization by the Nelder-Mead simplex method, minimization by -a derivative-free Powell method variant, and two small tools for resampling -data (i.e. for a bootstrap); the minimizers are merely convenience wrappers -(with result reporting) for optimizers included in scipy.optimize. Future -functionality will include more robust global search methods, including -genetic algorithms, simulated annealing, and differential evolution. See -[here](https://econ-ark.github.io/HARK/generated/HARKestimation.html) for full -documentation. - -#### 0.2.6 HARK.parallel - -By default, processes in Python are single-threaded, using only a single CPU -core. The HARK.parallel module provides basic tools for using multiple CPU -cores simultaneously, with minimal effort.[8](HARKmanual8.html#fn8x0) In -particular, it provides the function multiThreadCommands, which takes two -arguments: a list of AgentTypes and a list of commands as strings; each -command should be a method of the AgentTypes. The function simply distributes -the AgentTypes across threads on different cores and executes each command in -order, returning no output (the AgentTypes themselves are changed by running -the commands). Equivalent results would be achieved by simply looping over -each type and running each method in the list. Indeed, HARK.parallel also has -a function called multiThreadCommandsFake that does just that, with identical -syntax to multiThreadCommands; multithreading in HARK can thus be easily -turned on and off.[9](HARKmanual9.html#fn9x0) The module also has functions -for a parallel implementation of the Nelder-Mead simplex algorithm, as -described in Wiswall and Lee (2011). See [here](https://econ- -ark.github.io/HARK/generated/HARK.parallel.html) for full documentation. - -### 0.3 Microeconomics: the AgentType Class - -The core of our microeconomic dynamic optimization framework is a flexible -object-oriented representation of economic agents. The HARK.core module -defines a superclass called AgentType; each model defines a subclass of -AgentType, specifying additional model-specific features and methods while -inheriting the methods of the superclass. Most importantly, the method solve -acts as a “universal solver” applicable to any (properly formatted) discrete -time model. This section describes the format of an instance of AgentType as -it defines a dynamic microeconomic problem;[10](HARKmanual10.html#fn10x0) - -#### 0.3.1 Attributes of an AgentType - -A discrete time model in our framework is characterized by a sequence of -“periods” that the agent will experience. A well-formed instance of AgentType -includes the following attributes: - - * solveOnePeriod: A function, or a list of functions, representing the solution method for a single period of the agent’s problem. The inputs passed to a solveOnePeriod function include all data that characterize the agent’s problem in that period, including the solution to the subsequent period’s problem (designated as solution_next). The output of these functions is a single Solution object, which can be passed to the solver for the previous period. - * time_inv: A list of strings containing all of the variable names that are passed to at least one function in solveOnePeriod but do not vary across periods. Each of these variables resides in a correspondingly named attribute of the AgentType instance. - * time_vary: A list of strings naming the attributes of this instance that vary across periods. Each of these attributes is a list of period-specific values, which should be of the same length. If the solution method varies across periods, then ’solveOnePeriod’ is an element of time_vary.[11](HARKmanual11.html#fn11x0) - * solution_terminal: An object representing the solution to the “terminal” period of the model. This might represent a known trivial solution that does not require numeric methods, the solution to some previously solved “next phase” of the model, a scrap value function, or an initial guess of the solution to an infinite horizon model. - * pseudo_terminal: A Boolean flag indicating that solution_terminal is not a proper terminal period solution (rather an initial guess, “next phase” solution, or scrap value) and should not be reported as part of the model’s solution. - * cycles: A non-negative integer indicating the number of times the agent will experience the sequence of periods in the problem. For example, cycles = 1 means that the sequence of periods is analogous to a lifecycle model, experienced once from beginning to end; cycles = 2 means that the agent experiences the sequence twice, with the first period in the sequence following the last. An infinite horizon problem in which the sequence of periods repeats indefinitely is indicated with cycles = 0. - * tolerance: A positive real number indicating convergence tolerance, representing the maximum acceptable “distance” between successive cycle solutions in an infinite horizon model; it is irrelevant when cycles > 0. As the distance metric on the space of solutions is model-specific, the value of tolerance is generally dimensionless. - * time_flow: A Boolean flag indicating the direction that time is “flowing.” When True, the variables listed in time_vary are listed in ordinary chronological order, with index 0 being the first period; when False, these lists are in reverse chronological order, with index 0 holding the last period. - -An instance of AgentType also has the attributes named in time_vary and -time_inv, and may have other attributes that are not included in either (e.g. -values not used in the model solution, but instead to construct objects used -in the solution). - -#### 0.3.2 A Universal Solver - -When an instance of AgentType invokes its solve method, the solution to the -agent’s problem is stored in the attribute solution. The solution is computed -by recursively solving the sequence of periods defined by the variables listed -in time_vary and time_inv using the functions in solveOnePeriod. The time- -varying inputs are updated each period, including the successive period’s -solution as solution_next; the same values of time invariant inputs in -time_inv are passed to the solver in every period. The first call to -solveOnePeriod uses solution_terminal as solution_next. In a finite horizon -problem, the sequence of periods is solved cycles times over; in an infinite -horizon problem, the sequence of periods is solved until the solutions of -successive cycles have a “distance” of less than tolerance. - -The output from a function in solveOnePeriod is an instance of a model- -specific solution class. The attributes of a solution to one period of a -problem might include behavioral functions, (marginal) value functions, and -other variables characterizing the result. Each solution class must have a -method called distance(), which returns the “distance” between itself and -another instance of the same solution class, so as to define convergence as a -stopping criterion; for many models, this will be the “distance” between a -policy or value function in the solutions. If the solution class is defined as -a subclass of HARKobject, it automatically inherits the default distance -method, so that the user must only list the relevant object attributes in -distance_criteria. - -The AgentType also has methods named preSolve and postSolve, both of which -take no arguments and do absolutely nothing. A subclass of AgentType can -overwrite these blank methods with its own model specific methods. preSolve is -automatically called near the beginning of the solve method, before solving -the sequence of periods. It is used for specifying tasks that should be done -before solving the sequence of periods, such as pre-constructing some objects -repeatedly used by the solution method or finding an analytical terminal -period solution. For example, the IndShockConsumerType class in -ConsIndShockModel has a preSolve method that calls its updateSolutionTerminal -method to ensure that solution_terminal is consistent with the model -parameters. The postSolve method is called shortly after the sequence of -periods is fully solved; it can be used for “post-processing” of the solution -or performing a step that is only useful after solution convergence. For -example, the TractableConsumerType in TractableBufferStockModel has a -postSolve method that constructs an interpolated consumption function from the -list of stable arm points found during solution. - -Our universal solver is written in a very general way that should be -applicable to any discrete time optimization problem– because Python is so -flexible in defining objects, the time-varying inputs for each period can take -any form. Indeed, the solver does no “real work” itself, but merely provides a -structure for describing models in the HARK framework, allowing -interoperability among current and future modules. - -#### 0.3.3 The Flow of Time and Other Methods - -Because dynamic optimization problems are solved recursively in our framework, -it is natural to list time-varying values in reverse chronological order– the -solve() method loops over the values in each time-varying list in the same -direction that a human would read them. When simulating agents after the -solution has been obtained, however, it is much more convenient for time- -varying parameters to be listed in ordinary chronological order– the direction -in which they will be experienced by simulated agents. To allow the user to -set the order in which “time is flowing” for an instance of AgentType, the -HARK framework includes functionality to easily change ordering of time- -varying values. - -The attribute time_flow is True if variables are listed in ordinary -chronological order and False otherwise. AgentType has the following methods -for manipulating time: - - * timeReport(): Prints to screen a description of the direction that time is flowing, for interactive convenience and as a reminder of the functionality. - * timeFlip(): Flips the direction of time. Each attribute listed in time_vary is reversed in place, and the value of time_flow is toggled. - * timeFwd(): Sets the direction of time to ordinary chronological order. - * timeRev(): Sets the direction of time to reverse chronological order. - -These methods are invoked to more conveniently access time-varying objects. -When a new time-varying attribute is added, its name should be appended to -time_vary, particularly if its values are used in the solution of the model -(or is part of the solution itself). For example, the solve() method -automatically adds the string ’solution’ to time_vary if it is not already -present. Note that attributes listed in time_vary must be lists if solve() or -timeFlip() are used. Some values that could be considered “time varying” but -are never used to solve the model are more conveniently represented as a -numpy.array object (e.g. the history of a state or control variable from a -simulation); because the numpy.array class does not have a reverse() method, -these attributes should not be listed in time_vary. - -The base AgentType is sparsely defined, as most “real” methods will be -application-specific. Two final methods bear mentioning. First, the __call__ -method points to assignParameters(), a convenience method for adding or -adjusting attributes (inherited from HARKobject). This method takes any number -of keyword arguments, so that code can be parsimoniously written as, for -example, AgentInstance(attribute1 = value1, attribute2 = value2). Using -Python’s dictionary capabilities, many attributes can be conveniently set with -minimal code. Second, the resetRNG method simply resets the AgentType’s random -number generator (as the attribute RNG) using the value in the attribute -seed.[12](HARKmanual12.html#fn12x0) This method is useful for (inter alia) -ensuring that the same underlying sequence of shocks is used for every -simulation run when a model is solved or estimated. - -#### 0.3.4 Sample Model: Perfect Foresight Consumption-Saving - -To provide a concrete example of how the AgentType class works, consider the -very simple case of a perfect foresight consumption-saving model. The agent -has time-separable, additive CRRA preferences over consumption, discounting -future utility at a constant rate; he receives a particular stream of labor -income each period, and knows the interest rate on assets that he holds from -one period to the next. His decision about how much to consume in a particular -period can be expressed in Bellman form as: - -![Vt\(Mt \) = max u\(Ct\) + β//Dt E\[Vt+1\(Mt+1 \)\], - - Ct - At = Mt - Ct, - - Mt+1 = RAt + Yt+1, - - Yt+1 = Γ t+1Yt, - - C1- ρ - u\(C \) = -----. - - 1 - ρ ](HARKmanual4x.png) - -An agent’s problem is thus characterized by values of ![ρ -](HARKmanual5x.png), ![R ](HARKmanual6x.png), and ![β ](HARKmanual7x.png) -plus sequences of survival probabilities ![//Dt ](HARKmanual8x.png) and -income growth factors ![Γ t ](HARKmanual9x.png) for ![t = 0,⋅⋅⋅,T -](HARKmanual10x.png). This problem has an analytical solution for both the -value function and the consumption function. - -The ConsIndShockModel module defines the class PerfForesightConsumerType as a -subclass of AgentType and provides solver functions for several variations of -a consumption-saving model, including the perfect foresight -problem.[13](HARKmanual13.html#fn13x0) A HARK user could specify and solve a -ten period perfect foresight model with the following commands: - -MyConsumer = PerfForesightConsumerType(time_flow=True, cycles=1, - -CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98, - -LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90], - -PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02], - -Nagents=1000) - -MyConsumer.solve() - -The first line makes a new instance of ConsumerType, specifying that time is -currently “flowing” forward[14](HARKmanual14.html#fn14x0) and that the -sequence of periods happens exactly once; the next three lines (all part of -the same command) set the time invariant (CRRA, Rfree, DiscFac) and time -varying parameters (LivPrb, PermGroFac). Note that dir(MyConsumer) shows all -the attributes of MyConsumer. After running the solve method, MyConsumer will -have an attribute called solution, which will be a list with eleven -ConsumerSolution objects, representing the period-by-period solution to the -model.[15](HARKmanual15.html#fn15x0) Each ConsumerSolution has several -attributes: - - * cFunc: Optimal consumption function for this period as a function of (normalized) market resources ![m = M ∕Y - t t t ](HARKmanual11x.png). Can be called like any other function: -MyConsumer.solution[0].cFunc(5) returns consumption at ![mt = 5 -](HARKmanual12x.png). - - * vFunc: Value function (over market resources ![mt ](HARKmanual13x.png)); can be evaluated like cFunc. - * mNrmMin: Minimum value of normalized market resources ![mt ](HARKmanual14x.png) such that cFunc and vFunc are defined. - * hNrm: Normalized human wealth– the PDV of future income (ignoring mortality) divided by current period income ![Yt ](HARKmanual15x.png). - * MPC: The constant marginal propensity to consume (linear consumption function). - -The following command generates the plot for the perfect foresight consumption -function: - -mMin = MyConsumer.solution[0].mNrmMin - -plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10) - -To solve a version of this problem in which the sequence of ten periods -happens three times (yielding a solution attribute with thirty-one elements), -the user can do: - -MyConsumer(cycles = 3) - -MyConsumer.solve() - -To solve an infinite horizon problem in which the agent experiences the same -one period problem indefinitely, the user can do: - -OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02, - -DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000) - -OtherConsumer.solve() - -This instance is specified as being infinite horizon by setting cycles=0. Note -that the time-varying inputs are still specified as (one element) lists, even -though they take on the same value in every period; this is because an -infinite horizon model might consist of a multi-period sequence repeated -indefinitely, rather than just one repeated period. The solution attribute of -OtherConsumer will be a list containing one instance of ConsumerSolution, -representing the solution to every period of the model. - -### 0.4 Macroeconomics: the Market Class - -The modeling framework of AgentType is deemed “microeconomic” because it -pertains only to the dynamic optimization problem of agents, treating all -inputs of the problem as exogenously fixed. In what we label as -“macroeconomic” models, some of the inputs for the microeconomic models are -endogenously determined by the collective states and controls of agents in the -model. In a dynamic general equilibrium, there must be consistency between -agents’ beliefs about these macroeconomic objects, their individual behavior, -and the realizations of the macroeconomic objects that result from individual -choices. - -The Market class in HARK.core provides a framework for such macroeconomic -models, with a solve method that searches for a dynamic general equilibrium. -An instance of Market includes a list of AgentTypes that compose the economy, -a method for transforming microeconomic outcomes (states, controls, and/or -shocks) into macroeconomic outcomes, and a method for interpreting a history -or sequence of macroeconomic outcomes into a new “dynamic rule” for agents to -believe. Agents treat the dynamic rule as an input to their microeconomic -problem, conditioning their optimal policy functions on it. A dynamic general -equilibrium is a fixed point dynamic rule: when agents act optimally while -believing the equilibrium rule, their individual actions generate a -macroeconomic history consistent with the equilibrium rule. - -#### 0.4.1 Down on the Farm - -The Market class uses a farming metaphor to conceptualize the process for -generating a history of macroeconomic outcomes in a model. Suppose all -AgentTypes in the economy believe in some dynamic rule (i.e. the rule is -stored as attributes of each AgentType, which directly or indirectly enters -their dynamic optimization problem), and that they have each found the -solution to their microeconomic model using their solve method. Further, the -macroeconomic and microeconomic states have been reset to some initial -orientation. - -To generate a history of macroeconomic outcomes, the Market repeatedly loops -over the following steps a set number of times: - - 1. sow: Distribute the macroeconomic state variables to all AgentTypes in the market. - 2. cultivate: Each AgentType executes their marketAction method, likely corresponding to simulating one period of the microeconomic model. - 3. reap: Microeconomic outcomes are gathered from each AgentType in the market. - 4. mill: Data gathered by reap is processed into new macroeconomic states according to some “aggregate market process”. - 5. store: Relevant macroeconomic states are added to a running history of outcomes. - -This procedure is conducted by the makeHistory method of Market as a -subroutine of its solve method. After making histories of the relevant -macroeconomic variables, the market then executes its calcDynamics function -with the macroeconomic history as inputs, generating a new dynamic rule to -distribute to the AgentTypes in the market. The process then begins again, -with the agents solving their updated microeconomic models given the new -dynamic rule; the solve loop continues until the “distance” between successive -dynamic rules is sufficiently small. - -#### 0.4.2 Attributes of a Market - -To specify a complete instance of Market, the user should give it the -following attributes:[16](HARKmanual16.html#fn16x0) - - * agents: A list of AgentTypes, representing the agents in the market. Each element in agents represents an ex-ante heterogeneous type; each type could have many ex-post heterogeneous agents. - * sow_vars: A list of strings naming variables that are output from the aggregate market process, representing the macroeconomic outcomes. These variables will be distributed to the agents in the sow step. - * reap_vars: A list of strings naming variables to be collected from the agents in the reap step, to be used as inputs for the aggregate market process. - * const_vars: A list of strings naming variables used by the aggregate market process that do not come from agents; they are constant or come from the Market itself. - * track_vars: A list of strings naming variables generated by the aggregate market process that should be tracked as a history, to be used when calculating a new dynamic rule. Usually a subset of sow_vars. - * dyn_vars: A list of strings naming the variables that constitute a dynamic rule. These will be stored as attributes of the agents whenever a new rule is calculated. - * millRule: A function for the “aggregate market process”, transforming microeconomic outcomes into macroeconomic outcomes. Its inputs are named in reap_vars and const_vars, and it returns a single object with attributes named in sow_vars and/or track_vars. Can be defined as a method of a subclass of Market. - * calcDynamics: A function that generates a new dynamic rule from a history of macroeconomic outcomes. Its inputs are named in track_vars, and it returns a single object with attributes named in dyn_vars. - * act_T: The number of times that the makeHistory method should execute the “farming loop” when generating a new macroeconomic history. - * tolerance: The minimum acceptable “distance” between successive dynamic rules produced by calcDynamics to constitute a sufficiently converged solution. - -Further, each AgentType in agents must have two methods not necessary for -microeconomic models; neither takes any input (except self): - - * marketAction: The microeconomic process to be run in the cultivate step. Likely uses the new macroeconomic outcomes named in sow_vars; should store new values of relevant microeconomic outcomes in the attributes (of self) named in reap_vars. - * reset: Reset, initialize, or prepare for a new “farming loop” to generate a macroeconomic history. Might reset its internal random number generator, set initial state varibles, clear personal histories, etc. - -When solving macroeconomic models in HARK, the user should also define classes -to represent the output from the aggregate market process in millRule and for -the model-specific dynamic rule. The latter should have a distance method to -test for solution convergence; if the class inherits from HARKobject, the user -need only list relevant attributes in distance_criteria. - -#### 0.4.3 Sample Model: FashionVictim - -To illustrate the Market class, consider a simple example in the emerging -economic subfield of aesthemetrics, the -FashionVictimModel.[17](HARKmanual17.html#fn17x0) -[18](HARKmanual18.html#fn18x0) This module defines a subclass of AgentType -called FashionVictimType. Each period, fashion victims make a binary choice of -style ![s ](HARKmanual16x.png): to dress as a jock (0) or punk (1). They -receive utility directly from the outfit they wear and as a function of the -proportion of the population who just wore the same style; they also pay -switching costs (![cpj ](HARKmanual17x.png),![cjp ](HARKmanual18x.png)) if -they change styles rather than keep the same as the previous period. Moreover, -they receive an idiosyncratic type 1 extreme value (T1EV) preference shock to -each style in each period. Defining the population punk proportion as ![p -](HARKmanual19x.png) and the conformity utility function as ![f : \[0,1\] → ℝ -](HARKmanual20x.png), the current period utility function is thus: - -![u\(s ;s ,p\) = s f\(p \)+ \(1- s \)f\(1- p \)+s U + \(1- s \)U - c s -\(1- s \)- c \(1 - s \)s . - - t t- 1 t t t t t t p t j pj t-1 t jp -t-1 t - -](HARKmanual21x.png) - -Fashion victims are forward looking and discount future utility at a constant -rate of ![β ](HARKmanual22x.png) per period. To simplify the analysis, we -assume they believe that the population punk proportion in the next period is -a linear function of the punk proportion in the current period, subject to a -uniformly distributed shock. No restrictions are put on the function ![f -](HARKmanual23x.png); fashion victims might be conformists who like to dress -the same as others (![f′\(p\) > 0 ](HARKmanual24x.png)) or hipsters who like -to style themselves in the minority (![f′\(p\) < 0 -](HARKmanual25x.png)).[19](HARKmanual19.html#fn19x0) A fashion victim’s -problem can be written in Bellman form as: - -![ \[ \] - -V\(st-1,pt\) = E smt∈a{x0,1}u \(st;st-1,pt\) + ηst + βE \[V \(st,pt+1\)\] , - -](HARKmanual27x.png) - -![pt+1 = apt + b + πt+1, πt+1 ~ U\[- w,w \], η0,η1 ~ T 1EV. - -](HARKmanual28x.png) - -An instance of FashionVictimType is thus characterized by values of ![Up -](HARKmanual29x.png), ![Uj ](HARKmanual30x.png), ![cpj ](HARKmanual31x.png), -![cjp ](HARKmanual32x.png) and a function ![f ](HARKmanual33x.png), as well -as beliefs about ![pt+1 ](HARKmanual34x.png) as a function of ![pt -](HARKmanual35x.png) (summarized by slope ![a ](HARKmanual36x.png), intercept -![b ](HARKmanual37x.png), and uniform shock width ![w ](HARKmanual38x.png)). -Given this information, a FashionVictimType’s infinite horizon microeconomic -model can be solved by backward induction in a few lines; the “one period -solver” is given by solveFashion. However, while individual agents treat the -dynamics of ![pt ](HARKmanual39x.png) as exogenous, they are in fact -endogenously determined by the actions of all the fashion victims in the -market.[20](HARKmanual20.html#fn20x0) A dynamic general equilibrium of the -“macroeconomic fashion model” is thus characterized by a triple of ![\(a,b,w\) -](HARKmanual40x.png) such that when fashion victims believe in this “punk -evolution rule” and act optimally, their collective fashion choices exhibit -this same rule when the model is simulated. - -The search for a dynamic general equilibrium is implemented in HARK’s Market -class with the following definitions: - -sow_vars = [’pNow’] (macroeconomic outcome is ![pt ](HARKmanual41x.png)) - -reap_vars = [’sNow’] (microeconomic outcomes are ![st ](HARKmanual42x.png) -for many agents) - -track_vars = [’pNow’] (must track history of ![pt ](HARKmanual43x.png)) - -dyn_vars = [’pNextSlope’,’pNextIntercept’,’pNextWidth’] (dynamic rule -![\(a,b,w \) ](HARKmanual44x.png)) - -millRule = calcPunkProp (aggregate process: average the style choices of all -agents) - -calcDynamics = calcFashionEvoFunc (calculate new ![\(a,b,w\) -](HARKmanual45x.png) with autoregression of ![pt ](HARKmanual46x.png)) - -act_T = 1000 (simulate 1000 periods of the fashion market) - -tolerance = 0.01 (terminate solution when ![\(a,b,w \) ](HARKmanual47x.png) -changes by less than 0.01) - -The agents attribute has a list of 22 FashionVictimTypes, which vary in their -values of ![Up ](HARKmanual48x.png) and ![U - - j ](HARKmanual49x.png), and their ![f ](HARKmanual50x.png) functions. The -marketAction method of FashionVictimType simulates one period of the -microeconomic model: each agent receives style preference shocks ![η0 -](HARKmanual51x.png) and ![η1 ](HARKmanual52x.png), sees the current -proportion of punks ![pt ](HARKmanual53x.png) (sown to them as pNow), and -chooses which style to wear, storing it in the binary array sNow, an attribute -of self. - -The millRule for this market is extremely simple: it flattens the list of -binary arrays of individual style choices (gathered in the reap step) and -averages them into a new value of ![pt ](HARKmanual54x.png), to be tracked as -a history and sown back to the agents to begin the cycle again. Once a history -of 1000 values of ![pt ](HARKmanual55x.png) has been generated with the -makeHistory method, we can calculate a new dynamic fashion rule with -calcFashionEvoFunc by regressing ![p - - t ](HARKmanual56x.png) on ![p - - t- 1 ](HARKmanual57x.png), approximating ![w ](HARKmanual58x.png) as twice -the standard deviation of prediction errors.[21](HARKmanual21.html#fn21x0) The -new fashion rule is an instance of the simple FashionEvoFunc class, whose only -methods are inherited from HARKobject. - -When the solve method is run, the solver successively solves each agent’s -microeconomic problem, runs the makeHistory method to generate a 1000 period -history of ![pt ](HARKmanual60x.png), and calculates a new punk evolution -rule based on this history; the solver terminates when consecutive rules -differ by less than 0.01 in any dimension. - -### 0.5 Contributing to HARK - -This section provides an overview of how users can contribute new code to -HARK, what sort of contributions are most desired, and style conventions for -the toolKit. Before attempting to extend HARK, we strongly recommend that you -familiarize yourself with its features and models by looking at the source -code. - -#### 0.5.1 What Does HARK Want? - -The easiest and most straightforward way to contribute to HARK is by writing -new general purpose tools for inclusion in one of the top-level modules. This -might be a commonly known data analysis technique (e.g. a kernel density -estimator) for HARK.utilities, an optimization method (local or global) for -HARK.estimation, an interpolation method for HARKinterpolation, etc. If you’ve -found a technique useful in your own research and believe it could be of use -to others on entirely different projects, then it probably belongs in HARK. -Likewise, if you read through a HARK module expecting to find a certain tool -or function (because of similar functions that are present, or merely because -it’s very useful) but don’t, this is a good sign that HARK has a hole that you -can fill.[22](HARKmanual22.html#fn22x0) - -Secondly, the development team wants to expand HARK to include more canonical -models. Most of the models available in the toolKit at the time of its public -beta release concern agents with (geometrically discounted) CRRA utility over -a unitary consumption good, who receive permanent and transitory shocks to -income and have access to a single risk free asset. This is an important class -of models, but that description has an awful lot of qualifiers. In short, -there are many more common features of dynamic household models that are not -yet in HARK, but would be very valuable for the toolKit. New model -contributions should “play nicely” with existing models and tools, following -HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent -possible. For ideas of potential model extensions for HARK, see section -0.6.2.[23](HARKmanual23.html#fn23x0) - -Finally, contributions to HARK are not restricted to entirely new -functionality. While we have done our best to provide flexible frameworks and -useful tools, we have almost certainly overlooked an unexpected case that -would not be (properly) handled by the toolKit as is. Moreover, there might -even be flaws in our solution or simulation methods. If you find a mistake or -error, we strongly encourage you to contribute a fix for the problem, or at -least to flag it as an Issue on GitHub so that someone else might. The -“mistake” you find might not even be in the code itself, but a deficiency in -the documentation: a misnamed (or incorrectly typed) parameter or return -variable in the docstring, an incorrect (or missing) comment, even a -grammatical or spelling error. No matter how niggling, we want your fix. - -For those who want to contribute to the HARK project without programming a new -model or digging through the code for errors and omissions, consider reading -through the Issues page on GitHub to see what other users have flagged for -development. You might find several pieces of “low hanging fruit” for you to -quickly fix. - -#### 0.5.2 Git and GitHub - -Git is a distributed version control system that has gained increasing -popularity in the decade since its creation; it is designed to enable -collaboration and “non-linear workflow” among a (potentially disperse) -programming team. As HARK is to be developed by volunteer researchers all -around the world, git is an excellent fit for the toolKit. Originally designed -by Linus Torvalds as a command line tool, others have since extended git with -further functionality and a user interface more friendly to those more -familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK -project is hosted. This section provides a brief overview of how to use GitHub -to interact with (and potentially contribute to) HARK. - -To prepare to contribute to HARK, follow these simple steps: - - 1. Make a GitHub account: Go to [github.com](https://www.github.com); the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.[24](HARKmanual24.html#fn24x0) - 2. Install GitHub Desktop: Go to [desktop.github.com](https://desktop.github.com/) and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account. - 3. Fork the HARK repository: Go to the [HARK repository page](https://github.com/econ-ark/HARK), click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead. - 4. Add the repository to Desktop: Open GitHub Desktop, click on the ![+ ](HARKmanual61x.png) icon in the upper left, and select clone. Find the repository named yourusername/HARK, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine. - -You now control a “fork” of the main HARK repository, hosted by GitHub, and -have a clone of this fork on your local machine. You are free to edit your -fork of HARK in any way you’d like; other GitHub users can view your fork (and -fork it themselves), but they cannot make changes to your fork unless you give -them permission by adding them as a contributor. Changes made to the local -copy of your fork do not get automatically sent to the remote copy on GitHub -for public viewing. To make changes to your fork on GitHub, follow these -steps: - - 1. Make local changes: Edit your local copy of the repository in any way you’d like: add or delete lines from existing files; add, delete, or rename entire files or directories. - 2. Commit local changes: Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button. - 3. Push the commit: Click on the Sync button near the upper right to “push” your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced. - -The left hand panel of the Changes tab has a summary of the files that have -been changed, added, or removed since your last commit; the right hand panel -shows the specific changes made to each changed file. After step 2, a record -of your changes has been stored locally by the git system, but is not yet -reflected in the public remote copy on GitHub.[25](HARKmanual25.html#fn25x0) -The “timeline” summary of the repository at the top of the Desktop window -shows the history of commits for the repository: each dot represents a commit -that was pushed to the remote server, and each open ring represents a local -commit that has not been pushed to the remote server; the large broken ring on -the far right represents changes that have not been locally -committed.[26](HARKmanual26.html#fn26x0) The History tab lists all previous -commits, including the timestamp, contributor, and a record of changes made. - -Suppose you have mucked about in your fork for a while and have made changes -to HARK that you think should be included in the main repository hosted by -Econ-Ark. To make your dream a reality, follow these steps: - - 1. Isolate the changes: If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should isolate the desired changes in a new branch of your fork. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,[27](HARKmanual27.html#fn27x0) then edit this branch to include only the changes you want to push to the main repository. - 2. Issue a pull request: Go to [HARK’s GitHub page](https://github.com/econ-ark/HARK) and click the “New pull request” button. Click the text that says “compare across forks”, and select econ-ark/HARK as the base fork, master as the base, your fork as the head fork, and the branch to be merged as the “compare”. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you’ve done. Click the “Create pull request” button. - 3. Be patient: Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section 0.5.3 for procedures followed by the HARK team when evaluating outside contributions. - -#### 0.5.3 Submission Approval Process - -Before submitting to HARK, the development team would like you to make sure -that your intended contribution is listed as an Issue on the GitHub page. If -you are going to fix or address an Issue that is already posted, reply to that -post with a note indicating that you are about to work on it and an estimate -of when you will make a pull request for it. If you want to do something that -no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly) -describing what you intend to do.[28](HARKmanual28.html#fn28x0) This will both -help prevent two people from working on the same thing (and one of them -potentially wasting time on it), and lets the development team know what’s -coming. Moreover, if you propose a contribution that is (a) already in HARK, -(b) not in the spirit of HARK, or (c) a fix for something that’s not actually -broken, this gives the team a chance to let you know not to spend effort on -it. - -After creating a pull request to submit your contribution, it will be -evaluated by one or more members of the HARK team. Criteria that the HARK team -will consider when choosing whether to accept a submission include: - - 1. Does the contribution break previous code? If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility. - 2. Is the code well documented? Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section 0.5.5) and enough comments for a reader to follow the algorithm. - 3. Is the contribution relevant? The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn’t belong in HARK. - 4. Is the code demonstrated with an example? If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you’ve contributed a new function, consider providing a snippet of code demonstrating its use.[29](HARKmanual29.html#fn29x0) - 5. Is the code correct? Your code should actually do what it claims to do. - 6. Does the code use HARK conventions? To the best of your ability, contributions to HARK should follow the style guidelines in section 0.5.4. We don’t demand perfection, as these are guidelines rather than rules. - 7. Is the code well integrated in HARK? In the beta release of HARK, we have tried to present a “taxonomy” of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy. - 8. Is this already in HARK? If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK. - -The HARK team intends to release more specific guidelines in the near future, -so that contributors are better able to judge their submissions before issuing -a pull request. We are in the process of recruiting “czars” to oversee -particular aspects of HARK, providing their knowledge and -expertise.[30](HARKmanual30.html#fn30x0) While czars might directly evaluate -submissions as a HARK team member, their more important role is to help -establish these guidelines for their domain, more specifically nailing down -what we mean by “correct” or “well integrated”. - -#### 0.5.4 Naming Conventions - -Object naming conventions in HARK are fairly different than existing -standards, and differ somewhat between tool modules vs model or application -modules. The following conventions apply throughout HARK: - - * Functions and methods are always in “camel case”: no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. approxLognormal. - * Function and method names should accurately and concisely describe what the function does; the first word in the name must be a verb.[31](HARKmanual31.html#fn31x0) - * Variable and class names should not have a verb as their first word. - * Class names should use no underscores and capitalize the first letter of each word; moreover, a class name must include a noun. E.g. ConsPerfForesightSolver. - -When naming variables in model modules, the HARK team strongly discourages -using single letter names, like c for consumption. Instead, we encourage -contributors to use longer, more descriptive variable names using additional -words and common abbreviations to specify the variable more precisely. In -/Documentation/NARK.pdf, we list standard single letter variable “bases” and -an array of prefixes and suffixes to adjust them. Economic variables in model -modules should (usually) not use underscores, instead using camel case to the -greatest extent possible.[32](HARKmanual32.html#fn32x0) The development team -prefers this standard so that users can translate between Python code and -LaTeX script with minimal work. - -Conventions for naming variables in HARK’s tool modules are significantly -closer to more commonly used standards. Variable names should be in all lower -case, with underscores between words, e.g. data_to_match. The functions and -classes in these modules are more general and almost surely do not have any -inherent “economic content”; they are numerical or algorithmic objects, not -variables that might appear in an equation in an article for a (non- -computational) economics journal. Variable names in application modules (e.g. -the files that execute the cstwMPC estimations) are a mix of the conventions -for tool and model files, as appropriate for each variable. That is, variables -that are directly related to “economic variables” in model modules should -follow those conventions, while objects created solely for data manipulation -or reporting should use the style of tool modules. - -#### 0.5.5 Documentation Conventions - -The HARK team wants the toolKit to be as accessible to users as possible; our -greatest fear[33](HARKmanual33.html#fn33x0) is that a new user will open up -our code, get hopelessly confused trying to read it, and then never look at -HARK again. To prevent this tragic outcome, we have tried hard to provide -comprehensive, accurate documentation and comments within the code describing -our methods.[34](HARKmanual34.html#fn34x0) Moreover, HARK uses the Sphinx -utility to generate a website with [online documentation](https://econ- -ark.github.io/HARK/) for all of our tool and model modules, so that users can -learn about what’s available in HARK without digging through the source code. -When making contributions to HARK, the development team asks users to format -their inline documentation to work with Sphinx by following a few simple -rules. - - * The top of every module should begin with a “docstring” providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:[35](HARKmanual35.html#fn35x0) - -""" - -Specifies an economic model and provides methods for solving it. More specific -description of the key features of the model and variations of it in this -module. Maybe some comments about the solution method or limitations of the -model. Your bank account routing number. - -""" - - * The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as: - -def functionName(input1,input2): - -""" - -Concise description of the function. More details about what - -the function does, options or modes available, and maybe mathematical - -methods used. Credit to a source if you poached their algorithm. - -Parameters - -–––––––––– - -input1: type - -Description of what input1 represents. - -input2: type - -Description of what input2 represents. - -Returns - -––––––– - -output_name: type - -Description of the output(s) of the function. Might have - -multiple entries. If no output, this is just "None". - -""" - - * Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it. - -Finally, if you write a new model module, the HARK team asks that you also -provide a short mathematical writeup of the model as a PDF. This document does -not need to go into great detail about the solution method for the model or -the functions and classes included in the module, merely specify the economic -model and provide a summary of how it is solved. See -/Documentation/ConsumptionSavingModels.pdf for an example of this. - -### 0.6 Future of HARK - -HARK is in a state of perpetual incompleteness, always expanding to include -more tools and more models. As an open source project in the hands of many -contributors around the world, the HARK team cannot fully predict future -features of the toolKit. This section provides an overview of what we would -like to see in the near future, as well as potential mechanisms to encourage -researchers to contribute their code to the project. - -#### 0.6.1 Future Tools - -The current frameworks in HARK.core concern agents who have “isolated” dynamic -problems or interact with each other only through aggregate outcomes to which -they contribute atomically. We hope to eventually provide a framework for -models in which agents directly interact with each other; this may include a -system for specifying the state of networks and how connections are formed and -broken. HARK.core might also develop a framework more suited to industrial -organization models and dynamic games.[36](HARKmanual36.html#fn36x0) - -The HARK.estimation module in particular is very sparsely populated, with its -functions mostly serving as wrappers for “off the shelf” optimization methods -from scipy.optimize. There are plenty of non-standard or more complex -optimization techniques available, including global and/or stochastic search -methods. We believe that additional optimizers are “low hanging fruit” as -straightforward contributions to HARK and thus will be added in the near -future. Moreover, there are plenty of distributions that have no approximator -in HARK.utilities (or data generator in HARK.simulation), and these functions -are among the easiest for new contributors to write. The data analysis tools -in HARK.utilities only include functions that we happened to use for our -current research projects, and we expect that this will be the mode of growth -for future tools. There are some obvious holes that we believe will be filled -quite quickly; for example, we provide a rudimentary non-parametric kernel -regression function, but have not written a kernel density estimator to -generate PDFs from data. - -The classes currently in HARK.interpolation are all variations on linear (or -cubic) spline interpolation, and each is specifically written for a fixed -number of dimensions. As these methods use full tensor grids, they are highly -impractical for representing functions in higher dimensions, suffering greatly -from the curse of dimensionality.[37](HARKmanual37.html#fn37x0) We are quite -confident that HARK.interpolation will soon include techniques more applicable -to high dimensional state spaces, including Smolnyak interpolation, other -(adaptive) sparse grid methods, and regression-based interpolation. For lower- -dimensional spaces, HARK will also soon provide Delaunay interpolation -classes. - -The HARK.parallel module currently provides a simple interface for handling -ex-ante heterogeneity among agents. While useful for speeding up moderately- -sized problems, the libraries used are only able to access the CPU of the -local machine running the code. In the very near future, the HARK team will -provide an interface (and example) for using Apache Spark, which bills itself -as a “fast and general engine for large-scale data processing”. Specifically, -users will be able to easily scale up their computing resources and run HARK -code on an Amazon EC2 session or other cluster. - -#### 0.6.2 Future Models - -The economics models currently included in HARK mostly concern consumption- -saving problems with constant relative risk aversion consumers who experience -fully permanent and fully transitory shocks to income. This is obviously a -very small subset of all consumption-saving models (which are themselves a -small subset of all dynamic models economists might be interested in). -Consider some examples of variants, features, and extensions that are not -included in HARK, but could be in the future: - - * Consumers with other utility functions, such as CARA or expo-power. - * Consumers with time-inconsistent preferences, e.g. hyperbolic discounting. - * Income shocks that are neither fully transitory nor fully permanent. - * Consumers with a bequest motive or terminal utility function. - * Utility from leisure and endogenous labor supply. - * Investment in a durable consumption good such as housing. - * Exogenous shocks to expenses. - * Multiple consumption goods, as with endogenous medical care. - * Portfolio allocation between a risky and riskless asset. - * Job search intensity as a control variable. - * Endogenous health evolution and health investment. - -The HARK team is currently developing a module for mixing discrete and -continuous choice models, in the style of Jorgensen, Rust, Iskhakov, and -Schjerning (2015). Among many other models, this framework will enable HARK -users to easily develop features like: - - * Labor supply on the extensive margin: the timing of retirement. - * Selection of job type or sector (by riskiness, specific human capital, etc). - * Choice of medical insurance contract. - * Decision to rent or own housing. - * Endogenous default on debt (with “macroeconomic” pricing of debt). - * Household formation, dissolution, and reproduction. - -Finally, we hope to provide a generic solver for any discrete choice model -with T1EV shocks. The scope of HARK is limited only by what users choose to -put in it. We hope that within a year of its debut, HARK will include a -significant number of canonical models, providing more building blocks for -future researchers to create novel combinations and new extensions. - -#### 0.6.3 Bounty Hunting - -While HARK users are free to write and contribute any tools and models that -they believe could be helpful, building the toolKit in a “bottom up” style, -the HARK team hopes to provide mechanisms for targeted “top down” development. -The team is working to secure grant funding for HARK so as to offer “bounties” -for particular models, tools, applications, or frameworks. Suppose the team -decides that the model in Important Paper X should be included as part of -HARK; we will issue a call for contributions to fill this hole, offering a -modest prize of, say, $20,000 to the programmer who submits the best -implementation of the model, as judged by clearly stated -criteria.[38](HARKmanual38.html#fn38x0) The bounty would be announced as an -Issue on HARK’s GitHub page, as well as in a potential electronic digest of -HARK news. - -Bounties might be placed on items as small as a single data analysis tool or -as large as a complex estimation of a large general equilibrium model, with -prizes scaling accordingly. We hasten to emphasize that we cannot guarantee -funding for HARK, so the bounties (like everything else in this section) are -purely speculative. As economists, the HARK team understands the incentive -compatibility problems associated with asking researchers to contribute -significant amounts of their time, effort, and unique skills in service of a -tenuous concept like the Greater Good in exchange for even more tenuous Glory. -We acknowledge that economic researchers are also optimizing agents with their -own preferences over leisure and consumption, and agree that Greed is a valid -mechanism to expand HARK. - -#### 0.6.4 All Aboard the Ark - -As observant readers may have noticed, the HARK repository is hosted by a -GitHub account named Econ-Ark, with no explanation as to what this might mean. -Looking a few years into the future, the development team believes that HARK -might be the first piece of a broader collection of computational economics -resources. HARK is open source but somewhat akin to an exclusive club: -contributions are required to adhere to standards and norms so as to foster -modularity and interoperability among models and tools. Consistent with its -namesake ship, the Econ-Ark has a significantly lower bar for admission, with -a place on board for everything. - -On one level, the Ark might serve as a warehouse for any and all quantitative -models and tools, for any economic purpose, in any subfield, and written in -any language. Through nearly unanimous feedback, the HARK team understands -that many economists are willing to share code that they have already written, -but are reluctant to expend the considerable effort to translate and format it -for HARK. In the future, we will set up a mechanism for code like this to be -hosted on the Ark, with minimal requirements imposed on its authors (and -correspondingly minimal warranty for potential users). Relevant projects on -the Ark might become bounty targets for HARK, with users incentivized to port -the original code into Python and to meet HARK standards; direct access to the -original source code will significantly expedite the translation process, and -provide a suitable benchmark for testing and verifying the contribution to -HARK. - -On a different level, the Ark might also include cohesive toolkits like HARK, -but geared toward purposes other than heterogeneous agents macroeconomics -models. For example, the previously mentioned modules for networks among -agents (and other agent-based modeling), industrial organization and dynamic -games, and traditional discrete choice models might become their own toolkits, -with some interoperability with HARK. In support of this, some of the tool -modules currently in HARK might be “promoted” and become universally shared -across all Econ-Ark toolkits. - -The dark secret of HARK is that it is named for heterogeneous agents -macroeconomics, but designed and written by structural microeconomists. The -development team believes these subfields to have considerable overlap in -terms of methods and techniques, and that HARK might not be noticed by half of -its potential audience due to its name. In a general sense, the Econ-Ark gives -us cover to expand the scope of our efforts and/or rebrand the toolKit for an -alternative audience. - From 5d59179cff7f7a399686b3b16afe36996742e16d Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 29 Oct 2019 16:20:28 -0400 Subject: [PATCH 102/242] fixing sphinx index: intro text, uses Gentle notebook from DemARK, API docs render without placeholder. See #409 --- Documentation/index.rst | 64 ++++++++++------------------------------- 1 file changed, 15 insertions(+), 49 deletions(-) diff --git a/Documentation/index.rst b/Documentation/index.rst index 71ae56fc0..0258f5c79 100644 --- a/Documentation/index.rst +++ b/Documentation/index.rst @@ -1,6 +1,18 @@ -Welcome to HARK's documentation! +HARK Documentation ================================ +Heterogenous Agents Resources & toolKit (HARK) is a toolkit for the structural modeling of economic choices of optimizing and non-optimizing heterogeneous agents. + +HARK is software written in Python. +This is the documentation for that software. +It is aimed at computational researchers and other +software developers who are building new models with HARK, +or are contributing to the core software. + +If you are a student trying to learn economics concepts, +you might want to look at the `DemARK +`_ project. + .. toctree:: :maxdepth: 2 :caption: User Guide @@ -11,60 +23,14 @@ Welcome to HARK's documentation! :maxdepth: 2 :caption: Notebooks - notebooks/Gentle-Intro-To-HARK + submodules/DemARK/notebooks/Gentle-Intro-To-HARK submodules/DemARK/notebooks/KinkedRconsumerType .. toctree:: :maxdepth: 2 :caption: Python API - Placeholder - -.. autosummary:: - :toctree: generated - - HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUs - HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond - HARK.BayerLuetticke.Assets.One.SharedFunc - HARK.BayerLuetticke.Assets.One.SharedFunc2 - HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUs - HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUsBond - HARK.BayerLuetticke.Assets.One.defineSSParameters - HARK.BayerLuetticke.Assets.One.defineSSParametersIOUsBond - HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset - HARK.BayerLuetticke.Assets.Two.SharedFunc3 - HARK.BayerLuetticke.Assets.Two.SteadyStateTwoAsset - HARK.BayerLuetticke.Assets.Two.defineSSParametersTwoAsset - HARK.BayerLuetticke.BayerLuetticke_wrapper - HARK.BayerLuetticke.ConsIndShockModel_extension - HARK.ConsumptionSaving.ConsAggShockModel - HARK.ConsumptionSaving.ConsGenIncProcessModel - HARK.ConsumptionSaving.ConsIndShockModel - HARK.ConsumptionSaving.ConsMarkovModel - HARK.ConsumptionSaving.ConsMedModel - HARK.ConsumptionSaving.ConsPrefShockModel - HARK.ConsumptionSaving.ConsRepAgentModel - HARK.ConsumptionSaving.ConsumerParameters - HARK.ConsumptionSaving.RepAgentModel - HARK.ConsumptionSaving.TractableBufferStockModel - HARK.FashionVictim.FashionVictimModel - HARK.FashionVictim.FashionVictimParams - HARK.SolvingMicroDSOPs.Calibration.EstimationParameters - HARK.SolvingMicroDSOPs.Calibration.SetupSCFdata - HARK.SolvingMicroDSOPs.Code.StructEstimation - HARK.SolvingMicroDSOPs.do_all - HARK.cAndCwithStickyE.StickyEmodel - HARK.cAndCwithStickyE.StickyEparams - HARK.cAndCwithStickyE.StickyEtools - HARK.core - HARK.cstwMPC.cstwMPC - HARK.dcegm - HARK.estimation - HARK.interpolation - HARK.parallel - HARK.simulation - HARK.utilities - HARK.validators + api Indices and tables ================== From dcca4c4b9f152b5d52e445c0364390e3c0810204 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 29 Oct 2019 16:25:41 -0400 Subject: [PATCH 103/242] adding dependencies for sphinx, see #409 --- requirements.txt | 1 + setup.py | 5 ++++- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c58716718..af16adb0e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,3 +12,4 @@ funcsigs jupyter sphinx-rtd-theme nbsphinx +recommonmark diff --git a/setup.py b/setup.py index 45c35334c..ebcfda81c 100644 --- a/setup.py +++ b/setup.py @@ -154,7 +154,10 @@ 'joblib', 'future', # Optional 'funcsigs', - 'jupyter'], + 'jupyter', + 'nbsphinx', + 'recommonmark' + ], python_requires='>=2.7', From e105a2b163411b7a1b91492a0efa8d9a1dcbf3c8 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 29 Oct 2019 16:28:13 -0400 Subject: [PATCH 104/242] api.rst for the API docs. see #409 --- Documentation/api.rst | 48 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 Documentation/api.rst diff --git a/Documentation/api.rst b/Documentation/api.rst new file mode 100644 index 000000000..f4c6ca4af --- /dev/null +++ b/Documentation/api.rst @@ -0,0 +1,48 @@ +API Documentation +================== + +.. autosummary:: + :toctree: generated + + HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUs + HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond + HARK.BayerLuetticke.Assets.One.SharedFunc + HARK.BayerLuetticke.Assets.One.SharedFunc2 + HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUs + HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUsBond + HARK.BayerLuetticke.Assets.One.defineSSParameters + HARK.BayerLuetticke.Assets.One.defineSSParametersIOUsBond + HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset + HARK.BayerLuetticke.Assets.Two.SharedFunc3 + HARK.BayerLuetticke.Assets.Two.SteadyStateTwoAsset + HARK.BayerLuetticke.Assets.Two.defineSSParametersTwoAsset + HARK.BayerLuetticke.BayerLuetticke_wrapper + HARK.BayerLuetticke.ConsIndShockModel_extension + HARK.ConsumptionSaving.ConsAggShockModel + HARK.ConsumptionSaving.ConsGenIncProcessModel + HARK.ConsumptionSaving.ConsIndShockModel + HARK.ConsumptionSaving.ConsMarkovModel + HARK.ConsumptionSaving.ConsMedModel + HARK.ConsumptionSaving.ConsPrefShockModel + HARK.ConsumptionSaving.ConsRepAgentModel + HARK.ConsumptionSaving.ConsumerParameters + HARK.ConsumptionSaving.RepAgentModel + HARK.ConsumptionSaving.TractableBufferStockModel + HARK.FashionVictim.FashionVictimModel + HARK.FashionVictim.FashionVictimParams + HARK.SolvingMicroDSOPs.Calibration.EstimationParameters + HARK.SolvingMicroDSOPs.Calibration.SetupSCFdata + HARK.SolvingMicroDSOPs.Code.StructEstimation + HARK.SolvingMicroDSOPs.do_all + HARK.cAndCwithStickyE.StickyEmodel + HARK.cAndCwithStickyE.StickyEparams + HARK.cAndCwithStickyE.StickyEtools + HARK.core + HARK.cstwMPC.cstwMPC + HARK.dcegm + HARK.estimation + HARK.interpolation + HARK.parallel + HARK.simulation + HARK.utilities + HARK.validators From eade7112f5bf13c7573733cb7b08b855c1d25c68 Mon Sep 17 00:00:00 2001 From: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> Date: Wed, 30 Oct 2019 08:19:08 -0400 Subject: [PATCH 105/242] Minor edits to Portfolio model (#418) --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 21 ++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 13b21d636..331b67afc 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -285,11 +285,12 @@ def __init__(self, cFunc=None, vFunc=None, wFunc=None, vPfunc=None, RiskyShareFunc=None, vPPfunc=None, mNrmMin=None, hNrm=None, MPCmin=None, MPCmax=None): """We implement three different ways to allow portfolio choice. - The agent can choose - * any portfolio share ('continuous choice') - * only a specified set of portfolio shares ('discrete choice') - * With probability 1 (agent always gets to choose) - * With probability 0 < p < 1 (stochastic chance to choose) + The agent can choose: + + * any portfolio share ('continuous choice') + * only a specified set of portfolio shares ('discrete shares'): + * With probability 1 (agent always gets to choose) + * With probability 0 < p < 1 (stochastic chance to choose) We allow two choices for the description of the distribution of the stochastic variable: @@ -298,13 +299,13 @@ def __init__(self, cFunc=None, vFunc=None, wFunc=None, 2. A true lognormal distribution * The mean return and the standard deviation are specified - In the discrete portfolio shares case, the user also must - input a function that *draws* from the distribution in drawRiskyFunc + In the discrete shares case, the user also must input a function that draws + from the distribution in drawRiskyFunc Other assumptions: - * distributions are time constant - * probability of being allowed to reoptimize is time constant - * If p < 1, you must specify the PortfolioSet discretely + * distributions are time constant + * probability of being allowed to reoptimize is time constant + * If p < 1, you must specify the PortfolioSet discretely """ # Change any missing function inputs to NullFunc if cFunc is None: From 89054aa368b23a885ed2f8601361dbca21aeb118 Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 31 Oct 2019 13:52:04 -0400 Subject: [PATCH 106/242] Refactor 'readme' in docs into separate files to better use sidebar nav. #409 --- Documentation/index.rst | 6 +- Documentation/introduction.md | 13 ++ Documentation/license.md | 5 + Documentation/{readme.md => list-of-files.md} | 190 +----------------- Documentation/quick-start.md | 143 +++++++++++++ Documentation/warnings-and-disclaimers.md | 4 + 6 files changed, 171 insertions(+), 190 deletions(-) create mode 100644 Documentation/introduction.md create mode 100644 Documentation/license.md rename Documentation/{readme.md => list-of-files.md} (53%) create mode 100644 Documentation/quick-start.md create mode 100644 Documentation/warnings-and-disclaimers.md diff --git a/Documentation/index.rst b/Documentation/index.rst index 0258f5c79..21446eabf 100644 --- a/Documentation/index.rst +++ b/Documentation/index.rst @@ -17,7 +17,11 @@ you might want to look at the `DemARK :maxdepth: 2 :caption: User Guide - readme + introduction + quick-start + list-of-files + license + warnings-and-behaviors .. toctree:: :maxdepth: 2 diff --git a/Documentation/introduction.md b/Documentation/introduction.md new file mode 100644 index 000000000..ef113cbdf --- /dev/null +++ b/Documentation/introduction.md @@ -0,0 +1,13 @@ +# Introduction + +This document will tell you how to get HARK up and +running on your machine, how to get started using it, and give you an +overview of the main elements of the toolkit. + +Other useful resources are: + + * Documentation: [ReadTheDocs](https://hark.readthedocs.io/) + * User guide: [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf) + * In the [HARK repository](https://github.com/econ-ark/HARK) + * Demonstrations of HARK functionality: [DemARK](https://github.com/econ-ark/DemARK/) + * Replications and Explorations Made using the ARK : [REMARK](https://github.com/econ-ark/REMARK/) diff --git a/Documentation/license.md b/Documentation/license.md new file mode 100644 index 000000000..ee2dae560 --- /dev/null +++ b/Documentation/license.md @@ -0,0 +1,5 @@ +# License + +All of HARK is licensed under the Apache License, Version 2.0 (ALv2). Please see +the LICENSE file for the text of the license. More information can be found at: +http://www.apache.org/dev/apply-license.html diff --git a/Documentation/readme.md b/Documentation/list-of-files.md similarity index 53% rename from Documentation/readme.md rename to Documentation/list-of-files.md index 20e40d06b..d43320865 100644 --- a/Documentation/readme.md +++ b/Documentation/list-of-files.md @@ -1,181 +1,4 @@ -# Heterogeneous Agents Resources and toolKit (HARK) -pre-release 0.10.1.dev3 - -Click the Badge for Citation Info. -[![DOI](https://zenodo.org/badge/50448254.svg)](https://zenodo.org/badge/latestdoi/50448254) - - -Table of Contents: - -* [1. Introduction](#i-introduction) -* [2. Quick start guide](#ii-quick-start-guide) - * [Installing](#Installing-HARK) - * [Installing Anaconda](#Using-HARK-with-Anaconda) - * [Learning HARK](#Learning-HARK) -* [3. List of files in repository](#iii-list-of-files-in-repository) -* [4. Warnings and disclaimers](#iv-warnings-and-disclaimers) -* [5. License Information](#v-license) - - -## I. INTRODUCTION - -This document will tell you how to get HARK up and -running on your machine, how to get started using it, and give you an -overview of the main elements of the toolkit. - -Other useful resources are: - - * Documentation: [ReadTheDocs](https://hark.readthedocs.io/) - * User guide: [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf) - * In the [HARK repository](https://github.com/econ-ark/HARK) - * Demonstrations of HARK functionality: [DemARK](https://github.com/econ-ark/DemARK/) - * Replications and Explorations Made using the ARK : [REMARK](https://github.com/econ-ark/REMARK/) - -## II. QUICK START GUIDE - -### Installing HARK - -HARK is an open source project that is compatible with both python 2 and 3. But we recommend -using python 3; eventually support for python 2 will end. - -#### Installing HARK with pip - -The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/). - -To install HARK with pip, at a command line type `pip install econ-ark`. - -(If you want to install a release that is not the default stable release, for instance if you want to install a development release, you'll need to use a "pinned" release number: `pip install econ-ark==0.10.1.dev1`, substituting "0.10.1.dev1" for your desired release number.) - -If you are installing via pip, we recommend using a virtual environment such as [virtualenv](https://virtualenv.pypa.io/en/latest/). Creation of a virtual environment isolates the installation of `econ-ark` from the installations of any other python tools and packages. - -To install `virtualenv`, then to create an environment named `econ-ark`, and finally to activate that environment: - -``` -cd [directory where you want to store the econ-ark virtual environment] -pip install virtualenv -virtualenv econ-ark -source econ-ark/bin/activate -``` - ----- -#### Using HARK with Anaconda - -If you intend ever to use the toolkit for anything other than running the precooked material we have provided, you should probably install [Anaconda](https://anaconda.com/why-anaconda), which will install python along with many packages that are frequently used in scientific computing. - -1. Download Anaconda for your operating system and follow the installation instructions [at Anaconda.com](https://www.anaconda.com/distribution/#download-section). - -1. Anaconda includes its own virtual environment system called `conda` which stores environments in a preset location (so you don't have to choose). So in order to create and activate an econ-ark virtual environment: -``` -conda create -n econ-ark anaconda -conda activate econ-ark -conda install -c conda-forge econ-ark -``` -1. Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`. - -1. To verify that spyder has access to HARK try typing `pip install econ-ark` into the iPython shell within Spyder. If you have successfully installed HARK as above, you should see a lot of messages saying 'Requirement satisfied'. - - * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. - - * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)). - - * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone - - * If you don't want to clone HARK, but just to download it, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory. - - Once you've got a copy of HARK in a directory, return to Spyder and navigate to that directory where you put HARK. This can be done within Spyder by doing `import os` and then using `os.chdir()` to change directories. `chdir` works just like cd at a command prompt on most operating systems, except that it takes a string as input: `os.chdir('Music')` moves to the Music subdirectory of the current working directory. - -6) Most of the modules in HARK are just collections of tools. There are a few demonstration -applications that use the tools that you automatically get when you install HARK -- they are listed below in [Application Modules](#application-modules). A much larger set of uses of HARK can be found at two repositories: - * [DemARK](https://github.com/econ-ark/DemARK): Demonstrations of the use of HARK - * [REMARK](https://github.com/econ-ark/REMARK): Replications of existing papers made using HARK - -You will want to obtain your own local copy of these repos using: -``` -git clone https://github.com/econ-ark/DemARK.git -``` -and similarly for the REMARK repo. Once you have downloaded them, you will find that each repo contains a `notebooks` directory that contains a number of [jupyter notebooks](https://jupyter.org/). If you have the jupyter notebook tool installed (it is installed as part of Anaconda), you should be able to launch the -jupyter notebook app from the command line with the command: - -``` -jupyter notebook -``` -and from there you can open the notebooks and execute them. - -#### Learning HARK - -We have a set of 30-second [Elevator Spiels](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#capsule-summaries-of-what-the-econ-ark-project-is) describing the project, tailored to people with several different kinds of background. - -The most broadly applicable advice is to go to [Econ-ARK](https://econ-ark.org) and click on "Notebooks", and choose [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) which will launch as a [jupyter notebook](https://jupyter.org/). - -##### [For people with a technical/scientific/computing background but little economics background](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-people-with-a-technicalscientificcomputing-background-but-no-economics-background) - -* [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) - -##### [For economists who have done some structural modeling](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-done-some-structural-modeling) - -* A full replication of the [Iskhakov, Jørgensen, Rust, and Schjerning](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE643) paper for solving the discrete-continuous retirement saving problem - * An informal discussion of the issues involved is [here](https://github.com/econ-ark/DemARK/blob/master/notebooks/DCEGM-Upper-Envelope.ipynb) (part of the [DemARK](https://github.com/econ-ark/DemARK) repo) - -* [Structural-Estimates-From-Empirical-MPCs](https://github.com/econ-ark/DemARK/blob/master/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb) is an example of the use of the toolkit in a discussion of a well known paper. (Yes, it is easy enough to use that you can estimate a structural model on somebody else's data in the limited time available for writing a discussion) - -##### [For economists who have not yet done any structural modeling but might be persuadable to start](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-not-yet-done-any-structural-modeling-but-might-be-persuadable-to-start) - -* Start with [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) to get your feet wet - -* A simple indirect inference/simulated method of moments structural estimation along the lines of Gourinchas and Parker's 2002 Econometrica paper or Cagetti's 2003 paper is performed by the [SolvingMicroDSOPs](https://github.com/econ-ark/REMARK/tree/master/REMARKs/SolvingMicroDSOPs) [REMARK](https://github.com/econ-ark/REMARK); this code implements the solution methods described in the corresponding section of [these lecture notes](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/) - -##### [For Other Developers of Software for Computational Economics](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-other-developers-of-software-for-computational-economics) - - -* Our workhorse module is [ConsIndShockModel.py](https://github.com/econ-ark/HARK/blob/master/HARK/ConsumptionSaving/ConsIndShockModel.py) - * which is explored and explained (a bit) in [this jupyter notebook](https://github.com/econ-ark/DemARK/blob/master/notebooks/ConsIndShockModel.ipynb) - -### Making changes to HARK - -If you want to make changes or contributions (yay!) to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files (and it's a bad idea to mess with the original code anyway because you'll likely forget what changes you made). If you are adept at GitHub, you can [fork](https://help.github.com/en/articles/fork-a-repo) the repo. If you are less experienced, you should download a personal copy of HARK again using `git clone` (see above) or the GitHub Desktop app. - -1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). If you get a permission denied error, you may need to setup SSH for GitHub, or you can clone using HTTPS: 'git clone https://github.com/econ-ark/HARK.git'. - -2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))). - -For Mac or Linux: - -Install virtualenv if you need to and then type: - -``` -virtualenv econ-ark -source econ-ark/bin/activate -``` -For Windows: -``` -virtualenv econ-ark -econ-ark\\Scripts\\activate.bat -``` - -3. Once the virtualenv is activated, you may see `(econ-ark)` in your command prompt (depending on how your machine is configured) - -3. Make sure to change to HARK directory, and install HARK's requirements into the virtual environment with `pip install -r requirements.txt`. - -4. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`. - -### Trouble with installation? - -We've done our best to give correct, thorough instructions on how to install HARK but we know this information may be inaccurate or incomplete. Please let us know if you run into trouble so we can update this guide! Here's a list of platforms and versions this guide has been verified for: - -| Installation Type | Platform | Python Version | Date Tested | Tested By | -| ------------- |:-------------:| -----:| -----:|-----:| -| basic pip install | Linux (16.04) | 3 | 2019-04-24 | @shaunagm | -| anaconda | Linux (16.04) | 3 | 2019-04-24 | @shaunagm | -| basic pip install | MacOS 10.13.2 "High Sierra" | 2.7| 2019-04-26 | @llorracc | - -### Next steps - -To learn more about how to use HARK, check out our [user manual](Documentation/HARKmanual.pdf). - -For help making changes to HARK, check out our [contributing guide](CONTRIBUTING.md). - - -## III. LIST OF FILES IN REPOSITORY +# List of Files in Repository This section contains descriptions of the main files in the repo. @@ -390,14 +213,3 @@ Other files that you don't need to worry about: * Image for the front cover of the user guide, showing the consumption function for the KinkyPref model. - -## IV. WARNINGS AND DISCLAIMERS - -This is a beta version of HARK. The code has not been extensively tested as it should be. We hope it is useful, but there are absolutely no guarantees (expressed or implied) that it works or will do what you want. Use at your own risk. And please, let us know if you find bugs by posting an issue to [the GitHub page](https://github.com/econ-ark/HARK)! - - -## V. License - -All of HARK is licensed under the Apache License, Version 2.0 (ALv2). Please see -the LICENSE file for the text of the license. More information can be found at: -http://www.apache.org/dev/apply-license.html diff --git a/Documentation/quick-start.md b/Documentation/quick-start.md new file mode 100644 index 000000000..c55c8189c --- /dev/null +++ b/Documentation/quick-start.md @@ -0,0 +1,143 @@ +# Quick Start + +## Installing HARK + +HARK is an open source project that is compatible with both python 2 and 3. But we recommend +using python 3; eventually support for python 2 will end. + +### Installing HARK with pip + +The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/). + +To install HARK with pip, at a command line type `pip install econ-ark`. + +(If you want to install a release that is not the default stable release, for instance if you want to install a development release, you'll need to use a "pinned" release number: `pip install econ-ark==0.10.1.dev1`, substituting "0.10.1.dev1" for your desired release number.) + +If you are installing via pip, we recommend using a virtual environment such as [virtualenv](https://virtualenv.pypa.io/en/latest/). Creation of a virtual environment isolates the installation of `econ-ark` from the installations of any other python tools and packages. + +To install `virtualenv`, then to create an environment named `econ-ark`, and finally to activate that environment: + +``` +cd [directory where you want to store the econ-ark virtual environment] +pip install virtualenv +virtualenv econ-ark +source econ-ark/bin/activate +``` + +---- +### Using HARK with Anaconda + +If you intend ever to use the toolkit for anything other than running the precooked material we have provided, you should probably install [Anaconda](https://anaconda.com/why-anaconda), which will install python along with many packages that are frequently used in scientific computing. + +1. Download Anaconda for your operating system and follow the installation instructions [at Anaconda.com](https://www.anaconda.com/distribution/#download-section). + +1. Anaconda includes its own virtual environment system called `conda` which stores environments in a preset location (so you don't have to choose). So in order to create and activate an econ-ark virtual environment: +``` +conda create -n econ-ark anaconda +conda activate econ-ark +conda install -c conda-forge econ-ark +``` +1. Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`. + +1. To verify that spyder has access to HARK try typing `pip install econ-ark` into the iPython shell within Spyder. If you have successfully installed HARK as above, you should see a lot of messages saying 'Requirement satisfied'. + + * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. + + * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)). + + * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone + + * If you don't want to clone HARK, but just to download it, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory. + + Once you've got a copy of HARK in a directory, return to Spyder and navigate to that directory where you put HARK. This can be done within Spyder by doing `import os` and then using `os.chdir()` to change directories. `chdir` works just like cd at a command prompt on most operating systems, except that it takes a string as input: `os.chdir('Music')` moves to the Music subdirectory of the current working directory. + +6) Most of the modules in HARK are just collections of tools. There are a few demonstration +applications that use the tools that you automatically get when you install HARK -- they are listed below in [Application Modules](#application-modules). A much larger set of uses of HARK can be found at two repositories: + * [DemARK](https://github.com/econ-ark/DemARK): Demonstrations of the use of HARK + * [REMARK](https://github.com/econ-ark/REMARK): Replications of existing papers made using HARK + +You will want to obtain your own local copy of these repos using: +``` +git clone https://github.com/econ-ark/DemARK.git +``` +and similarly for the REMARK repo. Once you have downloaded them, you will find that each repo contains a `notebooks` directory that contains a number of [jupyter notebooks](https://jupyter.org/). If you have the jupyter notebook tool installed (it is installed as part of Anaconda), you should be able to launch the +jupyter notebook app from the command line with the command: + +``` +jupyter notebook +``` +and from there you can open the notebooks and execute them. + +### Learning HARK + +We have a set of 30-second [Elevator Spiels](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#capsule-summaries-of-what-the-econ-ark-project-is) describing the project, tailored to people with several different kinds of background. + +The most broadly applicable advice is to go to [Econ-ARK](https://econ-ark.org) and click on "Notebooks", and choose [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) which will launch as a [jupyter notebook](https://jupyter.org/). + +#### [For people with a technical/scientific/computing background but little economics background](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-people-with-a-technicalscientificcomputing-background-but-no-economics-background) + +* [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) + +#### [For economists who have done some structural modeling](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-done-some-structural-modeling) + +* A full replication of the [Iskhakov, Jørgensen, Rust, and Schjerning](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE643) paper for solving the discrete-continuous retirement saving problem + * An informal discussion of the issues involved is [here](https://github.com/econ-ark/DemARK/blob/master/notebooks/DCEGM-Upper-Envelope.ipynb) (part of the [DemARK](https://github.com/econ-ark/DemARK) repo) + +* [Structural-Estimates-From-Empirical-MPCs](https://github.com/econ-ark/DemARK/blob/master/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb) is an example of the use of the toolkit in a discussion of a well known paper. (Yes, it is easy enough to use that you can estimate a structural model on somebody else's data in the limited time available for writing a discussion) + +#### [For economists who have not yet done any structural modeling but might be persuadable to start](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-not-yet-done-any-structural-modeling-but-might-be-persuadable-to-start) + +* Start with [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) to get your feet wet + +* A simple indirect inference/simulated method of moments structural estimation along the lines of Gourinchas and Parker's 2002 Econometrica paper or Cagetti's 2003 paper is performed by the [SolvingMicroDSOPs](https://github.com/econ-ark/REMARK/tree/master/REMARKs/SolvingMicroDSOPs) [REMARK](https://github.com/econ-ark/REMARK); this code implements the solution methods described in the corresponding section of [these lecture notes](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/) + +#### [For Other Developers of Software for Computational Economics](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-other-developers-of-software-for-computational-economics) + + +* Our workhorse module is [ConsIndShockModel.py](https://github.com/econ-ark/HARK/blob/master/HARK/ConsumptionSaving/ConsIndShockModel.py) + * which is explored and explained (a bit) in [this jupyter notebook](https://github.com/econ-ark/DemARK/blob/master/notebooks/ConsIndShockModel.ipynb) + +## Making changes to HARK + +If you want to make changes or contributions to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files (and it's a bad idea to mess with the original code anyway because you'll likely forget what changes you made). If you are adept at GitHub, you can [fork](https://help.github.com/en/articles/fork-a-repo) the repo. If you are less experienced, you should download a personal copy of HARK again using `git clone` (see above) or the GitHub Desktop app. + +1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). If you get a permission denied error, you may need to setup SSH for GitHub, or you can clone using HTTPS: 'git clone https://github.com/econ-ark/HARK.git'. + +2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))). + +For Mac or Linux: + +Install virtualenv if you need to and then type: + +``` +virtualenv econ-ark +source econ-ark/bin/activate +``` +For Windows: +``` +virtualenv econ-ark +econ-ark\\Scripts\\activate.bat +``` + +3. Once the virtualenv is activated, you may see `(econ-ark)` in your command prompt (depending on how your machine is configured) + +3. Make sure to change to HARK directory, and install HARK's requirements into the virtual environment with `pip install -r requirements.txt`. + +4. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`. + +## Trouble with installation? + +We've done our best to give correct, thorough instructions on how to install HARK but we know this information may be inaccurate or incomplete. Please let us know if you run into trouble so we can update this guide! Here's a list of platforms and versions this guide has been verified for: + +| Installation Type | Platform | Python Version | Date Tested | Tested By | +| ------------- |:-------------:| -----:| -----:|-----:| +| basic pip install | Linux (16.04) | 3 | 2019-04-24 | @shaunagm | +| anaconda | Linux (16.04) | 3 | 2019-04-24 | @shaunagm | +| basic pip install | MacOS 10.13.2 "High Sierra" | 2.7| 2019-04-26 | @llorracc | + +## Next steps + +To learn more about how to use HARK, check out our [user manual](Documentation/HARKmanual.pdf). + +For help making changes to HARK, check out our [contributing guide](CONTRIBUTING.md). + diff --git a/Documentation/warnings-and-disclaimers.md b/Documentation/warnings-and-disclaimers.md new file mode 100644 index 000000000..a223f075c --- /dev/null +++ b/Documentation/warnings-and-disclaimers.md @@ -0,0 +1,4 @@ +# IV. WARNINGS AND DISCLAIMERS + +This is a beta version of HARK. The code has not been extensively tested as it should be. We hope it is useful, but there are absolutely no guarantees (expressed or implied) that it works or will do what you want. Use at your own risk. And please, let us know if you find bugs by posting an issue to [the GitHub page](https://github.com/econ-ark/HARK)! + From 0827d18e7b172fe489c30527c7ef06ca253a5b85 Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 31 Oct 2019 14:14:26 -0400 Subject: [PATCH 107/242] changing link in README to quick start to new quick start doc. #409 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1599aecd9..e42bb9bc4 100644 --- a/README.md +++ b/README.md @@ -232,7 +232,7 @@ If you are worried or don’t know how to start, you can always reach out to the **After your first contribution please let us know and we will add you to the Contributors list below!** -To install for development see the [Quickstart Guide](./Documentation/readme.md#ii-quick-start-guide). +To install for development see the [Quickstart Guide](./Documentation/quick-start). For more information on contributing to HARK please see [CONTRIBUTING.md](./CONTRIBUTING.md). From b16f3b5611079f5c43423e08d4b6a663b69ab90f Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 31 Oct 2019 14:35:26 -0400 Subject: [PATCH 108/242] Adjusted LaborIntMarg lifecycle figures Lifecycle figures for intensive margin labor model now plot across periods, using the median shock (more or less) rather than across shocks. These plots now have "nice" xlim and ylim, but infinite horizon figs do not. We can fix that later. --- HARK/ConsumptionSaving/ConsLaborModel.py | 69 +++++++++++++++++------- 1 file changed, 51 insertions(+), 18 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 0a8bad130..0d2ec2942 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -604,56 +604,89 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): LifecycleExample.unpackcFunc() LifecycleExample.timeFwd() - t = 0 - bMax = 100. + bMax = 20. - # Plot the consumption function at various transitory productivity shocks - TranShkSet = LifecycleExample.TranShkGrid[t] + # Plot the consumption function in each period of the lifecycle, using median shock B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: + b_min = np.inf + b_max = -np.inf + for t in range(LifecycleExample.T_cycle): + TranShkSet = LifecycleExample.TranShkGrid[t] + Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) C = LifecycleExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,C) + plt.plot(B_temp, C) + b_min = np.minimum(b_min, B_temp[0]) + b_max = np.maximum(b_min, B_temp[-1]) + plt.title('Consumption function across periods of the lifecycle') plt.xlabel('Beginning of period bank balances') plt.ylabel('Normalized consumption level') + plt.xlim(b_min, b_max) + plt.ylim(0., None) plt.show() - # Plot the marginal consumption function at various transitory productivity shocks - TranShkSet = LifecycleExample.TranShkGrid[t] + # Plot the marginal consumption function in each period of the lifecycle, using median shock B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: + b_min = np.inf + b_max = -np.inf + for t in range(LifecycleExample.T_cycle): + TranShkSet = LifecycleExample.TranShkGrid[t] + Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - C = LifecycleExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,C) + MPC = LifecycleExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp, MPC) + b_min = np.minimum(b_min, B_temp[0]) + b_max = np.maximum(b_min, B_temp[-1]) + plt.title('Marginal consumption function across periods of the lifecycle') plt.xlabel('Beginning of period bank balances') plt.ylabel('Marginal propensity to consume') + plt.xlim(b_min, b_max) + plt.ylim(0., 1.) plt.show() - # Plot the labor function at various transitory productivity shocks - TranShkSet = LifecycleExample.TranShkGrid[t] + # Plot the labor supply function in each period of the lifecycle, using median shock B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: + b_min = np.inf + b_max = -np.inf + for t in range(LifecycleExample.T_cycle): + TranShkSet = LifecycleExample.TranShkGrid[t] + Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - Lbr = LifecycleExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,Lbr) + L = LifecycleExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp, L) + b_min = np.minimum(b_min, B_temp[0]) + b_max = np.maximum(b_min, B_temp[-1]) + plt.title('Labor supply function across periods of the lifecycle') plt.xlabel('Beginning of period bank balances') plt.ylabel('Labor supply') + plt.xlim(b_min, b_max) + plt.ylim(0., 1.01) plt.show() # Plot the marginal value function at various transitory productivity shocks pseudo_inverse = True TranShkSet = LifecycleExample.TranShkGrid[t] B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: + b_min = np.inf + b_max = -np.inf + for t in range(LifecycleExample.T_cycle): + TranShkSet = LifecycleExample.TranShkGrid[t] + Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) if pseudo_inverse: vP = LifecycleExample.solution[t].vPfunc.cFunc(B_temp,Shk*np.ones_like(B_temp)) else: vP = LifecycleExample.solution[t].vPfunc(B_temp,Shk*np.ones_like(B_temp)) plt.plot(B_temp,vP) + b_min = np.minimum(b_min, B_temp[0]) + b_max = np.maximum(b_min, B_temp[-1]) plt.xlabel('Beginning of period bank balances') if pseudo_inverse: plt.ylabel('Pseudo inverse marginal value') else: plt.ylabel('Marginal value') - plt.show() \ No newline at end of file + plt.title('Marginal value across periods of the lifecycle') + plt.xlim(b_min, b_max) + plt.ylim(0., None) + plt.show() + \ No newline at end of file From 5b404b56ef4a2a407f2c1ee56fdcfef18c01b0a2 Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 31 Oct 2019 14:41:22 -0400 Subject: [PATCH 109/242] adding contribution guidelines to readthedocs. #410 --- Documentation/CONTRIBUTING.md | 8 ++++---- Documentation/index.rst | 1 + 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/Documentation/CONTRIBUTING.md b/Documentation/CONTRIBUTING.md index 1b489aee9..eec0e3083 100644 --- a/Documentation/CONTRIBUTING.md +++ b/Documentation/CONTRIBUTING.md @@ -1,12 +1,12 @@ # Contributing to Econ-ARK -### Welcome! +## Welcome! Thank you for considering contributing to Econ-ARK! We're a young project with a small but committed community that's hoping to grow while maintaining our friendly and responsive culture. Whether you're an economist or a technologist, a writer or a coder, an undergrad or a full professor, a professional or a hobbyist, there's a place for you in the Econ-ARK community. We're still creating our contribution infrastructure, so this document is a work in progress. If you have any questions please feel free to @ or otherwise reach out project manager [Shauna](https://github.com/shaunagm), or lead developers [Chris](https://github.com/llorracc) and [Matt](https://github.com/mnwhite). If you prefer to connect through email, you can send it to __econ-ark at jhuecon dot org__. -### How to Contribute +## How to Contribute We're open to all kinds of contributions, from bug reports to help with our docs to suggestions on how to improve our code. The best way to figure out if the contribution you'd like to make is something we'd merge or otherwise accept, is to open up an issue in our issue tracker. Please create an issue rather than immediately submitting pull request, unless the change you'd like to make is so minor you won't mind if the pull request is rejected. For bigger contributions, we want to proactively talk things through so we don't end up wasting your time. @@ -21,7 +21,7 @@ If you'd like to help with those or any other kind of contribution, reach out to We don't currently have guidelines for opening issues or pull requests, so include as much information as seems relevant to you, and we'll ask you if we need to know more. -### Responding to Issues & Pull Requests +## Responding to Issues & Pull Requests We're trying to get better at managing our open issues and pull requests. We've created a new set of goals for all issues and pull requests in our Econ-ARK repos: @@ -31,7 +31,7 @@ We're trying to get better at managing our open issues and pull requests. We've If you've been waiting on us for more than two weeks for any reason, please feel free to give us a nudge. Correspondingly, we ask that you respond to any questions or requests from us within two weeks as well, even if it's just to say, "Sorry, I can't get to this for a while yet". If we don't hear back from you, we may close your issue or pull request. If you want to re-open it, just ask - we're glad to do so. -### Getting Started +## Getting Started The [quick start guide](https://github.com/econ-ark/HARK#ii-quick-start-guide) in our README provides instructions for how to get started running HARK. This also serves as a setup guide for new contributors. If you run into any problems, please let us know by opening an issue in the issue tracker. diff --git a/Documentation/index.rst b/Documentation/index.rst index 21446eabf..12935f8d2 100644 --- a/Documentation/index.rst +++ b/Documentation/index.rst @@ -22,6 +22,7 @@ you might want to look at the `DemARK list-of-files license warnings-and-behaviors + CONTRIBUTING .. toctree:: :maxdepth: 2 From f5263b955a3ac6f94a65fa61e8d7a27de50f0b10 Mon Sep 17 00:00:00 2001 From: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> Date: Thu, 7 Nov 2019 07:45:15 -0500 Subject: [PATCH 110/242] Robustify conda installation command (#431) `conda install econ-ark` only works if the user has conda-forge as the default conda channel. Replaced it with `conda install -c conda-forge econ-ark` which will work generically. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1599aecd9..386b385ae 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,7 @@ The Econ-ARK project uses an [open governance model](./GOVERNANCE.md) and is fis Install from [Anaconda Cloud](https://docs.anaconda.com/anaconda/install/) by running: -`conda install econ-ark` +`conda install -c conda-forge econ-ark` Install from [PyPi](https://pypi.org/) by running: From bbfab2fcc2ac5a36d512f91eab89d14e013d0ebd Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 7 Nov 2019 12:36:15 -0500 Subject: [PATCH 111/242] removing Model directories from the API documentation --- Documentation/api.rst | 34 ---------------------------------- 1 file changed, 34 deletions(-) diff --git a/Documentation/api.rst b/Documentation/api.rst index f4c6ca4af..0c7113c05 100644 --- a/Documentation/api.rst +++ b/Documentation/api.rst @@ -4,41 +4,7 @@ API Documentation .. autosummary:: :toctree: generated - HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUs - HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond - HARK.BayerLuetticke.Assets.One.SharedFunc - HARK.BayerLuetticke.Assets.One.SharedFunc2 - HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUs - HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUsBond - HARK.BayerLuetticke.Assets.One.defineSSParameters - HARK.BayerLuetticke.Assets.One.defineSSParametersIOUsBond - HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset - HARK.BayerLuetticke.Assets.Two.SharedFunc3 - HARK.BayerLuetticke.Assets.Two.SteadyStateTwoAsset - HARK.BayerLuetticke.Assets.Two.defineSSParametersTwoAsset - HARK.BayerLuetticke.BayerLuetticke_wrapper - HARK.BayerLuetticke.ConsIndShockModel_extension - HARK.ConsumptionSaving.ConsAggShockModel - HARK.ConsumptionSaving.ConsGenIncProcessModel - HARK.ConsumptionSaving.ConsIndShockModel - HARK.ConsumptionSaving.ConsMarkovModel - HARK.ConsumptionSaving.ConsMedModel - HARK.ConsumptionSaving.ConsPrefShockModel - HARK.ConsumptionSaving.ConsRepAgentModel - HARK.ConsumptionSaving.ConsumerParameters - HARK.ConsumptionSaving.RepAgentModel - HARK.ConsumptionSaving.TractableBufferStockModel - HARK.FashionVictim.FashionVictimModel - HARK.FashionVictim.FashionVictimParams - HARK.SolvingMicroDSOPs.Calibration.EstimationParameters - HARK.SolvingMicroDSOPs.Calibration.SetupSCFdata - HARK.SolvingMicroDSOPs.Code.StructEstimation - HARK.SolvingMicroDSOPs.do_all - HARK.cAndCwithStickyE.StickyEmodel - HARK.cAndCwithStickyE.StickyEparams - HARK.cAndCwithStickyE.StickyEtools HARK.core - HARK.cstwMPC.cstwMPC HARK.dcegm HARK.estimation HARK.interpolation From c436a92fbff70272677f0e8758a3f7ee8f28f0ff Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Sat, 9 Nov 2019 22:07:44 +0100 Subject: [PATCH 112/242] Pass the RNG stored in AgentType to portfolio agents' methods for drawing risky returns rates. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 331b67afc..7201ae8bc 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -275,7 +275,7 @@ def LogNormalRiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) - return lambda: drawLognormal(1, mu=mu, sigma=sigma) + return lambda RNG: drawLognormal(1, mu=mu, sigma=sigma, seed=RNG) class PortfolioSolution(Solution): @@ -627,7 +627,7 @@ def getControls(self): return None def getRisky(self): - return self.drawRiskyFunc() + return self.drawRiskyFunc(self.RNG) class ConsIndShockPortfolioSolver(ConsIndShockSolver): ''' From 0bfc4b902917f7fc4f97f918348850dab99802ce Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Sat, 9 Nov 2019 23:17:14 +0100 Subject: [PATCH 113/242] Fix from RNG to actual number. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index 7201ae8bc..c66689474 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -627,7 +627,7 @@ def getControls(self): return None def getRisky(self): - return self.drawRiskyFunc(self.RNG) + return self.drawRiskyFunc(self.RNG.randint(0,2**31-1)) class ConsIndShockPortfolioSolver(ConsIndShockSolver): ''' From b7625726e17eea96b75c7e594659ce09c982f0cf Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Sat, 9 Nov 2019 23:18:11 +0100 Subject: [PATCH 114/242] Don't call the argument RNG. --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index c66689474..f88f25717 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -275,7 +275,7 @@ def LogNormalRiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) - return lambda RNG: drawLognormal(1, mu=mu, sigma=sigma, seed=RNG) + return lambda rngSeed: drawLognormal(1, mu=mu, sigma=sigma, seed=rngSeed) class PortfolioSolution(Solution): From 327cf3b1947c8d82392e4fcbc0f943a02aa5182d Mon Sep 17 00:00:00 2001 From: Gina Date: Wed, 13 Nov 2019 11:27:33 -0600 Subject: [PATCH 115/242] Create FUNDING.yml add NumFOCUS github sponsors button (recurring donations only) This feature is launching today at GitHub Universe! I've also included the direct link to donate to Econ-ARK as a custom link. cc @llorracc @shaunagm @jackiekazil --- .github/FUNDING.yml | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 .github/FUNDING.yml diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml new file mode 100644 index 000000000..696c25ff3 --- /dev/null +++ b/.github/FUNDING.yml @@ -0,0 +1,2 @@ +github: [numfocus] +custom: ['https://numfocus.org/donate-to-econ-ark'] From a8c027c1e0cd5eb5a6cc8c3af20d264b407569a7 Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 25 Nov 2019 16:22:34 -0500 Subject: [PATCH 116/242] adding link to PDF manual --- Documentation/_static/HARKmanual.pdf | Bin 0 -> 543441 bytes Documentation/quick-start.md | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 Documentation/_static/HARKmanual.pdf diff --git a/Documentation/_static/HARKmanual.pdf b/Documentation/_static/HARKmanual.pdf new file mode 100644 index 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--git a/Documentation/quick-start.md b/Documentation/quick-start.md index c55c8189c..222810ce8 100644 --- a/Documentation/quick-start.md +++ b/Documentation/quick-start.md @@ -137,7 +137,7 @@ We've done our best to give correct, thorough instructions on how to install HAR ## Next steps -To learn more about how to use HARK, check out our [user manual](Documentation/HARKmanual.pdf). +To learn more about how to use HARK, check out our [user manual](_static/HARKmanual.pdf). For help making changes to HARK, check out our [contributing guide](CONTRIBUTING.md). From 506f9bc811397420cf58c4bfe5cc2aa23855e0ef Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 26 Nov 2019 10:10:57 -0500 Subject: [PATCH 117/242] fix nbsphinx version to 0.4.3 to get compatibility with Sphinx v.1.6.7 --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index af16adb0e..4b938908c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,5 +11,5 @@ jupyter funcsigs jupyter sphinx-rtd-theme -nbsphinx +nbsphinx==0.4.3 recommonmark From 66d1ecd9556ff8c41e1a1ff42e87e5458e1698b0 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 26 Nov 2019 18:13:44 -0500 Subject: [PATCH 118/242] models moved from HARK subpackages into examples/ --- .../BayerLuetticke/Assets/One/EX1SS.p | Bin .../BayerLuetticke/Assets/One/EX1SS_nm50.p | 0 .../BayerLuetticke/Assets/One/EX2SS.p | Bin .../Assets/One/FluctuationsOneAssetIOUs.py | 0 .../Assets/One/FluctuationsOneAssetIOUsBond.py | 0 .../BayerLuetticke/Assets/One/Luetticke_wrapper.py | 0 .../BayerLuetticke/Assets/One/SharedFunc.py | 0 .../BayerLuetticke/Assets/One/SharedFunc2.py | 0 .../Assets/One/SteadyStateOneAssetIOUs.py | 0 .../Assets/One/SteadyStateOneAssetIOUsBond.py | 0 .../BayerLuetticke/Assets/One/__init__.py | 0 .../BayerLuetticke/Assets/One/defineSSParameters.py | 0 .../Assets/One/defineSSParametersIOUsBond.py | 0 .../BayerLuetticke/Assets/Two/EX3SS_20.p | Bin .../BayerLuetticke/Assets/Two/EX3SS_20_python2.p | 0 .../BayerLuetticke/Assets/Two/EX3SS_20_python3.p | Bin .../Assets/Two/FluctuationsTwoAsset.py | 0 .../BayerLuetticke/Assets/Two/SharedFunc3.py | 0 .../Assets/Two/SteadyStateTwoAsset.py | 0 .../BayerLuetticke/Assets/Two/__init__.py | 0 .../Assets/Two/defineSSParametersTwoAsset.py | 0 .../BayerLuetticke/Assets/__init__.py | 0 .../BayerLuetticke/BayerLuetticke_wrapper.py | 0 .../BayerLuetticke/ConsIndShockModel_extension.py | 0 {HARK => examples}/BayerLuetticke/README.md | 0 {HARK => examples}/BayerLuetticke/__init__.py | 0 .../notebooks/DCT-Copula-Illustration.ipynb | 0 .../notebooks/DCT-Copula-Illustration.py | 0 .../BayerLuetticke/notebooks/DCT.ipynb | 0 .../BayerLuetticke/notebooks/OneAsset-HANK.ipynb | 0 .../BayerLuetticke/notebooks/OneAsset-HANK.py | 0 .../BayerLuetticke/notebooks/TwoAsset.ipynb | 0 .../BayerLuetticke/notebooks/TwoAsset.py | 0 .../FashionVictim/FashionVictimModel.py | 0 .../FashionVictim/FashionVictimParams.py | 0 {HARK => examples}/FashionVictim/__init__.py | 0 .../Calibration/EstimationParameters.py | 0 .../SolvingMicroDSOPs/Calibration/SCFdata.csv | 0 .../SolvingMicroDSOPs/Calibration/SetupSCFdata.py | 0 .../SolvingMicroDSOPs/Calibration/__init__.py | 0 .../SolvingMicroDSOPs/Code/StructEstimation.py | 0 .../SolvingMicroDSOPs/Code/__init__.py | 0 .../SolvingMicroDSOPs/Figures/SMMcontour.pdf | Bin .../SolvingMicroDSOPs/Figures/SMMcontour.png | Bin .../SolvingMicroDSOPs/Figures/__init__.py | 0 .../SolvingMicroDSOPs/Tables/__init__.py | 0 .../SolvingMicroDSOPs/Tables/estimate_results.csv | 0 {HARK => examples}/SolvingMicroDSOPs/__init__.py | 0 {HARK => examples}/SolvingMicroDSOPs/do_all.py | 0 .../cAndCwithStickyE/Calibration/CRRA.txt | 0 .../cAndCwithStickyE/Calibration/CapShare.txt | 0 .../cAndCwithStickyE/Calibration/DeprFacAnn.txt | 0 .../cAndCwithStickyE/Calibration/DiePrb.txt | 0 .../cAndCwithStickyE/Calibration/KYratioSS.txt | 0 .../cAndCwithStickyE/Calibration/PermShkAggVar.txt | 0 .../cAndCwithStickyE/Calibration/PermShkVarAnn.txt | 0 .../cAndCwithStickyE/Calibration/TranShkAggVar.txt | 0 .../cAndCwithStickyE/Calibration/TranShkVarAnn.txt | 0 .../cAndCwithStickyE/Calibration/UnempPrb.txt | 0 .../cAndCwithStickyE/Calibration/UpdatePrb.txt | 0 .../cAndCwithStickyE/Calibration/betaSOE.txt | 0 .../Figures/ValueVsPermShkAggVar.pdf | Bin .../cAndCwithStickyE/Figures/ValueVsPi.pdf | Bin .../cAndCwithStickyE/Figures/uCostvsPi.pdf | Bin .../cAndCwithStickyE/Figures/uCostvsPiInv.pdf | Bin {HARK => examples}/cAndCwithStickyE/README.txt | 0 .../Results/DSGEmarkovFrictionlessBirthValue.csv | 0 .../Results/DSGEmarkovFrictionlessData.txt | 0 .../Results/DSGEmarkovFrictionlessResults.csv | 0 .../Results/DSGEmarkovStickyBirthValue.csv | 0 .../Results/DSGEmarkovStickyData.txt | 0 .../Results/DSGEmarkovStickyResults.csv | 0 .../Results/RAmarkovFrictionlessData.txt | 0 .../Results/RAmarkovFrictionlessResults.csv | 0 .../cAndCwithStickyE/Results/RAmarkovStickyData.txt | 0 .../Results/RAmarkovStickyResults.csv | 0 .../Results/SOEmarkovFrictionlessBirthValue.csv | 0 .../Results/SOEmarkovFrictionlessData.txt | 0 .../Results/SOEmarkovFrictionlessResults.csv | 0 .../Results/SOEmarkovStickyBirthValue.csv | 0 .../Results/SOEmarkovStickyData.txt | 0 .../Results/SOEmarkovStickyResults.csv | 0 .../Results/SOEuCostbyUpdatePrb.csv | 0 .../Results/SOEvVecByPermShkAggVar.csv | 0 .../cAndCwithStickyE/Results/SOEvVecByUpdatePrb.csv | 0 .../cAndCwithStickyE/Results/temp.txt | 0 .../cAndCwithStickyE/StickyETimeSeries.do | 0 {HARK => examples}/cAndCwithStickyE/StickyE_MAIN.py | 0 .../cAndCwithStickyE/StickyE_NO_MARKOV.py | 0 {HARK => examples}/cAndCwithStickyE/StickyEmodel.py | 0 .../cAndCwithStickyE/StickyEparams.py | 0 {HARK => examples}/cAndCwithStickyE/StickyEtools.py | 0 .../cAndCwithStickyE/Tables/CGrowCross.tex | 0 .../cAndCwithStickyE/Tables/CGrowCrossDSGE.tex | 0 .../cAndCwithStickyE/Tables/Calibration.tex | 0 .../cAndCwithStickyE/Tables/Calibration_1.tex | 0 .../cAndCwithStickyE/Tables/Calibration_2.tex | 0 .../cAndCwithStickyE/Tables/DSGEmrkvSimReg.tex | 0 .../cAndCwithStickyE/Tables/DSGEmrkvSimRegF.tex | 0 .../cAndCwithStickyE/Tables/DSGEmrkvSimRegLong.tex | 0 .../Tables/DSGEmrkvSimRegNoMeasErr.tex | 0 .../cAndCwithStickyE/Tables/DSGEmrkvSimRegS.tex | 0 {HARK => examples}/cAndCwithStickyE/Tables/Eqbm.tex | 0 .../cAndCwithStickyE/Tables/EqbmSlides.tex | 0 .../cAndCwithStickyE/Tables/RepAgentMrkvSimReg.tex | 0 .../Tables/RepAgentMrkvSimRegLong.tex | 0 .../Tables/RepAgentMrkvSimRegNoMeasErr.tex | 0 .../cAndCwithStickyE/Tables/SOEmrkvSimReg.tex | 0 .../cAndCwithStickyE/Tables/SOEmrkvSimRegF.tex | 0 .../cAndCwithStickyE/Tables/SOEmrkvSimRegLong.tex | 0 .../Tables/SOEmrkvSimRegNoMeasErr.tex | 0 .../cAndCwithStickyE/Tables/SOEmrkvSimRegS.tex | 0 .../cAndCwithStickyE/Tables/econtexShortcuts.sty | 0 {HARK => examples}/cAndCwithStickyE/__init__.py | 0 {HARK => examples}/cstwMPC/EducMortAdj.txt | 0 {HARK => examples}/cstwMPC/Figures/placeholder.txt | 0 {HARK => examples}/cstwMPC/MakeCSTWfigs.py | 0 {HARK => examples}/cstwMPC/MakeCSTWfigsForSlides.py | 0 .../cstwMPC/Results/BetaDistPYResults.txt | 0 .../cstwMPC/Results/BetaDistPYResults_py3.txt | 0 {HARK => examples}/cstwMPC/SCFwealthDataReduced.txt | 0 {HARK => examples}/cstwMPC/SetupParamsCSTW.py | 0 {HARK => examples}/cstwMPC/USactuarial.txt | 0 {HARK => examples}/cstwMPC/__init__.py | 0 {HARK => examples}/cstwMPC/cstwMPC.py | 0 125 files changed, 0 insertions(+), 0 deletions(-) rename {HARK => examples}/BayerLuetticke/Assets/One/EX1SS.p (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/EX1SS_nm50.p (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/EX2SS.p (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUs.py (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUsBond.py (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/Luetticke_wrapper.py (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/SharedFunc.py (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/SharedFunc2.py (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/SteadyStateOneAssetIOUs.py (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/SteadyStateOneAssetIOUsBond.py (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/__init__.py (100%) rename {HARK => examples}/BayerLuetticke/Assets/One/defineSSParameters.py (100%) rename {HARK => 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a/HARK/cstwMPC/MakeCSTWfigsForSlides.py b/examples/cstwMPC/MakeCSTWfigsForSlides.py similarity index 100% rename from HARK/cstwMPC/MakeCSTWfigsForSlides.py rename to examples/cstwMPC/MakeCSTWfigsForSlides.py diff --git a/HARK/cstwMPC/Results/BetaDistPYResults.txt b/examples/cstwMPC/Results/BetaDistPYResults.txt similarity index 100% rename from HARK/cstwMPC/Results/BetaDistPYResults.txt rename to examples/cstwMPC/Results/BetaDistPYResults.txt diff --git a/HARK/cstwMPC/Results/BetaDistPYResults_py3.txt b/examples/cstwMPC/Results/BetaDistPYResults_py3.txt similarity index 100% rename from HARK/cstwMPC/Results/BetaDistPYResults_py3.txt rename to examples/cstwMPC/Results/BetaDistPYResults_py3.txt diff --git a/HARK/cstwMPC/SCFwealthDataReduced.txt b/examples/cstwMPC/SCFwealthDataReduced.txt similarity index 100% rename from HARK/cstwMPC/SCFwealthDataReduced.txt rename to examples/cstwMPC/SCFwealthDataReduced.txt diff --git a/HARK/cstwMPC/SetupParamsCSTW.py b/examples/cstwMPC/SetupParamsCSTW.py similarity index 100% rename from HARK/cstwMPC/SetupParamsCSTW.py rename to examples/cstwMPC/SetupParamsCSTW.py diff --git a/HARK/cstwMPC/USactuarial.txt b/examples/cstwMPC/USactuarial.txt similarity index 100% rename from HARK/cstwMPC/USactuarial.txt rename to examples/cstwMPC/USactuarial.txt diff --git a/HARK/cstwMPC/__init__.py b/examples/cstwMPC/__init__.py similarity index 100% rename from HARK/cstwMPC/__init__.py rename to examples/cstwMPC/__init__.py diff --git a/HARK/cstwMPC/cstwMPC.py b/examples/cstwMPC/cstwMPC.py similarity index 100% rename from HARK/cstwMPC/cstwMPC.py rename to examples/cstwMPC/cstwMPC.py From ec5647244485850ec50f0425126ec0a44eba52ea Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 26 Nov 2019 18:19:34 -0500 Subject: [PATCH 119/242] don't import BayerLuettecke in HARK __init__ --- HARK/__init__.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/HARK/__init__.py b/HARK/__init__.py index 14522d5e7..625114886 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -2,5 +2,3 @@ from .core import * __version__ = '0.10.2' - -from .BayerLuetticke import * From 529fad4ebdd4084385202f25c454139c8082b32d Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 26 Nov 2019 18:19:56 -0500 Subject: [PATCH 120/242] correcting Params import path for FashionVictim --- examples/FashionVictim/FashionVictimModel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/FashionVictim/FashionVictimModel.py b/examples/FashionVictim/FashionVictimModel.py index 274d357fd..8b921ac80 100644 --- a/examples/FashionVictim/FashionVictimModel.py +++ b/examples/FashionVictim/FashionVictimModel.py @@ -15,7 +15,7 @@ from HARK.utilities import approxUniform, plotFuncs import numpy as np import scipy.stats as stats -from HARK.FashionVictim import FashionVictimParams as Params +import FashionVictimParams as Params from copy import copy class FashionSolution(Solution): From 1b8b65efee42df760f6bccc091f3b20f8ee93492 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 26 Nov 2019 18:22:25 -0500 Subject: [PATCH 121/242] correcting import path for cstwMPC example/ --- examples/cstwMPC/cstwMPC.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/cstwMPC/cstwMPC.py b/examples/cstwMPC/cstwMPC.py index 89fb19731..2490223b8 100644 --- a/examples/cstwMPC/cstwMPC.py +++ b/examples/cstwMPC/cstwMPC.py @@ -16,7 +16,7 @@ getPercentiles, getLorenzShares, calcSubpopAvg, approxLognormal from HARK.simulation import drawDiscrete from HARK import Market -import HARK.cstwMPC.SetupParamsCSTW as Params +import SetupParamsCSTW as Params import HARK.ConsumptionSaving.ConsIndShockModel as Model from HARK.ConsumptionSaving.ConsAggShockModel import CobbDouglasEconomy, AggShockConsumerType from scipy.optimize import golden, brentq From 1b1372723f8358c2aa541a967115a090c2044e03 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 26 Nov 2019 18:46:28 -0500 Subject: [PATCH 122/242] fixing up BayerLuetticke paths --- examples/BayerLuetticke/BayerLuetticke_wrapper.py | 12 ++++++------ examples/BayerLuetticke/__init__.py | 12 ++++++------ 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/examples/BayerLuetticke/BayerLuetticke_wrapper.py b/examples/BayerLuetticke/BayerLuetticke_wrapper.py index 11447ff64..6f92ba066 100644 --- a/examples/BayerLuetticke/BayerLuetticke_wrapper.py +++ b/examples/BayerLuetticke/BayerLuetticke_wrapper.py @@ -4,11 +4,11 @@ ''' import sys import os -sys.path.insert(0, os.path.abspath('./BayerLuetticke_code/')) + from HARK.core import AgentType, Market from HARK.simulation import drawDiscrete -from SteadyStateOneAssetIOUs import SteadyStateOneAssetIOU -from FluctuationsOneAssetIOUs import FluctuationsOneAssetIOUs, SGU_solver +from Assets.One.SteadyStateOneAssetIOUs import SteadyStateOneAssetIOU +from Assets.One.FluctuationsOneAssetIOUs import FluctuationsOneAssetIOUs, SGU_solver from copy import copy, deepcopy import numpy as np import scipy as sc @@ -266,7 +266,7 @@ def solve(self): ############################################################################### if __name__ == '__main__': - import defineSSParameters as Params + import Assets.One.defineSSParameters as Params from copy import copy import pickle import pylab as plt @@ -279,9 +279,9 @@ def solve(self): EX1param = copy(Params.parm_one_asset_IOU) EX1 = SteadyStateOneAssetIOU(**EX1param) EX1SS = EX1.SolveSteadyState() - pickle.dump(EX1SS, open("BayerLuetticke_code/EX1SS.p", "wb")) + pickle.dump(EX1SS, open("Assets/One/EX1SS.p", "wb")) else: - EX1SS=pickle.load(open("BayerLuetticke_code/EX1SS.p", "rb")) + EX1SS=pickle.load(open("Assets/One/EX1SS.p", "rb")) #Build BayerLuetticke's object FluctuationsOneAssetIOU=FluctuationsOneAssetIOUs(**EX1SS) diff --git a/examples/BayerLuetticke/__init__.py b/examples/BayerLuetticke/__init__.py index f7b730eab..4ae08c79b 100644 --- a/examples/BayerLuetticke/__init__.py +++ b/examples/BayerLuetticke/__init__.py @@ -1,6 +1,6 @@ -from HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUs import * -from HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUs import * -from HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond import * -from HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUsBond import * -from HARK.BayerLuetticke.Assets.Two.SteadyStateTwoAsset import * -from HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset import * +from BayerLuetticke.Assets.One.FluctuationsOneAssetIOUs import * +from BayerLuetticke.Assets.One.SteadyStateOneAssetIOUs import * +from BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond import * +from BayerLuetticke.Assets.One.SteadyStateOneAssetIOUsBond import * +from BayerLuetticke.Assets.Two.SteadyStateTwoAsset import * +from BayerLuetticke.Assets.Two.FluctuationsTwoAsset import * From b6fe2ff8e016e3fa7971786040255df1f0c96b6b Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 26 Nov 2019 18:47:26 -0500 Subject: [PATCH 123/242] fixing paths in Sticky --- examples/cAndCwithStickyE/StickyE_MAIN.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/cAndCwithStickyE/StickyE_MAIN.py b/examples/cAndCwithStickyE/StickyE_MAIN.py index ed91b0ab3..7801d7bfc 100644 --- a/examples/cAndCwithStickyE/StickyE_MAIN.py +++ b/examples/cAndCwithStickyE/StickyE_MAIN.py @@ -15,12 +15,12 @@ import csv from time import clock from copy import deepcopy -from .StickyEmodel import StickyEmarkovConsumerType, StickyEmarkovRepAgent, StickyCobbDouglasMarkovEconomy +from StickyEmodel import StickyEmarkovConsumerType, StickyEmarkovRepAgent, StickyCobbDouglasMarkovEconomy from HARK.ConsumptionSaving.ConsAggShockModel import SmallOpenMarkovEconomy from HARK.utilities import plotFuncs import matplotlib.pyplot as plt -from . import StickyEparams as Params -from .StickyEtools import makeStickyEdataFile, runStickyEregressions, makeResultsTable,\ +import StickyEparams as Params +from StickyEtools import makeStickyEdataFile, runStickyEregressions, makeResultsTable,\ runStickyEregressionsInStata, makeParameterTable, makeEquilibriumTable,\ makeMicroRegressionTable, extractSampleMicroData, makeuCostVsPiFig, \ makeValueVsAggShkVarFig, makeValueVsPiFig From 0b4970b421847ad801a48a0dd84576ad10c755da Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Tue, 26 Nov 2019 20:04:46 -0500 Subject: [PATCH 124/242] Tiny fix to getPercentiles (#438) Added two lines to utilities.getPercentiles to correctly handle case when data has size 0 or 1: return NaN vector of same size as requested percentiles of data. That is, there is no such thing as "percentiles" of an empty dataset or a singleton. This can come up when simulated or empirical moments are calculated, but no observations qualify. --- HARK/utilities.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/HARK/utilities.py b/HARK/utilities.py index 81553a252..f64c7e5a9 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -1025,6 +1025,9 @@ def getPercentiles(data,weights=None,percentiles=[0.5],presorted=False): pctl_out : numpy.array The requested percentiles of the data. ''' + if data.size < 2: + return np.zeros(np.array(percentiles).shape) + np.nan + if weights is None: # Set equiprobable weights if none were passed weights = np.ones(data.size)/float(data.size) From 5fae087d4342ad49af0be184bf6b098ad1f17314 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Wed, 4 Dec 2019 20:01:10 -0500 Subject: [PATCH 125/242] fix matplotlib import in non-GUI environment --- HARK/utilities.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/HARK/utilities.py b/HARK/utilities.py index f64c7e5a9..b90b7a035 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -13,12 +13,12 @@ import warnings import numpy as np # Python's numeric library, abbreviated "np" import math -try: - import matplotlib.pyplot as plt # Python's plotting library -except ImportError: - import sys - exception_type, value, traceback = sys.exc_info() - raise ImportError('HARK must be used in a graphical environment.', exception_type, value, traceback) +# try: +# import matplotlib.pyplot as plt # Python's plotting library +# except ImportError: +# import sys +# exception_type, value, traceback = sys.exc_info() +# raise ImportError('HARK must be used in a graphical environment.', exception_type, value, traceback) import scipy.stats as stats # Python's statistics library from scipy.interpolate import interp1d from scipy.special import erf, erfc @@ -1226,6 +1226,7 @@ def plotFuncs(functions,bottom,top,N=1000,legend_kwds = None): ------- none ''' + import matplotlib.pyplot as plt if type(functions)==list: function_list = functions else: @@ -1261,6 +1262,7 @@ def plotFuncsDer(functions,bottom,top,N=1000,legend_kwds = None): ------- none ''' + import matplotlib.pyplot as plt if type(functions)==list: function_list = functions else: From 2012e5b165e80831a114d3208d7f76ccee28f36f Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Wed, 4 Dec 2019 20:06:49 -0500 Subject: [PATCH 126/242] remove import of BayerLuetticke into namespace --- HARK/__init__.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/HARK/__init__.py b/HARK/__init__.py index 14522d5e7..625114886 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -2,5 +2,3 @@ from .core import * __version__ = '0.10.2' - -from .BayerLuetticke import * From a1df5ec2edf835a3fc5051827e3a52e98e3d5cbe Mon Sep 17 00:00:00 2001 From: Sebastian Benthall Date: Thu, 5 Dec 2019 10:14:45 -0500 Subject: [PATCH 127/242] split on non-essential deps into extra_requires() (#429) * split on non-essential deps into extra_requires() * correcting extras_require use; moving numpydoc to dev deps. #429 --- setup.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/setup.py b/setup.py index ebcfda81c..89b669f49 100644 --- a/setup.py +++ b/setup.py @@ -148,16 +148,19 @@ install_requires=['numpy', 'matplotlib', 'scipy', - 'Sphinx', - 'numpydoc', 'dill', 'joblib', - 'future', # Optional 'funcsigs', - 'jupyter', - 'nbsphinx', - 'recommonmark' + 'jupyter' ], + extras_requires={ + 'dev': [ + 'future', # Optional + 'Sphinx<=1.6.7', + 'nbsphinx', + 'numpydoc', + 'recommonmark' + ]}, python_requires='>=2.7', From a09d2f850a1d02f4016680402f96337cd48450c7 Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 5 Dec 2019 15:50:02 -0500 Subject: [PATCH 128/242] removing cAndCwithStickyE, see #335 --- .../cAndCwithStickyE/Calibration/CRRA.txt | 1 - .../cAndCwithStickyE/Calibration/CapShare.txt | 1 - .../Calibration/DeprFacAnn.txt | 1 - .../cAndCwithStickyE/Calibration/DiePrb.txt | 1 - .../Calibration/KYratioSS.txt | 1 - .../Calibration/PermShkAggVar.txt | 1 - .../Calibration/PermShkVarAnn.txt | 1 - .../Calibration/TranShkAggVar.txt | 1 - .../Calibration/TranShkVarAnn.txt | 1 - .../cAndCwithStickyE/Calibration/UnempPrb.txt | 1 - .../Calibration/UpdatePrb.txt | 1 - .../cAndCwithStickyE/Calibration/betaSOE.txt | 1 - .../Figures/ValueVsPermShkAggVar.pdf | Bin 18242 -> 0 bytes .../cAndCwithStickyE/Figures/ValueVsPi.pdf | Bin 17011 -> 0 bytes .../cAndCwithStickyE/Figures/uCostvsPi.pdf 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100644 index 51af6eb09..000000000 --- a/examples/cAndCwithStickyE/Calibration/PermShkAggVar.txt +++ /dev/null @@ -1 +0,0 @@ -0.00004 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Calibration/PermShkVarAnn.txt b/examples/cAndCwithStickyE/Calibration/PermShkVarAnn.txt deleted file mode 100644 index 11ba96a30..000000000 --- a/examples/cAndCwithStickyE/Calibration/PermShkVarAnn.txt +++ /dev/null @@ -1 +0,0 @@ -0.012 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Calibration/TranShkAggVar.txt b/examples/cAndCwithStickyE/Calibration/TranShkAggVar.txt deleted file mode 100644 index 4e6943788..000000000 --- a/examples/cAndCwithStickyE/Calibration/TranShkAggVar.txt +++ /dev/null @@ -1 +0,0 @@ -0.00001 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Calibration/TranShkVarAnn.txt b/examples/cAndCwithStickyE/Calibration/TranShkVarAnn.txt deleted file mode 100644 index 1c7da8c00..000000000 --- a/examples/cAndCwithStickyE/Calibration/TranShkVarAnn.txt +++ /dev/null @@ -1 +0,0 @@ -0.03 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Calibration/UnempPrb.txt b/examples/cAndCwithStickyE/Calibration/UnempPrb.txt deleted file mode 100644 index 30e2fb4d6..000000000 --- a/examples/cAndCwithStickyE/Calibration/UnempPrb.txt +++ /dev/null @@ -1 +0,0 @@ -0.05 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Calibration/UpdatePrb.txt b/examples/cAndCwithStickyE/Calibration/UpdatePrb.txt deleted file mode 100644 index 2a64fae10..000000000 --- a/examples/cAndCwithStickyE/Calibration/UpdatePrb.txt +++ /dev/null @@ -1 +0,0 @@ -0.25 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Calibration/betaSOE.txt b/examples/cAndCwithStickyE/Calibration/betaSOE.txt deleted file mode 100644 index d6fcd6a7c..000000000 --- a/examples/cAndCwithStickyE/Calibration/betaSOE.txt +++ /dev/null @@ -1 +0,0 @@ -0.97 \ No newline at end of file diff --git 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distribution of Python 2.7 (from -www.continuum.io), run Spyder, and execute the MAIN file; see notes below. All -packages used by the StickyE project are included in the base distribution of Anaconda -or included in this archive. The only exception occurs if you try to run the "beta -dist" specification of the HA-DSGE model, which is commented out in the parameters -file. To successfully run this exercise, you must install the packages joblib and -dill by typing "conda install joblib" and "conda install dill" at a command prompt. - -NOTE ON SPYDER: Anaconda comes with an IDE called Spyder. On most operating systems, -Spyder can be run by typing "spyder" at a command prompt. On Windows 10, this does -not seem to work, and Spyder needs to be opened from the Start menu (usually in the -Anaconda2 submenu). - -NOTE ON STATA: The MAIN file includes a boolean called use_stata. If set to True, the -regressions will be run in Stata; if False, they will be run using the statsmodels.api -package in Python. The only difference is that the Stata code is able to produce the -KP statistic, but Python is not. To successfully use this option, you must set the -stata_exe variable in the StickyEparams.py to point to a valid Stata executable. -You must set a valid path in stata_exe if the make_emp_table or make_histogram booleans -are set to True. - -NOTE ON MEMORY: The heterogeneous agents models are set to simulate 20,000 households -for about 21,000 periods, saving the entire history of several household-level variables -in in double precision. As such, running the SOE or HA-DSGE models is very memory -intensive. We recommend that you run the code on a computer with at least 32GB of RAM -to ensure that memory problems are not encountered. To run the SOE and HA-DSGE models -back-to-back on the same call to the MAIN file, we recommend that you have at least -64 GB of memory. - -NOTE ON TIME: On a reasonably modern CPU, the RA model can solve and simulate in about -25-30 seconds (and has basically no memory requirement). The SOE model takes about -15-20 minutes to solve and simulate (but see note on memory). The HA-DSGE mode takes -roughly 16 hours to solve the sticky and frictionless versions, and will display progress -toward finding the equilibrium aggregate saving rule after each iteration. The very -long run time for HA-DSGE is largely due to the extremely large number of periods. -Exercises that examine the relationship between parameters and value-at-birth (e.g. the -"cost of stickiness" exercise) take about 4 hours each to run. Time series regressions -take a few seconds to run in Python or two minutes in Stata (if use_stata is True). - - -StickyE project files include: - -1) StickyE_MAIN.py -This is the file to run to produce all the results and tables in the paper. -There are a number of switches at the beginning of the file for the user to choose -which results and files to produce. Note: Setting do_DSGE_markov=True will result in VERY long run time. - -2) StickyEparams.py -This file loads the calibrated parameters, defines non-economic parameters (grid sizes, -etc), constructs parameters calculated from the primitive calibrated parameters, and -defines dictionaries for constructing objects in the MAIN file. It also sets: -i) The number of periods and number of agents to simulate. This has direct consequences for run time. -ii) The Stata path - this must be set to a valid Stata executable on the user's -machine if you want to reproduce the Stata parts of the code. - -3) StickyEmodel.py -This file contains the model code, building heavily off the rest of the HARK toolbox. - -4) StickyEtools.py -This file does most of the post-processing after simulation. It runs regression, calculates -equilibrium outcomes, etc. It is a collection of functions that are called by StickyE_MAIN.py - -5) StickyETimeSeries.do -This is a Stata do file. The regressions are run in Stata in order to produce the KP statistic. - -Relative locations of other folders are defined near the top of StickyEparams.py. -Referenced folders include: - -1) ./Results (results_dir) -This folder contains saved data from the model simulations. - -2) ./Tables (tables_dir) -This folder contains the Latex code output by functions in StickyEtools.py. -Some of these tables appear in the paper. - -3) ./Figures (figures_dir) -This folder contains figures produced by functions in StickyEtools.py. -Some of these figures appear in the paper. - -4) ./Calibration (calibration_dir) -This folder contains one line parameter values that are read by StickyEparams.py -and by the LaTeX file that produces the paper. diff --git a/examples/cAndCwithStickyE/Results/DSGEmarkovFrictionlessBirthValue.csv b/examples/cAndCwithStickyE/Results/DSGEmarkovFrictionlessBirthValue.csv deleted file mode 100644 index 6afcc4cbb..000000000 --- a/examples/cAndCwithStickyE/Results/DSGEmarkovFrictionlessBirthValue.csv +++ /dev/null @@ -1 +0,0 @@ --24.311498913568347,-24.068031555558235,-23.950381149098533,-23.582359316383222,-23.270495399257591,-22.866481004855782,-22.274778904479014,-21.869074849354746,-21.482237363934196,-21.200148944861038,-20.982374811308251 diff --git a/examples/cAndCwithStickyE/Results/DSGEmarkovFrictionlessData.txt b/examples/cAndCwithStickyE/Results/DSGEmarkovFrictionlessData.txt deleted file mode 100644 index 08d1587fb..000000000 --- a/examples/cAndCwithStickyE/Results/DSGEmarkovFrictionlessData.txt +++ /dev/null @@ -1,20010 +0,0 @@ -time_period DeltaLogC_me DeltaLogC DeltaLogY A BigTheta Delta8LogC Delta8LogY Delta8LogC_me Measurement_Error MrkvState R -0.0 0.0015189105452846074 0.0027785416188439993 -0.0020592476157209544 41.42615307955284 0.99557930605502465 0.0040080354253672346 0.029980322405552307 0.0011488669372288762 0.0014619124040379835 7.0 1.0179217090649488 -1.0 -0.0064817738066116903 -0.0018613177614139054 0.0047031251502711192 41.786965772619631 0.99831453862132957 0.0097266720003424401 0.02392464416633297 0.0092957658900112428 -0.0031585436411597986 6.0 1.0177058101913274 -2.0 -0.0074113603832190733 -0.010552768816099595 0.0037207564381689946 41.88629386972233 0.99999510607391928 -0.0064871462247987066 0.022162170167449251 -0.0062743016804922835 -1.7135208279270469e-05 6.0 1.0176556208877661 -3.0 0.005609203171325905 0.0043221595779029398 0.00096908161811710904 41.840241418292528 0.99831453862132957 -0.00087083319423980621 0.022608569238057437 -0.0011671028217186685 0.0012699083851436901 6.0 1.0176788663460108 -4.0 -0.0041239884688787087 -0.0013823438089913265 0.0030593938974901869 42.310963201809784 0.99831453862132957 0.0080531209412596783 0.025516114222416755 0.0088169990472545812 -0.0014717362747436974 5.0 1.0174117557863434 -5.0 -0.0059950143776155707 -0.0080094125988614262 -0.00057036570621682176 42.12938778881378 1.0016785742323322 -0.0082824156399249338 0.0081935195965820769 -0.0086665641614716571 0.00054266194650215459 6.0 1.0175336077722219 -6.0 0.016783230511911718 0.01710403715179562 0.0070752150579137616 42.227284725734769 0.99557930605502465 -0.0053591346441785315 0.021876824316102498 -0.0018824820027285305 0.00022185530661825587 6.0 1.0174847971410252 -7.0 -0.0022893419681261629 -0.0020762576394592891 -0.01247957750602649 42.663450258631826 0.99557930605502465 0.00032263772371701749 0.0044183813339969055 -2.2370749218070252e-06 8.7709779513819328e-06 5.0 1.0172382636442268 -8.0 -0.015910832267591962 -0.01554520721906183 -0.0019784515639126015 42.971127452049963 1.0016785742323322 -0.018001111114188811 0.0044991773858052584 -0.011882997651809418 -0.00035685407057875067 4.0 1.017057610345188 -9.0 -0.0061305924646104237 -0.0073724807400585568 0.023162176276848554 42.908906841353222 1.0044324750173943 -0.023512274092833463 0.022958228512382696 -0.021385334749458196 0.00088503420486937237 5.0 1.017118833438424 -10.0 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-56.847792401,3.44465152065,0.276427085691,0.00965865960185,0.00743251267234,0.00387845391934,1.01458943395,0.597611960896,0.796007731615,0.863239758053,0.123056245946,0.0542195437699,0.0534679195361 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Results/DSGEmarkovStickyBirthValue.csv b/examples/cAndCwithStickyE/Results/DSGEmarkovStickyBirthValue.csv deleted file mode 100644 index 59d5d93c6..000000000 --- a/examples/cAndCwithStickyE/Results/DSGEmarkovStickyBirthValue.csv +++ /dev/null @@ -1 +0,0 @@ --24.321233175889635,-24.078152103307058,-23.961036115445367,-23.593752276046803,-23.282284372129595,-22.878201207882963,-22.286075315090393,-21.879602769758616,-21.491854325722905,-21.208419487452186,-20.990138513482528 diff --git a/examples/cAndCwithStickyE/Results/DSGEmarkovStickyData.txt b/examples/cAndCwithStickyE/Results/DSGEmarkovStickyData.txt deleted file mode 100644 index 8ce642e98..000000000 --- a/examples/cAndCwithStickyE/Results/DSGEmarkovStickyData.txt +++ /dev/null @@ -1,20010 +0,0 @@ -time_period DeltaLogC_me DeltaLogC DeltaLogY A BigTheta Delta8LogC Delta8LogY Delta8LogC_me Measurement_Error MrkvState R -0.0 -0.00029276852472726045 0.00096686254883099343 -0.0022877144175365198 41.694721832010508 0.99557930605502465 0.0094495305047415656 0.028397491686628775 0.0065903620166057608 0.0014619124040366692 7.0 1.0177843896668779 -1.0 -0.0047908198087595055 -0.0001703637635658839 0.0045196618392438692 42.030938846878108 0.99831453862132957 0.0086399204342419988 0.022167189339480024 0.0082090143239111901 -0.0031585436411569589 6.0 1.0175828815778716 -2.0 0.0013845258314990339 -0.0017568826013786565 0.0034901301008304149 42.07652755481665 0.99999510607391928 0.0037591763528691813 0.020405880661447249 0.0039720208971754101 -1.7135208279255064e-05 6.0 1.0175600408734768 -3.0 -5.5231205991435051e-05 -0.0013422747994132345 0.00050463401856440024 41.992717653156603 0.99831453862132957 0.0014781776280876624 0.020556159441370535 0.0011819080006090776 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a/examples/cAndCwithStickyE/Results/RAmarkovStickyResults.csv +++ /dev/null @@ -1 +0,0 @@ -55.69098757539448,3.42498371365486,0.3008797045721221,0.004871674373629889,0.007305685878208272,0.003889291072792226 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Results/SOEmarkovFrictionlessBirthValue.csv b/examples/cAndCwithStickyE/Results/SOEmarkovFrictionlessBirthValue.csv deleted file mode 100644 index cc9a4a680..000000000 --- a/examples/cAndCwithStickyE/Results/SOEmarkovFrictionlessBirthValue.csv +++ /dev/null @@ -1 +0,0 @@ --14.239039232212145,-14.040676227601795,-13.866533669712576,-13.514366785937964,-13.216857773146344,-12.864923285895442,-12.457899531631028,-12.105842565408437,-11.806097722617816,-11.550977598151178,-11.413149647448066 diff --git a/examples/cAndCwithStickyE/Results/SOEmarkovFrictionlessData.txt b/examples/cAndCwithStickyE/Results/SOEmarkovFrictionlessData.txt deleted file mode 100644 index 9ae1a3d93..000000000 --- a/examples/cAndCwithStickyE/Results/SOEmarkovFrictionlessData.txt +++ /dev/null @@ -1,20010 +0,0 @@ -time_period DeltaLogC_me DeltaLogC DeltaLogY A BigTheta Delta8LogC Delta8LogY Delta8LogC_me Measurement_Error MrkvState -0.0 0.00096888051088886429 0.0024796187792763685 0.00044519218945537897 5.0099097736159637 0.99557930605502465 0.013816632764326167 0.039137429108720923 0.010387489627524511 0.0017533443403946296 7.0 -1.0 -0.0095137929397222792 -0.0039722499344689993 -0.0015708638739008532 5.0610974590167181 0.99831453862132957 0.015026202933150067 0.024180755735993095 0.014509395778560072 -0.0037881986648586434 6.0 -2.0 -0.002900748764818703 -0.0066683963211602096 0.0040989404199996624 5.1037548022892585 0.99999510607391928 0.0016760350745119168 0.020446383402729494 0.0019313101348457096 -2.0551108517143049e-05 6.0 -3.0 0.0072238859475938777 0.0056802704791593731 0.0025627269385216506 5.1214955137956313 0.99831453862132957 0.010389313586761664 0.025092998480873475 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-7.49405615871,2.70959690528,0.332498021886,0.0103798754233,0.0097784963325,0.00838100286402,0.925733533807,0.790329129542,0.796007731615,0.863239758053,0.269394097769,0.0977443702736,0.0534679195361 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Results/SOEmarkovStickyBirthValue.csv b/examples/cAndCwithStickyE/Results/SOEmarkovStickyBirthValue.csv deleted file mode 100644 index 710960ecb..000000000 --- a/examples/cAndCwithStickyE/Results/SOEmarkovStickyBirthValue.csv +++ /dev/null @@ -1 +0,0 @@ --14.246438818594823,-14.048154293198802,-13.874094093855906,-13.521980295348582,-13.224525565709483,-12.871920540680181,-12.464188283491941,-12.111409511872299,-11.810789358488528,-11.554901595916261,-11.41682321470828 diff --git a/examples/cAndCwithStickyE/Results/SOEmarkovStickyData.txt b/examples/cAndCwithStickyE/Results/SOEmarkovStickyData.txt deleted file mode 100644 index b797931fe..000000000 --- a/examples/cAndCwithStickyE/Results/SOEmarkovStickyData.txt +++ /dev/null @@ -1,20010 +0,0 @@ -time_period DeltaLogC_me DeltaLogC DeltaLogY A BigTheta Delta8LogC Delta8LogY Delta8LogC_me Measurement_Error MrkvState -0.0 -0.00045690896608915899 0.0010538293022993861 0.00044519218945517081 5.1018992874572673 0.99557930605502465 0.011486677702874815 0.039137429108720714 0.0080575345660707925 0.0017533443403958348 7.0 -1.0 -0.0060174284178370896 -0.00047588541258000716 -0.001570863873900645 5.1260917804115662 0.99831453862132957 0.010754212040273062 0.024180755735993095 0.010237404885682713 -0.0037881986648612468 6.0 -2.0 0.0025179632900581839 -0.001249684266285897 0.0040989404199994542 5.1265469787518096 0.99999510607391928 0.006444137922761578 0.020446383402729286 0.0066994129830955373 -2.0551108517157178e-05 6.0 -3.0 0.0010790480708339212 -0.00046456739760165899 0.0025627269385218587 5.1177495781482518 0.99831453862132957 0.0054904564328321498 0.025092998480873475 0.0051351255161941889 0.0015230643599184154 6.0 -4.0 -0.0047463755462679724 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-20007.0 0.0096397335214692959 0.011372345039784904 0.01423965758302348 4.431808075965554 1.0044324750173943 0.082494527634111137 0.087698261166277547 0.082206923188518743 -0.0017502124566182674 10.0 -20008.0 0.012503345830609192 0.0098424851871117269 0.0047750451374231062 4.4548399265469021 1.0044324750173943 0.080854698524975177 0.079450989821146401 0.079345492152175012 0.00091064818687951177 10.0 diff --git a/examples/cAndCwithStickyE/Results/SOEmarkovStickyResults.csv b/examples/cAndCwithStickyE/Results/SOEmarkovStickyResults.csv deleted file mode 100644 index 9a005d928..000000000 --- a/examples/cAndCwithStickyE/Results/SOEmarkovStickyResults.csv +++ /dev/null @@ -1 +0,0 @@ -7.43072065583,2.70761108741,0.321321057031,0.00653672912957,0.0097784963325,0.00742204661077,0.927276563321,0.790594189786,0.796007731615,0.863239758053,0.269342076161,0.0980425218485,0.0534679195361 \ No newline at end of file diff --git a/examples/cAndCwithStickyE/Results/SOEuCostbyUpdatePrb.csv b/examples/cAndCwithStickyE/Results/SOEuCostbyUpdatePrb.csv deleted file mode 100644 index f4b17e622..000000000 --- a/examples/cAndCwithStickyE/Results/SOEuCostbyUpdatePrb.csv +++ /dev/null @@ -1,2 +0,0 @@ -0.025000000000000001,0.050000000000000003,0.074999999999999997,0.10000000000000001,0.125,0.14999999999999999,0.17499999999999999,0.19999999999999998,0.22499999999999998,0.24999999999999997,0.27499999999999997,0.29999999999999999,0.32500000000000001,0.34999999999999998,0.375,0.39999999999999997,0.42499999999999999,0.45000000000000001,0.47499999999999998,0.5,0.52499999999999991,0.54999999999999993,0.57499999999999996,0.59999999999999998,0.625,0.65000000000000002,0.67499999999999993,0.69999999999999996,0.72499999999999998,0.75,0.77499999999999991,0.79999999999999993,0.82499999999999996,0.84999999999999998,0.875,0.89999999999999991,0.92499999999999993,0.94999999999999996,0.97499999999999998,1.0 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b/examples/cAndCwithStickyE/Results/SOEvVecByPermShkAggVar.csv deleted file mode 100644 index eeb752732..000000000 --- a/examples/cAndCwithStickyE/Results/SOEvVecByPermShkAggVar.csv +++ /dev/null @@ -1,2 +0,0 @@ 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--12.804070961225381,-12.805300198452086,-12.806507033440289,-12.80769301393113,-12.808859519322692,-12.810007782566981,-12.811138913059432,-12.812253911404776,-12.813353684487685,-12.81443905815869,-12.815510785848003,-12.816569557121607,-12.817616006229406,-12.818650717147683,-12.819674229551726,-12.820687043753578,-12.821689620875004,-12.822682393730387,-12.823665764270903,-12.824640107096796,-12.825605772964838,-12.826563090974419,-12.827512368656132,-12.828453895705168,-12.829387945674023,-12.830314776198804,-12.831234630615198,-12.832147738894367,-12.833054318449383,-12.833954575239209,-12.834848705257871,-12.835736894027383,-12.836619317633547,-12.837496144299809,-12.838367533622023,-12.839233639014282,-12.840094605034123,-12.840950571335521,-12.841801671030902,-12.84264803126012 diff --git a/examples/cAndCwithStickyE/Results/SOEvVecByUpdatePrb.csv b/examples/cAndCwithStickyE/Results/SOEvVecByUpdatePrb.csv deleted file mode 100644 index 6c3a8926b..000000000 --- a/examples/cAndCwithStickyE/Results/SOEvVecByUpdatePrb.csv +++ /dev/null @@ -1,2 +0,0 @@ -0.025000000000000001,0.050000000000000003,0.074999999999999997,0.10000000000000001,0.125,0.14999999999999999,0.17499999999999999,0.19999999999999998,0.22499999999999998,0.24999999999999997,0.27499999999999997,0.29999999999999999,0.32500000000000001,0.34999999999999998,0.375,0.39999999999999997,0.42499999999999999,0.45000000000000001,0.47499999999999998,0.5,0.52499999999999991,0.54999999999999993,0.57499999999999996,0.59999999999999998,0.625,0.65000000000000002,0.67499999999999993,0.69999999999999996,0.72499999999999998,0.75,0.77499999999999991,0.79999999999999993,0.82499999999999996,0.84999999999999998,0.875,0.89999999999999991,0.92499999999999993,0.94999999999999996,0.97499999999999998,1.0 --12.945596659970885,-12.88512297964874,-12.861543847565502,-12.849847644921613,-12.843146641288104,-12.838883351301355,-12.836030710023095,-12.834031249393956,-12.832512225670678,-12.831384142896825,-12.83049253722878,-12.829781027249082,-12.829185454378113,-12.828689117017662,-12.82828294242832,-12.827926658485161,-12.827629224540392,-12.827358108999407,-12.827121297107437,-12.826912659118284,-12.826728164684914,-12.826557525686535,-12.826407586411495,-12.826275326142751,-12.826157059804643,-12.826047596564546,-12.825947730616537,-12.825852751745407,-12.825765790410024,-12.825686843603961,-12.825611910414908,-12.825542491337165,-12.825477848500084,-12.825417436781482,-12.825360454622381,-12.825308757255625,-12.825257742161522,-12.825208764692672,-12.825166345957767,-12.825124003614798 diff --git a/examples/cAndCwithStickyE/Results/temp.txt b/examples/cAndCwithStickyE/Results/temp.txt deleted file mode 100644 index 9eeeead05..000000000 --- a/examples/cAndCwithStickyE/Results/temp.txt +++ /dev/null @@ -1,8 +0,0 @@ -CoeffsArray,StdErrArray,RsqArray,PvalArray,OIDarray,ExtraInfo -.5850750157773537,.0053878791733095,.3422661009290046,,,1 -.8737330208980229,.0083663678985942,.3662175205486002,0,1.27455419456e-21,0 -.9727678900163255,.013669480494565,.3152824220595177,0,1.1873460863e-151,1 --.0001898490094191,2.36438267024e-06,.2163716845811734,0,0,.3703226375841939 -.7867886164121477,.023388139577672,.3703412204324246,6.70767319442e-46,.560499758550367,4.16256649948e-06 --.0062154281513593,.0510710458902886,,,, --.0000374119774333,7.85011970051e-06,,,, diff --git a/examples/cAndCwithStickyE/StickyETimeSeries.do b/examples/cAndCwithStickyE/StickyETimeSeries.do deleted file mode 100644 index 0c8700999..000000000 --- a/examples/cAndCwithStickyE/StickyETimeSeries.do +++ /dev/null @@ -1,152 +0,0 @@ -* This do file runs the macroeconomic regressions for the StickyE project. -* It is called by StickyEtools.runStickyEregressionsInStata(). - -clear - -global infilename `1' -global tempfilename `2' -global interval_size `3' -global meas_err `4' -global instruments = "L(3/4).deltalogc L3.delta8logc L(3/4).deltalogy L3.delta8logy L(3/4).a" - -*import data from file produced in Python -import delimited $infilename, clear -tsset time_period -global num_regressions = floor(_N/$interval_size) - -*use measurement error if required -if $meas_err==1 { - drop deltalogc - gen deltalogc = deltalogc_me -} - -*Order: DeltaLogC_OLS, DeltaLogC_me_OLS, DeltaLogC_me_IV, DeltaLogY_IV, A_OLS, DeltaLogC_HR, DeltaLogY_HR, A_HR -matrix CoeffsArray = J(7,1,0) -matrix StdErrArray = J(7,1,0) -matrix RsqArray = J(5,1,0) -matrix PvalArray = J(5,1,0) -matrix OIDarray = J(5,1,0) -* ExtraInfo is 1) Number of L.deltalogc significant coeff, 2) Number of deltalogy significant coeff, 3) num_regressions, 4) Rsq of instruments on deltalogc -matrix ExtraInfo = J(5,1,0) - - -forvalues i = 1/$num_regressions { - * Cut the dataset to just the observations for this interval - preserve - quietly keep if time_period >=${interval_size}*`i'-(${interval_size}-1) & time_period <=${interval_size}*`i' - - *OLS on consumption - quietly regress deltalogc L.deltalogc, robust - matrix CoeffsArray[1,1] = CoeffsArray[1,1]+_b[L.deltalogc]/$num_regressions - matrix StdErrArray[1,1] = StdErrArray[1,1]+_se[L.deltalogc]/$num_regressions - matrix RsqArray[1,1] = RsqArray[1,1]+ e(r2_a)/$num_regressions - matrix PvalArray[1,1] = PvalArray[1,1]+ e(idp)/$num_regressions - matrix OIDarray[1,1] = OIDarray[1,1]+ e(jp)/$num_regressions - - *IV on consumption - quietly ivreg2 deltalogc (L.deltalogc = ${instruments}), robust - matrix CoeffsArray[2,1] = CoeffsArray[2,1]+_b[L.deltalogc]/$num_regressions - matrix StdErrArray[2,1] = StdErrArray[2,1]+_se[L.deltalogc]/$num_regressions - matrix PvalArray[2,1] = PvalArray[2,1]+ e(idp)/$num_regressions - matrix OIDarray[2,1] = OIDarray[2,1]+ e(jp)/$num_regressions - *calc second stage R2 - qui reg L.deltalogc ${instruments}, robust - cap drop first_stage_predict_c - quietly predict first_stage_predict_c - quietly reg deltalogc first_stage_predict_c, robust - matrix RsqArray[2,1] = RsqArray[2,1]+ e(r2_a)/$num_regressions - - *IV on income - quietly ivreg2 deltalogc (deltalogy = ${instruments}), robust - matrix CoeffsArray[3,1] = CoeffsArray[3,1]+_b[deltalogy]/$num_regressions - matrix StdErrArray[3,1] = StdErrArray[3,1]+_se[deltalogy]/$num_regressions - matrix PvalArray[3,1] = PvalArray[3,1]+ e(idp)/$num_regressions - matrix OIDarray[3,1] = OIDarray[3,1]+ e(jp)/$num_regressions - *calc second stage R2 - quietly reg deltalogy ${instruments}, robust - cap drop first_stage_predict_y - quietly predict first_stage_predict_y - quietly reg deltalogc first_stage_predict_y, robust - matrix RsqArray[3,1] = RsqArray[3,1]+ e(r2_a)/$num_regressions - - *IV on assets - quietly ivreg2 deltalogc (L.a = ${instruments}), robust - matrix CoeffsArray[4,1] = CoeffsArray[4,1]+_b[L.a]/$num_regressions - matrix StdErrArray[4,1] = StdErrArray[4,1]+_se[L.a]/$num_regressions - matrix PvalArray[4,1] = PvalArray[4,1]+ e(idp)/$num_regressions - matrix OIDarray[4,1] = OIDarray[4,1]+ e(jp)/$num_regressions - *calc second stage R2 - quietly reg L.a ${instruments}, robust - cap drop first_stage_predict_a - quietly predict first_stage_predict_a - quietly reg deltalogc first_stage_predict_a, robust - matrix RsqArray[4,1] = RsqArray[4,1]+ e(r2_a)/$num_regressions - - *IV Horeserace - quietly ivreg2 deltalogc (L.deltalogc deltalogy L.a = ${instruments}), robust - matrix CoeffsArray[5,1] = CoeffsArray[5,1]+_b[L.deltalogc]/$num_regressions - matrix CoeffsArray[6,1] = CoeffsArray[6,1]+_b[deltalogy]/$num_regressions - matrix CoeffsArray[7,1] = CoeffsArray[7,1]+_b[L.a]/$num_regressions - - matrix StdErrArray[5,1] = StdErrArray[5,1]+_se[L.deltalogc]/$num_regressions - matrix StdErrArray[6,1] = StdErrArray[6,1]+_se[deltalogy]/$num_regressions - matrix StdErrArray[7,1] = StdErrArray[7,1]+_se[L.a]/$num_regressions - - matrix PvalArray[5,1] = PvalArray[5,1]+ e(idp)/$num_regressions - matrix OIDarray[5,1] = OIDarray[5,1]+ e(jp)/$num_regressions - if abs(_b[L.deltalogc]/_se[L.deltalogc])>1.96 { - matrix ExtraInfo[1,1] = ExtraInfo[1,1] + 1 - } - if abs(_b[deltalogy]/_se[deltalogy])>1.96 { - matrix ExtraInfo[2,1] = ExtraInfo[2,1] + 1 - } - - *calc second stage R2 - qui reg L.deltalogc ${instruments}, robust - cap drop first_stage_predict_c - quietly predict first_stage_predict_c - quietly reg deltalogy ${instruments}, robust - cap drop first_stage_predict_y - quietly predict first_stage_predict_y - quietly reg L.a ${instruments}, robust - cap drop first_stage_predict_a - quietly predict first_stage_predict_a - - quietly reg deltalogc first_stage_predict_c first_stage_predict_y first_stage_predict_a, robust - matrix RsqArray[5,1] = RsqArray[5,1]+ e(r2_a)/$num_regressions - *calc R2 of deltalogc on instruments - quietly reg deltalogc ${instruments}, robust - matrix ExtraInfo[4,1] = ExtraInfo[4,1] + e(r2_a)/$num_regressions - - * Restore the entire dataset for the next subinterval - restore -} -matrix ExtraInfo[3,1] = $num_regressions - -*Hard code variance of measurement error - better to pass this in from the data file -*Can replace this once new data files are produced -qui sum measurement_error -matrix ExtraInfo[5,1] = r(sd)^2 - -*Store results in a file -clear -svmat double CoeffsArray, name(CoeffsArray) -svmat double StdErrArray, name(StdErrArray) -svmat double RsqArray, name(RsqArray) -svmat double PvalArray, name(PvalArray) -svmat double OIDarray, name(OIDarray) -svmat double ExtraInfo, name(ExtraInfo) - -rename CoeffsArray1 CoeffsArray -rename StdErrArray1 StdErrArray -rename RsqArray1 RsqArray -rename PvalArray1 PvalArray -rename OIDarray1 OIDarray -rename ExtraInfo1 ExtraInfo - -export delimited $tempfilename, replace - -exit, STATA clear - - - diff --git a/examples/cAndCwithStickyE/StickyE_MAIN.py b/examples/cAndCwithStickyE/StickyE_MAIN.py deleted file mode 100644 index 7801d7bfc..000000000 --- a/examples/cAndCwithStickyE/StickyE_MAIN.py +++ /dev/null @@ -1,387 +0,0 @@ -''' -This module runs the exercises and regressions for the cAndCwithStickyE paper. -User can choose which among the three models are actually run. Descriptive -statistics and regression results are both output to screen and saved in a log -file in the results directory. TeX code for tables in the paper are saved in -the tables directory. See StickyEparams for calibrated model parameters. -''' -from __future__ import division -from __future__ import print_function -from __future__ import absolute_import -from builtins import str -from builtins import range -import os -import numpy as np -import csv -from time import clock -from copy import deepcopy -from StickyEmodel import StickyEmarkovConsumerType, StickyEmarkovRepAgent, StickyCobbDouglasMarkovEconomy -from HARK.ConsumptionSaving.ConsAggShockModel import SmallOpenMarkovEconomy -from HARK.utilities import plotFuncs -import matplotlib.pyplot as plt -import StickyEparams as Params -from StickyEtools import makeStickyEdataFile, runStickyEregressions, makeResultsTable,\ - runStickyEregressionsInStata, makeParameterTable, makeEquilibriumTable,\ - makeMicroRegressionTable, extractSampleMicroData, makeuCostVsPiFig, \ - makeValueVsAggShkVarFig, makeValueVsPiFig - -# Choose which models to do work for -do_SOE = False -do_DSGE = True -do_RA = False - -# Choose what kind of work to do for each model -run_models = True # Whether to solve models and generate new simulated data -calc_micro_stats = False # Whether to calculate microeconomic statistics (only matters when run_models is True) -make_tables = False # Whether to make LaTeX tables in the /Tables folder -use_stata = False # Whether to use Stata to run the simulated time series regressions -save_data = False # Whether to save data for use in Stata (as a tab-delimited text file) -run_ucost_vs_pi = False # Whether to run an exercise that finds the cost of stickiness as it varies with update probability -run_value_vs_aggvar = False # Whether to run an exercise to find value at birth vs variance of aggregate permanent shocks - -ignore_periods = Params.ignore_periods # Number of simulated periods to ignore as a "burn-in" phase -interval_size = Params.interval_size # Number of periods in each non-overlapping subsample -total_periods = Params.periods_to_sim # Total number of periods in simulation -interval_count = (total_periods-ignore_periods) // interval_size # Number of intervals in the macro regressions -periods_to_sim_micro = Params.periods_to_sim_micro # To save memory, micro regressions are run on a smaller sample -AgentCount_micro = Params.AgentCount_micro # To save memory, micro regressions are run on a smaller sample -my_counts = [interval_size,interval_count] -alt_counts = [interval_size*interval_count,1] -mystr = lambda number : "{:.3f}".format(number) -results_dir = Params.results_dir - -# Define the function to run macroeconomic regressions, depending on whether Stata is used -if use_stata: - runRegressions = lambda a,b,c,d,e : runStickyEregressionsInStata(a,b,c,d,e,Params.stata_exe) -else: - runRegressions = lambda a,b,c,d,e : runStickyEregressions(a,b,c,d,e) - - - -# Run models and save output if this module is called from main -def main(): - - ############################################################################### - ########## SMALL OPEN ECONOMY WITH MACROECONOMIC MARKOV STATE################## - ############################################################################### - - if do_SOE: - if run_models: - # Make consumer types to inhabit the small open Markov economy - StickySOEmarkovBaseType = StickyEmarkovConsumerType(**Params.init_SOE_mrkv_consumer) - StickySOEmarkovBaseType.IncomeDstn[0] = Params.StateCount*[StickySOEmarkovBaseType.IncomeDstn[0]] - StickySOEmarkovBaseType.track_vars = ['aLvlNow','cLvlNow','yLvlNow','pLvlTrue','t_age','TranShkNow'] - StickySOEmarkovConsumers = [] - for n in range(Params.TypeCount): - StickySOEmarkovConsumers.append(deepcopy(StickySOEmarkovBaseType)) - StickySOEmarkovConsumers[-1].seed = n - StickySOEmarkovConsumers[-1].DiscFac = Params.DiscFacSetSOE[n] - - # Make a small open economy for the agents - StickySOmarkovEconomy = SmallOpenMarkovEconomy(agents=StickySOEmarkovConsumers, **Params.init_SOE_mrkv_market) - StickySOmarkovEconomy.track_vars += ['TranShkAggNow','wRteNow'] - StickySOmarkovEconomy.makeAggShkHist() # Simulate a history of aggregate shocks - for n in range(Params.TypeCount): - StickySOEmarkovConsumers[n].getEconomyData(StickySOmarkovEconomy) # Have the consumers inherit relevant objects from the economy - - # Solve the small open Markov model - t_start = clock() - StickySOmarkovEconomy.solveAgents() - t_end = clock() - print('Solving the small open Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Plot the consumption function in each Markov state - print('Consumption function for one type in the small open Markov economy:') - m = np.linspace(0,20,500) - M = np.ones_like(m) - c = np.zeros((Params.StateCount,m.size)) - for i in range(Params.StateCount): - c[i,:] = StickySOEmarkovConsumers[0].solution[0].cFunc[i](m,M) - plt.plot(m,c[i,:]) - plt.show() - - # Simulate the sticky small open Markov economy - t_start = clock() - for agent in StickySOmarkovEconomy.agents: - agent(UpdatePrb = Params.UpdatePrb) - StickySOmarkovEconomy.makeHistory() - t_end = clock() - print('Simulating the sticky small open Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Make results for the sticky small open Markov economy - desc = 'Results for the sticky small open Markov economy with update probability ' + mystr(Params.UpdatePrb) - name = 'SOEmarkovSticky' - makeStickyEdataFile(StickySOmarkovEconomy,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats) - if calc_micro_stats: - sticky_SOEmarkov_micro_data = extractSampleMicroData(StickySOmarkovEconomy, np.minimum(StickySOmarkovEconomy.act_T-ignore_periods-1,periods_to_sim_micro), np.minimum(StickySOmarkovEconomy.agents[0].AgentCount,AgentCount_micro), ignore_periods) - DeltaLogC_stdev = np.genfromtxt(results_dir + 'SOEmarkovStickyResults.csv', delimiter=',')[3] # For use in frictionless spec - - # Simulate the frictionless small open Markov economy - t_start = clock() - for agent in StickySOmarkovEconomy.agents: - agent(UpdatePrb = 1.0) - StickySOmarkovEconomy.makeHistory() - t_end = clock() - print('Simulating the frictionless small open Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Make results for the frictionless small open Markov economy - desc = 'Results for the frictionless small open Markov economy (update probability 1.0)' - name = 'SOEmarkovFrictionless' - makeStickyEdataFile(StickySOmarkovEconomy,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats,meas_err_base=DeltaLogC_stdev) - if calc_micro_stats: - frictionless_SOEmarkov_micro_data = extractSampleMicroData(StickySOmarkovEconomy, np.minimum(StickySOmarkovEconomy.act_T-ignore_periods-1,periods_to_sim_micro), np.minimum(StickySOmarkovEconomy.agents[0].AgentCount,AgentCount_micro), ignore_periods) - makeMicroRegressionTable('CGrowCross', [frictionless_SOEmarkov_micro_data,sticky_SOEmarkov_micro_data]) - - if run_ucost_vs_pi: - # Find the birth value and cost of stickiness as it varies with updating probability - UpdatePrbVec = np.linspace(0.025,1.0,40) - CRRA = StickySOmarkovEconomy.agents[0].CRRA - vBirth_F = np.genfromtxt(results_dir + 'SOEmarkovFrictionlessBirthValue.csv', delimiter=',') - uCostVec = np.zeros_like(UpdatePrbVec) - vVec = np.zeros_like(UpdatePrbVec) - for j in range(UpdatePrbVec.size): - for agent in StickySOmarkovEconomy.agents: - agent(UpdatePrb = UpdatePrbVec[j]) - StickySOmarkovEconomy.makeHistory() - makeStickyEdataFile(StickySOmarkovEconomy,ignore_periods,description='trash',filename='TEMP',save_data=False,calc_micro_stats=True) - vBirth_S = np.genfromtxt(results_dir + 'TEMPBirthValue.csv', delimiter=',') - uCost = np.mean(1. - (vBirth_S/vBirth_F)**(1./(1.-CRRA))) - uCostVec[j] = uCost - vVec[j] = np.mean(vBirth_S) - print('Found that uCost=' + str(uCost) + ' for Pi=' + str(UpdatePrbVec[j])) - with open(results_dir + 'SOEuCostbyUpdatePrb.csv','w') as f: - my_writer = csv.writer(f, delimiter = ',') - my_writer.writerow(UpdatePrbVec) - my_writer.writerow(uCostVec) - f.close() - with open(results_dir + 'SOEvVecByUpdatePrb.csv','w') as f: - my_writer = csv.writer(f, delimiter = ',') - my_writer.writerow(UpdatePrbVec) - my_writer.writerow(vVec) - f.close() - os.remove(results_dir + 'TEMPResults.csv') - os.remove(results_dir + 'TEMPBirthValue.csv') - - if run_value_vs_aggvar: - # Find value as it varies with updating probability - PermShkAggVarBase = np.linspace(0.5,1.5,40) - PermShkAggVarVec = PermShkAggVarBase*Params.PermShkAggVar - vVec = np.zeros_like(PermShkAggVarVec) - for j in range(PermShkAggVarVec.size): - StickySOmarkovEconomy.PermShkAggStd = Params.StateCount*[np.sqrt(PermShkAggVarVec[j])] - StickySOmarkovEconomy.makeAggShkDstn() - StickySOmarkovEconomy.makeAggShkHist() - for agent in StickySOmarkovEconomy.agents: - agent(UpdatePrb = 1.0) - agent.getEconomyData(StickySOmarkovEconomy) - StickySOmarkovEconomy.solveAgents() - StickySOmarkovEconomy.makeHistory() - makeStickyEdataFile(StickySOmarkovEconomy,ignore_periods,description='trash',filename='TEMP',save_data=False,calc_micro_stats=True) - vBirth_S = np.genfromtxt(results_dir + 'TEMPBirthValue.csv', delimiter=',') - v = np.mean(vBirth_S) - vVec[j] = v - print('Found that v=' + str(v) + ' for PermShkAggVar=' + str(PermShkAggVarVec[j])) - with open(results_dir + 'SOEvVecByPermShkAggVar.csv','w') as f: - my_writer = csv.writer(f, delimiter = ',') - my_writer.writerow(PermShkAggVarVec) - my_writer.writerow(vVec) - f.close() - os.remove(results_dir + 'TEMPResults.csv') - os.remove(results_dir + 'TEMPBirthValue.csv') - - # Process the coefficients, standard errors, etc into a LaTeX table - if make_tables: - t_start = clock() - frictionless_panel = runRegressions('SOEmarkovFrictionlessData',interval_size,False,False,True) - frictionless_me_panel = runRegressions('SOEmarkovFrictionlessData',interval_size,True,False,True) - frictionless_long_panel = runRegressions('SOEmarkovFrictionlessData',interval_size*interval_count,True,False,True) - sticky_panel = runRegressions('SOEmarkovStickyData',interval_size,False,True,True) - sticky_me_panel = runRegressions('SOEmarkovStickyData',interval_size,True,True,True) - sticky_long_panel = runRegressions('SOEmarkovStickyData',interval_size*interval_count,True,True,True) - makeResultsTable('Aggregate Consumption Dynamics in SOE Model',[frictionless_me_panel,sticky_me_panel],my_counts,'SOEmrkvSimReg','tPESOEsim') - makeResultsTable('Aggregate Consumption Dynamics in SOE Model',[frictionless_panel,sticky_panel],my_counts,'SOEmrkvSimRegNoMeasErr','tPESOEsimNoMeasErr') - makeResultsTable('Aggregate Consumption Dynamics in SOE Model',[frictionless_long_panel,sticky_long_panel],alt_counts,'SOEmrkvSimRegLong','tSOEsimLong') - makeResultsTable(None,[frictionless_me_panel],my_counts,'SOEmrkvSimRegF','tPESOEsimF') - makeResultsTable(None,[sticky_me_panel],my_counts,'SOEmrkvSimRegS','tPESOEsimS') - t_end = clock() - print('Running time series regressions for the small open Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - - ############################################################################### - ########## COBB-DOUGLAS ECONOMY WITH MACROECONOMIC MARKOV STATE ############### - ############################################################################### - - if do_DSGE: - if run_models: - # Make consumers who will live in the Cobb-Douglas Markov economy - StickyDSGEmarkovBaseType = StickyEmarkovConsumerType(**Params.init_DSGE_mrkv_consumer) - StickyDSGEmarkovBaseType.IncomeDstn[0] = Params.StateCount*[StickyDSGEmarkovBaseType.IncomeDstn[0]] - StickyDSGEmarkovBaseType.track_vars = ['aLvlNow','cLvlNow','yLvlNow','pLvlTrue','t_age','TranShkNow'] - StickyDSGEmarkovConsumers = [] - for n in range(Params.TypeCount): - StickyDSGEmarkovConsumers.append(deepcopy(StickyDSGEmarkovBaseType)) - StickyDSGEmarkovConsumers[-1].seed = n - StickyDSGEmarkovConsumers[-1].DiscFac = Params.DiscFacSetDSGE[n] - - # Make a Cobb-Douglas economy for the agents - StickyDSGEmarkovEconomy = StickyCobbDouglasMarkovEconomy(agents = StickyDSGEmarkovConsumers,**Params.init_DSGE_mrkv_market) - StickyDSGEmarkovEconomy.track_vars += ['RfreeNow','wRteNow','TranShkAggNow'] - StickyDSGEmarkovEconomy.overwrite_hist = False - StickyDSGEmarkovEconomy.makeAggShkHist() # Simulate a history of aggregate shocks - for n in range(Params.TypeCount): - StickyDSGEmarkovConsumers[n].getEconomyData(StickyDSGEmarkovEconomy) # Have the consumers inherit relevant objects from the economy - StickyDSGEmarkovConsumers[n](UpdatePrb = Params.UpdatePrb) - - # Solve the sticky heterogeneous agent DSGE model - t_start = clock() - StickyDSGEmarkovEconomy.solve() - t_end = clock() - print('Solving the sticky Cobb-Douglas Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - print('Displaying the consumption functions for the Cobb-Douglas Markov economy would be too much.') - - # Make results for the sticky Cobb-Douglas Markov economy - desc = 'Results for the sticky Cobb-Douglas Markov economy with update probability ' + mystr(Params.UpdatePrb) - name = 'DSGEmarkovSticky' - makeStickyEdataFile(StickyDSGEmarkovEconomy,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats) - DeltaLogC_stdev = np.genfromtxt(results_dir + 'DSGEmarkovStickyResults.csv', delimiter=',')[3] # For use in frictionless spec - if calc_micro_stats: - sticky_DSGEmarkov_micro_data = extractSampleMicroData(StickyDSGEmarkovEconomy, np.minimum(StickyDSGEmarkovEconomy.act_T-ignore_periods-1,periods_to_sim_micro), np.minimum(StickyDSGEmarkovEconomy.agents[0].AgentCount,AgentCount_micro), ignore_periods) - - # Store the histories of MaggNow, wRteNow, and Rfree now in _overwrite attributes - StickyDSGEmarkovEconomy.MaggNow_overwrite = deepcopy(StickyDSGEmarkovEconomy.MaggNow_hist) - StickyDSGEmarkovEconomy.wRteNow_overwrite = deepcopy(StickyDSGEmarkovEconomy.wRteNow_hist) - StickyDSGEmarkovEconomy.RfreeNow_overwrite = deepcopy(StickyDSGEmarkovEconomy.RfreeNow_hist) - - # Calculate the lifetime value of being frictionless when all other agents are sticky - if calc_micro_stats: - StickyDSGEmarkovEconomy.overwrite_hist = True # History will be overwritten by sticky outcomes - for agent in StickyDSGEmarkovEconomy.agents: - agent(UpdatePrb = 1.0) # Make agents frictionless - StickyDSGEmarkovEconomy.makeHistory() # Simulate a history one more time - - # Save the birth value file in a temporary file and delete the other generated results files - makeStickyEdataFile(StickyDSGEmarkovEconomy,ignore_periods,description=desc,filename=name+'TEMP',save_data=False,calc_micro_stats=calc_micro_stats) - os.remove(results_dir + name + 'TEMP' + 'Results.csv') - sticky_name = name - - # Solve the frictionless heterogeneous agent DSGE model - StickyDSGEmarkovEconomy.overwrite_hist = False - for agent in StickyDSGEmarkovEconomy.agents: - agent(UpdatePrb = 1.0) - t_start = clock() - StickyDSGEmarkovEconomy.solve() - t_end = clock() - print('Solving the frictionless Cobb-Douglas Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - print('Displaying the consumption functions for the Cobb-Douglas Markov economy would be too much.') - - # Make results for the frictionless Cobb-Douglas Markov economy - desc = 'Results for the frictionless Cobb-Douglas Markov economy (update probability 1.0)' - name = 'DSGEmarkovFrictionless' - makeStickyEdataFile(StickyDSGEmarkovEconomy,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats,meas_err_base=DeltaLogC_stdev) - if calc_micro_stats: - os.remove(results_dir + name + 'BirthValue.csv') # Delete the frictionless birth value file - os.rename(results_dir + sticky_name + 'TEMPBirthValue.csv',results_dir + name + 'BirthValue.csv') # Replace just deleted file with "alternate" value calculation - frictionless_DSGEmarkov_micro_data = extractSampleMicroData(StickyDSGEmarkovEconomy, np.minimum(StickyDSGEmarkovEconomy.act_T-ignore_periods-1,periods_to_sim_micro), np.minimum(StickyDSGEmarkovEconomy.agents[0].AgentCount,AgentCount_micro), ignore_periods) - makeMicroRegressionTable('CGrowCrossDSGE', [frictionless_DSGEmarkov_micro_data,sticky_DSGEmarkov_micro_data]) - - # Process the coefficients, standard errors, etc into a LaTeX table - if make_tables: - t_start = clock() - frictionless_panel = runRegressions('DSGEmarkovFrictionlessData',interval_size,False,False,True) - frictionless_me_panel = runRegressions('DSGEmarkovFrictionlessData',interval_size,True,False,True) - frictionless_long_panel = runRegressions('DSGEmarkovFrictionlessData',interval_size*interval_count,True,False,True) - sticky_panel = runRegressions('DSGEmarkovStickyData',interval_size,False,True,True) - sticky_me_panel = runRegressions('DSGEmarkovStickyData',interval_size,True,True,True) - sticky_long_panel = runRegressions('DSGEmarkovStickyData',interval_size*interval_count,True,True,True) - makeResultsTable('Aggregate Consumption Dynamics in HA-DSGE Model',[frictionless_me_panel,sticky_me_panel],my_counts,'DSGEmrkvSimReg','tDSGEsim') - makeResultsTable('Aggregate Consumption Dynamics in HA-DSGE Model',[frictionless_panel,sticky_panel],my_counts,'DSGEmrkvSimRegNoMeasErr','tDSGEsimNoMeasErr') - makeResultsTable('Aggregate Consumption Dynamics in HA-DSGE Model',[frictionless_long_panel,sticky_long_panel],alt_counts,'DSGEmrkvSimRegLong','tDSGEsimLong') - makeResultsTable(None,[frictionless_me_panel],my_counts,'DSGEmrkvSimRegF','tDSGEsimF') - makeResultsTable(None,[sticky_me_panel],my_counts,'DSGEmrkvSimRegS','tDSGEsimS') - t_end = clock() - print('Running time series regressions for the Cobb-Douglas Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - - - ############################################################################### - ########### REPRESENTATIVE AGENT ECONOMY WITH MARKOV STATE #################### - ############################################################################### - - if do_RA: - if run_models: - # Make a representative agent consumer, then solve and simulate the model - StickyRAmarkovConsumer = StickyEmarkovRepAgent(**Params.init_RA_mrkv_consumer) - StickyRAmarkovConsumer.IncomeDstn[0] = Params.StateCount*[StickyRAmarkovConsumer.IncomeDstn[0]] - StickyRAmarkovConsumer.track_vars = ['cLvlNow','yNrmTrue','aLvlNow','pLvlTrue','TranShkNow','MrkvNow'] - - # Solve the representative agent Markov economy - t_start = clock() - StickyRAmarkovConsumer.solve() - t_end = clock() - print('Solving the representative agent Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - print('Consumption functions for the Markov representative agent:') - plotFuncs(StickyRAmarkovConsumer.solution[0].cFunc,0,50) - - # Simulate the sticky representative agent Markov economy - t_start = clock() - StickyRAmarkovConsumer(UpdatePrb = Params.UpdatePrb) - StickyRAmarkovConsumer.initializeSim() - StickyRAmarkovConsumer.simulate() - t_end = clock() - print('Simulating the sticky representative agent Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Make results for the sticky representative agent economy - desc = 'Results for the sticky representative agent Markov economy' - name = 'RAmarkovSticky' - makeStickyEdataFile(StickyRAmarkovConsumer,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats) - DeltaLogC_stdev = np.genfromtxt(results_dir + 'RAmarkovStickyResults.csv', delimiter=',')[3] # For use in frictionless spec - - # Simulate the frictionless representative agent Markov economy - t_start = clock() - StickyRAmarkovConsumer(UpdatePrb = 1.0) - StickyRAmarkovConsumer.initializeSim() - StickyRAmarkovConsumer.simulate() - t_end = clock() - print('Simulating the frictionless representative agent Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Make results for the frictionless representative agent economy - desc = 'Results for the frictionless representative agent Markov economy (update probability 1.0)' - name = 'RAmarkovFrictionless' - makeStickyEdataFile(StickyRAmarkovConsumer,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats,meas_err_base=DeltaLogC_stdev) - - - if make_tables: - # Process the coefficients, standard errors, etc into a LaTeX table - t_start = clock() - frictionless_panel = runRegressions('RAmarkovFrictionlessData',interval_size,False,False,True) - frictionless_me_panel = runRegressions('RAmarkovFrictionlessData',interval_size,True,False,True) - frictionless_long_panel = runRegressions('RAmarkovFrictionlessData',interval_size*interval_count,True,False,True) - sticky_panel = runRegressions('RAmarkovStickyData',interval_size,False,True,True) - sticky_me_panel = runRegressions('RAmarkovStickyData',interval_size,True,True,True) - sticky_long_panel = runRegressions('RAmarkovStickyData',interval_size*interval_count,True,True,True) - makeResultsTable('Aggregate Consumption Dynamics in RA Model',[frictionless_me_panel,sticky_me_panel],my_counts,'RepAgentMrkvSimReg','tRAsim') - makeResultsTable('Aggregate Consumption Dynamics in RA Model',[frictionless_panel,sticky_panel],my_counts,'RepAgentMrkvSimRegNoMeasErr','tRAsimNoMeasErr') - makeResultsTable('Aggregate Consumption Dynamics in RA Model',[frictionless_long_panel,sticky_long_panel],alt_counts,'RepAgentMrkvSimRegLong','tRAsimLong') - t_end = clock() - print('Running time series regressions for the representative agent Markov economy took ' + mystr(t_end-t_start) + ' seconds.') - - ############################################################################### - ########### MAKE OTHER TABLES AND FIGURES ##################################### - ############################################################################### - if make_tables: - makeEquilibriumTable('Eqbm', ['SOEmarkovFrictionless','SOEmarkovSticky','DSGEmarkovFrictionless','DSGEmarkovSticky'],Params.init_SOE_consumer['CRRA']) - makeParameterTable('Calibration', Params) - - if run_ucost_vs_pi: - makeuCostVsPiFig('SOEuCostbyUpdatePrb') - makeValueVsPiFig('SOEvVecByUpdatePrb') - - if run_value_vs_aggvar: - makeValueVsAggShkVarFig('SOEvVecByPermShkAggVar') - -if __name__ == '__main__': - main() - diff --git a/examples/cAndCwithStickyE/StickyE_NO_MARKOV.py b/examples/cAndCwithStickyE/StickyE_NO_MARKOV.py deleted file mode 100644 index 11ebb67aa..000000000 --- a/examples/cAndCwithStickyE/StickyE_NO_MARKOV.py +++ /dev/null @@ -1,264 +0,0 @@ -''' -This module runs the exercises and regressions for the cAndCwithStickyE paper, -but in models without the Markov growth process. The results generated by these -models do not appear in the paper. Without the growth process, there are not -strong enough instruments for the IV regressions to recover the serial corre- -lation of aggregate consumption growth after introducing measurement error in C_t. -User can choose which among the three models are actually run. Descriptive -statistics and regression results are both output to screen and saved in a log -file in the ./Results directory. TeX code for tables in the paper are saved in -the ./Tables directory. See StickyEparams for calibrated model parameters. -''' -from __future__ import division -from __future__ import print_function -from __future__ import absolute_import - -from builtins import str -from builtins import range -import numpy as np -from time import clock -from copy import deepcopy -from .StickyEmodel import StickyEconsumerType, StickyErepAgent, StickyCobbDouglasEconomy -from HARK.ConsumptionSaving.ConsAggShockModel import SmallOpenEconomy -from HARK.utilities import plotFuncs -import matplotlib.pyplot as plt -from . import StickyEparams as Params -from .StickyEtools import makeStickyEdataFile, runStickyEregressions, makeResultsTable,\ - runStickyEregressionsInStata, makeParameterTable, makeEquilibriumTable,\ - makeMicroRegressionTable, extractSampleMicroData, makeuCostVsPiFig - -# Choose which models to do work for -do_SOE = True -do_DSGE = True -do_RA = True - -# Choose what kind of work to do for each model -run_models = True # Whether to solve models and generate new simulated data -calc_micro_stats = False # Whether to calculate microeconomic statistics (only matters when run_models is True) -make_tables = True # Whether to make LaTeX tables in the /Tables folder -use_stata = False # Whether to use Stata to run regressions -save_data = True # Whether to save data for use in Stata (as a tab-delimited text file) -run_ucost_vs_pi = True # Whether to run an exercise that finds the cost of stickiness as it varies with update probability - -ignore_periods = Params.ignore_periods # Number of simulated periods to ignore as a "burn-in" phase -interval_size = Params.interval_size # Number of periods in each non-overlapping subsample -total_periods = Params.periods_to_sim # Total number of periods in simulation -interval_count = (total_periods-ignore_periods) // interval_size # Number of intervals in the macro regressions -periods_to_sim_micro = Params.periods_to_sim_micro # To save memory, micro regressions are run on a smaller sample -AgentCount_micro = Params.AgentCount_micro # To save memory, micro regressions are run on a smaller sample -my_counts = [interval_size,interval_count] -mystr = lambda number : "{:.3f}".format(number) - -# Define the function to run macroeconomic regressions, depending on whether Stata is used -if use_stata: - runRegressions = lambda a,b,c,d,e : runStickyEregressionsInStata(a,b,c,d,e,Params.stata_exe) -else: - runRegressions = lambda a,b,c,d,e : runStickyEregressions(a,b,c,d,e) - - - -# Run models and save output if this module is called from main -def main(): - ############################################################################### - ################# SMALL OPEN ECONOMY ########################################## - ############################################################################### - - if do_SOE: - if run_models: - # Make a small open economy and the consumers who live in it - StickySOEbaseType = StickyEconsumerType(**Params.init_SOE_consumer) - StickySOEbaseType.track_vars = ['aLvlNow','cLvlNow','yLvlNow','pLvlTrue','t_age','TranShkNow'] - StickySOEconsumers = [] - for n in range(Params.TypeCount): - StickySOEconsumers.append(deepcopy(StickySOEbaseType)) - StickySOEconsumers[-1].seed = n - StickySOEconsumers[-1].DiscFac = Params.DiscFacSetSOE[n] - StickySOEconomy = SmallOpenEconomy(agents=StickySOEconsumers, **Params.init_SOE_market) - StickySOEconomy.makeAggShkHist() - for n in range(Params.TypeCount): - StickySOEconsumers[n].getEconomyData(StickySOEconomy) - - # Solve the small open economy and display some output - t_start = clock() - StickySOEconomy.solveAgents() - t_end = clock() - print('Solving the small open economy took ' + str(t_end-t_start) + ' seconds.') - - print('Consumption function for one type in the small open economy:') - cFunc = lambda m : StickySOEconsumers[0].solution[0].cFunc(m,np.ones_like(m)) - plotFuncs(cFunc,0.0,20.0) - - # Simulate the frictionless small open economy - t_start = clock() - for agent in StickySOEconomy.agents: - agent(UpdatePrb = 1.0) - StickySOEconomy.makeHistory() - t_end = clock() - print('Simulating the frictionless small open economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Make results for the frictionless representative agent economy - desc = 'Results for the frictionless small open economy (update probability 1.0)' - name = 'SOEsimpleFrictionless' - makeStickyEdataFile(StickySOEconomy,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats) - - # Simulate the sticky small open economy - t_start = clock() - for agent in StickySOEconomy.agents: - agent(UpdatePrb = Params.UpdatePrb) - StickySOEconomy.makeHistory() - t_end = clock() - print('Simulating the sticky small open economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Make results for the sticky small open economy - desc = 'Results for the sticky small open economy with update probability ' + mystr(Params.UpdatePrb) - name = 'SOEsimpleSticky' - makeStickyEdataFile(StickySOEconomy,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats) - - if make_tables: - # Process the coefficients, standard errors, etc into a LaTeX table - t_start = clock() - frictionless_panel = runRegressions('SOEsimpleFrictionlessData',interval_size,False,False,False) - frictionless_me_panel = runRegressions('SOEsimpleFrictionlessData',interval_size,True,False,False) - sticky_panel = runRegressions('SOEsimpleStickyData',interval_size,False,True,False) - sticky_me_panel = runRegressions('SOEsimpleStickyData',interval_size,True,True,False) - makeResultsTable('Aggregate Consumption Dynamics in SOE Model',[frictionless_me_panel,sticky_panel,sticky_me_panel],my_counts,'SOEsimReg','tPESOEsim') - t_end = clock() - print('Running time series regressions for the small open economy took ' + mystr(t_end-t_start) + ' seconds.') - - - - - ############################################################################### - ################# COBB-DOUGLAS ECONOMY ######################################## - ############################################################################### - - if do_DSGE: - if run_models: - # Make consumers who will live in a Cobb-Douglas economy - StickyDSGEbaseType = StickyEconsumerType(**Params.init_DSGE_consumer) - StickyDSGEbaseType.track_vars = ['aLvlNow','cLvlNow','yLvlNow','pLvlTrue','pLvlNow','t_age','TranShkNow'] - StickyDSGEconsumers = [] - for n in range(Params.TypeCount): - StickyDSGEconsumers.append(deepcopy(StickyDSGEbaseType)) - StickyDSGEconsumers[-1].seed = n - StickyDSGEconsumers[-1].DiscFac = Params.DiscFacSetDSGE[n] - - # Make a Cobb-Douglas economy and put the agents in it - StickyDSGEeconomy = StickyCobbDouglasEconomy(agents=StickyDSGEconsumers,**Params.init_DSGE_market) - StickyDSGEeconomy.makeAggShkHist() - for n in range(Params.TypeCount): - StickyDSGEconsumers[n].getEconomyData(StickyDSGEeconomy) - StickyDSGEconsumers[n](UpdatePrb = 1.0) - - # Solve the frictionless HA-DSGE model - t_start = clock() - StickyDSGEeconomy.solve() - t_end = clock() - print('Solving the frictionless Cobb-Douglas economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Plot the consumption function - print('Consumption function for the frictionless Cobb-Douglas economy:') - m = np.linspace(0.,20.,300) - for M in StickyDSGEconsumers[0].Mgrid: - c = StickyDSGEconsumers[0].solution[0].cFunc(m,M*np.ones_like(m)) - plt.plot(m,c) - plt.show() - - # Make results for the frictionless Cobb-Douglas economy - desc = 'Results for the frictionless Cobb-Douglas economy (update probability 1.0)' - name = 'DSGEsimpleFrictionless' - makeStickyEdataFile(StickyDSGEeconomy,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats) - - # Solve the sticky HA-DSGE model - for agent in StickyDSGEeconomy.agents: - agent(UpdatePrb = Params.UpdatePrb) - t_start = clock() - StickyDSGEeconomy.solve() - t_end = clock() - print('Solving the sticky Cobb-Douglas economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Plot the consumption function - print('Consumption function for the sticky Cobb-Douglas economy:') - m = np.linspace(0.,20.,300) - for M in StickyDSGEconsumers[0].Mgrid: - c = StickyDSGEconsumers[0].solution[0].cFunc(m,M*np.ones_like(m)) - plt.plot(m,c) - plt.show() - - # Make results for the sticky Cobb-Douglas economy - desc = 'Results for the sticky Cobb-Douglas economy with update probability ' + mystr(Params.UpdatePrb) - name = 'DSGEsimpleSticky' - makeStickyEdataFile(StickyDSGEeconomy,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats) - - # Process the coefficients, standard errors, etc into a LaTeX table - if make_tables: - t_start = clock() - frictionless_panel = runRegressions('DSGEsimpleFrictionlessData',interval_size,False,False,False) - frictionless_me_panel = runRegressions('DSGEsimpleFrictionlessData',interval_size,True,False,False) - sticky_panel = runRegressions('DSGEsimpleStickyData',interval_size,False,True,False) - sticky_me_panel = runRegressions('DSGEsimpleStickyData',interval_size,True,True,False) - makeResultsTable('Aggregate Consumption Dynamics in HA-DSGE Model',[frictionless_me_panel,sticky_panel,sticky_me_panel],my_counts,'DSGEsimReg','tDSGEsim') - t_end = clock() - print('Running time series regressions for the Cobb-Douglas economy took ' + mystr(t_end-t_start) + ' seconds.') - - - - ############################################################################### - ################# REPRESENTATIVE AGENT ECONOMY ################################ - ############################################################################### - - if do_RA: - if run_models: - # Make a representative agent consumer, then solve and simulate the model - StickyRAconsumer = StickyErepAgent(**Params.init_RA_consumer) - StickyRAconsumer.track_vars = ['cLvlNow','yNrmTrue','aLvlNow','pLvlTrue','TranShkNow'] - - # Solve the representative agent's problem - t_start = clock() - StickyRAconsumer.solve() - t_end = clock() - print('Solving the representative agent economy took ' + mystr(t_end-t_start) + ' seconds.') - - print('Consumption function for the representative agent:') - plotFuncs(StickyRAconsumer.solution[0].cFunc,0,50) - - # Simulate the representative agent with frictionless expectations - t_start = clock() - StickyRAconsumer(UpdatePrb = 1.0) - StickyRAconsumer.initializeSim() - StickyRAconsumer.simulate() - t_end = clock() - print('Simulating the frictionless representative agent economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Make results for the frictionless representative agent economy - desc = 'Results for the frictionless representative agent economy (update probability 1.0)' - name = 'RAsimpleFrictionless' - makeStickyEdataFile(StickyRAconsumer,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats) - - # Simulate the representative agent with sticky expectations - t_start = clock() - StickyRAconsumer(UpdatePrb = Params.UpdatePrb) - StickyRAconsumer.initializeSim() - StickyRAconsumer.simulate() - t_end = clock() - print('Simulating the sticky representative agent economy took ' + mystr(t_end-t_start) + ' seconds.') - - # Make results for the sticky representative agent economy - desc = 'Results for the sticky representative agent economy with update probability ' + mystr(Params.UpdatePrb) - name = 'RAsimpleSticky' - makeStickyEdataFile(StickyRAconsumer,ignore_periods,description=desc,filename=name,save_data=save_data,calc_micro_stats=calc_micro_stats) - - if make_tables: - # Process the coefficients, standard errors, etc into a LaTeX table - t_start = clock() - frictionless_panel = runRegressions('RAsimpleFrictionlessData',interval_size,False,False,False) - frictionless_me_panel = runRegressions('RAsimpleFrictionlessData',interval_size,True,False,False) - sticky_panel = runRegressions('RAsimpleStickyData',interval_size,False,True,False) - sticky_me_panel = runRegressions('RAsimpleStickyData',interval_size,True,True,False) - makeResultsTable('Aggregate Consumption Dynamics in RA Model',[frictionless_me_panel,sticky_panel,sticky_me_panel],my_counts,'RepAgentSimReg','tRAsim') - t_end = clock() - print('Running time series regressions for the representative agent economy took ' + mystr(t_end-t_start) + ' seconds.') - -if __name__ == '__main__': - main() - diff --git a/examples/cAndCwithStickyE/StickyEmodel.py b/examples/cAndCwithStickyE/StickyEmodel.py deleted file mode 100644 index 7e15c74fe..000000000 --- a/examples/cAndCwithStickyE/StickyEmodel.py +++ /dev/null @@ -1,568 +0,0 @@ -''' -Models for the cAndCwithStickyE paper, in the form of extensions to AgentType -subclasses from the ../ConsumptionSaving folder. This module defines four new -AgentType subclasses for use in this project: - -1) StickyEconsumerType: An extention of AggShockConsumerType that can be used in - the Cobb-Douglas or small open economy specifications. -2) StickyEmarkovConsumerType: An extention of AggShockMarkovConsumerType that can - be used in the Cobb-Douglas Markov or small open Markov economy specifications. -3) StickyErepAgent: An extention of RepAgentConsumerType that can be used in the - representative agent specifications. -4) StickyEmarkovRepAgent: An extension of RepAgentMarkovConsumerType that can be - used in the Markov representative agent specifications. - -The Markov-based AgentTypes are imported by StickyE_MAIN, the main file for this -project. Non-Markov AgentTypes are imported by StickyE_NO_MARKOV. -Calibrated parameters for each type are found in StickyEparams. -''' -from __future__ import division, print_function -from __future__ import absolute_import - -from builtins import str -from builtins import range - -import numpy as np -from HARK.ConsumptionSaving.ConsAggShockModel import AggShockConsumerType, AggShockMarkovConsumerType, CobbDouglasEconomy, CobbDouglasMarkovEconomy -from HARK.ConsumptionSaving.RepAgentModel import RepAgentConsumerType, RepAgentMarkovConsumerType - -# Make an extension of the base type for the heterogeneous agents versions -class StickyEconsumerType(AggShockConsumerType): - ''' - A class for representing consumers who have sticky expectations about the - macroeconomy because they do not observe aggregate variables every period. - ''' - def simBirth(self,which_agents): - ''' - Makes new consumers for the given indices. Slightly extends base method by also setting - pLvlErrNow = 1.0 for new agents, indicating that they correctly perceive their productivity. - - Parameters - ---------- - which_agents : np.array(Bool) - Boolean array of size self.AgentCount indicating which agents should be "born". - - Returns - ------- - None - ''' - AggShockConsumerType.simBirth(self,which_agents) - if hasattr(self,'pLvlErrNow'): - self.pLvlErrNow[which_agents] = 1.0 - else: - self.pLvlErrNow = np.ones(self.AgentCount) - - - def getUpdaters(self): - ''' - Determine which agents update this period vs which don't. Fills in the - attributes update and dont as boolean arrays of size AgentCount. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - how_many_update = int(round(self.UpdatePrb*self.AgentCount)) - base_bool = np.zeros(self.AgentCount,dtype=bool) - base_bool[0:how_many_update] = True - self.update = self.RNG.permutation(base_bool) - self.dont = np.logical_not(self.update) - - - def getpLvlError(self): - ''' - Calculates and returns the misperception of this period's shocks. Updaters - have no misperception this period, while those who don't update don't see - the value of the aggregate permanent shock and assume aggregate growth - equals its expectation. - - - Parameters - ---------- - None - - Returns - ------- - pLvlErr : np.array - Array of size AgentCount with this period's (new) misperception. - ''' - pLvlErr = np.ones(self.AgentCount) - pLvlErr[self.dont] = self.PermShkAggNow/self.PermGroFacAgg - return pLvlErr - - - def getShocks(self): - ''' - Gets permanent and transitory shocks (combining idiosyncratic and aggregate shocks), but - only consumers who update their macroeconomic beliefs this period incorporate all pre- - viously unnoticed aggregate permanent shocks. Agents correctly observe the level of all - real variables (market resources, consumption, assets, etc), but misperceive the aggregate - productivity level. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - # The strange syntax here is so that both StickyEconsumerType and StickyEmarkovConsumerType - # run the getShocks method of their first superclass: AggShockConsumerType and - # AggShockMarkovConsumerType respectively. This will be simplified in Python 3. - super(self.__class__,self).getShocks() # Get permanent and transitory combined shocks - newborns = self.t_age == 0 - self.TranShkNow[newborns] = self.TranShkAggNow*self.wRteNow # Turn off idiosyncratic shocks for newborns - self.PermShkNow[newborns] = self.PermShkAggNow - self.getUpdaters() # Randomly draw which agents will update their beliefs - - # Calculate innovation to the productivity level perception error - pLvlErrNew = self.getpLvlError() - self.pLvlErrNow *= pLvlErrNew # Perception error accumulation - - # Calculate (mis)perceptions of the permanent shock - PermShkPcvd = self.PermShkNow/pLvlErrNew - PermShkPcvd[self.update] *= self.pLvlErrNow[self.update] # Updaters see the true permanent shock and all missed news - self.pLvlErrNow[self.update] = 1.0 - self.PermShkNow = PermShkPcvd - - - def getStates(self): - ''' - Gets simulated consumers pLvl and mNrm for this period, but with the alteration that these - represent perceived rather than actual values. Also calculates mLvlTrue, the true level of - market resources that the individual has on hand. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - # Update consumers' perception of their permanent income level - pLvlPrev = self.pLvlNow - self.pLvlNow = pLvlPrev*self.PermShkNow # Perceived permanent income level (only correct if macro state is observed this period) - self.PlvlAggNow *= self.PermShkAggNow # Updated aggregate permanent productivity level - self.pLvlTrue = self.pLvlNow*self.pLvlErrNow - - # Calculate what the consumers perceive their normalized market resources to be - RfreeNow = self.getRfree() - bLvlNow = RfreeNow*self.aLvlNow # This is the true level - - yLvlNow = self.pLvlTrue*self.TranShkNow # This is true income level - mLvlTrueNow = bLvlNow + yLvlNow # This is true market resource level - mNrmPcvdNow = mLvlTrueNow/self.pLvlNow # This is perceived normalized resources - self.mNrmNow = mNrmPcvdNow - self.mLvlTrueNow = mLvlTrueNow - self.yLvlNow = yLvlNow # Only labor income - - - def getMaggNow(self): - ''' - Gets each consumer's perception of normalized aggregate market resources. - Very simple overwrite of method from superclass. - - Parameters - ---------- - None - - Returns - ------- - MaggPcvdNow : np.array - 1D array of perceived normalized aggregate market resources. - ''' - MaggPcvdNow = self.MaggNow*self.pLvlErrNow # Agents know the true level of aggregate market resources, - return MaggPcvdNow # but have erroneous perception of pLvlAgg. - - - def getPostStates(self): - ''' - Slightly extends the base version of this method by recalculating aLvlNow to account for the - consumer's (potential) misperception about their productivity level. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - AggShockConsumerType.getPostStates(self) - self.cLvlNow = self.cNrmNow*self.pLvlNow # True consumption level - self.aLvlNow = self.mLvlTrueNow - self.cLvlNow # True asset level - self.aNrmNow = self.aLvlNow/self.pLvlNow # Perceived normalized assets - - - -class StickyEmarkovConsumerType(AggShockMarkovConsumerType,StickyEconsumerType): - ''' - A class for representing consumers who have sticky expectations about the macroeconomy - because they do not observe aggregate variables every period. This version lives - in an economy subject to Markov shocks to the aggregate income process. Agents don't - necessarily update their perception of the aggregate productivity level or the discrete - Markov state (governing aggregate growth) in every period. Most of its methods are - directly inherited from one of its parent classes. - ''' - def simBirth(self,which_agents): # Inherit from StickyE rather than AggShock - StickyEconsumerType.simBirth(self,which_agents) - - def getShocks(self): # Inherit from StickyE rather than AggShock - StickyEconsumerType.getShocks(self) - - def getStates(self): # Inherit from StickyE rather than AggShock - StickyEconsumerType.getStates(self) - - def getPostStates(self): # Inherit from StickyE rather than AggShock - StickyEconsumerType.getPostStates(self) - - def getMaggNow(self): # Inherit from StickyE rather than AggShock - return StickyEconsumerType.getMaggNow(self) - - def getMrkvNow(self): # Agents choose control based on *perceived* Markov state - return self.MrkvNowPcvd - - - def getUpdaters(self): - ''' - Determine which agents update this period vs which don't. Fills in the - attributes update and dont as boolean arrays of size AgentCount. This - version also updates perceptions of the Markov state. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - StickyEconsumerType.getUpdaters(self) - # Only updaters change their perception of the Markov state - if hasattr(self,'MrkvNowPcvd'): - self.MrkvNowPcvd[self.update] = self.MrkvNow - else: # This only triggers in the first simulated period - self.MrkvNowPcvd = np.ones(self.AgentCount,dtype=int)*self.MrkvNow - - - def getpLvlError(self): - ''' - Calculates and returns the misperception of this period's shocks. Updaters - have no misperception this period, while those who don't update don't see - the value of the aggregate permanent shock and thus base their belief about - aggregate growth on the last Markov state that they actually observed, - which is stored in MrkvNowPcvd. - - Parameters - ---------- - None - - Returns - ------- - pLvlErr : np.array - Array of size AgentCount with this period's (new) misperception. - ''' - pLvlErr = np.ones(self.AgentCount) - pLvlErr[self.dont] = self.PermShkAggNow/self.PermGroFacAgg[self.MrkvNowPcvd[self.dont]] - return pLvlErr - - - -class StickyErepAgent(RepAgentConsumerType): - ''' - A representative consumer who has sticky expectations about the macroeconomy because - he does not observe aggregate variables every period. Agent lives in a Cobb-Douglas economy. - ''' - def simBirth(self,which_agents): - ''' - Makes new consumers for the given indices. Slightly extends base method by also setting - pLvlTrue = 1.0 in the very first simulated period. - - Parameters - ---------- - which_agents : np.array(Bool) - Boolean array of size self.AgentCount indicating which agents should be "born". - - Returns - ------- - None - ''' - super(self.__class__,self).simBirth(which_agents) - if self.t_sim == 0: # Make sure that pLvlTrue and aLvlNow exist - self.pLvlTrue = np.ones(self.AgentCount) - self.aLvlNow = self.aNrmNow*self.pLvlTrue - - - def getShocks(self): - super(self.__class__,self).getShocks() # Get actual permanent and transitory shocks - - - def getStates(self): - ''' - Calculates updated values of normalized market resources and permanent income level. - Makes both perceived and true values. The representative consumer will act on the - basis of his *perceived* normalized market resources. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - # Calculate perceived and true productivity level - aLvlPrev = self.aLvlNow - self.pLvlTrue = self.pLvlTrue*self.PermShkNow - self.pLvlNow = self.getpLvlPcvd() - - # Calculate true values of variables - self.kNrmTrue = aLvlPrev/self.pLvlTrue - self.yNrmTrue = self.kNrmTrue**self.CapShare*self.TranShkNow**(1.-self.CapShare) - self.kNrmTrue*self.DeprFac - self.Rfree = 1. + self.CapShare*self.kNrmTrue**(self.CapShare-1.)*self.TranShkNow**(1.-self.CapShare) - self.DeprFac - self.wRte = (1.-self.CapShare)*self.kNrmTrue**self.CapShare*self.TranShkNow**(-self.CapShare) - self.mNrmTrue = self.Rfree*self.kNrmTrue + self.wRte*self.TranShkNow - self.mLvlTrue = self.mNrmTrue*self.pLvlTrue - - # Calculate perception of normalized market resources - self.mNrmNow = self.mLvlTrue/self.pLvlNow - - - def getControls(self): - super(self.__class__,self).getControls() - self.cLvlNow = self.cNrmNow*self.pLvlNow # This is true - - - def getPostStates(self): - ''' - Slightly extends the base version of this method by recalculating aLvlNow to account for the - consumer's (potential) misperception about their productivity level. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - RepAgentConsumerType.getPostStates(self) - self.aLvlNow = self.mLvlTrue - self.cLvlNow # This is true - self.aNrmNow = self.aLvlNow/self.pLvlTrue # This is true - - def getpLvlPcvd(self): - ''' - Finds the representative agent's (average) perceived productivity level. - Average perception of productivity gets UpdatePrb weight on the true level, - for those that update, and (1-UpdatePrb) weight on the previous average - perception times expected aggregate growth, for those that don't update. - - Parameters - ---------- - None - - Returns - ------- - pLvlPcvd : np.array - Size 1 array with average perception of productivity level. - ''' - pLvlPcvd = self.UpdatePrb*self.pLvlTrue + (1.0-self.UpdatePrb)*(self.pLvlNow*self.PermGroFac[self.t_cycle[0]-1]) - return pLvlPcvd - - - -class StickyEmarkovRepAgent(RepAgentMarkovConsumerType,StickyErepAgent): - ''' - A representative consumer who has sticky expectations about the macroeconomy because - he does not observe aggregate variables every period. Agent lives in a Cobb-Douglas - economy that has a discrete Markov state. If UpdatePrb < 1, the representative agent's - perception of the Markov state is distributed across the previous states visited. - ''' - def simBirth(self,which_agents): - ''' - Makes new consumers for the given indices. Slightly extends base method by also setting - pLvlTrue = 1.0 in the very first simulated period, as well as initializing the perception - of aggregate productivity for each Markov state. The representative agent begins with - the correct perception of the Markov state. - - Parameters - ---------- - which_agents : np.array(Bool) - Boolean array of size self.AgentCount indicating which agents should be "born". - - Returns - ------- - None - ''' - if which_agents==np.array([True]): - RepAgentMarkovConsumerType.simBirth(self,which_agents) - if self.t_sim == 0: # Initialize perception distribution for Markov state - self.pLvlTrue = np.ones(self.AgentCount) - self.aLvlNow = self.aNrmNow*self.pLvlTrue - StateCount = self.MrkvArray.shape[0] - self.pLvlNow = np.ones(StateCount) # Perceived productivity level by Markov state - self.MrkvPcvd = np.zeros(StateCount) # Distribution of perceived Markov state - self.MrkvPcvd[self.MrkvNow[0]] = 1.0 # Correct perception of state initially - - - def getShocks(self): # Inherit from StickyE rather than RepresentativeAgent - StickyErepAgent.getShocks(self) - - def getStates(self): # Inherit from StickyE rather than RepresentativeAgent - StickyErepAgent.getStates(self) - - def getPostStates(self): # Inherit from StickyE rather than RepresentativeAgent - StickyErepAgent.getPostStates(self) - - - def getpLvlPcvd(self): - ''' - Finds the representative agent's (average) perceived productivity level - for each Markov state, as well as the distribution of the representative - agent's perception of the Markov state. - - Parameters - ---------- - None - - Returns - ------- - pLvlPcvd : np.array - Array with average perception of productivity level by Markov state. - ''' - StateCount = self.MrkvArray.shape[0] - t = self.t_cycle[0] - i = self.MrkvNow[0] - - dont_mass = self.MrkvPcvd*(1.-self.UpdatePrb) # pmf of non-updaters - update_mass = np.zeros(StateCount) - update_mass[i] = self.UpdatePrb # pmf of updaters - - dont_pLvlPcvd = self.pLvlNow*self.PermGroFac[t-1] # those that don't update think pLvl grows at PermGroFac for last observed state - update_pLvlPcvd = np.zeros(StateCount) - update_pLvlPcvd[i] = self.pLvlTrue # those that update see the true pLvl - - # Combine updaters and non-updaters to get average pLvl perception by Markov state - self.MrkvPcvd = dont_mass + update_mass # Total mass of agent in each state - pLvlPcvd = (dont_mass*dont_pLvlPcvd + update_mass*update_pLvlPcvd)/self.MrkvPcvd - pLvlPcvd[self.MrkvPcvd==0.] = 1.0 # Fix division by zero problem when MrkvPcvd[i]=0 - return pLvlPcvd - - - def getControls(self): - ''' - Calculates consumption for the representative agent using the consumption functions. - Takes the weighted average of cLvl across perceived Markov states. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - StateCount = self.MrkvArray.shape[0] - t = self.t_cycle[0] - - cNrmNow = np.zeros(StateCount) # Array of chosen cNrm by Markov state - for i in range(StateCount): - cNrmNow[i] = self.solution[t].cFunc[i](self.mNrmNow[i]) - self.cNrmNow = cNrmNow - self.cLvlNow = np.dot(cNrmNow*self.pLvlNow,self.MrkvPcvd) # Take average of cLvl across states - - - -class StickyCobbDouglasEconomy(CobbDouglasEconomy): - ''' - This is almost identical to CobbDouglasEconomy, except it overrides the mill - rule to use pLvlTrue instead of pLvlNow - ''' - def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): - ''' - Make a new instance of StickyCobbDouglasEconomy by filling in attributes - specific to this kind of market. - - Parameters - ---------- - agents : [ConsumerType] - List of types of consumers that live in this economy. - tolerance: float - Minimum acceptable distance between "dynamic rules" to consider the - solution process converged. Distance depends on intercept and slope - of the log-linear "next capital ratio" function. - act_T : int - Number of periods to simulate when making a history of of the market. - - Returns - ------- - None - ''' - CobbDouglasEconomy.__init__(self,agents=agents,tolerance=tolerance,act_T=act_T,**kwds) - self.reap_vars = ['aLvlNow','pLvlTrue'] - - def millRule(self,aLvlNow,pLvlTrue): - ''' - Function to calculate the capital to labor ratio, interest factor, and - wage rate based on each agent's current state. Just calls calcRandW(). - - See documentation for calcRandW for more information. - ''' - return self.calcRandW(aLvlNow,pLvlTrue) - - - -class StickyCobbDouglasMarkovEconomy(CobbDouglasMarkovEconomy): - ''' - This is almost identical to CobbDouglasmarkovEconomy, except it overrides the - mill rule to use pLvlTrue instead of pLvlNow. - ''' - def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): - ''' - Make a new instance of StickyCobbDouglasMarkovEconomy by filling in attributes - specific to this kind of market. - - Parameters - ---------- - agents : [ConsumerType] - List of types of consumers that live in this economy. - tolerance: float - Minimum acceptable distance between "dynamic rules" to consider the - solution process converged. Distance depends on intercept and slope - of the log-linear "next capital ratio" function. - act_T : int - Number of periods to simulate when making a history of of the market. - - Returns - ------- - None - ''' - CobbDouglasMarkovEconomy.__init__(self,agents=agents,tolerance=tolerance,act_T=act_T,**kwds) - self.reap_vars = ['aLvlNow','pLvlTrue'] - - def millRule(self,aLvlNow,pLvlTrue): - ''' - Function to calculate the capital to labor ratio, interest factor, and - wage rate based on each agent's current state. Just calls calcRandW() - and adds the Markov state index. - - See documentation for calcRandW for more information. - ''' - MrkvNow = self.MrkvNow_hist[self.Shk_idx] - temp = self.calcRandW(aLvlNow,pLvlTrue) - temp(MrkvNow = MrkvNow) - - # Overwrite MaggNow, wRteNow, and RfreeNow if requested - if self.overwrite_hist: - t = self.Shk_idx-1 - temp(MaggNow = self.MaggNow_overwrite[t]) - temp(wRteNow = self.wRteNow_overwrite[t]) - temp(RfreeNow = self.RfreeNow_overwrite[t]) - - return temp diff --git a/examples/cAndCwithStickyE/StickyEparams.py b/examples/cAndCwithStickyE/StickyEparams.py deleted file mode 100644 index 14ca2d1a0..000000000 --- a/examples/cAndCwithStickyE/StickyEparams.py +++ /dev/null @@ -1,266 +0,0 @@ -''' -This module holds calibrated parameter dictionaries for the cAndCwithStickyE paper. -It defines dictionaries for the six types of models in cAndCwithStickyE: - -1) Small open economy -2) Small open Markov economy -3) Cobb-Douglas closed economy -4) Cobb-Douglas closed Markov economy -5) Representative agent economy -6) Markov representative agent economy - -For the first four models (heterogeneous agents), it defines dictionaries for -the Market instance as well as the consumers themselves. All parameters are quarterly. -''' -from __future__ import division -from builtins import range -import numpy as np -from copy import copy -from HARK.utilities import approxUniform -import os - -# Choose file where the Stata executable can be found. This should point at the -# exe file itself, but the string does not need to include '.exe'. Two examples -# are included (for locations on two authors' local computers). This variable -# is irrelevant when the use_stata boolean in cAndCwithStickyE.py is set to False. -# Using Stata to run the regressions allows the tables to include the KP test -# statistic; Python's statsmodels.api currently does not have this functionality. - -# NOTE: To successfully use_stata, you must have Baum, Schaffer, and Stillman's -# ivreg2 Stata module installed, as well as Kleibergen and Schaffer's ranktest -# module. These modules are archived by RePEc IDEAS at: -# https://ideas.repec.org/c/boc/bocode/s425401.html -# https://ideas.repec.org/c/boc/bocode/s456865.html -# You can also simply type "ssc install ivreg2" and "ssc install ranktest" in Stata. - -#stata_exe = "C:\Program Files (x86)\Stata14\stataMP-64" -stata_exe = "C:\Program Files (x86)\Stata15\StataSE-64" - -# Choose directory paths relative to the StickyE files -my_file_path = os.path.dirname(os.path.abspath(__file__)) -calibration_dir = my_file_path + "/Calibration/" # Absolute directory for primitive parameter files -tables_dir = my_file_path + "/Tables/" # Absolute directory for saving tex tables -results_dir = my_file_path + "/Results/" # Absolute directory for saving output files -figures_dir = my_file_path + "/Figures/" # Absolute directory for saving figures - -def importParam(param_name): - return float(np.max(np.genfromtxt(calibration_dir + param_name + '.txt'))) - -# Import primitive parameters from calibrations folder -CRRA = importParam('CRRA') # Coefficient of relative risk aversion -DeprFacAnn = importParam('DeprFacAnn') # Annual depreciation factor -CapShare = importParam('CapShare') # Capital's share in production function -KYratioSS = importParam('KYratioSS') # Steady state capital to output ratio (PF-DSGE) -UpdatePrb = importParam('UpdatePrb') # Probability that each agent observes the aggregate productivity state each period (in sticky version) -UnempPrb = importParam('UnempPrb') # Unemployment probability -DiePrb = importParam('DiePrb') # Quarterly mortality probability -TranShkVarAnn = importParam('TranShkVarAnn') # Annual variance of idiosyncratic transitory shocks -PermShkVarAnn = importParam('PermShkVarAnn') # Annual variance of idiosyncratic permanent shocks -TranShkAggVar = importParam('TranShkAggVar') # Variance of aggregate transitory shocks -PermShkAggVar = importParam('PermShkAggVar') # Variance of aggregate permanent shocks -DiscFacSOE = importParam('betaSOE') # Discount factor, SOE model - -# Calculate parameters based on the primitive parameters -DeprFac = 1. - DeprFacAnn**0.25 # Quarterly depreciation rate -KSS = KtYratioSS = KYratioSS**(1./(1.-CapShare)) # Steady state Capital to labor productivity -wRteSS = (1.-CapShare)*KSS**CapShare # Steady state wage rate -rFreeSS = CapShare*KSS**(CapShare-1.) # Steady state interest rate -RfreeSS = 1. - DeprFac + rFreeSS # Steady state return factor -LivPrb = 1. - DiePrb # Quarterly survival probability -DiscFacDSGE = RfreeSS**(-1) # Discount factor, HA-DSGE and RA models -TranShkVar = TranShkVarAnn*4. # Variance of idiosyncratic transitory shocks -PermShkVar = PermShkVarAnn/4. # Variance of idiosyncratic permanent shocks -#TempDstn = approxMeanOneLognormal(N=7,sigma=np.sqrt(PermShkVar)) -#DiscFacSOE = 0.99*LivPrb/(RfreeSS*np.dot(TempDstn[0],TempDstn[1]**(-CRRA))) # Discount factor, SOE model - -# Choose basic simulation parameters -periods_to_sim = 21010 # Total number of periods to simulate; this might be increased by DSGEmarkov model -ignore_periods = 1000 # Number of simulated periods to ignore (in order to ensure we are near steady state) -interval_size = 200 # Number of periods in each subsample interval -AgentCount = 20000 # Total number of agents to simulate in the economy - -# Use smaller sample for micro regression tables to save memory -periods_to_sim_micro = 4000 -AgentCount_micro = 5000 - -# Choose extent of discount factor heterogeneity (inapplicable to representative agent models) -TypeCount = 1 # Number of heterogeneous discount factor types -DiscFacMeanSOE = DiscFacSOE # Central value of intertemporal discount factor for SOE model -DiscFacMeanDSGE = DiscFacDSGE # ...for HA-DSGE and RA -DiscFacSpread = 0.0 # Half-width of intertemporal discount factor band, a la cstwMPC - -# These parameters are for a rough "beta-dist" specification that fits the wealth distribution in DSGE simple -#TypeCount = 7 -#DiscFacMeanSOE = 0.96738 -#DiscFacMeanDSGE = 0.96738 -#DiscFacSpread = 0.0227 - -# Choose parameters for the Markov models -StateCount = 11 # Number of discrete states in the Markov specifications -PermGroFacMin = 0.9925 # Minimum value of aggregate permanent growth in Markov specifications -PermGroFacMax = 1.0075 # Maximum value of aggregate permanent growth in Markov specifications -Persistence = 0.5 # Base probability that macroeconomic Markov state stays the same; else moves up or down by 1 -RegimeChangePrb = 0.00 # Probability of "regime change", randomly jumping to any Markov state - -# Make the Markov array with chosen states, persistence, and regime change probability -PolyMrkvArray = np.zeros((StateCount,StateCount)) -for i in range(StateCount): - for j in range(StateCount): - if i==j: - PolyMrkvArray[i,j] = Persistence - elif (i==(j-1)) or (i==(j+1)): - PolyMrkvArray[i,j] = 0.5*(1.0 - Persistence) -PolyMrkvArray[0,0] += 0.5*(1.0 - Persistence) -PolyMrkvArray[StateCount-1,StateCount-1] += 0.5*(1.0 - Persistence) -PolyMrkvArray *= 1.0 - RegimeChangePrb -PolyMrkvArray += RegimeChangePrb/StateCount - -# Define the set of aggregate permanent growth factors that can occur (Markov specifications only) -PermGroFacSet = np.exp(np.linspace(np.log(PermGroFacMin),np.log(PermGroFacMax),num=StateCount)) - -# Define the set of discount factors that agents have (for SOE and DSGE models) -DiscFacSetSOE = approxUniform(N=TypeCount,bot=DiscFacMeanSOE-DiscFacSpread,top=DiscFacMeanSOE+DiscFacSpread)[1] -DiscFacSetDSGE = approxUniform(N=TypeCount,bot=DiscFacMeanDSGE-DiscFacSpread,top=DiscFacMeanDSGE+DiscFacSpread)[1] - -############################################################################### - -# Define parameters for the small open economy version of the model -init_SOE_consumer = { 'CRRA': CRRA, - 'DiscFac': DiscFacMeanSOE, - 'LivPrb': [LivPrb], - 'PermGroFac': [1.0], - 'AgentCount': AgentCount // TypeCount, # Spread agents evenly among types - 'aXtraMin': 0.00001, - 'aXtraMax': 40.0, - 'aXtraNestFac': 3, - 'aXtraCount': 48, - 'aXtraExtra': [None], - 'PermShkStd': [np.sqrt(PermShkVar)], - 'PermShkCount': 7, - 'TranShkStd': [np.sqrt(TranShkVar)], - 'TranShkCount': 7, - 'UnempPrb': UnempPrb, - 'UnempPrbRet': 0.0, - 'IncUnemp': 0.0, - 'IncUnempRet': 0.0, - 'BoroCnstArt':0.0, - 'tax_rate':0.0, - 'T_retire':0, - 'MgridBase': np.array([0.5,1.5]), - 'aNrmInitMean' : np.log(0.00001), - 'aNrmInitStd' : 0.0, - 'pLvlInitMean' : 0.0, - 'pLvlInitStd' : 0.0, - 'UpdatePrb' : UpdatePrb, - 'T_age' : None, - 'T_cycle' : 1, - 'cycles' : 0, - 'T_sim' : periods_to_sim - } - -# Define market parameters for the small open economy -init_SOE_market = { 'PermShkAggCount': 5, - 'TranShkAggCount': 5, - 'PermShkAggStd': np.sqrt(PermShkAggVar), - 'TranShkAggStd': np.sqrt(TranShkAggVar), - 'PermGroFacAgg': 1.0, - 'DeprFac': DeprFac, - 'CapShare': CapShare, - 'Rfree': RfreeSS, - 'wRte': wRteSS, - 'act_T': periods_to_sim, - } - -############################################################################### - -# Define parameters for the small open Markov economy version of the model -init_SOE_mrkv_consumer = copy(init_SOE_consumer) -init_SOE_mrkv_consumer['MrkvArray'] = PolyMrkvArray - -# Define market parameters for the small open Markov economy -init_SOE_mrkv_market = copy(init_SOE_market) -init_SOE_mrkv_market['MrkvArray'] = PolyMrkvArray -init_SOE_mrkv_market['PermShkAggStd'] = StateCount*[init_SOE_market['PermShkAggStd']] -init_SOE_mrkv_market['TranShkAggStd'] = StateCount*[init_SOE_market['TranShkAggStd']] -init_SOE_mrkv_market['PermGroFacAgg'] = PermGroFacSet -init_SOE_mrkv_market['MrkvNow_init'] = StateCount // 2 -init_SOE_mrkv_market['loops_max'] = 1 - -############################################################################### - -# Define parameters for the Cobb-Douglas DSGE version of the model -init_DSGE_consumer = copy(init_SOE_consumer) -init_DSGE_consumer['DiscFac'] = DiscFacMeanDSGE -init_DSGE_consumer['aXtraMax'] = 120.0 -init_DSGE_consumer['MgridBase'] = np.array([0.1,0.3,0.5,0.6,0.7,0.8,0.9,0.98,1.0,1.02,1.1,1.2,1.3,1.4,1.5,1.6,2.0,3.0,5.0]) - -# Define market parameters for the Cobb-Douglas economy -init_DSGE_market = copy(init_SOE_market) -init_DSGE_market.pop('Rfree') -init_DSGE_market.pop('wRte') -init_DSGE_market['CRRA'] = CRRA -init_DSGE_market['DiscFac'] = DiscFacMeanDSGE -init_DSGE_market['intercept_prev'] = 0.0 -init_DSGE_market['slope_prev'] = 1.0 - -############################################################################### - -# Define parameters for the Cobb-Douglas Markov DSGE version of the model -init_DSGE_mrkv_consumer = copy(init_DSGE_consumer) -init_DSGE_mrkv_consumer['MrkvArray'] = PolyMrkvArray - -# Define market parameters for the Cobb-Douglas Markov economy -init_DSGE_mrkv_market = copy(init_SOE_mrkv_market) -init_DSGE_mrkv_market.pop('Rfree') -init_DSGE_mrkv_market.pop('wRte') -init_DSGE_mrkv_market['CRRA'] = init_DSGE_mrkv_consumer['CRRA'] -init_DSGE_mrkv_market['DiscFac'] = init_DSGE_mrkv_consumer['DiscFac'] -init_DSGE_mrkv_market['intercept_prev'] = StateCount*[0.0] -init_DSGE_mrkv_market['slope_prev'] = StateCount*[1.0] -init_DSGE_mrkv_market['loops_max'] = 10 - -############################################################################### - -# Define parameters for the representative agent version of the model -init_RA_consumer = { 'CRRA': CRRA, - 'DiscFac': DiscFacMeanDSGE, - 'LivPrb': [1.0], - 'PermGroFac': [1.0], - 'AgentCount': 1, - 'aXtraMin': 0.00001, - 'aXtraMax': 120.0, - 'aXtraNestFac': 3, - 'aXtraCount': 48, - 'aXtraExtra': [None], - 'PermShkStd': [np.sqrt(PermShkAggVar)], - 'PermShkCount': 7, - 'TranShkStd': [np.sqrt(TranShkAggVar)], - 'TranShkCount': 7, - 'UnempPrb': 0.0, - 'UnempPrbRet': 0.0, - 'IncUnemp': 0.0, - 'IncUnempRet': 0.0, - 'BoroCnstArt':0.0, - 'tax_rate':0.0, - 'T_retire':0, - 'aNrmInitMean' : np.log(0.00001), - 'aNrmInitStd' : 0.0, - 'pLvlInitMean' : 0.0, - 'pLvlInitStd' : 0.0, - 'PermGroFacAgg' : 1.0, - 'UpdatePrb' : UpdatePrb, - 'CapShare' : CapShare, - 'DeprFac' : DeprFac, - 'T_age' : None, - 'T_cycle' : 1, - 'T_sim' : periods_to_sim, - 'tolerance' : 1e-6 - } - -############################################################################### - -# Define parameters for the Markov representative agent model -init_RA_mrkv_consumer = copy(init_RA_consumer) -init_RA_mrkv_consumer['MrkvArray'] = PolyMrkvArray -init_RA_mrkv_consumer['MrkvNow'] = [StateCount // 2] -init_RA_mrkv_consumer['PermGroFac'] = [PermGroFacSet] diff --git a/examples/cAndCwithStickyE/StickyEtools.py b/examples/cAndCwithStickyE/StickyEtools.py deleted file mode 100644 index 10f91afa6..000000000 --- a/examples/cAndCwithStickyE/StickyEtools.py +++ /dev/null @@ -1,1185 +0,0 @@ -""" -This module holds some data tools used in the cAndCwithStickyE project. -""" -from __future__ import division, unicode_literals -from __future__ import absolute_import - -from builtins import str -from builtins import range -import os -import csv -import numpy as np -import pandas as pd -import statsmodels.api as sm -import statsmodels.sandbox.regression.gmm as smsrg -import matplotlib.pyplot as plt -from copy import deepcopy -import subprocess -from HARK.utilities import CRRAutility -from HARK.interpolation import LinearInterp -from .StickyEparams import results_dir, tables_dir, figures_dir, UpdatePrb, PermShkAggVar -UpdatePrbBase = UpdatePrb -PermShkAggVarBase = PermShkAggVar - -def mystr1(number): - if not np.isnan(number): - out = "{:.3f}".format(number) - else: - out = '' - return out - -def mystr2(number): - if not np.isnan(number): - out = "{:1.2f}".format(number*10000) + '\\text{e--4}' - else: - out = '' - return out - -def mystr3(number): - if not np.isnan(number): - out = "{:1.2f}".format(number*1000000) + '\\text{e--6}' - else: - out = '' - return out - - -def makeStickyEdataFile(Economy,ignore_periods,description='',filename=None,save_data=False,calc_micro_stats=True,meas_err_base=None): - ''' - Makes descriptive statistics and macroeconomic data file. Behaves slightly - differently for heterogeneous agents vs representative agent models. - - Parameters - ---------- - Economy : Market or AgentType - A representation of the model economy. For heterogeneous agents specifications, - this will be an instance of a subclass of Market. For representative agent - specifications, this will be an instance of an AgentType subclass. - ignore_periods : int - Number of periods at the start of the simulation to throw out. - description : str - Description of the economy that is prepended on the output string. - filename : str - Name of the output log file, if any; .txt will be appended automatically. - save_data : bool - When True, save simulation data to filename + 'Data.txt' for use in Stata. - calc_micro_stats : bool - When True, calculate microeconomic statistics like in Table 2 of the - paper draft. - meas_err_base : float or None - Base value of measurement error standard deviation, which will be adjusted. - When None (default), value is calculated as stdev(DeltaLogC). - - Returns - ------- - None - ''' - # Extract time series data from the economy - if hasattr(Economy,'agents'): # If this is a heterogeneous agent specification... - if len(Economy.agents) > 1: - pLvlAll_hist = np.concatenate([this_type.pLvlTrue_hist for this_type in Economy.agents],axis=1) - aLvlAll_hist = np.concatenate([this_type.aLvlNow_hist for this_type in Economy.agents],axis=1) - cLvlAll_hist = np.concatenate([this_type.cLvlNow_hist for this_type in Economy.agents],axis=1) - yLvlAll_hist = np.concatenate([this_type.yLvlNow_hist for this_type in Economy.agents],axis=1) - else: # Don't duplicate the data unless necessary (with one type, concatenating is useless) - pLvlAll_hist = Economy.agents[0].pLvlTrue_hist - aLvlAll_hist = Economy.agents[0].aLvlNow_hist - cLvlAll_hist = Economy.agents[0].cLvlNow_hist - yLvlAll_hist = Economy.agents[0].yLvlNow_hist - # PermShkAggHist needs to be shifted one period forward - PlvlAgg_hist = np.cumprod(np.concatenate(([1.0],Economy.PermShkAggHist[:-1]),axis=0)) - AlvlAgg_hist = np.mean(aLvlAll_hist,axis=1) # Level of aggregate assets - AnrmAgg_hist = AlvlAgg_hist/PlvlAgg_hist # Normalized level of aggregate assets - ClvlAgg_hist = np.mean(cLvlAll_hist,axis=1) # Level of aggregate consumption - CnrmAgg_hist = ClvlAgg_hist/PlvlAgg_hist # Normalized level of aggregate consumption - YlvlAgg_hist = np.mean(yLvlAll_hist,axis=1) # Level of aggregate income - YnrmAgg_hist = YlvlAgg_hist/PlvlAgg_hist # Normalized level of aggregate income - - if calc_micro_stats: # Only calculate stats if requested. This is a memory hog with many simulated periods - micro_stat_periods = int((Economy.agents[0].T_sim-ignore_periods)*0.1) - not_newborns = (np.concatenate([this_type.t_age_hist[(ignore_periods+1):(ignore_periods+micro_stat_periods),:] for this_type in Economy.agents],axis=1) > 1).flatten() - Logc = np.log(cLvlAll_hist[ignore_periods:(ignore_periods+micro_stat_periods),:]) - DeltaLogc = (Logc[1:] - Logc[0:-1]).flatten() - DeltaLogc_trimmed = DeltaLogc[not_newborns] - Loga = np.log(aLvlAll_hist[ignore_periods:(ignore_periods+micro_stat_periods),:]) - DeltaLoga = (Loga[1:] - Loga[0:-1]).flatten() - DeltaLoga_trimmed = DeltaLoga[not_newborns] - Logp = np.log(pLvlAll_hist[ignore_periods:(ignore_periods+micro_stat_periods),:]) - DeltaLogp = (Logp[1:] - Logp[0:-1]).flatten() - DeltaLogp_trimmed = DeltaLogp[not_newborns] - Logy = np.log(yLvlAll_hist[ignore_periods:(ignore_periods+micro_stat_periods),:]) - Logy_trimmed = Logy - Logy_trimmed[np.isinf(Logy)] = np.nan - birth_events = np.concatenate([this_type.t_age_hist == 1 for this_type in Economy.agents],axis=1) - vBirth = calcValueAtBirth(cLvlAll_hist[ignore_periods:,:],birth_events[ignore_periods:,:],PlvlAgg_hist[ignore_periods:],Economy.MrkvNow_hist[ignore_periods:],Economy.agents[0].DiscFac,Economy.agents[0].CRRA) - - BigTheta_hist = Economy.TranShkAggHist - if hasattr(Economy,'MrkvNow'): - Mrkv_hist = Economy.MrkvNow_hist - if ~hasattr(Economy,'Rfree'): # If this is a markov DSGE specification... - # Find the expected interest rate - approximate by assuming growth = expected growth - ExpectedGrowth_hist = Economy.PermGroFacAgg[Mrkv_hist] - ExpectedKLRatio_hist = AnrmAgg_hist/ExpectedGrowth_hist - ExpectedR_hist = Economy.Rfunc(ExpectedKLRatio_hist) - - else: # If this is a representative agent specification... - PlvlAgg_hist = Economy.pLvlTrue_hist.flatten() - ClvlAgg_hist = Economy.cLvlNow_hist.flatten() - CnrmAgg_hist = ClvlAgg_hist/PlvlAgg_hist.flatten() - YnrmAgg_hist = Economy.yNrmTrue_hist.flatten() - YlvlAgg_hist = YnrmAgg_hist*PlvlAgg_hist.flatten() - AlvlAgg_hist = Economy.aLvlNow_hist.flatten() - AnrmAgg_hist = AlvlAgg_hist/PlvlAgg_hist.flatten() - BigTheta_hist = Economy.TranShkNow_hist.flatten() - if hasattr(Economy,'MrkvNow'): - Mrkv_hist = Economy.MrkvNow_hist - - # Process aggregate data into forms used by regressions - LogC = np.log(ClvlAgg_hist[ignore_periods:]) - LogA = np.log(AlvlAgg_hist[ignore_periods:]) - LogY = np.log(YlvlAgg_hist[ignore_periods:]) - DeltaLogC = LogC[1:] - LogC[0:-1] - DeltaLogA = LogA[1:] - LogA[0:-1] - DeltaLogY = LogY[1:] - LogY[0:-1] - A = AnrmAgg_hist[(ignore_periods+1):] # This is a relabeling for the regression code - BigTheta = BigTheta_hist[(ignore_periods+1):] - if hasattr(Economy,'MrkvNow'): - Mrkv = Mrkv_hist[(ignore_periods+1):] # This is a relabeling for the regression code - if ~hasattr(Economy,'Rfree') and hasattr(Economy,'agents'): # If this is a markov DSGE specification... - R = ExpectedR_hist[(ignore_periods+1):] - Delta8LogC = (np.log(ClvlAgg_hist[8:]) - np.log(ClvlAgg_hist[:-8]))[(ignore_periods-7):] - Delta8LogY = (np.log(YlvlAgg_hist[8:]) - np.log(YlvlAgg_hist[:-8]))[(ignore_periods-7):] - - # Add measurement error to LogC - if meas_err_base is None: - meas_err_base = np.std(DeltaLogC) - sigma_meas_err = meas_err_base*0.375 # This approximately matches the change in IV vs OLS in U.S. empirical coefficients - np.random.seed(10) - Measurement_Error = sigma_meas_err*np.random.normal(0.,1.,LogC.size) - LogC_me = LogC + Measurement_Error - DeltaLogC_me = LogC_me[1:] - LogC_me[0:-1] - - # Apply measurement error to long delta LogC - LogC_long = np.log(ClvlAgg_hist) - LogC_long_me = LogC_long + sigma_meas_err*np.random.normal(0.,1.,LogC_long.size) - Delta8LogC_me = (LogC_long_me[8:] - LogC_long_me[:-8])[(ignore_periods-7):] - - # Make summary statistics for the results file - csv_output_string = str(np.mean(AnrmAgg_hist[ignore_periods:])) +","+ str(np.mean(CnrmAgg_hist[ignore_periods:]))+ ","+str(np.std(np.log(AnrmAgg_hist[ignore_periods:])))+ ","+str(np.std(DeltaLogC))+ ","+str(np.std(DeltaLogY)) +","+ str(np.std(DeltaLogA)) - if hasattr(Economy,'agents') and calc_micro_stats: # This block only runs for heterogeneous agents specifications - csv_output_string += ","+str(np.mean(np.std(Loga,axis=1)))+ ","+str(np.mean(np.std(Logc,axis=1))) + ","+str(np.mean(np.std(Logp,axis=1))) +","+ str(np.mean(np.nanstd(Logy_trimmed,axis=1))) +","+ str(np.std(DeltaLoga_trimmed))+","+ str(np.std(DeltaLogc_trimmed))+ ","+str(np.std(DeltaLogp_trimmed)) - - # Save the results to a logfile if requested - if filename is not None: - with open(results_dir + filename + 'Results.csv','w') as f: - f.write(csv_output_string) - f.close() - if calc_micro_stats and hasattr(Economy,'agents'): - with open(results_dir + filename + 'BirthValue.csv','w') as f: - my_writer = csv.writer(f, delimiter = ',') - my_writer.writerow(vBirth) - f.close() - - if save_data: - DataArray = (np.vstack((np.arange(DeltaLogC.size),DeltaLogC_me,DeltaLogC,DeltaLogY,A,BigTheta,Delta8LogC,Delta8LogY,Delta8LogC_me,Measurement_Error[1:]))).transpose() - VarNames = ['time_period','DeltaLogC_me','DeltaLogC','DeltaLogY','A','BigTheta','Delta8LogC','Delta8LogY','Delta8LogC_me','Measurement_Error'] - if hasattr(Economy,'MrkvNow'): - DataArray = np.hstack((DataArray,np.reshape(Mrkv,(Mrkv.size,1)))) - VarNames.append('MrkvState') - if hasattr(Economy,'MrkvNow') & ~hasattr(Economy,'Rfree') and hasattr(Economy,'agents'): - DataArray = np.hstack((DataArray,np.reshape(R,(R.size,1)))) - VarNames.append('R') - with open(results_dir + filename + 'Data.txt','w') as f: - my_writer = csv.writer(f, delimiter = '\t') - my_writer.writerow(VarNames) - for i in range(DataArray.shape[0]): - my_writer.writerow(DataArray[i,:]) - f.close() - - -def runStickyEregressions(infile_name,interval_size,meas_err,sticky,all_specs): - ''' - Runs regressions for the main tables of the StickyC paper and produces a LaTeX - table with results for one "panel". - - Parameters - ---------- - infile_name : str - Name of tab-delimited text file with simulation data. Assumed to be in - the results directory, and was almost surely generated by makeStickyEdataFile - unless we resort to fabricating simulated data. THAT'S A JOKE, FUTURE REFEREES. - interval_size : int - Number of periods in each sub-interval. - meas_err : bool - Indicator for whether to add measurement error to DeltaLogC. - sticky : bool - Indicator for whether these results used sticky expectations. - all_specs : bool - Indicator for whether this panel should include all specifications or - just the OLS on lagged consumption growth. - - Returns - ------- - panel_text : str - String with one panel's worth of LaTeX input. - ''' - # Read in the data from the infile - with open(results_dir + infile_name + '.txt') as f: - my_reader = csv.reader(f, delimiter='\t') - all_data = list(my_reader) - - # Unpack the data into numpy arrays - obs = len(all_data) - 1 - DeltaLogC_me = np.zeros(obs) - DeltaLogC = np.zeros(obs) - DeltaLogY = np.zeros(obs) - A = np.zeros(obs) - BigTheta = np.zeros(obs) - Delta8LogC = np.zeros(obs) - Delta8LogY = np.zeros(obs) - Delta8LogC_me = np.zeros(obs) - Measurement_Error = np.zeros(obs) - Mrkv_hist = np.zeros(obs,dtype=int) - R = np.zeros(obs) - - has_mrkv = 'MrkvState' in all_data[0] - has_R = 'R' in all_data[0] - for i in range(obs): - j = i+1 - DeltaLogC_me[i] = float(all_data[j][1]) - DeltaLogC[i] = float(all_data[j][2]) - DeltaLogY[i] = float(all_data[j][3]) - A[i] = float(all_data[j][4]) - BigTheta[i] = float(all_data[j][5]) - Delta8LogC[i] = float(all_data[j][6]) - Delta8LogY[i] = float(all_data[j][7]) - Delta8LogC_me[i] = float(all_data[j][8]) - Measurement_Error[i] = float(all_data[j][9]) - if has_mrkv: - Mrkv_hist[i] = int(float(all_data[j][10])) - if has_R: - R[i] = float(all_data[j][11]) - - # Determine how many subsample intervals to run (and initialize array of coefficients) - N = DeltaLogC.size // interval_size - CoeffsArray = np.zeros((N,7)) # Order: DeltaLogC_OLS, DeltaLogC_IV, DeltaLogY_IV, A_OLS, DeltaLogC_HR, DeltaLogY_HR, A_HR - StdErrArray = np.zeros((N,7)) # Same order as above - RsqArray = np.zeros((N,5)) - PvalArray = np.zeros((N,5)) - OIDarray = np.zeros((N,5)) + np.nan - InstrRsqVec = np.zeros(N) - - # Loop through subsample intervals, running various regressions - for n in range(N): - # Select the data subsample - start = n*interval_size - end = (n+1)*interval_size - if meas_err: - DeltaLogC_n = DeltaLogC_me[start:end] - Delta8LogC_n = Delta8LogC_me[start:end] - else: - DeltaLogC_n = DeltaLogC[start:end] - Delta8LogC_n = Delta8LogC[start:end] - DeltaLogY_n = DeltaLogY[start:end] - A_n = A[start:end] - Delta8LogY_n = Delta8LogY[start:end] - - # Run OLS on log consumption - mod = sm.OLS(DeltaLogC_n[1:],sm.add_constant(DeltaLogC_n[0:-1])) - res = mod.fit() - CoeffsArray[n,0] = res._results.params[1] - StdErrArray[n,0] = res._results.HC0_se[1] - RsqArray[n,0] = res._results.rsquared_adj - PvalArray[n,0] = res._results.f_pvalue - - # Define instruments for IV regressions - temp = np.transpose(np.vstack([DeltaLogC_n[1:-3],DeltaLogC_n[:-4],DeltaLogY_n[1:-3],DeltaLogY_n[:-4],A_n[1:-3],A_n[:-4],Delta8LogC_n[1:-3],Delta8LogY_n[1:-3]])) - instruments = sm.add_constant(temp) # With measurement error - - # Run IV on log consumption - mod = sm.OLS(DeltaLogC_n[3:-1],instruments) - res = mod.fit() - DeltaLogC_predict = res.predict() - mod_2ndStage = sm.OLS(DeltaLogC_n[4:],sm.add_constant(DeltaLogC_predict)) - res_2ndStage = mod_2ndStage.fit() - mod_IV = smsrg.IV2SLS(DeltaLogC_n[4:], sm.add_constant(DeltaLogC_n[3:-1]),instruments) - res_IV = mod_IV.fit() - CoeffsArray[n,1] = res_IV._results.params[1] - StdErrArray[n,1] = res_IV.bse[1] - RsqArray[n,1] = res_2ndStage._results.rsquared_adj - PvalArray[n,1] = res._results.f_pvalue - - # Run IV on log income - mod = sm.OLS(DeltaLogY_n[4:],instruments) - res = mod.fit() - DeltaLogY_predict = res.predict() - mod_2ndStage = sm.OLS(DeltaLogC_n[4:],sm.add_constant(DeltaLogY_predict)) - res_2ndStage = mod_2ndStage.fit() - mod_IV = smsrg.IV2SLS(DeltaLogC_n[4:], sm.add_constant(DeltaLogY_n[4:]),instruments) - res_IV = mod_IV.fit() - CoeffsArray[n,2] = res_IV._results.params[1] - StdErrArray[n,2] = res_IV.bse[1] - RsqArray[n,2] = res_2ndStage._results.rsquared_adj - PvalArray[n,2] = res._results.f_pvalue - - # Run IV on assets - mod = sm.OLS(A_n[3:-1],instruments) - res = mod.fit() - A_predict = res.predict() - mod_2ndStage = sm.OLS(DeltaLogC_n[4:],sm.add_constant(A_predict)) - res_2ndStage = mod_2ndStage.fit() - mod_IV = smsrg.IV2SLS(DeltaLogC_n[4:], sm.add_constant(A_n[3:-1]),instruments) - res_IV = mod_IV.fit() - CoeffsArray[n,3] = res_IV._results.params[1] - StdErrArray[n,3] = res_IV.bse[1] - RsqArray[n,3] = res_2ndStage._results.rsquared_adj - PvalArray[n,3] = res._results.f_pvalue - - # Run horserace IV - regressors = sm.add_constant(np.transpose(np.array([DeltaLogC_predict,DeltaLogY_predict,A_predict]))) - mod_2ndStage = sm.OLS(DeltaLogC_n[4:],regressors) - res_2ndStage = mod_2ndStage.fit() - mod_IV = smsrg.IV2SLS(DeltaLogC_n[4:], sm.add_constant(np.transpose(np.array([DeltaLogC_n[3:-1],DeltaLogY_n[4:],A_n[3:-1]]))),instruments) - res_IV = mod_IV.fit() - CoeffsArray[n,4] = res_IV._results.params[1] - CoeffsArray[n,5] = res_IV._results.params[2] - CoeffsArray[n,6] = res_IV._results.params[3] - StdErrArray[n,4] = res_IV._results.bse[1] - StdErrArray[n,5] = res_IV._results.bse[2] - StdErrArray[n,6] = res_IV._results.bse[3] - RsqArray[n,4] = res_2ndStage._results.rsquared_adj - PvalArray[n,4] = np.nan #Need to put in KP stat here, may have to do this in Stata - - # Regress Delta C_{t+1} on instruments - mod = sm.OLS(DeltaLogC_n[4:],instruments) - res = mod.fit() - InstrRsqVec[n] = res._results.rsquared_adj - - # Count the number of times we reach significance in each variable - t_stat_array = CoeffsArray/StdErrArray - C_successes_95 = np.sum(t_stat_array[:,4] > 1.96) - Y_successes_95 = np.sum(t_stat_array[:,5] > 1.96) - - sigma_meas_err = np.std(Measurement_Error) - - N_out = [C_successes_95,Y_successes_95,N,np.mean(InstrRsqVec),sigma_meas_err**2] - - # Make results table and return it - panel_text = makeResultsPanel(Coeffs=np.mean(CoeffsArray,axis=0), - StdErrs=np.mean(StdErrArray,axis=0), - Rsq=np.mean(RsqArray,axis=0), - Pvals=np.mean(PvalArray,axis=0), - OID=np.mean(OIDarray,axis=0), - Counts=N_out, - meas_err=meas_err, - sticky=sticky, - all_specs=all_specs) - return panel_text - - -def runStickyEregressionsInStata(infile_name,interval_size,meas_err,sticky,all_specs,stata_exe): - ''' - Runs regressions for the main tables of the StickyC paper in Stata and produces a - LaTeX table with results for one "panel". Running in Stata allows production of - the KP-statistic, for which there is currently no command in statsmodels.api. - - Parameters - ---------- - infile_name : str - Name of tab-delimited text file with simulation data. Assumed to be in - the results directory, and was almost surely generated by makeStickyEdataFile - unless we resort to fabricating simulated data. THAT'S A JOKE, FUTURE REFEREES. - interval_size : int - Number of periods in each regression sample (or interval). - meas_err : bool - Indicator for whether to add measurement error to DeltaLogC. - sticky : bool - Indicator for whether these results used sticky expectations. - all_specs : bool - Indicator for whether this panel should include all specifications or - just the OLS on lagged consumption growth. - stata_exe : str - Absolute location where the Stata executable can be found on the computer - running this code. Usually set at the top of StickyEparams.py. - - Returns - ------- - panel_text : str - String with one panel's worth of LaTeX input. - ''' - dofile = "StickyETimeSeries.do" - infile_name_full = os.path.abspath(results_dir + infile_name + ".txt") - temp_name_full = os.path.abspath(results_dir + "temp.txt") - if meas_err: - meas_err_stata = 1 - else: - meas_err_stata = 0 - - # Define the command to run the Stata do file - cmd = [stata_exe, "do", dofile, infile_name_full, temp_name_full, str(interval_size), str(meas_err_stata)] - - # Run Stata do-file - stata_status = subprocess.call(cmd,shell = 'true') - if stata_status!=0: - raise ValueError('Stata code could not run. Check the stata_exe in StickyEparams.py') - stata_output = pd.read_csv(temp_name_full, sep=',',header=0) - - # Make results table and return it - panel_text = makeResultsPanel(Coeffs=stata_output.CoeffsArray, - StdErrs=stata_output.StdErrArray, - Rsq=stata_output.RsqArray, - Pvals=stata_output.PvalArray, - OID=stata_output.OIDarray, - Counts=stata_output.ExtraInfo, - meas_err=meas_err, - sticky=sticky, - all_specs=all_specs) - return panel_text - - -def calcValueAtBirth(cLvlHist,BirthBool,PlvlHist,MrkvHist,DiscFac,CRRA): - ''' - Calculate expected value of being born in each Markov state using the realizations - of consumption for a history of many consumers. The histories should already be - trimmed of the "burn in" periods. - - Parameters - ---------- - cLvlHist : np.array - TxN array of consumption level history for many agents across many periods. - Agents who die are replaced by newborms. - BirthBool : np.array - TxN boolean array indicating when agents are born, replacing one who died. - PlvlHist : np.array - T length vector of aggregate permanent productivity levels. - MrkvHist : np.array - T length vector of integers for the Markov index in each period. - DiscFac : float - Intertemporal discount factor. - CRRA : float - Coefficient of relative risk aversion. - - Returns - ------- - vAtBirth : np.array - J length vector of average lifetime value at birth by Markov state. - ''' - J = np.max(MrkvHist) + 1 # Number of Markov states - T = MrkvHist.size # Length of simulation - I = cLvlHist.shape[1] # Number of agent indices in histories - u = lambda c : CRRAutility(c,gam=CRRA) - - # Initialize an array to hold each agent's lifetime utility - BirthsByPeriod = np.sum(BirthBool,axis=1) - BirthsByState = np.zeros(J,dtype=int) - for j in range(J): - these = MrkvHist == j - BirthsByState[j] = np.sum(BirthsByPeriod[these]) - N = np.max(BirthsByState) # Array must hold this many agents per row at least - vArray = np.zeros((J,N)) + np.nan - n = np.zeros(J,dtype=int) - - # Loop through each agent index - DiscVec = DiscFac**np.arange(T) - for i in range(I): - birth_t = np.where(BirthBool[:,i])[0] - # Loop through each agent who lived and died in this index - for k in range(birth_t.size-1): # Last birth event has no death, so ignore - # Get lifespan of this agent and circumstances at birth - t0 = birth_t[k] - t1 = birth_t[k+1] - span = t1-t0 - j = MrkvHist[t0] - # Calculate discounted flow of utility for this agent and store it - cVec = cLvlHist[t0:t1,i]/PlvlHist[t0] - uVec = u(cVec) - v = np.dot(DiscVec[:span],uVec) - vArray[j,n[j]] = v - n[j] += 1 - - # Calculate expected value at birth by state and return it - vAtBirth = np.nanmean(vArray,axis=1) - return vAtBirth - - -def makeResultsPanel(Coeffs,StdErrs,Rsq,Pvals,OID,Counts,meas_err,sticky,all_specs): - ''' - Make one panel of simulated results table. A panel has all results with/out - measurement error for the sticky or frictionless version, and might have only - one specification (OLS on lagged consumption growth) or many specifications. - - Parameters - ---------- - Coeffs : np.array - Array with 7 entries with regression coefficients. - StdErrs : np.array - Array with 7 entries with coefficient standard errors. - Rsq : np.array - Array with 5 entries with R^2 values. - Pvals : np.array - Array with 5 entries with P values (for the first stage). - OID : np.array - Array with 5 entries with overidentification statistics. - Counts : [int] - List with 3 elements: [C_successes,Y_successes,N_intervals]. - meas_err : bool - Indicator for whether this panel used measurement error. - sticky : bool - Indicator for whether this panel used sticky expectations. - all_specs : bool - Indicator for whether this panel should include all specifications or - just the OLS on lagged consumption growth. - - Returns - ------- - output : str - Text string with one panel of LaTeX input. - ''' - # Define Delta log C text and expectations text - if meas_err: - DeltaLogC = '$\Delta \log \mathbf{C}_{t}^*$' - DeltaLogC1 = '$\Delta \log \mathbf{C}_{t+1}^*$' - MeasErr = ' (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\\times \\xi_t$);' - else: - DeltaLogC = '$\Delta \log \mathbf{C}_{t}$' - DeltaLogC1 = '$\Delta \log \mathbf{C}_{t+1}$' - MeasErr = ' (no measurement error)' - - if sticky: - Expectations = 'Sticky' - else: - Expectations = 'Frictionless' - - DeltaLogY1 = '$\Delta \log \mathbf{Y}_{t+1}$' - A_t = '$A_{t}$' - - - # Define significance symbols - if Counts[2] > 1: - sig_symb = '\\bullet ' - else: - sig_symb = '*' - def sigFunc(coeff,stderr): - z_stat = np.abs(coeff/stderr) - cuts = np.array([1.645,1.96,2.576]) - N = np.sum(z_stat > cuts) - if N > 0: - sig_text = '^{' + N*sig_symb + '}' - else: - sig_text = '' - return sig_text - - # Make the memo text - memo = '' - if all_specs: - memo += '\\\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, ' + DeltaLogC + ' $= \mathbf{Z}_{t} \zeta, ~\\bar{R}^{2}=$ ' + mystr1(Counts[3]) - if meas_err: - memo += '; ~$\\var(\\log(\\xi_t))=$ ' + mystr3(Counts[4]) - memo += ' } \n' - - # Make the top of the panel - output = '\\\\ \\midrule \multicolumn{6}{l}{' + Expectations + ' : ' + DeltaLogC1 + MeasErr + '} \n' - output += '\\\\ \multicolumn{1}{c}{' + DeltaLogC + '} & \multicolumn{1}{c}{' + DeltaLogY1 +'} & \multicolumn{1}{c}{'+A_t+'} & & & \n' - - # OLS on just lagged consumption growth - output += '\\\\ ' + mystr1(Coeffs[0]) + sigFunc(Coeffs[0],StdErrs[0]) + ' & & & OLS & ' + mystr1(Rsq[0]) + ' & ' + mystr1(np.nan) + '\n' - output += '\\\\ (' + mystr1(StdErrs[0]) + ') & & & & & \n' - - # Add the rest of the specifications if requested - if all_specs: - # IV on lagged consumption growth - output += '\\\\ ' + mystr1(Coeffs[1]) + sigFunc(Coeffs[1],StdErrs[1]) + ' & & & IV & ' + mystr1(Rsq[1]) + ' & ' + mystr1(Pvals[1]) + '\n' - output += '\\\\ (' + mystr1(StdErrs[1]) + ') & & & & &' + mystr1(OID[1]) + '\n' - - # IV on expected income growth - output += '\\\\ & ' + mystr1(Coeffs[2]) + sigFunc(Coeffs[2],StdErrs[2]) + ' & & IV & ' + mystr1(Rsq[2]) + ' & ' + mystr1(Pvals[2]) + '\n' - output += '\\\\ & (' + mystr1(StdErrs[2]) + ') & & & &' + mystr1(OID[2]) + '\n' - - # IV on aggregate assets - output += '\\\\ & & ' + mystr2(Coeffs[3]) + sigFunc(Coeffs[3],StdErrs[3]) + ' & IV & ' + mystr1(Rsq[3]) + ' & ' + mystr1(Pvals[3]) + '\n' - output += '\\\\ & & (' + mystr2(StdErrs[3]) + ') & & &' + mystr1(OID[3]) + '\n' - - # Horse race - output += '\\\\ ' + mystr1(Coeffs[4]) + sigFunc(Coeffs[4],StdErrs[4]) + ' & ' + mystr1(Coeffs[5]) + sigFunc(Coeffs[5],StdErrs[5]) + ' & ' + mystr2(Coeffs[6]) + sigFunc(Coeffs[6],StdErrs[6]) + ' & IV & ' + mystr1(Rsq[4]) + ' & ' + mystr1(Pvals[4]) + '\n' - output += '\\\\ (' + mystr1(StdErrs[4]) + ') & (' + mystr1(StdErrs[5]) + ') & (' + mystr2(StdErrs[6]) + ') & & & ' + mystr1(OID[4]) + '\n' - output += memo - - if Counts[0] is not None and Counts[2] > 1 and False: - output += '\\\\ \multicolumn{6}{c}{Horserace coefficient on ' + DeltaLogC + ' significant at 95\% level for ' + str(Counts[0]) + ' of ' + str(Counts[2]) + ' subintervals.} \n' - output += '\\\\ \multicolumn{6}{c}{Horserace coefficient on $\mathbb{E}[\Delta \log \mathbf{Y}_{t+1}]$ significant at 95\% level for ' + str(Counts[1]) + ' of ' + str(Counts[2]) + ' subintervals.} \n' - - return output - - -def makeResultsTable(caption,panels,counts,filename,label): - ''' - Make a time series regression results table by piecing together one or more panels. - Saves a tex file to disk in the tables directory. - - Parameters - ---------- - caption : str or None - Text to apply at the start of the table as a title. If None, the table - environment is not invoked and the output is formatted for slides. - panels : [str] - List of strings with one or more panels, usually made by makeResultsPanel. - counts : int - List of two integers: [interval_length, interval_count] - filename : str - Name of the file in which to save output (in the ./Tables/ directory). - label : str - LaTeX \label, for internal reference in the paper text. - - Returns - ------- - None - ''' - if caption is not None: - note_size = '\\footnotesize' - else: - note_size = '\\tiny' - note = '\\multicolumn{6}{p{0.95\\textwidth}}{' + note_size + ' \\textbf{Notes:} ' - if counts[1] > 1: - note += 'Reported statistics are the average values for ' + str(counts[1]) + ' samples of ' + str(counts[0]) + ' simulated quarters each. ' - note += 'Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. ' - else: - note += 'Reported statistics are for a single simulation of ' + str(counts[0]) + ' quarters. ' - note += 'Stars indicate statistical significance at the 90\%, 95\%, and 99\% levels, respectively. ' - note += 'Instruments $\\textbf{Z}_t = \\{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \\}$.' - note += '}' - - if caption is not None: - output = '\\begin{minipage}{\\textwidth}\n' - output += '\\begin{table} \caption{' + caption + '} \\label{' + label + '} \n' - output += ' \\centerline{$ \Delta \log \mathbf{C}_{t+1} = \\varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \\alpha A_t + \epsilon_{t+1} $}\n' - else: - output = '\\begin{center} \n' - output += '$ \Delta \log \mathbf{C}_{t+1} = \\varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \\alpha A_t + \epsilon_{t+1} $ \\\\ \n' - output += '\\begin{tabular}{d{4}d{4}d{5}cd{4}c}\n \\toprule \n' - output += '\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} \n' - output += '\\\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} \n' - - for panel in panels: - output += panel - - output += '\\\\ \\bottomrule \n ' + note + '\n' - output += '\end{tabular}\n' - - if caption is not None: - output += '\end{table}\n' - output += '\end{minipage}\n' - else: - output += '\end{center}\n' - - with open(tables_dir + filename + '.tex','w') as f: - f.write(output) - f.close() - - -def makeParameterTable(filename, params): - ''' - Makes the parameter table for the paper, saving it to a tex file in the tables folder. - Also makes two partial parameter tables for the slides. - - Parameters - ---------- - - filename : str - Name of the file in which to save output (in the tables directory). - Suffix .tex is automatically added. - params : - Object containing the parameter values. - - Returns - ------- - None - ''' - # Calibrated macroeconomic parameters - macro_panel = "\multicolumn{3}{c}{\\textbf{Macroeconomic Parameters} } \n" - macro_panel += "\\\\ $\\kapShare$ & " + "{:.2f}".format(params.CapShare) + " & Capital's Share of Income \n" - macro_panel += "\\\\ $\\daleth$ & " + "{:.2f}".format(params.DeprFacAnn) + "^{1/4} & Depreciation Factor \n" - macro_panel += "\\\\ $\sigma_{\Theta}^{2}$ & "+ "{:.5f}".format(params.TranShkAggVar) +" & Variance Aggregate Transitory Shocks \n" - macro_panel += "\\\\ $\sigma_{\Psi}^{2}$ & "+ "{:.5f}".format(params.PermShkAggVar) +" & Variance Aggregate Permanent Shocks \n" - - # Steady state values - SS_panel = "\multicolumn{3}{c}{ \\textbf{Steady State of Perfect Foresight DSGE Model} } \\ \n" - SS_panel += "\\\\ \multicolumn{3}{c}{ $(\\sigma_{\\Psi}=\\sigma_{\\Theta}=\\sigma_{\\psi}=\\sigma_{\\theta}=\wp=\\PDies=0$, $\\Phi_t = 1)$} \\ \n" - SS_panel += "\\\\ $\\breve{K}/\\breve{K}^{\\kapShare}$ & " + "{:.1f}".format(params.KYratioSS) + " & SS Capital to Output Ratio \n" - SS_panel += "\\\\ $\\breve{K}$ & " + "{:.2f}".format(params.KSS) + " & SS Capital to Labor Productivity Ratio ($=12^{1/(1-\\kapShare)}$) \n" - SS_panel += "\\\\ $\\breve{\\Wage}$ & " + "{:.2f}".format(params.wRteSS) + " & SS Wage Rate ($=(1-\\kapShare)\\breve{K}^{\\kapShare}$) \n" - SS_panel += "\\\\ $\\breve{\\mathsf{r}}$ & " + "{:.2f}".format(params.rFreeSS) + " & SS Interest Rate ($=\\kapShare \\breve{K}^{\\kapShare-1}$) \n" - SS_panel += "\\\\ $\\breve{\\Rprod}$ & " + "{:.3f}".format(params.RfreeSS) + "& SS Between-Period Return Factor ($=\\daleth + \\breve{\\mathsf{r}}$) \n" - - # Calibrated preference parameters - pref_panel = "\multicolumn{3}{c}{ \\textbf{Preference Parameters} } \n" - pref_panel += "\\\\ $\\rho$ & "+ "{:.0f}".format(params.CRRA) +". & Coefficient of Relative Risk Aversion \n" - pref_panel += "\\\\ $\\beta_{SOE}$ & " + "{:.3f}".format(params.DiscFacSOE) +" & SOE Discount Factor \n" #($=0.99 \\cdot \\PLives / (\\breve{\\mathcal{R}} \\Ex [\\pmb{\\psi}^{-\CRRA}])$)\n" - pref_panel += "\\\\ $\\beta_{DSGE}$ & " + "{:.3f}".format(params.DiscFacDSGE) +" & HA-DSGE Discount Factor ($=\\breve{\\Rprod}^{-1}$) \n" - pref_panel += "\\\\ $\Pi$ & " + "{:.2f}".format(params.UpdatePrb) +" & Probability of Updating Expectations (if Sticky) \n" - - # Idiosyncratic shock parameters - idio_panel = "\multicolumn{3}{c}{ \\textbf{Idiosyncratic Shock Parameters} } \n" - idio_panel += "\\\\ $\sigma_{\\theta}^{2}$ & " + "{:.3f}".format(params.TranShkVar) +" & Variance Idiosyncratic Tran Shocks (=$4 \\times$ Annual) \n" - idio_panel += "\\\\ $\sigma_{\psi}^{2}$ &" + "{:.3f}".format(params.PermShkVar) +" & Variance Idiosyncratic Perm Shocks (=$\\frac{1}{4} \\times$ Annual) \n" - idio_panel += "\\\\ $\wp$ & " + "{:.3f}".format(params.UnempPrb) +" & Probability of Unemployment Spell \n" - idio_panel += "\\\\ $\PDies$ & " + "{:.3f}".format(params.DiePrb) +" & Probability of Mortality \n" - - # Make full parameter table for paper - paper_output = "\provideboolean{Slides} \setboolean{Slides}{false} \n" - - paper_output += "\\begin{minipage}{\\textwidth}\n" - paper_output += " \\begin{table}\n" - paper_output += " \\caption{Calibration}\label{table:calibration}\n" - - paper_output += "\\begin{tabular}{cd{5}l} \n" - paper_output += "\\\\ \\toprule \n" - paper_output += macro_panel - paper_output += "\\\\ \\midrule \n" - paper_output += SS_panel - paper_output += "\\\\ \\midrule \n" - paper_output += pref_panel - paper_output += "\\\\ \\midrule \n" - paper_output += idio_panel - paper_output += "\\\\ \\bottomrule \n" - paper_output += "\end{tabular}\n" - paper_output += "\end{table}\n" - paper_output += "\end{minipage}\n" - paper_output += "\ifthenelse{\\boolean{StandAlone}}{\end{document}}{} \n" - with open(tables_dir + filename + '.tex','w') as f: - f.write(paper_output) - f.close() - - # Make two partial parameter tables for the slides - slides1_output = "\\begin{center}\label{table:calibration1} \n" - slides1_output += "\\begin{tabular}{cd{5}l} \n" - slides1_output += "\\\\ \\toprule \n" - slides1_output += macro_panel - slides1_output += "\\\\ \\midrule \n" - slides1_output += SS_panel - slides1_output += "\\\\ \\bottomrule \n" - slides1_output += "\end{tabular} \n" - slides1_output += "\end{center} \n" - with open(tables_dir + filename + '_1.tex','w') as f: - f.write(slides1_output) - f.close() - - slides2_output = "\\begin{center}\label{table:calibration2} \n" - slides2_output += "\\begin{tabular}{cd{5}l} \n" - slides2_output += "\\\\ \\toprule \n" - slides2_output += pref_panel - slides2_output += "\\\\ \\midrule \n" - slides2_output += idio_panel - slides2_output += "\\\\ \\bottomrule \n" - slides2_output += "\end{tabular} \n" - slides2_output += "\end{center} \n" - with open(tables_dir + filename + '_2.tex','w') as f: - f.write(slides2_output) - f.close() - - -def makeEquilibriumTable(out_filename, four_in_files, CRRA): - ''' - Make the equilibrium statistics table for the paper, saving it as a tex file - in the tables folder. Also makes a version for the slides that doesn't use - the table environment, nor include the note at bottom. - - Parameters - ---------- - - out_filename : str - Name of the file in which to save output (in the tables directory). - Suffix .tex appended automatically. - four_in_files: [str] - A list with four csv files. 0) SOE frictionless 1) SOE Sticky 2) DSGE frictionless 3) DSGE sticky - CRRA : float - Coefficient of relative risk aversion - - Returns - ------- - None - ''' - # Read in statistics from the four files - SOEfrictionless = np.genfromtxt(results_dir + four_in_files[0] + 'Results.csv', delimiter=',') - SOEsticky = np.genfromtxt(results_dir + four_in_files[1] + 'Results.csv', delimiter=',') - DSGEfrictionless = np.genfromtxt(results_dir + four_in_files[2] + 'Results.csv', delimiter=',') - DSGEsticky = np.genfromtxt(results_dir + four_in_files[3] + 'Results.csv', delimiter=',') - - # Read in value at birth from the four files - vBirth_SOE_F = np.genfromtxt(results_dir + four_in_files[0] + 'BirthValue.csv', delimiter=',') - vBirth_SOE_S = np.genfromtxt(results_dir + four_in_files[1] + 'BirthValue.csv', delimiter=',') - vBirth_DSGE_F = np.genfromtxt(results_dir + four_in_files[2] + 'BirthValue.csv', delimiter=',') - vBirth_DSGE_S = np.genfromtxt(results_dir + four_in_files[3] + 'BirthValue.csv', delimiter=',') - - # Calculate the cost of stickiness in the SOE and DSGE models - StickyCost_SOE = np.mean(1. - (vBirth_SOE_S/vBirth_SOE_F)**(1./(1.-CRRA))) - StickyCost_DSGE = np.mean(1. - (vBirth_DSGE_S/vBirth_DSGE_F)**(1./(1.-CRRA))) - - paper_top = "\\begin{minipage}{\\textwidth}\n" - paper_top += " \\begin{table} \n" - paper_top += "\caption{Equilibrium Statistics} \n" - paper_top += "\label{table:Eqbm} \n" - paper_top += "\\newsavebox{\EqbmBox} \n" - paper_top += "\sbox{\EqbmBox}{ \n" - paper_top += "\\newcommand{\EqDir}{\TablesDir/Eqbm} \n" - - slides_top = '\\begin{center} \n' - - main_table = "\\begin{tabular}{lllcccc} \n" - main_table += "\\toprule \n" - main_table += "&&& \multicolumn{2}{c}{SOE Model} & \multicolumn{2}{c}{HA-DSGE Model} \n" - main_table += "\\\\ %\cline{4-5} \n" - main_table += " &&& \multicolumn{1}{c}{Frictionless} & \multicolumn{1}{c}{Sticky} & \multicolumn{1}{c}{Frictionless} & \multicolumn{1}{c}{Sticky} \n" - main_table += "\\\\ \\midrule \n" - main_table += " \multicolumn{3}{l}{Means} \n" - main_table += "%\\\\ & & $M$ \n" - main_table += "%\\\\ & & $K$ \n" - main_table += "\\\\ & & $A$ & {:.2f}".format(SOEfrictionless[0]) +" &{:.2f}".format(SOEsticky[0]) +" & {:.2f}".format(DSGEfrictionless[0]) +" & {:.2f}".format(DSGEsticky[0]) +" \n" - main_table += "\\\\ & & $C$ & {:.2f}".format(SOEfrictionless[1]) +" &{:.2f}".format(SOEsticky[1]) +" & {:.2f}".format(DSGEfrictionless[1]) +" & {:.2f}".format(DSGEsticky[1]) +" \n" - main_table += "\\\\ \\midrule \n" - main_table += " \multicolumn{3}{l}{Standard Deviations} \n" - main_table += "\\\\ & \multicolumn{4}{l}{Aggregate Time Series (`Macro')} \n" - main_table += "%\\ & & $\Delta \log \mathbf{M}$ \n" - main_table += "\\\\ & & $\log A $ & {:.3f}".format(SOEfrictionless[2]) +" & {:.3f}".format(SOEsticky[2]) +" & {:.3f}".format(DSGEfrictionless[2]) +" & {:.3f}".format(DSGEsticky[2]) +" \n" - main_table += "\\\\ & & $\Delta \log \\CLevBF $ & {:.3f}".format(SOEfrictionless[3]) +" & {:.3f}".format(SOEsticky[3]) +" & {:.3f}".format(DSGEfrictionless[3]) +" & {:.3f}".format(DSGEsticky[3]) +" \n" - main_table += "\\\\ & & $\Delta \log \\YLevBF $ & {:.3f}".format(SOEfrictionless[4]) +" & {:.3f}".format(SOEsticky[4]) +" & {:.3f}".format(DSGEfrictionless[4]) +" & {:.3f}".format(DSGEsticky[4]) +" \n" - main_table += "\\\\ & \multicolumn{3}{l}{Individual Cross Sectional (`Micro')} \n" - main_table += "\\\\ & & $\log \\aLevBF $ & {:.3f}".format(SOEfrictionless[6]) +" & {:.3f}".format(SOEsticky[6]) +" & {:.3f}".format(DSGEfrictionless[6]) +" & {:.3f}".format(DSGEsticky[6]) +" \n" - main_table += "\\\\ & & $\log \\cLevBF $ & {:.3f}".format(SOEfrictionless[7]) +" & {:.3f}".format(SOEsticky[7]) +" & {:.3f}".format(DSGEfrictionless[7]) +" & {:.3f}".format(DSGEsticky[7]) +" \n" - main_table += "\\\\ & & $\log p $ & {:.3f}".format(SOEfrictionless[8]) +" & {:.3f}".format(SOEsticky[8]) +" & {:.3f}".format(DSGEfrictionless[8]) +" & {:.3f}".format(DSGEsticky[8]) +" \n" - main_table += "\\\\ & & $\log \\yLevBF | \\yLevBF > 0 $ & {:.3f}".format(SOEfrictionless[9]) +" & {:.3f}".format(SOEsticky[9]) +" & {:.3f}".format(DSGEfrictionless[9]) +" & {:.3f}".format(DSGEsticky[9]) +" \n" - main_table += "\\\\ & & $\Delta \log \\cLevBF $ & {:.3f}".format(SOEfrictionless[11]) +" & {:.3f}".format(SOEsticky[11]) +" & {:.3f}".format(DSGEfrictionless[11]) +" & {:.3f}".format(DSGEsticky[11]) +" \n" - main_table += " \n" - main_table += " \n" - main_table += "\\\\ \\midrule \multicolumn{3}{l}{Cost of Stickiness} \n" - main_table += " & \multicolumn{2}{c}{" + mystr2(StickyCost_SOE) + "} \n" - main_table += " & \multicolumn{2}{c}{" + mystr2(StickyCost_DSGE) + "} \n" - main_table += "\\\\ \\bottomrule \n" - main_table += " \end{tabular} \n" - - paper_bot = " } \n " - paper_bot += "\\usebox{\EqbmBox} \n" - paper_bot += "\ifthenelse{\\boolean{StandAlone}}{\\newlength\TableWidth}{} \n" - paper_bot += "\settowidth\TableWidth{\\usebox{\EqbmBox}} % Calculate width of table so notes will match \n" - paper_bot += "\medskip\medskip \\vspace{0.0cm} \parbox{\TableWidth}{\\footnotesize\n" - paper_bot += "\\textbf{Notes}: The cost of stickiness is calculated as the proportion by which the permanent income of a newborn frictionless consumer would need to be reduced in order to achieve the same reduction of expected value associated with forcing them to become a sticky expectations consumer.} \n" - paper_bot += "\end{table}\n" - paper_bot += "\end{minipage}\n" - paper_bot += "\ifthenelse{\\boolean{StandAlone}}{\end{document}}{} \n" - - slides_bot = '\\end{center} \n' - - paper_output = paper_top + main_table + paper_bot - with open(tables_dir + out_filename + '.tex','w') as f: - f.write(paper_output) - f.close() - - slides_output = slides_top + main_table + slides_bot - with open(tables_dir + out_filename + 'Slides.tex','w') as f: - f.write(slides_output) - f.close() - - -def extractSampleMicroData(Economy, num_periods, AgentCount, ignore_periods): - ''' - Extracts sample micro data to be used in micro (cross section) regression. - - Parameters - ---------- - - Economy : Economy - An economy (with one AgentType) for for which history has already been calculated. - num_periods : int - Number of periods to be stored (should be less than the number of periods calculated) - AgentCouunt : int - Number of agent histories to be stored (should be less than the AgentCount property of the agent) - ignore_periods : int - Number of periods at the beginning of the history to be discarded - - Returns - ------- - micro_data : np.array - Array with rows 1) logc_diff 2) log_trans_shk 3) top_assets - ''' - # First pull out economy common data. - # Note indexing on Economy tracked vars is one ahead of agent. - agg_trans_shk_matrix = deepcopy(Economy.TranShkAggNow_hist[ignore_periods:ignore_periods+num_periods]) - wRte_matrix = deepcopy(Economy.wRteNow_hist[ignore_periods:ignore_periods+num_periods]) - - # Now pull out agent data - agent = Economy.agents[0] - c_matrix = deepcopy(agent.cLvlNow_hist[(ignore_periods+1):ignore_periods+num_periods+1,0:AgentCount]) - y_matrix = deepcopy(agent.yLvlNow_hist[(ignore_periods+1):ignore_periods+num_periods+1,0:AgentCount]) - total_trans_shk_matrix = deepcopy(agent.TranShkNow_hist[(ignore_periods+1):ignore_periods+num_periods+1,0:AgentCount]) - trans_shk_matrix = total_trans_shk_matrix/(np.array(agg_trans_shk_matrix)*np.array(wRte_matrix))[:,None] - a_matrix = deepcopy(agent.aLvlNow_hist[(ignore_periods+1):ignore_periods+num_periods+1,0:AgentCount]) - pLvlTrue_matrix = deepcopy(agent.pLvlTrue_hist[(ignore_periods+1):ignore_periods+num_periods+1,0:AgentCount]) - a_matrix_nrm = a_matrix/pLvlTrue_matrix - age_matrix = deepcopy(agent.t_age_hist[(ignore_periods+1):ignore_periods+num_periods+1,0:AgentCount]) - - # Put nan's in so that we do not regress over periods where agents die - newborn = age_matrix == 1 - c_matrix[newborn] = np.nan - c_matrix[0:0,:] = np.nan - y_matrix[newborn] = np.nan - y_matrix[0,0:] = np.nan - y_matrix[trans_shk_matrix==0.0] = np.nan - c_matrix[trans_shk_matrix==0.0] = np.nan - trans_shk_matrix[trans_shk_matrix==0.0] = np.nan - - top_assets = a_matrix_nrm > np.transpose(np.tile(np.percentile(a_matrix_nrm,1,axis=1),(np.shape(a_matrix_nrm)[1],1))) - logc_diff = np.log(c_matrix[1:,:])-np.log(c_matrix[:-1,:]) - logy_diff = np.log(y_matrix[1:,:])-np.log(y_matrix[:-1,:]) - logc_diff = logc_diff.flatten('F') - logy_diff = logy_diff.flatten('F') - log_trans_shk = np.log(trans_shk_matrix[1:,:].flatten('F')) - top_assets = top_assets[1:,:].flatten('F') - - # Put nan's in where they exist in logc_diff - log_trans_shk = log_trans_shk + logc_diff*0.0 - top_assets = top_assets + logc_diff*0.0 - - return np.stack((logc_diff,log_trans_shk,top_assets),1) - - -def makeMicroRegressionTable(out_filename, micro_data): - ''' - Make the micro regression or (cross section regression) table for the paper, saving - it to a tex file in the tables folder. Also makes two partial tables for the slides. - - Parameters - ---------- - - out_filename : str - Name of the file in which to save output (in the tables directory). - The suffix .tex is automatically added. - micro_data : [np.array] - A list of two np.array's each containing micro data array as returned by extractSampleMicroData - - Returns - ------- - None - ''' - coeffs = np.zeros((6,2)) + np.nan - stdevs = np.zeros((6,2)) +np.nan - r_sq = np.zeros((4,2)) +np.nan - obs = np.zeros((4,2)) +np.nan - for i in range(2): - this_micro_data = micro_data[i] - logc_diff = this_micro_data[:,0] - log_trans_shk = this_micro_data[:,1] - top_assets = this_micro_data[:,2] - - #Lagged consumption regression - mod = sm.OLS(logc_diff[1:],sm.add_constant(np.transpose(np.vstack([logc_diff[0:-1]]))), missing='drop') - res = mod.fit() - coeffs[0,i] = res._results.params[1] - stdevs[0,i] = res._results.HC0_se[1] - r_sq[0,i] = res._results.rsquared_adj - obs[0,i] = res.nobs - - #Expected income regression - mod = sm.OLS(logc_diff[1:],sm.add_constant(np.transpose(np.vstack([-log_trans_shk[0:-1]]))), missing='drop') - res = mod.fit() - coeffs[1,i] = res._results.params[1] - stdevs[1,i] = res._results.HC0_se[1] - r_sq[1,i] = res._results.rsquared_adj - obs[1,i] = res.nobs - - #Assets regression - mod = sm.OLS(logc_diff[1:],sm.add_constant(np.transpose(np.vstack([top_assets[0:-1]]))), missing='drop') - res = mod.fit() - coeffs[2,i] = res._results.params[1] - stdevs[2,i] = res._results.HC0_se[1] - r_sq[2,i] = res._results.rsquared_adj - obs[2,i] = res.nobs - - #Horeserace regression - mod = sm.OLS(logc_diff[1:],sm.add_constant(np.transpose(np.vstack([logc_diff[0:-1],-log_trans_shk[0:-1],top_assets[0:-1]]))), missing='drop') - res = mod.fit() - coeffs[3,i] = res._results.params[1] - stdevs[3,i] = res._results.HC0_se[1] - coeffs[4,i] = res._results.params[2] - stdevs[4,i] = res._results.HC0_se[2] - coeffs[5,i] = res._results.params[3] - stdevs[5,i] = res._results.HC0_se[3] - r_sq[3,i] = res._results.rsquared_adj - obs[3,i] = res.nobs - - paper_top = "\\begin{minipage}{\TableWidth}\n" - paper_top += " \\begin{table}\n" - paper_top += " \\caption{Micro Consumption Regression on Simulated Data} \\label{table:CGrowCross}\n" - paper_top += " \\begin{eqnarray} \n" - paper_top += "\\CGrowCross \\nonumber %\\\CGrowCrossBar \\nonumber \n" - paper_top += " \end{eqnarray}\n" - - slides_top = '\\begin{center} \n' - - header = "\\begin{tabular}{cd{4}d{4}d{5}ccc} \n" - header += "\\toprule \n" - header += "Model of & & & & & \\\\ \n" - header += "Expectations & \multicolumn{1}{c}{$ \chi $} & \multicolumn{1}{c}{$ \eta $} & \multicolumn{1}{c}{$ \\alpha $} & \multicolumn{1}{c}{$\\bar{R}^{2}$} & \n" - - F_panel = "\\\\ \\midrule \n \multicolumn{2}{l}{Frictionless} \n" - F_panel += "\\\\ & {:.3f}".format(coeffs[0,0]) +" & & & {:.3f}".format(r_sq[0,0]) +" & "+ " %NotOnSlide \n" - F_panel += "\\\\ & \multicolumn{1}{c}{(--)} & & & & "+ " %NotOnSlide \n" - F_panel += "\\\\ & & {:.3f}".format(coeffs[1,0]) +" & & {:.3f}".format(r_sq[1,0]) +" & "+" %NotOnSlide \n" - F_panel += "\\\\ & & \multicolumn{1}{c}{(--)} & & & "+ " %NotOnSlide \n" - F_panel += "\\\\ & & & {:.3f}".format(coeffs[2,0]) +" & {:.3f}".format(r_sq[2,0]) +" & " +" %NotOnSlide \n" - F_panel += "\\\\ & & & \multicolumn{1}{c}{(--)} & & "+ " %NotOnSlide \n" - F_panel += "\\\\ & {:.3f}".format(coeffs[3,0]) +" & {:.3f}".format(coeffs[4,0]) +" & {:.3f}".format(coeffs[5,0]) +" & {:.3f}".format(r_sq[3,0]) +" & \n" - F_panel += "\\\\ & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & & "+ " %NotOnSlide \n" - - S_panel = "\\\\ \\midrule \n \multicolumn{2}{l}{Sticky} \n" - S_panel += "\\\\ & {:.3f}".format(coeffs[0,1]) +" & & & {:.3f}".format(r_sq[0,1]) +" & %NotOnSlide \n" - S_panel += "\\\\ & \multicolumn{1}{c}{(--)} & & & & "+ " %NotOnSlide \n" - S_panel += "\\\\ & & {:.3f}".format(coeffs[1,1]) +" & & {:.3f}".format(r_sq[1,1]) +" & %NotOnSlide \n" - S_panel += "\\\\ & & \multicolumn{1}{c}{(--)} & & & "+ " %NotOnSlide \n" - S_panel += "\\\\ & & & {:.3f}".format(coeffs[2,1]) +" & {:.3f}".format(r_sq[2,1]) +" & %NotOnSlide \n" - S_panel += "\\\\ & & & \multicolumn{1}{c}{(--)} & & "+ " %NotOnSlide \n" - S_panel += "\\\\ & {:.3f}".format(coeffs[3,1]) +" & {:.3f}".format(coeffs[4,1]) +" & {:.3f}".format(coeffs[5,1]) +" & {:.3f}".format(r_sq[3,1]) +" & \n" - S_panel += "\\\\ & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & & "+ " %NotOnSlide \n" - - paper_bot = " \\\\ \\bottomrule \\\\\n" - paper_bot += " \multicolumn{5}{p{0.9\\textwidth}}{\\footnotesize \\textbf{Notes}: $\\mathbb{E}_{t,i}$ is the expectation from the perspective of person $i$ in period $t$; $\\bar{a}$ is a dummy variable indicating that agent $i$ is in the top 99 percent of the normalized $a$ distribution. Simulated sample size is large enough such that standard errors are effectively zero. Sample is restricted to households with positive income in period $t$. The notation ``(---)'' indicates that standard errors are close to zero, given the very large simulated sample size.}\n" - paper_bot += "\end{tabular} \n" - paper_bot += "\end{table}\n" - paper_bot += "\end{minipage}\n" - paper_bot += "\ifthenelse{\\boolean{StandAlone}}{\end{document}}{} \n" - - slides_bot = "\\\\ \\bottomrule \n" - slides_bot += "\end{tabular} \n" - slides_bot += '\\end{center} \n' - - # Make table for paper - paper_output = paper_top + header + F_panel + S_panel + paper_bot - with open(tables_dir + out_filename + '.tex','w') as f: - f.write(paper_output) - f.close() - - # Make tables for slides - slidesF_output = slides_top + header + F_panel + slides_bot - with open(tables_dir + out_filename + '_SlidesF.tex','w') as f: - f.write(slidesF_output) - f.close() - - slidesS_output = slides_top + header + S_panel + slides_bot - with open(tables_dir + out_filename + '_SlidesS.tex','w') as f: - f.write(slidesS_output) - f.close() - - -def makeuCostVsPiFig(uCost_filename): - ''' - Make two versions of a figure that plots the cost of stickiness vs updating probability. - Saves pdf files to the figures directory. - - Parameters - ---------- - uCost_filename : str - Name of data file, as a two line csv. First line is UpdatePrb, second line is uCost. - - Returns - ------- - None - ''' - data = np.genfromtxt(results_dir + uCost_filename +'.csv',delimiter=',') - UpdatePrbVec = data[0,:] - uCostVec = data[1,:] - - # Plot uCost vs Pi - f = LinearInterp(UpdatePrbVec,uCostVec*10000) - plt.plot(UpdatePrbVec,uCostVec*10000,color='#1f77b4') - plt.plot([UpdatePrbBase,UpdatePrbBase],[0.,f(UpdatePrbBase)],'--k') # Add dashed line at Pi=0.25 - plt.xlim([0.05,1.0]) - plt.ylim([0.0,30.0]) - plt.xlabel(r'Probability of updating information $\Pi$') - plt.ylabel('Cost of stickiness $\omega$ ($10^{-4}$)') - plt.savefig(figures_dir + 'uCostvsPi.pdf') - plt.savefig(figures_dir + 'uCostvsPi.png') - plt.savefig(figures_dir + 'uCostvsPi.jpg') - plt.savefig(figures_dir + 'uCostvsPi.svg') - plt.show() - plt.close() - - # Plot uCost vs 1/Pi - plt.plot(1./UpdatePrbVec,uCostVec*10000,color='#1f77b4') - plt.plot([1./UpdatePrbBase,1./UpdatePrbBase],[0.,f(UpdatePrbBase)],'--k') # Add dashed line at Pi=0.25 - plt.xlim([1.0,16.0]) - plt.ylim([0.0,35.0]) - plt.xlabel('Expected periods between information updates $\Pi^{-1}$') - plt.ylabel('Cost of stickiness $\omega$ ($10^{-4}$)') - plt.savefig(figures_dir + 'uCostvsPiInv.pdf') - plt.savefig(figures_dir + 'uCostvsPiInv.png') - plt.savefig(figures_dir + 'uCostvsPiInv.jpg') - plt.savefig(figures_dir + 'uCostvsPiInv.svg') - plt.show() - plt.close() - - -def makeValueVsAggShkVarFig(value_filename): - ''' - Parameters - ---------- - value_filename : str - Name of data file, as a two line csv. First line is PermShkAggVar, second line is birth value. - - Returns - ------- - None - ''' - data = np.genfromtxt(results_dir + value_filename +'.csv',delimiter=',') - PermShkAggVarVec = data[0,:] - vVec = data[1,:] - - # Plot birth value vs PermShkAggVar - f = LinearInterp(PermShkAggVarVec*10**5,vVec) - vBot = np.min(vVec) - vTop = np.max(vVec) - plt.plot(PermShkAggVarVec*10**5,vVec,color='#1f77b4') - plt.plot([PermShkAggVarBase*10**5,PermShkAggVarBase*10**5],[vBot,f(PermShkAggVarBase*10**5)],'--k') - plt.ylim(vBot,vTop) - plt.xlabel('Variance of aggregate permanent shocks $\sigma^2_\Psi$ ($10^{-5}$)') - plt.ylabel('Expected value at birth $V(W,\cdot)$') - plt.tight_layout() - plt.savefig(figures_dir + 'ValueVsPermShkAggVar.pdf') - plt.savefig(figures_dir + 'ValueVsPermShkAggVar.png') - plt.savefig(figures_dir + 'ValueVsPermShkAggVar.jpg') - plt.savefig(figures_dir + 'ValueVsPermShkAggVar.svg') - plt.show() - plt.close() - - -def makeValueVsPiFig(value_filename): - ''' - Parameters - ---------- - value_filename : str - Name of data file, as a two line csv. First line is UpdatePrb, second line is birth value. - - Returns - ------- - None - ''' - data = np.genfromtxt(results_dir + value_filename +'.csv',delimiter=',') - UpdatePrbVec = data[0,:] - vVec = data[1,:] - - # Plot birth value vs 1/UpdatePrb - f = LinearInterp(UpdatePrbVec,vVec) - vBot = f(1./16) - vTop = np.max(vVec) - plt.plot(1./UpdatePrbVec,vVec,color='#1f77b4') - plt.plot([1./UpdatePrbBase,1./UpdatePrbBase],[vBot,f(UpdatePrbBase)],'--k') - plt.xlim([1.0,16.0]) - plt.ylim(vBot,vTop) - plt.xlabel('Expected periods between information updates $\Pi^{-1}$') - plt.ylabel('Expected value at birth $V(W,\cdot)$') - plt.tight_layout() - plt.savefig(figures_dir + 'ValueVsPi.pdf') - plt.savefig(figures_dir + 'ValueVsPi.png') - plt.savefig(figures_dir + 'ValueVsPi.jpg') - plt.savefig(figures_dir + 'ValueVsPi.svg') - plt.show() - plt.close() - - - diff --git a/examples/cAndCwithStickyE/Tables/CGrowCross.tex b/examples/cAndCwithStickyE/Tables/CGrowCross.tex deleted file mode 100644 index 8d9861cce..000000000 --- a/examples/cAndCwithStickyE/Tables/CGrowCross.tex +++ /dev/null @@ -1,39 +0,0 @@ -\begin{minipage}{\TableWidth} - \begin{table} - \caption{Micro Consumption Regression on Simulated Data} \label{table:CGrowCross} - \begin{eqnarray} -\CGrowCross \nonumber %\\CGrowCrossBar \nonumber - \end{eqnarray} - \\ -% \begin{center} -\begin{tabular}{cd{4}d{4}d{5}ccc} -\toprule -Model of & & & & & \\ -Expectations & \multicolumn{1}{c}{$ \chi $} & \multicolumn{1}{c}{$ \eta $} & \multicolumn{1}{c}{$ \alpha $} & \multicolumn{1}{c}{$\bar{R}^{2}$} & -\\ \midrule - \multicolumn{2}{l}{Frictionless} -\\ & 0.019 & & & 0.000 & %NotOnSlide -\\ & \multicolumn{1}{c}{(--)} & & & & %NotOnSlide -\\ & & 0.011 & & 0.004 & %NotOnSlide -\\ & & \multicolumn{1}{c}{(--)} & & & %NotOnSlide -\\ & & & -0.190 & 0.010 & %NotOnSlide -\\ & & & \multicolumn{1}{c}{(--)} & & %NotOnSlide -\\ & 0.061 & 0.016 & -0.183 & 0.017 & -\\ & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & & %NotOnSlide -\\ \midrule - \multicolumn{2}{l}{Sticky} -\\ & 0.012 & & & 0.000 & %NotOnSlide -\\ & \multicolumn{1}{c}{(--)} & & & & %NotOnSlide -\\ & & 0.011 & & 0.004 & %NotOnSlide -\\ & & \multicolumn{1}{c}{(--)} & & & %NotOnSlide -\\ & & & -0.191 & 0.010 & %NotOnSlide -\\ & & & \multicolumn{1}{c}{(--)} & & %NotOnSlide -\\ & 0.051 & 0.015 & -0.185 & 0.016 & -\\ & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & & %NotOnSlide - \\ \bottomrule \\ - \multicolumn{5}{p{0.9\textwidth}}{\footnotesize \textbf{Notes}: $\mathbb{E}_{t,i}$ is the expectation from the perspective of person $i$ in period $t$; $\bar{a}$ is a dummy variable indicating that agent $i$ is in the top 99 percent of the normalized $a$ distribution. Simulated sample size is large enough such that standard errors are effectively zero. Sample is restricted to households with positive income in period $t$. The notation ``(---)'' indicates that standard errors are close to zero, given the very large simulated sample size.} -%\end{center} -\end{tabular} -\end{table} -\end{minipage} -\ifthenelse{\boolean{StandAlone}}{\end{document}}{} diff --git a/examples/cAndCwithStickyE/Tables/CGrowCrossDSGE.tex b/examples/cAndCwithStickyE/Tables/CGrowCrossDSGE.tex deleted file mode 100644 index faf4f6ca5..000000000 --- a/examples/cAndCwithStickyE/Tables/CGrowCrossDSGE.tex +++ /dev/null @@ -1,42 +0,0 @@ -\begin{table}[t] -\caption{Micro Consumption Regression on Simulated Data} -\label{table:CGrowCross} -\begin{center} -\ifthenelse{\boolean{StandAlone}}{\input \eq/CGrowCross.tex -}{} -\begin{eqnarray} -\CGrowCross \nonumber %\\CGrowCrossBar \nonumber -\end{eqnarray} -\newsavebox{\crosssecond} -\sbox{\crosssecond}{ -\begin{tabular}{cd{4}d{4}d{5}ccc} -\toprule -Model of & & & & & \\ -Expectations & \multicolumn{1}{c}{$ \chi $} & \multicolumn{1}{c}{$ \eta $} & \multicolumn{1}{c}{$ \alpha $} & \multicolumn{1}{c}{$\bar{R}^{2}$} & -\\ \midrule \multicolumn{2}{l}{Frictionless} -\\ & 0.033 & & & 0.001 & %NotOnSlide -\\ & \multicolumn{1}{c}{(--)} & & & & %NotOnSlide -\\ & & 0.002 & & 0.000 & %NotOnSlide -\\ & & \multicolumn{1}{c}{(--)} & & & %NotOnSlide -\\ & & & -0.109 & 0.011 & %NotOnSlide -\\ & & & \multicolumn{1}{c}{(--)} & & %NotOnSlide -\\ & 0.034 & 0.002 & -0.108 & 0.013 & -\\ & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & & %NotOnSlide -\\ \midrule \multicolumn{2}{l}{Sticky} -\\ & 0.022 & & & 0.001 & %NotOnSlide -\\ & \multicolumn{1}{c}{(--)} & & & & %NotOnSlide -\\ & & 0.002 & & 0.000 & %NotOnSlide -\\ & & \multicolumn{1}{c}{(--)} & & & %NotOnSlide -\\ & & & -0.109 & 0.011 & %NotOnSlide -\\ & & & \multicolumn{1}{c}{(--)} & & %NotOnSlide -\\ & 0.023 & 0.001 & -0.108 & 0.012 & -\\ & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & \multicolumn{1}{c}{(--)} & & %NotOnSlide -\\ \bottomrule -\end{tabular} -}\usebox{\crosssecond} -\ifthenelse{\boolean{StandAlone}}{\newlength\TableWidth}{} -\settowidth{\TableWidth}{\usebox{\crosssecond}} % Calculate width of table so notes will match -\medskip\medskip \parbox{\TableWidth}{\small Notes: $\mathbb{E}_{t,i}$ is the expectation from the perspective of person $i$ in period $t$; $\bar{a}$ is a dummy variable indicating that agent $i$ is in the top 99 percent of the normalized $a$ distribution. Simulated sample size is large enough such that standard errors are effectively zero. Sample is restricted to households with positive income in period $t$. The notation ``(---)'' indicates that standard errors are close to zero, given the very large simulated sample size.} -\end{center} -\end{table} -\ifthenelse{\boolean{StandAlone}}{\end{document}}{} diff --git a/examples/cAndCwithStickyE/Tables/Calibration.tex b/examples/cAndCwithStickyE/Tables/Calibration.tex deleted file mode 100644 index 73ff4ac6f..000000000 --- a/examples/cAndCwithStickyE/Tables/Calibration.tex +++ /dev/null @@ -1,36 +0,0 @@ -\provideboolean{Slides} \setboolean{Slides}{false} -\begin{minipage}{\textwidth} - \begin{table} - \caption{Calibration}\label{table:calibration} -\begin{tabular}{cd{5}l} -\\ \toprule -\multicolumn{3}{c}{\textbf{Macroeconomic Parameters} } -\\ $\kapShare$ & 0.36 & Capital's Share of Income -\\ $\daleth$ & 0.94^{1/4} & Depreciation Factor -\\ $\sigma_{\Theta}^{2}$ & 0.00001 & Variance Aggregate Transitory Shocks -\\ $\sigma_{\Psi}^{2}$ & 0.00004 & Variance Aggregate Permanent Shocks -\\ \midrule -\multicolumn{3}{c}{ \textbf{Steady State of Perfect Foresight DSGE Model} } \ -\\ \multicolumn{3}{c}{ $(\sigma_{\Psi}=\sigma_{\Theta}=\sigma_{\psi}=\sigma_{\theta}=\wp=\PDies=0$, $\Phi_t = 1)$} \ -\\ $\breve{K}/\breve{K}^{\kapShare}$ & 12.0 & SS Capital to Output Ratio -\\ $\breve{K}$ & 48.55 & SS Capital to Labor Productivity Ratio ($=12^{1/(1-\kapShare)}$) -\\ $\breve{\Wage}$ & 2.59 & SS Wage Rate ($=(1-\kapShare)\breve{K}^{\kapShare}$) -\\ $\breve{\mathsf{r}}$ & 0.03 & SS Interest Rate ($=\kapShare \breve{K}^{\kapShare-1}$) -\\ $\breve{\Rprod}$ & 1.015& SS Between-Period Return Factor ($=\daleth + \breve{\mathsf{r}}$) -\\ \midrule -\multicolumn{3}{c}{ \textbf{Preference Parameters} } -\\ $\rho$ & 2. & Coefficient of Relative Risk Aversion -\\ $\beta_{SOE}$ & 0.970 & SOE Discount Factor -\\ $\beta_{DSGE}$ & 0.986 & HA-DSGE Discount Factor ($=\breve{\Rprod}^{-1}$) -\\ $\Pi$ & 0.25 & Probability of Updating Expectations (if Sticky) -\\ \midrule -\multicolumn{3}{c}{ \textbf{Idiosyncratic Shock Parameters} } -\\ $\sigma_{\theta}^{2}$ & 0.120 & Variance Idiosyncratic Tran Shocks (=$4 \times$ Annual) -\\ $\sigma_{\psi}^{2}$ &0.003 & Variance Idiosyncratic Perm Shocks (=$\frac{1}{4} \times$ Annual) -\\ $\wp$ & 0.050 & Probability of Unemployment Spell -\\ $\PDies$ & 0.005 & Probability of Mortality -\\ \bottomrule -\end{tabular} -\end{table} -\end{minipage} -\ifthenelse{\boolean{StandAlone}}{\end{document}}{} diff --git a/examples/cAndCwithStickyE/Tables/Calibration_1.tex b/examples/cAndCwithStickyE/Tables/Calibration_1.tex deleted file mode 100644 index fcf8b8def..000000000 --- a/examples/cAndCwithStickyE/Tables/Calibration_1.tex +++ /dev/null @@ -1,19 +0,0 @@ -\begin{center}\label{table:calibration1} -\begin{tabular}{cd{5}l} -\\ \toprule -\multicolumn{3}{c}{\textbf{Macroeconomic Parameters} } -\\ $\kapShare$ & 0.36 & Capital's Share of Income -\\ $\daleth$ & 0.94^{1/4} & Depreciation Factor -\\ $\sigma_{\Theta}^{2}$ & 0.00001 & Variance Aggregate Transitory Shocks -\\ $\sigma_{\Psi}^{2}$ & 0.00004 & Variance Aggregate Permanent Shocks -\\ \midrule -\multicolumn{3}{c}{ \textbf{Steady State of Perfect Foresight DSGE Model} } \ -\\ \multicolumn{3}{c}{ $(\sigma_{\Psi}=\sigma_{\Theta}=\sigma_{\psi}=\sigma_{\theta}=\wp=\PDies=0$, $\Phi_t = 1)$} \ -\\ $\breve{K}/\breve{K}^{\kapShare}$ & 12.0 & SS Capital to Output Ratio -\\ $\breve{K}$ & 48.55 & SS Capital to Labor Productivity Ratio ($=12^{1/(1-\kapShare)}$) -\\ $\breve{\Wage}$ & 2.59 & SS Wage Rate ($=(1-\kapShare)\breve{K}^{\kapShare}$) -\\ $\breve{\mathsf{r}}$ & 0.03 & SS Interest Rate ($=\kapShare \breve{K}^{\kapShare-1}$) -\\ $\breve{\Rprod}$ & 1.015& SS Between-Period Return Factor ($=\daleth + \breve{\mathsf{r}}$) -\\ \bottomrule -\end{tabular} -\end{center} diff --git a/examples/cAndCwithStickyE/Tables/Calibration_2.tex b/examples/cAndCwithStickyE/Tables/Calibration_2.tex deleted file mode 100644 index 5eba41bc4..000000000 --- a/examples/cAndCwithStickyE/Tables/Calibration_2.tex +++ /dev/null @@ -1,17 +0,0 @@ -\begin{center}\label{table:calibration2} -\begin{tabular}{cd{5}l} -\\ \toprule -\multicolumn{3}{c}{ \textbf{Preference Parameters} } -\\ $\rho$ & 2. & Coefficient of Relative Risk Aversion -\\ $\beta_{SOE}$ & 0.970 & SOE Discount Factor -\\ $\beta_{DSGE}$ & 0.986 & HA-DSGE Discount Factor ($=\breve{\Rprod}^{-1}$) -\\ $\Pi$ & 0.25 & Probability of Updating Expectations (if Sticky) -\\ \midrule -\multicolumn{3}{c}{ \textbf{Idiosyncratic Shock Parameters} } -\\ $\sigma_{\theta}^{2}$ & 0.120 & Variance Idiosyncratic Tran Shocks (=$4 \times$ Annual) -\\ $\sigma_{\psi}^{2}$ &0.003 & Variance Idiosyncratic Perm Shocks (=$\frac{1}{4} \times$ Annual) -\\ $\wp$ & 0.050 & Probability of Unemployment Spell -\\ $\PDies$ & 0.005 & Probability of Mortality -\\ \bottomrule -\end{tabular} -\end{center} diff --git a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimReg.tex b/examples/cAndCwithStickyE/Tables/DSGEmrkvSimReg.tex deleted file mode 100644 index c3810c780..000000000 --- a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimReg.tex +++ /dev/null @@ -1,38 +0,0 @@ -\begin{minipage}{\textwidth} -\begin{table} \caption{Aggregate Consumption Dynamics in HA-DSGE Model} \label{tDSGEsim} - \centerline{$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $} -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.189^{\bullet \bullet \bullet } & & & OLS & 0.036 & -\\ (0.072) & & & & & -\\ 0.476 & & & IV & 0.020 & 0.318 -\\ (0.354) & & & & &0.556 -\\ & 0.368 & & IV & 0.017 & 0.107 -\\ & (0.321) & & & &0.457 -\\ & & -0.34\text{e--4} & IV & 0.015 & 0.000 -\\ & & (0.98\text{e--4}) & & &0.433 -\\ 0.289 & 0.214 & 0.01\text{e--4} & IV & 0.020 & 0.572 -\\ (0.463) & (0.583) & (1.87\text{e--4}) & & & 0.531 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.023; ~$\var(\log(\xi_t))=$ 4.16\text{e--6} } -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.467^{\bullet \bullet \bullet } & & & OLS & 0.223 & -\\ (0.061) & & & & & -\\ 0.773^{\bullet \bullet \bullet } & & & IV & 0.230 & 0.000 -\\ (0.108) & & & & &0.542 -\\ & 0.912^{\bullet \bullet \bullet } & & IV & 0.145 & 0.105 -\\ & (0.245) & & & &0.187 -\\ & & -0.97\text{e--4}^{\bullet } & IV & 0.059 & 0.000 -\\ & & (0.56\text{e--4}) & & &0.002 -\\ 0.670^{\bullet \bullet \bullet } & 0.171 & 0.12\text{e--4} & IV & 0.231 & 0.460 -\\ (0.181) & (0.363) & (0.86\text{e--4}) & & & 0.551 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.232; ~$\var(\log(\xi_t))=$ 4.16\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\footnotesize \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{table} -\end{minipage} diff --git a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegF.tex b/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegF.tex deleted file mode 100644 index c7803d0b0..000000000 --- a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegF.tex +++ /dev/null @@ -1,23 +0,0 @@ -\begin{center} -$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $ \\ -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.189^{\bullet \bullet \bullet } & & & OLS & 0.036 & -\\ (0.072) & & & & & -\\ 0.476 & & & IV & 0.020 & 0.318 -\\ (0.354) & & & & &0.556 -\\ & 0.368 & & IV & 0.017 & 0.107 -\\ & (0.321) & & & &0.457 -\\ & & -0.34\text{e--4} & IV & 0.015 & 0.000 -\\ & & (0.98\text{e--4}) & & &0.433 -\\ 0.289 & 0.214 & 0.01\text{e--4} & IV & 0.020 & 0.572 -\\ (0.463) & (0.583) & (1.87\text{e--4}) & & & 0.531 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.023; ~$\var(\log(\xi_t))=$ 4.16\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\tiny \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{center} diff --git a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegLong.tex b/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegLong.tex deleted file mode 100644 index ef62eaeb0..000000000 --- a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegLong.tex +++ /dev/null @@ -1,38 +0,0 @@ -\begin{minipage}{\textwidth} -\begin{table} \caption{Aggregate Consumption Dynamics in HA-DSGE Model} \label{tDSGEsimLong} - \centerline{$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $} -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.254^{***} & & & OLS & 0.064 & -\\ (0.007) & & & & & -\\ 0.989^{***} & & & IV & 0.073 & 0.000 -\\ (0.029) & & & & &0.161 -\\ & 0.733^{***} & & IV & 0.067 & 0.000 -\\ & (0.016) & & & &0.000 -\\ & & -1.78\text{e--4}^{***} & IV & 0.074 & 0.000 -\\ & & (0.04\text{e--4}) & & &0.089 -\\ 0.269 & 0.107^{**} & -1.07\text{e--4}^{***} & IV & 0.074 & 0.000 -\\ (0.193) & (0.054) & (0.32\text{e--4}) & & & 0.792 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.074; ~$\var(\log(\xi_t))=$ 4.16\text{e--6} } -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.585^{***} & & & OLS & 0.342 & -\\ (0.005) & & & & & -\\ 0.874^{***} & & & IV & 0.366 & 0.000 -\\ (0.008) & & & & &0.000 -\\ & 0.973^{***} & & IV & 0.315 & 0.000 -\\ & (0.014) & & & &0.000 -\\ & & -1.90\text{e--4}^{***} & IV & 0.216 & 0.000 -\\ & & (0.02\text{e--4}) & & &0.000 -\\ 0.787^{***} & -0.006 & -0.37\text{e--4}^{***} & IV & 0.370 & 0.000 -\\ (0.023) & (0.051) & (0.08\text{e--4}) & & & 0.560 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.370; ~$\var(\log(\xi_t))=$ 4.16\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\footnotesize \textbf{Notes:} Reported statistics are for a single simulation of 20000 quarters. Stars indicate statistical significance at the 90\%, 95\%, and 99\% levels, respectively. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{table} -\end{minipage} diff --git a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegNoMeasErr.tex b/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegNoMeasErr.tex deleted file mode 100644 index 7d77bcc52..000000000 --- a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegNoMeasErr.tex +++ /dev/null @@ -1,38 +0,0 @@ -\begin{minipage}{\textwidth} -\begin{table} \caption{Aggregate Consumption Dynamics in HA-DSGE Model} \label{tDSGEsimNoMeasErr} - \centerline{$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $} -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}$ (no measurement error)} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.257^{\bullet \bullet \bullet } & & & OLS & 0.067 & -\\ (0.071) & & & & & -\\ 0.511 & & & IV & 0.025 & 0.288 -\\ (0.338) & & & & &0.530 -\\ & 0.371 & & IV & 0.019 & 0.107 -\\ & (0.306) & & & &0.433 -\\ & & -0.35\text{e--4} & IV & 0.017 & 0.000 -\\ & & (0.94\text{e--4}) & & &0.410 -\\ 0.328 & 0.240 & 0.07\text{e--4} & IV & 0.025 & 0.565 -\\ (0.472) & (0.576) & (1.88\text{e--4}) & & & 0.513 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.028 } -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}$ (no measurement error)} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.845^{\bullet \bullet \bullet } & & & OLS & 0.714 & -\\ (0.038) & & & & & -\\ 0.802^{\bullet \bullet \bullet } & & & IV & 0.359 & 0.000 -\\ (0.049) & & & & &0.352 -\\ & 0.920^{\bullet \bullet \bullet } & & IV & 0.208 & 0.101 -\\ & (0.222) & & & &0.117 -\\ & & -0.98\text{e--4}^{\bullet \bullet } & IV & 0.083 & 0.000 -\\ & & (0.46\text{e--4}) & & &0.000 -\\ 0.732^{\bullet \bullet \bullet } & 0.119 & 0.16\text{e--4} & IV & 0.359 & 0.412 -\\ (0.086) & (0.176) & (0.42\text{e--4}) & & & 0.451 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.351 } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\footnotesize \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{table} -\end{minipage} diff --git a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegS.tex b/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegS.tex deleted file mode 100644 index 867a5e019..000000000 --- a/examples/cAndCwithStickyE/Tables/DSGEmrkvSimRegS.tex +++ /dev/null @@ -1,23 +0,0 @@ -\begin{center} -$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $ \\ -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.467^{\bullet \bullet \bullet } & & & OLS & 0.223 & -\\ (0.061) & & & & & -\\ 0.773^{\bullet \bullet \bullet } & & & IV & 0.230 & 0.000 -\\ (0.108) & & & & &0.542 -\\ & 0.912^{\bullet \bullet \bullet } & & IV & 0.145 & 0.105 -\\ & (0.245) & & & &0.187 -\\ & & -0.97\text{e--4}^{\bullet } & IV & 0.059 & 0.000 -\\ & & (0.56\text{e--4}) & & &0.002 -\\ 0.670^{\bullet \bullet \bullet } & 0.171 & 0.12\text{e--4} & IV & 0.231 & 0.460 -\\ (0.181) & (0.363) & (0.86\text{e--4}) & & & 0.551 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.232; ~$\var(\log(\xi_t))=$ 4.16\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\tiny \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{center} diff --git a/examples/cAndCwithStickyE/Tables/Eqbm.tex b/examples/cAndCwithStickyE/Tables/Eqbm.tex deleted file mode 100644 index 99afbc894..000000000 --- a/examples/cAndCwithStickyE/Tables/Eqbm.tex +++ /dev/null @@ -1,47 +0,0 @@ -\begin{minipage}{\textwidth} - \begin{table} -\caption{Equilibrium Statistics} -\label{table:Eqbm} -\newsavebox{\EqbmBox} -\sbox{\EqbmBox}{ -\newcommand{\EqDir}{\TablesDir/Eqbm} -\begin{tabular}{lllcccc} -\toprule -&&& \multicolumn{2}{c}{SOE Model} & \multicolumn{2}{c}{HA-DSGE Model} -\\ %\cline{4-5} - &&& \multicolumn{1}{c}{Frictionless} & \multicolumn{1}{c}{Sticky} & \multicolumn{1}{c}{Frictionless} & \multicolumn{1}{c}{Sticky} -\\ \midrule - \multicolumn{3}{l}{Means} -%\\ & & $M$ -%\\ & & $K$ -\\ & & $A$ & 7.49 &7.43 & 56.85 & 56.72 -\\ & & $C$ & 2.71 &2.71 & 3.44 & 3.44 -\\ \midrule - \multicolumn{3}{l}{Standard Deviations} -\\ & \multicolumn{4}{l}{Aggregate Time Series (`Macro')} -%\ & & $\Delta \log \mathbf{M}$ -\\ & & $\log A $ & 0.332 & 0.321 & 0.276 & 0.272 -\\ & & $\Delta \log \CLevBF $ & 0.010 & 0.007 & 0.010 & 0.005 -\\ & & $\Delta \log \YLevBF $ & 0.010 & 0.010 & 0.007 & 0.007 -\\ & \multicolumn{3}{l}{Individual Cross Sectional (`Micro')} -\\ & & $\log \aLevBF $ & 0.926 & 0.927 & 1.015 & 1.014 -\\ & & $\log \cLevBF $ & 0.790 & 0.791 & 0.598 & 0.599 -\\ & & $\log p $ & 0.796 & 0.796 & 0.796 & 0.796 -\\ & & $\log \yLevBF | \yLevBF > 0 $ & 0.863 & 0.863 & 0.863 & 0.863 -\\ & & $\Delta \log \cLevBF $ & 0.098 & 0.098 & 0.054 & 0.055 - - -\\ \midrule \multicolumn{3}{l}{Cost of Stickiness} - & \multicolumn{2}{c}{4.82\text{e--4}} - & \multicolumn{2}{c}{4.51\text{e--4}} -\\ \bottomrule - \end{tabular} - } - \usebox{\EqbmBox} -\ifthenelse{\boolean{StandAlone}}{\newlength\TableWidth}{} -\settowidth\TableWidth{\usebox{\EqbmBox}} % Calculate width of table so notes will match -\medskip\medskip \vspace{0.0cm} \parbox{\TableWidth}{\footnotesize -\textbf{Notes}: The cost of stickiness is calculated as the proportion by which the permanent income of a newborn frictionless consumer would need to be reduced in order to achieve the same reduction of expected value associated with forcing them to become a sticky expectations consumer.} -\end{table} -\end{minipage} -\ifthenelse{\boolean{StandAlone}}{\end{document}}{} diff --git a/examples/cAndCwithStickyE/Tables/EqbmSlides.tex b/examples/cAndCwithStickyE/Tables/EqbmSlides.tex deleted file mode 100644 index 11aa2d003..000000000 --- a/examples/cAndCwithStickyE/Tables/EqbmSlides.tex +++ /dev/null @@ -1,33 +0,0 @@ -\begin{center} -\begin{tabular}{lllcccc} -\toprule -&&& \multicolumn{2}{c}{SOE Model} & \multicolumn{2}{c}{HA-DSGE Model} -\\ %\cline{4-5} - &&& \multicolumn{1}{c}{Frictionless} & \multicolumn{1}{c}{Sticky} & \multicolumn{1}{c}{Frictionless} & \multicolumn{1}{c}{Sticky} -\\ \midrule - \multicolumn{3}{l}{Means} -%\\ & & $M$ -%\\ & & $K$ -\\ & & $A$ & 7.49 &7.43 & 56.85 & 56.72 -\\ & & $C$ & 2.71 &2.71 & 3.44 & 3.44 -\\ \midrule - \multicolumn{3}{l}{Standard Deviations} -\\ & \multicolumn{4}{l}{Aggregate Time Series (`Macro')} -%\ & & $\Delta \log \mathbf{M}$ -\\ & & $\log A $ & 0.332 & 0.321 & 0.276 & 0.272 -\\ & & $\Delta \log \CLevBF $ & 0.010 & 0.007 & 0.010 & 0.005 -\\ & & $\Delta \log \YLevBF $ & 0.010 & 0.010 & 0.007 & 0.007 -\\ & \multicolumn{3}{l}{Individual Cross Sectional (`Micro')} -\\ & & $\log \aLevBF $ & 0.926 & 0.927 & 1.015 & 1.014 -\\ & & $\log \cLevBF $ & 0.790 & 0.791 & 0.598 & 0.599 -\\ & & $\log p $ & 0.796 & 0.796 & 0.796 & 0.796 -\\ & & $\log \yLevBF | \yLevBF > 0 $ & 0.863 & 0.863 & 0.863 & 0.863 -\\ & & $\Delta \log \cLevBF $ & 0.098 & 0.098 & 0.054 & 0.055 - - -\\ \midrule \multicolumn{3}{l}{Cost of Stickiness} - & \multicolumn{2}{c}{4.82\text{e--4}} - & \multicolumn{2}{c}{4.51\text{e--4}} -\\ \bottomrule - \end{tabular} -\end{center} diff --git a/examples/cAndCwithStickyE/Tables/RepAgentMrkvSimReg.tex b/examples/cAndCwithStickyE/Tables/RepAgentMrkvSimReg.tex deleted file mode 100644 index 68703c040..000000000 --- a/examples/cAndCwithStickyE/Tables/RepAgentMrkvSimReg.tex +++ /dev/null @@ -1,38 +0,0 @@ -\begin{minipage}{\textwidth} -\begin{table} \caption{Aggregate Consumption Dynamics in RA Model} \label{tRAsim} - \centerline{$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $} -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ -0.015 & & & OLS & 0.002 & -\\ (0.077) & & & & & -\\ 0.387 & & & IV & 0.014 & 0.367 -\\ (0.390) & & & & &0.570 -\\ & 0.390 & & IV & 0.016 & 0.084 -\\ & (0.311) & & & &0.475 -\\ & & -0.26\text{e--4} & IV & 0.016 & 0.000 -\\ & & (1.11\text{e--4}) & & &0.493 -\\ 0.122 & 0.267 & 0.16\text{e--4} & IV & 0.018 & 0.547 -\\ (0.519) & (0.575) & (2.12\text{e--4}) & & & 0.572 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.018; ~$\var(\log(\xi_t))=$ 3.33\text{e--6} } -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.412^{\bullet \bullet \bullet } & & & OLS & 0.179 & -\\ (0.063) & & & & & -\\ 0.788^{\bullet \bullet \bullet } & & & IV & 0.183 & 0.001 -\\ (0.138) & & & & &0.532 -\\ & 0.641^{\bullet \bullet \bullet } & & IV & 0.128 & 0.085 -\\ & (0.163) & & & &0.171 -\\ & & -0.47\text{e--4} & IV & 0.075 & 0.000 -\\ & & (0.52\text{e--4}) & & &0.027 -\\ 0.632^{\bullet \bullet \bullet } & 0.118 & 0.10\text{e--4} & IV & 0.184 & 0.321 -\\ (0.223) & (0.280) & (0.79\text{e--4}) & & & 0.480 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.186; ~$\var(\log(\xi_t))=$ 3.33\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\footnotesize \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{table} -\end{minipage} diff --git a/examples/cAndCwithStickyE/Tables/RepAgentMrkvSimRegLong.tex b/examples/cAndCwithStickyE/Tables/RepAgentMrkvSimRegLong.tex deleted file mode 100644 index 48b1bf5f9..000000000 --- a/examples/cAndCwithStickyE/Tables/RepAgentMrkvSimRegLong.tex +++ /dev/null @@ -1,38 +0,0 @@ -\begin{minipage}{\textwidth} -\begin{table} \caption{Aggregate Consumption Dynamics in RA Model} \label{tRAsimLong} - \centerline{$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $} -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.056^{***} & & & OLS & 0.003 & -\\ (0.008) & & & & & -\\ 0.999^{***} & & & IV & 0.073 & 0.000 -\\ (0.034) & & & & &0.877 -\\ & 0.673^{***} & & IV & 0.060 & 0.000 -\\ & (0.015) & & & &0.000 -\\ & & -1.64\text{e--4}^{***} & IV & 0.072 & 0.000 -\\ & & (0.04\text{e--4}) & & &0.006 -\\ 1.626 & -0.030 & 0.97\text{e--4} & IV & 0.073 & 0.899 -\\ (1.323) & (0.139) & (1.95\text{e--4}) & & & 0.938 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.073; ~$\var(\log(\xi_t))=$ 3.33\text{e--6} } -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.567^{***} & & & OLS & 0.322 & -\\ (0.005) & & & & & -\\ 0.913^{***} & & & IV & 0.357 & 0.000 -\\ (0.009) & & & & &0.000 -\\ & 0.838^{***} & & IV & 0.321 & 0.000 -\\ & (0.011) & & & &0.000 -\\ & & -1.66\text{e--4}^{***} & IV & 0.253 & 0.000 -\\ & & (0.02\text{e--4}) & & &0.000 -\\ 0.798^{***} & 0.006 & -0.30\text{e--4}^{***} & IV & 0.361 & 0.000 -\\ (0.025) & (0.029) & (0.04\text{e--4}) & & & 0.320 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.361; ~$\var(\log(\xi_t))=$ 3.33\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\footnotesize \textbf{Notes:} Reported statistics are for a single simulation of 20000 quarters. Stars indicate statistical significance at the 90\%, 95\%, and 99\% levels, respectively. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{table} -\end{minipage} diff --git a/examples/cAndCwithStickyE/Tables/RepAgentMrkvSimRegNoMeasErr.tex b/examples/cAndCwithStickyE/Tables/RepAgentMrkvSimRegNoMeasErr.tex deleted file mode 100644 index 537ba0618..000000000 --- a/examples/cAndCwithStickyE/Tables/RepAgentMrkvSimRegNoMeasErr.tex +++ /dev/null @@ -1,38 +0,0 @@ -\begin{minipage}{\textwidth} -\begin{table} \caption{Aggregate Consumption Dynamics in RA Model} \label{tRAsimNoMeasErr} - \centerline{$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $} -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}$ (no measurement error)} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.017 & & & OLS & 0.003 & -\\ (0.078) & & & & & -\\ 0.400 & & & IV & 0.016 & 0.342 -\\ (0.376) & & & & &0.561 -\\ & 0.372 & & IV & 0.017 & 0.083 -\\ & (0.297) & & & &0.460 -\\ & & -0.27\text{e--4} & IV & 0.017 & 0.000 -\\ & & (1.08\text{e--4}) & & &0.479 -\\ 0.119 & 0.210 & 0.23\text{e--4} & IV & 0.021 & 0.522 -\\ (0.513) & (0.555) & (2.07\text{e--4}) & & & 0.586 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.020 } -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}$ (no measurement error)} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.790^{\bullet \bullet \bullet } & & & OLS & 0.625 & -\\ (0.044) & & & & & -\\ 0.826^{\bullet \bullet \bullet } & & & IV & 0.306 & 0.000 -\\ (0.070) & & & & &0.411 -\\ & 0.673^{\bullet \bullet \bullet } & & IV & 0.191 & 0.075 -\\ & (0.147) & & & &0.094 -\\ & & -0.47\text{e--4} & IV & 0.106 & 0.000 -\\ & & (0.42\text{e--4}) & & &0.003 -\\ 0.728^{\bullet \bullet \bullet } & 0.072 & 0.15\text{e--4} & IV & 0.305 & 0.277 -\\ (0.113) & (0.143) & (0.41\text{e--4}) & & & 0.437 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.298 } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\footnotesize \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{table} -\end{minipage} diff --git a/examples/cAndCwithStickyE/Tables/SOEmrkvSimReg.tex b/examples/cAndCwithStickyE/Tables/SOEmrkvSimReg.tex deleted file mode 100644 index cfef4af9f..000000000 --- a/examples/cAndCwithStickyE/Tables/SOEmrkvSimReg.tex +++ /dev/null @@ -1,38 +0,0 @@ -\begin{minipage}{\textwidth} -\begin{table} \caption{Aggregate Consumption Dynamics in SOE Model} \label{tPESOEsim} - \centerline{$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $} -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.295^{\bullet \bullet \bullet } & & & OLS & 0.087 & -\\ (0.066) & & & & & -\\ 0.660^{\bullet \bullet } & & & IV & 0.040 & 0.237 -\\ (0.309) & & & & &0.600 -\\ & 0.457^{\bullet \bullet } & & IV & 0.035 & 0.059 -\\ & (0.209) & & & &0.421 -\\ & & -6.92\text{e--4} & IV & 0.026 & 0.000 -\\ & & (5.87\text{e--4}) & & &0.365 -\\ 0.420 & 0.258 & 0.45\text{e--4} & IV & 0.041 & 0.516 -\\ (0.428) & (0.365) & (9.51\text{e--4}) & & & 0.529 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.039; ~$\var(\log(\xi_t))=$ 5.99\text{e--6} } -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.508^{\bullet \bullet \bullet } & & & OLS & 0.263 & -\\ (0.058) & & & & & -\\ 0.802^{\bullet \bullet \bullet } & & & IV & 0.260 & 0.000 -\\ (0.104) & & & & &0.554 -\\ & 0.859^{\bullet \bullet \bullet } & & IV & 0.198 & 0.060 -\\ & (0.182) & & & &0.233 -\\ & & -8.26\text{e--4}^{\bullet \bullet } & IV & 0.066 & 0.000 -\\ & & (3.99\text{e--4}) & & &0.002 -\\ 0.660^{\bullet \bullet \bullet } & 0.192 & 0.60\text{e--4} & IV & 0.261 & 0.359 -\\ (0.187) & (0.277) & (5.03\text{e--4}) & & & 0.546 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.260; ~$\var(\log(\xi_t))=$ 5.99\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\footnotesize \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{table} -\end{minipage} diff --git a/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegF.tex b/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegF.tex deleted file mode 100644 index 819cca05d..000000000 --- a/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegF.tex +++ /dev/null @@ -1,23 +0,0 @@ -\begin{center} -$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $ \\ -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.295^{\bullet \bullet \bullet } & & & OLS & 0.087 & -\\ (0.066) & & & & & -\\ 0.660^{\bullet \bullet } & & & IV & 0.040 & 0.237 -\\ (0.309) & & & & &0.600 -\\ & 0.457^{\bullet \bullet } & & IV & 0.035 & 0.059 -\\ & (0.209) & & & &0.421 -\\ & & -6.92\text{e--4} & IV & 0.026 & 0.000 -\\ & & (5.87\text{e--4}) & & &0.365 -\\ 0.420 & 0.258 & 0.45\text{e--4} & IV & 0.041 & 0.516 -\\ (0.428) & (0.365) & (9.51\text{e--4}) & & & 0.529 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.039; ~$\var(\log(\xi_t))=$ 5.99\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\tiny \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{center} diff --git a/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegLong.tex b/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegLong.tex deleted file mode 100644 index eca5ea2d0..000000000 --- a/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegLong.tex +++ /dev/null @@ -1,38 +0,0 @@ -\begin{minipage}{\textwidth} -\begin{table} \caption{Aggregate Consumption Dynamics in SOE Model} \label{tSOEsimLong} - \centerline{$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $} -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.350^{***} & & & OLS & 0.122 & -\\ (0.006) & & & & & -\\ 0.988^{***} & & & IV & 0.082 & 0.000 -\\ (0.025) & & & & &0.685 -\\ & 0.666^{***} & & IV & 0.070 & 0.000 -\\ & (0.013) & & & &0.000 -\\ & & -11.75\text{e--4}^{***} & IV & 0.075 & 0.000 -\\ & & (0.28\text{e--4}) & & &0.000 -\\ 0.784^{***} & 0.080^{*} & -1.38\text{e--4} & IV & 0.082 & 0.000 -\\ (0.101) & (0.046) & (0.97\text{e--4}) & & & 0.930 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.082; ~$\var(\log(\xi_t))=$ 5.99\text{e--6} } -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.598^{***} & & & OLS & 0.358 & -\\ (0.005) & & & & & -\\ 0.873^{***} & & & IV & 0.359 & 0.000 -\\ (0.008) & & & & &0.000 -\\ & 0.954^{***} & & IV & 0.317 & 0.000 -\\ & (0.013) & & & &0.000 -\\ & & -12.30\text{e--4}^{***} & IV & 0.164 & 0.000 -\\ & & (0.19\text{e--4}) & & &0.000 -\\ 0.785^{***} & 0.053 & -1.57\text{e--4}^{***} & IV & 0.361 & 0.000 -\\ (0.022) & (0.032) & (0.30\text{e--4}) & & & 0.332 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.361; ~$\var(\log(\xi_t))=$ 5.99\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\footnotesize \textbf{Notes:} Reported statistics are for a single simulation of 20000 quarters. Stars indicate statistical significance at the 90\%, 95\%, and 99\% levels, respectively. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{table} -\end{minipage} diff --git a/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegNoMeasErr.tex b/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegNoMeasErr.tex deleted file mode 100644 index f741ab077..000000000 --- a/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegNoMeasErr.tex +++ /dev/null @@ -1,38 +0,0 @@ -\begin{minipage}{\textwidth} -\begin{table} \caption{Aggregate Consumption Dynamics in SOE Model} \label{tPESOEsimNoMeasErr} - \centerline{$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $} -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Frictionless : $\Delta \log \mathbf{C}_{t+1}$ (no measurement error)} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.394^{\bullet \bullet \bullet } & & & OLS & 0.156 & -\\ (0.062) & & & & & -\\ 0.689^{\bullet \bullet } & & & IV & 0.049 & 0.206 -\\ (0.282) & & & & &0.571 -\\ & 0.466^{\bullet \bullet } & & IV & 0.041 & 0.062 -\\ & (0.194) & & & &0.382 -\\ & & -6.94\text{e--4} & IV & 0.030 & 0.000 -\\ & & (5.52\text{e--4}) & & &0.337 -\\ 0.465 & 0.260 & 0.60\text{e--4} & IV & 0.049 & 0.512 -\\ (0.400) & (0.335) & (8.75\text{e--4}) & & & 0.500 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.047 } -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}$ (no measurement error)} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.876^{\bullet \bullet \bullet } & & & OLS & 0.768 & -\\ (0.033) & & & & & -\\ 0.826^{\bullet \bullet \bullet } & & & IV & 0.392 & 0.000 -\\ (0.044) & & & & &0.314 -\\ & 0.868^{\bullet \bullet \bullet } & & IV & 0.276 & 0.057 -\\ & (0.161) & & & &0.135 -\\ & & -8.25\text{e--4}^{\bullet \bullet } & IV & 0.090 & 0.000 -\\ & & (3.30\text{e--4}) & & &0.000 -\\ 0.730^{\bullet \bullet \bullet } & 0.132 & 0.68\text{e--4} & IV & 0.392 & 0.317 -\\ (0.074) & (0.109) & (2.06\text{e--4}) & & & 0.362 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.383 } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\footnotesize \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{table} -\end{minipage} diff --git a/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegS.tex b/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegS.tex deleted file mode 100644 index 45589c727..000000000 --- a/examples/cAndCwithStickyE/Tables/SOEmrkvSimRegS.tex +++ /dev/null @@ -1,23 +0,0 @@ -\begin{center} -$ \Delta \log \mathbf{C}_{t+1} = \varsigma + \chi \Delta \log \mathbf{C}_t + \eta \mathbb{E}_t[\Delta \log \mathbf{Y}_{t+1}] + \alpha A_t + \epsilon_{t+1} $ \\ -\begin{tabular}{d{4}d{4}d{5}cd{4}c} - \toprule -\multicolumn{3}{c}{Expectations : Dep Var} & OLS & \multicolumn{1}{c}{2${}^{\text{nd}}$ Stage} & \multicolumn{1}{c}{KP $p$-val} -\\ \multicolumn{3}{c}{Independent Variables} & or IV & \multicolumn{1}{c}{$\bar{R}^{2} $} & \multicolumn{1}{c}{Hansen J $p$-val} -\\ \midrule \multicolumn{6}{l}{Sticky : $\Delta \log \mathbf{C}_{t+1}^*$ (with measurement error $\mathbf{C}_{t}^* =\mathbf{C}_{t}\times \xi_t$);} -\\ \multicolumn{1}{c}{$\Delta \log \mathbf{C}_{t}^*$} & \multicolumn{1}{c}{$\Delta \log \mathbf{Y}_{t+1}$} & \multicolumn{1}{c}{$A_{t}$} & & & -\\ 0.508^{\bullet \bullet \bullet } & & & OLS & 0.263 & -\\ (0.058) & & & & & -\\ 0.802^{\bullet \bullet \bullet } & & & IV & 0.260 & 0.000 -\\ (0.104) & & & & &0.554 -\\ & 0.859^{\bullet \bullet \bullet } & & IV & 0.198 & 0.060 -\\ & (0.182) & & & &0.233 -\\ & & -8.26\text{e--4}^{\bullet \bullet } & IV & 0.066 & 0.000 -\\ & & (3.99\text{e--4}) & & &0.002 -\\ 0.660^{\bullet \bullet \bullet } & 0.192 & 0.60\text{e--4} & IV & 0.261 & 0.359 -\\ (0.187) & (0.277) & (5.03\text{e--4}) & & & 0.546 -\\ \multicolumn{6}{l}{Memo: For instruments $\mathbf{Z}_{t}$, $\Delta \log \mathbf{C}_{t}^*$ $= \mathbf{Z}_{t} \zeta, ~\bar{R}^{2}=$ 0.260; ~$\var(\log(\xi_t))=$ 5.99\text{e--6} } -\\ \bottomrule - \multicolumn{6}{p{0.95\textwidth}}{\tiny \textbf{Notes:} Reported statistics are the average values for 100 samples of 200 simulated quarters each. Bullets indicate that the average sample coefficient divided by average sample standard error is outside of the inner 90\%, 95\%, and 99\% of the standard normal distribution. Instruments $\textbf{Z}_t = \{\Delta \log \mathbf{C}_{t-2}, \Delta \log \mathbf{C}_{t-3}, \Delta \log \mathbf{Y}_{t-2}, \Delta \log \mathbf{Y}_{t-3}, A_{t-2}, A_{t-3}, \Delta_8 \log \mathbf{C}_{t-2}, \Delta_8 \log \mathbf{Y}_{t-2} \}$.} -\end{tabular} -\end{center} diff --git a/examples/cAndCwithStickyE/Tables/econtexShortcuts.sty b/examples/cAndCwithStickyE/Tables/econtexShortcuts.sty deleted file mode 100644 index 71474b7f1..000000000 --- a/examples/cAndCwithStickyE/Tables/econtexShortcuts.sty +++ /dev/null @@ -1,630 +0,0 @@ -\providecommand{\aAgg}{\ensuremath{\mathsf{A}}} -\providecommand{\aboveMin}{\blacktriangle} -\providecommand{\adjPar}{\ensuremath{\omega}} -\providecommand{\adj}{\ensuremath{\mathrm{j}}} -\providecommand{\aE}{\aRat^{e}} -\providecommand{\aFunc}{\ensuremath{\mathrm{a}}} -\providecommand{\Age}{\ensuremath{Z}} -\providecommand{\age}{\ensuremath{z}} -\providecommand{\ALevBF}{\ensuremath{\mathbf{A}}} -\providecommand{\aLevBF}{\ensuremath{\mathbf{a}}} -\providecommand{\ALev}{\ensuremath{A}} -\providecommand{\aLev}{\ensuremath{a}} -\providecommand{\Alive}{\ensuremath{\cancel{\mathfrak{D}}}} -\providecommand{\Alt}{\grave} -\providecommand{\aMin}{\ensuremath{\underline{\aRat}}} -\providecommand{\ARat}{\ensuremath{A}} -\providecommand{\aRat}{\ensuremath{a}} -\providecommand{\ASS}{\ensuremath{\ensuremath{A}}} -\providecommand{\aSS}{\ensuremath{\ensuremath{a}}} -\providecommand{\ATarg}{\ensuremath{\check{A}}} -\providecommand{\aTarg}{\ensuremath{\check{a}}} -\providecommand{\BE}{\BRat^{e}} -\providecommand{\bE}{\bRat^{e}} -\providecommand{\BLevBF}{\ensuremath{\mathbf{B}}} -\providecommand{\bLevBF}{\ensuremath{\mathbf{b}}} -\providecommand{\BLevE}{\ensuremath{\BLev^{e}}} -\providecommand{\bLevE}{\ensuremath{\bLev^{e}}} -\providecommand{\BLevU}{\ensuremath{\BLev^{u}}} -\providecommand{\bLevU}{\ensuremath{\bLev^{u}}} -\providecommand{\BLev}{\ensuremath{B}} -\providecommand{\bLev}{\ensuremath{b}} -\providecommand{\bMin}{\ensuremath{\underline{\bRat}}} -\providecommand{\bRatE}{\ensuremath{b}^{e}} -\providecommand{\BRatE}{\ensuremath{B}^{e}} -\providecommand{\bRatU}{\ensuremath{b}^{u}} -\providecommand{\BRat}{\ensuremath{B}} -\providecommand{\bRat}{\ensuremath{b}} -\providecommand{\bTargE}{\ensuremath{\check{b}^{e}}} -\providecommand{\BTargTarg}{\Target{\Target{\BRat}}} -\providecommand{\bTargTarg}{\Target{\Target{\bRat}}} -\providecommand{\BTarg}{\ensuremath{\check{B}}} -\providecommand{\bTarg}{\ensuremath{\check{b}}} -\providecommand{\BU}{{B}^{u}} -\providecommand{\bU}{{b}^{u}} -\providecommand{\cAgg}{\ensuremath{\pmb{C}}} -\providecommand{\CARA}{\ensuremath{\alpha}} -\providecommand{\ccRat}{\ensuremath{\mathsf{c}}} -\providecommand{\CDF}{\ensuremath{\mathcal{F}}} -\providecommand{\CEndFunc}{\ensuremath{\mathfrak{C}}} -\providecommand{\cEndFunc}{\ensuremath{\mathfrak{c}}} -\providecommand{\cEss}{{c}^{e}} -\providecommand{\CE}{\CRat^{e}} -\providecommand{\cE}{\cRat^{e}} -\providecommand{\cFuncAbove}{\ensuremath{\bar{\mathrm{c}}}} -\providecommand{\cFuncBelow}{\ensuremath{\uline{\mathrm{c}}}} -\providecommand{\cFuncMax}{\ensuremath{\bar{\bar{\mathrm{c}}}}} -\providecommand{\CFunc}{\ensuremath{\mathrm{C}}} -\providecommand{\cFunc}{\ensuremath{\mathrm{c}}} -\providecommand{\cFunc}{\ensuremath{\mathrm{c}}} -\providecommand{\CGroOverG}{\ensuremath{\Upsilon}} -\providecommand{\CGroOverR}{\ensuremath{\Phi}} -\providecommand{\CGroPF}{\ensuremath{\Lambda}} -\providecommand{\cGroPF}{\ensuremath{\lambda}} -\providecommand{\CGroPS}{\ensuremath{\chi}} % Precautionary Saving boost to consumption growth -\providecommand{\chiFunc}{\pmb{\chi}} -\providecommand{\Chi}{\ensuremath{\mathrm{X}}} % capital chi is sometimes useful, and not native to LaTeX -\providecommand{\CLevBF}{\ensuremath{\mathbf{C}}} -\providecommand{\cLevBF}{\ensuremath{\mathbf{c}}} -\providecommand{\CLevE}{\CLev^{e}} -\providecommand{\cLevE}{\cLev^{e}} 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-\providecommand{\inc}{{y}} -\providecommand{\InvEpShkInv}{\acute{\psi}} -\providecommand{\InvEpShkInv}{\underline{\psi}} -\providecommand{\Inv}{{I}} -\providecommand{\inv}{{i}} -\providecommand{\IRat}{\ensuremath{I}} -\providecommand{\iRat}{\ensuremath{i}} -\providecommand{\itc}{\ensuremath{\zeta}} -\providecommand{\jFunc}{\ensuremath{\mathrm{j}}} -\providecommand{\JLev}{\ensuremath{J}} -\providecommand{\kapRent}{\ensuremath{\varkappa}} -\providecommand{\kapShare}{\ensuremath{\gamma}} -\providecommand{\Kap}{{K}} -\providecommand{\kap}{{k}} -\providecommand{\KFunc}{\ensuremath{\mathrm{K}}} -\providecommand{\kFunc}{\ensuremath{\mathrm{k}}} -\providecommand{\KLevBF}{\ensuremath{\mathbf{K}}} -\providecommand{\kLevBF}{\ensuremath{\mathbf{k}}} -\providecommand{\KLev}{\ensuremath{K}} -\providecommand{\kLev}{\ensuremath{k}} -\providecommand{\kPriceAfterITC}{\ensuremath{\mathscr{P}}} -\providecommand{\kPrice}{\ensuremath{\mathsf{P}}} -\providecommand{\KRat}{\ensuremath{K}} -\providecommand{\kRat}{\ensuremath{k}} -\providecommand{\kTargE}{\ensuremath{\Target{k}^{e}}} -\providecommand{\kTarg}{\ensuremath{\Target{k}}} -\providecommand{\labor}{\ensuremath{\ell}} % -\providecommand{\Labor}{\ensuremath{L}} -\providecommand{\labShare}{\ensuremath{\nu}} -\providecommand{\Leisure}{Z} % -\providecommand{\leisure}{z} % -\providecommand{\leiShare}{\ensuremath{\zeta}} % -\providecommand{\LGro}{\ensuremath{\Lambda}} -\providecommand{\lGro}{\ensuremath{\lambda}} -\providecommand{\LLevBF}{\ensuremath{\mathbf{L}}} -\providecommand{\lLevBF}{\ensuremath{\pmb{\ell}}} -\providecommand{\lLev}{\ensuremath{\ell}} -\providecommand{\LLev}{\ensuremath{L}} -\providecommand{\Lo}{\check} -\providecommand{\LRat}{\ensuremath{L}} -\providecommand{\MaxMaxMPC}{\ensuremath{\bar{\bar{\kappa}}}} -\providecommand{\MaxMinMPC}{\ensuremath{\hat{\underline{\kappa}}}} -\providecommand{\MaxMPC}{\ensuremath{\bar{\kappa}}} -\providecommand{\MaxMPS}{\ensuremath{\PatR}} -\providecommand{\Mean}{\ensuremath{\mathbb{M}}} % Mean -\providecommand{\mEss}{\check{m}^{e}} -\providecommand{\ME}{\MRat^{e}} -\providecommand{\mE}{\mRat^{e}} -\providecommand{\mFunc}{\ensuremath{\mathrm{m}}} -\providecommand{\MinMinMPC}{\ensuremath{\underline{\kappa}}} -\providecommand{\MinMPC}{\ensuremath{\uline{\kappa}}} -\providecommand{\MinMPS}{\ensuremath{\pZero^{1/\CRRA} \PatR}} -\providecommand{\MLevBF}{\ensuremath{\mathbf{M}}} -\providecommand{\mLevBF}{\ensuremath{\mathbf{m}}} -\providecommand{\mLevBF}{\ensuremath{\mathbf{m}}} -\providecommand{\mLevE}{\mLev^{e}} -\providecommand{\MLev}{\ensuremath{M}} -\providecommand{\mLev}{\ensuremath{m}} -\providecommand{\MPCE}{\MPC^{e}} -\providecommand{\MPCFunc}{\ensuremath{\pmb{\kappa}}} -\providecommand{\MPCPPF}{\ensuremath{\Pi}} -\providecommand{\MPCP}{\ensuremath{\pi}} -\providecommand{\MPCU}{\MPC^{u}} -\providecommand{\MPC}{\ensuremath{\kappa}} -\providecommand{\MPSFunc}{\ensuremath{\pmb{lambda}}} -\providecommand{\MPS}{\ensuremath{\lambda}} -\providecommand{\MRatE}{\MRat^{e}} -\providecommand{\mRatE}{\mRat^{e}} -\providecommand{\MRat}{\ensuremath{M}} -\providecommand{\mRat}{\ensuremath{m}} -\providecommand{\MSS}{\ensuremath{\breve{M}}} -\providecommand{\mSS}{\ensuremath{\breve{m}}} -\providecommand{\mTarg}{\check{m}} -\providecommand{\MU}{{M}^{u}} -\providecommand{\mU}{{m}^{u}} -\providecommand{\m}{\ensuremath{m}} -\providecommand{\M}{\ensuremath{M}} -\providecommand{\next}{2} % via search-and-replace -\providecommand{\Next}{t+1} % -\providecommand{\nFunc}{\ensuremath{\mathrm{n}}} -\providecommand{\NLev}{\ensuremath{N}} -\providecommand{\nLev}{\ensuremath{n}} -\providecommand{\NRat}{\ensuremath{N}} -\providecommand{\nRat}{\ensuremath{n}} -\providecommand{\Num}{\ensuremath{N}} -\providecommand{\n}{\ensuremath{n}} -\providecommand{\OLevBF}{\ensuremath{O}} -\providecommand{\oLevBF}{\ensuremath{\mathbf{o}}} -\providecommand{\OLev}{\ensuremath{O}} -\providecommand{\oLev}{\ensuremath{\pmb{o}}} -\providecommand{\ORat}{\ensuremath{O}} -\providecommand{\oRat}{\ensuremath{o}} -\providecommand{\Pareto}{\zeta} -\providecommand{\PatPGroAdj}{\text{\pmb{\Thorn}}_{\acute{\PGro}}} -\providecommand{\patpGroAdj}{\text{\thorn}_{\acute{\pGro}}} -\providecommand{\patpGrohat}{\hat{\text{\thorn}}_{\pGro}} -\providecommand{\PatPGro}{\text{\pmb{\Thorn}}_{\PGro}} -\providecommand{\patpGro}{\text{\thorn}_{\pGro}} -\providecommand{\PatR}{\text{\pmb{\Thorn}}_{\Rfree}} -\providecommand{\patr}{\text{\thorn}_{\rfree}} -\providecommand{\PatU}{\text{\pmb{\Thorn}}_{\urate}} -\providecommand{\patu}{\text{\thorn}_{\urate}} -\providecommand{\PatWGro}{\text{\pmb{\Thorn}}_{\WGro}} -\providecommand{\patwGro}{\text{\thorn}_{\wGro}} -\providecommand{\Pat}{\text{\pmb{\Thorn}}} -\providecommand{\pat}{\text{\thorn}} -\providecommand{\pDeadRate}{\ensuremath{\grave{\cancel{\mathsf{d}}}}} -\providecommand{\pDead}{\ensuremath{\mathfrak{D}}} -\providecommand{\pDieRate}{\ensuremath{\grave{\mathsf{d}}}} -\providecommand{\pDies}{\ensuremath{\mathsf{d}}} % Continuous time death rate -\providecommand{\PDies}{\ensuremath{\mathsf{D}}} % Discrete-time one-period death rate -\providecommand{\PDV}{\ensuremath{\mathbb{P}}} % PDV -\providecommand{\PGroAdj}{\ensuremath{\underline{\PGro}}} -\providecommand{\pGroAdj}{\ensuremath{\underline{\pGro}}} -\providecommand{\PGrouAdj}{\ensuremath{\underline{\underline{\PGro}}}} -\providecommand{\pGrouAdj}{\ensuremath{\underline{\underline{{\pGro}}}}} -\providecommand{\PGro}{\ensuremath{\Gamma}} -\providecommand{\pGro}{\ensuremath{\gamma}} -\providecommand{\phiFunc}{\ensuremath{\digamma}} -\providecommand{\PIHMPC}{\ensuremath{\varkappa}} -\providecommand{\PInc}{\ensuremath{P}} -\providecommand{\PLabor}{\ensuremath{P}} % Permanent labor income in levels -\providecommand{\Plabor}{P} % Permanent labor income in levels -\providecommand{\PLevBF}{\ensuremath{\mathbf{P}}} -\providecommand{\pLevBF}{\ensuremath{\mathbf{p}}} -\providecommand{\PLev}{\ensuremath{P}} -\providecommand{\pLev}{\ensuremath{\pmb{p}}} -\providecommand{\PLev}{\ensuremath{P}} -\providecommand{\PLives}{\ensuremath{\cancel{\PDies}}} -\providecommand{\pLives}{\ensuremath{\cancel{\pDies}}} -\providecommand{\pNotZero}{\ensuremath{\cancel{\wp}}} -\providecommand{\PopE}{\mathcal{E}} -\providecommand{\popE}{e} -\providecommand{\PopGro}{\ensuremath{\Xi}} -\providecommand{\popGro}{\ensuremath{\xi}} -\providecommand{\PopLev}{\ensuremath{\pmb{N}}} -\providecommand{\PopU}{\mathcal{U}} -\providecommand{\Pop}{\ensuremath{L}} -\providecommand{\PostITC}{\ensuremath{\cancel{\zeta}}} -\providecommand{\power}{\ensuremath{\eta}} -\providecommand{\pPDVFunc}{\ensuremath{\mathbb{P}}} -\providecommand{\PPDV}{\ensuremath{\text{PDV($P$)}}} -\providecommand{\pRat}{\ensuremath{p}} -\providecommand{\Price}{\ensuremath{\mathsf{P}}} -\providecommand{\ProdFunc}{\ensuremath{\mathrm{F}}} -\providecommand{\ProdFunc}{\ensuremath{\mathrm{F}}} -\providecommand{\prodFunc}{\ensuremath{\mathrm{f}}} -\providecommand{\prudEx}{\ensuremath{\omega}} -\providecommand{\prud}{\ensuremath{\eta}} -\providecommand{\pSav}{\ensuremath{\phi}} -\providecommand{\pShkMin}{\underline{\psi}} % -\providecommand{\PShk}{\Psi} % -\providecommand{\PermShkAgg}{\Psi} % New -\providecommand{\PermShk}{\Psi} % New -\providecommand{\permShk}{\psi} % New -\providecommand{\pShk}{\psi} % -\providecommand{\permShkInd}{\psi} % New -\providecommand{\pshk}{\psi} % -\providecommand{\PtyGro}{\ensuremath{\Phi}} -\providecommand{\ptyGro}{\ensuremath{\phi}} -\providecommand{\PtyLab}{\ensuremath{\mathrm{Z}}} % Labor productivity -\providecommand{\ptyLab}{\ensuremath{z}} % usually, log of \PtyLab -\providecommand{\PtyLev}{\ensuremath{A}} % Multiplies overall production function -\providecommand{\ptyLev}{\ensuremath{a}} % Usually, log of \PtyLev -\providecommand{\pZero}{\ensuremath{\wp}} -\providecommand{\QLev}{\ensuremath{Q}} -\providecommand{\q}{\ensuremath{\koppa}} -\providecommand{\RCpnd}{\ensuremath{\mathbf{R}}} -\providecommand{\RevFunc}{\ensuremath{\pmb{\Pi}}} -\providecommand{\revFunc}{\ensuremath{\pmb{\pi}}} -\providecommand{\Rev}{\ensuremath{\Pi}} -\providecommand{\rev}{\ensuremath{\pi}} -\providecommand{\rfree}{\ensuremath{\mathsf{r}}} % The net return on the safe asset at an annual rate -\providecommand{\Rfree}{\ensuremath{\mathsf{R}}} % The return factor on the safe asset - unfortunately mathfrak fonts don't come through for tth -\providecommand{\RFunc}{\ensuremath{\mathrm{R}}} -\providecommand{\RGross}{\ensuremath{\breve{\mathsf{R}}}} -\providecommand{\rGross}{\ensuremath{\breve{\mathsf{r}}}} -\providecommand{\RiskyAlt}{\ensuremath{\acute{\mathbf{R}}}} % The return on the risky asset -\providecommand{\riskyAlt}{\ensuremath{\acute{\mathbf{r}}}} % The net return on the risky asset annual rate -\providecommand{\riskyshare}{\ensuremath{\varsigma}} -\providecommand{\Risky}{\ensuremath{\mathbf{R}}} % The return on the risky asset -\providecommand{\risky}{\ensuremath{\mathbf{r}}} % The net return on the risky asset annual rate -\providecommand{\RLev}{\ensuremath{R}} % Rate of return (but better to use \Rfree or \Risky or something else more specific -\providecommand{\RnormWGro}{\ensuremath{\mathcal{R}_{\WGro}}} % Normalized version of riskless return factor -\providecommand{\rnormwGro}{\ensuremath{\mathit{r}_{\wGro}}} % Normalized version of riskless rate of return -\providecommand{\Rnorm}{\ensuremath{\mathcal{R}}} % Normalized version of riskless return factor -\providecommand{\rnorm}{\ensuremath{\mathit{r}}} % Normalized version of riskless rate of return -\providecommand{\Rport}{\ensuremath{\pmb{\mathfrak{R}}}} % Portfolio -weighted return -\providecommand{\rport}{\ensuremath{\pmb{\mathfrak{r}}}} % Portfolio -weighted return -\providecommand{\rProd}{\ensuremath{\mathit{r}}} -\providecommand{\Rprod}{\ensuremath{\mathscr{R}}} -\providecommand{\rprod}{\ensuremath{\mathscr{r}}} -\providecommand{\RProd}{\ensuremath{\mathsf{R}}} -\providecommand{\RSave}{\ensuremath{\ushort{\Rfree}}} -\providecommand{\rsave}{\ensuremath{\ushort{\rfree}}} -\providecommand{\RBoro}{\ensuremath{\bar{\Rfree}}} -\providecommand{\rboro}{\ensuremath{\bar{\rfree}}} -\providecommand{\R}{\Rfree} -\providecommand{\saveRate}{\grave{s}} % saving (income minus consumption) over income -\providecommand{\Save}{S} % Saving (income minus consumption) -\providecommand{\save}{s} % saving (income minus consumption) -\providecommand{\SDF}{\MLev} % Stochastic Discount Factor -\providecommand{\sdr}{\mRat} % Stochastic Discount rate -\providecommand{\Seniority}{\ensuremath{\mathsf{X}}} -\providecommand{\seniority}{\ensuremath{\mathsf{x}}} -\providecommand{\SeverancePayment}{\ensuremath{\mathcal{S}}} -\providecommand{\SeveranceRatio}{\ensuremath{\varsigma}} -\providecommand{\Severance}{\ensuremath{\varsigma}} -\providecommand{\SE}{\SRat^{e}} -\providecommand{\sE}{\sRat^{e}} -\providecommand{\sFunc}{\ensuremath{\mathrm{s}}} -\providecommand{\shk}{\phi} -\providecommand{\Shk}{\Phi} -\providecommand{\ShkLogZeroLogStd}{\sigma_{\cancel{\ShkMeanOneLog}}} % Std of that shock -\providecommand{\ShkLogZeroLogVar}{\sigma_{\cancel{\ShkMeanOneLog}}^{2}} % Variance of that shock -\providecommand{\ShkLogZeroLog}{\cancel{\ShkMeanOneLog}} % Log of that shock -\providecommand{\ShkLogZero}{\cancel{\ShkMeanOne}} % A shock whose expectation in logs is zero; cancellation of the nonzero mean for the mean one shock -\providecommand{\ShkMeanOneLogStd}{\sigma_{\ShkMeanOneLog}} % Std of that shock -\providecommand{\ShkMeanOneLogVar}{\sigma^{2}_{\ShkMeanOneLog}} % Log of that shock -\providecommand{\ShkMeanOneLog}{\theta} % Log of that shock -\providecommand{\ShkMeanOne}{\Theta} % A shock whose expectation in levels is always equal to one regardless of variance -\providecommand{\SLevBF}{\mathbf{S}} -\providecommand{\sLevBF}{\mathbf{s}} -\providecommand{\SLevE}{\SLev^{e}} -\providecommand{\sLevE}{\sLev^{e}} -\providecommand{\SLevU}{\SLev^{u}} -\providecommand{\sLevU}{\sLev^{u}} -\providecommand{\SLev}{\ensuremath{S}} -\providecommand{\sLev}{\ensuremath{s}} -\providecommand{\srate}{\ensuremath{\varsigma}} -\providecommand{\SRatE}{\SRat^{e}} -\providecommand{\sRatE}{\sRat^{e}} -\providecommand{\SRatU}{\SRat^{u}} -\providecommand{\sRatU}{\sRat^{u}} -\providecommand{\SRat}{\ensuremath{S}} -\providecommand{\sRat}{\ensuremath{s}} -\providecommand{\STargE}{\Target{\SRat}^{\null}} -\providecommand{\sTargE}{\Target{\sRat}^{\null}} -\providecommand{\STargTarg}{\Target{\Target{\SRat}}} -\providecommand{\sTargTarg}{\Target{\Target{\sRat}}} -\providecommand{\STarg}{\Target{\SRat}} -\providecommand{\sTarg}{\Target{\sRat}} -\providecommand{\Steady}{\bar} -\providecommand{\Stocks}{{S}} -\providecommand{\stocks}{{s}} -\providecommand{\straight}{\Pi} -\providecommand{\Surplus}{\ensuremath{Z}} -\providecommand{\surplus}{\ensuremath{z}} -\providecommand{\SU}{\SRat^{u}} -\providecommand{\sU}{\sRat^{u}} -\providecommand{\Target}{\check} -\providecommand{\TaxCombInv}{\ensuremath{\mathcal{T}^{-1}}} -\providecommand{\TaxComb}{\ensuremath{\mathcal{T}}} -\providecommand{\TaxCorp}{\ensuremath{\tau}} -\providecommand{\taxDep}{\ensuremath{\partial}} -\providecommand{\TaxFree}{\ensuremath{\cancel{\Tax}}} -\providecommand{\TaxLev}{T} -\providecommand{\TaxNetTrans}{\ensuremath{Z}} -\providecommand{\taxNetTrans}{\ensuremath{z}} -\providecommand{\TaxPaid}{\ensuremath{T}} -\providecommand{\TaxRate}{\ensuremath{t}} -\providecommand{\TaxUI}{\ensuremath{\tau}} -\providecommand{\Tax}{\ensuremath{\tau}} -\providecommand{\tax}{\tau} -\providecommand{\tBak}{\ensuremath{\pmb{n}}} -\providecommand{\TEatBak}{\ensuremath{\mathtt{q}}} -\providecommand{\TEat}{\ensuremath{\TEnd}} -\providecommand{\TEndBak}{\ensuremath{\mathsf{p}}} -\providecommand{\TEnd}{\ensuremath{T}} -\providecommand{\TermTime}{\ensuremath{T}} -\providecommand{\tFwd}{\ensuremath{n}} -\providecommand{\tHorOfm}{\ensuremath{\pmb{n}}} -\providecommand{\tHor}{\ensuremath{\mathsf{n}}} -\providecommand{\timeRate}{\ensuremath{\vartheta}} -\providecommand{\tinyAmount}{\ensuremath{\epsilon}} -\providecommand{\TLev}{\ensuremath{T}} -\providecommand{\TMap}{\mathscr{T}} -\providecommand{\tNow}{\ensuremath{t}} -\providecommand{\tShkAll}{\xi} % -\providecommand{\tShkEmpMin}{\underline{\theta}} % -\providecommand{\TShkEmp}{\Theta} % -\providecommand{\tShkEmp}{\theta} % -\providecommand{\tShkEmp}{\theta} % -\providecommand{\tranShkInd}{\theta} % New -\providecommand{\TranShkAgg}{\Theta} % New -\providecommand{\TShk}{\Xi} % -\providecommand{\tShk}{\xi} % -\providecommand{\tshk}{\xi} % -\providecommand{\tSS}{\ensuremath{t}} -\providecommand{\tThen}{\ensuremath{\tau}} -\providecommand{\uFunc}{{\ensuremath{\mathrm{u}}}} -\providecommand{\uInvEpShkuInv}{\underline{\underline{\psi}}} -\providecommand{\ULev}{\ensuremath{U}} -\providecommand{\uLev}{\ensuremath{u}} -\providecommand{\unins}{\ensuremath{\zeta}} -\providecommand{\uPDVFunc}{\ensuremath{\mathbb{U}}} -\providecommand{\uPmt}{\ensuremath{\mu}} -\providecommand{\uPPP}{{\ensuremath{\mathrm{u}^{\prime\prime\prime}}}} -\providecommand{\uPP}{{\ensuremath{\mathrm{u}^{\prime\prime}}}} -\providecommand{\uP}{{\ensuremath{\mathrm{u}^{\prime}}}} -\providecommand{\urate}{\ensuremath{\mho}} -\providecommand{\utilFunc}{\ensuremath{\mathrm{u}}} -\providecommand{\util}{\ensuremath{u}} -\providecommand{\ValAlt}{\ensuremath{\mathcal{V}}} % middle-of-period Value function -\providecommand{\valfn}{\ensuremath{\mathrm{v}}} % middle-of-period value function -\providecommand{\Value}{\ensuremath{\mathrm{V}}} % middle-of-period Value function -\providecommand{\VEndFunc}{\ensuremath{\mathfrak{V}}} -\providecommand{\vEndFunc}{\ensuremath{\mathfrak{v}}} -\providecommand{\VEnd}{\ensuremath{\mathfrak{V}}} % end-of-period Value function -\providecommand{\vEnd}{\ensuremath{\mathfrak{v}}} % end-of-period value function -\providecommand{\vEss}{\check{v}^{e}} -\providecommand{\VE}{{V}^{e}} -\providecommand{\vE}{{v}^{e}} -\providecommand{\vFirm}{\ensuremath{\mathrm{e}}} -\providecommand{\VFunc}{\ensuremath{\mathrm{V}}} -\providecommand{\vFunc}{\ensuremath{\mathrm{v}}} -\providecommand{\VInv}{\ensuremath{\Lambda}} -\providecommand{\vInv}{\ensuremath{\scriptstyle \Lambda \displaystyle}} -\providecommand{\vk}{\ensuremath{\lambda}} -\providecommand{\vLevBF}{\ensuremath{\mathbf{v}}} -\providecommand{\VLevFunc}{\ensuremath{\pmb{\mathrm{V}}}} -\providecommand{\vLevFunc}{\ensuremath{\pmb{\mathrm{v}}}} -\providecommand{\VLev}{\ensuremath{V}} -\providecommand{\vLev}{\ensuremath{v}} -\providecommand{\vNorm}{\ensuremath{\mathrm{w}}} % end-of-period value function -\providecommand{\VNum}{\ensuremath{V}} -\providecommand{\vNum}{\ensuremath{v}} -\providecommand{\vOptAlt}{\ensuremath{\Alt{\tilde{\mathfrak{v}}}}} -\providecommand{\vOpt}{\ensuremath{\tilde{\mathfrak{v}}}} -\providecommand{\VRat}{\ensuremath{V}} -\providecommand{\vRat}{\ensuremath{v}} -\providecommand{\vTarg}{\Target{\vRat}} -\providecommand{\VU}{{V}^{u}} -\providecommand{\vU}{{v}^{u}} -\providecommand{\Wage}{\ensuremath{\mathsf{W}}} -\providecommand{\wage}{\ensuremath{\mathsf{w}}} -\providecommand{\WAllLev}{\ensuremath{\mathbf{O}}} -\providecommand{\wAllLev}{\ensuremath{\mathbf{o}}} -\providecommand{\WAllRat}{\ensuremath{O}} -\providecommand{\wAllRat}{\ensuremath{o}} -\providecommand{\WAll}{{O}} -\providecommand{\wAll}{{o}} -\providecommand{\WBeg}{\KLev} % Wealth as of the beginning of the period (before R is received, not including Y) -\providecommand{\wBeg}{\kLev} % wealth as of the beginning of the period (before R is received, not including Y) -\providecommand{\Wealth}{\ensuremath{O}} -\providecommand{\wealth}{\ensuremath{o}} -\providecommand{\WEndRat}{\ARat} % -\providecommand{\wEndRat}{\aRat} % -\providecommand{\WEnd}{\ALev} % Wealth as of the end of the period (after C has been chosen) -\providecommand{\wEnd}{\aLev} % wealth as of the end of the period (after C has been taken) -\providecommand{\Wend}{\ARat} % Wealth as of the end of the period (after C has been chosen) -\providecommand{\wend}{\aRat} % wealth as of the end of the period (after C has been taken) -\providecommand{\wFunc}{\mathrm{w}} -\providecommand{\WGroPF}{\ensuremath{\mathsf{G}}} -\providecommand{\WGro}{\ensuremath{\mathsf{G}}} -\providecommand{\wGro}{\ensuremath{\mathsf{g}}} -\providecommand{\whumMin}{\underline{\hRat}} % human wealth -- individual -\providecommand{\WHum}{\HLev} % Human wealth -- aggregate -\providecommand{\wHum}{\hLev} % human wealth -- individual -\providecommand{\Whum}{\HRat} % Human wealth -- aggregate -\providecommand{\whum}{\hRat} % human wealth -- individual -\providecommand{\WLev}{\ensuremath{W}} -\providecommand{\wLev}{\pmb{w}} -\providecommand{\WMid}{\BLev} % Wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\WMid}{\BLev} % Wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\wMid}{\bLev} % wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\Wmid}{\BRat} % Wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\wmid}{\bRat} % wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\WMkt}{\MLev} % Wealth as of the middle of the period (after R is received, including Y) -\providecommand{\wMkt}{\mLev} % wealth as of the middle of the period (after R is received, including Y) -\providecommand{\wmkt}{\mLev} % wealth as of the middle of the period (after R is received, including Y) -\providecommand{\wNet}{\ensuremath{x}} % -\providecommand{\WNet}{\ensuremath{X}} % Total wealth -\providecommand{\WPre}{{K}} -\providecommand{\wPre}{{k}} -\providecommand{\wRat}{\ensuremath{o}} % -\providecommand{\WRat}{\ensuremath{O}} % Ratio to permanent income -\providecommand{\wTot}{\ensuremath{\mathbf{o}}} % -\providecommand{\WTot}{\ensuremath{\mathbf{O}}} % Total wealth -\providecommand{\wtot}{\ensuremath{o}} % -\providecommand{\Wtot}{\ensuremath{O}} % Total wealth -\providecommand{\xFer}{\chi} -\providecommand{\XFer}{X} -\providecommand{\xFunc}{\mathrm{x}} -\providecommand{\XLev}{\ensuremath{X}} -\providecommand{\xLev}{\ensuremath{x}} -\providecommand{\xpend}{\ensuremath{\xi}} -\providecommand{\XperGro}{\ensuremath{\mathsf{X}}} -\providecommand{\xperGro}{\ensuremath{\mathsf{x}}} -\providecommand{\XRat}{\ensuremath{X}} -\providecommand{\xRat}{\ensuremath{x}} -\providecommand{\yFunc}{\mathrm{y}} -\providecommand{\yLevBF}{\ensuremath{\mathbf{y}}} -\providecommand{\YLev}{\ensuremath{Y}} -\providecommand{\YLevBF}{\ensuremath{\textbf{Y}}} -\providecommand{\yLev}{\ensuremath{y}} -\providecommand{\YRat}{\ensuremath{Y}} -\providecommand{\yRat}{\ensuremath{y}} -\providecommand{\yTargE}{\ensuremath{\check{y}^{e}}} -\providecommand{\yTarg}{\ensuremath{\check{y}}} -\providecommand{\zAgg}{\ensuremath{\pmb{Z}}} -\providecommand{\zFunc}{\mathrm{z}} -\providecommand{\ZLevBF}{\ensuremath{\mathbf{Z}}} -\providecommand{\zLevBF}{\ensuremath{\mathbf{z}}} -\providecommand{\zLevBF}{\ensuremath{\pmb{z}}} -\providecommand{\ZLev}{\ensuremath{Z}} -\providecommand{\zLev}{\ensuremath{z}} -\providecommand{\ZRat}{\ensuremath{Z}} -\providecommand{\zRat}{\ensuremath{z}} - -\newcommand{\discRte}{\omega} -\newcommand{\DieFac}{\pDead} -\newcommand{\LivFac}{\Alive} -\newcommand{\PopFac}{\PopGro} -\newcommand{\popRte}{\popGro} - - -\providecommand{\NFALev}{\NLev} -\providecommand{\NFARat}{\NRat} -\providecommand{\NI}{\ensuremath{Z}} -\providecommand{\GDPLev}{\pmb{P}} -\providecommand{\GDPRat}{P} -\providecommand{\GDPGro}{\gimel} -\providecommand{\gdpLev}{\pmb{p}} -\providecommand{\gdpRat}{p} -\providecommand{\weight}{\omega} - - -\providecommand{\bi}{} -\renewcommand{\bi}{\begin{itemize}} -\providecommand{\ei}{} -\renewcommand{\ei}{\end{itemize}} -\providecommand{\reqd}{} -\renewcommand{\reqd}{\item[$^{*}$]} -\providecommand{\recm}{} -\renewcommand{\recm}{\item[\phantom{$^{*}$}]} - -\providecommand{\Severance}{\kappa} -\newcommand{\pd}[2]{\frac{\partial#1}{\partial#2}} - diff --git a/examples/cAndCwithStickyE/__init__.py b/examples/cAndCwithStickyE/__init__.py deleted file mode 100644 index e69de29bb..000000000 From 2eb6f690d68e2c1b9988c8f3eb31527fb66abcf0 Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 5 Dec 2019 16:47:14 -0500 Subject: [PATCH 129/242] changing relative imports in BayerLueticke One code --- .../BayerLuetticke/Assets/One/FluctuationsOneAssetIOUs.py | 2 +- examples/BayerLuetticke/Assets/One/SteadyStateOneAssetIOUs.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUs.py b/examples/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUs.py index 5fff2d1eb..86f16d8e8 100644 --- a/examples/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUs.py +++ b/examples/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUs.py @@ -19,7 +19,7 @@ from scipy import linalg from math import log, cos, pi import time -from .SharedFunc import Transition, ExTransitions, GenWeight, MakeGrid, Tauchen +from SharedFunc import Transition, ExTransitions, GenWeight, MakeGrid, Tauchen import matplotlib.pyplot as plt import matplotlib.patches as mpatches diff --git a/examples/BayerLuetticke/Assets/One/SteadyStateOneAssetIOUs.py b/examples/BayerLuetticke/Assets/One/SteadyStateOneAssetIOUs.py index 2eeea3a04..865c1e915 100644 --- a/examples/BayerLuetticke/Assets/One/SteadyStateOneAssetIOUs.py +++ b/examples/BayerLuetticke/Assets/One/SteadyStateOneAssetIOUs.py @@ -14,7 +14,7 @@ from scipy.interpolate import interp1d, interp2d from scipy import sparse as sp import time -from .SharedFunc import Transition, ExTransitions, GenWeight, MakeGrid, Tauchen +from SharedFunc import Transition, ExTransitions, GenWeight, MakeGrid, Tauchen class SteadyStateOneAssetIOU: @@ -560,4 +560,4 @@ def StochasticsVariance(self, par, mpar, grid): EX1SS = EX1.SolveSteadyState() # pickle.dump(EX1SS, open("EX1SS.p", "wb")) - pickle.dump(EX1SS, open("EX1SS_nm50.p", "wb")) \ No newline at end of file + pickle.dump(EX1SS, open("EX1SS_nm50.p", "wb")) From 56396b16e4b33bb76aaf498d899e6ad473999a34 Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 5 Dec 2019 16:47:54 -0500 Subject: [PATCH 130/242] correct paths to binder notebooks in BayerLuetticke --- examples/BayerLuetticke/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/BayerLuetticke/README.md b/examples/BayerLuetticke/README.md index 126f1c63c..73da650e1 100644 --- a/examples/BayerLuetticke/README.md +++ b/examples/BayerLuetticke/README.md @@ -8,9 +8,9 @@ The paper solves three models, represented here in three places: A Krusell-Smith model with a single asset, [OneAsset-KS](https://github.com/econ-ark/HARK/blob/master/HARK/BayerLuetticke/OneAssetCode-KS), which can be run by executing _SteadyStateOneAssetIOUs.py_ then _FluctuationsOneAssetIOUs.py_.. -A HANK model with a single asset, [OneAsset-HANK](https://github.com/econ-ark/HARK/blob/master/HARK/BayerLuetticke/OneAsset-HANK.ipynb), which can be launched on MyBinder using [this url]([https://mybinder.org/v2/gh/econ-ark/HARK/master?filepath=HARK%2FBayerLuetticke%2FOneAsset-HANK.ipynb](https://mybinder.org/v2/gh/econ-ark/HARK/master?filepath=HARK%2FBayerLuetticke%2FOneAsset-HANK.ipynb) +A HANK model with a single asset, [OneAsset-HANK](https://github.com/econ-ark/HARK/blob/master/HARK/BayerLuetticke/OneAsset-HANK.ipynb), which can be launched on MyBinder using [this url]([https://mybinder.org/v2/gh/econ-ark/HARK/master?filepath=examples%2FBayerLuetticke%2FOneAsset-HANK.ipynb](https://mybinder.org/v2/gh/econ-ark/HARK/master?filepath=examples%2FBayerLuetticke%2FOneAsset-HANK.ipynb) -A HANK model with a liquid and an illiquid asset, [TwoAsset-HANK](https://github.com/econ-ark/HARK/blob/master/HARK/BayerLuetticke/OneAsset-HANK.ipynb), which can be launched on MyBinder using [this url]([https://mybinder.org/v2/gh/econ-ark/HARK/master?filepath=HARK%2FBayerLuetticke%2FTwoAsset.ipynb](https://mybinder.org/v2/gh/econ-ark/HARK/master?filepath=HARK%2FBayerLuetticke%2FTwoAsset.ipynb) +A HANK model with a liquid and an illiquid asset, [TwoAsset-HANK](https://github.com/econ-ark/HARK/blob/master/HARK/BayerLuetticke/OneAsset-HANK.ipynb), which can be launched on MyBinder using [this url]([https://mybinder.org/v2/gh/econ-ark/HARK/master?filepath=examples%2FBayerLuetticke%2FTwoAsset.ipynb](https://mybinder.org/v2/gh/econ-ark/HARK/master?filepath=examples%2FBayerLuetticke%2FTwoAsset.ipynb) Other content: From c338171dfa627863b8af942ae654f4e59ac422f0 Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 5 Dec 2019 17:16:35 -0500 Subject: [PATCH 131/242] fixing import paths --- .../BayerLuetticke/Assets/One/EX1SS_nm50.p | Bin 37777 -> 27370 bytes .../BayerLuetticke/Assets/One/__init__.py | 4 ---- .../Assets/Two/FluctuationsTwoAsset.py | 2 +- .../Assets/Two/SteadyStateTwoAsset.py | 4 ++-- examples/BayerLuetticke/Assets/__init__.py | 2 -- .../BayerLuetticke/BayerLuetticke_wrapper.py | 3 +++ 6 files changed, 6 insertions(+), 9 deletions(-) diff --git a/examples/BayerLuetticke/Assets/One/EX1SS_nm50.p b/examples/BayerLuetticke/Assets/One/EX1SS_nm50.p index 5b860f7db8f9b8b5c5aa09de46b610590c039844..55aa318899af113637ca0b97b2a226db4099fe2d 100755 GIT binary patch literal 27370 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z?|Y-+CWTv3f{r>G2q|CT09_V$PQjW$)JqixB2e^tx4+t&Jf%q;ZA(0Z98ks8F)qjS_$B}3Ct#O-XJWN1G5r+a4^EU4TsBxQL z?nQMkO-^w|cPXo}&=`(qZ;c8q6Dasm=|N~HdN8+~O`02Avbq=`={2;~1Ue4g8hyZi zM2-0axU3TtJ+#H+1F*X=PG4&Yu#7DGgypLX0IQRlPYW^P;y0%7^Q?5(V%xNb0$?w;*Rm0%QGsgazLA8 z4+^C&*C4g+SH|1M0dXhg&0;R{H#+R}ZtLqmeE*3K%rbUjZl_b^%kC~9?5eUWC(2?m ze1FTfF)kJ#X~1NO?~S4P;AJqaQ*5pQUG(c37Ovt7t!-fJ(0Fn%%X}|DmMvfQIR1tT zKPckc6f%BjW987RC((^}7sLt0SN;w~#;MuKI z{7H!N`|uuh%#0SFJBW$oO>)`n--`gyu&=;@2@|Ah)q` G?Ee5% Date: Thu, 5 Dec 2019 17:16:50 -0500 Subject: [PATCH 132/242] fixing import path --- examples/BayerLuetticke/ConsIndShockModel_extension.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/examples/BayerLuetticke/ConsIndShockModel_extension.py b/examples/BayerLuetticke/ConsIndShockModel_extension.py index 17655cce5..19503a743 100644 --- a/examples/BayerLuetticke/ConsIndShockModel_extension.py +++ b/examples/BayerLuetticke/ConsIndShockModel_extension.py @@ -6,7 +6,6 @@ from __future__ import print_function import sys import os -sys.path.insert(0, os.path.abspath('../ConsumptionSaving/')) from copy import copy, deepcopy import numpy as np import scipy as sc @@ -161,7 +160,7 @@ def CalcErgodicDist(self): self.ergodic_distr = ergodic_distr.reshape((len(self.Dist_mGrid),len(self.Dist_pGrid))) if __name__ == '__main__': - import ConsumerParameters as Params + import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import plotFuncsDer, plotFuncs from time import clock mystr = lambda number : "{:.4f}".format(number) @@ -186,4 +185,4 @@ def CalcErgodicDist(self): end_time = clock() print('Calculating the ergodic distribution took ' + mystr(end_time-start_time) + ' seconds.') - \ No newline at end of file + From 0db0348040be5d04b2069d1a335a1609ebe5e79d Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 5 Dec 2019 17:44:19 -0500 Subject: [PATCH 133/242] cleaning up __init__.py --- examples/BayerLuetticke/__init__.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/examples/BayerLuetticke/__init__.py b/examples/BayerLuetticke/__init__.py index 4ae08c79b..e69de29bb 100644 --- a/examples/BayerLuetticke/__init__.py +++ b/examples/BayerLuetticke/__init__.py @@ -1,6 +0,0 @@ -from BayerLuetticke.Assets.One.FluctuationsOneAssetIOUs import * -from BayerLuetticke.Assets.One.SteadyStateOneAssetIOUs import * -from BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond import * -from BayerLuetticke.Assets.One.SteadyStateOneAssetIOUsBond import * -from BayerLuetticke.Assets.Two.SteadyStateTwoAsset import * -from BayerLuetticke.Assets.Two.FluctuationsTwoAsset import * From bc0041b7d4493821f580e51b91e85413f3c37b34 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Mon, 9 Dec 2019 09:42:43 -0500 Subject: [PATCH 134/242] Add which_vars to minimizeNelderMead (#443) Added option to HARK.estimate.minimizeNelderMead to select which parameters are estimated by passing a vector of booleans of the same size as parameter_guess. When not passed, it defaults to estimating all of the parameters (previous behavior). Not a breaking change. --- HARK/estimation.py | 37 +++++++++++++++++++++++++------------ 1 file changed, 25 insertions(+), 12 deletions(-) diff --git a/HARK/estimation.py b/HARK/estimation.py index 53ce5c354..62bc6ba66 100644 --- a/HARK/estimation.py +++ b/HARK/estimation.py @@ -14,11 +14,11 @@ import warnings -def minimizeNelderMead(objectiveFunction, parameter_guess, verbose=False, **kwargs): +def minimizeNelderMead(objectiveFunction, parameter_guess, verbose=False, which_vars=None, **kwargs): ''' Minimizes the objective function using the Nelder-Mead simplex algorithm, starting from an initial parameter guess. - + Parameters ---------- objectiveFunction : function @@ -27,18 +27,30 @@ def minimizeNelderMead(objectiveFunction, parameter_guess, verbose=False, **kwar parameter_guess : [float] A starting point for the Nelder-Mead algorithm, which must be a valid input for objectiveFunction. + which_vars : np.array or None + Array of booleans indicating which parameters should be estimated. When + not provided, estimation is performed on all parameters. verbose : boolean A flag for the amount of output to print. - + Returns ------- xopt : [float] The values that minimize objectiveFunction. ''' + # Specify a temporary "modified objective function" that restricts parameters to be estimated + if which_vars is None: + which_vars = np.ones(len(parameter_guess),dtype=bool) + def objectiveFunctionMod(params): + params_full = np.copy(parameter_guess) + params_full[which_vars] = params + out = objectiveFunction(params_full) + return out + parameter_guess_mod = parameter_guess[which_vars] # Execute the minimization, starting from the given parameter guess - t0 = time() # Time the process - OUTPUT = fmin(objectiveFunction, parameter_guess, full_output=1, maxiter=1000, disp=verbose, **kwargs) + t0 = time() # Time the process + OUTPUT = fmin(objectiveFunctionMod, parameter_guess_mod, full_output=1, disp=verbose, **kwargs) t1 = time() # Extract values from optimization output: @@ -47,19 +59,20 @@ def minimizeNelderMead(objectiveFunction, parameter_guess, verbose=False, **kwar optiter = OUTPUT[2] # Number of iterations performed. funcalls = OUTPUT[3] # Number of function calls made. warnflag = OUTPUT[4] # warnflag : int - # 1 : Maximum number of function evaluations made. - # 2 : Maximum number of iterations reached. - + # 1 : Maximum number of function evaluations made. + # 2 : Maximum number of iterations reached. # Check that optimization succeeded: if warnflag != 0: - warnings.warn("Minimization failed! xopt=" + str(xopt) + ', fopt=' + str(fopt) + - ', optiter=' + str(optiter) + ', funcalls=' + str(funcalls) + + warnings.warn("Minimization failed! xopt=" + str(xopt) + ', fopt=' + str(fopt) + + ', optiter=' + str(optiter) +', funcalls=' + str(funcalls) + ', warnflag=' + str(warnflag)) + xopt_full = np.copy(parameter_guess) + xopt_full[which_vars] = xopt # Display and return the results: if verbose: - print("Time to estimate is " + str(t1-t0) + " seconds.") - return xopt + print("Time to estimate is " + str(t1-t0) + " seconds.") + return xopt_full def minimizePowell(objectiveFunction, parameter_guess, verbose=False): From 8c37eb01fe926d36dfffb4f7bf074146ed222abc Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Mon, 9 Dec 2019 12:17:05 -0500 Subject: [PATCH 135/242] Moved NARK to HARK documentation --- Documentation/NARK/NARK-Add.bib | 0 Documentation/NARK/NARK.bib | 0 Documentation/NARK/NARK.pdf | Bin 0 -> 368651 bytes Documentation/NARK/NARK.tex | 337 ++++++ Documentation/NARK/econtex.bst | 1294 +++++++++++++++++++++++ Documentation/NARK/econtex.cls | 360 +++++++ Documentation/NARK/econtex.sty | 34 + Documentation/NARK/econtexBibMake.tex | 7 + Documentation/NARK/econtexSetup.sty | 236 +++++ Documentation/NARK/econtexShortcuts.sty | 624 +++++++++++ 10 files changed, 2892 insertions(+) create mode 100644 Documentation/NARK/NARK-Add.bib create mode 100644 Documentation/NARK/NARK.bib create mode 100644 Documentation/NARK/NARK.pdf create mode 100644 Documentation/NARK/NARK.tex create mode 100644 Documentation/NARK/econtex.bst create mode 100644 Documentation/NARK/econtex.cls create mode 100644 Documentation/NARK/econtex.sty create mode 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+\documentclass[12pt]{econtex} +\usepackage{econtexSetup} +\usepackage{econtexShortcuts} + +\usepackage{cancel} + +\begin{document} + + +\title{Suggested Naming Conventions in ARK} + +\forcedate{July 15, 2018} + +\vspace{2in} + +\keywords{ARK, Variable, Function, Object, Operator, Naming, Guidelines, Conventions} +\jelclass{None} + + +\maketitle + + +\begin{abstract} + Sharing of code is easier when different contributors use similar names for similar objects. While we will not enforce the + recommendations below, contributors can make their code more attractive to others + by using names consistent with our guidelines. +\end{abstract} + +\thanks{The \texttt{NARK} repo that generates this document +also contains \LaTeX~configuration files that permit you to use the same name for an object in +your writing (your \texttt{paper.tex} file) and in your code (helping to minimize confusion in translating between paper and code).} + + +\pagebreak + +\section{Principles} +Our aim has been to balance: +\begin{itemize} +\item Brevity (nobody wants to type long names over and over) +\item Uniqueness (in case you want to search-and-replace) +\item Mnemonic quality (it should be easy to remember what represents what) +\item Ubiquity (objects defined herein will appear in many projects) +\item Combinatoriality (easy to mix and match our recommendations) +\end{itemize} + + +\pagebreak + +\section{Variables} + +\subsection{Single-Letter} + +We generally discourage the use of single-letter variable names. The reasons for this advice are presented \textit{ad nauseum} in introductory computer programming texts (which, we know, few economists consult); here we will note only that the extent to which your code will be influential depends upon the extent to which someone else can easily read it, which is harder if you have used variable names which could mean almost anything. + +It is only slightly less objectionable to name a variable after a familiar letter in another commonly used alphabet (say, \texttt{delta}). Your future self (and other users) will not know which of the many possible meanings of $\delta$ you had in mind. + +But, because brevity is a virtue, a single letter \textit{in combination with a modifier or two} (`\texttt{hMin}' as the value of minimum human wealth, say) is fine -- so long as the reader has some reason to expect that the lower-case letter \texttt{h} signifies human wealth (as they will, if they consult Table~\ref{table:RomanLetters}). + +That is the spirit in which we offer preferred interpretations for the Roman letters below. The upper case version is an aggregated version of the variable (at the level of the whole economy, say, or of the whole market being studied), while the lower case indicates the level of an individual consumer or firm or other subaggregate entity. + +A few exceptions to these rules are explicitly noted below the table. + +When an alternative is needed with a meaning similar to, but distinct from, the definitions below, please use a multi-letter name to represent it. For example, please do not use $W$ for wealth (if some measure of wealth that differs from $\ALev$, $\BLev$, $\HLev$, or $\NLev$ is needed); instead use, say, \texttt{Wlth} or \texttt{Wealth}. (Some examples follow in a subsequent section). + +Finally, a few of the definitions below are actually prohibitions; these are based on many years of experience which have shown that use of the prohibited variable name generates more confusion than clarity. + +\begin{table}[h] +\begin{center} +\begin{tabular}{|lcl|} \hline +Letter & & Meaning +\\ \hline + \ALev & & \textit{A}ssets \textit{A}fter \textit{A}ll \textit{A}ctions have been \textit{A}ccomplished (end of period) +\\ \BLev & & \textit{B}eginning \textit{B}ank \textit{B}alances \textit{B}efore any \textit{B}ehavior (\textit{b}eginning-of-period) +\\ \CLev & & \textit{C}onsumption \textit{C}hoice \textit{C}onnects $B$ to $A$ +\\ \DLev & & \textit{D}ebt +\\ \ELev & & PROHIBITED: Too many possible meanings (expectations, effort, expenses) +\\ \FLev & & Production \textit{F}unction +\\ \GLev & & \textit{G}rowth +\\ \HLev & & \textit{H}uman wealth +\\ \ILev & & \textit{I}nvestment +\\ \JLev & & Ad\textit{J}ustment costs (e.g., in a $Q$ model) +\\ \KLev & & Capital or beginning of period nonhuman assets +\\ \LLev & & PROHIBITED: Is it Labor or Leisure or Land or ...? +\\ \MLev & & \textit{M}arket resources (the sum of capital, capital income, and labor income) +\\ \NLev & & \textit{N}et wealth including human wealth ($=B + H$) +\\ \OLev & & PROHIBITED: Too similar to the number 0; too many possible meanings +\\ \PLev & & PROHIBITED: Is it prices, permanent income, present value, profits, ...? +\\ \QLev & & Hayashi/Abel $Q$ (or similar asset price) +\\ \RLev & & \textit{R}eturn (see the variants articulated below) +\\ \SLev & & PROHIBITED: ``saving'' (flow)? ``savings'' (stock)? or the ``saving rate'' (ratio)? +\\ \TLev & & This is a tough one. See the discussion below. +\\ \ULev & & \textit{U}tility +\\ \VLev & & \textit{V}alue +\\ \WLev & & \textit{W}age +\\ \XLev & & e\textit{X}penditures (as distinct from consumption; e.g., for durables) +\\ \YLev & & Noncapital income (usually, the sum of transfer and labor income) +\\ \ZLev & & Lei\textit{Z}ure in consumption/leisure tradeoff +\\ \hline +\end{tabular} +\end{center} +\caption{Preferred Usages of Roman Letters} +\label{table:RomanLetters} +\end{table} + +\subsection{Exceptions to the Rules} + +The letter $\TLev$ is an exception to the rule that lower- and upper-case versions of variables are individual and aggregate quantities. We reserve the capital letter to designate the end of the horizon (death, or the end of the economy, occurs at the end of period $\TLev$). The lower case version $t$ is so ubiquitiously used as the current time period that we do not want to resist the overwhelming force of tradition to prohibit its use in that capacity. + +Finally, the following are exempted from the prohibition on single-letter variable names because they are used so frequently that the prohibition would be more trouble than it is worth: $a$, $b$, $c$, $m$. + + +\pagebreak + +\subsection{Strings} + +There are more objects that are likely to be used extensively in ARK projects than there are Roman letters. We present preferred usages for some of those commonly-needed variables here. + +\begin{table}[h] +\begin{center} +\begin{tabular}{|rcl|} +\hline +Name & - & Description + \\ \hline + \texttt{CND} & - & Consumption of Nondurable Good + \\ \texttt{Cst} & - & Cost of something + \\ \texttt{Dgd} & - & Stock of durable good + \\ \texttt{Dvd} & - & Dividends + \\ \texttt{Hse} & - & Quantity of housing (\textit{not} value, which is quantity $\times$ price) + \\ \texttt{Ilq} & - & Quantity of illiquid asset (\textit{not} value, which is quantity $\times$ price) + \\ \texttt{Lbr} & - & Quantity of labor + \\ \texttt{Pop} & - & Size of population + \\ \texttt{Tax} & - & Tax -- should be modified by \texttt{Rte} or \texttt{Amt} articulated below + \\ \texttt{Perm} & - & Permanent income + \\ \texttt{Tran} & - & Transitory income +\\ \hline +\end{tabular} +\caption{String Variables} +\label{table:Strings} +\end{center} +\end{table} + + + +\section{Factors and Rates} + When measuring change over time, lower-case variables reflect rates + while the corresponding upper-case variable connects adjacent discrete periods.\footnote{This + convention rarely conflicts with the usage we endorse elsewhere of + indicating individual-level variables by the lower and aggregate + variables by the upper case.}$^{,}$\footnote{If there is a need for the continuous-time +representation, we endorse use of the discrete-time rate defined below. Any +author who needs a continuous-time rate, a discrete-time rate, and a discrete-time factor +is invited to invent their own notation.} So, for example, if the time interval is a year and the annual interest rate is $\rfree=0.03$ or three percent, then +the annual interest factor is $\Rfree=1.03$.\footnote{In the rare cases where it is necessary to distinguish between a continuous-time rate and a discrete-time rate -- for example, when there is an analytical result available in continuous time -- the variable in question can be modified by \texttt{Cnt} or \texttt{Dsc}.} + + +\begin{table}[h] + \centering + \begin{tabular}{|ccc|} + \hline +Code & Output & Description +\\ \hline + \verb|\Rfree| & $\Rfree$ & Riskfree interest factor +\\ \verb|\rfree| & $\rfree$ & Riskfree interest rate +\\ \verb|\Risky| & $\Risky$ & The return factor on a risky asset +\\ \verb|\risky| & $\risky$ & The return rate on a risky asset +\\ \verb|\Rport| & $\Rport$ & The return factor on the entire portfolio +\\ \verb|\rport| & $\rport$ & The return rate on the entire portfolio +\\ \verb|\RSave| & $\RSave$ & Return factor earned on positive end-of-period assets +\\ \verb|\rsave| & $\rsave$ & Return rate earned on positive end-of-period assets +\\ \verb|\RBoro| & $\RBoro$ & Return factor paid on debts +\\ \verb|\rboro| & $\rboro$ & Return rate paid on debts +\\ \hline + \end{tabular} + \caption{Factors and Rates} + \label{table:Factors} +\end{table} + +We depart from the upper-lower case scheme when the natural letter to use has an even more urgent use elsewhere in our scheme. +A particularly common example occurs in the case of models like \cite{blanchardFinite} in which +individual agents are subject to a Poisson probability of death. Because death was common in the +middle ages, we use the archaic Gothic font for the death rate; and the probability of survival is the cancellation of the probability of death: +\begin{table}[h] + \centering + \begin{tabular}{|>{\ttfamily}cccl|} + \hline + Code & \LaTeX & Description & \\ + \hline + \verb|\DieFac| & $\DieFac$ & Probabilty of death & +\\ \verb|\LivFac| & $\LivFac$ & Probability to not die $=(1-\DieFac)$ & +\\ \hline + \end{tabular} + \caption{Special Cases: Factors and Rates} + \label{table:SpecialFactors} +\end{table} + +\section{Parameters} + Some parameters are worth defining because they are likely to be + used in a high proportion of models; others are subject to enough + constraints when used (such as the need for similar-looking upper- + and lower-case Greek representations), as to be worth standardizing. + +Programmers should +use the corresponding variable name without the backslash as the name of the corresponding object +in their code. For example, the Coefficient of Relative Risk Aversion is $\verb|\CRRA|$ in a \LaTeX +document and \texttt{CRRA} in a software module. + +\begin{table}[h] + \centering + \begin{tabular}{|>{\ttfamily}rcll|} + \hline + Name & \LaTeX & Description & Illustration + \\ \hline + \verb|\CARA| & $\CARA$ & Coefficient of Absolute Risk Aversion &$\uFunc(\bullet)=-\CARA^{-1} e^{-\CARA \bullet}$ + \\ \verb|\CRRA| & $\CRRA$ & Coefficient of Relative Risk Aversion & $\uFunc(\bullet)=(1-\CRRA)^{-1}\bullet^{1-\CRRA}$ + \\ \verb|\DiscFac| & $\DiscFac$ & Time Discount Factor & $\uFunc^{\prime}(\cLev_{t}) = \Rfree \DiscFac \uFunc^{\prime}(\cLev_{t+1})$ + \\ \verb|\discRte| & $\discRte$ & Time Discount rate & $\discRte=\DiscFac^{-1}-1$ + \\ \verb|\DeprFac| & $\DeprFac$ & Depreciation Factor (Hebrew \texttt{daleth}) & $\Kap_{t+1} = \DeprFac \Kap_{t}+\ILev_{t}$ + \\ \verb|\deprRte| & $\deprRte$ & Depreciation Rate & $\DeprFac = 1-\deprRte$ + \\ \verb|\TranShkAgg| & $\TranShkAgg$ & Transitory shock (aggregate) & $\Ex_{t}[\TranShkAgg_{t+n}]=1$ if $\TranShkAgg$ iid + \\ \verb|\tranShkInd| & $\tranShkInd$ & Transitory shock (individual) & $\Ex_{t}[\tranShkInd_{t+n}]=1$ if $\tranShkInd$ iid + \\ \verb|\PermShkAgg| & $\PermShkAgg$ & Permanent shock (aggregate) & $\Ex_{t}[\PermShkAgg_{t+n}]=1$ if $\PermShkAgg$ iid + \\ \verb|\permShkInd| & $\permShkInd$ & Permanent shock (individual) & $\Ex_{t}[\permShkInd_{t+n}]=1$ if $\permShkInd$ iid + \\ \verb|\PopGro| & $\PopGro$ & Population Growth Factor & $\texttt{Pop}_{t+1} = \PopGro \texttt{Pop}_{t}$ + \\ \verb|\popGro| & $\popGro$ & Population Growth rate & $\PopGro = 1 + \popGro$ + \\ \verb|\PtyGro| & $\PtyGro$ & Productivity Growth Factor & $\GLev = \PtyGro \PopGro$ + \\ \verb|\ptyGro| & $\ptyGro$ & Productivity Growth rate & $\PtyGro = (1+\ptyGro)$ + \\ \verb|\leiShare| & $\leiShare$ & Leisure share, Cobb-Douglas utility & $\uFunc(c,z)=(1-\CRRA)^{-1}(c^{1-\leiShare}z^{\leiShare})^{1-\CRRA}$ + \\ \verb|\MPC| & $\MPC$ & Marginal Propensity to Consume & $\cFunc^{\prime}(\mRat)=\partial c/\partial m$ + \\ \verb|\Pat| & $\Pat$ & Absolute Patience Factor (\texttt{Thorn}) & $\Pat = (\Rfree \DiscFac)^{1/\CRRA} $ + \\ \verb|\pat| & $\pat$ & Absolute Patience rate (\texttt{thorn}) & $\pat = (\Rfree \DiscFac)^{1/\CRRA}-1 \approx \CRRA^{-1}(\rfree-\discRte) $ + \\ \verb|\riskyshare| & $\riskyshare$ & Portfolio share in risky assets & $\Rport_{t+1}=(1-\riskyshare)\Rfree+\riskyshare\Risky_{t+1}$ + \\ \hline + \end{tabular} + \caption{Parameters} + \label{table:Parameters} +\end{table} + +Mnemonics: +\begin{itemize} +\item Hebrew \texttt{daleth} is the fourth letter of the Hebrew alphabet (as d and $\delta$ are of the Roman and Greek) and is an etymological and linguistic cousin of those letters +\item $\discRte$ is the lower case Greek letter \texttt{omega}, because people say ``OMG, I've got to think about the future.'' +\item You are invited to scrutinize $\Xi$ yourself to imagine reasons it could represent something to do with population growth. +\item The glorious letter $\Pat$ (pronounced `\href{http://en.wikipedia.org/wiki/Thorn_(letter)}{thorn}') enriched Old English, Gothic, and some other defunct alphabets; sadly, it remains in use today only in Iceland. It is useful because having to type the many symbols in the object $(\Rfree \DiscFac)^{1/\CRRA}$ over and over again is a \textit{thorn} in the side of economists working with dynamic models! (It is the `absolute patience factor' because if it is less than one the consumer wants to bring resources from the future to the present and is therefore absolutely impatient; for a fuller discussion of this terminology, see \cite{carrollTractable}.) +\end{itemize} + + + +\section{Operators} +A few operators are so universally used that it will be useful to define them. + +\begin{table}[h] + \centering + \begin{tabular}{|>{\ttfamily}rcll|} + \hline + Name & \LaTeX & Description & Illustration + \\ \hline + \verb|\Ex| & $\Ex$ & The expectation as of date $t$ & $\Ex_{t}[\uFunc^{\prime}(\cRat_{t+1})]$ +\\ \verb|\PDV| & $\PDV$ & Present Discounted Value & $\PDV_{t}^{T}(y)$ is human wealth + \\ \hline + \end{tabular} + \caption{Operators} + \label{table:Operators} +\end{table} + + + + +\section{Modifiers} +\begin{table}[h] + \centering + \begin{tabular}{|>{\ttfamily}ccc|} + \hline + \textit{[object]}\texttt{Agg} & - & Value of something at the aggregate level (as opposed to \texttt{Ind}) +\\ \textit{[object]}\texttt{Ind} & - & Value of something at the level of an individual (as opposed to \texttt{Agg}) +\\ \textit{[object]}\texttt{Lvl} & - & Level +\\ \textit{[object]}\texttt{Rto} & - & Ratio +\\ \textit{[object]}\texttt{Bot} & - & Lower value in some range +\\ \textit{[object]}\texttt{Top} & - & Upper value in some range +\\ \textit{[object]}\texttt{Min} & - & Minimum possible value +\\ \textit{[object]}\texttt{Max} & - & Maximum possible value +\\ \textit{[object]}\texttt{Cnt} & - & Continuous-time value +\\ \textit{[object]}\texttt{Dsc} & - & Discrete-time value +\\ \textit{[object]}\texttt{Shk} & - & Shock +\\ \textit{[object]}\texttt{Trg} & - & The `target' value of a variable +\\ \textit{[object]}\texttt{Rte} & - & A `rate' variable like the discount rate $\discRte$ +\\ \textit{[object]}\texttt{Fac} & - & A factor variable like the discount factor $\DiscFac$ +\\ \textit{[object]}\texttt{Amt} & - & An amount, like \texttt{TaxAmt} which might be lump-sum +\\ \hline + \end{tabular} + \caption{General Purpose Modifiers} + \label{table:General} +\end{table} + + +Shocks will generally be represented by finite vectors of outcomes and their probabilities. For example, permanent income is called \texttt{Perm} and shocks are designated \texttt{PermShk} +\begin{table}[ht] + \centering + \begin{tabular}{|>{\ttfamily}ccc|} + \hline + \textit{[object]}\texttt{Prbs} & - & Probabilities of outcomes (e.g. \texttt{PermShkPrbs} for permanent shocks) +\\ \textit{[object]}\texttt{Vals} & - & Values (e.g., for mean one shock \texttt{PermShkVals} . \texttt{PermShkPrbs} = 1) +\\ \textit{[object]}\texttt{Dstn} & - & Representation of a probability distribution (includes both Prbs and Vals) +\\ \hline + \end{tabular} + \caption{Probabilities} + \label{table:Probabilities} +\end{table} + + + +Timing can be confusing because there can be multiple ordered steps +within a `period.' We will use \texttt{Prev}, \texttt{Curr}, \texttt{Next} to refer +to steps relative to the local moment within a period, and $t$ variables to refer to succeeding periods: +\begin{table}[h] + \centering + \begin{tabular}{|>{\ttfamily}lcl|} + \hline + \textit{[object]}tm2 & - & object in period $t$ minus 2 +\\ \textit{[object]}tm1 & - & object in period $t$ minus 1 +\\ \textit{[object]}Now & - & object in period $t$ +\\ \textit{[object]}t\phantom{p1} & - & object in period $t$ (alternative definition) +\\ \textit{[object]}tp1 & - & object in $t$ plus 1 +\\ \textit{[object]}tpn & - & object in $t$ plus $n$ +\\ \textit{[object]}Prev & - & object in previous subperiod +\\ \textit{[object]}Curr & - & object in current subperiod +\\ \textit{[object]}Next & - & object in next subperiod +\\ \hline + \end{tabular} + \caption{Timing} + \label{table:Timing} +\end{table} + +\input econtexBibMake + +\end{document} + + diff --git a/Documentation/NARK/econtex.bst b/Documentation/NARK/econtex.bst new file mode 100644 index 000000000..d826b51ae --- /dev/null +++ b/Documentation/NARK/econtex.bst @@ -0,0 +1,1294 @@ +%%% BEJournals Bibliography Style +%%% Modified from econometrica.bst by KCB (see documentation below) +%%% Modified by Christopher Carroll to print full names of authors when available rather than initials +%%% For use with harvard.sty and LaTeX2e (NFSS) or LaTeX 2.09 +%%% 24-aug-94 +%%% +%%% Based on makebst, but with modifications by +%%% kcb@hss.caltech.edu (KC Border) +%%% Please report any problems to me. +%%% +%%% The main changes from the ouput of makebst are: +%%% 1. Addition of a function to replace repeated authors with a 3 em dash. +%%% (Search for compare.names, this function is portable.) +%%% 2. Modification of output.nonnull to allow commas inside quotation marks +%%% and no comma after year. +%%% 3.Eliminated feature (bug?) of the harvard style of citation. +%%% It creates a short label for subsequent citations. This alleged +%%% feature shortens both Aliprantis, Border, and Luxemburg (1991) and +%%% Aliprantis, Brown, and Burkinshaw (1991) to Aliprantis et~al. (1991). +%%% This creates the need to label these as +%%% Aliprantis, Border, and Luxemburg (1991a) and +%%% Aliprantis, Brown, and Burkinshaw (1991b), despite the fact +%%% that these entries have different authors. I used my own format +%%% for creating the labels, and used the same label for +%%% both the short and long forms. I know that the +%%% _Chicago_Manual_of_Style recommends using Aliprantis et~al, +%% but Econometrica regularly writes out three authors, +%% and it is rare to cite an article with more than four authors in +%%% Econometrica. (I couldn't find an example of even four being cited, +%%% but I didn't spend a lot of time looking.) +%%% 4. Addition of macros to handle editions. (My own idiosycracy.) +%%% 5. A new entry type called issue for special issues of a journal. +%%% (Another idiosyncracy.) +%%% 6. Many details to conform to Econometrica's quirks. +%%% (Search for KCB ) +%%% 7. I wrote this for use with LaTeX2e and the NFSS2. However, I did +%%% add some macros so it will work with LaTeX 2.09. +%%% (Search for \ifx\undefined\emph ) +%%% The remainder of this file was generated by makebst, except for +%%% modifications marked with KCB. +%% +%% This is file `em.bst', generated +%% on <1994/8/17> with the docstrip utility (2.2h). +%% +%% The original source files were: +%% +%% genbst.mbs (with options: `ay,har,nm-rev1,nmft-sc,dt-beg,yr-par,tit-qq,atit-u,thtit-a,volp-com,edby,blk-com,pp,ed,abr,and-com,nfss') +%% ---------------------------------------- +%% *** Econometrica style *** +%% +%% (Here are the specifications of the source file) +%% \def\filename{genbst.mbs} +%% \def\filedate{1994 July 1} +%% \def\fileversion{1.3} +%% For use with BibTeX version 0.99a or later, LaTeX version 2.09 +%%------------------------------------------------------------------- +%% NOTICE: +%% This file may be used for non-profit purposes. +%% It may not be distributed in exchange for money, +%% other than distribution costs. +%% +%% The author provides it `as is' and does not guarantee it in any way. +%% +%% Copyright (C) 1994 Patrick W. Daly +%% Max-Planck-Institut f\"ur Aeronomie +%% Postfach 20 +%% D-37189 Katlenburg-Lindau +%% Germany +%% +%% E-mail: +%% SPAN-- nsp::linmpi::daly (note nsp also known as ecd1) +%% Internet-- daly@linmpi.dnet.gwdg.de +%%----------------------------------------------------------- +%% \CharacterTable +%% {Upper-case \A\B\C\D\E\F\G\H\I\J\K\L\M\N\O\P\Q\R\S\T\U\V\W\X\Y\Z +%% Lower-case \a\b\c\d\e\f\g\h\i\j\k\l\m\n\o\p\q\r\s\t\u\v\w\x\y\z +%% Digits \0\1\2\3\4\5\6\7\8\9 +%% Exclamation \! Double quote \" Hash (number) \# +%% Dollar \$ Percent \% Ampersand \& +%% Acute accent \' Left paren \( Right paren \) +%% Asterisk \* Plus \+ Comma \, +%% Minus \- Point \. Solidus \/ +%% Colon \: Semicolon \; Less than \< +%% Equals \= Greater than \> Question mark \? +%% Commercial at \@ Left bracket \[ Backslash \\ +%% Right bracket \] Circumflex \^ Underscore \_ +%% Grave accent \` Left brace \{ Vertical bar \| +%% Right brace \} Tilde \~} +%%--------------------------------------------------------------------- + % This is an author-year citation style bibliography. As such, it is + % non-standard LaTeX, and requires a style option to function properly. + % The appropriate style file is: harvard.sty + % The form of the bibitem entries is + % \harvarditem[Jones et al.]{Jones, Baker, and Smith}{1990}{key}... + % There are style changes that may be specified in the text with + % the command \citationstyle. + % \citeasnoun{key} ==>> Jones et al. (1990) + % \cite{key} ==>> (Jones et al. 1990) + % \cite[chap. 2]{key} ==>> (Jones et al. 1990, chap. 2) + % On the first citation of any work, the full author list is used. +%%--------------------------------------------------------------------- + +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + } + {} + { label extra.label sort.label } %%%KCB: removed long.label + +INTEGERS { output.state before.all mid.sentence after.sentence after.block } + +%%%KCB: new output state, no.comma +INTEGERS { no.comma} + +FUNCTION {init.state.consts} +{ #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := + #4 'no.comma := %%%KCB +} + +STRINGS { s t } + + +%%%KCB: revise to use new output state +FUNCTION {output.nonnull} +{ 's := + output.state no.comma = %%%KCB: if in this state, write a space + { " " * write$ before.all 'output.state := } %%%KCB + { output.state mid.sentence = %%%KCB: add brace at beginning + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + } + if$ + mid.sentence 'output.state := + } %%%KCB: matching brace + if$ %%%KCB: test for no.comma state + s +} + +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} + +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} + +FUNCTION {output.bibitem} +{ newline$ + "\harvarditem[" write$ + label write$ + "]{" label * "}{" * write$ %%%KCB: replaced long.label with label + year duplicate$ empty$ + { pop$ "????" } + 'skip$ + if$ + extra.label * "}{" * write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {fin.entry} +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} + +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} + +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} + +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} + +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} + +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} + +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\emph{" swap$ * "}" * } + if$ +} + +INTEGERS { nameptr namesleft numnames } + +%%%KCB: Here is a portable set of routines to check for repeated authors +%%% and replace them with a 3 em dash +%%% According to _Chicago_Manual_of_Style_, a dash is used to +%%% replace an author's name only if the entire list is the same, so: +%%% Compare names. See if prev.name is identical to CurrList. +%%% If not, then just output CurrList. +%%% If so, replace with \BySame +%%% I use a conservative matching scheme, +%%% and check to see if full names are identical, +%%% even though we only use initials. +%%% This may mean that you have to correct your .bib database +%%% to ensure consistency. +%%% +%%% sample usage: +%%% search for {format.author} + +INTEGERS {currNum prevNum} + +STRINGS {PrevList CurrList prev.name PrevTemp CurrTemp} + +%%%KCB: Initialize prev.name ot some nonsense string: + +FUNCTION {init.prev.name} +{ +"My puppy Harley" 'prev.name := +} + +%%%KCB: Must EXECUTE {init.prev.name} after READ + +%%%KCB: compare.names +FUNCTION {compare.names} +{ prev.name 'PrevList := + duplicate$ 'prev.name := % make current list the new previous list + 'CurrList := + PrevList num.names$ 'prevNum := + CurrList num.names$ 'currNum := +% + prevNum currNum = %% If prevNum = currNum + { "" 'PrevTemp := %% Then take this branch + "" 'CurrTemp := + #1 'nameptr := %%% start with first name in each list + { nameptr prevNum > not }%% while nameptr <= prevNum +%% Format both lists the same way. +%% Use full names: bib file might need to be corrected + { + PrevList nameptr "{ff }{vv~}{ll}{ jj}" format.name$ PrevTemp * 'PrevTemp := + CurrList nameptr "{ff }{vv~}{ll}{ jj}" format.name$ CurrTemp * 'CurrTemp := + nameptr #1 + 'nameptr := } + while$ +%% Now compare : + PrevTemp CurrTemp = %% If PrevTemp = CurrTemp + { "\BySame{}" } + { CurrList } + if$ + } + { CurrList } %% If not prevNum = currNum + if$ %% If prevNum = currNum +} + +%%% +%%% +%%%KCB: End of new routine. + + +FUNCTION {format.names} +{ 's := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { nameptr #1 > + { s nameptr "{ff~}{vv~}{ll}{, jj}" format.name$ 't := } + { s nameptr "{vv~}{ll}{, jj}{, ff}" format.name$ 't := } + if$ + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + ", " * + t "others" = + { " et~al." * } + { " {and} " * t * } %%%CDC: removed \small + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + + +FUNCTION {format.names.ed} +{ 's := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ff~}{vv~}{ll}{, jj}" + format.name$ 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + ", " * + t "others" = + { " et~al." * } + { " {and} " * t * } %%%CDC: removed \small + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {format.key} +{ empty$ + { key field.or.null } + { "" } + if$ +} + +FUNCTION {format.authors} +{ author empty$ + { "" } + { "\textsc{" author compare.names format.names * "} " * } + if$ +} + +FUNCTION {format.editors} +{ editor empty$ + { "" } + { "\textsc{" editor compare.names format.names * " }" * + editor num.names$ #1 > + { " (eds.)" * } + { " (ed.)" * } + if$ + } + if$ +} + +FUNCTION {format.in.editors} +{ editor empty$ + { "" } + { editor format.names.ed + } + if$ +} + +FUNCTION {format.title} +{ title empty$ + { "" } + { title + "``" swap$ * ",'' " * + } +no.comma 'output.state := %%%KCB: + if$ +} + +FUNCTION {n.dashify} +{ 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ "in " } + +FUNCTION {format.date} +{ year duplicate$ empty$ + { "empty year in " cite$ * "; set to ????" * warning$ + pop$ "????" } + 'skip$ + if$ + " (" swap$ * extra.label * "):" * %%%KCB: added : + before.all 'output.state := %%%KCB: +} + +FUNCTION {format.btitle} +{ title emphasize +no.comma 'output.state := %%%KCB: +} + +FUNCTION {tie.or.space.connect} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ * * +} + +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} + +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { "vol." volume tie.or.space.connect + series empty$ + 'skip$ + { " of " * series emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} + +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { output.state mid.sentence = + { "no." } + { "No." } + if$ + number tie.or.space.connect + series empty$ + { "there's a number but no series in " cite$ * warning$ } + { " in " * series * } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition empty$ + { "" } + { output.state mid.sentence = + { edition "l" change.case$ " edn." * } + { edition "t" change.case$ " edn." * } + if$ + } + if$ +} + +INTEGERS { multiresult } + +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} + +FUNCTION {format.pages} +{ pages empty$ + { "" } + { pages multi.page.check + { "pp." pages n.dashify tie.or.space.connect } + { "p." pages tie.or.space.connect } + if$ + } + if$ +} + +FUNCTION {format.vol.num.pages} +{ volume field.or.null + number empty$ + 'skip$ + { "(" number * ")" * * + volume empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + } + if$ + pages empty$ + 'skip$ + { duplicate$ empty$ + { pop$ format.pages } + { ", " * pages n.dashify * } + if$ + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { "chap." } + { type "l" change.case$ } + if$ + chapter tie.or.space.connect + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.in.ed.booktitle} +{ booktitle empty$ + { "" } + { editor empty$ + { word.in booktitle emphasize * } + { word.in booktitle emphasize * ", ed. by " * %%%KCB: abbrv ed. + format.in.editors * } + if$ + } + if$ +} + +FUNCTION {format.thesis.type} +{ type empty$ + 'skip$ + { pop$ + type "t" change.case$ + } + if$ +} + +FUNCTION {format.tr.number} +{ type empty$ + { "Discussion Paper" } %%%KCB: changed default + 'type + if$ + number empty$ + { "t" change.case$ } + { number tie.or.space.connect } + if$ +} + +FUNCTION {format.article.crossref} +{ + word.in + "\cite{" * crossref * "}" * +} + +FUNCTION {format.book.crossref} +{ volume empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + word.in + } + { "vol." volume tie.or.space.connect + " of " * + } + if$ + "\cite{" * crossref * "}" * +} + +FUNCTION {format.incoll.inproc.crossref} +{ + word.in + "\cite{" * crossref * "}" * +} + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + format.title "title" output.check + crossref missing$ + { journal emphasize "journal" output.check + format.vol.num.pages output + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + note output + fin.entry +} + +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + format.btitle "title" output.check + mid.sentence 'output.state := %%%KCB: + crossref missing$ + { format.bvolume output + format.number.series output + new.sentence + publisher "publisher" output.check + address output + } + { + format.book.crossref output.nonnull + } + if$ + format.edition output + note output + fin.entry +} + +FUNCTION {booklet} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + format.title "title" output.check + howpublished output + address output + note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + format.btitle "title" output.check + crossref missing$ + { format.bvolume output + format.chapter.pages "chapter and pages" output.check + format.number.series output + new.sentence + publisher "publisher" output.check + address output + } + { format.chapter.pages "chapter and pages" output.check + format.book.crossref output.nonnull + } + if$ + format.edition output + note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + format.title "title" output.check + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + publisher "publisher" output.check + address output + format.edition output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + note output + fin.entry +} + +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + format.title "title" output.check + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + address output + new.sentence + organization output + publisher output + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + note output + fin.entry +} + +FUNCTION {conference} { inproceedings } + +%%%KCB: special issue of a journal with a special editor + +FUNCTION {issue} +{ output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + format.btitle "title" output.check + crossref missing$ + { journal emphasize "journal" output.check + format.vol.num.pages output + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + note output + fin.entry +} + +FUNCTION {manual} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + format.btitle "title" output.check + organization output + address output + format.edition output + note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + format.title "title" output.check + "Master's thesis" format.thesis.type output.nonnull + school "school" output.check + address output + note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + format.title output + howpublished output + note output + fin.entry +} + +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + format.title "title" output.check + "Ph.D. thesis" format.thesis.type output.nonnull + school "school" output.check + address output + note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + format.btitle "title" output.check + format.bvolume output + format.number.series output + address output + new.sentence + organization output + publisher output + note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + format.title "title" output.check + format.tr.number output.nonnull + institution "institution" output.check + address output + note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + format.title "title" output.check + note "note" output.check + fin.entry +} + +FUNCTION {default.type} { misc } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +%%%KCB: removed computer science journal macros +%%%KCB: added following macros for editions +MACRO {first} {"1st "} +MACRO {second} {"2d "} +MACRO {third} {"3d "} +MACRO {fourth} {"4th "} +MACRO {fifth} {"5th "} +MACRO {sixth} {"6th"} +MACRO {seventh} {"7th "} +MACRO {eighth} {"8th "} +MACRO {ninth} {"9th "} +MACRO {tenth} {"10th "} + +READ + +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} + +INTEGERS { len } + +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} + + +%%%KCB: Change format of labels: +FUNCTION {format.lab.names} +{ 's := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr "{vv~}{ll}" format.name$ 't := + nameptr #1 > + { namesleft #1 > + { ", " * t * } + { numnames #2 > + { "," * } + 'skip$ + if$ + t "others" = + { " et~al." * } + { " and " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +%%%KCB: removed FUNCTION {format.long.lab.names} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.label} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ +} + +%%%KCB: use entire year field as label + +FUNCTION {calc.label} +{ type$ "book" = + type$ "inbook" = + or + type$ "issue" = %%%KCB: need for new entry type + or %%%KCB + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + ", " + * + year field.or.null + * + 'label := +} + +FUNCTION {calc.short.label} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + 'label := +} + +%%%KCB: removed FUNCTION {calc.long.label} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { nameptr #1 > + { " " * } + 'skip$ + if$ + s nameptr + "{vv{ } }{ll{ }}{ f{ }}{ jj{ }}" + format.name$ 't := + nameptr numnames = t "others" = and + { "et al" * } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} + +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} + +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} + +FUNCTION {editor.sort} +{ editor empty$ + { key empty$ + { "to sort, need editor or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ +} + +FUNCTION {presort} +{ calc.label + label sortify + " " + * + type$ "book" = + type$ "inbook" = + or + type$ "issue" = %%%KCB: need for new entry type + or %%%KCB + 'author.editor.sort + { type$ "proceedings" = + 'editor.sort + 'author.sort + if$ + } + if$ + #1 entry.max$ substring$ + 'sort.label := + sort.label + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} + +SORT + +STRINGS { last.label next.extra } + +INTEGERS { last.extra.num } + +FUNCTION {initialize.extra.label.stuff} +{ #0 int.to.chr$ 'last.label := + "" 'next.extra := + #0 'last.extra.num := +} + +FUNCTION {forward.pass} +{ last.label label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + label 'last.label := + } + if$ +} + +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + extra.label 'next.extra := +} + +EXECUTE {initialize.extra.label.stuff} + +EXECUTE {init.prev.name} %%%KCB: needed for replacing repeated authors + +ITERATE {forward.pass} + +REVERSE {reverse.pass} + +FUNCTION {bib.sort.order} +{ sort.label + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := + calc.short.label +%%%KCB: removed calc.long.label +} + +ITERATE {bib.sort.order} + +SORT + +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ +%%%KCB: add defn of \BySame, can be overridden in document +"\ifx\undefined\BySame" write$ newline$ + "\newcommand{\BySame}{\leavevmode\rule[.5ex]{3em}{.5pt}\ }" + write$ newline$ + "\fi" write$ newline$ +%%% KCB: For those who still cling to LaTeX 2.09 +"\ifx\undefined\textsc" write$ newline$ + "\newcommand{\textsc}[1]{{\sc #1}}" + write$ newline$ + "\newcommand{\emph}[1]{{\em #1\/}}" + write$ newline$ +"\let\tmpsmall\small" write$ newline$ +"\renewcommand{\small}{\tmpsmall\sc}" write$ newline$ + "\fi" write$ newline$ +%%% + "\begin{thebibliography}{}" write$ newline$ +} + +EXECUTE {begin.bib} + +EXECUTE {init.state.consts} + +ITERATE {call.type$} + +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +"Done." top$ %%%KCB: Signal end for NeXTeX's TexView +} + +EXECUTE {end.bib} +%% End of customized bst file diff --git a/Documentation/NARK/econtex.cls b/Documentation/NARK/econtex.cls new file mode 100644 index 000000000..fdf0f7726 --- /dev/null +++ b/Documentation/NARK/econtex.cls @@ -0,0 +1,360 @@ +%%% LaTeX class for economics +%%% +%%% author: Christopher Carroll +%%% license: LaTeX Project Public License +%%% +%%% Modified from style itaxpf by Arne Henningsen +%%% version: 1.0 (09 July 2007) +%%% license: LaTeX Project Public License +%%% +%%% Further information is available at +%%% http://www.uni-kiel.de/agrarpol/ahenningsen/latex-bibtex.html +%%% +%%% This document class is based on the "scrartcl" class +%%% from the KOMA script bundle. For documentation, Google KOMA-Script Documentation +%%% +%%% The corresponding BibTeX style file "econtex.bst" should be used +%%% for the bibliography: \bibliographystyle{econtex} +%%% +%%% In addition to the options for scrartcl, this package includes an additional option +%%% titlepage = include a title page (with command "\maketitle", or "\maketitleWithForcedDate{}") +%%% +%%% If an abstract is defined by \begin{abstract}\end{abstract} before \maketitle is invoked, +%%% it will appear on the title page +%%% +%%% If the titlepage option is invoked: +%%% \keywords can be used to show keywords below the abstract +%%% \jelclass can be used to show JEL classifications below the abstract +%%% \thanks{text of the thank-you footnote} produces a footnote at the bottom of the page containing the text + +\ProvidesClass{econtex}[2016/05/27 LaTeX class for economics papers, handouts, and exam questions written by Christopher Carroll] +\NeedsTeXFormat{LaTeX2e} +% \RequirePackage{rotating} + +\RequirePackage{ifthen} +\RequirePackage{chngpage} + +% If unspecified whether pdf output (instead of dvi) is being used, determine that it SHOULD be +\newif\ifdvi\dvitrue +\@ifundefined{pdfoutput}{}{\ifnum\pdfoutput>0 \dvifalse\fi} + +% permit double spacing +\RequirePackage{setspace} +%\doublespacing + +% option to include a title page +\newcounter{IncludeTitlePage} +\setcounter{IncludeTitlePage}{0} +\DeclareOption{titlepage}{\setcounter{IncludeTitlePage}{1}} + +% pass any options on to the scrartcl class and load this class with some options +\DeclareOption*{\PassOptionsToClass{\CurrentOption}{scrartcl}} +\ProcessOptions\relax +\LoadClass[fontsize=12pt,english,numbers=noenddot,captions=tableheading,captions=nooneline]{scrartcl} +% pointlessnumbers = do not add final dot after ordinary section numbers +% tablecaptionabove = use \captionabove for captions above rather than below tables +% noonelinecaption = do not treat one-line captions differently + +% use new TeX encoding scheme (T1), which contains accented (European) letters, +% to make European hyphenations work +\RequirePackage[T1]{fontenc} + +% use babel package and set language to english +\RequirePackage[english]{babel} + +% spacing around formulas +\AtBeginDocument{ +\setlength{\abovedisplayshortskip}{6pt} +\setlength{\belowdisplayshortskip}{6pt} +\setlength{\abovedisplayskip}{6pt} +\setlength{\belowdisplayskip}{6pt} +\allowdisplaybreaks % Permits LaTeX to break long formulas across pages (?) +} + +%% Added the above info to the definition of normalsize +\def\normalsize{\@setfontsize \normalsize \@xiipt {14.5} +\abovedisplayskip 6\p@ \@plus 3\p@ \@minus 3\p@ +\belowdisplayskip \abovedisplayskip +\abovedisplayshortskip 6pt \belowdisplayshortskip 6pt +\let \@listi \@listI} +\normalsize + +% Don't indent the second and later lines of footnotes +\deffootnote[1em]{0.0em}{1em}{\textsuperscript{\thefootnotemark}} + +% spacing around captions and floats +\setlength{\abovecaptionskip}{6pt} +\setlength{\belowcaptionskip}{3pt} +\setlength{\floatsep}{0pt} +\setlength{\textfloatsep}{20pt} +\setlength{\intextsep}{15pt} + +\RequirePackage{calc} + +\newenvironment{Description} +{\begin{list}{}{\let\makelabel\Descriptionlabel + \setlength\labelwidth{75pt} + \setlength\labelsep{0pt} + \setlength\leftmargin{75pt} + \setlength\itemindent{0pt} +}} +{\end{list}} +\newcommand*\Descriptionlabel[1]{\textbf{#1}\hfil} + +\RequirePackage{cancel,verbatim, +amsmath,amssymb,amsfonts,amsthm +,threeparttable,dcolumn,multicol,multirow,booktabs % table environment extensions +,latexsym +,afterpage +,endnotes +,moreverb +,hhline % Allow double hlines +,tipa % Define more characters, in particular the \textthorn character +,color +} + +\RequirePackage{econtex} +\RequirePackage[mathscr]{eucal} +\RequirePackage[normalem]{ulem} +\providecommand\bmmax{0} % Prevent boldmath package from using up too much memory +\RequirePackage{bm} % Allow bold math +\RequirePackage{bbm} % Blackbord math + +% font for URLs +\RequirePackage{url} +\def\UrlFont{\ttfamily} + +\usepackage[econtex]{optional} % Enable the control of compilation using options passed on startup +\opt{EndFloats}{\RequirePackage[noheads,nolists,tablesfirst,nomarkers]{endfloat}} + +% page format, margins if PrintGeom option is invoked +\opt{PrintGeom}{ +\RequirePackage{geometry} +\geometry{letterpaper, tmargin=1.4in, bmargin=1.4in, lmargin=1.25in, + rmargin=1.25in, headheight=0in, headsep=0in, footskip=0.5in } +} + +\opt{JournalFormatting}{ % BEJM requires 1.5 inch margins +\RequirePackage{geometry} +\geometry{letterpaper, tmargin=1.5in, bmargin=1.5in, lmargin=1.5in, + rmargin=1.5in, headheight=0in, headsep=0in, footskip=0.5in } +} + +\providecommand{\textSizeDefault}{\normalsize} +\opt{bigWide}{\renewcommand{\textSizeDefault}{\large}} + +\providecommand{\abstractSizeDefault}{\small} +\opt{bigWide}{\renewcommand{\abstractSizeDefault}{\normalsize}} + +%% formatting section headers +\setkomafont{section}{\normalfont \Large \bfseries} +\setkomafont{subsection}{\normalfont \large} +\setkomafont{subsubsection}{\normalfont \itshape} + +% citations and bibliography +\RequirePackage[authoryear]{natbib} +\AtBeginDocument{\renewcommand{\refname}{\clearpage References} +\renewcommand\@makefnmark + {\mbox{\textsuperscript{\tiny\@thefnmark}}} % Restore footnote defn after titlepage redefines it +\ifdvi + \renewcommand{\ushort}{\underline} % ushort seems to not work properly for tex4ht (which compiles using dvi) +\fi +} + +% captions of figures and tables +\setcapwidth[c]{\textwidth} +% \setcapindent*{0pt} +\setkomafont{captionlabel}{\centering \bfseries} +\setkomafont{caption}{\centering} +\renewcommand{\captionformat}{~ } + +\newcommand{\forcedate}[1]{\newcommand{\@forcedate}{#1}} + +% titlepage with title, author(s), and date + +\renewcommand{\maketitle}{ +\ifthenelse{ \isundefined\@forcedate }{ + \ClassWarningNoLine{econtex}{{\protect\forcedate} is undefined in the .tex file; please define it and rerun}}{} + \ifthenelse{\equal{\theIncludeTitlePage}{1}}{ + \let\footnotesize\scriptsize + \thispagestyle{empty} + \vspace*{0mm} + \enlargethispage{0.4in} + }{} + \begin{center} + \begin{LARGE} + \begin{spacing}{1.00} + \textbf{\@title} + \end{spacing} + \end{LARGE}\medskip\medskip\medskip + {\normalsize \@date \par} + {\tiny{~}\par} + \ifthenelse{\equal{\theIncludeTitlePage}{1}}{ + \vspace*{3mm} + {\large + \lineskip .75em% + \begin{tabular}[t]{c}% + \@author + \end{tabular}\par}% + }{} + \end{center} +} + + +\newcommand{\maketitleWithForcedDate}[1]{ +\ClassWarningNoLine{econtex}{\protect\maketitleWithForcedDate{} is deprecated. To set a date different from the current one, just use the \protect\date{(string desired to signify paper date)}} + + \ifthenelse{\equal{\theIncludeTitlePage}{1}}{ + \let\footnotesize\scriptsize + \thispagestyle{empty} + \vspace*{0mm} + \enlargethispage{0.4in} + }{} + \begin{center} + \begin{LARGE} + \begin{spacing}{1.00} + \textbf{\@title} + \end{spacing} + \end{LARGE}\medskip\medskip\medskip + {\normalsize #1 \par} + {\tiny{~}\par} + \ifthenelse{\equal{\theIncludeTitlePage}{1}}{ + \vspace*{3mm} + {\large + \lineskip .75em% + \begin{tabular}[t]{c}% + \@author + \end{tabular}\par}% + }{} + \end{center} +} + + +% abstract, keywords, JEL classification +\newcommand{\keywords}[1]{\newcommand{\@keywords}{#1}} +\newcommand{\jelclass}[1]{\newcommand{\@jelclass}{#1}} +\newcommand{\aspublished}[1]{\newcommand{\@aspublished}{#1}} + +\renewenvironment{abstract}{% + \begin{spacing}{0.9} + \noindent {\tiny \phantom{.}} \\ % Trick to get proper spacing in html + \noindent \hrule height 0.4pt depth 0.0pt width \textwidth \relax + \vspace*{4mm} + \abstractSizeDefault + \noindent \textbf{Abstract}\\ + \indent +}{% + \noindent {\tiny \phantom{.}} \\ % Trick to get proper spacing in html + \noindent \hrule height 0.4pt depth 0.0pt width \textwidth \relax + \vspace*{0mm} + \ifthenelse{ \isundefined\@keywords }{ + \ClassWarningNoLine{econtex}{No keywords specified. + Please use the command \protect\keywords} + }{ + } + \end{spacing} + +\begin{quote} +\begin{Description} +\item[\textbf{Keywords~}] \@keywords +\ifthenelse{ \isundefined\@jelclass }{ +\ClassWarningNoLine{econtex}{No JEL classification specified. + Please use the command \protect\jelclass} +}{ +\item[\textbf{JEL codes~}] \@jelclass +} + +\end{Description} +\ifthenelse{ \isundefined\@aspublished }{ +}{ +{ + +\vspace{0.1in}\@aspublished} +}\end{quote} +} + +\setcounter{footnote}{0}% + +\medskip + +% avoid clubs and widows +\clubpenalty=1000 % penalty for breaking a page with only one sentence; set to 10000 to prohibit +\widowpenalty=10000 +% \displaywidowpenalty=10000 + +\hbadness 3000 % LaTeX default is 1000, which is too finicky - set to 10000 to get rid of complaints + +% increase stretchability of the spaces (avoid overfull hboxes) +\setlength{\emergencystretch}{3em} + + +\pagestyle{plain} +\thispagestyle{empty} + +\newcommand{\titlepagefinish}{\newpage +\renewcommand\@makefnmark + {\mbox{\textsuperscript{\tiny\@thefnmark}}} +} + + +\RequirePackage[multiple]{footmisc} +\RequirePackage{manyfoot,perpage} +\DefineFNsymbols{blank}[text]{{} {} {} {} {}} +\setfnsymbol{blank} +\DeclareNewFootnote{B}[fnsymbol] +\MakePerPage{footnoteB} + + +\renewcommand{\thanks}{\footnoteB} +\newcommand{\thanksFooter}{\footnoteB} + +\hfuzz2pt % Don't bother to report over-full boxes if over-edge is < 2pt + +%%%%%%%%%% Added by Amy Hendrickson, TeXnology Inc. +%% Amy change, Feb 28, 2011 ==>> + +%%% Generalization of the author and authorinfo commands, based on +%%% the original code. + +%% Sample use: +%% \author{Christopher D. Carroll\authNum \\ +%% \and +%% Jiri Slacalek\authNum \\ +%% \and +%% Martin Sommer\authNum \\ +%% \and +%% Another Name\authNum +%% \and +%% Still Another Name\authNum +%% } + +\newcounter{authornum} % Use either \num or (preferred) \authNum to denote authors +\def\num{\global\advance\c@authornum by 1\textsuperscript{\tiny \the\c@authornum}} +\def\authNum{\global\advance\c@authornum by 1\textsuperscript{\tiny \the\c@authornum}} + +%% \begin{authorsinfo}...\end{authorsinfo} + +%% Now used: +%% \begin{authorsinfo} +%% \name{First author: info} +%% \\ +%% \name{Second author: info} +%% \\ +%% \name{Third author: info} +%% (and so on for as many authors +%% as you have) +%% \end{authorsinfo} +%% (note: the \\ doesn't do anything, but makes it easier to +%% see the separate author names.) + +\newcounter{bottomauthor} +\def\authorsinfo{\global\c@bottomauthor=0\setbox0=\vbox\bgroup\footnotesize +\let\\ \relax +} + +\def\endauthorsinfo{\egroup +\footnoteB{\unvbox0} +} + +\def\name#1{\global\advance\c@bottomauthor by 1\textsuperscript{\tiny \the\c@bottomauthor}{#1}{~~~~}} + diff --git a/Documentation/NARK/econtex.sty b/Documentation/NARK/econtex.sty new file mode 100644 index 000000000..fcefb5736 --- /dev/null +++ b/Documentation/NARK/econtex.sty @@ -0,0 +1,34 @@ +%---------------Custom Definitions-------------- + +\def\liminf{\mathop{\rm lim\, inf}\limits} +\DeclareMathOperator{\Ex}{\mathbb{E}} % Expectations operator +\def\var{\operatorname{var}} % variance +\def\cov{\operatorname{cov}} % covariance +\def\std{\operatorname{std}} % standard deviation +\def\argmax{\operatornamewithlimits{arg\,max}} % argmax + +%---------------Custom Modifications---------------------- + +% \htline defines a thick horizontal line in table +\def\htline{\noalign{\hrule height 2\arrayrulewidth}} + +% \hdline defines a dotted horizontal line in table +\def\hdline{\noalign{.\dotfill{}.}} + +%To keep footnotes on a single page except in extreme cases +\interfootnotelinepenalty=5000 %from 0 to 10000 + +%To prevent hyphenation +\hyphenpenalty=5000 %from 0 to 10000, default is 200 + +%To prevent breaking math formula +\relpenalty=4000 %from 0 to 10000, default is 500 + +%To prevent breaking math formula after binary operator +\binoppenalty=10000 %from 0 to 10000, default is 700 + +%-------------------------------------------------------- + + + + diff --git a/Documentation/NARK/econtexBibMake.tex b/Documentation/NARK/econtexBibMake.tex new file mode 100755 index 000000000..dafc34bf8 --- /dev/null +++ b/Documentation/NARK/econtexBibMake.tex @@ -0,0 +1,7 @@ + +% \write18{if [ ! -f economics.bib ]; then touch economics.bib ; fi} +\write18{if [ ! -f \jobname.bib ]; then touch \jobname.bib ; fi} +\write18{if [ ! -f \jobname-Add.bib ]; then touch \jobname-Add.bib ; fi} + +\bibliographystyle{econtex} +\bibliography{economics,\jobname,\jobname-Add} diff --git a/Documentation/NARK/econtexSetup.sty b/Documentation/NARK/econtexSetup.sty new file mode 100644 index 000000000..29aa7a880 --- /dev/null +++ b/Documentation/NARK/econtexSetup.sty @@ -0,0 +1,236 @@ +\RequirePackage{wasysym +,psibycus % Greek language package, including koppa +,ushort +%,lscape % Pages in landscape mode go inside \begin{landscape}\end{landscape} % CONFLICTS WITH EPSTOPDF +} + +\newenvironment{CDCPrivate} % Environment for extra material not wanted in the public document +{\marginpar{\tiny beginCDC}}{\marginpar{\tiny endCDC}} % CDCPrivate + +\provideboolean{Web} +\setboolean{Web}{false} % reset to true if running via dvi; search for \ifdvi below + +\provideboolean{bigPrint} +\setboolean{bigPrint}{true} +\setboolean{bigPrint}{false} + +\provideboolean{wideMargins} +\setboolean{wideMargins}{true} +\setboolean{wideMargins}{false} + +\provideboolean{BigAndWide} +\setboolean{BigAndWide}{true} +\setboolean{BigAndWide}{false} + +% Options to set if being run from the shell (so autocompiled) +\opt{FromShell}{ +\setboolean{bigPrint}{false} +\setboolean{wideMargins}{false} +\setboolean{BigAndWide}{false} +} + +\opt{bigWide}{ +\setboolean{bigPrint}{true} +\setboolean{wideMargins}{true} +\setboolean{BigAndWide}{true} +\providecommand{\textSizeDefault}{\large} +\providecommand{\abstractSizeDefault}{\large} +} + +\opt{Aquamacs}{ +%\setboolean{bigPrint}{true} +%\setboolean{wideMargins}{true} +%\setboolean{BigAndWide}{true} +%\providecommand{\textSizeDefault}{\large} +%\providecommand{\abstractSizeDefault}{\large} +} + +\provideboolean{KoppaOn} +\setboolean{KoppaOn}{true} +%\setboolean{KoppaOn}{false} + +\ifthenelse{\boolean{KoppaOn}}{ +\providecommand{\koppa}{\text{\greek{k+}}} +\providecommand{\Koppa}{\text{\greek{K+}}} +\providecommand{\sampi}{\text{\greek{s+}}} +\providecommand{\cigma}{\text{\greek{c+}}} +\providecommand{\Cigma}{\text{\greek{C+}}} +}{ +\def\koppa{\mathbf{q}}\providecommand{\koppa}{$\mathbf{q}$}\providecommand{\Koppa}{$\mathbf{Q}$} +} + +% Keep the elements in the argument #1 together on the same page +\newcommand{\together}[1]{\noindent\parbox{\linewidth}{#1}} + +% html version of document must be constructed by running pdflatex in +% dvi output mode; code below creates an ifdvi construct that detects +% whether running in dvi mode and sets the options necessary to generate +% html version of document using tex4ht + +%\makeatletter +%\newif\ifdvi\dvitrue +%\@ifundefined{pdfoutput}{}{\ifnum\pdfoutput>0 \dvifalse\fi} +%\makeatother + +% dvi compilation only occurs if making the html version +\ifdvi + \setboolean{Web}{true} + \RequirePackage{tex4ht} + \RequirePackage{graphicx} + \RequirePackage[tex4ht]{hyperref} + \provideboolean{bigPrint}\setboolean{bigPrint}{true} % HTM output looks better in a larger font size + \providecommand{\wideMargins}{\null{}} % Wide margins doesn't mean anything for html documents + \newcommand{\CDCFig}[1]{\centerline{\includegraphics{#1}}} + \newcommand{\CDCFigSideways}[1]{\centerline{\includegraphics{#1}}} + \DeclareGraphicsExtensions{.png} + \providecommand{\textSizeDefault}{\large} + \providecommand{\titlepagefinish}{\newpage\textSizeDefault} + \providecommand{\abstractSizeDefault}{\large} + \providecommand{\ushort}{\underline} % ushort seems to not work properly for tex4ht + \let\footnoterule\relax + \makeatletter + \renewenvironment{abstract}{% + \begin{spacing}{0.9} + \noindent {\tiny \phantom{.}} \\ % Trick to get proper spacing in html + \noindent \hrule height 0.4pt depth 0.0pt width \textwidth \relax + \vspace*{5mm} + \noindent \textbf{Abstract}\\ + \indent \abstractSizeDefault + }{% + \noindent {\tiny \phantom{.}} \\ % Trick to get proper spacing in html +% \noindent \hrule height 0.4pt depth 0.0pt width \textwidth \relax + \vspace*{3mm} + \ifthenelse{ \isundefined\@keywords }{ + \ClassWarningNoLine{bejournal}{No keywords specified. + Please use the command \protect\keywords} + }{ + } + \end{spacing} + \begin{quote} + \begin{Description} + \item[\textbf{~~~~~~~~~~~~Keywords~}] \@keywords + \ifthenelse{ \isundefined\@jelclass }{ + \ClassWarningNoLine{bejournal}{No JEL classification specified. + Please use the command \protect\jelclass} + }{ + \item[\textbf{~~~~~~~~~~~~JEL codes~}] \@jelclass + \end{Description} + } + \end{quote} + \makeatother + } +\else + \RequirePackage[pdftex]{graphicx} + \RequirePackage[plainpages=false]{hyperref} + \hypersetup{ + pdffitwindow = false, % Don't rescale to fit size of window + pdfpagemode=UseThumbs, + pdfstartview=FitH, + pdfnewwindow = true, % Links open in new window + pdfproducer = {pdfLaTeX with hyperref} + } + \providecommand{\wideMargins}{\usepackage[twoside,marginparwidth=0in,left=0.5in,right=0.5in,top=0.5in,bottom=0.5in]{geometry}\usepackage{layouts}} + \ifthenelse{\boolean{wideMargins}}{\wideMargins}{} + \newcommand{\CDCFig}[1]{\centerline{\includegraphics[width=0.75\textwidth]{../Figures/#1}}} + \newcommand{\CDCFigSideways}[1]{\centerline{\includegraphics[width=7.8in,height=5.5in]{../Figures/#1}}} + \DeclareGraphicsExtensions{.pdf} + \ifthenelse{\boolean{bigPrint}}{\renewcommand{\titlepagefinish}{\newpage\large}}{} +\fi + +\newcolumntype{d}[1]{D{.}{.}{#1}} % Seems to need to come after hyperref + +\definecolor{darkblue}{rgb}{0.1,0.094,0.3} +\definecolor{darkred}{rgb}{0.3,0,0} +\hypersetup{colorlinks=true, % put a box around links + linkbordercolor = {1 0 0}, % the box will be red + pdfborder = {1 0 0}, % +% bookmarks=true, % PDF will contain an index on the RHS + urlcolor=darkred, + citecolor=darkblue, + linkcolor=darkred +} + +%% Define the title table as different from the regular table + +%\let\tabularTitle\tabular +%\let\endtabularTitle\endtabular + +%\newenvironment{tabularTitle}{\tabularTitle}{\endtabularTitle} +\newenvironment{tabularTitle}{\tabular}{\endtabular} + +\providecommand{\SolvingMicroDSOPs}{\href{http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/}{SolvingMicroDSOPs}} + +\newcommand{\handoutC}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/#1.pdf}{\texttt{#1}}} +\newcommand{\handoutA}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/AssetPricing/#1.pdf}{\texttt{#1}}} +\newcommand{\handoutI}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/Investment/#1.pdf}{\texttt{#1}}} +\newcommand{\handoutG}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/Growth/#1.pdf}{\texttt{#1}}} +\newcommand{\handoutF}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/MathFacts/#1.pdf}{\texttt{#1}}} +\newcommand{\handoutD}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/DSGEModels/#1.pdf}{\texttt{#1}}} +\newcommand{\MathFactsList}{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/MathFacts/MathFactsList.pdf}{\texttt{MathFacts}}} +\newcommand{\handoutM}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/MathFacts/#1.pdf}{\texttt{#1}}} +\renewcommand{\handoutM}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/MathFacts/MathFactsList/}{\texttt{#1}}} +\newcommand{\Aggregation}{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/MathFacts/Aggregation.pdf}{\texttt{Aggregation}}} + + +% If doing a web version, renewcommands link to the html versions of handouts +\ifdvi +\renewcommand{\handoutC}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/#1}{\texttt{#1}}} +\renewcommand{\handoutA}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/AssetPricing/#1}{\texttt{#1}}} +\renewcommand{\handoutI}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/Investment/#1}{\texttt{#1}}} +\renewcommand{\handoutG}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/Growth/#1}{\texttt{#1}}} +\renewcommand{\handoutD}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/DSGEModels/#1}{\texttt{#1}}} +\renewcommand{\handoutM}[1]{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/MathFactsList/#1}{\texttt{#1}}} +\fi +\renewcommand{\MathFactsList}{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/MathFacts/MathFactsList/}{\texttt{MathFacts}}} +\renewcommand{\Aggregation}{\href{http://econ.jhu.edu/people/ccarroll/public/lecturenotes/MathFacts/Aggregation/}{\texttt{Aggregation}}} + +\providecommand{\CRRALim}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#CRRALim}{\ensuremath{\mathtt{[CRRALim]}}}} +\providecommand{\ELogNorm}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#ELogNorm}{\ensuremath{\mathtt{[ELogNorm]}}}} +\providecommand{\ELogNormMeanOne}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#ELogNormMeanOne}{\ensuremath{\mathtt{[ELogNormMeanOne]}}}} +\providecommand{\ELogNormTimes}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#ELogNormTimes}{\ensuremath{\mathtt{[ELogNormTimes]}}}} +\providecommand{\EulersTheorem}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#EulersTheorem}{\ensuremath{\mathtt{[EulersTheorem]}}}} +\providecommand{\ExpEps}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#ExpEps}{\ensuremath{\mathtt{[ExpEps]}}}} +\providecommand{\ExpPlus}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#ExpPlus}{\ensuremath{\mathtt{[ExpPlus]}}}} +\providecommand{\FinSum}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#FinSum}{\ensuremath{\mathtt{[FinSum]}}}} +\providecommand{\InfSum}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#InfSum}{\ensuremath{\mathtt{[InfSum]}}}} +\providecommand{\LogELogNorm}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#LogELogNorm}{\ensuremath{\mathtt{[LogELogNorm]}}}} +\providecommand{\LogELogNormTimes}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#LogELogNormTimes}{\ensuremath{\mathtt{[LogELogNormTimes]}}}} +\providecommand{\LogEps}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#LogEps}{\ensuremath{\mathtt{[LogEps]}}}} +\providecommand{\LogMeanMPS}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#LogMeanMPS}{\ensuremath{\mathtt{[LogMeanMPS]}}}} +\providecommand{\MeanOne}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#MeanOne}{\ensuremath{\mathtt{[MeanOne]}}}} +\providecommand{\MultPlus}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#MultPlus}{\ensuremath{\mathtt{[MultPlus]}}}} +\providecommand{\NormTimes}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#NormTimes}{\ensuremath{\mathtt{[NormTimes]}}}} +\providecommand{\OverPlus}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#OverPlus}{\ensuremath{\mathtt{[OverPlus]}}}} +\providecommand{\SmallSmallZero}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#SmallSmallZero}{\ensuremath{\mathtt{[SmallSmallZero]}}}} +\providecommand{\SumNormsIsNorm}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#SumNormsIsNorm}{\ensuremath{\mathtt{[SumNormsIsNorm]}}}} +\providecommand{\TaylorOne}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#TaylorOne}{\ensuremath{\mathtt{[TaylorOne]}}}} +\providecommand{\TaylorTwo}{\href{http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/MathFacts/MathFactsList\#TaylorTwo}{\ensuremath{\mathtt{[TaylorTwo]}}}} +\RequirePackage{ifthen} + +\provideboolean{AnswersTF}\setboolean{AnswersTF}{false} % Default is no answers; run with \UseOption{Answers} from shell to generate answers + + +\provideboolean{ExamQ} +\setboolean{ExamQ}{false} +\newcommand{\ifExamQuestion}{\ifthenelse{\boolean{ExamQ}}} + +\provideboolean{SpaceBetweenAnswers} +\setboolean{SpaceBetweenAnswers}{true} +\providecommand{\AnswerOrBlankSpace}{\ifthenelse{\boolean{SpaceBetweenAnswers}}} + +\opt{Answers}{\provideboolean{AnswersTF}\setboolean{AnswersTF}{true}} + +\newcommand{\ifAnswers}{\ifthenelse{\boolean{AnswersTF}}} +\newcommand{\answer}[2]{\ifthenelse{\boolean{AnswersTF}}{ + +{\it Answer:} +\begin{quote} +#1 +\end{quote} +} +{#2}} + + + + + diff --git a/Documentation/NARK/econtexShortcuts.sty b/Documentation/NARK/econtexShortcuts.sty new file mode 100755 index 000000000..c31a9e7c1 --- /dev/null +++ b/Documentation/NARK/econtexShortcuts.sty @@ -0,0 +1,624 @@ +\providecommand{\aAgg}{\ensuremath{\mathsf{A}}} +\providecommand{\aboveMin}{\blacktriangle} +\providecommand{\adjPar}{\ensuremath{\omega}} +\providecommand{\adj}{\ensuremath{\mathrm{j}}} +\providecommand{\aE}{\aRat^{e}} +\providecommand{\aFunc}{\ensuremath{\mathrm{a}}} +\providecommand{\Age}{\ensuremath{Z}} +\providecommand{\age}{\ensuremath{z}} +\providecommand{\ALevBF}{\ensuremath{\mathbf{A}}} +\providecommand{\aLevBF}{\ensuremath{\mathbf{a}}} +\providecommand{\ALev}{\ensuremath{A}} +\providecommand{\aLev}{\ensuremath{a}} +\providecommand{\Alive}{\ensuremath{\cancel{\mathfrak{D}}}} +\providecommand{\Alt}{\grave} +\providecommand{\aMin}{\ensuremath{\underline{\aRat}}} +\providecommand{\ARat}{\ensuremath{A}} +\providecommand{\aRat}{\ensuremath{a}} +\providecommand{\ASS}{\ensuremath{\ensuremath{A}}} +\providecommand{\aSS}{\ensuremath{\ensuremath{a}}} +\providecommand{\ATarg}{\ensuremath{\check{A}}} +\providecommand{\aTarg}{\ensuremath{\check{a}}} +\providecommand{\BE}{\BRat^{e}} +\providecommand{\bE}{\bRat^{e}} +\providecommand{\BLevBF}{\ensuremath{\mathbf{B}}} +\providecommand{\bLevBF}{\ensuremath{\mathbf{b}}} +\providecommand{\BLevE}{\ensuremath{\BLev^{e}}} +\providecommand{\bLevE}{\ensuremath{\bLev^{e}}} +\providecommand{\BLevU}{\ensuremath{\BLev^{u}}} +\providecommand{\bLevU}{\ensuremath{\bLev^{u}}} +\providecommand{\BLev}{\ensuremath{B}} +\providecommand{\bLev}{\ensuremath{b}} +\providecommand{\bMin}{\ensuremath{\underline{\bRat}}} +\providecommand{\bRatE}{\ensuremath{b}^{e}} +\providecommand{\BRatE}{\ensuremath{B}^{e}} +\providecommand{\bRatU}{\ensuremath{b}^{u}} +\providecommand{\BRat}{\ensuremath{B}} +\providecommand{\bRat}{\ensuremath{b}} +\providecommand{\bTargE}{\ensuremath{\check{b}^{e}}} +\providecommand{\BTargTarg}{\Target{\Target{\BRat}}} +\providecommand{\bTargTarg}{\Target{\Target{\bRat}}} +\providecommand{\BTarg}{\ensuremath{\check{B}}} +\providecommand{\bTarg}{\ensuremath{\check{b}}} +\providecommand{\BU}{{B}^{u}} +\providecommand{\bU}{{b}^{u}} +\providecommand{\cAgg}{\ensuremath{\pmb{C}}} +\providecommand{\CARA}{\ensuremath{\alpha}} +\providecommand{\ccRat}{\ensuremath{\mathsf{c}}} +\providecommand{\CDF}{\ensuremath{\mathcal{F}}} +\providecommand{\CEndFunc}{\ensuremath{\mathfrak{C}}} +\providecommand{\cEndFunc}{\ensuremath{\mathfrak{c}}} +\providecommand{\cEss}{{c}^{e}} +\providecommand{\CE}{\CRat^{e}} +\providecommand{\cE}{\cRat^{e}} +\providecommand{\cFuncAbove}{\ensuremath{\bar{\mathrm{c}}}} +\providecommand{\cFuncBelow}{\ensuremath{\uline{\mathrm{c}}}} +\providecommand{\cFuncMax}{\ensuremath{\bar{\bar{\mathrm{c}}}}} +\providecommand{\CFunc}{\ensuremath{\mathrm{C}}} +\providecommand{\cFunc}{\ensuremath{\mathrm{c}}} +\providecommand{\cFunc}{\ensuremath{\mathrm{c}}} +\providecommand{\CGroOverG}{\ensuremath{\Upsilon}} +\providecommand{\CGroOverR}{\ensuremath{\Phi}} +\providecommand{\CGroPF}{\ensuremath{\Lambda}} +\providecommand{\cGroPF}{\ensuremath{\lambda}} +\providecommand{\CGroPS}{\ensuremath{\chi}} % Precautionary Saving boost to consumption growth +\providecommand{\chiFunc}{\pmb{\chi}} +\providecommand{\Chi}{\ensuremath{\mathrm{X}}} % capital chi is sometimes useful, and not native to LaTeX +\providecommand{\CLevBF}{\ensuremath{\mathbf{C}}} +\providecommand{\cLevBF}{\ensuremath{\mathbf{c}}} +\providecommand{\CLevE}{\CLev^{e}} +\providecommand{\cLevE}{\cLev^{e}} +\providecommand{\cLevFunc}{\ensuremath{\pmb{\cFunc}}} +\providecommand{\CLevU}{\CLev^{u}} +\providecommand{\cLevU}{\cLev^{u}} +\providecommand{\cLev}{\ensuremath{c}} +\providecommand{\CLev}{\ensuremath{C}} +\providecommand{\Cons}{\ensuremath{C}} +\providecommand{\cons}{\ensuremath{c}} +\providecommand{\corr}{\varrho} +\providecommand{\cPDVFunc}{\ensuremath{\mathbb{C}}} +\providecommand{\CPDV}{\ensuremath{\text{PDV($C$)}}} +\providecommand{\cPPP}{\cons^{\prime\prime\prime}} +\providecommand{\cPP}{\cons^{\prime\prime}} +\providecommand{\cP}{\cons^{\prime}} +\providecommand{\cRatBF}{\ensuremath{\pmb{c}}} +\providecommand{\CRatE}{\CRat^{e}} +\providecommand{\cRatE}{\cRat^{e}} +\providecommand{\CRatU}{\CRat^{u}} +\providecommand{\cRatU}{\cRat^{u}} +\providecommand{\CRat}{\ensuremath{C}} +\providecommand{\cRat}{\ensuremath{c}} +\providecommand{\CRRA}{\ensuremath{\rho}} +\providecommand{\CTargE}{\CTarg^{\null}} +\providecommand{\cTargE}{\ensuremath{\Target{c}^{e}}} +\providecommand{\cTargTarg}{\Target{\Target{\cRat}}} +\providecommand{\CTarg}{\ensuremath{\Target{C}}} +\providecommand{\cTarg}{\ensuremath{\Target{c}}} +\providecommand{\curr}{1} % Permits a change of notation to T-1 and T, t and t+1, or whatever +\providecommand{\Curr}{t} % Permits a change of notation to T-1 and T, t and t+1, or whatever +\providecommand{\CU}{\CRat^{u}} +\providecommand{\cU}{\cRat^{u}} +\providecommand{\debtLim}{\mathsf{d}} +\providecommand{\Debt}{\ensuremath{D}} +\providecommand{\debt}{\ensuremath{d}} +\providecommand{\DeprFac}{\ensuremath{\daleth}} +\providecommand{\Depr}{\ensuremath{\daleth}} +\providecommand{\depr}{\ensuremath{\delta}} +\providecommand{\deprRte}{\ensuremath{\delta}} % New +\providecommand{\DiscAltuAdj}{\ensuremath{\underline{\underline{\beth}}}} +\providecommand{\DiscAlt}{\ensuremath{\beth}} +\providecommand{\Discount}{\ensuremath{\beta}} +\providecommand{\DiscRate}{\ensuremath{\vartheta}} +\providecommand{\DiscRte}{\ensuremath{\vartheta}} % New +\providecommand{\DiscFac}{\ensuremath{\beta}} % New +\providecommand{\DivGro}{\ensuremath{\mathsf{G}}} +\providecommand{\divGro}{\ensuremath{\mathsf{g}}} +\providecommand{\Div}{\ensuremath{D}} +\providecommand{\div}{\ensuremath{d}} +\providecommand{\DLev}{\ensuremath{D}} +\providecommand{\Dvdnd}{\ensuremath{\mathbf{D}}} +\providecommand{\dvdnd}{\ensuremath{d}} +\providecommand{\edvdnd}{\grave{\dvdnd}} +\providecommand{\EEndMap}{\ensuremath{\mathsf{E}}} +\providecommand{\effUnits}{\ensuremath{X}} +\providecommand{\eFunc}{\ensuremath{\mathrm{e}}} +\providecommand{\ELev}{\ensuremath{E}} +\providecommand{\ek}{\ensuremath{\lambda}} +\providecommand{\EmpGro}{\ensuremath{\Xi}} +\providecommand{\empGro}{\ensuremath{\xi}} +\providecommand{\empState}{\xi} % employment state indicator variable +\providecommand{\EpremLog}{\varphi} % Not using regular \eprem because want to distinguish between \varphi = log E_{t}[\Phi_{t+1}] and \phi_{t} = E[\log \Phi_{t}] +\providecommand{\EPrem}{\Phi} % equity premium +\providecommand{\eprem}{\phi} % log equity premium +\providecommand{\EpShkInv}{\Ex[\pShk^{-1}]} +\providecommand{\erate}{\ensuremath{\cancel{\mho}}} +\providecommand{\error}{\ensuremath{\epsilon}} +\providecommand{\Err}{\ensuremath{Z}} +\providecommand{\err}{\ensuremath{z}} +\providecommand{\err}{\ensuremath{z}} +\providecommand{\Estdr}{\ensuremath{\sigma_{\risky}}} % Standard deviation of log return on risky asset +\providecommand{\Evarr}{\ensuremath{\sigma_{\risky}^{2}}} % Variance of log return on risky asset +\providecommand{\EVarr}{\sigma_{\Risky}^{2}} % Variance of level return on risky asset (when returns norm dist) +\providecommand{\expend}{\ensuremath{\xi}} +\providecommand{\ExplDel}{@!\#\$\%\&!} +\providecommand{\Ex}{\ensuremath{\mathbb{E}}} % Expectations +\providecommand{\FDist}{\ensuremath{\mathcal{F}}} +\providecommand{\fDist}{\ensuremath{\mathcal{f}}} +\providecommand{\FFunc}{\ensuremath{\mathrm{F}}} +\providecommand{\fFunc}{\ensuremath{\mathrm{f}}} +\providecommand{\FLev}{\ensuremath{F}} +\providecommand{\fLev}{\ensuremath{f}} +\providecommand{\fPP}{\ensuremath{\mathrm{f}^{\prime\prime}}} +\providecommand{\FPP}{\ensuremath{\mathrm{F}^{\prime\prime}}} +\providecommand{\fP}{\ensuremath{\mathrm{f}^{\prime}}} +\providecommand{\FP}{\ensuremath{\mathrm{F}^{\prime}}} +\providecommand{\GLev}{\ensuremath{G}} +\providecommand{\GovNW}{\ensuremath{N}} +\providecommand{\govNW}{\ensuremath{n}} +\providecommand{\GovSpend}{\ensuremath{X}} +\providecommand{\govSpend}{\ensuremath{x}} +\providecommand{\Habit}{\ensuremath{H}} +\providecommand{\Habit}{\ensuremath{H}} +\providecommand{\habit}{\ensuremath{h}} +\providecommand{\Ham}{\ensuremath{\mathcal{H}}} % Hamiltonian +\providecommand{\HARKcore}{\texttt{HARKcore}} +\providecommand{\hEndMin}{\ensuremath{\underline{\mathfrak{h}}}} +\providecommand{\hEnd}{\ensuremath{\mathfrak{h}}} +\providecommand{\hFunc}{\ensuremath{\mathrm{h}}} +\providecommand{\Hi}{\hat} +\providecommand{\HLev}{\ensuremath{H}} +\providecommand{\HLevBF}{\ensuremath{\mathbf{H}}} +\providecommand{\hLevBF}{\ensuremath{\mathbf{h}}} +\providecommand{\HLev}{\ensuremath{\pmb{H}}} +\providecommand{\hLev}{\ensuremath{\pmb{h}}} +\providecommand{\hMin}{\ensuremath{\underline{\h}}} +\providecommand{\HMin}{\ensuremath{\underline{H}}} +\providecommand{\hours}{\ensuremath{\mathfrak{h}}} +\providecommand{\Hours}{\ensuremath{\mathfrak{H}}} +\providecommand{\HRat}{\ensuremath{H}} +\providecommand{\hRat}{\ensuremath{h}} +\providecommand{\h}{\ensuremath{h}} +\providecommand{\H}{\ensuremath{H}} +\providecommand{\iFunc}{\ensuremath{\mathrm{i}}} +\providecommand{\IFunc}{\ensuremath{\mathrm{I}}} +\providecommand{\ILev}{\ensuremath{I}} +\providecommand{\iLev}{\ensuremath{i}} +\providecommand{\impg}{{\imath}_{\pGro}} +\providecommand{\ImpG}{{\Im}_{\PGro}} +\providecommand{\impr}{{\imath}_{\rfree}} +\providecommand{\ImpR}{{\Im}_{\Rfree}} +\providecommand{\Inc}{{Y}} +\providecommand{\inc}{{y}} +\providecommand{\InvEpShkInv}{\acute{\psi}} +\providecommand{\InvEpShkInv}{\underline{\psi}} +\providecommand{\Inv}{{I}} +\providecommand{\inv}{{i}} +\providecommand{\IRat}{\ensuremath{I}} +\providecommand{\iRat}{\ensuremath{i}} +\providecommand{\itc}{\ensuremath{\zeta}} +\providecommand{\jFunc}{\ensuremath{\mathrm{j}}} +\providecommand{\JLev}{\ensuremath{J}} +\providecommand{\kapRent}{\ensuremath{\varkappa}} +\providecommand{\kapShare}{\ensuremath{\alpha}} +\providecommand{\Kap}{{K}} +\providecommand{\kap}{{k}} +\providecommand{\KFunc}{\ensuremath{\mathrm{K}}} +\providecommand{\kFunc}{\ensuremath{\mathrm{k}}} +\providecommand{\KLevBF}{\ensuremath{\mathbf{K}}} +\providecommand{\kLevBF}{\ensuremath{\mathbf{k}}} +\providecommand{\KLev}{\ensuremath{K}} +\providecommand{\kLev}{\ensuremath{k}} +\providecommand{\kPriceAfterITC}{\ensuremath{\mathscr{P}}} +\providecommand{\kPrice}{\ensuremath{\mathsf{P}}} +\providecommand{\KRat}{\ensuremath{K}} +\providecommand{\kRat}{\ensuremath{k}} +\providecommand{\kTargE}{\ensuremath{\Target{k}^{e}}} +\providecommand{\kTarg}{\ensuremath{\Target{k}}} +\providecommand{\labor}{\ensuremath{\ell}} % +\providecommand{\Labor}{\ensuremath{L}} +\providecommand{\labShare}{\ensuremath{\nu}} +\providecommand{\Leisure}{Z} % +\providecommand{\leisure}{z} % +\providecommand{\leiShare}{\ensuremath{\zeta}} % +\providecommand{\LGro}{\ensuremath{\Lambda}} +\providecommand{\lGro}{\ensuremath{\lambda}} +\providecommand{\LLevBF}{\ensuremath{\mathbf{L}}} +\providecommand{\lLevBF}{\ensuremath{\pmb{\ell}}} +\providecommand{\lLev}{\ensuremath{\ell}} +\providecommand{\LLev}{\ensuremath{L}} +\providecommand{\Lo}{\check} +\providecommand{\LRat}{\ensuremath{L}} +\providecommand{\MaxMaxMPC}{\ensuremath{\bar{\bar{\kappa}}}} +\providecommand{\MaxMinMPC}{\ensuremath{\hat{\underline{\kappa}}}} +\providecommand{\MaxMPC}{\ensuremath{\bar{\kappa}}} +\providecommand{\MaxMPS}{\ensuremath{\PatR}} +\providecommand{\Mean}{\ensuremath{\mathbb{M}}} % Mean +\providecommand{\mEss}{\check{m}^{e}} +\providecommand{\ME}{\MRat^{e}} +\providecommand{\mE}{\mRat^{e}} +\providecommand{\mFunc}{\ensuremath{\mathrm{m}}} +\providecommand{\MinMinMPC}{\ensuremath{\underline{\kappa}}} +\providecommand{\MinMPC}{\ensuremath{\uline{\kappa}}} +\providecommand{\MinMPS}{\ensuremath{\pZero^{1/\CRRA} \PatR}} +\providecommand{\MLevBF}{\ensuremath{\mathbf{M}}} +\providecommand{\mLevBF}{\ensuremath{\mathbf{m}}} +\providecommand{\mLevBF}{\ensuremath{\mathbf{m}}} +\providecommand{\mLevE}{\mLev^{e}} +\providecommand{\MLev}{\ensuremath{M}} +\providecommand{\mLev}{\ensuremath{m}} +\providecommand{\MPCE}{\MPC^{e}} +\providecommand{\MPCFunc}{\ensuremath{\pmb{\kappa}}} +\providecommand{\MPCPPF}{\ensuremath{\Pi}} +\providecommand{\MPCP}{\ensuremath{\pi}} +\providecommand{\MPCU}{\MPC^{u}} +\providecommand{\MPC}{\ensuremath{\kappa}} +\providecommand{\MPSFunc}{\ensuremath{\pmb{lambda}}} +\providecommand{\MPS}{\ensuremath{\lambda}} +\providecommand{\MRatE}{\MRat^{e}} +\providecommand{\mRatE}{\mRat^{e}} +\providecommand{\MRat}{\ensuremath{M}} +\providecommand{\mRat}{\ensuremath{m}} +\providecommand{\MSS}{\ensuremath{\breve{M}}} +\providecommand{\mSS}{\ensuremath{\breve{m}}} +\providecommand{\mTarg}{\check{m}} +\providecommand{\MU}{{M}^{u}} +\providecommand{\mU}{{m}^{u}} +\providecommand{\m}{\ensuremath{m}} +\providecommand{\M}{\ensuremath{M}} +\providecommand{\next}{2} % via search-and-replace +\providecommand{\Next}{t+1} % +\providecommand{\nFunc}{\ensuremath{\mathrm{n}}} +\providecommand{\NLev}{\ensuremath{N}} +\providecommand{\nLev}{\ensuremath{n}} +\providecommand{\NRat}{\ensuremath{N}} +\providecommand{\nRat}{\ensuremath{n}} +\providecommand{\Num}{\ensuremath{N}} +\providecommand{\n}{\ensuremath{n}} +\providecommand{\OLevBF}{\ensuremath{O}} +\providecommand{\oLevBF}{\ensuremath{\mathbf{o}}} +\providecommand{\OLev}{\ensuremath{O}} +\providecommand{\oLev}{\ensuremath{\pmb{o}}} +\providecommand{\ORat}{\ensuremath{O}} +\providecommand{\oRat}{\ensuremath{o}} +\providecommand{\Pareto}{\zeta} +\providecommand{\PatPGroAdj}{\text{\pmb{\Thorn}}_{\acute{\PGro}}} +\providecommand{\patpGroAdj}{\text{\thorn}_{\acute{\pGro}}} +\providecommand{\patpGrohat}{\hat{\text{\thorn}}_{\pGro}} +\providecommand{\PatPGro}{\text{\pmb{\Thorn}}_{\PGro}} +\providecommand{\patpGro}{\text{\thorn}_{\pGro}} +\providecommand{\PatR}{\text{\pmb{\Thorn}}_{\Rfree}} +\providecommand{\patr}{\text{\thorn}_{\rfree}} +\providecommand{\PatU}{\text{\pmb{\Thorn}}_{\urate}} +\providecommand{\patu}{\text{\thorn}_{\urate}} +\providecommand{\PatWGro}{\text{\pmb{\Thorn}}_{\WGro}} +\providecommand{\patwGro}{\text{\thorn}_{\wGro}} +\providecommand{\Pat}{\text{\pmb{\Thorn}}} +\providecommand{\pat}{\text{\thorn}} +\providecommand{\pDeadRate}{\ensuremath{\grave{\cancel{\mathsf{d}}}}} +\providecommand{\pDead}{\ensuremath{\mathfrak{D}}} +\providecommand{\pDieRate}{\ensuremath{\grave{\mathsf{d}}}} +\providecommand{\pDies}{\ensuremath{\mathrm{d}}} % Continuous time death rate +\providecommand{\PDies}{\ensuremath{\mathsf{D}}} % Discrete-time one-period death rate +\providecommand{\PDV}{\ensuremath{\mathbb{P}}} % PDV +\providecommand{\PGroAdj}{\ensuremath{\underline{\PGro}}} +\providecommand{\pGroAdj}{\ensuremath{\underline{\pGro}}} +\providecommand{\PGrouAdj}{\ensuremath{\underline{\underline{\PGro}}}} +\providecommand{\pGrouAdj}{\ensuremath{\underline{\underline{{\pGro}}}}} +\providecommand{\PGro}{\ensuremath{\Gamma}} +\providecommand{\pGro}{\ensuremath{\gamma}} +\providecommand{\phiFunc}{\ensuremath{\digamma}} +\providecommand{\PIHMPC}{\ensuremath{\varkappa}} +\providecommand{\PInc}{\ensuremath{P}} +\providecommand{\PLabor}{\ensuremath{P}} % Permanent labor income in levels +\providecommand{\Plabor}{P} % Permanent labor income in levels +\providecommand{\PLevBF}{\ensuremath{\mathbf{P}}} +\providecommand{\pLevBF}{\ensuremath{\mathbf{p}}} +\providecommand{\PLev}{\ensuremath{P}} +\providecommand{\pLev}{\ensuremath{\pmb{p}}} +\providecommand{\PLev}{\ensuremath{P}} +\providecommand{\PLives}{\ensuremath{\cancel{\PDies}}} +\providecommand{\pLives}{\ensuremath{\cancel{\pDies}}} +\providecommand{\pNotZero}{\ensuremath{\cancel{\wp}}} +\providecommand{\PopE}{\mathcal{E}} +\providecommand{\popE}{e} +\providecommand{\PopGro}{\ensuremath{\Xi}} +\providecommand{\popGro}{\ensuremath{\xi}} +\providecommand{\PopLev}{\ensuremath{\pmb{N}}} +\providecommand{\PopU}{\mathcal{U}} +\providecommand{\Pop}{\ensuremath{L}} +\providecommand{\PostITC}{\ensuremath{\cancel{\zeta}}} +\providecommand{\power}{\ensuremath{\eta}} +\providecommand{\pPDVFunc}{\ensuremath{\mathbb{P}}} +\providecommand{\PPDV}{\ensuremath{\text{PDV($P$)}}} +\providecommand{\pRat}{\ensuremath{p}} +\providecommand{\Price}{\ensuremath{\mathsf{P}}} +\providecommand{\ProdFunc}{\ensuremath{\mathrm{F}}} +\providecommand{\ProdFunc}{\ensuremath{\mathrm{F}}} +\providecommand{\prodFunc}{\ensuremath{\mathrm{f}}} +\providecommand{\prudEx}{\ensuremath{\omega}} +\providecommand{\prud}{\ensuremath{\eta}} +\providecommand{\pSav}{\ensuremath{\phi}} +\providecommand{\pShkMin}{\underline{\psi}} % +\providecommand{\PShk}{\Psi} % +\providecommand{\PermShkAgg}{\Psi} % New +\providecommand{\pShk}{\psi} % +\providecommand{\permShkInd}{\psi} % New +\providecommand{\pshk}{\psi} % +\providecommand{\PtyGro}{\ensuremath{\Phi}} +\providecommand{\ptyGro}{\ensuremath{\phi}} +\providecommand{\PtyLab}{\ensuremath{\mathrm{Z}}} % Labor productivity +\providecommand{\ptyLab}{\ensuremath{z}} % usually, log of \PtyLab +\providecommand{\PtyLev}{\ensuremath{A}} % Multiplies overall production function +\providecommand{\ptyLev}{\ensuremath{a}} % Usually, log of \PtyLev +\providecommand{\pZero}{\ensuremath{\wp}} +\providecommand{\QLev}{\ensuremath{Q}} +\providecommand{\q}{\ensuremath{\koppa}} +\providecommand{\RCpnd}{\ensuremath{\mathbf{R}}} +\providecommand{\RevFunc}{\ensuremath{\pmb{\Pi}}} +\providecommand{\revFunc}{\ensuremath{\pmb{\pi}}} +\providecommand{\Rev}{\ensuremath{\Pi}} +\providecommand{\rev}{\ensuremath{\pi}} +\providecommand{\rfree}{\ensuremath{\mathsf{r}}} % The net return on the safe asset at an annual rate +\providecommand{\Rfree}{\ensuremath{\mathsf{R}}} % The return factor on the safe asset - unfortunately mathfrak fonts don't come through for tth +\providecommand{\RFunc}{\ensuremath{\mathrm{R}}} +\providecommand{\RGross}{\ensuremath{\breve{\mathsf{R}}}} +\providecommand{\rGross}{\ensuremath{\breve{\mathsf{r}}}} +\providecommand{\RiskyAlt}{\ensuremath{\acute{\mathbf{R}}}} % The return on the risky asset +\providecommand{\riskyAlt}{\ensuremath{\acute{\mathbf{r}}}} % The net return on the risky asset annual rate +\providecommand{\riskyshare}{\ensuremath{\varsigma}} +\providecommand{\Risky}{\ensuremath{\mathbf{R}}} % The return on the risky asset +\providecommand{\risky}{\ensuremath{\mathbf{r}}} % The net return on the risky asset annual rate +\providecommand{\RLev}{\ensuremath{R}} % Rate of return (but better to use \Rfree or \Risky or something else more specific +\providecommand{\RnormWGro}{\ensuremath{\mathcal{R}_{\WGro}}} % Normalized version of riskless return factor +\providecommand{\rnormwGro}{\ensuremath{\mathit{r}_{\wGro}}} % Normalized version of riskless rate of return +\providecommand{\Rnorm}{\ensuremath{\mathcal{R}}} % Normalized version of riskless return factor +\providecommand{\rnorm}{\ensuremath{\mathit{r}}} % Normalized version of riskless rate of return +\providecommand{\Rport}{\ensuremath{\pmb{\mathfrak{R}}}} % Portfolio -weighted return +\providecommand{\rport}{\ensuremath{\pmb{\mathfrak{r}}}} % Portfolio -weighted return +\providecommand{\rProd}{\ensuremath{\mathit{r}}} +\providecommand{\Rprod}{\ensuremath{\mathscr{R}}} +\providecommand{\rprod}{\ensuremath{\mathscr{r}}} +\providecommand{\RProd}{\ensuremath{\mathsf{R}}} +\providecommand{\RSave}{\ensuremath{\ushort{\Rfree}}} +\providecommand{\rsave}{\ensuremath{\ushort{\rfree}}} +\providecommand{\RBoro}{\ensuremath{\bar{\Rfree}}} +\providecommand{\rboro}{\ensuremath{\bar{\rfree}}} +\providecommand{\R}{\Rfree} +\providecommand{\saveRate}{\grave{s}} % saving (income minus consumption) over income +\providecommand{\Save}{S} % Saving (income minus consumption) +\providecommand{\save}{s} % saving (income minus consumption) +\providecommand{\SDF}{\MLev} % Stochastic Discount Factor +\providecommand{\sdr}{\mRat} % Stochastic Discount rate +\providecommand{\Seniority}{\ensuremath{\mathsf{X}}} +\providecommand{\seniority}{\ensuremath{\mathsf{x}}} +\providecommand{\SeverancePayment}{\ensuremath{\mathcal{S}}} +\providecommand{\SeveranceRatio}{\ensuremath{\varsigma}} +\providecommand{\Severance}{\ensuremath{\varsigma}} +\providecommand{\SE}{\SRat^{e}} +\providecommand{\sE}{\sRat^{e}} +\providecommand{\sFunc}{\ensuremath{\mathrm{s}}} +\providecommand{\ShkLogZeroLogStd}{\sigma_{\cancel{\ShkMeanOneLog}}} % Std of that shock +\providecommand{\ShkLogZeroLogVar}{\sigma_{\cancel{\ShkMeanOneLog}}^{2}} % Variance of that shock +\providecommand{\ShkLogZeroLog}{\cancel{\ShkMeanOneLog}} % Log of that shock +\providecommand{\ShkLogZero}{\cancel{\ShkMeanOne}} % A shock whose expectation in logs is zero; cancellation of the nonzero mean for the mean one shock +\providecommand{\ShkMeanOneLogStd}{\sigma_{\ShkMeanOneLog}} % Std of that shock +\providecommand{\ShkMeanOneLogVar}{\sigma^{2}_{\ShkMeanOneLog}} % Log of that shock +\providecommand{\ShkMeanOneLog}{\theta} % Log of that shock +\providecommand{\ShkMeanOne}{\Theta} % A shock whose expectation in levels is always equal to one regardless of variance +\providecommand{\SLevBF}{\mathbf{S}} +\providecommand{\sLevBF}{\mathbf{s}} +\providecommand{\SLevE}{\SLev^{e}} +\providecommand{\sLevE}{\sLev^{e}} +\providecommand{\SLevU}{\SLev^{u}} +\providecommand{\sLevU}{\sLev^{u}} +\providecommand{\SLev}{\ensuremath{S}} +\providecommand{\sLev}{\ensuremath{s}} +\providecommand{\srate}{\ensuremath{\varsigma}} +\providecommand{\SRatE}{\SRat^{e}} +\providecommand{\sRatE}{\sRat^{e}} +\providecommand{\SRatU}{\SRat^{u}} +\providecommand{\sRatU}{\sRat^{u}} +\providecommand{\SRat}{\ensuremath{S}} +\providecommand{\sRat}{\ensuremath{s}} +\providecommand{\STargE}{\Target{\SRat}^{\null}} +\providecommand{\sTargE}{\Target{\sRat}^{\null}} +\providecommand{\STargTarg}{\Target{\Target{\SRat}}} +\providecommand{\sTargTarg}{\Target{\Target{\sRat}}} +\providecommand{\STarg}{\Target{\SRat}} +\providecommand{\sTarg}{\Target{\sRat}} +\providecommand{\Steady}{\bar} +\providecommand{\Stocks}{{S}} +\providecommand{\stocks}{{s}} +\providecommand{\straight}{\Pi} +\providecommand{\Surplus}{\ensuremath{Z}} +\providecommand{\surplus}{\ensuremath{z}} +\providecommand{\SU}{\SRat^{u}} +\providecommand{\sU}{\sRat^{u}} +\providecommand{\Target}{\check} +\providecommand{\TaxCombInv}{\ensuremath{\mathcal{T}^{-1}}} +\providecommand{\TaxComb}{\ensuremath{\mathcal{T}}} +\providecommand{\TaxCorp}{\ensuremath{\tau}} +\providecommand{\taxDep}{\ensuremath{\partial}} +\providecommand{\TaxFree}{\ensuremath{\cancel{\Tax}}} +\providecommand{\TaxLev}{T} +\providecommand{\TaxNetTrans}{\ensuremath{Z}} +\providecommand{\taxNetTrans}{\ensuremath{z}} +\providecommand{\TaxPaid}{\ensuremath{T}} +\providecommand{\TaxRate}{\ensuremath{t}} +\providecommand{\TaxUI}{\ensuremath{\tau}} +\providecommand{\Tax}{\ensuremath{\tau}} +\providecommand{\tax}{t} +\providecommand{\tBak}{\ensuremath{\pmb{n}}} +\providecommand{\TEatBak}{\ensuremath{\mathtt{q}}} +\providecommand{\TEat}{\ensuremath{\TEnd}} +\providecommand{\TEndBak}{\ensuremath{\mathsf{p}}} +\providecommand{\TEnd}{\ensuremath{T}} +\providecommand{\TermTime}{\ensuremath{T}} +\providecommand{\tFwd}{\ensuremath{n}} +\providecommand{\tHorOfm}{\ensuremath{\pmb{n}}} +\providecommand{\tHor}{\ensuremath{\mathsf{n}}} +\providecommand{\timeRate}{\ensuremath{\vartheta}} +\providecommand{\tinyAmount}{\ensuremath{\epsilon}} +\providecommand{\TLev}{\ensuremath{T}} +\providecommand{\TMap}{\mathscr{T}} +\providecommand{\tNow}{\ensuremath{t}} +\providecommand{\tShkAll}{\xi} % +\providecommand{\tShkEmpMin}{\underline{\theta}} % +\providecommand{\TShkEmp}{\Theta} % +\providecommand{\tShkEmp}{\theta} % +\providecommand{\tShkEmp}{\theta} % +\providecommand{\tranShkInd}{\theta} % New +\providecommand{\TranShkAgg}{\Theta} % New +\providecommand{\TShk}{\Xi} % +\providecommand{\tShk}{\xi} % +\providecommand{\tshk}{\xi} % +\providecommand{\tSS}{\ensuremath{t}} +\providecommand{\tThen}{\ensuremath{\tau}} +\providecommand{\uFunc}{{\ensuremath{\mathrm{u}}}} +\providecommand{\uInvEpShkuInv}{\underline{\underline{\psi}}} +\providecommand{\ULev}{\ensuremath{U}} +\providecommand{\unins}{\ensuremath{\zeta}} +\providecommand{\uPDVFunc}{\ensuremath{\mathbb{U}}} +\providecommand{\uPmt}{\ensuremath{\mu}} +\providecommand{\uPPP}{{\ensuremath{\mathrm{u}^{\prime\prime\prime}}}} +\providecommand{\uPP}{{\ensuremath{\mathrm{u}^{\prime\prime}}}} +\providecommand{\uP}{{\ensuremath{\mathrm{u}^{\prime}}}} +\providecommand{\urate}{\ensuremath{\mho}} +\providecommand{\utilFunc}{\ensuremath{\mathrm{u}}} +\providecommand{\util}{\ensuremath{u}} +\providecommand{\ValAlt}{\ensuremath{\mathcal{V}}} % middle-of-period Value function +\providecommand{\valfn}{\ensuremath{\mathrm{v}}} % middle-of-period value function +\providecommand{\Value}{\ensuremath{\mathrm{V}}} % middle-of-period Value function +\providecommand{\VEndFunc}{\ensuremath{\mathfrak{V}}} +\providecommand{\vEndFunc}{\ensuremath{\mathfrak{v}}} +\providecommand{\VEnd}{\ensuremath{\mathfrak{V}}} % end-of-period Value function +\providecommand{\vEnd}{\ensuremath{\mathfrak{v}}} % end-of-period value function +\providecommand{\vEss}{\check{v}^{e}} +\providecommand{\VE}{{V}^{e}} +\providecommand{\vE}{{v}^{e}} +\providecommand{\vFirm}{\ensuremath{\mathrm{e}}} +\providecommand{\VFunc}{\ensuremath{\mathrm{V}}} +\providecommand{\vFunc}{\ensuremath{\mathrm{v}}} +\providecommand{\VInv}{\ensuremath{\Lambda}} +\providecommand{\vInv}{\ensuremath{\scriptstyle \Lambda \displaystyle}} +\providecommand{\vk}{\ensuremath{\lambda}} +\providecommand{\vLevBF}{\ensuremath{\mathbf{v}}} +\providecommand{\VLevFunc}{\ensuremath{\pmb{\mathrm{V}}}} +\providecommand{\vLevFunc}{\ensuremath{\pmb{\mathrm{v}}}} +\providecommand{\VLev}{\ensuremath{V}} +\providecommand{\vLev}{\ensuremath{v}} +\providecommand{\vNorm}{\ensuremath{\mathrm{w}}} % end-of-period value function +\providecommand{\VNum}{\ensuremath{V}} +\providecommand{\vNum}{\ensuremath{v}} +\providecommand{\vOptAlt}{\ensuremath{\Alt{\tilde{\mathfrak{v}}}}} +\providecommand{\vOpt}{\ensuremath{\tilde{\mathfrak{v}}}} +\providecommand{\VRat}{\ensuremath{V}} +\providecommand{\vRat}{\ensuremath{v}} +\providecommand{\vTarg}{\Target{\vRat}} +\providecommand{\VU}{{V}^{u}} +\providecommand{\vU}{{v}^{u}} +\providecommand{\Wage}{\ensuremath{\mathsf{W}}} +\providecommand{\wage}{\ensuremath{\mathsf{w}}} +\providecommand{\WAllLev}{\ensuremath{\mathbf{O}}} +\providecommand{\wAllLev}{\ensuremath{\mathbf{o}}} +\providecommand{\WAllRat}{\ensuremath{O}} +\providecommand{\wAllRat}{\ensuremath{o}} +\providecommand{\WAll}{{O}} +\providecommand{\wAll}{{o}} +\providecommand{\WBeg}{\KLev} % Wealth as of the beginning of the period (before R is received, not including Y) +\providecommand{\wBeg}{\kLev} % wealth as of the beginning of the period (before R is received, not including Y) +\providecommand{\Wealth}{\ensuremath{O}} +\providecommand{\wealth}{\ensuremath{o}} +\providecommand{\WEndRat}{\ARat} % +\providecommand{\wEndRat}{\aRat} % +\providecommand{\WEnd}{\ALev} % Wealth as of the end of the period (after C has been chosen) +\providecommand{\wEnd}{\aLev} % wealth as of the end of the period (after C has been taken) +\providecommand{\Wend}{\ARat} % Wealth as of the end of the period (after C has been chosen) +\providecommand{\wend}{\aRat} % wealth as of the end of the period (after C has been taken) +\providecommand{\wFunc}{\mathrm{w}} +\providecommand{\WGroPF}{\ensuremath{\mathsf{G}}} +\providecommand{\WGro}{\ensuremath{\mathsf{G}}} +\providecommand{\wGro}{\ensuremath{\mathsf{g}}} +\providecommand{\whumMin}{\underline{\hRat}} % human wealth -- individual +\providecommand{\WHum}{\HLev} % Human wealth -- aggregate +\providecommand{\wHum}{\hLev} % human wealth -- individual +\providecommand{\Whum}{\HRat} % Human wealth -- aggregate +\providecommand{\whum}{\hRat} % human wealth -- individual +\providecommand{\WLev}{\ensuremath{W}} +\providecommand{\wLev}{\pmb{w}} +\providecommand{\WMid}{\BLev} % Wealth as of the middle of the period (after R is received, not including Y) +\providecommand{\WMid}{\BLev} % Wealth as of the middle of the period (after R is received, not including Y) +\providecommand{\wMid}{\bLev} % wealth as of the middle of the period (after R is received, not including Y) +\providecommand{\Wmid}{\BRat} % Wealth as of the middle of the period (after R is received, not including Y) +\providecommand{\wmid}{\bRat} % wealth as of the middle of the period (after R is received, not including Y) +\providecommand{\WMkt}{\MLev} % Wealth as of the middle of the period (after R is received, including Y) +\providecommand{\wMkt}{\mLev} % wealth as of the middle of the period (after R is received, including Y) +\providecommand{\wmkt}{\mLev} % wealth as of the middle of the period (after R is received, including Y) +\providecommand{\wNet}{\ensuremath{x}} % +\providecommand{\WNet}{\ensuremath{X}} % Total wealth +\providecommand{\WPre}{{K}} +\providecommand{\wPre}{{k}} +\providecommand{\wRat}{\ensuremath{o}} % +\providecommand{\WRat}{\ensuremath{O}} % Ratio to permanent income +\providecommand{\wTot}{\ensuremath{\mathbf{o}}} % +\providecommand{\WTot}{\ensuremath{\mathbf{O}}} % Total wealth +\providecommand{\wtot}{\ensuremath{o}} % +\providecommand{\Wtot}{\ensuremath{O}} % Total wealth +\providecommand{\xFer}{\chi} +\providecommand{\XFer}{X} +\providecommand{\xFunc}{\mathrm{x}} +\providecommand{\XLev}{\ensuremath{X}} +\providecommand{\xLev}{\ensuremath{x}} +\providecommand{\xpend}{\ensuremath{\xi}} +\providecommand{\XperGro}{\ensuremath{\mathsf{X}}} +\providecommand{\xperGro}{\ensuremath{\mathsf{x}}} +\providecommand{\XRat}{\ensuremath{X}} +\providecommand{\xRat}{\ensuremath{x}} +\providecommand{\yFunc}{\mathrm{y}} +\providecommand{\yLevBF}{\ensuremath{\mathbf{y}}} +\providecommand{\YLev}{\ensuremath{Y}} +\providecommand{\yLev}{\ensuremath{y}} +\providecommand{\YRat}{\ensuremath{Y}} +\providecommand{\yRat}{\ensuremath{y}} +\providecommand{\yTargE}{\ensuremath{\check{y}^{e}}} +\providecommand{\yTarg}{\ensuremath{\check{y}}} +\providecommand{\zAgg}{\ensuremath{\pmb{Z}}} +\providecommand{\zFunc}{\mathrm{z}} +\providecommand{\ZLevBF}{\ensuremath{\mathbf{Z}}} +\providecommand{\zLevBF}{\ensuremath{\mathbf{z}}} +\providecommand{\zLevBF}{\ensuremath{\pmb{z}}} +\providecommand{\ZLev}{\ensuremath{Z}} +\providecommand{\zLev}{\ensuremath{z}} +\providecommand{\ZRat}{\ensuremath{Z}} +\providecommand{\zRat}{\ensuremath{z}} + +\newcommand{\discRte}{\omega} +\newcommand{\DieFac}{\pDead} +\newcommand{\LivFac}{\Alive} +\newcommand{\PopFac}{\PopGro} +\newcommand{\popRte}{\popGro} + + +\providecommand{\NFALev}{\NLev} +\providecommand{\NFARat}{\NRat} +\providecommand{\NI}{\ensuremath{Z}} +\providecommand{\GDPLev}{\pmb{P}} +\providecommand{\GDPRat}{P} +\providecommand{\GDPGro}{\gimel} +\providecommand{\gdpLev}{\pmb{p}} +\providecommand{\gdpRat}{p} +\providecommand{\weight}{\omega} + + +\providecommand{\bi}{} +\renewcommand{\bi}{\begin{itemize}} +\providecommand{\ei}{} +\renewcommand{\ei}{\end{itemize}} +\providecommand{\reqd}{} +\renewcommand{\reqd}{\item[$^{*}$]} +\providecommand{\recm}{} +\renewcommand{\recm}{\item[\phantom{$^{*}$}]} + +\providecommand{\Severance}{\kappa} +\newcommand{\pd}[2]{\frac{\partial#1}{\partial#2}} + From 3e495375f7245a31754e5a482c86462f0bbddd6e Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 9 Dec 2019 14:52:37 -0500 Subject: [PATCH 136/242] adding back in Parameters because these are used by LifecycleModel --- .../SolvingMicroDSOPs/EstimationParameters.py | 173 ++++++++++++++++++ 1 file changed, 173 insertions(+) create mode 100644 HARK/SolvingMicroDSOPs/EstimationParameters.py diff --git a/HARK/SolvingMicroDSOPs/EstimationParameters.py b/HARK/SolvingMicroDSOPs/EstimationParameters.py new file mode 100644 index 000000000..430e6f286 --- /dev/null +++ b/HARK/SolvingMicroDSOPs/EstimationParameters.py @@ -0,0 +1,173 @@ +''' +Specifies the full set of calibrated values required to estimate the SolvingMicroDSOPs +model. The empirical data is stored in a separate csv file and is loaded in SetupSCFdata. +''' +from __future__ import print_function + +# --------------------------------------------------------------------------------- +# Debugging flags +# --------------------------------------------------------------------------------- + +show_PermGroFacAgg_error = False +# Error Notes: +# This sets a "quick fix" to the error, AttributeError: 'TempConsumerType' object has no attribute 'PermGroFacAgg' +# If you set this flag to "True" you will see the error. A more thorough fix is to +# fix the place where this error was introduced (Set to "True" and it will appear; +# this was almost certainly introduced when the code was extended to be used in the +# GE setting). An even more thorough solution, which moves beyond the scope of +# fixing this error, is adding unit tests to ID when changes to some code will +# break things elsewhere. +# Note: alternatively, decide that the "init_consumer_objects['PermGroFacAgg'] = 1.0" +# line below fixes it properly ('feature not a bug') and remove all this text. + +# --------------------------------------------------------------------------------- +# - Define all of the model parameters for SolvingMicroDSOPs and ConsumerExamples - +# --------------------------------------------------------------------------------- + +exp_nest = 3 # Number of times to "exponentially nest" when constructing a_grid +aXtraMin = 0.001 # Minimum end-of-period "assets above minimum" value +aXtraMax = 20 # Maximum end-of-period "assets above minimum" value +aXtraHuge = None # A very large value of assets to add to the grid, not used +aXtraExtra = None # Some other value of assets to add to the grid, not used +aXtraCount = 8 # Number of points in the grid of "assets above minimum" + +BoroCnstArt = 0.0 # Artificial borrowing constraint; imposed minimum level of end-of period assets +CubicBool = True # Use cubic spline interpolation when True, linear interpolation when False +vFuncBool = False # Whether to calculate the value function during solution + +Rfree = 1.03 # Interest factor on assets +PermShkCount = 7 # Number of points in discrete approximation to permanent income shocks +TranShkCount = 7 # Number of points in discrete approximation to transitory income shocks +UnempPrb = 0.005 # Probability of unemployment while working +UnempPrbRet = 0.000 # Probability of "unemployment" while retired +IncUnemp = 0.0 # Unemployment benefits replacement rate +IncUnempRet = 0.0 # "Unemployment" benefits when retired + +final_age = 90 # Age at which the problem ends (die with certainty) +retirement_age = 65 # Age at which the consumer retires +initial_age = 25 # Age at which the consumer enters the model +TT = final_age - initial_age # Total number of periods in the model +retirement_t = retirement_age - initial_age - 1 + +CRRA_start = 4.0 # Initial guess of the coefficient of relative risk aversion during estimation (rho) +DiscFacAdj_start = 0.99 # Initial guess of the adjustment to the discount factor during estimation (beth) +DiscFacAdj_bound = [0.0001,15.0] # Bounds for beth; if violated, objective function returns "penalty value" +CRRA_bound = [0.0001,15.0] # Bounds for rho; if violated, objective function returns "penalty value" + +# Expected growth rates of permanent income over the lifecycle, starting from age 25 +PermGroFac = [ 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, + 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, + 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, + 1.025, 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , + 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 0.7 , # <-- This represents retirement + 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , + 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , + 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ] + +# Age-varying discount factors over the lifecycle, lifted from Cagetti (2003) +DiscFac_timevary = [1.064914 , 1.057997 , 1.051422 , 1.045179 , 1.039259 , + 1.033653 , 1.028352 , 1.023348 , 1.018632 , 1.014198 , + 1.010037 , 1.006143 , 1.002509 , 0.9991282, 0.9959943, + 0.9931012, 0.9904431, 0.9880143, 0.9858095, 0.9838233, + 0.9820506, 0.9804866, 0.9791264, 0.9779656, 0.9769995, + 0.9762239, 0.9756346, 0.9752274, 0.9749984, 0.9749437, + 0.9750595, 0.9753422, 0.9757881, 0.9763936, 0.9771553, + 0.9780698, 0.9791338, 0.9803439, 0.981697 , 0.8287214, + 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111, + 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111, + 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111, + 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111, + 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111] + +# Survival probabilities over the lifecycle, starting from age 25 +LivPrb = [ 1. , 1. , 1. , 1. , 1. , + 1. , 1. , 1. , 1. , 1. , + 1. , 1. , 1. , 1. , 1. , + 1. , 1. , 1. , 1. , 1. , + 1. , 1. , 1. , 1. , 1. , + 1. , 1. , 1. , 1. , 1. , + 1. , 1. , 1. , 1. , 1. , + 1. , 1. , 1. , 1. , 1. , # <-- automatic survival to age 65 + 0.98438596, 0.98438596, 0.98438596, 0.98438596, 0.98438596, + 0.97567062, 0.97567062, 0.97567062, 0.97567062, 0.97567062, + 0.96207901, 0.96207901, 0.96207901, 0.96207901, 0.96207901, + 0.93721595, 0.93721595, 0.93721595, 0.93721595, 0.93721595, + 0.63095734, 0.63095734, 0.63095734, 0.63095734, 0.63095734] + + +# Standard deviations of permanent income shocks by age, starting from age 25 +PermShkStd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, +0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, +0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0, 0.0, 0.0, # <-- no permanent income shocks after retirement +0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, +0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +# Standard deviations of transitory income shocks by age, starting from age 25 +TranShkStd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, +0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, +0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0, 0.0, 0.0, # <-- no transitory income shocs after retirement +0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, +0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +# Age groups for the estimation: calculate average wealth-to-permanent income ratio +# for consumers within each of these age groups, compare actual to simulated data +empirical_cohort_age_groups = [[ 26,27,28,29,30 ], + [ 31,32,33,34,35 ], + [ 36,37,38,39,40 ], + [ 41,42,43,44,45 ], + [ 46,47,48,49,50 ], + [ 51,52,53,54,55 ], + [ 56,57,58,59,60 ]] + +initial_wealth_income_ratio_vals = [0.17, 0.5, 0.83] # Three point discrete distribution of initial w +initial_wealth_income_ratio_probs = [0.33333, 0.33333, 0.33334] # Equiprobable discrete distribution of initial w +num_agents = 10000 # Number of agents to simulate +bootstrap_size = 50 # Number of re-estimations to do during bootstrap +seed = 31382 # Just an integer to seed the estimation + +# ----------------------------------------------------------------------------- +# -- Set up the dictionary "container" for making a basic lifecycle type ------ +# ----------------------------------------------------------------------------- + +# Dictionary that can be passed to ConsumerType to instantiate +init_consumer_objects = {"CRRA":CRRA_start, + "Rfree":Rfree, + "PermGroFac":PermGroFac, + "BoroCnstArt":BoroCnstArt, + "PermShkStd":PermShkStd, + "PermShkCount":PermShkCount, + "TranShkStd":TranShkStd, + "TranShkCount":TranShkCount, + "T_cycle":TT, + "UnempPrb":UnempPrb, + "UnempPrbRet":UnempPrbRet, + "T_retire":retirement_t, + "T_age":TT+1, + "IncUnemp":IncUnemp, + "IncUnempRet":IncUnempRet, + "aXtraMin":aXtraMin, + "aXtraMax":aXtraMax, + "aXtraCount":aXtraCount, + "aXtraExtra":[aXtraExtra,aXtraHuge], + "aXtraNestFac":exp_nest, + "LivPrb":LivPrb, + "DiscFac":DiscFac_timevary, + 'AgentCount':num_agents, + 'seed':seed, + 'tax_rate':0.0, + 'vFuncBool':vFuncBool, + 'CubicBool':CubicBool + } + +if show_PermGroFacAgg_error: + pass # do nothing +else: + print("***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***") + init_consumer_objects['PermGroFacAgg'] = 1.0 + + +if __name__ == '__main__': + print("Sorry, EstimationParameters doesn't actually do anything on its own.") + print("This module is imported by StructEstimation, providing calibrated ") + print("parameters for the example estimation. Please see that module if you ") + print("want more interesting output.") From 88970e2a6dcb6f850faa334e176f8d1ce0c98643 Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 9 Dec 2019 15:09:08 -0500 Subject: [PATCH 137/242] adding LifecyleModel from DemARK --- .../LifecycleModelExample.ipynb | 443 ++++++++++++++++++ .../LifecycleModel/LifecycleModelExample.py | 156 ++++++ 2 files changed, 599 insertions(+) create mode 100644 examples/LifecycleModel/LifecycleModelExample.ipynb create mode 100644 examples/LifecycleModel/LifecycleModelExample.py diff --git a/examples/LifecycleModel/LifecycleModelExample.ipynb b/examples/LifecycleModel/LifecycleModelExample.ipynb new file mode 100644 index 000000000..59b6dee18 --- /dev/null +++ b/examples/LifecycleModel/LifecycleModelExample.ipynb @@ -0,0 +1,443 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# The Distribution of Assets By Age\n", + "\n", + "National registry data on income and wealth from Scandinavian countries has recently become available (with a lot of security) to some (lucky!) researchers. These data offer a uniquely powerful tool for testing (and improving) our models of consumption and saving behavior over the life cycle.\n", + "\n", + "\n", + "But as of this writing (in March of 2019), the data are so new that there do not seem to be any published attempts to compare the data to the implications a standard life cycle model with income uncertainty, constraints, and other modern features.\n", + "\n", + "This notebook is an example of how one could counstruct a life cycle model with the HARK toolkit that would make predictions about the model analogues of the raw data statistics that are available. \n", + "\n", + "For example, the papers have shown information about the growth rate of assets at different ages over the life cycle. Here, we show how (under a given parameterization) we could produce the life cycle model's prediction about the distribution of assets at age 65 and age 66, and the growth rate between 65 and 66. \n", + "\n", + "The parameters of the model have not been optmized to match features of the Norwegian data; a first step in \"structural\" estimation would be to calibrate the inputs to the model (like the profile of income over the life cycle, and the magnitude of income shocks), and then to find the values of parameters like the time preference rate that allow the model to fit the data best.\n", + "\n", + "An interesting question is whether this exercise will suggest that it is necessary to allow for _ex ante_ heterogeneity in such preference parameters.\n", + "\n", + "This seems likely; a paper by [](http://econ.jhu.edu/people/ccarroll/papers/cstwMPC) (all of whose results were constructed using the HARK toolkit) finds that, if all other parameters (e.g., rates of return on savings) are the same, models of this kind require substantial heterogeneity in preferences to generate the degree of inequality in U.S. data.\n", + "\n", + "But in one of the many new and interesting findings from the Norwegian data, have shown that there is substantial heterogeneity in rates of return, even on wealth held in public markets. \n", + "\n", + "[Derin Aksit](https://github.com/econ-ark/REMARK) has shown that the degree of time preference heterogeneity needed to match observed inequality is considerably less when rate-of-return heterogeneity is calibrated to match these data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "code_folding": [], + "lines_to_next_cell": 2, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***\n" + ] + } + ], + "source": [ + "# Initial imports and notebook setup, click arrow to show\n", + "\n", + "import HARK.ConsumptionSaving.ConsIndShockModel as Model # The consumption-saving micro model\n", + "import HARK.SolvingMicroDSOPs.Calibration.EstimationParameters as Params # Parameters for the consumer type and the estimation\n", + "from HARK.utilities import plotFuncsDer, plotFuncs # Some tools\n", + "\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "code_folding": [], + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Set up default values for CRRA, DiscFac, and simulation variables in the dictionary \n", + "Params.init_consumer_objects[\"CRRA\"]= 2.00 # Default coefficient of relative risk aversion (rho)\n", + "Params.init_consumer_objects[\"DiscFac\"]= 0.97 # Default intertemporal discount factor (beta)\n", + "Params.init_consumer_objects[\"PermGroFacAgg\"]= 1.0 # Aggregate permanent income growth factor \n", + "Params.init_consumer_objects[\"aNrmInitMean\"]= -10.0 # Mean of log initial assets \n", + "Params.init_consumer_objects[\"aNrmInitStd\"]= 1.0 # Standard deviation of log initial assets\n", + "Params.init_consumer_objects[\"pLvlInitMean\"]= 0.0 # Mean of log initial permanent income \n", + "Params.init_consumer_objects[\"pLvlInitStd\"]= 0.0 # Standard deviation of log initial permanent income" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Make a lifecycle consumer to be used for estimation\n", + "LifeCyclePop = Model.IndShockConsumerType(**Params.init_consumer_objects)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "code_folding": [], + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sb/projects/econ-ark/HARK/HARK/interpolation.py:1710: RuntimeWarning: All-NaN slice encountered\n", + " y = np.nanmin(fx,axis=1)\n" + ] + } + ], + "source": [ + "# Solve and simulate the model (ignore the \"warning\" message)\n", + "LifeCyclePop.solve() # Obtain consumption rules by age \n", + "LifeCyclePop.unpackcFunc() # Expose the consumption rules\n", + "\n", + "# Which variables do we want to track\n", + "LifeCyclePop.track_vars = ['aNrmNow','pLvlNow','mNrmNow','cNrmNow','TranShkNow']\n", + "\n", + "LifeCyclePop.T_sim = 120 # Nobody lives to be older than 145 years (=25+120)\n", + "LifeCyclePop.initializeSim() # Construct the age-25 distribution of income and assets\n", + "LifeCyclePop.simulate() # Simulate a population behaving according to this model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "code_folding": [ + 0 + ], + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption as a function of market resources while working:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption functions during working life\n", + "\n", + "print('Consumption as a function of market resources while working:')\n", + "mMin = min([LifeCyclePop.solution[t].mNrmMin for t in range(LifeCyclePop.T_cycle)])\n", + "plotFuncs(LifeCyclePop.cFunc[:LifeCyclePop.T_retire],mMin,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "code_folding": [ + 0 + ], + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Define the saving rate function\n", + "def savingRateFunc(SomeType, m):\n", + " \"\"\"\n", + " Parameters:\n", + " ----------\n", + " SomeType: \n", + " Agent type that has been solved and simulated.\n", + " \n", + " \n", + " Returns:\n", + " --------\n", + " SavingRate: float\n", + " \n", + " \"\"\"\n", + " inc = (SomeType.Rfree -1.)*(m-1.)+1.\n", + " cons = SomeType.solution[0].cFunc(m)\n", + " Saving = inc - cons\n", + " SavingRate = Saving / inc\n", + " return SavingRate " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "code_folding": [ + 0 + ], + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Create a Giant matrix gathering useful data:\n", + "# 't_now', 'aNrmNow_hist', 'cNrmNow_hist', employment-status in date t, in date t-1, aLvlGro_hist, Saving rate\n", + "\n", + "w, h = 1, LifeCyclePop.T_cycle\n", + "giant_list = [[0 for x in range(w)] for y in range(h)]\n", + "SavingRate_list = []\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\") # Suppress some disturbing but harmless warnings\n", + "\n", + "for t in range(1,LifeCyclePop.T_cycle+1):\n", + " #aLvlGro_hist[0] = 0 # set the first growth rate to 0, since there is no data for period 0\n", + " aLvlGroNow = np.log(LifeCyclePop.aNrmNow_hist[t]/LifeCyclePop.aNrmNow_hist[t-1]) # (10000,)\n", + "\n", + " # Call the saving rate function with test value for \n", + " SavingRate = savingRateFunc(LifeCyclePop, LifeCyclePop.mNrmNow_hist[t] )\n", + " \n", + " SavingRate_list.append(SavingRate)\n", + "\n", + " # Create elements of matrix list\n", + " matrix_list = [0 for number in range(7)]\n", + " matrix_list[0] = t\n", + " matrix_list[1] = LifeCyclePop.aNrmNow_hist[t]\n", + " matrix_list[2] = LifeCyclePop.cNrmNow_hist[t]\n", + " matrix_list[3] = LifeCyclePop.TranShkNow_hist[t]\n", + " matrix_list[4] = LifeCyclePop.TranShkNow_hist[t-1]\n", + " matrix_list[5] = aLvlGroNow\n", + " matrix_list[6] = SavingRate\n", + " \n", + " giant_list[t-1] = matrix_list\n", + " \n", + "# Print command disabled to prevent giant print!\n", + "#print giant_list" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "code_folding": [ + 0 + ], + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Construct the level of assets A from a*p where a is the ratio to permanent income p\n", + "LifeCyclePop.aLvlNow_hist = LifeCyclePop.aNrmNow_hist*LifeCyclePop.pLvlNow_hist\n", + "aGro41=LifeCyclePop.aLvlNow_hist[41]/LifeCyclePop.aLvlNow_hist[40]\n", + "aGro41NoU=aGro41[aGro41[:]>0.2] # Throw out extreme outliers" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "code_folding": [ + 0 + ], + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the distribution of growth rates of wealth between age 65 and 66 (=25 + 41)\n", + "from matplotlib import pyplot as plt\n", + "n, bins, patches = plt.hist(aGro41NoU,50,density=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "cite2c": { + "citations": { + "6202365/7MR8GUVS": { + "DOI": "10.3982/QE694", + "URL": "https://onlinelibrary.wiley.com/doi/abs/10.3982/QE694", + "abstract": "In a model calibrated to match micro- and macroeconomic evidence on household income dynamics, we show that a modest degree of heterogeneity in household preferences or beliefs is sufficient to match empirical measures of wealth inequality in the United States. The heterogeneity-augmented model's predictions are consistent with microeconomic evidence that suggests that the annual marginal propensity to consume (MPC) is much larger than the roughly 0.04 implied by commonly used macroeconomic models (even ones including some heterogeneity). The high MPC arises because many consumers hold little wealth despite having a strong precautionary motive. Our model also plausibly predicts that the aggregate MPC can differ greatly depending on how the shock is distributed across households (depending, e.g., on their wealth, or employment status).", + "accessed": { + "day": 5, + "month": 2, + "year": 2019 + }, + "author": [ + { + "family": "Carroll", + "given": "Christopher" + }, + { + "family": "Slacalek", + "given": "Jiri" + }, + { + "family": "Tokuoka", + "given": "Kiichi" + }, + { + "family": "White", + "given": "Matthew N." + } + ], + "container-title": "Quantitative Economics", + "id": "6202365/7MR8GUVS", + "issue": "3", + "issued": { + "year": 2017 + }, + "language": "en", + "note": "Citation Key: carrollDistributionWealthMarginal2017", + "page": "977-1020", + "page-first": "977", + "title": "The distribution of wealth and the marginal propensity to consume", + "type": "article-journal", + "volume": "8" + }, + "6202365/B9BGV9W3": { + "URL": "http://www.nber.org/papers/w22822", + "abstract": "We provide a systematic analysis of the properties of individual returns to wealth using twenty years of population data from Norway’s administrative tax records. We document a number of novel results. First, in a given cross-section, individuals earn markedly different returns on their assets, with a difference of 500 basis points between the 10th and the 90th percentile. Second, heterogeneity in returns does not arise merely from differences in the allocation of wealth between safe and risky assets: returns are heterogeneous even within asset classes. Third, returns are positively correlated with wealth. Fourth, returns have an individual permanent component that accounts for 60% of the explained variation. Fifth, for wealth below the 95th percentile, the individual permanent component accounts for the bulk of the correlation between returns and wealth; the correlation at the top reflects both compensation for risk and the correlation of wealth with the individual permanent component. Finally, the permanent component of the return to wealth is also (mildly) correlated across generations. We discuss the implications of these findings for several strands of the wealth inequality debate.", + "accessed": { + "day": 17, + "month": 3, + "year": 2019 + }, + "author": [ + { + "family": "Fagereng", + "given": "Andreas" + }, + { + "family": "Guiso", + "given": "Luigi" + }, + { + "family": "Malacrino", + "given": "Davide" + }, + { + "family": "Pistaferri", + "given": "Luigi" + } + ], + "genre": "Working Paper", + "id": "6202365/B9BGV9W3", + "issued": { + "month": 11, + "year": 2016 + }, + "note": "DOI: 10.3386/w22822", + "number": "22822", + "publisher": "National Bureau of Economic Research", + "title": "Heterogeneity and Persistence in Returns to Wealth", + "type": "report" + } + } + }, + "jupytext": { + "cell_metadata_filter": "collapsed", + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/LifecycleModel/LifecycleModelExample.py b/examples/LifecycleModel/LifecycleModelExample.py new file mode 100644 index 000000000..1b3a572c2 --- /dev/null +++ b/examples/LifecycleModel/LifecycleModelExample.py @@ -0,0 +1,156 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: collapsed +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.2' +# jupytext_version: 1.2.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# +# # The Distribution of Assets By Age +# +# National registry data on income and wealth from Scandinavian countries has recently become available (with a lot of security) to some (lucky!) researchers. These data offer a uniquely powerful tool for testing (and improving) our models of consumption and saving behavior over the life cycle. +# +# +# But as of this writing (in March of 2019), the data are so new that there do not seem to be any published attempts to compare the data to the implications a standard life cycle model with income uncertainty, constraints, and other modern features. +# +# This notebook is an example of how one could counstruct a life cycle model with the HARK toolkit that would make predictions about the model analogues of the raw data statistics that are available. +# +# For example, the papers have shown information about the growth rate of assets at different ages over the life cycle. Here, we show how (under a given parameterization) we could produce the life cycle model's prediction about the distribution of assets at age 65 and age 66, and the growth rate between 65 and 66. +# +# The parameters of the model have not been optmized to match features of the Norwegian data; a first step in "structural" estimation would be to calibrate the inputs to the model (like the profile of income over the life cycle, and the magnitude of income shocks), and then to find the values of parameters like the time preference rate that allow the model to fit the data best. +# +# An interesting question is whether this exercise will suggest that it is necessary to allow for _ex ante_ heterogeneity in such preference parameters. +# +# This seems likely; a paper by [](http://econ.jhu.edu/people/ccarroll/papers/cstwMPC) (all of whose results were constructed using the HARK toolkit) finds that, if all other parameters (e.g., rates of return on savings) are the same, models of this kind require substantial heterogeneity in preferences to generate the degree of inequality in U.S. data. +# +# But in one of the many new and interesting findings from the Norwegian data, have shown that there is substantial heterogeneity in rates of return, even on wealth held in public markets. +# +# [Derin Aksit](https://github.com/econ-ark/REMARK) has shown that the degree of time preference heterogeneity needed to match observed inequality is considerably less when rate-of-return heterogeneity is calibrated to match these data. + +# %% {"code_folding": [0]} +# Initial imports and notebook setup, click arrow to show + +import HARK.ConsumptionSaving.ConsIndShockModel as Model # The consumption-saving micro model +import HARK.SolvingMicroDSOPs.Calibration.EstimationParameters as Params # Parameters for the consumer type and the estimation +from HARK.utilities import plotFuncsDer, plotFuncs # Some tools + +import numpy as np + + +# %% {"code_folding": [0]} +# Set up default values for CRRA, DiscFac, and simulation variables in the dictionary +Params.init_consumer_objects["CRRA"]= 2.00 # Default coefficient of relative risk aversion (rho) +Params.init_consumer_objects["DiscFac"]= 0.97 # Default intertemporal discount factor (beta) +Params.init_consumer_objects["PermGroFacAgg"]= 1.0 # Aggregate permanent income growth factor +Params.init_consumer_objects["aNrmInitMean"]= -10.0 # Mean of log initial assets +Params.init_consumer_objects["aNrmInitStd"]= 1.0 # Standard deviation of log initial assets +Params.init_consumer_objects["pLvlInitMean"]= 0.0 # Mean of log initial permanent income +Params.init_consumer_objects["pLvlInitStd"]= 0.0 # Standard deviation of log initial permanent income + + +# %% +# Make a lifecycle consumer to be used for estimation +LifeCyclePop = Model.IndShockConsumerType(**Params.init_consumer_objects) + + +# %% {"code_folding": [0]} +# Solve and simulate the model (ignore the "warning" message) +LifeCyclePop.solve() # Obtain consumption rules by age +LifeCyclePop.unpackcFunc() # Expose the consumption rules + +# Which variables do we want to track +LifeCyclePop.track_vars = ['aNrmNow','pLvlNow','mNrmNow','cNrmNow','TranShkNow'] + +LifeCyclePop.T_sim = 120 # Nobody lives to be older than 145 years (=25+120) +LifeCyclePop.initializeSim() # Construct the age-25 distribution of income and assets +LifeCyclePop.simulate() # Simulate a population behaving according to this model + + +# %% {"code_folding": [0]} +# Plot the consumption functions during working life + +print('Consumption as a function of market resources while working:') +mMin = min([LifeCyclePop.solution[t].mNrmMin for t in range(LifeCyclePop.T_cycle)]) +plotFuncs(LifeCyclePop.cFunc[:LifeCyclePop.T_retire],mMin,5) + + +# %% {"code_folding": [0]} +# Define the saving rate function +def savingRateFunc(SomeType, m): + """ + Parameters: + ---------- + SomeType: + Agent type that has been solved and simulated. + + + Returns: + -------- + SavingRate: float + + """ + inc = (SomeType.Rfree -1.)*(m-1.)+1. + cons = SomeType.solution[0].cFunc(m) + Saving = inc - cons + SavingRate = Saving / inc + return SavingRate + + +# %% {"code_folding": [0]} +# Create a Giant matrix gathering useful data: +# 't_now', 'aNrmNow_hist', 'cNrmNow_hist', employment-status in date t, in date t-1, aLvlGro_hist, Saving rate + +w, h = 1, LifeCyclePop.T_cycle +giant_list = [[0 for x in range(w)] for y in range(h)] +SavingRate_list = [] + +import warnings +warnings.filterwarnings("ignore") # Suppress some disturbing but harmless warnings + +for t in range(1,LifeCyclePop.T_cycle+1): + #aLvlGro_hist[0] = 0 # set the first growth rate to 0, since there is no data for period 0 + aLvlGroNow = np.log(LifeCyclePop.aNrmNow_hist[t]/LifeCyclePop.aNrmNow_hist[t-1]) # (10000,) + + # Call the saving rate function with test value for + SavingRate = savingRateFunc(LifeCyclePop, LifeCyclePop.mNrmNow_hist[t] ) + + SavingRate_list.append(SavingRate) + + # Create elements of matrix list + matrix_list = [0 for number in range(7)] + matrix_list[0] = t + matrix_list[1] = LifeCyclePop.aNrmNow_hist[t] + matrix_list[2] = LifeCyclePop.cNrmNow_hist[t] + matrix_list[3] = LifeCyclePop.TranShkNow_hist[t] + matrix_list[4] = LifeCyclePop.TranShkNow_hist[t-1] + matrix_list[5] = aLvlGroNow + matrix_list[6] = SavingRate + + giant_list[t-1] = matrix_list + +# Print command disabled to prevent giant print! +#print giant_list + + +# %% {"code_folding": [0]} +# Construct the level of assets A from a*p where a is the ratio to permanent income p +LifeCyclePop.aLvlNow_hist = LifeCyclePop.aNrmNow_hist*LifeCyclePop.pLvlNow_hist +aGro41=LifeCyclePop.aLvlNow_hist[41]/LifeCyclePop.aLvlNow_hist[40] +aGro41NoU=aGro41[aGro41[:]>0.2] # Throw out extreme outliers + + +# %% {"code_folding": [0]} +# Plot the distribution of growth rates of wealth between age 65 and 66 (=25 + 41) +from matplotlib import pyplot as plt +n, bins, patches = plt.hist(aGro41NoU,50,density=True) + From 90db54121234196edba789627a40252334218f8e Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 9 Dec 2019 15:13:19 -0500 Subject: [PATCH 138/242] removing SolvingMicroDSOPs -- will remain in REMARKs --- .../Calibration/EstimationParameters.py | 173 - .../SolvingMicroDSOPs/Calibration/SCFdata.csv | 10476 ---------------- .../Calibration/SetupSCFdata.py | 89 - .../SolvingMicroDSOPs/Calibration/__init__.py | 0 .../Code/StructEstimation.py | 392 - examples/SolvingMicroDSOPs/Code/__init__.py | 0 .../SolvingMicroDSOPs/Figures/SMMcontour.pdf | Bin 37857 -> 0 bytes .../SolvingMicroDSOPs/Figures/SMMcontour.png | Bin 53409 -> 0 bytes .../SolvingMicroDSOPs/Figures/__init__.py | 0 examples/SolvingMicroDSOPs/Tables/__init__.py | 0 .../Tables/estimate_results.csv | 2 - examples/SolvingMicroDSOPs/__init__.py | 0 examples/SolvingMicroDSOPs/do_all.py | 148 - 13 files changed, 11280 deletions(-) delete mode 100644 examples/SolvingMicroDSOPs/Calibration/EstimationParameters.py delete mode 100644 examples/SolvingMicroDSOPs/Calibration/SCFdata.csv delete mode 100644 examples/SolvingMicroDSOPs/Calibration/SetupSCFdata.py delete mode 100644 examples/SolvingMicroDSOPs/Calibration/__init__.py delete mode 100644 examples/SolvingMicroDSOPs/Code/StructEstimation.py delete mode 100644 examples/SolvingMicroDSOPs/Code/__init__.py delete mode 100644 examples/SolvingMicroDSOPs/Figures/SMMcontour.pdf delete mode 100644 examples/SolvingMicroDSOPs/Figures/SMMcontour.png delete mode 100644 examples/SolvingMicroDSOPs/Figures/__init__.py delete mode 100644 examples/SolvingMicroDSOPs/Tables/__init__.py delete mode 100644 examples/SolvingMicroDSOPs/Tables/estimate_results.csv delete mode 100644 examples/SolvingMicroDSOPs/__init__.py delete mode 100644 examples/SolvingMicroDSOPs/do_all.py diff --git a/examples/SolvingMicroDSOPs/Calibration/EstimationParameters.py b/examples/SolvingMicroDSOPs/Calibration/EstimationParameters.py deleted file mode 100644 index 430e6f286..000000000 --- a/examples/SolvingMicroDSOPs/Calibration/EstimationParameters.py +++ /dev/null @@ -1,173 +0,0 @@ -''' -Specifies the full set of calibrated values required to estimate the SolvingMicroDSOPs -model. The empirical data is stored in a separate csv file and is loaded in SetupSCFdata. -''' -from __future__ import print_function - -# --------------------------------------------------------------------------------- -# Debugging flags -# --------------------------------------------------------------------------------- - -show_PermGroFacAgg_error = False -# Error Notes: -# This sets a "quick fix" to the error, AttributeError: 'TempConsumerType' object has no attribute 'PermGroFacAgg' -# If you set this flag to "True" you will see the error. A more thorough fix is to -# fix the place where this error was introduced (Set to "True" and it will appear; -# this was almost certainly introduced when the code was extended to be used in the -# GE setting). An even more thorough solution, which moves beyond the scope of -# fixing this error, is adding unit tests to ID when changes to some code will -# break things elsewhere. -# Note: alternatively, decide that the "init_consumer_objects['PermGroFacAgg'] = 1.0" -# line below fixes it properly ('feature not a bug') and remove all this text. - -# --------------------------------------------------------------------------------- -# - Define all of the model parameters for SolvingMicroDSOPs and ConsumerExamples - -# --------------------------------------------------------------------------------- - -exp_nest = 3 # Number of times to "exponentially nest" when constructing a_grid -aXtraMin = 0.001 # Minimum end-of-period "assets above minimum" value -aXtraMax = 20 # Maximum end-of-period "assets above minimum" value -aXtraHuge = None # A very large value of assets to add to the grid, not used -aXtraExtra = None # Some other value of assets to add to the grid, not used -aXtraCount = 8 # Number of points in the grid of "assets above minimum" - -BoroCnstArt = 0.0 # Artificial borrowing constraint; imposed minimum level of end-of period assets -CubicBool = True # Use cubic spline interpolation when True, linear interpolation when False -vFuncBool = False # Whether to calculate the value function during solution - -Rfree = 1.03 # Interest factor on assets -PermShkCount = 7 # Number of points in discrete approximation to permanent income shocks -TranShkCount = 7 # Number of points in discrete approximation to transitory income shocks -UnempPrb = 0.005 # Probability of unemployment while working -UnempPrbRet = 0.000 # Probability of "unemployment" while retired -IncUnemp = 0.0 # Unemployment benefits replacement rate -IncUnempRet = 0.0 # "Unemployment" benefits when retired - -final_age = 90 # Age at which the problem ends (die with certainty) -retirement_age = 65 # Age at which the consumer retires -initial_age = 25 # Age at which the consumer enters the model -TT = final_age - initial_age # Total number of periods in the model -retirement_t = retirement_age - initial_age - 1 - -CRRA_start = 4.0 # Initial guess of the coefficient of relative risk aversion during estimation (rho) -DiscFacAdj_start = 0.99 # Initial guess of the adjustment to the discount factor during estimation (beth) -DiscFacAdj_bound = [0.0001,15.0] # Bounds for beth; if violated, objective function returns "penalty value" -CRRA_bound = [0.0001,15.0] # Bounds for rho; if violated, objective function returns "penalty value" - -# Expected growth rates of permanent income over the lifecycle, starting from age 25 -PermGroFac = [ 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, - 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, - 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, 1.025, - 1.025, 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , - 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 1.01 , 0.7 , # <-- This represents retirement - 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , - 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , - 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ] - -# Age-varying discount factors over the lifecycle, lifted from Cagetti (2003) -DiscFac_timevary = [1.064914 , 1.057997 , 1.051422 , 1.045179 , 1.039259 , - 1.033653 , 1.028352 , 1.023348 , 1.018632 , 1.014198 , - 1.010037 , 1.006143 , 1.002509 , 0.9991282, 0.9959943, - 0.9931012, 0.9904431, 0.9880143, 0.9858095, 0.9838233, - 0.9820506, 0.9804866, 0.9791264, 0.9779656, 0.9769995, - 0.9762239, 0.9756346, 0.9752274, 0.9749984, 0.9749437, - 0.9750595, 0.9753422, 0.9757881, 0.9763936, 0.9771553, - 0.9780698, 0.9791338, 0.9803439, 0.981697 , 0.8287214, - 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111, - 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111, - 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111, - 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111, - 0.9902111, 0.9902111, 0.9902111, 0.9902111, 0.9902111] - -# Survival probabilities over the lifecycle, starting from age 25 -LivPrb = [ 1. , 1. , 1. , 1. , 1. , - 1. , 1. , 1. , 1. , 1. , - 1. , 1. , 1. , 1. , 1. , - 1. , 1. , 1. , 1. , 1. , - 1. , 1. , 1. , 1. , 1. , - 1. , 1. , 1. , 1. , 1. , - 1. , 1. , 1. , 1. , 1. , - 1. , 1. , 1. , 1. , 1. , # <-- automatic survival to age 65 - 0.98438596, 0.98438596, 0.98438596, 0.98438596, 0.98438596, - 0.97567062, 0.97567062, 0.97567062, 0.97567062, 0.97567062, - 0.96207901, 0.96207901, 0.96207901, 0.96207901, 0.96207901, - 0.93721595, 0.93721595, 0.93721595, 0.93721595, 0.93721595, - 0.63095734, 0.63095734, 0.63095734, 0.63095734, 0.63095734] - - -# Standard deviations of permanent income shocks by age, starting from age 25 -PermShkStd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0, 0.0, 0.0, # <-- no permanent income shocks after retirement -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -# Standard deviations of transitory income shocks by age, starting from age 25 -TranShkStd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0, 0.0, 0.0, # <-- no transitory income shocs after retirement -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -# Age groups for the estimation: calculate average wealth-to-permanent income ratio -# for consumers within each of these age groups, compare actual to simulated data -empirical_cohort_age_groups = [[ 26,27,28,29,30 ], - [ 31,32,33,34,35 ], - [ 36,37,38,39,40 ], - [ 41,42,43,44,45 ], - [ 46,47,48,49,50 ], - [ 51,52,53,54,55 ], - [ 56,57,58,59,60 ]] - -initial_wealth_income_ratio_vals = [0.17, 0.5, 0.83] # Three point discrete distribution of initial w -initial_wealth_income_ratio_probs = [0.33333, 0.33333, 0.33334] # Equiprobable discrete distribution of initial w -num_agents = 10000 # Number of agents to simulate -bootstrap_size = 50 # Number of re-estimations to do during bootstrap -seed = 31382 # Just an integer to seed the estimation - -# ----------------------------------------------------------------------------- -# -- Set up the dictionary "container" for making a basic lifecycle type ------ -# ----------------------------------------------------------------------------- - -# Dictionary that can be passed to ConsumerType to instantiate -init_consumer_objects = {"CRRA":CRRA_start, - "Rfree":Rfree, - "PermGroFac":PermGroFac, - "BoroCnstArt":BoroCnstArt, - "PermShkStd":PermShkStd, - "PermShkCount":PermShkCount, - "TranShkStd":TranShkStd, - "TranShkCount":TranShkCount, - "T_cycle":TT, - "UnempPrb":UnempPrb, - "UnempPrbRet":UnempPrbRet, - "T_retire":retirement_t, - "T_age":TT+1, - "IncUnemp":IncUnemp, - "IncUnempRet":IncUnempRet, - "aXtraMin":aXtraMin, - "aXtraMax":aXtraMax, - "aXtraCount":aXtraCount, - "aXtraExtra":[aXtraExtra,aXtraHuge], - "aXtraNestFac":exp_nest, - "LivPrb":LivPrb, - "DiscFac":DiscFac_timevary, - 'AgentCount':num_agents, - 'seed':seed, - 'tax_rate':0.0, - 'vFuncBool':vFuncBool, - 'CubicBool':CubicBool - } - -if show_PermGroFacAgg_error: - pass # do nothing -else: - print("***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***") - init_consumer_objects['PermGroFacAgg'] = 1.0 - - -if __name__ == '__main__': - print("Sorry, EstimationParameters doesn't actually do anything on its own.") - print("This module is imported by StructEstimation, providing calibrated ") - print("parameters for the example estimation. Please see that module if you ") - print("want more interesting output.") diff --git a/examples/SolvingMicroDSOPs/Calibration/SCFdata.csv b/examples/SolvingMicroDSOPs/Calibration/SCFdata.csv deleted file mode 100644 index c26c88fc2..000000000 --- a/examples/SolvingMicroDSOPs/Calibration/SCFdata.csv +++ /dev/null @@ -1,10476 +0,0 @@ -wealth_income_ratio,age_group,weight --7.278331,1,.7919394 --3.352119,1,1.077116 --2.806893,1,.6973649 --2.053807,1,1.283598 --1.629471,1,.8323343 - -1.42016,1 ,.818994 --1.274307,1,.8566858 - -1.11702,1,.7354656 --1.091087,1,.9976413 --.9850314,1,.7813509 --.9656265,1,.8889729 --.9253121,1,1.057292 - -.899695,1,.9466035 --.8299303,1 ,.890588 - -.812595,1,1.869985 --.7882341,1,.8057681 --.7869177,1,.8014933 --.6918034,1,1.634311 --.6831914,1 ,.881384 --.6659861,1,.9229636 --.6609519,1,1.142049 --.6391214,1,1.008117 - -.599839,1,1.076386 --.5505417,1,.8633595 --.5389788,1,.6785128 --.5188019,1,1.081038 --.5126372,1,.7898264 --.5109465,1,1.280624 --.4946782,1,.9332498 --.4766116,1,1.566128 - 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266.7173,7,.0109874 - 298.0587,7,.1067118 - 303.0951,7,.0029558 - 411.0751,7,.0073425 - 451.2466,7,.3640336 - 565.0374,7,.3550345 - 652.0066,7,.0064149 - 702.8777,7,.0049825 - 835.3079,7,.0124518 diff --git a/examples/SolvingMicroDSOPs/Calibration/SetupSCFdata.py b/examples/SolvingMicroDSOPs/Calibration/SetupSCFdata.py deleted file mode 100644 index 445f7ad03..000000000 --- a/examples/SolvingMicroDSOPs/Calibration/SetupSCFdata.py +++ /dev/null @@ -1,89 +0,0 @@ -''' -Sets up the SCF data for use in the SolvingMicroDSOPs estimation. -''' -from __future__ import division # Use new division function -from __future__ import print_function -from __future__ import absolute_import -from builtins import zip -from builtins import str -from builtins import range - -import os, sys - -# Find pathname to this file: -my_file_path = os.path.dirname(os.path.abspath(__file__)) - -# Pathnames to the other files: -calibration_dir = os.path.join(my_file_path, "../Calibration/") # Relative directory for primitive parameter files -tables_dir = os.path.join(my_file_path, "../Tables/") # Relative directory for primitive parameter files -figures_dir = os.path.join(my_file_path, "../Figures/") # Relative directory for primitive parameter files -code_dir = os.path.join(my_file_path, "../Code/") # Relative directory for primitive parameter files - - -# Import modules from local repository. If local repository is part of HARK, -# this will import from HARK. Otherwise manual pathname specification is in -# order. -try: - # Import from core HARK code first: - from HARK.SolvingMicroDSOPs.Calibration.EstimationParameters import initial_age, empirical_cohort_age_groups - -except: - # Need to rely on the manual insertion of pathnames to all files in do_all.py - # NOTE sys.path.insert(0, os.path.abspath(tables_dir)), etc. may need to be - # copied from do_all.py to here - - # Import files first: - from EstimationParameters import initial_age, empirical_cohort_age_groups - - -# The following libraries are part of the standard python distribution -import numpy as np # Numerical Python -import csv # Comma-separated variable reader - -# Set the path to the empirical data: -scf_data_path = data_location = os.path.dirname(os.path.abspath(__file__)) # os.path.abspath('./') #'./' - -# Open the file handle and create a reader object and a csv header -infile = open(scf_data_path + '/SCFdata.csv', 'r') -csv_reader = csv.reader(infile) -data_csv_header = next(csv_reader) - -# Pull the column index from the data_csv_header -data_column_index = data_csv_header.index('wealth_income_ratio') # scf_w_col -age_group_column_index = data_csv_header.index('age_group') # scf_ages_col -data_weight_column_index = data_csv_header.index('weight') # scf_weights_col - -# Initialize empty lists for the data -w_to_y_data = [] # Ratio of wealth to permanent income -empirical_weights = [] # Weighting for this observation -empirical_groups = [] # Which age group this observation belongs to (1-7) - -# Read in the data from the datafile by looping over each record (row) in the file. -for record in csv_reader: - w_to_y_data.append(np.float64(record[data_column_index])) - empirical_groups.append(np.float64(record[age_group_column_index])) - empirical_weights.append(np.float64(record[data_weight_column_index])) - -# Generate a single array of SCF data, useful for resampling for bootstrap -scf_data_array = np.array([w_to_y_data,empirical_groups,empirical_weights]).T - -# Convert SCF data to numpy's array format for easier math -w_to_y_data = np.array(w_to_y_data) -empirical_groups = np.array(empirical_groups) -empirical_weights = np.array(empirical_weights) - -# Close the data file -infile.close() - -# Generate a mapping between the real ages in the groups and the indices of simulated data -simulation_map_cohorts_to_age_indices = [] -for ages in empirical_cohort_age_groups: - simulation_map_cohorts_to_age_indices.append(np.array(ages) - initial_age) - - -if __name__ == '__main__': - print("Sorry, SetupSCFdata doesn't actually do anything on its own.") - print("This module is imported by StructEstimation, providing data for") - print("the example estimation. Please see that module if you want more") - print("interesting output.") - diff --git a/examples/SolvingMicroDSOPs/Calibration/__init__.py b/examples/SolvingMicroDSOPs/Calibration/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/SolvingMicroDSOPs/Code/StructEstimation.py b/examples/SolvingMicroDSOPs/Code/StructEstimation.py deleted file mode 100644 index 478d99531..000000000 --- a/examples/SolvingMicroDSOPs/Code/StructEstimation.py +++ /dev/null @@ -1,392 +0,0 @@ -''' -Demonstrates an example estimation of microeconomic dynamic stochastic optimization -problem, as described in Section 9 of Chris Carroll's SolvingMicroDSOPs.pdf notes. -The estimation attempts to match the age-conditional wealth profile of simulated -consumers to the median wealth holdings of seven age groups in the 2004 SCF by -varying only two parameters: the coefficient of relative risk aversion and a scaling -factor for an age-varying sequence of discount factors. The estimation uses a -consumption-saving model with idiosyncratic shocks to permanent and transitory -income as defined in ConsIndShockModel. -''' -from __future__ import print_function -from __future__ import absolute_import - -from builtins import str -from builtins import range - -import os -import sys -import csv -import numpy as np # Numeric Python -import pylab # Python reproductions of some Matlab functions -from time import time, clock # Timing utility - -# Import modules from core HARK libraries: -import HARK.ConsumptionSaving.ConsIndShockModel as Model # The consumption-saving micro model -from HARK.simulation import drawDiscrete # Method for sampling from a discrete distribution -from HARK.estimation import minimizeNelderMead, bootstrapSampleFromData # Estimation methods - -# Find pathname to this file: -my_file_path = os.path.dirname(os.path.abspath(__file__)) - -# Pathnames to the other files: -calibration_dir = os.path.join(my_file_path, "../Calibration/") # Relative directory for primitive parameter files -tables_dir = os.path.join(my_file_path, "../Tables/") # Relative directory for primitive parameter files -figures_dir = os.path.join(my_file_path, "../Figures/") # Relative directory for primitive parameter files -code_dir = os.path.join(my_file_path, "../Code/") # Relative directory for primitive parameter files - - -# Import modules from local repository. If local repository is part of HARK, -# this will import from HARK. Otherwise manual pathname specification is in -# order. -try: - # Import from core HARK code first: - from HARK.SolvingMicroDSOPs.Calibration import EstimationParameters as Params # Parameters for the consumer type and the estimation - from HARK.SolvingMicroDSOPs.Calibration import SetupSCFdata as Data # SCF 2004 data on household wealth - -except: - # Need to rely on the manual insertion of pathnames to all files in do_all.py - # NOTE sys.path.insert(0, os.path.abspath(tables_dir)), etc. may need to be - # copied from do_all.py to here - import EstimationParameters as Params # Parameters for the consumer type and the estimation - import SetupSCFdata as Data # SCF 2004 data on household wealth - - - -# Set booleans to determine which tasks should be done -local_estimate_model = True # Whether to estimate the model -local_compute_standard_errors = False # Whether to get standard errors via bootstrap -local_make_contour_plot = True # Whether to make a contour map of the objective function - -#===================================================== -# Define objects and functions used for the estimation -#===================================================== - -class TempConsumerType(Model.IndShockConsumerType): - ''' - A very lightly edited version of IndShockConsumerType. Uses an alternate method of making new - consumers and specifies DiscFac as being age-dependent. Called "temp" because only used here. - ''' - def __init__(self,cycles=1,time_flow=True,**kwds): - ''' - Make a new consumer type. - - Parameters - ---------- - cycles : int - Number of times the sequence of periods should be solved. - time_flow : boolean - Whether time is currently "flowing" forward for this instance. - - Returns - ------- - None - ''' - # Initialize a basic AgentType - Model.IndShockConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds) - self.addToTimeVary('DiscFac') # This estimation uses age-varying discount factors as - self.delFromTimeInv('DiscFac')# estimated by Cagetti (2003), so switch from time_inv to time_vary - - def simBirth(self,which_agents): - ''' - Alternate method for simulating initial states for simulated agents, drawing from a finite - distribution. Used to overwrite IndShockConsumerType.simBirth, which uses lognormal distributions. - - Parameters - ---------- - which_agents : np.array(Bool) - Boolean array of size self.AgentCount indicating which agents should be "born". - - Returns - ------- - None - ''' - # Get and store states for newly born agents - self.aNrmNow[which_agents] = self.aNrmInit[which_agents] # Take directly from pre-specified distribution - self.pLvlNow[which_agents] = 1.0 # No variation in permanent income needed - self.t_age[which_agents] = 0 # How many periods since each agent was born - self.t_cycle[which_agents] = 0 # Which period of the cycle each agents is currently in - return None - - -# Make a lifecycle consumer to be used for estimation, including simulated shocks (plus an initial distribution of wealth) -EstimationAgent = TempConsumerType(**Params.init_consumer_objects) # Make a TempConsumerType for estimation -EstimationAgent(T_sim = EstimationAgent.T_cycle+1) # Set the number of periods to simulate -EstimationAgent.track_vars = ['bNrmNow'] # Choose to track bank balances as wealth -EstimationAgent.aNrmInit = drawDiscrete(N=Params.num_agents, - P=Params.initial_wealth_income_ratio_probs, - X=Params.initial_wealth_income_ratio_vals, - seed=Params.seed) # Draw initial assets for each consumer -EstimationAgent.makeShockHistory() - -# Define the objective function for the simulated method of moments estimation -def smmObjectiveFxn(DiscFacAdj, CRRA, - agent = EstimationAgent, - DiscFacAdj_bound = Params.DiscFacAdj_bound, - CRRA_bound = Params.CRRA_bound, - empirical_data = Data.w_to_y_data, - empirical_weights = Data.empirical_weights, - empirical_groups = Data.empirical_groups, - map_simulated_to_empirical_cohorts = Data.simulation_map_cohorts_to_age_indices): - ''' - The objective function for the SMM estimation. Given values of discount factor - adjuster DiscFacAdj, coeffecient of relative risk aversion CRRA, a base consumer - agent type, empirical data, and calibrated parameters, this function calculates - the weighted distance between data and the simulated wealth-to-permanent - income ratio. - - Steps: - a) solve for consumption functions for (DiscFacAdj, CRRA) - b) simulate wealth holdings for many consumers over time - c) sum distances between empirical data and simulated medians within - seven age groupings - - Parameters - ---------- - DiscFacAdj : float - An adjustment factor to a given age-varying sequence of discount factors. - I.e. DiscFac[t] = DiscFacAdj*DiscFac_timevary[t]. - CRRA : float - Coefficient of relative risk aversion. - agent : ConsumerType - The consumer type to be used in the estimation, with all necessary para- - meters defined except the discount factor and CRRA. - DiscFacAdj_bound : (float,float) - Lower and upper bounds on DiscFacAdj; if outside these bounds, the function - simply returns a "penalty value". - DiscFacAdj_bound : (float,float) - Lower and upper bounds on CRRA; if outside these bounds, the function - simply returns a "penalty value". - empirical_data : np.array - Array of wealth-to-permanent-income ratios in the data. - empirical_weights : np.array - Weights for each observation in empirical_data. - empirical_groups : np.array - Array of integers listing the age group for each observation in empirical_data. - map_simulated_to_empirical_cohorts : [np.array] - List of arrays of "simulation ages" for each age grouping. E.g. if the - 0th element is [1,2,3,4,5], then these time indices from the simulation - correspond to the 0th empirical age group. - - Returns - ------- - distance_sum : float - Sum of distances between empirical data observations and the corresponding - median wealth-to-permanent-income ratio in the simulation. - ''' - original_time_flow = agent.time_flow - agent.timeFwd() # Make sure time is flowing forward for the agent - - # A quick check to make sure that the parameter values are within bounds. - # Far flung falues of DiscFacAdj or CRRA might cause an error during solution or - # simulation, so the objective function doesn't even bother with them. - if DiscFacAdj < DiscFacAdj_bound[0] or DiscFacAdj > DiscFacAdj_bound[1] or CRRA < CRRA_bound[0] or CRRA > CRRA_bound[1]: - return 1e30 - - # Update the agent with a new path of DiscFac based on this DiscFacAdj (and a new CRRA) - agent(DiscFac = [b*DiscFacAdj for b in Params.DiscFac_timevary], CRRA = CRRA) - - # Solve the model for these parameters, then simulate wealth data - agent.solve() # Solve the microeconomic model - agent.unpackcFunc() # "Unpack" the consumption function for convenient access - max_sim_age = max([max(ages) for ages in map_simulated_to_empirical_cohorts])+1 - agent.initializeSim() # Initialize the simulation by clearing histories, resetting initial values - agent.simulate(max_sim_age) # Simulate histories of consumption and wealth - sim_w_history = agent.bNrmNow_hist # Take "wealth" to mean bank balances before receiving labor income - - # Find the distance between empirical data and simulated medians for each age group - group_count = len(map_simulated_to_empirical_cohorts) - distance_sum = 0 - for g in range(group_count): - cohort_indices = map_simulated_to_empirical_cohorts[g] # The simulated time indices corresponding to this age group - sim_median = np.median(sim_w_history[cohort_indices,]) # The median of simulated wealth-to-income for this age group - group_indices = empirical_groups == (g+1) # groups are numbered from 1 - distance_sum += np.dot(np.abs(empirical_data[group_indices] - sim_median),empirical_weights[group_indices]) # Weighted distance from each empirical observation to the simulated median for this age group - - # Restore time to its original direction and report the result - if not original_time_flow: - agent.timeRev() - return distance_sum - -# Make a single-input lambda function for use in the optimizer -smmObjectiveFxnReduced = lambda parameters_to_estimate : smmObjectiveFxn(DiscFacAdj=parameters_to_estimate[0],CRRA=parameters_to_estimate[1]) -''' -A "reduced form" of the SMM objective function, compatible with the optimizer. -Identical to smmObjectiveFunction, but takes only a single input as a length-2 -list representing [DiscFacAdj,CRRA]. -''' - -# Define the bootstrap procedure -def calculateStandardErrorsByBootstrap(initial_estimate,N,seed=0,verbose=False): - ''' - Calculates standard errors by repeatedly re-estimating the model with datasets - resampled from the actual data. - - Parameters - ---------- - initial_estimate : [float,float] - The estimated [DiscFacAdj,CRRA], for use as an initial guess for each - re-estimation in the bootstrap procedure. - N : int - Number of times to resample data and re-estimate the model. - seed : int - Seed for the random number generator. - verbose : boolean - Indicator for whether extra output should be printed for the user. - - Returns - ------- - standard_errors : [float,float] - Standard errors calculated by bootstrap: [DiscFacAdj_std_error, CRRA_std_error]. - ''' - t_0 = time() - - # Generate a list of seeds for generating bootstrap samples - RNG = np.random.RandomState(seed) - seed_list = RNG.randint(2**31-1,size=N) - - # Estimate the model N times, recording each set of estimated parameters - estimate_list = [] - for n in range(N): - t_start = time() - - # Bootstrap a new dataset by resampling from the original data - bootstrap_data = (bootstrapSampleFromData(Data.scf_data_array,seed=seed_list[n])).T - w_to_y_data_bootstrap = bootstrap_data[0,] - empirical_groups_bootstrap = bootstrap_data[1,] - empirical_weights_bootstrap = bootstrap_data[2,] - - # Make a temporary function for use in this estimation run - smmObjectiveFxnBootstrap = lambda parameters_to_estimate : smmObjectiveFxn(DiscFacAdj=parameters_to_estimate[0], - CRRA=parameters_to_estimate[1], - empirical_data = w_to_y_data_bootstrap, - empirical_weights = empirical_weights_bootstrap, - empirical_groups = empirical_groups_bootstrap) - - # Estimate the model with the bootstrap data and add to list of estimates - this_estimate = minimizeNelderMead(smmObjectiveFxnBootstrap,initial_estimate) - estimate_list.append(this_estimate) - t_now = time() - - # Report progress of the bootstrap - if verbose: - print('Finished bootstrap estimation #' + str(n+1) + ' of ' + str(N) + ' in ' + str(t_now-t_start) + ' seconds (' + str(t_now-t_0) + ' cumulative)') - - # Calculate the standard errors for each parameter - estimate_array = (np.array(estimate_list)).T - DiscFacAdj_std_error = np.std(estimate_array[0]) - CRRA_std_error = np.std(estimate_array[1]) - - return [DiscFacAdj_std_error, CRRA_std_error] - - -#================================================================= -# Done defining objects and functions. Now run them (if desired). -#================================================================= - -def main(estimate_model=local_estimate_model, compute_standard_errors=local_compute_standard_errors, make_contour_plot=local_make_contour_plot): - """ - Run the main estimation procedure for SolvingMicroDSOP. - - Parameters - ---------- - estimate_model : bool - Whether to estimate the model using Nelder-Mead. When True, this is a low-time, low-memory operation. - - compute_standard_errors : bool - Whether to compute standard errors on the estiamtion of the model. - - make_contour_plot : bool - Whether to make the contour plot associate with the estiamte. - - Returns - ------- - None - """ - - - # Estimate the model using Nelder-Mead - if estimate_model: - initial_guess = [Params.DiscFacAdj_start,Params.CRRA_start] - print('--------------------------------------------------------------------------------') - print('Now estimating the model using Nelder-Mead from an initial guess of ' + str(initial_guess) + '...') - print('--------------------------------------------------------------------------------') - t_start_estimate = clock() - model_estimate = minimizeNelderMead(smmObjectiveFxnReduced,initial_guess,verbose=True) - t_end_estimate = clock() - time_to_estimate = t_end_estimate-t_start_estimate - print('Time to execute all:', round(time_to_estimate/60.,2), 'min,', time_to_estimate, 'sec') - print('Estimated values: DiscFacAdj=' + str(model_estimate[0]) + ', CRRA=' + str(model_estimate[1])) - - # Create the simple estimate table - estimate_results_file = os.path.join(tables_dir, 'estimate_results.csv') - with open(estimate_results_file, 'wt') as f: - writer = csv.writer(f) - writer.writerow(['DiscFacAdj', 'CRRA']) - writer.writerow([model_estimate[0], model_estimate[1]]) - - - if compute_standard_errors and not estimate_model: - print("To run the bootstrap you must first estimate the model by setting estimate_model = True.") - - # Compute standard errors by bootstrap - if compute_standard_errors and estimate_model: - - - # Estimate the model: - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print("Computing standard errors using",Params.bootstrap_size,"bootstrap replications.") - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - try: - t_bootstrap_guess = time_to_estimate * Params.bootstrap_size - print("This will take approximately", round(t_bootstrap_guess/60.,2), "min, ", t_bootstrap_guess, "sec") - except: - pass - t_start_bootstrap = clock() - std_errors = calculateStandardErrorsByBootstrap(model_estimate,N=Params.bootstrap_size,seed=Params.seed,verbose=True) - t_end_bootstrap = clock() - time_to_bootstrap = t_end_bootstrap-t_start_bootstrap - print('Time to execute all:', round(time_to_bootstrap/60.,2), 'min,', time_to_bootstrap, 'sec') - print('Standard errors: DiscFacAdj--> ' + str(std_errors[0]) + ', CRRA--> ' + str(std_errors[1])) - - # Create the simple bootstrap table - bootstrap_results_file = os.path.join(tables_dir, 'bootstrap_results.csv') - with open(bootstrap_results_file, 'wt') as f: - writer = csv.writer(f) - writer.writerow(['DiscFacAdj', 'DiscFacAdj_standard_error', 'CRRA', 'CRRA_standard_error']) - writer.writerow([model_estimate[0], std_errors[0], model_estimate[1], std_errors[1]]) - - # Make a contour plot of the objective function - if make_contour_plot: - print('````````````````````````````````````````````````````````````````````````````````') - print("Creating the contour plot.") - print('````````````````````````````````````````````````````````````````````````````````') - t_start_contour = clock() - grid_density = 20 # Number of parameter values in each dimension - level_count = 100 # Number of contour levels to plot - DiscFacAdj_list = np.linspace(0.85,1.05,grid_density) - CRRA_list = np.linspace(2,8,grid_density) - CRRA_mesh, DiscFacAdj_mesh = pylab.meshgrid(CRRA_list,DiscFacAdj_list) - smm_obj_levels = np.empty([grid_density,grid_density]) - for j in range(grid_density): - DiscFacAdj = DiscFacAdj_list[j] - for k in range(grid_density): - CRRA = CRRA_list[k] - smm_obj_levels[j,k] = smmObjectiveFxn(DiscFacAdj,CRRA) - smm_contour = pylab.contourf(CRRA_mesh,DiscFacAdj_mesh,smm_obj_levels,level_count) - t_end_contour = clock() - time_to_contour = t_end_contour-t_start_contour - print('Time to execute all:', round(time_to_contour/60.,2), 'min,', time_to_contour, 'sec') - pylab.colorbar(smm_contour) - pylab.plot(model_estimate[1],model_estimate[0],'*r',ms=15) - pylab.xlabel(r'coefficient of relative risk aversion $\rho$',fontsize=14) - pylab.ylabel(r'discount factor adjustment $\beth$',fontsize=14) - pylab.savefig(os.path.join(figures_dir, 'SMMcontour.pdf')) - pylab.savefig(os.path.join(figures_dir, 'SMMcontour.png')) - pylab.show() - - - -if __name__ == '__main__': - main() - diff --git a/examples/SolvingMicroDSOPs/Code/__init__.py b/examples/SolvingMicroDSOPs/Code/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/SolvingMicroDSOPs/Figures/SMMcontour.pdf b/examples/SolvingMicroDSOPs/Figures/SMMcontour.pdf deleted file mode 100644 index 958c22f074ca35be9305c7adbda8d768e7469a55..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 37857 zcmZs>Wmp{1(lrVMcX!trhTzUXaCaSC2MO*@@Zj#jJwbxI6KrsIcXvr3$<6uB``-Kg 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b/examples/SolvingMicroDSOPs/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/SolvingMicroDSOPs/do_all.py b/examples/SolvingMicroDSOPs/do_all.py deleted file mode 100644 index 1fb92f330..000000000 --- a/examples/SolvingMicroDSOPs/do_all.py +++ /dev/null @@ -1,148 +0,0 @@ -''' -Run all of the plots and tables in SolvingMicroDSOPs. - -To execute, do the following on the Python command line: - - from HARK.[YOUR-MODULE-NAME-HERE].do_all import run_replication - run_replication() - -You will be presented with an interactive prompt that asks what level of -replication you would like to have. - -More Details ------------- - -This example script allows the user to create all of the Figures and Tables -modules for SolvingMicroDSOPs.StructuralEstimation. - -This is example is kept as simple and minimal as possible to illustrate the -format of a "replication archive." - -The file structure is as follows: - -./SolvingMicroDSOPs/ - Calibration/ # Directory that contain the necessary code and data to parameterize the model - Code/ # The main estimation code, in this case StructuralEstimation.py - Figures/ # Any Figures created by the main code - Tables/ # Any tables created by the main code - -Because computational modeling can be very memory- and time-intensive, this file -also allows the user to choose whether to run files based on there resouce -requirements. Files are categorized as one of the following three: - -- low_resource: low RAM needed and runs quickly, say less than 1-5 minutes -- medium_resource: moderate RAM needed and runs moderately quickly, say 5-10+ mintues -- high_resource: high RAM needed (and potentially parallel computing required), and high time to run, perhaps even hours, days, or longer. - -The designation is purposefully vague and left up the to researcher to specify -more clearly below. Using time taken on an example machine is entirely reasonable -here. - -Finally, this code may serve as example code for efforts that fall outside -the HARK package structure for one reason or another. Therefore this script will -attempt to import the necessary MicroDSOP sub-modules as though they are part of -the HARK package; if that fails, this script reverts to manaully updating the -Python PATH with the locations of the MicroDSOP directory structure so it can -still run. -''' - -from __future__ import division, print_function -from builtins import str, range - -import os, sys - -# Find pathname to this file: -my_file_path = os.path.dirname(os.path.abspath(__file__)) - -# Pathnames to the other files: -calibration_dir = os.path.join(my_file_path, "Calibration") # Relative directory for primitive parameter files -tables_dir = os.path.join(my_file_path, "Tables") # Relative directory for primitive parameter files -figures_dir = os.path.join(my_file_path, "Figures") # Relative directory for primitive parameter files -code_dir = os.path.join(my_file_path, "Code") # Relative directory for primitive parameter files - -# Import modules from local repository. If local repository is part of HARK, -# this will import from HARK. Otherwise manual pathname specification is in -# order. -try: - # Import from core HARK code first: - from HARK.SolvingMicroDSOPs.Code import StructEstimation as struct -except: - print("**************** Manually specifying pathnames for modules *******************") - # It appears that the current module is not part of HARK, therefore we will - # manually add the pathnames to the various files directly to the beginning - # of the Python path. This will be needed for all files that will run in - # lower directories. - sys.path.insert(0, calibration_dir) - sys.path.insert(0, tables_dir) - sys.path.insert(0, figures_dir) - sys.path.insert(0, code_dir) - sys.path.insert(0, my_file_path) - - # Manual import needed, should draw from first instance at start of Python - # PATH added above: - import StructEstimation as struct - - -# Define settings for "main()" function in StructuralEstiamtion.py based on -# resource requirements: - -low_resource = {'estimate_model':True, 'make_contour_plot':False, 'compute_standard_errors':False} -# Author note: -# This takes approximately 90 seconds on a laptop with the following specs: -# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz - -medium_resource = {'estimate_model':True, 'make_contour_plot':True, 'compute_standard_errors':False} -# Author note: -# This takes approximately 7 minutes on a laptop with the following specs: -# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz - -high_resource = {'estimate_model':True, 'make_contour_plot':False, 'compute_standard_errors':True} -# Author note: -# This takes approximately 30 minutes on a laptop with the following specs: -# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz - -all_replications = {'estimate_model':True, 'make_contour_plot':True, 'compute_standard_errors':True} -# Author note: -# This takes approximately 40 minutes on a laptop with the following specs: -# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz - - -# Ask the user which replication to run, and run it: -def run_replication(): - which_replication = input("""Which replication would you like to run? (See documentation in do_all.py for details.) Please enter the option number to run that option; default is in brackets: - - [1] low-resource: ~90 sec; output ./Tables/estimate_results.csv - - 2 medium-resource: ~7 min; output ./Figures/SMMcontour.pdf - ./Figures/SMMcontour.png - 3 high-resource: ~30 min; output ./Tables/bootstrap_results.csv - - 4 all: ~40 min; output: all above. - - q quit: exit without executing.\n\n""") - - - if which_replication == 'q': - return - - elif which_replication == '1' or which_replication == '': - print("Running low-resource replication...") - struct.main(**low_resource) - - elif which_replication == '2': - print("Running medium-resource replication...") - struct.main(**medium_resource) - - elif which_replication == '3': - print("Running high-resource replication...") - struct.main(**high_resource) - - elif which_replication == '4': - print("Running all replications...") - struct.main(**all_replications) - - else: - return - -if __name__ == '__main__': - run_replication() From f0d18a44ff52f8115f16751fe53b1695424c9641 Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 9 Dec 2019 16:01:23 -0500 Subject: [PATCH 139/242] moving cstwMPC back into HARK because of downstream dependencies --- {examples => HARK}/cstwMPC/EducMortAdj.txt | 0 {examples => HARK}/cstwMPC/Figures/placeholder.txt | 0 {examples => HARK}/cstwMPC/MakeCSTWfigs.py | 0 {examples => HARK}/cstwMPC/MakeCSTWfigsForSlides.py | 0 {examples => HARK}/cstwMPC/Results/BetaDistPYResults.txt | 0 {examples => HARK}/cstwMPC/Results/BetaDistPYResults_py3.txt | 0 {examples => HARK}/cstwMPC/SCFwealthDataReduced.txt | 0 {examples => HARK}/cstwMPC/SetupParamsCSTW.py | 0 {examples => HARK}/cstwMPC/USactuarial.txt | 0 {examples => HARK}/cstwMPC/__init__.py | 0 {examples => HARK}/cstwMPC/cstwMPC.py | 0 11 files changed, 0 insertions(+), 0 deletions(-) rename {examples => HARK}/cstwMPC/EducMortAdj.txt (100%) rename {examples => HARK}/cstwMPC/Figures/placeholder.txt (100%) rename {examples => HARK}/cstwMPC/MakeCSTWfigs.py (100%) rename {examples => HARK}/cstwMPC/MakeCSTWfigsForSlides.py (100%) rename {examples => HARK}/cstwMPC/Results/BetaDistPYResults.txt (100%) rename {examples => HARK}/cstwMPC/Results/BetaDistPYResults_py3.txt (100%) rename {examples => HARK}/cstwMPC/SCFwealthDataReduced.txt (100%) rename {examples => HARK}/cstwMPC/SetupParamsCSTW.py (100%) rename {examples => HARK}/cstwMPC/USactuarial.txt (100%) rename {examples => HARK}/cstwMPC/__init__.py (100%) rename {examples => HARK}/cstwMPC/cstwMPC.py (100%) diff --git a/examples/cstwMPC/EducMortAdj.txt b/HARK/cstwMPC/EducMortAdj.txt similarity index 100% rename from examples/cstwMPC/EducMortAdj.txt rename to HARK/cstwMPC/EducMortAdj.txt diff --git a/examples/cstwMPC/Figures/placeholder.txt b/HARK/cstwMPC/Figures/placeholder.txt similarity index 100% rename from examples/cstwMPC/Figures/placeholder.txt rename to HARK/cstwMPC/Figures/placeholder.txt diff --git a/examples/cstwMPC/MakeCSTWfigs.py b/HARK/cstwMPC/MakeCSTWfigs.py similarity index 100% rename from examples/cstwMPC/MakeCSTWfigs.py rename to HARK/cstwMPC/MakeCSTWfigs.py diff --git a/examples/cstwMPC/MakeCSTWfigsForSlides.py b/HARK/cstwMPC/MakeCSTWfigsForSlides.py similarity index 100% rename from examples/cstwMPC/MakeCSTWfigsForSlides.py rename to HARK/cstwMPC/MakeCSTWfigsForSlides.py diff --git a/examples/cstwMPC/Results/BetaDistPYResults.txt b/HARK/cstwMPC/Results/BetaDistPYResults.txt similarity index 100% rename from examples/cstwMPC/Results/BetaDistPYResults.txt rename to HARK/cstwMPC/Results/BetaDistPYResults.txt diff --git a/examples/cstwMPC/Results/BetaDistPYResults_py3.txt b/HARK/cstwMPC/Results/BetaDistPYResults_py3.txt similarity index 100% rename from examples/cstwMPC/Results/BetaDistPYResults_py3.txt rename to HARK/cstwMPC/Results/BetaDistPYResults_py3.txt diff --git a/examples/cstwMPC/SCFwealthDataReduced.txt b/HARK/cstwMPC/SCFwealthDataReduced.txt similarity index 100% rename from examples/cstwMPC/SCFwealthDataReduced.txt rename to HARK/cstwMPC/SCFwealthDataReduced.txt diff --git a/examples/cstwMPC/SetupParamsCSTW.py b/HARK/cstwMPC/SetupParamsCSTW.py similarity index 100% rename from examples/cstwMPC/SetupParamsCSTW.py rename to HARK/cstwMPC/SetupParamsCSTW.py diff --git a/examples/cstwMPC/USactuarial.txt b/HARK/cstwMPC/USactuarial.txt similarity index 100% rename from examples/cstwMPC/USactuarial.txt rename to HARK/cstwMPC/USactuarial.txt diff --git a/examples/cstwMPC/__init__.py b/HARK/cstwMPC/__init__.py similarity index 100% rename from examples/cstwMPC/__init__.py rename to HARK/cstwMPC/__init__.py diff --git a/examples/cstwMPC/cstwMPC.py b/HARK/cstwMPC/cstwMPC.py similarity index 100% rename from examples/cstwMPC/cstwMPC.py rename to HARK/cstwMPC/cstwMPC.py From 26b323856dd98c7b9ddeabe2df706b7374a9ab2c Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Mon, 9 Dec 2019 19:19:15 -0500 Subject: [PATCH 140/242] remove deprecated RepAgentModel file, it was used in cAndCwithStickyE but it is now updated to use ConsRepAgentModel --- HARK/ConsumptionSaving/RepAgentModel.py | 11 ----------- 1 file changed, 11 deletions(-) delete mode 100644 HARK/ConsumptionSaving/RepAgentModel.py diff --git a/HARK/ConsumptionSaving/RepAgentModel.py b/HARK/ConsumptionSaving/RepAgentModel.py deleted file mode 100644 index 6dbae740a..000000000 --- a/HARK/ConsumptionSaving/RepAgentModel.py +++ /dev/null @@ -1,11 +0,0 @@ -''' -This file appears to be an old version of what is now ConsRepAgentModel.py. -Its previous contents have been entirely removed and replaced with a universal -import from ConsRepAgentModel. Whenever a user imports from this file, they -will get a warning that they should import from ConsRepAgentModel instead. -''' - -import warnings -from HARK.ConsumptionSaving.ConsRepAgentModel import * - -warnings.warn('Please import from ConsRepAgentModel rather than RepAgentModel. This module will be removed in a future version of HARK.') \ No newline at end of file From f953536d1218a9099ad216918f6d98957452d68d Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Mon, 9 Dec 2019 19:30:10 -0500 Subject: [PATCH 141/242] remove py2 specific imports --- HARK/ConsumptionSaving/ConsAggShockModel.py | 4 ---- HARK/ConsumptionSaving/ConsGenIncProcessModel.py | 4 ---- HARK/ConsumptionSaving/ConsIndShockModel.py | 6 ------ HARK/ConsumptionSaving/ConsMarkovModel.py | 3 --- HARK/ConsumptionSaving/ConsMedModel.py | 4 ---- HARK/ConsumptionSaving/ConsPrefShockModel.py | 4 ---- HARK/ConsumptionSaving/ConsRepAgentModel.py | 4 ---- HARK/ConsumptionSaving/ConsumerParameters.py | 1 - HARK/ConsumptionSaving/TractableBufferStockModel.py | 3 --- 9 files changed, 33 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index fd1c99702..52a2bbd28 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -4,10 +4,6 @@ basic solver. Also includes a subclass of Market called CobbDouglas economy, used for solving "macroeconomic" models with aggregate shocks. ''' -from __future__ import division, print_function -from __future__ import absolute_import -from builtins import str -from builtins import range import numpy as np import scipy.stats as stats from HARK.interpolation import LinearInterp, LinearInterpOnInterp1D, ConstantFunction, IdentityFunction,\ diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index 748e0b1fd..36234e1a8 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -4,10 +4,6 @@ ConsIndShockModel by explicitly tracking persistent income as a state variable, and allows (log) persistent income to follow an AR1 process rather than random walk. ''' -from __future__ import division, print_function -from __future__ import absolute_import -from builtins import str -from builtins import range from copy import deepcopy import numpy as np from HARK import AgentType, HARKobject diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 01e0e2f7a..86aa387df 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -12,12 +12,6 @@ See NARK for information on variable naming conventions. See HARK documentation for mathematical descriptions of the models being solved. ''' -from __future__ import division -from __future__ import print_function -from __future__ import absolute_import -from builtins import str -from builtins import range -from builtins import object from copy import copy, deepcopy import numpy as np from scipy.optimize import newton diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py index d1f90280a..2d03ed80f 100644 --- a/HARK/ConsumptionSaving/ConsMarkovModel.py +++ b/HARK/ConsumptionSaving/ConsMarkovModel.py @@ -4,9 +4,6 @@ include a Markov state; the interest factor, permanent growth factor, and income distribution can vary with the discrete state. ''' -from __future__ import division, print_function -from __future__ import absolute_import -from builtins import range from copy import deepcopy import numpy as np from HARK import AgentType diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py index 75b2502ad..cd4578815 100644 --- a/HARK/ConsumptionSaving/ConsMedModel.py +++ b/HARK/ConsumptionSaving/ConsMedModel.py @@ -1,10 +1,6 @@ ''' Consumption-saving models that also include medical spending. ''' -from __future__ import division, print_function -from __future__ import absolute_import -from builtins import str -from builtins import range import numpy as np from scipy.optimize import brentq from HARK import HARKobject diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index 636c8c3dd..a0b6f98a4 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -6,10 +6,6 @@ 2) A combination of (1) and ConsKinkedR, demonstrating how to construct a new model by inheriting from multiple classes. ''' -from __future__ import division, print_function -from __future__ import absolute_import -from builtins import str -from builtins import range import numpy as np from HARK.utilities import approxMeanOneLognormal from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, ConsIndShockSolver, \ diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py index 1f58c2c3b..e0d985ce9 100644 --- a/HARK/ConsumptionSaving/ConsRepAgentModel.py +++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py @@ -4,10 +4,6 @@ take a heterogeneous agents approach. In RA models, all attributes are either time invariant or exist on a short cycle; models must be infinite horizon. ''' -from __future__ import division, print_function -from __future__ import absolute_import -from builtins import str -from builtins import range import numpy as np from HARK.interpolation import LinearInterp from HARK.simulation import drawUniform, drawDiscrete diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 83d6f827b..8bdfcd66d 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -3,7 +3,6 @@ consumption-saving models. These models can be found in ConsIndShockModel, ConsAggShockModel, ConsPrefShockModel, and ConsMarkovModel. ''' -from __future__ import division, print_function from copy import copy import numpy as np diff --git a/HARK/ConsumptionSaving/TractableBufferStockModel.py b/HARK/ConsumptionSaving/TractableBufferStockModel.py index 057b4c3d5..1fb135f89 100644 --- a/HARK/ConsumptionSaving/TractableBufferStockModel.py +++ b/HARK/ConsumptionSaving/TractableBufferStockModel.py @@ -20,9 +20,6 @@ Despite the non-standard solution method, the iterative process can be embedded in the HARK framework, as shown below. ''' -from __future__ import division, print_function -from __future__ import absolute_import -from builtins import str import numpy as np # Import the HARK library. From 44e378348ca09125d701674b199038a96967dab0 Mon Sep 17 00:00:00 2001 From: llorracc <@users.noreply.github.com> Date: Tue, 10 Dec 2019 10:07:17 -0500 Subject: [PATCH 142/242] solution_distance and completed_cyles as agent attributes of agent - useful for debugging --- HARK/core.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/HARK/core.py b/HARK/core.py index 3c762bf6e..e1129aafd 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -806,6 +806,8 @@ def solveAgent(agent, verbose): if infinite_horizon: if completed_cycles > 0: solution_distance = solution_now.distance(solution_last) + agent.solution_distance = solution_distance # Add these attributes so users can + agent.completed_cycles = completed_cycles # query them to see if solution is ready go = (solution_distance > agent.tolerance and completed_cycles < max_cycles) else: # Assume solution does not converge after only one cycle solution_distance = 100.0 From e435d64141f3f33ac14924ca34f2494da9b92775 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 10 Dec 2019 10:15:35 -0500 Subject: [PATCH 143/242] fixing import paths in BayerLuetticke notebooks --- .../notebooks/DCT-Copula-Illustration.ipynb | 89 ++++-------------- .../notebooks/DCT-Copula-Illustration.py | 14 +-- .../notebooks/OneAsset-HANK.ipynb | 92 ++++++++++--------- .../BayerLuetticke/notebooks/OneAsset-HANK.py | 8 +- .../BayerLuetticke/notebooks/TwoAsset.ipynb | 4 +- examples/BayerLuetticke/notebooks/TwoAsset.py | 78 ++++++++-------- 6 files changed, 120 insertions(+), 165 deletions(-) diff --git a/examples/BayerLuetticke/notebooks/DCT-Copula-Illustration.ipynb b/examples/BayerLuetticke/notebooks/DCT-Copula-Illustration.ipynb index 7f4d74649..ca191477d 100644 --- a/examples/BayerLuetticke/notebooks/DCT-Copula-Illustration.ipynb +++ b/examples/BayerLuetticke/notebooks/DCT-Copula-Illustration.ipynb @@ -253,9 +253,7 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "code_folding": [ - 0 - ] + "code_folding": [] }, "outputs": [], "source": [ @@ -265,7 +263,7 @@ "\n", "# Relative directory for BL codes \n", "sys.path.insert(0,'../../../..') # comment by TW: this is not the same as in TwoAsset.ipynb. \n", - "from HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset import FluctuationsTwoAsset" + "from FluctuationsTwoAsset import FluctuationsTwoAsset" ] }, { @@ -524,7 +522,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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BGbzoNMZ8ik3jU7D76AD2AiTQZZpd2G3Vhn+L2JNkZH8FYc9jUc7vHYI9p8W4G69cXtKocoOI3IndyaFGd0CGiW2EZJkx5rVUxo/AVkS7PTfjym+c/bDE5HDDLEplhXOnvgBbq/58OpP7LG0JKBeIfRcxEVuTsQq2ZvVszciVrxKRZtjX6Lp4OhalUuM8JnoW+DQvZ+TgncXseVFxbJHlWWyx4VZgqEcjUiqTRGQ9tmTpCWPMsfSmV8oTROQ+7KOlYmSh3pOv0GJ2pZRSysfpnblSSinl4zQzVyqbiG3vvYXzf08R2eLhkLKV8/rUarGNd8x3Xoc7lYH5vxfbxny+Iml0VSzJ2idXKrO0ApxSOcAY8zm2idssE+/p+nQYtgGdJi6VN4ulMf1ljDF3pT+VUioz9M5cqXxAsqc7yarANn0LQynvo5m58npiuxV8R2zXoWfEdsvYIdk0g0Vkl9iuCteKS3ec4kaXrimsM71uWAuLbf/+pIjsl2TdSYrTBWqy35Bqt7oi0kBse8zRzjJXi+3WNlNdn8q/XbA+JyKHgAinFavXReQvZztGisgTbu6DLdiGTl5xYnhInK5knfH+zm+c6DJPL7HdxpZNvg1cipd7i+0O94yILEma1pkmza6DU4gxaT+/6eyzEyLytIhUFpHlzjo2isj1afxOd7rfTfVYdLZxql2mpqFlattBKXdoZq58RV9s86pFsV1yThOn3WKxzbmOxrbWVhLb0tNiEXFtQ7sZtgWnctguIl8U2w51amKBx7Ftktdx5nvPZfy72Daha2K7YeyA7eYws8ZhW1Urge0+8mngojHmf9ji+mlOu9CFjDEJYvt0ngQ86czTF/hQ7PvfScKcuKth3wlv40x3kzGmMLaXq1XuBGeMqYdtVW+0E8OkZOMTsE3cthORPiJSE9ujVQ+X5nBT0hW7b8pjW7cb5TLuc2zToRWxLcOl1GJZcs2wLW2Vwbbo9xZ2Oz2G3U47gPfTmD+9/Q5pHIvOOp/CHg9lsC2kNSN9aW0HpdKlmbnyFbONMaudJiMnYE+kSR0sPAh8YoxZZ4yJdzKardhmdJPsNsZ87Ixfi21ushGpMMasMsasd6Y/gm3WsTVc6nClJ/CKMeaI00tUak3nuusitgOeisaYOGPMWqfpzNQMBd4zxqw0xiQaY8KxPUT1cZkmDhhmjLngNJhxEQgBaolIiDHmqDFmcxbjvsTJtHtgM7gFwDvGmPSaeh1pjDlujDmNbW63EYDYCmGtsN2Vnnba9x/tRhi7jTETjTEJxpjvsU0W/2CM2WGMiXNdRyq/IdX97iKtY7EP9ljcaIy5iO3U6Igbcae4HZRyl2bmyldcurtzyeSSunasyJXdev7J5V2Xpto1pNha1klF2B87w9LqjrQ0EMzl3TW6061oWh7EpsdVIrJPREaLSFoVVNPr+hQgyrh092iMWQG8iG0j+6hTnJvdmcZP2G1fDtuudnqSd0eatE/d7To4reVByt3OptolaDr7/Yp1pHAsZrbL1NS2g1Ju0cxc5QUHubJbz6pc3l95qowxd7kUYQ9yBqfVHelx7F2u6zqTrz+5NLvVNcbsM8b0N8ZUwD4GeJh/77Iz0/VpivMZYyYYY27DFgFHYFsmzE4vYe/+12Lv0DPL3a6Ds5s73e+mJSe6TFUqXZqZq7xgKvCIiNwotkvGB4H6pN9LWlpS7Y7UeT48ExgptovOIthuQtOSZre6ItLXJYM/BcTzb092men69ArO9mkqIsHYZ8NnXNaBUyFuRDq/I1Vi37F/HtvTVg+gjWSyz/ds7Do4o9zpfjctOdFlqlLp0sxc+TxjzExgJPaZ8Qlsd4h3G2PcKd5MTXrdkQ7FFq3vxHags5C0u5FNr1vdVsBGETkHrMFeLMxwxmW469NUFMJW5jqO3U53YCte4WTwme5qVESuxhZJDzHG/O48407q871OZpZJ9nQdnFHudL+blmzvMlUpd2jb7ErlABF5CHjGGFPT07G4Q2x/7c8YY+7xdCypEe06WKlUaQtwSuWMhsAf6U7lJYwxy4Hlno7DlWjXwUq5TTNzpbKZ2C5CQ3HvvWiVuuLYNgPKYp9jfw8849GIlPJSWsyulFJK+TitAKeUUkr5OM3MlVJKKR+nmblSSinl4zQzV0oppXycZuZKKaWUj9PMXCmllPJxmpkrpZRSPk4zc6WUUsrHaWaulFJK+TjNzJVSSikfp5m5Ukop5eM0M1dKKaV8nGbmSimllI/TzFwppZTycZqZq1wlIiVEZL6InBOR/SLSI41prxeR5SISLSJ/iEgnd8dnZV53xiul0uYtaT2/0Mw8HxGRAE/HAIwDLgJXAz2B8SJSK/lETqxfA98CJYCBwGcicl1647MyrzvjlfJ2mtbzYVo1xugnFz/AMOBP4AywHejkMq4i8BVwDDgBfJjWcGecAa51+T4VeM3leyTwH2ArEAsEZDKG54B5yX7L+8B7GfjtBbGJ+zqXYTOAMSlMWxs4C4jLsCXA6PTGZ2Ved8brRz/ufDStez6t56eP3pnnvj+BpkBRYCT2KrKsiPhjry73A2FAeWBWasMzuM7uwD1AMWNMfEZjcJbxGdBWRIrBpavlbsB05/u3InIqlc+3zjKuA+KNMbtdYtsCXHG1ngrBJt7MjM/KvO6MVyo5TevemdbzJk9fTeT3DxABdABuwV4hByQbn+Jwl/HuXK33z0oMLtN9Dwxw/m8HbM/gb20KHEk2bACwIoVpA4G9wPPO/3dgr/R/SG98VuZ1Z7x+9JOZj6b13E/r+emjd+a5TET6iEhE0pUs9gqyFLbIa7+xV9OuUhueEQezGEOSaUAv5/9e2GKzjDgLFEk2rAi2CPAyxpg4oCP2LuMI8AzwJXAovfFZmded8Uq5Q9O659N6fqKZeS4SkcrAp8DjQEljTDHgN2yx0EGgUgoVV1IbnuQ8UMDle5kUpjFZjCHJAqCuiNTGXq1/7rLc70XkbCqf753JdgMBIlLNZZn1gN9TWpkxZqsxprkxpqQx5k6gKhDuzviszOvOeKXSomnde9J6fqGZee4qiE1sxwBE5EH+fbYTDkQBY0SkoIiEiEiTNIYniQB6iIi/iLQFmudADAAYY2KAucBMINwYc8Bl3F3GmEKpfO5ypjmHrXAzyll+E2yRX4pX/SJS14mhgIg8C5TFFi2mOz4r87ozXql0aFr3krSeX2hmnouMMduBd4A1wN9AHeBXZ1wCcC9wLXAAW0zUNbXhLosd6ow/hX39Y0F2x5BsEdOceTJa7JbkUSAUOAp8AQw2xvwOl674X3SZtjf2hHMUaA20McbEujk+K/O6M16pVGlaB7wnrecLYoxJfyqlHCJSCdgJlDHGnPZ0PEqpnKFp3bfonblym4j4AU8DszRxK5V3aVr3Pd7QSpDyASJSEFtUtx9o6+FwlFI5RNO6b9JidqWUUsrHaTG7Ukop5eN8tpi9VKlSJiwszNNhKOX1Nm7ceNwYU9rTcaRG07JS7kkrLftsZh4WFsaGDRs8HYZSXk9E9ns6hrRoWlbKPWmlZS1mV0oppXycZuZKKaWUj9PMXCmllPJxPvvMPCVxcXEcOnSImJgYT4ei8qCQkBAqVKhAYGCgp0NRSqnL5KnM/NChQxQuXJiwsDBExNPhqDzEGMOJEyc4dOgQVapU8XQ4VlQUdOsGs2dDmZQ60FJK+YRsSMt5qpg9JiaGkiVLakausp2IULJkSa8p9THGcPCpFzCrVsGoUZ4ORymVBWdfHo5ZmbW0nKcyc0AzcpVjvOXYMqGhiJ8fFWdPQxITYfx4EIHQUE+HppTKABMaCiIUmvwpYrKWlvNcZq5UXhafkMio9xaytmYd4oOC7MACBaBnT9i3z7PBKaXcdvjUBZ54bS4Lrm9ObFCwHZiFtKyZeT728MMPs3379iwvJzIykpkzZ2Z4vn79+jF37twrhrdo0SLTjYhMnTqV0qVL06BBA6pVq8add97J6tWrL5vm7bffpkaNGtSvX5/GjRszffp0OnXqRP369bn22mspWrQo9evXp379+lfM60mx8Qk88cVmzu9fzE2hu/CPuwghIRATA0WK6HNzpXyAMYaZ6w5w59hfWB7tz/U1KhAUH5fltKyZeR6RkJCQ4XkmTpxIzZo1s7zuzGbmOaVr165s3ryZPXv2MGzYMO677z527NgBwMcff8zSpUsJDw8nIiKCH3/8EWMM8+fPJyIigokTJ9K0aVMiIiKIiIjg1ltv9fCvsc5fjOfhqeu5Zsd43gz8FJFSyMCHYe1aGDQIjhzxdIhKqXQc+uc8vSeF8+L8bdStUJQfnmxGdc4jjzyS5bScp2qzuxq58He2/5W93fDWLFeE4ffWSnOa6dOn8/bbbyMi1K1blxkzZlw2Pjw8nKFDhxITE0NoaChTpkyhevXqTJ06lfnz5xMdHc3hw4fp1asXw4cPJzIykrZt23LDDTewadMmatWqxfTp0ylQoABhYWF07dqVpUuX8vzzz1OjRg0GDRrE+fPnueaaa5g8eTKFCxfmlltu4a233qJFixa88MIL+Pn58frrr9OiRQvefvttGjVqRKFChRg8eDDfffcdZcuW5Y033uD555/nwIEDvPvuu7Rv357IyEh69+7NuXPnAPjwww+59dZbGTZsGDt27KB+/fr07duXIUOGMGzYMFasWEFsbCyPPfYYjzzyCMYYnnjiCZYuXUrFihUJSiomTsGMGTN4+OGHiY+PZ/LkyTRq1Ijq1auzevVqSpcuTWJiItdddx1r1qyhdOnUmx1v2bIlAwcOZMKECYwdO5Y33niDFStWUKRIEQCKFClC37593T0EPCL6QhwPTV1P9UNzeTZwDtTrDq98AP7OK3Ljxnk2QKVUmowxzAw/wBuL7E3F651q0+PGSrYezpzZMPUeqFw8S2lZ78yz0e+//85rr73G8uXL2bJlC++9994V09SoUYOVK1eyefNmRo0axYsvvnhpXHh4OPPmzWPr1q3MmTPnUlHzrl27ePTRR9mxYwdFihTho48+ujRPyZIl2bRpE926daNPnz68+eabbN26lTp16jBy5EgCAgKYOnUqgwcPZtmyZSxevJjhw4dfEde5c+do1aoVv//+O4ULF+bll19m6dKlzJ8/n1dffRWAq666iqVLl7Jp0yZmz57NkCFDABgzZsylu9mnnnqKSZMmUbRoUdavX8/69ev59NNP2bdvH/Pnz2fXrl1s376d6dOnp1mEff78eSIiIvjoo4/o378/fn5+9OrVi88//xyAZcuWUa9evTQz8iQNGzZk586dnD59mjNnzlC1atV05/EWx8/G0n3CWrYcOkXTzo/BXW9Bx/H/ZuRKKa928OR5ek5cx0vzf6NBpeL88FQzet5U+d8Ktf6BcPsIMIlZWk+evTNP7w46JyxfvpwuXbpQqlQpAEqUKHHFNNHR0fTt25c9e/YgIsTFxV0a16ZNG0qWLAnAfffdx6pVq+jYsSMVK1akSZMmAPTq1Yv333+fZ599FrBFyknLPXXqFM2bNwegb9++dOnSBYBatWrRu3dv2rVrx5o1a1K8Iw4KCqJt27YA1KlTh+DgYAIDA6lTpw6RkZGAbZTn8ccfJyIiAn9/f3bv3p3idliyZAlbt2699Dw8OjqaPXv28Msvv9C9e3f8/f0pV64crVq1SnVbdu/eHYBmzZpx+vRpTp06Rf/+/enQoQNPPvkkkydP5sEHH0x1flfGGLem8zZR0RcY+OlyepyeRuXuY2hauypwjafDUkq5ITHR8Hn4Af773Q78RPjvfXXo1riizcSNgVVjbUZ+6xNQOeuP8/JsZu6tXnnlFVq2bMn8+fOJjIykRYsWl8Ylf/Up6XtqwwEKFizo1nq3bdtGsWLFOHr0aIrjAwMDLy3Xz8+P4ODgS//Hx8cDMHbsWK6++mq2bNlCYmIiISEhKS7LGMMHH3zAnXfeednw7777zq1YIeXfXLFiRa6++mqWL19OeHj4pbv09GzevJnrr7+eIkWKUKhQIfbu3ev1d+eRx88x5NPveStmFNf5H8YvNBLw7piVUtbBk+d5fu5W1uw9QdNqpRjTuS7lizmvmyXEw3fPwMapUOcBm7Fnw2uvWsyejVq1asWcOXM4ceIEACdPnrximujoaAt1TGIAACAASURBVMqXLw/Ymteuli5dysmTJ7lw4QILFiy4dDd+4MAB1qxZA8DMmTO57bbbrlhu0aJFKV68OCtXrgTsM+eku/SvvvqKkydP8ssvv/DEE09w6tSpTP2+6OhoypYti5+fHzNmzLhU6a5w4cKcOXPm0nR33nkn48ePv1TqsHv3bs6dO0ezZs2YPXs2CQkJREVF8dNPP6W6rtmzZwOwatUqihYtStGiRQFbA79Xr1506dIFf3//dGP++eefmTBhAgMGDADghRde4LHHHuP0aVuf4uzZs0yfPj0TWyPn7Dxymmc+nsf42BeoFngMv55fwjWpl2IopbxDYqJh+ppI7nz3F347HM2bneswvf+N/2bksWfgi242I2/6DHT6JFsyctA782xVq1YtXnrpJZo3b46/vz8NGjS4IsN+/vnn6du3L6+99hr33HPPZeNuvPFGOnfuzKFDh+jVqxeNGjUiMjKS6tWrM27cOPr370/NmjUZPHhwiuufNm3apQpwVatWZcqUKRw/fpxhw4bx448/UrFiRR5//HGGDh3KtGnTMvz7Hn30UTp37sz06dNp27btpVKBunXr4u/vT7169ejXrx9Dhw4lMjKShg0bYoyhdOnSLFiwgE6dOrF8+XJq1qxJpUqVuOWWW1JdV0hICA0aNCAuLo7JkydfGt6+fXsefPDBNIvYZ8+ezapVqzh//jxVqlRh3rx5XH/99QAMHjyYs2fP0rhxYwIDAwkMDOSZZ57J8LbIKREHT/HfSbOYyOsUDg3Ev9e3UP4GT4ellErHgRPneW7uFtbtO0mz60oz5r46lCvm0vhLQjxMuxeitkK7d6GRe48J3SW++jyxUaNGJvm7yDt27Lh00vY1U6dOZcOGDXz44YeXDY+MjKRdu3b89ttvHorMu2zYsIGnnnrqUglEbsvJY2z1n8cZMG0DtQucYnrxiQTf/wmUzPozchHZaIxplA0h5oiU0rJSviLpbvzNxbsI8BNeaVeTLo0qpNxi5PpJUKwSVGuTqXWllZZz9c5cRCYD7YCjxpjazrASwGwgDIgEHjDG/JObcSnfMGbMGMaPH+/2s3Jfsmz730ye+TkVSjTg/Yc7Ely4a7YVvymlckbk8XM8P28r4ftO0qJ6af57Xx3KFk3WFOveFfauvNrt0PihHIslt5+ZTwXaJhs2DPjRGFMN+NH5nu/069fvirtygLCwML0rdwwbNoz9+/enWGfAl30dcZj1M0cyM2AkX920h6uLhGhGrpQXS0w0TPl1H23f+4UdUad56/66TOnX+MqMPGImfNYZfn7TVnTLQbl6Z26M+UVEwpIN7gC0cP6fBqwA/pNrQSnlQTPXRnLu2xd5IWARcTU6UPAm727ARqn8bt/xc/xn7lbCI0/Ssnpp/ntfXcoUTfZmjzGwYgz8PAaqtoAHpuf4Bbo3VIC72hgT5fx/BLg6tQlFZCAwEKBSpUq5EJpSOefTn3ZScvkz9AhYRXyjAQTe/T/w850XTPSxmcpPEhINU1dH8tYPOwny9+OdLvW4r2H5K5+NJ8TDN4/Dli+gfk9b2S0g9dYus4tXnTmMrY2XalmEMWaCMaaRMaaROy1/KeWNjDG8/cMuFixZxr0B64hv8TIB97zlUxm5Yyr62EzlA3uPnaXrJ2sY/e12mlxTiqVPN6fzDalUcvPzB78AaPEidBiXKxk5eMed+d8iUtYYEyUiZYGUWzVRKg9ITDS8/k0Ek9b+RbfGzfBruRH/EpU9HVam6GMzldclOM/G3/phF8EBfvzfA/Xo1CCFu3GAUwchPhZKXQvtP8j1ei/ecCvwDZD0oLAv8LUHY/GYiIiINFtI6969O3Xr1mXs2LGpTrNixQratWsH2FfdHn/88SzFdPfdd6fYwMyIESN4++23s7Ts/Cg+IZH/zlxMj03dee/6Xfz3vjo+m5Gnwa3HZiIyUEQ2iMiGY8eO5V50Srnpz2Nn6fLxal5btIOm1Uqz7Onm3NcwlbvxvyJgYmuY1z/bWnTLqNx+Ne0L7FV7KRE5BAwHxgBfishDwH7ggdyMyVtERESwYcMG7r777ivGHTlyhPXr1/PHH3/kSizGGIwxGWp+VaUtNj6B/02dxyMHn6NIUCJVW96a8kkhDzHGGBFJ8bGZMWYCMAHse+a5GphSaUhINExatZd3luwmJNCfd7vWp0P9cqmn191LYE4/KFAiW1t0y6hcvTM3xnQ3xpQ1xgQaYyoYYyYZY04YY1obY6oZY243xlzZBmpmTbnnyk/4p3bcxfMpj9/svMN87sSV49wwffp06tatS7169ejdu/cV48PDw7nlllto0KABt956K7t27eLixYu8+uqrzJ49m/r1619qyjTJHXfcweHDh6lfvz4rV66kRYsWl3pUO378OGFhYW5vkmPHjtGmTRtq1arFww8/TOXKlTl+/Pillub69OlD7dq1OXjwIGFhYRw/fhyA119/neuuu47bbruNXbt2XVre+++/T82aNalbty7dunVzO4785PzFeN7+ZBJDDw6hQEgwIQOXIpVu9nRYOeVv53EZ+thM+Zo/jp7l/o9X88Z3O2l2XWmWPt2MjqkVqwNsmGybZy15DTy8DK7yXKNl3vDMPM9I6gJ19erVlCpVKsW22ZO6QA0ICGDZsmW8+OKLzJs3j1GjRqXYAhzAN998Q7t27YiIiMhyjCNHjqRVq1a88MILLF68mEmTJl0at2fPHqZNm8bNN1+e0WzcuJFZs2YRERFBfHw8DRs25IYbbBOjY8aMYd++fQQHB2e6zfe8LPpCHC9M/Iaxx18kpnAlCg1YCEXLezqsnJT02GwM+fixmfItCYmGT1fu5f+W7qZAkD/vdatP+3pp3I2DrbW+ZTZc2xrunwLBhXIv4BTk7cz8wUWpjwsqkPb4giXTHp+CrHaBmhtWrVrF/PnzAWjbti3Fixe/NK5y5cpXZOQAK1eupFOnThQoUACw7aMnqVu3Lj179qRjx4507Ngxh6P3LcfPxtJnUjh7joay+6bh1Lm9py2KyyP0sZnKC/44eoZn52wl4uAp7qx1Na91rEPpwsGpzxAfC/ExEFIUesyGoELg7/ms1PMR5DNpdYHqroCAABITbUf2MTEx2Rabu92pulq0aBG//PILCxcu5PXXX2fbtm0EBOhh9dc/5/lh/NMUvFCLiX27Uee6K+tC+DpjTPdURrXO1UCUyoT4hEQ+XbmPsct2UzDInw+6N6Bd3bJp342fPwmzetp+yPt8DaHFci/gdHhDbfY8IytdoCbvRjQtYWFhbNy4EYC5c+dmKMYmTZrw5ZdfArBkyRL++Sf99jyaNWvGggULuHDhAmfOnGHhwoUAJCYmcvDgQVq2bMmbb75JdHQ0Z8+ezVA8edG+o9Fs+LA3D178gvfr7qf5ddomglJeISoKmjfnz21/0Hn8at5cvJNW1a9iyVPNuTe9YvWT+2BSGzi8ARr28bomlzUzz0auXaDWq1ePp59++oppnn/+eV544QUaNGhAfHz8peEtW7Zk+/btKVaAS+7ZZ59l/PjxNGjQ4FIFNXcNHz6cJUuWULt2bebMmUOZMmUoXLhwmvM0bNiQrl27Uq9ePe666y4aN24MQEJCAr169aJOnTo0aNCAIUOGUKyY91ypesKuQ39zYHxn2ics42iDIZS9/y1Ph6SUciSOHIVZuZK1/YZy8J8LfNijAeN7NUy7WB3g0AaYeDucO27vyOvcnzsBZ4B2gZrPxMbG4u/vT0BAAGvWrGHw4MHZUrEuv0jrGNvyxwESPutCfXZxouloSrd+IpejS5l2garyvdBQSOmRZEgIXLiQ9rwJ8fDRTZAQB73mQalqOROjG7ymC1TleQcOHOCBBx4gMTGRoKAgPv30U0+HlCes/vM4j0zfxgeBJTh5xyeUvqmrp0NSSjkS//iTjV0eovb6nwiNj4UCBaBTJ0iv8StjbOW2bjMhtAQU8t5HZpqZ5zPVqlVj8+bNng4jT1m99lee/fYAZUuWo2b/ryiVvBtEpZRHvbbpFFUvCI0SLtq78ZgYKFIEypRJeYbEBPjhJTCJcNebULp67gacCXnumbmvPjZQ3i+lY+uXH7+l5vdd+CD0U2YPvIWrNCNXyqtMXLmXyb/u44aQOGTwYFi7FgYNgiNHUp7h4nn4sg+sGw/il+P9kGeXPHVnHhISwokTJyhZsmSebypT5S5jDCdOnCAk5N9+i1d8M42bNj7LPwGlqfHwpxQsmDu9Iyml3PPNlr94bdEO7qlblupvLAE/J18YNy7lGc4esy26Hd4IbcfAzYNzL9gsylOZeYUKFTh06BDacYPKCSEhIVSoUAGAn2b9H013jOZg8LWUffRbQoql2J+IUspDVv95nGe+jODGKiV4p0s9/PzSucFLTIDp7eHkXug6A66/N3cCzSZ5KjMPDAykSpUqng5D5WHGGP7v+6202z6RPYVu4NonviIwtIinw1JKudh55DSPTN9IlVIF+bR3I0IC/dOfyc8fWr0Cha6CCl778keq8lRmrlROSkxI4LVvtjJ53V9caDCeYffdjH9gOu+nKqVy1V+nLtBv8noKBgcw9cEbKVogMO0ZfpsHcRegQS+o4bstNea5CnBKZSunxaj4/fvY8l5nGm4axiNNw3jxgWaakSvlZaLPx9FvSjjnYuOZ2r8x5YqlUSHVGFg1Fub2hy2zwGki21dpZq5UWkaPxqxaxemuTWhw+ieKX3sTw+6uqRUslfIyMXEJDJixgcjj5/mkzw3UKJPG46+EeFj0NCwbAbU6Qc+54Ofb2aEWsyuVEpcWowQosS4K1kGTkLeg7yjPxqaUukxiouGZL7cQvu8k73dvwK3XlEpj4gSY1QP2/ABNhkLrET6fkYPemSuVsr17ie3SlcRAeweeEBwEPXvCvn0eDkwp5coYw+hF21m0LYqX77me9vXKpT2Dnz9UaAz3/B+0GZUnMnLQO3OlUvRXaDE2HLpAu3hIDArCPy4+7RajlFIeMXHlPqb8Gkn/JlV4uGnV1Cc8uhNiz0DFxtD8udwLMJfkjUsSpbLR4U2LmfPBCxT45zhHezyIX3h42i1GKaU84pstf/H6d7ZRmJfvSaOTrX0rYdIdsHCIz1d0S43emSvlYt/Pn1H+p6HcQznifviRMpWusiNSazFKKeURbjcKs3UOLBgMJa+BHrPzTLF6cnnzVymVCXu+HUvl5Y+z068awQMWc31SRq6U8ipuNQpjDKx8B756GCrdDP0XQ7FKuR9sLtE7c6WAnbNeosbOD1kTdBPXDJ7NVSWKezokpVQKDp+6QN/J4ek3CmMM/BUBdbpAh3EQkLfbhdDMXOV701ZHsmvbBVoXuZPGT0yjSAHt+UwpbxR9Po5+k8M5H5vAnMG3pNwoTOxZW9GtSFnoPBH8AvNs0borzcxVvmXiLvDFwsUMDw+iTc0eNOlWn5AgTRJKeaOkRmH2nzjP1P6NU24U5swRmPmA7Yd84M95/m7clZ65VL6UcP4fDozrSIezO9hT/0te6tKQAP+8f/WulC9yq1GYozvh8y5w/gR0mWrfJ89HNDNX+U7sP4c5Nr4d5WP3s7jaCF7t2lybZ1XKS7nVKEzkKtuqm38wPLgIyjXI/UA9zGsycxF5CngYMMA24EFjTIxno1J5zbmoXZybeC/F40/xY4MPad+xh6dDUkqlId1GYRITYckrUKgM9JwDxSvnfpBewCvKFUWkPDAEaGSMqQ34A908G5XKa06cjWXxtDfxjz/P2mbTuUsz8hwjIk+JyO8i8puIfCEiIZ6OSfmeryMOp94ojDEQf9FWbus2Ex76Id9m5OAlmbkjAAgVkQCgAPCXh+NRecjBY6fo8vEaXjnXmZ3tv6V167aeDinP0otzlR1W/3GcZ+ds4aaUGoVJ6vVsTj/bcUqRshCav18n9YrM3BhzGHgbOABEAdHGmCXJpxORgSKyQUQ2HDt2LLfDVD7qr1WfYcbdjN/ZKKY/dAtNbqjv6ZDyA704V5m2I+o0j8ywjcJM6JOsUZjYs/b5+IbJUKoatl9D5RWZuYgUBzoAVYByQEER6ZV8OmPMBGNMI2NMo9KlS+d2mMoHHfj+Hcote4zjUpyPHryNRmElPB1SnufOxblemKvUHD51gX5TXBqFCXVpFObM3zD1HvhjqdPr2ch88Q65O7xlK9wO7DPGHDPGxAFfAbd6OCbly4wh8sthVFo3il/8b6b04EVcV7mip6PKF9y5ONcLc5US10ZhpvZvfHmjMMbAF93g+G7o9gU0fshzgXohb6nNfgC4WUQKABeA1sAGz4akfNnWOa9Td/t4vg9uS+PHplCqSAFPh5SfXLo4BxCRpIvzzzwalfJq6TYKIwJ3/c++P16+oWeC9GJecWdujFkHzAU2YV9L8wMmeDQo5bMmrtxLr03VmFr0UW57coZm5Lnv0sW52Bf4WwM7PByT8mKJiYanv4wgfN9J3n6g3uWNwmybCz+/Zf+v2Fgz8lR4RWYOYIwZboypYYypbYzpbYyJ9XRMyreY8/+w/uNBvLVoC01qX0v3J16jcGiQp8PKd/TiXGVEUqMw3207cnmjMMbAqrEw7yHYuwIS4jwap7fzlmJ2pbIk/pRt1a1ezH6eqdWEh3o0xD+1/o1VjjPGDAeGezoO5f1SbBQmIR6+f87WWK/dGTqOB/9UekdTgGbmKg+IPbKTMxPbUzgumq9rvceALr20eValfECKjcIYA3P6ws5v4banoNWrWmPdDZqZK592dl84iTPuxyQYlt88hS533e3pkJRS6YmKIrpDZ8bc8ig31bzm8kZhRKD63XBNK62xngGamSufdfRMDC/P/4PH40ty9I5xtL9N32ZUyhecHPYKxTas5YXA0jQfM9c2CnNsF5zcB9XbQoOeng7R52hmrnzSkd9/peuiWI6eKUGPnou5vcbVng5JKZWe0FCIiSGp6ab2q7+BAkEQHAQjykBwEbimZb7qhzy76IMI5XOO/PB/lJlzN80vLGXmgJtooRm5Uj7hxLYdLG3QmgtJmXWBAnB3UxhSCAqWhn4LNSPPJM3MlW+IioLmzTny6aOUWTOS5X4303fA0zSolL87V1DKV5yNjaff94c4LsGEJMRBSAhcuABH10KNG+GhJVA8zNNh+izNzJVvGDkSs/IXykyYyDeBban++DyuKafNgCrlC2LiEhg4fQPbo07TuoRBBg+CtWvh7vrgVxZ6z4cC2m9CVugzc+XdnGds4PSNtCGO9hu+hNe+sVf1SimvlpBoeGp2BKv/PMHYrvW4asQcOB0Fpa6Fb9YDoq+eZQPdgsqrmT272dnq3kvP2ExoKPTsCfv2eTgypVR6jDG8vOA3vv/tCK+0q0mnasEwtR1M7wBxMbaddc3Is4XemSuvlRD9F39/1okj/8B1CRcxISFIbCwUKQJlyng6PKVUOt5Zspsvwg/waItreKhGAky63XZjev9kCAzxdHh5il4SKa8Ue2QX/3zQgiIXDnJNAMigQcjatTBoEBw54unwlFLpmLxqHx/+9Afdb6zIczVPwaQ2EHsW+i2CGtq4U3bTO3Pldc78GU7iZ50hEX66ZQr3jnRJ+OPGeS4wpZRbFmw+zKhvt9O2Vhle61gHmdUdQotBr3lQoqqnw8uTNDNXXiUqcidFZ9xLtCnEn3fO4N5btVU3pXzJT7uO8uycLdxStSTv3l/Ddnh03wRIuAgFS6W/AJUpWsyuvMb2v07T4bODjOd+/u6ykOaakSvlUzbu/4fBn23k+jIFmVbha0I+aw9xFyCkiGbkOUzvzJVX+GPxh7y4JgS/4Cq0GzSG6mUKezokpVQG7P77DP2nrqdSYT/mlp5EUPjXcONA8A/ydGj5gmbmyrOMYdfsl6i+cxwDgu6gwaPTKVcs1NNRKaUy4ODJ8/SetI7S/uf4pth4gnetgzaj4dYnbC9oKsdpZq48JzGB7ZMHUfPQl/wU2obbHptO0UKakSvlS46fjaXP5HAuXEzgx8qfE3Jos331rHZnT4eWr2hmrjwi8WIMuz7uQc2TP/JDsW40f3QcIUF6OCrlS87GxvPglPVERV/gs4duolDha+HMEQhr4unQ8h2tAKdy3cX4RJ6bG8HJY0f4odxj3D7kY83IlfIxSe2tlzryC8tqLKRR5eJQ8hrNyD1Ez6AqV509GcUzc3/jh70XueaOKQxueR2iz9SU8ilJ7a1XiJzLmKDJ+J2pCbGnIaSop0PLtzQzV7nm+KHdxEzuSO/44rS5fxb3N6ro6ZCUUhlkjOHl+duosfNDhgZ+BVVbwwPTIFjfQPEkt4rZRSRYRPqJyAIROSAiZ52/X4vIgyKivcmrNB3cuQEz8U4KJ5yiwB2vaEbuQZqeVVa8s2Q31TePZmjAV1C/F/SYrRm5F0g3MxeR/sB+4CFgM/Ak0NH5uxHoD0SKyIM5GKfyYbvWL6XorPYY4Mh982l4W1tPh5RvaXpWWZHU3npitTsxLV6ADh+Cf6Cnw1K4V8zeBmhujNmVwrivgFEiUh0YDkzJzuCU7/vx97+o9O2TnPIrivReQPWq1T0dUn6n6VllyuLVm4n4bg5ta3Wmb8+7ET+t6+JN0r0zN8Z0T0r4ItIllWl2GWN6ZHdwyrfNWrefAZ9t5q0SIyg4eBkVNSP3uNxKzyJSTETmishOEdkhIrdkZXnKs9aF/0rdHzozJngK77avaNtbV14lo6+mvSkiVZIPFJH2WQ1EE3/eYYxh5dRXSVw4lNuuLcXYQZ0oeVV5T4elrpRj6Rl4D1hsjKkB1AN2ZMMylQfsWvsd1y+6nxC/REzfbwkpepWnQ1IpyGhm/jTwpYhcekgiIo8Ak7IhFk38viwqCpo3J/7QYX756FGaRr7H9SWESb3rUzBYX5rwUjmSnkWkKNAsaTnGmIvGmFNZWabyjKhVn1FlcW9O+pWAh5dRMOwGT4ekUpGhzNwYswBYB7wDICKjgReBllkJQhN/HjB6NGbVKo490Izmx2ay6er7qT90DoFBWjHaW+VUegaqAMeAKSKyWUQmikhB1wlEZKCIbBCRDceOHcvi6lS2i4oipklTvvlmBb/JdQQMWEqJ8td6OiqVBjHGpD2ByNNABBBhjDkpIkHYE8A/QCngLmPM4SwFIVIfmABsx96VbwSGGmPOJZtuIDAQoFKlSjfs378/K6tV2SE0FGJirhweEgIXLuR+POoKIrLRGNPI+T830nMjYC3QxBizTkTeA04bY15JafpGjRqZDRs2ZGWVKjslJhDTtztBn89lzg13U3/BdKqXL+HpqBSXp+Xk3LkzvwP4AjgmIgeAucAeoDrQLasJ3xEANATGG2MaAOeAYcknMsZMMMY0MsY0Kl26dDasVmXZ3r2c6/wAMYH2DjwxKBB69oR9+zwcmEpFbqTnQ8AhY8w65/tcbPpW3i40FPwDCPlsDn7G0HXDIqpXKGmHK6/mTm32tsaYq4GKwGAgHJv5XgS2icgREfk+i3Fo4vdRu86dYdv+PwmKv0hicDB+8QlQpAiUKePp0FQKciM9G2OOAAedV9wAWmNL3ZQ3O3eCc6/Ux9QOJC7AqedSoIBenPsIt2smGWP+Av4CFiUNE5Fi2Ey3flaCMMYcEZGDIlLdeW1GE78P2Lh5I1ct6EbVswc53bMnxZ59FiZMsJXhlFfLyfTseAL43CnG3wtoIzTe7OQ+Tk/sQFDsX6wv0IDGCRvso7KYGL049xFZqmbsVFJb7nyyShO/D1nx84/UWv4ggX6G6AXLKVX9Vjti3DjPBqYyLTvTszEmAkjx2Z7yLhfjE9kw83WuP3eCt8r8j/+U+QYZPAgGDtSLcx+SbmYuIj8Drxpjfk5jmmbAKGNMi8wGoonfd3z7zRyabXyCi/6F8O//DcUq1PR0SMpNuZWelW84Hn2GR7/4jc2H7uXpG7vxYsfW+A/u/+8EenHuM9y5M38D+Mh5F3UZtvj7NFAEqIktEo/HvrOq8rDERMMb3+1A1i3nhoJXUfyRhYSUrOzpsFTGaHpWABz68RPiVr3PgbhXebvbbXSorw07+bJ0M3NjzA9ALRG5E+gA9AKKY19l2QwMcaZReVhsfAIjv/iFmb+fp9+tj3PVHW/hH1LI02GpDNL0rDCGnbNepMauj1jnV5/JD91GzSqakfu6jFSA+wHQRJ4PnYmJY+FHw/hP9EzqNPuMbnfVRETbZvZlmp7zp4S4i2yb0J/6xxayIvR2ag+aRqmielGeF2SoBTgRmS0ibXIqGOV9jkZfYPHYgfQ4PZEz5ZvR/Y7bNCPPIzQ95y+nzl/k+/cfsxl5mX40eeZLzcjzkIzWZm+D7e/4EhG5A2iaWutOynf9+fcpdkx4kC4JyzlcrQcVun8Ifv6eDktlH03P+cTuv88wYPoGYk61omSj+rTo9JinQ1LZLKMdrfgDccmGrSPZCUH5vo37/+Grj4fTLmE5fzcYSvkeH2lGnvdoes4HVq5dw9aP+hAbG8tHA9tyi2bkeVJG78zXA3cDC1yGnQYKZ1tEyuOWbv+bJ77YRIXC99C3RVOuvvF+T4ekcoam5zwsMdHw5fx53LF1KHX8/GneqyKlKxf3dFgqh2Q0Mx8OfCUiCcaYhc6wu7EtSak8YP7KTQQseYFGpYfwbv9WlCqkvZ7lYZqe86izsfFMm/whDx15jTPBV1Hwoa8pdnU1T4elclCGMnNjzK8i8igwWUQSsa+zVAEezongVO4xxjD52xW0Wv8I5QKiuf2uwoRqRp6naXrOY6KioFs3Dn40hVkLp/L0hfc5Uaw2pQfMRwppx1R5XYabczXGzHM6YmiO7TJxkzHm92yPTOU8J/HHz/yCD1aso8fuJykcmIB/n68Jrnyzp6NTuUDTcx4yejRm5SpW9xvChnb3crLKvVzV4xMIKuDpyFQuyFTb7MaY80BWe0pTnjZ6NGbVKiJ69KZ/i+34BRUk9OHvkau1edb8RNOzjwsNtR2iAAJ03bCIrhsW2Y5S+s3wbGwq12S0NrvK0EFe5wAAIABJREFUC0JDQQTGj0cSE2n0y3KKjjpC4dGHNCNXysec2b6LDTe3JiHAns5NSLB2W5oPaWaeH+3dy7n7H+BiYCAACSGhNvFHRno2LqVUhuw8cpr+c8K5Jm4TfgmJmKBA5GKcdluaD2lmng/toCB/RO4gKD4OExiA/8VYTfxK+ZivNh3iuXGz+PD88xS9cAHp0R4JXw+DBsGRI54OT+WyLPVnrnzP2j+Ps23GMww4u4OLLa8l6M3PYco07bNYKR8RG5/AqIXb+XzdAYaUjaZUXBB+y9ZB2bp2Au22NF/SzDwfWbzlIKfnDWGA33LOjX6Egve9Z1t1a3Sjp0NTSrnh4MnzPDZzEycO/cGg5jcz5I678E94TGusK83M84sZa/cz5ZulLApeQ8zNT1PwjldtJTillE9YsesoT87aTJ/Er3ky9Ev8an8H/n7grxm50sw8zzPG8N4Pv/HuigPcfn09uCeckFKVPB2WUspNCYmG937cw7jluxhb+AvaX1wEtTpD2XqeDk15Ec3M87D4hETGzPmFTtuHUDWsA3f3GkmAv9Z5VMpXnDx3kaGzNrN+z2G+Lj2J2mdWwa1D4PaR4KdpWf1LM/M86sLFBEZOX8SgA89SPiCamq2bIZqRK+UzIg6e4tHPNnL83EU+a7SXWr/9Cne9BTcN9HRoygtpZp4HnTp/kVETv2TYiZcoGmwI7LsIKjTydFhKqfRERWG6dWPuc2/z4ppjlCkcxLxBt1KnfFu4pSWUv8HTESovpbdqecxfpy7Q/6MfGH7ieQoXCCF44FLNyJXyEXEjRmJWriTmleH0qXSCFYVeok7ocVtZVTNylQa9M89Ddv99hr6TwzkbE8TRJiOpdmNbKFbR02EppdLjtK8e6HztHfE9DPoeAgS6GY+GpnyD3pn7uqgoaN6ciPU7mD7+DRrGb2H2I7dQ7Y4BmpEr5SP++W0nPzZszYUAp9vhAODGq2DnVih1rUdjU75BM3NfN3o0ZuVKzOAuvMZHvBW2nprling6KqUQEX8R2Swi33o6Fm92OiaO3t8d4G+CCfl/9u47PKoqfeD496QnkAQIoSgl9J6CoCBIUwSURYpIC0VENIBgWxZsLBYWV36LHRekBUGQqiirBAEBQUKAgLSAQsRQQ0uvk/P74w4xkELKJDOTvJ/nmYeZe++ceeeSM++95557jindSOQmILAfNGpt7fCEnbCpZnallCMQAZzTWve1djw27bZpD4P2H4X94OG2AUZaNzQhzKYAxwE5usxHcnomTy7eR9TFBHpWAzV+HHTwhr2JcOmStcMTdsSmkjlS+Qvv9GlOBo+n4Y5NOGVmoV2dUIOegP/7P2tHJgRKqTrAo8A7wItWDscmpWaYeDo0gjNnz/Jjsy34vv4luJl/+sZYNTRhh2ymmT1H5f/c2rHYOq01/4q8Qfi1TBxNWWgXJ1RGFnh7y8xnwla8D0wFsvJaqZQar5SKUEpFxMbGlm1kNiDDlMXE5QeI+f0oP1WbRb0/v4ELkdYOS9gxm0nm3KHyg/wAgDG04zurfuK7Hb8Q5JYBz4SgwiNk2kNhM5RSfYHLWuv9+W2jtZ6vtW6ntW7n6+tbhtFZnylL8/yqSOKidvBD5TfxzEqA0d9Agy7WDk3YMZtoZs9Z+ZVS3fLbTms9H5gP0K5duwp3v0Zapol3Qr9jbPTLjK/qhe/Pe1EOjsZKmfZQ2I5OQD+l1COAG+CllPpCax1s5bisLitL84+1h0k+solVbu/j6FkPRqwGn0bWDk3YOVs5M79Z+aOBlUAPpdQX1g3JtiSlZfL2/BVM/mMitVzTqDH8s78SuRA2RGs9XWtdR2vtBwwFtkoiNy6Pzdx4lDX7Y+h0fxccWzwK47ZIIhcWYRNn5lrr6cB0APOZ+ctS+f9yPSmdD+fPY9qNd8CjOm5PfQ3Vm1g7LCFEIWmteW/TUTLDF/J056d46tFWoKRZXViOTSRzkb+LcamM/PwXZscvx1S1IV5PbQBP6eQm7IPWejuw3cphWN2CsIPc/8skOjsfRTd7FKXk/nFhWTaXzKXy/yU6NpEnF/7M5WSNacgXeDWq9detK0II23fhApd79eHBh5Lx87pCVr9PcGjykLWjEuWQzSXzCu/CBRg6lKi5/+XQxhnM0uepNG4d/vUqVo9fIcqDs+PHUffIIaq6esCX3+HQuJu1QxLllCRzW2MentX9mZ488Wg81wOfpWodH2tHJYQoCvMIjfXML50jkqFJd3Bzg5QUq4Ymyidb6c0u3N2NaQ7nzUNpTb2IGJgZT9VhH4KD/DcJYU+iv17OxlbdSHU2T5zi4QEjRsCZM9YNTJRbkiVsxenTxPTpj8nJ+C/Rri5S+YWwN1kmkr7+O367n6SKRzqupgzjbDw1Fby8ZIRGUWokmduIL/5IZ/uFNJRJo11dUBmZUvmFsCfpSWR+OYJKB+cTqh8hwKca6tln4ZdfZIRGUerkmrmVaa1Z9/VarkR8xwMOKWSNfwankGdh/nyjM5wQwvYlXkavGILD+Uj+mTGariNfw2tmjb/WywiNopRJMrcirTVrV/yXvidfI86jFtV27sLJo4qxUiq/EPbj9HYyLh5jQvoLPNB3FN2b17jze4SwIGlmtxJTlmb9grcZcHIasZWa4Dt5O843E7kQwj6k3ABgecp9dE7+P+p0GMTo+/2sG5OokCSZW0FapolNn7zIwPNz+KPq/dSZEoZD5erWDksIURSHVsL7/hz8ZRtvfH2UVs2a8tqjLawdlaigJJmXpQsXMD3QhZc+/IFNFzw5Ubs/DZ/7GuVa2dqRCSEKS2vY/i6sf4bk6q2Z+L/rNKlRmY+Gt8XJUX5ShXXINfMylPbaazj/vIv7kj7G9b//pXn7utYOSQhRFKYM2Pg87Awl63+VCR4wknR3LxaOaU9lV/k5FdYjf31lwTwalHn4CEYe/B/cW09GgxLC3uxfApFfkHWqBZyI4PEfvqDV+uXcXcXd2pGJCk7ahMrA5Z1bSWzjjb556CSjQQlhX7Q2/u06GWbG47BxHw5aM3z/JgLqVTUO2IWwIknmpezcqUiyvg3GxSUdTEpGgxLC3lw6Cot6Qfx5bhw5wY52PUlxkmFahW2RZF6KTl1K4JsvP8OJDNK826NCQmQ0KCHsyentsKg3XP+DixcvMHDDGWKynHEzpcuBubApcs28lBz94yIjQ3/F0WEQD4+cSqN/NvxrpQwII4Tti/wSvpkEPk2IemgRI746T3qmid6+yjgwHz9eRmoUNkOSuSWZ5yI/+9xj+P76IY1d3+Hf4/vjV72StSMTQhTF4a9gw7PQoAs/3/M+45efwtvdmS+fvp9q/+z113ZyYC5shCRzSzLPRV4veQ8H+t3HB0/1pLYkciHsT5OHoeV4Ymfv4KXOe6jbsB5LnryXWt5u1o5MiDxJMrcE861nAAogIoO2EbtgVn259UwIe5GRCj+/D52moN28ObL8PK0O7uMtr3Xc9+9VeLk5WztCIfIlydwSTp/m2hOPUO2XSMgE7e6OGjgQ5syxdmRCiMJIuQErh8MfP6N7vYFKz6CNeVXPn9aDu8st40JkZGQQExNDqvkgXghLcnNzo06dOjg7F/4AUpK5BSyLTsMpsQZDTQrcXFFpadLDVQh7cOECPD4A+magM/7gx5azmBlShX/8uJDep/bglJZq3H42YMAtB+cxMTF4enri5+eHUsqKX0CUN1prrl69SkxMDA0aNCj0++TWtBLQaYlELniWf3+9j6YO6ZjGP4OSW8+EsB+vvAR79pK14QSvesxg3AE/mrdtRvf2jXDKyP/2s9TUVHx8fCSRC4tTSuHj41PkVh85My+OCxfQTzzOhe7ptFGnmNzYH/93fvxrkgXp4SqEbcvRzwXAITyZWeGTecvFFYfUFNSGq8ZBeQG3n0kiF6WlOH9bksyLIev16aifd1MjxZXVz83hqZEhODhIxRYCQClVFwgFagIamK+1/sC6Ud0m/AfS356H3rAe1/Q00l3cYMAAXN7/DygF69b9ta0cnAs7IM3sReHuDkrhsHApSoPT/jSGjnkOh0oe1o5MCFuSCbyktW4JdAAmKqVaWjmmv/y6Br1+AFF/nMA5Ix2Tiysumem4VKsi/VxKybhx4zh27FiJy4mOjmbFihVFft+YMWNYs2ZNruXdunUjIiKiWLEsWbIEX19fgoKCaNKkCb169WL37t23bDNnzhyaN29OYGAg7du3JzQ0lAEDBhAYGEjjxo3x9vYmMDCQwMDAXO8tKknmRZAadYoD93Yly8l8Fi7jMguRi9b6gtb6gPl5AnAcuNu6UZl9+y78bSi/JjTkyg1XLgwbg2P4XunnUgQmk6nI7/n8889p2bLkx3PFTealZciQIRw8eJBTp04xbdo0Bg4cyPHjxwH47LPPCAsLIzw8nMjISH788Ue01qxfv57IyEg+//xzHnjgASIjI4mMjOT+++8vUSw20cxu081y5lHdUj55l/F7NL3SPQgyIeMyC1EISik/IAjYe9vy8cB4gHr16pV+IFrDtlkwcyb6rImT231pHLaCu+tWMdaXoCl95sajHDsfb6FADS3v8mLG31oVuE1oaChz5sxBKYW/vz/Lli27ZX14eDhTpkwhNTUVd3d3Fi9eTLNmzViyZAnr168nLi6Oc+fOERwczIwZM4iOjqZ3797cc889HDhwgFatWhEaGoqHhwd+fn4MGTKEsLAwpk6dSvPmzXn22WdJTk6mUaNGLFq0CE9PTzp27Mh7771Ht27dmD59Og4ODrzzzjt069aNOXPm0K5dOypXrkxISAibNm2idu3azJo1i6lTp3L27Fnef/99+vXrR3R0NCNHjiQpKQmAjz/+mPvvv59p06Zx/PhxAgMDGT16NJMnT2batGls376dtLQ0Jk6cyDPPPIPWmueee46wsDDq1q2Li4tLvvtx2bJljBs3jszMTBYtWkS7du1o1qwZu3fvxtfXl6ysLJo2bcqePXvw9fXNt5zu3bszfvx45s+fz9y5c5k1axbbt2/Hy8sLAC8vL0aPHl3YP4Eis5Uzc9ttljOP6ub45IO0/+O/dKuqZcIUIQpBKVUZWAs8r7W+JdtpredrrdtprdsV9ANpMe5u8OBrEJGB0vB4xP8ItOOpS48ePcrbb7/N1q1bOXToEB98kPvcp3nz5uzcuZODBw/y5ptv8sorr2SvCw8PZ+3atRw+fJjVq1dnNzVHRUUxYcIEjh8/jpeXF59++mn2e3x8fDhw4ABDhw5l1KhRvPvuuxw+fJg2bdowc+ZMnJycWLJkCSEhIWzZsoXvv/+eGTNm5IorKSmJHj16cPToUTw9PXnttdcICwtj/fr1vPHGGwDUqFGDsLAwDhw4wKpVq5g8eTIAs2fPzj6bfeGFF1i4cCHe3t7s27ePffv2sWDBAs6cOcP69euJiori2LFjhIaGFtiEnZycTGRkJJ9++iljx47FwcGB4OBgli9fDsCWLVsICAgoMJHf1LZtW06cOEF8fDwJCQk0bNjwju+xFJs4M9daXwAumJ8nKKVuNsuV/CJLcd02qptLRDKTI5YaZ+Rb/2dsIx1jhMiTUsoZI5Ev11qvu9P2pUZrUIrUk7/za//Haf1rJO6ZaXneO15cdzqDLg1bt25l8ODBVK9eHYBq1arl2iYuLo7Ro0dz6tQplFJkZGRkr+vZsyc+Pj4ADBw4kF27dtG/f3/q1q1Lp06dAAgODubDDz/k5ZdfBowm5Zvl3rhxg65duwIwevRoBg8eDECrVq0YOXIkffv2Zc+ePXmeEbu4uNC7d28A2rRpg6urK87OzrRp04bo6GjAGJRn0qRJREZG4ujoyMmTJ/PcD5s3b+bw4cPZ18Pj4uI4deoUO3bsYNiwYTg6OnLXXXfRo0ePfPflsGHDAOjSpQvx8fHcuHGDsWPH8thjj/H888+zaNEinnzyyXzfn5O+Oe+9FdjKmXm2/JrlzOvGK6UilFIRsbGxpRvI6dOk9+mKNh/umFzd5Pq4EIWgjPtqFgLHtdb/sVogGSnGqG7HN/Lm/utE6Wq4mTIqzCWy119/ne7du3PkyBE2btx4y33Lt9/6dPN1fssBKlUq3DwTv/76K1WqVOHy5ct5rnd2ds4u18HBAVdX1+znmZmZAMydO5eaNWty6NAhIiIiSE9Pz7MsrTUfffRR9nXnM2fO8PDDDxcqzpvy+s5169alZs2abN26lfDwcPr06VOosg4ePEiLFi3w8vKicuXKnD59ukixlIRNJfOCmuWgjJrmLlyArl25GHsZLoeDCbJcXHHMSC/3lV8IC+kEjAR6KKUizY9HyjSC1Hj44nGI+I7Ex58n7MdDtPfIQIU8Wy4ukfXo0YPVq1dz9epVAK5du5Zrm7i4OO6+2+h3uGTJklvWhYWFce3aNVJSUtiwYUP22fjZs2fZs2cPACtWrKBz5865yvX29qZq1ars3LkTMK453zxLX7duHdeuXWPHjh0899xz3Lhxo1jfLy4ujtq1a+Pg4MCyZcuyO915enqSkJCQvV2vXr2YN29edqvDyZMnSUpKokuXLqxatQqTycSFCxfYtm1bvp+1atUqAHbt2oW3tzfe3t6A0QM/ODiYwYMH4+joeMeYf/rpJ+bPn8/TTz8NwPTp05k4cSLx8UYqS0xMJDQ0tBh7o3BsopkdbKhZ7q230Lt2sXv869RLaozfsACqT31Z5i0WopC01rswzzlkFcnX4IuBcPFXkk+3x+P4Nt7yWUfDHT9AORnYqVWrVrz66qt07doVR0dHgoKCciXsqVOnMnr0aN5++20effTRW9bde++9DBo0iJiYGIKDg2nXrh3R0dE0a9aMTz75hLFjx9KyZUtCQkLy/PylS5dmd4Br2LAhixcv5sqVK0ybNo0ff/yRunXrMmnSJKZMmcLSpUuL/P0mTJjAoEGDCA0NpXfv3tmtAv7+/jg6OhIQEMCYMWOYMmUK0dHRtG3bFq01vr6+bNiwgQEDBrB161ZatmxJvXr16NixY76f5ebmRlBQEBkZGSxatCh7eb9+/XjyyScLbGJftWoVu3btIjk5mQYNGrB27VpatGgBQEhICImJibRv3x5nZ2ecnZ156aWXirwvCktZs40/OwijnWMpcE1r/Xxh3tOuXTtd3PsD83TbiFDZckyuIIQ9Ukrt11q3s3Yc+bFoXU6Nh4UPw/N7jW61t7NQfT5+/Hj2j7a9WbJkCREREXz88ce3LI+OjqZv374cOXLESpHZloiICF544YXsFoiyltffWEF12Vaa2a3bLHfhAgQEkNI5ILutIsvdXa6RC2FvXD2hemeyWrXgl8AupDgZ12NlTAhRFLNnz2bQoEH861//snYohWYTzexWb5Z76y10eDjuPqBNoF1dcZCZz4SwH9dOw7kYmPQ6WfX94PAJqlZLxM2U/2QpFdWYMWMYM2ZMruV+fn5yVm42bdo0pk2bZu0wisQmkrnVuLlBWhpgPpK4YvyrtDY6yMg1ciFs35Gf4bGecCYFNDiwA4BmV/801mdlSX0W5V7FTuZDhqBDQ9FK4aA1We7uOAwcaNx7KkfwQti+2Cjw72yMG5mXESOkPosKoWIm89vOyJW5E6BDSoo0xQlhL1xdIY/7jzWgHB2NM3Kpz6KCqJjJ3HxGjgKlQTs6onr1Al9fu773VIgKpUMd9I7TmJQDTjrLSOLmBwMHGvVZmtZFBWErvdnLhnkKU0JDzWfkxmJlMkH9+rBkya3zGAshbM/NerzjNApw0lmAOYkPHgwTJkBmpnEvudTnXCIjI9m0aVO+64cNG4a/vz9z587Nd5vt27fTt29fwLjVbdKkSSWK6ZFHHslzgJl//vOfzLHAkLsVQcU5MzfffpYefQqXS8ZoSXJGLoSdidoPtT3Q5zWkpaG4rR7Hx9v9gDClLTIykoiICB55JPfdvxcvXmTfvn389ttvZRKL1hqtdYEHF6JwKkYyv3AB7roLgJzD/iuTCX74wTiKF0LYtpTr0KId6FvvY7WJerz40dzLWvWHe5+G9GRYPjj3+sDhEDQCkq7CV6NuXffkd3f8yOJMgdqgQQPeeOMNUlJS2LVrF9OnT8+eQAXg4Ycf5ty5cwQGBvLRRx/x+uuvZ09deuXKleyR4gojNjaW4cOHc/78eTp27EhYWBj79+8nMTGRXr16cd9997F//342bdpE165diYiIoHr16rzzzjssXbqUGjVqULduXe655x4APvzwQz777DOcnJxo2bIlK1euLFQcFUX5T+Y5Orvl0qQJNG5ctvEIIYouvxEabyri5Br27uYUqLt376Z69ep5js1+cwpUJycntmzZwiuvvMLatWt588038xwBDuCbb76hb9++REZGljjGmTNn0qNHD6ZPn87333/PwoULs9edOnWKpUuX0qFDh1ves3//flauXElkZCSZmZm0bds2O5nPnj2bM2fO4OrqWuwx38uz8p3MC0rkSsFDD0GO+XqFEDYqM+9ZswAYPdro72JNBZ1Ju3gUvL6ST6HOxHMq6RSoZWHXrl2sX78egN69e1O1atXsdfXr18+VyAF27tzJgAED8PDwAIzx0W/y9/dnxIgR9O/fn/79+5dy9Pan/HaAc3cvOJGHhMh1ciHsgbs7ZGblva5ZM+M6uciloClQC8vJyYmsLGPfF+f9+SnsdKo5fffdd0ycOJEDBw7Qvn377OlShaF8JvM7Ncn17i09XYWwB0oVXJdbtqyQ9bgkU6DePo1oQfz8/Ni/fz8Aa9asKVKMnTp14quvvgJg8+bNXL9+/Y7v6dKlCxs2bCAlJYWEhAQ2btwIQFZWFn/++Sfdu3fn3XffJS4ujsTExCLFU96Vz2ReUOWvVw+k56QQ9q9PnwqZyOHWKVADAgJ48cUXc20zdepUpk+fTlBQ0C1nsd27d+fYsWMEBgZmz+Wdn5dffpl58+YRFBTElStXihTjjBkz2Lx5M61bt2b16tXUqlULT0/PAt/Ttm1bhgwZQkBAAH369KF9+/YAmEwmgoODadOmDUFBQUyePJkqVaoUKZ7yziamQC2OfKdNVAXM1+LubpyVV9AfAFEx2fUUqAXVZyv+dtnzFKhlJS0tDUdHR5ycnNizZw8hISEW6VhXUdjrFKiW8+mnaP4aqvmW6i6JXAj7snnzLfU5W+fOVghGFMXZs2dp3749AQEBTJ48mQULFlg7pHKt/PVmDwlBTZqEzvpreEfAGMdZErkQ9qVnz+ynt9TnnTutEY0ogiZNmnDw4EFrh1FhlL8zc4CsLJSDA2r4cHAwf0UL9sQUQpQd5eqKqloV9e67ULWqcWBuA+z1EqWwfcX52yp/Z+Zw67W05cutF4cQouRyHohPnWq9OHJwc3Pj6tWr+Pj4oAq6ri9EEWmtuXr1Km5ubkV6X/lM5kIIUYrq1KlDTEwMsbGx1g5FlENubm7UqVOnSO+RZC6EEEXk7OxMgwYNrB2GENnK5zVzIYQQogKRZC6EEELYOUnmQgghhJ2z2xHglFKxwB932Kw6ULQxCG2DxF227DVuKFzs9bXWvmURTHEUsi6D/f4/SdxlqzzHnW9dtttkXhhKqQhbHsYyPxJ32bLXuMG+Yy8qe/2uEnfZqqhxSzO7EEIIYeckmQshhBB2rrwn8/nWDqCYJO6yZa9xg33HXlT2+l0l7rJVIeMu19fMhRBCiIqgvJ+ZCyGEEOWeJHMhhBDCzpXbZK6U6q2UilJK/aaUmmbteG5SStVVSm1TSh1TSh1VSk0xL6+mlApTSp0y/1vVvFwppT40f4/DSqm2Vo7fUSl1UCn1rfl1A6XUXnN8q5RSLublrubXv5nX+1k57ipKqTVKqRNKqeNKqY72sM+VUi+Y/06OKKW+VEq52cs+txRbrcsg9dlKMUtdzkO5TOZKKUfgE6AP0BIYppRqad2osmUCL2mtWwIdgInm2KYBP2qtmwA/ml+D8R2amB/jgXllH/ItpgDHc7x+F5irtW4MXAeeMi9/CrhuXj7XvJ01fQB8r7VuDgRgfAeb3udKqbuByUA7rXVrwBEYiv3s8xKz8boMUp+tQepyXrTW5e4BdAR+yPF6OjDd2nHlE+vXQE8gCqhtXlYbiDI//y8wLMf22dtZIdY6GBWlB/AtoDBGLHK6fb8DPwAdzc+dzNspK8XtDZy5/fNtfZ8DdwN/AtXM+/BboJc97HML7gO7qcvm+KQ+l27MUpfzeZTLM3P+2nE3xZiX2RRz00kQsBeoqbW+YF51Eahpfm5L3+V9YCqQZX7tA9zQWmeaX+eMLTtu8/o48/bW0ACIBRabmxQ/V0pVwsb3udb6HDAHOAtcwNiH+7GPfW4pNvF/URhSn8uE1OV8lNdkbvOUUpWBtcDzWuv4nOu0cThmU/cMKqX6Ape11vutHUsxOAFtgXla6yAgib+a4QCb3edVgccwfsDuAioBva0alMiT1OcyI3U5H+U1mZ8D6uZ4Xce8zCYopZwxKv5yrfU68+JLSqna5vW1gcvm5bbyXToB/ZRS0cBKjKa5D4AqSimnPGLLjtu83hu4WpYB5xADxGit95pfr8H4QbD1ff4QcEZrHau1zgDWYfw/2MM+txRb+b/Il9TnMiV1OR/lNZnvA5qYewq6YHQ0+MbKMQFG70pgIXBca/2fHKu+AUabn4/GuPZ2c/koc6/MDkBcjuakMqO1nq61rqO19sPYn1u11iOAbcDj+cR98/s8bt7eKkfLWuuLwJ9KqWbmRQ8Cx7DxfY7RJNdBKeVh/ru5GbfN73MLstm6DFKfyzBkQOpygZ9Q1h0ByrDDwSPASeB34FVrx5Mjrs4YTUCHgUjz4xGM6yE/AqeALUA18/YKozfv78CvGL0hrf0dugHfmp83BMKB34DVgKt5uZv59W/m9Q2tHHMgEGHe7xuAqvawz4GZwAngCLAMcLWXfW7BfWCTddkcm9Tnso9X6nIeDxnOVQghhLBz5bWZXQghhKgwJJkLIYQQdk6SuRBCCGHnJJnKaTzQAAAgAElEQVQLIYQQdk6SuRBCCGHnJJmLElNKRSulgq0dhxCiZKQu2y9J5kIIIYSdk2QuhBBC2DlJ5sKizMMVfq2U+s48+YQQwg5JXbYvksyFxSilagE/AeeBflrrRCuHJIQoBqnL9keSubCUVsAeYLXWOkRrbbJ2QEKIYpG6bIdkbHZRYuZpFN2AK0AHOYoXwj5JXbZfcmYuLGUaxqxEYUqpqtYORghRbFKX7ZAkc2EpmcAIjOn9tiulalg5HiFE8UhdtkOSzIXFaK2ztNZPY8wrvFMpVc/aMQkhik7qsv2Ra+ZCCCGEnZMzcyGEEMLOSTIXQggh7JwkcyGEEMLOSTIXQggh7JwkcyGEEMLOSTIXQggh7JwkcyGEEMLOSTIXQggh7JwkcyGEEMLOSTIXQggh7Jwkc2GzlFLVlFLrlVJJSqk/lFLDC9i2hVJqq1IqTin1m1JqQFHWCyFKl4Xr83alVKpSKtH8iCr9b2DbJJmLPCmlnKwdA/AJkA7UxJjFaZ5SqtXtG5lj/Rr4FqgGjAe+UEo1Lcx6Icq78lSfc5ikta5sfjQr3dBtnyRzG6aUmqaU+l0plaCUOpbz6FQpVVcptU4pFauUuqqU+rig5eZ1WinVOMfrJUqpt3O8jlZK/UMpdRhIUko5FTOGvyul1t72XT5USn1QhO9eCRgEvK61TtRa7wK+AUbmsXlz4C5grtbapLXeCvycY9s7rRei1El9tlh9FnmQZG7bfgceALyBmRhHp7WVUo4YR61/AH7A3cDK/JYX8TOHAY8CVbTWmUWNwVzGF0BvpVQVyD7SHgqEml9/q5S6kc/jW3MZTYFMrfXJHLEdAnIdyedDAa1LsF4IS5P6bNn6/C+l1BWl1M9KqW6FLKf80lrLw04eQCTwGNARiAWcbluf5/Ic6zXQOMfrJcDbOV5HA2NLEkOO7f4HPG1+3hc4VsTv+gBw8bZlTwPb89jWGTgNTDU/fxijOe+HwqyXhzys8ZD6XLz6bN7mPsATcAVGAwlAI2v/n1rzIWfmNkwpNUopFXnzKBfjyLQ6UBf4QxtH2jnlt7wo/ixhDDctBYLNz4OBZUWMIxHwum2ZF0alvYXWOgPoj3EGchF4CfgKiCnMeiHKgtRny9Rn8zZ7tdYJWus0rfVSjGb4R4oYU7kiydxGKaXqAwuASYCP1roKcASjuelPoJ7K3aklv+U3JQMeOV7XymMbXcIYbtoA+CulWmMcyS/PUe7/1F+9UG9//M+82UnASSnVJEeZAcDRvD5Ma31Ya91Va+2jte4FNATCC7teiNIk9dmy9Tmf76kKWF/uSTK3XZUw/kBjAZRST/LXNaNw4AIwWylVSSnlppTqVMDymyKB4UopR6VUb6BrKcQAgNY6FVgDrADCtdZnc6zro//qhXr7o495myRgHfCmufxOGM2BeZ4RKKX8zTF4KKVeBmpjNDsWar0QpUzqs4Xqs1KqilKql3m9k1JqBNAF+P4O379ck2Ruo7TWx4D/A/YAl4A2GE1JaK1NwN+AxsBZjOanIfktz1HsFPP6Gxi3hmywdAy3FbHU/J6iNsndNAFwBy4DXwIhWuujkH028EqObUdi/BhdBh4Eemqt04qwXohSI/UZsFx9dgbexjgouQI8B/TXt3auq3CU1vrOWwlRDEqpesAJoJbWOt7a8Qghik/qs22TM3NRKpRSDsCLwEqp+ELYN6nPts8WRgUS5YwyBoi4hHHPam8rhyOEKAGpz/ZBmtmFEEIIOyfN7EIIIYSds9tm9urVq2s/Pz9rhyGEzdu/f/8VrbWvtePIj9RlIQqnoLpst8ncz8+PiIgIa4chhM1TSv1h7RgKInVZiMIpqC5LM7sQQghh5ySZCyGEEHZOkrkQQghh5+z2mrkQovzKyMggJiaG1NRUa4ciyik3Nzfq1KmDs7OztUOxCEnmQgibExMTg6enJ35+fihVoSfDEqVAa83Vq1eJiYmhQYMG1g7HIqSZXQh7lJECGeX3rDU1NRUfHx9J5KJUKKXw8fGxnZaflBslLkKSuRD2JvkahPaHDc9COR7BURK5KE028/d1aCV84A/nI0tUjCRzIeyJKROW9IXzB6BFP7CVHyQhRNHtXwLrn4Fa/lCtZM39ksyFsCeOTtD5eRi5HloPtHY0wkaMGzeOY8eOlbic6OhoVqxYUeT3jRkzhjVr1uRa3q1bt2IPCLRkyRJ8fX0JCgqiSZMm9OrVi927d9+yzZw5c2jevDmBgYG0b9+e0NBQBgwYQGBgII0bN8bb25vAwEACAwNzvdcmNOgK7Z+G4LXg5l2ioqQDnBD24PetkJ4ELf4G/k9YOxpRikwmE46OjkV6z+eff26Rz76ZzIcPH26R8kpqyJAhfPzxxwBs27aNgQMHsm3bNlq0aMFnn31GWFgY4eHheHl5ER8fz/r161m/fj0A27dvZ86cOXz77bfW/Aq5ZaTAgWVw79PG2fijcyxSrCRzIWzdoZXw9USoHQDNHgWHitWgNnPjUY6dt+wU2i3v8mLG31oVuE1oaChz5sxBKYW/vz/Lli27ZX14eDhTpkwhNTUVd3d3Fi9eTLNmzViyZAnr168nLi6Oc+fOERwczIwZM4iOjqZ3797cc889HDhwgFatWhEaGoqHhwd+fn4MGTKEsLAwpk6dSvPmzXn22WdJTk6mUaNGLFq0CE9PTzp27Mh7771Ht27dmD59Og4ODrzzzjt069aNOXPm0K5dOypXrkxISAibNm2idu3azJo1i6lTp3L27Fnef/99+vXrR3R0NCNHjiQpKQmAjz/+mPvvv59p06Zx/PhxAgMDGT16NJMnT2batGls376dtLQ0Jk6cyDPPPIPWmueee46wsDDq1q2Li4tLvvtx2bJljBs3jszMTBYtWkS7du1o1qwZu3fvxtfXl6ysLJo2bcqePXvw9c1/CoHu3bszfvx45s+fz9y5c5k1axbbt2/Hy8sLAC8vL0aPHl3YPwHrSL4GK4ZAzD6o7Q/1Olis6Ir1qyCEPdEadv6fcU2t/v1G03oFS+TWcvToUd5++222bt3KoUOH+OCDD3Jt07x5c3bu3MnBgwd58803eeWVV7LXhYeHs3btWg4fPszq1auzm5qjoqKYMGECx48fx8vLi08//TT7PT4+Phw4cIChQ4cyatQo3n33XQ4fPkybNm2YOXMmTk5OLFmyhJCQELZs2cL333/PjBkzcsWVlJREjx49OHr0KJ6enrz22muEhYWxfv163njjDQBq1KhBWFgYBw4cYNWqVUyePBmA2bNn88ADDxAZGckLL7zAwoUL8fb2Zt++fezbt48FCxZw5swZ1q9fT1RUFMeOHSM0NLTAJuzk5GQiIyP59NNPGTt2LA4ODgQHB7N8+XIAtmzZQkBAQIGJ/Ka2bdty4sQJ4uPjSUhIoGHDhnd8j824Hg0LH4YLh+CJpRZN5CBn5kLYpqws+N/fYd/n0GYwPPYpOOV/9lOe3ekMujRs3bqVwYMHU716dQCqVauWa5u4uDhGjx7NqVOnUEqRkZGRva5nz574+PgAMHDgQHbt2kX//v2pW7cunTp1AiA4OJgPP/yQl19+GTCalG+We+PGDbp27QrA6NGjGTx4MACtWrVi5MiR9O3blz179uR5Ruzi4kLv3r0BaNOmDa6urjg7O9OmTRuio6MBY1CeSZMmERkZiaOjIydPnsxzP2zevJnDhw9nXw+Pi4vj1KlT7Nixg2HDhuHo6Mhdd91Fjx498t2Xw4YNA6BLly7Ex8dz48YNxo4dy2OPPcbzzz/PokWLePLJJ/N9f07aXu/eOB8JyweDKR1GfQ31O1r8I+QwXwhbpBQ4OEOnKTBgft6J/MIF6NoVLl4s+/gEr7/+Ot27d+fIkSNs3LjxlnuWb7/t6ebr/JYDVKpUqVCf++uvv1KlShUuX76c53pnZ+fsch0cHHB1dc1+npmZCcDcuXOpWbMmhw4dIiIigvT09DzL0lrz0UcfERkZSWRkJGfOnOHhhx8uVJw35fWd69atS82aNdm6dSvh4eH06dOnUGUdPHiQFi1a4OXlReXKlTl9+nSRYrGatHhw9YSnNpdKIgdJ5kLYluRrEBtlJPPe/4Keb+bZtJ6Vpbnyj9fQu3bBm29aIdDyrUePHqxevZqrV68CcO3atVzbxMXFcffddwNGz+ucwsLCuHbtGikpKWzYsCH7bPzs2bPs2bMHgBUrVtC5c+dc5Xp7e1O1alV27twJGNecb56lr1u3jmvXrrFjxw6ee+45btwo3mAjcXFx1K5dGwcHB5YtW4bJZALA09OThISE7O169erFvHnzslsdTp48SVJSEl26dGHVqlWYTCYuXLjAtm3b8v2sVatWAbBr1y68vb3x9jZ6bY8bN47g4GAGDx5cqA5/P/30E/Pnz+fpp58GYPr06UycOJH4eKM/RWJiIqGhocXYG6UoNsr4t0EXmLgXfJuV2kdJMhfCVlyPhoU9YcUTYMrIdQ/5jeR0vjl0ngwXVxwcHai+bBEqKwvmzTO2dXe3TtzlUKtWrXj11Vfp2rUrAQEBvPjii7m2mTp1KtOnTycoKCj7jPeme++9l0GDBuHv78+gQYNo164dAM2aNeOTTz6hRYsWXL9+nZCQkDw/f+nSpfz973/H39+fyMhI3njjDa5cucK0adP4/PPPadq0KZMmTWLKlCnF+n4TJkxg6dKlBAQEcOLEiexWAX9/fxwdHQkICGDu3LmMGzeOli1b0rZtW1q3bs0zzzxDZmYmAwYMoEmTJrRs2ZJRo0bRsWP+Z5tubm4EBQXx7LPPsnDhwuzl/fr1IzExscAm9lWrVhEYGEjTpk2ZNWsWa9eupUWLFgCEhITQvXt32rdvT+vWrXnggQdwsJU+JVrDjjnwyX3GnSgAjqU7Brwqy2sQSqlFQF/gsta6tXlZNWAV4AdEA09ora/fqax27drp4t6/KITNyXlNbdhKqN+RrCzNsQvxbDtxme0nYzl49jpZGhqb4pm9O5TAA9txSk0FDw8YMADmzIFatXIVrZTar7VuZ+mQLVWf86rLx48fz/7RtjdLliwhIiIi+5aqm6Kjo+nbty9HjhyxUmS2JSIighdeeCG7BcIaSuXvLMsEm16GiEXQ5gl47BOL9XcpqC6XdQe4JcDHQM62kGnAj1rr2UqpaebX/yjjuISwnt+2wFejwb0qCUM38NP1qmxffYjtUbFcSUwDwL+ON5N6NKFbM18C6lTBceIO+CUd3NwgNRW8vPJM5KVsCVKfRTHMnj2befPmZfdoLzfSk2HtOIj6Djo9Dw/OKLM7UMr0zBxAKeUHfJvjSD4K6Ka1vqCUqg1s11rf8cKCnJmL8kBnZZG4eCBp187xiscb/HjOEVOWxtvdmS5NfenW1JcuTX3x9XS99Y0DB0Lt2jB+PMyfb3SGW7cuz88orTNzc9l+lLA+l7czc2E/LP53dmQtrHkK+vwb7htvuXLNbOnMPC81tdYXzM8vAjXz21ApNR4YD1CvXr0yCE0Iy4tPzeDnk7HsOnGWLb8lkhQ/AgA/tyqEdK1B9+bG2beTYwFH9DkT9yeflHLERVKo+ix1WZQrpkxjqOXWg8C3BdRsWeYh2EIyz6a11kqpfJsKtNbzgflgHM2XWWBClIDWmhMXE9geFcv2qMtE/nGFGQ4LecIphsRGH9O5RTO6NvOlhqebtUO1qILqs9RlUW6cPwhrxsLji+CuIKskcrCNZH5JKVU7R7Nc3jdPCmFHEtMy2XXqCj+dvMy2E7FcjDfuQQ6s5cJ3NT6j8Y1dZHV6gQ8euq+8zXwm9VlUHKe2wFejwMMHnD2sGootJPNvgNHAbPO/X1s3HCGKTmvNqcuJbI8yknfEH9fIMGk8XZ3o3KQ63ZvVoFtdqLFxtHEk/+j/4dB+nLXDLg1Sn0XFcHA5bJwMNVrAiDXgWeYdUG9RpjflKaW+BPYAzZRSMUqppzAqfU+l1CngIfNrIWxDAaOsJaVlsvnoRV5Z/yud393Gw3N3MGvTCa4np/NU54asHN+BA2/0ZF7wPTzRvi41wqbApWMw5AsoB4lc6nPeIiMj2bRpU77rhw0bhr+/P3Pnzs13m+3bt9O3b1/AuNVt0qRJJYrpkUceyXOAmX/+85/MmWOZWbsqlJM/wNcTwK8zjNlk9UQOZXxmrrUels+qB8syDiEK7a23wDzKmv7kE36PTWR7VCzboi6z78x10k1ZVHJxpHOT6kzq0ZhuzXyp7Z3P4C2PvAfJV6HuvWX7HUqJ1Oe8RUZGEhERwSOPPJJr3cWLF9m3bx+//fZbmcSitUZrXeDBhSiGRj2g1yxjLnIbmTPBRobLEcLGuLsb17LnzTMmPZk3D+XgQJ27fHj7u+PEJqQxppMfK56+j4NvPMx/R7Zj2L31cifykz/Ady8ZI0L5NCo3ibzMLX409yN8gbEuPTnv9QfN9zAnXc29rhBCQ0Px9/cnICCAkSNH5lofHh5Ox44dCQoK4v777ycqKor09HTeeOON7JHLbg5letPDDz/MuXPnCAwMZOfOnXTr1i17RrUrV67g5+dX6F0SGxtLz549adWqFePGjaN+/fpcuXKF6OhomjVrxqhRo2jdujV//vknfn5+XLlyBYB33nmHpk2b0rlzZ6KiorLL+/DDD2nZsiX+/v4MHTq00HFUGOnJ8O2LkHjZGM2t40SbSeRgG9fMhbA9p0+TMGkKLhu/wTUjjRQnVyLbdef862/y8/2tubtKIYZO3b8Uvn0BarWBtARw8yr9uIVF3JwCdffu3VSvXj3PsdlvToHq5OTEli1beOWVV1i7di1vvvlmniPAAXzzzTf07duXyMjIEsc4c+ZMevTowfTp0/n+++9vGSr11KlTLF26lA4dbp1mc//+/axcuZLIyEgyMzNp27Yt99xzD2AM5HLmzBlcXV2LPeZ7uZV0xZiH/Nx+o2m99UBrR5SLJHMhbpNpymJ+VBJVopMYmpmOycUVt8wMOgY1gEfa37kArWH7bPhpNjR+CAYvBdfKpR94efbkd/mvc/EoeH0ln4LX56GkU6CWhV27drF+/XoAevfuTdWqVbPX1a9fP1ciB9i5cycDBgzAw8Poed2vX7/sdf7+/owYMYL+/fvTv3//Uo7ejlw7A18MgvhzMGQZtPibtSPKkzSzC5HD0fNx9P/0Z/79fRQtHFJIfeppHMP3op59tvBTjX4/zUjkgcHGOOuSyMulgqZALSwnJyeysrIAivX+/BR2OtWcvvvuOyZOnMiBAwdo3759rsljKqRLR43Jj1KuwahvbDaRgyRzIQBIzTDx3g8n6Pfxz1yMS2PeiLYE7duKx4L/QkCAMcpaPsOl5tLkYeg6DR77uNRnShKloyRToN4+jWhB/Pz82L9/PwBr1qwpUoydOnXiq6++AmDz5s1cv37H+ano0qULGzZsICUlhYSEBDZu3AhAVlYWf/75J927d+fdd98lLi6OxMTEIsVTLnnWhlr+MHYz1LvP2tEUSJK5qPD2/3GNRz/cySfbfmdA0N1sebELfdrULlohiZfhV/OPceMHofv08jYYTIVSkilQu3fvzrFjx/LsAHe7l19+mXnz5hEUFJTdQa2wZsyYwebNm2ndujWrV6+mVq1aeHp6Fvietm3bMmTIEAICAujTpw/t2xuXjUwmE8HBwbRp04agoCAmT55MlSpVihRPuXLyB8hMA49qMHId+Da1dkR3VOYTrViKTLQiSiopLZP3fohi6Z5o7vJ2518D29ClqW/RC7ryG3wx0LjtbHIkVC5GGaWoNCdasQSZaKV40tLScHR0xMnJiT179hASEmKRjnUVSa6/M61hx3uw7R3o+SZ0Kt588aXF1idaEaLM7TgZy/R1v3I+LoXRHf34e69mVHItRnX4cx+seMI4Cx/1jc0lclF+nT17lieeeIKsrCxcXFxYsGCBtUOyb6ZM+O5FOLAUAoZBhwnWjqhIJJmLCiUuOYO3vjvGmv0xNPStxOpnOtLOL3dP5UI5scmYYMGzFgSvNe4jF6KMNGnShIMHD1o7jPIhPcmoyye/hwdehh6v2d1lMknmosL4/sgFXv/6KNeS0pnYvRHP9WiCm7Nj8Qu8fsYYl3n4V3JGXgq01ig7+0EV9uOWS8xx5+DPvfDof6D9U9YLqgQkmYty73JCKjO+Psr/jlykZW0vFo9pT+u7vYtXmNZw7bRxFt5xok0N51ieuLm5cfXqVXx8fCShC4vTWnP16lXcnB2MOu3b1Ojv4m6lTn8XLsDQobBqFdQq3jjvksxFuaW1Zu2Bc7z17TFSMkz8vVczxndpiLNjMW/iMGXAN8/B8W9hwm6oUk8SeSmpU6cOMTExxMbGWjsUUU65kUqd78dCx/HQIcR6iRxumQOCTz8tVhGSzEW5FHM9mVfWH2HHyVja1a/K7EH+NK5RgsFb0hJg1Ug4vQ26vwredS0XrMjF2dmZBg0aWDsMUV6d/AFWj4FKvtC4Z5l+tNaa+NRMLsen0qi+Lw5paX+tnDfPeLi5QUpKkcqVZC7KlawszbJf/uDd708AMLNfK0Z2qI+DQwmaahMuwvLHjelLH/sEgoItFK0Qosxlz5nQGoavBs+aFis6Jd3EpfhU45GQxqW4HM9vLo9PJTXDGPXPd9wCXt26kF6nfsE9Mw08PGDAACjGtLSSzEW58XtsIv9Yc5iIP67Tpakvswa0pk5Vj5IXvOcTuHra6OjW5KGSlyeEsI4jP8Ogp+Clh2HMKnAteJCdmzJMWVw2J+TL8alcik/jojkxX443ll+MTyUhNfcQuG7ODtTycqOGlxv+dapQ09OVWt7G65qerrRI+gm3qJ3G2XhqKnh5Feu6uSRzYfcyTFks2Hma97ecwt3ZkTmDAxjU9u6Sd5zKMoGDIzz4BgSOgBrNLROwEMI6Pl0OZ7PgaD1w9SQrS3M1KT3HWbM5YSekcjHOeH05IZUriem5inJyUNTwdKWmtxuNfCtzfyMfani5UcvLjZpebtT0cqWGlxtebk4F/xbFXYNnn4Xx42H+fKMzXDHICHDCrh05F8c/1h7m6Pl4+rSuxczHWlHD063kBR//Frb/C0Z9DZWql7w8K7LHEeCEsJj0ZHRlT5S5aTunVEdnmr+8/pZlSoFPJVdqerlmn1HX9HLNTtA1zcm6modLyS7fFYOMACfKndQMEx9tPcVnP52mqocL80a0Lfp46vnZtxA2vQx3BVmmPCFEmUpMyyQi+hqHok7T+/BkGk+qxOHNd9M86gzumWmkubhy5N4H2TthGm/61aWGp5Goa3m7Ub2ya/HveLEiSebCPuS4DzMi1YWpaw9zOjaJx++pw2uPtqCKhwVuEdMatr4NO+dAk14weDG4FH0qSSFE2bqZvH85fY1fTl/l13Nx1NaXWOYym7vUNb5p/x73XdyD2/GT4OaGa3o697Spzz3DHrB26BYjyVzYh7feQu/aRcSTz/NE4Eju8nYndOy9xZsYJT+7/mMk8raj4NG54CjVQwhblJiWyb5oI3HvPX2NX8/FYcrSODsqAupU4eWOXjx19DmcyUQN38iAeh1g9Q8WuTZtq+SaubBt7u5GD8/baDc3VBHvw7yjpCvw62q471m7G5e5IHLNXNi7hNQMIv64zi+nr/LL6WscuS15d2joQ4eGPrStXwUPFyejlW3bO9BmMPg2s3b4FiPXzIX9On2a6xMm4/btRtwz0zC5ueM4aCCqGPdh5inxMux6H3rONDq6dQixTLlCiGJLSM0gItqcvM/cmrwD61ZhQrdGRvKuVxV3lxzzKxxeDXcFQvUmxmQpFYgkc2HTjmR5cOzPFB43paNd3XBMTyv2fZi5XP3dmIc84RL4D5YOb0KUldvGIr8leZuveWdp7py8b9Iafn4ftvzTuI20f/GGRLVnksyFzTpxMZ6RC/cyNyWOpCfH4Tl5ouWudcXshxWDjedjvpVELkQZSp8xE+eduzjw1Au82WfiLck7qG5VJnZvXHDyzinLBN9Pg/D50Ppx6Pt+2XwJGyPJXNikU5cSGLFgLy5ODjTY8T2ePuZe5Z98UvLCf9tijLNeyReC10H1xiUvUwhxR1lubjikpXHz3pN7Nq3k600ryXRxJfxoDEGFSd45ZaTCuqfh+DfQcRL0fAsc7O+2MkuwmW+tlHpBKXVUKXVEKfWlUsoCI38Ie/R7bCLDFuzFwUGx4ukO1Pex8O1hlWrA3ffAU2GSyEuJ1Gdxk9aa3b9f4akl+7hv3AK+adWNdBfjz0G7u8OIETj9Ec39jasXLZEbJRgdV3vNgl7vVNhEDjZyZq6UuhuYDLTUWqcopb4ChgJLrBqYKHPRV5IYvuAXtNZ8+XQHGvmWYKaznLQ2Zjxr1ANq+8PojeWqx7otkfosANIzs/j28Hk+33mGYxfi8ankwsj+99EzdScux3eAmxsqrZh9YOJiwKWyMW3p6I1yGyk2kszNnAB3pVQG4AGct3I8ooz9eS2Z4Qt+IT0ziy/Hd6BJzcJNgnBHpkzY9BLsXwIj1hqTpUgiL21SnyuoG8npLN97lqW7o7mckEaTGpV5d1AbHgu8GzdnR/j0Ssnu9750FL543OjnMmyFJHIzm9gLWutzSqk5wFkgBdistd58+3ZKqfHAeIB69eqVbZCiVJ2/kcKwBb+QlG5ixdP30byWl2UKTk+GNWPh5P/ggZeg8YOWKVfkqzD1Wepy+XM6NpHFP0ezZn8MKRkmHmhSnfcGB9ClSfVbJxpZt+6v50XtA3NmJ6wcAS4e0ONVywReTthEMldKVQUeAxoAN4DVSqlgrfUXObfTWs8H5oMx0ESZBypKxcW4VIYt+IW45AyWP30fre7ytkzBSVdhxRNwbj88Mgfufdoy5YoCFaY+S10uH7TW7D1zjc93nuHHE5dwdnCgf9BdjO3cwHIH5DcdWQfrn4FqDWHEGqhS17Ll2zmbSObAQ8AZrXUsgFJqHXA/8EWB7xJ273JCKsM//4UrCWksG3cf/nWqWK7wmKjpJ44AACAASURBVHC4fAyGLIMWf7NcueJOpD6Xc+mZWWz69QKf7zrNkXPxVKvkwnM9mjCyQ318PV0t/4EZKRA2w+i4OnQFeFSz/GfYOVtJ5meBDkopD4xmuQcBGd+xnLuSmMaIBXu5cCOV0KfupW29qpYpODUe3LygWR+Ycggq17BMuaKwpD6XU3HJGawIN66HX4xPpZFvJf41sA0DgszXwy0tKwvQ4OwOo78Gz9rGc5GLTSRzrfVepdQa4ACQCRzE3AQnyqfrSekEf76XP68ns3jMvbT3s9CR9m8/wtqnYPBSaNhVErkVSH0uB24boS36ShKLfz7DVxHG9fDOjavzr0Ft6NrEt/Tm9M5Mh68ngFsVeOQ9o3ld5MsmkjmA1noGMMPacYjSF5ecQfDCvZy+ksSi0e3p2MjHMgUfWglfTwTf5lC9qWXKFMUi9dnOmWcpvPzyK7z+8ATCjl/CyUHxWODdPNW5AS1qW/h6+O1S42FVMJz5CR58o3Q/q5ywmWQuKob41AxGLdrLqUuJzB91D52bVC95oTnHZW7QBYZ8AW4W6kQnREWSY5ZCBdRcvpj5yxeT6eLKtdgb1PAqg7F/4i/A8sEQexz6z4PA4aX/meVAxR0uR5S5xLRMxiwK5+j5eD4d0ZZuzSzUBH7yByORt37cuI9cErkQxXLt1+Ps7diLFCejE1umqxuZQ4fj9Ed02SRyUyaEPgbXTsOwVZLIi0DOzEWZSE7PZOzifRyKieOT4UE81LKm5Qpv2gseXwQtB1To4RyFKIktxy4xbd0pXkx15F5TOtrNDaf0dKjqbZlZCgvD0QkefsuYN+HutmXzmeWE/PKJUpeSbuKpJRFE/HGN94cE0rt17ZIXmnzNGDzi6u/GaG6tB0kiF6IYElIz+Meaw4wLjcDX042/1XJAhYSgfvnFGKnt4sXSD+LEd3DQfOdi016SyItBzsxFqUrNMDF+WQS/nLnKf54I4G8Bd5W80Bt/wheD4PoZY+5in0YlL1OICmjv6au8tPoQ52+kMKFbI6Y81ATXKRv/2sASsxTeScQi+O4lqNMeAoaBQync4lYBSDIXpSYt00TIF/vZeeoK/37cnwFBdUpe6MUjsPxxY5jW4HXQ4IGSlylEBZOaYeI/YSdZsPM0dat68NUzHWlnqdtDC0tr2PYO7HgPmvSCwYslkZeAJHNRKtIzs5i4/CDbomKZNaANT7SzwNCL5yNh6d+M2ZLGfg81W5a8TCEqmKPn43hx1SGiLiUw/L56vPpICyq5lnEq0Bq+mWQ0rQeNhL7vy4QpJSR7T1hcpimLKSsPsuX4Jd58rBXD77PQRBrVmxrDsnZ/BbwtcJYvRAViytJ89tPvvL/lJFU8XFg8pj3dm1tpUCWloIofdP0HdJsusxj+f3v3HR5VtTVw+LfTQ0mAUAxSAkqHFJpSREARRER6L4IoVfRa+MCG4pWLyhU7inQEpaMgIkFB4NJL6D0JEAgdQoDUmf39cSYBIUDK9Kz3eeZJ5pwzMyuT7KzZ++yzlxVIMhdWZTJr/jVvF7/vPcM7z1SjT4OQvD/prp+hSmtjidZ23+b9+YTIZ2IvXOf1+bvYfvwyz9QK5t/talK0oI/9A7l+Aa6cMCa4Pf6m/V/fjcn0X2Ed8fHoJo8z5oc/WbrrNCOfrsqAx/K4/KLZDCvfMSolbfneOnEKkY9orflx03Ge/mIdR84m8kW3cL7uEeGYRH4pBqY8ZVyFkpZs/9d3c9IzF1ahx4xBr1/Hw4mf8fr4Lxj0eB5nmKenGkuz7pkH9QZA49esE6gQ+cS5q8mMWLibNYfO0/jh4nzaOZTgQAcVKYnfBT92AnOasRiMtx0WoMlnJJmLvLEs/6gwln/sHfU7PPk7+PlBUlLunjMl0ViXOXoNNH8XHntdzqkJcT+3FEdZds7MO0v2kpxm4oO2Nej9aHnbFUS5n2OrjfbsXxR6LYMSVRwTh5uTYXaRN9HRnGnTIXP5R12gAPTsCTExuX/O5AS4GA3PfQNN3pBELkR2WIqjrO89nGFzdlK+WAF+G/4YfRuGOC6RgzFjvUh5eGGlJHIbkp65yJPT/kVYfyaFTpblH1VyMgQE5G75x4RTRr3iwDIwbIvULRYiO24rjtJ41XxiV8032uOwXI6OWUPKNfAtZHwoT08G/yKOiyUfkJ65yLU0k5lhc3ZQ9NplEp9/IW/LP8Zth+8fgzVjjfuSyIXInuhoTrRqlzk6Zvbzh549UXkZHcsLsxn+eBsmP2mUMvX2k0RuB9IzF7n28e8H2XHiCqlzFxAYallvPTfLPx6JhHl9jOIKYd2tG6QQbm5uXBppZ1LoYRkd80hNyf3oWF6lp8IvQ2DPfKj/EvgUtH8M+ZT0zEWurNh7hsnrY3i+YQjPhOahcMrO2TCnKwQ9DC9EyjrrQuTArE3H+b+Fe6jikYT5pYH2LY5yu+SrMKezkcifGA1PfyLLs9qR9MxFjp24eIM3F+wirEwgo1pXzf0TXT0Nv71mrK/e9UfwLWy9IIVwc1PXxzBm2X6eqFqSWh+uwsvbkjjtURwlK8vfhJh10G6i1CF3AEnmIkeS00wMmbMdBXzdoza+Xrn45K21MUM9oDT0XQbBYeDlgEUshHBR3/99jP/8fpCWNUrxVffa+Hg5wSDrk+9DWFd4qLmjI8mXnOAvQLiSj347wN5TV/lvl3DKFiuQ8ydIS4b5fSFqjnG/bD1J5ELkwNd/HeE/vx+kTWgwX/dwcCKP2w6/DANTOgQESyJ3IEnmItt+3XWaWZuOM7BJRVpUL5XzJ0i6Aj92gP2/GNeSCyGyTWvNZ5GHGb/yMO0jHuTzruF4ezrwX/iRSJjRBmLWwvXzjotDADLMLrLp6LlrjFy4m7rli/JGy1ws/JBwyqhDfuEIdJwCtTpZP0gh3JTWmk/+OMTENcfoXKcM4zqG4unIhWCi5hg98lI1oOcCKJyLD/fCqiSZi/tKSjUxdPYO/Lw9+apHRM57A8kJRoGF5ATotQAqNrVFmEK4Ja01H/12gMnrY+jxSDn+/VxNx67otuk7WPF/RjvuMsuoZigcTpK5uK/3ftnL4XOJzOhXP3eFGvwC4dHBUKEJBIdaP0Ah3JTZrPlg6T5mbDzO8w1DGP1sdZSjlzcuUxciesMzn8l8FyeSrS6WUspXKfW8UmqJUuqEUuqa5esvSql+SilfWwcqHGPetpPM3x7Hy80r0aRyiZw9+MAyiNtmfN9wmCRyJyHt2TWYzZq3l+xlxsbjvPhYBccm8rRk2LfY+L5MXXjua0nkTua+yVwp1R84DrwA7AReBdpZvm4H+gOxSql+eQlEKVVEKbVAKXVQKXVAKdUgL88n8u7gmau898teGj4UxCtPVMrZg7dOgXm9Ye2ntglO5Iq0Z9dgMmtGLNzNT1tOMKTpQ7zVuprjEnnGxNX5/eDsPsfEIO4rO8PsLYDHtdaHsti3CBijlKoCjAam5SGWL4AVWutOSikfIBfXPQlruZaSzpAfd1DYz5svukVkf7KN1rD6IyOJV24FnabaNlCRU9KenVl8POauXXm/x7ssiE3l1Scr8coTlRyXyK+ehh87WiauTjYmvAmndN9krrXOXCxbKdVZaz0/i2MOAble8kcpFQg0AZ63PF8qkJrb5xN5o7Vm1KI9xF68zpwXH6VE4WyOuprSYdmrsHOWcU6tzefgKdMynIm0Z+dm+mAMav16KidO4M3PvmRos4cdF8z5QzCrAyRfgZ7z4aFmjotF3FdOL1L8WClV4faNSqm2eYyjAnAemKaU2qmUmqyUumOFfqXUS0qpbUqpbefPy3WNtvLj5hMs3XWa15+qwqMVg3L24BuXoMkIaPuVJHLn57D2LG35Nv7+oBSe33+Hh9b0jvqdoc0rGdsd5XQUmNOg33JJ5C4gp8n8NWCeUso7Y4NSaiAwJY9xeAG1gYla6wjgOjDy9oO01pO01nW11nVLlMjhZCyRLbvjrvDh0v00q1KCwY9ns+jJjUuQeMZI3l1nQfO3jeVahbNzWHuWtnyb6Gj2Nn0ms4wpBQpAz57giDKm1y8YX8O6wrBtxnLLwunlKJlrrZcAm4H/AiilPgTeAvL6sS0OiNNab7bcX4Dxz0DYUcKNNIbM3kHxQj581iU8e9eyXjlhXEP+cw/jfLlUSXIZ0p6dx4qLiqgrZnxNqeDnB8nJjiljun0GfB5qLNMKcg25C8nObPbXlFLNlVLFLJteAx5TSv0FPAc01FrvzUsQWuszwEnLxBuAJ4D9eXlOkTNaa95YsIszCcl83bM2RQtm47KTs/uMRH79HDz1b+mNuwBpz84n+vw13pi/i4fM1zEPHAiOKGOqNfz9KSwdDuUehRK5WOVROFR2Tmo+BfwfUFwpdQqIAo4AjYAWWutTVorlZWC2ZeZrNJCnS2NEzkxZH0Pk/rO826Y6tcsVvf8DYtfDTz3ApyD0WwGlqts+SGEN0p6dyI3UdAb/uANvT0W5v1fgVcRyjtyeZUzNJvh9BGydDKHdjGvIPb3v/zjhVLIzm70VgFKqNBBhudXGmJ26Ryl1HtiptX46L4ForaOAunl5DpE7249fYpylnGL/RiH3f4DZDH+8bazH3GsRFClr8xiFdUh7dh5aa95atCdzdcUHizhostuun41E3nA4PPkBeEj9LVeU7enGWuvTwGngt4xtSqkiGP8Iwq0fmrC5+HjSOnfhvSbDKV2kOJ90Crv/9axmk3FevPtP4OUHBYrd+3jhlKQ9O96sTcdZEnWa11tUzvnqitYU1h0KBEGVVo6LQeRZnq4d0lpfAf6y3ISL0WPG4Lnhf/RICiDs1zkE+t9jaC1jMZgLh6HTNAgobb9AhV1Ie7afHScu8+Gy/TSvWtIx15JfjYdfX4Y2E4yRNUnkLi87E+D+Vko9fp9jmiil1lgtKmFblmta1XfGNa09dyynZpkid7+m1ZRuTIxZ+yn4FjYSu3BJ0p4d7+K1FIbO3sEDgX5MyO5VI9Z04YgxcfXERrhy3L6vLWwmOz3zscC3lmtRV2HMSr0KBADVMWaqpmPMihWuIDqaK0OG47tsKf7pKegCBVDt28P48Xcem5YEC/rDoeXQ5E1oJteQuzhpzw5kMmuG/7yTi9dTWTS4IYEF7DzRLG47zO4EygOeXwalI+z7+sJmsjMB7g+ghlKqJcalK72AosBljEINwy3HCBeRXLwk68+l0tqUivbzQ93rmtb5/eDwCmg9Huq/aP9ghVVJe3as/648xP+OXuSTjqHUfDDQvi9+YjPMageFShoTV4OyuSiUcAk5mQD3ByCN3A18FnmY2hfOc7pbX8r836swaRLEx2d9cON/QVg3qNHOvkEKm5L2bH+R+8/y7ZpjdKtXli71HHAFSMlqUL0dPPm+cSWKcCs5mgCnlJoLTNZaR9ooHmFjm6Iv8sO6aLqP+55W7WsZG2+/pvXcAYhZC48MhHKP2D9IYRfSnu0n9sJ1XpsXRc0HA3i/rR0rj2kNu+dCtbbGam7tJ9rvtYVd5fSCwhbAhls3KKWesiwDKZxcYnIar8/bRbliBXi7dbWsDzqxCaa2hHWfGXWMhTuT9mwHSakmBv24HQ+lmNizDn7edlryOGM9iMUDYZuUInZ3OU3mnkDabds2A/2tE46wpTFL9xOfkMRnXcIp6JvFoMzB32Dmc1CwBLywEvyL2D9IYU/Snm1Mnz7NqfD6XDx2gs+7hVO2mJ3KuqenwqIXYdM38MggeHSIfV5XOExOk/lWoPVt264Cha0TjrCVlfvOMH97HIObPkSd8lks17p9BsztBaVqQP8/oGh5+wcp7E3as40defn/qHhgJ5Njl9OsSkn7vGhKIszpAnsXwBOjodU4WdUtH8jpojGjgUVKKZPWeqllW2uMlaSEk7pwLYVRi/ZQo3QArzxR+e4HPvQEdJlhrLcu8gNpz7bi7w/JyWS0trBlP4H6yaiIlpRk29e+ft6Y9/LctxDR07avJZxGTkug/g8YAkxVSp1VSh0EFgEf2SI4kXdaa0Yu3ENiSjoTuobj43XLr9xsgvjdxvd1+kKPeZLI8xFpz7ZjPnqMdfVakORtx/rkiWeNCW/FKsLwHZLI85kcj71orRcC5YHnMRp9ba31LCvHJaxk/rY4Vh04y4iWVahc6pbR07QkmNcHprQwapKDDMXlQ9KebWPeqXSOp3nhl26n+uSno+C7xrDWsvCTfCjPd3K1NrvW+gbwu5VjEVZ28tINPli6j0crFqN/owo3dyRdNsqXntgIrf4DRco5LkjhcNKerevCtRT+8/tBJnHDqEs+cOC913LIq2N/wdze4F8Uqj9nm9cQTi9PhVaE8zKZNa/P24WHUozvHHZz/eeEU/BjR7h0DDpNgZodHRuoEG7mo98OcCM1naA/lqJKWkbDbFWffPc8WDIYileBXgshINg2ryOcniRzNzV5XTRbYi8xvnMYZYrecjnM9umQEAc9F0DFe9bbEELk0PojF1i88xTDmz/MwyVtfFFAQhz8MhTKPgrdZsulpPmcJHM3dCD+Kv9deZiWNUrRsfaDxkZTGnh6Q9ORxvKssi6zEFaVnGbinSV7CAkqwBB7lDUNLGOssV6mHnj72f71hFOTGU9uJiXdxL/mRhHg783Y9rVQSsGBZfDNI8YQu4enJHIhbODb1UeJvXiDf7erZbtV3tJTYdFA2LvIuF/hMUnkApBk7nY+izzMwTOJfNyxFkGFfI1lHOdZJsd4SaMXwhaOnktk4t/HaB/xII0rFbfNi2QsBrP755tXoAhhIcPsbmRLzCUmrY2mW72yPFG1JKz+D/w9Dio9BZ2ny+UqQtiA1pq3Fu+lgI8Xbz9zl5oHeXXtnFGH/MxeeO4biOhlm9cRLkuSuZu4lpLO6/OjKFPUn3faVIfN3xmJPLwXPPu5cb5cCGF187fHsSXmEuM61KJ4IV/rv0DSFZjyFCSege4/QeWW1n8N4fIkmbuJD5fuJ+5yEvMGNqCQrxeE9wDlAfVfAqUcHZ4QbunitRTGLj9AvZCidKlroxrl/kWMSasPPwll6trmNYTLk3Pmri4+nkv1GvDXmt280rA49faPM1Z38ws06pFLIhfCZj5afoDrKemMbV/r5loO1nL0z5vLLTcdKYlc3JMkcxeX/O77FNm+mf/snMXw48OM68hPRzk6LCHcW3w8CfUbsm7tXgY2eYhKpax8TfnuecZkt1XvW/d5hduSYXZXZanKlDE//ckNK2ED4OsD7zZwZGRCuL30Dz6g8LZNvO1ZglYTulv3yTd8BSvfgZDHoPM06z63cFtO1TNXSnkqpXYqpZY5OhanFx3NsSfbkuxlmdjmraDDMxB73LFxCWHhlu3Z3x+Uwuv77/HQmnabfsXPx8vYnldmM/zxtpHIq7czlmf1C8z784p8wamSOfAKcMDRQbiCPaYCbL6Yho8pHe3tASagVDnbVWUSIufcrz1HR5PcuStJXjYobapNcG4/1B8InaaBlw1mxgu35TTJXClVBngGmOzoWJxdYlIqs2b9QHDKVVIHvITash0GDYYzZxwdmhCAG7fn4GD2JGp8TamYfa1U2vTGJbh+wbh8tPvP8PTHUo5Y5JgznTP/HBgB3HUmiVLqJeAlgHLl8mfZTm1KJ+r7F/kkZQlHvv4Gv2aWxSNsVZVJiNy5Z3t21bZ88tINLkWfZPNTXWjw8ai8lza9fNxYDKZAcei3XHrjItec4uOfUqoNcE5rvf1ex2mtJ2mt62qt65YoUcJO0TmR1BucntSJx64sYfuDvan0eA9HRyTEHbLTnl21LU9YdZjhnd+lwtzpEBZmfIhetCh3T3Y6Cqa0gGtnofk7chmpyBOnSOZAI6CtUioW+BlorpT60bEhOZlr50me/DTBZ9YwPXAI4S98JUNxwlm5ZXs+dCaRxTtP8XyjEB4IzGOdgyORMK01ePpA/5UQ0sg6QYp8yymygdZ6lNa6jNY6BOgG/KW1lsWHb5F6eg+mc4d40/NNWr8wGk9rL1AhhJW4a3v+9I9DFPL1YvDjeaw6aEo3Zq0HPQQDVkHJqtYJUORrznTOXGTl+gUoWJzRe0uwPPkLvujXjJIBUv1MCHvafvwSqw6c5c2WVShSwCd3T6I1mE3g6QW9FhiVDH2tvNiMyLecLplrrdcAaxwchnM4sBQWDWRLvf/y05YABj4eStMqJR0dlRDZ5g7tWWvNxysOUbyQL/0aheTuSdJTYekroM3Q/jso4jqT/oRrcIphdpGFTd/B3N6kBFXljf95EVGuCG88VcXRUQmR7/x9+DxbYi7xyhMPU8AnF/2f5KswpzPsmgPFKlo/QCFwwp55vmc2GytAbfoGc5Vn6HWhP1eUidndIvD2lM9eQtiT2az5ZMUhyhbzp2u9XPSmr56G2Z3h/EF47luI6Gn9IIVAkrnzOfw7bPoG6g9knLk3W3ed4LtetSlbrICjIxMi31m2J5798Vf5vGs4Pl45/DBtNsGsDpAQBz3mwcNP2CZIIZBk7jy0Nq4zrdIa+vzCXylVmTRjO70fLU+rmsGOjk6IfCct7hTl2remwfPv0zasdM6fwMMTWn8CfkUgONT6AQpxCxm3dQaXj8PkJ+HsPjhzhtTe7zJ26hqqBQfw9jPVHB2dEPlS9PCRhMbs4dMDv+SsVvnu+bBpovF9hSaSyIVdSM/c0U5HGXWL05Mh+SrmcV/jtfF/vJASSP3ff8bP29PREQqRv1jKC2dMNy3z8wz4eQb4+UFS0t0fpzX87wtYNdooX1r/JaN3LoQdSM/ckY6surkK1PvnIKQhHt99h4fWdN/+Gw+VLGyd0opCiOyLjubok21zVhnNbILfRxiJvEYHo3ypJHJhR5LMHSVmndEjD6oIL0RCTAxHnnzWNqUVhRDZllKiJLuumvE1pRq98ftVRtMa5j8PWyZBg2HQcYoUTBF2J8ncUco+Ao3/Bf1+h4Bg5pxIY/OFdHxNqejs/AMRQtjEoh2nKHjlIvHdn4dNm2DQoHuXF1YKQhpDq3HQ8iOpmSAcQs6Z25MpDf7+GB4ZDAWD4Il3Afgl6hRvL9nDfI8k9MBBeAwamPfSikKIHEs3mfnu72MEvvwJvwxtZCTqu5UXvhQDCSeNSW6PDLRvoELcRpK5vaRcM4bijkYaSznW7gPAnwfO8vq8XdQLKUaNjavw9LGcZ5P65ELY3W974jl+8Qbf9aqDuldJ0lPbYU5X8PKHl7eDVy7XaxfCSiSZ28O1c8YqUGf2wLNfZCbyjccuMmT2DqqXDmBK37r4+8iEGSEcxWzWfLv6GJVKFuKp6qXufuCRSJjXBwoWh16LJJELpyDJ3NYuRcOs9kZC7/4TVG4JQNTJKwyYsZVyxQowo199Cvt5OzhQIfK3VQfOcuhsIhO6ht39uvKds+HXl6FUDei5AArfI+kLYUeSzG3NpxAUKA4dp0KZOgAcOpNI36lbCCrky48DHqFoQflkL4Qjaa35ZvVRyhUrwLOh91jt7cQGqPAYdJkFfgH2C1CI+5Bkbisnt0DpCChUEgasMibSxMeT3LEzrzQZjl9AMWYPeIRSUptcCIf739GL7IpLYGz7WnjdXtDIbILr56HwA9Dmc+NSNBlaF05GrqGwhR0zYWorWP+5cd8ykeb6O6Px2bSB/n/O4scXHpHiKUI4ia9XH6FUgC8d6zz4zx1pScb58aktjUmsnt6SyIVTkp65NWkNf38Ca8bCQ0/Ao4OM7ZblIQtaDuuybRk8EHD/5SGFELYVH09iu04cqzeYgV0b4ut1yyTUG5fgp+5wcjM8/TH4FnJcnELch/TMrcVsgmX/MhJ5WHfoMRd8C2Mya36cs5pfazQlyVtWdxPCqXz4IQW3buTNzXPp8cgt9cqvnDRG107vgM7T5Dpy4fSkZ24tl6Jhz3xjVbcnRoNSxFy4zpvzd7Ht+GWmlSyG34G07C0PKYSwLctoGRg9mi7bloGv983Rsj/egsQz0HuxsbqbEE5OeuZ5lZ5qfC1eCTr8Ah+uxhx/hun/i+HpL9Zy+Gwin3UJo2kRjRo0KHvLQwohbCs6Gnr0INXHmICq/f3/OVr27BfQ/3dJ5MJlSM88L66dg9mdIKyHcX78y6no9etZ1X0o7zd4gaZVSjCuQygPBPrBokU3HyeruwnhWMHBJPr4UzAthTRvX7xTUsB8FVa/ZgyrFyhm3IRwEZLMcyM+Hjq1g6evAxeg6SuQMhgABTy1djGxaxej/fxQ/WSCmxDO6OTBWHZEtKblF+9S4qv3IGoFNKlvzFqXqmfCxUgyz41Rr8LGLWhTIf4cM5sFHypaTf8vLQ9vwj89BbO/Px4dOqDGj898SFpaGnFxcSRbztMJYW1+fn6UKVMGb29ZTfB+Ll9PpeOTb9C6VjC9vDdBtU3QqgV0nwN+gY4OT4gck2R+P/Hx0K0bzJ0LFSpkTpoBUJuv8WTL53jMy4dDrTrid9CY4OaRknLHBLe4uDgKFy5MSEjIvQs4CJELWmsuXrxIXFwcFSpUcHQ4Tm/mxuMkpZn4v8BI+P1DqNrGqEPuLYs4CdckE+Du58MP0evXc2jIm7z80UJ+rdGUVC/jM1Cqrx+X2nXG50Qsod7J95zglpycTFBQkCRyYRNKKYKCgmTkJxuSUk3M2BhL86olKRn+tFGSuPMMSeTCpTlFz1wpVRaYCZQCNDBJa/2FQ4LJ6Ilv3gwpKUZ8QJXFP/LV4h8xKQ88AO3nh09qKsWCi0NwcLYmuEkiF7bkLH9fTtWes7BwazSNk9bQq8lrUCoInh7n6JCEyDOnSOZAOvC61nqHUqowsF0pFam13m/3SCw98aNPteNQ3CWeOLgR//QUtBeo+mXxDKxpDLe/9BJMmmQkfyHErZynPd8eWMwxnu1Zj16dTGjPNkADR4ckhFU4xTC71jpea73D8n0icAB48N6PsjJ/f2MN9YkTUWYzlVYsos3eNfilp4AXKBNQsyUsX270vMPCjK+39siF1QwYMID9+/P+vz82NpY5xlWyLAAAFt5JREFUc+bk+HHPP/88CxYsuGN706ZN2bZtW65imT59OiVKlCAiIoJKlSrRsmVLNmzY8I9jxo8fT9WqVQkPD6devXrMnDmT9u3bEx4ezsMPP0xgYCDh4eGEh4ff8Vhn4RTtOSvJV0nr8zgBJxK4uC8UVV4SuXAfztIzz6SUCgEigM1Z7HsJeAmgXLlyt+/Om+hoTK+9TtrCRfilpZDu54dnIR/Ugzeg1zNw7AE4e866r5lPmEwmPD0973/gLSZPnmyV185I5j169LDK8+VV165d+frrrwFYvXo1HTp0YPXq1VSrVo3vvvuOyMhItmzZQkBAAFevXmXx4sUsXrwYgDVr1jB+/HiWLVvmyB8hR+7Wnm3alrPi7wfJKfhb7gat2GB8eJf6CMJNOFUyV0oVAhYCr2qtr96+X2s9CZgEULduXW21F7acJ9/lEUh4WiomH1+8UlOhTgUY2gKe+Qw8cpaM7uWDpfvYf/qOHy9PqpcOYPSzNe55zMyZMxk/fjxKKUJDQ5k1a9Y/9m/ZsoVXXnmF5ORk/P39mTZtGlWqVGH69OksXryYhIQETp06Ra9evRg9ejSxsbG0atWKOnXqsGPHDmrUqMHMmTMpUKAAISEhdO3alcjISEaMGEHVqlUZNGgQN27c4KGHHmLq1KkULlyYBg0a8Omnn9K0aVNGjRqFh4cHH330EU2bNmX8+PHUrVuXQoUKMXjwYJYvX05wcDBjx45lxIgRnDhxgs8//5y2bdsSGxtL7969uX79OgBff/01DRs2ZOTIkRw4cIDw8HD69u3L8OHDGTlyJGvWrCElJYWhQ4cycOBAtNa8/PLLREZGUrZsWXx87l4Za9asWQwYMID09HSmTp1K3bp1qVKlChs2bKBEiRKYzWYqV67Mxo0bKVGixF2fp1mzZrz00ktMmjSJCRMmMHbsWNasWUNAgFEnOyAggL59+2b3T8Dp3Ks926wt303kNMxD+pF2wIxveppRH6F9e7jl8lEhXJlTDLMDKKW8MRr+bK21fceuP/wQvW4dQTu3sPOZLniuXm7MSPevYtQvtmIid5R9+/bx73//m7/++otdu3bxxRd3zkeqWrUq69atY+fOnYwZM4a33norc9+WLVtYuHAhu3fvZv78+ZlDzYcOHWLIkCEcOHCAgIAAvv3228zHBAUFsWPHDrp160afPn34+OOP2b17N7Vq1eKDDz7Ay8uL6dOnM3jwYFatWsWKFSsYPXr0HXFdv36d5s2bs2/fPgoXLsw777xDZGQkixcv5r333gOgZMmSREZGsmPHDubOncvw4cMBGDduHI899hhRUVH861//YsqUKQQGBrJ161a2bt3KDz/8QExMDIsXL+bQoUPs37+fmTNn3nMI+8aNG0RFRfHtt9/Sv39/PDw86NWrF7NnzwZg1apVhIWF3TORZ6hduzYHDx7k6tWrJCYmUrFixfs+xhXYpD1HRUGRIrB7d/YfY1luWTfqxprA5nib0tFSH0G4IafomStjGu4U4IDW+jO7vfAtxRYUUD7hLOV/mwt/zIPrieBT8N6Pz6X79aBt4a+//qJz584UL14cgGLF7lyqMiEhgb59+3LkyBGUUqSlpWXua9GiBUFBQQB06NCB9evX065dO8qWLUujRo0A6NWrF19++SVvvPEGYAwpZzzvlStXePzxxwHo27cvnTt3BqBGjRr07t2bNm3asHHjxix7xD4+PrRq1QqAWrVq4evri7e3N7Vq1SI2NhYwFuUZNmwYUVFReHp6cvjw4Szfh5UrV7J79+7M8+EJCQkcOXKEtWvX0r17dzw9PSldujTNmze/63vZvXt3AJo0acLVq1e5cuUK/fv357nnnuPVV19l6tSp9OvX766Pv5XWtu+U2ptN2nN8PDRqBDduQI8esHfv/R9z4SjM7ghPf8Imz7qknrvC4fY9qfreGzJ5Vbgdp0jmQCOgN7BHKRVl2faW1nq5TV81OhreeIPUBYvwSU1G+3iiqigYO8ZmidyZvfvuuzRr1ozFixcTGxtL06ZNM/fdftlTxv27bQcoWDB77+GePXsoUqQI585lPSfB29s783k9PDzw9fXN/D49PR2ACRMmUKpUKXbt2oXZbMbPL+trhrXWfPXVV7Rs2fIf25cvz/6fWlY/c9myZSlVqhR//fUXW7Zsyeyl38/OnTupVq0aAQEBFCpUiOjoaHfonVu3Pd9+yd2+fTe33e3D0Nn9MPM50GYIKM1XS49wpM8Y1o1oBt6eUh9BuB2nGGbXWq/XWiutdajWOtxys20iBwgOJsHbH6/UFMxeHqg0E1RuDG1G2fyl7a158+bMnz+fixcvAnDp0qU7jklISODBB41Jx9OnT//HvsjISC5dukRSUhJLlizJ7I2fOHGCjRs3AjBnzhwaN76zylRgYCBFixZl3bp1gHHOOaOXvmjRIi5dusTatWt5+eWXuXLlSq5+voSEBIKDg/Hw8GDWrFmYTCYAChcuTGJiYuZxLVu2ZOLEiZmjDocPH+b69es0adKEuXPnYjKZiI+PZ/Xq1Xd9rblz5wKwfv16AgMDCQw0lv8cMGAAvXr1onPnztma8Pf3338zadIkXnzxRQBGjRrF0KFDuXrVOL187do1Zs6cmYt3w7Gs2p79/e++b9eurLefjoLpzxinx/r9zvaUB9lw7CIDm1TEz9v1T5kJkRWnSOYOEx+P58IFxIaWQw0oCJ2fBHNRR0dlEzVq1ODtt9/m8ccfJywsjNdee+2OY0aMGMGoUaOIiIjI7PFmqF+/Ph07diQ0NJSOHTtSt25dAKpUqcI333xDtWrVuHz5MoMHD87y9WfMmMGbb75JaGgoUVFRvPfee1y4cIGRI0cyefJkKleuzLBhw3jllVdy9fMNGTKEGTNmEBYWxsGDBzNHBUJDQ/H09CQsLIwJEyYwYMAAqlevTu3atalZsyYDBw4kPT2d9u3bU6lSJapXr06fPn1o0ODuly35+fkRERHBoEGDmDJlSub2tm3bcu3atXsOsc+dO5fw8HAqV67M2LFjWbhwIdWqVQNg8ODBNGvWjHr16lGzZk0ee+wxPDzydxPNKFWaZf87LOzOZH/5OMxoCz6FoN9ySC9MwaeepJL5Gj0escOseSEcRWvtkrc6derovEroN0CblNI7W7XX+tCKPD/fvezfv9+mz29L06ZN00OHDr1je0xMjK5Ro4YDInJOW7du1Y0bN3ZoDFn9nQHbtBO02bvd7tmW/fy0NgbT77z17Kl1fPw/jzebtV7zidaXT2ittb7Qu782ofTutj2y9f4J4czu1Zad5Zy5fVkmvgVY7oavWAxVFss1pyLXxo0bx8SJE7N9rlxkU3Q01K+PjovjjsVqZ8+GhQuNNnt8AxQqBUEPweNvZrbxIMuhtX6dA2qOtHHhtvLnGN6+HZjrlUBnfJQpUAB69oSYGIeG5ayef/75zIVObhUSEsLe7MwqzgdGjhzJ8ePHs5wzIPIgOBjatAGMRd4zhts1QMGCRpuNWQc/doTfXr/5uOhozj/bkaSMuuTSxoWby38986TLEDkI9FW0SaF9ffCQa06FcE6WHvbtvXIFcP26key9gM/rQ4dJmfvTS5Zi88U0WptS0X5+KGnjws3lr555cgLMag/H9pK035PIOi1RdylZKoRwApYJcCnevpmbTOqWf1vVfGFMOPRdCoVKGtvi47lYtwEFzpzmZJc+0sZFvpC/euZXTkLCKRKP1aPgjZWUKxOECg+Xa06FcFbBwRAQgE9aChqjR+6hzaSj8ESjLit4+Y+biRxIee99SuzaSvpj7Sj30zTjmnRp48LNuXcyj4+HZ56Bo0dh/Xp45BFITqYwRwGotmQ2qNkyKUYIZ5XFMLsCvDLOnp9JhsKljDYMkJxMRh/+qbWLwcND2rfIF9x7mP3DD9E7d0JiIjzXAqKjSW/7HKaM1aNkUky2REVF3XOFtO7duxMaGsqECRPuesyaNWtoY5nINH36dIYNG5anmFq3bp3lAjPvv/8+46V4hvuwDLMne1mW+fX1gRIFSfU27muAChUgPByWLiW5aDGSMo6V9i3yEffsmd+25joAseegdGky1n/Snp4yKSaboqKi2LZtG61bt75j35kzZ9i6dStHjx61SywZ11TmZPlV4cIsw+y+6anGMHtKKpxPJWMFfwWWGe0x6BYtMnvlMulN5Dfu2TOPjobSpf+xalTG9yrjZjKB2Qzff2//+ACmPXPnbcsPxr7UG1nv32m5hvn6xTv3ZcPMmTMJDQ0lLCyM3r1737F/y5YtNGjQgIiICBo2bMihQ4dITU3lvffey1y5LGMp0wxPPfUUp06dIjw8nHXr1tG0adPMimoXLlwgJCQk22/J+fPnadGiBTVq1GDAgAGUL1+eCxcuEBsbS5UqVejTpw81a9bk5MmThISEcOHCBQA++ugjKleuTOPGjTl06FDm83355ZdUr16d0NBQunXrlu04hBPx94fvvstst7e6fVU4destOdk4Vy6T3kQ+4X4986x65dz5jyC/1TPOKIG6YcMGihcvnuXa7BklUL28vFi1ahVvvfUWCxcuZMyYMWzbti3La81//fVX2rRpQ1RU1B37cuqDDz6gefPmjBo1ihUrVvxjqdQjR44wY8YMHn300X88Zvv27fz8889ERUWRnp5O7dq1qVOnDmAs5BITE4Ovr2+u13wXDhYdjX79ddLnz8M73QS+3tCpC3h6wqxZmFEobUZB5gQ5/P2hQwejbUuvXOQT7pfMs1tS8sYNxw7B9fvt7vt8Ctx7f8Gge+/PQl5LoNrD+vXrWbx4MQCtWrWiaNGb6+SXL1/+jkQOsG7dOtq3b0+BAgUAY330DKGhofTs2ZN27drRrl07G0cvbCI4GFPBAnilmyzD7GlGuz1zBjV4MOrcOfSCBTcTuacnpKTI8LrId9wvmcfEQJMm6KNHMz+tw82euQZU8+ZQtqwMwd3mXiVQs8vLywuz2QxAsmWExBqyW071Vr/99htr165l6dKlfPTRR+zZswcvL/f7k3dr/v543f53NHHizRnqHTqghgyBgwfh7FkoVQqqVpVa5SLfcb9z5hUrgiWRA3eca1MAVarA9OmwaJG9o3OYvJRAvb2M6L2EhISwfft2ABYsWJCjGBs1asS8efMAWLlyJZcvX77vY5o0acKSJUtISkoiMTGRpUuXAmA2mzl58iTNmjXj448/JiEhgWvXruUoHuEEoqNJ7tyV9IyFYjw9/zlDfdEi4xryP/+EvXuNr998k6/athDgjsk8Ovru+5QyLmPJhz3yvJRAbdasGfv3789yAtzt3njjDSZOnEhERETmBLXsGj16NCtXrqRmzZrMnz+fBx54gMKFC9/zMbVr16Zr166EhYXx9NNPU69ePQBMJhO9evWiVq1aREREMHz4cIoUKZKjeIQTqFgRv/lz8dLGaA8mk1FgpUIFx8YlhJNROrvnmJ1M3bp1dcas6X9Qd0x1uyk+3mHn0Q4cOJBZt1pkLSUlBU9PT7y8vNi4cSODBw+2ysS6/CSrvzOl1HatdV0HhXRfd23LYAynp6Tcud3XN3OiqxD5xb3asvudQNy5Exo2RCcl/XOG64MPyoQYJ3fixAm6dOmC2WzGx8eHH374wdEhCUeLiSG5YWN8Y6Nvni6rVAnWrnVgUEI4H/dL5uHhUKECav9+wJLIixaF+vUdGpa4v0qVKrFz505HhyGcSXAwfliG2H18IDUV0tPlg7kQt3G/c+YAly9DjRowd67x1c/PKSbEuOopDeEa3PbvKyIChgyBLVuMr+Hhjo5ICKfjfj1zgNOnb37fpYvj4riFn58fFy9eJCgoCHWv8/pC5ILWmosXL+KXUXDEndz6QVyqnwmRJfdM5k6oTJkyxMXFcf78eUeHItyUn58fZcqUcXQYQggHkGRuJ97e3lSQy2mEEELYgHueMxdCCCHyEUnmQgghhIuTZC6EEEK4OJddAU4pdR44fp/DigM5W1PUOUjc9uWqcUP2Yi+vtS5hj2ByI5ttGVz39yRx25c7x33XtuyyyTw7lFLbnHkZy7uRuO3LVeMG1449p1z1Z5W47Su/xi3D7EIIIYSLk2QuhBBCuDh3T+aTHB1ALknc9uWqcYNrx55TrvqzStz2lS/jdutz5kIIIUR+4O49cyGEEMLtSTIXQgghXJzbJnOlVCul1CGl1FGl1EhHx5NBKVVWKbVaKbVfKbVPKfWKZXsxpVSkUuqI5WtRy3allPrS8nPsVkrVdnD8nkqpnUqpZZb7FZRSmy3xzVVK+Vi2+1ruH7XsD3Fw3EWUUguUUgeVUgeUUg1c4T1XSv3L8neyVyn1k1LKz1Xec2tx1rYM0p4dFLO05Sy4ZTJXSnkC3wBPA9WB7kqp6o6NKlM68LrWujrwKDDUEttI4E+tdSXgT8t9MH6GSpbbS8BE+4f8D68AB265/zEwQWv9MHAZeMGy/QXgsmX7BMtxjvQFsEJrXRUIw/gZnPo9V0o9CAwH6mqtawKeQDdc5z3PMydvyyDt2RGkLWdFa+12N6AB8Mct90cBoxwd111i/QVoARwCgi3bgoFDlu+/B7rfcnzmcQ6ItQxGQ2kOLAMUxopFXre/78AfQAPL916W45SD4g4EYm5/fWd/z4EHgZNAMct7uAxo6QrvuRXfA5dpy5b4pD3bNmZpy3e5uWXPnJtvXIY4yzanYhk6iQA2A6W01vGWXWeAUpbvneln+RwYAZgt94OAK1rrdMv9W2PLjNuyP8FyvCNUAM4D0yxDipOVUgVx8vdca30KGA+cAOIx3sPtuMZ7bi1O8bvIDmnPdiFt+S7cNZk7PaVUIWAh8KrW+uqt+7TxccyprhlUSrUBzmmttzs6llzwAmoDE7XWEcB1bg7DAU77nhcFnsP4B1YaKAi0cmhQIkvSnu1G2vJduGsyPwWUveV+Gcs2p6CU8sZo+LO11ossm88qpYIt+4OBc5btzvKzNALaKqVigZ8xhua+AIoopbyyiC0zbsv+QOCiPQO+RRwQp7XebLm/AOMfgrO/508CMVrr81rrNGARxu/BFd5za3GW38VdSXu2K2nLd+GuyXwrUMkyU9AHY6LBrw6OCTBmVwJTgANa689u2fUr0NfyfV+Mc28Z2/tYZmU+CiTcMpxkN1rrUVrrMlrrEIz38y+tdU9gNdDpLnFn/DydLMc75NOy1voMcFIpVcWy6QlgP07+nmMMyT2qlCpg+bvJiNvp33Mrctq2DNKe7RgyIG35nq9g74kAdpxw0Bo4DBwD3nZ0PLfE1RhjCGg3EGW5tcY4H/IncARYBRSzHK8wZvMeA/ZgzIZ09M/QFFhm+b4isAU4CswHfC3b/Sz3j1r2V3RwzOHANsv7vgQo6grvOfABcBDYC8wCfF3lPbfie+CUbdkSm7Rn+8crbTmLmyznKoQQQrg4dx1mF0IIIfINSeZCCCGEi5NkLoQQQrg4SeZCCCGEi5NkLoQQQrg4SeYiz5RSsUqpXo6OQwiRN9KWXZckcyGEEMLFSTIXQgghXJwkc2FVluUKf1FK/WYpPiGEcEHSll2LJHNhNUqpB4C/gdNAW631NQeHJITIBWnLrkeSubCWGsBGYL7WerDW2uTogIQQuSJt2QXJ2uwizyxlFP2AC8Cj8ileCNckbdl1Sc9cWMtIjKpEkUqpoo4ORgiRa9KWXZAkc2Et6UBPjPJ+a5RSJR0cjxAid6QtuyBJ5sJqtNZmrfWLGHWF1ymlyjk6JiFEzklbdj1yzlwIIYRwcdIzF0IIIVycJHMhhBDCxUkyF0IIIVycJHMhhBDCxUkyF0IIIVycJHMhhBDCxUkyF0IIIVycJHMhhBDCxf0/VMoTkgYZPMAAAAAASUVORK5CYII=\n", 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" ] @@ -671,9 +669,9 @@ "output_type": "stream", "text": [ "Input: plot the graph for bottom x (0-1) of the distribution.\n", - "0.99\n", + ".6\n", "Input:choose the accuracy level for DCT, i.e. 0.99999 in the basline of Bayer and Luetticke\n", - "0.99999\n" + ".9999\n" ] } ], @@ -814,7 +812,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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VR7HvJpvZR/6gd1v95i3x/rQ8vGDjb/7+PSfC8oP9/ZNrZvlm9qmZnRAy3czsFjPb4e+Xh8zsQzO7I2SeiWb2rp/GNjP7bcixDDT9+X6k42VmjwIzgV/5edzof36Oma00swIz21/f3Xgzu9TMVvvz7jazx80sNWR6tpndbmYfm1kh8E3/84vMbLl5A+StN7PLG9jX5/vbUGhmbwJ9wqZnm9kV/v89zewFv9Yq37wmVjPN7GTgMWCkn06hmc3yX5X+ftqK92Stump9zjWvRizfzF4zs36R8hC2/web18TrFmBWyLpH+vPV+91vaNtFRNoF55xeeumlV60XYMB+4HT//bPAJuAe//3lwBr//+GAAwIXPN3xahP+NyS9+f70/kAS3qjJG4BEf/oCvFG0z/LXnYI34vphvJqIeOBYvMeoXllHns/18/ELf77dfr77+9N7+NOPD1suH++xrABfABcDvYGtwHjgx8Bfo9xv6XgjU/8KSPaX3wr8PGSebOCKetIYClzo74OueI/TzQnZV1f66/gakIgXMFUAd/jTAyOgX+Pv60F4o9Pf3ojjtQBvcLzQfO0Cfugfn2RgVj3b8G/AcXg3tUYDXwK/DdsH2/1tMH87v+7ne6a/3HS8gQBPr2Mdo/BGJL8CSMAbPb4IWBBpX+M1X3sLSPPXORYY4U+bDWwOS3+Wv5+e9Y9rSvi+CdmXC4Fj/H35Cl7AGvF4hywz2H9/B94AmaHrrve7H82266WXXnq1h5dqNkQkIuecAz4CzvHvpp4F3IZ3QQhwDhB+Z/tO59x+51wB8AwwDcC8PhHfA/7LObfXOVeOF2wMwGvOFPCic+4j5ykG/gt4wTn3mnOuyjm3AXgU72I7ksCd3X/10x2PdxEbGE26m/83P2y5PLyLRID/8Nf7AXA/UAJcB/zczH5lZp+Y2YtmdkwdeTgfKAfucs6VOefWA/cB/1nH/LU457Y55153zhU750rw9vtQvKZd4G3/4865lc4bt+IBvECAkOmrnXOPO+fKnXM7gd9Se79FPF71KMe7yO3vb9uCerbhHefcOufVLm0G/gycHTbb//rb4PztvAF4xDn3qb9cBt6xq+t4XwpkOOeeds5VOufeA15tIP+9gXF4j37/yjmX1cA2A/zCOZfv6q/VutM5t8fflz8Hvm5mA6NIuy4Nffcbu+0iIm0ioa0zICLt2gd4d3xfwrsgfxH4i5n1xrtwnBM2/+6Q/4s4cnE/wv+7JqwFVCIwJOR9dlh6I4CzzOySkM/i8O6IR3LY/3uPc24feJ1vgRV+E57A9PSw5Xrg1argnFvHkYAKM3sP+BlwEnAmXtD1H8Dv8Gp3wg0BcvxgLWBL2HbWyw/Ofo93Z70HUO1P6uv/HYRX04GfZ2dmoftkBHCq32QrmCzeHfJQdR2vunwDr8lPppnl4tX2ROwwb2ZfB27HuyOf7K97X9hs2WHvR+A1xftpyGfxwKd15GdwhDSy8PZPJA/gfef+DxjgNz26yTm3t475wdv3dX3fQmVH+H8wNYPAxmjou9/YbRcRaROq2RCR+nwAnAhcArzvnKvCu/C7Fq/JyCdRphO4MB7jnOsR8kpxzj0bMl91hOX+HrZMd+fccXWsZ5X/N+Jopc65PGAbMDXwmd8+vjsROmyb9xSrXOfcm8AUYIm/DxbiNf+JZDswzGpGVSOJ7oI14Lf4tT7Oue4cCVQCae4EAn1M8NcVGszk4DXLCd1v6c65tEbkIfxY4Jxb7Zz7Ll4zrWuA35rfhyeUmSXh3WV/Dhjqb8MvQvJf1zpy8JqChea7m3PuvDryuBOvSVKo8Peh+S9yzt3qnJuI18RrEF4AEikvIYu5iN+netYb+H+H//cwkBoyPbzGI9K6G/ruN2rbRUTaioINEamTc24r3kXyjcD7/scf4jUT+cI5VxRlOvvwmun82cwGgdeZ28wuNrP6LoD/DFxqZv9uZolmlmBmE8zsjDrWkwO8DfzSzHqZWTe8vhPvhuT1r8AvzGyEmXXHa+L0rnMuOzQtv7P0L/Ca9oBXOzHLzLoC5wF1PSb1Lbw7+beYWZJ5T7H6BfBEPdsZrjtQDBzy9899YdOfAq42s+PN6/T9U2pewD4JTDOvU38XM4szs5Fmdm4j8rAHr68F4AUQZvYDM+vjX3wfwrtIroqwbBLePjjknCsxswl4TwhryMPAT/yO0fH+Ok8ws7qadz0HzDCzy/zvxjnARXUl7n+PxpvXWb8QKA3J/x6gn/+daIpfmffQhMB36gPnXKBWYzlwmZmlmVlfvO9kqD3AUD9IC2jou9+obRcRaSsKNkSkIR/gdVT+OOR9d2r312jIj4CNwAIzOwxkAt+mjloIAOfcWuACvGBnN14znHkcaU4Uyff9+bLxAoJiarb5vxd4A1iKd3c4Hq+Tbbi/4HWo3u+/fxWvQ/tuf/6b6shzPl5n3XPwOnG/i3fx//t68hzudo508l4DLKLmRf2TwJ/wAqu9eE1qvsDrMIxzbg9ek6+L8PbDIbxOyyMbkYeH8AKWPDNb53/2XWCDeU+Peh34tXOuVu2Wc64Qr/brfn/eP+EFm/Xy+x38CK+2YT/evn4Ir0N3pPk3A9/C2195wE/wnhpWl1F4x74Ab7+U4AWC4H2/3wey/G2OGNDW42m8Wr/teMHW90Om3YZ3/HbjdS5/LmzZF/zl9vjrHtHQd78J2y4i0iYsutphERFpr8ws0Jb/5865Bi/qRUREWotqNkREOiDzxrHoYmYpeE/2SgHeaeNsiYiI1KBgQ0SkY5qL14RqN94Tss5zzh1q2yyJiIjUpGZUIiIiIiISE6rZEBERERGRmFCwISLSBsxsoplt9B9dG/jsj2a238wKzaxfW+YvGmY228zqegRwXcs4Mzutnun3mtlvmp87ERFpDxRsiIi0jQeA+5xzFQBmdgpwFTDeOZcWGAG9E7oPuC4wHouIiHRsCjZERFqZP9DfqdQcb2EksNs5l9vENM3MEloif23J7+T+Dt4I5SIi0sEp2BARaX0XAYucc8UAZnYT3oBsI/0mVB/5nw8zs9f8plXbzexhfwRz/OnOzG4ws2V4gxdGHGnbzC4ys+X+gHHrzezykGmDzeyfZpZrZvlm9qmZnRC2/CVmtsxffo+Z3R02/Xoz22Fmh8zscX+E7vpMNrOlZnbYzL4ws2PDpr+PRsMWETkqKNgQEWl9U4EvA2+cc/cDc4CtfhOqs/xaireAPcAw4CS82pAHw9L6D7yRvdOAleErMrOvA0/gjUTdC/gB8KiZne7PEgf82V/HMcAK4OVAXxIz+zfg/4A7gD7AWGqO5zEM6I83OveJeKPCX9rA9s8Gvumntx34Y9j0TGCimSU1kI6IiLRzCjZERFpfT6CggXmmA2OAnzrnipxzO4HbgKvMzELme9A5t8U5V+WcK4uQzg3AI865T51z1c65DOBp4EoA59w259zrzrli51yJv46h/roBfgw85px70zlX6ZwrcM59FpJ+CXC7c67MObcZ+JA6alhCPOCvtwyYF2H+AsCAHg2kIyIi7ZyCDRGR1ncI6N7APEOAXOdcUchnW4AuQN+Qz7IbSGcE8Au/CVSemeXh1SwMBDCzPmb2pJltM7MCvJoGQtYxHPiqnvT3OeeqQt4XAd0ayNPuBubvDjggr4F0RESknevwnQlFRDqglcCsBubZDvQ1s5RA3w68TuSlQGgn8uoG0skB5jnnHqhj+m+BAcAM59xuM+vGkZoF8IKZMXUsGysTgXXOufJWXq+IiLQw1WyIiLS+14CTQzt7R5ABbAZ+Z2YpZjYQ+A3wD+eca8S6HgZ+YmYzzSzezJLM7AQzCzRd6o7XufyQmaXhPXo21J+Aa83s38wswcy61zdORgv5OvBqjNchIiKtQMGGiEgrc86tBxbjdeyua55K4AJgMLANL/hYAvysket6D/gR3rge+/GaMD2E16Ec4HagH3AAWAMsAqpCln8LrxP6PcBBYCPwr43JQ2OYWQ/gPOCxWK1DRERajzXuBpmIiLQEM5sIvAhMCgzsJ2BmvwWqnHO3tXVeRESk+RRsiIiIiIhITKgZlYiIiIiIxISCDRERERERiQkFGyIiIiIiEhMKNkREREREJCYUbIiIiIiISEwo2BARERERkZhQsCEiIiIiIjGhYENERERERGJCwYaIiIiIiMSEgg0REREREYkJBRsiIiIiIhITCjZERERERCQmFGyIiIiIiEhMKNgQEREREZGYULAhIiIiIiIxoWBDRERERERiQsGGiIiIiIjEhIINERERERGJCQUbIiIiIiISEwo2REREREQkJhRsiIiIiIhITCjYEBERERGRmFCwISIiIiIiMaFgQ0REREREYkLBhoiIiIiIxISCDRERERERiQkFGyIiIiIiEhMKNkREREREJCYUbIiIiIiISEwo2BARERERkZhQsCEiIiIiIjGhYENERERERGJCwYaIiIiIiMSEgg0REREREYkJBRsiIiIiIhITCjZERERERCQmFGyIiIiIiEhMKNgQEREREZGYULAhIiIiIiIxoWBDRERERERiQsGGiIiIiIjEhIINERERERGJCQUbIiIiIiISEwo2REREREQkJhRsiIiIiIhITCjYEBERERGRmFCwISIiIiIiMaFgQ0REREREYkLBhoiIiIiIxISCDRERERERiQkFGyIiIiIiEhMKNkREREREJCYUbIiIiIiISEwo2BARERERkZhQsCEiIiIiIjGhYENERERERGJCwYaIiIiIiMSEgg0REREREYkJBRsiIiIiIhITCQ1Md62SCxERqY+1dQbaMZVTIiJtr85ySjUbIiIiIiISEwo2REREREQkJhRsiIiIiIhITCjYEBERERGRmGiog7iItFMVFRXs2LGD0tLSts6KtJAuXbowePBgEhMT2zorIiLNpnLq6NOUcsqcq/dBHnrKh0g7lZWVRbdu3ejduzdmelhRR+ec48CBAxw+fJgRI0aET9YBrpvKKZF2SuXU0aWp5ZSaUYl0UKWlpTqBH0XMjN69e+sOoIgcNVROHV2aWk4p2BDpwHQCP7roeIrI0UbntaNLU46ngg0RabL4+HiOP/744Cs7O7ve+YcPH87+/fsBSEtLa/b6TznllIifz549mxdffLHZ6YuISMemcqrtqYO4iDRZ165dWbVqVauvt7KykoSEBBYtWtTq6xYRkY5D5VTbU82GSGfw/PNwxRU1X3/7W0xWNW/ePObOnRt8f8EFF7BgwYKol9+yZQsnnXQSkyZN4rbbbgveWVqwYAEzZ87kwgsvZMKECcCRu07OOebOncu4ceM455xz2LdvX8S0N2/ezDnnnMOUKVOYOnUqW7ZsaeJWiohIi/rgg9rl1P33x2RVKqdal4IN6bCGDBnCihUrIk775S9/ycMPPxxVOtOnT2fdunUtmbX2JyfHCzjmz/dezz0HX37Z7GRLSkqCVdMXX3xxC2QUbrjhBm644QYyMzMZPHhwjWkrVqzgkUce4auvvqrx+SuvvMLGjRv58ssvefLJJ+u8k3T55Zdz3XXXsXr1ahYtWsSAAQNaJM8iIpGonGqE/fu9silQTj3zDHzxRbOTVTnV9hRsSId06NAhdu3axfjx42tNy83N5cknn+Saa64Jfnbw4EEuvvhiUlNTGTZsGM8880xw2s9+9jNuv/32Vsl3m7nuOkhJOfI+KQl++ctmJxuonl61ahWvvPJKs9MDWLx4Md/+9rcB+N73vldj2vTp0yM9bo+FCxdy2WWXER8fz8CBAznrrLNqzXP48GF27twZLGy6dOlCSug+ERFpQY0tpx599FGmTZtGcnIys2fPrjF/pyinvv1tCL2w7tIF7rmn2cmqnGp7CjakQ8rMzGT48OF07dq11rR58+Zx3nnn1Zh23XXXkZSUxN69e5k/fz7XXntt8C7RhRdeyMcff8yePXtaLf+tLjUVbrnFCziSk2HOHOjbNyarSkhIoLq6Ovi+oUfk3XrrrcG7Tg1JTU1tdv5ERFpDY8upgQMHctttt3HVVVfVmr9TlFPx8V6zqbQ07/9zz4Vjj43JqlROtS4FG9IhrVmzhtGjR3P99dfTt29fBg4cyPvvvw/AO++8wxlnnBGct6ioiJdeeonf/OY3pKWlcdpppzORu68AACAASURBVHHhhRfy1FNPAd6dgxNOOIF33323Tbal1cydCwkJEBfXIrUadRk+fDirVq2iurqa7du3k5GRUe/8d999d/CuE8BJJ53ESy+9BMBzzz0X1TpPP/10nn/+eaqqqti9ezcff/xxrXm6devG4MGDefXVVwEoKyujuLi4MZsmIhK1xpRTAJdccgkXXXQRvXv3rpVWpymnvvMd6NEDzFqkVqMuKqdal4IN6ZAyMzNZtmwZ559/Pnv37uWaa67hvvvuC04bN25ccN6vvvqKhIQExo4dG/xsypQpNdq/jh8/ntWrV7feBrSF1FR48EG49daY1WoAnHrqqYwYMYIJEyZw/fXXM3Xq1EYt//DDD/P73/+eyZMns3nzZtLT0xtc5uKLL2bMmDFMmDCBK6+8kpNPPjnifE899RR/+MMfmDx5MqeccsrRfZdQRNpUY8qpaHSKcio+Hh57DK69Nma1GqByqrXp0bfSIa1Zs4Zbb72Vf/3XfwVgwoQJfPrppwDk5eXRrVu34LyFhYV07969xvLp6ekcPnw4+L5bt27s3r27FXLexn70oxZNrrCwsNZnZsb8+fMjzh/6fPNIywIMGjSIL774AjPjueeeY+PGjQDMmjWLWbNmRVy/mfHoo482mN8xY8bw0UcfNTifiEhzNaacikanKafOP997tRCVU21PwYZ0OM451q5dy5NPPhn8bO3atcHHzPXs2bNGIJGWlkZBQUGNNAoKCmqc6A8fPkyPHj1inHOJxvLly5k7dy7OOXr06MHf//73ts6SiEijNLaciobKqfZD5VTjqBmVdDhZWVkAjB49OvjZypUrgx23Jk+eXOORc2PHjqWyspJNmzYFP1u9ejXHHXdc8P369euZMmVKrLMuUZg5cyarV69mzZo1LFy4sMZxFhHpCBpbTkVD5VT7oXKqcRRsSIezZs0aJk2ahJkFP1u5cmXwJHzeeefxySefBKelpqZyySWXcPvtt1NUVMTnn3/Oa6+9xve//33AewrF8uXL+frXv966GyIiIkelxpZT4I04XVpaSlVVFVVVVZSWllJZWQmonJKOTcGGdDiZmZk17u7s37+fPXv2MHHiRACuvPJK3n77bUpKSoLz/PnPf6akpIR+/fpx2WWX8Ze//CVYs/HGG28wa9YsBg4c2LobIiIiR6WmlFN33XUXXbt25d577+Xpp5+ma9eu3HXXXYDKKenYzDlX3/R6J4q0V7fccgv9+vXjxhtvbHDeGTNm8MQTTwQLgY5i/fr1EQeLko6tjuNqkeYVQOWUdFAqp6Sjamw5pWBDpIPSSfzopGCj0VROibRTKqeOTo0tp9SMSkSa5dVXX8XM2LBhQ1tnRUREpBaVU21LwYZIJzF/Pgwf7g0gPny4974lPPvss5x22mk8++yzLZMgBDtFiohI56Fy6uikYEOkE5g/H66+GnJywDnv79VXN/9EXlhYyGeffcYTTzzBc889F/x8wYIFnH766Zx//vmMGzeOOXPmUF1dDXjjnvzkJz/huOOO4+yzzyY3NxfwBkO68cYbmTZtGo888gjZ2dmcddZZTJ48mbPPPptt27YB8I1vfCP47PrHH3+cyy+/vFa+9u7dy8UXX8yUKVOYMmUKixYtat6GiohITKmcOorLKedcfS8Raae+/PLLqOcdNsw57/Rd8zVsWPPy8PTTT7urrrrKOefcySef7JYtW+acc+7jjz92ycnJbsuWLa6ystKdc8457oUXXnDOOQe4p59+2jnn3J133umuu+4655xzZ5xxhrv22muDaV9wwQVu3rx5zjnnnnjiCfeNb3zDOefcnj173KhRo9zChQvdmDFj3IEDB2rl6zvf+Y576KGHnHPOVVZWury8vOZtaCuq47g2dK7uzC8RaadUTqmcct5h1ElcpCNqzEncLPJJ3Kx5eTj//PPde++955xz7pFHHnH//d//7ZzzTuIzZ84MzvfEE0+4G264wTnnXFxcnKuoqHDOObdlyxY3ZcoU55x3El+wYEFwmd69e7vy8nLnnHPl5eWud+/ewWnz58938fHx7vXXX4+Yrz59+rjS0tLmbVwbUbChYEPkaKFySuWUc46Etq5ZEZHYGzrUq5KO9HlTHTx4kI8++ojMzEzMjKqqKsyMBx54AKDGYFaR3kf6PDU1Nap1Z2Zm0rt3b3bt2tXE3IuISHuicuropT4bIp3A3XdDSkrNz1JSvM+b6sUXX+T73/8+OTk5ZGdns337dkaMGMGnn34KQEZGBllZWVRXV/P8889z2mmnAVBdXc2LL74IwDPPPBP8PNwpp5wSbF87f/58Zs6cGUz3nXfeYeXKlTz44INkZWXVWvbss8/mL3/5CwBVVVXk5+c3fUNFRCTmVE4dveWUgg2RTuDyy+Gvf4Vhw8DM+/vXv3qfN9Wzzz7LxRdfXOOzb37zm8GnfZx44onMnTuX8ePHM2LEiOC8qampZGRkMHHiRD766CNuv/32iOn/8Y9/5B//+AeTJ0/mqaee4pFHHqGsrIwf/ehH/P3vf2fgwIH87ne/46qrrvLahIZ45JFH+Pjjj5k0aRInnHACX375ZdM3VEREYk7l1NFbTmlQP5EOqj0PlrRgwQIefPBB3nzzzVrT0tLSKCwsbINcdQwa1K/RVE6JtFMqp45OGtRPRERERETaBXUQF5EWN2vWLGbNmhVxmu4WiYhIW1M51XpUsyEiIiIiIjGhYENERERERGJCwYaIiIiIiMSEgg0REREREYkJBRsiHYxzrsaruro6+KqqqqKqqorKykqqq6trPdc7FtLS0uqctmvXLr71rW81mMY999wT1brmzZvH3LlzAXjsscd48skn65x3wYIFLFq0qM7pr7/+Ovfeey8As2fPDg7gFK3wPJ9yyimNWl5E5GilckrlVCgFGyLtROCEHDgJV1RUUFZWRklJCUVFRRw+fJiCggLy8/PJz8/HOUdVVVWNk3jgxB5IL/Q9WfPh1eHwTJz3N2t+zLdp4MCBUZ0coz2Jh5ozZw5XXnllndPrO4lXVlZy4YUXcvPNNzd6vQHhea6vwBARORoEypTQcqq8vJzS0lKVUxGonPIo2BCJscYEEQUFBRw+fJjCwkIWL15MUVERZWVlwTtAAHFxccTHxwNgZhFf4et3WfNxGVdDcQ7gvL8ZV7fYidw5x89//nMmTpzIpEmTeP755wHIzs5m4sSJgHe355JLLuHcc89lzJgx3HTTTQDcfPPNlJSUcPzxx3N5hKFi//GPfzB27FimT5/O559/Hvz8jjvu4MEHHwTgD3/4AxMmTGDy5MlceumlZGdn89hjj/HQQw9x/PHH8+mnnzJ79mzmzJnDjBkzuOmmm2rcfQL44IMPmDZtGmPHjg0O8hQ+zwUXXMCCBQsi5jlw56yufbFgwQJmzZrFt771LY499lguv/zyVrmjJyLSkGiDiJycHHbt2hUsp4qKiigqKqK0tJSKiooml1NVVVVUb31K5dRRWk5pnA2RJgqvJg6vKg7cwQmcfEOXA2qccM2MuLi4GifgyspKEhMTWyazq2/BqoprflZVDKtvhRG1T5yN9fLLL7Nq1SpWr17N/v37OfHEEzn99NNrzbdq1SpWrlxJcnIy48aN48c//jH33nsvjz76KKtWrao1/+7du/n1r3/N8uXLSU9P58wzz+RrX/tarfnuvfdesrKySE5OJi8vjx49ejBnzhzS0tL42c9+BsATTzzBjh07WLRoEfHx8cybN69GGtnZ2WRkZLBlyxbOPPNMNm/eXOf21pfn+vbFypUrWbduHQMHDuTUU0/l888/57TTTqt334qINFVTy6nAsuHlVEFBAampqXTv3r1F81lRUUGX1bepnDpKyynVbIiEiVQTUVpaWqMmIlBFHFoTUVRURHFxcZ01EYFXQkICCQkJxMfHExcXFwwywu/0tKji7XV8vq1Fkv/ss8+47LLLiI+Pp3///pxxxhksXbq01nxnn3026enpdOnShQkTJpCTk1NvukuWLGHWrFn07duXpKQkvvvd70acb/LkyVx++eU8/fTTJCTUfQ/l29/+dvBuW7jvfOc7xMXFMWbMGEaOHMmGDRvqzVtd6tsX06dPZ/DgwcTFxXH88ceTnZ3dpHWISOfW1BrzwsLCqMqpQFkVXk4BsSurSlROwdFZTqlmQzqN+u7uOOc4cOAA6enpEasMI93hiXmA0JJShkQ+YacMbdVsJCcnB/+Pj4+nsrKyRdJ96623WLhwIW+88QZ33303mZmZEedLTU2tM43wY2lmJCQk1LjjV1pa2qx8xmr7ReToEKkmoqqqqkaZFVoTsWXLFkaNGhV1jXm7pnIKODrLKdVsSIcXfocn0M60uLi43pqI4uJiSkpKKC8vp7Kykk2bNgVPztHc4ekwJ3DATb4bF59S87P4FNyUu1sk/ZkzZ/L8889TVVVFbm4uCxcuZPr06VEvn5iYSEVFRa3PZ8yYwSeffMKBAweoqKjghRdeqDVPdXU127dv58wzz+S+++4jPz+fwsJCunXrxuHDh6POwwsvvEB1dTVbtmxh69atjBs3juHDh7Nq1argOjIyMhrMc3P3hYgcfeoqp6KpMS8pKamzJiI/P79Va8wDN96as/2B/iBlZWXB7XHOUTXxf1RONaCjllOq2ZB2K7wWItKdnYYem9eYmoiOFkA0yvDv4QDW3Oo1qUoZgpt8Nwy7DFdd3eRtr6ysJDk5mYsvvpjFixczZcoUzIz777+fY445Jurq16uvvprJkyczdepU5s8/0hlwwIAB3HHHHZx88sn06NGD448/vtayVVVVXHHFFcEnn1x//fX06NGDf//3f+db3/oWr732Gn/84x8bzMPQoUOZPn06BQUFPPbYY3Tp0oVTTz2VESNGMGHCBMaPH8/UqVMbzHNd+6Kp1d0i0n41VGNeXznV4WvM8S6iA4FD4G/o/2lpaRQVFQHUCIACwVJJSQlJSUleWkMvIy4uXuVUPTpqOWUN9DLXo1KkxdUXRER6NF4k4W1HW+LkvHTpUk488cRmp9NSGsrPtm3bOPbYYyNOCy/EotGUE/nq1av50Y9+VONOijTP+vXrGT9+fPjHHefqo/WpnJIW15QgYuPGjYwaNYr4+PhaQUTo35YUq3IrEESEBxC5ubnExXmNYpxzxMXFkZSURHJycq2/ycnJ7Nmzp8FyqqyszOsg3qVLgw9FUTnVPjS2nFLNhrSYxgQRBw4cID4+nh49etRIoyPf4enoQqvno9nvjz32GH/4wx94+OGHY501EZEWEcsa89LSUuLi4urt/NvWqqurg53JI9VGlJeXB4OA8ACie/fulJaW0qtXL/r3799i5XOXLl2orq4O9jWoL+AIHLtorw9UTrUP7fcXIe1G+Im5uTURZkZpaSlJSUl1PnFBmq+xBUGgLXGXLl2iWn7OnDnMmTOnyfkTEWkpLV1j3pSbXW15c8w5V29zptAgIrwGonv37jU+q2878vPzSUxMbPFtDdSQVFRUBMuhutYROJ7R5EPlVPugYKMTi0UQEe0JyMw0oFk7FnpsVLskIm2lqUHEqlWrgm3nO3KNeSCIiBRAlJWVUVRUREZGRjCICG3C1K1bt+D/iYmJweZPzc1PrPafmdG1a1cqKiooKiqiS5cuEWuJqqurqaysJCEhoUlNhqX1Kdg4CrVUEFHfaJ/SPrT0iT80LQUcrU8BuHQWsb7ZVV1d3a5rzp1z9TZnKisrq1ETEVrzkJaWRu/evUlKSmLNmjXt/ml30ZRTocc5MTEx2Hk8tLa9oWVVTrWOppRTCjY6iMDBbejkHGkU0G3btpGSkkKfPn3aVRChC6vmSUpK4uDBg/Tq1avu45ifD598AqGPvktLg3POgSgLYgUdrSPQl6mhglWkPYu2P0QsmzO1pYaezlReXk61/2SlxMTEGs2Z0tLS6NWrV/CzaGoiWnvfNPYGV1TlVARxcXGkpqZSXl4erOWoL3hUOdU6mlpOKdhoY4EfSOgJONogIlTo4+QC78Ontae7PDoZNF+fPn3Yv38/ubm5taYFnhJCRQXp69bh4uJwSUnE5+dTMWgQRZs21VomMHBUfZ0bddxiq0uXLgwePLitsyFSQ3Nudh0+fJjdu3czduzYVr/Z1ZI1v4GaiLqaMwUuiletWlXriUypqan06tUr+FlLNGfqKBoqpwLfg4qKiuAYIeECndoD44lUVVUF+2xEonIqtppSTinYiJH6gojADyX0xF3XjyM0iGjqD0j9I45OCQkJHHPMMRGnlZeXk5KS4n1vnMMWLoRBg2DHDqovuAD69Km1zJ49eyguLmbkyJG1poU+ASRQxS0iHVtr3OwKnCta+5wRKPeiab5TWVkZsQYi8FmgJiIhISEYQCQlJZGSkkLPnj2DzZwyMzOZMGFCcNyIo1FjA7iGyqmuXbsSHx9PZmYmAwcOpHv37hHnra6uZtOmTRw8eJABAwZQWlrKqFGjauUtkL/AAIcKPNoHBRuNVN/JubKykvz8fLp16xbx5Bz6I22JICJa7THYaI95Olq50aMpXrSE/V/lMHTimIiBBhAsUCMJfF8DnRUDo9XqRC7S/tRVTpWXlxMXFxesxWxOEBGttjrXB2oi6quNCGx/eHOmlJQUevToEfxMN1dqisV5v7Ssgi/W5JCWlsopx9e+4RUXF8e4ceM4ePAgmZmZdO/evVbgE1pOVVRURP3EKok9BRu+0BNyeJvT8JqISMsGvuAbNmzgxBNPbJUgIlq6sO98vLt1VXyybCMvvLeStxdsIKG6infOOItaw/CEaOg7G5heWVkZPJF3piYBIm2puTXmy5cv54QTTujQN7sCNRF19YcoKyujqqqK4uJi1q1bF6yJSE5OpmvXrvTo0SMYVLR0ENEZytqW2r59Bw+zaOUWVm7YRUZmDqs37qCyqpqkxHieuf8qzjk58kCAvXr1YvTo0eTk5LB69eqINUmB73RocysFjG2r0wYbb7/9NkOGDGH48OENBhGhtRENnZzbY2e2znAC7KwiPVt96dptvPP5Rhau2sGBvKIa83/zjhf451/nMnRAr1pp1VezESr07lFZWZlqOURiZNGiRQBMmjSpwXIKGq4xb4u+e9GWP4Ebe/U1Z6qqqgKo1ZypS5cudO/ePfhZfHw8y5cvZ8qUKbrIbAecc3yVs4+MzBwyMnNYkplN9s6DEectr6ji+zfP44WHfsRpU0dFnMfMGDBgAF27dmXp0qWMGzeOPmE19qHlVFZWFgMHDqRr164qp9pIpw02Fi5cyIwZMxg6dGir3eFpK+0x2DCzBqvwO7PAHcvCwsI6OyMGgoOkpCR27i/m4+Xb+CBjKzv3FdSZ7q7cfC654a/88/G59OmZVmt6Y34DobUcVVVVUT89RUSis3LlSpxzHHfccS1STrVFOWBmVFVVUVRUFDGAiBREhHauDgQRSUlJjRqZuz2We0eDaPpslJZVsGrDDi+4WJvD0rU5HCooiXodJWUVXPqzJ3j1D9cwbeKwiPOYGccccww9evRg3bp15ObmMnbs2FrBpZmxb98+evfuHRw4UOVU6+u0wUZycjIVFRWd4kunC/v2JfTuXX0Fb0lJCdu2bavRjjjQGTE5OZlduQW8/MFqXv5gEV9u2RP1+jdvy+XbP/0bb/zpWtJSkoOfN+XJLYH5169fT//+/enXr5865Ym0kOTkZAoLC9ttOdVQc6bKyspgDWhWVlaN2ohu3boFz2WNCSIaQ8FG69ifVxistcjIzGHNVzspr6iKevku1ZUcU1GzFr6iPJ5v/fR/eeNP/8WkMQNrTAuthe/SpQtTp05l+/btLFmyhOOOO4709PQa8zvngkGIauPbRqcNNpKSkqgIHXtAWt3RVhBUV1dHDB4C7wPft/j4+BqFbuhIr6F375YuXcqECRNqrONgfhHPv7eGl99fxZLMnCbvw5Xrt3PFL/7B//v9f5KU6K0v+LjcRgqcsOPi4qioqAjWcuhELtI8SUlJlJeXt1h60T6hqaHmTJWVlUDkc1laWlqN5kzl5eVs2LCBiRMntth2REPnn9hwzpG18yBrF24kY60XXGzZvr9ZaXarKueC/CxK4uJxGCnVleQkdeeNgjQuuf5x3n7sOsYM61djmfCO4UOHDqVXr16sXbuWvn37MnLkyOA84U3i1eew9XXqYKMlT+LtWXusTu5IBUGgX0R4EBEogMvLy4MX6klJSXTftYuU4mJSExLomZTkdU4bM4b4IUOatN3FpeX887MvefmD1XycsYmKyujvGNVnwdJNXH3HM/z9N1cQFxfXrGfSV1dXB5t4VFdXU1paGuyU15GOtUh70tI3xZxzFBcXB0euDq+VCL8hEj5qdegNkWh/121V/rTFettjWdtc5RWVrNqwkyWZ2SzNzGHx6q3kF5a16DpyE1PYkpxOz8oy8hKSGVxeyPIUL7jIPVTIN+Y+xtuPX8fwgb2BuvsXpqWlMX36dLZs2UJGRgaTJk0iJSWlxo20wHKBm4Mqp1pHpw42yspa9gfTXh2NJ8CWEP589YqKCrKzsyP2iwg8GjHw6tatG3369AkWvqEnqriSEhKWL8f18jph26FDVIwciWvEyayysoql6/fw938+zzuffUlxSWwC41c/XE3v9FR+d9M3mxVshN450qMHpTO66qqrePPNN+nXrx9r166tNd05xw033MDbb79NSkoK8+bNY+rUqfWmGe1NsaqqqnpHrQ4EESUlJWzZsoUuXboEA4nU1NQazZla+req8qdjOZhfxNK124IduVdv3ElZeWXM17s8pT8X5W2hugr2JXRlV2JqcNqu3Hwu+vHjvP2X6xjYz2siVdf3NC4ujjFjxpCXl8eqVasYOnRoreAkvJxSbXzsddpgI9BnozNojyf7WOcp2n4RoU81cc7V6hfRlCrW6okTcUuXesFGSQmuWzfcsMid3EI558hYm8NL76/ijY/XciC/qMFlWsITLy+iT880Lj1nLHb4sDcAYGgfn+Rk3EknQT0n4kjPOwc9elA6j9mzZzN37lyuvPLKiNPfeecdNm3axKZNm1iyZAnXXnstS5YsqTfNuLg48vLy+PDDD5k4cWKtGtZAGRYXF1erOVNKSkrws0AQsWLFCiZMmBCzPhKRdKaajY7IOceSDXt5e2kOX3y+gtUbtrfJftuXmML2pDTGlubxQs8xtcqb7J0HuOjHj/H2Y9dRXV3dYHnSo0cPpk+fzoYNGygsLAyOQB4qtJZDtfGx1amDjaKi1rmYa2tH00m3urq6zgGaIvWLCH2qSUMdEg8dOkS/fv1qfd5oqalUTZtG/LJlUFFB5UUXQT1By/qte3jp/VW8+uEatu051Pz1N8F9T7yHVZdy1axjiXvzTVxSEsTHY4WFuGHDvGCjHpFqRTQQoHQmp59+OtnZ2XVOf+2117jyyisxM0466STy8vLYvXs3AwYMqDHfp59+yo033ohzjtLSUpKTk9m9ezc33XQT3bp1qzHYXGNrDdviYSFtGWy0hY5S1q7YnMsri7byxhfZ7DrgXQsdN6QfiYm7KG+FmoxIlqX0p9zia9RqhPoqZx8XXf84f/7F+fTp1fAo7QkJCUycOJEDBw6wbNkyxo4dW6uMV2186+i0wYb6bLQvgR96pD4Rgb+BC9rwO3iBwjdWzQCaonrSJOIzMnB9+0as1dixN4+X31/FSx+sZv3W6J8kFUv3z/uU/r3Tuer007GMDBg0CLKycOeeW2+tBtT/JCs9IlcEdu7cyZAhQ4LvBw8ezM6dO2sFGyeffDJLly4lLi6OTz75hJdeeom77767RfIQ6JvVmtqy/GmLbW3P1mYf4LXFWby6OIucvYdrTV+3PZ9pJ05l+eKluOrWP2a5iSl8mDi03nnWbtrFdfe+zj/+57tRp5uYmMgJJ5wQfETuuHHjat1wDK2N37dvH/379ycxMbHxGyERdepgI1bNqJrT9j0W2vpkH/54xLKyMvLy8igrK2Pfvn0R+0UEHo0YeN/hnhqRmkrVrFleUyo/3wfzi3jt40xe/mA1Gc14klSsVDvHTQ+/zYg7v8vZ1dW4gwdxAwbgRo9ucNmGnmSlgQBFohN6ERSrp1G1JjWjalubd+XzyudbeW1xFl/tzGtw/mVbD3HyqdNY/OnSVshd02Ru3su1d7/C63+6jq5dogsIkpOT+drXvsbOnTvJyMhgwoQJ9OjRo8Y8gXJq06ZN9PL7XKqcahmdOtiIRc1GtI8WPBpEejxi6PvA4xFD+0UE2hIHRosdMWLEUduWv3ryZIpKyvnn+6t46f1VfLJsc4s9SSpWKiqruPyuF3nzsklMy1xM9Y9/XG8TsIDGjD4OevSgdD6DBg1i+/btwfc7duxg0KBB9S7T0g8y6WxjLh3twUZd27dt32FeXbSVVxZlsS4n8kjdAd0rSkh0NculJZuqOfnUqSz+fEWL5bWlLV23nSt/OY/59/8w+Pj2hpgZgwcPplevXmRmZtKrVy9GjRoVsQyKi4tTbXwLUrDRwtrj3ZTG5im0X0Skx72G9osI7RMR/oz1+joh7tu3j+Li4qMm0DhQUMq23EJy9hUG/+7YX8juzZvY8NW2ts5eoxSVlPPtF9fx/EUn8rWRI4nmFNuYADtw9yjw6EHVckhncOGFF/Loo49y6aWXsmTJEtLT02s1oQrX0jXwbdWMqi10tj4buw8WeU2kFm1lxebox704f+9aBpTlU2FeWRxPNS8MOIEvsuI4cdpxLF22LlZZbpJj+veiX7+eJKWksamwmlv//jkPXHNGo9JISUnhxBNPJCsrK/iI3NTUmv1EVBvfsjptsBGrp1EFLqLa00V04AcTqV9EeGfr0H4RoYFEczolthXbtAkrKKjxmRs+HNe7d6PTKi6rZFtuIduCwcRhP7AoYltuIYUlkb9LA3v1pWePQxzKq90+tj0aObg3/fuks37Lbs5+KpNuL9/BtOOGMmPycGZMGs6044bSLbVLreWaUpsXuPDJNB5k5gAAIABJREFUy8ujqqqKfv366e6RdFiXXXYZCxYsYP/+/QwePJg777wzWMbMmTOH8847j7fffpvRo0eTkpLCP/7xjwbTTE5O7vDNqNpKWzUZa037C0p5c+UBfvPmWyzZuJembO7K9MH0yz3Mrq49SKsspcri2NWlO87B6r2VTJw4irVrt7R85qM0bEg/Bg46huqEJLbtL2b3wWL27K0C8gH437fWMHPSIC48peHmvqHi4uIYNWoUffr0YfXq1QwePJghEcbCUm18y+i0wUasajba4s5RoElSXZ2ri4qKqKio4NChQ8F+EaGjvfbu3btN+kXEej/FbdhA/OLFuHT/udwFBVRcfTVECDaqqqvZm1/OZ+v2kLPvcLB2IlBTkZtf2qQ87DpYwvhjj6NoxXLKy9vno5aP6d2NUUP7kb1rP1t3HmLrziNPxDpcVMbHGZv4OGMTAPHxcYwf2Z+TJg1n+uThzJg8nGEDejW56aCZUVRURHFxMenp6Xr0oHRYzz77bL3TzYw//elPjUqzpWs2OlszqqNRXmEZb2Xk8OrirXy2djdVzezIvSm1H6ce3ELXqnJ6VhTz6jFTcOZdB5RXVpNdnMyoEYPYkrWzJbJfL4szRg8fSN9j+lJOAln7CskpKCVna82bhv3LChhWcqR52Lw7/sa0X3+bgaee0Oh1pqenM2PGDDZu3MiKFSsijnSv2vjmU7DRwlr6bkroYE2RAolI/SKSkpLo2rVrsDaipKSE/fv3M3bs2BbLV3O1xo+0+uSTic/MxA0cCAUF7Ot5DFvLU9i2KKtWMLHzQBGVVQ7Y2uL5WL+jgOknTSNj4eIWT7up0romMXHsIIpLK1i7eTd7DmZFtVxVVTVrN+3mq+x9rN64kz8/+wnTJw3nm6cNbNaAgIHRxzXAksgRLV1OtcXNsLZyNNXiFJZU8M6yHF5blMWCNTspr2y5gLEqLp7Pe43i4j2r2d61Jzlde9Vad9f0Xgw4poTde+rv/9FYiYnxjBk5hJ59e7Evr4i9hY5NheVs2lx/R/b0ylK+t2sFeYldAehZUczjv3PcNuN4EhMa36okPj6eCRMmkJuby7Jly4LXVeECvx/VcjRepw42YtmMqiGBfhF1BRKBAiZ0sKZAIBHaLyKau8Cd5RG/4Qq79eDdXpN4PbOU7ANVfFkSBwv+2SZ5ydiazykzT2RRGz7hIy4ujsljB5GUlEDmpl18sSan0WmMG9aX3j1SWbdxO0vXZgOwasMOBrx/kJO+ezruhz9sdJrV1dXBYCPwPnD3SLUc0pkdDU+jkqYpKa/k/eXbeXVxFh+u3E5JeeweLrIptR/bu/bks16jgrUaoXLzSxnWfxjpxaXkFxQ3eT1duyYzetQg0tLTKShzbNlzmC8PVcGhxgUxm1L6sCWlNwmumqL4JMw5nizphXvqC/7nh6c2OX99+/YlPT2dhQsXkpmZyfjx4+t8RG6gnFJtfHQ6dbDRkk/5CDCz4JOY6gokAk1OwjtXp6en13jUa0t9edtjAROrPDnnWLzpIC9n7OTd1XsoLk8A0hjaqwupBwopKm2bwYoAFmcXccK041jeyh3uRg/tS//e3fkqJ5dVGxtfFd6vZxpjhvVl2+4DbMzeG3Geu/J6Mrm6O2c3IX+hTbA0wJLIEbGo2egszajaqtyLdp3FZZXsOFDMzoMl7DxYwq6Dgf+L2XWwhKSEOHK27aaojv6ALakqLp4XBkylLL7ux8jm7Ctk/LgJFK9aRUVFdOVo924pjBo5mC5paRwsrmLLngIy91XDvuYNXussjvf6HMsVu5aRVlVGRo+hHEpM4Y+vrmDm5EF8/YThTU470DKkV69eLFmyhAkTJtCzZ88a84SXU6qNb1inDjYaU7MRqV9EeDARGPK+rKyMlJSUGk9o6t27N0lJSW32CLX2Fmy0tJz9RbycsZNXlu5i58GSWtO3lSfwtQlD+GxFdM2FYsE5+PJgHGPHDOWrTbF9QlXfnmmMHd6f3fsL2Lz9AJu3H2jU8smJ8UwZN4iy8sr/z96bh7dx3ve+n5kBMNhBElzAnRQXSVy0S5YcyXbc2k6dxkm6pO5pFjeNu5zcJzk56b3NPec0T5r6dDlt6tsmvelycptuttvUzuM0dRzvjiVrFyWR4iJxXwFi39fB3D8oUqJISgAI2pLNz/PoEQEM3hkAM+87v+37o/fKDPO+0E23VxD41afP8fL9h9naXJXXvlbr0XF9g6VFg+N2El3YZJN3gs3Ixvp4twrEs1kVZzCxYEB448uMiJmrRkUwtvr9hyGTRLx63Hsayjg3ME1U3PjmcjczNBYZmA6yrXMbQxcvrdr0r6LcRmNjDRq9EVc4xbgrTM9cCihu+hXAZVMF8zozlakIb5duARbW2N988mWO/vkvU203Fzy2IAjU1tYuSeTabDba2trWXKcuX75MRUUF5eXlm1GONbhjjI0XX3yRL37xiyiKwuc+9zm+8pWvLHt9cnKSz3zmM0vKNn/0R3/Eww8/vOZ416t83FgXcaMRcWNdxGJEQq/XY7PZlh5LkkR/fz8NDQ2YzYWf6MXmvbrAhBNpXuhx8typGc6M3tpTcmEqzL17mnjz3PjGH9waxFMKfqudqoowLvf6vDs3YtRr6WqrIZ7IcGnUybHz+RtWWxsrKCsxcenKLKd6x/N6byiS4Jd++zu8+p0vYC/J/fxfS73teu9RKpXaLMrb5H3HprFROBs9T8x44wzNhpZFJC5PefDHp5gPJUkr+X/P5nScXx1+DVnJoAogDMHPVVr579I2Yhp5Az5F/gy6ktx1915OHj1DbY2d2loH6PTM+hNMe6K4p+LASodfsVEFkRcqOmiM+/FrjUvPe0MJHv+zl/jB738cUVzfOWAwGNi/fz/j4+OcPHmSrq4uLBbLsm0Waw2BzWj8TbgjjA1FUfj85z/Pyy+/TF1dHfv37+eRRx6ho6NjaZsnnniCT3ziE/zWb/0W/f39PPzww4yPjy8bJxAI8Kd/+qfMzs4yPj7O4OAge/bs4Xd+53fo6OhYZkiYTKa86iIWuR3VPm63BUZwu5HPncMQiSDOzy88abOR3b79lu/NZlXevuzluVMzvNTryjuP9cJMlF1tlZy/Ml/IoRcFdyhJa2M7och54vH1pfKJokhXWzWyTkvflTlO9U3d+k034LBbaKkvZ3TStWaaVK6Mz3j55Ff+nue/+Rs5N1q6lZLV9dKDmw2WNnk/UezzfDONan2kMlle6Zvn6ePTXJoOYRQUxuejRRs/ojUwYaqgIerBo7dSEQ/yRsrGrrvbeLv39ujXtKXaglxawf0feYDXTl5hZiIKFO87yIcRUwUjpooVzx/tneGPnznF//2f7lr3PgRBoLm5mfLycvr6+qiurqaxsXHZmnW9yMlmNH517ghj49SpU7S2trJly0Ko7NFHH+X5559fZmwIgkDoak+FYDBITU3NinFkWebQoUPU1tZiNBr5zd/8TZ5//vmiHuvtqPZxuxkbhEKYf/ADRJ0OTWkpQiBA5p574CbGxth8lGdPTvP9M7M4A4XJ0AIoWRVPSkNtuZkZT6TgcdbLsDPMnr176Hn7xKrh6FvRVFOKvcTI2EyAi5fn8n6/XqdhR3vNUpqU0xPMe4y1OH5+lC/+4ff49ld/OaftV0ujupHNBkubbLJ+bru1YAMp5tww5Y3xzPEZnjs9gy96Lf2ptEyPSZ8sai3giYp2toVmENUs+myaU+WthGaidLdU0jvy7jjJttSWUl9Txkwww1wggWsyjEEn0VBdwuTczZWj3i3+5F9Pc7i7lsNdtXm9b63rw2KxcODAAa5cucKZM2fo7u5Gr1/oOXW9yMlmNH517ghjY2Zmhvr6+qXHdXV1nDx5ctk2X/va13jwwQf55je/STQa5ZVXXlkxjsFg4MMf/jAA0Wh0w6RvbzfP0e22wKhbtpBqaUHxehdkaRWF7OHDK7YLxdL8sGeO507N0DNevAktGEuzpaoMbzBOIr1xCh+34tx4kEOHD3D8JydvvTFQYpapq7ISiKQYnwsyPpe/gdCxpQqbWU/fcP5pUvnw9Atn2Oqd5r/uqVx6Tm1vRz10aMW22Ww2r8ghbDZY2uT9QzHn7ttxfdpI1vPdZZQsr15y8y8npnn7im/VhnnTvgTdW8o5OeAsqKHeaswbShi01rLHN8bJ8ja8eisoKq6YQG2FhRn3O9Mg1mE3s7WpEm9CZcwdwzWx3DkXTynUVVUx4wquu9fHRpDNqvz6N17izSd/Ka+b/ZtF2iVJYtu2bXi9Xs6ePcuWLVuorq5e8Z7NaPxK7ghjIxeefvppHnvsMb785S9z/PhxPvWpT9HX17fmD/xeaup3K247q1oQSN57L9J3v4swP4+yaxdqeTmwEHl4a9DDc6dmeKXPRTK9MQvj6HyMAzsb+cmZ4vfVyIfjo2E6ulroX6NDq0HW0NVWQzKl0DfipG80v0JvgDKrTHtjFbPuIP0j+UdBCuX3Ts7RPnqJR0wJ8HrJPv74qsZGvg0BN6UHN9mkMG7H9WmjKNTJNuuP8y8nZnj29Azu0K3vEXqnwxzprOYnfcWbW09UtNMQ83Cq/FpX7EAsTWO5DXMkQWSDFKpsJpnu9moSqsTgXIQzU8vTo7o9YzwyeRLhuu/1kdY2vh5zbMjxrJeOy+f411/r4+EdVsSRhTU2e/gwNDWt+Z7FKMXNsNvtHDhwgIGBAebn58lkMqsWj29G469xRxgbtbW1TE1dy0Wfnp6mtnZ5aOw73/kOL7640EPh0KFDJBIJPB4PlZWVrIZGo0FRiu/Vvt2iCIvcbseUaWoiW1UF0SjZI0e44gzz7MkZnj8zy3yo+JLEq9EzEeLIrgbeOv/u5sJeCRvo7Gzh0qWFyVAQBDpbHKjZDGNzIU5fms57TL2sYWd7LfFEir7hWU5cHC/yUd8agwB/nShj1qjyqF2D+UMfWnW7XCb3G7l+Ip+ZmcFkMmG329+3E/km712KeU6/m+tTvk6FdxIlq/LGgJt/OT7DW0Mebuao12eSy262AXqmYPeWMnpGi6O6NG8o4Z+b78EvLxfamPDE2NFaS8+liaJFEww6iZ1baxC0MgNzEXpm1i7unjOVYU7HcRpKyQoitVEPL6WstNSVMjJdXMGTYlCTDNJ4dgi3z0Gbz4fg85G9++6bvifX81Sr1bJjxw7m5ua4dOkSgUAAo9G4YrvNaPwCd4SxsX//fq5cucLY2Bi1tbU888wzPPXUU8u2aWho4NVXX+Wxxx5jYGCARCJBRcXKwqFFNmrSux2NjdvymESR4f1HeHU2yT9+9zK9U8WrGciHS844nc3lXBrzvCv7B0grWVxZC7s6mzFoBUamPPSNFFaovX1LFRajjkvDc5y8+O7I/G5vqcZiMdE7NMObCZU3Z+GrmmY+9q2X+dWP7md/Z8Oy7ddzEyIIAsFgEFEUSSQSm1GOTTa5CYsFrO8Hcln3XMEE3zs5w/dOzuAM3trJJWUVfvXSj7Ako2SvNr9TBJF/7HgIp76cKqsWV6g4UYcbDY1FLk6GOLSzmaM9hUflJVFgR5sDs9XMkDNKrysF3DqK4zHYOFXRzi7PKEHZREhnpKdsCxVmA3pt6F1NS74eQRRorbPja3kQ/ZsejodlIudn2X7fHmqu1v6uRb7rUXV1NZOTk0xPTxMKhWhra1tRGL4Zjb9DjA2NRsO3vvUtHnroIRRF4bOf/SydnZ189atfZd++fTzyyCN84xvf4PHHH+fJJ59EEAS++93vvis/5O2YE1uosSG43Yj9/cueU202srt2FXQcnkiKs5Mhzk2GOD0ewBXW02wvYWDmnUvtuZG0ohLOSth14N3gRuuSKFBTZqTMoseoX9A0jyUzeMJJnP44XtVMS5WZlq5KSuacXBmeyul3qy630FRbxtScn4F3ME3qeqwmme5tjcx4wgxM+oHlXq5YJstTPzrHUz86R2eLg8ce2c8nHtyFzazPqUD8ZixK5y5KEG5KD26yyeqIorgk5f5OsrgGvdPX5GrzZzar8taQl385Mc0bA568IgSKKDFQ1she1xBzJju2ZISIzojHYIN4hmqrgVBCIZ7a2HuAU6MB7t7RwNsX84vKb2sqx15mYcKX4rI/A/6b909ajbequ9jvvkJlPMC/NR8mLWmYDaa5a287b54YyHu8YrBoXFSUWkgoMDwX4YonzRWgTG7ggG8UT1jh4R49LX/4Kv/nz3VzcOvqWS+FRNpFUWTHjh04nU5OnTpFZ2cnVqt1+THe0AgwEAi8r6Lxd4SxAfDwww+v6Jvx9a9/fenvjo4Ojh07lteYG+Htvx1zYguObESjaJ57DvXqRSP4/SiHD+dsbARiaXqmQpydXPg36VupIjXmTXDvrhpePZd/Z+ti4Y8ptDU5CA3NkhbWF94URYHqMiN2ix6TXgMIxFMKnnACpz/OlC/B1CrfwyLDzgjDAFiwtnWzrdpMNhpiqHeIcPKax8xIlq72amJZuDTsZM79zkeGRElk59Z6RI2G80OzHMtx4cuk0zz30ln+/O9f5iuPP8TuZtO6JtzrZQdhsxHgJu8til0g/m6sT+/Gfm+cU9yhJM+enuVfT0wz4y9c0fBsVTv7nINIWQVrKsYLWw7B1X3NhRR2tpRzYmDjVaMuTOemUFVdZqC6woI3KTEVyzIVi61rvx6DjdMVbWwPTHGh/FqU4OxkmN3b6ugZzD/tN18EAVpq7VTarcuNC8/KVK6Xqrr5oHuQnpJGxk0VjF+Y5dULs3xgexX/1891c2939bLtCzGKFx1ejY2N2O12ent7cTgcNDU1rRhr8fHFixc5dOjQ+ybKcccYG3cKt2XKUoHHpDY1ke3sRPB4UEtKIJNBue++NbePJDP0TIU5dzV6MeKOkcteB9xxdreU0TNS/C6juXLFn2F/hcTbnlsfsSAIVJcaKLfpMcpaBHHBoPCFk8z54sz4EszcxKDIlVBc4dToghEh12zlnrQLjZIg5nPTm5E5NfTuRDEaauzU19i5POmj50pu6V4Wo0yzw4g/EGVo5Jph+X888S/s3ebg//nKL1JSUlLQ8VzvidqUHtzkvYYkSSiKUhTD+d2KvL9bxsZ0IMmPR2Z5eyRARlF4/bxz3eOGdSbOOLbxwakehktqmbBULXu9dybK4U4HRy+tf183I62oTIcUKmxa3MHlqVt2m56tjRWEMhJjnjghP0DxfvdX6nZzurKdtHTtFlJVwZfVYTPrCUbWv/5dz42Ri8HpIMM+hWHfretEXHobz9TdRb91eZ3vsQEXH/2fLg60VfC7v7yLIx2Oq58jf2Pj+veYzWbuuusuhoeHOX36NN3d3RgMhpWf6epa9X6Jxm8aG0XmdmyatJ6JPvPAA+j+5m8glSK7cyeq45rqRCKtcGE6vJQaddkVpYCmqWRViAkSZWYtvsjGqGzkQq9g57BxhqMxGUEAR4mRcpsek35hEkikrxoU/jizgSSzgeIXslsNGqrLjFj0GlKpJJJWjzeUYNYf4ydCJWjgsFUlOp9/+Hs9GPU6urfVE0lkuDTiYtKdWxOnzuYKLAYtF4amuDi0evTl7KCTB3/j2/y3X/8Q//nRe5Gk/KJLqy0Om9KDm7xXWFROXO2GJV/ercj7O2VsZLIqF6dDHB0O8MagF2dEQZe5lh97ZIuF48N+MuL6bn3OVrWzwz3CT+p2LkU1rqd3JkJ3Yym9ExtbNB1NqVRZSrCkgwiCQHdbNUkkhuYiXHBuXF5wWGckrFtZDO0Jp9jd2czRk+tLpxJEgWaHDaMsIeiMTHgSa0YucuElR/eyx1UlBrZU2xBFkSlvjF/+s6O89vUHaa+x5iXFvsiNqVeiKNLe3o7f7+fcuXM0NTVRU1OzNO7imnVjNH7ROfZe5L35qXJkI6zI91JkA0Btbibb3Iw4NET8yL30Xk2JOjcZpH8uSqZIihjBhEJrg40zAzdXAtlIaqwSRnMFD4kCc6KeiyMe5nIoGswXgYXQdoVNj1HWkM1CJJFmzh/HF04SmrnekFh5U/+2voGu7iR9vVeKfmw3sr2lmhKbhd4RJycv5ZbqVmLR09lUwazLx6XLuXU0jycz/MMPzzIwFeAvf+djeV2ba+XYbkoPbvJeQJblohkb78U0qlA8w/HRAMdG/JwcCxBOXitSrvfN8PM9P1pSj9KJcNBYyp9seWhd+wzrTPxTx4P4ZMuqr2eyKr5klgqrjLvI6opGWaK6zES5Wcsu9zDpeAxbtYkLo17SPU6G7M1kNXJR95kPPZNhDu5s5sSF3AVKBFGgtsxIQ23lUlrUaCANKMD6+4rYDBLbGuzotBpm/XGmvDF6Rrz8j95/oyy50D9k6hf+ie0HmhB+/dcLckyttq6UlpZy1113MTAwgNvtpqOjA51Ot2Y0Pp1Oo6rqe3Kdel8bGxvBu14gns0i+G+w/m9x4aiqSiSpEExkCMYzBBMZQlf/D8YzhKo+gKrZzgv/OkUqs3EL1WQoy327anitZ3bD9nEjZr1ER50NfyzN6HwMF9cW9O1ba6m2aBmb8TE2l38kwaCTqC0zUmqW0WhEUhkFfyTFjDfGrC/OrG9ticGbkUVgVrFSWVHKvLv4nrNSm5HtrXXMeSMMTPq4sdh7dVS6WxwYtAIXhqY5di63Jox6FNqbKknIVi5P+bjyUi8f2NXMr3wodxGCWxWYb0oPbnIno9PpSKeLE/F9r6RRjXvjHBv2c2zET+9MeM2I+pytirhWT1KrI6HV4wi4mG3vQkYkmVnf9+DTW2/6eiCWob5ETzCaIpVHyF8SBRylRipK9JiNOnRaDQoQTWXxx9L4Yxn8KoQDaX7p5GuUxkMkNDrulsEVStFX2nDLfWw04yEVh92M0xtZ9XVBFGipKaW81ExKlbgyF2E6lmb6SnHWM4tBS3tdCUa9FlcgzoQ7Rs/E8sh6VhQJaQ20h2aZ19sI+MKMX57BVl6O4CpMDXI1NBoN3d3duFwuTp8+TXt7OyUlJav25YBr0fj3Ws3h+97YKLZCxrttbAgjI+j++q/JShIjWhseVYe/rIqLbds5n5pZMB6uMyqC8QzhZAblFoesl4wYpAypDRYyGfIk6G4qpXd8Y8PP22utmPUa+mfDnB1fPb1nLpBkLpAEdOzurMcmiwyOe3D6lkcbyq16HKUGzHoNAhBNZpgPJHAF4ww7wxTDM3MjvkiK7a3b8fpOoNzqx8sBURJprSvFYDJzaXQ+Z5WTcpuR9oYyhsdm6R2cyHl/LXpwZNNclO1cnM8A1+p1fv87r/HRezswG3Q5jZVL2HvRe7QoPbgZ5djkTqGYDWjv1DSqjJLl/HT4qoERYDqQW01ARtJwtGUfD/X/BLeljIyk5XVLMwfry3jz4sbWVABMBTIc6HBwtHd5fV2ZRUdVqYkSiw69TguiQCKjEohn8EbSxLMqk1EVoklg9chIRtLw45ZDfHzwdWasVWhiHk607COySnrTO4k5FeNnr5yk0aJhYtoJKiQ0Wnr2fBBrhZ14BkZcEYZ9GYZ9xUkH1uskttWVYDXJ+KNphp1heqduPfb3Gw6yzztCStRQkQjxhGY7v5PamKyXqqoqSkpK6Ovrw+lc/dx7L9ccvq+NDa1WSzqdRqfL7aYmF95tNapMYxNH63byrKUVCZVzsavh1AlgovDJNaFAS7mZ0Ewkp6LvQlFUyMo6SkxaAtHi1m+UW3S0OizMBBJcduVWc7DImGchCqHTG/nIPbXEQlGm3GFmvTE8oQSeUHEL4nJhYCbMoSMHOf7G2wWPUe8oo6GugitTXi7PRlgtbetGBAF2tTmQBJXzA1O87cmtsF+WNezq3EIokWVg3M2IyEKU/Aac3ghPPnWU3/21+3MaNx/p3MXrczPKscmdgk6nI5ksTirO7ZxGpWRV3JEUrnCK+fDC/65QCk8kxZg7uqqaYS4MOVo5PHKG2oCTl7ffQ1Ir0+eMsrPBxIXJ/NaBQginsvz8PS3Mh1OEEgreaIpYKosrBS5vGih8nbvg2MqDIycwJ2MkFRXfoXtgdPVowjtFRtRwyDWIxZ1lj82IGgozntbztB+UYG4R71uhEWFbXSllNgOheIZhZ5j+2QiQ32efMFdwxt7CAc8wYa2BF2ztZL4/yJd/qrwox3kjsiyzZ88eRkdHcblcBINBbDbbiu3ei9H497WxsegxKqax8W5M5tlslkgswStX/Lw4HMJbtmfptR3pSS5qV9eTzpcRb4K9TTbOrBEJKBbeaJqdWyt5swhyuJIo0FVvQxAF+mfCnB4rbLKrKdVTZzcw7Ixy9Iqflkojo84wmUIq4ovI8dEwe/d1cfZMX87vMcg6dmyvJ5pQ6BtxMuXNLSLhKDPTUlfK6OQ8PZfGc95fU205tbVVXBr3cHIwt/D0Xz5zjE/f00ZjW/0tt823oG+zwdImdxKLTrFi8G4JmCwKbLgTCVyhFK5wcsmYcF01LDyR1LJ6vY6pQSpD8xiA+606pr1xBiubmS6rXXM/q7EY3Tgycpq+mq1Lz3sVDfV2A1PewtJZb4ZWEtjXZkfVahjzJhgOpglHUkV3oKU0Wl5qOchj53/AD7bexwm/wL5WO2eGvUXdT64YdBJdbdVckj7EgeM/YkAx0yYlOH/oQyjBwlOCREGgvdZGZYmRaErh8myAy/MxmF+fjC8sRDcOeq7wb40HSUo6/v2sk3tb9HR0rHvoVREEgerqavx+PwMDA1RUVNDc3LxqWtV7KRq/aWwUKTy9SLHTqDKZDMlkcs1/8YzKuYCWs34tsczKk3C+op4KRcUdLs7nHHLHaCjVM7kOnfKc9jMf576d1bxxoTB519oyPfV2E6PuGH0zhaUxaUSB7gYrqYzKwFx4WbH4yHyM+4xxXgnrCxq7mAyFtNQ7yphy3jzCsG1LNaWlFvqGXTkXe0uiQGu1Ga1Gon/UhdOVW6d1rVZid9cWYmnoG51n3J+f9npCUfnq373J3//BJ2+5bSFNmG4sHo/FYpSXl9+xE/mTqRmHAAAgAElEQVQm710WC8SLwbvhDBtyx/jOIPh7xpn05x6hscZDHL58Ar+pBGZgTzrIqP3WzofVGKhuY66kiqT2WuF0PJ2lzm5iPphcd/3GInazhs6mUlxxldFQBljIOw4nFTrqrbw9WHwj4IJjKyfqdvB2/Q4APAkVi0FDOP7ONG8UBeisL8FslLnsitI3G2NYbqFb0lEWDzKrMfHvhlYONluIBCP0jd36OxAE2OKwUm03kVJURp0RRj1xRj3FNwwnzBU8ue3DnLc3Lz3X/+1nUX/8FJrrlBGzP/3TqDf0eiuUxWjFYpRjUSLXaFyZAnd9NN7n81FRUXFHRjne18aGLMtF8xgtkmsa1WJO3o3Ggzo1heLxLHV5lSQJoboayeFAlmWMRiOlpaXEshI/Hg7zyniAeHrtiTKhCjTZ9XjCSVTWfyOVVlR0OgmdJORV9FYII4EU2+qsDE7nltcpa0S6Gm3EU1mG5iI4Q4VFMRw2mYZyIyPzUc5Prr3vHlMtOy0JLsxufCj+ZkQSGSrr2zG4TxK/4TcpKzGxbUsNTl+UwSkfTOVWC1NbYaXJYePy+BxD4+6cj6W+2k5Dg4OBCS+nhtbX2Or5E6O8dmqI+w8seCNd/ihWo4xBXj5tFWJsLLKoc37lyhUsFsumRO4mtx13qrExF0ryj2ddHBtfmEOrTBq0Uop0juvGxcYu9o+cJWSwok8l6NfXkm1rhUD+a3ZWlPCZSlc8Px1IcrCjct31GzuaSigtMXDZHWfIt3B8kqJQGfYsNKAA/H64x6rnJyHdqnK5hZLSaPnHXT+79NgbTbOztWJFnUixaa40UVtuZsKXYNibBO81QzKhlfmPlrv57MUf8hf7fhFFlBiejwEiH9zfzMiEh8n55U7AKquW1jo7KgJj8xGm/Emm8jBO18PJyq3LHo+ltLhef4O6zoWmhYLLRfbnf75o+1tcs0RRpLW1lfLycs6fP099fT11dXWrSrmrqkp/fz8HDx5Ep9PdccXj72tjY6MiG4qiEIvF1oxGLKZ96HQ6dDodsiwjyzIlJSXYTpzA+NprYLMtmAbhMOnPfpZsaysAs6Ekz/Z5eGM0mLPs7Lg/SbtdYshbHO/NdDDJzgYrp8c2Np0qrajIFiNmQ4zITbw0LQ4TdovM0FyU8xOFFZxJokBzqYjeaKJ/Nowrh0hQVhBJWEqwGdMEYxunaZ4Lo64oB/bt5tTJc4iiQHd7HVqdzIUrc7zdm5v8rCQK7N1aTSKZovfyDDOzuRkLkiTS3lyJzmjl4oiLqd7idJAts+r5xj/9hL/99/MooszZ4Xn+xycP8as/s1wzfb0iD6qqLk3cm4bGJrcbd5IalaqqeMNx/vWCm1dHw1wfMHBFM7SUCgzm0DgVICYbOd2yl7uGTyNls3y/+yNktToMWuWmDrZ86XNGuWtrOSeHcovaLmKSJfa2lRNWVGaDKVzzy73uFWEPv/n636EKIioCmmyGjM3G6f2fIZ7d2AjqhekIO5vLuDBW3Ea5drNMjU1E0RgY88RxRtdeb0/UdmGPB+mpWn4j3zsTQSMbeOhgOaFAFFHSMOWN4gwkOD26scIwuXK0soOesbewJjNYtSJqSwvqoUNFG//GNaukpIQDBw4wNDRET08PnZ2dyPJK+WJRFJf157iTeN8bG/kU3i3qIK9lRKTT6SXZsmQyiSzL6PV6ZFnGarUuGRU3s0iFBx5AOH0ataYGNZ0GnY7srl2MeON8v8/DyclQQX0ofIqWSqvKfKg4N8V9c1G2OkwMOTfWq+8Kp9i/3cHr55bfwFoNGrbV2vBFU4y5Y4x7CkvrqrLJNFUYGZmPMeJPgT+/lKv5UIpdnXW8eXq0oP0XA0kSqLebyFoMfOjBu7k0MMqFkdyjEU3VJdSWm7k4OMmpi7l/jpqqEpqbahia9DMwGwOKE+LusEqYmxpIKQsqJicuzqNcPen/9c2hFcYGrE89ZNH4vxMn8E3e+xSzQHw9AiaL69pa/1KZLKd9Wt72aEgoC9fS3Rd/Qsf4tXoyQYDWPUf4obU9p31ebOziruHTeKx2xssbUMMpOmqtnBsvTqHxIjORNA0VRibdt64BaK4y0VRtY8yfYMi39u/itFUybm+gKjRPwFSCIzDPa7W72dtewdENSKe6kYgiYJIloslVVDjyQK8V6W4sQxUEBp1RhgMqucz1CY3Ms9uWi3xIokBHnRWdVuKyK8q2Ciuvns3NGfZOkpY0fLf5CM2XX6Oz3kb2C1+4ZQuBfFgtGq/RaOjs7MTtdnPmzBna2tqorKxc9p7FdepOXKvuGGPjxRdf5Itf/CKKovC5z32Or3zlK8te/9KXvsTrr78OQCwWY35+nkDg5hPS9R6jxSKctf4pysIFq9Vql4wGWZaxWCyUl5cjyzI6nY5QKITT6WTr1q032/WaqJWVKPv3I124AOk0Zx/6RZ496uLi3Ppu6tOqQGOpHncoVRQ1KRWIpLOYZYnIOiezWzEwH+Oebgdv9TnZXmvFKGvonwlztsAFRxQFuuosqED/TJj5ddazXJwOc3hnHUcvFMejvxaiIFBbbqKyxIBB1pBSVHyRFDO+OFPBNFPBhXPZXr6Fu1sFzp/tIxZffTHU6zTsbKsiEolzaXiW8dyUbhElkV0djcSSCpdngsz2rr+IH8Ck17GzzUFwdBpbUyNzI9OMsTJ/9UT/LFPzYeorV2+mVQjrScPaZJONppgR+NXSqG7lRFvctyRJy9Y+vV6PzWZDp9NxYibBUz1u3DcUQDvLHNzVfxxXmQOASr8LZ5kDkygSTd06OhGTjbzecQ9eSxnq1Wu03xllZ72VCzlIm+ZKPJ2lrtSIy59YtX5DIwrsbbUj6TWMeBL051KYLAi82nGEz/3kn4nKKTKSxJnmXWTccerKDEwX2GcpV+bDKfa0V/JWAelUggCd9TasJv1CHcY67z8ay41UlxmY8CWQL55nu3uMDhZUpbpSKY6L5ZyqyM0A3UhMeg0tDhOyVmS+7jDjk8dwBbRsad/JWtVChRjvN1tzKioqsNlsXLp0ifn5ebZt24ZGo7nj16k7wthQFIXPf/7zvPzyy9TV1bF//34eeeQROq6TC3jyySeX/v7mN79JT0/PqmO99tprvP3220xPT3P06FFOnDhBV1cXX/jCF5alNMmyjNlsXvo71xbyS5N5IgGZDLF0FlcsQyYLGVmPklXJZFUyqkomy7XHyrXnlfYj4IJzBge98zZykSPNhYlAsqhqUt5omu0OEz0Fpi7lgk4SaC43YjVoePiuBk4OuvEXGE2ptMo0VRoZd8fonS5u74vxYIb6SgtT8+sfVxDAUWqg1ChRarOQUVX80TTT3hizoTSzoZunVHgjKbwRKNvSwb7Zfs4G4kTVhUmqVY1Q2dpA37SPk+dHcj4mR7mNli21jMwGOTdSvND8ltoyDLJIRpRBb2LSUE54xAerGBqwkP787WeP8sRvPFS0iXdxEr8TvUWbvPdZTxrVohMtlUqRSCSIx+NEIhH6+vpu6kS7PhKv1WrXvDYuzEb47puzjK4hSzte04KnpBxdJoUunWK0poUhQwXbyw2cyVEZ8GLTykjmdDBJpVVXtEg9rF6/UWHV0dVchjOaYTySgUh+RdeT9jrGyhvonhng+7sfJi4bQFFpLN94YwNgYMLH/9f7D+C5FkmJaWX+YPcvMWuyr9i+scJEfYWZKX+CEW8KvIV/vwvZB1ZCSeVqytXC2rgzFuAX+l7Cf7UpYk02yljtBwvez3rQSAJb60qwW/WEEgojrghXPNc+81/ueISkpGPufx3lO91pDnqHl71f7e4me//9ea8dt0r91el07Nq1i5mZGU6dOkVHRwd6vX7T2NhoTp06RWtrK1u2LBTrPProozz//PPLjI3refrpp/m93/u9VV+TJInt27fzwAMPEIvF+OQnP8mhIubiLebE6v74jxHcbrJamW/t/yR+2ZzfQI0HaJWSUOT7+KlwiiqrDleRJukBV4yd9RYuTBXv5r2+VKbSKpPIZJn0JxjzJ5aaWNsqLOzZqmfOHaF/8tZGkyBAV70VQViQvnWPFjf8vkg0qdBaYWXOE8m5lgagutRAVZkRk16HokIglmbGG2M+qjAfzYK78Bt7XzTNhZIWunQuSjIhJufdDGT1DA/m1qFdEAV2bm9E0uk5f8WJs684UQyNRmTPtjriqSw6WSYQjTHqjKLm6D17qcfJI6dO0dXVhdmc53W1Cvn06dhkk5txqwj85OQkn/nMZwgEAiiKwh/90R/x8C0UbtYqEL+VUuHiDc31RoRWq0Wj0dDc3JyXEw1AeustxDNnAAjE05ydivBSWRujrbvXfE9WlDjWdYSffft5AE523g3AkCdOd52lYKdPNJWl2a7HG06t2T28EPqcUQ5uKycSz1BeauSKJ06/ex3Ki1ejG6WxAGeady09PeyJc6CllFMjG1ujkJR0TJXV0uD3MytbsabjJCQdTsO1Yvkyk45t9SUEEwqj7hjz63Aeaq6mSWm1EkOuKBdmVva9ONa4m5+79ApJSUdWEMikJdwfuB+m3pn+IM1VFuoqTKQUGJmPMOpNMOpd/Te+WLFQK0s8zd987zStg89R0ehYSGXy+VC+8IWCagZziVIIgkBdXR1lZWX09vZisVjuaIfYHWFszMzMUF9/LYhVV1fHyZMnV912YmKCsbEx7r9/9YZg995779LfTz31VNGVORZzYpUDB9D86Efoaxx8eu4sf950763ffAMjikxLucRIEeXekhmVxlID80VKpwKYDqaoMGlwRwuT2isxaGiwG5BEgdlQElc0g2uNsdKKyqA7Dkgc3FGNLpvl1JCbxA0Fg+UWHVsqTUx44/QVOYqxFsO+FB/Uhng5uTLFp8Kmp8ZuwmTQklUhEE0x408wH1OYj61/khVQqbUbqbDq0UkCiZTCnD+OK5DglK4KdFXsdFgojwTxhG5+U19eZmFbaz2jzhDnx4q3GDrsFlrqK5jzxdAajEyHwszO5J+7PDwXRkxquXjxIvX19eu+hu/08PQmtwe5ROCfeOIJPvGJT/Bbv/Vb9Pf38/DDDzM+Pr5irEAgwPDwMDMzM5w5c4aTJ0/y4x//mMcff3wpyqHRaJYZEotKhYspvaud04qi4HQ6MZlMy54PJxUuzkVIZFQSmSyJTJZkOks8kyV59XHpUJSDr53FW1qBoqhonE6iH1zb0FhkvKYFv6WMoLkEV1n10vPehEKJUUMgVti6MeZNsLuxOJF6Afj03Em2JHykR1R84YXGe7H2g0yUFya3u8hkeT3/7/2/SkJnWPa8K5bBatAQ2mCJ2mfqD/InzkFmMwLWVJy/7/optLKG7sZSBFFkcC5KzzqdhdenSQ25b36/ktLoeK7zp/lkz7+DIPC9rgdJWG1IYgRlA7QLSo0SNaU6bCU2pn0JnOEkzmj+58ybdbu4NPgKlX6F1vpS9JJE9uMfL2j9yOc9RqORAwcOMDg4iN/vJxKJYLevjErd7twRxkY+PPPMM/zCL/xCTrJgxZQUXGQxjUo5fBjNyy9DKsWBufPs3n43PXFtXmOpCwOiFQXShVSFr8FEIMneZhtniqQmFU0ptJQb8ERDOcnr6jQCjWUykpomJcjMBJIMFtCcZyqwUI/QVGWkwT3FUEKLLRVDQqVXchTcwG89nC9t5D7fOOlYgkxpGX6bHWcohS+RxbeKl6cQLAYNdXYjVr2WrKoSiKSY8kaZnF/4txYX9A4qKhvpUrz09V5e/qIg0FJvp7S8gvNXnBwtUhQDAXa21aDRaokmFTKSjtlwkFHv+qQmX/jeMX73zz/P4OAg8Xh8Xc05N42NTYpBLhF4QRAIhRY8x8FgkJqamlXHevPNN/nhD39IbW0tOp2O6upqPvrRj9LV1bWuxl4rmvqFQgipFGZV5e0z04wHkkRMVrLiyvVTNDVQZS5HTCqYtAKecgdj1S233GdWlPjRoY+Q0iy/PsNJhZZKE+fXYSz0u2K0VZq4cpN5by0WvPAWyiwy7ngGYSKFo/cMAUsZdbJEctbFa9sPF3xs13OjoQEQjGfobizh2GB+Klj5Mm1z8GZJK/fM9uKqqEJ/5ANY3Qkuza2vIZ7NoGVrrYVgQmHcey1NKhcWoxuimuXNLfuJB5LsbS3l1OX1O7eMeg3b60owGXW4QkkmPTFGg0CwsPOstlRPrd1IWlH5t/kP8dkzz3I5GKT2v/5nrFYraipV9DSqG1mMcsRiMfr7+7n77rs3pW83gtraWqamrikWTE9PU1u7ehfRZ555hr/8y7/MadxiqnwsslSzYbGQeeABNP/2b6h1dfzKkS30vzZNMpOf0eCJZeiuNXOuiGlKAFPBFNU2eVmjuvUw4omzt97CmTVCoTemRk0saaWvb/8WWaRREpEiMmUVdkJOP6NZA0UL2+SIUSvQaswQCia5XLuNLt84PaIN3zpC8CIqdeUmyi0yOo1IPJlh9mq0IhgpzEh2h5L4RAuHP3g3x944TqnNxPa2eiY9UUZcQfDklmJ1KyxGHd1ttTj9MWSTiVBCYXAuCHPrM7jqSdIspfmeupVH5yJ0dnbi8Xg4ffo027ZtK8jjk28H8k02WY1cIvBf+9rXePDBB/nmN79JNBrllVdeWXWsj370o3z0ox8F4K/+6q9IpVLs3bt33cd443mu+/a3ESYmQKvlSymFcxMBfvBTjzJR17bivVlR4sTuD/KhN58FBOY/9itLRdu3wlNSuerzI94EuxutBdf9ZVWIK2rOqksGrUhnvRWTQctcJI0vncV3tZfD6a672T14iqjBjCYSwNm5h9nS6luMuD76nVG21ZgZnC1eCpFeK+KwyZSadRh0EiowZn2Yw8+NcOIDH+bsdOE1oBpRoLFUpKTEtmaaVC6kNDr+edeH0Slp4tqFxriKL8jPREcJRq+tbR7ZwmXbzTvGa6SF7uLlNgPh5ELdRb8zSqG1rkatSFOFHovJwIwvzmwgwdzVtPOB6h38gvwi+kyKL1wS+LOnn8OqF7BPTiLOz6NWV6Pu2XPLfRQaDZFlmc7OzjvO0IA7xNjYv38/V65cYWxsjNraWp555hmeeuqpFdsthplyrcEopn75Itd7jpTDh9G88QaZj3yEMouej3fYeeZi/l6McX+SOpvMdJEMA4CkotJg0+MKJQuS0l2NIV+SBrPEZETJKzUqHwSgvlRPmUlLJKkw4U8wkNFBaQMATbYIE34VpQgNDHOhucJIRYmBQWeMoVQWDAuFzRftW7DJErtKtLx1YfqWjaxsRi21ZUYseomsCu5AjBlfnHFXhHFX8RYim0GkySigVQQePryXl88Pc+xScQwMgPbGSkptJiZdASLJFIGUwOjl3GV412KXSUWXznDeWMucIEI4zWN/+iqvfLoVnSiye+9eent7cbvdtLe35zWRb0Y2NnmnePrpp3nsscf48pe/zPHjx/nUpz5FX1/fTc8/WZaJRDYmnz1z771o/+mfUOvqMEajOLIy047mNbefqG8jYC1DFUSOWuppNmgZW6M4PFemQ2mqLLqcehuthieaZrvDTM/E6p7rEuNCobJWJzEdSuFMZCGxci0NWkrp2XaAXYOnkLIKl+5+EDFC0dbH1VABSadBIwp51frpNALVNj0lJg3ZdAKL1Uo8ncUfS+OLpvEms3iT1/8upXgO/QrD1iYOOixMOMPM+XP/3RbTpMa9CaZjaaaLkPp7omHXssdtU0P8zrlnmE0tXAuWdJwXa3bzBztWNtNz2DS01pWTVmF0PsqYL8nYTWSIb4YoQGuVGbtVJhDLMOwKM+hKACu/n5Sk5a92PEJJMoJ+aoLkl/8Qau3UZ1JImQzZj38cJQdjo5A6wesbAd6J3BHGhkaj4Vvf+hYPPfQQiqLw2c9+ls7OTr761a+yb98+HnnkEWAhqvHoo4/m7KXcqKZ+SznkFgupL30JtaICgJ9uLeH4ZJiJQH4XhaIuSLKJRTQMACaDSfZnPJyUyvN+r1YUsBkkzLIGoyyhlUQkUUASBOqyWc5OhApKjVoNk06kyW5EEgVmgklmQilm1ihwH7dWcW+dxGu96+tefTN0GpEdjSXE0ioj7hiza9wIRJIKvS6Fzm21aDMpTg/NI4lQZzdSYdGjkQSiiQxz/jjzwQT+cHGjbLV2I7XlZox6LYlMlll/HHcoyeUkkARB1dEopLhCful9NyJrNWxttJPKSmh1WhRJy3wM5iLrU1EzyBr2dDTiimXpm4/CDVlSw74U//O//wOf+OQe9EeOsG/fPiYmJjh58iTd3d05F49vqlFtUgxyicB/5zvf4cUXXwTg0KFDJBIJPB7PMj39G9kIp9gi2T17UH/wA4hGETweGh77JHUh05prVFaUeO3uj6AKAllBRJQEdJJAah1V2olMloYSPe5IqrD1TVUZmQ6w36HnwtX6vPISPXWVJrKCwHwCpqMZyMHZdbrrbvb3HWOwuZOLhgr2V+o4uYFF3MZkDO2VST5Wrufc1bTfsGzCbSpDIwlU22TKzDqMsgZBhERGJRBL442m8aWy+FIpQIQcjNHLjoWUt6H5GHqdliMdFo4PetY0cmwGLdvqLATi+adJFcKxxt3MnP8PVIMGT0JFUrN8r2nBcVxu1dNaY0PSiEz5Ftaxs2sYl7lQbtZiN4DeYGDSl2TYHWM4hx4rAG/XLqijyZkUn+t7gVBcoqqsBGMmSfbTn85pjEKi6Xe6U+yOMDYAHn744RWqHV//+teXPf7a176W15gbVbNxfU6s6nAs/S0KAo/tqeT3X5/Ke1J1RtLsrLHQM1PcC37MVkVtMMiMZEZQVSx6DVbDggEha0Q0ooggLBg8qUyWWDpLOKkQTSmEMxDOrD6Jt1Wb0QrQPxspKKOpvkTGbtYRTS1EL/IxXMYiCtU2DXPB4hbeOWwyzQ4LI+44vbO530h7oyk6qi38zEETb52bZMwZYcxZPE+lJAo0VpipLDWg02mIJNLM+BP44hl8NzlfVEGgvMzOlfnCUhjqHSXUO8oYnvEh6mSSSYHBqfXXAdVW2mhudNA/E+Hk+Mrj12QV9mU8hEUdf1dzEI/LxN+ycO01NTVRVlbGxYsXqauro76+/paT+p0+iW9ye5BLBL6hoYFXX32Vxx57jIGBARKJBBVXnVFrsRHr1BI6HZlHHkH3v/832aoq2LeXz0UUvvbyxJoqT67KuqW/3dEM3TVmzq4zzXfyqiz76QLqCPdN9/GpU99H1kpIAmSULP1VW3j6w5/Le6ygpZQXjnyMaUcTAN6kgkWWCG9QL6nu6X4eO/oMmIz8siSiTcQZaO7mb+9/DE8kRSCtEvBf9Q4VkUQmS58rxm/6z9I0MYjvalquKAiMHriHofa9DLminJ9+ZxSiABJaPd/repBf7/shUlphsqWL6g/sQwonmfDEuDBduEqWrBHZWmvFqJOYCyaY8sZZUB7O//M5bHqaqixkEfi+9xE+ffx7RDMx7D/706j19bCYmi+KoF3dkZfNZtGu8dpa3Onr1B1jbGwEG5VGdTN1nMZSPT/dUsJLw/kXL0+FU1SadcwXmK+/Gs1RD7JZS0twlrOWepIIuOMKxNc3uS4WbzdXmbBoRXpnwjc1sIxakSa7Aa0kMhtKMhtOMxsu7LfJZGFLUyWui7PrjgQtSOfa0Go1DMxFODeZ+6JaZdVRW6LnsjPC2atFkF3bahgaceINFbZ4yBqRZoeFMqseSRIJxjNMeWPMRTPMFeB5umBtYLvZw8BobgXboiiwe2stGVVYyK3V6ohlNZwvQq+VnVtr0RlNXJgI4hpe6U0sNWnpqjYxMuzkjOmax/h4r5cXHv9dPvylX0bt6MBqtXLXXXcxNDRET08PXV1dNy0e35S+3aQY5BKB/8Y3vsHjjz/Ok08+iSAIfPe7372lMbyRkQ1YiG5k6+rI/MzPgFZLU6mWD28r4wcDucluD/sSbLHr15QPzZVhX5LGMj0Tq6RlWfUSVVaZEpMWg06DKAqksiqRdJZYyQ6yPf+B01KC2axHnJikv2VnwcdxYduBpb8jKYVdzTbeGixeb6Hriew9gGbwJdzoSGt0NKtunq0/UHBKWb74oikemeglWWYnk84iB328FD5CXxFrSHJFFMDzwQfIjr1OpRTlvzXex1CBaoiCAM2VJiqtenzhOBO+JH0FGis6jcjWGhtWkw53JMWUL7FUqzLUcICfP/tDjCE/2ZdeQfvGGwtvymahtJT0Cy+ALK8Ys5A1505fp97XxoYsy4TDxY0UrNah9UY+3mnn7GwEb56Sf2lFpd62fmNDALZWGklnVabkqwWNFgfbzBoUJcuAszjpTwBzoRRzLKT0lBs09M2Gl+oXyvULjfZi6SyzYeWWknn5MBlIct/Oal47n3/3VFhoSNRZX8JcKMXlPIu8t1aZ0EgLfT2cN6QjDLvj1Fl0WA06xlw3P/eMskRLtZUSs0wWAV80xbQ3zkQgxUSgeIuRUFoB3NzYKC8xsbWpitG5IKpGB4hM+MOM+9anKmWQNbTUlZLUmLk0HwVWGuEtDgtVpQYuTAQ4PhEB7UJ61NaYC4deoLekkdfn9DxsuSY5LEkSHR0dzM/Pc/r0abZu3Up5+erpgtlsNq9+A5tssha3isB3dHRw7NixvMbcCCGTG3ZA6r/8F7Bal576WKedszORNdNVr0dVAWH96VSZrIq9zEBNmQGdRgRBIKGoBJMK8XSWFDCfVCF5g+FltHBq9z3cdfZ1IooNU2kJl1p3rbqPQhjxJ2kuNzBWJAl6rSTQVWshkV1IZX6286f4yMl/J6HVM2itQT64F4c/gbOINZo6SaDKJlNq1CLrRLLqQprvRfk+/P2vEc1qKJPSDJQ2cMGxtWj7zYW6Mj11ZXqmAgkGoyr/2PUhOmaHcNx3gLkBJ7Ho8vU3I0gL1sQN2M06mqsW1oZxT4xxT5zxAn8zh02mudpKWoFRd4xhbwJWMaZTGh1/v+sRdjiHeCg1TbM+C2YzeDxk7757VS+orKQAACAASURBVEMDNtOo3ndseM3GGsgakV/ZVcFfvJ3/jfBUMMWOGjMXC/A8iAJsqzQRz2SZDK783HNXu6PuarASiacZLsLNv0YAsw7kdIys28tuJY2QSjOiL8efNuH3bpzHbjKcplHOMJHM/TRvcZgotxoYmIvmpQAmawQ6ayy4wykGb6G4NCcYsFo07LLInB9eEAywW2QaKs1YjDrSWRWnP44rlGLYkwDP+jyGt2I8qNJdoqN3FQOmq9WBQa9nzBkErQ5BNnBudP1evspSA+0t9QzMRRn0pblROUQUBHZvKSOjwqWpIBPe+NXnYV9zKerEBIO1rSyWtg+W1CL94R+CzYbyxBNwVa2jsrISm822VDy+devWFRP2phrVJrczGx3ZAKCkZNlDrSTyuf0Ovv7aJLm0sfHE1pdOZVQz3O3Q4UvEqNBq6J+LkdTpyWhzk7M+3/0BDvT8hJKAh5Mf+SSKZn11aNeTVaG8dP3GRqlRQ5vDzGw4xbD/miFxZusBHjz3EuZEhH/+qU8xHkih12rYt8XAmTyb0NoMGiotMhaDhCSJpJQsgXgGbzSNN6HgTdyQsSDpeXHXA3zkzAtoRHju0C+ueiNfbPRaka56CxkVRr0J+uevfbevdhzh9e0fIOuO8eXpN7jv2L+TvGrEiqrKD5oP8he7fx6dJNJWY8Fi0OAOJRn3xDg3XpjcvV4rsrXGiqqk8CVUvHE4n2OTwZfb7ubl1kP0Tp7hLwa/hyQIEI2S3b8f4dQpANS6OrhO5rpQNSpBEO7Ytep9b2wUexLP9UTYVW1mb62ZswVIx83HMthkkWAytw44GlGgzphF0RoYz6E4feqqIbK30YY7lGRyDdUKrQQWLRgk0IgqkgAIAllEUqpALCMQS6tEsxBFgqsSdiXJCKnYxuTAXk8yo9Jcb2fqip+ssPaFrdOI7GgowRuOMR3MMB3MfcGstOioK9VzxRVdSpXKhVhKYVuznbpKCxfGfHjCKQZdMaB4UaV8kPRWYMHwsahpune0MhdIkFI1mGQ9ESXMsf71RTEAdrTXIJvMXJgIcGJk5cJgkjXs2mJn2hfjwnUd4k2yhr3tlbgiaQb8CbAuL7wdNVSQff7HSFvbVyyWsiyzd+/epeLxrq4uLNdFQe50j9Em7202wimWC63lBh5qK+XFHHsfDPsStNj1jOSRTiUAexusdL/1Ajv+4T/IaLRIIjyQTHG6+zAv37dSiWg1YlejG90DZ3i7vpuOchOXnOsTqLieqWCSfQX2pmquMFBukRn2JhhYxYGX1Ol5ac+D7By7yJXaBcnhRCbLsC/JvpZSRuYi+GPX7lMkASqtOuxmHbJGJBaPIegMeKNpwkmFmUgqr1KEox0f4Gd6XmLOVoH+yEEY2piUMYB2h4lyq8yIN87ltZxogkBWWHAWvebo5D7tj4iXlBCKpanLhPEfPEKLRctcRGVgtvDMlIYKI/V2M/GMyqg7xmXPOiJJgsCr9XuYHnmRxstDCIqC5oknFl4Lh8n+2q+h/PZvL22+Gdl4nyHL8sZ7jG7Cf9pZQb8rRjyTX9vMeDrLFiFOD6uH6BbRSQJbK0344mnm4wqk80vbWjBMVHZVioQSCqogkkUgmRWJplUSCoSv/lud1V1iAdlMR2CY07qGvI6nEMbSGu6Xg7ySKl3x2rKC77n8Fqb2KiM6SaR/Nowrj3C3VU3Rtc2BM5xm0BOnrcKA5x3Kz70ZI6ZK7jfMoU8n8dc2INlsyBmJwUkfrLOYfVFVaj6WpX8+Cu6VRka93UhjlYW+qSAnh691Fa8pM9JeX8rQfIyem3SCT0kaxlJa2vv60HV2kv6bv0E9cmTp9cXicfv/z957x8l11vf+79On1+0723clq3dZtmSDLRsXbGE6GDvB4HAvOIGQ+wsxN4H4JpRcICQ/LjfYECAhwYCBBIwNxgbccFXBVrOklbS9l9np5bT7x2hXWm2bWa1si+jzeu1L2p3nPHPOmTnP9/l8y+cbDnPw4EFqamqor6+fEnS4qEZ1Ea9XnNcC8QXwzjVl/K4/yVByYTtp22AJAposFNVPanmFi5BXYzhlcKBlA6ueeoSJYAW2IFAXG+TQ8oUlRM/Eng1v5EjbBgxZIZo1USXIL6FPy4rHuP7ob9GN05PqksLTy7bN6DciCbA64sUUBLqiOUaz80dFnl21g92XXDrDUXJ8LEtTlYf1mshExiCeNRhJ6sR0m9hUdERgNpnWYpHWXHz3incz5gszlMgTcMmU9ZzEn5m+3p4MRYi6/CXPH3AprKj1MJE16YvlGCwhfby9spmTDStYNngCvyJwyFHNd/VKiJb+wbo1meW1PpyaQt9ElqF4npH00qXRm6LEF9tu4CuZccy8QRSVsEtBMgzMd72rUMchCHAq++V8dh1/PeK/NNl4rTxGkwg6Zd62Osx3Xyq9D8FJycuqRAeHvDMb3jhkkeUVLoaSOifOQQO9zqegKRK9cZmGcpXd3ZMFVueuv3ukoplq0WQgef6773XWtFB/ootuyYsArK5fXMG3JgusqPYynsxztERyEpF1muP9HIks5+AZNTHtIxlW1gc43P3qdTsXBagrc1MZcOJ2yMiAse93RGuaOOYuJyspyBlIDJ9b/42acj8tjVUc7p9dVQoKn0U+l6UzmmcgfppkrGoIEvS7ONiXYG93cYV9R8oaaRvejxCLIT70EFZZGfaKFdPGeL1etm7dyrFjx9i3bx+rV6++4Bfxi/j9xvmIwBfbwViVRT64pYrPP95T1Ko/ljZYXT1/OlWVV2VZlYe+hM7wKSXDaFkVx1dspOn4ASxB5HhtG5mmVjg77Wce5FUH46FCg7hY1qQ1pHF4ZOnqHrKJNB/43U+J5ixMQcSpZ+kLVPP0sm1TY9yKwLIqF+N5OFlCXZ0tiuTF6c7DljInHqdMRzSLpkkcXSIp+dmwp21z4T95izWNfq796U/Z2P8K6VMN99z5DP/7DR/gqaYtRc0nCbCqzodDkzg+kuHQUGnnrsoiK6o9ZHWLf9t0M5/68RcQBHhq13vAKt4p1FzpIeAQyCPRFc3zylAGWLra0ElossCKGi+Z1qv507paNj39MG8+/DhiLgkeN8oNN4BtY910E+YXvrDoNKpSFaxeT/gvTzZey8gGwFXNfp7vTiyKFMTLa3AnM6RkJwBOGeq9EiM5aD8HZZCmoANRFArFgZlC1GUwqRNyyYyXWNQ+FyxBRFEEJFHHLC2wUzJytsCqkERdPEpfZUPJBd/lXpX6U6lSczWOmgsra7143ArHhtO85F4Os9jOqgrfeSMbggC1ISdVQRcutVAcaNiQzlv0RjNEJ4lPqGXacYYFGxwGv13E1+h0qlSM4VlUpTRFYmNLmLGz6lskATa1lZEXZE4Mp+iOl6Ye8pUr/4D7BJuH/+1PkL71rUL9Rk0N+Kd74yRJYsWKFYyMjLBnzx6cTudFsnERr1ucD6dYsWQDYEWFi6tbAvx6lrTH2RB67Bd8vP8Y0cxpW7F3+RaOX7KBLY1BhtIGfbMoDe7Zdh2tr+xDxub5y2+gPuQkeg6qSP0pC68KSxU4Hg+U8/TKy1n7yh76HQEqY8P858YbsEWRuqCD6pCD46MZjk8s3kZKAqyodmPYIB45wrVP/QfiKSn998oCRyUf/3J1cb0cFov20SzaZTex6qfHGXf6cRg50oqD5+sWVviqCWg0Vrjpj+eLStk+G/VBB2U+jZOjaQ5N2obKZl6pXYYzn6V8vJ97jz9M7Iy0sl/XbeTRpoKCmM+lsKzah6rI9ESzDCTzFKZZWhl8gEjQQX3YRVbP0zWe5ZWhUw5ILcKxTbu4uv05rFwSKRIBWYZYDGvtWujsxNb1i2pU/5Vw3lU+ioAoCPzBxgr+5tfdc+qaz4W4pNGW6ua4o4xI2MGQpdFzDs6PppADURBmVSDJmTat5W5ePIdGOmdjLC+wvs7H3iWQTZ0NdUGNSp9GUrfpcrdQ1SLgjeUZKDLtqa3ChUMROdSXYLiEVClZEtjQ6CdjUqh3WSAF4dhImrYyjfZzyRk9haqAg6qAA5dDxqEq2EJBIWYknueV4XhRxZ6TUCWZYhdphyqxaVUjw2mbw8PJWVOlyv0Oltf6OdqfYPeJ03nBHk1k0/IquqNZXhkpTVNeFAU0h4wkiQwKhSjfBAoBPYd0772Yt946g2xMnU95OT6fj+eee46uri6CweBFVaqLeN1hqcnGQvLss+Hd68p5aaA4BUVTlGk5vp8BZwDbhmBinOjV12BVeuhLzn18tKyK9hUbcaWTDNY0IcTy1PhU+otQxJoNugW1bplEfuk2mr/Z/Ca2te/Bk00S9QTRL7ucdZrKyfEsE8OL95i7FJFLqtyMZgy6TtVMhp0elnUeJuNwYQki4UyczsZNS3Upc8KwbDKbN9HxVISa+DCqqfP1Le8gL89erO+QRVbXezERODma4WCJtTJORaQlrBHP6vQn8vSfzQ4FgW/svAPJMtl8Yi+bO3/HhOxEN8GfT3F43Q62r6ggkbM4OZLm0BKqaZ4JWYTWcgcVATcTWZ0Tw2lG5ugr1Rus5jfLLueyjn20HT+BaBogy8hf+AJ85jN4/sf/QGhqKun9L/QI/H9py/papVFZlkUulyOXy2F0dyOnUlztVGjXHdi2ja0oCKKAKIiIooAoiQjCqTb1gngq56+gkmGJ9WzIpXgFJ7lFhgiaQw4QhAUX9c6JHG3lTtqXUKK2YyJHmUdldAl6h8iiQEuFC69DZjilM5Y2GD+jCG0gayM7Fa5ZX8UT+4dm7ZyqSgIrazxEUzrHSlw0fU6ZNQ1++mJ5jpagIGUj0Chkaae0moEyr0ok7MapSsiyiCCK2AhE03kOD2XIG+e26B4J1OGd6JinJgeqy31EqoIcH83PmSp1Sa0fv0djf1eU59tPp0o1VHior/RwuETlLwBJFtE0GUkUZuQ5H6tqYUuyF+umm6Cxcd55NE0jFAqhaRqdnZ0sX/7qyj5exEUshKW2U8UoJp4NpyLygc1VfPGp3gXH7l+5hSt3/xKPx4mUz5OrbONQwxpsc+H17elr3lnYmAkCNhByKYsmGwB9GagPaHQvwss+G8YCFfzukq1cue9XfOP6u+hIWZBafBZBmVuhIeygO5an/azshrFgBc+vu5LNh55lwhsilc9wYte7lrq/36w4OprlqWvezZ3f/wLjTh9PNG+dMaa10kWF30HHeHbuYu95UOUWCLoVehMWRxZwtI15QwD8ZvUbeMcLD6KqIophMeL28uOqjZglpEOXggqvSkulG0kS6I1m6ZjI0TFRXITvH994Bw+tuop//c+/xp2YAFVFGB0FScL3xBM4NA2K7DgOF9WoLmicD7JhmiaWZRGNRqcIRS6XI5vNks/np8LXmqahSRItn/0sSirFbapKV1Uz37v0FnrKIrOm24B16ufMi/CA6iGoiLQ6JA4OJotuZNcSdmDZlNQ8T8tnEQXOuVneJHKGTWtQWzTZ8GgSzeUuJEmkJ5ajN6HDPNdjWHAilufSNZUMDiY4cSr0WeZRCCkGg2mR35UYaakLO6gvd9O+iELzSRxxVdBYmaFzaPa0gZBHoS7sxuWQAQFJEhFEgYxucXI8SyK79GHivKSwodrDU7N0kV27rAaHp5AqNdI7k3zKksDG5jJSeZOj/XEYPn1f1jeHcbo0DvUnGO8t7X4pqoyqSoji3AvusaoWtvaMYP3ZnxU1p23b1NbW4p8jAnIRF/Fa4nyQDcsqwTGVTKJ+8YtsSaX47GiGtG5hahq73/MhcsFwYfMjnCoQt20MG/re/FbW//wBBNvix9tvn1FAPRdyDte03/sSeskKV9Mh4BFsrvvtT9D00xtaS5B4Ysu1JN3FPfNBp0xDyIlh2zy3480481msK3Ygds7frHYuNIUd+F0KHdEsx+a5tt9su5HNB58lEB/jwLKN9JZHEPviS2Z/50P/qnUcqGzl8eatU1ENv1Mm4gdbddE7kWM4U9r67ZAFmkMKCR0GEgajueLtVtij0FIR5PGd7+TNj/47qgD/smInkdjQ1JiYw8OE0zfPLPNDlgQiPpFI2EvehuPDafb1LC7zQpcUXqlq44WmjVx96ClIJAok2uOh7NFHMTQNYeVK7M2bi5rvYmTjAkapalSmaU4RhzOJxOQPFBbyXC7H6OgomqbhcDjw+/0FcqFpM74s8q5dyD/5CXYkQuvYMJ888nN+86G/4OFjsZJUqtK6RVq3aCl3Y1sWx+fRBJ8kGX3x0utVhtHYGFHYs0g99dlwPJpjbbSL/cGGosZX+VRqgw4ypk3PRI4T0dJdPf3xPKomcqMyRl/K4mAsxIggMoPMzYPVdV6cToVjQ+lFk4xJWMAlDSE6h5L4nTJVARW3JqGoKoIgIssipi0wFM8tabOnhSCnTkdHnJisWdPEQFzn8Igxa6pUwKWwujFEx3CKfZ2n6zU0WWTz8gpieZsTo2mIlrZ50hwyiiIt6NXZEutizfBxzGuvxW5tLWrui2pUF/F6xlKrJpacRuVyISSTCD09NHp9dA6Mk9Wc9MlurMzsYc8Dq7ex4lcPYodDdDWvPKfzlSQRgcXLkvQkdW7r/B3q4ABZ1YF0KnLyxNY3zXtcyCXTEHSiWzYDCf10b6pgBT9++4chabGlKUD7YLKoWkZRgBVVbiwBemN5Rosofh8LVrBn9eVs/93jPHb5LkZSOhvq/exdwnTmudAZzfGTD36Svb1p1ocFbvv1dzASp52ZuqTwLzveOxV1mA8tZU48DpnjoxmOjRdPMARgRY0HhypzYizD4eE0J1e9gWse/xGOXJL/vvuH/MFLPwPAYeR4tm49f/Wmj5Z0nSGPQmtFIUNgJJnn5EiagXNUqar0adSXucgZFl+69kOMVUV4xy+/g2CaCIkEIqA+9BD22Bjml76E3da24JwXycariEceeYSPfexjmKbJnXfeyd133z1jzAMPPMA999yDIAisW7eO+++/f875zvQY6bpOPp+flUxMRiQkSZoiEJqm4XK5CAaDOBwOFEWZ+iLs3r2btiK+PADGjTciP/QQ5POFRjDvu5Urm/xsqPHw4JEoL/YmS1pkR08temtrPAzGcwyfUS/QHNKwbGFRJONMDKdN/A6JWAlKIQthvKwKVzY7pX5xJkQBmstdBFwK4xmDoaTOkUU2ulNEaAw5UTs76JY8vBJpY3nnYV4yi3uIVVlgfWOApGHRHc3BOd7LSYTdMi6nys5tjSiSgFO0GBhNcbg/M2u616uFo6EGWscPUoPB4erl7Oud/b7XlTmpK/exv2tiWqpU2KexpqmMk2MZ9veXRsgEETRNKaSIzUMEJAHetKqC2y6LsMK7CeXl72AUGdWAC38Rv4jfb7zmaVSiiP6+96F97nMoXg8hcYSf7Xwr1jyN8wzNwdO7bifn8REJOqZ6Ny0GIymDFZUuDpeoaDQJS5J5/I1v45offZ2oL0w4NsKTm64l6ZrpAZ8kGHnLZjChT9VPzIX+hE5l0Eml1zpdIHwWHLLIimo30axB9yJSwh7bfjMD5bUMlhdq0qzxMS4Z6p5mF8Y9QcaL2PQXA6cEIaeASy10hV9W46QrZlDZeYyq2DAZxYFiGhiSxL1XvX/OeXyaREuFi7G0QddEabV4AZdCW5WH0ZROd1wHTtvZrObkO9fdgWiZfPjhe0mjYgkikmXyk5VXLzi3KEBLhYvqgBPTtukcy/By37mRCwForXAT8qqMp3V6olkO9CfwOiQalzXwZMP7ufqFXxAaH0SwrELCdDKJ8NRTcPfdGPfeC+Xl877HhW6nLhiyYZomd911F4899hiRSIQtW7awa9cuVq487TVpb2/n85//PM888wzBYJDh4eEZ80SjUf7xH/+R3t5ejh07xokTJ9i4cSP33HMPLS0tU2TC4/EQDofRNA1VVUv2ehat9hEMYtx0E8q//ivmpk1Yq1YB4NUk3reujMvqPPzw0PisRdtzQQCGUwYuTWFLyEk8rRNLZ+lPLE2qTdawuKTSwwtL6F2JOrysHj/Gi/5GoFAw11LhQpEl+uJ5BlIGA6nFnb9LEagPOrBt6IxmaR/NgKdq6vWjjSu5PjbAL0fVOYld0K2wss5Hz0SOI0tYs1JvxAk3Rzg6nKYrmqWt0k1nNEcqb1Hn1TCsV7fJnyBAQ5mbmrALNT5BdCiBP+zkQbsKzmoiKQDLqhyIkkz7cJaB2GmS0Vrjo6bcy6H+ZNHStZOQpFP1GNLMeowz4dEkrlvuZ2eTwrZ1l0z9XX/ssZK64F7oKh8X8fuNpRYyEUWxtDQqwFq/HquhAaG7m0BNOb3rZ+bwn42eFRsAKFelRZ3nmUjrNrIoLNrx8nzTenaWl+GdiGGJIs9svGrqtTK3Ql3AQc60GUouTDDORjJvUd97nK/+7OvEEtkpG5IOlvHLj95DbzzP8XOQoJ/whfnt5munft/4+IPc/NyDjAsFqVxnPsOj63by7Z1/WNK8mmhT5pbwOmRURcJEJK7bxHMWSSBpAAaUhZ0YQobvbns7H/3VN0g6PARSE/x4002kHO5pcxY23S6cqkT7SOkR/3qfiMel0p+GV+aR+n1h9XYANo91sPbJhxFsm2FPiJjmoW20C1sQOBmsxRIL3z2vJrK82ofXJRPLGBwfTp1zHY9DEVlW5cHUswwmTXriOXriOSp9KusjPhBAEgVSeYt9cZsP3fElvn7vHxNOncoIsG3I5xH37gVj4b3NhW6nLhiy8eKLL9La2kpzczMA73nPe/jpT386jWx84xvf4K677iIYLDRwq6iomDGPpmls3ryZW265BVmW+cQnPsEDDzywpOc66TkqlqAYN96I/OtfY9x664xNUnPIwZ/vqOY3J2McG8sV8mKxC8Xhto1pFdQjJn90y2Yy+ypvQ1/SoN6nlryALoTOiRzNYQcnz0FidxKyKBByylheD28cOsxYy3K6Ba0knfKz4XdI1Po1coZFVzTHkQWUQo75q7ksnOe5Y4lpik0NZU5qy1wcGz73VKkzsbzSjSwLtI9IWH0jbIj2017VzKGh059/+nxrAgNuTaKt2ofHIZHM5YnmBeJZk/hIBtAgUE2/t5w1Rw5zQCp4ztwYrF9RS/9EppASdUqxShBgU1s5olzoYTJUIslQFAlVk+etxwAoc0ncvr2Bt22sJhEdJZ0+yyj9F+vMehG/35AkCdNcuijyYgrEp6Ibn/oU9kc+wub6AL/tKU6WdiRtUO+T6I4v/hry0QmuG+3gxNjpdTzrcNFZX5yggyXJ/Hz7Lbzn+//AT3e8HUd5mGVBB1nDZnARBONsjAYrcIwM4VBVsoJEMB3j+UjzvPUYi8Uzm3Zyze5HSKkeLEC2TB5bt3PO8aoIFV6FoFtBU2QMBGJZk2jGKJCKHJCzmaNQlNGUQTDgpHvT5Yw9/2OCqQl0WeGXa05HEQJOiaYyF8NJnY4SiZXXIdMUUonlLcayNuMl9N7690038Q/PPkLlaB9OI8tXH/4c2KCZOp/5g78lt3otyVSSuCFzYODcU7/DHoWmcjeGZXNyLM2xkTTYEPHLVAkmiqridjkZTxv0Tky/D33hCF/a9af87QOfQTYNkCRsjwfrfe+D6uoF3/tCt1MXDNno6+ujrq5u6vdIJMILL7wwbcyxY8cA2L59O6Zpcs8993D99ddPG+Nyubj55psBGB0dxSiCUZaKkhfzYJDs3/89BAKzviwKAlc1+dk3MMhYpvTz7Uvo+FVYSr5hAx6HgihkFyxWk0WBsEvB75RwqRKyKGDZMJFMY0oq8axJHhioakQqj9AQcnKyxJQbgDK3TKVHJZk36ZnIlRx2HzRU3rSphl/t62dVxIeqyRwbThNdIpIhCdAclEib0BHNUuGEZWUyfSkfBz0zCxXHdZkyr8ZoYmm8moIA9WE3tWUuFEViImPQN5Gle4GielOSCdZW0NQ1QiQf42BF67Qu3w5FYHm1ixQqR0vsYQKgaTKKunA9xqoaL7dtq6VJSxAd70e0yqYUOs4FF/oifhG/31jqWqJFkQ0K0Y38XXdh7tjBZYLEnoEU2SI6hQOIkoSAgV2i4t4kKga6ufqbn2ebomHYBaGS/qpGvvpHf7PgsYooUO5RyG7fQdfYcUbfcBMWIp3n4Mw6G66KECdveCsrHvkPrGCYVCrJI9vfsmTzn4mRUBXPr97B5QefJmPAnuYN9JZFUCSBKr9GyFVoxmvYEMsajGUMEjYkkhawuGuOZgx8PjePvem93Hn/F/jWjvfiNHOsckooksDJsSyd8TgJp7foOVvKnQTcGh3RLCcXSUSjvjC/23EDqx5/CNUywO3BnU8zFG5if3kzsansi8UT3aYyFxV+jYmMQfd4hoMDSRRJoLXchSqJZHNZVEVmNGMzOqHDxOwkXBDgibVv5F+HTnLHb/4NERC8XsyPfKSo87jQawsvGLJRDAzDoL29nSeeeILe3l6uvPJKDhw4QGCOTfz5kr6dVPuQpBLCx6eiMXNBEgWub/Pz3f1j846bDaYNNXqCGMUvBMVgMKmzvtbLwYEkYbeCT5NxqiKyJGJZNjnTJpEzSeRMcnah1oP0mQ+9BPr0RcCUZE7GdDZGvBwZSpHW5/fu13hVgi6ZaMagP55ndJGpVgBhh03Aq/G27fX8cv8wi12Yz4YmC7SVaYxmLLqSJm0VLqrCMsfHMiTm4TE2sKrRz5MHZqYDFgO3JtFa7cPjEEmks0zoEom8VXQaWJVfo8rvQE7GiSZgo9fgp3brVOuNKgdcckktRwaSnExYlNI4SRBAcyxcjyEKcOWyMLdti7CxYfI5riQaDbNv3z6CwSBut3vO44vBUhCWi7iICwUlq1FNQhQxb7wRAAdwWcTD43PIXZ+NWN4m4oaeRfptuptW0F/TRHl6gjHJRSg6zOM7bp42JuiUCblk3KqEJIrkDYN4ziBjiuRsGMzD966/gxqvgjuaJbWAbVkIfodEJKCRMwvRm8c2X0frYw/iHB1m/5pLp2osVWdApAAAIABJREFUzgd+ueMt7Dj8DB7F5qU3v5O2ai+jaZ24AfGzahyWCvGcyf51l/Obnl1Ia1bxrX/+M3QEbAQkyySnaNzx4X9Cn6MfB4Bblbik2kM8bzGQyDOUObeUZE0WePrtH+ToG27k/f/7TwiP9SLmdUYFjS99+xPokswXd/4RHeH6oueURVhe7cXtkOmPTTYGzONzSKypLeyhxFOS613jGZK5uUncpFk5075889oPsLbzIBv7DsO73gW1xX1PLnQ79ZqTjVwuhyzLC27Ma2tr6enpmfq9t7eX2rM+pEgkwqWXXoqiKDQ1NbFs2TLa29vZsmXLrHNqmnZeyMZimiYVg7WVLp72J06rYpSAIU8ZTUN9dLjKlux8lvllRFlkU7WTV6IGmbQBS1RicDKaozrgIJ836TlDfUmgoJvu0SSGknM0ASoBtX6NmoBGIm8ykjI4OZHDo4ookoBeapfFsxByyzSXuzkxmuF4VGdltYfyAKdyRYszBpJa/CNaX+aiJuRElkXiOYv+WI6ehA4JKNy5uY2rLApUekSqQx4My2YgnmM8bTCeSQIihGshXMvOw/sYyImEZYuDrtaSGzIWW4/hkEVuXl/F+y6tpT7smvF6MBhky5Yt7N69m0QiQSQSKY3cn4ULWb/8Is4/irVT5wtL+d1cKvu0tdbNi32pojftpigjCWbJzWsBbFHkyWvfxdvv/wfK3GBUVqJedSXrJYmsYRPLmeRNm4m8zcS0Jn4zI5b9CZ2gR8Wtm9MEVIqBRxWpDzowgaGkQe8ZdZAZj5/dV7yZHY/9iOO73nkuzvR50VbmxF3bxr71VxIws7SH60inlp5cqJJAhUfB55CRBIGcYTGa1nnwLX9ELp1np7ecYGqCjObEl47zzPJtcxKNprCTkFejM5rl6BKkloU0gYZyN5Ioks5b/NZRRfXm63n3z7+FDbjzafzZJLYgMOpeuHA+4JRpqXRjI3B8ODmVrlflU1lf58MwbRRJRLdtOkbT5Of5Es9GMM4e8Pn/9jm+9tR9BIuMasCFH4F/zciGrus8/PDD7N27F03T2LJlC1dccQUu18yNBcCWLVtob2+no6OD2tpavv/9789Qmrrlllv43ve+xx133MHo6CjHjh2bqvGYDYqinLfIxvkgGwBvXhbga7sX5+mWqioQ4taiJQQn0Rp2Ig0NMGxWggmCJbFl+BV2VxSnwFUsxtIGiiSwvsZNImeiSSJ98dwpZYvFo9anUOGRSeg20ZzNybOkc5N5i01qkuczi/OYR4Ia5T6NY0NpXhlKsarGy0TOXJREb+9YGpdskzamL1xuTaKl0kPAo5G3bQZieRI5k2Njxb2HR5NoKHPiUuVC2tl4hpGszUj/zBBwlV8johg4jr5CPhLixXSIHihJi1JRpEJ/DGn+xTLsVnj3llresbmGgGtutRsoRCZra2uJxWK8+OKLrFmzBo/HU/xJXcRFLIBS7dSFgEVHNs6CKolsr/Pw6MniHA4ZS2RZmYr5xJO0vbJ32mtdzas4uPHKeY9Prt+I8dsGgieP8vO3f5Du1Cx9p4rERNZEkwQ2GGOs/t4/F+RwJ8/T5eVHb/sw5im1LYcs0BB0YGMzkrbm7YK++8qbGSuv5US4gWAigzU0gi85XSJ8LFhB2lnaOiUKsLLSDYLAUMpgIq/zs7d+CMG2aNAkKp/6DXZ2+tq/b8VWEkX3EZEoc6s4FAHLgnjWZDStM5IyGJmRLSBQEfbww6vey10//Qo5S8UWBH506fS0Macickm1h7Rh0xfPM5I7tyiGgE1bmRNNFjANnZGkwcQZ/aV+eeXbuObZh/Bmk1TGR5Fsi2FPiP/7w0/TEa7jr2/8+LT56kIOaoJOEjmDzrEMhwZTCECFE6rDPnTTxqFKJHIGXeOZedPFFyQYQEuZiytag1zZGmLw2EsEP/a9kq7/ItlYJL75zW9y9913s2HDBnK5HPfddx9ve9vb+OxnPzvrhkGWZb761a9y3XXXYZomH/jAB1i1ahWf/vSn2bx5M7t27eK6667j0UcfZeXKlUiSxBe/+EXC4fCc53C+IhBLtZjPhsaAxuoKJwcXKHieDeO2wsoqiUODiws/tIQLXvPhlAGByqm/26JI97K1bHKL7B9Ioy9CMcSlCAScMi5lsqbDJps3iGdNuuM6jW6BfYuUPgQod9qEHQJZZBKmSHdy/s9HUSSEtIUtFP9wL6t0oyoix4bTJHSLtXVeBhI6R+fpebIQ8oLEerdJj+yjNuxEVSQmsib9sRy9KZPeVHH3pDqgUeXTEEWB4XiW4aQ+ayd4UYCGsJMKvwNRFBhJ60xkDDotFdrWAeB/uYOYUtxmS9UKSifCAkXfLeUu3rctwo1rKlHl4u+5bduUl5fj8XjYv38/DQ0NMyKeF3ERi0Wpdup8YSnt1FLavU01bp7vSxHPFefGj+sWFXqWTS/8ipS3sBF2J2KMVtbNeYxTFlle7mQ4bfDU9e9l0yMPMLBhG/WSuKgo/yRypk0/Tm7tPEDaBENScGWS9EZaEGSJWpeFLArEbYXBdHHXl3V5OLzpSrBhRZWXld/5e7YceIacWpB0d+Qy/GTne/j5G99R1HyqJLCqyk1Ktxk66xwMpRBFiJkWf/rE93AODZBXVATbRjZ0TtQtm0E2VEmg0lNIeRZFgaxhMZbWSeWtktSZhlM67muuYeSJ79Ew3MW+pnU0D3fSPNxJhVfF63Px66pVtI+fe71hwCHRFHZhmAaZTIbulHwqOjadBMW9Qf7iE9/g7m9/imVH9mFTkMNtHuuhO1hDa7QXdeVyvC6VwXiOoUSekXQCVRJYXulGEUVMIJ9JEs0a9C/Qz2ohgiEKsLLaw6ZqjbL8ENvWVlF+St528Fjp9+FCV6MSFlh0lnwnPqnS1NbWxn333cfVVxcUDXp7e7n55pt5//vfzx//8R+/auHqjRs38uSTTy7pnAcPHqS5ufm8eb9G0zpffnZwUeFotyIyEs8WXdgHBUUsVZEYKqIeotIt0zWWntGDQ6CQ4+pzSDhO5enH4glEzcVE1iCzQChesC3qO17hpRKiJ7U+mUqvSsKA8TmaT80Hz8Q4e8fnHyOJAqtqPKR1i+5oloBTprXSTWc0Syp/boTToWfZEO/F3rCeRw8WX6ujSAL1IQdBt4ZuFdKpYnMICyiiQEulG68mkDMMJnRxwToZdc8+fueae0MvCAWSUUwTvnU1Tm6+xMNbt69YVKrIyZMncTqdVFdXYxgGhw8fBmDlypXIcnG+lGeffZbLL798Wq+c1yEu5njNjYt2qgScOHGCQCAwryOuFOwbSPFwe/Ey6BHN4g1/8WEEy8QSJVQ9xz/9f///jO7hogArK5wkdXuavRJNA0uSkQTwqeI5p+W88eff5Yonf8KYJ0Q4GeXRu/6S/c3rOcflG1kE//7f8cH7Pk3c7Ue0LdyZJJ/66FeI+udPZ/aoEssr3YyljaLS1LY8/0ve+R9fY1Tz4koleKV5Nd+589OE3SoOWcS0bRKnohVL2bbpxq493HjfZwsd1Z1OLEEEXceQZP7bn/8LWc256Llbwk48DglFFInnCjWZxeCSE/v5zJc/gmTksQQJQYCYL4RpwW1/9FVGfWX4T3WEN63Cc64qIn0TOcbT86ejLUQwZFFgQ8THFa1BtrcECbsLhDCXy3HgwAG8Xi9tbW08//zzXH755cXfDOC5555j27ZtKIrymqVzFoE57dRrFtkYHx9n1ameErquE4lEePDBB7nhhhu49dZbpxjghYjzmUYFUOZS2Bbx8EyR0oNnIqVbLK9083Lfwsc2hjScqsxg0oB8ccW/QymDSMhJvWWhmzZ50yKZt5jIGMRyJrFpHjC50MywCNiCSG/DclrQOZGZ+0FrCGlUeDViWZOxjEFXYvGJs/5IBYzPnrLmUERW1ngYjOc5Mpym0qeyqcHP8bEMh84hAgPgxWDN6EkGIy2crFwLSYvL20I82z478/E5JOpCThyKSDJv0TeRpSeWn7WRlkeTaC534XUppHWL/niW0ZzJ6JQTZ2HDVj5HcyZREtA0GVkS563HkEWB61ZXcPu2CC5jAsMwFp2TfmZoWZZl1q5dS19fHy+++CKrV6/G55vZuOsiLqJY/D7aqaW2T+vLVLp/8RKpzOn1xhJEepavxVRm5vCPGBLPX/9urv7x18GGJ9/07hlEoymooSkSo7M4iSypsG0xbYjmLC4JKRwZL732IuyScSsSybe/A+n5X1CVi9Nd3cCehnWLzc6aBsMCx6Wb6Xi4jYb+E4imxdObds5LNMrcCo0hJwNJvaSozb7NV3PDI98lZOZBhF/fcBsp3SZ1jinHc0EAKjwqXZt38OPIfaz6t3tZfWwfKacLt2HwyNYbF0U0HIrIsnIXNoW6ve6J0p12XcvX8cw77+TyH/4zipHHsgV8sULX+6/9x9/w5Q/9HalwFYIg0DuRIZ2ff48gCPOnR6kibKzzcPUllVzWFMDrmLmt1jSNTZs20dHRwe7duxeV+TLpALlQawtfdbIxecMikQgdHR1UVlaiKAqmaVJXV8fQ0NBM3fzziPPxwS1lGpX8jW8gRqOn/yCK6O94Bzub69hbgvTgmRhI6oRcMuPp2QlEQ1DDpRVIRqxIkgGgigJ1AZX+hI4miwwmMkWH14uBKckkJIVWt8jxM1KTGkMOKrwq0azJeMYguYjaiNnQlzRoq3LTPnhaQiXokmkud9ExnuXgQIqGsJNI2En7aIbD8zQhKgYhl8zyKg99CZ0TofXTXstlMngwSSJR7deo9KmIosBYSmcwni80KpwF5V6VurATpyoTzxkMJfP0pw2Y+uxL//47wwE44+1kRURVZaQF6jFcisBVTRofuW4N1cHC5qKnJ3pO0YTZ+tnU1tbi9/s5cOAANTU11NfXX7AL9EW8Nng92qlSejfNh8U09Zt3vlyOXf/+D8RiaUxZRjQtwOZfP/VPJIMzN9Y50yZ31VWkH/kBip7jpS2n+zWEnTKRgMZQyiBTZG+RVDTJlsw4HXFzKsSV9IXIOV04RYsKnwO3IiGJoFuQzJlkTbuwGdcNED3secNNXPazf+eFD95ecp+e+TCcsfjVjbfxofs+jSWK/PKKW2YdVxcoOMl64/mSe364JYtIuY/Hb/oD3v7tv+Pk1jdwrKJpKU4fAEmEKq9GyCUjiQKxVIa0rZAzbfoyNkZNMy+/8VY+d2wfjlwG2TQ4HlnGqo4DAPSWR4h55lfbrHCJhF0SqqZhI9A5nqFUPlzhUaj2OeiJ5bjvujsJdbSz9vnHkLDBNnHoGWr6O3jr977MX77nr+ecp5j6C68mcVlzkCtagrhTfbQ21S3o2BIEgebmZoLBILt372ZoaIjKysp5j/l9wqtONiY3Fh//+McZGRkhn8+jqiqSJJFOp/F4PEvawOi1wFLmxEr79yPt2YPt84FlIWQy6O98J25V4qomH78oIXw9CcOCSMDBeHp6dKM+oOF1KPQndeJ68SRDEqHWJTKStek4pV2eM20q3AKpvI1pL93irZs2pgLrajxoish4xiCaNUksEcE4G01VXtoHU9QGHFT4VY4NFZr7La8s9Ko4MZZleA7SViwqvSrNFS66Y/qcjQyDY4PcmB7iN5WrGcsYs/ZbEYDIKeKlqBLjGYPxtEF3PM9SyfgC6GvXIu/tRxDFQtH3AvUYkaCDWy+NsGtdJWPDA3QeeRnf2rW43e5z3kDNVTTn8XjYunUrR48e5aWXXmL16tUoyvwF5xcJyUVM4vVmp1RVRdd1VHVuWdFiseSRd48H9d3vQPr2d4n5y/BERzm26YpZicYkBvICT739A9jxBDmHC0WwafBCCrGodN0zccUPv86K536N4HRg2SDnsuzf9V6evul2cmeSinnw0lW7Cj1ALt3KGWHec4Zu2bi2beHEz5fTX1E3I6rRVubE7ZDpi5cWyQAod8n4HRK9CZ3OiTw9m69mwzO/4LdbruET//n3JFOn50s5PfzbWz8y1VF7LmiSQLVfw+co1HRkdIvRjEHKhNSU8pbMmZmLQykddfUKXm7dwM49j6LLMn/y4y8D4Mxl+Jcb7uTh7TNJlizCsnIXoiCApRPPQ0+ytJQ4AahyWridDgZSFu1jGRSxcF//8/2foKb7GGX9XYX3M01EM8OlR1/k3b/9IT/Y8c7T8xRBMMJuhe3NhQLv9XU+5FN2b//+3pJsx6Rce39/P2NjYyxfvvz1nBa1ZHhN0qji8Tjvf//7Z/y9q6uLD3/4w1MdwF8NnK8C8aWaV7/zTsRDh7DDYYSREYzrrsNuaABgR72X53uSRLOlG73ehE6jlKPT1KiT8vj8bvrzkChBClDEpkwxSNoyvemZ15u0VdbXudnXHSs5qVoUCiHloFPGffwYksdDrr6eZL6QlhXyKuzvP/eOoPNBsU0cY8O8rTzPuGEwntS4pMZLzrRLkq+dC3VBB7VBJ12xHCejsxua5sEOFEWip76V0boWmi2Tl0515pZFgaYyJyGvio3AcCpPMm/RlVh6nXUBUGURRRaJWQLbNtTS1T7AhDX3Irum1svtl9Vx1SVlSKcWZlddHX6/n5dffpnm5mYsy1qQBMyH+RQ6JEli5cqVDA4O8uKLL7Jy5coZa8v5THe8iAsbryc7paoquVxuycjGUguYGO98J74f/JB4PoeIzZ5r3jrveNOG5MZNnIxbtAUkdEEmuUjuduTKG1iz72mSvgCibWGOm+zedDW5Eooas14/L+y6DS1roUoCb7nvb2l9Zd8ZIwQeveWD7L3ixpLPbzhj8d077mbUkMC2qYwOsizsxBYExvrGSEkyhIr3cFd7FNyaTG88f8opWFhbTUXhvv/xDwTGh7n93v9FHgFDkHDkM3TVtBTqKc6AWxWp9mm4tcI2MKlbjKeNU/LBpdmPKr+DH1z9PladPICWz5KXVSTLJKO5eHLD1dPGhlwydQGNnGHiUCS6JnLkjNK+j5oEyyo8xE7VocRSFi4Z6oJOxlM6qiIyVlnDVz76Jf7XJ9+FcMY6b4oiXRX1RRGMGr/GFa0hrmgJsqraM+vYxfS/EASB9evX093dzYsvvsjaU86332e86mTj0Ucf5Sc/+Qm33347l156KalUCpfLhSRJrFixghUrVrzap7TkWMrF3Fq7FmvVKsRjx8Cy0G+/feo1WRS4rtXP9w/OXcUsCgVvhYSFJos4FBl1aAB5fAxXNkVActDVuJJkCU4VEagPqEzkTEZz86fA9CV0NtX52NMzuzyiTytI7nk0CUUWsWxI6xbxXEGPPZqHaP2ywuAzJAcHEjrlboWRJdYXlwRoCDpwTYwxYMj0esqoT3bRmIniallBEonjvedGcprLXJT5VLon8nTMkVNb6xFQBYFB7XRBvC0IVGs27kqJjMvDYFInathEz0NURwBURUSRCk0aBWYuyhutKL9hes66KMAbl5dx+2UR1tXNLrvo8/nYsmULBw8eJJfLEYlEFn2exURGqqqq8Pl8HDhwgPLycpqamqaOudAVPi7i/OD1ZqeWsgHtYiPvtm2j6zrZbJZcLjfj3+ptW6n4j5+x56q3ECuvBmw0UUCTBTQJMAz8XheSKCAJApIAlYHT0fDFoDGgwqZ1DLatJHzyKLJtsX/LG4hV1Cxqvpxp0xLS6GpZzcqXniXp8SPYNq50gp6WlYuaU7dsmpqrGe+OcY05ym1f+RMMQcIWBETLxBJF/uZvvkvWNb+6WZ1fRZZE+hM647k57pkgMBGu5Lkdb2bb84+QUFwI2PzyLR9gWYULtypjQqF+MmsymrUYzZ7792ogoTPR0MKdf/EdPvvPn2Blx0Fk02A4UM5ff/uvAHj6rR/gxIbLkSQR3bLpjRuU0ggWCqlSYafIQNLkxHghClLr03AoAhOpHJl0mpytcvKUClasYTkPvO/jvPvfv4wF6LJCd3k9u5dtndNuVDltrlsb4crWEC3lC4v8lGpDJp89QRBoaGggEAjw8ssv09jYSE3N4r63FwJeNbJhmiaSJPGFL3yBt771rXzrW9+ioqKChx56iF27dtHU1IRt278Xxn9JJXUFAf3OO3F86EMYN92E3dg47eX1VS5GUjqDKQPLBtO2MSwbw7TRLXuGYpVug15RDRXVxIBQfxeuTLIo3e9CQz2VZN6iO178Jr83abC5zks6X/BkIAjkDJuJdB4dkaQJybRJKV2QbKA+4FgSsiFQqFOx8xnSgoOhlEGZL0Rb+0Fi4Sqija1MVs1UOSVeXiTZuKTSjdup0BvPk5rFwApAS5kD0xYYOivCVDPWR62Vpr9lJdnOfrr1c/dwnglRKEQuZFlEFmcnF2fDtW4VvFwooHcqIrvWV3HrpRHqQgsXBiqKwvr169m3bx8dHR2Ew2GcztILCovVHne5XFO9evbu3cuaNWvQNO2C78p6EUuL16udmkyjWgrMFnm3bRvDMOYkEoZhTJ2Hpmk4HA40TSMUCqFpGpqmITc1IZ7oIP6e99IY1NBNe3pEW1FIGzZnpuBIAtT5FXpihWtzJiYQjTM2oIJAyh+aUUcRckqUe+SpqP7L7/hDrvncJzAFgc53vO+c7k8iZ9Gx/Xqu+sX9SKaBlstyZO1lDNc0Lmq+Ko9ChUdhfa2Pl6MqV0Zaqek9SdbpxpVKsH/djjmJhgA0BjVMm1PpZcXZyCevfy+XvfgoZWaG/uo6jqzaip2xILP0fcWgQKhWVXt4rtPku9f+IZ/9+p8jmwaaYdA43oeSz/FUWRmxnMlEieRGAFrLXciSQHc0R1I3C38LO8kYJqpUsFsZWyJtSJwtTvfYDbdz+ZMPEuk5Tlpz8e2df4h9xrMrUJConYxgdB3ex+WXFe8AK3UtONtm+f1+tm7dyqFDhxgbG2PFihVFKyleSHjVrmjSoK9atYp3vetd7N27F6/Xy6FDh7j00ktpaioUNL3aREOSpCkDs1RY6pxYa+1a9Ntuw7jlFmzbJp/PTzMGDVaO0bzCgOEoee7xmgYiepphCv0b5kKdXyFnQk8JJAMgoEk0hTTSukVvwsDKnunJOLfPejipo4osWqKwzq/hd0qMpk1G0iZOUaIx7CBn2oykDTpb1844JlfixyoAq2s8SIrEQEInqs9OMtrKnOQsm/7EdE9PjVuipvMIAy2r6Dv1bFT4HXSdY+BMFEGVJRRJRBKFQt5sqVBVyjwqV9TYfPjG9ZT5SpN6FgQBv99PIBBg3759LFu2rGR1n1IWelEUWb58OaOjo+zZs4fly5fj8/kueOfGRSwdXq926lwiG5NEYtJexGIx8vk8sViMXC43RWIURZlGJAKBwNTvRW1+QiGs+/+drXmbR07EiqoSM23ImjaNAZXe4QS3/9WdKPnsVH8jwbb44V98meHGQnRblQSaQxoTOWNa+vBo20qGl60iXVaB2lQPw4uXw03pFvXVAR6/4Vau//E3sEWBJ958W0lz+DWJ+oCKbhWiCENpA69LwZ81+PlNd/BH9/4VgmWCAL+6/tYZx4sCNIUcZHSb/nmaCM6GlpCGUt7I/h03sPUX3wd/BR/5P5+Yej3pDfCvd/zlkhbCAyQMG1GA/S3r6WxeRXPHIbyZBJJlEHf72fq9e1np9PC19909pSg2H5yKSGuZi1jOpO+U7K1bFalwioxlDBRZRLdhcA6H4+QTKkgy/+cv/om//J/voTdYwwttW5BFgfURL1e0hNjRelqiFqCrxOsu1WE1WzT+bCXFNWvW4PV6Zz3+QnWOver0KRAIcP/999PZ2cnhw4eZmJh4TQvCNU0jn88vyqs6FxZLNuYNU19+OebAAMLgIKqqThkETdPwer28pVbl2UGd/YtYZGOKi6AiosniDI96rU/BtKEvUdqC50knuCTaw2jbSkZPFVC3hR0cHT03TfQzkbVgZd9RXqpeXvQx1V6FsFslmjEYz5qkDYuGoAPLFhhM5ulNzE+mJrImkYBG7wKSgqIA6yJeDIRCE8TsTHYgCdBW7iSVt2e8b4UTKn0OxnLQ37Zm2mte0SpZmlE6RS7kcyEXFK6rLuBgebmLS8pdfOpNTby0by9+5+LqLmzbxufzUVdXx/79+4lGo7S2tha9mVtMZKKsrAyv18v+/ftxu90X7OJ9EecP52KnHnnkET72sY9hmiZ33nknd99994wxDzzwAPfccw+CILBu3Truv//+eeectFOz4Uwicfa/k0RCluUp4jA5XyQSmSISS/YMSBIhJ7y5NcAvTsSK6hFh2YV6gcYqL8c37WDFc78m7QugpZMkg+UMNxRSSVtCKrplM56dxRYJAr/9k7/CkmXMrElYMFCPt08bktccjEWKU2nKmRYvXV6IbnS0rS0qqqFKArVu0GSJuCHOkO1N6RatZS4OXrKOvkgLre37ee7yGxgtP92zSBYLZCqWs+gtwbEnAK2hQiPWAgkzeWHXbSi5DKuf/Dn5oR4sUUTLZTnZumah6UqGQKHn1FVtQWRR4OHP3cvW//t3XPHrHxWuy9BZe2QPPdVNCxapn6kqNZkqVelR8DlkxlI6iiQgyzKdc9hgkZkb8mhZNT/4n18hLap88tpWLmsKzipRuxiUKnIyVzR+Uv3O7/dz8OBBIpEIkUjkvLdSeLXwqkc2rrzySh555BECgQCf/OQn2b59O62trcD0XLZXC5Meo6UkG7NJCy5JmHoB79K1LeCQE7zYn5p33GxI6xaaLFAfKNQSVHsKahQDJXpV1FyG1eNdxBpbGaqeLt/qVpfeG5itqVuwpVeFW6bCoxLPWYxlDNJGjoaAg2qvxmBKPyOaUNz3bpnDpHeO1xRJYF3ER8qw6Z9DVUUWBdrKHMRyFt2x6QalLqBS5lYZShmMzcFnlHgcXIF5z1EShUJB9ylysdhnShSgIeCgTMpQpRrs3LQcn1ObNuZcUpEmF15VVdm0aRMnTpxg7969rF27Fk3Tij6+VGiaxubNmzl69CixWIxMJlPU+13E7zfO1U6Zpsldd93FY489RiQSYcuWLezF4EAZAAAgAElEQVTatYuVK0/n+7e3t/P5z3+eZ555hmAwyPDw7L18ANLpNL29vcTjcX7wgx9QVVXF9u3byeVy5PN5bNtGkqQpe+FwOPB6vZSVleFwOFAUZcZ5Dg0Nkc1m5/ScLgX8Dok3t/r5xYkYiSJCzzaFnhmeW/8A+/lfI5gmsp7nmbd/gEqvgs8hndWjaSby3tPSo6v6j7DlMx9FP9XrQTJ0sm4vX/vHHxXl0Y/nLOqq/XzvQ59mIjx38bYoQGNAw6tJjGcNshbMxoUmMZEzWR/x8sjNd3DHfZ/mV296D1o2XbC9fo2xjEHPuFWU5x8KFqvWZeFyuWdkJiTClfziv/8VTk2h7lcPkVcdiDY8+JY/OueoRtAhEXTKeNRCTZ9pQyJvMZwxsGwbcBJ9+4fY9tTPUPNZvMkYCBAZOMk3P7mLv/3jL9MZOV2PKAANAQWXQ6MrmqV9rKCt3hRyYFqF1HBVltAx6EnNNPjC5M8s16VicmWTn2tuuAWvNv99XcymvlQbtNB4r9fL1q1beeWVVxgbG2P16tWIonjBO8VedbKxc+dOdu7cOeuYsz+ApdIVnw9LVXh3pncpkUiQSCQYGxub4V060yj4/X4qKyuniEQp1yru3YvyrW9xphi11dTEFX/+52iywNPdM5v2CdiokogiCaiigCQKyKKAKBQWTUEQECgsJAdKjJBIAqwsd5JNWoxUzEw/AhjLmIRdEmPppYtkjXpCNCtwcnz6+YacMtU+lZRuMZIy6JzIU+fXqPSqjKQNhkusETkT2mA/UDHtb05FZG3Ex3jWpGeO6IgqCbSVORnLmHSdRTIaAhpBl8Jw2lhQ+jHvD84Qm/p/7L13nF11nf///Jx++/ReksnMpEzaJJmEKoI0wxIJICCydJAmVkB+irroT9bFxYa6rA13LawKigVURBFETZkkkwQSSG8zadPbLad8/zhzb6bcmbl3SprzejwGMnNP+Zxzz/m8P+/2eimSQFVkVFmMy7mQBUzL8lDdl7mozPHgUd1IVGNjI5vXu46A33+sxng872n/fYUQVFZW0tzczNq1a5k1a9aoKsfjOXc8ktTZ2cmGDRs455xzTsta2SmkjvHaqdWrV1NZWUlFRQUA1113Hc8///wAZ+Pb3/429957b4LNKi9v4FwSx+c+9zl+97vfUVJSQmNjI9OmTWPBggUUFRWh6zqapo3p2Z9onY3hENBlLvf3smrNNrr6Cad15RbQnZN8Ab8vlMeRCy6h9DfPcnT6TDwXnEtvxB7V0RiMnTMXUF1YiqelmajHi6ernfqLr0p7kb23ci4AmUcamVv/KvHIVoYho2Znsf68y+g1HXrToD8/2muRe8HZPJHxTc79++941x9+QkyScRyQbIuNi9/Jjz4wvA4EuPa6OttDzLbpjDpERyiBfmXFTdz4pxfIaG+mNTOP8t1bKN+zFYD1i86jdRQmrJAuk+1V8KoysrDp7u1F0V3HqHkEzypQUcYPVt7LLc9+DcWM4QgJxTRxRJi6hldpzsgllpFFVa6Xo11hDvc60BtGlwXTsjwc7Y66axRZ4khHlNbI0DXJSE5Gvl/lgspMzL0b0bpb6G4VBAoKRrzWsdiTsTSIj7a9LMvMnTuXpqYmVq9ezaxZs075ct8Tblmbm5tpaWmhs7OTWCyGx+OhuLiY7OzsIV/6aOnpp59+mgceeIDiYjcted9993H77bePeP5UnA3LspJmI4aLLjmOg2EYFBYWout60ujSuOH1orz2GrbfD0Igtbdj9hmvpcV+dFli0+EeTMfVpoiaFhYSNn2iSiPQAuqyINsj05xEvXUwBDArx8DBjdqgjtw3Up6h0zzBYlhBQwXCBHWZ0gydsOlwsMvlHs/3q9TkeWkNW7RGbFoj4zeyR0pn4GnspNdyDWpNUYBDPSa7h+FJNxTBjGwPR3rMIcwr0zMNgh6lzwFKzWB15OTjOdiNoquIPmaXsT5fiiSYlmVQqJlMC8mcNbscXUk+qRUVFeH3+2loaKCysjIhSDRRzkYc2dnZLF68mI0bN9LW1kZFRcWwxx9rZqP/+Q3DYO7cuVOOxhSGRap26sCBA5SWliZ+LykpYdWqVQOO9fbbbwNw9tlnY1kWn/3sZ7n00kuHnPORRx7hkUceAeCee+7hqquuYunSpeO+luNZlhF89qe8+/vfp131YjkOWk8PWy65glW3fAhwtRYMWUJXBKosoQpovPYGyl7/E1tuuIPmMc7XJhI7brmbBf/xGeRYDFPV2HD+irSO0dJrUZ6hsactir+jlct/+g3QNGwhkMNhDlfM5O9np0+FC2AhqFk2hzfsCOf88WeE+xTU9Ugv6864aNj9ZAHVOR6ilkNLipT3LdkF7Dr/3dQ++wNk8wArn/svhOMgWya7ps8Z4Gz4VYkcv4pHldBldykfsx3awhbNiSCaBimQFYQt+O0F11K1603OX/0iwrYxor0oVoz3/v5/UCun87u8SxKlUhm6ID/ooakjiiwLDF3hwDCBu0Q/RhK7UJXj4V2VGczNd0tk/9YoU1dXx6ZNm2hra6O6unpYmzEW4od0M/vpbF9YWEgwGKShoQHTNE/pcqoTZl3b29t59dVXefHFF9m0aRPNzc0oikJ+fj7Tp0+ntraWSy65JBEhSiU9DXDttdfy5JNPpjwOVVV5++23OXLkCAUFBQMciUgkknj4+mckfD4f2dnZw0aX9u/fjyRJk5qmtmfPxnzHO5DXrMEJBnFiMaJ33pn4fEGBl30dUXYm6hpTfxkilkOeX6UzahMdwSmpytbRZEFHGgahO2YjC4awZKULxYyR5ZHx+w10ReIdFSFae032t0fxqhJz8rx0x2xaeq20mLNSgalq1E3PImbbHOiMsWsYJ8OrSlRkezjYFRviZFRkGQQMmSM9FkfSiIpJgD/o5ZwMD40HWmiy02OlcmuCDWbm+ZiZ62VGtgddkdizZw+yLA/raMQRDAZZunQpGzdupL29naqqqnE5G8M5C4ZhsGTJErZt28a6deuYN29eUo2BiRAFFEKc8lGjKUwO0rVTqcA0TbZt28Yrr7zC/v37ecc73sGmTZvIyBi+NFJV1Ullo5osmFdfjfrMMwRDQVp7Y0iOTedV11AScPsv7H7DsGw31xzOKuS1x77J4YKSsSafAdi55Bym5xSQs38nr1x3N9FRqGWTIajLzMn1IOUtZt+8JRRsf5OI148mK7z+vrvSOpahCAr9KlHboSNi0xmzsebMYfvsRVS+WY8tybTkFLJ13hlD9lUkQXW2Qa/ppBQEHIx1772NyIGDzNqyhphqoEd62VM5F7u2llmajKZISEJgOdAdtWjqNNPWxhqM1rBFYVDj6ffez5LNrxPo6UAAimViAcv/+99Z90A5LJhPLBqh1xKofYyIu4ahcx/OyZAELCoO8K7KDMoyhgY8VVWltraWXbt2sXbtWhYsWJC0bHasJcET0bMxHHw+H7W1taxevZr169ezZMmScWlTnSicEGfjjTfe4JOf/CRbtmzhggsu4N57701w7Tc2NrJq1Sp+/vOf8/LLL/PJT34ycaNHS0+niv/8z//kZz/7GZFIhAMHDnDw4EEuuOACrr32WjweDxkZGRiGgaZpY1qEHK/JPHbPPcg33YRoaxsg9hfHRRVBnnmjJe0UNLgMGrNzDRoO9g75bFpII2jItIYtwmZ61xk2HapzDLYcSa1MS5UEWV4Zv+aWCB3T4ZAII/A4oMgCQ5Hx6Qq5fo29rdG0WbNSgRKLMvPQToz5s7ENDy+8mVzfxKfJTM8yaOyMsnPQpFmZbeDVXQascBrlZBIQNGQMGZdKCqhp2kZTfs3IY5agMtvrlkXluc6FKg99ptNZtKuqyqJFi9i+fTv19fXj6tkY6bxx9qjDhw+zZs0aampqhizIxpvZGO/+Uzh9MRY7VVxczL59+xLH2L9/fyLTHkdJSQnLli1DVVWmT59OdXU127Zto66ubtixjNQgni4mQ9RvODilpZjvfjfKiy9ihKMcOPN8jhSUjRpt6iidTr4usbs9wlnffIyS+r8P+LzhvTfz1qVXjniMiCN44323sOi7Xx81qyELyOlT5NZktwKg13SDbdleme2tEf527Z2s/P8/ghruoT2/mF0Lzxz1+iXhUt/qsqAlbA1oGrcdVzTu5ZW3UbllHcKxeeGqDwygZVVlwcxsg66YM6ThPB3sC+TS9dknqLpnJdl7d4Fjkxnt5l8/fy9hIfPcTQ/SnD92vaPhUJ7pYUNPLj/89De4+xOuRpgAZMvEE+7mwg0v89KC+RiKRIctJbIc/TFSqZRHkThrWpDzKzLI9I68ABdCUFFRQTAYZO3atcOKvE526f5YabODwSDl5eWnbAb+uI46bth/85vfcPbZZ/PLX/4y6XbXXHMNAE8++SQdHa4YXCrpaYBnn32WV199lerqar785S8P2CeOW2+9lfvuuw9d17nlllu48847mTt37kRcInD8JnN7zhysZctQXnuN2B13DPlcVySWV4b46ZstY8okHOkxmZfvYdMh1+EoDqjk+hSae60xqZbHYahDXzRNFmR7FHy6hCoJTAd6ohadUZse00GSbLIUBb8myDBkcn0KXVEL0yahKh5HSUhl69GJE7orD2nk+BW62h16ChbQAxCxqY0cZr1+rN46pMuUZhrs7xjqZEzPUDFUifYY9IzFyVDEkHpjOTA0UqfJgmkZGrPz/VRm6XTuf5vcbI2KipwRJ9F0J1khBFVVVRw+fJgNGzbQ3t5OKJRcxG8kpDLx5uXl4ff72bhxIwUFBZSXlyfGOuVsTGGiMR47Fddy2bVrF8XFxTzzzDNDmKauuOIKfvKTn3DLLbdw9OhR3n777VEzIxPpbEyoDlQKiN12G8qLLyIci23X3JTyfr0xB1lAS3klVX96gYjPDw7oPV20TKsa/QBA45nnsXH6wgFZjYAmkeVV8KkSkiSIWQ7dMRsH6DYdus2B83NbxKLYr3Jg5nwOVc6mdHO9m9UYYd6IN1B39tmw4ZSZOqM2hWctYvvsRczYup4VP/kaK575OpIAQ5HY8K4reP2y8emGlARVHCBs2my988Oc84m7sYUgsH8PIcdtRu/xTVwVhgCyve71K5KgMKjzqlzDmTVnsvCNPqdRSES8Ada954Y+JyoJOxPDOxkhTXBhdTZnlYeSridGQk5ODj6fj4aGBgoLCykrKxsg8jrZzsZYgnPxfbKysk7ZRvHj6mzEjfpDDz0EQG9vb1IWqPiNve+++9I6/uWXX8773vc+dF3nqaee4qabbuJPf/rTkO36e7OaphGJTKwCsyRJCXapyUb0wx/GWrZsSFYjjlyfyjunBXl5V3IF75EgmSZhU2JunoEqSRztNceUwh2MzojNoiIPh1s68Pj8dMVcZ6HLtIk6DtkemYAmk2nIWI5Dd9QmYjn0mDapVBzFG5rHg2yvTFlII+a4pV8tYQv0genZ/ICGiNiEDIU8n0xTjzPIyXAo8Tp4DI1uWyaSRrJFAgKGhEeRhm1qjGTnoLf2ULVzM9U5BhVXXcr0TAPLdGvKZVnGLljMW2+9xYYNG5g3b96wUZGxTrJ5eXl4vV7efPNNSktL01YDT3Xi9Xq9LF26lK1bt9LQ0JDosZiIMqopZ2MK/TEeO6UoCk8++SSXXHIJlmVx6623UlNTw6c//WmWLFnCihUruOSSS/jDH/7AnDlzkGWZxx9/fFQihIlUED/eVJpOaSnm8uW07t5NcHYVnaPQi8cRsx2K/BrbLrychT/7PsKykKNRDs5ZyJFZqdG3xiSVWdWFxCzQDx/E7Oom1q92q62oDGcUjS0HsAWEDIW/Xn8Py577ftKshqEIMlUbW0j0WKRcHtsRtVnzoUfY+8ufcumvn8bRDWwHlGiEIzljV5SOOxk9MZt8v4rpOKyvu4DqqhpC+3cjmyaybbK9egFLXnuBw4XlvLXwrLTPo8uCPL+CT5UxHYf2sEXEchJEJ8WZBj5d4pefeZKa689ENaPEVI2/nf1u3tKzhhxvpH6M8kydecEY8/IMSoozh3yeKjweT4LxadOmTdTU1Lj28jjYg7Gc43SwU8c9HxNfHPz617/mr3/9K2eeeSaFhYUEAgFCoRCZmZl4vW6zVHwyF0KklJ7uP2HffvvtPPjgg4yGiVRmjeN4TuZOZSVmHyXjcKjJ9bD3SDvtpsBr6CgSyCLOQiWO1Wbu3o0tJEyvj5juwQr5ALdpbFvz+PUxJMtVE+0um0YEQW6mnxyvTmfUwnKgN2bTHbOJ2YwrcxJXGE33G9CETXWeD1UWtEUs2kahbNSwOE9rZy057O9HxyeAmbkeZFmiNWzRnUaSK+5kGMrIVHdZu7ex7Omvc1P9X1FUhZ5//AMn210Q9b9zkiQxe/ZsDhw4wJo1a1iwYEHi/eqP8SzaJUmirq6ON954g/b2dmbPnp3yxJjOeSVJYs6cOQmGjnnz3AXHeJyN460EPYVTA2O1UwDLly9n+fKBjcOPPvpo4t9CCJ544gmeeOKJlMczkc7G8WKj6o/oww+zY80aKjK0YZt+k8EBLN1gw3tvoe4H3wAB666/c9T9APK8CoptocgKXZEIl9xzPWpvT4JWVo6G+eNnvkrjoqE9EkPGbzlkeGQaZ87l+Ye+xDWfvpviLevd47hUjvz1zo/zxrvfm/K19b/GYFkhLbfcQeSVX6JEehG2TVt+EduWvCPt48WdjO6YTYHf1clKkI/IMv/3X89x23Xn4T3UCEJQsWUd1ZtWsf6sS1NyNoKKRV7QVfTuidm0hy3aIzbtfb2bsoBcr4KhuM3lMcuhIseLbXv43v3/zh1f+Thh3cOL/3LzgOMO52QIYH6hjwsqM6nM9rBz507kCZizJUmipqaG/fv3s3r1aubPnz/gPZ4sjMXWxp2NUzWrASfQ2QB45ZVX+PGPf4zjOBQVFZGfn09OTg633nor55577oAvPpX0dFNTE4WFhQD86le/Yvbs2aOOZyIn8TiOZ01sqpidIVjXqWE64GaJ44vjfkvyorKk+3bFbHI8Ckd7x5itsW3K92zFysmhpcwtFzAUgc+yMB2BJkvs65i47yBsOhT4VZq6RjdqElAelPBpgh5bpdu0YYTL9EgOBVs2Es7No6uymoAk8O7poCtqI4DZeR6QJJfzPJZeudRomQxwS7VqC7zUfPhhlA0bIBgk+v734wxDnxlHcXExfr+f9evXJ6WUHW+GIK6AumfPnoRTExcQGwljWezHF32bNm0a97t7OkSMpjDxGKudmiycypkNADQNR9PI8ihkEcPasXPAxzFfgJ6CoVH8nphDvldl24WXU/vT79FSXjlqVsOjCEqCBl1RB0dSiNmQHzB4+9KV1PziR5i6gRwJ05OdR9P8JSlfQlfUZlqGxraWCAfOficlOzYT0w2itoMajbBvwehOS38IoCig4lEkOqM2SsjHX957Bxf+4MvIlsnGd15O4c4tAIS9flqKp414vLiT0RW1KQyo2P2djH5wZJlV93+SCz55H5aQkM0YwnHANrnkp9/k99fck9hWlwX5fpf21nIcWsMmUUsecFyPIsg0FGTJzaIIAX5dRhIQi9pEbbffJMOQWVV3IdXnX0nE8NHWx4KVTIQP3JLgM8qCnD8jgzz/MXKQdGxVKs95SUkJgUCADRs2UFpaelzKqKYyG8cB8Rt2+eWXc/nllwNw8OBB/vGPf/C9732PH/3oR5xxxhmce+652LaN3JfiTCU9/bWvfY1f/epXKIpCVlYWTz/99KjjmQxn43jXxKYCjwwByaLTHttXnuMbm7NRIEXRjx6kfY5rIJRYlKJsPz0xB6uvpCegj7/saTByvPKIzka2ZpHrlbBkDROJbodhCbtkAfk+FVkStEcsjs5flPgsajucax9lX9TBKi2lLWKTDoVKOk7GwnwPMzJ1hBBEvv1t5He9CxSF2Ac/OGT7pHWuoRBLlixhw4YNdHZ2Duh9mIhaVSEE06ZNIxQKUV9fn5JOxliby/1+P3V1dfzlL39h06ZNiZKUdDE4Kj2FKcDY7dRkYSIz8Cdakbim4a9kP/wQluFmYqVohO6iUv743z9Lur1HlbB0gz9/7HP0ZA8fVBHA9JBBzIau6MDr6445qDfeiv3r/0OYJpJtUX/jvTgpNNsKIKDJ+DQJRRIsyPfw5sUrqX3mO4iYiRaLsuPci+goGtofmgwhXSLXqxK2HKKWQ6wvg+7TZNa/83Iu/J+v4O1s4+xnv8vZz30PORajJ5TB1596MenxspQYHq+XzqhFYUDDIbmT0R+bz7qYqlkLKd26HinSiy0r1P79JY5Mr2bHXR9BkQS9pk1b2KItYvfZNRc+ySLTb+A4rmNjKAKPKuEAwnRp9SNJzt/dF5D74a2fRCCGdTKCusx5FRmcMz2EXxv6Xo2lv3A0hEIh6urqWL9+PbFYbFJ7N8YSYDsevSSTjZOirb2goIArrriCCy64gB/+8IeJZrnBX8ho6enHHnuMxx57LK1z67p+SpdRDThvczPGLbdAdz8FcY+H8Le/jZBlsqTImJ2NHtNJi7I2yyMTNGTawjLh0ukIy6R0xxYi8xbQOcgQ2JNwq8JJBIAydInyTB1HuI15o3Xq5HhkfJrsNvjFhmaqQocbyWs5jJVXQIM/E9KgAE7VyQjqErX5XioydaR+2zlVVZhXXomTlTVqVqM/dF2nrq6ON998k82bNycW6RM5mWVmZrJ48WIaGhro6Ohg2rRpwx57PGVMcZHMzMzMRFlVf7HBVHA6RIymcHyQqp2aDBiGkWhCHy9ORBlVf4Quu5jYv38BYia2pqOaMbatvH7Y7TsiNkFd5uAIWYg8r4JfU+kdgR2xzRti31XXM+N//pvO/CJ2n3PhkG0kAUFNxqtKyJLAtN3mcbtPITu+jSfoo/7a2znj6a/hSBL1141c2iULKA5oKLKgK2rTkaREtztmEwwa/OIjj/Ger30KJRbDUlUcIVh1+Q1Dti8Jqli2TVdUkKe7i/1U+0QcWeYv3/oR77toAbIZQ7ZMZMsiZ892rr59Bd/82nMAKAKyvAqGIhG1bDojFgZu83rMdhDCXRv0pNCjatoOM/O8HO6M0p6kTDrfp3BRdTZ1pQEUaWLITNLZVtM0Zs2axebNm6mvd8Vrk9GtjxdjbRA/1e3UCRt9d3c3u3fvprm5md5el+0oGAzS3NzMd7/7XcDV1phsTEaD+IlyNpzMTOjtRTp4ENHejjh0CNHVhZPjMhFliCijyCgMi5jtUBoa/cULaBIVmW50pa1vQikgSnHbUTrnLyIqhkYqek0HrzqxXnvUEhhWDF0W1OQZ1BZ7yfKrdMTshNFIBq8qUR7SKPCrRGxoCVsDGgoVAaUBjcrOo3inV9C1+Ax6S6eRKaX2rAogZEjkBVS3kX2YSccQJkvzFK6alUllljHA0Ygj8sQTRPtEv9JBvFY1FAqxdu1awuHwhEdODMOgrq6O3t7ehCBRMoyHNheOqYDX1NSwceNGmpqa0tr/dJjEpzB5OB3t1InObEheLy23340cjSJFI8T8Qfa9a2RxvGwjeZDMowiqMj3IkjKioxHHnmtuJBoIUX/jvQhVIdOQKQqolAY1Cnwqfk3GdNym7dawyyQ1OBhmO5DtUdh6yUpiHi+7lp1HxB9A72hL/NB3f7M9MpVZOvl+N5PRNUof4LRMgx2Lz+HlGz8COMixKJaq8cZZF6N3dyHHopQEVYqDKu0Ri4Am4VXhSI81olBvf6iSYFpIQwv5WbfyxsTfHdzKA92KccHff0u2R8ajuv2DmixQJQlZkohKGoe6TVp6rRGDjwL6+kLdvhap70cfxB41O8/L8vwwHz0zlzPLgyM6GpCeAzEW+xIKhSgrK2PNmjW0t7entW+qY5oqozoOiN+03/72tzzwwANcdNFFFBYWUllZSVZWFq+99hozZ84Extf4mSpOq54NSSL6sY9hPPAAqCpEIkQ/8hHoUzAXOBQHNPYMI0I3GnR7+OiFoQiKAyqtESuhbJppyPg1ha6oSqTYN+KxA0TpYfxCNVq4h9yWQwR6O5lRPo19Ho0ey6FnhAVBvEwqHO7BlIykyqw5HoWQrhAxwbTBLBjIupTf00arMXzJkACChjQqU1ZAk1iY7yV2cCflwbKkTkYC45h8hBCUlZXh9/upr6/H5/NN+PsWb+hubGxMNOANzjxMlJMTDAapq6tj8+bNtLa2MmvWrJQmZ9u2T0mBpClMLk5GO3W6lFEBBG64BvPb30Jvb2Xdbfdja0MF1vqjK2qjySIhMiuAaRmGS30eS/1aooEM3v7fZ+kN5uCzIGZDbAwq5R1Rm6K8IC9+5qtc9ql7qHztJZw+/SLJNFn/+Dc5cuZ5dMfstERv46W8m85bzqXffgxfewtR3eCDd1+GYtscnLuI//vCdygKqEgCmiMOkFoJnyygJKjRE7Np69Pe2vf/fYZ5v38Oo6MVgasqnrl3BzP/8Ev2LD6bw4HslMlaXJravn+P8E5Mz9Rp7o6xMN/DRdXZlGZ62LDhSErngMnLbMCx9z5Ot97Q0DAiy+JY3qN/VmfjuI8+fsMuvPBCvvKVr1BcXMyGDRv4whe+wHve8x6ampq4//77ASa9DhYmh43qRPZsWBdfjF1QgGhthexszL6ys7iBmRYce1qww5YIDeqeViSYnqkR0GWawxa2A14ZijwuS/ZokZw4skPBtMcT1N0sxOwcDzW5HmbF2ijMy0CZM5vexUtRCvIxRzh9tkemPKQRMhQ6YzYx2RjAYOVRBNNDBtOCHlRJoSc2fBlZ5pHkEXUBZBgS+fFMxjAIaBLnlvq5enYm1dnGSJVVE4qsrCwWLVpES0sLR48enZRzFBUVMXfuXBoaGjh06NCAzyZyElVVlYULF+L1elm9enUiEj0SptioppAMp7OdOtFlVABGwMehW++iJ7dg1KwGuPNuoc+1XYv+/gduue58zr94GRdduox3L1/GmR+5ddRjZBkyISiElDcAACAASURBVE2hOaeEkEcbt0J22HLoqlnAtgsuw5EE6AZCkjCDIfbOq6M7SentqMc0HcpCKrai8urVd+BIEqbXD5pOTFHZu3wlmR6Fo70WPSkK6koCykIqOV6F9oibqZf6RAwjNqz/wlcGbm9Z5K9fhdk2clQ/nrXon7kYqf9NFQ65aowss40rsluojOxE6jkmjDsZDkS6zkb/7eN06y0tLWzevDlpBnMswbIpNqrjjKysLFauXMnKlSsTf3v++efZuHHjqE2lE4nJymycsMhRPLtx772JrEZ/ZHsV/KpE1xgmQiSJ3JBBe7uJwCFXt7GFnIh8yI5FpupgyQa9TnovxUjBKUm4aeugrqArrhMTseyEI5GYdHMLBuzXYzqUBjT2dR77fr2KRI5PIdon5DQ4iyGAQr+KR1HojTmETUiFRNerDKXrCxkSxiiZDH9fJqMqa1BPxnFsCPN4POTl5dHR0cGbb76ZclYgHQSDQZYuXcrGjRtpb2+nqqoq8Z5M5HXGm9QzMjJYt24dVVVV5I3Q03I6RIymMHk4He3UyZDZAPDeegOvLDlvxKyGAAxZoCluf1t1lof2mkWIaBRL13EkCaWnh6MLhu/n8CiCoKrQ3c/IZL38Egue+vKA+9BSXcOqh7+Q8vhNG0qDKi133I300i+xbJdy/c2b7sHWR2fiGw55fpW97TH23Xwn2/dsofz1PyKEIOb10RW1Kfr9r9mz7B1ERxHiE0BJwNXXiGdXBK4t7YrZhGMO2R6VA2dfSNe0Cvy7XYYwU1XZfua7aCkdKDTZv7oplTlbcSwCIkKWYpHrEeR4NTweA10PYhjlRKNRduzYQXd3d1rlTsfL2QA3kDBv3jz27duXYFnsr7kzlmDVWAglTgc7dVI0iMfxnve8h3A4zEc/+lGefvppLMua9KiRrut0dXVN6DFPdOTIuvhioo88kshqwEADUxbSePPo2HQzIrZNjmajqDLhvmZzSUBJQKPXdEbMJIyEqOWQ5ZHpidquQ6TJKJLAdtyeDgdc2t6YQzoKGt7dO5EDueRn+RNsUsnSwiFNQjcjGL4AMdulXEwHcl95kMDNuHiSsGj0h1+VWFDgpXqQk3GiIEkSlZWVtLa2Ul9fz4IFC1Jqjktn0aKqKosWLWL79u2JBjwYexnKSOfOyMigrq6OTZs20draSlVVVdLJ+nSIGE3h+OJE2KmJLqM60ZkNgEyvRuH0Iizb1XtyS337RPQcsOyhmeSABo1FJRy44N0U/+kFLN3A1jR2XzlUZVsAhtmDqgYHOBoAPQXFBPfvIapqIARKuJemurNHHK9HFgQNGV2WAUHEdLAc6M7OZedlV1L53I+JhjLZddmVY74nkgCvInNWaYDuqMVbd3yQqpd/DY6D48B5X38UJRLhmaeeo2X68M5GccClwe1PbJLlUQibrpZVlqHQETlW4rX2Wz/kvOVnIxwwdQ9/ven+tJ0LXXLI90oUBTRKQwbZvpHth23bLFiwgJ07d9LW1pZy79Nkl1EN0fnoKzkOBoOsW7eOmTNnkpOTM+z2qYzpn7GM6oQ5GwcOHODll19m4cKFGIaRiK7u37+frVu3AmOrh0sXpxMbVQKSROzmmxO/Oo6DaZqEw2EOHz6M0htB4Ed2LCTHQWAjA4rsMnDIkowkSUiSjHT4ME5PL2YgSCwjCxuVDC+oEhzujhLUZWxHDGGYShfFAQ0loLOn022CDFukTn01CJ7mw/iPHkY1YziazszeFvbFhgrZaZKgwK+hCMltLlQ1xpLwAbBLy1hqBNjf3oWtDL/w8KluJqM6e2QnYzwR//HUkVZUVBAIBFizZg3z5s0jGBy9vC1dGsKqqioOHz7MmjVrxrXoGe0eaZrGokWL2LlzJ2vXrmX+/PlDtD/G26A+hdMbJ5OdmkhRvxNlnwa/s1VZOm8ciaU87/bG3PLXbTfeRfGff4cS7uXt999BLBgasF2GJiMLQUQJJmU77Jw1l+a6s8hct4qYqmHpBluvO1aKpUiQoSt4FBkhXMmkmO06QD029A94ZRoqb11/BxW/eXbMWQ2vIhHQZXpiDlFbENJkNEnGmTePQ1U15G1/E1PXUWIx9teeQcv06qTHKfArKEIMqFzI1N2m966oRY5HpTN6TIAvjtaCUra/+ypm/P4X7FlyDm3Tq4Zjgk/Ap0oUBlSK/CpFAZWMYZr4R4IQgurqao4cOcLGjRupra1NKjrbH+k6G+ks0kfaPiMjgyVLliSy8xUVFWMuiToeDsrJhhMm6rd//37uvPNOMjMzKS8vp7KyElVV2bhxY4LX/Hjc3NOBjco0TSKRCOFwmHA4PODfeb/4BcaRI8hCkG/b+P1+lBUrWFazgKOmh6PhY9SzkgBFEqiyQOn7t5Sfi7qxASc/G8fnRkscx92nPKTS2BUmfa3uY8jVBH5NI+rIOCI9el1w2aO8ipTIgkQsB7OgaIBAlI6F1B0jPr3meVUCmkLYdFwjMo7xexQZQwjk3Dz8QsBbb7K3pGLIdj5VYkG+h+osw1WcPQkRnwBzc3Pxer00NDQwffr0hFBmMozVKcrLy8Pn8/H666+zf//+YRvwRkIq0R4hBDNmzCAjIyOp9sfpEDGawsTjdLZTJ8q5TlY26VElyjSTrt/+AWEfi2w7skLzuRfgJCmxEo6EXVrKgfMvpejVlwZkNTRZkKkrdEedkbRZAdh550dYcs/1KJEI+666jpzyImQhYdoOff3TpCItZTuCcE4e2656P7n1fyd3/arEZwfOvZB9F1+edD9JQJahIBBuk7sjyNRluqNOQidEVyReePQb3HDTpWjdXSAEEb+fc5/8PFsvuZIjVXMAyPPKGIrUR6nr7htQ3fvdHrUISRYIdYiTAX2Nu0Jmy799Cd+Bvbx+84eSjtevSWRrDl6rm4UzSghNoD6WruuUl5ezfv16Zs+eTVZW1rDbHs8yqmTjXLx4Mdu2bWP9+vXDZsxHwlgbxE91IpPj7mzEv8hly5YRDodpb29n9erVNDQ0sGPHDm677TZuvdWNMByvSXwyMhsTlaa2bZtoNJrUkYhGoziOgyzLGIaR+AkGg+Tl5WEYBv7HH0f5y1+wNQ3btlFsm94rrsDKyaAIV/FzW7OJ5QzVuzBtQNaJ1i7t+8PQ8YV0mfZI+tSPGa1HyW5vITx3IfF4neUIivw6+zqHGlVBn2OhSshCYDkk0ti9o/RVRJCpfGsNVmY2VmUVESv9Mqn+UCWBR5ZRk5TfZPZ2sbff7z5VYn6+h5lpOhnHW8RncLTF5/Mleiw6OzsTPRaj7ZcOfD4fPp+P5uZm2tvbmT179oRFoQYjOzubxYsXs3HjRtra2qioqEi8p1POxhQG45/BTp0syLe6KH30AWwhgSQjLNfQ/OP5VzGTOBuW4y7St975YQ5c9C+JrEauRyFqQneKWfauWXPpWbIM3+q/sfeGDxA140Vc6SFsOgQ1CUdRKX31Jew+oUDJNDmysG7I9h5FIqjJ9JpuT2CmLqFKoi/4NfD8ERPMsjK6c3LJOLAXW5KY8dofEY7N7jPPJ6g4aJhEbZloHxmLYkWQBHTFdIKSRUBRiQ6z1JP7NXTbms7q/3mOlhbX/gY1icKARlFApdCvEtRlWlpaaGmJTaijAe5cHgqFEvpMhYWFlJWVDbvtZFHfpnJsSZKYOXMmBw8epKGhIW0tjqkyquMI0zQ5cOBAYrFx0UUXcdFFF52IoZxQBfH+5U3JMhOWZSGEQNd1DMNI/D8jIwPDMNA0bdQXI/rxjyPX1+NoGk53N1ZNDdY55yQ+96oSQsBYfaOAlp6zocuCIr9B1FdKuHToZCIJGVm44zKUY45F2HSwHeiJwWgGwZAFgab9GI0HEKEMYsWlqJmZ7J1elY649xB4FRlDlkcsf/JJ7th8zYeYX13IzOLMkzaTMRiDnyVFUaitrWX79u2sW7eO+fPnD4mujNcpEkIwf/589uzZk2jAG1zqNBzSNSSGYbBkyZLE9cybN++0mMSnMDk43e3U8cZwGX8nPx975UrEL36JpetIPSYHV7wXM3P46HavCVmlhTRl5ZDd0YzHNAlbDiqgIggXFI0olhrSZXRFZu/HPkPmP17BONiIcbAx8Xm4sJhoVk7K1xbQFN6+5iaqfvoDhG2D4xDzBdj1L1e7147bMyEJQVfUwbLdMq2emEP3KDYtW4U/ffTzrPz4zUQ9PiTHobW8kugZy5BkBbNvGWfIAq8q0RoxyPTIOI6g1xzdyeiPAp9KWdB1MJKpd080Bo8hPke/8cYbdHV1JSUrOR7Ut6mgoKAAWZZpaGigsbGRoqKi0XdifKJ+p3LJ7wlxNnbu3Mmll17KokWLqK6u5gtf+AKmaSa+5ONp/Ccrs+E4DrZtD3Ai+jsSsVgMx3FQVTXhRMTVkOP/noimQ3vxYuzaWsSaNSAE0YcfHjIJBzSJ5t6xeRuSSO27kgSUBnRsRyZqQbKCUFm4qdryoIdDPbGUHAtdFvg1GV2WkITAsl3nhGkziEybcez80bGVIKiShFeRUUag9BswHuC8p75IdVUR9rs+MqZzwtgX8fGM2lhqQpMh3mNx8OBB1qxZM0QrYyIyMHEGqVAolLTUaTiMxVGQJInq6upEz4iu61POxhSS4mSzU6erswFg3v9BjF//CtuMgSyz78YPJN1OkdzMsiK5C+YSQ2LBpZcixaI4ffZSikZZ97WnaVt0xpD9NQmyvCpRS7h6SdMqML7zdZZ+8d+wPG6vgNLbw76V17P1gc+mfG0xC2KBENuuuYmZP/4ujhC8ccu96D4PQd1lNuyNQaYhkaW7zlJXihmYoKHxRt05RD1e9J4uBILAoUYuu/pC/v7wYxyuO5NMr05L2MKDIM+j9vVsDD2+3DdXD56zswyJqiyXwn4kTFbGvf9x4wxQu3btSkpWciLLqAbD4/GQnZ3N4cOHaWtrS4nJcUpn4ziiurqav/3tb/h8Pvbs2eMORJISP8cTY228cxyHaDRKR0cHR44cYd++fWzbto1NmzbR0NBAe3s769atY/v27Rw9ehTTNPH5fBQVFTFnzhyWLFnC0qVLqa2tZc6cOVRUVFBUVER2djY+n29C2U0iDz6IiMUIl5ZinXvukM+D+tgnj6g1+kNU7NcoDXgxbTlps56IRcloO4whCyKWQO1jJRkMTXKbA4v8KiUBnUKfQUjXkYWCaUtELTFsv0csJw+5tyelaxKAT1HI0jVCmpq0XGowAjrM+P5XmXf3+1jw+2exbxud933UcaQ46VmWRXd3N83NzTQ2NtLY2Jh2z9Boqd2CggLmzZtHQ0MDhw8fTnm/dJCZmcnixYvZvn07u3btGvUaxmP48vLyWLRoEe3t7TQ2No6+wxT+6XCy2alTvYxqJGfDKSvDXrECra2VthVX4S8rJttQyDIUMnWFoKbgUxR0SUFCxrZlYpaEpuk0XfIecBxsVQPHIZxXQNuCoeVLuV6FoKERtQbOGYevvwWhasQbEm1FZd/VN6R1bVHb1fF4+5qbULs6MdpbWfitx3n3+Qs4++y5FL/4HAauw5FKH8gASCp+TaJ9fi3CcXCE6xB5WpvRCnJxhETUcsj3qkQtktLay0Ikshn950y/JlGbr7Oo0DOqozGZGDyXCyGoqKigvLyctWvXDmAMPZmcDdu2URQlQYm7Zs0awuGRmT7Ho7NxKuOEsVEVFBTwgx/8IPF7qjfyd7/7HR/60IewLIvbb7+dT3ziE0m3e/bZZ7n66qtZs2YNS5YMz8E9XOOdZVnDljfFYjGEEKiqOqC8KScnB13XEw2EixcvTumaJhv24sX0rlzJvrPOoizJQx7QJcZaX2Q7kGEotISHzqA5hkJA14haDEuJG9y9HWHoREqnJf5mCZlCr0LYdjAUCUlIxzIWDH+sESFJ+I8eor10+rCb6JKER5GHTTEPhipBpkeQ4XGb6vngB+D7TxG9804IhUbdfyTEjbLjOMRisQHZsf7P4+CeHU3TsCyLNWvWUFtbm3I9aSrOSSAQoK6ujoaGBjo7O8fMxjESDMOgrq6OrVu30tDQwNy5c1GU5NPUeCdgj8dDRkYGkUiEvXv3UlExtLF/Cv/cGKudmmhMBpHJ8cZo5cXRD34Q47W/0nTT3UTM1OaUqAUdd91L0QvPQl+D+Y67Pgb9gnU+VRDQ3YV4MvTMmU/3kqV4//E3EHD43Avpnl6Z+oX1werpRvGoHKk7i+z1q8AByXYzDO21Z4Gc/nLLpwq8qoyheNh47a1c+I/XELaN2tuDrWqcc+MVvPT8K3TnFPc1hw+EBEmF9nRZMCNTpdCvTOpifLzHzcvLw+v1snHjRiorK8nLy5tUNqqx9ngIIZg+fXoiOz9Sk/tY7Nbx7uGcDJxQnY14yUeqN96yLO69915eeuklSkpKqKurY8WKFcyZM2fAdp2dnXz1q19l2bJlw563qamJvXv38tprr7F7925uv/12br75ZnRdTyzg+pc3BYPBxO+KMvILatv2SSGa1B+djz1G9LXXUL/3vQF/twsK0JYvR5dhDH3eAPg622lRfYnfA5pMrkcjYolhJ3hDASsSJlY1c8DfRSRM5pFGnOoqjnaZY9btSAZPW+sQZ0MCvIqC1leGNRoEbhYj0yPh0wZlH3w+el58Eae4OOUxJSu1C4fDdHR0sGHDhgFObfwnEAgknNzB745t24k+oHjZUyAwsvgTpD6ZaZrG4sWLeeutt2hoaGDGjBkTPglKksScOXNobGxMXIPP5xuy3URMwI7jUF1dPSrd4hT+eZGunZoMnC4N4iPZRaesjN7X/0pGWHCoK3X7Gcsroumyqyj5+f/SW1TKoT5FckmAYfWiK6Fh7VAc++97iKo1rj7Gjjs/POo5hWOjSw6a7FLFm44MahYqJvu/9HWy/uV8cByEbdO4fCXhwtRtggAyDAlZSEQsCJsgC5XIuy7C8ngTGXphmcQyMsl75U/suvpfBxxjOCdDkWBaSKU0qJ5UvYQjzeV+v58lS5bQ0NBAV1fXpGc20qkqGew4ZGVlJZrc8/PzKS8vH3L+f9YyqhPqbPS/eak8FKtXr6aysjIRgbzuuut4/vnnhzgbjzzyCA899BCPP/540uPs37+fe+65h7KysoTzcM011zBv3jwCgcC4v9QTrrORBEIIPDt2oH/uc6D3MXxEIlh1dfQuX05Al4j0jG1lLx86BCUVaJKgOGAQs8SwjosiuanbiCVQjIELyNAbG5BLirGqZyKNg452OKjWsexLIoshCZIXbQ2EJh/LYigjTNLOoOh4PEOW7GcwAYBhGPh8PrKzs+nt7aWmpgZdH15ddyTk5ORgGMaAiNBISGdSliSJ2bNnc+DAATZu3DjmMY6GoqIi/H5/wqnJz88f8PlETMCnQ+PdFCYX6dqpycDp3rORgCxTfO/NlLzyar8dYf89D3DoxjuT7mLZ0HXXfVi/fTaR1cgwZDRZxrRTy+z2zJlPb2kZ3l07WfjAsX6RropqGv7jWxiKwNOnQ2XZbmBOk8FQBLKQsGz6tEJkrPwyuurOIPOlFwBB/p9/R96rf6Sroor6p/5v2DGoEoQMxe0lsY+RP0oCvKpAV7wcvvDdFP76Wfe22DZ6WwvewwcH3sIkToYASoIK0zM0NHnsz+/xzmzEEQ9ybd26lfb29pTZPie7jCrZ9qNl56fKqE4Auru7ExHLkWg1458dOHCA0tLSxOclJSWsWrVqwD7r1q1j3759XHbZZcM6G2VlZfzmN78BYMeOHTz44INcfPHFE3JNcOJ4zEeCEIKWs87CLi1FHDkCqgqyTPTBBwEIaIKjqbU0HDumGUPraEfWdMo8Eo5iDBtBEkDQEMQsaYgjYsjg372NWF1dopjLjplgmWNKPSeDBMjVM8nxaDimjUghehEfc6ZH4NOGfqfpljj1ZxLTdX3Y8iBwv6/xTi79I0Ld3d1MmzZt2GdzLBNgcXExkiTxxhtv0NzcnFJT9+BzjoZgMEhdXV1CSKk/Be9EOhtTmMJwSNdOTQb+aZwNwD7vPNS/vIqpqOA4SGaMjmUjq3v3ZBey7TNfZPa/PcTczz9InIHE8gfY/MLr2J7RM5eH/+2LTH//SjyN+xGSQIrFCC9aSkBVsBxX5VyTBEIWqJJLwevqNCU51t0fIfMPv8VWFaRwL5Jl0T0teWlWQBMYikzEdKlu49BkVzekN+aWi3lUiebHn6TgN88hHAdbkjG9Pt5+/+3A8AxT+T6ZykwNj3pqz3PxjHdLS0tCAHA05sLJoL4dfPxk9qN/dn716tUDiFXGk9k4GdeWqeKEORuxWIxHHnmEmTNnJpqjZVkmIyODrKwsNE0b0xfy0Y9+lKeffjrlfU6HSTwVCCFwZJnoww+jP/AARCLYc+YkaHCDuoTActk+ZAlFckX94pF/N5jvPugO4HR2YQd8EPIDxXiwaB2G0SqgCRykIY6IY8XIDhhYCGIzB2anUFWM7hbCwcwxXa8iuRkISXLHLoSAkmIyOtppU0Y2PHpfFiOoO5ixCOHuMB3NAx2J+DOTTonTiUA8IrRlyxY2b95MTU1N0nGNNWLl9/vJzs5m+/btdHV1UVZWNuEpblVVWbRoEdu3b6e+vp758+ejadqElVGdDN/TFE5OTIadGgtUVZ3wMqrjnaVJ1dmIve99aE88gdTdA6ZJxxnvoHdmzcCNHAeBiWNbfbztDr1nnwWaDj0xHFlGikbonVGdkqMBEFtUR8/5F+F79WVsScPWBEfv+TABTSbqstmmXGrcvWAJLYvPJGPjWqRIBCEEps9P2Y+/y95rb0ZS3OyLwC017t/y6FHdXHvY7KsE0F3ilIgF6F52XnczM37yfWxdZ8c1N2NnZCEz1BHO6GOYmkhNjIl8ZgY/C6keV9d1ysrKqK+vZ+7cuYRG6I+cTOrbVI5fVFREIBBg48aNVFRUUFBQMOaejVPdTp0wZ0OSJJqamnj55Zc5evQo7e3tnH322eTn55OXl0dhYSFFRUXk5uZy4YUXAm4kdd++fYlj7N+/n+J+9fGdnZ1s3ryZd77znQAcPHiQFStW8Ktf/WrYJvGxslGdaoi/EOaKFWiPPYbU1ET0k59M0OCqsqAo6PZZ9IfT9zOERco/sA9A2I47G/d78TwqaJI7UQ/YFggZEpajY43AZaVmBAinkC2VJVAkCVki0XcxbAQ/GEJpPISZMah5y7GJdB5Ft3tp72qlZYQSJ8MwUFV1Ug31RE7q8SjL3r17Wbt2LQsXLhzSOD7W88XpmxcsWMCbb745okMzGOlMunEK3jhl7dy5cyckK9G/wW8KUxiMsdipyRrHRCK+8D8ZnI3+elPxH98NN1D4zW8iJJmmu+9FsXuQZQVZlpFkBYQMYqDmjyBI070fp+RLj4IQOIrKgQ89POq4VNnVqIjagqYPfYKqV/+E0tNN58IlGK+/hgH0Vs+he15tytdq2rDvno+Sddt7gRiOJDP9+9/AzMyi96ZbiUkKsX6OiyRcJ8O03OyGTwO/7gboBjs4jQ99lpIXn0eybXbccMeQ79CnCiqzNHK9J7RwZVKRlZVFbW0tDQ0NlJeXD6txcTzYqEZ7N+PEKps2bUqUgE31bBxHyLLMT37yEwBWrVrFlVdeidfrpaKigvXr1/PnP/8Zx3EGTOJ1dXVs27aNXbt2UVxczDPPPMOPf/zjxDFDoRBHjx5N/P7Od76TL33pS6OyUf2zOBuO44CiEP3sZ1GfegonEEBqaEhsEyyt4IgcHNPxLUkhY2s9bbPno0rg6+vLGOxo+HWBKsvYiFEpc436tXTWDuRKlwQosoQsQJLcCFC6BtNzuInOuLNhhlHMbrxyjP1H9zJz/nx0vWzEEqdTEUIIysvL8Xq9SRvHx+NsxEu+ampq2Lt3b8rifGM5Z15eXqKPIyMj47T7nqZwcmEsdmqyMJF9gMezrzBOghGLxThy5AhHjhxJOBWDM8Tx4A7/+q9I//u/9MxbBIvPJBWZTwdB+NrrsL75JdTWZjqWnkPPvIUj7hPQIWa5joYmg1QzBzM7F23XdnxvNODbsgkRiXD4+lvTcjYAzPPfRSQnH/3oIZBc8dzesgpyv/6f7LvfZdFUJdAVQdiMOxkC1REkYa9NwJ8RYsenv0js0MCgmS4LKjJVitJkmEoHJwMrUrw0yuv1Jkpsu7q6BpTYxnEiejaSQVVVamtr2blzJ+3t7cRisbSUx8ciBHiy4YRb6j/+8Y/cfffdXHnllfztb3/jO9/5TqL2++jRo2zZsiWxraIoPPnkk1xyySVYlsWtt95KTU0Nn/70p1myZAkrVqxI+/z/LM4GHDNW5mWXofzsZ3iXL8fxeAAQPT3It91Oy4Ofw3LSf6ilnm70WJjMzmYiWXlD+zIU8GoyFhKptqFLqoImu6VQbj3qxPTD6K1H8Tz3Y/zXXYEnPwD4sG2bQwebkrIenQhM1qSem5uLx+MZ0jg+HhHB+H5xh8bv91NfX09NTQ0ZGRkj7juWaE3cyNTX12NZFjNmzDjloz5TOLmRjp06FTCRzsbgrER/dr3+JBhx2vhQKERWVtaoGeLIf/0XnrvvYWbtsV4HR1XY8/TPicyZm3QfyWPQdPsHmfboQxjbtjL3kmV9+6nsfOLb9FbPBlx7JEuCqOUGrHyau8C3HEHjE9+i/H0rcIRws/W6zsFb7kn7vqiSoPGDH2f6Zx5AmG4JXHD9KpTuTo5+9GEk4WpuxGz3/NERiFUA4rwkQkh0vucqmrpcKmTh2EzP1CkPnVwMU5OJ+DOjKAq1tbVs27aN9evXM3/+/HE1Y6drk9LNzs+YMYPGxkbWr1/P3LlzR7SP4xnXyYgT6my88MIL3HXXXXzqU5/izjvv5KmnnuLqq6/m5z//OVlZWeTk5HDuIBG65cuXs3z58gF/e/TRR5Me/5VXXhl1DP8szsZg4xK76y7kP/8ZEa8DVlXMm28ioEFbBMBBEq6qtyRwG+bo69ro+48D0NqKrek4oQD2OedCzMTpV3+qklA2KgAAIABJREFUCAh6ZCwEVgqsT3FIAvT5c8lXLdpIPQKQDIpw+prtHHxf+U/8X/oisY99jGjm6JSwpyOSNY6PN7PRH9nZ2SxatIgNGzZQWlpKSUlJyvumCkVRKC0tpampKeVMyhSmMBaMxU6d7BBCpMTo4zgOkUgkKT13NBrFcRwURUmUmuq6niDBMAxjAI3o1q1byc/PJxhMLXtun3MOTnk5cn09jqYhLBNLDRKtGF4Dw7Sh65p/peDpb6EdasJWNYRl4egGkaISJBFf2INpCwwFEIKYfWweCi89k47l7yH4q58DECkupfTfH8H2+tj9mf/AMTxJzy0JkDERjoVuGMRsQfuNt2F/4RGkiCv0JiwL77atlDxwL41f+obLApmkVGrwcYeWSskIIM9woPUAFZmzUrqn48VEBcHiz95E2BwhBNXV1QNsQZzKPN1eh8nOhIBru+bPn8+mTZsoKiqitLR01GNMORvjxF133cWXv/xlrrrqKizL4gMf+ACrVq3i85//PI8//jiKokx62k6WZSxrjAITpxAGOxvWGWdg19QgrV8PkoS1fDlOZSUBG6KO6MtuHLvvDv1k//oHxAb1PniPHiKckY+wbUJexT12Gk4GgMcMo+oaZGdBb687E6fIfS0JB01yHQvXwTgWEQLgtpuxX3+V6F13pTWm443Jfu4HN46PNU073Dg9Hg9Lly5l06ZNdHZ2MnPmzCGT5UQ0vWVlZZGRkUF9fT2zZs1KmxFrClMYDSeDnZpoxAX2klFzxx0L0zQRQqBpWlJGPU3T0rrmsWRTYo98Cumaa90eDFnlyP0P4IwQVHAQeIIe9jz6BDPuvxVwcIRE0x0fxMgMAG4GId4jEbMFyVjWW+68l9BzzwCgHWxCbzpALCcPpGN2SBZgqG6GxCYuNusGxuJlUJoic+jGOyj89teP3QfHxrdtC/Ke3UTKkwuJuuXBwy/GvargjGIPdqSb/W0nF81+qhhrr9xw71phYSFer5f169cnbMHJ0LOR7Bzx7PyWLVvYtGkTNTU1I+p7nA69hSfU2XjuuecS/RSyLGPbNt/73vc488wz6e3tJRAITPokfip/eekg2UQf/fSn8VxzDQCRhx4C3GZrXXboMdNfBIruToRlkv33P2MvPQNbSk9/QZMFuiohhP/YHz0etH27iCYVRYo7FsecC2W0YWdn0/v882mN61RDOnoZ8cbxgwcPpl1HCiNPzrIss2DBAnbt2kV9fT0LFiwYcPzx1qHGJ/rMzMyEkFJHR8eIFL9TmEK6OBns1FjgOA7RaHRIaVM4HKa9vZ0NGzYMyEoYhkEoFEr8e6L7ocbibNjLlmHOnY+2dhVmZjZtV18/6j6yJGg/6zyiRSV43t7C/2PvzcPjOKu077uq90WtfW+tlmXtu7LAyzbhnYAhnjAkEJaEEDxsAcIXCAlDJgmBwBsScg0z4WMJMOGDyesMgWC2BJIZIEMm2NbSrc22ZFuyLC9xZFuy1Ju6q+r7Q37K1d3V3VXV1avqd12+LFvdVdVS93nqfs459+GMRphDAei/8y9gzRZ4P/IPYCldWDYjkkB3PwIdXTAeOrgpdGg9Lr7hb1D9wi/hefs7wVhtCLHYzNhHJog4Fkb9Zl8hy1G4+KUvo/oHj2+WZAEAw8A8exCsNbpkN57IoCnAYQQKzWSdo7GW5qHy2fA+j/ceKiws5NcCj8cj2y5WbTeqWFAUBZ1Oh66uLpw4cQL79+8Py8jEek4uk1GxMTQ0FPbLIr/kJ554gi+JyPXUUbYgFuiZq64C63QCq6swffWrlx/7utfD+8GPYTP0cdDTmx7eOmIjy3HgcMl9ChRohoGO5YDyUqC8FOxrZ7FWIL3RXEdzsBh0MX/XpuVXsVFdC33AB6PNzAsLAx1mfpVXpCuokz6L48ePy5o4TkiUnaAoCs3NzSgoKIg6frKvUShWEg1SSkSuB3KN1KFknXr++edxxx13gGEY7N69G/fcc4/osX/+85/jhhtuwIEDB+IamYjBMIxoeRNpxiZZCaGjnsPhgNlsxtzcHLZt2xb35kZtlPaJ+L/0JZh2vg1UIICWv72aHAyn7/sa1q95W9TjQ+zmuV79wG1o+tJnAZZF6eOPgWIZMGXlOPLhj26mJRJw6rs/RtMbB0H5fQBFoeQ3vwD17B4cHrwKofrLG2IULmfQN4IhsNTmYD7dpTkZ0BmxetPNKPy//x8AgDWasHzTrQiVXx5SerkfI/q69DRQaAIcpogs/RYmXrw2m80YGhrC9PQ0fD4fqqqqJB9XSWYjWVFeV1cHh8OB8fFxtLa2ory8PKnjZSsZExvnz59HSUmJ6C+2q+ty89dLL72EK6+8MmVTilNJNuwCJGLjM5+B+VOfAv3b327+B8uC0evhuPUfQOtNoBSIPbpEWtMTBcBipKCndTFVg256CiWPPoSqqQlQtTXw/uIXQLGy2RsasSF1pFInjhOkvsfLy8thtVrhdrt5v/Fky6jI0ESCcJASETbxGv7T5cajkbsoWacYhsHtt9+OF154AU6nE8PDw9i1axc6OsJnCa2treFb3/oWrrzyypjnn5qawuzsLBYXF3Hx4kXs2rULV111Fd761reCpumoGT/l5eV8ViLe55KUUaUTpWKDvvpKeK68Gpbxkc3eB25zo2ujSbx3gwPgMAP+m2+F/9/+XxiXFgGDERxN47U7vySpJNekA3Tt7djo6IJxZgocRYMK+BFo3Iaivc/g4sc+CcpqBcttlkwFWcBIA3qaA8cFodebwIFC6NLLPffIt+B4Zs9mj6TBgDOfvHPztcURGSYdUGQGbIbs2VTLhXsaYDMD2d3djfHxcSwuLqK6ulpS1l5J2ZUaG+KFhYW8s9bKygpaWlpy4ucsh7SnDUiw+fznP48f//jHOHLkCJaXl+H3+3mLvKWlJfzlL3/BY489hsceewzr6+vpvsykSae1oBTI9bAsC5/PhwsXLuD06dOYGx6Gv7oaLMeBoSgwBgMm//7v4fV4FAkNAKD9iXO7ZgMFm4mGXhdDaLAs7J/7DIpfPwTLi7+H7uyroM6dAyQ2F+YD6Q42pHH8+PHjmJ+flzbtV0YplM1mw/DwME6ePInZ2VneqUYpsVLeNTU16OzshNvtxquvvhrz+bmycGqkn2TWqf3796OlpQXNzc0wGo246aabsFekdPOf/umfcPfdd8c1NnjuuecwPT2NkpIS2O12fO1rX8Odd96JK664AkNDQ+jq6kJLSwucTifKy8tRUFAgaQZQJtYnpeekKGDlX7+zObCP3sy2e67dCV1rC6yGzbkSNgMFq4GGWU/DpNeB1hlgNelx/NF/Bc0woNfXQXk9KPr3H6Fh1zWw/uVP4ucCB7uRAq3TgQOF81/5xub/swyoUAjmY0dQ8d1/RijEgeE251PZLp2XonWg9WboDGZwkX2KegNO3vNlgKawfOMHwVZUbg7MFanDtxqAGjvgdAB2Y/YIjVyDoiiUlpairKwMIyMjWFtbS/icVLpRJYL0UXIch7GxsbwzLkp7ZoN8sHbv3o3Pfe5zeOyxx9Db2wun0wmDwQCPx4PXXnsNJ0+eBEVRuOuuu1BSUpLgqNlHpsRGPBtCj8eD0dHRMOcQW0EBvPfeC9PnPw9qYwPMrl1o27kTIegRSnw6ceKkAQ06CmZDvDpKDjowMNAh6NdXQbHsZrS12xH48pclN4prKIMEPKkD+uTesJNp4HNzczh8+HBSVsPxzu1wOPidotXVVVEP9nxw+NBIDcmsUydPnkRdXR1/LKfTiX379oUdf2xsDCdOnMA73vEOPPLIIzGv46677uK//v73v4/m5mZVXNdompbkRqUmyayJ9pYGBN6+E+Znfw7QNEINjSj6wXcAvR6+930QsNlAHBI54pQICrrhKxAqLIZ++SzA0jAfnAIAMCVlUecw6wGa1oUZmjBvejOY6hroTp/a/A+WAeX1ovmWv8eJvS+CkfhyGA7wfPSTODd7EK996vNRsYjCprAoMm+WZGUrubZBw3EcHA4H6urqJGXt0+FGFQ/irHX27FmMjIygq6tLsntbtpOxMqrXve51eOWVVzA2NoZnnnkGY2NjOH/+PIqKitDT04Obb7455UOSUgkJ5vEcBuTCcRyCwWBUjS4RFaSsRMw5xGw2Y2xsDMPDw9EHvvFG4N57QXk8YHU6GO+5B1RbJ0K3flTRdbJV1cC6HxCUvtEUYDHS0MW5uaPBwIAQ6EtLReCRR2D42c8AlgVXUoKQgjkqWw01BC4Z0Hf8+PGYE8eF55MbbElAPXLkCBYXF7G+vg673Z74iREkEgtE2Bw5cgSjo6Po6emJalDXxIZGPFKxTrEsizvvvBNPPvmkrOcRm3Y1+ixyKbMBbBqXcJ/6JKin/y9A0yj63rcBbnMjyvuu94C1id+QGYxmnP36Y6j56C2b/8Ew2GjeDuvLfwZYFoGuHtAUB6uBBsOvPJuyRUcBDGhc+D+PoexDN11+HRwL+tw5mMcOwD8gsp7yx7iclaAoGjojjVOPPA5W8COgqcv9GAnNTTRkQ9Ynod37+vo6mpqaRNetTIsNQkVFBWw2GyYmJlBfX8//fy4JvUgy2iDOsiwGBgYwMDAg+n0SmFL9A07F8RU5b1xKz4uJieCleRiRNoQOhwMmkwkmk0n5jRNNg+3ogO5Pf4Jxz6bdn257K3wKxQb0ehhPL2GjcRuATYtBgy52NzcFDgYEoYsc9+dwIHDffTA98ICW1UgzFEWhsbERNpstbuN4MsG2uLgYXq8XbrdbUWOclHpZiqKwfft2nD17FgcOHEBXVxcKCwsBaGJDQxpy16na2lqcOHGC//7S0hJqay+76a2trWFqagpvfvObAQBnzpzBrl278Ktf/Spuk7jJZOLXgWSROmdDTZIVOGxvL5g3vQn0vv2bd+mcDuvvvwVsaXSWQnjOjev+DqGyMuhfOwsAMM4fReVD9+LV+78Ouq8HVEQ2Q09xl+ZCbTozMte/a3POBylrYVkYF46Ci4gd4eJCPCbaDBTWNjZNV4pMmxPMc6npW60ehXQhvF6StT906BAmJibQ1dUVtRmcyTKqSEjZMWl0T/fnVW0yKjak3CikC7XfNGKBVcqkVaGQsNlsKC0tTThpVQ38//zPsL3udUAwCHAcqAvnNxvxFJyTpgCbxQgdmZehF98VZxkGeoRg1EVVuPIEd+8GdfFiyrMa2dRfk03EmjhOSGbxYVkWJpMJbW1tcLvdWFtbi7njFOv5Uh9bUVEBu90Ot9vNDxoUXnsu7xhppBa569Tw8DDm5uYwPz+P2tpa7NmzB0899RT//cLCQiwvL/P/fvOb34xHH300oRuV0WhEIKCO12kuNYgL8X//+7D19AC+AEDT0J08gdJ/uBmeG98H/9/uFH2OjqZw5qvfhPNjN29eBxMCZzTCsjgP5tBBBC9NI6fAQUcDDEdDR7Ew6Ghw1Obv/uLXH0Hh5+4AAHAUDd9V/wuBvsG4Dd7RcDDqgUqDek3fuVbWlG4ifz7EROTEiRM4cOAA+vr6wsoSlbhRyc2EyEGv16O7uxsvvfQSDhw4gCuuuCJnh9dmVGxkC0ajEcFgMCnHK7ESp6NHjyIUCkkqcVKz3ErR9Tc1IfTOd0K/dy9gMCB4112gKC6s0Y2mLv3hG9s2HQYoigKoyyMAKYoC2lthevl/4I2RZtaBwZGDLuxo3Q5KF8clwm7Hxn33qfdC45BrQVuYCfP5fPzfOp0u4ZAgOYhNHCc/q2RmZZCbfbLjdPjwYbjdbnR3d0u6drkbBGSQ0vT0NFZXV1FfX59zv3ON7Eev1+Pxxx/HtddeC4ZhcNttt6GzsxP33XcfhoaGsEvhxgkpo1KDXCujInDV1WAaGqGbPQxwHCwv/h4URcUUGpsnprH+TkF2gmVBBQJw/PB78L31bQh2dF7KZtBgOMCk5wBKF7b2+Xd/AvaHvgLd+WXAYsH5e78CnYSUhI7iYKQ5GHUcDDSXU1mMfCDW+lRXVwebzYbR0VF0dnaiqGjTQTPVblRKxCHHcTCbzWhra4PBYJD13Gwi58RGIv/y7373u/j2t78NnU4Hu92O73//+1GWg5GQIB5PbMgtcdLpdPx0Y6PRmBOpx+A73gH9f/wHEAzC8L3vQd9/JQyvuxq0Tt5gHIKOi077UWAvlUxxYELqlATkKyzLhr3PiKCIlQkrKChAaWkpVlZWMDIygv7+ftlD+mIRq3E8mcxG5OyC9vZ2LC0tSRpwFPl8qRCL3+PHj2NychIWi0XRtWtoxGPnzp3YuTP8BvjBBx8Ufeyf/vQnScc0GAxbuoyK4Pvud2G/5m8AnQ4Ux4EpKkawvh66M6fBVFVHPZ6jaOgoBuc+ezfKvvGVS//JARyLss98FGdf/AuCVbVR2Yywawew/p0nUPi+d8M/MISNgRhZKI5F0L+OEocNRnozU5JP5FomJd71lpSUoL+/H263Gw0NDaipqUl5z4aS9ZJsqhUWFubEfWQsckpsSPEvf//734+Pf/zjAIBf/epXuPPOO/H888/HPa7RaMRrr72G8+fPw2azRZU6MQwDmqbDhiPZ7XaUlZXBZDKJljh5PB4UFBTkVMqLJTXJDAP62DE4/vebEPzpU+BufK+i41GlpZe/XluDvsCM8OrYrQ3DMFHClQgKj8eDsbGxqOm+lZWVcaf7MgwDm82GoqIijIyMoKenR1HztRhijePJLD5igdfpdMJut2N8fBxtbW0oFbyHIlFa+kj6UXQ6HWZnZ3H+/HlUV0ffpGhoZBMmk0m1zEaullEBAIYGEapvgP74AgCA9qyj6u93Yv3dN+H8v3xP8EAOTDAAk9EIg4mG547PXRYbAJ/h0I+Pgb6uJiqbQaAvZe1DO98J7+6P4/x7bw47h4G+nL1YOb8M/+oqLCXbkn+deU46REui9YlkuycnJ7G2tsbf60lFSY+H3NedL72FOSU2hP7lAHj/cqHYENqEeTyemL/Y733ve/jd736HkydPYn5+Hh/84Adx3XXX4T3veQ/MZjOKi4uTKnHKxM5RsnD19Qh94APQ/+xnQCgEymIB09urfBhLXR2wsQHLj74Pw9oKgnd9Puzb2TaLRG0ie3SEf0gAMZvNsFgsfFldVVUV7xwmd6KwEDLcy+12J7xpl4OwcXxkZAQlJSWKsyexAm9RUREGBwd555BY5U7JBmG73Y6KigqcPHkSlZWVeRHQNfIXrYzqMmv/51EUv++GzeP6N50P2euugzXkBaybNrigKMB06RaH47DBUvAPDMM8doA/Dr2yAvt/Po+Lu/5O9Dw6Kvym2PvIY2ACDCw0C6NuU2QIQxOdQ7v+SkhFZiOV70Mp16vX69HX14cjR47A4/GAYRjJ93xKejyUZjaA3Cv1FpJTYkOKfzkAfPvb38Zjjz2GjY0N/Nd//Zfosa655hq8/e1vR01NDd7znvfgwQcfRENDg2rXmomdIzUI3H039M88A3AcmPZ2sEaDJLHBXUpLk7/1TzwB6sgcip97DvpTJ7F+8GDKrz3dxLIh9vv9UT06FosFJSUl/HyTVPbokIBUUFCAwcFBuFwu+P3+MEecZCGN4wcOHEgqsxHruWazme+vmJ6eRkdHR1SQTnbhY1kWer1e9NgaGtmG2pmNTJRRqXVO3TuuDevBQDAIx83vg/fuf4T3nnvFTg6LnsL5n+5BTcflrANnMmH9rn+MejjJZgjjC8WxMFAhmM25tYmY7aSyNEtq2RJxLTx16hSftZdiMS1XPCh5rVpmI4u5/fbbcfvtt+Opp57CV7/6Vfz4xz+OekxLSwv/tZo7RoRc3bWnVlcBvx8AoJuehvUtb0Hwt8+BaW4G9HpwHAtw3KXXxvH/jqKyHJYvfB6gKGx8+tPApQassHNlsUrnOA6hUCisTyJypolerw8rcyorK+PFRCaDg/B9ZzabMTQ0hImJCXi9XrS0tKj2c7fb7aitrcXZs2dhNBrDGselkCiI0jSNrq4uLC4u4sCBA+jt7Q0rS0w2COeajaPG1kbLbFyGBgf/3V+E5Stf3vyPUAgwGsFUVoLyrIG6dKPo9XphtlhA0zRovQ5cdQ38//ttML/wPKDXw/+e94F11oUdOzKbAW5z0KwOwawsAU7nOpqMMBCWDXu9XgBAc3NzysWG3KGznZ2dksp4lRxfyZqVL+tUTomNRP7lkdx00034xCc+kfC4xI1KTTKxc6QGbHc32OZm0MeOAYEAqNOnoPv8/4ONn/8cCEqfKc687W1gGxtBLy5i41Ofivm4TAkyoXuYz+fj/7jdbmxsbIDjOBgMhqgGbIvFkjMN/wS9Xo/+/n4cPnxY1F88md8BRVFoaWnB8vKypInjQqQEaoqi0NDQALvdnrRzSCT5smOksTXQxEY4oY98GCBiAwBYDgWf+SRWr349mLZ2ABz0Oh101Kb9LQXAYNLj4re/D1NXCyiaxvrdX+KfLpbNALdpaEJHzoCKQy5uMkolXsxNVDYszPSbTCZcvHgRk5OTaGtrS5nYUNIjUVhYiKGhIbhcLng8nrChemLHT/VcDvIasnlzVgo5JTYS+ZcDwNzcHLZv3w4A+O1vf8t/HQ8tsyGAohB4+GFYPvxhwOMBTKbNgXoKjuP/zndAz8+LZjVSDcdx2NjYiHJx8vv9/O9a6B5mNBphMBjQ1tYGo9GY0x9ssWunKAptbW1YXFwUdapKphSKWO1KmTguhGVZyVZ+paWlvHNIfX09amtrkxYLmtjQyCXU3BSjKAoMw6hyLDnnVHNNpEpKwLS3Q0dKdENBcMXFsDz7M2z805dBURRMIvGFqqyA5wtfAn1umc9qiGUzaDDQy8xmpHvdSOc9RigUwsbGBv83WVcDgUCUmEhUNswwDCorK/Haa6/B5XJJ3piV+3qVbkiZTCYMDw9jamoKa2traG9vF10r0jFxPF/WqZwSG1L8yx9//HG8+OKLMBgMKC4uFi2hikQTG+Ewb3wjuOJiUB4P2I4OUBcugB4bA9vfL2sSETs4CHZwMOb3k/kZcRwXZkUsFBTBYBAURYWJCYvFguLiYlgsFlH3MIZhcPr06aRmrWQDiYJZfX09LBZLmFNVMu9Tcj6pE8flXGskxDlkamoKFy9eVE1s5LKw1Ng6qLlO0TSNUEh6ploNUrEmBv/hH6C7887LgmBlBfTx43E/0zqagueuLwJQL5uRD5DMhFjpMNlUYhgGFosFVquVHzhsNpsVx+HGxkYYjUZMT0/D6/WqbneeTPabpml0d3djYWEh5iZaujIbmtjIAIn8y7/1rW/JPmYqyqhy0Y2KoPvrX0GfOAEOAH3oECw33ghwHNZPnQLSNHxQbK4JCYKhUGhz10pgRZzOaeu5jtCpqr29PSn/7shgLpw4vn37dpSXl8d9rtzz6vV69Pb24tixY7h48SKCwaDiQUf5EsQ1tgZaGVU0odtuA750L+Db7AGAyYSN+8TnmRB0FMBgc98scp2guZDsbEauEKsHkYiJyB7E0tJSWCyWsB7E+fl52O32uHFdLmVlZbzdubBMVox0ig1g8/3R1NQEu92OkZERdHd3h22ipduNKpfJObGRCkwmEwKBgKrHzFU3KgBg3vQmsPX1oBcXAY8HnMmEjU98QlWhwbIsGIbBysoKLly4ECYoxAbWkbkmRExoJIfQqaqmpkbxccQEA5k4TmpeGxoaYlrXKlkIKIrCtm3bcPLkSYyPj0vKooiRL0FcY2uQD25Uqq+JNA3/ww/D/JlPAzodQu++EdwlV8nNjEV0Ml5vMkLHsAgEBa9fxWxGJtZ9YmgitjknZmhisViyytDEYDCgo6MDLpcL27ZtQ2VlZczHplNsEISbaMLr09yopKOJDWytMipJb3aaRuChh2D++MdBeTwATWPjjjtknUdsYJ1w+jVN0wgEAlhZWUFBQQEcDgcqKiriDqzTUBfiVOV2u/ndLbmBMNZzjEYjhoaGMDMzkzLrWjINfGJiAs3NzaiqqpJ97fkQxDW2BiaTCT6fT5Vj5UtmAwBCt34Y3MMPg3r1DJgHHoSOTtw7oRPOxlAxm5FKVyWxMqe1tTV4vV6srq5Cr9fzM5vMZjOfwVZTTKRqzgZFUXyZrMvlgtfrFXU3zJTYADY30YaHh/n5T0qctDQ3qi3OVnGjIsFeyocjdN114O69F/D7sfGxjwERFnBSB9aRXZSioqKoIYlTU1NoaGiQ5GetkRr0ej26urrwyiuviDpVJSJedkJs4riw5lVpZkMIWQAmJiawtrYmy9pXToO6hkamMRqNWF1dVeVY+SQ2QAHB7z0B+pVXgMZGSaKBoiiA46DHBnRZ0JshdEeMzE7Eckd0OBwoLi7GuXPn0NbWlumXIBsSp4X3JAaDAYODg5iensbBgwfR1tYWdqOtRGyoeaNuMBgwMDDAOzums4wq10vDNbGBrZPZkHNNIb8fFz/9aTjuvReLb3kLfK+8Aq9eDy8uOxAJA5/SgXXZ8jPKluvIFAUFBSguLsbo6KhkNykgcfCPnDhOmtLJc9VYCMgCMDc3x5dVScmO5Ut6WmNroOamWKbKqFICx2Ljja+H/m+vFRUam4NmN2dCbc6FYsGxHIwIpa03I1JMCLMTkWKCZCccDge/psb62a2urubMnA2pxyTzlY4dO4bx8XH09vby8TyTmQ3h9bW3t2NpaQlnz56F3+8Pm/+k9vWosSmXDWhiA1tHbBAi6zuFgY/Ud7Y+8QSc//Ef4AwGbHvve0EFgwhefTV8v/mNajdo2fYByrbrSRfkfSp0qurt7YXNZpP0XCk/N7HGcTUXAoqi0NraijNnzmD//v2Srj9fdow0tga53iAOpHZTh2ND2NQV7CWBweLokSMoLS1BYWFh1OPV/NQTMeHxeODxeHD8+PGwNRUIt1o3m818tj9Zq/VcjV+JNqm2bduGU6dO4cCBA+jv74fZbFaUSUjVz8fpdOLYsWNR858SXY/WIL6FUbPxjpAJH3OCWErW5/PB4/FgdHQUAERywl56AAAgAElEQVQH1gnrO2m7Hdi7F/SlaeKczQZm9+68eNPnO8n0XZA6X5fLhfb2dpSUlEh+biIiG8dTEUSrqqpgtVrhcrnQ2toa1zUlX4K4xtZAzXUqE2Ij1aYpzIY/6v9CoaAq5xTObRLLTFAUxbsghkIhVcVEoutKJ2puEJFrj3fMmpoamM1mjI6O8hnrVGU2lPwsycBcl8uFhoaGhGYrSrL5ZIZJrqOJDWwGcbVqYQmp9DEXC3yBQICv7wSid1EcDgfW19fR3d0tbeBaRwdCb3oT9C+8AIplwZWUIHT99aq+jmzO/mwlIgOy0Kmqrq4OtbW1cZ8rJ3gKG8cvXLiQlBNWLBwOB9/It7a2hqampphuWJrY0MgV1J6zkRduVBKQck4pQ2CFVuux5jatrKzg7NmzMd2UNKJJJAhKSkrQ19fHD3RNpdhQIqQsFguGh4cxOTmJtbU1tLa2xjyOkkxLvvQWamID2VdGFTmwThgA4w2sS7SLIlcdb3z5y9D/+c/gaBqBr3wF0G7Msh4l7zmxIEucqiYmJuD1emM2XisJ0KRx/K9//SsOHTqEwcFByT0iUjEajRgcHMShQ4fgdrvR3d0d9f5PRT2vhkaqUHuCeCYaxDPVJ0LEhFBIxFtTrVYr34eozW26jNqlr1KPabPZMDQ0hJGREVnnSIfYADYzHH19fThy5AjGxsbQ09MjKhA0N6otTrqH+okNrCPBT2xgnVqBT+4CQ7IbupkZ1bMaBC2zoS4cx8n+mcYKsiRFTJw3uru7o4JeMrMyrFYriouLoxrH1YKmaXR0dGBpaQn79+9HX18fLBYL/30ts6GRS+RDz0aqEG7QCQXF+fPnsby8DL1eD6PRyDdfa2Iisyi5sTcajejq6sLY2BgOHz4cN4Og5DxqDADcvn07zpw5gwMHDoj2DSrJUuRLb6EmNqC+2GBZFsFgEF6vF6dOnQoTFEIxQQKfcGCd3JpEOShZYALf/S5w4UJKshq5/uHJBGLpfuEOHbCZwZKTLYgXZCmKQltbGxYXF0Xta5MJ0BzHobS0FCUlJZImjivF6XTCbrdjbGwsrA9FExsaucRWLqNiWTYqM0H+kMyEcIPOZrOhrKwMNE2jrKwMpRHW7aki30RcJOlwo4oHRVEoLi4GgJgZa6XHVms9EPYNtrW1hb33NDeqLY7cIC5lYB0JOoWFhVkzsE5JIORKS6NmbGikDiImGIbBq6++GtWICFyuHbZYLPwOncVigV6vRygUwurqKi8MpMwwkRIAYzlVJSs2aJqGxWKRNHE88rlyKCoqwuDgINxuN6qrq1FXV5c3O0YaW4N8KKOKdU6xbD+JfZHZfovFInmDThsQm/3IzT7QNI0dO3bgxIkTGBkZQX9/f9yNtXRlNoQ4HA5+TVtfX+d7TTQ3qhzj+eefxx133AGGYbB7927cc889Yd9/7LHH8IMf/AB6vR7l5eX40Y9+hIaGhpjHi3T5IGJCbBclcmCd0HVCOLDu/PnzOH/+PJxOZ2p+CHlCPu8CiUFsh8Vqh4lFosFgQDAYhM/ni9mIGI+SkhLY7XaMj49LsuSTY18b6VSVTD2pcMdGysRxJdcshPShzMzM4OLFi/zGgIZGLmAymfgYkSzpFhtETAQCgZjZfuGaKhQTyTbHbrU1JtVkMrMhfGxdXR3MZrNqZbhKZnjEw2QyYXh4GNPT0/yaptSNKh/WqZwTGwzD4Pbbb8cLL7wAp9OJ4eFh7Nq1Cx0dHfxj+vv7MTIyAqvViu985zv4whe+gKeffjrsOBcvXsSf/vQnLCws4OWXX8bhw4fxu9/9Do899hisVmtY4CM3e3IG1mXzBPFsIV93lIViQmyGSeTwpsLCwrDmfpZlMTY2hsbGRsXX4HA4MDAwgPHxcbS0tKCioiLmY+UE2UinqmQzG5HDnOJNHBeiNADrdDp0dXVhcXERr776quq9WhoaqSKbh/qxLCua6ff5fGAYhreGDQQCYBgGBQUFacn25+saQ0i3yUUq7h+S6asgG2But1uSVXs85K4pUq6bDCgka1pBQYGiMipNbGSA/fv3o6WlBc3NzQCAm266CXv37g0TG295y1v4r6+66ir89Kc/jTrO2toaRkZG0NjYiDe84Q0wmUz45je/KXkSZCKy7cYeyM5rykViDUT0+/3gOA56vT5MTBQUFPBiVU5tqlLIc4kl3/j4OPx+P+rr60UfL3fBinSqUopYEKUoCo0xJo4neq5UKIpCQ0MDTp48iYmJCfT19aWtpltDQymZbBCPFBPCuEcyhMIyJ7HSYb/fj9nZWdTV1anyGqSQ7jVvK6yx2ZLZIJANsPHxcUmzLtS4DkD6GiRc0yYnJ2VnYDSxkSFOnjwZFqycTif27dsX8/E//OEP8fa3vz3q/2tra/Hggw8CAMbGxjA6Oqqa0AAyY/OXiGwLhNl2PQRhT44wQxEIBPgBO0RImM1mPt1PBiImi5o/E4PBwAsDn88n6uChZHeMOFX98Y9/xNTUlKhTVSLinVe4YyU2nE+NHT2dTofu7m6trlsjJ1BbbAjXJ4Zh+FlNsfoQhdn+wsJCVFZWyspMZGu815BOqjIbch4rFvfNZjM/W8nn86G5uVn2+qCkjErO48vLy1FWVob5+XkYjUbJs1jIeXI9S5fXq+xPf/pTjIyM4M9//nPcx6Vizkaqp6UqQQv2m5BdOuHC6vV6sba2hv3790On04UtrMQi0Ww2p22HQc3AQtM0ent7MTs7K2phq/TGndRZE/vaeGVPYiTasSkoKMDw8LBo47gauz0sy8Jms8FkMiV1HA2NdJBMGVWkqYnP54PX68XIyIjkPsRkybamdA35pGLORuTXSs9PNsAOHTqEqakpdHZ2yroeuWuKkjVIp9Oho6MDCwsLWF9flySKtMxGhqitrcWJEyf4fy8tLYlOOH7xxRfx0EMP4c9//nPCm4nIBnE1yMYgl23XlKrrIc2IYk3Ykbt0FosFRUVFKC8vx8bGBgYHB1W/Hrkk8zOJ9VyKorBjxw4cP34co6Oj6Ovr4xsvk11AYjlVSbnWROeN1TiuxqKnDfXTSCWpMDKJ1SAulo0VMzUhGdni4mIsLy+jv79fNTGRiGxbf1LFVniNaqLmLAyaptHe3s6vc2pkTdR6PLB5b2I0GjEwMIDDhw9Lsu/VxEaGGB4extzcHObn51FbW4s9e/bgqaeeCnvM+Pg4Pvaxj+H555+P2xhLyLYJ4qkiX26sOI6LWT8c6WwSq344kmAwmDUf6GRughOlWxsaGngHDzLkTo2b7vLycphMpjCnKqnXmwhh4/jo6Ch6e3tVC8D58pnQyC7UMjIhrK+v49ixYwgGg3jkkUdwxRVXoLS0lBcTkdlYKaYmx44dS5vQALZGZiPf44laGzTC34nag/dIj4TFYuHLqoTDXJM5thCl08ApiuJF0dLSEj8AMNY1amIjQ+j1ejz++OO49tprwTAMbrvtNnR2duK+++7D0NAQdu3ahbvuugvr6+u48cYbAWzuvP7qV7+KecxUlVFlW88GkH27LmLXQ2ZNiDk6RQ5wslgsaXM2yRcqKythNBoxNjaGnp4e1RYQh8MR5lQllnFUSmTj+LZt2/IiAGvkJ2oZmQDA61//ev797/F4+Hrv+vr6tJZ2JstWEBsa8knVlO/KykpYLBaMjY2hq6sLhYWFcR+fCjeqROdwOp2w2WwYGxtDR0cHP7AwmevKVnLyzmznzp3YuXNn2P+RZm9gs4RKDmpPEAeyM8hlyzURMbGxsYHz589jdXWVFxVETBiNRj7lT6bBpnrCeraQjvKe4uJi9PX1we12o6KiQrXzRTpVtbS0qPpahLM+pOxWaWhkArWMTADg5Zdf5r8eGBjAHXfcod6FppFsWX80sgu5652cx+p0OvT19cHlcmHbtm1xm7LTldmIfE5xcTEGBgbgcrlQX18ftUmniY08IlVlVNmW2UhXsOc4DsFgULTMidQcG41GBAIB6PV62O12vglb6uA6jeSx2WwYHBzE/v37kx6IJETYqCfWkJ4sBQUF2LFjB6anp7GwsCBp4riGRrYi1cgk19EyG+qT7teWio0wOccUDoKVitVq5Y1GfD4fGmPMr0pXz4bYc4hN/eTkJNbW1rBjxw7+cZobVR5BbnzVJN/dqIiYiGzC3tjY4AfXCe1hibsJGVwHAHNzcygtLU1qEE8+ks7GZZPJhKamJszPz+Po0aOKLAPFoCgK7e3tWFxcVORUlQi9Xo/Kykqsr69LmjiuoZFOUmFkkutk6mYpEzfk6STXb0LlouT1GgwGDA4OYmpqCjMzM2hvb486jpIyKjWngev1evT19eHo0aN8iTMxccmH37EmNqCVUYlBBtdFigkyBVuv14eJCYfDwc+aSMfgOg31oGkatbW18Pl8qt+4K3WqSgRpihU6j/T29ioSNNr7UENtUmFkoiGfdH+28z2W5FJmI/Jeh6ZpdHd38zfzvb29YT2eSsqoku3ZiISiKLS0tODMmTN843i+oIkNpCZAZLvYIFaJYk3YwinYpAlb7cF1GrFJtyUr2aHp6OjA/Pw8xsfHowJxMih1qooHCdqRjeOxJo5raKSTVBiZaMgnG9dhjU0iy4TkPi8RYsclN/OnTp3iM+5kmHM6xIbUbEhVVRWsVitcLhcYhpF1jmxFExspItNBTmyI0/LyMs6ePctbHgrFRGlpaUYG12kLQeYR1oQ2Nzfzgbi/v1+10g6hU1V9fT1qampUuWYCaRyfmJjA9u3boyaOJ3q+hobaqG1koqGRaXItsxHrXqampgZmsxmjo6Po6elBQUFB2sqopF6/w+HA0NAQXnrpJSwsLKClpUXWubINTWykiFTfyJAp2GJN2MLBdcIhTizLorCwMK4jg0bmSSagKxFvkeerqamByWTiA7FamQKhU5XH40lKaIotDAUFBRgaGhKdOC72fE1saGx18l10Z8KoRdtAk4fcn5daNrklJSXo7e3FxMQEWltbFTWIK9mYlXMOk8kEq9WK9fV1rK2toaioSPb5sgVNbGQpZAq2WN8EERMmk4kXE4WFhaiqqoLZbI45qGltbS2vFxYNZSJXLMiWlpaiu7sbbrc7pv+3EoROVcLpxmpcM3B54vj09HTc/pN8sRPU2FqQm2e1Blrmu9gA0nvzvxV+lmoP9QOk/9zkbBJJeazdbsfQ0BDGx8dhsVgSzuJQei3JQFEUurq6VDVYyQSa2MgQHMfFFBNkCrZQTEiZgp2IbCtbyrbryRbkDjna2Njg3zsejweVlZWy+iJi7dAUFBRgcHAQ4+PjaGpqQlVVVdTzlEBRFNra2vDqq68qdqqKd8NF0zS6urqwsLAQs3FcExsauQixaSd15smwFeJvvt/85wpCO3yyTpWWlqK8vDytPRtikA2qv/71rwgEAqivr5f0vK0g1NVEExspguM4sCyL1dXVqCbsyCnYZrMZdrudb8ImdmdqsxUWl3wkFApFvYeIMxgAfgAiEaYzMzPo6OiQLDjivSdI6ZPL5YLf7w/zKE+23IvY7ipxqpLi6tHU1BSzcVz4fG3B0MgVTCaTJjZkkO+vMZvmbDAME3OdirTDN5lMmJubA8uysns2YlVuyLnWSHQ6HWpra7G8vAy3243u7u6E50nHhlU+vXc1saGQSKUu/HtjYwMURcHv9+PMmTMwm82wWq0oKSmBxWLJ2BTsbAy82XY9mYD035D3z8WLF7G6uoqRkRGwLMs7gwmzXBaLJWxmCSEYDKK8vByTk5Nobm6W1J+TqPZU6FF+6NAhfuCQ0ppV8pppmlbsVCX13BUVFbBYLFGN41pmQyMXUdOmnabprBs8mw/k65wNlmXBMAxWVlZ4YUHWLCIChOtUWVkZLBaLqB1+MBhERUUFpqenYTQaUVZWJvk61GgQj0V1dTUYhuENUuJl3NOR2cindSonxcbzzz+PO+64AwzDYPfu3bjnnnvCvv/SSy/hs5/9LCYmJrBnzx7ccMMNko4rfPNwHBe2oyy8GRTuKAsdnYqKimCxWPgp2AcOHMCOHTvUffFJkG1iI5t2lFP5cyGlTsLgTP4OhUJ8Mz95HzkcDmxsbMiavE1uGjiO49PC4+Pj2NjYQF1dXdKvgXiUz83N8Ts/yWY2yHOVOFWxLCs5AyjWOJ6MUNLQyBSkjEoNsm09SAVb4TWqBdlAjVynhKXdfr8fKysrsNlskvpEIxGuU2QT65VXXgHLsqitrU24nsgtMVYyEVzqbKh0ZTbyZZ3KObHBMAxuv/12vPDCC3A6nRgeHsauXbvQ0dHBP6a+vh5PPvkkHn300bjHunjxIubn5zE/P49AIICPfvSj6O7uxtVXX81/GKTuKGvkPsn8TsWyXGSiOoCw/htSMkeyXJGsr69jdXU1KsiQlDOwGYTEFlHSQGo0GjE4OIiJiQkEAgFs27Yt5uuTGtAoikJraytOnDiB0dFRdHZ2JiU2hOeMdKpqaWmJe2y5QTiycdzpdPJzOjQ0cgW1xUa+ZzbSLTayPZ4Ih/UK16qNjQ1+o4qsU8JqDLKx43K50NTUFHfHX846RVEUampqsLy8zJf/Jor7qRIbQvEgzLjHMkjRMhvyyDmxsX//frS0tKC5uRkAcNNNN2Hv3r1hYoPUlSf6JX3961/H6dOn0dTUBJ1Oh3e+850YHByUpLBzkWzb5cm264mHcG5JZHYicqI6cbUwm82yhClZ+BmG4bMhYs8lMzHIzbLw3yzLgmVZbGxswGg0oq+vDzMzMzh48CDa29tFjyf3d1BXV8cHYqVmBWJOHkKnqsnJSXR1dcX8DCtxAhE2jh88eFC1aeYaGulCTbFB03TOxF8NaQgt8YWCQljqJFynHA4H30MhtSma9KMKv5a7TgGbwoeInNraWvh8PrhcLvT09MTMlKQjs0EgGffx8XE0Njaiuro67PFysutKEf5sc/2eNOfExsmTJ8PKQpxOJ/bt26foWF//+tf5r1966SW88Y1vVM3iMxvZCjtZSuE4DgzD4MKFC1GCQmxuCdnxkTNRXZhCFv6JDCJGoxEejwfr6+soKioK24FPdC6apkHTNB/IjUYjOjo6cPTo0ZiBXEmqtqKiAgDgdruxuroqyzIw3jkpikJ7ezuOHz8e16lK6Y4PaRznOA4LCwtYX19XZaK5hkY6IA3iapBLmz1KybfXKCzJ9fv9OHfuHJ8JJyW5wix6cXEx/7XSdYp8DWz+PNfW1lBYWMiLB7LhJGedIhti6+vrWFlZQVVVFVpaWrC4uIjR0VH09/eL3sinU2wAmxn34eFhuN1ueL1eNDc3h5Xayz2+XLTMRh6iZuOdkGyyR8u3wCsHYQ+OWEM/AAQCAbz22muwWCwoKChQZDUsJYUMIGy3RyxIDwwM8ClrcmMvB71ej1AohEAgAKPRyAfysbEx9PX1hQVype+JgoICFBUVYXp6WtLUbiGJMhMNDQ2wWq0x62aT/Vw5HA6Ul5djdnYWV155ZdZ8RjU04pEPPRvpXhMz4diUDLHWKdIrSlwshSJi+/btKVmnhGsUTdNoa2vD5OQkWltb427SRPYpCv+Q/g/ioFhSUoKCggIwDIP6+noYjUa+QTvSdU2uc5XcMiqxx5OM+8GDB8NmNymZOC73fa+JjQxSW1uLEydO8P9eWlpCbW1t0sdVM4gTSDDPlhuZbBMbal8PKXWKDNRC6z0SpCMb+oPBIGZmZtDa2hr3HJG7PkpTyIkwmUy84AiFQpKapiPR6/VgGIbPcMQK5Erfo+Rn2tvbi/HxcQQCATidTsnPTXTOeE5VyQZhlmVhNpvR2tqaNZ9PDY1EqF1Gle5Md7rXxHR/tqWcj2VZUYtYJSW5586dw8rKSpTQSNU6ZbPZ0N/fD7fbjfr6ethstigxIWbJbrPZUF5eHtPan2Q69Ho9qqqqYDQaMTo6ir6+vrCNJjn3C0oyD/FmN3V0dPCzm/r6+pLqCZH7nHxYo3JObAwPD2Nubg7z8/Oora3Fnj178NRTTyV93FSJjWxSptkmNuQSOVVdGKiF1nskSJO5JakodSL/VpJClorBYMDAwADcbjdCoRDq6+tlH0On04GiqLiBPBmxQVEU33w9MTEBn8+XsLlb+NxExHKqSvaGJZ+CuMbWQc0MfCbWg0w0bGcisyEmJOKV5JJ1S+46xTAMWJZFKBSK2YhNURS/DsgtdQoEAqLZCYZhMDMzg4KCAr6kmBjoSO3/EELTNJ+N5zgORUVF6OnpgcvlQldXF1+mK6fkV40yKiGkBJc4VTkcDtkTx5WKjXwg58SGXq/H448/jmuvvRYMw+C2225DZ2cn7rvvPgwNDWHXrl04cOAA3vWud+HChQv49a9/jfvvvx/T09Nxj0t2t9Uk2xrwsk1sRF6P0HovUlAIByGSIK3Eeg8QTyGTkqNgMMh/uBOVOqUDnU6Hvr4+TE1NIRgMhtWMSiVRIFd64y7cLSPXeejQIUxNTaGzszPuz0lOEBVzqko2COeTpaDG1iHX3aiybQ1SQqQtvlhZ7uHDh/ld/WRKcsn5IkudSMw2m82Yn59HTU0NLBaLrHUq0upW+Iest0QEWa1WFBYWorq6ml9vGYaB2+2G0WhUlHmPhKxTDMMgFArxWRSXy8WX6aY6syHl8eSeY2xsLGxQrJTjy11z8mmdyjmxAQA7d+7Ezp07w/7vwQcf5L8eHh7G0tKSrGOaTCY+/acW+RBY1UZovbeysgKWZXH27NmoKaPCek65gxCVpJD1ej2qq6sxOzuLnp6erPqAkxkXBw8exOHDh/mhenKPESuQm83mpDIbBNLcvbCwwPeGxFpc5S4EkU5VyQZhJW5WGhqZxmg0qrZOZWIzLBNlTUpeo7AkV/g3KXWKtMUnJbmhUAhHjhxBT09PwnOoUepkNpvR2dmJmZkZ9PT0wGq1ir4Or9cbVeoUb70ls8LiQTaYJicnEQwG0dTUlPTvV+hUBVzeaBobG0MwGJSd2ZCzRsjZwCoqKkJZWRmOHz8Oi8Uiqa9SyZqTT+tUToqNVKCmywch28RGOq4nMvUqDNSRU0ZpmobVaoXT6ZSVeo234yNEbqlTU1MTFhcX4Xa749rvZQJyI3/06NGwJjU5kMcTwUEC+csvv4xz587JdmWKJRgaGxthNptx4MAB0SY/QFl6WOhUdfToUUVlZcmcX0Mj05hMppwuowLS37Ad6xpiWZmTUidh30Rpaakk90Fhpiieq1MkNE0rKnXiOA4mkwlOpxMjIyMoLy/np3sLXwf5U15eLttFMR40TaOnpwczMzOYnZ1VpQdOuDEWDAZhMBj4oayhUAhVVVWSjiP3Rl3JBhjZXPN6vWi8NHIh3vVoZVQaKXGjykQDXjzUWFyIy4RY3wRxmYjc8YlV6nTixAno9fqYN6NquDrJpb6+HjRNw+12o7e3N+sER0tLCxYWFhQLIqHgIIG8rKwM586dg16vR1NTk+RjxQvOwt6Qnp4eFBQUSH5uIhoaGnD69GlMT0+jv79f0bwMInw1NHKJXC+jSlc2hZQIeb1eeDweLCws8OuUWEmuw+FARUUFLBaL7JJccj5SXhUMBvldeBLjItcoYaluImKVOkUOjK2qqsKrr76KHTt2oKSkRPEMJLlQFIWOjg7Mzc1JGswnBeE6RQxOBgYG8Je//AWLi4soKipSrS9Q6eOFw3OnpqZw8OBBtLW1xTyG5kalASC1blTZgtTriVWPGstlgjRiyxlwQ8QEaW5LlauTEpxOJyiKgsvlQl9fX9bdlDY2NsJgMGB8fBy9vb2yBwtFBnIA2LFjB06cOIHDhw9L3p1KlKYuKSlBb28vJiYm0NbWFuUmlcyCROx8xZyqpJCOgUwaGmqT60P91FwTyQ6+2DpFSoRomkYwGORLhMg6laqSXLIuLi0t8f11ic5FBvGJCQqhO1Wki6KYO1VNTQ0mJyf5XpF0QVEUtm/fjoWFBUxMTKC7uzvpNZo8Xziktri4GBzHJRz6CigTD0rECSlzPnr0KMbHx9HT0yMq9DQ3Kg0AW0tsCINbZD0qy7J8tkG46xMruMVCSqmTyWTC6dOnUVFRkVJXJyXU1taCpmmMj4/H7T3IFLW1tdDr9fz1iQ2+iwdFUWE7fxRFobu7G4cPH5YUyAFpwdxut/NTWBsaGvgprGr0XBQUFIQdW06TYj4FcY2tQz64UUnNpghLciPXq8iSXLPZjLKysig3pJWVFZw9exaVlZUxzwGoW+rU1dWF6elpLCwsoLm5WdbMCWHfhBx3KoLdbkdPTw8mJibQ2dkJh8Mh6/nJQNyaTpw4AZfLpUplQOSQWo7j0NTUhOXlZdX7AuWuScLHk6qDkydP8tbyJpMpqeMDWmYjL0mF2MhUGVVkcCMBen19HYFAACsrK2EWsUqmjALyBgMJ08ckANTU1CAYDOLw4cOq7ISoTXV1NSiK4m/os20nvLKyEnq9HmNjY+jt7YXFYgEAWI79Y9RjD+k+Bp/PB6/XG5Z+t1qtKC8vh8FgAMuyaGtrw/z8PJ81iSeypO4EmUwmDA0Nwe12w+/3o7GxUTXrWoPBgOHhYUxMTMDr9WLbtm2qZGU0NLIRk8mElZUVVY6VqTkbhEg3JOHuvrAkVzhvQq77IBE3ZK1Sc+aEEOEgPp/PB71ej6WlJSwtLcFoNIpWA5BGbLWx2Wzo6+uD2+1GW1sbioqKVD9HPOrq6vh1Kd66aXn2XVH/53vXs2H/Fv5cPR4PbyrT1NQEk8mEkZERDAwMiG62paOMKvLxtbW1MJvNGBkZiSofVpLNz6d1ShMbl8i1zEYwGIza7SF1nBRF8cHNbDbDbrejrKwMgUAAFy5cwPbt2yWdIx0D7BoaGsCyLKanp9HV1ZV1O81VVVVhJVXZIjiIoHACcFYAOP2ruI9vY76H01VfiJmhIjtHRqORD+Sjo6Po7++PmTWRE5yJm9TMzAwOHjyI4uLipK1rhQYAQqcqKVmZfHL50Ng65NoEcaH7oM/nw9raGg4ePMi7DQnXKTrhvREAACAASURBVKvVqrr7IMdxWF9fRzAY5LMSSrLo8WZOkCyLMDvhcDhQX1+Pubk5FBYWJmweVhuLxYLe3l643W7s2LEDxcXFaT1/dXU1mv/6SUCeKSgsz74L+1seFP25Wq1WtLe3w2Aw8INuDQYDRkZG0NfXF+XEla4yqkhKS0vDfvZlZWX88ZVkNvJlndLExiWyzY2KWNapMWVUeEzh9UhNIRPxoMQtQwpNTU04evSoas1lalNZWRmW4ZBbsqQEsexEslSf/wZ8zV8T/V7ktHFhIO/v7+ezJkLkBnOaptHZ2YmjR49ifn4+ZmmDFCIDt9Cpiiw+8X5P+ZSe1tg6ZJsbFSnJjTQMEboPCtcps9mMhoYGFBcXy74RlGoYIlyniouLUVlZiSNHjqC7uztu826imROkEVuYZUnUUN7d3Y3JyUkcP34cDQ0Nkl+vGlgsFvT19cHlcqG1tVV2X5ukc4hkJ5LliiP3AYjOchBYluUb8MvLy2E0GvleCWEmIdVlVPHWELvdzjto+f1+OJ1OxWIjWzY4k0UTG5dItxsVx3Ex61HVmjIKhAdp0owt9jqF2YhM9E00NzfjyJEjOHToUFxHh0xRUVHBC454u/1ySIWgkHpOMdEROW2cBPKxsTHVHKVIbavf78fS0hLq6uoU/SxjnbuhoYGf8Nrb2xvTqUoTGxq5iNo9GwzDxH2MsCQ3UlCIuQ8mKsm9cOGC6IZYKrPojY2N/GZWQ0MDf/1er5d/TQCSmjkRC9I8PDExAYqikrLrVoLZbObnKW3bto3fZZdKKsSEnHOLCY7IIbUFBQW8qBKahSjpwVBTnJDyYVLiW1hYqLlRaWy+MUjQUQMSMD0eT5hVLJkwSnZKSKBWa8povCBtt9vh8Xiwvr7O7yxlyxuZ3ITOzs6q5tetNuXl5WFN45ENYJFkQkxIxXLsH0UFh9RADiRXT1pYWAiapmOmwKUQ6/1BPkfxnKryKYhrbB3UdqMKhUJhdfHCdYq4DwrXKWG/gZJ1CrhcshlPTCgpdRJu4Ik1Ym9sbGBlZQUVFRV8r5qaMydiQWZREMFRV1eXsnOJYTKZeMFBsgFCMikoEpFIcJCZURaLhTcLaWpqQlVVVcrnbEg5Phl8ePjwYczPz6O0tFTy8ck58sXIRBMblzAajVhdXZX1HOGUUTHrPRLEiVUd+TsZ6z3ytRhS3DLIjWNnZ2darfGkQFEUWltbcejQIRw5cgQtLS1Z9yErLS0NK6kqPvVg4idlKYkEh1ggb25u5sufkmnyJkLG6XRifHwcnZ2dqjYyOhyOuE5VmtjQyEWUZDYirVWFhiEMw2B5eZkXE6TfQK77IDlPolInm82G06dPY/v27Yqy6FJnTgg38Igw4jgOMzMz0Ol0qK2tlfy61IAIDrfbDYqi4HQ603Zuy7PvggXAmwDgZNpOqxrxBAcAfmPMaDRiaGgI4+PjvHNVqiaIA9LXP4qi0NbWhsnJSZw+fRoNDQ2Ss/n5tE7lrNh4/vnncccdd4BhGOzevRv33HNP2PcDgQBuueUWjI6OorS0FE8//XTcJi2xHSPSFCYWqIXWeyRQk3kTZKdkfn4edrs9aichkng2sbEGAyktdTKbzXxat6+vT7QWP5OQD+bMzAyOHTuGbdu2ZfR6xLITtQBqKwCcit+UnQvEExwAwho5SSAPBAKor69PqnmNWCw7HA4MDAzwaf6KigrlLyYCs9mM4eFhuN3uKKcq8rnKNjGrkV+kYp0iGQcC2dEXW6fileSura3B6/VKHuSpRqlTU1MTDh48iJMnT6K5uVn0HGrNnBC7ro6ODkxOTmJxcTHtJU2RgkNNwZPN2Qk1kJLhIENqBwcHMTExAYZhJE8bB5RlNuTce5WUlICiqIQlvpHXpImNDMIwDG6//Xa88MILcDqdGB4exq5du9DR0cE/5oc//CGKi4tx5MgR7NmzB3fffTeefvrpsONwHIfl5WUcO3YMBw4cwNGjR7F7927cfvvtolNG5VrvkQa8dLg6ycFms6GzsxNut1vUDzrTkEVhamoK8/PzsqZaKyGby51SDdXwBliZ5+DVvT3qe/EC+cbGRpifvVyEQdRisfBCxu/3q3oToNfrMTAwEOVUlU87RhrZiZrr1IULFzA/P4+XX34Zc3Nz+PCHP4yPf/zj/BohtFaVWpLr9XrjGoaQf0d+xpMpdQI2+/Ompqbg8/lgsVhE7W6tVmuYMLJYLKp8XimKQldXF9xud0YyHDqdjncqoihK8mygfBcTUkiU4RAanPT19WHfvn04deoUamtrJa9TqS67Ii5lLpcLHR0dCV3CNDeqDLN//360tLTwOyM33XQT9u7dGxbE9+7diwceeAAAcMMNN+BTn/pU1JtjbGwMX/ziF9Hc3Ay/34+CggK8//3vR2dnpyqlThzH8btLBKmDgVKNw+FAa2sr3G43BgYGsm5oHUVR6OzsVMXJYyuLCSFUwxtifi+e4ACiA/nMzAzOnTsna+dISORn0WAw8M10Pp9P1Z4dMacqTWxopBq11qmlpSXceuutvCW10WjELbfcgu7ublgsFsWlTsTVR6wsK5l1ikz2Jg3YXq+XLy8GNrMzVqsV58+fh8FgQF1dXcpmTohBMgwulwt6vT4pVzwlEMHhcrkAbM6b0sREHLZfXvstU5+Fr+ufox4iNm28vLwca2trcLlc6OnpSXrAoBhKGsqFJb6NjY38oFsx8mmdyq47TImcPHkyrMnK6XRi3759MR+j1+tRWFiIc+fOhbkxDA4O4g9/+AMA4Pe//z1efPFFvPGNbxQ9p5JSp9raWrjdbpSWlqKwsFCFV64uJSUlaGhogNvtRl9fX0o+jMkgdPKgaTpuY50mKMKJJyxikUhwCAN5R0cHxsbGcPLkSTidTtnvHbEgStM0ent7MTs7i4mJCdUHPQqdqkgpooZGqlBrnaqrq8N//ud/AgCmp6fx0EMP4S1veYvoOeVk0R0OBxYWFviNNqlZdLkzJyorK6MmewOb/RdjY2OoqKhIu70nueEfHx8HTdMJS53VwPzly78zqqcI/wsAFlN+2txje/yNxXiCQzhtnGVZVFdXY2Njg58ZlUkbWfLZAC6X+LpcLvh8PjQ1NYl+TjWxkYcYjUY+CyG11AmIn0I2GAx8jWZ/fz/MZnPqX4hMKisrEQwGMTU1hZ6enqxL2ZFdKNvCvcCxTF9NdqJEWMQinuAQBnKDwYCysjJcvHhRUSCPlYKmKAo7duzA8ePHMTo6qvogRVJesm/fPly8eFGRC5aGRqYwGo3w+/38OkX+iJU6AYhb6mQymXiXpIGBAb6cNtHMCQBhDeVSZ05EYjAY+Bv+7u5u2O325H44MtHr9fz5dTpd0nMohGIiHlRPeid6ZzUJhEUsYgkOYsrj8Xhw4cIFFBQU8PbqZGZUpu7DIoUDKfE9ePAgpqen0dHREXUPqblRZZja2lqcOHGC//fS0lJU7SV5jNPpRCgUwurqalzbsfPnz+O5557DfffdB6PRqFqpE5l66Xa7MTg4mHXlSsDmjtvGxgYOHjyI9vb2jL2xtexEYtQUFrGIJTiA8OF/DMOgtLQUOp1OdiBPVO/a0NAAs9nMlz2paWTgcDhgtVoxNzcHg8GgalO6hgYhFeuUz+fDK6+8grW1NT4bobTUiTRiFxcXY9++fXA4HLyTYipmTohBDEsmJyczciNIykLHx8fR3t6esAJBqqCIRBMYUCwsYmGZ+iymLJ/my/Qis2pVVVWw2WwIhUKorKyEwWDA6Ogoent70y5sAfFmb5qm0dHRgYWFBYyNjaGvry/sHlHLbGSY4eFhzM3NYX5+HrW1tdizZw+eeuqpsMfs2rULP/7xj3H11VfjmWeewd/8zd/EDZLvfve7MTs7iwceeACPPvqoqtdbVFSEhoYGTE5Oore3NyvfPE1NTTh8+DCOHj2KlpaWlJxDExPySIewiIWY4CA7nl6vFx6PB8vLy6ipqeGnjcsJ5FKCaGVlpehQwWSnHgObO79DQ0NpmQavsTVJxTrV09ODhx9+GJ/5zGfwk5/8JOZnKN7MCaFDldVqhd1uB03TWF9fx9DQUNrLae12O78hNzAwkPZSF5PJxPdQXPXlT/D/b7k6fvNuIra0wFBZWMSiy/eveK3pqzGzasJp4yUlJXylidpW61KIVy3T1NQEi8WCAwcOhG2uaW5UGUav1+Pxxx/HtddeC4ZhcNttt6GzsxP33XcfhoaGsGvXLnzkIx/BzTffjJaWFpSUlGDPnj0Jj3vPPffggx/8IH74wx/iIx/5iKrXXFVVBZ/Ph8OHD2flhGxSvjI1NZWULaAmKJSRSWERCyvzHFxHt/E3KQDCXG9qa2tRWFiIYDAoO5BLdfIoLi5GX18f3G43duzYgdLS0qTmewgxGAxZmWnUyA9StU7dcsstmJ6exv3334+dO3cCAKqrq2XNnBDjyJEjOHr0KFpbW9X7IUikqKgITU1NfMlxKgUP97a+qP8zA7hK8G8lQmPLios0CYtYlC/cCwAx+ziEQ2ptNhsGBgYwPj6O7du3p6VXh5Bog62qqgomkwljY2Po6upCYWFhXrlRUQl2CZPfQswx/H4/3vrWt+Lee++N2SyuFDJUyG63J+WulEpYloXL5UJ1dbWoS4ImJpSTjYJCKuvUtaKBkmVZhEIh6HQ66PV6BAIBSYF8ZmYGNTU1kneXAoEAXC4XnE4nKisrMTo6iiuvvFLx6/mf//kfvO51r4PBYMiVnaP8WHFSw5ZZpx5++GH84he/wMbGBs6cOYO2tjbceOONuPbaa3lhIXcYH4HjOExOTqKkpCStQ+eELC0t4dy5c0n1D5q//Bb4Xrmg6LmayBAhw2JCKlRtG7zFHxf9HsuyfEaPiI/x8XE4nU6+tJGsCVKR+/hDhw6hvLw84RRxr9cLl8uFlpYWLCwsoL+/X3Y/VAaJ+aHVtvUiMJvNeOaZZ/C2t70N//7v/67qjAdiwTk+Pg6LxZKVteI0TeP1Rc8APmgN2TLJZTGRCDv3e3gRfxZHKBSCyWQKm+Iay8de7o6NyWTiZ3x4PJ682e3R0JDDRz7yEXz2s5+FyWTC2toarrnmGvT09MS1z5QKsRsfGxuD2WwOc8RKF06nE4FAAIcOHYpbAZCod8JydbEswSFXZOSEwMgRkaAGVG0bAMB64buigiNy2jiZGeV2u7GxsZHyWV6A9P4Lq9WKoaEh3qlKaxDPY6qqqvCjH/0IH/rQh/Cb3/wGDodDtWMTd6WxsTGYTKaMWOJq2Qll5LOYkEIia1w5gVxJKZRer0dfXx+mpqbg9XrzqnlOQ0MKQgFQUFCAp59+Gtdffz1+/vOfSx4QFw9iCUsERyYaaZubm2H95d8Dh2I/RkoqK5aAEIqQnBIZW0g8xIMIi1jEExyRQ2r7+/sxNTWFQ4cOqdILGA8565XRaMTQ0BD++Mc/YnZ2Ft3d3Sm9tnSgiY0YDAwM4Atf+AJuu+02PP3006qmsIglrsvlUt1ph6AJCvlsdTEhBTnTxoWBfMeOHWHiQqlQoGkaLS0tGB0dxfj4OHp7e5Pqu8iHHSONrUtTUxP+5V/+BTfffDN+85vfqLKWGI1Gfr6R0BJXbSxTn43+z7njkp4rdtPPTaxIO2+2CgxNTISRSFTEI1GGQziktru7G7Ozs/D7/ZLXJSXCRG6zNzFx0Ov1OHfuXFZWwshB69lIwAMPPIDV1VU89NBDqh97dXUVhw4dUuTAoYkJ5WiiInliWeOS2lgAfO347OwsAoEAurq6+GDrcrmwfft22Gw2+ef2ejE7O4uKigosLi6iv79f9g0RqbdVWt+eAXLiIjPEll+nfvCDH+APf/gD/u3f/k219/P58+dx9OhRDAwMKNpss174LgCAOxknRSFEotCQg1QBIkZKRIYmKERJRljEI14PB8uy4DiOdyR86aWXYLfbo+xnYz1/3759uPrqqyVfi9vtxrZt22RlC/Opt1DLbCTg/vvvx3vf+1785Cc/wc0336zqsQsLC9HY2IjJyUn09fVFvZk0QZE8mrBIDXKmje/YsSPKRzwZRynS71FTUwOTyYTR0VH09PRkpORDQyMb2L17N6anp/GNb3wDd999tyrHLCkpgd/vjzvwlQgKMSSLjBQSSzDEEiGqCQxNVIiSKlERi3gZDuGQWpJFqKmpwcjICAYGBuLaoivJzG/1sl9NbCSAoig8+eSTuOaaa9DS0iJLySbCcOMQnAAarq8GDoo84Orcr9NLJ5qwSC9ypo03NjbyU1wHBgaSCrzC55aWlqK7uxtutxsdHR0oLk5cIpHq2lwNjUzwyCOP4LrrrsPevXvxd3/3d6occ9v5b2CbDsB0jAeI3DwqEhkpyGrEQ/WshSYuoki3sIhFLMEBgHemCgQCAICamhp+nerr64PVahV9npL5F2pZtucqmtiQgNVqxc9+9jO84x3vwNNPPy17BgXDMJuTWj/8ZvHv//I0dNdHu4lwr0yC0gSHKJqwyA6kThs3Go1hgVyn0ykOvJFBu6CgAIODgxgfH0dTUxOqqqriPn+r7zBp5Cd6vR579uzBNddcg8bGRvT29io6juXZd4l/Q+SGmggLcmOZDdmMtKCJizCyRViEUdrBf2nFS1gLvh4+nw9er5efOi6cS1NaWopgMIiysjIYDAaMj4+ju7tb1CBIyfyLZNadfBApmtiQiNPpxBNPPIEPfehD+PWvfx1WssFxHDY2Nvg3sti0VovFgjNf34P2L94k67xbWXBogiI3iCc4SK03yXCQQL5//354PB5FzadiQdtsNvN2gX6/H42NjbKer6GRDxQWFmLPnj244YYb8Oyzz6KyslLS8ziOQygUgtfrxZmrv4OmVz4R/SCSfYgjOhSR5qyGIjRxASBLRQVBIC7EKDC8jIMHS/h5NEVFRWFzacjMKGDzcxQ5TFaIkixFPk0DV4ImNiTi9Xpht9vxhje8Abt27UJzczMGBgYwMDAAYLMZ1mq1Sp7WGkms7Ea+oQmI/CSR4KAoCsFgEBzHobCwEDabDTMzM+jo6EBJSYmsc8XaVSKWu7EcsAhbPehr5DctLS345je/iVtuuQV79+6F2WwGsPm58fv9YZthXq8Xfr+ft6wmN2LA5b6GqJKjOKJDFtksMjRxkdPCIhZX9JyHlxUf1ix0VOQ4DhaLBYODgxgbG4vKmCvt2ciHDIVSNLEhkU9+8pMIBoNobm5GVVUVrFYrrr/+elRXV8t6AwV/NgLDjUOi38vlcipNRGjEExzCya1kV2hwcBAul0tS6ZOQeGKBpml0d3djbm4Obrcb3d3dUU46wqC/lYO/Rv7BsixGR0exvLyMkpISvPWtbwVFUbjtttvQ0dEBs9nMb4qVlJSgtrYWZrM56vPka3oW5onNwXmqi45sFBlbXFxktbAAFIsLMaz0SwAgKjqE1rihUAgGgwHDw8P8kFpSQq8ksyFXoORbb6EmNiTy5JNP8l+zLIt3v/vd+O///m+8973vlX0sJYIj02hiQkMKVuY5AOLWuDRNQ6fTwe/3IxQK8YOLXC5XWCBPRKKgTVEUWltbceLECYyOjqKvry/MWUQro9LIZ37wgx+gqakJH/jAB/DLX/4S27dvx6233ir75sh//x/DJnUnLTqyTWRsUYGR9cICUFVcxMJKvxRXcEQOqZ2YmEAgEEBLS4uiNUSuQMm3hnJtzoZC1tfXcc01/z979x0W1Zm/DfweelOagghIB5EiWBNXXYw1mmhUYjQkmGiiKf6im7Zu4mZ138SYZnTjJnFjdjXGSIwbjaYXJcUaDaAotqFJEQtFOlPO+wc7JwzMwDAzB5jh/lzXXMqUcw6W58t9njYRr776KkaOHGnUMfQFDn1hQ+reDQYKMqeiqtFt5jG1HK4RGRkp7i9z6tQpuLq6Ijw8vMMGtqysDNXV1QgPD+/wGq5evYpLly5prSxSU1MDuVzeJoT0cNZTdcyPdUoPhUKB6dOnY+nSpZg+fbpRx2gZOFrSu6JT6x/ie0rIsMJwYRHBwRBdEC700TesSrNnlEwmg729PWQyGXJycqBWqxEYGIjCwsJO7eyt2TPDUEqlEidPnsTo0aOtYj8ohg0TFBQUYObMmdi9ezf8/f2NOkZXBg6GCepq54pjxGEbzs7OWvtw6GvIY2Ji2m1YS0tLUV9fj9DQUIOuoaqqCtnZ2YiNjYW7uztu3ryJgoICxMfHM2xYB4usUw0NDRg/fjwaGxuhVCqRnJyMNWvWIC8vD/Pnz8eNGzcwfPhwbN++3aR/p+Xl5Zg0aRI2b96MmJgYo44hTEsAoHv37S7bYbuzelC4sJpQYA7dGCz06ShwAL9vUpubm4vr16/DxcVF0rDR1NSErKwsjBw5kmGDgF9++QXPPPMM9u/fr3dN5o50JnDoCxsMEtRTdWa38dzcXNy8eRPx8fF6dy0uKSlBU1NTuytOtbmGujpx13J7e3sUFxcjNjaWYcM6WGSdEgQBtbW1cHNzg0KhwNixY7Fx40asX78ec+bMwfz58/HII49g6NChePRRHatDdcK5c+cwf/58fPbZZ+jfv79x1/u/wAFIHzr0DtkyRDeEDIaJdvTAcKFLe4Gj5W7jNjY2OH/+PEpKSjB27Fixd74jnQ0bDQ0NOHPmDIYPH27Uqo3dRG+d4sBlE40dOxZLlizB0qVLzT6hR7W3tM1zwpHTZj0HkdQ08zha08zhkMlkaGpqgiAICAsLQ79+/XDy5EkoFAqdnzNmVQ8XFxeMGDECubm5KCsr45wN6nYymUxcQl2hUEChUEAmk+HAgQNITk4GACxcuBB79+41+VyDBw/GunXr8MADD4j7CnT6er/OFH9ff6QC9UcqtF4XTlWKD1MY9fmIoN8fEpD5D273Qf/jPaTtw0JoJo63plncRFOnVCoV+vXrB3d3d5w4cQINDQ2SXI+1zS20nu+kGz344IMICQnByy+/bNTnFZ+c6NT7dQUOoeBno85N1BXaCxytG/LAwEAEBQXpbciNbYQ1E9IrKytRWVlpdat9kOVRqVRISEiAj48PJk+ejLCwMHh4eIhLpgcEBKC4uNgs55o2bRpmzZqFP/3pT2b7t68rdAAwKXTI4j3ER4fMHDAYJjrJQoOFPvoCB4A2dcrV1RWDBw/GyZMnUVNTY/Zrsbalchk2zOSVV15BZmYmPv30U6M+ry9w6OrdILJE+gIH0LYh9/X11duQm7JKh62tLUJCQiAIArKzsxk4qFvZ2toiMzMTRUVFOH78OM6dk3YH7uXLl8PW1hbvvPOOUZ/X9G5cPqPdO9Je6DA7M/ViMFR0kgX3WnRGe4FDEATU1dXh+vXrEAQBnp6eiI+PR1ZWFiorzftvXbPEu7UEDi59aya2trb46KOPMHHiRISEhCAxMbHTx2hvSVxDCAU/c+4G9Wjt7cWh2VCpqakJdnZ2Wg15TEwMPDya73Sq1WqDx8nqIggCfHx84OvrazUNOVk2Dw8PTJgwAUeOHEFlZSWUSiXs7OxQVFRk9OIjushkMrz99tuYNm0aIiIiMHny5M4f4+tMIHCIGDgCY36f91R/pELnfA6zMCFcMEgYwUrDhCFcbH6CvDhCXEmxrq4OarUadnZ24mInXl5eUCgU6NOnD4YNG4aMjAxEREQYPSeqNWsbRsWwYUZ9+/bFxx9/jNmzZ+PTTz+Fn1/n98swNXAQ9XSG7DauVCoBQGdDbmojrPm8u7u70ccgMtW1a9dgb28PDw8P1NfX47vvvsOf//xnTJgwAbt378b8+fOxbds2zJo1y6zndXBwwK5duzB58mQMGjQIUVFRHX5GqVRq7Tpe/+1/4TplLoDmXo72AodwqlJ7SFQXTOBmuOikXhws9Anzv4j8K4Ph6+sLZ2dnnZvDanYbd3R0xIgRI8TN/8xxg8DawgZXo5LAwYMHsWrVKuzbtw/Ozs5GHaNl4OjMqlQAV6Yiy6AvcADNDa1SqYStra2483hGRgYCAgJQX1+PPn36wNfX16jzFhcXQ6FQIDQ01KQeki7GLhj9LLJOnTp1CgsXLoRKpYJarca8efPwwgsvIDc3F/Pnz0d5eTkSExPx4YcfSrIaTXZ2NlJTU7Fv3z54eHigsbERDQ0N2qGivh5qtRq2trbiHd2WS1mXhf/eg98ycADaK1ZJHTYYLgzEUNFp+lapAn4PHJq5h4IgIDMzE56enggJCRF7zgVBwNGjR3HrrbcafN4bN27g2rVriI6OtopVExk2JLJ582akp6djy5YtBg3VUKvVbRr52FUpADq/5wbDBlmKjgKHroZcpVIhODgYPj4+Rp2zqKgIarUawcHBDBvWgXWqE2pqarB161bI5XIcPnwYly9fhqurK1atWoW4uLg2oULfEtQahYG//wDbMnC0Hk4lBg4zzLUgPRgmJNPe0rhKpRI2NjZiPTlz5gzs7OwwePBgyGQyqFQq/Prrr7jlllsMPt+1a9dQUVGBqKgohg1q3xNPPIH+/fvjqaeeAtC8vGHL3ZQ1v1cqlZDJZG0aed+lUwAwbJB1M6SHo2VDfvjwYbi6uiIhIcGoOReFhYWQyWQICgpi2LAOrFOdUF9fj48++ghhYWEIDQ3FRx99hPz8fKxfv97oYxoSOAzt3WCY6AADRbfpzG7jFy9eRH19PeLi4qBSqZCRkYFRo0YZfK6rV6+iqqoKUVFRVlGnemXYuHz5MlJTU1FWVgaZTIYlS5Zg+fLlKC8vxz333IP8/HwEBwdj165d8PTs/GS3Cxcu4Oeff8bFixfxwQcfwM3NDT4+Pli7dm2bbmgXF5d2/yHZ3z3CqN3EGTjI0nS0+Z+mIT937py4B0dcXFynx7Xm5+fD3t4egYGBVtGIk3XWqa4iCAIWLlyI4cOH4+GHHzbqGC3DBmBA4Phf2GCw+B8GCIth6G7jNjY2yM/Px/Xr1zFkyBCcOXMGI0eONPg8V65cQW1trbgRrYVg2GiptLQUpaWlGDZsGKqrqzF8+HDs3bsXTFpScAAAIABJREFUW7duhZeXF1auXIl169ahoqICr7zySqeP/+OPP+LUqVMICwuDt7c3HnvsMbz99tud2tq+Jacdd+p8nmGDrI0hu43L5XL4+/ujsrIS169fR0JCgrgvgSFyc3Ph7OyMgICATn2umzFs6GeVdUrfTbHVq1fjvffeE1e9Wbt2LaZPn27SuRoaGjBlyhSsXLkSSUlJRh3DkMBh1rDRE35Av3G27XM94bpIUp3Zbby0tBR5eXmwt7fvVNgoKSlBY2MjwsLCGDasxaxZs7Bs2TIsW7YM6enp8PPzQ2lpKZKSknD+/HmTj3/hwgXMmzcPe/fuNXqcua7A0V7YABg4yDLpChyCIKC+vh61tbXIz8/H4MGD4eHhgdLSUhQUFCAxMdHgSbSXLl1Cnz59MHDgQIYN62CVdUrfTbFdu3bBzc0NTz/9tFnPV1ZWhqlTp2L79u0ICwsz6hj6AofJvRv8AZ56mPYmjiuVSgiCAHt7e9ja2qK4uBg5OTkYM2YMXFxcDDp+UVERVCoVQkJCrCJsWM+6WkbKz89HRkYGRo8ejbKyMnG52gEDBqCsrMws54iMjMSrr76K1NRUNDY2GnWMhpT9ZrkWop7ORfUVioqKcOHCBWRmZuLo0aM4duwYzp8/j/Lycvj4+MDBwQEqlQp+fn4IDw/HyZMnUVdXZ9DxrW1JQbJOfn5+GDZsGIDmJaCjo6PNtpu4Lr6+vti6dSseeOABozcoG3T5LM6UO+JMeXPw1+zFoWvDv3b1gs3jyLJ1ZrdxDw8PuLu7IyMjAzdv3jTo+NZWp6znOzFCTU0N5s6diw0bNqBv375ar8lkMrNu+DVlyhTcfffdWL58udG7FrcOHMKR0+2+Xyj4GULBz0adi6g7Rfqdhre3N6KiojBq1CjccsstSExMRFRUFIKCgmBrays25P369UNMTIzBDbm1NeJk/VreFAOATZs2IT4+HosWLUJFRSd/kG9HQkICnnvuOSxatEjc66azbq/NAAC9gUPcVfxiQfPXxS12TWe4IAvSUeCws7NDU1MTmpqa4OjoiMTERGRnZ+PGjRsdHtva6pT1fCedpFAoMHfuXKSkpGDOnDkAmu/slJaWAmjuwjZ2yJM+y5Ytg7OzMzZt2mT0MXQFDkNCB5GlCfQ4DmdnZ50NbsuGXKFQwN3dHQkJCQY15IIgcOdwshitb4o9+uijkMvlyMzMhJ+fn7jaobnMnTsXf/jDH7Bq1Sqjj6EJHETWrr3AodnS4Nq1a83vdXHB8OHDceHCBVy5cqXd42rqlLXUql4ZNgRBwOLFixEdHY0nn3xSfH7mzJnYtm0bAEiyc6tMJsOmTZvw7bff4uuvvzbpWKq9pVDtLRW/1oQOfcGDvRxkiVxUX+l9TbP/hlKphEKhgKurq0ENubXdMSLrpe+mmK2tLWxsbPDwww/j+PHjZj/vqlWrcO3aNbEeGiPGy4Ahw617N3RNuCbq4VxsfsKVK1eQl5eHM2fO4Ndff8WRI0eQkZGBoqIiAIC/vz+amprEieKXL19GQUGB3mNaW53qlRPEf/nlF4wbN05r2cy1a9di9OjRmDdvHgoLCxEUFIRdu3bBy8vL7Oe/ceMGJk2ahC1btiA6OtqoY7TcYRzo3C7jnDhOlqYzm/+p1WpkZGTA19cXgwYNavP+06dPIygoCF5eXh1uWNaDWMftLWlYZZ3SLEnr5eWFDRs2iM+XlpaKcwvffPNNHDt2DGlpaWY/f319PSZNmoS//e1vGDt2rEHXq1AoxD2k6urqYJ/0+8ImrSeLtztRnEOpyALllUaJWxq0XnykdZ0CgFOnTsHV1RXh4eFtejAuXrwId3d3+Pn5WcVCJr0ybPQEZ8+eRUpKCvbt2wdvb2+jjtE6cGi0Dh4MHWQNOhM4AP0NeVZWFsLCwuDh4cGwYR2ssk7puym2c+dOZGZmQiaTITg4GJs3bxbDh7mVlJTg9ttvx86dOxEcHAxBENDQ0KC1Ka3moVl9p+VeUi4uLqgY0jzPRG/YANoGDoYNslDtrVLVepNamUyGnJwcqNVqDBkyRKsn4/z58/D29oavry/DBpnmiy++wBtvvIE9e/YYvbTZBd9Y8fcx453avN5R8GDgIEvSmd3G9TXkGRkZiIqKQt++fRk2rAPrlAS+/vprXLx4EYcOHcKhQ4fg5OSE5ORk3HHHHVphQrNBbXtDPoRpCeLv21sGF2DgIMvXUeBovdt4bm4uqqqqMHToULEm5eTkwNfXF/3792fYINO98cYbyMnJwcaNG42eCNQycGi0Dh7thQ4GDrIkhvRwtNeQnzx5EjExMXBzc2PYsA6sUxJYu3YtBgwYgLCwMJw5cwZffvkldu7cafT/GV2Bo93eDYCBgyxaZzb/KyoqQnFxMRITE+Hg4IAzZ87A398f3t7eDBtkOkEQsGjRIsTFxeGRRx4x+jivuSaKv5/lphB/317oYOAgS2bIbuMtG/KSkhIkJiYiKysLcXFxcHV1ZdiwDqxTXeCvf/0r6uvr8fe//93oY2gCB8MG9RadCRxXr16FXC5HYmIiLl26hMDAQHh7e1tFnbKeqe4WSiaT4d1338W+ffvwww8/dOqzml2Vy8vLtZ7/rOb3IVlnfmow7FhcqYosjL6VqmxsbGBraytuqqRWqxEQEIDg4GCcOHFCHGpFZOkuX76MCRMmYMiQIYiJicHGjRsBAOXl5Zg8eTIiIiIwefJks+zFsWbNGuTl5eGjjz4y+hiyrzM7fI/WvhtcnYosnL6lcTXzC1tu/ufj44PBgwfj5MmTaGxstKo6xZ6NHuLq1auYMmUKtm3bhoiICPF5zTrNmsl4ml8bGxshk8ng6OgojptNGzxT/JwxvRsAezjI8rQ3rEqpVIoTV21tbVFRUYETJ05g1KhR6NevnyU15uzZ0K/X1qnS0lKUlpZi2LBhqK6uxvDhw7F3715s3boVXl5eWLlyJdatW4eKigq88sorJp+vtrYWEydOxLp16zBq1CijjuG0ZoLW1+3O29BgDwdZuPbmcSiVSqjVajg4OMDW1hY1NTU4evQoYmJiEBgYyJ6N3mDRokXw8fFBbOzv8yJWr14Nf39/JCQkICEhAV9++aVJ56ipqUFpaSlSU1ORnJyMBQsWYMWKFTh69ChOnDgBuVyOqqoq2NrawtfXF9HR0Rg9ejRGjx6NhIQEREZGIjAwEM/o2Uipvd6NjjYEJOrp2tuLo/WdI09PTzg5OSE7OxuVlZVdeJVE5ufn54dhw4YBAPr06YPo6GgUFxfjs88+w8KFCwEACxcuxN69e81yPldXV+zatQvLly/H5cuXjTpGw98Odvgerd4NIitg6G7jCoUCbm5ucHd3R25urrghoKVjz0YHfvrpJ7i5uSE1NRXZ2dkAmsOGm5sbnn76abOc46mnnsL169cRHh6OiooKnD17Fv/85z+NXs5QM3+DvRvUW7TXuwH8vlKVnZ0dfv31VyQmJsLR0RHOzs5ddIUmY8+Gfr2+TgFAfn4+xo8fj+zsbAwaNEgM04IgwNPT06zh+vDhw3jyySexf/9+uLq6GnUMTQ+Hrp4NgL0bZH3a690AtOtUVlYWBg8eDAcHB/Tp06eLrtBkeuuUxUxx7y7jx49Hfn6+pOd44403tL5eu3Yt1q9fj9dee82o4z1Tm6E1YRxo7t3QtTQu0Ny70TJwCAU/M3BQj9ZRuGhNMz5WoVBArVbD2dnZ6OWmiXqampoazJ07Fxs2bEDfvn21XpPJZEavdKjPmDFj8Nhjj2HJkiXYvn27UcMRG/52EM57Zpv1uoh6ko7CRWuaOqVUKqFUKmFvbw8XFxeJrq5rcRiVkTZt2oT4+HgsWrTILJPvWvrLX/6CyspK/Pvf/zb6GPqGVGmo9pYafWyirlBne7v4uNYwHj8c90BJ9a0dBg3NxmPl5eUoKirCxYsXkZWVhePHjyMjIwN9+vQRJ44TWTqFQoG5c+ciJSUFc+bMAQD4+vqitLS5jS8tLYWPj4/Zz5uamorIyEi89NJLRh+jfvae37+4WKD1WpuhVJwsTj3Q4d/6olrxB9Spx7d5tEcQBDQ1NaGyshIlJSW4dOkSTp06hV9//RW//fYb7OzsIAgCOhh9ZDE4jMoA+fn5uOOOO8RhVGVlZejXrx9kMhn++te/orS01KRgoEtDQwMmTZqEVatWYfz4zqXjllrvwdGyd4Mb/lF360wPRUNDAzIzMxEeHg5vb280NTWhrq5O61FfXw8A4sIJmoejoyNu3LgBuVyOS5cu4dKlSxg2bBgWLVok1bdmbhxGpV+vrVOCIGDhwoXw8vLChg0bxOefeeYZeHt7ixPEy8vL8eqrr5r9/CqVCrNnz0ZycjKSk5ON+nx9fT36f3f/70+2Gk4FcEgVda/2gkNRURHKysowdOjQNvthCIIAhUKhs05plrzV1CjN5phVVVVinbp48SIGDRqEp556Supv0Vy4z4YpWocNQ18zVWlpKaZNm4aPPvoIISEhRh2jvbABtD93A2DgINN1dsiThq6GuqamBuXl5bCzs4Obm5tWoHByckJlZaXYUMvlcsjlchQVFUEQBPj7+yM8PBwRERGIiIjA0KFDMWDAADN/t5Jh2NCv19apX375BePGjUNcXJw4lGnt2rUYPXo05s2bh8LCQgQFBWHXrl3w8vKS5Bqqq6sxceJErF+/Xpys3pJSqdRaTVHze80S1JoftGJPtZoDyTkc1IU6O+SppeLiYhQWFiIgIEC8CVZfXw+1Wi0OhWpZp6qrq5GbmyvWKLlcjsLCQiiVSgwYMECrTsXFxWHQoEFm/E4lxbBhitaBorS0VJy8/eabb+LYsWNIS0uT5Ny//fYbli5div3797cZi2uoloGjMzuLAwwbZBhjAwUAvXd+9DXUVVVVeOaZZ+Dp6Qlvb2+xoVapVGJDHRkZicjISERERCA4OFjcd8OCWfTFS4x1qhsJgoCMjAzMnz8fqampqKysxLx581BXVwe1Wg1bW1utu7eaX1vPmXLeMxvCqeZJ7Lo2+9Pgpn9kLFMChSY0t36o1WrY2dnBxsYGeXl5iIyMRHBwMOrr65Gbmyve+Lp06RIKCgrQ1NSE/v37i2FCU6dCQ0Nhb29vtXWKYaMDCxYsQHp6Oq5fvw5fX1+sWbMG6enpyMzMhEwmQ3BwMDZv3mz0ylGG2LVrF7Zt24a0tDSj11vWBI6OwgbAwEG6mTNQaO5yagKF5ocQTaBo3VDL5XIUFBRAoVCgf//+CA0NxdGjRzFs2DCsXLnSWhrq9ljtN2YGvb5OdZd3330Xmzdvhre3N1xcXFBRUYEFCxZg7ty5cHFx6XS90rsHB6B/Hw4GDmrB1EDRsgdO81CpVFqhWROYm5qakJeXJ9apc+fO4ZdffoGXlxcGDBiAiIgI8eZXREQEwsLC4OTk1CvrFMOGhVi9ejWqqqpMmoxnaOBg2Oi9yhUTkJmZiaioKHh6enbqsx3d+WndUDc2NrYJFHl5eWhqaoKXl1ebhjo0NFSroVYqlVi1ahWee+45o3v9LIjVViczYJ3qJoIgaP3gtGXLFnz77bf4z3/+Y/QPVJ3e9I9ho9fRFSiuXr2K/Px8JCYmtrvSoEql0lmn9AUKlUolBgpNvcrNzUV9fT369u2rNeQpMjISdXV1ePTRR/Hpp58iNDRUyj+Gnohhw9Kp1Wrcc889mDRpEu6///6OP6CH/d0j2jzXUdgAGDisSUc9FI2NjcjMzERERESbcd6dbaiVSqXehtrDw0PnnR8XFxdrvvNjLP6B6Mc61Y5Fixbh888/h4+Pj9ZeUe+99x769+8PoHmex/Tp081yvhUrVsDDwwPPPvus0cdoGTjYu9E7GdNDce3aNeTl5SE+Pl7nza+W84Ra1ilBEFBQUNBmvl9tbS3c3NwQFhbWZtiTm5ub3jpVUFCAgQMH9sbl1Rk2rEFdXR0mTpyIF198EbfeeqvRx2kdOAwZSgUwcFgSU4Y8qVQq3Lx5E2fOnIGnpydkMplRDXVNTQ3c3Nza3PnpqKEmnfiHpR/rVDu6YmPalpRKJe68806kpqZi1qxZRh+nzaZ/AMOGlTFlyJNardY55Km+vh6NjY1wd3eHp6enWKdsbW1RWFioVaPkcjkqKyvh4uKCsLAwsVZFRUUhIiIC7u7urFOdw039rIGLiws++eQTzJgxAx9//LHRKxQoPjmhs4eDLIspgUKtVutspBUKhRgofHx8cPbsWahUKkyePFlnQ33z5s02DfWMGTPYUBP1EF2xMW1LdnZ2SEtLw2233Ybg4GAMHTrUqOM0/O1gmyFVet04y8DRQ5krULQMFk1NTbCxsYGTk5N442vAgAGwt7dHYWEhDh06hI0bN2L06NG4cuUKbty4AScnJ4SEhIg3viZOnIjIyEh4eXmxTnUB9mxYoGPHjuGJJ57A/v374ebmZtQx2LthGUwNFLru/DQ1NcHW1lZrUrbmzs/ly5e1wkReXh7OnTsHNzc3DB8+XAwUmtWeND0fJDn+IevHOtWB1isqrl69Glu3bkXfvn0xYsQIvPHGG52eo9WRixcv4u6778aePXvg6+tr9HG0dhnncrg9kiZQCIKAixcvQq1WIyoqyqDaoFar0dDQoLNOyWSyNnXK3t4excXFWsNy5XI5rl69CkdHRwQHByM8PBwA8Omnn2Lnzp1ISEgwapd76jQOo7I2O3bswK5du7Bjx44O/xPp2q+gvr4ew/7+oPgeho3uY2qgMFdD7eDggJCQEK1hT1FRUbCzs8Ndd92Fl156CePG8e+/mzBs6Mc61YHu2JgWAH744QesXr0a+/btg6Ojo1HHYNjoGQztoRAEARcuXIAgCGLgEARBZ51qbGwEgDZ1ytHREVeuXGmzgMiVK1dga2uLoKAgrRtfUVFR8PX1bfOz0LFjx1BXV4cJEwzsISNTMWx0FV2T8crLy3HPPfcgPz8fwcHB2LVrl1nuIj333HNQKpVYvXo1BEHQ2kym9U6Vrfcr0OxW6TR/NADDwwbAwGEMXYEiPz8f1dXViI2NbfcOkDENdWlpqVZDnZubiytXrsDOzk6rodYECl0NdUvV1dVwdHSEg4OD6X8YZAyGDf1YpzrQXRvTAsDbb7+Nw4cPY/PmzUb3goqBo6OwATBwmMCUIU+CIKCxsRF1dXWora1FcXExlEqluKt2yyFPLi4ucHR0xPXr19vM9SspKYFMJkNgYGCbnnR/f3/2UPRsDBtdRddkvGeffRZeXl5YuXIl1q1bh4qKCrzyyitGnyMnJweHDx/GxYsXsX37djg7O8PNzQ2bNm3SuXlSR/85nXbcqfN5ho3OMaaHQrMyU3R0tNhQtxcoNCHR2dkZZWVlbe78lJaWwsbGRmdDPXDgQDbUlothQz/WqQ5058a0APDYY48hICAAK1asMPoYzntmM2yYgbkCRctHQ0MDAMDR0VHrZ5BDhw7hq6++wj333KO1Y3ZRUREEQYC/v79WnYqIiMCgQYOM3k+Muh3DRldq3bBHRUUhPT0dfn5+KC0tRVJSEs6fP2/08b/66itcunQJERER8PPzw5IlS/Daa69hxAjjJ33rChz6wgbQewOHKUOe9DXUVVVVUKvV8PDwgKurq9bmduXl5bh06ZJWoCgqKgIA+Pv7t1nlKTAwkA21dWLY0I91qh09YWNahUKB6dOn45FHHsHttxvehrZevjSm7h9arzNs6HalMgFnz55FQkICnJycOv5ACy1HSbQeeg1oBwpNnbp582abHorCwkKoVCo0NTXBxcUF999/v7jKU3BwMGxtbTnfz/owbBgiODgYL774Iu677z6TjtM6bHh4eKCyshJA839kT09P8WtzKCgowMyZM7F79274+/sbfZzWgaO3hg1TA0VnG+qqqips3LgRFy5cQExMDHJzc8WG2s/Pr82dHzbUvRL/svXrVXUKMF+t6krl5eWYNGkSNm/ejJiYGPF5zXzC1otZqNXqNnv3uLi4wKfgr1rH7a2Bo6MeioqKCly4cAGJiYlthr/qmsfZcti1g4NDmzpVV1fXJlAUFBRAoVCgf//+bepUaGgo7O3tIQgClixZgpSUFM6dsH5c+rankMlkZv8hMSgoCP/85z+RmpqK/fv3w8XFxSzHFY6cbjdwWDJTA4WhDbWnpyf8/PxQW1sLuVyO06dPi3MoNA21j48PwsLCUFtbi8LCQqxbt05sqBkoiMjSVVRUQC6X495778U999yDiIgIeHt746GHHoKdnZ04TNTV1RX9+/eHs7OzONa/tfrYDXDObh6OpTNoWBFThjx5enoiKCgIJ0+eRGBgoNirXl9fD7VarTWP093dHQMGDEBDQwNyc3Nx7tw5rRUJm5qa4OXlJW7COmbMGCxcuBChoaFwcnJqt07JZDK89957rGW9HMNGF/D19RXHyJaWlsLHx8fs5xg7diyWLFmCpUuX4oMPPjDqP3ZDyn6t3o32goZQ8HOP792os71dXIYvMjKyU38mnQkULRtquVyOnJwcsaHOz89HU1MTvL29xYZ67NixePDBBxEaGgpHR0fxutRqNVatWgUfHx9OxCYiq/HXv/4VTU1NiIiIwL333otjx47hlVdegbe3d3dfWrczJVAA0FunNIHCwcEBR48exciRIxEcHAylUqm1GqHm14aGBri7u4t1asSIEViwYAHCwsLg4uJiUlhg0CAOo2qhZdd0XV0dFixYAKVSiY8//rhT+1m0Hkb1zDPPwNvbW5wgXl5ejldffVWS7+Hpp5+Gk5MTnnvuOaOPoQkcHfVq9ISw0VEPhSAIOHv2LFxcXBASEtLm9Y4a6tYreCkUijYNtVwuR0NDAzw8PMSGWtOVHB4eDmdnZza2ZCr+A9KvV9UpwHy1qqWuXElxw4YNyMrKwqZNm4xuG10q3tX/Yg8cRmVKqGg9d6XlUDM7O7s2dUqtViM/P18c9nTy5ElkZWWhb9++6Nu3b5vFQ8LDw+Hm5sY6RabinA1DaBrwSZMm4c4778SIESOwadOmTk241TUZ76677sK8efNQWFiIoKAg7Nq1C15eXpJ8DyqVCrNmzcL8+fMxZ84co4/jtOPOHhM2TBnyBABNTU3IysoSG2JDGuq8vDyt8am5ubmora2Fm5ub1rKxmlDh6urKhpqkxH9c+vWqOgWYp1a11hUrKWoIgoCHH34YgwcPxmOPPWb0cfQGjm4KG60DRWVlJc6fP49hw4bB3t6+3c+qVCqdgUKlUrWZu6K5gVVQUKA1h0Iul+PmzZtwcXFBWFiYVqg4cOAATp48ibS0NK5KSFJh2DBEcHAwFixYgLS0NDz66KN49tlnu/uSjHLz5k3cdttt2LhxIxITE40+jnNux70j5gocpgYKXXd+6uvrxYZas1RsfX09xo8fD5lMhvz8/DYNdXV1NVxdXds01BEREejbty8DBXUX/sPTr1fVKUC6WiX1SootNTU1YerUqVixYgUmT55s9HHaBA6Jg0ZneyiuXbuGgoICsRa3rlF1dXVQKpWwsbFpEyhsbW1RWFioVaPkcjnKy8vh7OyM0NDQNr0Unp6eeuvU0aNHccstt5j8Z0CkB8OGIYKDg9HQ0IB+/frh6NGjRndH9wRyuRxz5szBp59+atKShoYEDl10hRBTA0VHd35ab27XuqG+cOECfvjhB9jb26N///6dbqip5/nkk0+wevVq5OTk4Pjx43qXf/7666+xfPlyqFQqPPTQQ1i5cmUXX6nJ+I9Sv15VpwDpalVXr6R47do1TJkyBf/+978RFRVl9HHEwGGmoGHKkCe1Wt2mRlVUVKCxsVFraXNNnXJwcEBRUZHYk67Zj+Lq1atwcHBASEiI1tDcqKgocfd3oh6GYcMQwcHBWL16Nb755hvk5+fjyy+/NMv41O5y8OBBrFq1Cvv37+/0Wtst2d+tf/+OljuPa4Zd1YeuNfpcKpWqzRKI7d35sbe3x+XLl9s01NeuXYOjoyOCg4PFIU8RERHw8PDA/fffjw8++ABxcda50lZvkpOTAxsbGyxduhSvv/66zrChUqkQGRmJ7777DgEBARg5ciR27tyJIUN63rjudvAnC/16VZ0CpKtV7YUNoHmFo4qKCpPP01J2djZSU1Oxb98+k4YXu9j81Kn3mxoodNWppqYmnTe+HB0dsW3bNnzzzTeYNGkScnNzkZubiytXrsDOzg5BQUFaw3OjoqLg6+vL4U4WytC5Ts8++yy++OILqNVqTJ48GRs3brT0EMmlbw1lZ2eHHTt2YOnSpUhKSsJ3330nyepRXWHChAlITU3F448/ji1btpj0j/jMTw3i72PGO0HxyQkAgALNd6cKCwuRGJxoUOPYmYbaw8MDDg4OKCkpgVwux6+//ioGirKyMtjb22s11LfeeiuioqLg4+Oj91o+/fRTiw6R9Lvo6OgO33P8+HGEh4cjNDQUADB//nx89tlnlhY2iLR0Ra3qipUUY2NjsXr1ajzwwAP473//2+HcBn3q1OPbBI469XhcunRJXJGwM9RqNRoaGnTWKZlMplWn/Pz84OjoiCtXriA3NxcZGRlij3ppaSlsbGwQEBCAGzdu4Mcff8STTz6JyMhIDBw4kIHCCq1btw4TJ04U5zqtW7euzVynw4cP49ChQzh16hSA5hVFf/zxRyQlJXXDFUuPYUMHGxsbvPfee3jyyScxbtw4fPfddxg0aFB3X5ZRli5diuzsbKxfvx5PPfWUUcdQfHICLZtpRavX+/fvj/r6epw9exYxMTGQyWTtBgobGxuthnrAgAFaDfVvv/3WpqFuGSjmzZuHyMhI+Pn5GdVQ61qViqxXcXExAgMDxa8DAgJw7NixbrwiIvOQulbNnDkT27Ztw8qVK7Ft2zbMmjXLbMdufZ6cnBz8+c9/xvr1640+jq7eirCwMGRnZ6OoqAgBAQFarwmC0KZO1dfXo7GxEQC06pSvry+cnJxw48YNXLp0SdwzSS6Xo6ioCDKZDP7+/mKdmjVrFiIiIhBR2q73AAAgAElEQVQYGChO3FepVFiyZAliYmLg6+tr9PdJPdtnn32G9PR0AMDChQuRlJTUJmzIZDI0NDSgqalJXGrfmv9NcBhVL6BUKjFjxgwsXrwYd9xxh9mO2/rOT3FxMVQqFezs7CCTyeDk5NRmF9Lr16+32YW0uLgYgiAgICCgzaTslg019U6TJk3ClStX2jz/0ksviT/8JCUl6R1GtXv3bnz99dfYsmULAGD79u04duwYNm3aJO2Fm5dF961LjHXKDLp7JUVBELBw4UIMHz4cDz/8sFmPW1tbi9OnT8PDwwM2Njaoq6sTA4WuOlVVVdVm8ZDCwkKoVCr4+fm1qVPBwcGwtbW19CEwZCaGznV6+umnsWXLFgiCgGXLluGll17q6ks1Nw6j6s3s7OyQlpaGiRMnIigoqFNzFQRB0NmVrOvOT1RUFNLS0tDQ0IARI0bg7NmzYkN9+fJlqNVqrYb69ttvR0REBIKCgthQk17ff/+9SZ/39/fH5cuXxa+Liorg7+9v6mURWZWdO3fqfP6HH37okvPLZDL861//wpQpUxAREdGp4SSCIIg7ZLd8NDQ0D/91cnKCh4cHzp07B19fXyQkJKC6uhpyuRynTp0SlzcvKCiAQqGAj4+PWKcmTJiAJUuWIDQ0VLyRRpZBygVE2rsJ1pJMJtP5b+bSpUvIyclBUVERAGDy5Mn4+eefMW5c9+9fJgWGjR4kODgYffr0ga2tLezs7HDixAmzHdvT0xNpaWmYN28e9u7dqzX2tmWgaNml3LKh1gSK/v37w8nJCTdv3oRcLkdWVpYYKAoKCqBWq1FcXIwxY8bgj3/8IyZOnIhHHnkEISEhbKgtmCGN9uXLl5GamoqysjLIZDIsWbIEy5cv74ar1TZy5EhcvHgReXl58Pf3R1paGj766KPuviwiasXJyQm7du3C1KlT8eGHHyIsLEx8TRModA17AgBHR0exTnl7eyMgIAB1dXWQy+U4c+aM2KP+008/oV+/fhgwYIC48erYsWPx4IMPIjQ0FI6OjqxTViI2Nhaffvopli5dqvc9KpUKjz/+uNYCIjNnzuxwTl97N8EMmeu0Z88e3HLLLeJKcrfffjuOHDnCsEFd4+DBg+jXr5/ZjysIAlxdXZGSkoLZs2cjPj4eXl5euPPO5t3CDWmo5XI58vPz0dTUBG9vb70NdWlpKZKTk/HYY4/B0dHR7N8LdT1DGm07Ozu88cYbGDZsGKqrqzF8+HBMnjxZ0onYe/bswf/93//h2rVrmDFjBhISEvDNN9+gpKQEDz30EL788kvY2dlh06ZNmDp1KlQqFRYtWoSYmBjJromIjCMIAmQyGVasWIG7774bt956K+zs7LBgwQIAgIODg1inPD09MXDgQCgUCnFp89zcXHFVwoaGBri7u4t1auTIkbj33ntRUlKCv//97/jiiy/g7Ozczd8xSam7FhAxZK7ToEGD8N577+Evf/kLBEHAjz/+iBUrVhh9zp6OYaOXmDp1Kuzs7MSl9Wpra/Hkk0+iX79+kMvlOH/+vNhQ5+XloaGhAR4eHmJDPWrUKKSkpCAsLEzcvVSfgQMH4scffzR6VRHqeQxptP38/MQ9Xfr06YPo6GgUFxdLGjZmz56N2bNnt3l+4MCB+PLLL8Wvp0+fjunTp0t2HURkuvvvvx9Xr14VVxYsLS3F6tWrERQUhLy8PK35frm5uaitrYWbm5s45Ck2NhZz5sxBREQEXF1dddap+Ph4cbI3kRQLiKxcuRLz5s3D+++/L851AoATJ07g3XffxZYtW5CcnIwDBw4gLi4OMpkM06ZNE2/+WiOGjR5EJpNhypQpkMlkWLp0KZYsWWK2Y3/77bfi7wVBwJQpU3DXXXdprZ4RHx+P5ORkhIeH622oDcWg0bvl5+cjIyMDo0eP7u5LISKJmHvo74cffqj19eLFi5GcnIxBgwYhLCwM4eHhiIqKwp133omIiAj07dvXqDql2c2bLJ8hC4h0NW9vb51znUaMGCEuVGJra4vNmzd39aV1G4aNHuSXX36Bv78/rl69ismTJ2Pw4MEYP974jYf0kclk+PLLL2Fra8s1vklkrka7pqYGc+fOxYYNG9C3b19zXiIR9TBSDf0FgPfeew8qlYo3ryyclHP+uICIZWDY6EE0/8B9fHwwe/ZsHD9+XJKwAbDngdoytdEGAIVCgblz5yIlJQVz5swxw1URUW9lY2PDG2JWoKfO+QO4gEhX4f/iHqK2thbV1dXi77/99lvExsZ281VRV/vkk08QExMDGxubDockqFQqJCYmmnXvFFMIgoDFixcjOjoaTz75ZHdfDhFJTDP0d/jw4fjXv/7V3ZdDPVR0dDSioqLafY+fnx+GDRsGQHvOnyn27NmDgIAAHDlyBDNmzMDUqVMBACUlJeIcvpYLiERHR2PevHlcQEQC3NSvh8jNzRUnuiqVStx77714/vnnu/mqqKvl5OTAxsYGS5cu1btJncb69etx4sQJ3Lx5E59//rmk19Vy1ScPDw+dqz798ssvGDduHOLi4sS7kWvXruXEbPPgWpz6sU51k+LiYq2hv2+99ZZkvfFk+drbfLWl/Px8jB8/HtnZ2RyKa1m4qV9PFxoaiqysrO6+DOpmhqz6BDSPK/3iiy/w/PPPY/369RJflWGrPo0dOxYd3LwgIivSlUN/yXSGbnIHNPecjxgxAv7+/gbdzOKcP2oPwwaRBVqxYgVeffVVcegdEVFXqq2thVqtRp8+fcShvy+88EJ3Xxa1w5C5ExobN25EdHQ0bt68adCxOeeP2sM5G0RdbNKkSYiNjW3z+Oyzzwz6/Oeffw4fHx8MHz5c4islItKtrKwMY8eOxdChQzFq1CjMmDED06ZN6+7LonYYMncC+L3n/KGHHuqCq2rGOX/WjWGDeh1DJ2FXVlYiOTkZgwcPRnR0NI4cOWKW83///ffIzs5u8zC0q/nQoUPYt28fgoODMX/+fBw4cAD33XefWa6NiMgQmqG/WVlZOHPmDOcYWhFNz7m5VgIzZKL2oUOHsH37dhw4cAAJCQlISEjQ2piVLBuHUVGvY2hX8vLlyzFt2jTs3r0bTU1NqKur66IrbN/LL7+Ml19+GQCQnp6O119/vc1mWERE1PuYOneiZc95enq6Wa6Jc/6IPRvU6xjSlVxVVYWffvoJixcvBgA4ODjAw8ND8msz5A4QERFZJql71tlzTj0RwwaRDnl5eejfvz8efPBBJCYm4qGHHkJtba3k5509ezaKiorQ2NiIsrIyfPPNNwC07wC1lJSUJPmyt0REZB6anvWOVu3S9KyfO3cOWVlZBq9UaKqXX34ZRUVFyM/PR1paGm677Tb2nJPJGDbIKpk6CVupVOK3337Do48+ioyMDLi6umLdunUSXzUREVmz7uxZZ885dReGDQv39ddfIyoqCuHh4fxhuAVTu5IDAgIQEBCA0aNHAwCSk5Px22+/SXnJRERWiXWqc6TqWWfPOXUXhg0LplKp8Pjjj+Orr77C2bNnsXPnTpw9e7a7LwuA4eNS33zzTcTExCA2NhYLFixAQ0NDF16lfgMGDEBgYCDOnz8PAPjhhx8wZMiQbr4qIiLL0pPrlFTYs06kjWHDgh0/fhzh4eEIDQ2Fg4MD5s+fb3BjJjVDxqUWFxfjH//4B06cOIHs7GyoVCqkpaVJfm2GdiW/9dZbSElJQXx8PDIzM/Hcc89Jfm1ERNakJ9YpqW+GsWedSBvDhgUrLi5GYGCg+HVAQACKi4u78Yp+Z+jmQUqlEvX19VAqlairq8PAgQMlvzZDu5ITEhJw4sQJnDp1Cnv37oWnp6fk10ZEZE16Yp3qyTfDAPask/Vh2KBu4+/vj6effhqDBg2Cn58f3N3dMWXKlO6+LCIismLdeTOMPevUG3FTPwvm7++Py5cvi18XFRXB39+/y85v6uZBFRUV+Oyzz5CXlwcPDw/cfffd+PDDD7mmNxGRlejuOmWsljfDnJ2dMWXKFLPcDDNkgzvg9551ImvAng0LNnLkSFy8eBF5eXloampCWloaZs6c2WXnN3Vc6vfff4+QkBD0798f9vb2mDNnDg4fPizxVfd8wcHBePHFFzFhwgS4ubkhLi4Op06dws6dOxEeHg53d3c89NBDUCqV3X2pRETt6q46Zeok7ZY3w0pKSlBbW8v9JlphrSJDsWfDgtnZ2WHTpk2YOnUqVCoVFi1ahJiYmO6+LIMNGjQIR48eRV1dHZydnfHDDz9gxIgR3X1ZPcK2bduwb98+hIeH48EHH8Ts2bMxceJEZGVl4caNGxgxYgQmTJiAlJSU7r5UIiK9uqtOff/99yZ/XnMzDIB4M4w979pYq8gQ7NmwcNOnT8eFCxcgl8vx/PPPd/fliAwZlzp69GgkJydj2LBhiIuLg1qtxpIlS7rzsnuMJUuWIDo6Gvb29rj33nuRm5uLl156Ca6urhg0aBCSkpLYxU5EFqGn1qn2tLwZJggCfvjhhy7bxduSsFaRIRg2SBKGrvi0Zs0anDt3DtnZ2di+fTscHR2765J7FD8/P/H3Li4usLW1Fe+waZ6rrq7ujksjIrJovBlmPqxVZAgOoyIiIqJew9BJ2mvWrMGaNWu68tKIrBJ7NoiIiIiISBIMG0REREREJAmZIAjtvd7ui0RE1CVk3X0BPRjrFBFR99Nbp9izQUREREREkmDYICIiIiIiSTBsEBERERGRJBg2iIiIiIhIEgwbREREREQkCYYNIiIiIiKSBMMGERERERFJgmGDiIiIiIgkwbBBRERERESSYNggIiIiIiJJMGwQEREREZEk7Dp4XdYlV0FERGQc1ikioh6MPRtERERERCQJhg0iIiIiIpIEwwYREREREUmCYYOIiIiIiCTBsEFERERERJJg2CAiIiIiIkkwbBARERERkSQYNoiIiIiISBIMG0REREREJAmGDSIiIiIikgTDBhERERERSYJhg4iIiIiIJMGwQUREREREkmDYICIiIiIiSTBsEBERERGRJBg2iIiIiIhIEgwbREREREQkCYYNIiIiIiKSBMMGERERERFJgmGDiIiIiIgkwbBBRERERESSYNggIiIiIiJJMGwQEREREZEkGDaIiIiIiEgSDBtERERERCQJhg0iIiIiIpIEwwYREREREUmCYYOIiIiIiCTBsEFERERERJJg2CAiIiIiIkkwbBARERERkSQYNoiIiIiISBIMG0REREREJAmGDSIiIiIikgTDBhERERERSYJhg4iIiIiIJMGwQUREREREkmDYICIiIiIiSTBsEBERERGRJBg2iIiIiIhIEgwbREREREQkCYYNIiIiIiKSBMMGERERERFJgmGDiIiIiIgkwbBBRERERESSYNggIiIiIiJJMGwQEREREZEkGDaIiIiIiEgSDBtERERERCQJhg0iIiIiIpIEwwYREREREUmCYYOIiIiIiCTBsEFERERERJJg2CAiIiIiIkkwbBARERERkSQYNoiIiIiISBIMG0REREREJAmGDSIiIiIikgTDBhERERERSYJhg4iIiIiIJMGwQUREREREkmDYICIiIiIiSTBsEBERERGRJBg2iIiIiIhIEgwbREREREQkCYYNIiIiIiKSBMMGERERERFJgmGDiIiIiIgkYdfB60KXXAUREbVH1t0X0IOxThERdT+9dYo9G0REREREJAmGDSIiIiIikgTDBhERERERSYJhg4iIiIiIJNHRBHEi6qEUCgWKiorQ0NDQ3ZdCZuLk5ISAgADY29t396UQEZmMdcr6GFOnZILQ7kIeXOWDqIfKy8tDnz594O3tDZmMixVZOkEQcOPGDVRXVyMkJKT1y/wL1o91iqiHYp2yLsbWKQ6jIrJQDQ0NbMCtiEwmg7e3N+8AEpHVYJ2yLsbWKYYNIgvGBty68O+TiKwN2zXrYszfJ8MGERnN1tYWCQkJ4iM/P7/d9wcHB+P69esAADc3N5PPP2bMGJ3PP/DAA9i9e7fJxyciIsvGOtX9OEGciIzm7OyMzMzMLj+vUqmEnZ0dDh8+3OXnJiIiy8E61f3Ys0HUGxw4APy//6f9+PxzSU61detWLFu2TPz6jjvuQHp6usGfl8vluOWWWxAXF4dVq1aJd5bS09Mxbtw4zJw5E0OGDAHw+10nQRCwbNkyREVFYdKkSbh69arOY1+6dAmTJk3C0KFDMWzYMMjlciO/SyIiMqsTJ9rWqZ07JTkV61TXYtggixUYGIjffvtN52t/+ctfsGHDBoOOM2rUKJw5c8acl9bzHDgArFkDvPBC82P1amDvXpMPW19fL3ZNz5492/TrBLB8+XIsX74cp0+fRkBAgNZrv/32GzZu3IgLFy5oPb9nzx6cP38eZ8+exQcffKD3TlJKSgoef/xxZGVl4fDhw/Dz8zPLNRMR6cI61QkZGdp16m9/A95/3+TDsk51P4YNskgVFRUoKSlBdHR0m9euXbuGDz74AEuXLgUANDY2YvHixQgKCkKfPn2QkJCAr776Snz/008/jRdeeKHLrr1bPPkk4Oj4+9cODs2NuYk03dOZmZnYs2ePyccDgCNHjuDuu+8GANx7771ar40aNUrXcnv46aefsGDBAtja2mLgwIG47bbb2rynuroaxcXFYrFxcnKCi4uLWa6ZiKi1ztQpALjvvvvg5+eHvn37IjIyElu2bBFf6xV1auFCwNPz96+dnIB160w+LOtU92PYIIt0+vRpBAcHw9nZuc1rW7duxfTp08XXlEolAgMD8eOPP6Kqqgovvvgi5s2bJ04SmzlzJg4ePIgrV6505bfQtby8gP/7v+bG294emD8fGDRIklPZ2dlBrVaLX3e0RN7zzz8v3nXqiKurq8nXR0TUFTpTp4Dmno78/HzcvHkT+/btw6pVq3Dy5EkAvaROOTgAf/874OoKyGTA6NHAiBGSnIp1qmsxbJBFOnXqFMLDw/HEE0+gf//+GDhwIL777jsAwFdffYU//vGP4ntdXV2xevVqBAcHw8bGBnfccQdCQkLERtzJyQnDhw/HN9980y3fS5d59lnAxgawtW3uqpZIcHAwMjMzoVarcfnyZRw/frzd97/00kviXScAuOWWW/Df//4XAJCWlmbQOcePH4+PP/4YKpUKpaWlOHjwYJv39OnTBwEBAdj7v+FjjY2NqKur68y3RkRksM7UKQCIiYmB4/96oGUyGWQymThev9fUqcWLAWfn5ptir70m2WlYp7oWwwZZpNOnT+PEiROYMWMGysrKsHTpUrzyyivia1FRUXo/W1ZWhgsXLiAmJkZ8Ljo6GllZWZJfd7fy8gJWrmxuzCXq1QCAP/zhDwgJCcGQIUPwxBNPYNiwYZ36/IYNG7B+/XrEx8fj0qVLcHd37/Azs2fPRkREBIYMGYLU1FTceuutOt+3fft2/OMf/0B8fDzGjBlj3XcJiahbGVOnHnvsMbi4uGDw4MHw8/PD9OnTxdd6RZ1ycABefx2YPl2yXg2AdaqrcelbskinTp3C888/j6lTpwIAhgwZgp9//hkAUFlZiT59+uj8nEKhQEpKChYuXIjBgweLz/fp0welpaXSX3h3++tfzXq4mpqaNs/JZDLs2LFD5/tbrm+u67MA4O/vj6NHj0ImkyEtLQ3nz58HACQlJSEpKUnn+WUyGTZt2tTh9UZERODAgQMdvo+IyFTG1Km3334bb731Fo4cOYL09HSxpwPoRXVq4cLmh5mwTnU/9myQxREEAdnZ2bjzzjvF57Kzs8Vl5jw9PVFdXd3mc2q1Gvfffz8cHBza/Ievrq6Gh4eHtBdOBjl58iQSEhIQHx+Pt99+G2+88UZ3XxIRUacYW6eA5k3oxo4di6KiIrzzzjvi86xTPQfrVOewZ4MsTl5eHgAgPDxcfC4jIwN33XUXACA+Ph4XLlzAyJEjxdcFQcDixYtRVlaGL7/8Evb29lrHzMnJwX333dcFV08dGTdunPUPFSAiq2ZMnWpNqVRq7bHAOtVzsE51Dns2yOKcOnUKcXFxkMlk4nMZGRkYOnQoAGD69On48ccftT7z6KOPIicnB/v372+zMkhDQwNOnjyJyZMnS3/xRERk9Tpbp65evYq0tDTU1NRApVLhm2++wc6dOzFx4kQArFNk2dizQRbn9OnTYoMNANevX8eVK1cQGxsLAEhNTUVCQgLq6+vh7OyMgoICbN68GY6OjhgwYID4uc2bNyMlJQX79+9HUlISBg4c2OXfCxERWZ/O1imZTIZ33nkHjzzyCNRqNYKCgrBhwwbMnDkTAFinyKLJBEFo7/V2XyTqqZ577jn4+PhgxYoVHb539OjReP/998UiYClycnJ0bhZFlk3P36tM13sJAOsUWSjWKbJUna1TDBtEFoqNuHVi2Og01imiHop1yjp1tk5xzgYRmWTv3r2QyWQ4d+5cd18KERFRG6xT3Ythg6iX2LEDCA5u3kQ8OLj5a3PYuXMnxo4di507d5rngGhehYWIiHoX1inrxLBB1Avs2AEsWQIUFACC0PzrkiWmN+Q1NTX45Zdf8P777yMtLU18Pj09HePHj8eMGTMQFRUlTnoEADc3N/zpT39CTEwMJk6ciGvXrgFo3gxpxYoVGDFiBDZu3Ij8/HzcdtttiI+Px8SJE1FYWAgAmDVrFj744AMAv0/yb62srAyzZ8/G0KFDMXToUBw+fNi0b5SIiCTFOmXFdUoQhPYeRNRDnT171uD3BgUJQnPzrf0ICjLtGj788ENh0aJFgiAIwq233iqcOHFCEARBOHjwoODo6CjI5XJBqVQKkyZNEj755BNBEAQBgPDhhx8KgiAIa9asER5//HFBEAThj3/8o/Doo4+Kx77jjjuErVu3CoIgCO+//74wa9YsQRAE4cqVK0JYWJjw008/CREREcKNGzfaXNe8efOEN998UxAEQVAqlUJlZaVp32gX0vP32lFb3ZsfRNRDsU6xTgnNf41sxIksUWcacZlMdyMuk5l2DTNmzBC+/fZbQRAEYePGjcJTTz0lCEJzIz5u3Djxfe+//76wfPlyQRAEwcbGRlAoFIIgCIJcLheGDh0qCEJzI56eni5+xtvbW2hqahIEQRCampoEb29v8bUdO3YItra2wr59+3ReV79+/YSGhgbTvrluwrDBsEFkLVinWKcEQeA+G0S9waBBzV3Sup43Vnl5OQ4cOIDTp09DJpNBpVJBJpPhtddeAwCtzax0fa3reVdXV4POffr0aXh7e6OkpMTIqyciop6Edcp6cc4GUS/w0kuAi4v2cy4uzc8ba/fu3bj//vtRUFCA/Px8XL58GSEhIfj5558BAMePH0deXh7UajU+/vhjjB07FgCgVquxe/duAMBHH30kPt/amDFjxPG1O3bswLhx48TjfvXVV8jIyMDrr7+OvLy8Np+dOHEi3nnnHQCASqVCVVWV8d8oERFJjnXKeusUwwZRL5CSAvzrX0BQECCTNf/6r381P2+snTt3Yvbs2VrPzZ07V1ztY+TIkVi2bBmio6MREhIivtfV1RXHjx9HbGwsDhw4gBdeeEHn8d966y385z//QXx8PLZv346NGzeisbERDz/8MP79739j4MCBeOONN7Bo0aLmMaEtbNy4EQcPHkRcXByGDx+Os2fPGv+NEhGR5FinrLdOcVM/IgvVkzdLSk9Px+uvv47PP/+8zWtubm6oqanphquyDNzUr9NYp4h6KNYp68RN/YiIiIiIqEfgBHEiMrukpCQkJSXpfI13i4iIqLuxTnUd9mwQEREREZEkGDaIiIiIiEgSDBtERERERCQJhg0iIiIiIpIEwwaRBdIsWS0IAgRBgFqtFh8qlQoqlQpqtbrNut5ScHNz0/taSUkJkpOTOzzG2rVrDTrX1q1bsWzZMgDAu+++iw8++EDve9PT03H48GG9r+/btw/r1q0DADzwwAPiBk6Gan3NY8aM6dTniYisGesU65QGwwZRD6JplFUqFZRKJRQKBZqamtDQ0IDa2lpUV1fj5s2bqKqq0mqw6+vroVQqxYa79QMAkLcD2BsMfGTT/GveDsm/n4EDBxrUOBraiLf0yCOPIDU1Ve/r7TXiSqUSM2fOxMqVKzt9Xo3W19xewSAishbt1am6ujrU1NTorFMtwwbrVLPeUqcYNoi6QGdCRFVVFaqrq1FTU4Pa2lrU1taioaEBCoUCarUaAGBj0/xfVyaTQSaTQRAE8fcymfa+OiqVCurcD4HjS4C6AgBC86/Hl5itIRcEAc888wxiY2MRFxeHjz/+GACQn5+P2NhYAM13e+bMmYNp06YhIiICzz77LABg5cqVqK+vR0JCAlJ0bBX7n//8B5GRkRg1ahQOHTokPr969Wq8/vrrAIB//OMfGDJkCOLj4zF//nzk5+fj3XffxZtvvomEhAT8/PPPeOCBB/DII49g9OjRePbZZ7XuPgHA999/jxEjRiAyMlLc5Kn1e+644w6kp6frvGbNnTN9fxbp6elISkpCcnIyBg8ejJSUlC65o0dEZAhT6lRdXR3+P3tnHt7GXef/1+iWbEu2ZdmOj9iJncOJcydO0ouWwrJAW0pbCmVLYXcLW55lF/Zkd/ktFFjoLrsLFMq5dCnL0ZYe9G6hJW1TmsNXnMRxbMd3bMu2LFmWdR8zvz8cKZYt+ZTsJJ7X8+SJrZn5zkgaf77z/n4uv99PMBgkEokAM+ep6f9mnLv7l0jyPHVFzlNynw0ZmSWQaHVm6upNdAVHFEW8Xi8ul4vCwsLYsVGDGzW+CoVihhFeKoFAAMOpL0DEG78h4oWTX4B1Mw3nQnn66adpamri5MmTjI6Osm/fPq677roZ+zU1NXHixAm0Wi2bNm3ir/7qr/j3f/93HnroIZqammbsb7Va+dKXvkRDQwMmk4kbbriBXbt2zdjv3//93+nu7kar1eJ0OsnOzua+++4jMzOTv//7vwfg4Ycfpr+/nyNHjqBUKnnkkUfixujp6aG2tpbOzk5uuOEGOjo6ktquoCIAACAASURBVL7f2a55ts/ixIkTnDlzhqKiIq6++mrefvttrrnmmlk/WxkZGZmlEH1YnO5RiHodRFHE6XSi0WjQarUzjl2OeQqAk/+CIM9TV+Q8JXs2ZGQSMNUQR1d4AoEAPp9vxgqPy+WKrfC43W68Xi+BQCAW1gSTKzyiKOLxeFAqlSiVSlQqVeznqPFerAGfc+XBez7J632LOt90/vCHP3DXXXehVCopKCjgHe94B3V1dTP2u/HGGzGZTOh0OrZs2UJvb++s4x4/fpzrr78ei8WCRqPhwx/+cML9tm/fzp/8yZ/wi1/8ApUq+RrKhz70IZRKZcJtd955JwqFgg0bNrB+/XpaW1tnvbZkzPZZ1NTUUFJSgkKhYOfOnfT09CzqHDIyMjKp9JiPjo7i8/lic1I65qk5kecp4Mqcp2TPhsyqIpEnIhKJxHkkbDYbubm5M44D4gztQld4VjRkxlCa2GAb1i7rZUxdNVMqlYTD4ZSM++KLL3L48GGef/55vva1r3H69OmE+2VkZCQdY/r3KAgCKpUqJhgB/H7/kq4zXe9fRkbmymEhHvNExy7WE5E2EZGA6PXHPVTL8xRwZc5TsmdD5opguiciuroz1RMRXd2Z7onw+Xxxnoju7m4UCsWM1Z3oCo9CoUj/Cs8imO1aItVfRVIa4l6TlAakHV9LybmvvfZaHn/8cSKRCDabjcOHD1NTUzPv49VqNaFQaMbr+/fv580338RutxMKhXjiiSdm7COKIufPn+eGG27gP/7jPxgfH8ftdpOVlcXExMS8r+GJJ55AFEU6Ozvp6upi06ZNlJeX09TUFDtHbW3tnNe81M9CRkbmymQhHvPm5uZ5ecxT5YlI1WJYJBLB6/XidDoZHh4mEonE3qPX6415VaLzrtfrjb0fafvX5HlqDi7XeUr2bMhc0iRb3fH7/ajV6rhVnkTHTvdEzMfwpktETF1xWnbKP4qkUMCpL0y6qg2lSNu/BmV3wRSvzUIJh8NotVo++MEPcvToUXbs2IEgCHzjG9+gsLBw3u7XT33qU2zfvp3du3fzy19eTAZcs2YN999/PwcPHiQ7O5udO3fOODYSiXD33XczPj6OJEn89V//NdnZ2dx8883ccccdPPvss3z3u9+d8xrWrl1LTU0NLpeLH/7wh+h0Oq6++mrWrVvHli1bqKqqYvfu3XNec7LPYrHubhkZmUub+XjME81Ts3nMR0dHWbdu3bK+j1kXrC6IhmAwGPd/9OdQKBS7dq1WG5f/oVQqUavVM+bWqfmMWq0WdflHCYXDqM98CXzx85Qkiouem+V5auXnKWEONSuXSpFJC7O5iKf+nkxENDY2snfvXoA4I50K6urq2LdvX0rGmsrY2Bijo6Ns2LAhJeP19fWxefNmALxeL3q9PvYZTBVaXq8XnU4XqwySjMUY8pMnT/LJT34ybiVFZmmcPXuWqqqq6S9fOi60Sw95npJJC8nmqcWKiKn/z0W65qHpRCIRgsEgnZ2dGAwG1Gp1nJiYKiKiAiL6f/SfRqNBpVIlfG9T56npTF2A8/v9SJIU88io1eqEx8jz1KXBQucp2bMhk1KmG+ZkBnqpnoio8bucuNTLnE69vvkY8x/+8Id85zvf4dvf/nY6L0tGRkYmpSx0sau3t5fCwkK0Wu2iPebLTVRETPVATBUR0Tj8qIiIJofr9XoyMjJigiKZiEglgiCg1+sJhUKxqIVkYkOepy5PZLEhMy9mExHTm/RE929tbY1Tvpe6cV4OlvM9L+ZcUdf/fCaY++67j/vuu2+xlycjIyOTUtK12OX1TpZjTVYhaDkRRTFhGFP056iIEAQhzvOg1WoxGAyx16bb+Pb2dvLz88nOzl6pt4ZarY7ls/j9frRabcJ5aCGCQ56nLg1ksbHKWaiISEQi4yxJUmylRObyIVpzPfq9rUZBKCMjc2mxXB7zlWS6iIiGNk3NiYCLImJqSFN2dnbs52huxOVK9P1JkoTH40Gv1yd9johEIpdksRaZmchi4wplumFOJCLcbjfBYDDpSsZSjLP8hz+TdCSIR8dMFjOM3Y7iyBFQqSAadmY0wsGDScecmvcxfaKWSS+XeqidjEwqWcxiV0dHB6WlpbHk48tBRCRLqo7mREiSNCMnQhAEsrOzY6+lW0Sks4DJYsaO5oH4fD5UKtWMZoMAoVAoLr/jUvvur1QWM0/JYuMyYz4iYr6eCJ/Ph9vtxmw2L9flyyyRqRMXwMjICCaTCVEU8fl8cWFsGo0GrUaD4tw5BLMZlEpwOGDHjqTjJ1sZBNmQpxtJkrDb7eh0upW+FBmZJZEujzkQW+FfTq95ogWdqC1OlhORTERotdp5iYjR0dErYm7WaDQ4HA5yc3PnPYdE91MoFGRkZBAIBGJejmS5mvI8tTwsdp6SxcYlQjIR4fF40Gq1SzLOybjcEqyvZCRJSrj6Ff09GAzOmLjUajV2uz1W2zu6GhYdLxoSZTCbyezrI5Kbi9rhYNxgQExS3i4a75usu6lsyNOLTqejpKRkpS9DRiYhqVzsWoonIp0ewEQiwu1209XVFduWTESYTKZYTsTlHs6UKvLy8hgdHcVms83YFvV4qK1WlGNjwGRolCAIiIWFRCyW2L6iKBIKhWK9RGByvhIEYYbwlD/39LKYeUoWG2lk6irzbMY5URfQKE1NTezduzfOMKfyD0kO20gvkiTFGjdFm/h0d3fHTWRRg6vRaOJKChqNxhlu9WRML5MYDAYxGAyMj49z4tw5dgMWhQKuvZbcBDXAo/T19SEIAqWlpTPeR3SCVavVslCVkbmCWKyIOHnyJDsueEqXK5xpseNGH1YTJVVPXdBJlBOhVqtjydNz2eLLlXSFUalUKgoLCxNuCwaD6PV6VB4Piro6pOxshoaGyIpE0O/YAdP6jITDYdrb2/F4PGzdupW+vj5MJhP5+fmx9zB1PpXnqUsHWWwsgqjBnWqAFyoi4KJRjv5BJCvxmi7DfSUazOVCkqRYp/JELvRAIBD7/tVqNVqtNlbpKSMjg9zc3NhElk6DmJOTg2TMpQ4DpSdbqLj1VhIXFLz4vhJdT/QejCYxqtVqlEqlfA/JyFzCpNsTEYlElr0IyPSQpqkiIlFOxFQRMd0TMd8FnfHxcTIyMhLmDcgsHWnTJgYPH+PX57zcmK0lw2KEtWtn7KdSqdiyZQs2m436+noMBkNczunUAjWBQACVSrUspXtl5kYWG1OYTUREQ1KmGm5BEBgcHCQjIwOj0TjDKEerJCyF6B9OusSG7NmYiSRJeL3ehAIiEAgQiUQAYklr0X96vT6uKsj0SdhmszExMRFbhUklEVGiq3+UzvOjnOu10d4zRPfAGB19NmxjbgyREFu1YT7xRi1/fMP+pLHAc91rCoUi5q2JRCKyl0NGZplJhccc4sXDpZhYnSy0dHx8nLa2trh5eDavsGyj5sdKPAsEgmFeebuJx15u5PXac0REifYyDV/9x+sncwyTYLFYMJlMHDt2jN7eXnJycuJCf6P3cjgcRhRF+R64BFi1YuPIkSMUFBRQWFgYJyKmM/XhK5GICIfDRCKRpDHuSyWdgmC1iY1ok6NkIiKar+Dz+ejq6oqrUZ6VlRXXKXWxLHVCH5/w0XHexrleW0xYdJ6f/DkcSf5w4VWqqQur6fzxUZRKFbu2lLFhw4YZBng+wja6XRRFent7KSkpkVePZGTSwKlTpxAEgYqKillFxLlz5ygoKIgtes3lMV9JoiIimRdittDSrKwsJiYmKCkpIScnZ1kfIFfDXLlc90pjy3kee6WB3/z+FOMTvrhtj/YF+bgig71zjKHRaLBYLCgUCmpra6mqqiInJye2fbo3XvZyrCyrVmw8++yzHDhwALPZvCRPRLof2NM9/pVgQJOVFoz+i1YvUSgUM5ocRV3jUREhCAJ1dXVUV1ev2PuJRET6hia9Ehf/jdJx3obN4V7S2A6Xl3/+wVv84J9zcDiOs23bNjIzM2Pb5+tFi/7N9PT0UFBQgCiKV2wss4zMSnH48OFYDtVs81T0tZXsazQ1Py2RgAgGg4iiOENEaDQaMjMzMZvNsddnExF2u33ZV6plu7Z0hkdd/Pp3J/j1K4209Ywk3S8swdd/+gZP79o8r3EtFgulpaU0NzeTk5NDRUVF3L0R9cZ7PB58Ph8FBQWyl2MFWLViQ6vVEg6Hl2ycozdyuki3Z+NSZrYKTV6vl7q6uriEvoWWFlwOhMFB8HgAUDkcaH0+yM0FkwmX28+5vpFJIXFBVHSeH6V7wE4gGE7bNY25vNz3wLP84ut3c/r0aYqKili7du2iQ/YUCkXc6pGcyyEjkxq0Wi1er3fOeSqd88RUETHdG+Hz+aivr4+JiGh+WtT2ZmZmxomKVDzkrTaP/HKRjs80EAzz27fP8tjLDbxed47ILN73qRyqPccbte1cX7Nx1v2i85XBYGDfvn10d3dTW1tLdXV13CKaIAj4fD6GhobIzs6Wcw5XgFUrNjQaDcFgcMnjRN106eJKDKOabfKab4Uml8sVq9J1KaNoaSF85Cj9Xom+gWHcw3a+WrSdw05pyV6KpeB0+fjoP/2cJ755L16vl8bGRqqrqxclNqYm5UVry8vuahmZpaPRaBi7UBJ0NqKCfyHMZYeneiLUanWcHY4WuXA6nezevXtVrBSvBoGTKpvd1NrPYy838Jvfn2TM5Zv7gAR84TvP8db//e2s99b0MPf169eTl5c3YxEtSnSuiuYcyt745UMWG0vkcg6jSvXY0QpN0YkqFArR09Mzo0JToslroStgqUi+TweSJNHWZ6eh1Up96yCdJ89xoKGJfo2RjWYNA4M+fuMbRxRWfnJ2uf186G9+wpPf/iTryyzU19djBHJHRxFcrth+UkYGFBXNOd70PKbo6pGMjMzi0Gg0sTDQ2Zhqy6MiYraciLlERPS1+djh5RYaK7FIdinONZcaI44JnvjtZJjU2e7hJY93psPKz5+r5eO3Hki6T6LFMaPRSE1NDefOnaOhoYHq6mp0Ol2s0uLUnEPZG798rFqxodVqZbGxgLGnl3mdT4UmSZLmrNB0uTM24bsgLKw0tFk50T6EyxOI28ekz2Od34lncJzRXQcQ+ybgElklc3n83P7ZH/Prb97Lvn37aH7sMcTf/Q7WrZt8iHC7kQ4cQPzQh+Y13lQvRzAYlJPyZFYVkUiEvXv3UlxczAsvvBC3LRAIcM8999DQ0IDZbObxxx+nvLx81vGmzlNTF3OmCwiHw8Hw8DBdXV0IghBnhzUaDQaDgZycnJin+HK2w3IYVXpYjFc7GArz27dbefyVBg4db5+1SMli+NqPX+G2d+8kKyNxt+pk16xUKtm8eTN2u52GhgYqKiriBMV0b3y0YpU8T6WPVSs25rtiNBcKhSJWxSgdpNuwiqKIz+ebs0KTUqmMK/M6nwpNY2NjFBQUpO3al5tIRKSlZ5T61kEa2qw0tFrpHJg7xKHeWEK1d4RRtYGX7SoO7NvKsdrmZbji+THhDXDH3/wPj//3veTt2oVw+jS9bjcF69ZhCIcRr7pqwWPKpQdlViMPPvggVVVVuKZ4BqM8/PDD5OTk0NHRwWOPPcbnP/95Hn/88Rn7nTt3jieffBKr1UpDQwPDw8P86le/4rvf/S4GgyEuJyLaZ0ChUJCRkcGaNWuW422uKCvl2ZAFzkVOtQ3w6IUwKce4N23nGXFM8K3/O8QXP/2+hNvnEkhms5mamhpaWloIBAIYjca47dFjo60NZG98+li1YkOr1eJ0Opc8zqXq2ZhaoSmRiIiq+ej/c1VoWo3YnF7qz04Ki/rWQZrODeP1L1yg2tUGGjPX0K3LQRQUHDs7xMH91Rw9fukIDo8vyJ1/+xP+87Pv5qb3v5+SV15huLWVicpKzEVFLOYOkBssyawm+vv7efHFF/nCF77AN7/5zRnbn332We6//34A7rjjDj7zmc8kfFhSq9VUVFRw7bXXsmfPHpqamvjKV74y67knJiZS9j4uB+QH/+XHNubmyd+d4PFXGmnpHFq2837/sTf5xK0HWLsmd8a2+Xhj1Go1O3bsoLW1lcHBQSwWS1yfKdkbvzysWrGh0WgIBAJz7zgHi0nMWwjTxcZsFZqitctnq9AU/V2tVuP1eunp6WHr1q1pu/7LhVA4wunOERrarPzu7bN0/+AkfcPjKRv/9ZyKuN+PtgxxVc1WjtSeSdk5lorXH+Rvv/UKxi/czq25uRRLEl3799NZW8u2bdswGAyLGneql8PpdGI2m2Uvh8wVx+c+9zm+8Y1vJH3wHxgYoLS0FJgMNzWZTNjtdvLy8uL2Ky8vj4VXhcNhGhoa5jz3Sq68p6vpbDLkh8D0kOh7DIUj/O5IK4+/3MBrx9pSHiY1H/yBMF/+/os8/NWPzdi2kHsvOzsbQRDo7u7GZrOxYcOGOC+G7I1PL6tabKQijCpVRj5ZZRCXy0VbWxuRSGRe3VIXUl1htbqGhc5OIuMTNHbbOXGml+YuG2+MilglzbJex5Gzw1xVs4UjtS3Let7ZCAQj3Pu1pyn45EEOlAcpv/pqsp1OmpqaKCsro6ioaFGTffSY06dPc/DgQbn0oMwVxQsvvEB+fj579uzhjTfeSNm48y1kku5Fr2Qstlx2Ks65nKy2ufL0uUEee6mBp187iX3cs9KXw1OvNnHfh69jX3VZ3OsLufckSUKj0bBnzx76+vqora1l69atcaFVU70c58+fx2KxoNfr5XkqBaxasaHVapdFbEyv0JQorGm2Ck0Gg4Hi4mJyc3PTorJXkwENR0QOt4zQ+/ALqI4fY0TQUpyjY2t3D2+v2Qe65RUbAEfOjnBwXxVH684u+7mnk23KxGw2kmvO5d9awvzzn1zNQSZXhGpqajh79iw2m40tW7ag0Sz8s4oacrn0oMyVxttvv81zzz3HSy+9hN/vx+Vycffdd/OLX/witk9xcTHnz5+npKSEcDjM+Ph4XDhHIuZbyGSlHoZXsny6TGpxTvh5+DfHefr3pznTYV3py5nBv3z7WV79yV/HvbZQsRGdf8rKyjCbzTQ3N1NQUEB5efmMErmDg4NkZWWhVCplL0cKWLViIxWlb6MVmrxeL0NDQzNERLRC03QREU3qm0+FJofDkbJmSNNZDQ954YjIH87aeL6+n5cbBxnzBLH4jfwpGgb0ubgmfKyp3kmf27Ri13i01caBvZs5Vt+a/pMJAoUFuRQWmDFkZSEpVbj8ItYxH46JAE4fdPYHgADt3/w9h795B/nZBlQqFdu2bWNoaIi6ujo2b94854NS8ku4WHrQ7/fLXg6Zy54HHniABx54AIA33niD//qv/4oTGgC33HILP/vZzzh48CBPPvkk73znO+e85+frgU93c9lkrJYytFeUZ0MUEaxWEEXCEZFXT/Ty6OFWXm3qXZEwqflS19zLU6+e4PZ374q9thCxEV3UjZKZmUlNTQ0dHR3U1dVRXV0dFyocLZUbLZErz1NL47IQG3/2Z38Wc1M3N89MqpUkic9+9rO89NJLGAwGHnnkEXbv3j3rmLMZ8UgkMmty9dQKTYIgxERHNKRptgpNC+VSKX17ORERJd5uHeG5ukmB4XDHi0qbzkiLsYT1nmF0kSCPKHZw9cFy3j62cvkTx9tHqdm1kdoT7SkZT6VSUlqcjzkvF384RIYxhzFvhH67h6FAmKERCUZmVsyZytCYl3u/+Xt+86X3o1ROit3CwkKys7Npbm7GZrMt+v6RSw/KrAa++MUvsnfvXm655Rb+/M//nI997GNUVlaSm5vLY489Nufx810US3dz2dnOK4c0XfpEIpGLzzIuF8IvHuWXVjWvuQ3Udfau9OXNm/u/9yLvv64anVYNLM6zMRWFQsHGjRsZGxubESocFRtRIS9745fGZSE2PvGJT/CZz3yGe+65J+H2l19+mXPnznHu3DmOHz/Opz/9aY4fPz5jv3A4jNVqZWBggKNHj3Lu3Dk+97nPcffdd6NUKmPiQ6FQzEiuTlahaXx8nOHhYdauXZuW957OSeRKMtqiKHG03cZzdf282DCIfWL25P/jeZVsd/Zy3mCm15BHb4+HA3s3cay+bZmuOB5JAuvZHv5TdZ6hgZHY6w6ljp/mJU/gzzDoKC6yoNIoyTZb8EcEbBNBrGNeuoMS3YMXHlRGHIu6rsOnB/j6o3X86937Y6/pdDr2nT3LxPHjGMbGiDzzDBqtFvGOO5B27lzQ+HLpQZkrjeuvv57rr78eIK6KlE6n44knnljQWAvxbMhiY/UxW8GYaNVJmLw/NBoNIUnJs40OHutcy1hg8jPcU2ymYcC+km9j3pwfGuP7jx3mbz9+I7B0sRElJyeHmpoaWltbGRkZYevWrTGxAbI3PhVcFmLjuuuuo6enJ+n2Z599lnvuuQdBEDhw4ABOpxOr1Tqj5vipU6f4yle+QlFRUSzh+rrrrmPdunVkZWUtamU13StKsmcjOaIocezcKM/X9fNiwwA21/yri9l0Ro5YNtKRVQgXvvPGwSB7tq2n4XRXui55VuwKHeKQjXV5RvpsTgpCPvoysgAw55pYU2gm02REUKlxB2HYFWDE6aPdA3iAsaWXck7EN58+Qc3mQt6zd0pyXmEhJq8Xl0aDbXSUbJ8PbX7+rCVyk91rculBGZnEzLdq4mrK2Vgt55QkCY/HM6PiZPTn6PUkKhgT/Tn6TGOf8PPjV9p4+NVWXN548Tqqz0ehcCCKl8ezwLd+9nvuPrCO/OwMVHY7CpsN9HrInVkadypTxUMiVCoV1dXVjIyMUFdXN0OcyN74pXFZiI25mFpSEKCkpISBgYEZYmP37t0888wzAJw4cYIHH3yQ2267bUnnvlT7bMyXS1VsCJ2dKOvr414T164lcuAAtR12fvIHB3WPvsTwuH/R53ijIN5jEBElzjoF1pea6Tq//Cs9XqWGQ5ZNfCTUz9qNFZjGR+mvvgmjX4XdG8I+Bowtf2UQSYK/ePAQb/737ZTlT1bukK6+Gl54AcHjoUirxbpxI+cHB6nOzUWnW1i31yhTSw9G3dVyUp7MamYhYVQrYctXyqNyOTO9B9Z0MREN0/b5fPT09MSEg16vj+V6arXaednG4TEf33vpLP93qANvIHHz4d6gigMHdnLsyImUvs90MeEN8PV/+SHf3WZgzeAg6sOHESwWxM99LrZwmIi5xEaU/Px8TCYTb731Fq2trVRVVSUskSt74xfGFSE2FsN8q3zMxXL32Uj12JcsWi2K06eR8vORgJG2Xp4pCPDQU2NYx3xpO20gJDKiNlJZrqKjZzht54lSaDGxtiQfjcGAwyfSYC3hhpNPYxx1cMhYylGHBCy9atpScboDfOI/X+W3X78VjVoJBgOh974X7Y9+hKKggIJPfAKlWk1DQwOVlZUJO8eLojinsZcbAcrIXGS+VRPlMKqVZ2rlyWRCAoiVr58aqp2TkxP7PWrv6urqFt0D6/yoh+8+38KjhzsJhOa+L7omlGjUKoKhxILkUuP/BiN8qkpDhsUCCgXSVVfNKjRgYSFX0UI+JpOJ48ePs2XLFrKzs2PbZW/8wrkixEa0pGCU/v5+iouLZz3mUuuzsRLjL6fRliQJbzCCxx/G7Q/jDkRw+8N4ApO/T309+toumwFdWw9BnZ7BjlEe2qjCp0qf0IjiC0m4dCZKCkP0Dy0u1yERKqWCtcVm1qyxEFZo6HcGGBkPYB+OABcagQkafl9Yzc39DfTuvhahZ5xLZV490WHjn//3bf77L64DIHLwINJPf4q4fz+sWUMeYDQaOXPmDDabjc2bN8cVSViIsY/u19LSQnl5OVlZWbKXQ2bVsZA+G3IYVXrx+/04nc6EQiISiSAIAiqVKiYaouXrzWZzLKQp3Tas0+riwedaePJIN+HI/D+jkfEA27dt5FTjpdPzaTZECb7QOMa3t0iQk4O0Y8ecxyymH0xJSQm5ubk0NzdjNptZv3593Hc41RvvcDjIy8uTvRxJuCLExi233MJDDz3ERz7yEY4fP47JZJoRQjWdVJS+BVlsTOfs4ARP1VsZdng42TVG5PlDuAMRvIEwCw0JPRYu5WPWNgSFAuu2ffgi2pRe62zYJ4IU5xSQFwgyOuZe1Bg5RgPrygoxZGXiCkLXiIfzYYnz5/1A8vCv43kbsGsyOW1XcPWOMv7QdOlUC3n4lRYOVK3hQ9dtQNLrsd5xB+Ybb4xt12g07Ny5k4GBAWpra+NWhBZq7AVBwOPxyKUHZVYtarVarkaVgFSdM7oynSysKbogGQwG0el0ZGVlxYRE9OdUVZ5cCi3nnXz72WaeO34ecZGfTeeEGqMxA5dr5Zv4zYcOUUd97whld9wBSUJ3p7IYsSEIAhkZGezbt4/u7u5YidyMjIy4fQRBoKWlhf3796NWq2UvRwIuC7Fx11138cYbbzA6OkpJSQlf/vKXY0bgvvvu433vex8vvfQSlZWVGAwGfvrTn8455nwT7+Yi3StKl0POhtsf5oWmYZ6qH6RlcPLB3JKlwROUcPoW/xkPZubRbSykxD3KW5nlrNOp6R5e3IP/YhgY87G+tIxAsJMJz+y5IQqFQHmJhYICM6i1DLlC9Dt8nLYD9oVds0+l5XRuOQCNvRNsKjXTtgI5JMn43A/eZNs6M+vyM/CvWzcjMU8QBEonJrAIAh3PP4/bZKK4uBixvHzBK3uSJMUERrT0oNxgSWa1oFQq5yUiVluC+HxIFNK02ATrzs5OTCYTeXl56XxrC+ZEp51vPdvMb08MLNkD7gmIHNi5jWOHj6Xm4tJAhkHH5q0bsIdU9DjCfElZwvu3bGNuqTGzz8ZCUCgUVFRUkJeXx8mTJykpKaG0tDRhKd1wOBzL5ZDnqYtcFmLj0UcfnXW7IAh873vfW9CYqQyjWq05Gw09Tp6qt/K70yP4QiL7Bs7wZ7bO2HajVuD1rPUcz6lY9DleL9lJjn8CW0RJgUpNnlHHqGvxSeELxdTazE2a3MlTcgAAIABJREFUCayO/ljDo2ZDPufzSqlYV4jRZMQbUdAz6uO8P8z5gcmGeKkiFJEIKHVk6NV4fCufuwHg8Ye55z9+x8v/9v6k95DyRz8io6eH7Xo9Hrcbz8QEkW98A2GBK4BTu74CspdDRiYBqylnAyY9DePj4wmFRHRej4Y0RYXEYhKso1xqduZo6wjffKaZN5uHUjruiQEfBfm5DC+yVHq62Lq1kqz8Qk71e2gYDgOTuSX9ES3//XIbX/jw7H3VYHGejemYTCb2799PW1sbjY2NVFdXo9VejLiIzlXReUrO5bjIZSE20oEcRrW4sR3uIM+dGOKpeitdNm/ctnFtJiWuEayZk52ljTY7a3Zdh+Bg0asuNn02Nv1kGM7weICK/Cy8gRDeQGRxAy4QU8jLjvYj7C20EAmGyA15+Z/KfXQr1nDWCTjT73IecPjYs7GEYye7AdBFgqik+AcLt1I7Z4JcKmkfcPL3/3OEv35XfsLtkdtuQ/nQQ1BSQobdTmDTJo4FAijC4QV3fZ1a63xq6UG5wZKMzCRXgmcjWYL1dG9EOBxGqVQSDAZjQiJRgvWVyKGTg3zr2TMcb7elZfxASKR8SxXDI2+nZfyFUJifS+WWjfS6JM6MemFiPOF+337mNB/YV0z1+plFSaYy32pUc6FUKtmyZQs2m436+vqEBVGiES+yl+Miq1ZsyNWo5r9aI0kSRzrGeLJukNfPjhJKknjWkVvCUKYZpSSikET6TAW84jNy1WYjb59NjXHsHPGwbb2FutahBeeALJScTC2aquvQhLvojWgpyhVoHNLwWyk/7eeeTkO3k/3VJRw/fZ5/a3seS8BNRLhovB6o/CPOZc5ubFPNb450U54Du3bN3CYdPAiPPgpuN4Lbjfrv/o5tJSU0NTVx8uRJtmzZgkajmfMciSpYTfdyqFQq2cshc8Uyn/v6UvdsRCKRpHkRgUAgdu2JEqyj3oloKWyr1Uo4HI4rd78crFQFLEmSeKm+nwefO0NTd/o9DvU9E6wrL6K7ZzDt55qORqNmfUUJurxiTvW5GOqaezEvFJH47A/e5GefvYri4uKkfy+p8GxMxWKxYDKZaGlpYWRkZMb9IXvj41m1YkOlUsXqWS+Fy9mzMRdD436errfyTOMQA2Nzhy6JCiVvrN3NbW2vIwAvVV4NgkBD3wR7K83Ud6Qm7+D0eRc7yjI50ZP6/I1MnYrKNQaMOTmcG/FxYlzCkL+L93QcQfRKNO26kUhkZapNtA77KS00cWRoPTcNn6Zfn01W2E9EUNBlSG8scU6WjmKLEVOmDhRKJgIRrA4vD75hp2XsDf7lju3sWDcld0OtJnLXXSi//nWkvXuRtmxB4XaTk5NDQUEBdXV1bNq0ac4Y6GQTxHQvhyRJV/SKpozMbKzkPBEMBpmYmJg1wVoQhDgRsZQE60s5T2TRRCLgcsW/5vHy9JEeHnzuDGf7E6/qp+VSRIns8nWwjGJj48YyzMWltFh9tHqC4FnY+23o9fLM0W6u2TDK1q1bUavVM/ZJtdiAyQiZHTt2YLVasVqtjI2NkZOTE9sue+MvsmrFRqq+7OUQG8u1YqV8+mkCNgd9Dj/1XQ7abD4Ole1lJGP2zpxT6cgtwaE3EVSq6DFdrAh2zuZjc7GR1gHXLEfPn7ZRkWu3FvLWmaXHrOo0SnZV5qPVa+mweRkISAwMXQwRayiq4p09dYyrtFgrt6PtHCEQXv5VRG8gQlGhiTcLt/DekTOoxTC5QS/fK7+OiCI1AsiUoaJsTS6ZGXoQFEz4wwyO+RhzBxkf9sPwTNH56slBXj05yE17S/in27ezpfRC9amDB5F27kS86y648Hei6++ncHgYcyhEz69/jV+no6imBkV1ddJrWkgjQLnBksxqJB2ejekhTdM9E9FeOOPj4zEPRLIE61RyqfbZWApCfz+qxx8HlYqQBI+NaPjPkUzOhwZW5HpO9LioqlrP2bNdaTtHdnYmm7ZsYjSsoX3IDZ1LE1Q/OGTlj/eVx6ogTn3oh/SIDZi8H4uKiujq6qKjowOj0ciGDRsSlshdzd74VSs2IDVu0XTfLOk2rCER6nrHqe91oT/URV7rKRwZ2eRrBCrtdl6ovGZB44kKJc9XXo2oUMblEATCIq6QREG2jmFnahK8G/tc7N9k4XjbwkO0NCoFOystZGbq6Rz10eEMgzOxp8uv1vHChmuZ0BjoHg9x9Y4SDjX0LfXyF0XHkJv9uzfz8tBWPjLYQJ8+hwbT2gWPk59jYE2ekUyDFklQ4PJNigqnJ0izdfbSvMl4ob6flxoGuHX/Wj5/2zY2FBmJ/Nu/wYVVS0mSMLS1oXrqKVQmE5uB4MgIfZ2dmP/pn8jKylrwOWFmI0BJkjAYDKvKkMtcuczH/i9knkjUwXrq76FQCEEQUCqVcVWadDodJpMp5o1QKpV0dnaSnZ2N2Wxe6tucN5d7ud2EY5eUYMvM5dcTJmpdCp4bSF2RkUWTXQikVmwolAq2b9+EIsvM6fMujvcHgaWHswMM2L383x+G+afbdnH69GnMZjMVFRVxD/rpnBMUCgV79+6lp6eH48ePU11dHTenTfdyRHM5Vss8tarFxuVAqg2rKEm0D3up75sUGKcGICy2A5Bnqeae9jN4tAb87lG6dx7ArTEs+BxWY+KkYYcnRJlZz4QvNQnekgRtwz5u1TmIdMQbxfasNbSZ4hs7KhUCOyryyMnOoNvup9sVgXnWFD9RVBX7+fSghx0VZk52rkw52touJzUH3sHoC638es3uWb0ahbkZZOmV5OflIAoC494wAw4fo74Qo4NewJv02MUgShJPH+vl2do+PnRVOZ+/bRtl+ZmT20QR9zXXIL31FhiNoFSiUSrJu/deTjc3s2bNGsrKyhZtfAVBIBwOc+rUKfbt2ycn5cmsGhQKBZFIhFAoNGeCtUKhiImI6P9TqzQtJBxxpXJFlpt0PhAePefgsaP9/L6njJAIZmUEvcKPT1zZh9CzAxPs2rWFEyeW3uivbO0aitaXc240RJM9APb0hIX94MUWPnp9Jfv27aOzs5O6ujq2bduGXq9Pm2djKoIgsG7dOvLy8mhOMqdFf+7o6CAvLw+z2bwqvPGrWmxcDopyMcZcGB4G38VO20PjAeoCeo4PBzlx3oXLn/hBfzTLTNuaSiqHuwkj0FyxC7MX7O7UrDwA9Np9VJfl0HhuNCVJ1sGwiHbMwV1DtfShB8AccPO9jX8ETDpXqsvNFJgz6XOGOO8Jcd6ztApSogRBhYYsvYoJ39LzfhaKJEF7SM/3d3+QlqAGQQBzlob83Ey0aiVhEdxBCbtXZMQfZiQAnc6xtF6TXqNkrSUTs0mHWqUiEIpQ2+3ktv9+izfufxdZejWSJCFlZiJ++MMofv5zEATEm24ic9069peV0d7eTkNDA9XV1ejm0aQpEdGHqaiXQy49KLNc+P1+rrvuOgKBAOFwmDvuuIMvf/nLcfs88sgj/MM//APFxZMLIZ/5zGe49957Zx1XqVQyMTGBz+eL9YdKlGDt9Xppbm6ekWA9VVikWnyvpg7iqWTcG+I39YM8drSf7mlVHe0RJQc2WHi9bXSFru4iTqURQSEgLWKyzszUs3V7FU5RR9vgBL1d6e+RFYqI/N1PjvLi/e9lw4YNOBwOGhsbqaysXBaxESUrK4v9+/dz7tw56uvr2bZtW9ycJggCfr8/5mVcDfPUqhYblwOJDKs3GMETjOAORPAELvwfjOAOhPEEI1T++hFMPR34VVoc7iB+t48na26lK798zvMdX7+H6v5W3tq4n35Rw7piHeMddsJJKlAthuYBNwer8nm7ZSQl472Ws4n3ZDXglTQEA0H8Sg2uHXt4V5GZwYkwQxNBhgZTu4I/MhFkT1URbzQubziVABSYVORlKdFaKtkZCHPW6sMRlnDYwuQEnVSND2ICon4dj0rLyZyylJxfq1ZQasnEYtKjECQ8viDusALrmI8+Z5A+50xh+o1nW/jqR3bEjL34vvehePxxCIUQb7sNmBTVmzdvxm6309DQkLCc4HyIVq+amsshlx6UWQ60Wi2HDh0iMzOTUCjENddcw3vf+14OHDgQt9+HP/xhHnrooVnHuv/++zl+/DgjIyN0d3fz7ne/mw984APcfvvtcQnWUSGhUqmoq6tjV6LScGlktYiNVJ2zqdfJY0f7efnkMP5Q8kXEnpAavVrAF1pZUdU94qGmZge1x5rmd4AgsG1rJbq8fE73ezjeHwKWtz/U2y3DPH64g4/khzGHQuzX6+k8fJiwx4NYUADzCNdd6HedaH+FQsGmTZtwOBw0NDSwbt06ioqK4o6J5m2shnlKFhuXOFEj127z8rP6IXptXqzjs8dzrtds4NbxVqw5FspMIUbR02ueX5nA0SwzL25/F5356wDotvu5arOFw2dSIwyiNPS62L8xj+Pti1+9ydApydJryNBl0Shewx93vE1EI/LU1j9mGD3DKRYY0zk96OHAlgJUr7/OfntH3Lb63AreLNy65HOolQJlFj1mo46wpGDAGWDcF2bcCSCiEBTs3VzA2e5RHJ4wpV4H/3rmN3hUk42GMsIBTmWXLlhsqJUKSvMzsJj06DUqghGJ0Ykgg2M+BsZDDIzPfwL52Zvd3HlVGYX6CzGzWVmIH/sYjI2BxYJQW4vimWcAyAfMkQj9RiOnb7llwUZ/el+O6Gty6UGZdCMIApmZkyGDoVAolvuwGO6++24+/elPY7FYuPHGG/nFL36B0WhM5eWmhCsxfyLVeAJhnm8c4rGj/ZwdnJjXMU5vmK3rc6hvW/nmen1eNVqNmkAwuc1fU2imfGMlveMSp8d8MytrLTP/+vN63p99EtOYDa1Wy5ZIBJvVSltuLlU33DBnfuBCe3LM5jXJzc2lpqaGs2fPMjIyEquWNXVhbGrO4ZXq5VjVYuNyMFgTQZFXD52jY7ARCdieocZs99JnKsRlSDz5dFvKGM3KI8PvJjjmZviPPkhEmH9MYHPplrjfzwx7uWpTHkdS7NY9M+Rhd0UuE74Qeq0KnUaJWqVEpVKgVAiT7lsmw5ZCIgQjIoGQiDck4g1EiK4LeYBDpTvYffYoRmMmrxkX37F8oQz7oCpLz1WtbQzrTAAU+Mc5bq5c1HimDDUVhUYy9Gpc/gg9o14G3CIDbl/C/UUJTp53o9XpeUdFFvWtAq3GNeQGPTjVBkq9Dh5bezDp+ZQKgVJLJvnZevRaFQ7nBCGFlkGHF6srjNU1v8lxNiKixD/9somH/7QqZsDFD37wYqdHpRLFoUNIubkgCKhHR1n7iU8g5OZitVpxOp1kZ2fP61zTjb5celBmOYlEIuzZs4eOjg7+8i//kv3798/Y56mnnuLw4cNs3LiRb33rWwn7RVRWXrQfqWpAmw6uyDK0SVjo+2wdnODRo/0832jFs4gcRatXicmgYdy7st/9kNPPwS0VHG1qjXtdp9Wwbcdm/OosmvtdWHvSu7i3EEbG/Xxl7V6+6X4NqaQErFZ827ez/qqraG5upqioiLVr1ya9lxaaTJ6oH9RU1Go127dvZ2hoiNraWjZt2jTjHFe6N35Viw1IXzm0pRIRJV5udfDLRhvvPFrLB3tO49VOJmu/Xwjy7V23JRUbkkLBHzYf5M6jT2Mz5nFIvYaKHD2dtsQPrPOhcyzAlhIjLf1LX7HQShE+KvZiUAp4BkTGPQF8YThZuhWfRr+oMf1qHS9V30BYraWsKJuO4fR39gZw+cKMbNvF+TNvIEgSCDCoz+V43sZ5HV9iNlCcl4FKrWR0Ish5h4+zwws32oGwREOvizxLDiffeSsfeOFhAHozzDTllKEQIC9LRYnFSFaGjogIY54g/Q4fw+4ww+6poiL1eShNPWM8UWflvVWZF1+88Hcn7d6NtHMn9PVBZuZkeNWdd1JsNtPd3U1bWxtms5n169fPaXyTGf2pXg6/3y97OWTSglKppKmpCafTyQc/+EGam5upnlLW+eabb+auu+5Cq9Xyox/9iI9//OMcOnRo1jFlsXFpnHM+BEIRXj45zGNH+znRu7QkaG8I9mwp4lB9z5LGSQVnxyBLJTARlths0mPctp2WkSB1Q2FgZb0YyXj4jJt7LLns8HoRAgEcV13FhsxMampqaG9vp7GxkW3btiVsLrvQ58L57l9YWEh2djbNzc14vd5V1QhwVYsNtVpNOBxO2ABmJTkz5OHHx6z0XGikV1d1gIrBDkZyCsnwe2hRqTHuqobB5A/U3ZYyevLW0rB+FwFByUQwQrZeidO3uCpQYVEiICjJN2oZcS2uLF+BScvW8hwcbj87Hn2EbO84AZUGsxpsvjDNxVVzDzILjWU7ACjRKlErhaSdzlNNt0dJ2ztv4aqXfwnAjza8i5By5p+WUgHllgwKzZlEgH6HH7s7iH0gdYlzdneQF5RF3FRezubhHr7/zj+hwmDC7hWZCImcHfIBixedS+Gh1/o4UJZAhAkCkU9+EtXf/z0EAkTuuAMulNJUKBTs27eP7u7uWGURgyF5hbTZ3N+rvfSgzPKRnZ3NDTfcwCuvvBInNqaWiL333nv5x3/8xznHksXGTC61qIQem4fHjvbzm3orTm/qchTabX5ys3Q4JlJTLn6xBFFw7YZSWtwqWoUM6Lt0vBjJiIgSf0M1h9qeQXzXjQQsllhJ56qqKkZGRqirq2Pz5s0zSjfP5amYzkL21+l07Nmzh7fffpuTJ0+ybdu2uBDJK9Ubv6rFhlarJRgMrozYcDjQfP/7MKUykk+XwfcPfIjXBuON1bB5DT2F6yh0DKEL+njumtuwekWKTFoGk+RvSAoFTx64lbBq8r2NecNUmPVMBLxEFlkGyukLU1aYxbg3NO+mdgoBdq3PITNLS5fdT5t90mge2n49769/iaGcQiTnMGf33YBbl7Go65pOvzPAvsocjixjvOuLxg3szc7F6w3EvBoZOhWVa4wYMzR4giLdI24GPSKDntSuBGXqlBSb1OTlGnH5I/TafTxU/k7u1tTyRNCCFApjCPm5yt7FVHMVUKiot2yI64eSTtz+CN97fYhHtm+esU3avRtp0yaE9nak8nJUt98Oosi2sTE0OTls3LgR8z//M01NTaxdu5bi4uKExnc+7u/o9kgkEhMcq6H0oEx6sdlsqNVqsrOz8fl8vPrqq3z+85+P28dqtbJmzWSz0+eee46qqrkXWDQaTawT96XGcjadnXrO5SZOVEUiKOrqEIMh3hoVebg9zLFRkXTIH08gwu7Na/h9XXcaRp+bTL2KPZsKGLD7aRaKUIStcAm0AJkvdcMBfll2kI+8731IVmvcvZOfn4/RaOT06dOMjo7GNeJLl2cjiiAIaLVaKisraWlpwWKxsH79+oRhVVeKN35Vi43oilFGxtIfchccjpWdjaRSoRAEwiYTvW39vCWpeG1t4knl2Naruft3P2U4p5CewnVIYZGcLA1aT5BAOLGZiwqNKJ12HztKMmnsW3wcfq/DT82mPN6aI2HcYtSwzqLGr9Qz5A1DIH5l5lT5Nm44/QYZfjeSoKBp6wHU7tR5I84MeSg2KhhwLc9E6IkIvHnjhwj7/OxfW8KYN0TvqI92mw+WEL6WCItRS1l+Bhq1CocnzHmHjz439E0JhTqTX8m/WNazt0BP3Tk7eX4X/9rwKEGlCgkBbSSEXWfk4zf87YLyeZbKqy1jvHV2hGurpvViEQQin/3spNjYtg1hcBDJaEQIhxEGBhCvv57s7GxqampobW3FZrOxdevWGS7w+a4wTV098vv9RCIRsrKyLltDLrPyWK1WPv7xj8dE7J133slNN93EF7/4Rfbu3cstt9zCd77zHZ577jlUKhW5ubk88sgjc44bXRS7FFktYVRTCYRFXjnay899eTgFDc6xIFIaH6VahrzkZxsYcS6fNyEnS8uercV0OYK0OCIgTNrZ6vUFnD+78Ca6y02WXs2ujYVMhCS+OGZmh5CFJA3OmBt0Oh179+6lu7ub2tpatm/fjsFgWPDz3EI9IdFjsrKyqKmpobOzk9ra2hme++leDo/Hg9FovCznqVUvNlKxYhS9GWI3wPg46v/9XwSvF4c3xJlhLwGtniPvvYuAzoAoSUgSFOTt5ZraX+FdoyQwOMbhG9+f9BzD5jU0r9tGa3k10oWb2joRpLrUREO3c97Xetrqobook+bBxYfudJ13cEuBRO2UpnZujQGP1sCudTkYjVq6HH4mm6Amjv8PqTS8vu16Pnr4cV7c88d0hrXsKjNQ2zX/9zIbogTG7EyGPROEIxI7ra2YAvHvuTWvnOHMvCWdR6dWsLXEiEIlcHRUh0mvIhOJrp7UNS0qNespys1AoVQw5AoyNB7gjHVuASMJCkY8Inq1QF9WPkcLqthp72JUZ6TQ6+DximtnbQiYLv7l0ZO89q/vRKuOP7dUVYV0YaVXvPlmhJdeQlAoQKdD/OhHAVCpVFRXVzM8PExdXR2bNm0iL+/id7jQKiKCIBAKhWhpaWH37t1otdrL0pDLrDzbt2/nxIkTM17/yle+Evv5gQce4IEHHljQuHIY1cqfE2AiEOHlt/v5df0QY/61RN3EuzPCvDmRvkcpf0hk+3oLI429aTtHFLNRx+4txbSPBjg5OHOOafao2LUxnxPtqa1OmQoEAbZXWDAZM2gZcNF4/uLi270/rOWr785MaNsFQWD9+vWYzWaampooLy/HZDKlrBrVbMcoFAoUCgUbNmzA6XQm9dxH7/mGhgauuuqqtPTMSTerXmwEAkv3CUYb78W+/KwsBK8Xwekk12gkx+ei3S9x1iOA9+IKf19mCWUZZixd3YgbNzKYVzLreX574OYZr7XZfOwsNdJ0fv6hOX1O/6whWHOxv7OBD5z6LbdrdTg9IYxCmL591/L0tg8w7AkxPDq/+NJT5duotHZSu3EfAK0jHiwGAZs3NRNJvzNAzYVwqut6G7nmfBMTFzqiZwR9fPUdn1yU2FApBLaUZGHQqeiy+2i3XzTKo+4Qo8C1O4roGXCi6Ormc01PoREv5sqMaTP50v57Ej7oKxUC6/IzyTfpCEuT72HUE2J0lvyc2bB7QmwtzaK+y8WvNlzPXls7+nAAr1rHayXLW5M/Stewm+//9hx/c9PMcKookXvuQfXSS2jsdqQ1a1Ddf39sm6TTUfD//h+mPXtobm7GZrOxceNGlErlgquIQLxAuVQLRsisXhYiNpb7/l2JDuLL/fdpHQ/wv/VODnV48CeIIujUZJOlCzGRpFluKmgZ8lKQrWXYmZ4YpoJcA+uKMhnwqWmao2S8MiMDpUJYdDh2qlmTa2BzuYXB8SCdDh84Zi5Y9to8fOvNEO+4Ovk4JpOJmpoaWlpasFqtacvZmHrM1Ht5qud+ZGSE6urqGZ77y01gTOXyvfIUkKoVoxkrLQoF4VtuAb8f9HqqTErarnn3jNh4SaHg+O4bEBUKXtt8DWU52kWdv88VpDRn/h2XfSERlUpAp17c199UVk1Yo0NcU4ihsgxBp+XFoh2MehbmJQqpNDx+7Z249ZM1ryOSgCU3E0UK55KWYS/r8zN4ftM7cGkyGMjKZ0KTQWteOS2W9fMeRxBgU1EmV20yU2gx0O0McGbIgy9JY6Z2mw9Jr2PL/s3kB1xkhLwoxTAFXgdutS4mNDQqBVtKTFxdZWF3ZR452RkMTIQ50e/m9ICbsQV+ponoHBMptWTQYSqi3rKRMvcIv6y8Hp9qcffbQlEIsNaSwY4yEwc2mbmquoiXz9p5ttGa/KCSEsLveQ+SUgmhEEJtLcLp05P/nzoFBkMs0c5gMFBbW8vExMSijX603vnlbMxlrkzmO0+t1IP/lZog3j7s4YvPnuP2H57gpVZ3QqEB4EbFzsqlecjnIizClg1Fc++4QEosmbzrYCWq7Gw6XcpZGw1G6bX7uXbX2pRfy0LQqBTs37KG7ZVmPIKGup5xBsZm9/afGAjyrRfbZ91HpVKxfft2cnJycDgcOJ3zi7RYrMiffkzUc19SUkJdXR0jIxc9SNP7clxurHrPRirCqBQKxQzjJ27dilRYiNDbi2JtKR+4/RrOvNHP9JSE82vW8/I7P8xAXglmlRK9WpH0ATYZwYhEtl6NQa3AO89jra4gVQUZnOqfX/6GQoD1eTosRjUhjLTYrmbnmePo1ApOrqlkIDc1hrB3LMDuchP13akJQ4qIEjq9inZzMc35FWx09KEJh3h4961IwtwPlmV5eorNeoYmQgy6gwy653+/hCISjeOw/pqbufn4c7QLahQKgVPX38zVpflMBCL0jvroHgvSPZa+MImQCMUWI+dtHn654QYKfWO8VrKLPN84Vw+3xO3rUS3N42Ex6SjJyyDLoEESBFz+CINOP2MhkTGXCK4gMPleP//rM3TZPHzujyoSGs/An/4pffn5bMrPR/nAA5NlcV0uxA99CIaGABBMJsrKysjNzaW5uRmDwTBnw6bpLEagyMgsF/PN2VgtIU3pPmdtt5OfHxukdgGhsB02Lwa1gDeNHb+bBzysLciib3jpvY/KCrLYsL6AM0NeTg4s3Gve7xYxZWgY9yxveF9lcTalhdmcG/ZwZmjhuZAPvtzG9rJs/mjHmln3M5vNuFwu2trasFgsrFu3btYH/FTPIfn5+bESuTabLdaX43Kep1a12EhV4l3CihwXvBvq736XyC23UGkx8IGtZp5utsftJikU9JZuAMDuDVNVmBEXazhfRtwhNhdn0bgAAxlpPMHdngGGptiaLstaugomu4ebdAKlJiU6vZqJiAq/KDB4IUKqfvu17Go+huQP0n/r+yGSOqXdPRbAkqXBNpEaQ3Z+zE/Nhlyetd/Al1//Pl05JTTnJ2/8l6sX2Fyaw5g/TL8zgMO6tJ4dx6sOcsuZ37PN4+JQ0RZelyywhCT9xdA8MMHuCjONnfCZqz+NqFCwdmKEf2x6Er9Sg4RARthPa3bJvMRGlk5FYY6W7AwNBoMBTyiCdTyAyxeYQHLeAAAgAElEQVSmwxEEx/y+ux+93kPPqJf/uHMrumk5HJHcXNw1NYhbtqD4wQ8QOjoQbDaUP/4xyp/8BPx+xGuvJfK975GVlcX+/ftpbGxkYGCAoqIidLr5efuiYVSX42qRzJWP7NlI/zkjosTvz47y82ODtI8sPBHbF5bYtSGPt1vSlzwdFiXWl+YtSWxUFJkoX2vhzJBnUSIjyrgvTM32tbx6tGPRY8wXY4aGnRsKcHhD9Iz6GVpAjup0JAk++9MGXnx3JhufezRum7hnD+KnPnVhPynWiK+jo4P6+nq2bduWdE5JR/iiRqNh165dDAwMUFtbS0VFhSw2LlfSFkZ1AXHrVkJ//ueIF5Jeb67KpWXYS+ss1Ym6HAF2FmfStIjeC+2jfnaXmWicZzOh4rEhrjv+PL4sE76QRI7PxaGbPkbxphz8osCIN8wEMJFgMX8iM5vG6oPkOm0MFa5FO+xOWhVrofhDIptz1AgDg3Gv+zQ6JnSZSY6anbPDXnI2VvJG9z6Olm6f4dUwGVRsLsrCFxHpsfs5PbQ0gZGpVbJxTSYhUaLH4eeJre/iziNP8/qeP1rSuEvBGxEm+49ciJ5syy7hSEEV1Y4+HNpM8v3jPLz5PXHHqJUCa/MzyTcZ0GhUBCIiNneQEVeQIT8M+cNgX1op39+eHmFgzM/379lBvvFiaFfMgGu1iJ/+NMrPfx4pLw+MRhBFCAQQ3/tesNtBp0ORkUF+fj4ej4eGhgYqKiooLCyc8/yX+4qRzJXNfD3wV8qD/3xY6DlFUSQUCuH3+wkEArF/Lo+f17t8/L4njH2JrSx6nUGydCom/Klvihrl9ICbiiITnYML8/xvXJtDSZGZM1YPpxaZ/zed5iEv69aY6LamrhhKFEGAHZX5GLMMnBlwLamC5nQm/GHuPernZfs4hqJ8EASEgQGk9RfDqqNzj0KhYOPGjdjtdhoaGtiwYQP5+fkzxlxMruB8EASBkpIScnNzOXnyZKy7+OWILDZSIDaSrigpFIj791/8VRD4i/2F/OvvenEHk98wgxMhiowaBl0Lv7ZOh4+CDJjeQFspTD4AZ+lUZOpU6LUqpKJ3IXa9hSonj0xRxOPOoqGqhpB7foluf6h5D8pImIA/wp6ybI50ji34epORWXeMB//wK+zhyYdAdSREe/76/8/em8fHdZZn/9+zzb5r321Z8ibLS7w7GzGJkziJsxCcAIEQUtrQpIXf21J4XxoKvKUtpWlLA30pISUBSgIJJAESHEw2Z7Ud7/siybZ2aSSNRrOdOdvvj7FkydpGixOb+Pp89LGlOeeZZ5Zznue6r/u+bh5ad9+kxtNNC4fLzo+X34p+OpfNacs4SSEK1IWTHJxERGswFElgbpEHmyJS353iaPgMqXxr3mpiTg+uJfPhQBcze5q5a+/zSNaZ70G3089/rPzYlOYwFpp6UqyaV8jr+0/XSggCP5pzDd956wd4tSTdwQK6Fy/myoI8dAu642laIyodKehInRvbRVkUKA46sMki3/j1Eb54fRUVOZki/sHRInP9eoSTJ6GvD/HxxxE6OkBRkL/yFTAMrHnz0J96Csuy8Hq9VFVVceDAATo7O5k3bx6yPPqt7iLZuIjzGdkamXyQlI3BMAxjCIE4+6d/I2iz2bDb7djtdnJeeIEtqp/nbeXs65MGAjBTQUw1WFyVw+v726c81mgwLSgpCWVNNmpm5pCbH+RQW5yeaSIZ/dANi/KyvGklG8U5bopDdnoNG8e7UtB1btK0joZT/OWMm/hh+ybw+yEYxLr88oHHzyYPOTk5LF++nP379xMOh5kzZ86Q3kwTdUGcKFwuF7W1tezZs4dt27Zx6aWXjrmmnY+4sGY7zTjXysZICLkU7l1eyHfebBn1mLRh4XOKKGIm3z5bzGypI5iMsrDYQ8Iw0ExoLiijzZuHqp9pOpQG0jr0Ck7eXLqONdtexKOI/HL5jWhK9kXDuqwM9PI4GU1TGnTQ1DM9nU4PlM0jIjuJOxwkBJnC3g5enb16SmM29qRYVRWgL2ngOO0kdSScZG7rca7obRty7MlQKXX5M7IatzrfRdBr41REpWEUtxDV5mD77OUIEY3ZxR564k6WtxwkqdgxBAm/GmNbcc2UXl82ONqRYFahF49TweexI1BEU9sb1NTt4t+qN7Bq5xYY1J4q4vCyuWrNtDy33yFRFLDh9zgxTYtIUqc1kqKtT6PttHx2rH0Pj//JEgr9jqEkwG7H+MIXoL0d8Ve/go4OrJwcsCyERAKzrAzxV79Cys1FrKpCURQWLVpES0sL27ZtY968eQSDwRHnda6iUhdxEdOBbNepP0ZlQ9f1AdLQr0rE43EikQjbt28f2OT1k4j+H5/Ph8PhwGazDWvYuaMxyj/bl1MvZtauSxzdbE25Rnr6CaOhO3XO1Y39zTHmlAY40jR6OtGiqjx8IR+H2xJ0TDENeCwcbIuzfF4R2w+NYfYxDuyKyNI5hZiizMHmKIe7TODcd0x/Pubmu8Is/qLjIMaf/inYh6rqZ5OH/rSmxsbGgZ4YHk8m0+K9CFj1B9Kqq6svOKIBF8nGe0Y2Bsu4pYrKygKJre2jKwjhpMncPDv72rO3uruxZTe1e94gavfgUgS0nihP3Xofqdk5o56zt3Y1K3e+TJeo0LN89aSvcd20KAtMH9lI2l28sHAtd+z9PS2mQpc7yO7yBQOPiwL4nDIeu4TLJmFTJOyyiCQKCKKAZYFpWWimhapbJDWDRNqkIWYwL9/FvsbeAeeN2uZD3PHurwc6mHtTcX5w2cfHJBslQTslQSedcY3WuEZrIrvFxQLyclwcbQnxQvVlXFP3DhGXF6eu8rPa6yb9fo2FPI/ErJIANkWiM57GJom0dMapb8qk6sXmXs9dqsUpfwEPv/DPJGUbIODSkuwonj9hsmGTRUpDTkJuG7IkEE+btEdTRBI69d0adI+eEtLaq/LZx/bw2L1LkEfKgy0owNy4EfGpp6CpCSGZBFFE3LQJ4ZlnsD/wAMaMGUDmuiwpKSEYDLJv3z5CodCIea8XlY2LOJ+RbRrVhaRs9DcpG6xADE5x6n+9siwPIRFutxu3242u6yxcuHBC1+3J7iSPvN3C9lNR4EzD227FgaQyzLxlMoipBotnhcZtejsVWBYUpPo4MsJjl8wuwOn3cLQ9QUvbe9MEULfZkSVhIGMgW1SXBCjK91HXmWTvOHa75wK5PjtvBBZwWU+C2kGqBoxegyEIAuXl5QNrSklJCWVlZe+J5XT/OuV0Os/p85wrfKDJht1un7amfolEYkgUZjQZtz/acmOVk8Z4kpYxUpYaYxY1hS4OjHPTyHUr1JR42S5dw4zDu+jJLSClJjELfdRVjh0tTzndvL7qelSHE9HlREilmOw9tzGaZmmFjx0np5bD34/t81Zz+5HXqErF+cV1H2N2RZCUZpJIGyQ0EwPo1aFXN4DsPc4PdiTIDzpxSwIHWmJsnn8F1x58jYjTi2SZJBUHW2avGnZewCVTXeAmppk0RlR6J5l21dCdYllVkF/G13JN/TvkJiPsLpjNsZyKSY13NkpCDspynCg2hdZoku6EMSSlC6Ag6MSpSDR2JzmWU8Hfrf0cWBa7iuYws7uZqN1NTlLg8cUbxnyuPI9MUciNyyahmRbdMY223hRNEZWmSXrCn+xKct+P9/DQrTNH3EwYX/gCxu23o9x6K9TXg2kiNDUBkP+976G/+ir85jcDx7tcLpYvX05DQwPbt29nwYIFuN3ugccvko2LOJ/hcDjo7u4e97jzRdkwTZN0Oj1sHewnE4aRuVcPTmuy2+34/X7sdjsOhwNZlkfdvCWTSSRJyvqajSQ0Ht/eyqZDXYzUGqJTcrGkwsa7J6Zn3arvVs997YYcotbbyb7TpQzLq3KR/T6OdybhHKW8joamHpUrq0O8dLhr3GP9bhsLq/LpVU3qOxK0TtNeIVvIosCiyhwUm8yhlhjvmn6+PusGnrLZGPxtG4889Hf+Pnz4MLt37yYvL+89IxsXqgp/wZCNTZs28fnPfx7DMPiTP/kTvvzlLw95/NSpU9x9991EIhEMw+Cf/umfWL9+/ZhjjqdsWJY1KoFQVZV0Oo1lWaTTaRKJBF6vd+Bm2X/jtNvto94U/8Kr8nebT5EeKyLQ3MIDm36Gnj5DimJOHz+75pOIisLqmQG60yYtMR0KSmmoXkjpicMomsrejZ/BkJXRxz6N3YtPs/o+jbn5Lg5NoXYhkjbJI83VW57Brp1ROTRZ4bkVNw301BgLuR6FyjwXbTEXv1t0NUsPbaVl0TLiEY2eLBWE8dCV0OkCVswKcqhZ4g9zL2PdoS0APH3JDSRtmeiBQxGZW+QBUaChO8mhMYr7JwJDlujx5rC5chW3HHl1SqpGachOnlfC5XbR1pemJ6FztFsFRt/st/elCThlqgs9HGs7bUYgCPxoyc384+aH8asxOtwhDEGgtv0YTlkklOOhe8ZsUuk0fapOdxJ60xa9UyymHwn1nQn+9ZUm7l1kG/6g0wnV1Rif/Szyt74Fvb2QToMkIXd0IGoawm23oT/9NJy+9kRRZNasWeTm5rJnz54hXVovulFdxPmMibhRnWuycXZ9RDQaJRKJsG/fviGBtbPTmtxu98D/p5oCki2pSusmv9zTwS92tY9rCR/VLSRhetSNePrcqxsAPpeNNUISIy+P+rQC07Q2TQYnYhYhUafbHP7ZioLAolm5uD1ODrbE2NU4cfObqaI8z03QCd2awuGOoe/TvnCaZ3e0cuuyM/b92QSgJEmipqaG9vZ2Dh06RF5eXtbzmcx1eqEHxS4IsmEYBvfffz+bN2+mtLSU5cuXs2HDBubPnz9wzN///d+zceNGPve5z3Hw4EHWr1/PiRMnxhwzlUpx/Phxnn32WZYtWzYkAqPrOoIgDJNxPR4POTk52O12bDYbgiBw/PhxQqEQoVBoQq+r2Gfn44vzeGzH6DelsN1LeXcLfaaIKtnwxyMcL61mbrEPr8dOW3JoRP/dNddRdXgnvYFc3ildwAyXjRM92UeYO+PahPp1nI1Y2mRuRYiaxoP4470kbQ7sukpKcfL0mo+Mep4AzCl043LInOhJcfS0NcibSz7E7rnL6YsaeBwyJQEH+1um72Z1qCNBwOfg1LqbsA6/QVqSeW3uGuYXewbmcrx7+vNH2/vSrJmXw9OxDxN2+jmWU8GaU7tZe+LdIcftz5/Fs3OvGvhdEGBmvouioBNLEGiJpommdE7GgNjESGIkqeOySdSW+dh3ugP9vsLZNBVXsrjpAIV9MR5+7buZuiFNIy0rfOwT/0pKyb6BZDYIeRRKg048ThkEgUhKoy2aZkdrknm5ArWjiHPmn/4p2ty5KJ/6VIZwGAYCIIXD8OqryB/9KPp//3emAPA0+rvEHj58mM7OTmpqai74m/hF/HFjIm5Uk02jsixrYE0cK7DWXx/hcDiG/FtVVTVmYG06MR7ZsCyLl4/18KOtLXRm2RepI6axuMLPjglYx4+Fc6luBNwKl5S66Y0qSL1RdmrvTXPWsRAV7SwLivx+kLhRkuumqiyH5ojKsW51zPTZcwGnTWJxZQ6qCUfa4nSpACPP4aHfHWNdbT5ue2ZLPJG0qIKCAhKJBI2NjRw7diwri9rJFJRf6OvUBUE2tm3bRlVVFZWnrcnuvPNOnnvuuSFkQxAEotHMhqm3t5fi4pGbzD3xxBN8+9vfRhRFkskkBQUFxONxVqxYQSAQGBJ9yfbLNln5Wmhr45pdb2Ac76G+K8O2E04PuxZeNnCManfy0orruGH7Cxyz+8lRo0RvvwNNkulODk8d6ioo5eDC1ZyctQBTVpBkCVFgRPl4JMTSJlUhB/vbJ69uHIuZ5F19Gx/69WP0uv3kRzp5ful1qCMUn3vtEnOK3ERUk7a4BmcpF6Yk0+fxD8wtlk6ztNzL0YZOVPPM52OIEoY0sa+zKGSe322XSHlzabjuFrokB548H6eiaQpPnKRYHfo+NOWWkp5AEf1YaE8apEK5PO26BgDJMrm6YRu9tkyKjz8do8lfQHWhm4KgAwOB5l6ViGoQmaYols8hUxB0UJzjor49RktPiu9dchs32/0E4xHmd9QRdXoJ6BovzL1ySkRDEqAkZKfA78Ruk0jpGRvdSFLnZFSF6HBSvOl4inuvtpBHaivvdGJdey3GTTch/fKXkDz9nlgWqCriW28hvv025nVDVaP+Lq0dHR1s376dYDCIz+eb9Ou6iIs4l5iqsjG4PmIkMjFSfYTD4RgxsHY2EokE0Wj0Pc0jP3seQlsb0ksvgWmy13DyfbWII+bEC74TpoUAk04jHoxzoW7ML/dTlOehoSfF0ZQFNi+l+Qpiazrr9f1cYq+riPmJZgKWhl45i0PtiWlLqZ4I5pb6cNtFTvZoWdv8dval+f7LDfx1IIz0ve+RG49jmiay241VU4Pxla+Meb4sy1RUVKDrOtu3b6e2thaXa/Tv4GRMSc6149W5xgVBNpqbmykrKxv4vbS0lK1btw455mtf+xrr1q3j4YcfJh6P84c//GHEse68804+9rGMvegjjzxCPB7n3nvvndL8Jp0rm0ig/OhHXO900tCTxp7oo2n2QtRr1iEKmXElQaDPvhph/yvUaBHqZ9ewN1A25rCvrL8rEwIHwgmdeQXj130MRl23SrHXRsskm+pZQP2S1Vzy0jME4hGSdgdb56wccszMHCe5PhsnIyrHuyeW2x947WUe3vQoiBIpzUS0TI4WV/PvH/0iPoeMxy7itGUKxhVJRDy9UTWsTFfvlG6S1E1iaQPLgpgBsaTBY4vXM9OvED/dVPEvf/s9SrpbSMuZVB6HluLfb/pLdlRdMqn35Wz0qQYr5uTw8p7MgvRm2SJOBorJNxNYdjtGWuadK2+iS7Po6pgeclHos1PotyOIAm1Rla64RndrDIcs4pZFNBMOFVRxqKCKee3H+ZfffIucWDd+NU5htJMvvvJIZq4zLuGtmUtHfR6/S6Y05MTnUjKBAFWntTeVqbHpyv61hJMmvz/YyfoFw73N+2H84AdQWIj00EMZoiGKoChYFRWYH/7wqOfl5+fj9/vZtm0b8XickpISFGX8tMOLuIjRkEqluOKKK1BVFV3Xuf322/n6178+5BhVVfnUpz7Fjh07yMnJ4ec//zkzTpsajISxyMbg+oj+usFwODxAJAzDQBAEFEUZotBPNrB2Ns6HPhtR2cFb7RabA9U4TW1SRAOgNZpm8Qwfu6axdsPnlIkmJ69uOG0iy2fnkpYkmiIq0bNq75osB6vm+HjrcHiq050SPHaJS3ygKx5e77WTfo+K0/sRcNtYMCNEd0LnZFeSidRw9uOx10+x8VPVzOzpwXK5sEQRenqwqqrGPdeyLCRJoqKigpycHHbt2kVlZSVFRSN3Kp+MSnGhuyZeEGQjGzzxxBN8+tOf5q/+6q94++23+eQnP8n+/fuHfaCDPyybzUZPz9R7Q0zakaOyEuOyy5D27ydUXUTP0QZ2XX0rpijRL4ZrFhDKZ9dl67l005NsvfyGASIxxoSG/NqVNCeUGmUBDptEeXMdN/3hCQTzzIXbHcjnZ7d+btwxWlXYf80G1j35fR676i5UxY5dFphf7CFtQnM0Tc8kuygdnrkATVIgGESUZWwd7Rxdehlut23SReP96EqZKKKFZgr87pJ13PPST+h1+XCkk8TtLvbMqJ3UnEdDQyTN8lkBHHYZ1bB4pm8jn9v0X5BWefqS9XRIU7NkzHVL5LklHE7nGXIxgrSf0k3sDokcl0BXIvNdPlRQxf7Caj58/B0Ey2LNyV0AuLUk+wszXe8zaoWToEvC1FMoDhc9qkl3XKOpLw3T0AX+p9uaua4mD3G0770oYnzpS3DqFNIvfgGCgOXzYXzxizAOebDb7RQXF5NIJDh48CCXXDI9RPIiPpiw2+28/PLLeDweNE3jsssu4/rrr2fVqjOGE48++ijBYJDjx4/z5JNP8qUvfYmf//znw8ZKJBI0Nzdz8OBB6uvrefDBB7njjjswTRNVVQdSPfpJg67rOJ3OATViOuojxsP7VZRumha7W2K8fDzC9sY+tLzMdWu3DPzpBL3CCLVeWSBtCdOubmzZP3F1Y2aBh3y/SMSyU9+nA6MTlohm4lDEAXfF9xI+h8yiyiAnIyr7VQP8bq6caWPzrtGt/acLogCzC12EQn4OtvSxq3Fqjf/Susm3tvXwn9ddh/ib32AGAuByYY5T+wsZItBvsRwIBFixYgUHDx4kHA6P2OdpKmlUFyrhuCDIRklJCY2NjQO/NzU1UVJSMuSYRx99lE2bNgGwevVqUqkU4XB4xG6P/ZguN6qpWA5qGzfi2LmTUDxCy6zZtFfMHvG4Y0svx1BsBBbOpyU8sU16UjepynNOqHNoSzRNTWUJhR2NJB0uDEkmEO2mpSB7x6R3qldgW9PIiWWXs6LIT3NfmobI1DafZQE7+aWlvLv6Wla+/SKGx0vU7uLNuVPrwdGPqGpQFZI5FDZ4e+5qbn/7WRzpFC41yQ+vuXOgr8hUURp0UBpyEk7oKKJAU0cfnX0a9TOW8FFvDqF4hF6nh3tf/emQ896duYg9FaMTnjyPQpHfjigKdMQ0wnGNaMSAyPh1Lr0pg5Dfjm7p9J6Oxj22/DaqwydxaklEAdxGGux2lgQEZp16md25lezzzaE3agISJCbnQDUWGntSvHyki6vn5o5+kNeL8Z3voO7ejauhAYqLMTeM7aTVD8uyyM/Pz6rb+EVcxFgQBGHAe1/TNDRNG7Y5eO655/ja174GwO23384DDzwwLEd8+/btfOELX6C0tBSAeDxOZWUleXl5+Hy+EesjTpw4gcvlmnDt4FTwXpONjlial451s/mojeiBU8MeVwWJuV6B7ZMs62vuVVlY7mPPqelRN+q6slc3ZFFg2ZwcFIeNhu4UzSpkEzDrTuismpvLq/vObUH6YARcCgtnBKjvSXHgrJTrg91pVs3N553D52Y+xSEXVSV+TnYlaYxpNManL1Vr8/4O3rz9epb/+tconZ2Y990H3vFNbc6+fhVFYeHChQN9nmpqavAPqh2crLJxMY3qHGP58uUcO3aMhoYGSkpKePLJJ/nZz3425Jjy8nJeeuklPv3pT3Po0CFSqdS47gDvR1O/s2HNmoVxySVIW7bg/9pfQXpk1qo5nBxZ8SHsqolbEYlPMIrRGNUo9Npom0Ck+aTsZfvKq1m27WV6/CE02cZrq67P6twCj0JAlmi/+zMo4TMF35OBJMC8AjcIAi19Gg2RNJ2rrmfF1s3Ye7vZ9tHPoiuTi2SNhC5dxmuHPuDp1bdw36ZH6AjkD0sFmyg8Cswu9KALEq19GnWD0seK87yIQoz2aJofXPlJCqIdVHQ1c/u7vyZuz6gbrnSShryhKXR5XoVcpwgC9KYFwnGN3inUc3SnoKrES0d3kjy/HdfclXxn2SOs2PxLbt/8Y7ypGFGHh3W/fhSHppJcezf7iuZM+vmyxU+2NrNmZoB9rTHC8TTXzx9B6QgGOfIv/0Ltgw9CFqpGP/qjTBfyjfwizh8YhsHSpUs5fvw4999/PytXDr1vDE4LlmUZv99PV1cXublnyPTy5ct58803gUzN4iOPPMI999wz5vO+F25UZ+O9Ihu7mmP85mAX+9rip1WH0aO7p2QvbptGPD1xZRvAHKk+bJLIRt0oCDiorQzSnjBoSuiQmPhaebI3TY5HoSvLgvjJIuRWWDAjQF1Xasy6zh5DoDjooGWa+m7ZZZHFlTkYosjh1tg5rQX5hzc6eezSy/BvfScrVQNGJgL9fZ4CgQD79u2joKCAGTNmDBg5TFShuEg23gPIssx3v/tdrr32WgzD4DOf+Qw1NTV89atfZdmyZWzYsIGHHnqIz372s/zbv/0bgiDw2GOPjfthThfZmGozJe1jH8Py+chbsYh5hyMcGkO5UA2LmUE7+yeYw29aUB0+Qdn+Q0P+3plXwomKuSOeE0ubtF17M+b2V8np6WTngjV0hQpGPFYRBWaEHPgcClHVoCdl0GVIELMIeZ0UeE0Otsexp1NUnzyEMGiB0hQbh2fUDEv/8jskqnJddCWNjLXv4Ll5A7yzch2L9rzB3iVXIPTqTNealzYF5hV72dYQ4e25q9mw/XmeWbkBQ5KRjKHzsAQBU5RGGQkUSWB+kRebTeJUT4rGmAkM/650J3UKcj2IQpx95TXso4ZgrIdr971Cwu5ENnSiTi+Hll7JolwvgiDQEUtnyIU6dfm8yG+n0G9D09LELIl5M4Jsrev39heoW3gt6958Flc6iWiaeJN9CMCqo9tYfHIf/3HdfXQERlcRJwNJAq9NxumQ6dMMbn10N8bpSsigS2HNzOEdwdN5ecSffx5nzuiNLM/GhX4Tv4jzC5IksXv3biKRCLfeeiv79+9nwYIF4584CibSQfxCaeqXFZJJwj/5BY87q2hSvLSmRSxh/Os0oVvMK/Lw7snJOUs1RlRqS73sa5paWk4/6sIpfA6JaOoM+REEWFwZIhhwUted5HDX1BThpGaycFYOr+xpm+p0R0Su18b88gDHwkn2Z1GPEUsbFBd56ehNoU/hK1lV5KU418OxjkRWzzsdONoW44ll13DnpWvIzULVgLHTotxuNytWrODo0aPs2LGD2traSadRXcg1hRcE2QBYv379sL4Z3/jGNwb+P3/+/IFIULbI1lJwPEz1hmvNmIF2//0AfGiml6NdqTH9vttiOvluhY74xOZuO3SQj/zmESK205HyRIwXP3zHqGQDYL/hZObqD7Po9ReGqRpBp0xZwJFJ2Ynr9KgWPerwRTGhmZmN6YwATdv3cd9T/3q66FpA0dP0uX08+MC/D7hJzQg5CLlsNEXVMdOutlxxM3sWXkqHJrKgwM3sx/8fM5uPDzlm06U3s3/O6IXMo6EhopLjVuiKw9fufJC4w83Mtga+8bOvI5sZwiFYFjGHh+YMZMYAACAASURBVD+/7z+GOVRV5bnI9dlp7dNoiesQ1xkrGgfQk9TJDbkQBWiOqPR4gry+9Gqu3b0Z0TR5fsU1BJsb0Jozx+veXPAEJvzaAHI9NipynCiKRDihZQrrojogAgaKKA7xne9zennsyk+w6shWrjz8FpJpkLQ5qGk+jC7JzGk9iiFKdPmy3+QPhtMm4XPKyKKIapjEVAMLSJqQHKG3yrN7O0YkG6ZpIng849c1nXXORbJxEdONQCDAVVddxaZNm4aQjf604NLSUnRdp7e3l5wxyPFEyMYfi7KRNkx+UxdnU3AV+mmCMT/VwV5p+DU/Epr7NByySGqSO11RGT2ANFHENZMl9PAaPvyCxtIiB72hXNr7NDrD09cb42g4QUWui5Ph6duU57glZuQ7aIzB/gn2U2qNW1y1tIzN2xvHP3gQ3DaRxVV5RFWT+s4ErdOYJpUtnjqi8uFPzCUXEP7wB8QXXhjyuLVsGebHPz7w+3hKhSiKzJ07l3A4zLvvvktpaelFN6oPEqYzjWq6IkoBh8yyEjdbm0a/sC0gxyVPmGzsW/ohrtjyHH2igoGELtnYuuLqMc8xLThw1Y0c8RXRk1PAzKCDoEshoVmEE/owxWGsOTdGNQqWLuD4liWU1R8k6gkQ7O3ipZXrERWF+QVuDAvaYhrR9PiRHtXupKMgk46Q0C0EUWD2iYP0uXwIWLhTMX51zV1Zze9saIZFdb6broYIcWcmB/tUXhm9bj8uNU7K5sQbj7K/fP4A0cj32piZ56ZXNehK6PRO0GULMg5VCyqCzMhVOdGd5BdLb+BDOzfjSiW54ZWnWGv/LQDOdJLfLF3Po9eMnVrRj6BLYUauC7tNoiep0xnXaOgd/bvfldBYVO5n56Do4G+XXs/b1StYcnIfObEenGoCZzpF3O7iwV99m8cv/xhPr751RIvjwfA5ZbyOTE+N/m7wJhBJZV/Uv7MxyqmeJOXBoZabH0SXj4s4f9DZ2YmiKAQCAZLJJJs3b+ZLX/rSkGM2bNjA448/zurVq3n66adZu3btmN+/bINiU1XYJ4NzQTb2tMb5n90ddMZ1GKRkxBQnojmSLjwcsbTB/GIPOydZe3GyJ0VNiZcDzdOjbuiBADdYOodtORwVZeib/nQnw4RZRd5pIRt5HpmZhW5O9Oo0TGGvf6gjyYo5eWw70jnmcYKQ6bOlyNAUg91N733jP4D5JV78HhuH2/r46qZmHr2nEL/Lhfj221in0/KFzk6MQYYPkH1fjtzcXLxeL7t27ULX9QmtVxf6OvWBJht2u33a0qh0ffqa96wp87C3LUlyjKhMR1xnZtBOx6l2rvrd/yDpZ25eaZuDl278FGnHUCcj1enijctv4uqXnyaumby1Yh2xQZFxSQCPLdN3wqmI2GQRRRIQBQ+2m6+npDtNn2bS1zv5G2V7TGfHjZ9g3nf/D0lNxXQ4SNx4I4WmneYp3IC7kzrNN28ktf33aIoNVyrOocpa6kurJz1mXXeKYr+dlt4MaTAkmZ9dcQd//sL3URU7AhbPf+h2llX4Mz0womnqJ9BAcTAKvQp5bhvtsTQNEZV8t0wsZZD0BHl61S3oosRdr/+cpOJAwEI2DTZdsm7U8bx2iZl5bmTBoE/LuGydjE7su26MkLLQ5cvh6ZU385Ftz5Hb141gmbhTcRAE7n7tZ/iTUX7w4XvQZQVJFAi6FZw2Cd0wiaZ0dEtANUGdhk7wz+3t4C+uHGpY8EFslnQR5w9aW1u5++67MQwD0zTZuHEjN95445CU33vvvZdPfvKTVFVVEQqFePLJJ8ccM9t16oJVNk6f35XQ+NmeMDtHMTLptHuZ61U42JHdZrrztPmGNskmFDb71NWN2nIfOUaCE1IxpT3NqNa53XIdDSeZU+TiSOvkCEdJyEllkZfDHQmO90z9Hm0BUVWn2GbQkh7+fub57MwrD9ESTdMYmf7mudnAZxeoKnTRp4uc7FXh9HpfF1a5/3/28Z8fX0Zwzhzo6gKHA3JzMW+4YcgYE1lD7HY7s2fP5tixY2zdupXa2toBU4mxcNGN6gLG+VAgPhIcssjlFR5+Xzd2SEFAwLDZqDq0E1lLoys2bGqSaCB3VNekPcvWctWbv8Ur6/TdchuzFAVLlEmbkNItLEAFVA04qwi9PGDjZI9K2rAIdbaQE24d8nhPTgHh/NJxX19beRXh2kuo3L6Fn99wL0dSCtnFq8YZ1xng9RXX8qG3XwABfn3VnRNKpzkbpgVFIecA2QDYOnsFH9/yc8qj7Rxc9iESs2YTiU6OJLltIjOCDhKaSVtMo1c9c7PtiOssrwrx+uEwv1x9KwDzmo6wrG4nLjVBfeFMltXtZFndTnRRZsuyqykrCuJ1KsQ0g7aYRtMUiwWboyrziz0cPKtj+4/WfoqjxdV8+6d/i2SZSFhYloVPjbFx67M4PE7+6+rPoJsWfWmTvnT/Zzv1m6RNFin223HbRfa09PHnvziAQ5GIpQ3+8cZq2mIfvMK7izh/sHDhQnbt2jXs74NTfh0OB0899VTWY06kqd+FpmzIjz+OcPQoW/Ln8uPCpaSksXPSJ7L9jaQMFpR62TVJdaOhO8XcIjeHWyeWPiQKsHRWEMUu09Kn0SdlNpINvnxy0wbh5Ln9jFweOwKJCdn3lue4CLktmhIiByaYLjUe+iyJWW6LDtVEF0QUy2BhRQDR6eBIe4Id0+T8NRFIokBtmQ+bTeJwe5wjXSOvlQda+viLJw/wn5/5LP6vPQjxOMZ994HbPeS4iXQc7z/e7/dTXFzM3r17KS8vp6SkZMwxLvR16gNPNt5v69vRsKTIxd7j7chHjg75e9rlpqM802SmVzWoLA3xxtqPcNWLTxD3BlA0ldfW3YE5AtlwyQJzKvPYcf1GHMkYYm4OkS6VbF3FT0U1cj0KqbTBoh2vsPb3T5I8nWLkTMR446pbeXHDZ0Y8N9clU+RTMC2BrqTOlmvvxNXeyq6VY6dxTQSRlEHzzR+Ft1/gWPncKaka/ajrTjEz10lDOEl5yEFxwMnLGz7NHU/+O/tD5fzvhx4YcvzRmTU8cfN9o44nClCZ48AmiTT2qmMqIad601w+O4ctR7sAePLyjayo24Ev2UdlewN/+upPUEyduNPD7ivX05Y0aBuhq/xU4HYOd/nyO2WaV1/JK8euY+07LyCSoRGmZWFLqyzd9Rr6VZ+eEtEDsMtQ6LHhdduIJnWiqg6WQHdCozlqYhjmgCmAaVrc8d+7sRC5ao2JV8o+KnmhR4wu4o8b2a5T75eyMRX0VM/nJ/IsDvlLKI13c5yxyUZrn0Z1rpNjWdY6RFIGosCkO2y7XdkX5CqSwMrqECkEOhM6pId+ZmlJoarES/h49ygjTA9aYwargiZv94y/MZ2R56Ykz8WRjgSx+Lm7/9X7CllvniQSVan3F3E4YkLkvW38B1AUdFBZ4KExkuRYls1l9zZFeUDw8f2KStwdrcNUDZg4Eeg/3ufzsWLFCg4fPkw4HKampmbUIvCLZOMChs1mQ1Wn3hfgXNzkRUFgXboF739+bSAdSlGThEtm8ou/eWjguEjKpGXNWta8+gyeaA99vhBHFqwYNt68PAeaCR0JnY4rThd6qyazvBZ1fdnfZLqSBg5ZoH3djaS2/Iaky4MlCIimybY1Zwr4RQHyHRYhtx0NiV7VoD1+ZiPcUV7FT//3d5jttrGzOcbGX/wHi/cMKvAXYPOHN/LKVR/Jem4AzfYAT2z4UxoKK7FpKrNPHEQYRKY02caRGTWZ7qAjwCELOCWTkMc1kErmlAV8LjstfWnqelQa5qyk7Z6vAXD7psdI2l1YgoA7GWfX/FUjjlvoVXAJGn2mQvMElJDGPo0l5T72NfWhLJjHiSWr0esO4uiLZBobqgZv1FxGeeMxNMVGfensKW/yB6M3pXFpdQgDgYRmcCqcIKmZtCZMvvGRL1PWdJzZTUczjbAEkZjTw08+/MkJz8FjkygOOLArAj1JnVhKxzBNulMGLfHEMGIxEvr/eqA1zqqZ2RfOX+iFdxfxx41s16n3Q9mYMFJn1Ns9HSmeiOYR92euXDnLoJciZ3+thhM6tSVe9kzSWaquK0V1gZtj7aNH+z0OiWWzQvSkTZoSYwd76rqT5LkEOhPnlhTGFDs2K0VaGDnoMqvATUGOiyPtCQ6NYWE7HSgNOihJdlFfWol2qo2ec5xKdjZsssiCUi8JVaWpz2R308SVlN2NUb605jP8c6WK/fHHkZ5+esjjuZdeivDFL2Y93uA1R5ZlFixYQFtbG9u2bWP+/PkEg8ONEC70deoDTzbOBzeq0VBy1Wo6ymdgRnpRXR68Xe3svPrWIceohkVpUYi3PnQr65/5Lzbdcu8QVSPPLVPktdE1SsQ7ZtmYlwN1EZ30WBZYg5DSLeodIU6tvZ6Kl54HBPYuuYJEQRGzg3acikhPyiClW4THaExkSjJJI2ObWzerlmU7XqHPE0CwLDzxKPWVE7eLjKoG8fU30twco6y1gf/14/+LZrMhiiKKoaM5nPzgX38OdjsCAhYWmmmR0i3imol2+j2IGxA3ztjUlvnttMfSGFZm3sdm1oBlcbxiPhXNx1FtDlIOJy9detPAXIalSSExkXQxUYCqHCceh0w0bdKVMvj2zV8g1NPBt/79zynoacESBC7f/QpX7nwJQ5K576tPoI1ToD0WHDJUhlw4bBJR1aA1qnJqUC1NbamPbQ2RzC+CwAN//l2e+Ic7yYl1Y4oiYV8uW2qvHPM5Ak6ZAp8dWbTo6E2SMgQkWaQ9ppLQhioWlmVlbWksCDDbZ+KyXazZuIg/HpzPblQTgdDcjPLII+imxW9LL+F3xQuHPN7kzqFMgca+sZOlGnvT5DtMOlLZXbNx3ZpSV/CA1wYjkI0ct0JNuYeOlMWJLOsNLQRmFvrprI9McjbZoVNycqnSziv60AaP1UUecgNOjnYk6DnHJKMs5KAw6OR4V5LD9ozbWlWJlxMt7813dGaei+IcF/XhBEcm6folAPOKvdgUke3tcb6dLuUri+3w059iFWTaAAjt7cTmzMEzQWXjbFWwsLAQv9/Pvn37CIVCVFZWDlmXLvR16gNPNs6HPhujQpJw3P9n6H/7d1iCQCyQQ/2i4ZHz9riGcuXVvBHtGlA1ZFFgQYGTrqQxKtHoR3fK4nP/9lconR3op6PGhizzzKf/hqaZ80aemgjvfOgW5r7+IlgWHXd+gpBbIaKaRCbQ9yGhmdQWudm96DKu3/RTbGoSRdeom7WAkxUTbxaX65bJ99lZY5PoLqzl5LzFlNUfJOHyIMZ6efWKm2lUxdNFKdmjsTfNwhIvu5v6zixagsCvrrub//XDv8Wd6GP3/JXMa9hHoVdBEUWO2gLUM/HXkO9WKPLb6FUtIppFRNMp90v0pAxUm4PWgnJeXbaOW15+krRsQ9E1bJrK/qrFXLXtRepLqzleMfLndjZskkBF0IHPIdOXStMW02mIjB5FTelDFwrV5uQv7v8u//2v9yAg8OOrPzVgYQyQ61HI99qQRIGW7hi6ICMIAm19aZKagW5YgIWVNidGLMjY5Zb47Syv8HPTgnzyvXbeeustFpb4shvkNC50l4+L+OOGLMtZGZCc78qGVVhILJTHoyUridmcIx7jddlgHLIB4LGJdGRZT9we05hf7OFAy+Qcjo6Fk5Q6TZqSmY1egRtmFLgIGwqn4hPfODdE0szKc1HXeW43+405JfjbeukVbMwrcOEPujnamaArywL7yaI8x0FBIJPqFj1rk3/cFmBBucH+c1Sn4bZL1Jb7iWkmDeEEHZNQMQD8dpHykJ3WaJpjgz6n53a3s+iGam6urIS2NrAsrDlziNXUTGgNGY04OJ1Oli1bRn19Pe+++y61tbU4nc6Bcy7kdeoDTTamy43qXEaUfB++grbv5OJtauR3n/kbTGn4R2ZaEMjx8/INnwRgVsiOIol0jiPpDkAQ6MwrofpkHUlfANIaipYkOG8WAZ8DSRQQAMOy0ExQdRPVsNA9Bey//Dr8Vpp6d/6kQ0cpAwSbjd9ddxcbn3oYBIHfXfeJCY0xK8eB36XQEddpjun47RKaofO79Z/izx7+EpKuYcgKb12xYXKTJFNDsaTUy85BkvyxGfM5UVpNTf1eLt27hSVHd2BaFnY1xd7a1fy/z35jjBHPQBEsSj0gSDIxS6Y1PnTTINgcCKQH3uLn1t7BvPq9zGg6TrA3jCWIzKnfT+2xXTxx/T2jkg1JgLKAg5BbxjAF2vpUmqJpyNKpqi2WpirPxfFBN+DmvDIevPub3P36k4SvWU+tLdPJNq4ZmBa09KZJaAa6AZBpvjgRYgHgVESKfHaWlvu5sSaP0uDIm5XJ4EKPGF3EHzcEQchqk3HeKhuqivKDH3BQ9PNW/iLqPJnmn+U9bZzy5A05tLE3Tb5HpmMcS/VOTabYK9LSl919y5iiOUWR2ovLFCnzitTnlNKuT208v9cO55hsxESFtXKYllApx3SB9nP8fDNynOQG7BwPp+gdQ0kIhlwwzWRjbrGHoNfOsY44+1onRyplUWBekRdBFDjSHuNweOTv1rderGPOpz7HvK//DVgW+oYNKM3NyE4n2O3g90MoNOK5/RiroFwURaqqqujp6WHnzp3MmjWLwsLCC7628ANNNs73NCoAJIme224i+dzmEVUNyKTbdCcN5uU5sMsSnQmdlDGxCNeOGz/GvD1vYrMrCLFedmy4iy6bO8MExsDWj96LVxEQkuANt3Pz976KMshVSXV5+OUX/pGUZ/Roc1wzqS08o2505Jdwqnx8RUAWYV6BGwSB7pRBKn5mgepVDcpCDupm1XCisob5+7fy25vvJemeWNT7bJzsTXPJacLRnyb16qe/wNbDh9j4zP/DImORK1oWL15957jjlXgkQm4b3WmIm4xK2GIaLCz2sOd0dK4rWMAX//oRvvmdB1h85F2wTPyxCAgCG1/8MUsPbuVvP/8wAAHZoCToxG6305XQaI9ptE+wR8tg5PvtJNIGNjOF2+2mO6Vz9JLL+OrSSzHiJolIAv30928yxMImQZHfQblL59bFJSyaOXLX+unCRbJxEX8MEEXxvCQbUWSen3U5Bx0ZYlHc3kRLqAjF7xuWYWsB+R7buGQDBPJ9tqzJRnM0TZkXGidRulFuxCguCWHXoM7mn/gAI+BURKVmCmrLWHApIgvKfMR1i7qcJfhMA9qnr3ng2ajMcxL02jnelaInPL7cdCqisnJ2DltPG59MFl6HyOzTlrWNkRSNE7R270eh305ZjotTXUkOd4zvxKXqJl+ud/DTWbPxnGpA/vrXqSajSoiqinnppRgPPzzmGNmsOcFgkBUrVrB//37C4TCGYVzQ69T7TjZUVUWWZaQJuMdMF6arQPxcy9fJJYtwXH8L1b2gmxYmGecf0xrqsmETh2amrv75D1j0h2fO/EmAvR++hbfu/LMh43sVKFxYTeOKyyl782VMt4eDHxrarX00WJJE1ITygESTFsTd24M90Yduc2BLJejJL0V1je8hnbYAReHxT32ZuMuLIxln7pGdCIMWz7Ri4+D8FfhcCkVOE11x0j1GylY4oTMn38nvb7ybvI5m7KkE//evbxlyzM7la3nmjr8cdQxFFPDYJFw2EYckIAoZm9eVJQ4OhLWMm5S/DFaWEYh2ceOmn2DvixDOKaSy4SCVDQcxRZE3V12HerrQ3++QmJnjJKFn3FLas0wHcDvOcoYSBH6y4T6qH/48rlQcEQsDAVcqTkV3E3/3m3/nsbv+mnBKor7XACYf2cpxyxR67JgWtPalKc110xsT0EWJzlgKMDGMyRALkXyvjYUlXtbPz2NOvotTp07R1taG3W5nZs70KRhjIdvo8UV8MPF+rlNAViRiOpvLThqxGMqTT0IyiQm8Hark9wULUB1nFAyPlHktTaKLfIc4rDltYzRNwCETSY1NOBq6U/gUi6iW3XXr9/to7Ms+ou53SlwyI0Rz1EezIFISbqIp67PHh6hIU6olORvlOQ5KQy4ao+qghq0CotuBx5EmNk7gcKKYle/C77FR15Wiq2tiPTJMRUQWLHRrYvdcUYAFZX4cdpHDbfFR1YfxoEgCNSU+0obF8Y44XYmJKS2NPSn+Yd39fGO+iHL//RnOHAhAVxfmxo3jnm+aZlb3EkVRWLx4MU1NTRw5coREIoHX653QXM8XvG9kQ9M0nn/+eXbs2IHdbmf58uVcfvnluFyu8U+eJiiKct6nUfWPX5rnweWHV070jVrInTYtRAFmBGyciKQ5tXAFC196jngwU5zl7u3m1MIzTlWiAGUuC11S6FUNDt12F2VvvcLxW+5Ac0/sC62ZYCg23rj1M1z90++gKzYUNcWWj/7JqM5PgxFLn67dsDJ2tUUtDXzmsW+SVmxYgoCia2geH0/94BnaNJE+C9DHf8/b4jrFq5fxz8U/pPL4Pq566WlSjoxHtlNNEK1ZTGXIgSKSkUQNg0QqDZJCyoC0JZC2IH0WqREQKfbKNEbPLIivXbaB6zY/SSjRgWgafOTZ7yOaJoassGP5Wgo8LvxeN51Jk5bYxG/8HQmdipCDk91nbuyHK2v5ypd/yLf/4dMoaRXJMsEwCXS2Uv3G71nmK+aFaz424edyKiJlAQd2WaQznkYzLKKqQUIzSOkmLb0pLEQMzSDH50A3TMKRsaNnspBp4lRT6GH9/FwWFPuQxOGLzYwZMwgGg2zfvp1AIIDPNzU16iIuYrI4H9apbPG+KBuWhRyLQSRT8KzpBqdMOw2BYro9QXb6y4ed0pRTQkiw6NYyZhFnkw3DhEK7wXg93kwEKnPd7Mmygd3JiJpVrYQiCqyqChJJWzT2aQNdzJtzS5kpGjREpqf7d2ufxuIKH7tOTj6lSBYFaku9CLJIY2+aY93D37SepM6SyiCvHwxPZboDqM5343Ur1HWnCKuTa8TXEde5XAjzipU3/sFk1IdZhR6aoyp13ZNXacpznBT47NSFkxyYZLpVkUcg6LbxdpfBE0Yxn7jjDqQf/hB0HXJzIRhEOHAABAFr9myQh2+zJ+IsJQgCZWVlNDc3c+TIEQCKi4snNff3E+8b2Xj00Uf58pe/zJIlS1BVlf/6r//itttu45vf/GZW3RSnA5IkTcvN+b0gG5ZlUeC2cd0sHy819FGw+Xnsg6M0AjQtu5S+wlL60ibVITvH5i6is6KKUMspLCBcVknTvMUAFHkU3HaR+KDGfX3FZbzz+Qdpr72EMq+NyNEGgq2NQ+bSm19EpHho12bIpC2V+hQOrVrLpc/+CHdvN92F5TTUrsz6dWqWMOCJ3lo8k0NzllJVtxd8PuR4H1vW30lzeuIyom5ZLJsVoj1/Nd2/rSTYcgpTEOkN5bF18ZVY8bMjaLZxu0dZCMg2G167RZ+aIQ4pp5sXrv0EV7/8NJ5EL5psx6UlabjyWirtJrFIlHZpapH68pCL1qhKdciB36kQ1wyaXdW8tOYm1r36S8TTcTLRsghEu7jj2e9zaPYSGirmjjmuKEChW8YlgynKhBM6Cc0kljaIpoyBDuBnq2n9OJs0yJJAjkthXqGHa+fmckmZj21b32HNmuG2zCPB7/cTCoUIh8Mkk0nmzJnzvkWVL+KDi/NhncoW74eyITQ2UvTzp9h/uIUmfwF7iuegz8v0TpJMA3cyQdw2lJhZgohT1kATOdWr4hINEubQa7tbl3DKBslxAkqNfTo+h0Q0y6i9xzH2lmdphR9RkWkdpd7R63bANJENgKSRIQz6BBuB5LoVqos8dCZ0moatX8NR36OyYlaAbXWTd8GaXejG7VSo707RoU5dJQmXV+Kp7yImDu/lBBn1YWGFH0SBo+1xdjdPzr7YqUhU5zvoTWq0RNO0TiLdymOXmF3gIZLSaexJEVYz7/n3Xz/JvHUfYf6jjyIdP44VjSLfe2/mpGQS/Sc/wVq0aNh4kyn2FgSB5cuXX7Dr4HtONvoLYx566CF+9atfsXbtWgCampq46aabePTRR3nggQcuqDf0XKdRDSYzAYfM9VV+Ul95ntDenWgOF2Ahp1XCVfPpK8x08O5OGczOdbDtI/dww799BRDYets9OGSRmSE7EdUYQjT60bzycgCcksD8V55n2XM/IX26cZ8tGWPvutvYcs//N8o8M+rGm7fcw/pH/ylrVaMfsXSmdmNPaxy3IlJ3z33M/9oDqOk0KdnOrqsmUtxtkStryJJE0pAIGyamYfDCbX/GJx7+PyiayoEll1N9YNvAGZ2F5fTkl0xovjNynOxviQ1I4ZvXfpQ9q67mmw9+jGC4GUsQmLXlRaq2vIglCPzDw8+TyiKt7GyIApT6FNw2kWvn5tKR0PHYJXbV9QDww7u+hCfey2XbN59+9WCKIr2+EI0ls0YcM2AXKQm6sCxoiabQLYjrkDIN0oZFR586QC7GWgoFMnnCV1aHuKo6yMqKILYJeOGPOq4gMH/+fMLhMNu3b6e2thb3WZ1bB+N8zFe/iAsTF+I69V4ViEuvvIJ45AitNi9v51RyZOPfoNvs+JJRdPHM+2GIEvmJ7mFkAyBqKLiVjN14WY6HI51Do9VpA6pynexrG1uF0AyLqhwnu5qzi1LX96jD1GHIROsLQy5aYxoYo2+kW/o0Zue5ODpNxdbhhM6SCj/bG7IjAfOKPPjcCicjKsfHaAg7ErrTFkUBO61juA2OhLlFbux2hRM9qQw7miZEkVljj/J7LXfI34sDCjMLfZzoSnJ4Cs5Zs/JcBNw2jncmOJStddlZqM5343XKHO1IjFh4bpjw4GutfHPdjSzd8oeMuuH3Q28v1ty5WAsXjjDq5Hpm9Kde2Wwjk7PzHe+bstHd3U1NTQ2QkapLS0v59a9/zfXXX8/HP/5x8vKyk9emiunI036vlI1+uBQR519/HvP+vyDhDWCPRQlX19A5Z2hfiq6kQWDVCrorqjFNE9uaFRQKApEsohJ9aRM2fATtxafR7I7Tu49KJgAAIABJREFUjft09l5726jndKcMir0yh1atxZZK0FC7EiWVwHaW1Jr0+EZ01QIIuhVWlHvpSBgc8M5mwexFzNn5Bi/e9ZeknaNvNPshC1DqkUgjENdk+mNQFgJBn5OTtcvpLKqg+ORRrnzhf1j1yrMAOFIJXl3/CV7c+OfjPsdgtMV0Fpd42N0cozLHic8p0x538dq6O7n+F99Dl20gCEhplbr5yyhsPE5XQSl9gdzxBwdCTolcl4QiCsQ0k6ZBDQG7kjoBh0QkZYAg8MO7vsTs+n3kd7VhIZCyOXn6pj9BP913ZUhqVCxNIq3T3ZcASUHVLRKmmSEpo6gXg2GTBPIVjY+trGRBkQd5hHSoqaL/5lpRUUEgEGDPnj3MnDmToqKiEY8fy+HjIi5iMriQ1qnpDHpZloWqqgM/1q5dyAcPouk6enMb+2cs5uD/z955x9lZ1mn/ez/19HOmnOmTyaRNJpNCQhqGIk0QFEEUsSCIiAWxoKgr6mJZ3QWx7LLKvu6iq8K6rq+issprjSAoKZBCElInyfQ+c/o5T3v/eGbO9JpJRMz1+cwnkzPPcz/tPPd9X/fvd12/VRcT6uvE1Ny6PjFviIq+dlojZfl2YiXVaBbjUn9NBxaENA72ZGiJGfhUidSYxa/OlIkqCYxpOqPJ9p8MRd2tnJBct6AoGVbWVdCctF2iMQOo6vwSzJ6BFF5hk3Ymnnz6NYmGKlfw3Zk06M7NTWeaNm0WlAboHMgyk5Ja9RUBNE3meF8WZupsOUscr1xMyZFWEqqHlYujxDM5Tvbn2DXHIow+VVATkckKnZN9GU7OkliBq6ks9UFGaC7B6pt6+760xX2Lr+Lf3/UOgm9/G2L3bshmIZNBPf980DTMf/kXnJXD87O5mJIMEZS/1jHuLxbZqKqqorGxkdLSUlRVxbIsqqur6ejoIJU6c2Xs54MknO5c2YnIjDhvE56GOjh4FNuxaLz5dqJ+FUkCCYEk3JQkIQTt9/w9GcMibYM+0EfV/l2j2sr5g3SuPnf8dVVXcuDCV7Ni6y8AOLrxwglTqEZClSUsVeO5y11S8sYvf4yqwy9gKW4HrZgmv3nLney84g3uMYDygERIF5hImAhCskGLDSD4w/W3UdjZgn+gj3d/4qZRx9p10dU8+2rX8SmoSZQHVfozFn2GW7thLOI5m+UlXn502z2UNR3hDf/xRSxFQTgOOVXnz5dOXa1cEhDSZYK6jE+V0GSRtwXeUBPieL9BfNAj/o+vfjNrnv0Npc3HCPV2gRAsPLSbd3/hPfzh6rfxxI3vn/weSoKqsIpHETiOm9860YBrO1BfFuBPxwcASAQifOkDX+O+z92EYpvEQoUcuehVLA+7Vdw741nShk3WtEkZNjlbkMnY2E5uRtGLYr/K+QvDXLy0kCKfyjPPPMM5ladPrDaSPITDYTZs2MC+ffvo7e1l+fLl41aVz7pKncV84aU2To08p8kw03HIcRxyuRyZTCZPJkb+blkWsmGgAZW//z2GrPNiZT19ZeegOA79q0roCbuLJd4xEUwnHBn1/4wjqA6rHO0dP+nrTJnosiBrOSyM6OOiG8mczbKol33TFJ9LmzbLSnzsmmF042iwlEXxXmoUk46yapqSsyNoHQmD+lLfvFXejqGwNt3GM57RUfVSPyyqKKR5lOD71NAcy3G+L8UfkpNrjhoqg8iKxIn+rBvqPo0I+jTOLbH5neNnX/vcnbmWl/nx6SoHO5Mc6beB2UUyBFBX5sejKRzqTNIYd4Dp73lJUKMyonOkI871v+nlxjd/lDfd/xECsgk+H2QyYBg4xcUuAdFdYv7XXjNjLjjjZGNoMvDhD3+Yrq4ucrkcmqYhyzKpVIpAIIA1RRjzpYjTnSs7YeRECIw77yTwzttoa1hLfNU6pMEvr+tWRX72mI1WEVAFxLMUNB7igi9+AtPrdjZyNkOivIrHH/rhuOMOZC2yb7yJ+id/iXActl/39mnPtTttUuJX6BzspPZcdDUVxw6Q9QYQlgVkOLLhAsp0g6AuYys6tpBGdQ2m6uWcMsGu9jTttXX825e+yzm//znR5kZyHlfzoGXTxApLKPErhD0y3SmTjhl0jJ0pk/Dq5eyprGXV9t9T//wfkU2DI6s2saz9KJ7uRnK6SmrT+SiSewstB7KmQ8a08xPylGmTGnG4Yq8CDK+MpUIF3P/A/+Vjd72eyuMvYiPwJuMIHBYdeI4r/+tBnnjzaMJR4lco8ErIQpDI2TTHpr8ee4x/fKZuOb941yd49be/zC9efztZRSdtOGRsE8OGjnjWjV7Y09cy9ygSdVEvr64ror7s1KIXcyHjY8mDqqqsWbOGpqYmtm3bxqpVq0blzZ8q2fhb6/zPYnLMdZxqamri7W9/Ox0dHQghuP322/ngBz84aputW7fyute9jtraWgBe//rX85nPfGbK81FVFcMwpkyhEEJgWdaEBGLod8uyEEKgaRoejwdd19F1naKiIjyKgrexERGLof/Xf9Gr+TlWUMFTV7wJU1EJ9nUxUFhKVW8bQ6al7eES/EaapOQBoFPyEPXKo2o8xbJWXos3EhnTYUFE53BPhs7kxFGMyfYdi86kiSaLSc1ThlDtEyxN9qCVeHjeUz11o1PAERJCMCvnvanQFK0mMpAmgcLqkI0TjdIcy3FkAsH3qeJYMEp9zwkOeEZH12sLJPyhIE1ziAbMFlG/SmWBTmN/lj1li9lc4OE3u9tn1UZIl1gU9dKbcTjak2G2BAOg0K+yKOqnPZYdbGN6yJKgviyAYdsc6UzRnTQAQZFP5rdKOU+/4z6+8cg9ePbtc4v+FRSgXXwxztKlGD/7GfC3uTD2F0mjisVi3HLLLeM+P3HiBO9973spKCg48yd1CjgTaVQTkRl740bMq15N/DVvdAUTUyBnu1GE9tXrGVhQi6+7E9PnR9g2L9xwy6T7eWuqOHDRVWip5LRRjSE4RhpwU3f2n3cZF/7oW3jSSTTLpOnq64guW4DlTK3BTpoOy4s9vNidwZYV9lx4Fa/88X8gGzkk2yZeWkHm0svImk6e2MwURT6VsEeh+fb3s+Y9f0BPxlh8cBeLju5FzuVIRQr59sO/nFWb/VkTryJGCxqF4LF3fIz3fO52JMtEMbJYssKiAzsRjs2y3c9w8twtVIZUNNmNYnQkDcxZ8NZEzmJNsUzGkYkZkDZsnrz8BjqKK3l2yblkTUE8Z8xQe+EQVuFV9aVcuChCgU+d1T2YCnNJcZpoHyEECxYsIBKJsGfPHhYuXJh35pjrMc7iLCbCXMYpRVF44IEHWLduHfF4nHPPPZfLL7+cFStWjNruggsu4PHHH5/xuWiaRjqdJp1OI4SYkExYlkU6nebFF1/MkwiPx4Pf788Ti7HRQNHTg3juOeQdO5CffpoThRVsv/h1dL/xQ2Q9PmTbBscGSSKUTTEAdAYLUR0bY9ClSVclkiN4l0eRGFlAI56zWRB2HRLHYiDjkomUYVNbqHNoTJ2GvozFsmIvL3ZN7UAUz1rUl/rzdYhGQpcFa6pCeDSXBLUVhBC2TWk8RocyN1ex7pTJyjI/e9umr8swE6g4vFLq4YVAOU16cMZFVucCW0hoJRH8/TmSksZqPYNVWkrzQJbe00w0ogGV6gIPR/syHBkR7TrSl+GSao3fNU193ZKAFRVBZFniYHuCfTP1jR/TRn15AFmWONSZYvcMhedlIY3ysIfjvWn2D0ZhZAF1ZQEGYnHaUha9qFBcyz+/8SN87KFPwOCY5PT34zgO8uc/j/XBD/5NpvyecbLxq1/9iscee4ybbrqJTZs2kUwm8fl8yLJMfX099fUTVz5+KeN0C8QnDY8LQe6++4iaNuJkimUPP0jZU78ZtcnJ17yBxuvfRs5yKAtotCZy7Lr5Ds6/71PImTSZcIQTF14+6bH7syZNN99BxgJffw9L/vz7UX83dC8HXnnVKLKTEjpFXkHWMCmP6Jy47Q7WfPULWIrC86+/aUb5ou71uaLo5piBqek8df1tXPnoP+M48Psb3kUsmc1/gU1Nn5JwSQJqIzqK7DpwBXSJo6ULObz5EhZt+wMCB0tWELLJjuvfMcMTHIZpu5XbXxgjRDu0ajPfuevLvOOBu5BsG8UywYLFB54j+KNvsHX5Ipp91bMmTAUemSKfQn/GwlT9VOiCtCMzkDLY156gdckGN3oxzffSq0qsKPVxZV0ReqqLnq4uNLOFoDa/hH8unetUqz+hUIiNGzfm06rq6+vnnAf7t9bpn8X0mOs4VV5entcUBYNB6uvraWlpGUc2ZoKHHnqIp556iqamJvbt28fFF1/M1VdfzVvf+tY8mSgsLMz/LssyO3bsYM0E7jeTQXrmGdRHH4UjR/jx++4lXhAlUVCEJWTKThymuW4NlY0HOLlwOR2lVXhyaTKalwq/TNNgymgCDZ8iSA0utLTFDUK6RGyEXXhuktBEPGdTW6BztDdLf9aeMIox2b5j0Z8ZHQVZXORlQZGXvoxN3HSIm8MEyJEkSqJBOvrmnkWRsdzJ5ozHswlQ7FNYFPXTNqBzpDhCSSrOwOnNXAKgzxfmst4DHFUKOOGJwsDpJRmlQZWKiIdjvRkOTRJBOCZ8bDFO8LQ6XpNXEtSoKfbR3J/h4BxF4wVeicWlIZr7MxyahrwOQZUEdWV+tx5HV4rOQV1PaVCjPKxzojfNgfYEjMkweKxiHZcvWcvagzugu9v9644diH37cGpqcFasOBvZOF2wLAtZlrnvvvu47rrrePjhhykpKeHxxx/nmmuuoba2drAg2OxV+qeCv0aB+FjoikR5QCFZXom/tRlj0LFHTSVJVowMFbvX2rr+FSRKy4mcOMrOd30YR5l8BdtBUB7xcnTAxDfQyyXfuh9LVQGBbORIRUuJXX0NHl1Bk6W8hkEW0JbMYSI4cP7lLP3uv9F2zkbKn/vzqEJ9pu7hxAWXwwTP3HJcH3bTcgh5ZJqvfC3pn/wHoa52rvrqp7EHV+kk2+Z3t32U3VeNL6ajSYLaAh3LgYzlkB2cfA8NSL+66/Ms3P4UV//j3Xj7e0BIbH7km2x+9CHal6/i55/++qT3ZixMxxlfpEkI9rziCpoeW0nN4T35j2XToOTAblb9y5c49JF/As/0K2yygKqQhhCu+N+wHQq9Cu1xm86swHZMZEViaVmAF9sSE6ZJSQLKghqvXFTAltowEe/wsz9+vJvKykqEEGzfvp3Vq1dP6f40G5wOIqAoCqtXr6a5uZlt27axdOnSOTl8/K11+mcxOeZznDp+/DjPP/88mzaNt//+05/+xJo1a6ioqODLX/5yXoQ+Eq94xSu46KKLqKqq4sYbb+TrX/86paWl83atAPbmzdhbtyKlUiw4cZBGRaW3aiEVR15goLQSbJtYWTXCtjFllQpd0GxALOfkJ/YOgmKfwslB8woHdzFkJNnoTVtUBlVa4uNF2JlBkjKQsagt8HB0TOpQZ9JkceH4z8eiN22yNtmGV1cRBQV0ezTak1M4S+VkyoMybTOsQj4WfRmLVRWBGWtFRqIyrFMR0WmOGZwYyAGSOzwXRPD2ZUjPJrw9C0Q8MouKffQmsjT6z0Hq6xtXxX0+UeiBIq9ER9bh8AzSlNoWLWf94QPs8FSg2BYN1WEsIXG4Mzkn0bgsCVZUBEhnspwcMGccxSjUHWpKwhzrSbG/3Y1eycLVhhh54jHx96bYr1JV4OXzt3yBDTt/xyXP/ZpNR3Yi+vshmUT56Ecpu+46pPvum/X1/DXjjJGNoUlDQ0MDN9xwAzt37iQYDLJv3z42bdqUz1890wO/JEn5AWauON0rozMhMzVhlT9fdjV1//lNpGwWYZkkqhfSOWhlC5A2HQIYJCSVXbe8n3P+81+njGrkIcmsjCqIkjX0n3c+kV07Mf0BnGSCg7e8D1VTsRwGU4iGz7NQcegxBY6i8rsvPIje18OrP3wLlq7jCIFsGGSDYU5uuXScRa4uCwq9CiAo8Cjs6UiBrPHr995Dza4/s+ZXP8bUdIRtI5sGx9ZfMGr/gCaxIKyTMm2SE3i1p0yH0oBKRwKObb6YeHEZ0eOHyPh9KEYW2cjRtahu+nszAomczcICjca+4U5oyLL2//3z93jrmy/B39+T/5tsmSze9ScUIzdoYTwxIrpMsV9hIGsRz1pE/SqFXuhOWTiYg9Xk3crdJg6SEPg0mcSg65hXEayuCHLFskKWFvuQJtFeDE3uKyoqCAQC7N69m8WLF8/LBGeuEYTp9hkqeDTkVjXb9/gs2TiLkZivcSqRSHD99dfzta99bVxRynXr1nHixAkCgQC/+MUvuPbaazl8+PC4NlaPsM3UNG1eCtCOhRONYm/ahIjFWP2H/2XnRa9BTyWIRStIBCKU9XfQXlhOmZOhDZ1eVCThkDBsygIqrYPkoTdjocoCY3CZvz1h4pHd1f8hKPLE73Jv2qImrHFiIEd2kjDBZPsOQTgOG3OdeBaUcEyL4Ijp32kHN6V2rmQDXNI1E8esISwq8lLgV2mJGZwcGE+8BnIOKyuCbD85MOdzmvC4g3WZmmO5wXQ2AZKgqjrKieNzLyo4GYp9EmVhnZMxk7ZZBE1sIRDlpbzq2EGeK6tjf+fcCviVhXWqCryc6E3zYl7IP/V3SJUFy8sCpA2LY91p+geJyVAUw02fmjhtTuCwvDSIA7T0p3EcByUc4mdrr+TZhedw89ZHuG7bzyGXg1yOsu9/n1wiAd/+NszSyvavNRJ/xkfZSCTCo48+yvHjx9m/fz/9/f1/UUH46erE5xNCCEzTJJFI0NPTQ0tLC8eOHWP//v08//zzbNu2jRd370TB5ODN70XOZZFsmxdvvXNcxKAg6ApqWzds4YmvfgeERMX2p6l65vfDP3/+A3I2gwBqQx6KPB4iuk4iZ7Pr7XfgAFImjREM0XTpVZOedxqViOKu0MQrquluOIe2dZvAcbB0L5aisvfN78QZrLDpVQQVAZXygIYqy8RzDvGcTcJwqC92heHHz93C02+7A8PjRTIN5FyW/RddRbzEDb0W+WQaol4KfSqxnD2l/iHsGZyYShJ/ePfHyfqD6KkU/t4uPIkY5zz2CLe/5ZWU739+xs8qpLttFngklhRqLCvS8CgSzXh49O++jjPY4TlATtPZftWNpIORce1IAmrCGgsjGhaQNW2KPApCEjTHDTqTBqZtY1gOlj1aqOg4DnVRLzevL+POepuvXFnFB86vpq7EPynRGNpvqCMLhUJs2LCB5uZmDh06dMqRu9OdrhQMBmloaCCXy7Fnzx5Mc2a5CGfJxllMhFMZpwzD4Prrr+etb30rr3/9eKvwUCiUNza46qqrMAyD7u6pqztrmkY2e3pSXayLL4ZQCF0W1B17geJkH4lwIaWqjVHq2tiaPjfCmTEdygNuNNQYQQyylkNFYDhKatpu2u5IdCRNin0Tr28O9Q1dKZOa8PjJV0vcoHqCzwFqCnS2LPATr11CVzjKot6WmV46rQmDitDc6xbEsm50YzosL/VxTlWIpAXNMWNK7dyJgRz1JaceUdZlwZryAMtL/fTnHE4M5MalfDWnbJZGT63Q7EiUhzTWVgfJCZnjA+a0wv6RCCgOSyMKqUgx8eUrsCaovD0VVFmwpjrEioogvSk3itGfnn4cqI54WFsdwqvJ7GtLcKw7jSQcGsoDLC3x0ZXIsbslzsAEbRX7Vc6pChHQBGlzkHDbDntbEwR1hbqoj66CMr78ug/xrUtv4c9L1gMgLAv9Jz9BvvvuWV3jXzPOeGTjwgsv5IknniASifB3f/d3bNmyhSVLlgDDYs0zydyGyIbXO38v3Gwx5CAyJPgb+a9hGGQyGfr7+0cJ/UKhENFoFI/Hg6IoCCHoSpk8f9nV1H3nGxih8KioxhAShkNEl+nPWtiqhpqMc+EX7kbYNraiuJECI8fW7z2Or3QRmcH3K21CVVCnadkKulevo/zPT7L7vXdjq1N31CYKIU0Qy7kD9fO33Mmr7r4Nychhery0vPaNVARUbMetIxHLTWxbazhuzYnetIWpe/jTDe/isoe+hJpJserXP2HN736GENB67nk8+aVvzui+x3M2HlmQsRwaN13ED778Xd72gTchGzkMRUPLpHAMGS2ZxBPrJxMaTwoUCfyqhFeR0BSBIgTn1/ixHDfsurt9eGXmxKoN/Om1b+UVP/++6ybm8fL0daP1IWFdJjoYxRjIWEQDClGf6+7Vm7GxHXvSOhiqJDi33EdDkUpRyI8sy+zZ0zLj92ksIVBVlXXr1nHkyBF27tzJ6tWr51xQ6ExM6iVJori4mHA4nHerCgantuYdeV5/rStGZzF/ONVxynEc3vnOd1JfX89dd9014THa29spLS1FCMG2bduwbZuioqIpz0vTNAxj/ipXj4QTjWJu2oRoaGBlQw0/1qoRNhiaTk/aotDj9rtFXoWetJkvBtubsYj6hp2nYjlrVBppd8oYp2kYcg4ci46kSVlApT1hIMkT9xNBjwIjLGC9qmDDgjA9aYveETzQiETGp7NOgQKvSuspCLJ7Y2k8wiYzpk6GJGBlWQB7cGwmO/Pnl3XAK9mk7dn3mSUBleqIh/akSdMEaWtj4VVOPWWrPKRRGtY52puZUbrUSES8MgsiXo52pzmZcEBI9PojbJZ6+U339NdfVeChLOyhsWfyyMNYaJKgImCD6uVkX5aWmEvky0IaZSGdwx2xvAh8LCQBdSUBHKCpL43tOHgkh+M9aYr9KstLA5zsS3OgI4njOJQEFLLdCR6+7BYaTuxjcUcj0XgP2Dbyv/0bjqZh33//lOf7crDKPeNk49JLL+XSSy+dcJuxk5EzId48nZ04uF+SiYhEvliS4yDL8jj3kKKiInRdR9M0jh49SmFhIYWFhVMeq9gr4/N52PWxz2P6AxPqIABCupIv7Gf4gxx+9XUs+8X/xdJ0tFSCgYsuQ64ZJhpDSBkQ9Snse8edaPEY3u4O1t/3qVHbNF51PT0r1w5fPwJdEvlQc8/ylfSuPIeKZ5+i87wLqfvhd4bbL6vg5JXXTnwfHagM6fSlUzjAC5e9jvMf+QayZYLHg4VAMg3aNpw/5T0a12ZYy3vAd9at4uQ5m6nd/iSqkcUx3fSm6z57B7ve/wlOvOlmNzIgXEF4zrLJmA4OkLEhM4YoqZIYN9j+4r2fZvm2rUS6Wtl5xRtJRooGU600ZCHoTpukDZsir0JfxqI1bmI7Ns4UblJByWRzpZ+VlREkIeYcqZvM/Wnp0qV0dnayY8cOVq5cOS4tZK5tzzeGOuSqqioikQh79+6luro6r0OZ7LzORjbOYginOk49/fTTfO9732PVqlWcc845AHzxi1/k5MmTALznPe/hRz/6Ed/85jdRFAWv18sPfvCDad+N0x2Bt66/HoQgIEmUHh0gZdi0JwyKPHI+hUkf/DeWtSj1K3QkTVR52HkqkbOpHDT0ADettjKocXLERL4tYRDSJGK58RPcgCYDBq1xg7KgSvuYiXJTf4bSTIwOT4jVqTa0JYvpnqCidb8/Qq1X5ljfzO5XW8KgKqTRPEfCkUBh1UAj20OuPlKzTRoWFJAyHTrmWHE7kbOpiSi82DszIiCA2oiCpkh0ZqBxAuevydBvqdSXSByYQ8pSRVijJOSSjPgsSUbYI7Mg4qGxNzuuxgrAMW8hW9Iv8LS3atzfdFWiJiRhyxon+rJ0zLAgY02hm8Z2pCvFyQRAFkXA8rIAGdPmaHeKjvhgmtkYFPlVqgu8NPdnSBkWYa+C7TjsbY1T6XPbONyZpCcVx6NIlAU1uhI5OhMmi0oLaNj5DI5l8fG3f4Grt/0vl73wB8LpOMqDD5Kbhmy8HMapv1gF8SH09PTQ29tLPB7HMAy8Xi+VlZUUFRWdESZ3Kp34yCqrhmFw8uTJUWRiaNV0JJHwer0UFBTkicRMvkAzFaALIagJq+w/dzMA4YP7KP/Dr0dtk6yoovk1b8CrSHkR2v433szSJ36CarvalSPvuHPSVSHhyBj1K/j9P3+PzffeRfXWJ7AGK8jKhkHzRVeMvUs4CKoCKlkLLBz2v+tDRI4dIvrMHyja9gwAkpGjffOFk5INgKThsCLq5VBPhtKwyjP//B0avnE/pTtd0bmtqQjLZOlPHqW3roGeFTNxZRGUB1S8qoQEHLj3nyh839sInziKcBzUdBKERP23/5WBSDGNF796Bm26MGyH2gJ9lMUfwLfue4S3ff4O9rzpNpYU6sSyFgMZkxK/mh/E+7NTRzEkAfXFHjZUBug8fpiqcDhfZ2UkZjPJn2rbkpIS/H4/e/bsYcGCBXm72ZniTJGNofcpEAiwceNG9u/fT09PDw0NDSgThOVfDitGZ3H6MdNx6vzzz5+2r37/+9/P+98/eVHPiXDa031HaJ1Wl/rYetzNV1cVQUfCxK8KOpMmAU0ikRuuN9SRNPFKJmnbfbfGahcSY6IdtgMlIZVYz/iUsNa4kY9eB3VlHNlwhMRCkWa5R6arYtmU1umGzayiGyGvckp2s+0lVUTjCWozfXQvWEJr4tQtpXotnZVlghfaJ3df8kgOC0IyGRT6TTG1n/wUCGqz6wMrwholQZ2jfRlis41keGSqwh4a+zLjrI7Hom9xHVWNLTTrblbBwiIvRUGdo10pGmMWMH1qoUcR1EQUkoZLWJv63WOWhzWiAY2jnYlJCwpKApaVBEBAU68bxfBrMid60xT6VOpKAzT3ZWhJGTjJBNGAhmk79KUMwGFVRRCPKpGzbPat2Egsa4KQOVhZx/pjzxNOx7FePf2c4uWQ7vsXIxsDAwM8+eST/PKXv2Tv3r309PSgKAqlpaXU1taydu1arrjiChYtWpTf54knnuCDH/wglmVx22238YlPfGJcuz/84Q+59957EUKwZs0aHn300SnPQ9f1CTtxx3HyKUwTRSaGLEWHiIRt22iaRjAYzKc6zdeXYzZuVxUBhSN9OXKWg7eznWXfeyif6iRls7S0SHUVAAAgAElEQVRddBlNr3kDUZ/KycHQoVEUpffaG6j4/n/QcdnVJBctnbR9y4GIRyNtZdn/jjsof/YpbE1DyWVJ1i5GXHwxFZKE40DWMDGRAEHaBJ/mFtVLLl/JL374W6646Wo83Z3YqoZkauy77YOTHheg0KMQ0mRsB+KGTefi5eTeezdX33INcjaDaXlZ+9ADyLkce29535RkQ5MFhR6FjOlQGVJJGza6LDhWWM6vv/Agb7jJ7QAE4Dg2/p5O1nz3G3TWryZZVjlpu2Ph00YLliUBBUuqeeJ7j5NKmYicm54wkLVoTZg4uGJv2554oPSrEpur/KyIetEG0w06ppjIzxfZAPD7/fkq3v39/TNqcwhnorMce/6yLLNq1SpaWlomTasaOq+zhOMsJsJcxqnTgdMdgR+J8oCKrgiKvTJdSRO/JghpCknDIKIrJHI5ulOmm16VsfBJkB5cgO9NW6OKug5kLSqCCi3x4VlwR8LEo4i8C9UQHKDYp9KbtmiO5fLEYwgb0m2wpIZMbHrr04GsRW2BNuPoRnvCpCaicWIWEYEhKMJhRbIDRZPZEV02c4YzAwwkc4TJMcDo9NXqiE40oNESM+iewABltmjPSTRkO9mnl0y5XWVYJxrUONqbITbLgoMR7yDJ6M3OONUqI6ssDUJZy2H6qxZyIibTMkNSuLDQQ9jnRjEO97rfP1WCutLhKEb7JG0FVYfFpWE3ipEzifjUfBRjWYmf+rIAhzqT7G6JoyuCAs0hYQq6EjmqIjp1pRFwYCBjsGuEA5YqS7z9if/g1t9/L0+Ge776VcYnaI/GWbIxR+zbt4977rmHAwcOcMkll3DHHXdQVeWGylpbW3n22Wf50Y9+xG9/+1vuuece1q5di2VZ3HHHHfz617+mqqqKDRs2cM0114zyLz98+DBf+tKXePrppykoKKCzs3PC4zuOQ29vL01NTfT19fHwww9TUVHBBRdckK+yCm7O+hBxmK44UiwWo6ys7LTcr9mQDVkSVAcVjvYbtL/ilSQWLMLT3Yml6ciKwsFb7wQgZbguGkWaQJNVjt74Tgp//UtOvOU2PG2jBXa5SAG2d9gtSRaCZQVeuvR6es67kOjTW3FkmRff81GS5sj1pNH3KGu4ofis5eCoKnvf8xE2/MMnUBMxYrVL8fR2Uf7H32KrGp3rX4EjyxToQwRDYNiQMaHYpxEfcDur/iXLaT93MxV/etLVnFgmjiKTLK2g8unf0br5IpwRz8qrCMK6SzI0SZCDERaNrny7b0k9+699Kysee8S9/wC2TfGhfWx46H623vu1GT0LcItN+RSBLEv47DSO5qM/YxH1SZQFVDqSBrGYMWUUA1yx+OaqAFUTiBmnIgnzSTZg2G72+PHjtLW1kclk8Hg889L2qWKyDrmyspJwOMzevXupqqqiqqoqfy4vh078LE4P5jJOnS7oun7aBOITYXWJl90daRwswroyWOEbulKG22/aDh5VQAZitoJHclNJwV3IGQlrTCaQYTtUhbRxEV9wU5oCkkPCFpR2NNMbKidgZVkfsOitdBfByrJtDPjD015DznZmVH18CD5NAWZHNupLvICgI7wc4djU5jI0ZufubDkWWUllSbqXnd4yFMtgRaIDa1kdbUmT5ByI0VSQKiuQOnPYE/SHVWGdoqDGsd4MA6dCMqaJZIyELgsWhBV67HKiuSy77enHGb8msbTET1/a5GRfBgaLFEb9ElG/SnPMnDSKIQRU+hy8Xi8n+zJYtkNAlznekyaWMakr9dM6kONQZwrHcSj2azg49CQNTAErKgL4NRnTdjjaNVRZ3EVlWOfeq5ewtCSA8s135JO0zFe9ioPt7UQti9ra2knHyJdDBP6Mko2hgf3xxx9ny5YtPPbYYxNud8MNbr2EBx98kFjMtWXbtm0bS5Ysya8g3Xjjjfz0pz8dRTa+9a1vcccdd+Qru5aUTMzS7733Xv74xz9SXV1NX19fPi+9pqYmL7h+KWG2dTwWhDViWQdZUuj4wN0s+vu7UbIp4pu2ULh2DYUCBAKCGl0pNySeKy7lmf/5DfX/+CnK/t/PsHU3NUrKZml53Zs4+LHPIQko8ipkTHfSr8uCI7d9gNInf4OlKpT+7glKf///3JOQJY7e/D4ypcMFeiwHCnSF9pT7ErZecBm5YJhQTxeBk41s+uzdCMtEwuH53+/E8PsxbMiOSXuVx9ga/umT/8SWez9E+Y4/AQLT42HjVz+LIyR+9PifsWQvQVXCr8lkTQdNkshiM5AdfU8NG6Jemc60xdN3f55lT/wYJTOcS2pLMgM1i6d+VoBPlfAqAlWWkIS7UtjYl8UyNAq9CknDpn1w5W86wfe6ch/nlPkG85lnj/kmGzBsN9va2srOnTupr6+fVk/0lyQbMJxWdeDAAXp6eli5ciWKopwlG2cxDqcyTp0unGnXxEUFOs+2JAlp0qCg2yHqd61uywPKoCOem16VNCDqFbQmXVbRkTSJeGT6B31vu1LmKCE5uOJyRWKcW6Bpw4KQysF+k6ZIKatTnfhKi+gNDAvouypq8GRNMs7UfWIsa1Mb0Tg6w+hGR9JkYUTn+AyqaFcFVQp9Cr3ZYZ2eIyQcXUPJGoMR/flBfyTK5T0nOBQup7lyMcyyAOxMMSB5OJcutjNc0LU6olMYcElG/xkiGaoEy0r8dCQMTsYt0H2kFtWz5sABdgcmTt9dVOwl4FE43Jliz2AVeVVydRSpQSvb7tTEz7XQp7Kg0NViOIrAzGXAcXihNc6SEj8N5QEOdqbY3ZJAlV0xfG/SoDuZoyyksrEmRCwWx3Ycnj0+MLzUKuDKFcV88OJaNGX4+2AeOIDq9bqRje99jw3BIIcPH+a5555j1apVE5qwvByMTM7orHroZn384x8HIJ1OT+gCNcTiRua1trS0UF09XKCuqqqKZ599dtR+hw4dAmDLli1YlsW9997LlVdeOa79z372s/nfb7/9dl73utexbt26U7iy04vZkg1NFiwMa7TFHQYuuoJc9AE8LSdpfs9HMEe5W8j4ZJOU5X7maDqt19xAye9+iSO7Xw1HsWm95o0ENQlVkkaJxgOaSnvtUo6/8e1U/fx/qP7Zf7v7OQ4IwdGb3zfu3BI5h2KPQnfGxJFlnvvovWz4h4+jJRNuXSPLoe3aG0l5Q0xYlQ7IWRCQLBK2O9ikSsv506fu59o3vBLJNPM/AwtqueDLn+bYBz5OsqTcPX/HZiA7uejOp8qQtkCS+PG//5Qb3vaqoYeA4fPzwhtvRpUEPlXCowgUScAgWchaNlnTcT9DYNkOJu7KXqFPxUHFtm26U8aUgu+oT2FzVYAlhfqEOoyxmG4iP99kA8inDa5atYrdu3dTWlpKTU3NX1SIPd0xZFlm5cqVtLa2sm3bNlauXHmWbJzFOJzKOHW6MNM0qqGx4lQnJJIQrCzx0tiXJZYzKA+o9GVc/cXISt0FXje9qj/rIEvDUYywPkw2APQRQnKAlGGzIDxxmlNnLItumJwbayLo1zgeGB3FsIVEoWbTOoMIQnaW0Q2POnVf4JNtqgMSSSENEo0xx0NhVYWH51tn5oo0FQKyzZKIRkcyQNxXQzozfxGTyZCrqkJpjlFe4KUwqHOsN0PfXEhGyENj3+xIhiYLqvyCflPiyJg0KweBWLyQgpN99OmuLbAPk6Vemz5H5fiIc6wM6xQHNY51p3ihbSothh+E4GRPCst2COoyjT1pQh6ZMm+OuKVwpMuNYhT5VSThpkl1xHLUlwUIeCRsx9VytMeB/uTg9St88orFbFw4SXKUEDz78MOcq6oQDiMBdXV1dHV1sX37dlasWJFfMB/Cy2GcOuNL+EMd4c9//nP++Mc/ct5551FeXk4wGCQcDlNQUIDP56bsDHXmM+04TdPk8OHDbN26lebmZi688EL27t1LJDJ5Rtx85sKertXbuVQoL/RJJHIW8axM00c+TcHvniC9bMW47TyKSto08kWQ+tesJ76sgdABt9p1/7mb8axbS8Z0V/5HImdBiU/h4Af+jkTtElY88DkcRUHKZWm5+vWjohojYdmuZaxXkdCuuILDNdWsfMtrkfr73cjAsUM03Hkzmaoajn78cxO2EVYkRhbwTFQuoOnCy6n53S+RTPd5hpuOE2prpusNb2GgqJyYPb2zR9YSCMfGpykoK+tpufJ1VD7xU2xVo/Gm2/EVRjBtt0CNbQ/q8Ry3erhhA0Jg2mAMjrpDTy3/rxBUhXWO941eZZEErCj2sr7CT9EkXvSTYarvxmy+N3OJgng8HjZs2MCLL77Inj175k2IPZe6HjM9RkVFBeFwmD179hAKheZs53sWL1+cznFqLphpZEMIgW3bp1Skdgj1xR6eb0+hy4KBrEXGtCkZNLAo86u0Jgy6kyYyDlnbNdoYcqLqSBr4FEHKHCryZxDWpVELPSnDmVDELRyHS7KttK9eg5lOoVsGWVkdtU1a0vGrDklj6n4ilnULrR7rnVl0ozNlUdt9ksbiBaM+VyRYHIKc5Cc5DXNpT5gsLBjfx88UJQGV8qBGWyJHS06AqhFXNRpEP7tzp3fKFgp6uHyVl6caB06NZMxCNK7JgqVRH50Jg5a0xWSil4TmY63aRFN7Oz5hczC6kL1ZNwNDk6CuLEAyZ9HYk6Y1Nn0UI561KParCCHY2xpnSdTHyooghzqSnMwqKMKhQHNI2zI9SYNoQGPjwjASgoxh8vzJGCNN1TYuDPPpK5e4Fs3TILNwIc4rXjHqs2g0SjAYZPfu3USj0VFpVS8HbeFfjGwAbN26lUcffRTHcaioqKC0tJTi4mJuvfVWLrjgglEdeGVlJU1NTfl2mpubqawcLdStqqpi06ZNqKpKbW0ty5Yt4/Dhw2zYsGHS85lMID5bzNeK0lRtzxYVIYmjPTaxLa8kvnELJf/9n4jciJdQkuh+zRsI+z30Dy06CcGRO+5m7QdvQQI6P/CxcRa4I2E7EpoErVdey7L/81XUgX4cSRoR1XDwKTIeRaDJErIkEAjK0OjL2uQsyC1tIF6/ioJtT2MrKuGd25BMg77zLpz0uLLmQTKyo1asnvzHh7juhksIHD/qXp6RwxKCc971Znoee5J0+XgLvZHwqQKfLPA4CmkkLNvhuX/4OoW7d6AkExy58RZX1SEgZ7s5wY4zTCrGEovJIITIV9sNqBIbK/00lAwLvueC+dBsTNXOVO1KksSKFStoaWlh+/btrF69Gr/fP+n2s21/ppjN6o/f72fjxo0899xz9PX1UVtbe5Z0nEUecx2nThdmOk7NdayYCJossazQQ1fKtaMt8StkB8nDkLg7ZzsUKBY9pkIyZ+fJg+VAWVAdrFbtfhbxyKPIRn/Wopo0JxmOGjXoOYJBiXTJYuRcDsPro0SFptTohSIHQVlA5mjf9AuFiXQOMeiKOBPogeG+SzgODSVeco4gaU4hqhvbhiKhyYLcJBXRJ0JVWKPAq9CeNGlNGIy1X02GClB7MzOuVj5T6LKgttDDQDpLX9ahP+uwqiLA3taJowJjUeCVqZyl8BvcNOGlUS+dSXNcJGMi1BZ4sKxKyv0DxDv7yCka1QPtFAU9HNGLJo1iCAELQjKaqtI8kMOyHUK6zLGeNLG0wdISP53xHEe73crfBT4FxzQYyAn6coJKn0W02IOme+mIZzkxgoR5VYnbN5dRK/dwzjnLZ3ztk2Fo8W5sWtXLwfr2jJ/90A177Wtfy7PPPktTUxM7duzgk5/8JEIIHnnkEfbv3w+Qd3wC8g+gsbGRXC7HD37wA6655ppRbV977bVs3boVgO7ubg4dOjStS8h85cLOZyc/X20rkqAqPPiILZPKB++j+oEvUPW1L1H91X+g+oEvICfjoHgo9g6vhKXWrie7YiXJ5SsxkfC9uC//M4qs4Pa9RV6NoN9D+/s/gpqIgd/Phvs/xZa73sGWu95JxfZnUCQV25ExLImcJbDGFEA6cs8XsTXdTcMS4CgKXZddRXj7M0jpCdxHhEyZ1+XKsoBSn0LYo7Lvi18ftZmcyyJlM5z3vreMLrM9An5VosSnIpDJWIBjUur3EPF6SCLzm69/l2c+8wADngCG7WA57o/pOFi4yQE2ownHVBDAmlIvN6wo5N3rS1hb7j8lojFZfYzJ/jYZZhN9mKjdyspKGhoa2LVr1zhzhtmSh7mEjWd7DFmWqaysJBgMsnfv3lkd6yxe3pjrOHW6MNMIvCRJ83o+q0q8+bSpnOXQl7Eo8soMZC1KBiOwKVtCALGcTdmIKuLdKRNVGn4f2xMGXmXM+zmodSnMxLhQ7kMtipAJhsl6/JT1tbubWGKc6Bxc3Ycupr/WjCNTW6DP+Jo7i8pZGu+gNtbO6rCg3yAfoZkpYlmbhlLf9BsCtQU6DWU+sjZ5Ld9ESJoOK2bY5kxQ7FNZWeYn5NNoTVokB+2L3bpRErXq1NqQAq/MyjI/OUtwuDuDOUMSpMqCFaU+wl6Fo71Z4mNFmWOwsMBDdUTn5ECWFjVIR0kVJREPr23fRXOkjN1yhGRufBuFPre6d5FfI2k4qLJAltwoBkKwqiJIznLY3RKnM56hIqQR0GX6UiaGLVhfE2ZTTYRoQYgD3Tm2nRjIE41lUR8/uHUNv7xjA5fXFczrYoMkSdTV1bFgwQK2b99Ob2/vWYH4fKGsrIxrr72WSy65hO9///t5gjBysqEoCg8++CBXXHEFlmVx66230tDQwGc+8xnWr1/PNddcwxVXXMGvfvUrVqxYgSzL3H///TOqzDofZGOok5+P8PVkbc8Ffk1Q7BN046Xt1juo+D9fw1E1pEya3stfQ67C1cFYtoRftdEVCdsWHPzHb7DqTVey8u3X4AxZ56ZTHP7Sg/Re8dp8+wFNoKsKQkj0vuYGqr5+H2pnB0pfH+AgbJuOm9897rxMGwKqIDEYBk/WNbDr4R+x7qZrIOtgqxqLv/IF5GSCvd98hL5XXDSuDZ8iURPSMAc91SWgveFcDr/1XSx95Fv57YTjEGg8ysL/+S7Hb7g5/3lQk/AoEikD0oZNUBM4tiBFgITh4DgOhR6VbN0KTtRObgc8EQRu6N2nSIQ9MqU+habDBzjv3DWnLfo1EU5nZGMiMhAKhdiwYQN79uxhYGCAJUuW5FM7ZkMe5hrZUFV1+g3H7BOJRFi4cOGs9juLvz3MZJw6XdB1nXg8Pu12873oFdRlyoMayZxFe8Kg2CcjDa64D+nJso5EsQe6MmCNOHbWcigPqpwcrPpt2rCgv43G8LBrYzYYYku6hWxVNTF9tNtQrKQcMZie6rPS5Bj9dwdBZVjjWP/0gumcNTPthgAW23EKpDQvli2hR5r7FKkjYVIT0TkxgeBcAEuLPUiSoCdtkTBnJvpOmk4+Kj4XCKC20IOmyrTFDU7GJiawhu0QKCkkerSZLs/oIq6jIhmzEX7LgqVFXrpSZr6Q7lRYWODBcaBpYHjbBREd23E4IZYiKhfTsO8F9kWGU96EgLqoDyFJNPakMG2bsEfhaHeOlJFhaUmQnmSOxh43ihHxKhT5ZdpiWVoHsiwp8VHoVeiPxRlIGRzuHjaIUSS4sBzufs3qURqu05XRMjKtyufzzXpse6nhL0Y2kskkXV1dBINBfD4fXq+XUChET08PTz75JJdffjmWZY3K/77qqqu46qqrRrXzuc8N5/QLIfjKV77CV77ylRmfx8s5sjGEkoCrI0i//Wb4zjeRjBxCVoh94C58qo1pWui6jkCjb9DX3Couoe2m26l+6AH3HEwDM1xA3ysvB1yNQaFPwXQEliPwa4IBW+XF/3yMhusvASEQhkFqeQOxzROnQ2myBMbwikRs/XmkFtTiO3HMrb2RTpOpqGLgnPV50bksIKBJZDMZdE0nrMjkLIFhGbQNWs0d+PCnWPzf38lrNwAsr5fOLZcAENIkdFkiZULGcAjqLlVJ5dxQu4NLNNyBSUKWJid6sgCPLAjoMlGvQrFPIaBJE0Yq2h3jtHRK02k2zlRkYwiapnHuuedy5MiRfCj4TEU2ZrvPyyEX9ixOH+YyTp0OzHScmu/IBrhR2N82uhEIWQg6kiZBTaIrZeZ1GEN6vp60RbFXoTvtTp4TyQwCkU9h6tO8LOtvpgADs7ISoyyK3pwgqY+3NTVUnRJV0JG2cHQ/qmGPSyEayDn4VYnkWEHhGMRyNrURnaOT6CgiHpkFEY2saZMyNdoLi1iSjXPQnPtzdQAPFqpwMBz3+mUBdVEvhg1906zoT4SkYbOixMfuttkJ0L2qRG2hh0TOoTtjDfsUT4GE4bCgqohkW5yU6qFQcagoCsxa+K1KgiXFXrpT5qT3fyQqgwqKoowiGdURDRCjPnMkiUg0gpwxCScHWBjroKmgnJiTIVoZRZUFL7QmqC3ysqRApinusKc1jsChIqQTz1r0p00s22FddQhFElg2vNiZJJYBcIlGeUjj769awvKyIH19fTz33HMsX748v5B9OsXbQ2lVu3fvpq+vj0WLFv3VpvuecbIx9GD+93//l7vvvpvLL7+c8vJylixZQmFhIU899RR1dXXAmbH4ms/IxkuVbAghKPILWuwQXe+8g7Ivf46+a99EsnpwZU5V84NFxCPRP9gRddxwE5UPP4jIZnGERPO7P4SjewjqEqoiYzpixDFct5FsTS39F19J4S8fA1kmtmELxY/9N44Q9F96JVZw2Fkka4lxq00v/uODrHvz1SgpN//S03ySCzYvpfmeL9B787uwbVdQZikaNkpeT+JRFMAlF46qse0r/87mD7hRDEeWab3sNWiLFuKT3AKDWcshpAmcPMlwCYbD+NUvWQg8ssCrShR6ZEp8MiFdwae+NCap87myMl/OVUN20h0dHezYsYOysrIzotk4E9GQs3j546U4Ts3GjWo+UepXXTMPn6Ar5RKNgCYTz9kENIWBbI6YAQUa9OWAdAIGoxBxR6as9Qiyz0dVLk52aR2B7gy9Vcvy7SeqagkZWWLS+Pcw67gLW4btUBZQaRpThM1yoCKkcniCiuTj2urqRpJ82JKbfSDhsLjIg0+V6MtY9GVGT/57PUGKLOhJz91qNmbLrO4+zN5QBfU9J4jXLKIrPXuSMRKJWBLNyJJTp08NKw1oRAMqbQmT5vjsr6NH8rDZOEZ3WuZocfWsNRlLij10pyyOzYBkLIjo5LJZ2pNDycmujkUSo0nGSMiS4OreF2kSXnIIIok+DvsL6G8dYElFmP60yfHeDI7jEPRIFAc02geytAxkqS30sKI8gO04JLImB0ZUahfAFfVFfPiSWnR1OFuloKCA9evXs3v3bgYGBqitrT3tegpJkigvL0eWZV544QU2b978kph3zBZnnGwMPZTLLruMr33ta+zatYvnnnuOH/7whxw+fJiGhgb+9V//FeC0pCSNxXxGNk5X7u58DCAeBQIa9Lz5ZiKP/TeOLFN+37AFsBkppOv2OzEdkfc/t31+Wm59Pwu+/kUsf4DUmrVUNB/GcgRGeSWEhomD6Qh02a2J0Xr7hyj+yX/hSBIl//VtAORshhcatpIeQTYcx01lGikajK3dSM+Fl1L05G/cmKgk4cgy1qJFyDkDU9HJmCDGuJOYtkARMJRa23HRq+hrWEPBvt3YHg8dH/oYhu3ex6DmRjLcFK7JSQZA1CdRHVbwKOOtL18qmK+ifrOZrM9029LSUgKBADt27CAcnr4Q18j25xqlmA1eDsK7s5h/vBTHqb+EZsO2bbLZLIv9Nvv63AmgYmXpSCjI2HTEHRTbxJQUpHQSZD/9aIRiPcRCRSxsP05VkY+emiUMTeUykSIkx8YeUS9JD3ghNX4ynDEdSnwq7UmDeM5ClcS46EZv2pxZdCMQYXFLI33eEAvSvcSXLidjOWSsiSf/tgMRn0xv2pxzUXDJMokEdVYR4+TSlXNsZTRSqodVfYfZWTJxaq8kYHGRFyEE7Ulz0lSp6VAdUlFliTalgWI7i9k9M7IyRDJ60taMKrhXR3TkPKEYNAQKaSiy4OQkNU9KAyp2Lk1nSRXB/m7U4gqK25o5mhKsOfkCh6rqXIG741AR1omls8QzNqZlsLoyhK66BjDHulN0JYbvT9grc9dF1QRTLaxbt2TCY+u6zvr16zl06BDPP//8qCKxM8FcnRaDwSCLFi36qyQa8BdMoyosLOS6667juuuuy3/205/+lD179kyrs5hP6Lo+L9a3L+U0qiEUeSGZC9D8pa+z9A1X4sgSIBCmQWZJHV2334mDIKjL+XSqjhtuouYrn0ft72XFrW4RK5FO0XvLu+n85GhbWq8mkU3bZJbWMbDllQR2bUcIwDAYOO9C0kvHuzVIrols/v8+RdD2wIMUbVgOjoOUzeBoGjXvfCuN93+D7GvfMOG1ObiCt/YRVTsPfuM/2XD1BXRcdjXJ8mqCqnvE5HQkw3Eo9slUhxW80/iuv9RxJtyopoPf76e2tpampib279/P8uXLp53gn243qpH7/LV23mdx+vFSGqfm243KcRwMwyCTyZDJZMhms6N+H3o3PB4PuqZjWGH8iiBhqSgSFCuC1pygpL+LtsJy+oWHJU0HyaoeggM9+HWbxJqV9AORluP0l7u59dlgmAIVenLD5zmQtQloEonceMKQ/f/svXd0ZHl17/v5nVy5VMpZ3VIHdbc6h0kwgxcDZnw9+A3YHq7NgBkMw8Nes94YLg7A9eXZ42tjHB74OWFgzDLhPowNF4+xAXvAeELn3NOtzpK6W1lVpQqnTnp/HFW1Qil2q8OMvmtpLVXVOb9z6tQ5v/377r2/ezt+lauC41EXVuiZtnh2PGiIqHTPowOIZ8ZojCg4sQiDes2sfZwmYyzv0FGpLyhyMh2tcQ0tYzKyei1VF7u5vOgRZsdoy2qMdJ68ej39LKxJtCUCjOYd+rNLi55osqAlppEpuBNNGB1AMCIbNOV76TVmbyOgSII1VQGGc/aCSEZDREGbli6V0FxChk5vuvz+1SGViK5wcTRHlSWXITEAACAASURBVAqxgMKZtdto6j2LFArSYSf5Xn0X4UySJmeUq9Ea+pImNQHBuuYQkiyTLzgcuJwuNZQUwM7mKB9/pINYQCWbzXL69Nw2QZIk1q9fz7Vr13j11VfnbK8wHUuxbcV97mZbdUcIxIt4+9vfTj6f55lnnuFLX/oSjuMsu9dI13XGxxdW4m0uLEeubBE3i2woElQEYKRrG+N77id4cC+epoFncO3XPl7aruBCUIWsBVI4xLU/+QtqP/Y0FEVsqsrYu94zY/zJpQXPfu45tt2/ATHuV35QkqOs+6XHGH3oLQy856nSdqbjC8VVWcJ2/a6foraO9K77iOx70f/+loWnqsS/90+kHnwYJ1reQx5QFAQWiYCC58F4dSNH/uZ/4bW1EVAXQDKAuOZhDV1ibcuNl7G7VSjeG8WFw+QmZMvRQXyx24L/fDQ2NuJ5Hvv27WPLli0Yxsw87SJuRTWqyce5myfxFdxa3A47tRTNhm3bU0jEZDJhT8zlmqah6zqGYWAYBpWVlT650PUZ38m8muH8tSQZT6Hx0hmGErVImkFKC9HQd56miI4WCzDSsQG5YGJOPt9wZMpYeU9MqOOuI6jIZclGzvZK/T3GC27ZzuOjeZugKpEtE92QBKyvCpCPygzLtbSeP83Z+vZ5r2URqYJDRBOkCwuzwVVBheqwwmjewQyEARhqaacmbzFg3Zx5JmeE2Hj5OAdqOmjSXGKJCFfHZxd8z4e4LhGkQAa91CtlMhwP6lpquXo1gyNPXTZOJRkL0WSoqIpET7JAMV2qPqJhqBIXR/OMlnH+VoVU4obC+ZEcUUMhEVQZyEBdRGJ1QuWStAbPg7q+c9zDGD1OgX49yqbLxzE2d5EUgiupAlcnfTdDlfjQA828fUvdlGMtxo7U1dVhWRZnz56lr69vRjuGcrgRbeHdjNtGNvr6+vjBD37A1q1bMQyDQCBATU0Nvb29vPrqq8DSwk2Lxc1q6nc3RDYA4jqkTbj2zG+y+r3vRFgFCs1tpN70lslHRFdkVNlDlmTGfuoxKv76z9BPnQRJkH7rT1NYNXOydjxBQIWcBU4kRnLPG6j49+/iSRLBU8cReOQfeCMRQyDhZ0n5z7TqGx0P0qZffeTi73+Wrp/Y4Q/seYhCgfi//ysDp0+S2XXvjGMrEuiKoDmqMZxzUSU/XcrascsnGdbcJKMy4KdLCdvk7MDNafJ4M1FMZ5i+aMjn86RSKQ4dOoSqqhiGgWVZDA8Ps2mTH7a/E8hGcbJsaWkhGo1y4MCBsp1Slzr+5GMs9z4reP1gKXaqp6eHJ554gv7+foQQfOADH+Dpp5+eso3neTz99NM8//zzBINBvvSlL7F9+/ZZz2M62Sg3H5imyfDwMCMjI8iyjCzLJRKh6zrRaJSamhp0XUdRlIU/X7kcynPPse3b3+HQRz5DwHXBdVl75RxKZhy5uRlFdRhpX0fWsTHGRsjHE1QM9DEU8hfb6VglEQXSE2Qib3tUGDIjk3QSY6YzazqUNVFx0HQ86kMqPempc7TtQfNwL6ejDVPeb4yoxAKKP+bEInl41VoqhViwFsN2oTaskB6Z2y4EFcGqhM5IGf0HkkRdIshAf678zouEAMKRAJvzg1wMNpJaAskQQFNURZEEV8dt8mjMVbx9GJU9hUFeDPjNehUJ1lQFGVkgyaiPqBiKVKpOBn46VFCTuThaXgtSGVSoCKg+ydBlqkIq54Zz1IRUajWboYzAw6ZSBeXyJa42thNOjXDvpX0Mpi1SepAjg3nyqk5R09lRHeBTP7WWhnh5Z9diyYCu6zQ0NDA0NMTY2BidnZ1z7r9UbeGtSNdcTty2pn69vb184AMfoKKigtbWVjo6OlBVlaNHj/LTP+2XVr0Vi4DXQzWqqWNBdRDy9+3B2rQZ46Uf47S2suqP/JQoV9VI/eozeMEIjuP4AmwB/f/tv9P2rp8GzyP07//C2m3tIEn0/j+fJ3v/9bK0AUVCkVwEgmu//xkq7vlXEBLCsUFVkTyP8PP/m9Qjb5/RmEIS118U2jooVNWgDV3v1yByOaq//lyJbAg8gppAEgLL9XUjAVWlWXVxXG+iZvncJCNhCJpjKiHNv9duQAt4Qyh6IculNDiOgxBiigcyGAySSCQwDIOjR4+ybdu20mRkmiajo6Ps27dvUdG25SQbnueVzq+yspIdO3Zw5MgR6urqaGlpmTHWrSIOK2RjBeVwI3ZKURQ+85nPsH37dtLpNDt27ODhhx9mw4YNpW3++Z//me7ubrq7u3nllVf40Ic+xCuvvDJlnGQyyQ9+8AN6enp4+eWXOXXqFPfccw/PPvsskUikRCKKJKiiogLP86iurr65KV6ahnz6NNJ4it0/+AeGW9cg79hBqOciufZ2MtV1aOkxhG3hKSoBQyEPpBtakYXvFQeQpakps+Ua3oW18mQja7lUhxQGMjbjlouEhzut6d1wvJpgNkPWCBHMZeioC5OUZo7nCb/Yh8gtrC8SwKjpUafbXDNnLpkkAWsqdXK2x/B0kjF5jPzUSl1LgSz8vhyW4zEUWEdcdlHG7BmRnrmgy4LmmMZ4KVVq4ehv7aDj5Cn0fIbhygbOL2ABXBP0G/9endRDJCrbaJLLQAbIzCRKFYZCVVjl/EieqKFQE9Y4P5KnKqiypirAhZE8HioVARlDkelLFQjUNdF19D8ByFVWk1rXyDknQD7voNgWj+1o4IMPrpq4D2fHYsmA67ooisLmzZu5fPkye/fuZcuWLVPK407ffil26m4vZHLLyUbxR9yzZw/5fJ5kMsnevXs5cuQI586d48knn+R973sfcOvIxs2IbNwNaVRFGArkCoKx/+tj1L34H4S+/y/wb98H18ULBkn96jOAXxe72Ck2e98bcWJx5OQYkmmClwdZxuzcODGqR1AVOMgYQkaVBMn6ZsYefQfxf/z/wAHPdan+w99l/P4HfbIxDa4nMBSvVGHq3Oe/TufPvKn0uafrXPvQMxgK2IUcih7E8fxro8l+nw3LEziegix7CGw8z6NcSfIKQ9AyiWQsJzzPo1Ao4DgO/f39UwiFaZp4noeiKFMWD/F4vEQsFuLRmD45NjU1lYTZyWRyQeLsxZKNG+mbUSzpd+rUKY4ePcqmTZumfM8byWtd7D4rZGMF03Ejdqq+vp76et/7G4lE6OzspK+vbwrZ+Na3vsUTTzyBEIJ77rmHsbExrl69WtoPYHx8nBMnTtDc3MyDDz6IEILPfOYzhMPhWc97aGjopl2DEmQZ6+d/Hq2vj40v/xsvvfO/UjAC5BqbySdqiF44Q6qjk3hmjFElRsqIouJhSTJxXSotwNMFl7AqMT6x+M/ZHonp0Y28S0CRyJVZPTsTb5nO9a7lk2HrBs0DVxDjgzgtrSSl2UuEjlsurXGt1OF8QXA8QlaOjHp9Edka11BlQbJM+td0ePiVs5ZCNlRJsKpCI2t5jEyqZjXmSHRV6RwamD+yUBmQSQT8dLRyqVLzIaBIvuZlfSt7Rxwsee7Fb1yxMRSJwfz1OTlhSEQNhctJ8MpcsqDsUR81uJQsEHVkasMqF0byVAQU1lYFuDCaZzRvEzdksPIMZ0GXXTbW+M0OzY41nDcqcQUgJDb2nOO9f/ZxunpPceVjH0N66Lfn/Z5LcaQV9RStra1Eo1EOHjzIunXrqKqqKrv969EpdlvSqGzbpq+vj1AoRCgU4uGHH+bhhx++Hafyuots+OOBKnmYb3iI5IeeJvLc50GSwPNIPfWreBP5tX58wvW1GEJw+Qtfp/UX/w+E6+IBqbf+F7RLF8HMIVpbcDyBJnt4SLgINMnh6u/+EfFvfcNPhXJdkGWEbZP40l8x8t4PzDg3TZlEcDZvJ7tuI8HTJ/BkmexPPIzcuQ7HE0hqAOHZ6IqCg8Byr08OnudheVAdNkjmCtiTenlUTEQywjeRZDiOUza9qZgbLYQokdp8Po+u68TjcXRd9/ub3KBeYLZ7I5FIEAwGOXnyJG1tbVMWMrONsxyVq2YbW5IkNm7cSG9vb8kbFAwGlzR+cZ/X4yS+guXBzbBTFy9e5NChQ+zZs2fK+319fTQ3N5deNzU10dfXN+UZbWxs5BOf+AQAx44d48c//vGcRAOWz+nl7tyJ19iIPjxMzcs/ZLx5FSNd24ld6CbX2ARAIVYBlosLRHWZUdMlY7lTohuqLMGkSMP0IIY3sW85spGxXGqCCgNZGzdf8MedpB8IWSaVQYVkKE6uTN+O6UgVXKK6TGqB/S4sPUDLtW5OVa2iZvQaVQGZEbcBc4Gds4FSB/bhBZa/1WVBW1wnZToMzRKFGDI9tvWe5FDThhmfCaA5piEJuDZuk1mCY7VCg5DiMWZ5XBm3QY+wIdDPkUJ5slEbUgnrMhfHTIo676qgQtSQuThqMmrOXG8FJJemiiDnR/KYjktdWOPSqEnckFlXHeTCSI6U6RDVJKIBlZ4xE4FCZ3UASRI4rse5kTxOqAY8j0hymI8++0Gae8+WjjHwwANcOXaMjRs3zunAWywZmG5DiuVxjx49ytjYGO3t7VNs2Y3YtrtZW3hbyMb58+f5yZ/8SbZv387atWt59tlnsW279IPdSuP/eqpGNRm65FJwJVL/59NEvvw3CDOPp2oU1m/A+Pfv41ZWUdi8FUMRpdSi3K57seob0c+eRghB5HvPE/unfyR37wP0f/UfMGRwuP4Qq5JEIRYnu2MPwf0v+7VubZvgKy9iru7wBefTmmE5U9KqoP8vvkjbm+/F03SufuQTCECXPbKFPGhBzAmSUbw+06+SJktkLIe4LmiJL55kLKRiiyRJpSiEYRil3GjDMKY0+9q3bx+tra2LOv5CUZyEpi80JEkqdfROp9OsWbPmppTJvZlN+pqamohEIhw6dIi1a9dSXV29JO/PUj1Gd/MEvoLlw43aqfHxcd7xjnfwJ3/yJ0Sj0Tm3nQ/LUY1qUVAUrJ//efTTp2l7+d/Z99BbEa5LoaUVU1LRsMjbaqnJX7rgkwzLhZh+PXqRKrhThNxZyyWuy4xNWvAn8w6GLMiXCUm7E+I+MxCm4dxpelr88q+tqQFEUxPJRJTYSD8LKfnien437IWSDQArGmP3wGl6VnUysoQceg9oygwzLM1dvSikSsSFia0GGSxTEng6hjs3s/bsGc7U+r2zDNeiqSpM2nQZyCw+kiLwqFRtZFkh7ciY05ZI2Xgta86doztyXVxdP3qNcCHLhbrVpehNIqBQEZC5MGqW1ciENYmGmMGF4RzDyXHiiqA3BRFdYl11kIujec4O5wipgsqQxuUxk3TBZG1VgExqDESQ7qEc9gThC6gSH95Tz8bWbf56YwKptWvpePBB+vv7SwVK5kpzulFHmq7r7Nixg+7u7lJj22IzvltV/OROw20hG2vXruXFF1/kYx/7GJcuXQL8ift2eBg1TcM0F1/WbjrupjQq8CMbAg+3ugZz170Evv9dEIKqZz6MME3s1jau/HAfijypeQVw7Xc+Q+vj/wUkCck08VSV8Q99GEWAw9TfTygqnm1z6blv0Lmx6fr7tkXiK8+R27KdscefmHFuUc3Pq7VcyHZ0MvqeX0YdGkRatxbXExRcgaKH/GvieXPk3XpUBiSaohoRvfy9NVlomc/nGR8fJ51Oc/jw4ZJxLwqvi39VVVWllKc7xSs+eSISQkw5L0VR2LZtG93d3Rw6dIjNmzeX7Xi83JqNubaPxWKlTqnJZJJIJHJLiMNrwWO0guXBjdgpy7J4xzvewS/8wi/w2GOPzfi8sbGRnp6e0uve3t45K9nczg7iRbg7d2K9971ImzYRqYyiuZA0JQwcnIkGr8VUJ8fzG8SO5l1ytq+uKM7ThjK1atR00+bi5+zny+Tyj1suNU6eATlAtrIaPZehPdVPsqOzdIBkopZaVdC/gEV60nRpjqozyulOhiygMazi5nNYzU0I1W8Uu1SMRCup7L3GcLx6xmcxXaY+rDKcs8l5gfJCwzLwJAmnuZmOi+eQHYsrqzcsMVVK0BjRSBUcxgtysVjUzOMBkdUtGH1JYslhYlaO840dDEz0TqkwZBJBhYtjJqP5mb9DSBXURzR6UhbjpkNDVOfSmIcmHFbFFK5mXc4O5wiqgsaYweXRPJkxk/aEga5IuB4M2BpXBn3BvSYLntxZz9ZGPyuj8PLLaJOiiad+7/fokiSam5sJh8McPHiQzs5OEonEzO92kxxpkiSxbt26EsHp6uoiGo2upFHdatTV1fHcc8+VXt+uC/l6TKPyxwRN8jBdwfAf/CmNezb5qU75PJ6qknz6I8C0VCog88CDWO1rUM91I5wcIp+j5vHHsNau5+p/HJh2DAnJLeDGYphtq9Evnr/+oetQ/VefpfALv4iQZUDgen75XIGH4zioki8aH/7U72M5Hrj+PTJbFKMIWXhEdQir4Ll+itNQOj9DgO04DpIkTRFeBwIBDMNgw4YNqKr6mlmECiFYu3YtV65cYd++fWzdunWGZ+d2kg3wn8WdO3fS3d3NuXPnFi1yXUmjWsHNxlLslOd5PPnkk3R2dvLMM8+U3ebRRx/lc5/7HI8//jivvPIKsVhszjTHhdqp5bRDKAr2z/m9lpoLLicHC8WDYnkyQVWQtTyimkSq4DJecJGAggMVhsRI3icYKXOqLmPcconpMsnJ0Q3TQZNFWRE5Y2NQGUArmGzqP8u1XffP2MRyKdsAsBzyDhiT0neLqDBkqoIqOdvFdAHdT/FM1zVRn89y1VuaYNeTZVrMMYa5TjYSAYWaoC8eH1gASZqOsOQQyYzgxgOcCLfPIHDzodKQ0LwCGTSujC+MpBghg/srUxyzw5wL+ymBFekRKkMKF/LhKdGqIoKKoDGm05MskDId6iMqF0dNAopgfXWQCyNZLqcdNAGtFQa9YyYXR/KsThgEVJ9kXBjNk7NcQKBIgndtruGB1dMiRVu24K5ejXT+PO62bRSqqkr2p6KiYs4CJUuJbMw1LxQb2x49epTm5mYikcgtSRG+03Bb+2wUPTC38yK+3krfToYuu5iuhNPcwugnfoeK//vjiIlrEfjuP2H84F8Z+/VPEmhsImv55ERRJK599q9pfuQhfxBFwVNV8rvvRb56Bad+aulBBRdZgYEvf53mN+ya8plTU4cjFGTPJxWqAIQHCGSh4AHZggNIeNI8UQzPAytDIT1MLj3KtUJhivC6+JdIJErkolzeZi6XY3R0tBTyfK2hoaGBYDDIwYMHZ5SeXe40qoVsXyRFruty5coVGhoaiEQi8+5XPKcVsrGCm43F2qn//M//5Mtf/jJdXV1s3boVgGeffZbLl/22bk899RSPPPIIzz//PB0dHQSDQb74xS/OOeZC7dSyko1JCGsSQVWgShKpAqheAVkYTHYB2S7EdIkx0yVvU4pueEBQnSoCnz4zuB5USC79zsw5I1XbSOvZE7gtq0nV1qB7DqaYOpcXHI+aoELfAhbOluvRYGc4TxDFc2mSCsjxOOOWS2oW4bcWDUNy6RkRw+3rSFwdQiuYxDpaGMp7SyIZcdVFU2RStspovZ+iu8ktcCw1/75FPYcABrM2Oea3ebLwS9iajstIzmEkVElNq8W6vS8ybMuc6+hirIxw3FAEzTGd3lSBZN6mLqxyacxEyzusrw7SkzTpHs4he77ovidpcmEkz6oKjbCm4AKXx0zGCz6BkQRsDWb44FtnLxdtHz+O/La34Xzxi3iXL5ctUHLy5EmOHz/Ohg0bSuuBpWg25qsUFQqF2L17NydOnKC/v3/OHlOzHeNut1O3lWxMvni3KyftZkU2ljN8vVxjK8KbiFpIjP/iL5H47d8A1wUzT/B//wNCkhn7rd9GlgQh1cNGwvNA2r0bpyKBPDoCto2wbcJf+zLqhXMMfPOfS+PLnkU0EsYDMms2lCIiAF4gQOqTn0KT/f4cLpJfFHHCVhUNpiYLbNfDnsV+OlYeN5dE9UwCukZFZQyjsRZN05Z0P90KQ327EY/H2bFjB4cPH6apqYmmJj/FbTHffSmRjcVMlsUc92PHjtHW1kZDQ8M8eywtjWqlGtUK5sNi7dQDDzww77MkhODP/uzPFnwOd0Ia1XQ0RxW6J3pPCPxqU4YCGcsjrArGLa+UQmU6XimtCvz0JV0WmBORi3TBLUVEikjlLLRCgULwuihexaO19wJWQzMpI4inqkQ0gVlGD5A0XSoMeWbPizLIuILN5w8zsnYDuWB0pnJ9GtKF62L1pSBsm6wavsix9bsYzC/e5tSGFSQhGM07TM9SSnsSNSN9DCTKR8oMRdAYUUkXXIYWeP5BRVAVUhjJ2VydROCqgwqqJ9G/5wGUY0dxlKmLbt1zaKkK0ZcqMJpzqAv7kQxVcmhP6PRnbLqHcygC2ioMekZzXBgr0BLTUV0LM5elN6+TzDsIfJLx0OoYP7e5lpdeemnuk5YknH/5FwDcixdnLVDS09NTivYbhrEsUXsAWZbp6uri1Vdf5dq1a6xatapUEGU+vBbIxm09+0wmU/q/3I/luu6yL/5erwJxf1zQKZAZ7ceoCJN/x8+CEIiJY1rbt1PxvX8iPHyNsKEQNyRUWcJBYvC5r08dy7aRhgYx/u17CDwCioeq6QhJQpEE8YDEyP/6x9KBze27yW7dheP5BMbzvCl/RUiSVKYutoeTHSNCmvYqnTXNNbS1NFNbW0ssFrvhCk+vldSpuVD07AwNDXHq1KnSAuV2plFNhuu6GIbB7t276e/vn3KOc+2zEtlYwc3GnWKnbnsa1TTEDBlVFgQkF0vSCCoCTfafJVnyr1PB8aMbAJOKAuIBYW1qNELOTW145+gGlcP9pdcNhQx14SDjazdiVtVSkRkFIF3wiOvlNRTqhD2bC00DPVQYGqK5BSs4d7WvyQiqi9dtyJ5DtWIhRaMM73mAttDC9xVAQ0SlNqyQNN1ZSZQjK9Qpjl/9cRIqAzIdCR1N9pv4levWPh2VAZnmqIrtefSlLXK2hyT8ZomVAYXhnMO1vIcnKwQb6pBtfy2lmVnWnTmMYhUYzvmRjJGcRU+yQEelgaFJXBgrkLdcVlUYKLLE+ZE8YclhU22AsKGQ9mSumArJvI0AdjVF+Nzb1/DzW+qWZKPL7SOEoKWlhXXr1nHgwAFGR0dvavGTcscr9sE5dOgQg4ODizrG3bw2uW1W1rIsPvGJT/CXf/mXfO973+PEiRMlxlecVG/FxX29CsSL0CSX4auXMGSXzF98wX/TccCyUA8fIvSxjyB3nwVJBkmhOL8W7nsD7mRW7jhop04Q+tH30WXwhIxjW+SzWRzbRkgKufpaUhOC8OGPf2oGsZgNE3YLTfKoDHi0RMFOXUOZTb22ggVBlmW2bNmCpmkcPHhw0ZGNG+mzsdDxFUUpeZz2798/57O61K7jd/MEvoLlxZ1ip1RVveMiG+BHN5j47ors6zF0GcYLHiHVfz9v+4q/nO2ViAf4lalU6fp1SykG0Wu9U8ZP1jURGx2kfbAPN141JeCQS9RhyBNV+DyBXOYnyNoe9eHyKS5RTaIlopNraccKRRivayLR37fg754quETlhdkgATRHVRIhA0sJlK6Zpeg0RudOwSku7qtCCiN5h6Q5/+872rKarr5TCNehNdlPa0wjZ3tcSVvzNgCUxASpCSmMmQ5Xxi0cz49uNEc1AorE1XF7BtlJ1jSwq+c460/tR7cKDFbV03i5m2Q6z6UJcXdYlzk/apKdEOYbqsy5kTwxQ2FzXZCQ4jGUtXh1IMtQxicZ6yoN/mvNMI+uUudtyLdUFHUcp0+fZnBwcNkdaYFAgF27dnHp0iW6u7vntb0r1ahuAJIkcfXqVX7wgx8wNDREMpnk/vvvp7a2lpqaGurr62loaKC6upo3v/nNy3YeN1MgfjeSDZi4kXFBNci/6xcxvvJl/4O8iReLov/d3+J0rMFtakaRBUXd1+hvforKj39k8kAE//aLjP/33yWTTyNLEnoggKr6uaDBYAjz9/6AoS3bMLftnO+skABF8tAkj7jmoU+6W+/2B+9OgRCC9vZ2+vv7OXLkCJlMhlBofnfbUrqsLpWcCCFYtWoV0WiU/fv3z9CaTMZS06hW7qcVlMOdYqckSVqQDbiVkQ2AREBGAIpbIF3QMBS/3LjpFImEh+l4RHVB0vSmFFdyPUhcOMNg65rSe5rrgOdSMdRPpKEOZTiJkk4ytH7zjGN7QhBSJfKOjel4JAJK2TKx46ZDSDhkPN9TJuPRFNXJFPyUr8lQK2dWJ5oLldeukKpunnOb2qCCLEuky0QSPEDL54glkyRjUwtiiOJ52u6cncnLQcYjqsKagUv0NnfAAtKlFOHSENUZyzv0T6oElgjIBBWZaxmLvnT5LJCgIqgMyIx2babx1WNELpzgYOduktFKOi6dYqihjfOjAjxoiWuM5BwuJS1qQgqrE0FsF0ZzNlcKKhR8ktFaYfD0/Y0ENYVCoYEjR44wPj5OW1vbsszXxWj//v37S8dZiM1aim2TJAlN09ixYwdnz57lwIEDbN68eVat6GshAn/bzl6WZb761a9y5MgRvvnNbxKLxQgGg6xevZru7m6+8pWv8OlPf5pPf/rTy3oer2eBeHFs8D0vEi6Z3/+jSZ96iGQS/RtfR6T9yuWO65VIVeapX8HT9Otb6wbjH/1NJFUmFosSjsZQVW0iTQokSUFUVpJ+8oPTzsJDFi6G7BJRHSp0m2rDoSrgENddgupUolHa63Wgr7hVqK2tJRAIcPjw4QV1Ib4VaVTTJ9fKykq2b9/O6dOnuXTp0srvv4Jlx51ipxaKWx3ZAKjW3dJCQlck0qaLJvnpTYYy4cGfWCtnLY/Y0NXSvsm6ZtSsb1sqr1wm7rl0DPSht66moAbJNq8ivb6LgFz+Wc9YHhWGTyLSpkdInbmkcRHE+3xxfu3lc9Tm0owXylczzAQi6Cxch5FsWU3lQPloSFSTaI1pmC5TSv1OR8EIUicKKNZEqfV8jmbNIaR4DObsJCyZDQAAIABJREFUOfedDlUSNEVVoobCYNcOEqH5/ckVhkxj2He49KUtMpavs6kPq9QEVcbyLlfGrbJVeBMBmcaoguV5mK5HuDpB77Z7yO/exfbT+6hMDnK2YzOjRpjWwcvEdImLYwVUSbChSqMyqJIybU4P5ehL+d+/LqzyB4+08xtvaiWo+edfXJhns1mOHz++bPe4LMs0NDRgGAb79u0jn8/Pu8+NFEsRQrBmzRpaWlrYt28fyWRy1n3udrJxWwXiAN///vf50Ic+xGOPPcaLL77I5z//+VLJy2I++XLibhCI32yyYdt2qbdENpsll8tx4sQJAuEYjW1rsO57APXFH1/fwXWJvfUhRi73I8sKhmJTcH3PS+qz/y+xDz7pb6dr5J/8Zf+hEMUytTOPH5D9SIoi+SJ1+e5+hl4zkGW5VBIwk8nMKAk4GctNNmbbvhh+PnXqFMcW0A12BSu4GbjddmqhuNWRDYAKzaNPSAQUQdp0USW/uVrBdAnIfknZnO0Rv3qZsfoWpFQaqnzxsifLtPSew6prxuxYRxYwrvXhUwFR2kZTIOeUt6+2A8pEOyh/QXN93yKylbV0dB9ltGvnvFQiEQpwtUyPj9kQt3IMT3qtSYKGqEZygSlPAOmmVtafOEym4DKyag2DnjqzRNcc0GRBbUghbfpVoooYXb2WzqOHONWyccr2AqiLKHieX4nKX+cLdElQE1ZJmg79szQDFEBdWMHxPEZyDgFFJaLJDGYddBma3Bxjjo3UuY4xL0rb2WOkIxVcqGkhnkyysaUax/XIWg49aWsiswIqAgoPBgf5yTfuKTv3S5LEhg0buHz5Mvv27Vu29ZbneVRVVREKhThw4MCckXRYemRjMmpqagiHwxw5cqRUtGV6Od67nWzc1rN//vnned/73sdHP/pRPvvZz/KBD3yAd77znQwPD5d+8De84Q3Leg6vtT4bnudhmibJZJKBgQEuX77MmTNnOHr0KPv27WPv3r0cOXKEixcvMjY2VmoA19bWRnNjPXgeqb/92tRBXRcKJvrf+uUZFVmgSh66KmE+/m6cpmaQJLL/7TeRwhEQUimaMe3sUDEJqw4h1UOXV4jGnQZd19m5cyepVIoTJ07MOqEvp4huvu1lWWbTpk1UVFSwd+9estnsgsddwQoWizvBTi0UtyOyIUmCgJVEkwUeEBAO6ZyFks+SzloYQwMAOKZvZ1Pt64j1XqC29zxNtonZuRkmlQ7N1zUS0aY++zkbIrNIGywXFMsXl+ddQWQanai0cyRUGblx7nSnItKF8hGS2TDavo6KoasI16U5KBE3FMbyzhzNZmeiJjOK1daOGjQw9fKdrcvBUISvfVAEwzmHQpnwg7lmHdH0CACaXaAlKIhpMJCxS2lnEU2QkP36xH1pq6x4XJN83UlEl0iaNrosocnC13S4Hk0RFQ/oU8NkKirRM2m2KFliqsDUDDYf/jFN3ccx+65ydqTA5ZQNnkdYk/mNh1r4n29rJ6bOnZ4rhKC1tZX29nay2Syp1AJq/C4SRduWSCRKkfRi2eq5tl8oZrNtwWCQ3bt3MzY2xrFjx3AcZ9597ibc1sjGU089xR//8R/zjne8A8dx+OAHP8grr7zC7/zO7/DpT38aRVGWXRijKMqUH3WpuFWaDcdxpnS8ntykzrIshBBomjalUV00GsUwDDRNm3HDuq7L1atXS3n6Fi7UVuO2tiFdunjduVIoIG1Yjy4DioywXBxPIDyX1N9+hdiT7yH/yx8CIcpGMyQcVAqLcdas4DZBkiQ2bdrEpUuX2L9/P1u3bp2RS3orBOLzbd/c3Ew0GuXQoUOsXbt2wWOvYAWLwZ1gp2BheqTbEdkQQLjvPGNNIYz0OOOhMHImQ+RCNyPb7yHcewkznqBiPEXN2RNYkRiKa5Ne30XRzacGLSaXfsgUXHBtkK4zDNP2EJ6LJ8pEMvUwYclj3HJx8za6lcUygjRdPsv4hi1YQmDFKoiPDTEWis/cfxqimkxmoelLQtA0dIVQZQVpV2P2drMzEdclJCFIyn6UTG8RC2pGGFQlKgMyY3mH4dzc6xcrEGTtwFFGLp7h6rrN9OUn5nLPpTasIRATJXxnEdLrEjFdZjBrM245RDSZ/oxLb9qiJqgQ02EgazNuuSjCL4ebMh0G1mwkMnCV+two2vAAyUCUs2u3+WlEnocuC566p5ENtYsoyTWBRCJBKBTi+PHjtLe3U1tbu+gxZsPkZ7kYST9x4gSpVIoNGzaUXUMt1pE227NcLI/b09PD3r172bJlC8FgsHROd7O28LaSjW9+85vs3OkLhWVZxnVdvvCFL3DvvfeSy+WIRCLLPonfrMl5oQK+ueB5HrZtzyASuVyOdDrN3r17kSSpRCJ0XScSiVBdXY1hGCiKsuhrNX17VVgINUD+n/6Z4KZO/7wAZ+cu3Hvu9V95oCsC23FxXQ935x5Gj7xalmi4roMmOTe9ctTtMKp3C27G8yKEoK2tjVAoxL59+9i8efOU5nq3Q7NRDrFYjF27dnHkyBFM03xNVO1YwZ2FO8FOwcI0arcjsqEeO8aaj/8Wg/e/mf77HiK/dTfxw/tJru9CGxnCUGTa+i6S3X0f3vAghUiUvKqhDFzBrvH75+SNMLKVw5Enmp0JiZCmMCWTR5KJ63KpV8d0KJLv/LJDYaq6T0BlFeMbt07dJpGABaQ2jedtAvkMOWP2hbAuCxIBhYIDqT0PEBIu4wsUcgdkQUSXJ9Ksrv+uZjhK+4UzvFrdVna/sCpREfB7h8xHMgDCmiCoyAxv303oWh81yUGuiRoaL50h1bGewezsKag1QQVZ8iMgQdUnHANZm5RwqQup5B2XoYn+JkFFENJkRnJ+tCRhyNSrDvSep1BRibu2noteEE8IVNfhPT/+ezo/9WuEw4snGkCpYe/27dtLwvHVq1fflGdwuiOtSAAuXbrEvn372LJly5SmfEuxbfM1AZzsSFuzxi+gcLfbtdtKNnbu3Dnlhyr+wH/9139d+jHvltDRQha/ruuWohLToxPFVC5VVad0vK6srETXdU6ePMmuXbvmHH+p5z0FnodXyOG1tuLeex/SSy9CIIDz+3+AIstTtldVvzSk6UyQjGlf33Msei+cZm1H+00/7xXMj5tBxqqrqwkEAhw9epSOjg5qampKY9+qalTzoSgefOGFFzh06BBdXV3zTubTcbdP5CtYPtxNdup2OGEK69ZBPE7di//GhXc9SaDvMuHKBIZbQO3vI7vnftRXjwNgVVaj2jk/olFTV+oqDqDqATznesWqnCMRVH1ReREZC0KqmFFFCiBvQ9WVS7imhdyyCjszPmObnCsR0ShbGWoyPEmm8tJ5etd1zfis0lDQFEGm4DFeuH4eBSSiikNqDlGILKAqqJAy3Vn1HMnWdmrOnWdgoiM4+GLzmCEzklsYyYhoEoYiMZJ3yNkOyDJmVTUNQ8cJ9YzRvW5r2f1kAfVhhaztMWba1IRUIrpMf8bGkP00qpGczbUJTUtMl5AlwUjOIWfb1IQUAoqE40HWguEd94NlgyzTkc2w7XP/kzd/7+ukursXIcOfieLzqKpqKdXpyJEjdHV13bCGr1zkoeh8i0QiM3Qcy2Xbio60Y8eOkc/n73rn6m0jGyMjIyQSibIXfdOmTaX/f/SjH7Fnzx50XZ+x3Z0EIQSO4zA+Pj4ltan4v+M4CCGmpDcFg0ESiQSGYaCq6pxi3FsLDzybwt99DWPtarxt23Hvua/suciyhC4E+SkhZw8FGzOfxDLnr+awguXBzfK2hsNhdu3axeHDh0mn06xevXrRpW+Xm5wUI34NDQ3s27ePrq6uKZGY2c5pBSuYC3ebnbrZkQ3P8ygUCmUdZJMjidE3v5n13/gGnX/3V6Tue4j0A28idOwQma7taCND5NdvwnBM8pKGowRQhYfl+mSiqMW2Xd8TnzKvP5euxxRCAiAJAbhMV1AL2yJs5sk2tmLHKgj3XiJf2zDjOxlK+TK005Fe04meSWOGIuhmDlUTGMEIpuNRKMycO1wPgtksWQfswMzO0NUBGdOBsVkiM9e/oEw0qDNo21QMXSFUV8OIF1wQyYjqEposMZp3yNr+9poEcUMmKQwGtu4i3HMJ4dh48vXlnyFDSFjkJY0x06HCUMhaLn1pi5gu0RhRGZhU+rYqKGPaHknTRZ7oAyILX7OTytskS9dHoLo2b/n0x9j0g28D4OzeDdXVYC+dbkwn/52dnTM6gd+MsaejWBHxyJEjNDY20tzcvKy2TdM0tm/fzgsvvMD+/fvZsWMHgcDCNT13Em452Sj+MB/5yEd48MEHuf/++4nH44TD4VIZ2sHBQS5evMjevXv50Y9+xN/8zd+UJvHvfve7PP300ziOw/vf/35+/dd/vexx/v7v/553vvOd7Nu3rxQCv9HzLk660ydc0zSxbRvHcXAcp0Qo4vF4iVjcCNu+XV5Xr7GBwheew928ZfYsVCEhy4DlVwARuKgUkPC48VaJtx4rC9DyUFWVHTt28Oqrr3L06FE0Tbst1ajm2h6grq6OcDjM0aNHWbVqFfX19TftGCt4/eBG7dRyQJZlHMeZ05YsNrLhum5Zm5bP50sl4TVNmxJtj0aj6LqOrutIkoRpmpwWAn3/fmr+5VuM/Oy70XsvkunahnG1F28in97TdITt4QGq7OsSshZost9pHCBngS5T6uVUcHwCkp60uDcdiCSHSMeqS+9FhENwfIx813bCp44wFqsgs74LVYLp0ousBUFVTImYlIOjGzScPIwVjpJa24mHwHTm3icXidHw6jEur1pfei+KjRowSC2A4JSgqmw8uZfzOx5gIe66uCEjS4KxvENmos6wLkHMUBjL2xNExZ/rxptbWb/vFU6t2UZl7wWCmsS16kYKsiAW8KMYmbTfA0PgV6tKF3w9Rk1QIVVwGMo6BBRBS1TDm6j+NZy1mJxFFlQEb2mP0hitIfzIt69fo69+deHXYRaUm7ubm5tLFaQ2bdpELBZb0tjzkYHpOg7HcW5a8ZNyKDqp29raboq++HbhlpON4g3y/ve/n1/7tV/jj/7oj9iyZQtNTU2oqkomk2FwcJC+vj6EEHz0ox8lkfAb7TiOw4c//GG+973v0dTUxK5du3j00UfZsGHDlGOk02n+9E//lD179iz4vNLpNLlcDkVRZky+tm2XhNdFrYRhGMRisdLrdDrNlStXWL9+/fwHu0vguQ7u4+9akNxNljxwHRSskr/pbtVVvFYWoDd7MV0sPdjT08PZs2eJx+cXWhZxM6tRzbd9OBxm9+7dHDt2jLGxMdatW1d2rNdChY8VLA9uxE4tF4qVE+fybE7WDs6mASySieL9PznavhQNoBACV1Gw3vMetM9+lob/+C59P/teAJzGRgqOwFD8NKfgRApU8f+s5U3p/F0kIpMX9VmLGaTBCkTQRodw9QA1vRfIdW2jUNcIQKGxFeE4eLJMWGVGp2uAsJkjK83u/VYlqMyPk9t+DyI5jLeI0ibJ9V00HTvEUE0jcTPNaEMb5gKJhiJBhaGQrq0nG68kOjpMKl456/YVhowQTEnJMmSI6kVdR/noQUhy2Xj4P3h1y70Euk9SYTkMNq0mm7H9NCrLY2iiUlVIgbCuMJS1GcjaxHSZ2qCM6/nRnIGMzWQOljAkfmpNnKhxfXk5/sUvEv6lX7oe1bjB6NtskfVEIsG2bds4cuQIbW1tczqbZsNC7OZkHcfVq1cpFAoLjqYs1S5XV1cvOj34TsJtS6O67777eOmllzh48CDf+MY3OHjwICMjI8TjcTZv3sy73/3uGR1Z9+7dS0dHB6tXrwbg8ccf51vf+tYMsvGJT3yCj33sY3M2Wnr22Wc5cOAAPT09XLp0iYcffpgnnniCt7zlLSXvTU1NDbquL+gHvh3CvOWG5zp4kuz3zPC8CdLh17T1PA88l6JYQ/EcxKKK/a1gOTCZ3C2X5765uZmhoSG6u7sJBoML8iDdisjGZOKgKApbt27lwoUL7N+/ny1btszwOi+2otYKXn9Yip0CeN/73sd3vvMdampqOH78+IzPX3jhBd7+9rezatUqAB577DE++clPznkuuq5jmmaJbLiuOyPanslkGBsbY+/evYD/HEzXABYdZDerP03RqeS86U3k161Da25GTXkIF3K2IKhej1zkHV8X4Hh+2pQkfBISmpROlbf9aEZRD+EBxvAgVsX1SIaaSRO51ou1ag25zdunnI8djROWXdIFMO3rx5uMrKxjDA+RT1TN+D5VAQXHFWQVPydfqapBdZwZEZLZEHBtgpIgnkky2rJ6YTsBlQEF0/b8NDJJxgsEqL58rizZqAzIuEBqEskIKIKIJjNaimTMRIUhY1sOQ1t2UXX8IFvPHeHIqi603DgtJw4w1LWNq+M+yajQJWRZYihrk7FtqgIyYU3G8Tws16U/M/UYzVGFn2yPoykz51TnZ36GkUCA8a4ucn195HI5XNels7NzyffhbPYhGAyya9cujh49yvj4OO3ti9OMLtT2FHUcV65c4ejRo2zatGlBDrgbcXLdzY7Q2yoQd12X7du3s3379rKfFxdOxQvc19dHc/P1WtlNTU288sorU/Y5ePAgPT09/NRP/dScZONtb3sbjz/+OE1NTdxzzz388Ic/vKHvcrd68eeC59g4zsLyKss9AndK75HXK5YzTUjXderq6jh58uSCPEi3oi9HOVHf6tWriUaj7N+/n40bN04xBiuRjRUsBIu1UwDvfe97+ZVf+RWeeOKJWcd9wxvewHe+8505j33hwoVSnf/+/n6efPJJOjs7efTRR6doAHVdJxAIEIlEyOfzbN++/ZYtTEpzsaLgtbQAUBOGy2N+PMB0AI8Jsbe/KM5YHrbrRzfGCx6m7Tf9Ki6dbdudeO1/h1wwQs2RfYhgEKuxGbuxiUJ9PfJAf9lzcjx/b9fzy9iOmlMXxp6iEh+8yrVJZCOoCsKqQn6aybOQqNAlBrIFmOWaCiCu+wVUspZMcvMOggNXGF3A9YtqfgrUeBkdSHrdJlqOHuRyx0ZwXapUD1vVGJtEMoKKIKxJE+Lx8vY6EZCxHD8CkjAUnEyWwW17CF7rY/vx/+Rw1330btoBtkO1AQXJv2YCl4awgiZJOEC24DA8SXMigA1VOvc1BigUCqTGRmZE0orzrNHSgmGapayQbDa7ZI3FfJpBRVHYtm0bZ86c4fDhw4seezF2oRjlOH78OM3NzTQ1Nc25/evVyXVbycZ8F3yxk6XrujzzzDN86Utfmnfbbdu2TXl9owuzlcXvCu40LOaeXuy963neDA9SR0fHvA2ZFjP+zSInxW6wR44coaGhgebm5lJfnOIx7maP0QqWF0uxU2984xu5ePHiDR/7+eefp6+vj5aWFioqKvjgBz/I7t27Z/WgFlN+b+X9XM72abIgbkCm4JG1IaD4egxF+A36DNmPcuQsX8BccJkqFkci9uoRnHAEI52ksK4T0dpCrnbSQk6S0fNZyrXkLYrNxwseluMh8GakQmU61qNm0jiqRo0ukRPBGUSjiJwNledeZbijc8r7huwv9PO2v81kKXu2poH6k0e42j51nyJ0WRDV/cpUM0Ivk+C0tFF/dD+5xiZGq+v9i4Wf3hRU5y6DmzBkCq4vSk8EZGxPMJx3CETCVF+6wHBtI7mH3kLD4cOIzDjDTasZlKvRhU1zREcID88TjOUt0qWm6h4SHm3yOFXOKN6gx7EReUoErVj4ZrYImuu6xGIxKisrOXDgwIzS6gvBfPe4EIJ169bR29vLqVOnyOVyCxJXL8X2hEIhdu/ezfHjx0mlUqxfv37WeWMp1ateC7ir6FVjYyM9PT2l1729vTQ2NpZep9Npjh8/zkMPPURbWxsvv/wyjz76KPv3759z3KLg70bwWkyjuhl4rTwodypu1vVdappT0YPkeR6HDx/GvoEKIzdyPgsV9SWTSY4fP47jOCuRjRXcVrz00kts2bKFt73tbZw4caLsNh/+8Id59tlneeqpp6irq2PNmjVzpmrclqZ+sxwzEQTb8/UPORs0BZSJdac38Wx7gDIh2sjmHYyrvQCELnSjxyIoVZXkt+3EDYYw65sIXD4/5Ri51tXIsz7C/ri2J6g4eXTGp64RoP7kESodi6wanDcJ2FrTSbznAsJxSJw5QaVjAhLjBZ/clN2nZRVaOjnlPWmi8Z0kpClpUOUQUgSBfBZtzVrCyTEAIn2XqT1znLwDI2U6lQsgEVAIaTJjpouhCHTZ7zAu8FOwTNtjqLENKZ+n9sDLaO2riOsKcqHA6r0/pObMccbHx7mWynMlXSBtTaRLC5d7Ey6Pd+g81NnIjh072L17Nzt27GDjxo20t7fT2NhIIpEgGAzOmyKVSCTYunUrx44dY3BwcM5tJ2Mx1RDr6uoIBoMcPHiQ0dH5Y01LtYWyLLN582YCgQD79+/HNMuXyFlsJcfXip26rZGNxWLXrl10d3dz4cIFGhsb+drXvsZXvvKV0uexWIyhoaHS64ceeog//MM/nLcaVVF4N71L8mKwEtmYiRVv8e3FYiMbi50AJ0cF1q5dy5UrV0ph8Rstz7cctctlWWbTpk309vayb98+2tvbXxOT+AruPmzfvp1Lly4RDod5/vnn+Zmf+Rm6u7vn3EdV1XmdYrfD6TWb7ZOEoCYEQxkPC59YmDYlsXiokCGjhcjbkDjwIq4RRKRTRHJp8ms3YMoygeOHGY9PEt5XThXhe0LCUPwIynQUnOsidKemDlwHJH/xKzyPSs2jcP8bkd2FVfjxhERsPIUYGSTbuXlB+9jhKLUXD9ET8XVtCTuHHYxMKe9bDqok8MwMeRGCej913HAd1h56iXNb95AtM28VSUbedhnLOyQCMo7rkwxDhrgKKcslb/t6S901sQyd/l33Ee65SDCfZdXwCcZCMfpb1uA4MkyUx41qEm/riFEVuvkC5VAoxM6dOzl8+HCp39hCsBjbpmkaXV1dHD58mKampjlTnZayuJ9sC1etWkUkEmH//v1lq2ItNo3qtUI27qpvoCgKn/vc53jrW99KZ2cnP/dzP8fGjRv55Cc/ybe//e35B5gFuq4v6iYvh5XIxkyskI3lR7lFdvH1cpKNcts3NDTQ2dm5YA/SXFiu6lVCCJqbm1m/fj2nTp2iUCis3KcruOWIRqOEw2EAHnnkESzLmuIoKwfDMOa1U7fjXp7rmBFdoMh+GpXlQCg1hDOWgoKJ6QhCp46ROHcK0dCIvWMn1kM/gafrMOERN9vXInnX7aoZiaFOe8wLjh8tKAe157K/X20D8ZPHAAiMDZMIqphCw1M1lEBgSkWscggogkBqhPFtu4mMzP07TUd6wxZqj+6n+moPGT08Z/lcAVQYkt9bRPO7a0sC4sLCjifIbN1FIDU2Y5+ELqHLMJq3EU4ByfNTq+yCiWZn/Z4Ylk/4orKDIkmYcgAjoFPfd4HwtT7cSARz5y6udW7GUTUQEnVmil/aUsW7t1TddKIx+b7RNI2dO3fiOA6nTp2a13G7FNtmGAa7du1iaGiIV199ddZj3AytY1VVFdu2bePkyZP09fVN+ez1Gtm4677BI488wpkzZzh37hy/9Vu/BcCnPvUpHn300RnbvvDCCwvqsVGMbNwIViIb5bEiEF9ezHUNbjXZAIjH4+zYsYPTp0/T29u74PFuxvksZkKOx+N0dnaSTqc5d+7cyr20gluKa9eule65vXv34roulZWzlzgF307Nlppxp0L09dHw7a/iXhtA5LJ4w6NEjh+m6j++T7z3PGEzg7mhi8KqDowzp/x9Jj2LbiCIMYVdCNRpmTmu55OZcsg1thLsu+TvGQxRdewAanV1qToW+ClQcXNmt3GYWOjrEo4ncKr8fiHZTVsIKAuoVoTfYTtuZTHqG0nXzS0cjmoSQVUiZXolCYffpE+QQsPVDZxQmPorPoEShQLRK5eQPZtR00F2LWQ8Mp6KKsskdAlP1rCUIJIQVAVkNEki5ciosqA2qBBWJZyODoa272Fg4xbMyhoaogbr//07/NrbNvILv/0UQe3WLBOLjVk1TePgwYNzpuQuZsE+eVtZltmyZQuKonDw4MGykcKbVVilqGkcHBzk5MmTJWf0UqL2xe3vZsfYXZVGtVxYIRvLg7v5wbjZWM57406JbBRR9CAdO3aMdDrNunXrFjzuUs9nsd4i8A1PbW3tDaVPrmAF5fCud72LF154gaGhIZqamvgf/+N/lBY2Tz31FN/4xjf48z//cxRFIRAI8LWvfW3e+/dmaAtvKbJZAu99L+LMGarf/QGym7bhbN2KPDqIufkBHMNAAFIqiRuNISa6buc71qHgYXsTmgt3ahfxvAMSDi7XWYdtWoAMZRZxWjZDzrYI5cdxqyoxxcxtcqEYlQf3Mdx1veLY/8/em4fHlZd3vp+z1Kk6tWjfJWuzZO1SabPdC00aQjp0hr6Z5PIEbvIQwvQQCH0HJk9g8kxyydAhgckdspAmmTAhgQyBDjeEdIdAN91pGhq6sa19t+Xdlhd51VbLWe8f5VNd2lVSyXbZ9X0ePbZUp37nnFre9/d9l+8bCC9gZmUTWrHntSUZ3/x1wmrWmretSOBzSWhmrFwMbzZUZVPa/xMutK5WNPNIAi7RZilhyKBo6diGzqLgW/2yNray5yc/5FpdI6G8IvLHRrjZ2kFIUPDKAtlybIJ4NBpT9MpXJeY1i6vh2CC+IlWOS9lfj1gx9XpZQhBEKr/7LR741G8i3vJXS3/912ve425BEAT27t0bL8nt7OxcV6lqu75NEATq6uq4dOkSR44coaOjA5/Pt+7xO4Esy3R0dCyTYL9fy6gyZIPUkY1MGdVqpBsBS8frXWkYE4d63QmyAW9GkE6ePMnAwEDSr+tuDwF0niNJUlydKoMMUoWvbzIl+amnnuKpp55Kas1U+KnbCq8X/bHHkK9fp/C5f+Dkf/wNTEnCrm/E8vnwjgwS6jmI59KECNXqAAAgAElEQVQ0oaxsInuqcekRdJcbSRJwAtumLeCRbRJVXSXBwrITyIasEBgZYKG1k5UwCgspODbGUvcBBNtGEexlmQ0HkZY2vJdmMNwefDevsVTX+KYW7wqEs/LIH+nnWlNH7A+WRcAjI4sCIT0m8btq/dYgnutXiOTF5oUIpoFkRtEVL7oVs10iFqpkExZcCNLqIIhfMODGNSIPPMKeN17lTGM7s+3d+CJLBLwCNwyJsGHGSIZXYj4aIxkeWaDYJ2PZNthwIxKTBXaQ54LmL/4hlV/8Yvxv5t692NXVa78Au4yysjJUVV13GngywaX1fFVJSQmqqjI0NERjY2M8s5jqzf1KCXZRFHc9kHY3Iv3pUgqQiojRvfBh2A2k2+Yddue9vB2vg23baJoW/ywnc87dGLrnRKn27NnD0tISS0tLu3o92yEb90LEKIP7A2lHNgDjV34FwedDioTxv/xd1MkxzMIiPBOjaLV1YFlo1bXxidKSJzZ4M7qiD8NaYcosw0aIRpafa9WAPpuAG+TcHOyiopj8rSAgCyDYq1mE7faQfXkGWVFiRGMTmBXVeGYvUjDaj9820ExhTZIRv2a3h7xrV8CyyD02jkeLYrv9saG5xEquZEkibMmsnFyliALZbpEwLiRZRLlykRtv+1n2TE9QODpA2OXhuiUhAAVqrEzqashEAoq9MgFFxLZhLmJxLWzGX898VeLxugDN2TbXP/ShZcpWkW99a9PXIJVY6a9yc3Pp7OxkfHycy5dXz1NJRSAtOzubnp4ejh8/ztmzZzc9fidw+jgWFxfXvJ/14PipdN9jZjwtqcts7DbSbeOe7l+OVGOnr4dt20SjUebm5rh8+TKnT5/m2LFjLC4ucvjwYfr6+jh69CgjIyPxBu3dzGxsdaNeXFwcjyBt1gSbiN2O/mTIRgbphLQrowLskhL0J57AliTyv/K/0OoawDAwKyox8gpQLB1LkPAose9u1HxzU6Ik9GYYloBbetP/2S4F7+RyOVutohLv8Vjfhyc0T8AjotsilkdF21OF99Y5dFska2xw2XOlpUX8eojQgYcIXDrHprBMvOdOkjd7gYXgfnRla+p7UnYWla+/ynxDK5o3JhDgP3MCnyywoNmrJHRFAXI84i0lLwufAvOle4gWlZB/5HVCBx/GCmQjRkIU972BS4tw9ZbEbZFXJuCOPdchGU4vSI5H5GdrAzy0x4+cYAOXXn8d2P2sxlb3Mk7fw9mzZzl9+nT8eclmNjay8263m56eHm7evBnvrditvYvX68Xv93P16lUmJye3VA1zr/ipTBkVsQ/b3d545/SEZDbw9y6czEQkEiEcDi+bwuqoJimKEh+c5PV64xODOzs7EQQBwzDQNI3JyUlKSkruWBnVSkiSRHd3N0NDQywtLVFZWZnSz/JOMhuZ71QG6YB08FNrwfiVX0F+8UXEvXtxuUQkWyNaWIwiWtiiAlZi5kJAkWwi5q3J4wmQjx4jWvdm/5e+pxrB0LHlN1WSRMMg9+gI4bYOdGv59zpqxsqxIgaEgr34J8dY3NeM9/hRzOpqou4YYYjsawJDA3ntXi63YCErEpGuA2DoqPM3CQfWn30iCzEJ3qgJocq9ULmXsrEh5nwB3DeucrOlE9Yo6xL1EG7Vx0LUIuAWWdBsoppNQBEwbIWb+x8CXaPk4llyr1zgRO9bUK/OUuJexCouBgHmwhYm4OztA4rIAxU+PPJyWxnf/Le2svTSS9jNzevez25hPZ/icrno7u5mYmKCiYkJmppiQxJT6ducKeCnTp3iwoULmObW5JC3A9u26ejo4PTp0/T399PR0bFh32CGbNxD2Ip++Z3Gbjeg7waR2c1rTreGfOdao9HoKiIRDofRdX0ZmVBVFa/XG5/E6nK51nx/NE1DkqRlj7ndbnp7e+nr68OyrC29t7vZkO3cu3Nd4+PjjI+P09zcnDIjut3MRoZoZJAuSMcyKohlNyJf/Sp2UREBU+BGRLjVCyGgWyCLNoYVIxmaKaCZbzaEe6cnCdXHNpeR+kbcp04Qra4FwCgswjs6CB4vUl0tllvBaG+Hm1dZWYbkwJqbR9INzJw8jOJSct54laUH3hqX2gXApZA33sf1luX9H7ZlkKMqRAwRw3E9sgvvlctrkg2vLCCJsf6NpYTthRsD2dTJuz7LTNtqtUyPJKBIAkuCLzZvA5u5qPXm33UbRBnf7AWk2ctce/s78Z48RsWPX8FU3OByMZ+dj4kYJ3E+l8DBMh8+9/pD9uLCIgcOrHvMbmIjHySKIi0tLZw6dYqBgQEqKiq2pUa1EZzeipmZGQYGBujo6IjLU6caoihSW1tLIBDgyJEjtLW1kZW1tuBAhmzcQ0jFnI3dRrptru9HOGVOiSTC+b+u64TDYaamplBVNZ6ZyMvLQ1VVZFne1sZ3vc+ELMs0NDQwNjYWjwZtZLB2M7OReKwoirS2tnLmzBn6+voIBoMpUYPajkHeTjYkgwzuFJIpo7rbsuB2cUw21iODIIBLtNEtAVmw4r0ZztXaCKjHJgjta0LLyQVdA1fMRrgW54laFurMWfB6kIuK0PPy0VzuGIERBaI5+Zh6FMnlXnUdVlY2nmOThGUX3gtnIStrOdG4hXB7F/4T0yxW1wEQmL9GJCsnpi61ApGGZnJHBrnR1I60OI/X68ZQPLHMjPnmvXmVWGtK1JTRg70AZB2bZL4qdg7J0Al4XSzqIAg2trbEAl6kW/K7i5qNbtnIAvgUgfmiMuz8YnKO/BhR1/FKAqf2NaF7/ViGBZKIRxY4WK6S5U79ML5UY7PPrEMGvF4vx44do6BgZY/O9tZdCVmWaW5uZmRkhPr6egoLCzdceycoLCzE6/UyMjJCVVUVZWVla57jXvBTGbJBekSMbkeWIJ0yG3cCiWRiZXbCyUy43e54mZPP56OgoACPx4MgCAwPD9PR0ZHya9ooGpSbm4uqqgwODsb1xZNdZ6fHryU9WF1djc/no6+vj7a2NgKBwJbPvRZ2okaVQQbpgK36qbu95Dag2Cxot+YeiLGp4lI0hO7xopw5iVZVix7IBsPAKCrB23+IUNcBsCxE2yL76BhaeyzrYADm4s1leQxBlPAKOlHLWlMKV1AUso9PstAdi+C7sYiaK14rUURWXLgvXUANLRCub9qwwVXwquT3vc58Rw8hyR0nGS7xzYnp4TV4oj8aYR7IHTxMpKqWJcOHX7aZx4uteMlSRDTTZuHWhPQst0DEsJmL2qgyuAUTJAk9N59IZTXeUIibsoz7xnUe+vtn8D/zp1t4R+6OftCtfmZLSkoIh8OcOnWKsrIycnLWL2FLZt3E47OysuJTzZeWlqiqqlpzjVR8z3w+X1wqfn5+nn379i3zZfdKBj5DNkiPxjtRFDMlSbsMh0xomsbFixc3JRN+vz9OJjbLTBiGsWsqV+ut6zxWW1vLxYsXN9Qtv51kw0FhYSGqqjIyMkJdXR1FRUVbPv9OrseBZVm4XHd/xC+DDCCWgV9cXHv4XCLudnvulWFu0UBeXET3+pBu3sB9/Bihgw/jujqLVlWLUVqOOnCIcLAHPb8Qz6lphKJCzJ79yLOXYk0It77vsj8Hl2gRSSAMhuLB33eIxa798b+JWhRVEdGqa7GLS5CWFjB9AaRLlyCvaFWGwzV/E5euMx/cz0bwmlGiFVUIBUX4T04zX9+E/+g4gldlqaZ+WQnVMtg2IrDntZe5+OCjBCZHCJeUMVdYjAcT3dBZEmJ9JLHh3QLzURtJgGyPgG0BsofFrgMQDoEk4RVFOj/8f1HS/zpWZSWhJN6X272hXUuyfavXoKoqZWVlTE5OUlNTQ0lJybrHbsc3OCXNPT09TExMrFv2m6oSJ1mWCQaDnDx5clUfx73SW5ghG6RH493t6NlINe42p2dZ1rIyp8QMhdMQJkkSmqZhGMYyMpGOm9JEI1taWorH46G/v3/N+tDtGuStYCOD7Pf76enpYXh4mMXFRWpqarZlVLeTpbhXamEzuD+w1XLfO2V3V9oQ27YxDGOZvY0sLrL3gx8k5+0/R+jhtxLa/xDq9FHC7Z0Ic3OEO7qRZi+hXjiLUJiPoEewq6txHx0nkhubg2AUlSBbOkbC9sXQLeTwEobvTbsW7urFc+Iokb0NeCdHMZtb0MTYcyx/AE9kkSVAKykjMHCEhY5Y/4S0MI87N0Ak2I108wbSwhxmYPmcB4ipZQkIhAVPbCelenFfOE/e2AA3b5VJrQVRAFUWMCyBUEsHfsukbPgIF5rawTLJ7/8Jc10HwKW+WTIVtbGxCSggCgLYsYyQhR3L3rg9VPzdF9n3//7em/e/yayXdIeiKPT29jI0NEQoFFrXd+ykDMnpFXHKfjs6OnC73yzPS2UG0ZGKX9nHca/4qQzZIH3KqHZraGA6Mua1HOpKMpFY6uSQCbfbHe+ZWItMLC4ucvbsWfbs2ZPS690t57+VzIaD3NxcgsEgw8PDq2pRd7PsYrO1FUWhu7ubqakpRkZGaG1tTfoc28lS3CsRowzuD2zVT4mieFsGzNq2ja7rRCIRDMPgzJkzcfuraRq2bccnpDvZ4IKSElz796N+95+5+YEPIV67SqSpFfnyReTzZxEDWciRJUKPvA0A5dosmj+A1tSGiI11q2DKEl2YkRDSLclZW5JxzV7BLPdg34oIC5aJKxTCNdxPpGv15l9z+1AWF9C8frS99UjzcyiXLmDV1KDZsc2dmZOLv/8wc+1vNnILlonqllcMGbRxXzhHpLUD96njseaMFRtEtwSyKBAxYkTB4wLbElgM9iKEQ1S89gqzrZ3c7HkAdA3l0kXYU8Vc1MYjg1sSsIkpdyWe2y1BSxbkJBANq6QEu60tqffyTiMZH+SUFsmyTFdXF5OTk4yNjdHS0rJqY75T3+aU/fr9fvr6+mhvb4+X/e4GESgqKsLn8zE8PExNTU2GbNxLSGUZ1W5t2tJR2SnV6yaSiYWFBZaWlrhy5Urc2YmiuKzMKRAIUFRUFC9zutO4XcMC46oia3wWfT4fPT09DA4OEg6HqaysXPfYVF7jZmuLokhzczNnz56Nq2il+hwrca/UwmZwf2CrfipVJbebSXE71+TxeLAsC0VRyMnJwePx4Ha71/1uWR/5CNKrrxJ44V+IVtciSyKCS0bbfwBL14jkF+IKLaCrfrT8IlyChY6ELNixCdi3CIc7EsVaCmHdynjotXX4D7+O7XJhFxSgVdagd/XgGexb+wYFAdfFC2h792EGssj+3r8y/47HV5VThYNdeM6eJlJRjffMCbSaumWbfa8LooZApCxmS6M1dWQPHGGurQtBi+Kbv4FZUhofVOhxQeTWlHFZEPFFF4lqOguP/Ts8E6O4L11EMHXmO3oQNYMsrwvs2F0nlmQJQE2ORIFPBtwYLS3I4+Oxa/7mN5N6r2Mvx521hcna8ETRkebmZs6cOUN/fz/BYHBZ4ClVdt4JTI6MjLB3716Ki4t3zW/6fD7279/P6Ogouq5TWlqa8nPcbtz5HdhdALfbzcLCwo7X2c3GvHQkG8nCIRNrScOaprmMTFiWhc/no7i4OKVk4m54HZLFVjMbDpxa1NHRUcLhMPv27dtVspFMZKayshKfz0d/fz9zc3NkZ68uX9jpORzcKyofGdwfSKZBfKvDwlaWla7sUUuc66OqKrm5uXg8HhRFWWYvBgYGKCoq2pIdtmtrMX/6p8n/62eYfeZLGKWlWIXFuI5NYZeXY9k2ltePaMcyGYYtIAg2hi3gmZ4iUh+b7m3l5OIeHyEqSnhOTWMVFhHtPYB8cQa9oip+vkh7J5JoY1qr7Zu2rxHfcD+iDeHHfg7l8iW0khWKQJKMcvMa0pXZWKP6LRehhBcQA4E1FapEt0L2oR8TbmgilFeI9/o13Hn5cYUqjxwjHmEDQm4/uEEdH0ZamMc0TC71PkT2wCEky8A4+ACaLcUH8gFkK7CvYPl7EHnjDfzZ2VgVFUllNe4WbCez4cDJPni9Xo4cOUIwGMTr9Sa97mbw+/309vbGy35LSkp2zYc4fRz9/f2cOXOG0tLStCzndpAhG6SujCpdCcFuZjYSYVnWKqeW2DORSCZUVSUrK2vdzMSpU6fw+/27ooN9uxu5U7lu4vu40XsqSRIdHR1MT08zPDxMeXn5Hc1sJCI3Nxefz8fExATV1dVbiursZKhfBhmkA7aa2XDs+Vr21vlxBCvWU89LVoo7WR+i/fqvo774Ijn//A1ufvhjaIBRvw8b4ZYsrogs2mhWTApXOXcGrWwP0Zo6lKlxtMYWxPl55NAS4vlTRLoTGsFXDmSTJJSpccL7Vg+qc586juRxE95TA7KMMndzFdlwCyZW9V7k2Uu4rs5i+LPwjw2w1HVgzWF83mPjaOWViIGb+If7WezoIZSbj2DFGr11e/nAQlUGy7IJt8SUCv2HXyf/hy9h+gPYioeQDtxKtkgCNBXIeJW1+9MWL1+GbQTebod6mZMpC4fDhEIhFEWJE4Jkr2G9Y539wuDgIM3NzeTm5qb83lwuF11dXRw9epSpqamkfEiy+yxBEMjLy0PXdaanpwkGg8le7l2DDNkgdWVU6UgIUomVzi0cDhMOh+nv74+TCcexeTyeDclEBqnBZrrl+/bt49y5cxw9epS8vLxduYbtDAyUZZnu7m5GRkZYXFykrq5uwzW2O9QvQzYy2A184AMf4Nvf/jZFRUWMjY2tety2bT760Y/yne98B6/Xy5e//GW6uro2XHNlUMw0zTWJxI0bNxgZGUGW5WVkYjftbbL+ya6tJfqFL0BbG6InB5dtoyMiCzaGKSBFQxgeL56RASLtnWh7qlC//zJmaTniwgKewSNYNXuJHnwQ8drVZZPEtepaXJjo9psbcq2xBc/4KJGmWD+YGA7hDqjoe+swBQHv2DBLTe1Em1vxDfWz1NaJEI2iWhpRbwDy8jHz8vGNDKBfv8JS74Or7kkWYhVY4YZWPCMDGIVFRA88gDJ9DPXMCUINzYS8PiDGHdwu0Eyn/0JANSIoP/4RtkfFZ5lcbG4Hy4rdG1CW46Iia5PI9hpKg7cL65XdrTW01u12c+nSJTRNo7q6Ov78nZINgKysLLq7uxkcHIxL1qaaSImiSFNTE9PT05w7d45IJLKmyuNa172doFhOTs6aMzjSCZkdHqkb6rebjXl3A5ExTXPNMqfEzEQimcjOzubKlSsEg8HMPANuf8Zkq8Z7z549RKNRzp49S1VVFT6fL6XXmCwRcEiALMt0dnZy7NgxhoaGaGtrW3eTtN05GxmykcFu4P3vfz9PPfUU73vf+9Z8/Lvf/S7T09NMT09z6NAhPvzhD3Po0KFVxy0uLvLqq69y+vRpDh06xMjICPv37+ezn/0sWVlZy0qcnH4JiJUj7nR2TTLYjn8yH3oIAL9tcTMEYiSC4fPjHjiMmZuHVVaBXteA68Q0ypXL6L0HIBrFaG5B6T+EkR/r1bDyC1Bsg2ji6WcuQGFxfBgggJ2bi6BpqMcmMFrb0G0xPklQq2tAvnwJo7gEq7gEZfooos9LtLzy1pNthHOniTa34+n7CeHEG7EsVEUkaoJtgluyY+pa0Si+wSPYHg83OmIN6p7rVxCLCwnf6tkQiU0aFwSwXSpWTQ2h4nIE08QViaCpXrwnjxP80/+G8dw/J/X6phqJggAryURiD48jCKCqKnl5eXEBlkQfYBgG5eXlnDhxgsnJSRobG5Mjq5ts2j0eD729vYyMjCAIwqazOLaL4uJi5ubm6O/vp7W1ddOy3/s5KJYhG2TKqJy114uUhcPh+Ac+kUw4zs3j8axLJk6ePLkrRCMdMj2JuBvUqDaCE/UcGhqiqakppVmOZKM5idctCAINDQ1cuHAhXourquqOzwEZNaoMdg+PPPIIp0+fXvfx5557jve9730IgsDBgwe5efMmFy9eXFUyGAqFOHToENXV1Tz88MNomsaf/dmfbRgQkGX5tqhRJWIn9tj7/l9FDmtc/69Po+2tQ6/di2tiHNf0FGRlgywRfegRAJTBfqKFRWhtnZjRMJI7ZgsMC6TwEuatzIFRvgd1sI9w8JacbTSMcu0KnmNTLD3606uuwfZ4cF+JkQ358iVchkG4NBZJlq7O4rp2lWhjCwDRrl5cV6+gFxTiPjmNXVtLxBRwS7Gyr4gJimgjXp0l9MBbwDTJfeVFtKycWOmVHhv2p0i3hDyIjQ2JmkB1HegGtsuFV9dp+L/fT+Gr32Px2LFtvbZbhfPe6bq+ikgkqou5XK5l6mLOHmBlD89WIIoibW1tHD9+nKGhIfbu3ZuSzIYDJ1jV19fHxYsXqaqqSvmm3bIsVFWlubmZ4eHhTct+7+egWIZskNoyqrs5s7EWmQiHw9y4cYPh4WFkWY5HJLZKJjK480gF2YBYNKiuri6efk5V2nY7AwNXGteysjK8Xi8DAwPxWtxEbCdidDdPWc7g3sbMzMwyee2KigpmZmZWbVSKior4/d//fQAmJyf5/ve/v2nm8U4EYnZyTtvrRX31VbwvfRfhUgdSVgDB4ybywM8iz5zDqK5FGRtFa2lDC3YiRcOYbhXmFuEW2UCSUY5NEQ52x36/1ZPi63sDs7Yes7AIPdiNsLCAOH8TK2t1pDva1oH/tVdYevCtIEm4Zs4hHZtAq2uMEw0A2+1BPT6F+8QxlnoO4ro8i1sLE6msRSTWgxE1BaioxHX6BO5zZ1h89GdQ+g/jGxlEnr+J3tmFkJODZrKs8RsbRMGm5jd+ldyXvwOAGQzCBkPrkoFhGHECkUgqFhcXsSyLGzduLMuWZWVloarqtsjEViAIAvX19Zw7d46xsbFlPRwbYau2WxAESktLuXr1Kn19fQSDwfiwvFTAuQ6v17uscXy9st8M2bjPkarMxp2e8p1IJlaWOjlDz1ZmJkpKSrBtm9ra2pSXzzjXncHuNoin4pzOZt1JPw8PDxMOh6mtrd3xdW+nZ2Ot43Nycuju7mZoaIiKigoqKiqWPed+NeIZ3B9IRvo2nTIb+kc/ivzaa+T8xZ8SfeV1LMvCrKjEdXQKzJjUk1lbC6YJkoQgxb6zcnYerqlxtMZY47fW1YOn/wiybWE0NmN296IMHEErLIqfyw4E8Bz5CaHu5VPBJcFCPn0S7YG34BsZZKmzB+XcGWzVi5mzPLDhunIZu6oK6eIMvr7XCbV3YXlL8USXMFVfjGgQIx16Ti6L1XtxH59CWbiJJrnB5UIzbGzjTRlfiGU7yrJAdbnx/eiV+N/D3/jGll/LxCGKK/cAztyTxIBiIBBAVVVu3LiBpmlUVVVtfpJdwJ49e7Asi+PHjxMKhbZEOpLxbfn5+Xg8nvicjPWEZZL9DCf6EGfmx0Zlv9vJwN8rqokZskH6lFEZhsHS0tKaPRNrkYnc3FxUVcXtdm+YmbgXPsj3K9bayG80Z2MjOMc66efJyUnGx8dpbm7e0Wck2U39RlkKhwyNjo6yuLhIQ0NDPKN4v9bCZpB+KC8v59y5c/Hfz58/T3l5+YbPSUb6Np0yG1ZjI8Zb3oL8wgtkfe6zLP7Kr2FWViO4XGiNTcg3rmHk5qPM3UDLysV0uXEP9hMNdoMogWkiLi7gOTaFnV+AVrM3PlBPa+9EvjiDUfrma6t19eI6fRK9uhYhGsWlSBiCjL63HgC9oZHA97/H0tseA8A9OUF0XxMAnjMniFbVIl2dRfT5kXQdIbSE/9gkoc5esGON4rIYa/wWcnLx/eS12JA/vx8kF0vN7diCAIYBsguPCyqyQE6wRUsnT+IvK8Ps6lqW1XACimuVOtm2jSRJy4co3lIXc7vdG9q63WiiThY5OTnk5uYyODi4af9DMvbe2awXFhbi8XgYHh6msbGR/Fs9PyuPTTYLv1KCt6GhgZmZmTXLfu9nP5UhG9w9ZCMxKpFoSBwt9KtXrxIIBFY1X3k8nh19GNOt/yEdcSd6NpLBSiPoDEo6ffo0AwMDdHR0bFvjOxVlVIlwZHtPnjzJwMAA7e3tGenbDNIKTzzxBM888wzvec97OHToENnZ2ZtKPCuKQjQa3XTtdMtswK3sxksvob76MvOf+B3kK7MYdfW4rlyO3YtpYiAg3LyBnZMLuobn0I8RbHBdPIfevR/9YEwhSjl3Gq38VoRelpGuX1tGNpAk5GgUcWIMq7QUw1MQf0i5fAFLltEefBhx7iZWdg5yNIx+4xrus6eJBLtxX76AnpNLtLAYz2AfgeNHmX/gEYTQEt7pSSLBbiKmgCKCJAqY7R2I584Rrt4Lho6EjSFI5H/rWfLfdgCxsTF+/nh1QjTK4a9/nby6OiJjY2sGFFO5B7gbYNs2brebxsZGhoaGqKuro6ioaN3jt9PfEQgElg21TcyOw/YCY2sdX15evmbZ73Yz8HcDGdwpMmSD26dGtZJMOBGJaDS6zJA4kYlEQ3L69GmysrIoKChYd/3tIh3JRrpe8+3EdjMbib/X1NSgqmq83lVV1aRf91STDefa9u7dy6VLlzhy5AiSJGXIRgZ3Dd773vfy6quvcvXqVSoqKvjUpz4VL4H60Ic+xOOPP853vvMd6urq8Hq9/O3f/u2ma7rd7qTmbNxO7PScVmMj0U9/GrOiAt/QYZZau2PZgKiGWVCI+8gbRA8+jOsHryC7XOgHHkAaOIK+/0HEK7PLJF8tt4oQDmGrsVIcvbUdZWwEvaoG183r2HsqEHxeXDPnCe1rAEC8fAnXwhxafUN8Hc/Qa4QOPoylevGNDbG4/yHUQz8m3PsAiALqcB+Rrh7kc2dwD/YhaRpLBx5EiETxeBUgpjKl+bMQKypjmQyPSsnnfp+SL/wJAGP/9m8sjY+vEmFRVRU9J4fcgoItVSfsFHeif23l+RL7H3p6ehgaGiISiVBZWbnquclmNhKPdbvdcaWqUChEfX39tqsBNjo+NzeXrq4uhoaGqKyspLy8/L4OimXIBqnLbNi2TSgUig+uWVkvuTLF6dQRbiUqsdsOJN027sBdfNIAACAASURBVLuFdHsdNhvql2zPxlooKSnB4/EwMDBAa2srgUBgW1K2yRy/1fVLSkrwer0cOnSImzdvUpJEI6Xz+qR7xCiDuw9f//rXN3xcEAS+8IUvJLXmVv3UnchspOKcxnveg/s//Sfkf/xHon/zNcSZGbT9B/B951/A50N8/TXMzi7MhQVwuTCbWhC0KFZhEa7+I2jdMXlZq6gY9xs/InLgIYSlRVynToKho1y8gH6LXFBVA1U1qMMDWOEQeksH2grbYbS24Rk4jNbYglVYSOCNH7Lw6M8gXziPFI0QCfaApqEcnUJoaCLkC6CODSEuzKG3tGJmZWObIgJg+fy4Ls5Qe/ABXPNzAIRbWgjU1lK4jgjL9evX1yz1uR+gKArd3d2Mjo4SiUSWEQJIfibHSv8jSRLBYJBjx44xPDxMW1sbkiQlTQY2822qqi4r+y0qKrpvy6jS8g5eeOEFGhoaqKur47Of/eyqx//4j/+Y5uZm2tvbefvb386ZM2c2XG+rjXeGYbC4uMiVK1c4d+4c09PTjI6OcuTIEQ4fPsz169e5dOkS8/PzSJJEQUEBdXV1dHd3s3//frq7u2lubqa2tpaysjLy8vLwer1b+iDtdj/IbiHdNu+Q3hPEEzfPO81sJCInJ4fOzk7Gx8eZnZ3dtpTtbhyflZVFIBDg9OnTnDlzJqnPXIZoZJAuuFd7NhKhv//9EAiQ8/TvIpo6ntd/iPlTP4XR1Y0oy9i5eUjzNwGws7JxHZ0AwGhqRrx4Mb6OVVCI57VXESQZva0DvbMHs2IProEjuKbG8fzkdVyXLmC0diBbNvaKhmFhaRH3xBhiTi7K+AhiKET4px8j8OK3sbOy0GrrEG5ex3VqmvBP/TRmQSGBH72KuLiAKIiYqh9sAVEAQRQJuC32/H9fihMNAPO558jPz8fn8913ao9rfVZW2n2nXNayLEZGRpaR2VQMAHT6K/Lz8+nr64tXmKTaV8myTDAYRBRFjh49uuW1HdwrZCPtMhumafKRj3yEl156iYqKCnp7e3niiSdobm6OH+NoK3u9Xv7yL/+ST3ziE/zDP/zDums6Rvzq1avxZtOVPROJSg5OmnNl89Xx48fJy8vblUnM6TowMIM7h2SHJG1mNJ309uDg4JZqx5NZOxHbNa7d3d1MTU2lpKk9gwzuNkiShGmamx6Xjj0bDqyuLszubqTXXkM5cwqzphYzOwfXGz9GC3YiXJmNEYTxEYzmNvSmVqTTJzGra1FGR9ACfpSpcfSOLuzSMlxnTqI1tiCfmI73gViCiHFrkjjEyqyE+XnsrCwA5JvXsLJyiO5/APeh17Fr62KD+954Df3gg9iShOfwT8DtJtrRhXz2NK6Zs9jlFWAYhFvaEbRY8FL0uCnxgUuSsH7v97A/9zkEbsnZbtCPcLtxN8iAryd20tjYyJkzZ+jv7ycYDOJyuVKWtYeYEpbX66W/v599+/btiq9yJH5t2+b8+fPU1NQkJfN7L/iytLuDw4cPU1dXR21tLYqi8J73vIfnnntu2TGPPvpo/I08ePAg58+fX7WOpml87GMf4+d//ud5/PHHGRwc5Bd/8Rfp6+tjcXERWZYpKCigvr4+npno6uqKZyZKS0vjak/OB2G3sw/puPZuIR2veTewmdFN5ZAkiBHzYDCIYRhMTk5u6T1Iljxsx/E5wYDW1lZ8Ph99fX0pKY3MIIO7BVv9TqRzZgNA+63fQlBV/P/905j7GhGuX0Pv7kW8cgXXieOx88kusKxYr8W5s7j7D4FXxT08gN57EBQFOxBAjITxvPEaVl092gMPYRUWoRybXHY+OzsHz/gI4qWLuIcHsXIL4MZ1xKuzRB96BMvrRR0dIvrgI1g5uQS+/xJ4PEQ7uvAcfgNZEkCS0RsbEYqKQdfB4yH/f32BPVngSkhaLL36KrbXS/gf/zElr9W9hI3sflVVFZWVlfT19REOh1OS2UhEfn4+7e3tTE1NJTVzLVlflZubS2FhIYODg1y7dm1Lz7lXMhtpdwfrDUNaD1/60pd45zvfuervLpeLX/iFX+Dzn/883//+96mvr+cHP/gBjz32GDU1NWuSic2QrtmHdC3RSifcichRqg2yA0EQCAQCyLLM0NDQptHW25HZSOy/qKmpoaamhr6+PhYWFpJaJ4MM0h3pTjasri7Mjg7EhXnUr3wJ18QEKArS5YvovQeQpo9i5RfgeeFfIT8f/a2PYpVXYOXmob/lp1De+DHi2dMog/0Y3b3YKyoN9PZOhLlYKZawuIB84hiSaeC+MIO+rwH3Gz+CnDyskjKUsWFEXSP6zn+HMjaM+sr3iD76DuT5OdQfvQq2hXjpItHuXgTdgKIilIWb1D3YQtHX/2b1zXV1sXTp0l2V1bhbsJmfKC4uprGxkYGBAaLRaMp9m9/vp6WlhaWlpWWy1BthO4E0VVXp7u7m+PHjWzrPvaJGlXZkIxl89atfpa+vj49//OOrHhMEgUceeYTKykq8Xu9dIX17p9aG9OytyGBjQ7pbZMMxsPX19RQWFsbrXVOx9naOXwuFhYW0t7czOjrK7OzsjtbKIIN0QjqXUTmI/PVfY9fUoP7VFzBrahHPnEGQJDyvfA/5+nVE08R4/F24RkcAEK9dQ756BTG8hNXegXz5EnpXDwBmcyuuyfE3r/XmDTw/+THKxCiCqmJVVWNn5yBFw6iH3iD6wMOI16+hTI2ht3VgFRYhTR9Fmb0E2dlg6AiChWAakJNNtKEJIaph+3x4X/s+9QdbcV+6SPhb30rZ67HbuFvLqFYiNzeXYDDItWvXmJub2/DYZNZ1IMsy+fn5XLt2jampqU0/09sdWOvxeOjp6eH69etMTExs+n290+9NKpB2ZGOrw5Befvll/uAP/oDnn38et9u94Zoulyup1Nl6cPo9dgOZ7EN6407M2dgtspF4bEVFBXV1dfHyw7WQKu3yZOH3++np6eHMmTOcPHly3fcg8/nP4F5CupMNXddZEEVmn3oK27YIfPxjKGMjmI1NaI89Htvkaxri2TMIfj+ef30Oq7Uds7sX1+QEdiAAxcUx6VwH4RBK/2FcE2PY5RXoBx5AnjmHPDIEpoke7EJ7+K2Yzc2or3wPvCp6UysYBu6RQQRZIfqOdyIaeqwMKysHRAGzsBgMExSF7G99ndIPvBcAq7ISu74+Ja9HBsvh8/koKChgZmZmzRL5lUim58FRDe3o6ECSJIaGhjAMIyVrw3LfJkkS7e3tuN1u+vv7U7IHvZuRdg3ivb29TE9Pc+rUKcrLy3n22Wf52te+tuyYwcFBfv3Xf50XXnhhw6EwDkRRTImhTNU6ayGdsyYZxHCvlFGtPNapd11vMuudyGw4cCQUp6amGB0dpaWl5b5Tfsng/sLdXkblzJtaOQU7UYhFVVU83d3klJXh/t53EAwNOz8fva0DQRKRL85g5+djNrdgVVUjLMxjZ+dg+2KqUmZ1LcqRw2g9+1H6DmOVliJemMEMh3EN9qG3dhB97OeQf/QDXMeOIkbCWHn5GHvrEc6fww5kIQ8PIoXDaAceBNvG/fprMbt08wbRBx5GWphHMA0Ej0rFWzoxvvEstiQhmGZaZTXuFiRj952hs6dOnSISibB3796UBN0SS5bq6+uZmZmJz5jyJMxyWXn8VrEykCYIsXlRfr+fI0eO0N7ejn+FMtq9grQjG7Is88wzz/DYY49hmiYf+MAHaGlp4ZOf/CQ9PT088cQTfPzjH2dxcZF3v/vdAFRWVvL888/v+rWlKyHYzbUzJGZ3caczGw4SJ7NGIpFl2cbbpUa1HhzHdPbsWfr6+ujo6Ng025lBBumKO0E2ErMppmmuIhKJ86Y2U3VMhP27v4vwS7+E+3svABD+jY9iPPJTSMePYdQ3IB85hNF7APm1H6A//FbM2jqU772AnRVAmF9A6Ys9Lh2fBo+KZOhEgz1IJ48jX7yA0dwK16+hdXRy6+KxvD7UF76N1tGNVlqGdPY0gk/FVlyImo1dWoq0sIDl9qDMXqI0WA/FxehNTSxNTCD/y7+kXVbjTigerfQJyfogWZbp7OxkcnJyQwXCnQwALC8vR1VV+vv7aWtrI+uWYlni2qkQPykuLsbr9TI8PExDQ8OuDG++00g7sgHw+OOP8/jjjy/729NPPx3//8svv3y7LwlI3zIqSD9SkG5lZbdrzsZ2X5OdDkmC2GTWnp4eRkZGCIfD8WjT7VCj2goqKyvx+Xz09/dnpHEzuGex22VUpmmuIhLXr1/HMAzOnj2LJElxIuEMr3WmYCf7nTMfewzjiSeQvv99lO+9QOjTn0UaG8EMdiEPDcbnY5gNjUijwwheL8bb34H88ovY2dlIc3OI//Y9tEceBZcLeXQE9eUXiL7jZ9H31gEgjw0jzpxDECXM2r1Ybe1Ybe0IR4+i/OgHiNhYpWXYRSVohYUIhgHz80jXrlL+6EEAlpxMRmkpxgc/mLoX+z7CdgJegiDQ1NTE6dOnGRwcpKOjA1mW1zx2u9eQl5dHMBhkeHg43qe4nbUhRk7Wy6wHAgF6e3sZGhpicXGRqqqqe6rENy3Jxt0KURQ3rO/bCdJtc53B7cFaQ/3WeyyZdbZ7rBNtcsqWWltbU2qQd4r8/Hw6Ozu3pKKVQQZ3G7ZC3ndazuvMmVorO+GcO5FM5OXlIYoiiqJQUVGx7fOuCVEk8pd/ia+9HUEQUP/8Twn/xw8BICzMgeLG9eJ3oLgELBtDknH1HUb/mXcijQyhPfAw0uQEwpVZXGfPoD/wELjdYBjIg31IC/MYrR2IZ0+j9RwA00SeGEW8ehVu3sAoq0AIRzBr67AjIURDB9WLMjpM0RM/E3u9ioqwW1pSe9/3OLYy1G+z5zvHOgqEHo+HI0eO0NnZuazkaTviJyvh8/no6elhaGiIUChEVVVVfO1kA2kbHa8oCj09PYyPj8ezNQ7SfZ+WIRspRLqWOmWIzJtItwwPxK7Z2RAsLS1RWFiIoihJG++tYrO0dGK0qb+/n0AgcMd6NtaC1+ulo6ODQ4cOMT09TXNzc9p9TjO4P6EoCrqub1gGuFmG3bIsotHommTCNM04mXB+cnJyKCkpwePxrBsECIfDO763dREIoP/yL6N8/vO4n/17Qp/4r0g/eR27oQnhwnnsrKwYOVBVZFFE7+pBvHAeaWEe+8J5hNwclGNHY9mNW7M53OfPob31UUxJAl1HOjGN8uJ3EDwebI8HsrIQJRHRstA7OhGWFhCjGnZODtkf/TC+Z7/65r2vmPOVjrgdalS2baNpGpFIhMXFRXJzc5d9jnca8CotLY03W3d0dMR7H1IVSHOIwNjYGBMTEzQ1NW2rZ2Oz40VRpLW1lTNnztDX13fbxR52CxmykUJk5mxksB52Ysxt28YwjPiGIPHfpaUlAG7cuIGqqsiyzPDwMMFgMCWlUTu5l+rqalRVZXx8PKmmt+1Ei5KFKIpkZ2cjy3J8g5VBBqnECy+8wEc/+lFM0+TJJ5/kt3/7t5c9/uUvf5mPf/zj8f6mp556iieffHLDNRVFQdO0TXuOdF3nxo0bqzIUhmHEpTed7ERWVhZFRUV4PJ5VJShbxW6WEANo/+W/oPzN3yAsLZH97v+DpT/6Y4ylBURNwxYljEffhnT8OFZJKcqL/4rxtndg7KlC6j+M0d0L0SjC0Ulcp09jPPgwdiCA/MaPEHQdQRCx3G4kwHYrWLV1WMXFmJqGeO4sGBr4/EgXLqAUBZYRjcWzZyEnZ9fuO92w0k85/3ek0d1uNx6PB5fLxdjYGC0tLeTm5gLJ+8i1js3Ly6Ojo4Ph4WGamprIy8tLqW8TRZG2tjZOnjzJwMAAbrd7V/oRBUGguroar9fL0NAQCwsLq4RX0g0ZspFCpCshyJCNOw+nDnoloXCaKl0u17LShZycHFRVZXZ2FkEQ4hsWXdcpLi5meHgYv99P3oqBVushmfc/GadQXFzMpUuXOHHiBIFAgOzs7E2fk2y0aDtEzpE4rKmpweVyJfXcDDLYDKZp8pGPfISXXnqJiooKent7eeKJJ5aVRQD80i/9Es8888yW11UUhVAohKZpSJK0KjthGEY8OHH16lU8Hg+BQGDHZGIz7LoPCQQIPf883re9DWlqEu9TH2LxW9/G3FuHMDeH8uq/Ybs9WJWVWA+/FSQZcXIc4do1XD95HaOzB+nmDfT6BohEcL32A4SFBYyqKsQb1xHKyjCbmrBsAUwTwTQRlhahrAxBNwj8zm9x8/IF3P/7fxP94Adxf/GLGA8+eN8RjZUldg6hiEajWJYVb/5XVTVOZFVVRVGUZTZa13XKysoYGxujrq6OoqKibalGrQW/3093dzeDg4NUV1enpIwqEY6C1MWLF5mcnNyS4qmDZANpBQUF+Hw+RkdHOXjwIKqqbvm5dxsyZCOFSGdCkG5kI90Ikm3bmKbJjRs3VhEKJ7LuEAlVVbfcVJloRC3LwrZtfD4f3d3dvP766/j9/i0pW+xWfwfENkj79u1jfHycvXv3UlxcvOHx22koTzYzkSyhySCDZHD48GHq6uqora0F4D3veQ/PPffcKrKxFXzjG9/gpZde4vTp0wwMDPDOd76Td73rXfzyL/8yHo8n/h13IsahUIiTJ09SfxsVkW6HPbZ6erBaWhDHx5HPnsHzF3+O9o7HsHr2Q3YO5oGDSKOjWEVFKC/+K2ZZOcZj70T88WsIV6+AaeD6t5cQcnOwsgKI4q0+ywceRrx+HTxexLOnsffsAQEEXcPzlS+hfvq/Iek6Ay+/TAeg/4//gV1RgfEf/sOu3u/thGPTbdsmGo2uSShWltipqkpeXl78963aYMdPOYIig4ODaJqW0oCXx+OJN1snU+KXjG8rLS1lZmaGo0eP4na7dyWQ5hC43t7eNaV30wkZsnELkiRhGMaOIj/pXEa1W0gnQrAT2LaNruur0sfhcBhd17EsC8uyUBQFj8cTL11QVXXLzdDOZ8u27fjrapompmkuGwjknMfJKrjd7k0bN3eTbNi2vcr4V1dXp2z97RAHh9BkCEcGu4GZmRn27NkT/72iooJDhw6tOu6b3/wmP/zhD9m3bx9/8id/suw5Durr66mtraW6uponn3yST3/60/EG1bWQ7kP9NkLkf/5PvG95CwDuZ/+eyNN/CHNz4PYgP/dP2EUlyFdmMX/h3Qhv/BhpbBTB7UYcH8bOygZFxlLciFkBzJY2hJMnELRobLyxLCEqCu6nP4n6zJ8RGh/H/3u/A4D2a7+GnWCnjY99bNfvdbeg6/qqgNeNGzewLItz586hKEo88OX3+yksLEw6K+YQCljur1Ye4xCO4eFhgC1Lvm7FR8iyTFdXFz/4wQ84fvw4ra2tmz4nWd/jcrmorq7etUCac/y9UOabIRu34Ha70TRtR2RjNw1uug4MvJeQqB+/sh7VKXVKzE44UR+Xy8X8/DyXLl3aNNq4klAkvi8rFTgEQaCoqIixsTH8fn88neuQD0EQ2LdvH+fOnUPTNGpqam77TA7nnkRRxOVy0d3dvazBbq11ks1UbGcuR6pneWSQQbJ417vexXvf+17cbjd/9Vd/xa/+6q/yyiuvrDqus7Mz/n+Px4OmaRuueyfs+e06p9XRgf6+9+H6u79DWFrC/YdPYzzwIPj8mO94DFtyIR3+CcLFC0jXr2FbJnZ+IeKVWczqGlBVuHYNVB+CYSD4fCDLiBcv4H/7w7hOnQLA7OyEigr0f//vkf/lX9A+9zmEkZG08JObleQ6QxMTS3Id31VaWrqlc6z0U+sFfBw/5QR2nB9nDU3TUBSFYDDIoUOHuHz5Mnv27EkZKRBFEZ/Ph6IocWncjYJ728mqq6oaD6SFQiGqq6s39LPb9W3pHhjLkI1bcBrvvF7vttdIV0KQIRsxOCotkUiECxcuLCMUjhxrYt9EYWFhvNRpq8Zxp0Y60VhD7HPb1dXFyMgIhmFQUVGBYRgsLS2xtLREcXExwWCQiYkJpqamaGxsXHeDvxvN5CvXdhrsTpw4weDgIO3t7asI/nZ6NjJkI4O7CeXl5Zw7dy7++/nz55cNugSWNXw++eSTfOITn9h0XUeNaiPcy5kNgOgf/RGur34VLAv1C58n1LsfS1URb9xAGOhH8Hph+ijU1CCcOY3VEcTCBtmFLQiIto2tRREkEVGLkl2Wh0vTMBOyv+FvfjN2rq98hahtw1200XNKnVZm0FeqiTmEYisluTdv3lz22FpBr7V8hPO7Y8MTs8Wb2VdnVIBDOEpKSrh+/TrDw8O0t7dv+PxkA1579+7l8uXL9PX10dnZiaIoKVl3ZSBtYmIiHkjb6YDBxPXvBWTIxi1sxYhvht1U5NhttY/dJjJ3Ayt3Sp1WGmmnsVIQhHg5nWVZZGdnbyr5uNY5VmYlnH6NxPNAzNhKkrSMVDh/3wiJ0pXOj8vlYnp6mhMnTuD1elFVlUAgEDeqLS0tTE9Pr2vId7uMKvF4QRCoq6vjwoUL9PX1EQwGt62LDjsro8ogg91Ab28v09PTnDp1ivLycp599lm+9rWvLTvm4sWL8Ujy888/T1NT06brOkGxjXAvZzYA8HoJ/8VfoH4oNm9D/ud/Qnv/BxCuX0MoLEQQQJqdxa6qwg5kIV28iF1SgnD9OkJJKVTXYJsG7k/9P6h//qcA2KpKeGICf3Y25sGDkFjOc5t9l+OnTNPk8uXLq0pyBUGIqzrtRE0ssdTJsiwMw1hzD5QY8ErGT20FsixjGEa8wby0tBRd1+nv76ezs3Pd+9mOvyovL8ftdtPX10dHRwc+n29H6648XhRFWlpa4r1VHR0dq8RHtltGdS8gQzZuYStGfDOka/bhXspsGIaxZt+E04CWWI/q9XrJy8tDVdW4UZibm+Py5csb9jhsJzuRm5vLtWvXOHXq1LrlQw5WkqLEH8fZOE15Xq+X7OxsSktLaW5u5tixY4iiSENDA4IgxA2506R95syZNQ15qpRAkjm+rKwMj8dDf38/7e3tBAKB+PG7bZC3kw3JIIOtQpZlnnnmGR577DFM0+QDH/gALS0tfPKTn6Snp4cnnniCz3/+8zz//PPIskxeXh5f/vKXN113K37qXs9sAJjvfS/Gf/7PyOEwrn/+J8z/891YogjFxdjFJZCVA6qKUFuLvbAQ69uYn0N8+SXEkUHEZ7+GKxKJrxf5ylcAWLx2DdYJLKXyHleW5Dr/JpbkOj5gZUluMnYatuan8vLymJqaIjs7G7/fH/dbuwlHNS0UCrG0tMT169cpKyujqqoKRVE4cuQIXV1da8o8bzc4VlBQgKIoDA0N0dzcHJfddWBZVlLqhCt9jyAI1NTUoKpqnNQkVstsh8zcK72FGbJxC4qixLWgt4t0bhBPl8yGI70XCoXQdZ1QKBSvR3VKnRLrUbOysvB4PEnpYTuGeatkYqsp5MbGRk6cOMHY2BiNjY1omkYoFFpGJlb2fzg/iaRoo/toamqKn6OlpSU+R8JJVTuG3EknO4Z8tzMb6zmuRF30hoYGCgoKdlSmtVVk1Kgy2G08/vjjPP7448v+9vTTT8f//5nPfIbPfOYzSa15t2Y2bjvBEQT6/+IvOPBrv4YAyEODGI++DauoCPHCDHZXD0yOI0gSVlkZrj/7Y9yf/tSyJaK/+ZvYxcW4nn0W82d/NvbHFEkDrxycmPjvWiW5jppYYqnTkSNHqN5ASCOVpU5ut5v29nZGR0dpbW2NB35S8TqsFTRLlHR3fFxJSQlZWVnouk5paSmKotDf308wGFxV3r4Tf5WVlUVXVxdDQ0PU1tYua+pOlW8rKSlBVVUGBwdpaWkh55ZE8nYCafeKn8qQjVtIVRlVOhOCuwG2HZsyulY9qmEYiKKI2+3GMAwURSE3NzdusJP9Eq+llmFZFouLi+i6HleB2E6p08q62pU/P/rRj8jPz4+XPCX2f+wkouSUKJ09e5ahoaF4T4QgCHEBhLUM+e0so1oJv99PT08PQ0NDcdK423WtGTWqDNIRjpDJRrgTn+k74UNClZVYfj/S4iJMTsCTH4xNEl+cR9Ci2HsqYW4O99sfwTUxEX+etWcPwrlz6J/8JIgixoc/nPS51yvJjUQiy0qdnI30dkpyYWuqTpCaUie/309HRwcjIyM0NjbGN8gbYaW/Tgyerdc/4vV61/VzTjmXbdvk5ubS2trK4OAgra2tq6Rld+KvVFVd5nMcdbdUlvBmZ2fHSU1NTQ0lJSWZMqoMtmbEN0M6E4LbuXaiRGzivyunjKqqGteQd6ZjO5iZmQFYd6rmdhuxc3NzKSwsZHp6mra2tg2/6OuVOjmfo0RnEwgEKC4ujtfVnj9/ntnZWaqrq3dl0FZlZSUulyuuwKEoSrw+di1DfifJBhCXQBwZGWFxcXHLa0NGjSqD+wepKPfdDdypgFXowgX8WVm4/vXbmL/3Kbg8Cz29iL/x64gvvYh8+TIAtteLEAoR/ehH0Z9+GjQNNvn+J5bkOtn08fFxdF2Pl+Q6fiqxJNcJ7mwFG/kpZxq3MxBvLcGQVNswr9dLMBhkaGiI+vp68vPz49ex8ifRXyf6OUfSfTt+TRTFeDbeMAz8fj+dnZ0MDQ2xb9++LUvjbgVOU/fo6CjhcJiGhoaUZ9Udparh4WGWlpYyZCOD1BjxTBlVDI70XiQSQdM0Tpw4EVd5SpTecwx1dnY2Ho9n1ZTRjeBcr2macSPt3MvKe0um1AmgpqYmnhmor69fFrlxflZKCKqqGp/qvZX7qKioQJIkBgcHCQaDuzLFurS0FJfLFW9WcxzAWoY8mSj/TuT7NoIkSQSDQX74wx9y7NgxgsHglp633TKqe8WIZ3D/IBUZ+N3AnSIbtm1jlJcjz8wgmiZWTw+ut78VaWI89rgkIZgmoW9/GwoLsSsrYw3fbne8JHetwFdiSa7jpxRFYe/evfGehq1gJ6VOTU1NTExMEAwG1+xbSBXWEhzxeDwMDw+jKMoyMpE4FTyZ0uRk4Nhlx095PJ74fnkNugAAIABJREFURHCnxCqV52pvb4+Lp6iqmvJAmizLdHZ2MjU1xcLCQlLfk3vJT2XIxi3c72VUySCxRGiloV6ZOnWyBT6fL2WlThDbmF69epWioiIkSdp2qdNaRMJRjdJ1nYGBAUpKSvD5fPHIVbL3sR5KS0uRZTlOONaT49sJCgoKkGWZoaEh2tra8Pv9wGpD/tprr3HlyhUqKys3XXM3MhsOBEHA7/eTnZ0dV6ra7HXZbmYjmXKGDDK4G5DJbKxGZHISf1YWygP7l/3d9niY/8Y3WGpoICQIMT81NbVMFTCxbyInJ2fDUqfZ2dk1A0m7VeqUn5+PKIoMDQ2tUuxLBskKjuTk5FBaWookSYyOjrJnzx5KSkq2de7tIpFw6LqOy+WKTxvfaW/tSghCbB7V2bNnOXnyJFlZWUk/fzM45PHKlSsMDw9v2d9nyMY9iFSpUd0LmQ1HJWItMqFpGoIgxFWdElOna0nvzc3NkZubu2ZKdSczJ0pLSwmHw5uqOzmqH2s1YgPL7sPn88VLtpxMw8zMDJcuXaKurm5XNqeFhYVIksTAwMCOHMpGyMnJoa2tjbGxMZqamuK1r4mGPCsri4sXL2Lb9obTiWFncza2Asuy2LNnD4FAIE44Npp/k1GjyuD/Z++8w9sqz/7/PbK2vPe2LEt2vOSRhJBSeDMbwkjal5FBidMEeAlQQlnh+rVAUgqk9AICpINZKCtJKZDSNy8USiHMpLEs7z1lxfGI7XjIkizp/P4w5yBZw5oe8vO5Ll1J5OPzHCnSfZ/vc6/FAhEbtmtOTk7CYDCAWrMGoZ9+CgsA7X//N84rlTj/wx9OpeSOjNjYd6ark7tY+ylGpLjrp3xNdYqKisKSJUtmFBzWg/xmKsT2pOFISUkJKisr2RlOs4m14GAanDBpT8zr8mdkJT09HefPn0dzczMiIiJ8mrnmCOa+KSMjw2X7XWtIN6ogxF/dqBZKZMPaOJ0/fx5jY2MYGxuzMU7Td33cMU7WMEbabDYD+F5U+CPVCQDkcjmamppQU1OD5ORkO2M7vTuVNyHglJQUcDgcNvoQiPqK6Oho1qEUFhbOaIC8gSn+q6yshFwuZ3NfGUNOURSUSiUaGhpgMBigUCicvj+eRgW8mcpKUVPT0QUCASoqKhy2KbQ+3pvIRjAYcMLiYrGJDSbVafpcJIPBgLGxMVRXV0/Z9qefRszJk7Bs2gSRWAwZn48sH1KdHBEaGgqtVousrCyfCrE9JTIyEjk5OVCpVMjKymI30FwVYjObZr42HOFyuSguLkZ1dTXMZvOMG1H+hrl262njSqUS//73v9mOi/5874VCIWJiYpwWpfsKTdNISEiASCRy2n7XmmDyU0RsfIdAIAiqNCqapp3mozLGyTolSCgUIisry2PjNFMImc/nY2BgAImJiV51y3AnBDw5OYmxsTGkpKSwXT9EIpHfIhFJSUmgKCqg9RWRkZHIz89HVVWVz60Hu9Ly7J5L19RBJBKhtLQUlZWVMBqNiI2Nxfj4OHQ6HXQ6HSiKQlFRERoaGlwa8kCmUQG24sRRRw9Hx3tbsxEshpywOBAKhbhw4cJcX4Yd3vo+Z90Hp99EMzfSUVFR7HwepvkFuwGUmel0HX+kOmVnZ6Ourg4ajQZZWVkev9aZmKkQm8vlor6+HqmpqewgP28LsT0hJCQESqUSNTU1aG1thUwm89puikV1Ln+um7D3XUxXSOtp4wKBABKJhPXJ/vL1NE0jLCyMrWVUKBSIi4vzy7mtCQ8Px9KlS6FWq5Geno7k5GSHx5E0qiBkoQ31Y27CHQ2wm956j5k3wRin6V/M8+fPY3h4GCKRyG5dX1KdKIpCfn4+KioqIBQKER8f7/C1MVEWZmaG9ewMd0LANE2jqakJOp0OaWlpAbmBZMRSRUUFSkpKAiI4wsLC2F7n1ulOjnAkKFzRlZaHiU/eY514Q0MDBAIB+15mZWWxDnnJkiVob293asi9CV/7Ik6Yjh5qtRoTExOQSqU2P/c2shEsRpyweFiIkQ1nfmp6Sq5QKHTafdDZmgy++il3bAFFUcjLy0NtbS3a29uR6ULcOMK6ENs6rdfRjChnUfiRkRHU1dUhOTnZ72k+ruBwOCgsLER9fT2am5tdRr5nEhSuEIvqHAoOwHbaOADIZDJotVp2ZpQ/ah4ZXyIWi9kaEb1ej7S0NJ/PPR2hUMh2YNTpdGzEzJpg8lNEbHyHv8SGP7GeMsoYp6qqKrvBb8yuj7dTRmmaZguGPemW4U50gsPhoKioCCqVCjqdDnw+32kI2NuZE0yBV2NjI5qampCdnR0QwZGQkGCTUhWIgm6JRIKioiL0Zy+Fv/cvs/dugf69b9jOJjU1NeDxeMjIyABF2U4bd2XI/Z0rOx1H5+dyuSgtLUVdXR3q6uqQm5trE2In3agIiwFPGpkE+nvKwGwWGQwGdHd3sz7LWffByMhIj7sPOkt10uv1Np2avE3JdRdGcNTU1KCjowNSq6F77hZiM2LCOkrj7s58eHg48vLyUFlZCaVSGZCUW2dQFIXc3FxIxPUA6gO2DiM4nA0EnJiYYLsqpqSkgMfjsX7K0YapJ1j7Ej6fz4qBiYkJlwLLW5hOVY2NjaiqqrJrt+/pRPP5DBEb3zEXLQUdTRmdvtNhXTcREhKC7Oxsj1vOzRRC5vF4uHDhAsxmM9sj3FMjzexeTS/EZgQcj8dDZ2cn4uPjERMT4/cQMEVRyMnJCbjgiIuLs0mp8qUloafRiemk5XsudoQ/WQnqQzUAoLCwEI2Njaivr0dubq7dtHFnhnw2iqsd/d9xOBzk5+ejra3NJoWCzNkgLBbc9VNMpMEfNtBR98HpKbnMdXE4HMTExHhVL+BuqhPjn1JTU9kW2SEhIbPyfWaEVWJiIlpbWzEwMAAej4eJiQkA8LoQ2xPCw8NRUFCAqqqqgAkOX6IT/ly/psbkcCAgAHZIbXx8PPh8PlQqFZRKpU8pyNO/M0w79oaGBna6ur8/ZxRFYcmSJejq6rLrwBhMfoqIje8QCATQ6/V+Pef0nQ5rQ23des963oSr1ntardZhNwpfQ8hCoRCZmZmor693mv9oXajn7cwJg8GAiooKJCUl+WQQnMEIjoaGhhlDvb4QGxvLRjhKSkqcCg768mKbf2tqnUfOvBEO3kJfXgzRyijoH/43cnJy0N7eztaKMC2EXRny2doxdQRFUcjKykJPTw/+85//oKSkxKvdn2Ay4oTFg7uNTDxJ6XU3JZfxU0xKrnX3wcnJSbZRhzP8neqUlJQEi8WCmpoaFBUVufVa3Xkvps+ccBaFT01NxdmzZxEdHT3jAFh/ExYW5pPgmGsx4S4Xr+BCN6Fw+DPrIbXh4eFsA5SZiq5d4egzyUR0Ojo6UF5eHrC6zfT0dIhEIpw5cwZKpRKhoaFBVVtIxMZ38Pl8jIyMePx71lNGGSNdWVlp11qVMVC+ThkNRKoTAMTHx2NiYgIqlQopKSlOhZFYLIZQKGRTtkQikdtGViAQsAYhPz8/YIJjyZIlARcc0dHRyMnJAX/zCrhbpTObgsIRopW2Blh4YDX0D/8bMpkM3d3dUKvVUCqV4PF4Lg35fOiQkZSUBKFQiPLyckRHR3uc0kZa3xIWIu5GNpgBs8zGkXVKrvWf/krJZcQNE52wjlA4OpaiKK9mI00nJSUFJpMJtbW1KCgocOta/TkROzk5GZWVlTh37pxLoRUIwsLCUFhYyKbfMDOUGBaKoJgJZ3Uc06eNi8VidvhfVlaW0xpRV7jyC1KpFEKhkI30B2LQYlxcHDtQccmSJUHlpxas2Pjwww+xd+9emM1m3HTTTXjggQdsfm4wGLBjxw6Ul5cjJiYGR48etcmvnI6zblTTp4xa56M6mjLK5XKRk5Pj91QnYMpIj46OIjw83CsjbT1zwjrlyVoYWSwWaDQaZGRk2M2c8AcikQhKpRKVlZUODaQ/YARHfX09WlpaIJfLvb45nh6dsCbS2wucJaaLC4fHvPcTTPzkPaSmpoLH47HpSQKBwKkhFwgEbOvcuSQqKgrFxcU4ffq0x91IGME016KJENwEwk85qi10lJLLtLF2lJLLzJvwJtUJsK+dYNY3Go2siGB8VKBbxGZkZKClpQUNDQ3sDZrBYLBL6XVWiB0REcG+F57ag5CQEBQVFUGtVoPD4czq8DuxqA5iEbB2TQSArllbdy5wJTiA74fUMnUWFRUVMBqNSElJ8WidmaL2iYmJEAgEKC8vd1vcekpYWBj7GrhcrtdRmvnGghQbZrMZt99+Oz7++GOkpqZi+fLl2LRpE/Lyvv8wvvzyy4iKikJLSwuOHDmCffv24ejRo3bnslgsOHfuHNra2tDQ0ID9+/fj+uuvh16vh8lkAofDsenqFBUV5XKKdHd3d0BSnZhCa7VajaKiIoedKJh2t452bRwZWabfs7WRpWkazc3NGBkZQUJCQkC+TGKxGIWFhaiurnb6WnyFCX26IziEB1bb/HvimyG/X89s4I64sIZSTsklRnAkJCSwgkOpVLL/L9MN+ZdffomBgQHExMT4/TV4ikQiQVJSEvr6+iASidzuGkLSqAiBxp9+iqZp9Pf3o7m5GVqtFr/85S9x/fXXw2Kx2HUfZNJXmRapns7EYdbz1E9lZmaiqakJRUVFAf1uOSrENhqNGBgYQF9fn01qsreF2J7ACI6KigpQFIWEhAS/nVssdlKIPUfT2ucSdwQHM6R26dKlqKys9Dg13p0UYWaTS61WezTE2ZNOpQKBAMuXL8dXX30FrVbrVZRmvrEgxcbp06chl8shk8kAAFu3bsXx48dtjPjx48exf/9+AMC1116LO+64w+6DVF5ejptvvhlJSUkAptKPLrnkEqSmpkIsFntUvDwbqU5CoRA5OTmoqKhARkaGjcF15HC8mTlBURQUCgXq6urQ0dHhcXs/dwkNDWVzTouKinzuIuEIRnCIfr3Go99zdNM+HwWIp+KCgREZNuf6TnBER0ez3U6s531YG/K4uDgMDw/73HPdX3A4HCgUCvT09ECn07nVHICIDUKg8Zef0mg02Lx5M+Li4hAWFoaRkREoFAokJycjMjLSYUru0NCQw5vr6WKC+bsjOByOR6lOKSkpMBqNaGhoQG5urk92wVE7dEdpX9aF2FlZWWhsbERERMSsD59jht8xEQ53ZzM4FRMzMdN7u9DFiJPXJxbXQ6fLtXve0bTx4uJi1NbWejRt3F2/IJFIUFJSgq+//hpardatCIqntY4hISGIjo6GxWJBS0sLcnPtX/dCYkGKDa1Wa7ODmZqailOnTjk9hsvlIiIiAufPn7dJ/1i6dClUKhWAqXD3v/71L/z0pz91uq47qU4CgQCDg4OIiYnxOtXJOjrhbOZEe3s7ZDJZQLpdMDfpVVVV6O7uRmpqql/OO52wsDDk5uaisrLS5xzI6dEJf+Psxn42RYi34gJwLDDszl9zFyYKDrE1GlVVVcjJyWHDuIwhB4Ds7GxotVq2k9VcCg6LxQIul4uioiI0NTWxKXquBDYRG4RA4y8/lZaWxvqpmpoaPP7449i5c6fTdZlohMlkYje+GKxveAKR6iSVStHQ0MD6J2d4UojtSTv0goICVFZWgsfjzXoNBY/HQ3FxMRvhYP4PvRYUvjCXYiTAvmAmwWE9bXzJkiU4f/482yrYnSHC7voyLpeL8PBw9Pb2Qq/Xz7jx5o3PoWkaUqnU75PM54IFKTYCAZ/PZ3M6fUl1KigogEqlQmhoqMMCaE+MrFAodGpke3p6cO7cOSQlJQXkpokZ4qNWq8Hj8fwaGrYmIiICOTk5UKvVLgfziN77id1zdNVwQK7JE1wJAF+FiC/iAnBPYEBhuwPICA6xWMxOUc3MzERcXBzMZjPGx8cxPj4Oipoa2NjS0uK2IQ8UTBEd041Mo9HMOOiJiA3CQsRdPyUUCjEwMID09HSvBth5C1MvV1lZia6uLkRFRdn4OJ1Ox25YeFKI7S4cDgdKpZLNd5+N9JPpYmLVqjAA/d895ikLvFbNleCwnjZOURQkEgmio6NRXl6OkpISl58vT8QGk5peXFyM+vp61NbWIi8vz+n3y5sujoyfCkT632yzIMVGSkoKNBoN++/u7m67MBZzTGpqKkwmEy5cuOAyx7y3txeffPIJ9u/fz35YvEl14vP57G69VCq16XzhTyOblJQEg8GA+vp65OXlBWRnmclFValU4HK5AcvRj4qKglwuR8T/bgnI+ecKT6MhvooLhhlFhsJ1ioGo5i40RdyDiYkJ8Pl8dvifWCxmO6rxeDzQNA2FQoGuri6nhtyTPFVvmX6zlZaWxnYNKSoqctgW0lqgEAiBIBB+anx8HN988w30ej14PJ7TVCeFQoGKigoMDw8H7IbbWTt0Zv5GS0sLIiIi2DboERERdu3QA4F1DQWXy0V0dLTP55yT6ATBJc4EB/B9W1ymZiM9PR18Ph//+c9/UFpa6jSLwpNNKEY8cDgc5OXlob293Wb+ky/ntl4jWDbFFqTYWL58OZqbm9He3o6UlBQcOXIEb731ls0xmzZtwmuvvYaVK1finXfewZo1a1wauG3btuGbb77B4cOHcd9997lc352ZE2KxGG1tbcjKynI4c8IfZGRkoLGxkV0nEDC5qIzg8CWc5yg6weBNohaljJwX0Q1P8ZeosMabKIbD86QsAQDk4H+hEV/Hdqmqra1FREQEpFIpm6LBTBt3Zcg93c3xRpw4MuJxcXEQCARQq9UO+67Ph/a9hOAmEH6qpKQEd911Fx588EEcOnTI5fpKpRLl5eUQCoUIDw/3+Pp9nYhtNBqhUqkQFxfn1fq+wOPxWMGRm5s7o98iYmJhIhbXY2REwUbNrCNoTHfR2NhYmM1mJCYmgsfjoby83OUmlCeRDcbvUBQFmUyGs2fPOm2N641wCCY/tSDFBpfLxeHDh7FhwwaYzWbs2rUL+fn5eOihh7Bs2TJs2rQJu3fvxo033gi5XI7o6GgcOXJkxvM+9dRTuOqqq3D8+HGUlpZicnISIpHIxshaz5yYbmSnz5zo6elBb28vkpOTA/KBYdJGAl1bwefz2U5YM7WrdSUoAgFzk70QRYc/8DWKAXwvMKaTxvkrdOJbAUzduNTX17PT2adPG3dmyD0VG/7c/QkPD2fb9UqlUrYRBEOwGHHC/CRQfuq+++7Dzp078cILL+CWW25xehyPx2PnMDjbzbUuxJ4enWDOMb0Qm2meMtP3h8/ns23Oi4uLA9IExBXMXCe1Wo21a+d7o3KCt4SHN6OhwczW90RGRkIsFrN1rEwNB5OdUVBQALVajYKCAoci1F2/4Mi3JScnQyAQsFF163slb4RDMKX7UjPsJC7wlgbu88QTT+DLL79EV1cXNBoNMjMzUVZWhrVr19oYW09nTrS2tsJkMiEnJydAVz7lMJgOVe52wfCGsbExxHX86vsnmjsDtpY3LBbBEUiB4Qhd1JTgoGkaLS0tMBgMbG6qxWKByWQCl8sFj8fDyMgIqqurWUNuMplQXl6OFStWuLWWp8cDgFqthkKhcDpF12QyobKyElFRUcjMzARFUfj666/xgx/8gE1FWQAQZeScReOnGAwGA370ox/h/vvvx+rVzptj0DSN3t5etLe3Iy0tzaZekKkRtPZvzMPT+RuuuHDhAurr61FaWurx8E1PINEJgrO0KsZPhYSEgMvlQq/XQ61WIzs726YZA+MX3GF0dBQdHR0oLCx0+LOqqirk5uayaXxjY2NobW31aNr96dOnUVJS4lFH0TnGqZ9akJGNQHDllVdi27ZtSE5ORltbG6677jpcffXVPue7ymQy1NTUQKPRuD0DwFOsayv4fL7PnQtENXc5fn76E27c1M6mIFmoaVXu4I80KU8EhjXioT9BF3Ur2xa5o6ODLQpnjDfT9SY0NJQtLM/OzkZ4eLjHQ8O8CTW7+h0ul4uSkhKbIj4CYSEjEAhw7NgxbNiwAYcPH2YHmFlHKqxrBPl8PjQaDWQymV8KsT0hIiICWVlZqKysRGlpqdc3TURMEGbCVeG49ZBaoVCIZcuWQaVSwWg0etW5zFWkIiwszC6q7m0a1QLZDJsRIja+Iz8/n/27QqHA7373O+zYsQPHjx/3qSUr07VHpVJBJBIFbPIyj8eDUqmEWq2GUql0ussLOBcTAcH6BngWhEcwCQ5/12H4gnjoT0BMHnSWyyCVSsHn86FSqVBcXAwej2dnyBlD62kKoTcdO9z5HaaIr6OjAyqValYK1wmEQDA2NoabbroJHR0d0Ov12LZtGwoKCnDfffchIyPDaY1gc3MzRkdHA9ZZ0BVxcXEwGo2oqamBUql0+n0lgoLgK+7M4mCi8cuWLYNarYbRaIRUKvVonZmEgFAoxPLly6FWq6HX6xEVFeV1GlUwpPwSseGE9evXo7GxEXv37sUf//hHn/6zmXZ8KpUKAoHAYUtcfyASiVBQUIDq6mqsDp0593fWYW6MAyw6FqrgcEtcALMmMFhivo8EiDknobNchuTkZPB4PKhUKhQVFUEoFAL43pDzeDx258hgMLi9VCAiGwzMlGORSISqqiq22xaBsJAQi8U4cOAAMjIyIBQK8e677+L555/HsmXLXEYr5HI5Kisr0dPTY1e/NBukpKRAoRgB0DDraxMWF+4IDmZIbWlpKaqrq9HY2OjRJpQ7m1xcLhelpaWora3F8PCwV/4mGIQGQGo2XELTNPbs2QOpVIo777zT5/ONj4+jurra5wF2wHc7zU6gtQvEmAdYdMxnweG2sGCYLYER416Kkc5yGQBgeHgYDQ0NKCwshEQigcVigdlsBkVR4PF40Ol0OHPmDJKSktya7K3T6dDY2IiSkhK3L/nUqVNYunSpR2khJ0+eREhICC666CKXDQ/mEcHhcQLDovZTAPCb3/wGvb29+O1vf+vyOJPJBJVKhezsbERGBqZoWiyqs30iSG6WCAsPVzUcZrMZANgIYGNjI7RaLVavXu3W5tX58+cxMDDgVj0uTdOorq7G8PAwLrnkErdTCYOptpCIjRmYnJzExo0bcdttt+Hyyy/3+XyDg4NobW2dMXfVlZjwlHkvPgIoOuaD4PBYWADu1cPADwLDTXHhCEZwjI2Nobq6Gnl5eYiIiLAx5JOTk2hpaYFIJMLk5CTy8/NdGk1viui+/fZbXHTRRR4Z46+//holJSUICwtb8IV3BOKnaJrG9u3bcemll7qcLg4Aer2enQcgFos9XotNdXJnF5gIDcIc40pwMMMx+Xw+OBwO/v3vfyM8PNzprAxr+vv7MTQ0hOzsbLeuY2BgAB0dHTCbzS4HzloTTGKDpFHNAI/Hw9GjR7Fu3Tqkp6f7XFwaHR0NvV6PmpoaXNy2f+rJ724s/Zr6YsX088478RHA9KrZTKnySlQAbgsLdh1vPyc+CAtHMClVoaGhKC4uRmVlJRQKBTuUjGk5yEwVZoYeFRcXO73B93bKqjezPKa3qiYQFioUReGVV17BunXrkJWVhUsvvdTpsUKhEHl5eaiurkZpaanDDotu1U5QlHuCg0CYQ9ydNs7j8SAQCJCYmMgOqXUlCLxp684MuXQ1cDZYIWLDDWJiYvDWW29h+/btOH78uNdF3sIDUy0KZd89MO3mlNY2BExwWDNvxUeAREcgZnF4LSwAj8UF4IXA8LOwcIaYcxLj5ksREhICuVyO+vp6NlrADP+Kj4+HxWJBZmYm+Hw+O/TIkSH3tvuGp2KDiAxCsCESifDXv/4VGzduxNtvv+2y4JUZ0FldXY3i4mKb74NYVDMVK6LciPiRyAVhAeDOtHGj0QiappGSkmLjp5zNh/HUVzF+x3rgbH5+fsDSGecbRGy4SW5uLh599FHs3LkT7777rkeFPjRNw2AwQOjsgOZO9gZ0tgSHNfNOfASog5VPAsFbZkNYALMmLhwhCfkCX6gkEIlESEpKQl9fH2JjY7FkyRLWkDPTxmcy5N5ENjwlmKayEgjWJCcn45VXXkFZWRk++OADl5O7ExISoNPp0NTUhCVLvrc5uomC7wQHTcQEIWhgBMfk5KTNpHFmoOXk5CQkEgnMZjPi4uLYBihKpdJhUx9PN62s/U54eDhKS0tRUVGBrKysGTvEBYO/IjUbHvLUU0+htrYWzz77rM0HwGQy2UxgZR5MJx6BQACRSITio3tszmdzA2x1YzrbgsMVcy4+gHk3QNAhXggLYP5GLTyFqeGwWCyoqamBRCKBTCYDRVEwm80wm83g8/kICQnB8PAwamtr7Qz54OAgent7kZvreBfKEZ4MYgIAo9GIyspKLF++3K496DxmQVzkHEH81DSOHj2K119/HW+//bbLmiSaplFbW4uIiAibOVBiUc13f+MQwUEIKj7/fAxisZidOC4SidiJ49OH1I6NjaGystJmOB+DVqvF5OSk2y1zHR0/OTmJiooKJCQkICPD/v6B8W3B4KeI2HATi8WCnp4etLS04Fe/+hU7HXn37t2IiYlBSEiIzQRW5oPs6EPCpFMxLATBMZ05FSDzQXjMlrAA5q24cAQjOGiaRkPD1GdkyZIloCjKqSHPy8tDVFQUgKkOH/39/TY7rTPhqdjQ6/Wora3F0qVLfe4KN4ssCE8zRxA/5YCHHnoI4+PjeOSRR1weZ7FYUF5ejpSUFAiFQnajTFmo/+4IIjgIwYWzlCrAftq40WhERUUFZDKZTQSiu7sbZrPZoUhwRHd3NywWC9LT0+3Wq66uhlAotOnYSNM0vvnmGyI2Fhu9vb246aabIJPJkJ6ejvfeew9btmzBDTfcwM4Y8IRgEBzTmRMBMhvCgwgLj7AWHG1tbRgbG0NhYSE4HI5TQ56VlYX4+Hj09/djcHDQrXaCDJ6KDev2ukRsBAXETznAYrHg+uuvx4YNG3DDDTfAYDDAaDTaRN6ZSeM0TWNiYgJxcXGIiIhgN83i4zq+OxsRHITgYibBYTabbSaPV1RUICkpiY0AdnV1gaK2GaYEAAAgAElEQVQom4igK1wdT9M0mpqapkS+Usn6ylOnTmHlypVEbCxm+vv7sX79erz66qtutz6bjjuCYyGJjeksOPHhpahgWKziwhGM4AAAjUaDvr4+tp2gK0MuEAhw4cIFKBQKt9fyVGxYt9clYiMoIH5qGp988gk+//xzNDU14eOPP0ZUVBR++MMf4vbbb3cafR8bG0NNTQ1KS0ttahKnUqqI2CAEJzPN4mD8FE3TqKqqQlhYGLKystDZ2Qkul4vU1FS31uno6ACPx0NKSorTY7q6unDu3DmUlJSAoiiUl5djxYoVRGzMV/R6PS677DIYDAaYTCZce+21OHDgANrb27F161acP38eS5cuxeuvv+7TBOHq6mq2EI9JA/GUYBccwBymXLkSHj4KCwYiMFzDiI5z586hq6sLxcXF4PP5Tg05RVEIDQ2FXC53ew1PxcbIyAg6OzuhVCoX0gTxBeFp5ogF6acCyWeffYaxsTHIZDLweDxce+21OHbs2Iy7sMwsgNLS0mkdqr4b1LcwbngIBI+YSXAwQ2opikJdXR0oioJQKIRQKERycrJba7S3t0MoFCIpKcnlcX19fWhpaUF+fj6ampqCprYwKPs/CgQCfPrpp6isrIRarcaHH36Ib7/9Fvv27cMvfvELtLS0ICoqCi+//LJP6xQWFuLhhx/Gzp07YTKZvDqH/uF/2/zbUXvWeVGg7QNzJpYUGc4fPkClLGEfHhGTt6iEBjDVGhcAEhMTkZWVBZVKhYmJCXA4HISEhMBisWBychIURaG4uBhGoxH9/f2YYRPEJ2aj4xWBMJesWrUKV111FfLy8qBQKPCnP/0JO3bswNjYmMvfi42NRUJCAurq6my+g7qJ7+wWmatBCEKczZVh/BRN02y6YV5eHvh8Ps6ePevRGu52QYyPj0deXh6qqqrY4bjB4K+CUmwwu6PAVLU/czPz6aef4tprrwUAlJWV4f333/d5rc2bN2Pt2rV44IEHfD4XAys4rHbmg0FwLOQIjc8CY5GJDGsYwRETE4Pc3Fyo1WqMjo46NOQpKSngcrmorKxkDa0rvBEl3s7yIBD8gV6vx0UXXYSioiLk5+fj4YcfBjC187lixQrI5XJs2bIFRqMRAGAwGLBlyxbI5XKsWLECHR0dHq+5cuVK3HHHHbjllltgsVhcHpuWlgYul+t8HSI4CEHITIKDoijWT8nlcohEIrS1tWFyctKt83vidyIjI5GXl4exsTH09/e7/RrmM0Hrcc1mM4qLixEfH4/169cjKysLkZGR7Aj61NRUaLVav6y1b98+jIyMeB0pmR7dsCGIBAewsESH1wIDWPQCYzqM4IiIiIBSqURNTQ2GhobsDLnFYkF8fDyio6OhUqlmNOTeThwnYoMwV3gaeX/55ZcRFRWFlpYW/OIXv8C+ffu8WvfGG2/EkiVL8Jvf/GbGY7OzszE0NITe3l72OTa6ARDBQQhKXAkOLpfL+imz2Yzw8HDExcXhzJkz0Ov1Dn/PGk99lVAoRHR0NNv1aqETtB43JCQEarUa3d3dOH36NNuGMxBQFIWXXnoJx44dw+eff+7VOVymUwWZ4ADmbx2KXyIYRGQ4hBEcEokEJSUlaGpqQn9/Pys4jEYjRkdHAQDp6elIS0ub0ZB7IxyI2CDMJZ5G3o8fP46ysjIAwLXXXot//etfXqcZPvbYY6irq8OxY8dcHsfhcFBYWIiOjg5cuHCBfd5GcBAIQYgzwQHATnDExMQgJycH5eXlGB8fd3leT/2OxWIBl8tFaWmpy1k5C4Wg97iRkZFYvXo1vvnmGwwPD7O1Fd3d3S67AniKUCjEX//6VzzwwANoa2vz6hzuCo5gYb5EOUiK1Owh5pxkZ2lERESgtrYWX3zxBU6dOoWmpiZYLBaEhobCbDYjMTFxRkPui9gIhjxYwsLEk8i7VqtlC7u5XC4iIiJw/vx5r9blcDh488038Yc//AHl5eUuj+XxeFAqlairq7MR/KR+gxDsOBIcZrMZY2NjGBwchFarxfnz50HTNKKjo6FUKqFWq22E+XS8ERvBtCkWPK/Eiv7+fgwPT92oT0xM4OOPP0Zubi5Wr16Nd955BwDw2muvYfPmzX5dNzExEa+++ip27tzp8kNnDZOvPjw8jJ6eHtTdaJuKFYwF49OZbdFhLS48WpdEL/xCWlwtACAuLg7Lli2DRCJBYmIiSkpKkJubi7CwMHbnaCZDTtM0iWwQFhyzGXmfTlhYGI4dO4Y77rhjxiJXkUiE3NxcVFVV2TRBIYKDEOyIxfWoq6vDmTNn8O2336K8vBzt7e0YHR2FQCBAdnY2JBIJJicnERYWhpKSEtTW1jqtsfDUVwWbn+LO9QUEgp6eHpSVlcFsNrODjZjOHFu3bsWvfvUrlJSUYPfu3X5fu6SkBPv27cOuXbtw7NgxtuOOXq9nhygxA5X0ej1omgaPx2P7nkskEvTf/XfEPbXJ/uTNnX5r2TofoVKWeC2kAiZWiKgICDkZ7Wxb3OLiYtTV1aGpqQkKhcKmhoPL5bKGXK1WIzs7G7Gxsex53O3wYY03AoVACASOIu9cLtcm8p6SkgKNRoPU1FSYTCZcuHABMTExPq0rlUrx3HPP4cYbb8QHH3wAsVjs8hrT0tJQU1ODoqIiUBQFmqYxfEGOyIiWKcFBooSEIGTZMgoXLhSBx+M5/DkzpJamaQiFQixbtoytNZzeEtdTX0XExgJAqVSioqLC7nmZTIbTp08HbN0LFy7gk08+QXt7O/r7+3HRRRfBZDLh4MGDSE5OZgVFTEwMRCIRhEKhWx8mumrYdv4GpqIbNjfY1jfF5+v89ZJmnfmQVkUExuwg5pyEznIZOBwO21O8trYWeXl5bEGeI0NuNBpZQ+5tGhVJoSLMFf39/eDxeIiMjGQj7/v27WMj71u3brWJvG/atAmvvfYaVq5ciXfeeQdr1qzxy+f3sssuw0033YQ9e/bg1VdftTun9SaZyWSCwWDA119/zeaP8/l8iEQiLC2lieAgBC0RES1O53BYD6ZlNgqWLVsGtVoNg8GAzMxM9lhv0qiCyU8F5VC/uaK7uxuvvvoqZDIZZDIZfve732HdunVscZ+nWA/8czTsD7C6OfflBnkBixO/QMTFnMJEOGiaRmdnJ4aGhqBUKtmooPXwP4vFArVajdjYWEilUoyNjaGtrQ1KpdLt9bq6ukBRFDIyMpzuWM1Dgsfr+J8F5aeqqqrsIu8PPfQQ2trasHXrVgwODqKkpARvvPEGBAIB9Ho9brzxRlRUVCA6OhpHjhyBTCbzy7VcuHABt99+O4Cp/v6lpaXIyMhgi9aFQiE7aVwoFEKj0SAuLs5majI78A8ggoMQtDgTHMD3EY6QkBC27qqmpoZNt6Ioio3Mu4oiWtPX14cLFy4gJycnKPwUERsBRKfTYd26dfj1r3/t0YRja2YSHE6jG4FkIYsTIizmJYzgAKYKYnt6elBUNBW+ni44gO8NeWJiIrq6ulBYWOj2Wh0dHeDxeEhLSwsKI04gfspTaJrGypUrIRAIIJPJUFVVhUsuuQQ/+9nPkJmZyU5Lno7ZbEZ5eTnkcjmio6PZ58mEccJiwZNp401NTTAYDCgoKIBarUZubi5EIpFb65w7dw7j4+NQKBRB4aeCJyHMAzQaDVavXo28vDzk5+fjmWeeAQDs378fKSkpKC4uRnFxMU6cOOHTOmKxGH/9619xzz33oLPTu25S1h2qZiwWny0RsFAKpadf53y+1kWOmHOSbY2bkpKC9PR0qFQq6PV6u2njAFBYWAiaptHc3Oz1nI1gClETCJ5AURS+/fZbfP755/jzn/+MTz/9FF9//TUmJibA5/OdfjdCQkJQVFSExsZG6HQ69nlSME5YLHgybTwnJwdhYWGoqKgg3ajm+gLmAi6XiyeffBJ1dXX49ttv8fvf/x51dVM36r/4xS+gVquhVqtxxRVX+LxWSkoKXnzxRZSVlWFsbMyrczgc+jefZm/Mh5v6+XANBJ9hBEd8fDyys7OhVqsxPj7u0JAvWbIEYrEYAwMDNp1yZiLYjDghuPBmM+zxxx+HXC5HTk4OPvroI4/XjIiIwJEjR3Dbbbfh3LlzLo8VCAQoKChAVVWVzdBNIjgIiwV3p41bLBZkZmYiKSkJw8PDbk8bB4LPT5E0KgCbN2/GHXfcga+++gqhoaG49957/b7GW2+9hbfffhtvv/221x8gJqVq3qRTeYqnkZf5+joIAYdJqxodHUVNTQ3y8/MRHh4Oi8UCi8UCmqbB5/MxNDSE9vZ2mM1mlJSUgM/nz3ju5uZmREZGIjExkU3LWgCQMIxzgspP9fT0oKenB6WlpRgdHcXSpUvx/vvv49ixYw79U11dHbZt24bTp0/j7NmzWLduHZqamrwaBPbpp5/ioYcewt///ncIhUKXx/b19UGj0aCkpMTGp5GUKsJiwVUdB9PchMfjISQkBF9++SUoikJpaalbqVTBVlsYPLLJSzo6OlBRUYEVK1YAAA4fPgylUoldu3ZhaGjIb+ts374dSqUSBw4c8PocDiMcCwlH0QdXD8KihYlwhIWFoaioCHV1dRgcHGTrNpidI5PJhPDwcMhkMpw5cwYTExMznjvYunwQgoukpCSUlpYCmPr85+bmsgP+HHH8+HFs3boVAoEAmZmZkMvlXnddXLNmDW644Qb8/Oc/n3FKeXx8PGJiYtDQ0GBzLIlwEBYLM00b53A47MyokJAQ5OXlQaVSYXR0dMZzB5ufWtRiY2xsDNdccw0OHTqE8PBw7NmzB62trVCr1UhKSsI999zj1/UeeeQRtLS04MiRI349L4EQjDCCQywWo6SkBC0tLejt7QUwlTs+OTnJDvqLi4tz25AHW3iaELy4sxlmPWEcsJ0+7g179uxBZGQkDh06NOOxGRkZoGkaXV1dNs+zgoNACHJcCY6QkBCEhISwKVWRkZEoLi5GVVUVBgcHXZ6XmQcVLIJj0XrcyclJXHPNNbjhhhvw3//93wCAhIQEhISEgMPh4Oabb/b7TA4Oh4PXX38dzz//PP7zn/94dY4FH90gEDxAzDmJoaEhDAwMICIiAg0NDfjiiy9w6tQpNDQ0wGg0Ijo6Gmaz2W1DTsQGYSEw25th1jzzzDP4/PPPZ2ySQlEUcnNzMTAw4HhyMoluEBYBjOCgaRp6vR6Dg4Po7u5Gc3MzampqoFKpQFEUTCYTJBIJli5disbGRnbzzBHB5qeC55V4AE3T2L17N3Jzc3H33Xezz/f09LB/f++991BQUOD3tUNDQ3Hs2DHceeed6O7udvv3aJrGxMQEBgcH0bz8OdcHL+TWtATCNFJiqmE2mxETE4PS0lKEhYUhLi4OJSUlyM3NRUREBIxGIyYnJ90y5KQbFWG+48lmGDNhnMF6+ri3cLlcHD16FI899hhqampcHsvhcKBUKtHa2moTVdRN5JG6DcKiQSyux6lTp1BfX4/+/n5YLBZERUVBoVDgoosuwvLly2EymWA0GsHn87Fs2TJ0dnbaRQUZgk1sLMoC8S+//BKXXnopCgsL2f/Mxx57DG+//TbUajUoioJUKsXzzz+PpKSkgFzDV199hXvvvRd///vfIZFIAEx9uCYmJqDT6Wz+NBgMAKa6gIjFYnbIUtq537Dns5u8TWoeCEGG9fC/+vp6cDgc5OTkgKIou6FKZrMZFRUVSEpKskkxAYDKykpkZWUhMjLSqyLaOYLctTknqPwUTdMoKytDdHS0TSpTT08P64+efvppnDp1CkeOHEFtbS22b9/OFoivXbsWzc3NfvlsNzQ0YOvWrTh+/Dji4uJcHjs+Po6qqiqUlpaCw+Gw/ksm81/tI4Ew35lp+J/1zCiaplFVVYWwsDBkZWXZbIA1NjYiJiYGCQkJQdHIZFGKjbnEbDajqqoKra2tOHr0KNs1ZOvWrbjoootsJrYyfwoEAqe7sKKauwAQsUFYHFgLjtbWVuh0OhQUFIDD4bhtyJlJruHh4URsBAdB5ae82Qx79NFH8corr4DL5eLQoUPYuHGj367no48+wuOPP47333+f7fZG0zQMBoPd5tjo6CgMBgPCwsIgkUhYP5aZ6To/nUAIJtyZNs7hcNjhf8zohby8PNZP1dfXIyEhAXFxcURsEDzHYDBgz549yMrKQlZWFj766CPExsb61KVKVHMXERuERYP1tPGuri4MDAxAqVSCy+XaRTgcGXKVSoXc3FyEhYURsREcED8VACwWC9rb29HS0oKXXnoJGo0GfD4fmzdvRklJCQQCgc2mmFgshlAoRE9PDwYHB1FYWGizSeaqkJZACDbciXBYTxtn0hCVSiVCQkJQV1eHpKQkxMbGErFB8B2z2YzNmzdjy5YtuOaaa7w+j3joT7ZPELFBCGKsBUdPTw80Gg2Ki4vB5/NnNOQVFRUoLCyERCIhYiM4IH4qAIyPj6OsrIzdGPvwww+Rn5+P++67b8abn8bGRnC5XGRlZdk8TwQHYbHhTHQwfgoA+Hw+OBwONBoNenp6UFJSgsbGRqSlpSE6OpqIjWBGo9Fgx44d6O3tBUVRuOWWW7B3714MDg5iy5Yt6OjogFQqxbFjxxAVFeXTWiMjI1i7di0OHTqEkpISr85hJzYAIjgIQQ8jOgYGBtDS0oKioiKIRCKXhtxisWDp0qUQiUREbAQHi9ZPzSZGoxGXX3457rzzTvzoRz9yeSxN01Cr1UhMTLSpeyRig7AYcSU4rIfUcjgc9Pb2oq2tDUKhEHK5HFFRUUHhp4Kn1N3PcLlcPPnkk6irq8O3336L3//+96irq8PBgwfZAry1a9fi4MGDPq8VHh6Oo0eP4rbbbrPpiOUJuqhb7Z8kXakIQQ4ziyM2Nha5ublQq9UYGxsDh8NBSEgIO/zPYrEgLS0NGRkZGB0dhdFoJN2oCAsWjUaD1atXIy8vD/n5+XjmmWcAAIODg1i/fj0UCgXWr1/PzuKgaRp33nkn5HI5lEolVCqVx2vy+XwcO3YMBw4cQGNjo8tjKYpCYWEhNBoNhoeH2efZmy7SEpewiHAmsqcPqTWbzUhISEBOTg7Onz8PvV4/y1caOIjYcIKzKa7Hjx9HWVkZAKCsrAzvv/++X9aTyWR45plnsGPHDremIDuCCA7CYoQRHBERESgsLER1dTWGh4edGnKBQMCKEgJhIeLpZtj//d//obm5Gc3NzXjhhRewZ88er9aNjY3FG2+8gd27d884lIzL5aKwsBD19fU2Po0IDsJixJNp49HR0QgPD0dDQwO7YbDQIWLDDaynuPb29rJh4cTERJdDWTxl1apVKCsrw+23344Z0tuc4lRwENFBWASEhoaipKQEDQ0N7JCx6YKDy+VCqVQGVQ9zwuLC082w48ePY8eOHaAoChdffDGGh4e9jqLn5+fjwIEDKCsrw+TkpMtjRSIR8vLyUFVVBZPJxD7PThgngoNAAGA7bZzpVsUUiwcDxNvOwPQprtZQFOX3VIxbbrkF8fHxePLJJ70+h0PBARDBQQgadJbLbB7WCIVClJaWoqOjA2fPngUwJTgYQ07TNMLCwiAWi+fi0gkEv+LOZphWq7WZN5OamgqtVuv1mldffTU2bNiA+++/f8ZjIyIikJGRgerqaptNNCI4CIsBnS6XfcwE00VxcnISZrMZIpHI7r5zobJgStznAmdTXJnhSj09PYiPj/f7uk899RSuuuoqfPDBB7j66qu9Oocu6lbHRePn60jhOGHBMF1IuAuXy0Vubi5qamrQ19cHoVAInU4Ho9EIHo+HyclJ8Hg8P18tgTC7zPZmmDX33Xcfdu7ciRdeeAG33HKLy2MTExMxPj6OpqYm5OTksM/rJvIgFtVNCQ5SQ0VYoLgjJBxB0zT0ej10Op3Nw2AwsMNqvc1ymW8QseEEmqaxe/du5Obm4u6772af37RpE1577TU88MADeO2117B582a/r83lcnHkyBGsWbMGGRkZUCqVHp/D5QeUCA7CPMJbQeHKUANTKRzR0dHo6+vDZ599hl27duHs2bNobW3Fc889hyVLluAnP/mJP18KgTBreLIZlpKSAo1Gw/5ud3c3UlJSfFqfoii88MILbEH66tWrXR4vk8lQU1OD7u5upKamss+zgoNAmMf4IigMBoOdn2KKv5lBzmKxGLGxsRAIBDh37hxaW1vx/PPPIz4+Hjt37vTjK5kbSOtbJzib4rpixQpcf/316OrqQkZGBo4dO4bo6OiAXENTUxOuv/56vP/++w4jKDRNw2g02kxwZf5O0zR4PB4ulf3H8cmJ2CDMItMFhdlsRnV1NSIjIyGVSp3+nieGWiQSgc/n49y5c2hra0NLSwv758DAAHp6erBy5UpkZ2dDLpfjBz/4AfLz8wP5sv0J2fZ1zqLzUzRNo6ysDNHR0Th06BD7/H333YeYmBg88MADOHjwIAYHB/HEE0/gf//3f3H48GGcOHECp06dwp133onTp0/75Vp6e3uxYcMG/OUvf4FcLnd5rNlshkqlgkwmQ3R0NPvdTk31X+0jgeAtOl0uaJpGW1sbxsfHUVBQ4FZtn/W9mPWDaYwgEAhs/JRQKER/fz/rn1pbW9Ha2oqenh5wOBykpaVBLpdDLpdj+fLlWLZsWaBfur8gczYWKh999BH279+P22+/HSMjI7jkkktsPsR8Pt/mQ8z8af0FcZhOBRDBQfArnkYoLBYLamtrIRQKkZaWZmOgZzLUfX19Tg11RkYG5HI5FAoFsrOzkZ2djcTERDzxxBPQarU4fPhwIF5+oCFiwzmLzk95uhlG0zTuuOMOfPjhhxCLxfjzn//s1xuYyspK7Nq1Cx988AEiIyPZ5y0WC/R6vc1m2Pj4OIaGhiASiVifJRaLkZNDusMRAo+7EYqOjg4MDw+jsLAQISEhoGkak5OTDgUFMyeD+SyLxWIIhUIMDw+jtbUVzc3NrJ/q7u4GRVFISUmx8VMKhQJpaWkLvSCciI2FxpNPPom33nqL7aITGRmJK6+8Etu3b2dvuDzppkMEB8Ef+JLy5Gjnh7kRsVgsSEpKgkQiYQ310NAQWltbbQSFVqsFTdNITU31ylCPjIws1II7IjacQ/zUHGIwGNDe3o7XX38d//znPyGTyZCdnY3Vq1eDoig2+mi9GWYymVBbW4vS0lLw+Xz2XGToH8EfeJvyBMDGT/X29mJkZAQCgQAAwOPxbASFSCTC6OiojY9qbW1FV1cXzGYzkpKS7PyUVCplZ0AFIURsLDTGxsYgFovB4XBA0zT27NkDqVSKO++80+tzEsFBcAed5TKYzWZ2ArC7ud3e7PyMjIygubkZb7zxBpqbm5GQkACtVsuKj+mGOiMjI5gNtSsW3Qv2AOKn5pArr7wS4eHhkMvlaGhogFgsxkMPPYTExESX39P+/n50dnaitLTUNhJPBAfBDXwRFM78lMVisRMUg4OD2LdvH2699Vb09fWhtbUVbW1t6OzsxOTkJOLj4+38lEwmY1uuLzKI2AgEu3btwj/+8Q/Ex8ejpqYGALB//368+OKLiIuLAzAV2r7iiit8XmtychIbN27Ebbfdhssvv9zr8zgUHERsLDrciVCYzWZUVlYiPj7epqDTE0MtFAoxNjaGtrY2m50fxlAnJiayhlqtVqO3txfvvPMOJBLJYjTUriBvhnOIn5on0DSN7du349JLL3WrqLWrqwujo6PIy8uz+b4TwUEAgPb2aHR1daG4uNjj7oEmk8nOT+l0OlgsFnC5XLsIhV6vt0vN7ejogNFoRFRUFOrr63HrrbeitLSUFRQCgYD4KVuI2AgEJ0+eRGhoKHbs2GEjNkJDQ3Hvvff6fb3z589j3bp1ePHFF5GX571AsBEcRGgELd6mPAHfC4qxsTF0dHSwuzTeGOqYmBgoFArI5XKbnR9Hhvqtt97CFVdcYZP3TQBAxIYriJ9ygKebYY8//jhefvllhISE4Nlnn8WGDRu8WndiYgLr1q3Dww8/jB/+8Icuj6VpGg0NDRCJRHaNIojgWBzMFKHo6+tDZ2enQ8FhMpls6oGYh9lsRkhIiJ2fMplMaG9vt2ke0tbWBr1ej4iICDs/JZfLIRKJQFEUPvnkE7z//vsLteZvtiBiI1B0dHTgqquumhWxAQD19fXYvn07jh8/jtjYWK/OQcRG8NA3UorQ0FCvftednR+miPPkyZOora3F1VdfbWOo29vbodfrERkZaWeos7KyWENN8BnyJjqH+CkHeLIZVldXh23btuH06dM4e/Ys1q1bh6amJq+LVc+ePYuNGzfi7bffdtltDpgqIler1UhNTbXrukgER3DgS8qT2WxGd3c3uru7kZCQwHYwYwSFdZMBkUgEmqbR0dFhU+/HdJcKDQ1lI+nWaU/uRtJpmib+zDVO3xwyZyMAHD58GH/5y1+wbNkyPPnkk4iKivLbuXNzc/Hoo49i586dePfdd22K69zF6cA/wrzEWYRifHwc1dVVKCwsdCo4zGazQ0ExfedHIpEgNjYWFosF7e3tbHSCERYTExMYGRnBv/71L1xzzTVQKpW49tprIZfLScoTgTAPueyyy9DR0eHWscePH8fWrVshEAiQmZkJuVyO06dPY+XKlV6tnZycjJdffhllZWX44IMPXDZl4HA4KCwsRHl5OYRC4UJt4LDo8VVQOIpQmEwmcDgciMVihIeH47PPPsOaNWuQlpaGzs5Ou8Ls0dFRSCQSZGVlQS6XIycnB1dffTUUCgXCw8N99lPEz3kPERt+Zs+ePXjwwQdBURQefPBB3HPPPXjllVf8usYVV1yBhoYG3HPPPXj22We9+gLoom6FmHPSr9dF8B5vUp4kEgkKCwtRVVXF9rd3ZqiZBzMTpqurizXUzJ+jo6MIDQ21MdSbNm2CQqFAWFgYzGYzfvazn2HFihVYt26dX18/gUCYHRxthmm1Wlx88cXsMampqdBqtT6ts2zZMtx777246aab8Pbbb7uMkvB4PCiVSlRWVqKkpImvX+EAAB74SURBVARCoRDA1A0siW7MH8bHl3h9w22xWBwKCqPRaBehiIyMREhICDQajV2t369//Wukp6cjJyeHjVBs2LABCoUCUVFRRBDMU0galY9MT6Ny92e+wkw4LygowK233ur1eYjgmD18qaGwWCwOi7KNRuPUuXU6hIaGIj09HSKRiDXU03d+hoeHIRKJIJPJ7GZRREZGzmioLRYLKIoiBn32IW+4c4ifcsJ0H9Tb24vY2Fh2M6ynpwevvPIK7rjjDlx88cX46U9/CgDYvXs3Nm7ciGuvvdbna3jooYcwPj6ORx55ZMZjh4aG0NzcjKVLl9qIEyI4Zg9nEYq2tjbodDrk5+c7tf/WgsJaWBiNRlAUZZfyxOfzodFobOonWltb0d/fD4FAAKlUygoKhUKBnJwcqFQqPPzww/j888/ZlrSEeQNJo5otenp6kJSUBAB47733UFBQEJB1KIrCH//4R2zYsAFyudzrnWad5TIiOPwIIyhomkZjYyM4HA4UCoVbN+fOdn4mJyfB4XBs+tRHRkaCy+Wiu7sbra2tqKiowOuvv46UlBTodDoIhUIbQ71q1Srk5OQgJibGJ6HgyWwXAoEwv0hISGD/fvPNN+Oqq64CAKSkpECj0bA/6+7udrvl9Uzs378f1113Hd58803ccMMNLo+NiopCamoqampqoFQqWVtFIhz+xZuUJ5lMhpaWFtTV1UEqldr5KkZQMHNVxGIxEhISwOfzcfbsWbS1teHMmTOsoOjt7QWPx7MZwrpy5Urk5OQgPj7eqa/ZsGEDCgsLidBYYJDIhg9s27YNn332GQYGBpCQkIADBw7gs88+g1qtBkVRkEqleP7551nxEQj6+vrwox/9CK+++iqys7Pd/r3pQ9YUaa0Bu8Zgw90IBU3TqKurg1AoRFZWFoDvJ+o6CiU72/nRarV2Oz+MoWbyqxUKBTgcDp588kn87W9/Q05OTiDfAsLsQyIbziF+ygnTIxvWm2FPP/00Tp06hSNHjqC2thbbt29nC8TXrl2L5uZmv00zHh8fx9q1a/H4449jxYoVMx7f3NwMiqLY9FBgyp5KJA1+uZ7FgC81FDRNO/RTer0eRqMRZrMZqampkEgkEIlEEAgE6O3ttetI2NPTg5CQEKSnp9tF0pOSksjmVfBBulG5g1QqxW9+8xs2lLxQqK6uxs6dO/H3v//dphidpmm2c4P1LsTExAQA2A1ZS4+vm6uXMO/wJeXJ2lCPj4+zO4bMMDrrnR9/G+qqqiqIRCIoFAqvr58wLyFiwzmLyk8B7vkqTzfDHn30Ubzyyivgcrk4dOgQNm7c6Ndr1mg0uOqqq3Ds2DGkpaU5PMbaZzU1NUEgEIDD4dj4rB/+UOTX61rI+CoomPd6uqAAYOOnxGIxBAIBBgcH0dLSgpdffhn9/f0IDw/H2bNnQdM0UlNT7fxUamqq3wQrYUFAxIY7LESxYbFYoNVq8dprr+H48ePIy8tDamoq1q5dCwAQCATsTa31oDVHN6qLLZ3q5GkxSkpKvDKGznZ+DAYDANgJivfeew8ajQarVq2yERRarRYURdkZaoVCQQw1wRoiNpyzqPwUsDB9FQB888032Lt3L37/+9+js7MTSqXSJscf+H4TTCgUQqvVQiaTISEhYdFOGbcWFBMTE1Cr1S47EFozXVBMf6+Z+wPre4MLFy7Y1fppNBpYLBYkJSVBLpdDLpfjn//8J9LT0/Hss88u1mnZBHtIzUawcvnll0MgEEChUCAtLQ2Tk5PYtm0bpFKpxyHKYKzfcBWhSE4+i6qqKhQVFTl8r9zd+RGJRIiNjYVIJMLQ0BBaW1tRV1dnY6iBqaGMarUamzZtwpVXXgmFQoH09HQ24kEgEAjByIkTJ/DSSy+hs7MTAwMDKCsrQ3FxMXJychAVFYWUlBSHm2CJiYmoqKhAREQExGLxHF194HE3QiESiVBYWIjq6moUFxezcyWsU6Kt/RRN0xAIBOxmY1RUFJKTkzE6OorW1lZUV1fbdHoymUxISEhgN77Wrl2LW2+9FZmZmXaC4uc//zn++Mc/koYhBLcgkQ0rrHeLdDodtm3bBpPJhKNHj3o9OG02oWkaN954I1asWIHdu3d7fZ6FJjh8SXnq6OjA8PAw0tPTHaaaOdr5YQz19JZ80w01E6FgDPXExAR+8pOf4IUXXkBGRoa/Xj5hcUC8uXMWlZ8CFp6v0mq1MBgMSE9PB5fLxQMPPMB2xJqJkZER1NfXo7S01GaC9EKLbvia8jQ5Ocn6p6GhIfT19UEkmkopm54SLRKJMDExYdPavLW1FR0dHTAajYiJibEZwpqdnY3MzEwIBAIiHAi+QNKo3IEx4OvWrcPVV1+NZcuW4fDhwz6nsuzatQv/+Mc/EB8fzxbqDQ4OYsuWLejo6IBUKsWxY8f8MvxPr9dj/fr1+H//7//hv/7rv7w+z3wTHL7WUFgbamtBQdM0O0k7JyeHLXjT6XR2hrqzsxNGoxGxsbEOp2XzeDy3WseSojiCF5A7AOcsKj8F+M9XeeKbaJrG3r17ceLECYjFYrz66qsoLS316votFgt+/OMf45prrsF111034/G9vb3QarUoLi6e1+lUvggKAE79lMViAY/HsxEUExMTuOuuu3DHHXegr6+Prflrb2+HXq9HZGSkQz8lEomIoCAECiI23EEqlWLbtm04cuQI9uzZg/vvv98v5z158iRCQ0OxY8cO1qDff//9iI6OxgMPPICDBw9iaGgIv/3tb/2y3rlz57Bhwwa8+eabkMlkXp9ntgWHtaBw5lycMZOgmG6oRSIRDAYDa6DffPNNTExMwGw2w2AwICoqyqGhFgqFxFAvYDwR+SMjI8jLy8OPf/xjHD58eJav1A7yoXPOovJTgP98lSe+6cSJE3juuedw4sQJnDp1Cnv37sWpU6e8fg2jo6NYs2YNnnrqKSxdunTG49vb22EwGLBkyRKb52dbcFy4IIdKpUJOTg4iIyM9/v3pfoqJqFssFnC5XDs/ZbFY2C6EzAYYM/MiPDwcra2tuPnmm5Gfn4/s7GzI5XJIJBLipwhzAREb7iCVSqHX6xEbG4tvv/3Wr+Ho6S0Ic3Jy8NlnnyEpKQk9PT1YtWoVGhsb/baeSqXCLbfcgn/84x8IDw/3+jz+FhyeRCi6urowMjJiM0TImaAwm83g8/k2hfBisRgmkwnt7e0207KtDTUjJrKysvDyyy9jw4YNuPvuu4mhDlI8Efl79+5Ff38/oqOjidiY3ywqPwX411e565v+53/+B6tWrcK2bdvsjvNl7c2bN+Nvf/sbkpOTXR5L0zRqa2sRHh6O9PR0m5/5W3DMFKHQ6/WoqKiAUqmERCKx+zkTLZ/+MJvNdoJCLBaDpml0dnba+KnW1laMjo4iNDQUWVlZ7MwkZvMrLCwMFEXhtddeQ3t7O/bv3+/X94BA8AJSIO4uBw8exEcffYT169fjxIkTfkltckRvby9rpBMTE9Hb2+vX85eWlmLfvn3YtWsXjh49OqtdjXxJeQK+N9R8Ph86nQ7ffPMNuFyu3c5PWFgY4uPjQdM0Ojo67NKexsbGIJFIWEOdm5uLzZs32xhqa3784x/j3XffJUIjiDl+/Dg+++wzAEBZWRlWrVrlUGyUl5ejt7cXl19+Oc6cOTPLV0kgzEygfJUz36TVam1a1qampkKr1fokNqRSKZ577jnceOON+OCDD1wWgVMUhby8PKhUKojFYsTGxrI/83bon7dpT0KhELm5uVCr1cjIyLDZBDObzQgJCWH9lEQiQWxsLDgcDrq6uuw6PQ0PD0MkEkEmk7H1fhs2bEB2djYiIyNn9EdlZWWwWCxevQ4CYbYgYmMaXC4Xb775JruL8/HHHyM+Pj6gawaqm8N1112Huro6PPjgg3jssce8OoezDlWquni7cLYnmEwmh9Oypxvq9PR0fPXVV2htbcWVV15pZ6gvXLgAkUiErKws1lBv3LgRCoXCLUNtjUAgYHftCHODu2lOXV1duOmmm6DRaEBRFE6cOAGpVDrj+d0R+RaLBffccw/eeOMNfPLJJz6/JgIhEMyGr5qNTkOXXXYZdu/ejT179uDVV191uR6Hw4FSqYRKpYJQKHQrouNLHYXZbHbop0wmEzgcDoRCIc6cOYPCwkKkpqaCy+VCo9HYRNFbW1sxMDAAoVAIqVTKRihWr16N7OxsxMTE+PwekzpAwnyHiA0HcDgcvPjii7j77rtx6aWX4uOPP7YL2/pKQkICO821p6cnYILmoYcewpYtW/CXv/wFO3bs8Ooc0yMVU0XVtejq6nL5vpjNZqeh5JCQEJuUp+joaNZQTxcUg4ODGBgYwMcff4zVq1ezLfmys7MRHR1NIhFBxMGDB7F27Vo2zengwYMOIw87duzAL3/5S6xfvx5jY2M2znbdunU4d+6c3e88+uijNv92diP1hz/8AVdccQVSU1P98IoIhMARCF/lzDelpKSwbbwBoLu7GykpKT6txXDTTTehtrYWTzzxBPbt2+fyWD6fj4KCAtTU1KC0tBR8Ph+A96LCYrE4TM01Go12fioyMhJ8Ph9nz55Fa2srmpubcfr0afzyl79EaGgoBAIBpFIpu/F1ySWXICcnB3FxcUQQBBFPP/00XnrpJVAUhcLCQvz5z3+GUCic68ua15CajVliel7sfffdh5iYGPamanBwEE888URA1tbpdFi3bh0eeeQRrFy50i/ntFgsqKioQGpqKiQSiZ2xnpycZCMU1saaz+eju7vbbuenr68PPB7PxlArFArWUI+OjmLDhg145513yE1gEONOLVNdXR1uueUWfPnllwE5/w033IAvvvgCHA4HY2NjMBqNuO2223Dw4EGfXpuPEEXtHOKnfMBd3/T/27v3mCavNw7g30IH4yKKGwi2oHJRKzhRIJqMORgWFK9chhdYvDUaNybL1M3Jko1ki8aoG+oStxAvYQ5UMoGhYwJOl6nIGCAhqPGCEeoFdYAIArb09wehPxlFXia9UL6fhMTS0nPeGM7D0/d5zjlx4gT27t2rbRBfv349iouLB2wearUa8+fPx3vvvYeFCxf2+fpHjx6hurpa0MGsHR0dOu9QtLe3w8LCokevn7W1Ne7du9ejNPfevXuwsLCAu7u7Nk5NmDABp0+fRk1NDfbv388Pv8ycUqlEUFAQqqqqYGNjg9jYWERERGDFihXGnpopYIO4MS1duhRnzpzBw4cPMWrUKCQnJ2PRokWIjY3F7du3MWbMGBw9ehQjR47U2xyUSiUiIiKQkZHR7zMeeluo29ra8PTpU9jb2+P111/XJhbW1ta4e/euzoXa0tISY8aM6XYWxfjx4+Hi4tLnJz+tra389MDMjRgxAg0NDQA676A5OjpqH3fJyspCamoqrKysUF1djVmzZmHbtm2C+pL6m+QfPHgQJSUlbBA3bYxT/1F/YpNGo0FCQgLy8vJga2uLAwcOICAgYEDn09jYiNDQUOzevRt+fn59vr6mpgaNjY3w8fGBRqNBa2urzoRCJBLpTCgePHjQI04plUqIRCJIpdIeZyZJpVKd64xGo0F+fj7kcjmTDTOnVCoxY8YMXLp0CQ4ODli0aBHWr1+PsLAwY0/NFDDZIKC4uBgJCQnIzc3tUev6fELxfGLxooW6rq4O5eXl2LJlC0JCQnDv3j3cuXMHIpEIbm5uOhdq3koe3IT2VHzyySc4ceIEOjo6IJfLkZKSog3CLypzWr58ebfkwtHREfX19d1el5mZidWrV6OsrAzu7u5YvHgxIiIiBB1k+ejRI51/SJWUlGDfvn1ITU3t9nomG4MC45QZuX79OqKjo3H8+HG4uLh0e05XQlFXV4eOjo5uJ2U/fwjrP//80yOhqKmpgUajwejRo3vEqa6DB4l6k5KSgqSkJNjY2CAsLAyHDx829pRMBZMN6mzK3r17N3JychAUFARbW1vMnDkTbW1t2mY3IQv17du3odFoIJFI4OXlBQsLC5w6dQpHjx6FTCYz6M5XZFhCto49f/48Nm3ahD/+6NxYICgoCFu3bkVwcHCf7y+kzKmoqAiffvopzp49CwBIS0tDUVERvvvuu4G5SNPEZKN3jFNmpKOjAxkZGdi5cyfmzp0LtVqNiIgItLW1AYDOOPX5559j5MiRcHd373YIq0qlwqhRo7qVPHl7e2Ps2LEQi8W8C2EGGhoaoFAoUFlZCZFIhP379w9Yubgu9fX1iI6OxpEjRzBixAi8++67iImJQXx8vN7GHES49e1QJ5fLUV9fD3d3d6jValy9ehUKhQIymQwNDQ24ceMGrly50i2hUKlUcHFx0S7Ucrkc77//vs6FOj09HW1tbUw0zJyQrWNFIhFaW1vR3t6uPWxx1KhRgt5/wYIFOHToEDZv3oxDhw7prN0ODAxEQ0MDHjx4ACcnJ5w+fXrAyzmIyPDi4+NRVVUFqVSKkSNH4u+//4ZCocDEiRPx5MkT3LhxA5WVld0Sivb2djg7OyMrKwuLFy9GaGgoVq1aBU9PT7zyyitMKMxcYmIiZs+ejczMTLS3t6OlpUWv4xUUFGDcuHFwcnICAERFReH8+fNMNvrAOxtDhEaj0S66HR0dePPNN/Hw4UPY2trCycmpx61kDw8PLtSD0LFjx/Dll1/i8uXLKC4u7vWP8Ly8PCQmJkKtVkOhUGDz5s2C3l9ITwUAbNy4Eampqdo673/vBNUboWVO+fn52LBhAzQaDfz9/fHDDz9od6UxU/xF7B3jlJl4Pk4BQHR0NEpLS+Hg4ABHR0d4e3trD2L19vaGp6cnXn31VYhEIty6dQsbNmxAZmYm49YQ0djYCD8/P9y8edNg/+cXL17EqlWr8Ndff8HGxgYrVqxAQEAAPvzwQ4OMb+JYRjXYjB07FsOGDYOlpSXEYvGAHyxWV1cHa2trODg4cGE2I5cvX4aFhQXWrl2LHTt26Ew21Go1xo8fj/z8fEilUgQGBiI9PR2TJk0C8PI9FdevX0diYiKOHDkCoPOu2vbt2/HWW28N5KUONfwl7R3jlAHpik1Ce7n6q6mpCU+fPoWTk5OgOPXvZIXMW3l5OdasWYNJkybh0qVL8Pf3R0pKis5T3QfSF198gSNHjkAsFmPq1KlITU2FtbW1XsccJHr95WO3rgn7/fffUV5erpcTjJ2dnTF8+HAuzGZGJpNhwoQJL3xNcXExvLy84OHhASsrKyxZsgTZ2dna5wsKClBZWdnja+HChdo9+AH0ej7M8ePHMWPGDNjb28Pe3h5z5szBhQsXBvZCicho/h2bus7HuXbtGkJDQwdsm+hhw4bB2dlZcJxiPBtaVCoVSktLsW7dOpSVlcHOzs4gW5QnJyfjypUrqKysRFpaGhMNAZhsEA0xSqUSbm5u2sdSqRRKpVLQz3b1VADotafC3d0dZ8+ehUqlwrNnz3D27FnIZP/9FF8iMm3Z2dlYvnw5gM5erqysLCPPiAxNrVZj6tSpmDdvnsHGlEqlkEqlmD59OgAgJiYGpaWlBhufhGOyYaJEIhHCwsK09ehkHo4dOwYfHx9YWFj0eseqpqYGISEhmDRpEnx8fJCSktLt+VmzZsHX17fH1/N3J/Rl8+bNyM/Ph7e3NwoKCrS9HiUlJVAoFAA6F3xPT09MnjwZU6ZMwZQpUzB//ny9z42I9E9XbLp//z5cXV0BAC4uLrh//74xp0hGkJKSYvAPlVxcXODm5qbdsbCwsFBbDkymhbtRmag///wTEokEdXV1kMvlmDhxImbOnGnsadFL8vX1xc8//4y1a9f2+hqxWIydO3di2rRpaGpqgr+/P+RyuXYRLSgoeKk5SCQS1NTUaB/X1tZCIpEI+tnXXnsNhYWFPb4fEBCgbd62tLTE999//1JzJCLTpCs2PU8kErGcaYipra3FiRMnkJSUhF27dhl07D179iAuLg7t7e3w8PDAgQMHDDo+CcNkw0R1/fHn7OyMyMhIFBcXM9kwA0I++XF1ddV+Sjhs2DDIZDIolcoB+8QmMDAQ165dQ3V1NSQSCTIyMvDTTz8NyHsTkXnTFZu6erm6zsfR1ctF5uujjz7C9u3b0dTUZPCx/fz89NLXSgOLZVQmqLm5WftL29zcjFOnTsHX19fIsyJjuHXrFsrKyrQ1qX05fvw4pFIpLly4gLlz5yI8PBwAcOfOHURERADovHOyd+9ehIeHQyaTITY2Fj4+Pnq7BiIyD73FJiG9XKRffZXf6ktubi6cnZ3h7+9vkPFocOLWtybo5s2biIyMBNC528KyZcuQlJRk5FkNDULPqQA6G+ICAgIgkUiQm5ur/f6Lto7tCsLBwcG9bk3b5cmTJ3j77beRlJSEqKiol7gqMgOsS+kd45SB9Babejsfhwzn7t27uHv3brfy26ysLL33MHz22WdIS0uDWCxGa2srHj9+jKioKPz44496HZdMEs/ZIBJCyDkVXXbt2oWSkhI8fvy4W7IhRF/JxrNnzzBv3jyEh4fj448/7td7k1listE7ximif1m4cCESEhIgl8sNNuaZM2ewY8eOfsdDMhs8Z4NICCHnVAD/b4jr2oFpIGk0GqxevRoymYyJBhER9Ut/y2+J9I3JBg0aQraNBYCGhgbExMRg4sSJkMlkejlQrqshzsKif79CQnoqzp07h7S0NJw+fRp+fn7w8/PDyZMnB/waiIjIvDx58gTR0dH49ttv4eDgYNCxg4ODeVeDdOJuVDRoCNk2FgASExMxe/ZsZGZmor29HS0tLd2eF9JT8SLPN8SdOXOmX9cQGRmprXl+3ujRo7UJRVBQEPoobyQiIhOUl5eHxMREqNVqKBQK7VlEhvDs2TNER0cjLi6OfX5kUphsDHLGXNgMTci2sY2Njfjjjz9w8OBBAICVlRWsrKy6veZlz6k4d+4ccnJycPLkSW1DXHx8PBviiIh0GCpxSq1W44MPPkB+fj6kUikCAwOxYMECgxw0x/JbMmUsoxrEuha2X3/9FVVVVUhPT0dVVZWxp2VU1dXVcHJywsqVKzF16lQoFAo0NzcP6Bhbt25FbW0tbt26hYyMDLzzzjtMNIiIdBhKcaq4uBheXl7w8PCAlZUVlixZguzsbIOMzfJbMmVMNgYxYy5s/ya0n+Kbb76Bj48PfH19sXTpUrS2tnZ7ftasWfD19e3xJfS6VCoVSktLsW7dOpSVlcHOzg7btm0TfB1CeiqIiEgYU4pT+qZUKuHm5qZ9LJVKoVQqDTJ2V/ltRUUFysvLUV5ezphFJoNlVIOYroXt4sWLRpmLkH4KpVKJ3bt3o6qqCjY2NoiNjUVGRgZWrFihfc3LljhJpVJIpVLtLhwxMTH9SjaE9FQ8Lzg4GMHBwf95vkRE5syYcWrTpk345ZdfYGVlBU9PTxw4cAAjRowwyNhE9H+8s0EDQuiWsSqVCk+fPoVKpUJLSwtGjx49oPNwcXGBm5sbrl69CgAoLCw0SL0sERGZFrlcjsrKSlRUVGD8+PHYunWrXseTSCSoqanRPq6trYVEItHrmESDAZONQWywLWwSiQQbN26Eu7s7XF1dMXz4cISFhQn+eaElTnv27EFcXBzeeOMNlJeXY8uWLQN+LURE1DdjxqmwsDCIxZ0FHDNmzEBtba1exwsMDMS1a9dQXV2N9vZ2ZGRkYMGCBXodk2gwYLIxiBl6YXvZfor6+npkZ2ejuroad+7cQXNzc78aqyMjI1FbW4u2tjbcv38fv/32G4CeJU5+fn4oKSlBRUUFsrKy4Ojo2L8LNbKxY8fiq6++QkhICOzt7TF58mRUVFQgPT0dXl5eGD58OBQKBVQqlbGnSkT0QqbyB/j+/fsxZ84cvY4hFouxd+9ehIeHQyaTITY2Fj4+Pnod05gYq0go9mwMYs8vbGq1GqtWrdLrwvay/RQFBQUYN24cnJycAABRUVE4f/484uPjB2J6ZuXQoUPIycmBl5cXVq5cicjISISGhuLSpUt49OgRAgICEBISgri4OGNPlYioV/qOU0LOTfr6668hFosNsl5GREQMqcZsxioSgsnGIDeYFjZ3d3cUFRWhpaUFNjY2KCwsREBAgLGnZZLWrFmjPVdk2bJlOHz4MIqKimBnZwc7OzsEBwejpKSECzgRmTx9xqm+PgQ7ePAgcnNzUVhYCJFIpJc5DGWMVSQEy6hoQAjpp5g+fTpiYmIwbdo0TJ48GR0dHVizZo0xp22yXF1dtf+2tbWFpaWl9o5Q1/eampqMMTUiokEhLy8P27dvR05ODmxtbY09HbPEWEVC8M4GDQihW8YmJycjOTnZkFMjIqIhKCEhAW1tbZDL5QA6m8T37dtn5FkRDT1MNoiIiMjsXL9+3dhTICKwjIqIiIiIiPREpNFoXvT8C58kIiKDYGdr7xiniIiMr9c4xTsbRERERESkF0w2iIiIiIhIL5hsEBERERGRXjDZICIiIiIivWCyQUREREREesFkg4iIiIiI9ILJBhERERER6QWTDSIiIiIi0gsmG0REREREpBdMNoiIiIiISC+YbBARERERkV6I+3heZJBZEBER/TeMU0REJox3NoiIiIiISC+YbBARERERkV4w2SAiIiIiIr1gskFERERERHrBZIOIiIiIiPSCyQYREREREenF/wBXLh3I5DuTqAAAAABJRU5ErkJggg==\n", 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\n", 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7WrH+A3jf3afM7PiWZ1tEQh1ARXo0854SO8U5tyzG/CV4dxZv6tCMiXQCMzsXuNw5N6Kz89KVmNmJeMF7+l7U7DWXxhC8QHCQX7siItKjNdXBT0REeqbxeHeI92lmNhav+dBqvE7tNwGL4hVoiIjsixRsiIjsQ8zsDbzR2iL79+yLcvCeydIfr/nk03jPfxERkXaiZlQiIiIiIhIX6iAuIiIiIiJxoWBDRNqNmQ0xM2dmA5tfWszsCDN718xKzeyOzs5PT2Jm1/kDP4Tev29msUa3amsaT5vZVf7/Xebcj9x3EZHOpGBDRHosM/uJma01s3Iz2+yPzR+al2hmvzGzHf6P/b+a2f6B+ZP9dXeZ2U0R253bTsHBzcC//QfexXqCc6cysyVm1pbnT3QpzrmRzrlFzS1nZrPNrEWd5/0HLv5673MXTnuemT3fXtsTEekKFGyISJfmPyitLetdB/wY71kKvfAepPdqYJG5wKnAkXjPGYEvnx0B3lPfL8d7MNwMMxvvb3c4Xufqa9qSrwgHA++2w3Z6jLZ+3h2pO+RRRKSrULAhIk3yn4xcFvqBZWY/9JuLfM1/f4CZ1ZnZAYHVpprZB36NwbNm1j+wvQwzu83M1pvZbjP7t5kFn268xMzuMLMnzawEf3Qg/+nSy/x1PjGzy83MYuS5D14wcKlz7k3nXL1zbpdz7qPAYnOAW51zn/oPaLwKOMnM8vz5hwDPOud24z1M7BA/vT8CP3HOVbTg2I02sxf9hyp+6jdvSfTnFeEFG3/0j+/Xo6w/0D8+O8ys2MyWmtmEwHwzs2vM7DP/uMw3sxfMbF5gmXwze8bfxiYz+1Xgsww1/ZkZ7fMys7uBKXhPuy4zs4/86V83s3fMrMTMdjZ1N97MzjSzVf6yW83sXjPLDMzfYGbXm9lL5j1g7gx/+mlm9pZ5D6JbY2YzmjnW3/L3oczM/gnsHzF/g5md7f+fY2aP+bVWxeY1sZpi3tOhF+A9Eb3MfxX4r1r/OH0K7Pa3E63W5yTzasSKzexvZpYbLQ8Rx3+geU28rgEKAmkf7C/X5Lnf3L6LiHQq55xeeumlV8wXYHhPdT7Wf/8osA642X8/A3jX/38I4IDQD57eeLUJfwhs72F//gF4T07+Bd5TkpP9+UuAErynVhveE55H4D1R+VQgETgM78Fos2Lk+SQ/H1f7y231832AP7+PP39sxHrFeE9GBngd+A7QF/gUOBz4CXBfC49bNt4TpX8OpPrrfwpcGVhmA3B2E9sYjPfU4wy84Wr/B9gYOFaz/DTG4T2h/SqgBpjnz8/Fexr3+f6xPgjvSdDXt+LzWgJcF5Gvz4Fz/M8nFShoYh++CYzEu7l1CPAB8KuIY7DZ3wfz9/MEP99T/PUm4T2N/tgYaXwF76nfZ+MN6f4NoBxYEu1Y4zVfewrvqeUGDAOG+vNmAx9HbL/AP06P+p9rRuSxCRzLV4AD/WP5BF7AGvXzDqwz0H8/D+/hosG0mzz3W7Lveumll16d+VLNhog0yTnngBeBr/t3U78GXIf3gxDg60Dkne1fOOd2OudKgEeAIwDM6xPxX8BFzrkvnHN78IKN/njNmUIWO+dedJ4K4CLgMefc35xzdc65D4G78X5sRxO6s3uiv93D8X7EPuRP7+X/LY5YrwjvRyLAuX66zwO/BiqBi4ErzeznZvaymS02swNj5OFbwB7gJudctXNuDXArcF6M5Rtxzm1yzv3dOVfhnKvEO+6D8Zp2gbf/9zrn3nHO1QC/wQsECMxf5Zy71zm3xzm3BfgVjY9b1M+rCXvwfuQe4O/bkib24Wnn3PvOq136GPg9cHzEYn/w98H5+3kp8D/OuaX+eivwPrtYn/eZwArn3EPOuVrn3LPAk83kvy8wHG8I+LXOufXN7DPA1c65Ytd0rdYvnHPb/GN5JXCCmQ1owbZjae7cb+2+i4h0KD3UT0Ra4nm8O75/xftBvhi4x8z64v1wvCBi+a2B/8v58sf9UP/vuxEtoJKBQYH3GyK2NxT4mpmdHpiWgHdHPJpS/+/Nzrnt4HW+Bd72m/CE5mdHrNcHr1YF59z7fBlQYWbPAlcARwFT8YKuc4Hf4tXuRBoEbPSDtZBPIvazSX5wdjvenfU+eE+7Bujn/z0Ir6YDP8/OzILHZCgw2W+yFd4s3h3yoFifVyyn4jX5WW1mO/Bqe6J2mDezE4Dr8e7Ip/ppb49YbEPE+6F4TfF+FpiWCCyNkZ+BUbaxHu/4RPMbvHPuf4H+ftOjq5xzX8RYHrxjH+t8C9oQ5f+BNAwCW6O5c7+1+y4i0qFUsyEiLfE8MBE4HXjOOVeH98PvQrwmIy+3cDuhH8aHOuf6BF4ZzrlHA8vVR1nvTxHr9HbOjYyRzkr/b9SnljrnioBNwPjQNL99fG+idNg2bxSrHc65fwJjgP/4x+AVvOY/0WwG8qxhVHUwLfvBGvIr/Fof51xvvgxUQtvcAoT6mOCnFQxmNuI1ywket2znXFYr8hD5WeCcW+Wcm47XTOt84Ffm9+EJMrMUvLvs/wcM9vfh6kD+Y6WxEa8pWDDfvZxzJ8fI4xa8JklBke+D+S93zl3rnMvHa+J1EF4AEi0vgdVc1POpiXRD/3/m/y0FMgPzI2s8oqXd3Lnfqn0XEeloCjZEpFnOuU/xfiT/FHjOn/wCXjOR151z5S3czna8Zjq/N7ODwOvMbWbfMbOmfgD/HjjTzL5tZslmlmRmI8zsuBjpbAT+Bfy3me1nZr3w+k48E8jrfcDVZjbUzHrjNXF6xjm3Ibgtv7P01XhNe8CrnSgws3TgZCDWMKlP4d3Jv8bMUswbxepq4P4m9jNSb6ACKPSPz60R8/8MzDGzseZ1+v4ZDX/APggcYV6n/jQzSzCzg83spFbkYRteXwvACyDM7Admtr//47sQ70dyXZR1U/COQaFzrtLMRuCNENacO4DL/I7RiX6aE8wsVvOu/wOONLOz/HPj68BpsTbun0eHm9dZvwyoCuR/G5DrnxNt8XPzBk0InVPPO+dCtRpvAWeZWZaZ9cM7J4O2AYP9IC2kuXO/VfsuItLRFGyISEs9j9dR+aXA+9407q/RnB8BHwFLzKwUWA18jxi1EADOufeAU/CCna14zXAW8mVzomhm+sttwAsIKmjY5v8W4B/AG3h3hxPxOtlGugevQ/VO//2TeB3at/rLXxUjz8V4nXW/jteJ+xm8H/+3N5HnSNfzZSfvd4HXaPij/kHgd3iB1Rd4TWpex+swjHNuG16Tr9PwjkMhXqflg1uRh/l4AUuRmb3vT5sOfGje6FF/B25wzjWq3XLOleHVfv3aX/Z3eMFmk/x+Bz/Cq23YiXes5+N16I62/MfAd/GOVxFwGd6oYbF8Be+zL8E7LpV4gSB45/dzwHp/n6MGtE14CK/WbzNesDUzMO86vM9vK17n8v+LWPcxf71tftpDmzv327DvIiIdylpWKywiIl2dmYXa8l/pnGv2R72IiEi8qWZDRKQbM+85FmlmloE3slcG8HQnZ0tERARQsCEi0t39GK8J1Va8EbJOds4Vdm6WREREPGpGJSIiIiIicaGaDRERERERiQsFGyIincDM8s3sI3/I2tC0u8xsp5mVmVluZ+avJcxstpnFGvo31jrOzI5pYv4tZnbj3udORES6AgUbIiKd4zfArc65GgAzOxr4IXC4cy4r9OTzfdCtwMWh57CIiEj3pmBDRKSD+Q/4m0zD5ywcDGx1zu1o4zbNzJLaI3+dye/c/jTek8lFRKSbU7AhItLxTgNec85VAJjZVXgPYjvYb0L1oj89z8z+5jet2mxmd/hPLsef78zsUjN7E++hhVGfsG1mp5nZW/6D4taY2YzAvIFm9m8z22FmxWa21MwmRKx/upm96a+/zcx+GTH/EjP7zMwKzexe/8ncTRltZm+YWamZvW5mh0XMfw49BVtEpEdQsCEi0vHGAx+E3jjnfg1cAHzqN6H6ml9L8RSwDcgDjsKrDbktYlvn4j3ROwt4JzIhMzsBuB/vCdT7AT8A7jazY/1FEoDf+2kcCLwNPB7qS2Jm3wT+F5gH7A8Mo+FzPPKAA/Ceyj0R72nwZzaz/7OBM/ztbQbuipi/Gsg3s5RmtiMiIl2cgg0RkY6XA5Q0s8wk4FDgZ865cufcFuA64IdmZoHlbnPOfeKcq3POVUfZzqXA/zjnljrn6p1zK4CHgFkAzrlNzrm/O+cqnHOVfhqD/bQBfgIscM790zlX65wrcc4tC2y/ErjeOVftnPsYeIEYNSwBv/HTrQYWRlm+BDCgTzPbERGRLk7BhohIxysEejezzCBgh3OuPDDtEyAN6BeYtqGZ7QwFrvabQBWZWRFezcIAADPb38weNLNNZlaCV9NAII0hwNomtr/dOVcXeF8O9GomT1ubWb434ICiZrYjIiJdXLfvTCgi0g29AxQ0s8xmoJ+ZZYT6duB1Iq8Cgp3I65vZzkZgoXPuNzHm/wroDxzpnNtqZr34smYBvGDm0Bjrxks+8L5zbk8HpysiIu1MNRsiIh3vb8BXg529o1gBfAz81swyzGwAcCPwgHPOtSKtO4DLzGyKmSWaWYqZTTCzUNOl3nidywvNLAtv6Nmg3wEXmtk3zSzJzHo39ZyMdnIC8GSc0xARkQ6gYENEpIM559YAy/E6dsdaphY4BRgIbMILPv4DXNHKtJ4FfoT3XI+deE2Y5uN1KAe4HsgFdgHvAq8BdYH1n8LrhH4zsBv4CDixNXloDTPrA5wMLIhXGiIi0nGsdTfIRESkPZhZPrAYGBV6sJ+Amf0KqHPOXdfZeRERkb2nYENEREREROJCzahERERERCQuFGyIiIiIiEhcKNgQEREREZG4ULAhIiIiIiJxoWBDRERERETiQsGGiIiIiIjEhYINERERERGJCwUbIiIiIiISFwo2REREREQkLhRsiIiIiIhIXCjYEBERERGRuFCwISIiIiIicaFgQ0RERERE4kLBhoiIiIiIxIWCDRERERERiQsFGyIiIiIiEhcKNkREREREJC4UbIiIiIiISFwo2BARERERkbhQsCEiIiIiInGhYENEREREROJCwYaIiIiIiMSFgg0REREREYkLBRsiIiIiIhIXCjZERERERCQuFGyIiIiIiEhcKNgQEREREZG4ULAhIiIiIiJxoWBDRERERETiQsGGiIiIiIjEhYINERERERGJCwUbIiIiIiISFwo2REREREQkLhRsiIiIiIhIXCjYEBERERGRuFCwISIiIiIicaFgQ0RERERE4kLBhoiIiIiIxIWCDRERERERiQsFGyIiIiIiEhcKNkREREREJC4UbIiIiIiISFwo2BARERERkbhQsCEiIiIiInGhYENEREREROJCwYaIiIiIiMSFgg0REREREYkLBRsiIiIiIhIXCjZERERERCQuFGyIiIiIiEhcKNgQEREREZG4ULAhIiIiIiJxoWBDRERERETiQsGGiIiIiIjEhYINERERERGJCwUbIiIiIiISFwo2REREREQkLhRsiIiIiIhIXCjYEBERERGRuEhqZr7rkFyIiEhTrLMz0IWpnBIR6XwxyynVbIiIiIiISFwo2BARERERkbhQsCEiIiIiInGhYENEREREROKiuQ7iIhJFTU0Nn332GVVVVZ2dFelB0tLSGDhwIMnJyZ2dFRHp5lROSTy0pZwy55ocyEOjfIhEsX79enr16kXfvn0x00BBsvecc+zatYvS0lKGDh0aOVsnWWwqp0SiUDkl7a2t5ZSaUYm0QVVVlS7g0q7MjL59++oupIi0C5VT0t7aWk4p2BBpI13Apb3pnBKR9qRrirS3tpxTCjZEuqnExETGjh1Lfn4+3/ve96ioqGj1Ns477zw++OADAG6++eYG844++uh2yaeIiOybVE4JKNgQ6RgPPwxDhkBCgvf34Yf3epPp6emsXLmS9957j5SUFBYsWNDqbfzxj39kxIgRQOOL+GuvvbbXeYyX2traDk/DOUd9fX2L1q2rq4tHlkRE4kflVLtSOfUlBRsi8fbwwzBnDmzcCM55f+fMaZcLeciUKVP4+OOPAbj99tvJz88nPz+fO+64A4Dy8nK+9a1vMWbMGPLz81m0aBEABQUFvPnmm8ydO5fKykrGjh3LjDkc3GYAACAASURBVBkzAMjKygLgzDPP5KmnngqnNXv2bBYvXkxdXR1XXnklEydOZPTo0dx7771R83baaacxYcIERo4cyX333ReenpWVxWWXXcbIkSM5/vjj2bFjRzhPl156afhu2IoVKwCYN28eM2fOZPLkycycOZOqqirOOeccRo0axbhx43jppZcAmD9/Pj/84Q8BWL16Nfn5+Y3upsXK+5IlS5gyZQrTpk1jxIgRbNiwgeHDhzNr1izy8/PZvHkzjz76KKNGjSI/P5+rr766wf5cfvnljBkzhuXLlzN37lxGjBjB6NGjueKKK1r9mYqIdBiVUyqn4llOOeeaeol0WQMHDnRvvfVW1Hlz58518+fPb9F2Jk6c6N57771Wpf3BBx+0fOG8POe8y3fDV15eq9KMlJmZ6Zxzrqamxk2bNs39/ve/d2+++abLz893ZWVlrrS01I0YMcK9/fbbbvHixe68884Lr1tUVOScc+64445zb7zxRoPtRW7/8ccfd7NmzXLOOVddXe0GDhzoKioq3L333utuvPFG55xzVVVVbsKECe7TTz9tlM9du3Y555yrqKhwI0eOdDt37nTOOQe4hx56yDnn3C9+8Qt38cUXh/MUyuvLL7/sRo4c6Zxz7oYbbnDjx493FRUVzjnnbrvtNnfOOec455xbs2aNGzRokKusrHR1dXVuypQp7vHHH3cTJkxwy5Yta5SnWHl/6aWXXEZGRng/1q9f78zMLV++3Dnn3JYtW9ygQYPc9u3bXU1NjZs6dap74oknwvuzaNEi55xzO3fudMOGDXP19fXOOecKCwujf4hRxDi3mrtW78svkS5L5ZTKKZVTTjUb0j0VFhby+eefc/jhhzeat2PHDh588EHOP//88LTdu3fzne98h8zMTPLy8njkkUfC86644gquv/76+GV206bWTW+h0B2eI444gsGDB3PuueeybNmy8H5mZWVx+umns3TpUkaNGsVzzz3H1VdfzdKlS8nOzm5xOt/85jd56aWXqK6u5umnn+bYY48lPT2dZ599lgcffJCxY8dy5JFHsmvXLtatW9do/TvvvJMxY8Zw1FFHsXnz5vAyCQkJTJ8+HYCzzz6bZcuWhdc566yzADj22GMpKSmhqKgIgGnTppGeng7AsmXLOPvsswE47LDDyMvLY+3atSQkJLBw4UJmzpzJcccdx+TJkxvlqam8T5o0qcGQfnl5eRx11FEAvPHGGxQUFNCvXz+SkpKYMWMGr7zyCuC1TT7jjDMAyM7OJi0tjXPPPZfHH3+cjIyMFh9vEekZWltO3X333RxxxBGkpqYye/bsBsurnGqayqmuXU7poX7SLa1evZohQ4aEv9BBCxcu5OSTT24w7+KLLyYlJYUvvviClStXhqtqR44cybRp07jgggvYtm0bBx54YPtndvBgr0o62vS9EGoL2xLDhg3j7bff5l//+hfXXXcdxx9/fIsLrrS0NAoKCnjmmWdYtGgRZ555JuDVit51112ceOKJMdddsmQJzz//PMuXLycjI4OCgoKYQ+YFR7iIHO0i9D4zM7NFeV63bh1ZWVl8/vnnUefHyvuSJUsapdHSNNPS0khMTAQgKSmJFStW8MILL7B48WLuvvtuXnzxxRZtR0R6htaWUwMGDOC6667jmWeeobKyssHyKqeapnKqa5dTqtmQbundd9/lkEMO4ZJLLqFfv34MGDCA5557DoCnn36a4447LrxseXk5f/3rX7nxxhvJysrimGOOYdq0afz5z38GvC/fhAkTeOaZZ+KT2V/+EiLvGGRkeNPb2ZQpU3jyySepqKigvLycJ554gilTpvD555+TkZHB2WefzZVXXsnbb7/daN3k5GRqamqibnf69Ok88MADLF26lJNOOgmAE088kXvuuSe8ztq1aykvL2+wXnFxMTk5OWRkZPDhhx/y+uuvh+fV19ezePFiAB555BGOOeaY8LxQW91ly5aRnZ0d9Q7XlClTeNhvT7x27Vo2bdrE8OHDKS4u5pJLLuGVV15h165d4TSCWpL3aCZNmsTLL7/Mzp07qaur49FHH21wroWUlZVRXFzMySefzPz581m1alWz2xaRnqU15RTA6aefzmmnnUbfvn0bbUvllEflVPcsp1SzId3S6tWrefPNN/nZz37GHXfcwY033sitt97KCSecwOrVqxk+fHh42bVr15KUlMSwYcPC08aMGcPLL78cfn/44YfH74vmd2Tj2mu9KunBg70LeGh6Oxo/fjyzZ89m0qRJgDdk4Lhx43jmmWe48sorSUhIIDk5mXvuuafRunPmzGH06NGMHz8+fHEM+cY3vsHMmTM59dRTSUlJCW97w4YNjB8/Hucc/fr148knn2yw3kknncSCBQs4/PDDGT58eLiaF7w7MStWrOCmm24iNzc3fOEGr2AdN24cNTU1/OlPf4q6rxdddBEXXngho0aNIikpiYULF5KamsqFF17IxRdfzLBhw7j//vuZOnUqxx57LLm5ueF1W5L3aPr3788tt9zC1KlTcc7xrW99i1NPPbXRcqWlpZx66qlUVVXhnOP2229vdtsi0rO0ppxqCZVTKqe6bTnVVIeOFvcUEelgRx11lPvtb38bfv+Xv/zFHX/88c4555KSktyaNWvC81555RV3wAEHNFj/vvvuc8cdd1z4/TXXXBPuxNUSrep4J1FFdvQLCXYG3Bepg7g6iEvP0JpyKujaa691P/jBDxpNVznV8VRORacO4tLjOed47733+Pa3vx2e9t5774XH4c7JyaG0tDQ8Lysri5KSkgbbKCkpoVevXuH3paWl9OnTJ845FxGRfUFry6mWUDkl3ZWCDel21q9fD8AhhxwSnvbOO+8wduxYAEaPHs3atWvD84YNG0ZtbW2DEShWrVrFyJEjw+/XrFnDmDFj4p11CSgrK4s6fcmSJRxxxBEdnBsRkfbT2nKqJVROdTyVU+1DwYZ0O++++y6jRo1qMBLEO++8E74In3zyyQ36Y2RmZnL66adz/fXXU15ezquvvsrf/vY3Zs6cCUBVVRVvvfUWJ5xwQsfuiIiI9EitLafAexp0VVUVdXV11NXVUVVVFX5CtMop6c4UbEi3s3r16gZ3d3bu3Mm2bdvIz88HYNasWfzrX/9qMHTg73//eyorK8nNzeWss87innvuCdds/OMf/6CgoIABAwZ07I6IiEiP1JZy6qabbiI9PZ1bbrmFhx56iPT0dG666SZA5ZR0b+aca2p+kzNFuqprrrmG3NxcfvrTnza77JFHHsn9998fLgRaYs2aNVEf1CSyt2KcWxZtWQFUTkk3pXJKuqvWllMKNkTaQBdxiRcFG62mckokCpVTEi+tLafUjEqkmzIzLr/88vD72267jXnz5rV7OjfffHOD90cffXS7pyEiIj2PyikBBRsiHeLhh2HIEEhI8P5GPIuoTVJTU3n88cfZuXPn3m+sCZEX8ddeey2u6e2NUGfKjkzDOUd9fX2L1q2rq4tHlkRE9prKqY6xL5ZTCjZE4uzhh2HOHNi4EZzz/s6Zs/cX8qSkJObMmcP8+fMbzduxYwdnnHEGEydOZOLEibz66qvh6SeccAIjR47kvPPOIy8vL1wInHbaaUyYMIGRI0dy3333ATB37lwqKysZO3YsM/wnyWZlZQFw5pln8tRTT4XTnD17NosXL6auro4rr7ySiRMnMnr0aO69996o+Y+WXmj7l112GSNHjuT4449nx44dABQUFHDppZcyduxY8vPzWbFiBQDz5s1j5syZTJ48mZkzZ1JVVcU555zDqFGjGDduHC+99BIA8+fP54c//CHgdd7Mz8+noqKiQZ5i5X3JkiVMmTKFadOmMWLECDZs2MDw4cOZNWsW+fn5bN68mUcffZRRo0aRn5/P1Vdf3WB/Lr/8csaMGcPy5cuZO3cuI0aMYPTo0VxxxRXNf9AiInGmckrlVFzLqaae+BfXxw+KdGOteTJrXp5z3uW74Ssvb+/ykJmZ6YqLi11eXp4rKipyv/nNb9wNN9zgnHPurLPOckuXLnXOObdx40Z32GGHOeecu/jii93NN9/snHPu6aefdoDbsWOHc865Xbt2Oeecq6iocCNHjnQ7d+4MpxOZrnPOPf74427WrFnOOeeqq6vdwIEDXUVFhbv33nvdjTfe6Jxzrqqqyk2YMMF9+umnjfIfKz3APfTQQ845537xi1+4iy++2DnnPbH1vPPOc8459/LLL7uRI0c655y74YYb3Pjx411FRYVzzrnbbrst/JTdNWvWuEGDBrnKykpXV1fnpkyZ4h5//HE3YcIEt2zZskZ5ipX3l156yWVkZIT3Y/369c7M3PLly51zzm3ZssUNGjTIbd++3dXU1LipU6e6J554Irw/ixYtcs45t3PnTjds2DBXX1/vnHOusLCwUR70BHE9QVykPaicUjnVVcqppL0PV0SkKZs2tW56a/Tu3ZtZs2Zx5513kp6eHp7+/PPP88EHH4Tfl5SUUFZWxrJly3jiiScAOOmkk8jJyQkvc+edd4bnbd68mXXr1tG3b9+YaX/zm9/k0ksvpbq6mn//+98ce+yxpKen8+yzz/Luu++yePFiAIqLi1m3bh1Dhw5tsH6s9BISEpg+fToAZ599Nqeffnp4nbPOOguAY489lpKSEoqKigCYNm1aeP+XLVvGT37yEwAOO+ww8vLyWLt2LaNHj2bhwoWMHj2a888/n8mTJzfap1h5T0lJYdKkSQ32IS8vj6OOOgqAN954g4KCAvr16wfAjBkzeOWVVzjttNNITEzkjDPOACA7O5u0tDTOPfdcTjnlFE455ZSYx1dEpKOonFI5Fc9ySsGGSJwNHuxVSUeb3h5++tOfMn78eM4555zwtPr6el5//XXS0tJatI0lS5bw/PPPs3z5cjIyMigoKKCqqqrJddLS0igoKOCZZ55h0aJFnHnmmYBXW3rXXXdx4okntkt6wYdiBf8Pvs/MzGzRfq5bt46srCw+//zzqPNj5X3JkiWN0mhpmmlpaSQmJgJek4IVK1bwwgsvsHjxYu6++25efPHFFm1HRCReVE7tXXoqp5qmPhsicfbLX0JGRsNpGRne9Paw33778f3vf5/7778/PO0b3/gGd911V/j9ypUrAZg8eTJ/+ctfAO/uSGFhIeDdGcnJySEjI4MPP/yQ119/PbxucnIyNTU1UdOePn06DzzwAEuXLuWkk04C4MQTT+See+4Jr7N27VrKy8sbrNdUevX19eE7No888gjHHHNMeN6iRYsA765QdnY22dnZjfI0ZcoUHvYbGq9du5ZNmzYxfPhwiouLueSSS3jllVfYtWtXOI2gluQ9mkmTJvHyyy+zc+dO6urqePTRRznuuOMaLVdWVkZxcTEnn3wy8+fPZ9WqVc1uW0Qk3lROqZwKiUc5pWBDJM5mzID77oO8PDDz/t53nze9vVx++eUNRvu48847efPNNxk9ejQjRoxgwYIFANxwww08++yz5Ofn89hjj3HggQfSq1cvTjrpJGprazn88MOZO3duuNoVYM6cOYwePTrc8S7oG9/4Bi+//DJf//rXSUlJAeC8885jxIgRjB8/nvz8fM4///xGI2M0lV5mZiYrVqwgPz+fF198keuvvz48Ly0tjXHjxnHBBRc0KLSCLrroIurr6xk1ahTTp09n4cKFpKamctlll3HxxRczbNgw7r//fubOncv27dsbrNuSvEfTv39/brnlFqZOncqYMWOYMGECp556aqPlSktLOeWUUxg9ejTHHHMMt99+e7PbFhGJN5VTKqdC4lFO6aF+Im3QXR+WVF1dTWJiIklJSSxfvpwLL7wwfDepq8jKyqKsrKzR9IKCAm677TaOOOKITshVx9FD/VpN5ZRIFCqn4kflVOvKKfXZENmHbNq0ie9///vU19eTkpLCH/7wh87OkoiISJjKqZ5HwYbIPuTQQw/lnXfe6exsNCna3SLwOr+JiEjPpnKq51GfDen26uvraaY5oIiISKdROSX7MtVsSLdUX19PTU0Ne/bsoaamhpSUFJKTk0lISAi/IoefExER6Sj19fXU1tayZ88e9uzZE7WcgsZDpYr0NAo2pFtwzoUDjJqaGurq6oCGF2nnXIMRGcyMxMREzCwcfOiiLiIi8RK8EVZXV4dzjoSEhAZPUw6WU845qqqq6N27t8op6bEUbEiXFQwwQhfu0IU4WHMRnB6aFqqujhwOLnQ3KRSE6KIuIiJt1dSNsGA5FfwbLHcqKir48MMPGTduXHhasOZDtfTSE6jPhnQpobs+5eXlbNq0idLSUiorK3HOkZiYSGJiYosuvsGgJHjBDm2/urqaqqoqqqqqwgVEd2tTm5WVFXPe559/zne/+91mt3HzzTe3KK2FCxfy4x//GIAFCxbw4IMPxlx2yZIlvPbaazHn//3vf+eWW24BYPbs2VEfWtSUyDwfffTRrVpfRGRvOOeoq6ujqqqK0tJSSktLqaqqor6+Pnwzq6VBQmRZFSyn9uzZ06Ccqq2tVTnVBJVTXZeCDel0zjlqamqoqKigpKSEsrIyKioq2LhxY3is7eYu3C25+Ea7qAPhi3p1dTXV1dXs2bOn/S/q6x+GJ4fAIwne3/UPt892YxgwYECLLo4tvYgHXXDBBcyaNSvm/KYu4rW1tUybNo25c+e2Ot2QyDw3VWCIiLSHUABQWVnJ+vXr2blzZ/hGWGsDjKY0VU7V1NSEy6nq6mpqampUTsWgcqprUbAhnSIywCgvL2fPnj3hfhaJiYlxz0O02g/4ss1tqPYjdFEPtb9t9YV9/cOwYg5UbASc93fFnHa5kDvnuPLKK8nPz2fUqFEsWrQIgA0bNpCfnw94d3tOP/10TjrpJA499FCuuuoqAObOnUtlZSVjx46N+tTVBx54gGHDhjFp0iReffXV8PR58+Zx2223Ad4TYEeMGMHo0aM588wz2bBhAwsWLGD+/PmMHTuWpUuXMnv2bC644AKOPPJIrrrqqgZ3nwCef/55jjjiCIYNG8Y///nPcJ6Dy5xyyiksWbIkap5Dd85iHYslS5ZQUFDAd7/7XQ477DBmzJjRre4MikjniHYjrLq6ml27dlFbW9uiG2F7K1Y51e619CqnVE7FkfpsSIcJjswR7EsRvIAGdcYXLbJ9bWSHvtraWgoLCxsFHU0WNquuhbqKhtPqKrzpQxtfPFvj8ccfZ+XKlaxatYqdO3cyceJEjj322EbLrVy5knfeeYfU1FSGDx/OT37yE2655RbuvvvuqE9m3bp1KzfccANvvfUW2dnZTJ06tUGb4pBbbrmF9evXk5qaSlFREX369OGCCy4gKyuLK664AoD777+fzz77jNdee43ExEQWLlzYYBsbNmxgxYoVfPLJJ0ydOpWPP/445v42leemjsU777zD+++/z4ABA5g8eTKvvvoqxxxzTJPHVkT2PcEmTDU1NTjnGvzYb29tCVSilVNAowFSQv1JIpePSuWUyqk4Us2GxFV9fT3V1dWUlpZSUlJCRUUFdXV14WrnUEftSF2lQ1zkXaXa2lq2bNkCfBmIhC7oobtJjYKkik3RNx5reissW7aMs846i8TERA444ACOO+443njjjUbLHX/88WRnZ5OWlsaIESPYuHFjk9v9z3/+Q0FBAf369SMlJYXp06dHXW706NHMmDGDhx56iKSk2Pcuvve978Wsrfr+979PQkIChx56KAcffDAffvhhk3mLpaljMWnSJAYOHEhCQgJjx45lw4YNbUpDRHqeUDlVVlZGcXEx5eXl1NTUkJCQQFJSUqNyKvRDvqtoqpa+oqJC5ZTKqU6nYEPaVWTHuZKSEiorK9vUca4ram4Eq6gX9YxB0RfOGBynXDaWmpoa/j8xMbHRKF1t9dRTT3HxxRfz9ttvM3HixJjbzczMjLmNyONpZiQlJVFfXx+eVlVVtVf5jNf+i0j31NYbYd1FU7UZKqdUTnU0BRuy16IFGG0dmaOra+3dLOccbvQvcYkZDacnZsCYX+51fqZMmcKiRYuoq6tjx44dvPLKK0yaNKnF6ycnJ1NTU9No+pFHHsnLL7/Mrl27qKmp4bHHHmu0TH19PZs3b2bq1KnceuutFBcXU1ZWRq9evSgtLW1xHh577DHq6+v55JNP+PTTTxk+fDhDhgxh5cqV4TRWrFjRbJ739liISM/V3I2wjuh/0VTeOpPKqeapnNo76rMhbRK6cIeegRG6UxJ8hkV7pNGey3WaIf+FA3j3WqjYDBmDvAt73lkQaE8LLW8+VltbS2pqKt/5zndYvnw5Y8aMwcz49a9/zYEHHtji6tc5c+YwevRoxo8fz8MPf9kRsH///sybN4+vfvWr9OnTh7FjxzZat66ujrPPPpvi4mKcc1xyySX06dOHb3/723z3u9/lb3/7G3fddVezeRg8eDCTJk2ipKSEBQsWkJaWxuTJkxk6dCgjRozg8MMPZ/z48c3mOdaxaGt1t4h0b6FyqqKiosEd6MhnYLRWV2tG1S5UTjVJ5dTesWa+MD3s2yR7I9RxrrS0lJqamnCVXzzuBtXV1bFy5UomTJjQ7LJ79uwhNTWV5OTkds1DNBUVFXz00Uf07t2bww47LC5ptOSivmrVKn70ox81uJMiPcOaNWs4/PDDIyd3/2rB+FE5JWHRboStXbuW/v37k52d3S5l1dq1a8nNzaVPnz4tXqempobk5GRSUlIaTK+qquL9999vUVnXWuvWrWPo0KGkp6e3+7ZVTu3bWltOqWZDmhQKMEJPR3XOsW3bNhISEjjooIM6O3s9UvAGQOj/YPvbBQsWcOedd3LHHXd0Sv5ERLqSaOUUNHwSd3P97dqS5r7MOUdVVRWpqalR+4fce++9KqckTMGGNNLcELWJiYkNqqTjZV+/mAeFCtDk5GTmzJnD+eefDxAellFEZF/S0UPUBuma66mrq2vwPlhmq5ySIAUbAnz5ILs9e/aEH14X+QTTjtRVL0ydddGMDO6aq/0QEelpQuVUTU1NoxthTV33emQfi25A5ZSEKNjYR4WGvQtduEN3KFrSca6rXbg766LV1e7WhD6TyIt65P/SdXWl75VIV9AeN8K6WpnVkeK9360tW1ROdX9tOacUbOxDggFG6MIdfBiQvugtl5qayu7du9lvv/267HGLvKuki3rX5pxj165dpKWldXZWRDrN3twI6yjdJXhJTk6muLiY9PT0LnHcolHtR/fS1nJKwUYPF48hajvqQtuSNKqrq/niiy9ISUlh//3377CL6oEHHsi2bdvYsWNH3NMCwk0Gmnr6aVvpgt51pKWlMXDgwM7OhkiHCpVTtbW1VFZW8tlnnzFo0KB2CzC6S3DQ3rKzs9m6dSvFxcVx2X51dXWDB9G1t9AzUEDlVFfSlnJKwUYP1NzIHHurI770TaVRXV3N9u3bwz/099tvP/bs2cOHH35IZWUlmZmZ9OnTh5ycHHr16tWunQVDxzIpKalDfxRu2rQJM2PQoBhPeW2FyGps+PKuYbxGbhERCYp1I6y+vp7du3czZMiQzs5it5eQkEB9fT1f+cpX4rL91157ja9+9atxKSucc+Hth6ic6r4UbPQQ8Q4woqXXkaqqqsIBRkJCAv369WPkyJGkpqZSU1NDSkoKycnJOOcoLy+nqKiITZs2UVpaSnJyMjk5OeTk5JCdnb3XtQOd9YTZ9vocI6uoQ59lXV1dg9FFghf1rtJ8QUS6r5aUU6F+Ge0pHjUb+2JNSTTxKheCTbxDQgGqyqnuR8FGNxY5RO2mTZvIyMggNzc3rkP/dVSVdH19PRs3bmTnzp0kJCSQm5vLqFGjGj0UKTJvWVlZZGVlhWseqqurKSwsZPv27axduxYgXPORk5MT12rg9hLP4x2tfWzoLmNtbS2rV69m1KhRmBmJiYkN7ijpwi4iTWntELXdocmTrnudI7LMCZZTwWVCTcRV+9F1KNjoZpobmaMjvljx3H5FRQXbt29n586dVFVVkZyc3GyA0ZzU1FQOPPBADjzwQMDr/1BcXExhYSGbN29mz5499OrVKxx8ZGZmdsmLU0fmKRiAlJeXh99HXtRDF/TQxb0rHjcR6VhtHaI2pDvUbLQXXTPbLlYtffCcg4bPCFM51TkUbHRxrRmZw8w65GF7oXy1l/Ly8nCAkZKSQm5uLmPGjGHlypUMGDCg3dIJSUpKom/fvvTt2xfwCsbS0lKKior4+OOPKS8vJz09nZycHPr06UN2dnbcHxLVnM4eZre52o/QxT2ySjtyHRHpmdrrWU3d5XrR2jIwdL2U+GlpOaVa+o6nYKMLihyiNhRANDcyR0d9Wfb2LlGoX8X27dvZtWsXqamp5ObmMm7cuDb3p9ib/CQkJJCdnU12djZ5eXk456isrKSwsJAtW7awZs0aEhMTw02uVGB4ot1VilalrQ59Ij1PvIaojcf1ob1rNlqaR+ccJSUlbN++nd27dwOQlZUVrkXv1atXu+UpVvr7+vW2qdqPTz/9lNzc3PBAMiqn4kfBRhfR1BC1Lb1wd+WqYuccZWVl4QAjPT2d3NxcBg8eHJfhXPeGmZGRkUFGRgYHHXQQAHv27KGoqIgvvviC3bt389prr5GdnR0uNNLT0+Oap+5QaMS6q6QOfSI9Q3CI2lANxsqVKxk/fry+y77IAKNXr17hsi4lJYWamhoKCwvZuHEjpaWlJCUlUV1dza5du+jTpw+JiYmdvQs9WrCcqqqqCk+vr69vspwKriut17V+5e1j2nsEqY4KNlqajnOO0tLS8EU31Hl9yJAh3e6CGmrelZ6ejnOOkSNHUlJSQmFhIR988AFVVVVkZmY2uGPVnhem7hBsRKMOfSLdWyjACHXwrq+vD39HExMTGzwLoauKd9nonKO4uJjt27dTWFgYDjC+8pWvhI9NTU1NgwFMQsOYl5SUsHr1ar744gvWrl2LmTUYwGRv+it2Zx01CE2wvFEtffwo2Ohg8RyitiODjViCd3UKGuj+fgAAIABJREFUCwvJzMwkNzeXoUOHdrsAoymhZlU5OTnAlzU3hYWFbNiwgdLSUlJTUxsMuduT9r+tWtKhTx3PRTpXU+VUZE10d/huxqMZVX19PUVFReGyrnfv3uTm5nLIIYe0qixPTU0lNTWVESNGAF5QUlRUFK79qK2tpXfv3g1q0VvTjKs7fD6dJdbx0fDw7U/BRgeIHKI2pL2fgdFZHcSDd3WKiorIyspqdFenpzMzevXqRa9evRg8eDDgPRuksLCQbdu28dFHH+3VHaueWmg016Fv3bp19O/fn/T0dHXoE4mj1g5Ruy9yzlFUVMSuXbvYunUrOTk5bQowmpKcnEy/fv3o168f4P1+CI2euGbNGiorKxv1++iJ18KOKPNamkZz5VRwOZVT0SnYiJNQx7mioiL27NlDVlZW3KPfjqzZqK+vDz+7oqioqM13dbqb1lwA09LS6N+/P/379we8u/eRd6yCQ+5mZGQ0ue3OuGh1Rh+g4IW9pKSkwZDFwWVUpS2yd/Z2iNp46UpD34YCjGBZl56ezuDBg8nNzW3XfEaTkJAQsxZ9/fr1lJWVkZqaSp/sbNLf/g+9Bg+h91ePiXu+4q0rBRvRtLSWvqKigqysLJKSkvbpckrBRjuJNTJHSUkJVVVV9OnTJ+55iHewEao23rx5MyUlJTjn6NevH4ceemiPDjDaS1JSEvvvvz/7778/8OWQu4WFhaxdu5by8vJG/T5Cx7WzOv53do1KaJCEaH0/muvQt69e1EWa0l5D1PZkkQFGdnY2ubm54bJu/fr1nZa3aLXoxR+tYcfvb6d803rKzPjkxG+TcezxJCcnd1hrh/bWUcFGe/12iVX7sWrVKo488shwWbWvdjxXsLEXWjJEbUf+CI9HsBGswSgpKaFPnz7st99+pKWlMXz48HZNa18THHJ3yJAhOOeoqKigsLCQTZs2hUcqycnJoby8PO7DJEbT2cFGrM6nLe3Qp47nsq8LlVMVFRXU1tY2+O501QCjvfPUkrIxdDNtx44dUQOM9shfu5fPe/aw6y8PUfj3v0Jo6GHnyPz330lLSaFw7MROGT2xPXRE2RPqIB4voW2H+mvuyx3PFWy0UmuHqO3IfhTtFWzU19eze/dutm/fTmlpKTk5OfTv35/DDjsMM6O4uJjKysp2yLEEmRmZmZlkZmYycOBAAKqrqykqKmLnzp2sW7eO9evXNyg00tLS4pqnzg422tqmVk+SlX1Z5BC19fX1bN68mZSUFPr3779PnvfRysZgJ+/i4mL69OkTt9r69g40Kt5bxRcL7qRm65ao86v+vpic8lJSCk5k2PDh4X4fW7dupaqqqkG/j6ysrC53TnT1ZlRtEav2Y1/oeK5gowX2ZgSphISEDmsCszfBRl1dXTjAKC8vJycnh4MOOojevXtHPdG76vM84q2jL06pqakccMABFBUV0bdvX3JycigqKqKoqIgtW7ZQXV1Nr169wh3P27vQ6Oxgo63DajbXoU9PkpWepqkhaoOjSO2L53dwn6MFGAcccADDhw9v1bFpbRnYXse9rryMHf/7B0pefBaayUPVC89gO3aQcO3/Y7/99mO//fYDvuz3sXv3bj755BPKyspIT09vMHpiZzeN7m7NqNoqWhPhnlhLr2AjhsiROUJaOzJHR3XabktadXV17Nq1i+3bt1NRUcF+++3HoEGDmh3dorud5D1F6ILTt29f+vbtC3z5LJPCwkI++eQTysvLSUtLCxcavXv33qshdzs72GjP9FvaoW9fqNKWnqG1Q9TuqzeJAMrKylizZk24OXBbAoyQ9rwmtGZbpcuXsv2Pv6euqLDF6yS8+zZbfnUDA678OQl+TXiw30deXh7OOSorKyksLGTLli2sWbOmwfDuffr0ITk5udX7tjd6Ys1GS7S0nAqVVd2lll7BRkBTQ9TuzYgFHXmBby6t2tracIBRWVlJ3759ycvLa/Ud8a5UaHWlvMRLrH00M3r37k3v3r3Jy8sDCBcan3/+ebjQCNV89Onz/9l78zC3yvNs/D7naN+XGUmzLx5v4xWwWZI0H6GkFEjcJpCWNIWkkPT6UpIfbdMWSgklfGmTfmm+q00JvZqUBtKa2iFp40JTAmkh7LYZ4xjb2DNje+yZ8YykGUmjXTrL+/tjfM5IGu06R8tY9z9g6eh9X2nOeZ/3fpb7sbWU5K6SDcMKRT8yC8/9fr+UrraWQtpttC6qlaitZ0pvMyCz3nBxcREGgwHDw8NSOnArgV1cgO+730bs8JtVfT5+dAwzj9yPngcfBWOxrnqfoigYDAYYDAb09PQAANLptCTze+bMGRBC6prCWw8oXbMhB8qJ0geDQeh0OklUphnt1GVPNpRW5qBpuq41G/nAcRwWFhbg8/mQSqXgdDoxNDQEo9FYtVfncjjgNxMqOfTr9Xro9Xp0d3cDyG4Sde7cOfA8X3aTqEaTDaC+kbTcjX1+fh4Gg0HyEmu12ob/Hm1cfpBDovZyIBuF6g0tFgsIIbKpQtbLBhJCsPTT57Cw93sQ4vGaxkpOnMaFP/sSeh/+S6g7S0v2ajQauFwuSd6X53kphXdmZgbpdBrJZBLT09Ow2+1VnycK4XKNbJSDXDu1sLAAp9MpFf43o5267MhGIYlapZQ5GpVGxbKsRDBYloXT6cS6detgNBplmaeN+qPa371Qk6hQKIRTp04hkUjAaDRK0Y9cyd1G57Q2EoIgSDUdQPveb6N+kNsRpmT9YCMPbbkEw+FwrKo3jMfjTe0gy7e21MwFeP/+b5A8dVK2ediLM5h+8A/R8+WvQts/WNFnM1N4161bB0EQ8Prrr4PneUxMTCAWi8FgMGSl8NZiO+p1T62FPV3MAGhmO3VZkI1MgpFMJnHhwgX09/fXRfqvnmSD53lEIhEcPXoUHMeho6MD69evh8FgkH2uZt64lUSjjKqcv3dmk6ihoSEQQhCLxaRmg5FIBBqNRop6rHVvaDEomcbVRhuZyJSoZVk26+Agh51aS5GNTIIRjUZLCpq0UjSesCwC/74fgR/tB+HY0h+oEFxgAdNf/iP0/OlXoN+0pepxxHqBwcFBDF6Sbk8kEggEApiZmUE4HJak28UU3tw6omJo1ahDIaQXFqC51GNLbrSCnVqzZKOQRC2wHHIaGhqqyzqU3uDT6TT8fj98Ph/S6TRUKhVGR0cV1dFulo1bzMmdn58HwzDo6OhoGQ3xaqDUxktRFEwmE0wmE/r6+gAAyWQyq6nVm2++KUU+7HY7tFqtImtpNuRu4mvJ+LXReOSTqL148SIoikJPT4+s95tSkQ3RHsj9bOSOWaliotJQ4rdMnD4J7+N/g/TMBdnHzoQQjWLmKw+i60sPwrTrGlnGzKz7yJRuDwaD8Pv9mJiYAICy7chaIBviPcLHEzhx5z0Y/ad/gLbLI/s8bbJRZ5QjUVvvQ7ISG3wqlZIIBgB0dnZi8+bNSKVSmJ+fX7OHbWD57ycW/S0tLcFut8PtdkOtViMSieDkyZNIJpMwm81wOByw2+0wGAxrZtOqF3Q6HTweD0wmEwBgdHRU0mmfnp5GOp2G2WyWjIbc+brNglbYxNtoLZQjUZvZfE8uKOUkEh1qSjwnuQSjXMXEfGuU87vLvtelUvB+99tYeuE/gTpFn0g6hYv/91G4//d9sN7wK4rModVq4fF44PEsH7A5jsPS0hICgYBkRzLrBzNt9VohGzRF4fin7kJieg7xM+faZKNVUakyR71vXrk2uWQyCb/fD7/fDwBwuVzYsmVLlmdAjOAojUYobC0tLcHn8yEYDK6SLWRZVqpLEK+PRCIIBAIYHx9HLBaTGhg5HI6mbGBUCo1M36IoCiqVKktyVxAESXJXiXzdZkErbOJtND+aQaJWqSi73A41MdJz4sSJiiTZWxXxsUMw/+O3sBQJ139ynof32/8PfHgJjl//hOLTFbIjmbbaaDTCbrdXpJrYrBAEAfTfPY7YxHkAQOLMWdg/cJ0y8zS5nWpJslGrMkc9D8q1GI5EIiERDJqm0dnZuYpgyDVXs0H0/k1MTCAYDMJsNsPlcmFkZKTkQ5UpByvmksZiMQQCAZw9exaRSEQ6GDscjqyC6HLW1SiD14h5C0kD0jQNq9UKq9Wala8bDAalfN1G67TLgcy/91o86LShHKqVqFVKwVDpNKpakNvzSRAE9Pb2wmq1yvLcNaNt5EJB+J74e0TfeAWNPiYu/PMT4JdC6Ljrs3Xd5zLtCADJVodCIczNzSEUCuHw4cNZzQYrqftoNC78xdfAHTst/Tt+5lzJz8TGJ6Eb6AWjLV9auBWEXFrnr3YJU1NTePTRR/HNb36zafWEM1HpJhePxyWCoVKp0NnZiW3btpXF8uu1oSo1j9jV1Ov1IhAIIJ1Ow263Y926dTU9SJk1Cf39/VmFbBcuXEA4HIZGo5E6rDZD99RcNNJQlvN8FdNpF0kez/Ow2WxSzm6rpPs18/7SRnMilUrh4x//OPbu3QugMolapciGkhGTasbNJRiZPZ/eeecdWSPQSqRR1TJe+KUX4XvyHyBEo7KtqVYE/+NH4JZC8Nz7h6BqaARbCzJttdlsxszMDNatW4dQKASv14vx8XFQFJVV99GsEZC5p76P+WeezXotUYJspP2LOHHnvbjiJ09XRDZaobaw5cgGwzDw+Xw1sdt6M/dShiMej8Pn82FhYUFKB9qxY0fFnuB6kg05EY1Gpe9vMBjgdrsxNDSEI0eOoEMB9YZ8hWzJZDKrEV4tKhpKoFERlVqaHuXTaRfrPubm5pBIJLLqPloxva2NNvJBrVZLwhWVQql9vBlITDGCkfvsN1skQi6QhYvQBM7U3DdDCUR+/t8QImF0/dGfga7gsKsERNsj1g9m1n0Eg0GEQiGcP38eHMeV3TeqXgi8+CKmvvntVa/Hz00V/IzAsnj7l+8AI0QgJOIAnGXP106jUgBarRapVKrRyygbhTbiWCwmHbC1Wm3VBKOcuZRArfNkfn+dTgeXy4WBgYGqjLMc0Ol06OrqQldXF4Blr3ymigZFUbDb7VCr1Q2Tj2xkzYYcYBhGih6JY0ejUQSDQZw9exbRaBRarTar7qONNloRtaQsNQMpkHNcjuMkgpFIJNDR0YHBwcGiohKtUhxfDYTjr0MV86P/jj24sO8/6lYQXi5iRw5j5it/ip4/fRSM2Vzwukb9nmLGR6G+UfF4XKrRFPtG1dN2Ro8fx/ifPAwirP59+HAEaZ8fGlfnqveOfvTTiJ/1wnmltWIi2iYbCkCr1SKdTtc0RiNqNsRcRJ/Ph8XFReh0OnR2duKKK66QzWve7GlUiUQCXq8XCwsLktdbzu8vJzQaDdxuN9xuN4AVb8rFixcRCATw5ptvwmq1SopXSkvBNmpjVzKiQlEUzGYzzGYz+vv7ASzfI6FQCPPz8zh16hRisRhOnTrV9CHzNtqQC81cW1HuuJkEI5lMwul0YmhoqGzVumYiB/lQ9foiAZDp5Rx+VcyPvt/8KKb3/QfQZN81efo9TD/0JfQ8/BdQO1cfjEUoeYgv1/YU6hsVCAQwNTWFSCSS5cSyWq2KOTVT8/M4ec/vQWD5gtfEz5xbRTZO3fsAAq8dB6NnoNIx4BOVk41mry1svlNeCbRSZEP03CYSCRw+fBgGgwEulwv9/f2KHbCbLY0qmUzC5/NJqW9ut7vmCE4jIHpTVCoVNBoNNmzYgFAohGAwiAsXLkihXJF8yF2P0Gg1qnpBr9dDr9ejq6sLPM/j0KFDcDqdq0LmYs5uPWWNm3UTb2NtQSnVKCXHFYvhFxYW4Pf7swiGKJ9dzZhyr7HRoN47mEUs1PEF9HziI5j9wbNFPtUYpGcuXOo2/hfQ9vavel/p37Na25NbowlAEi8RnVgMw8Bms0l92ORwYnHxGI7fcRe4WLLodYkz52C77mrp3xe+9R3MPPUTAIDWsXwuqibFrtntU8uRDY1GA5atraumUk2IgBXZVZ/Ph0AgAKPRCJqmcdVVVymeIlTPMFqxjSaVSkkEg6ZpuFwubN++fU14pcXvzTDMKgk/sR5hLfX6aKT6liAIYBhmVcg8HA4jGAyukkoUQ+bNHk5uo41iaCU1Ko7jkEwmcfr0afA8j46ODgwPD8NoNNY0rhLkoOFkIxEFdf7kqpe1qQAsN38I4f96qQGLKg5uwY/ph/4IPQ9+BfoNm1e93wyRjXIgOrG6u7sBACzLIhgMYnZ2Fu+88w54nofVas2q+6h0rSc+eRdSC6GS18bPnJX+f+G/foaJL6/Udmjty2SDzyAba6HfCNCCZEOODVNuskEIQTgclvpAmEwmuFwuDA0NgWEYHD58uC61CI1Mo0qn0xLBAJb7gJSrorUWkBnKBeTv9XG5RDZKzU3TtKRoJV4Tj8elCFM4HIZarW6q4v422qgEzaYalQuO4yTFxFQqBUIIBgcHJSEIOdD0TfhQOXlhJsZACfnTazo0aZhv/whmf/icHEuTFUIkjJlHHkD3H38Zxit2Sa83a2SjHKjVanR0dECv1+Oaa64Bz/OSE+vkyZMVi5ec+t3PI3Zmuqy5E2eXFamiJ0/j3Tv/BIRf/h0pFQW1edlWCZfSqIREAgv/9G247v2jWr5uU+CytMKi56gWDyghBKFQCH6/H8FgEBaLBS6Xq2aZ1lpRT+8Ny7Lw+/3wer0QBCFvo8HLFaV6fUSjUej1+op6fVxuZKOcZ5SiKBiNRhiNRklZLJVKIRQKYWFhAZOTkyCEZHmtdLryVFZqUeJqo41qoWRko9pxWZbFwsICfD4f0uk0Ojo6sG7dOhiNRpw6darsZ6oSNHMaVcXjpZNgzr1b9BItG0TXx2/F3L/9Z42rkx8klcLs1x+B594/hOWDNyy/prBtqOf4mT2hxPdE8ZIzZ85k2Wux7kO0TWcfeRTBN4+UPW/8zDmk/Ys48qufBp9c6ROntaul9QjxBAghmP7T+7BWTNBlTTYqhZgq4/P5EAqFYLVay2o0Vy8CUI/IBsdx8Pl8iEQi+MUvfoHOzk5s2rSpZXomNAqV9PrIV8S2FgvElZpbq9WuKu4XU9xmZmaQTqdhNpuluo9CXqtWUPhoo7lRzT3cLGpUmQSDZVl0dHRg/fr1MBgMWdcpkZ615kj+xDuguNLCNno+BM+v3Yz5A/9Vh0VVCI5D9L+fg2n9MOiuQcWnaySZyRUvEe11KBSS5PEZhoHh568gUmE0ig2G8PYNv4F0ILsuQ+tYyQIREnHM/7+vInn+PIzrRyoav1mfncuSbFSy6QqCgFAoBJ/Ph6WlJdhsNrhcLqxfv77pDiJKkQ2x8M/n8yGVSsHpdEKv12PXrl2lP7zGINcGWKzXx9zcnFTEJpKPRnnZmz2yUQ5UKlVWfY2Y4lbKa7VWcmXbaAxUKhU4jmuafknljCtGq/1+f1GCkTuu3OSo4ZEIOcFzoMbfLvtyA8Jwf/QmeJ/9qYKLqhyuj9wEi9sA6uwRoGtwTUQ2yrUvmfZarPvw/eS/MPkvP6x4XiEmILXgz3pNY1VJ9RoAkDh+FKnJZdUySrM2jukt+S1q3TRKeY4EQUAwGITP50M4HIbdbofb7cbGjRub+vAh54aa2XwpkUjA6XRKYXOe5xEMBmWZp40VFOv1EQqFcOTIETidTulQXA9Fr1aMbJRCZorbwMBAQa+V2WwGx3GSWkkzP/ttNB9E5cRKn9N6p1HlEozOzs6SBCMTShzkm0U9qhAqWt/Zd0GlKlMXMtJRuG75MHw/ebGK1ckLSqtF32/fBq0QBQQeCM6DBOYAc0dL74m12JfIsWM4++BXgDy9NIpBSApILWQLHDF6BtaNJlD0pbVQlEQ0AIBeIzWHa+NbVIh8nhjxAO3z+RCNRmG329Hd3Y1NmzbV/EDVU5qzlg1aEASJYMRisYLdXZvdEKwVZPb6iMVi2Lx5s1QMfe7cOQiCoHivj7VINnKRz2slCh4sLCxIaiWDg4MYGhpSfD1trA2IZKNS6Vcl06hEpNNpKVrNcRw6OzuxYcOGqtJhW4FsNMxmCQLw3ltVfdSkjoP8yofgf6FxKlW6/l703Ho96GQ4+40z74DsuFHRuZXe/6vNFkjOzuLk3Z+HwFX2jBKWIDmfnUpHqynYN5tAq1YiLGpdtnOCKiEu1CoR+JYkG7WqSYkpEjzPIxAISIdrh8OB3t7euneclAvVbKhiFMfr9SIajcLhcKCvr69lf4O1CkII1Gp1lgwsz/NSr4/p6WmwLCt7r4+1kEZVDTQajdTFfOfOnRAEoWGd49toTeh0uqp6Qil1ME6n00in0zh69Ch4nkdnZyc2btxY8z6h1HqbXY2qGMT+UtyZYxiJh0t/oADMBhbkxg9i4WevyLi68mD/pffBuakHVC7RAADvFBALtXwaVaXjJ8+dwbt3fBp8srL2C4QnSM7l7AU0YNtkBqNbIRMqnXolwnEJVAkT2Cq1hS1JNtRqNViWrUpWled5pFIpTExMgGVZOJ1O9Pf3VyxF2owod/25dSh2ux09PT2wWCxrorur0miW+6TcXh+i4lU1vT4uV7KROz9N0y3XiLKNxqLaBrRyPm/pdBp+vx8+n08iy3IQjEwoYQ+UeO6VjpSwLAufzwev1wsAcLvd6E/6apuIEFhMAsj1H8Diy6/VNla5oCj0fPI2GLQswBW+f+lzxwDYFFtGM9VsAEDohedw5pG/AhsrXeifNY9AkPKmQXJ8Vdb1JknqFgAYjQo0k70eiqaWo2NF0Gg7WS5akmyIm3i5ZCO3sykA9Pb2wuPxNM3BUWmIUr2ikpbNZmuJOpRmQzOrQinR6+NySKMqhFbZxNtoTmg0GqTTlR1M5EAuwejs7MTmzZuh0+lw+PBh2ZUDL+fIhni28Hq9YFkWLpcLo6Ojy1LAc2dBL/lLD1IKhMBio8C972osvXGo9vGKQGW3o/83PwImtVTyWnpuEurOHYqtpVnSqLjAIha++7eY+8nLSIUrcx4QQpBeYCGks+9l04AeOufK+ZVWMWDUq9OlKJpCIh7DzMxMwebAud+jWc9zLUs2Sm3iomyf2Hgos7PpxMQEdDpdXf8ojTg4ic0GvV6v1AvE7XbXrKTVrDfzWkY1948cvT4q9f7IiTbZaKOVUW0aVTVIpVJSkbfY80gkGEqjFWo2APnIiyAIkprd/Px8wYJ66mR1tRr5QBECu0uL2LbN4N59T7ZxM2HavhWe67aCKoNoLK9JgCs2r8hagOZIo4q+9hIWv/8PWDrrRWIxWfH4XIgHH8+OTOjdWhi6V55LiqHBaPLXZVA0BQ3NIM1xksPQaDRKTkWz2dwydqplyUa+TTyfbJ+ooJQJpQrwCkHujuXFkNnNPBAIwGQywe12l+wF0srgeX7Nfjc5UajXR2b37dxeH+00qpX52yS7jUpQbRpVuRAJhs+3nKqTGcGoJ1qBbNQ6HiFEqm2MRCLQarWw2WxYv359/n1h8SIof3kdpcsFTQT0r3dhQaNFeOyorGO79twMS6cOVLoy1ayOuA+ETYNSV57SXgqNJBuCwGPxH/4G0ddfRjyQQHQ+VvH4QkwAu8RlvaaxqWEeXolOUBQFlVZVuN8HTYEGweDgoOQwFEViRJvNMAw4jsPi4uKq/lzNhJYnG2LI2O/3g+f5snXB65kOo/R8osfa6/UiHo9jenoaLpcLQ0NDTXvj1QqxsH1+fh7RaFTKqXc4HHA4HLDZbGuKgCix8WaqMPX09ABY6fUxPz+PU6dOgWVZmM1mybiq6ijD145stNHKKCcCXylSqRR8Ph/8/uX0nM7OTmzZskURJbpy0QpkoxrkZgbYbDZ4PB5s2rQJPp8PyWSy8CFRxqhGFngOHQM2gOxA+Mgvah6O1unQ99u3QcNHlmVtK4SK8MD5E8DIFTWvJRdKR9WL2ZfA3/0loocPIRVJYWm68gJ/IUlWSdyqDAysG7JTl1U6dVEbR9EUCL/yd6EoCkajEUajUerPtbi4iMnJSfh8PoyPj6O3txcjI5U1AqwHWpJs0DSNJ598Ejt27MDAwEDFqhqNimzIDZFgLC4uQqfTwe12w2AwYMuWLbLP1QwQN//5+XmEQiHY7Xb09vZCr9dDo9FI3ifxoKxSqWQnH408ANdj3txeHxMTE+B5Hn6/HxMTE6AoCjabTYp+KFk03ejDfqPnb6O1IVdkI5dguFyuhhOMTLRKzUa540WjUcmuGo1GeDyeyjIDwovA7EQNqy0BnkXHkAOE24rIseNVD6Mf6Ef3Lb8EOhmpbT3njoEMbwdFy+vYVJpsFiIzge98A5HDh8AmWQSnloAKl7EscZv93NNqCrbNJtCqHKJBF7fpywXixUmgSqWC0WjE5s2bl+dqUpvVUmTj8ccfxzPPPIPx8XH82q/9Gq6++mqJ3VWCRpANueaLx+OS4dFqtXC5XOjv75c8zufPn5dlnmZC5uYvpoVl1p2w7LIHQavVwuPxwOPxAFg20oFAQOrInRn5ELtEtwoa5eWjaRpms1n6TTmOQzAYlHp98DyfRT7kPAA1OrLRyHqVNloftZANQRAwPT0Nv98PiqKaIoJRCEpFNuqJRCIBr9cr2VW3243BwcGCmQHFvjP13kEovnoujc4NLhB+M6InKq/hMF63G54t/bUTDQBIxYCZcaB/c+1j5aDY/ksIgXDiDdBb3lfV/ZKvQHxp/xNYeuUV8GkewbNLIHxl93VBidvNZjDalXspn/JUPtA0nRXZyIdWSfetiWw8//zzuO+++8DzPD772c/igQceyHr/lVdewe///u/j2LFj2LdvH26//Xbpvaeeegpf/epXAQAPPfQQPv3pTwMAxsbG8JnPfAaJRAK33HIL/vZv/1b68VwuF/bu3YtHH30Ut99+e1VEA6h/iFbs61EtEomERDDUajVcLheuuOKKuqa01BuJRAKpVAqHDx+WSESxzT8ftFptlpdeJB9il+hWIh+NOnjnHrhVKlXden00OrJQbdOnNtoAKk+jEnsziKqJFEVh69atVUkoJsxmAAAgAElEQVS8F4PcewlFUeBLHIiqGVPpyEY6nYbX64XP5wNN03C73di5c2dt0dp4GDh/ssbVlgk2BdfmbhCOQ+x0mZEUmkbXHR+DUcuC4ivrFVEUZ4+C9NXeADkTpf7+ws+fAblwGoTnQO34X1WNn7ne6PM/QuC5H0PgBQSmlsCnK7uni0rcmlbOaoWUp/KhnMjGmicbPM/j3nvvxYsvvoje3l7s3r0be/bswejoqHRNf38/nnzySfz1X/911mcDgQC+8pWv4O233wZFUbjqqquwZ88e2O12fP7zn8d3v/tdXHPNNbjlllvw/PPP4+abbwYAiazUmgvbCmlUYug8cyPcsWPHmtb6F+tvvF4vKIoCRVG1b/4ZWEvko14odTDJ1+sjHA4jEAjU3Ouj0ZGNVtnE22hOlBPZSCQSUs0hTdNwuVzYtm0bjh07hu7ubtn3ICXESlqlZoMQAo7jJBvD8zxcLhe2bt0qW8SIOv02qCpqH6oGm4R7+wDmeR7xybNFL1U5Hej/xK1lydpWjGhwudGfZ0i2IYvdp/wb/wFy4TQAQDj2KuAeAO0ZrHr8+Bv/g4WnnwIIEJqJgotX2LSPELAL3GqJ20FDlsRtMeWpfMit2ciHRjvlykXVZOPQoUMYGRnB8PAwAOCOO+7AgQMHssjG4OAggNWhsJ/+9Kf48Ic/DIfDAQD48Ic/jOeffx7XX389wuEwrr32WgDAXXfdhR//+McS2RBRay6snGlN5c5XzsaZTqclggFAMjxye7aaCfl0ysV0gcOHDytKrnLJRzKZRCAQwOzsbFHysZZrNnJR6XNC0zRsNhtstuVmT7X0+mj0Jtro+dtobRRyihUiGJn7vGijlCIbcqLWyH0+yLlOnucRCASwtLSEd955R5bO6XnXl04CZ2ov2q4Y6QQ8V67DHMcjMZU/jdq8czvc14yWLWtbFc68UxeyIRx9CWTyF5kXQnj130F95HdB6Y2rri82Pk3TSB4fg+873wIRCHTuDhjiQCqYqGgcLsSDi2eTAr1HC0PXCoktpTyVFxQAnit6Sauk+1ZNNmZnZ9HX1yf9u7e3FwcPHqz6s7Ozs5idnc1KjRJfz0WtZIOmaXBc8T+gnChGbsSuo2IDpmYr/lMCgiAgEAjA6/UiFouho6MDIyMjqySK6w2dTofu7m50d3cDyE8+tFqtYgeBZkWtPVlK9frQ6XRS2pXFYpHma/QmKgjCmo4ktqEsdDod4vFlKVExFXZhYQEMw5R0JClxgAdaJwoB1FanJqoVer1eRKNRWK1WGAwGXHGF/KpJEsbHQHH1b+IIAEjF4blmI+YEAckL2ZK7tl/9ZXT0WCqWta0YwXmQwBwoR5csw+UjG8L4GIRjeTqpJ6IQXvsx6Bt/q6LIuco3A+8//R0IxwMUBevGfqTDKQThLXudBSVuh7Kj+KWUp1aBuvRs8e0O4g1Dq6VR5RoOseGgz+eTvPmN0EevJwghWFpagtfrRSgUgsPhQF9fH8xmc9Omp+QjH1NTU1hcXMSbb74JrVYrRT4yD8lrCXLXLRTr9TE9PZ3V6yOZTMJqtco2d6VolU28jebE0tIS3nrrLUxNTeG2226Dy+XC9u3byyKwStkoJcZtlmhJro2x2+3o6emBxWJBMpnE5OSkrGvMAseCmjii3PhlgErG0HXdKC5yPFIXL4LW6+H+jY/ARKeqkrWtBuTMO4qRDWHmNISD/1X4+rmzIMdfB7XtA2WNLyzMg3nqcfCp5ZQp87ZRqFU09B2mstdYUOJ2Yx6J2xLKU7mQPl9GzUaznqEyUTXZ6OnpwfT0CoOemZmRtPrL+ezLL7+c9dnrr78ePT09mJmZKTlmrZ1ZG9Fng+M4zM/Pw+fzIZ1Ol9UPpNUherHn5+cRCARgNpvhdruxYcOGlng4ciF64BmGwfr166XIx8zMDJaWltYk+VC6bqJYrw/REzw7OytFPurZ66NNNtqoBk8//TS+9a1vIZlMYnR0FF/84hfhcrkqGkOpVN+1FtkghGSpFVoslrw2RvGO5GePgUopHDkoA1Qyiu4PbsfihBudV4/KozZVCbxTOPbGz2Fw9Ujpx9X2+sq0PcLCDISf/wgoVTR+9GVQrn5Q7v6i1/GRJbDf/jr4+HJXcONgL7QMC4o2QGcvj2wQLo/ErYaCbbMZNLNy75WrPFV4IgIi8AWlhVultrBqq717925MTEzg3Llz6Onpwb59+/D000+X9dmbbroJDz74IILBIADghRdewNe+9jXpkPbWW2/hmmuuwfe//3188YtfXPV5OdKo6hHZ4Hkei4uLCAaDWFpagtvtztvRfK0hU0ZQp9PB4/FU3WCw0UXCxZAb+RA99GuJfDTi9xd7fcRiMam4PBgMYmFhoa69PtpkY+2hlIJiKpXCXXfdhbGxMTidTuzfvx+Dg4N48cUX8cADDyCdTkOj0eAb3/gGbrjhhrxz7Ny5Ez/5yU/w85//HGNjYxUTDUDZNKpWiGyU2nPi8Ti8Xi8WFhag1+vhdrsxPDxct+c1a32CAOr04brMWw6oRASmT3wS9Ok86UZKzw1gVMdiwWTK6ncl1ujZbLayzwGi7REiixBe3AuUo3hGCPhX/w3MR34XlC6/I1dIJeF95A/ALS0TMUOnFSQSgnbdckRGay3jfMZQoIkOwMo5lKIB2yYzGO3KPViJ8lRR8DxQJtloVlRNNlQqFR577DHcdNNN4Hked999N7Zs2YKHH34Yu3btwp49e3D48GF87GMfQzAYxLPPPos///M/x4kTJ+BwOPDlL38Zu3fvBgA8/PDDUrH4448/Lknf3nzzzauKw4Hlw0g0Gq126YpGNsRiNLGbt9PphNVqRXd3N+x2uyJz5qIRB0SxuN3r9YKmaXg8njUvz5sLvV4PvV6fRT4CgYCUHiRGRhwOB8xmc0tsEI0ke+LcGo0GbrcbbrcbQP16fbSKx6iN8lCOguITTzwBu92OyclJ7Nu3D/fffz/279+Pjo4OPPvss+ju7sbx48dx00035a0nBCCNV0sEXsk0qlaIbOQbM5VKSVK1arUabre7bBujaDbDhfdAxRQsvK4Q7OBWxLQm6Ax2MPFg3edn5ifhHr1O6s2UTqelM9H4+Dhoms5qtluIfBBCQLEpCP+zD2ArSJuPRyC8fgD0DXesrvkQePj+z5eQ8i43yNQ5TIDAQ2UyQsxyYrRqqM06sJFkwSk6tgwgdiGG2PmQ9Jp1Q7bEbaXKU5kghECtW3GiEZ4HVcCn1iq1hTWdBG+55RbccsstWa89+uij0v/v3r07Ky0qE3fffTfuvvvuVa/v2rULx48X74qp1WqxuLhYxYqXIfdGLhY8+3w+RKNROBwODAwMSEo7ExMKdhPNgRLShoWgtIxgM6KS31av16Onp0dKDxLJx4ULF1qGfDSSbBTKRS3V6yOdTsNqtUretGpVZ1rFY9RGeShHQfHAgQN45JFHACxLrX/hC18AISSrsHjLli1SH6Bie10ttYVKHY5bJY1KHFMUUPF6lwt23W43tm/fXrFCo6KpoO+VJ4xTLyQHtwEAEoPbYTr58/ovQBCAc8eAzdcBADQaTVaz3XQ6LaXJiuQjM/IhkkfCs7C88UMgWXl6GpmdBDnxJqit71t5jRAs/tWfITF1YXldFgNoGgABjH2erM+b3BYEi5CNzu0DSPlXznXmIQO0jmw1uYqVpzKg0jDQGDLu8SJRnVaxUy3pdm6GAnFBEBAKheD1ehEOh+FwOKRitNwbrJ41IkrPJQgCFhcXkUgkcOTIEXR2dmLDhg2K1Z40cxpVpcglH/F4HMFgMC/5yHcfNQKNjmyUs4lW0uvDbrfDaDSW9Z1aZRNvozyUo6CYeY1KpYLVasXi4iI6Ojqka370ox/hyiuvLOlUqSXdV6nIRiukUXEch3A4jHA4jLm5ObhcLoyOjtYsoKJIk8CLZ0At+WUbt1bwji6ksbxnJdRGGGgGdD37fog4fwJk5CpQ6tWkMDdSLZIPv9+PiYmJZfJhs2Hg1MugYuGqlyC88xIodz+ozmWF0+DjX0f0xAkAgNqghUrDSDUgKr02qx7E0GlGcNKXd1xjlw2WoU6o9FMAAH2XFoau7HuzYuWpTFCAzprtICNF5G9bxU5dlmSj2s2REIJQKASfz4dQKASbzYauri5s2lS8c2Y9+3ooVQgnEqulpSU4nU5oNBrs3r1b8eLhtYzcwuh4PI5AIIDz588jEolkkY96ChpkotGRjWo20WK9PiYmJsru9dEqm3gb9cOJEydw//3344UXXih5bTOSjWZNoxKdWGL6sU6nQ2dnpxSFaoY15h33vbdkH7MWpDZdK/0/IQLCXaOwzb5b/4VwaeDCCWBdaanhXPLBsiz4n+2FKhYq8ckSIAL4V/4NzEc+h6VnnsTSm28AABitChqTVpKU1fe4VxWe653mgsN2XT0CSqMBo9dAY1fDPJjtaK1GeUpaMiEwOvPUjBTZC1rFTrUs2ahXgTghBOFwGF6vF8FgEFarFS6XqyJFpXqrX8kxl6jyISpJWa1WuN1ubNy4ERRFIRgMrnkyUAhKfW+RfIi9ZjLJRywWw9jYGBwOB5xOZ90kgxsd2ZBj7kK9PoLBYNFeH60iKdhGeShHQVG8pre3FxzHSc4V8fqPfexj+P73v49169aVnO9ySaOqlhgRQqReGJFIJCv9eH5+vq69sKqBLrIAyp8/TbwREHQGJLXGrINz2t4FNIJsAMC5YyBD2wuqKBUCffgnoPzTpS8sB7ElRP7xrxB8cwwAQKkZ6GzG5b4al6DrWF1Lq3UUUKSiAXN/ByiKgtqsgXVDtqOqFuUpIhAYOgx5bU4lkY1mtVktSzaUjGyInlCRYJhMJrjdboyMjFTtaa1nGlUtyKfysW7dupZgzvVAPUljJvmIRCLYvHkzAoEApqamEIlEoNfrs2o+lNhk1gLZyEVmr4++vr6CvT6SySTC4TD0en3V8o1tNA/KUVDcs2cPnnrqKVx33XX44Q9/iBtuuAEURSEUCuHWW2/F17/+dbz//e8va75a7NRa7bOR67yz2WzweDyrsgPkJkVKjGefPy3beHIgveUDq78jTSPq6IcpcKH+C0rGgNlxoG9z2R/hj74EMnlMtiUk/EEsHr3UX4WmYHSaIbArB3darwOdJwqhK6BI5dm1DhrL8nvGHhOiF1Y+W4vyFCEEeru+8DmrSGO/dmRDQSgR2RA9+T6fD4uLizAajbIdtOsZ2aiG2KRSKamLOcMwFal8KIl6R4SaGZn9KHp7e6UDstLkoxXTqCpFoV4fY2NjWFhYwNTUFNRqddmHzDaaE+UoKN5zzz248847MTIyAofDgX379gEAHnvsMUxOTuLRRx+VRFBeeOGForK2tdiptdZnI7MXhtFohMfjKeq8a/a9n4kGYQzNNXoZEghFIWF15023ibuGG0M2AODMUZDe4mnmIoTTb4Pk6w5eJVKhCBaPnZEiPSaXNYtoAICpL38DQkarhtqkBRvNfn47t/SA0i3Xaqn0KwpQarsFSBUuKC8GQgg0Jk1RotKu2WgQ5CQbsVhM8uQbDAa4XC4MDg7K6smsZ80GUJ73PVdJyu12Y9u2bRWrfLTRGBQjH+fOnUMkEoHBYKiZfDSabDRqbp1OB41Gg02bNkGj0YDjuKYNT7dRPkopKOp0OjzzzDOrPvfQQw/hoYceqmiuWms2WiWNqtCYmf2WtFotPB5P2bZVznWSVBz0wjR0fHWHwXzQn/sFmmk3YDdeXfCMQdQ68CYHmGigzqsCEA0C3inAM1T0MuHCaQiHnpdtWjaawMI7E1JdhtFjh8Cyq65TmfQFGwUa3RaEoivF/1qbAVrrSpoTo1khG64P7IT/fypXJSOEQKVVQaMvce4qUuTfJhsKotY0qmQyiXg8jsOHD0Or1cLtdqO/v18xT36906gKzSU2GfR6vUgmk+js7MTGjRurlgZto3mQj3yINR8i+TAajbDb7XA6nQWLonPR6DSqRm6imZt4K+iYt9FcuBzTqDL7LTEMA5fLhZ07d1b8/MhKNqIBkGQE6xACc+p18L2jgKmGnlexMLTzZ2RZm1xIdo0ABX4uigISA9thOvFyXdck4cw7RcmG4J+G8Grp7uDlgkuk4D9yWopiGNw2kDxEQ+fuAFVkTkOnBaEzK2Sj94ObQOtXVKdozfJ5kdZpYB7qQTWaZDRDQ2cprbJGikjftopi52VDNhKJBHw+H/x+P1SqZf3jeqUKNVL6Nl8R3tDQUNnyn41GJbnA5SDw0sswbd8BjbM+DRYbBYqiYDQaYTQapboEkXyIRdGZkY9C5KPRZKOR92ireIzaaE5cLmlUPM8jkUjg6NGjsvZbkpNsgBAQawcQ8UN97EUI9i4IvVtAzI6Kx6NOHwJF6pepUApczwawJX6qhMoAI6MBxVfvpK0awXmQwBwox+qUJSG8COFnT5fXHbwM8CwH/5HT4JNpqC1GdLz/KqS8fiTPry4417mdRcfSO7OLxK09dtAZjln6EoF2f/AqUOpqzpEEenuZLQPaalSNQbmbeDKZlAiG6GXZsWMHGIbB2NhY3WoS6i19K/YZ8Hq9CAQCZUv0VgqlD4PljB2LxXDx4kUQQuByueBwOIr+XRdf+Bkix05i4L7fq2pNjT4AV4tS5EOMfOSSj0anUTVyE23Vv3UbzYFmlL6VyxZlRskTiQQEQZA1Si7Xc0eSMYBd/huoaQppZx8085Ogg3Ogg3MQbB4IfaMg5o4SI11CKgGcla+AWQ4k1+0seQ0hApKDO6A/c7gOK8qDM0eBHLIhJGMQnv9eZd3Bi0DgeSyMnQYXS8KxaxTmzeshCACl168iG5RaDaaEapQuQ5GqY2sfGIMWVIY90vUu1/dZN/QDVGV2ihACgzO/8tSqdWy9Eur+whLQbTUqBVFsE0+lUvD7/fD5fKAoCi6Xq+G1CPUiG/F4HLFYDO+++y7MZrOiSlKNPIim02l4vV74fD6oVCp0dHRAp9NJUqYAJIlYm80m5QgLLIelN94CISr0fO4zUCnUiLAVkI98xGIxBAIBnDlzBtFoFEajEYlEArFYDFarte5/62Y47Dd6/jZaFwzDVO2dp2laEenXWlJ6BUGQouTRaBROpxNDQ0PQaDQ4efKkrOm4ckVgSG6dApcC2zUC9ewp0ADo0Dzo0DwEqwtC3xYQS2fxASfGQHGrU3IaBcHSgRRdXopa3NyJhiVMe8+BRIOgLqWvCVwawk+eAJIJWYYXBAGLRydAaTXo/fj7oLKYl+2HzgCVybycS5ZxPxn78xeGZ0KboUjluWoQdM55gaIIDH0eqDQqCBXU+BJCoLcVUZ7KgMrVBcfn/iCL5OSi0U65ctGSZEOn02WlUaXTaYlgEELQ2dmJLVu21BzGlQtK1mykUinp4K1Wq6FSqTAyMgKr1arIfI2C6Embm5sDy7JZBe0sy0KlUqGra3kDYVkWgUAAXq8Xp0+fhkqlgtPphH56Bnw0CkIIph97AkN/8sUGf6vmQaYcbH9/v0Q+xsbGcO7cOcRiMRiNRjidTjgcjrqk4bXKJtpGG3KjWdKoCCFYWlqC1+tFKBSC3W5HT08PLBaL9PyzLNuUjQIBgESDq19jU+C6NkAzNy69Ri/5QC/5IFg6lyMdVvfqwTgW1PiRmtckJ1Kj7yv7Wl4QkO5aD83chIIrKoIzR4EdH4IgCCDPfw+ILskyLCEEoZNTMG9aB8O6AelgLrAcaNOyk1m/fScSv3hH+ozGYkLBIpdLUOk0UBu1IAC0Fh2oHIc14dJw/9KlpoVl2ilCCLRmbVkSuZRWB+e9D4A25JfhFZFpJ5vZOdaSZEOr1YJlWfzLv/wLtm7dCp7n0dnZic2bN0OnK11sU2/InSfLsqykJEUIgdvtxvbt26HRaPDee+/VTS5U6ToUsXO53+9HJBKB0+nEyMgIjMbiD59arc7qSJpKpZaJyov/La197sf/CfvnfhtWm62pH9BGQSQfGo0GO3bsAEVRUuRjcnIS0WgUJpNJSrtSgnw0Q2SjjTZqQS2RDaXUqEqRGFEGXpSqtVgscLvdBRvZNkpOtxRIMgZw+TMgBJ4F2zkItX8q63U67Ad94ucQzB3LpMPmWXnz7C9ApeXxxMsBotYiobdUVFgdd61rHNmYHQfZeDXIq/8OEvDJNmxyKQ7HB3aDMeTEbVQr5EC3eVQiGxqHDVQJoiHC6LHAPtIFxrA65YkSeBjcyzU/lKo0eSCEQK1TQa0rIxJFUXDc/f9B3d1X8tJWccq1FNmIRqP48Y9/jH379mFychLj4+P4+Mc/DkOTp8PI4aUSPfvz8/NIpVJwuVx5yVWz65OXg3g8jvn5ecngdXd311RvotVq0d3dDf97pyEmJlDRAE791WPgPvIh6dDsdDphyLOpZOJyOwCL37dQ5ENJ8tFMm+jl9Ddvo/FQUo2qUHpWvoauw8PDJZ9BpWxOzWSjhNQrT9GgrG6olryr3qMjC6BPvgLB5JQiHdSpBtU7FEB69P0V/0ZpAvDmDjCRBYVWVQQCD+Hl/SC+WdmG5GkVjJtXk2DC86BMNunfKoYAjArgORi6C/fGyYWh0wzbUAfoXCIDLEdQKOkfy9GNIs8sraKhNZfnDDffejv0V15b1rWtciapmWw8//zzuO+++8DzPD772c/igQceyHo/lUrhrrvuwtjYGJxOJ/bv34/BwUHs3bsX3/jGN6Trjh07hiNHjmDnzp24/vrrMTc3J+WAis2TpqamcPbsWXzzm9/EJz/5ySx99GZGtZtxvhzZ4eFhmEymgp+pF9mQe55MyUSVSgWPxwOz2YwNGzbIIjuavHAByfPnpX/Tahp47RDe/7WHEIvFsLi4iPHxccTjcZjNZjidTjidzqaMlNUbhbyZ+cjH4uIiJiYmEIvFZCEfrbCJttGG3FBqH88dNzcNt5qGrkpFNmpFvhSqnCvA6c2gU3HQyUjeK+joIuj3XoVgtEHgGqDkVAAEQMLZU/RwWwjx/m0wn3hJ/kWVgMCmQQKLso1HCAFjtuS9VwQ+RzadZ6HfeSUSR8cq6vJtGegEo9eCyleTwSw/IwQAKAoUQ4MU+XvobeVVzOh27IJlzx1lr3F5+ua3kzWRDZ7nce+99+LFF19Eb28vdu/ejT179mB0dFS65oknnoDdbsfk5CT27duH+++/H/v378enPvUpfOpTnwIAvPvuu/j1X/917Ny5oqqwd+9e7Nq1K2u+rVu3YuvWrbUsuSGoJCROCJGUpILBYN4c2WJoJbKRGa1Jp9OrGgtevHhRjqUCAIKvvbHqNdY3B++PnoPn9o/CZDJhYGBA+v0DgQCOHz+OVCoFm80m1Sq0kR+Z5EP8HaPRKAKBwCryUU4EqY02LmcoGdlgWRazs7Pwepc9+plpuNWgGdOoiqVQZUHgkXZ0Q+M7B7oImaBjIWBoI4SJ46CKNFirF7iRK8FXeX8kVTqYVBpQdSRPhOVAIvkJXbXg9WZo9PmdgZTRvOo13boR0IHKzhSGTgsYY4HMGfoSARHrJWg6b3JWJcpTKk8PHPf8/pq0jTWRjUOHDmFkZATDw8uyXHfccQcOHDiQRTYOHDiARx55BABw++234wtf+MKqsM+//uu/4o47KmNyrYRyNs7MTuYmkwlutxsjIyMVp5E0exqVWHA4Pz+PcDgMp9OJdevW5a3DkPO7hF59bdVrKhOD89/8Ljy3fzRrTqvVCqvViqGhIQiCgFAohEAggPPnzyOZTEKn00kysfWQT27mv2chUBQFs9kMs9m8inyMj48jFovBbDZLkY9mIx+t+Ju30Xyo9p6Wu2aD4zgsLCxgenoa6XQafX19GB0dlSVy25Rko5Ju2VwarHvdJYWqwnPSiTCwfhvI6aNVr0suJHo3Vf1ZQggSQzthmDgk44oKg1PrgOCc7B3XKZ0+r0qTwHKgjKuJs0oNULbVJKQQls+pAJVPaIhRLStcAdJ/qTxSukQg0DvKU56CVgfTPb8PWt/cZQHVoqaT0uzsLPr6VgpYent7cfDgwYLXqFQqWK1WLC4uoqNjRdd6//79OHDgQNbnfud3fgcMw+C2227DQw891FQHkUpRaOMU+4D4fD5oNBq43W4MDAxIUq1yziU3Kp1HrMNYWFiA2WyGx+PBxo0b6/J35aIxRMbeWfU6RVFIzs4i9MZh2N63O+9naZqWDsQjIyOYmZlBKBQqKbPbRjZKkY94PJ6VdtXoOqxWyYNto7nBMAw4jqvYKSFHnZ8gCFm9MDo6OtDd3Y1UKoX+/v6axlYadSUbAAibBNe9AZqLp4teRyeWwI1sAzX5btVrqxUxqxtpsnLWrQYJYwf0gOwEIB+iXZugsnignxiTbUzOYIVanf8AT7n6gHR81eskslTx96X1uvx2gMl4nqmVyEbWfIRAa9GCKaN4HBQFbs+ncHoxhOTs67BYLJItrERSupltVsMLxA8ePAiDwZCVHrV371709PQgEongtttuwz//8z/jrrvukn3ueh0oMkPiLMtKtQliH5AdO3bIUpdQT5Tzu7EsC6/XK9VhuN1uXHXVVXU/kC+9dRCkQIdStYXB+IP/F1e//ExZY9E0DZPJhMHBQQCrZXbVarVEPiwWiywFzmvx4FuIfGTWziSTSUxPTzck8tFMxelttC7EnlCVko1q06gIIVKdXyQSgcPhwMDAgNSkMxAIIJGQX1FJ7mezFrJBklGgihQhgUsj7R6Gxnu26HWqdBRR9yD03qmq1lcryNYP1Px784KAdM8maGdPybSq/IjYB8CZ7GCtnVD7L0AV8ssyLjHbQDOrbTqhGYBN5v9MsLKieMJxUJltq98Qi8FFiP+fEdkghECtV5enPAXAsuc3YfmVWzGIZdsTiUQQCARw8uRJJJPJrCwAvV7fkueBmshGT08PpqdXOjPOzMygp6cn7zW9vb3gOA5LS0twOlfaxO/btw+f/OQnV30GAMxmM37rt34Lhw4dkp1siJt5PQ6+giAgHpx9RygAACAASURBVI/j2LFjSKfTcLlcsoWwc1HPNKp88wiCgIWFhYJ1GJVAru+SL4VKBKOlETt1BvGz52EYHlj1PuE4JC/OQ9/fu/zvnPUUktmdmZnB0tISdDqdVO9hNptbcpOoBzLJx+DgIAgheO2118DzPE6fPo1EIlHXDbdVurK20dwQyUYpue5cVLL35db52Ww2dHV15VXwa/Y020xUTTYqjGpkQiAEnL0LquBc0euMWhoxRxf0geLXyQ3BaEVKo69I7rYQEp2DipINgVYjae+VJGhjm66D5dBzoGpV5tSboeLTQL6zm6MbSIRXvUwEAYiVXzNCyHLoiMrnJGByXhPTqDLsBaNmoDWV1+dNf+W1MN/6CenfNE1npXJn1pG+9957q8hHqzzPNZGN3bt3Y2JiAufOnUNPTw/27duHp59+OuuaPXv24KmnnsJ1112HH/7wh7jhhhukDVAQBPzgBz/Aq6++Kl3PcRxCoRA6OjrAsiyee+453HjjjbUsMy+U3nRFJan5+XlEIhFQFIWNGzdWbHQqRT3TqERk1mGIZLKUala9QHgeodffLHqNykhj/I+/ip0/+u6lLuNvInT4HQRfehPxs+dB0mls+ce/huND15ecT5TZ7e7uBrCcPhYIBHDu3DlEIpGqiqTXYmSjFCiKAsMwGBwclMiH6O05deqU4uSjHdloQw5otdqsBrTlopzIRmYvjHLr/FqFbNTyLJdWoSoOVmMArzFBm44WvIYCgd6kB6G7QC3Uj3CktrxfFqIBAGkB4ApI/8qBQPd2MAyBGH/gdSYkNuyG4dTBop8rBdY9CEOygKoVKVC8HwpU9LsRIhQuDM8lOWIalfg6Vb7ylKq7D/bf+WLR+z23jjTXFsZiMRw7dgx2ux2dnZ1N29C5JrKhUqnw2GOP4aabbgLP87j77ruxZcsWPPzww9i1axf27NmDe+65B3feeSdGRkbgcDiwb98+6fOvvPIK+vr6pAJzYNkzfNNNN4FlWfA8jxtvvBGf+9znVs1dbS6sCCXUPkQGOj8/L3Vb7evrA03TmJqaUpxoAPU1JvF4HLOzs1JRez3rMMpF9OR74ILFjY/awiD4+hjOfOUvEXz5FXDBIBiLBSlfFIRlAQAz336iLLKRC4PBAIPBgN7e3ix52ExvfSmZ3cuRbOSCoihYLBZYLJa6kI822WhDDoiRjUpRyD4lEgl4vV74/X7odDq43W4MDg6WHaFXSuWqWQrEq02hypqbCOBsbqiDHOgCKTkAwICAWKzgU0lQkdoITjkgDIOkyVmV3G0hJPq2wKwA2Ujp7YhZumFgsn+/pHsY6oVpqBeqU5rkdSao2HjewnBicgDJWN7PkXD5fx9CCAjHgcpnj2lmdbFMRmSDEAKDo7x6Q8pgRMe9D4DWlV+TsTzdii3s7+/HW2+9hcHBQQQCAfj9/rVJNgDglltuwS233JL1Wmb/C51Oh2eeyZ8Pf/311+Ott97Kes1oNGJsrHQhUbW5sCLkVPvI9TB5PB6sX79eOqwkEom6EQClyYZYh7G0tASO49Dd3d2QOoxyUSyFSgRFU9BYafj/fUWkgA+HYd65DeHDy8oj4WOnkTh/ASSP4kS5yCcPGw6Hsbi4iOPHjyOdTsNqtUppV9VKUV4OKEQ+FhcX84aaKy04b5ONNuRAtZGNzH08swcRwzBwuVzYuXNnVXV+rRTZqIps1JBClQkGAtKuQWgujoMmhQ/3FJcC4+4Gn06CSinbXTy9+TrZiWIMKugZzXJakkwgABZ6d8GYDoIYchysFIXYhmtgDT4HimcrHpvtGYF+aQ7Q5Ln3DRYgtppUkFQSqJDw0xptfmdVvvOmaCdoGgaHoTy7QdFwfu4PoHJ1VbSuXIilAKItbGab1fAC8WpRbS6siFrVPirxMNVzg1diLlHVZG5uTqo5sVgs2LBhQ0VKCZVCju8SevX1sq6jNfSqCIKQ4a0ivIDzf/VNmB78Y9miDJnh0eHh4VUyu4IgwG63N62nQklU+nfPJB/F8lxFIlfqvm2TjTbkQLWRDUEQkEwmcfToUfA8D5fLha1bt0KbT4azAsihclVoXLnHq3QPIITUnEKVBTYFtntDyboGKh0HPbAewpmToPj83dnlQNI1CAjy2naaphHv3w7LubdlGzPiXIeUwQFdai6vkLCg0SM+ei2M776a593C4DV6UGoN6DxEg9AqoEBjRgQqK0onHAfGlsfmUtRKb43c1wEY1g0iffpkWXNYPvZb0G29sqJ15UMr1Ra2NNmoxmMkoppwsuhh8vl8oCgKbre7LA9TK5KNzJSwpaUlOByOrDqMcDjc9B6ylNeH+PhEWdfSGhpcjAejW3lYU7MzMI5uROzkshzi4ksHYfhSKr93QwbkyuxyHIdgMAi/349IJIK33nrrspHZrTV1LF+eq0g+RIWPYvKCrbSJt9G8qIRsiE1OvV4vkskkeJ7Hxo0bZXXoyN2/QylUZcdSsZpTqHJB2BTSnhFo5ieLXkcnI8DIVgjjvwClwO/LDm4BJzPRkMa2ukAgjwwuz2gQ8GyDLhmAoC1836acfdCsvwrqCuRw2Z710ARmAVWeldo9eckGEQSQaIWF4QDofGe63MJw8fpLtkFtMaOcu0+/632w3PzxstdUDK3kFGtZsqHT6aryGIkodzMTmyF5vV5wHAeXy4UtW7ZU5GFSyptUaK5ajEk8HpciNmJK2IYNG1pS1ST0WnlRDQl5nlmVYWXTEdIcIt9/Gqa75ZdhzgeVSiUVfMViMezcuVNxmd1mgdx1KvmaNebKC2aSj1baxNtoXpRyiolCIl6vF9FoFE6nE0NDQzCZTDh8+LDskeNW2LerBYnIk0KVC0HgwTl7oVqcKXodnVgCWb8dGP+F7GtIDmyTfUwRvCAg3TsK7Ux5XvliCHTvgKDSQpvyQUDxBnqxvs2wzIyDTpQmA7xaB6GjC6q4H0C+6EKBD0aWKqtxIQS0ocAzV8K5l1e5Kgfq3gHYP/PF8tdTAq1kp1qWbCgZ2RAEQTrUxWIxdHZ2Yv369VU3GqunN6kaYyL2/pifnwfDMPB4PE1Rh1Gp/GMuyqnXyASjo8EnBTCalYc3dvo9aLvdSF1cLqILP/ezupENEeLBu1yZXafTKenqtyqU3kRz5QVzyUc0GgXDMJidnZVki9too1Lki2yI6n1er1cSEunp6YHFYlH8mV2rkQ1CCEiefH25wKm0oMxOMJECKkiXwCTD4Ie3AGdPyDY37+hCmlLWFic6BmomG0mDExH7ECiBBSkS1RAhCAISu2+G8ZUflLyW7V4HtXdqRfEpA8RoBQoQFhIqn4CKheF5U6hoWlKdKoR8a8sawmiC8/ceAF1jKmQm2mSjDqg2F1ZELtnIlW91OBzo6+uTpTdCM6ZRiXUY8/PzSKVSFecEN9JDlhp7DVwiDf3u9xd8cPlEEkuHKstDpSgKyOGfFAD9QI9ENrhQDMmXXwE+fWc1S68a+e7BQjK7Z8+erVpmNxONPJTUW4Erl3x4vV74fD6kUimcOHECg4ODq3oItdFGKYh2Smxa6fV6EQgEYDab4Xa780aNlYSSUXY5n9lK7UtyaREamVOoskAEsCYHqFQCdJ7u1JmguTiE3hFgpnjqVblIbbpWlnGKIS0QcHYPVMH5qj6/XBR+FUBRMCYWQMqMyKVoFdSbr4PmvcLy9LxaC6FrALrJsfyF4UZ7/sJwlgWSlRXtU2p1XqWrfClUAIBMEZdiZIOm4fjdL0HV6a5oPaXQJht1QK1kQ9x0I5HIKgMgt3xrvclGIWOSK82bGbJvFfAzZ5E68H2kNHaE9z8J/bW/BO37PgRmYDjruvDbYyBV3B+MnobAEtAZeaHxyXEwRgP42LKRCf/zD+pKNsq9d+SQ2c2dt1HRkWbYRA0GA4aHhzE8PFy16l0blzdisRh+8IMf4N1338Wv/uqvwu12Y3h4uGH3tlK2SBy3nmQjMyLfqwc6lBbv4zmkO/qhmZ8ELRQuBKcA0AwB7+oF5SueelUKRGdAUmuUrbdGMSR6t8BcJdmIdKxHWm8HADCUkOuzK4qYZxiq2XHQ4fxRI7ZrGFQsDFqdp2aCopdrdfKh0sJwngdjKRDBzlMYTggBNCt2tFhkw3rbndCN7qhoPeWAENIytYUta0FrIRuJRAKRSAQLCwsSwVDSADQ6spFIJDA/P1+yDqPWeZQGSSUR/8F3AUGARseATcQRf+mniL/0U1C9A7DdeCvM7/tfoHX6ilOoRFA0BcLygGpl4yDJBEzbdmDprSMAgOS5OSwe+DfYb/0oaFXl8pOVohojXkxm99133wXLsiVldhtJNhrdW0QQhKbeuNtobnzve9/Dd77zHQSDQXzoQx/CPffcA7vd3uhlKdZnQ257UGi8zIh8MpmE2+3G1i1boJo/BXB1qIvkUmC71kM9+16+Ej8J1KWmcLy1A9TSQtXTpbZ8oG52NklrYNToQacriwbwjBYBz1YAgDodgaCtLN2cEILYlb8C88v/uuo9gVFD6BqE7sKJ/Pux3ZOfbBACElmqaA0QBFCFCsPzzU3TK7K3KFyzYbjmgzD/yq+VvZZK0AxOuXJx2ZCNXK1ylUqF7u5ueDweBVe5jHqHyQkhktfH6/WCpml4PB5ceeWVsnlo6/Gdcg1O8rmnQS55K6iQD6S7D9TFaQAAmTmP4JOPY3HvPwLbrsLSofJVLnLB6BkQgYCiV75jcvK9rGumv/X3iL36IozbtoNSq6F2d8P8y7fkDiUb5Ejlyyezu7i4mCWz63Q6YbfboVKpLuvIRluNau3i+eefx3333Qee5/HZz34WDzzwQNb7qVQKd911F8bGxuB0OrF//34MDg4CAL72ta/hiSeeAMMw+Na3voWbbrop7xybNm3Cs88+i71790Kr1TYF0QCUj2zICXE8sYfO/Pw8gsEgHA4HBgcHpToqkohA4Crv2VD1utgk0q510PnOFL2O4lkwnS7wqQSoAs3mis5DUUhY3bI28Ss6HyFIDl4Bw/gbFX1usecKCMyys0rPhkDUlde3saCQ2n49tMdezn7dMwRQFFQaNcDmOe8VSG8i0TDAF+gmnv8ToA0FGsEWUqHSZZMqKk8PLuLphf2uz1ewjsrQaDtZCdY02eA4Dn6/H16vV9Iq37ZtGzQaDaamplrmj1QuMotcvV4v3G53xcpZlaCekQ323cNg38neBHUGFXLvAJpNA0feBNXRAUxXNxfFUCDQABlCdkIqDY1JjXR02ajFZkIw2M+Bu0R2KIaB/ordUDk6q5u0CJT4nTNldgFIMruBQACTk5NSDQPHceB5vu5iAc0Q2Vhr+0Mby/Ky9957L1588UX09vZi9+7d2LNnD0ZHR6VrnnjiCdjtdkxOTmLfvn24//77sX//fpw8eRL79u3DiRMncPHiRdx4440YHx/P+2xcd911AGpP95UbrUI2xHTgqakp+P1+GAwGeDwejIyMrHou5WrkVxkELBk7YY0VT9Wh0gnQ/cMQzp4GVWFNCbvx6rqpWIqIG2zQgwKVt0PGaiSNHYja+pf/QYTsGoYKEbF5QJk7oIksR4IEWgW+ZxjqZBRUHqJBdCYgnj96UUlhOAAQlgNjsax+g8qOXmQhJwpC5aZaGUwQPnE3KI0y5y+gtexUS5ONfGpUYpjV6/UikUigo6MDGzZsWKUkpVQ4ud7IrcPQ6XSw2WzYuHGjovPWK42KEILwzHkI//7kqrC1OhoEGR5B+uzqQjwb4vDrdRASyarmtV+5HsGx41kHXpWJkciGwBEIrABBtfywE55H/Kc/hOWTyngxlD54izK7nZ3LZCkzH/rgwYN1l9lt9CYqdmZtY23h0KFDGBkZwfDwco3XHXfcgQMHDmSRjQMHDuCRRx4BANx+++34whe+AEIIDhw4gDvuuANarRZDQ0MYGRnBoUOHJGKRDzqdDuFwWNHvVAmU2kfksgeig3Bubg6JRAIajQZXXHFFwYi87I38KoDaYAKnoqBa8hW9jk5GgXWbIYwfq6gHR7JrBGWe+WWDIAhI92+B9sLxktcSUFjo3bXS1C65AKKr/mBN0zSiOz4E22vPgMalqIZKDSZSICpkcQKx0Op1cRwQLz+SRAgBpWLy11wUKgzPm26VYa8YBuT2z4CyOcpeRzVopXTfliYboseIEIJQKASv14twOAyHw4GBgYGi8p+trjeeWYdhNBqlOgy/3494vLhahhxQ+gZPpVKIx+M4/u4xDB18Hrp0fu+gWk3lb6STjsN2xTYE3jhc+eQUBYtFQIjQALVyjxBBAKNjwCeXw7NsjAWtpkFfksqNvX0Qpt/4HOhCG1SVaMR9qlar0dHRgbm5OezatQvJZBKBQKBuMrvNENko1ayzjdbD7Ows+vr6pH/39vbi4MGDBa9RqVSwWq1YXFzE7Owsrr322qzPzs7OFp2vVon2Rj8H5aIWe0oIwf/P3psGSXKV57+/k0vtS1f1vkx3z6JRzwzaRiOJ5cKVhEBIhrF1xWIIC4zAxmb1FhfZyMTfBIRlHHG/gB38WT4IjCwEDrP4XgkwN3z5WyCEVqQZSbP1rF29VfVSe27nfqiu6qqupau6qzc0T8RIM1mZebIqM88573mf53kTiQSTk5NkMhm6u7s5cOAAL774Yslpry5yKbA3j0JVDkWA5Q2j5DOFgKLRvtlF5L6r4WRzNTiswSswt2h6konuairYWOzZj+FZtol1yTw261TpazrG4bfgeu7/xRraB7aFWiOYkwgw6iwkzrWmkZFOQV9TE7WsdqWEGta+5S5W4Xf9IYnhvai/5YtyrWDHBhsul4vx8XG+//3v09/fTzgcbslJarMzG+2YMJbrMIQQNXUYmxlEtbsdx3GYnZ1lcnIS0zQRQnDlQgwxU39AFwsz6Lv3Yo5X82c1I45wuZAtDvaefbsRZoaO4Q7mLy6vmkkbvFEXqYmCgM5IWbjD7pK+IzsZx37m5yg33NpSe6thqyYc5e16PJ4qm914PN42m92V2OpOdKvbv4zfDqwn2CiOUTshw7aWcSeVShGLxUgkEkQikTVZzW8NhaoMjoURHcQ1dQZllaBHzS9i7X0N4vTqE/nc3uvadYUtw3QkVnQQLVF/3LU0D4meQ6V/K1YO29ueWkSZcA/KtTcjNA2vkUapkd5JaT4CZu1gQy62KAy3bUQtqrmq1haGC1GTWlXMjPhefwvBN/8O8bNnN3zc3knawh0XbJw+fZqHHnqIr33tawwODnLNNddwww03tDwxUBQFy6pvX7ddUCwwGIvFyOVyq1Yw36xgo13tFP3nY7EYc3NzdHZ2snfvXvx+Pyd//jPE44+teg5dF9Tq5kU2Rcfhq5h7ojWxuBINAPOER0LMn58rVRZ3LAd3t5fMdA7Hklj5QobDsR1URQUpST/+MzraHGzA1nQijYKcos3url27SvcwkUiUbHaL1bibtdltpe3NwOVg47cTg4ODXLiwLOa6ePFiVf2U4j5DQ0NYlsXCwgKdnZ1NHbsS69FsFHULv03BRj6fL9Wwcbvd9PX1sXfv3jW9a1tJoaqAZRAb/N/w5OfpmP4NqqwvTFbNNHJkDHnu5br7OKFO8srWZlUzgwcINQg24oOHkeryNfrdAto1nXIcbH+okNWoY8UbiHTW1GvITApaNAtQPJ6mheFAzaxG4UQCffcVRP7gT4DNGUN20ji17qt87LHHuPLKK9m3bx8PPPBA1ef5fJ73vOc97Nu3j5tuuomzZ88CcPbsWbxeL9deey3XXnstf/Inf1I65umnn+aqq65i3759fPKTn6zoxH7605+ye/du/vqv/5q77rqL22+/fU0/9namURV1GCdOnODXv/41c3NzjI6OcuTIEUZGRhoKvrfz9yqHYRicP3+ep556inPnztHZ2ckNN9zAvn378Pv9yHyO6C8fbcqJQ1mcxbX3ipqf6fbCqpU9VyLqXf79fF3+0t+lU9ju6y50Nna+0Ls6tiz95umTZ3BiZ1tqbzVs1f1sdsIvhCAYDDIyMsLhw4d5/etfz/DwMPl8nhdeeIFf/OIXHDt2jMnJyaZXebe6E91JK0aX0TxuuOEGTp48yfj4OIZh8PDDD3P06NGKfY4ePcqDDz4IwPe+9z1uvfVWhBAcPXqUhx9+mHw+z/j4OCdPnuTGG29s2N56Mxs7oS+HxuOObdtMTk7y3HPP8eKLL6KqKtdccw1XXXUV3d3da3/Pt5BCVQ4p4eXMLi549nJ85Pe4OHIrOV9XzX0FoGAi+kfrni9/8PUbc6EtIKfoOG5/zc+ygV7SHbsqtiltizTApatI28JILiKy1Xon6fJCje0AMtEihcqyUHw1KFRC1KmtUV8Er3h9dP3p/1myz11ZA2MjsNXjZCtYV2ZjPc4eAHv37uW5556rOu+f/umf8rWvfY2bbrqJO++8k8cee4w77rgDoBSU/Ou//ivxeO0iMM1gOwrEa+kwarlvNMJ2zmwUxfuxWAzTNOnt7eXaa6+tyY3P/T8PoyerxV/1oKtOTe2GyCwSvv4a5p98pqnzeIZ3oeSWV0w690fJPLEsNnNsB3+vj9RkBjvvlChUjuWg6ir5uSTGLx7Fc3d7heLbLbPRCGux2W1X2+3CTurEL6N5aJrGl7/8ZW6//XZs2+bee+/l0KFDfPazn+XIkSMcPXqUD33oQ9xzzz3s27ePaDTKww8/DMChQ4d497vfzcGDB9E0jX/6p39aNeuwnszGdhyj6mHleFDUUU5OTpJMJusatawHW06hWsKca4BsWsVBB2kyS5TZ7psJKHk60+OEp49VUoGkg+JxYUd7ITFVcS6pu8l6w5tSxK8hpCQ7ei3+Vx6v3IxgduhwxTa3KtduPSwlrkyiUBBwqb/VHBsHUJN1Aodwd+2shmO3JgxHghAIrQVheIO6Wvr+q1EinaV/b4Z4eydpC9cVbKzH2aMeYrEYi4uLJSHe+9//fr7//e+Xgo0i1iu8a1RpeyNQ76GzLKvk+lNPh9FqO9ttNSyZTFbQpPbs2dOwarl57GnMp1sryicW47ivuJL8yVeqPnMpmUJH1sT9DuwZhNxE6d+KquDyuTEyS2YEDmhuDV+nh8xsDtuw0Twa0pGlCXLmxRdx/U4WxVMn3doitlKz0Y4JdzM2u8XPOzo6UFV1U1aFGmGr27+MjcOdd97JnXdW1sT53Oc+V/q7x+Phu9/9bs1jP/OZz/CZz3ym6bZebcFGOp1mcnKSeDxOKBSiv7+fsbGxDTGQ2BYUKmBKFmp1LWR1It7lSXfKcZPyjqHtvpKoNUvX9PO48ksLaLaJGo1iGzlILU+cjYOv3zbjd9YbxidEhYPWQu8BTHelRaxHNWlQUL0hVDuPN5sguDCB4Q2TCfbhSAOkJGjUcXGrF9jMJVoK0hoLw+vV1qhHodIQXbuq9r+c2VjGuoKN9Th7AIyPj3PdddcRCoX4/Oc/zxvf+EYuXbrE0NBQxTlrOX6s1798K1LUxUljUYcxOTlJNpulp6eHgwcPronbvhLbJbNhGAZTU1NMTU3hdrvp7+9vKkvjzCfIfv/BNV2TrthVdTcARDJB6LqrWHx6dScQt1YdwHYd6GTimQmQlAr9+fv8ZGZzWLlCsAFL2g1NJT0xg/WThzGvf+uaNQvl2Eoa1Uagls1usTbMK6+8gq7ruFwuNE3bss50J1kKXsb2xXoWxbbjwlEtGIZBJpPh+PHjJR3G6OjoxmpNcsltQ6GazhcWztKGSsSrAJUBouUIppVupvveTFDk6E6dITj7EsLIoQ4MY589AUtui9nOoU0r4rcaHMchP3wVnnO/AcDSvcz1HKjcSTrIeq5QTUA3s0jVhZAZ3Jk5XJk5bJcXqXtQawjDZagbjNpum3Kx+eBTSok0LUSkxtis1BOGU5NaBUDXEGJFgLJZmY1XRbCxHvT393P+/Hk6Ozt5+umn+b3f+z2OHTvW9PEej2ddmY2tcKNaWFhgenq6VAV1NXvetWArg41ympRhGPT19XHNNdc0neaTjkP2374B2TVa9y7M4tl/gNyJl6o+8rpMVnO717u7UJIzVR2N7tEL2Y10vqTbcPl1XEGd3HweT0dBQyNtiVQlZjJDdGKc6asMXnjhBUzTLNGGotHomrJWOzmzsRp0Xae3t5fe3l4Acrkcp0+fZnFxkV/+8pcbbrNbCzupE7+M7Yvf1sxGuXOgYRgIIdizZw9dXbW1Cu3GdslqJFyDmOnlfiJnufBo9SbfgqT0kvQfwhU6SJc5TXT6OdQ9B7Befh5777XY2+x+ZyODpWBjduh6pFI5drmFWdAxrAWOjWbnkWUTeAFoRhaMLE4gApaByKUp9fgeH6Sr530yl4EW54OKx11HGF4noGhQnE/07q7athUC8e28QLauYGM9zh5CiJLQ+frrr2fv3r2cOHGCwcFBLl682PCcsP7MxmZNyrPZLFNTU2QyGS5evNj0Cv9asRWrYeUWhtFodFWaVD0Yj/8Ee7yaBtUKNIxCsLDyN1icJXjNa0g+X992MLh/N8KeqvlZx0iY6ePTpGcz+KI+hCII9PlJnJzHzJjoviVRmC0RmiB7IcaItcDuG27Atm3m5+eZnZ3l9OnTKIpSmjyHw+FVn4XtLhBvN4qFKf1+P6OjozVtdouBWztsdmthJ3Xil7F9sV0F4mt5t1cWkC2nxJ44cWLN1N9WUaBQbQ+9xrTsrfh3IqMxEARW+WkNWzCh9BLru50ONYOS9xDYtWfjLnSNMB2J2TmEadhkQtXzML2mD2Rz0K1c4WeqM/4J6YCqIQMd2FKC46AsBZkrn92WheG2hRYI1/6wrjC8DkPBE0CEumscs/Hj505aFFtX71Du7DE4OMjDDz/MQw89VLFP0dnjda97XYWzx8zMDNFoFFVVOXPmDCdPnmTPnj1Eo1FCoRBPPPEEN910E9/85jf5xCc+UdV2O2hUG7VqVEuHEQwGGRsb2/AOebOCDcdxmJmZ4ezZsyWa1FotDAHIZ1ASE6vvtxoWEbcWvQAAIABJREFU43iuGKud3QgIkg0O9fgE9XbwRry4Ai6MlEE+nccT9OCJuFHdKqmJDJF9hY7LsR2EKkhPJuh4/v9DueJaVFUtBRdQoB0kEgkuXbrE8ePH8Xg8dHV10dnZid/vr+5It2jSv5VUovLvvNE2u7Wwkzrxy9i+aIf1bbtRHCOafbfLjUsCgUCpgGz58Zu6yJVNgr31tvW2I0oUqiIcKbDRUZuchEtgzvbB0BvR9CSq2WiE2hpkBg+wIGvU0HBMhFj7fdCt7NLfBA71rVGFlEjdA6aJkplHKhrS5QKhFJ47x4F046KK5ZBSFijRNauAa7UpVPWoVYDoGa25fTPGkK02UmkFq858s9ksuq7XnCSvx9nj5z//OZ/97GfRdR1FUfjKV75SEpD+8z//M3/4h39INpvljjvuqBKHw/oF4u0ONlbqMLq7uyt0GJOTk9tCS7EelNf8KFZqv/rqq3HVsYJrBcr4b/B4IReOIhfWt2qlka+Z3RDzUwQOHSB1rDoQUQMB1GTtrEYRwYEg8RNx0lMZ3IFCCjY8HMTMmDiWg6IVOhZFU9HdKgvPvoA78F1c+69G27UHZalzc7lc9PX10ddXEBZmMhlmZ2c5efIk6XSaUChUCj6K2b+t6lC2qt16HXXRZrdotVtcbY3H4yXKWjgcLmU+1vpsXg42dhYajVNbiXYU9Ws3ikFMo+e7vICsoij09fVx/fXX19VhbGox2W2S1ZiwI5iy+jdcyLqI+lpf8U87fkINl8O2Bnl3B5ZR/T01JwtrlOUotoFaVJULgdRcYNV/T2zdi2IX3gXhWIichVRUpO5GLs63pnORsrbdLdQXhvtq2wBL4PmJeYL5U1VMhcsC8UrU7ZmFEK5HHnmEp59+Gl3XOXz4MG95y1uq6DFrdfa4++67ufvuu2u2feTIEV58sXGVTbfbTS63dmFSOzpHKSXJZJLJyclVdRibxb/diE5/JU1qdHSUeDyOz+drS6BBNoVy8WWEAO/YATK/enz1YxphMVHQbrxyvOojX9RNrTWQ4MH9CBqnYgPdAbKJbMl9SqgCb9SDN+pBOhLbLBRzsnImoaEo0jLJ/ucPSP/435G2BE1D8XjROrvQe/txH7wG1+E34fP5GB4eZnh4uDR5np2d5fnnn8eyLLxeL1JKLMva1MnUVmc2mulE22GzWwvlnfhOWTl6NaLZcWqrsJ5xaqMm8PXoWbWMSxoVkN2Ma10JKSUyvT30GpNOT026VMZU8GTz+Lyr/27lSOYFbgHubVbDMe9orBS9IyUuYbMqX6wOdDNb8e9GwYaj6jguN4pReYxwbEQ+g/R6cbp6kLPTq16NlBJpWagdNShUQqlL6apnhSs6+ji47/oqpkI0Gl0X86ZZ/FYEG8CHP/KRj3DkyBEsy+Jf/uVfeOtb38oXv/hFwuE6XLdNxFZmNnK5XCmt7PV6m6qHsVOcRYowTZOpqSkmJydxuVxVNKlEon2rS8r48wV+JuB2O+S6+3BmalcObRaaky10HivEayIxgf/KK0i/crJiu7fDxaoKcqD7ym7o6sXrVUn85tTyeRWBUETBAteR5JM5Ar0dtU+SSSDHE5gzZ9Guel0p4wGVk+e9e/di2zbnzp1jcnKSX//61xWUrHA4vKET4a1M0a7VP3wtNrv12t8pnfirHNt6nPJ4PNtOIF4+FrWyYNbsOTcU24VCJQWLdNbfQQtBTW/E+hBCYLs6wV57/bB2Q2geTKv6GfTqEtVc49jgOOhWZQDu1KtrAdi6C0d1VwjJK67RsVG9XuTwbpxMBjk71TDoELqOqNW317uGBuOQ6Bkt0cj7+/uBQpY1Ho+TTqd59tlnCQaDJZpvuzWGO0lbWPXrCiGELPQaf17UWABMTU1x9OhRvvrVr/Lnf/7nW56u3mzr26IOY2qqQLXp7e3luuuua/p32C6WtI1QTpPK5/P09vY2pEm15ftkFlEmTpT+qQiBb98+UusMNkjO4dk/VjO74e90U176R6gK6vwlUJp7UfX+foJBSLx4piJ9q2gKtlHIbmRmFusHG0tw8ga88gS85o1191FVlXA4jGEYjI2Nkc/nicfjXLhwgRdffBG/318KPtrdkW1lsNGutlfa7BqGwdzcXIXNbnEgCIVCpY77crCxvbFTxqmiffNasFGaDUVRyGazTExMtLRg1gibltnYJhSqOX0AadafhM5ldXy6ATXsWxshZbvxyrrygE2HpXrBrn4G3cLCXuM5NTuHWPG7rHS5KsJBFIIMIeoGG0UI6aB6PQ2DDmnbqKEa+hOo6UIlpQR3ndoamgs6q0XzXq+XoaEhpqamOHjwILZtE4/HN0RjuJPGqVp3WFB4QzquvPJKoDBA9/b28sMf/pBbbrmFe+65p8Q33ypsRlG/WjqMAwcOrOkB2awigmvp9FOpVKkQUyQSYXR0lGCwzgu5jnZqQT3zXEXRIACXbqMOjmBfOreuc2vCRLjdyJVB6fwU7rCX/EIhLevr8qM0GWgA+PwaYBEc6SM5vixqF0IgVIG0JY7lkE2k8Ebr0zmkZeGceQGlQbABlRNvt9vNwMAAAwMDpSJa5R1ZUa/Q2dm5borbVmc2NqITdblcVTa7iUSCCxcusLi4WLLZte21DqWXsUnYEePUetBuNyrLspiZmWFhYYGTJ08yMDDQ0oJZI2xGsOFIsDKptcoE2oozqUB9RTMgpcCWGqpoTbth2WB7wmhmdYXszYYUKjmjes6iAPYKGlQrWEmhAgoshBpw3F4czd1wn6pT1Qk65JKjVW1heL3aGqI+taprGNEgAJJSoqoqXq+XQCBQV2PY0dFRyra3Ombv9GCjiPOnT5/uGhwcxOVyYVkWvb29JBIJFhcXt7wT3yg3qqLbzeTkJIlEgkgkwvDwMMFgcF0Tr+2W2ahFk9qzZ8/mPrjpBUTsdNVmIQS+3cMk1xls+EZ2EYh6SJ2PkY4lkEWBmRAE+gLLwUa0jlisBmSkC7dSSON3jA2zOD5RsXqiqEppopqanG8YbCAlcnEOJxFDifY32K32pF8IQSAQKHVkjuOwsLBAPB7n/PnzOI5TWkGJRCItF9n6bchsrAaPx1MRvBVT4Pl8nscff5xAIEBPTw+7d1f7qF/GtsC2HqfWg3bQqKSUzM3NEYvFSKfTdHd3EwqFuOKKK/DVE8muAZsxvk0uCNKug+y2nl6jUqA9sKUgo/VWyRhWYj7rIuo1W85SpB0fYbY+2MAVQNZYz/W7bMiv7V4L20R1agRgQuAIBaWM9lxsoRhsrJbZqDrliqCDmUkUn7dObY06U+F6FcMB0TvasP1amsdaGsMizbeoMSwGHs1oDHeStrDqm8jlCi3/18LCwr/k83ncbjeappHJZAgGg9ui0FC762zU0mGsy8p1BTarYnmjTr/4YMdiMbLZ7Ko0qbW20yzU089UpVOLcKk21sAw2sT5NZ3btf8Qem4OdI3w3l0Ehvsrgg5VBVfQg5HM4fY3rw3QBgZKf1cUBW9PhNz0smBRCIGiKji2g21Y5JNZ3MH6HZY0LeSL/w1veteavmc5FEUhEokQiUTYt28flmWRSCSYmZnhxIkT6LpeynqEQqFVO6ffxsxGIwghSja7Fy5c4HWvex2pVGpdRhSXsTHYKePUerCebHh5trqjo4OhoaHSO3/8+PG2/zYblbkv9kE5E2ILClKqTPhew2CmsYHMRmLONYSVXr1vypoKwq+CbC1LmjYEQd2D4mxdvyOBnF1MHlZCsfOrxVl14bKydQNFR3OhmLmyf7tBLFOsyoMNU/ehm80V/y0GHVZnD4pV4zcVon5tDb3O3MgXRgSiDdttZgwrr7kFlMbseDxe0hg2qsm14zMbQogOKeW3gX8p337x4kU+8YlPlPjPW4n1cGGhcJNt2yYWi5Vsafv6+tqWVl6JrcxspNNpYrFYiSbVjsrl6/4+yQRiarzhLqHhAdKxC1U0q9UgO3vxiMpUrVoj6Aj2Bcm41JJlbTPwB3QoY6t2XrWXi//560rPeVWUdklOzOG+sn6w4Zgm6tQ5HNtGqZN5WOukX9M0enp66OnpAQoBddGlaXFxsVQcr6j3aFe77cB28A8v2uxuB6HxZVRjJ4xT64GiKFhW82JowzCYnJxkenq6YbZ6I8aijTynEILzcQUpC/3BtBHE49tDZ+ZMW9trFlMrCvk1QsZw4dNbpxzl1RDeLQw2hMuPbVbfT7cqcRpY1DaE46DVmuwvQWo6VAQb2jKFCpBlz3GqazeR2LGmm5ZCIDUd4dS49npZDa2xMHzVNtcwhq0csw3DIB6PMzExwUsvvYTL5SqN2cUFlR0bbAgh3gL8H0KIhy3LIpVK4ff70TSN/fv3s3///i24zPahuLo/OTlJJpMhn88zNjaG11t/QtgObHawUfRJn5ycRNd1+vr6Np8m1QDqmWdXTYV73Qq5vWM4p6rrYtSF24uvO4yQtbmypaBj9xDJqQX08zEUtwuhqSi6VnCq0HUUXUfRFBSXq/C524XweAouFvnlFRXN48IV8mMml7cJIVA0BcdysHIrOjdR+g8AZnwOayGJ+J9/hwh3ol3zWlyvuan579sCPB4Pg4ODDA4OluiC8Xicl156iVwuR0dHR0V9ildbZuMydg5+28cpaI5GZds2s7OzTE5Olihk11xzTUMnt40YizYic1+8zkRasJir7AsuGD243BmC+XUaibQIRyrM5GrXXKiFuYyKL1w7Q9AIi4aGR4i6mf+NhomLWjwxj2rhrNEMTLWNCprUSpSLxB2hojgWpl5msrKUfbAVDcsbQtK88a7U3QhPCKbT1R/Wq63hqUMzFAr0jKzeZhvGz+KiwUqnq7Nnz7K4uEgul+PChQulgsDbGaVfWQihSilt4M+BHwPvPXHiBD/72c+47bbbGBsbK3gUb0Khknajlg5j165dpNNpRkdHN+UaNqPORpGfm0qleO655+jt7eWqq65qTy2MFVjXgLU4izLdnB4j0BdhcVxr2u7QPzaGlptfdT/t4GGiV0nqJUKFJ4CYvVi1PWNraPnK9G3n1XuJ/fdvKrMbiihJWK28heZeetVk6T+Ff1o20rIhNw3xaWRmsSrY2IhJf3lxvNHR0ar6FFJKdF3H4/FsycR/O2Q2LmP74bd5nFqJetQkKSXz8/NMTk6STCbp6upqSYOxEWPRRtGoLFtyIVF9HyWCcTnKlVoGt9WEZ3mbEHcNYqWb75ckAkvqaKK1bIDtgOXpQDe3oKaIqpM3a9xLKZGrCMMLcwIHIaqz9C6rMe2pnCbluApWt1LVyz7XcDw+0qFBpFBrFu6te26XB+GPwMp5R73K4I2E4ZF+hN6cSVC7x7Ci09XQ0BBSSv77v/8bKSUnTpxAURRuvPHGtrbXTpSHdMW7dhz4N+AzwWCQEydOcODAAcbGxgB2VAeez+dLaeVaOozNnMxsZGYjnU6X+LmhUAi3282RI0e27WRNPf1M0/vqKuSHRnGfO7Xqvp5D1zQVaKR6Rgipje+FzGcQqg52ZYbEo9g4qo5Stt3dEUR1u3CM5W0l7YblYGbzy8HGKnASM8iZC4juXcvXsgkT75X1KUzT5OTJkySTSZ544omq9O1GX8/lzMZl1MGOG6eaqdhdCyuzBZlMhsnJSWZnZwkGg/T19TE2Ntbyu7iTaFSxRQ3Trv39bEdwRhvjCuc5tFr0mA3AdAsUqiLmMzqdPqNloXjK9hJh84ONhayDWqMeod8lkTXcqYqQ0sHCBVLicQpBiaOoSKEibAvVXuUeLblNOYBAYrlWBM9CYPrDmIEoUggcRUVtYhFSspTZaKW2hrt+MCF6t4dZiBACVVUZGRlhZGRk2873iqj1SyeA9wL7nnvuORKJxI4S2hXt/cp1GNdee+2aCoS1E+3ujMtpUpqm0d/fX3LMeeaZZzb8wVvrSpaYn0apkTFohN6RXuYnL0G+/qqKOjiCy1xcNa8qBnYT7Ait3qh0kB3diPhExWZFSMxIb9V36BgbqSjyB4XshuZ1YWUM6GguxSkNA/v4r9D+912r77yB0HWdYDBIIBBgeHi4lL4dHx8nmUwSDAZLwcdGUBC3Wi9yGdseO2acKpqZtPqeKIqCaZpcvHiRqakpNE2jr6+PkZGRlp3lyrERWYgNWUzT/MymGn/PnKVy1n0Ve7P1zUbaBYfWKFRF5CxlaaW/NaF41oSwy4tir91mtlVIIdA8gZoJA9XJ1f2FHUdiKH6QEktouJcyOUJKFMdAM3OrU56EwFE0HE1HALZWPeHPu0IoSCQK+e5R9FQCLT1fKgpc8zvpHhxVq+FUXDt7IaUDrjrV33UPRLanw91OCjaKz9H/Au4EEl/84he58cYbOXToELC82rhdvlTxOqSUpXoYRXu/zdBhtIJ2dPDl3zOTydDT08NrXvMa3G53xT6bpQ1ZC5TTT7d8jCoknkNXkXvmydo7BEJ4w26EvYqneUcXdISbXmGyjByaEFUCdVWrPkFwVw+JF05XpHWFEIRHeli8MNtcg0uwL55BtQyEVqC/bdXEu7zdlenbZDJJPB7n2LFjGIZRofdoR2C/lZmNnVSV9VWIHTdOtRpsOI5Tsq/OZrN4vd6qfn492Eh9RbsgJTi+EZph5SdNF5d8r2Eo80Lb2q+FuD6EZaztmUqbLvxrEIpnlRD+TQw2RB27WyEtHDNf851yHMipARAKji2xFDeOLVBEoTqhFCqa3ZxzqKO5QICle2rSm0x3CIUlwbeiYgej2P4O1MxCIehwqgM62+PH0b04UmJ3D6PPnC8EHvVqazSoZk73CKLJeh+XUYnSr7pUjRUp5f+i0JEDvLt855WrKVu9+qhpGh/96Ee56667GBkZYdeuXZtC8VgL1tPBZzKZkptUOBzeNt+z1e8jEjGURGxNbXkDGvlgGJlc4T+uKHh3j6AaqcYncHsRA8MI0XzAp0qHlOojaFWKyjRp4XQPwUxldiM42kdyfPn7qW4dbyRAemoO6ciCjqMJ2PPzcOoZGHttadtWBRu1JvxCCEKhEKFQiN27d5dMF4qZD6CU9ejo6FhT0HBZnH4ZtbDTxilorgBtsdjX5OQk8/PzdHZ2Mjg4yMLCAsPDw229np1Ao5pJCtAa1Chaub8RwOPbR1dmdbrtWjEle9Z87HxGxb8GU7tkXsWnKIg1m822BsPRqCUMD7pB5ivfISklNhqGVqA7CcfCUgp/N3DhoRBgKLaB0qT9r63paFYOeyWFisIqQ3EMlSiUAlFFwQ5ECkFHdhE1NYdSRq9yPEvZKCGQkR7ykR7E4hzuxKXqCuONhOGsXlvjMuqjnvWtKHbqMzMzTE9PMz8/j2EY+Hw+hoeH6e/v35IOPJfL8aUvfYnvfve7XLx4kXvvvZc3velNbVv12Si02hlbllUquldMn4+Ojq6aPt+se9Ly4CIlSgtajZVQBHgPHCTz5C8rtrsPXoXLWEUgKBTEngMtBRpF+IMhmKt2sDAVhZXr9x1jIyyOx0odmOYtZCZCu7qxcga6rwmhvhA4uRxy4iRiKdjYKlpPs5O0lV7hhmGQSCSIxWK89NJLparcnZ2dTVsub6fMxmVsT2zncaocjWpCZbPZUn0nv99Pf38/+/fvRwhBMplkbq79nP3tTqMybbg03/r7d8HowrJn6FPbXxDPXiOFqgiJwHRc6Epr2hJHgqlHcJnxNbfdNDQPhrX0XEhZseovrEoKlZQSQ3iwFVf5xtL83xDeUrChm81b+CpIHM1d4UxVhCO00tjqCAUpRKXJlxDYvjC2N4SSS6Gl5gqidq1y3BWADEVgfpKaLmH15liBKMJ32QZ9ragZbEgppRCi4/vf/z6PPfYYx48fJ5FIoKoqXV1dDAwMMDY2xtvf/nauvvpqHnvsMT71qU9h2zYf/vCHue+++yrOl8/nef/738/TTz9NZ2cn3/nOdxgdHeWnP/0p9913H4Zh4HK5+Md//EduvfVWAG6++WZisVgp9fyTn/yEnp4edF2nq6uLH//4x7z97W/nrrvu2vaBBjTXwZdXe61Hk9qpMCbO4J+fWtc53B5BLtqNk5gBQNt9BW5joXYqtAh/GIb3IpQ1Dq5mFoKdkKzs7F3SIOfy4zaWA5GVRf70pWDD5fdgKiDUpdUYUQwKK/+u93aiuj2IvkGEaSATE4jowLagUbUCl8tFX19fqXpzJpMhHo9z+vRpUqkUoVCoFHx4PLWFeJczG5exGlodp7YKK4MNy7JKejshBH19fRw+fLiqvtNGmYpsdxrVxYSC7azl3RdMew7QIV/CY7U34Ijru7DXSKEqYi6j0+2vTUVqhKTlpnNdLTcHW/UWbLCAcHaCBd8gAF5N4phlmQIH8qq/4Ai1BCkllliep1iKC9sSqMJGbZJCJaVEsQwMb0f1Z4BTFoBIlsbPWhACxxvE8ATqUqulUGq7WdXTarB9hOFF7DRtYb3MxpXA5++77z5uueUWPvaxj7Fr1y5UVWVqaoqnn36aJ598kieeeIJPfepTfOxjH+OnP/0pQ0ND3HDDDRw9epSDBw+WzveNb3yDSCTCqVOnePjhh/n0pz/Nd77zHbq6uvjRj37EwMAAL774IrfffjuXLl0qHfftb3+bI0eOVFybqqp88IMfBAqdeC6XW5c2Y7MmNY0643KXkXA4XFHtdbuimcHFtm2mp6eJTUxwaPHkuttUBHiv3E/6lzMokS68bomQZb+R7oZoDyIYBo8PqQnwhjA8HXim196+4/JUicuEEHh6B5AXKs9bXuRP8y53XLp3das8xeUCJGTT4PEWqFQ3Dqx63EahXe9GsSr3rl27SnSReDzOCy+8gGmaRCKRkt6jOOHaSuvSy8HGzkAr49Rf/MVfcPPNN2/JdbrdblKpFJcuXWJubo5sNkt3dzcHDx6sG2zDxtmlb2ca1WJWEG+iOnc92FJwRh1jv/NsWx2qptdBoSrCsBUQGq0KxfMW2O4AqrUKVXg9EAq5JacpxcrjtRbJGwFyrjBuYZauOG9rWJqnalxQHAtTqZyom8KN25pruhaGcCykUCoK+RVRCg4o5iLEUsDR6ISiKqtRfj4pBKLskZXSqe9CpajQtbWmLSux07SFFcGGEEKRUjrAzcBvXnrppXfWOugd73gHAA899BDHjx9n37597NmzB4Df//3f5wc/+EFFsPGDH/yA//E//gcA73znO/n4xz+OlJLrrruutM+hQ4fIZrPk8/mmV/Kb4cI2QrEzX4+zRyttlXfG5atbiqLQ39+/bpeRzUS9waUoHo7FYiwsLNDd3c1VfWF8icYe283CrTvkhkbxREMInxcZjCC9PhS3C5TKF04AqWAfGXQ8uqeiOmlLyKXAG4RssnK7kQa3t8IlS/O40EN+rGQG3decFzeAcLmWDQ/SKfD6EYuzSMvccZmNRhBCEA6HCYfD7NmzB9u2S3qP06dPlyhZhmFs2crN5WBje2Mt49RWOFVJKXnuuec4duwY99xzD3/7t3/L2972tqb1dhuZ2diONCpHwvkaNTVaRd5WGHdfzb7sM23ROhQoVM3VMFkNqbxO0N1asAGQJUCA5oINzUhjuVqkfLmCJWF40JhFKArhXIy8K4RTVlsjp/jQRfV9dqiet+SFh44GFcMrICVqUatRS4RekdVQloTn6xibhFiy2i27F/VqbgBEB0uGLc1gM8aunTZOVQQbSx04Usr/CZBKpT4XCFSLtIod1fve9z6+973vMTQ0VPpsaGiIX/3qVxX7X7p0iV27ClGhpmmEw2Hi8ThdXV2lff7t3/6Nw4cPVwQaH/zgB1FVlbvvvpv777+/qoNuxIVtBpsZbAghsG275CaVSqXo6elZdXVrp8A0TSYnJ5mamsLj8TAwMFDgHgPar37QtnaEEPhvugmhiVUHbMcfJbOU2s13DOKeOb3WRpH+MGJFsCGQyK5BuFQpSux8zV6mnzyG6mqutgaA8JVl5yyz8EcIOPMc0tu3rQTi7USR8lLsCwzDIB6Pk8/nefLJJ/H5fCXKld/v35TfYaetGL3asJZxamn/Tb2Xd999N5qmMTAwwOc//3ne+MY3tnT8RmY2tmOwMbUgyJntuT8pU+eC7yqGM8+v+1ztoFAVsZDTCHkUZAOr1lpI5hX8moaQq9SVcBx8ySnmI3tq1qSrNUGVQN5ernLusVNIIXCZKUIijeMUtpuOglJjquRIMIWnKs+gO3nUJsuNS+kgHBtLr2apSEQlZav0Dq/9npRnSgrtryYMb41CtRlj544ONmBZdCeEePMnPvEJXvva19LX10dHRweRSISurq6SyNNxnLZ04MeOHePTn/40P/nJT0rbvv3tbzM4OEgymeTuu+/mW9/6Fu9///srjltvsLGRhfbKkclkmJ2dZXFxka6uLgYHB7c1TUpKiZ1aQNM1cPtrRvvF+59IJJiYmCCbzdLX18c111xTYX0qpsYRyUT7rs3tQ+hKU91M0tdd+vuiK0x3jSJ9TbebSyN0N5iVz5uQBlJRCkTWJXiiQXx99WqT14bi84OugVm4PplJI8IRmDgJe7fG13srMgsul4v+/n7Gx8d57WtfW6rvcfLkSTKZTIXeY6O0TDutE381otVxSoj6ixOJRIL3vOc9nD17ltHRUR555BEikUjVfg8++CCf//znAbj//vv5wAc+QCaT4V3vehenT59GVVXe8Y538MADDwDwyCOPoGkaf/mXf7mmyf1OolEVM/e2A7885eHKfoOugNO0zXjehNhCe9+5uOHD49tPT+bEus4z5ayfQrUMgSVdqLSWZZdAXuvAYza2UdfMLJqR4aWFIQJeh7CWJKwml+QJEsWRrOQEC92HZRWeB8XOo0gHqRRqXXiTE6T9he+/6ATxatXPoyW1mvc5aDY/7quWUairUSOacVaIxeXSF1hPZkOiFAKOctSzbHf7INzaM1DsczYSO22cqrX0Wgxxzeeff55HH30UwzDo7e0lEokQDof5gz/4A44ePQoUMhkXLlwoHXzx4kUGBwcrTjhL3fmjAAAgAElEQVQ4OMiFCxcYGhrCsiwWFhZKzjUXL17krrvu4pvf/CZ79+6tOAYgGAzyvve9jyeffLJmsNEOGtVGYCVNyuv1Mjg4WCq8t52RM2xSaYuuuRcKVbQDHeCPFP5oLnK5HFNTUyXnl+Hh4drUAOmgnn62rdfmRPsQTXBe7VAfebHceThCwYwMos+eXWPLEhnqQsQvVWwVtons3gVT5yq2R16zF0wTx7SQtg2WjXScwoqWU1mLAwCXu0ALW1hyn8mmkaEOhGWgp+cw/dWTn43GVluGKoqC3+/H7/czPDzM2Rno0OeJx+M8//zz2LZdofdoV4ZyMwaKy1g3WhqnGt3PBx54gDe/+c3cd999PPDAAzzwwAP8wz/8Q8U+iUSCv/u7v+Opp55CCMH111/P0aNHcbvd/NVf/RW33HILhmHw5je/mUcffZQ77rijpD9a66LYRmQgYGNpVImUyvm4jgQCXpuhDrupoONcXMGR7X/nLhlR3J5BwrlLq+9cAzYas22iUBURT2v0+Gh5YT5punDL+kwfADNroiDpMc6z4NlDwu4kbkXRMenRZvCRxpaVNWgKwu7C8xDKTSFVvdSInk8iPFEcRVvKLlQ/Nw5KtabRsXFb1S6OtSClg2LnMXzVC3QScMQKK+tSkNC+zEbdQAOgZ7Tl8WAzAoEdH2yUpah/XtyWSCR46qmn+Na3vsW///u/c8MNNxT35YYbbuDkyZOMj48zODjIww8/zEMPPVRxzqNHj/Lggw/yute9ju9973vceuutCCGYn5/nd37nd3jggQd4wxveUNrfsizm5+fp6urCNE3+4z/+g9tuu63q4tuR2WhnpyulZH5+nlgsVkWTmp6eJpNpj25hIyGlZD6ZxxJujGAf7mQMFqYLf4Cso7Bgq3S4g3h7e9l7xRV1zyUmxxHp+fZdmzeIlNbqL76iMu+unpzPuyN0KxegRuGfpto380gEygq7PKGrVQZ6qqaCpqLW8y5QdYyuAfSZi0jDQISCBSeMxfmCQ4aUkMuA20t49jTxwA1ruub1YCtF2ivxmwuCqUXBm/Z3sLejg71792JZVknvcerUKVRVLWU9wuHwmgOG7fS9L6M2Wh2nGuEHP/gB//Vf/wXABz7wAW6++eaqYOPHP/4xb3nLW4hGCxOit7zlLTz22GO8973v5ZZbbgEKWbnDhw9z8WJl/Z21LopthGsUbJxA3HEcZlOF9+ZCXGe0W3JqRuHivMNQxKbLXx10pFIpzk+myGhDNc7alivjrL2L/XoGr9m6jfCsPtQ2ClURhiUQio6UrWXZTRtsdwjNqm31bjlgpm1QYJBLnM/uJ+IvjJcWLnRpomLjWCqLIkhQzSBUnZy5PAfyGgs4ngDLMmzw5OaYcO3G7aqmRNlS1JzyB6140xXdFdtEKhqOWq2JcES1jsKhKBRfn2ajGLRIKQvay3q79oy2fPrNWKjbaeNUU6TyaDTKW9/6Vm699VZuueUWgsEgQKlK65e//GVuv/12bNvm3nvv5dChQ3z2s5/lyJEjHD16lA996EPcc8897Nu3j2g0ysMPPwzAl7/8ZU6dOsXnPvc5Pve5zwEFi1u/38/tt9+OaZrYts1tt93GH/3RH1VdVzsyG+3odLPZLLFYjNnZWYLBIAMDA1WTnc2ibK0X6ZyJtWR/N6t106/MoJTxLr2Kg1dxwEngOALO5woZj0AEysVdjoN6ps1ZjUjP6pxVwAz3Y4sa6VihYnUMoCUu1DiqmQuwyHnD+LKVAZQws8hID8xNN3+qvlGM/r24EjEUnw9MA+HxIr0+yBRWhGQ6hfAFcGXnOTc+jm3bdHV14fP5Nk27sB1W+E9MFgINEMymBIORwnukaRrd3d10dxfocvl8nng8zoULF3jxxRfx+/10dnbS1dWF1+tt+rvstBWjyyig0TjVCFNTU/T39wPQ19fH1FS1RXe57hAKGf1y50SA+fl5fvSjH/GpT32qYvt6MhsbFWxsZGajiHMzOrt7DHKmwqlphUt6IegIufLMzEwTi8XQXR6c8FU1yx20C44UnFH3s49ncNPaBH+6rRSqZSQNnYDeOqU3g58QtYONZ6eHuVkU3BGDzgLJrCDoAU0FcPCSQUEiyJN1epmxutEVB6+axaOaaE4ORdo4S/UrJIVgw20kyas6blEr2KhNofKbzVkPSynRrDymO1AVVKy0uy1tL2Y21kOjKs9sCEFNgQtAqBvhab64ZBFbkdnYDmN1I9QNNoQQvpdffpmOjg78fj+BQKCUFv7Sl77Eu971LmzbRtM07rzzTu68886K44vBA4DH4+G73/1uVRv3338/999/f832n3766VUvvl0C8bXAsixmZmaIxWIIIejv7+f666+vS+XYqJR4O+FIyWJqOXhzhMqMGqXXqT2JVpCQniv8mQY0V4luJZIJRGaVYnutXFsgAo65egejuZjXQnU/XvB20ikuVvtrNwm1TgcigmFkC8GG1dkPmo7tCaDl05BJIwMhhC+AXAo2MA2kaaCoKge9BkkheOWVV8hms3R0dNDV1UU0Gq3QyLQTW02jArgQh3OzSiljHk/BYB1GmdvtZmBggIGBQm2SdDpNPB7n5ZdfJpvNEg6HS5kPl6u+s8jlYGPnoNlx6m1vexuTk5NVx3/hC19Yeb41PfOWZfHe976XT37ykyVnxiLWE2xsBDayzsZsskzIi+DsjIvdPSYOkDUVTk4rYBlEXV6uuupqptMephY3/l0zbIWT6gEO2C+iNulQZaMxk1u7rX4jzGcUgh2tC8VTeUFAd6GssPU9n4yQzoDXW9CCCCR7lHPEMiN0B03CymIpI2+j0iumSIu9WFIlaQVYNGG3eQypLY0lxTpQSISU9DnnyVBtw+4gqihULjvTtO2wcGyQEruWMFwoS45RK7aXMhtrR0Ww0SirscbaGpcF4tWoKxAHbrztttu45ZZb6OnpYWRkhJ6eHn74wx+WVni2eiKy2QLxlTSp7u5uDhw40FSdj41KibcLUkpmE4vYTuXDawQGcDIZlHwTtnuWAQtThT/rSXHWur5QFJrIauTCA9XCrzJYQsMO96POT6zpOnThYPo60DMr6GH17HFrweXF8BRWXfNDV6Kefqbwaxn5ApVK0wtuVCwJxUMdBJOTRI68meHhYRzHYX5+ntnZWcbHxxFClFbw10MfqoWtfMenF+HlSbXiUYqnxFIQ1PhYIQSBQIBAIMDIyAiO47CwsEA8Huf8+fM4jkM0GqWzs5NIJFKxSLDTVoxejWh1nPrP//zPuufq7e0lFovR399PLBajp6d6NXtwcLBEtYKC1rC8bscf//Efc8UVV/Bnf/ZnVceuNwPfbmwUjcqwNTJGZd/rSMHZaZXeUAq3Z2mc1PwkHD+5aYdaGoCNgin8jLsOsM841tT+s/oQTpspVMsQmNKF1rRQfIlaKxQMNYzHmSl9kjF1js/2MqRUZuxHlXOcyu8h5LUIuZYX/qRQ8MgcAZEkJQvjkBAQNmdwyh2Zlore2ULF4+TJOE5FBsCSAqWmMLz5aueKlcfWPTWDCkfUXguXpfBmjTTZ4v/F0pys3sKTqkHn2uh9myUQ30ljUy3NRvFePPXVr36V559/nueee46f/exnHDt2jIGBAR599FGALa8J4fF4NkUgns1mmZycZGZmpi5NajVsVxqVYRhL322W7oG91e+8ECR8Q3TmX27p1ZZInI5elHVWDQdwQl1NBRrS7WNRWd1fPOnromONwQZQs1CQEEBnL/Li6sGG07+71Gk74S6kohZWeNJJhNsD/sCyUDxTEIortolcmEGEu1EUhWg0WuKPF+1iL1y4wLFjx/D7/SU72fXYKm9lZ2Yrfp6/UN2/WI5gPgORFm3kFUUhEokQiUTYt28flmWRSCSYmZnhxIkT6LpeynrYtr2jVoxejWjnOFXUFN533308+OCD/O7v/m7VPrfffjt/8zd/w9xc4b38yU9+wt///d8DhQz9wsICX//612ue3+PxkEi0z5FvvdioYCNt16ab2FIlkQky4DErePYZU8Gjg8BZH/++BSSdEFO+K+nNvLLqvu11oapGPKXR2yRDRwCaMDFxs2jouCnFAjw1NYLtCPrclZP8DjkHSKxcHp8rXXauwr3vEdOlYMPtpNCdPHllmRVQLHqX80ZRzDyB/Awpb2/pc1vq1Ys+joOn2eKDjoPqmOS84aqPCna3tfvgktZirZq8UlZDgNZASdC5C6E2b19f0cblzEYV6v6SUsoUwB133FHa9vjjj/PYY4/h97c40m8QNpJGVax+XUy99/X1NaRJrYbtFGxIKYnH48RiMfL5PP39/YzuHSOTry2czgovVqAbPTVT8/N6sAMhxMI0Yr3FngLhprQameBAUzzOvOLCCfWgLDZPeyqHZufBF4bMCl6qnYMm7HXNSKWVrRkdwD17AUwTaVvg9ZcJxR3IZQpByKmn4Po7qs5XtIvt7+9HSkkqlWJ2draiQneRctXK89tuGpXtwJlphQ6fpDtU/5nIGpALXFP383hKEPGv75nSNI2enp7SKnYulyMej3Pu3Dni8Thut7tUYDAcrh4ML2N7oB3j1H333ce73/1uvvGNbzAyMsIjjzwCwFNPPcVXvvIVvv71rxONRvnbv/3bkuj8s5/9LNFolIsXL/KFL3yBsbExDh8+DMDHP/5xPvzhD5fOv90yGxvlRpUy67s25UyF6Xmdno7KgCNnKgRckDM3b2Fjwojg9uyiI1dfu1dwodoYClURpi1A6NCEUFzg4BF5TOnGcsDydKCb8xxP9JPMF6hP3UplsKFImxHlIn2+bAV1TF0aSz0sZzf6rXMFgXbFPSj83dI86JZBIDtLytMDQpQbKlYgYM+hNEkNE7aBo+oF96sVcOoV2JOy7PlZ4/NSHqy0sbZGOS5b31ajpbDtDW94A47j8JGPfIRHH310y79su2lUUkoWFhaIxWIkk0m6u7sZGxtriibValsbjVoTxWw2y8TEBPF4nI6ODkZHRwkGg5iWw2S8sU3drN5Hn5IorMA3fxU4XUOoM2sUZANGoLPUOTZCGhdp0XzdhaS/l/Aagw2EQHoCiBXBhnBsZM8QxMbrH+sLYborOzhj6Apc8UsI6UAui/AHkR4fZMuE4l4/LMwiHRtRq7JS6dIEwWCQYDDI7t27SxW6Z2dnOXnyJLqul7IexToE9dDOYGNqQfByTGU+LRACbjlgEqzxWlk2/PK00jBonE0J9vW2913yeDwMDg4yODjI+Pg4juNg2zYvvfQShw4duhxw7CC0Ok51dnbys5/9rGr7kSNHKrIV9957L/fee2/FPkNDQ6v26+sdp9qNdo5FxWKusViMtHVdw/lfKq+gJTUiAavi/U4ZCiGPJLtp8ZjgnD2Iy5XBZ9Sm/MzquzaQQrWMZN5F0LV6sKEIiY6JkDZSqKQdL1bW5Nx8oV9SsOig2vlxj3IWl1oZdCtIbBRUnFJ2o8OcxnZVZsGlULAVrTAGCIHqmHjzCbKeTmzUml10oEnHr5Iw3FOtryzY3dajUInSs7P2zMbS8apWWMyrtY8ngBLqqvlZM7gsEK9GI4F45Ctf+QpXX301Pp+PQCBAV1cX586d4+WXXwa2puhXOdpVZyOXyxGLxZiZmSEQCNDf38/Y2Fhbb95G1vRYieJgUqxaXhSyA4UsxuhoxQr3Qmr1gdASGtngIL6F8y1di+P2oKg6Yg3F9CSAv7k8s9k52pI7RU51EwxEUVJrozfIXArh8oKRrdgulMbCNbtvd/V1KhqWN1TQgaSSSF8A4Q8gl4INjDzSMgvj+NkXYM+1TV/nygrduVyO2dlZTp8+TSqVIhQKlT5fKZpuR7CRNeD58xrTCwqKKrGcwvkeP6lzywETd9miluPAL04p2E7jNpO5QhEw98bo4pFS4vP5Su/KRgnwL2P9eDWMU+3GesciKSWJRIJYLEYul6O3t5eDBw9x+oXVA/L5jIquSgJeu6IfXMyphD02mU36mRwpOKPs40o1i25XW9JPOd01jmo/FrIKYbeCs0o2QEFiKzoBJ0mSDpI5wcvTQ6VV/j7XPKqoPkeEODncSFkZHNhCRZUOHnJEnSk0O09+pbGKEGTd0aU2CpPaYG6mEGxItUqvodm5Qta/CQinMB+oLQyvk9UA8upyQCQDnVhmGs3O1dR81ENR8+F4Q5CpXSTxQl5l+skn6erqorOzs+UizJthrrLVi/2tolFmI/SZz3wGl8vF0NAQIyMj+Hw+jh8/zjvf+U5gdUvBjYbH4yEeb16MVA7btkmlUkxPT+Nyuejr6+Pw4cMlJ5N2Y7MzG8lkksnJSRYWFujq6qqbockbNtn86pkDgLgSxeOaRTFaqRcicbp3oU6eaeGYArL+TtxNPGKmvxOjhkf3akgH+wiuMdhAgAxGq4v8WTlk1wDM1taEGB21ecDG0H60E08uCcWNgmhtpVA8GIZLJ1sKNlbC4/EwNDRUWpFdXFxkdnaWZ599FsdxSkLzjo6OdXWYUsKpKYUXL2o4EgIeSSq3fK50XvCLkxpvGrNQlcL+vzqjkLeaaU8QTwkGIhvzPu20FaNXObb9OLXdgo21jkWZTIbJyUlmZ2cJh8OMjIyUsqPTCw6S5iiaM0kNTZV43JXXsJBTCLltsubmvG+mrXDGdYgrcs+gyOWMvbUJFKpyzKdtQr7G31kIiSM0vCyQpIPzcR+Gtfxcd2q161lpjolim5iavsKFa7m9AessjuaumrBLIbA1d0FIXSzyZ+XQ84vk9epV/5AVb47YJCWqlccqt8ovfgTYNexui8dZFYWrBJbbj4Uf1cigW7mmFhxLmg9voE6wIRi5/k30o5SotYuLi6WFjGa0kJeL+lWjkWbjHBTEp7/+9a/5zW9+w6lTp7jnnnv40Ic+BGz9INxqJ76SJlXkug8PD2/gVRawGcGGaZpMTU2RTqc5d+4cg4OD7N+/v+59klIy30RWowQhWPAPETFOtHRdjipQ3D5EvvkgRQoF1bd65VYpBIveta1CZRQvPncAtRmnrVptm9mCgMyuDNaE1187uxHuqkpVF+EEOrC9QbRsEtIphLsTfAHkYplQPBhGGBnkYgIRqq622iqEEITDYcLhcKlIXlHL89JLL2EYRsmlpxWdViIteHpcYy6tIJBEAw7zmepOMZFWeOqMyo17bZ49L0jlm+9PZlMwsEFF1XdaJ/5qxk4Zp9ZDo2r3KmkrY1G5drFo8T4yMlKl/UqkW1uki83r7Oo00bTy6xCkDBWfbje56LB+ZEyN896rGck8W5ooz+rDm0KhKiJtBQjYiygN9HTKUqCgYjOfEsyny7OtEp9XQrb2saqRxdIqg6dyXYXLTGK7qvt3W2goQmIuVecoIpybIu1aEWw4Dl6zOat7KR0Ux8bQq8f3Ak2qdt9rifoZZtvlw3b5UM0smpktXG29eU9xe70+PtKLcHtxQ4WVej0tZCQSqVqkvpzZqEbNHkIIoQB7JiYm8Pv9vOENb6io8L1d0GwnnsvlmJycZHp6uoImdenSpU27WRtVZ6NoxzsxMUEmk6G3txe/38/BgwdXzdJk8xaG2Vo17ZTwE/RF0TItZASEwO4cQJs41Xw73g582ur3xgz11V8JaeK6Eu4o3WsMNnAcZLgHkViRxcgnC0UOU5X8Vat3pOHprI6+QrBh5pG2DT4fFIMNx4Z8FlxLQvHDb13bNTeApmn09vbS21twHHnyyScBqmp7dHZ21ny2TAteuKhxemp5cOoOOcRT9e/jxTkV8yRkjNZqNMXTzVngrgU7rRN/teK3bZyqhXJKbLuwGo2qmPGMxWIsLi42pV1MpFunGhaqjBsVc0tHCvK2iqrYq9Ip24U5w4Pbd4D+zEvA5lGoirClQiprEQo0CDZEISizFB2/kwKWKU8el0O3L81CNkyY6mJ6mpHB9IaQKKXnSMVCUqjerdgWZg2HRUv3FSptiEpnKJeZRjdTmHqB4iylRDhO08Jw1TKwVVdBM7ECtYr4LTVCTlt9wcvWvdi6F2EZuMx0wZxm5btTzGxUVQhZ+rinWhheTwsZj8c5depUia5cpFxtVmZjJ1F8683SAsD//dGPfpSuri6+9rWvYdt26UHdLgNxo068qFWYnJzEcZyaNKnNpDa1u85GPp8nFosxPT1NMBhkaGioxCuMx+OrtiWlbEqrUQtxdz892bmWXKakkFjeMFp29cqitlDwhIKrn1PRWHR1NH0NteAEu5H5OCLfWCBfD7buRfWG+P/Ze/MYyc7zvPf3nbW27up9m559hhzOkCIlzVCSJSeyZYq6lkwZsK4k38QQQjGBjQhJvChRINqQjRASAsdODAtQADmwbm5wKcWyLTmAJUu+UmSNRXJIDkUOySGnZ+2eXqbX2uts33f/qK7qWk5VV3VX90xb8wADTNdytjrnfb/nXZ5XFDaiOkII6B9C1ZENt7e1E/NGD2It3UDznJICVbynNFG8UMoIqVwOEYlB6hZKSsQOP4e6rjM5OUkkEqmZ7XHlyhU0TauklHt7e1nMaDwzZVKsKoEY6Q1aEg0A05AdEw0APxCkCtC3efKrY+yGbOFddAV73k9thjIx6Oa5NPN7ZRn0hYWFSs/Svffe2xbRWc5uTaXx2qLJkREPVbULLxDopgYq6KgWfzuYd5NEogfpKc6xXNy6XPhWkfci9Dbt9lMVuVpP2OyLrnChasBev13A0gKyZpJkyORuzXcQMiAwDPT1311QKhezvBTSsMKH562rRCm0mmZsAQwWZ5g3TxBIUero0LX1AX/N1wQSQd7soa+whhtt9NuK9X6NsO8KrSET0Wr1oQwLx7AQgYflZmtIR5k4hfpPw4KBxuGF9ajvhXQcp6bkyjAMbNumWCxuS36+FfaanwolG0qptBDibUeOHMm++WapZOZ2z9QIQ30ZVX1EZmhoiHvuuYdYk3IcTdPw/fb6FbaLbhAbKSXLy8vMzs7i+z7j4+OhfSbt7CtX8PCDrR2PKyxysVESucaJvE0hBEFvf1tkg+H9ocOC6uEkx5FNjFMnx+X07SOy0FlpGICK9DCTvA+t/z7GV17HvHV5400vB6YNXmmRoQYnkMYmUQhNw+sfw751HXLZUqN4LIFaJxs4BVTgl3IGN16FQw+EbsYPFM+/qXjHiY1pyJ4f4Dg+jufj+gG+HxBISSGIc3g0QiSk5aU6otpstseNGzdYXPNZsU4jq1LtA/GA5WzrH9HQJBFz69mJpYygL9b9YMFeG5b0k4q94qcikci2yUY3UZ1ll1KysrLC7OwsnucxNjbGQw891FHE1PUhU9zqokdwbdHk0HAt4Sh6GiYOCnOXnkXBdX+cPqsX2UE5Z7fgawk0LY8MUXsUbNjIQJjEtAJR3aUQWICi3yxl5o2oCSE6LIJSdsPXa9UapRAYXr7UO1GHatUnKbSGLEDEzYDrYphG5dhcI0bEbwzauZpNwe5HaRq+MEDMII3GBbgURlO526IeltVooz9DN3Gi/SB9LCeLpuSGGlXYvoYPtFR7bAbbtmtKrq5cucLa2lpbJVdbxV7rLWzVs7G1UO8uohwxymQyLC8vV8qkxsbG2orI7GZmYztlVLlcjrm5OZaXlxkYGODYsWMta+g3Oy8pFanc9hoW16xRrNwtrA4mwGqmgRfvx8w1l8dThoXSNzcjyoyQNjbPfrSDlJEgYkbAa3eaK0jDYrb//pLxVHBz8AEGe4ZJ3HgJ4RURSpVkcG+WCEgw0l5fkDtyEOG7gMCUAcKyId5TKqNSgOeBHUHNvIGoIht+ICm6Po7jcXU+QAQBb1yT2KZC0Xxklu+7PHclwqEhOFBXhtuqfKPc79TbP871101k1eRgQ2VJFyLQJE0NoGmSqL29MqidkMCFu2VUewl7xU9ttUF8J3xUOcg2NTVV8SlHjhwhkWhzwlwdlrI6W555QKl0anrZZP+QW2OpPKJERQGfzsU/toIgUDwzZTM0viu7a0DRt7C0xsaLmmyBEPiaxT19t/jx8iRRSzFglchGxIYMPfTQOFhWdwtokV4UGwEo3XcRgV9qDq9DdRBP0ShFLlCMeDdYso5UXnP1aIVsSCBv9eGZMXRR+lUFCh+r1KsR0hgumyzyFZRmgIS83jY0o5RNkQHlezXMt4WVUHUKIQS2bTM0NMTBgwc3LbnaKknYa36qJcWSUqKUuiOjRYVCgR/84Ad873vfY2FhgaeeeqpjNandlKPttIyq3Jg3NzeHruuMj49z5MiRtm6uzRxUJu8im03laRNKaKxExhgrdjaJW/X2o3JrlbRwPfy+sRZL1A3ke8a7l2IXGk7fPuzFy5t/llKZ183kKXxtw0j7EpasUQrHfprB+VfRV2cRyl+PngicRJvdzFaEwqEH8HUbK7tE78p1RDLku06Om1dv4Fqx0m9dVZo6WLdmaGXKoobLSlFxdVGwkIYHD4C1/ghtVivuePCjKZN8FdGImgohYi0bPAWS+DaJBqxL4Ppgd1lAbq9FjH7ScSf7KehOGVU34Pt+xacUCgUOHjzYtk9pheXM9q+7FwhmV0wmBmqH/hVkdNckcWdmAy5cUfyjIR/N3BlVylZYyemMhcTP6n25J2wmoiv8mEkMQzFgbpCLrNFHjx9CNrwCmgrwNQt93ffaXrp5CVXVwl8JEdrfEHXWEFEftd5n4WmR9SxGH0rT0YSq7KsMHxPP7mnQLStN9Q6/Dz2ti2VINedVt794H6JdP70Jqn3nZiVXnahcVWOvZeBbPlHVRqh8w98JJ/cXf/EXfO5zn+Phhx/m1KlT/Lf/9t+2tJ3bMfuiFcIa806ePNlxzV+rfQWBJLPNrEYZXnSEQOTQ2ymNKh+bruElh7FSjQP1pGHRZJZP7efsBDm9u8X6aSvJcBvTvyWCpeG34JuNmvKBFKRUBG/0Afp6RojNXIDhSZTQQ5vhmkGjNKXcjQ+iVq6HkgWhG/TPv8zC/nesK2+0vfka6EJh6x6utCi48MwUHBstKRxjdHEAACAASURBVD21IhtSwvNXDVZyVRkNTWEZahNVKUk82q3G7nUJ3L7uRn73WsToJx13qp8qYzuZjW7MxKhWYBwZGeHkyZO8+uqrjIyEy3B3iqUt9mvUo+hpLKyZjCS9msj3bkjiOkXJi2+U7p2VZYehsd0nG74UICxQtfdKpqgTr6pq8zSbPpEiYRaIawGGVjUdPGoRkthAsK5KFbEp5wRMN49vh5dQVS/E63s2ytCUpL84y3J0PwY+6IJCbKCSxahHgF6S0TWiENRmcJoN8UMpXD18/bNtq1+frRk5tN0tViClbBr8qC+56kTlqn4fe8lPtTzSTCZTMXRCiAYDLqWsvP+tb32Le++9l2PHjvGFL3yhYVuO4/Cxj32MY8eO8Y53vINr165V3vv85z/PsWPHuPfee/n2t79deb3ZNt///vdz/vx5fuM3fmNbF3u3y6ia7ct1Xaanp3n++eeZmZlhZGSEM2fOcPjw4S01F7XaVyrnbv8h3dgRi9HDrFqj+FoHqe54T2jK1O8damuRkO0Z67yjeBNIoeH179v0cwvJ4+TM5k3pQujkA4sFc4DZyTMUoj24g53n5TXAkC5OpMmgLE3DLqyVBlRsE1Gj1rldmocXr0KgGp/5Ml66rnNzdeM3FEKRjMlNiUYiotrqx2kHAsUbszov39DJtl8Btyn2mhH/SUcnfup2oBtqVJ3CcRyuXbvGuXPnmJ2dZXx8nDNnznDo0CEikUjX/J5SW28OD0PO0VjNGnV2rSSJaxk746uVkrx00SdYv0XeuOYig85UGruFVLG2V2YpYyHqTtsTJV9738AtBq1aJcWorcgRXmJtuPnKPBHhuwjptVFC1Ug+avbnpkiQJkYOE79lzMtfj20rs3afClGTSalGIPTmErXbvR2q7YTQYLi1WmQnaFdBrqxydfjwYc6cOcM73vEOhoeHWVlZ4dy5czz33HNcuXKFVCrV8MzuNT/Vkr4/9dRTlXrOAwcOYFkWfX19DA8PE4/HKycaBAH/8l/+S77zne8wOTnJmTNneOyxxzh58mRlW3/yJ39Cf38/U1NTPP300/y7f/fv+OpXv8prr73G008/zauvvsrs7Cw/93M/R7nZr9k2y/0K3Zogvhuodxr1U1i30pjX7r7K8PyAXKHzSd7NIGXAot+Hb/aBCXGZYjiYI+GvYMrmv4vQBH7fKFaVZGxgRtCszaNJQayfYjfTqlVI2f0MadPrdZ2NyIycpGBuHg0Umg56D8K2WbHfhp5fYSvV0BpQSI5hF9ZCDZcmFD0rV8kMHmn8cgeIGQ5rTryyDyEg64BKnmYxA2N13Oq1GY2pW7XOYbintcQtKOK2auo3OoZSCKFxK61zKw2vz+oM9yiOjvqMJhWRbTxG1Ub8ToqQ30U42vVTtwu7VUYlpWRpaYm5ublNBUS6hUxR4HZ5JsZaXsdYnzJePlapBO4OSeLOzjrMLG7YMy8wcHIu0d7dG+xXRqao0R/RkSrAcTVurkR4YKJuPpXQ8IXFiLVGWtRlJoQgY/QRD2nULpdSBcLE9vKhg/ygtndio1E8/Jr7hr2u9LT5ufmsq1vVibq0krstGM095/apZ9VBD0wgzEbitVVslQh0UnL1D4psFAoFvv71r7O6usr09DQPP/ww4+PjJJNJBgcHmZiYYGhoiH379nHs2DGOHCktej7+8Y/zjW98o4ZsfOMb3+Bzn/scAB/5yEf41Kc+hVKKb3zjG3z84x/Htm0OHz7MsWPHKvr+m21zOyofsPtkA0rXdG5urmYKa09Pdxqdq/cVRjbWst0tfC044Ffd7DktSU5LgglRmWEkmCXhr2DJkN8oGiEwbfR1taYgOYjerGRH6BTNXjLmIDLeh04Xw9hVCISO3zeBsTLd8F5h6BiLbRCNMqQSFKRFPKLja8MgHNiCvK5mmEjdRJchqmmaTjJ1nfTA4W0tIAxNYmk+nqpdoQuh8cYczKfggf2ga3BlQfD6nEG1oW5H4jZqKbpZUm/qGgup6g0KCp7ihasmXgB9McXkQMCpyc6f771mxH/S0a6f+shHPtI1JZhOYJrmllUP2+n1y2azzM3NsbKywuDg4KYCIt1Et0qoGrabMVCBS09C31DUCwSGqSGQLSQvOkM+7/Lim43bujHrc29vyBd2AcXAwqDA1aUYUgkiRuO942k2kSDDgNUYGLNjBoTM1xNKoXlF/IhVUqGyG++R+qF65essmw3aM6OlpvNNlv6KKrJRRS4UNFWUlIiaHouG8xECIxLDL7Y/LLhuAxv/7WIJFXRPlrZVyVU6nUYpxfj4OP39/dh298jSTqCl5f0v/+W/AHDp0iU+9KEPoes6Dz74IFNTUzzzzDN4nodSin/+z/85+/fvr3xvcnKSZ599tmZbN2/erHzGMAySySTLy8vcvHmTd77znTXfvXnzJsCm29xuZmO3yqiklCwuLpLP57l48SLj4+O8/e1v37GGxrDzKro+Rad7Mr8CyIu+pia/oPUwY5zgSjpGUktxzL7BiLiFLYvrx6jh942gL04T2DF0o/ZaeLpN3uwnZQ6T0Qc2DKCnGLFd1A6RxFR0kEExU5PGd/v3M2dtXmJVD6UEOU8nYVmkXUhGQBU7IxwCcOKDxDILIW8KdL9IvLhEPjK0rdKyiOHieY3pACEgXYC/vwSjvfD6rFkTWRxMbC5xa5sSs4slEDoBC6lawxq1FF4g8IJ1Uu/Ci5d1TkxI9A5t/l2ysbfQrp/6wAc+QF/f9ubybAXbCQQ0UzH0fZ+FhQXm5+cxTZOJiQmOHj266/dtN5rDm247H0MXGeKJjWe94GkkbCi63WmOvXwDXL/xms0tw3HPQ7sNQ9OWszqBH6Xg6ggUltH4+3vCJkoGvb7GCjAsHanpaCEZesPN49kJhGyiQlW3uK+QDb3xOijANyLo+Eiltfw9AjZkbcuZDUdZZLVeekW4T3T01pmlRCJGIj5E4BYppldxsyloc7BgCevHa0agf6yD722OnWjerh8s+OKLLzI4OMjy8jJXr17lPe95zx2did80zPPMM8/wxBNP8IEPfICzZ8/y/ve/n3//7/89APl8ngsXLnDlypUdP9AwbCc9DTuf2chms8zOzrK6ulpRGnjrW9+6Y/sro/6GU0qRymz9OoVhLa8QLVZxhqa4tlQyFimZ5IVCSaa1R8twzLrOqHYL21L4kQSypw9NaDhmgqw5wKo5iqs1i8wJPOIYYV1wXYAvDILkOPpaqcQr6B1lJnpkywt5pQQZV6fHskm5kLRBdZjh8HuGUOmF0EMQukE8NYtvxXG30TQfMxwyXvNoqFIwtwYR08fxdUCjNyrJFJqn2AEsQ3a11lqTLou52vO09NKED8cr3Y+mrigUJam8RrYIyQ4vS7cnNt/FzqMdP9Vs3tKdjGofpZRibW2N2dlZ8vk8o6OjPPDAA1jW7kjDhmGnMhtl3Mr1cCjqUh38zjoavRFFYZuJ+lTK583GJHYFS4suIxO7TzakgtX1iewRMwi1+65m09TuCoFvxbGKjekN3S2gSR/PjDeUUIVnGcqZDaMhgxHopTIsQamJvFV2o5zVAChKkwXtKIFmIaRHLyH+UCmCJo3hpVMUxGKl9YVuRYgPjRMbGMHJpHAyK0ivjZujfGFHDiK6PDxyNwbuKaUYHBxk375SIPRO91ktycbZs2d5/PHH+ZVf+RWefPJJvvWtb/GpT32KL33pSzz44IPEYjEefvhhgiBgenrjqZ2ZmalcgDL27dvH9PQ0k5OT+L5PKpWqXKhm391sm9slGzuR2QiLOB07dgxN01hZWenqvpqh/rwKjo/rd49UCSHw9CaNy+V9uiaBanzYMrKH88X7AUhoWQ4lr+JoMaz+vkoD2WZYdWxG7fABSN1AJjZE39osMj7AdOKeLjSjCzKuVkU4FMrpIPUrBL4dx3RDjLIQBKaNJhSmLG5ZJtDSA3QCggZRwppdEbUkliHJuwZeIAhU82tjaKU5H92ygVFTMbtSG+3SCFCBQz4oLSQ1IQl8WYm4ZgqC5BYG/93phvsuNtCun9qLEELgOA5Xr15lcXGR3t5eJicnt6XP3y34Aazldz6Tcm2pccp4uqhvSxJXBpJzr7b2iVMzkuExGT5pegcRMcBfz9BGzHAfp4SOFHrz+Ul2ONnQVIAmfdxoEqNhRpZoICAb+xM4di8RZ0N50jc3fI1GgGoiKCIReJhIJfCVTsG3EHpp8ngzfuJtIjiTiEfR6tWkNJ1IcoBIcgCvkMVJr+LlNw9KdmO2Rj12Q5Z2r/UWht5ZQpTo7a//+q/zq7/6qzz55JNIKfnABz7Ao48+yn/8j/+RhYVSWYeUkjNnznDp0iWuXr2K67o8/fTTPPbYYzXbfOyxx/jKV74CwJ/92Z/xsz/7swgheOyxx3j66acrBvXSpUs8/PDDbW3TMAyCbahGdCuzUY44vfbaa7z44ov4vs/999/PW97yFoaGhnY9tV1NNpRSpLLdzWrk/NYRQtuA2dTm0basTHAheIBL3lHyQSfNeIIiOxeldDQLd+gwM8mT259QXkGJcJh2hJSKIKzOmg+dvvHwpn8jimcl8KWGrnz0Fo35myFitHef6BokbB9TD2jmLboxtK/22GBu1aip1RYoemMCb/1eECh8p8DC2sZvtrjqbCugsBeM+E8qOvVTewlBELCwsMDCwgLT09NEIhHe/va3c+LECZLJ5B1xX67kdFSLYEP3UJoyXu9FU0WNqLm1Z/vadEBqkwSz60GhyxUB7cCt0nAJ69coI2ihEx8YkVAFKSUESjNq+ibKaDZUrwxnfShgGb6x4cM0VMMsjgCdHHHW6Kcoo7jSQiq95t6VYfePUjhaa/+eiLd+34wmSIzuJ7n/OJHkUOhUcAXQM4iIdb85ZzdKcfdauW/o3apUSR/ty1/+cqUhW9M0giDgc5/7HL/4i7/I/Pw8o6OjKKUwDIM//uM/5tFHHyUIAh5//HFOnTrF7/zO73D69Gkee+wxPvnJT/Irv/IrHDt2jIGBAZ5++mkATp06xUc/+lFOnjyJYRh88YtfrPQyhG2zGts1uNslG47jMD8/z8LCAolEgomJiTvGEZQXWNmChx90sV7eMMk7zY2cQDG3FqHTwQ9XlyOcGvfw2+SOqR3ObryiTjFkFCHoXp8LCDIO9NgR0q6g1wLlNk6MDYM0IwSahaFq1cRcK8Fq/AA9zi3cSB+WzFNUGqqdgSV1iBnupkSyciYCYpbENiSpgkEgN4ye1qWhfWXYhuJW2qhzTIr+uKrM+RAooqbk+mrt8U/PrZFfuERvby89yRGm1sb5xyclt2Fm1110GZ36qb2ATCbD3Nwca2trDA4OMjw8TCKRYGysuzXlzVB0IdJmVdZSZvcWOlIJri+aHBx2S43DQFkSN2r4uEH7x1LIB/x4qr374dpNn5Otk/hdhUDVXNdmmQ2AghajR4Z0ggNoGr4ZwXRrM+iBYYMQaCpAshFtbtao7VQG15ZIimslsN3Mer9Gbc+HhgQFnjBxiOIqC18ZoQMBywj7FVrK3QIR28Js04Brhkl0YIRI/zBuLo2TXiFwyj5XdL0xvIzdKMX9B0E2ynjLW96CUqpy4cok4Pd///crxq/82s///M/z8z//8zXf/73f+73K/yORCP/zf/7P0P189rOf5bOf/WzD62Hb7Ca2UkYlpWRlZYXZ2Vk8z2NsbKztyeW7PdNDSkW6iwpUQsByobXiga5pZFqQkebQWMlF6I20qzQlKBDHDpPd2CZ8rYf5bIyFbIT7RjOooJvRrWrCoei1FMpt75xXew8xnLpU85pj9aCETlHvQRMCV4sRCfIU9Dh0mJWxdW9d6aV9A6Zr0B/zKbgaOVcHVBeH9oGpKVYyRqWsoIyhnlqn3GNLLs83Hne8b5yfun+Y5ZUU/+vlAZQw+frZFG8bn2ZoaJC+vr49ZbDvohGd+Kk7EZ7nVYJWkUiE8fFxjh8/jhCC6enpXSVKSxkNQ1eMtTEos5vzNdqBLwUzyyb7BjemjEslyBUlpi6hmYRqFaSUvPBagGzzkt5ahXsdF93enb6Y6hKq0t/NyYajxUkEqaaL2sCKNZCNclO4AAJhoq0Hr+pVqEovbvRNlC+XE0liuRl8I9LweQ1Jjjh5lSBQtVnoZmjIjClF0dgkq5HovKpBCIGdSGInkvhOgempiwwlLRg60PG22sFuEIG91lvY9OkUQgwUi8VQOa1jx45V/v+DH/yA48ePMz7e+eCybmA7hriTzEY+n69I1vb393PkyBESia1MT9h5lMlGOuciu+iolGbjy+YOxtAU15e2rk0+l7YZiDttZ2LSjsWIbaDCZGG3CKFpvLo0AJSa3l5bSBL1Vzgw4qF1axodgoyjSNhRUg5EhcJUrQlNgSjpvn0MrV2qWci7Zkk22TVi2MpFCIGrR4kGOfJ6oqPGNyEgojsUOippW89y2BLLlHiB6FqznS4U2aKOU6fjP9wjWawiGn3RgEuz4fvMFARKCb57cbhSVnDyUIRkpJe5uTlef/11otFoRbt8LzYR/yRjr/gpqF0c1M9ZGh0d5S1veUtDs/duyrMD7BuQ/PV5k7ce9hnvb22Hd7o5PAyOr3H1Wo6euGRweL38RbcxTEkQbC6Je+1GnoXVzojDrUWP8cndIRtu3RisVpkNhCAQZml6dwjKpVSiSqEpqOqzQIgKiwgrq/JrglUbqlSeGScIUaeCEuHwVftN9bIusFUa8Nf8+7quEY1sT+LVsKPMpQocvO/BHevH2Y0GcdhbZb4Nd5gQQlNKSeA3fv/3f59f+IVfYHBwkGQySTQaJQgCFhcXmZqa4tlnn+WHP/whTz311G014lvFZoY8CAJu3brF3NwcmqYxPj7O4cOH7/hIqBCCQCryW+2eC4GmCRbzrQ1uwTPxQ5rCO8HNtSijPe02TwuWszDQxfXhsjuAF9Q60YIxwItTq5w64BCNdOu3F6S9GI5nIg2Iegqb5r9XQcRB08hHBog7G0IDRWt9RovQcLCxlYMQGp4eJVYhHO0bpJjhdkw2yjC0UqSxK/RWSRzfIOfUXu/hnoDFKrnN/ljAmzeb/ybpvOAvnjMorCtV3b/f5y0HAUYr5TX5fJ6lpSVef/11isUixWKRW7duMTAwcFuVfu6iOfaan7IsC8cp9Q9Vz1k6cOAAPT09TZ9RIcS2+hJbISwyKgS88x6fr521eeyMy8RAuH/MO4KCe3v84MyNNQIRYyJV4OghG83Q2pLELRY9Xrncedb98oxkbFy2VF/sBjShWErV7iNitg6ktSqlkrqBb0Ux19UPFSCNDXumKYmkRCPCS6jC1QmLkT5EQ3P5+j47yIorRQM5dDcROEnEY11bYO/kOm43GsSrsRdIR8OTt27AAf7vF1544bP/43/8D06dOsXk5CSGYVAsFllbW2NpaQkhBJ/85Cc5ceLELh92dxBWRqWUqtTNplIphoeHue+++4hGuzNNdDdSX0IIPKl3Z9G3DldFWpbX2AZcX97+wmytYDKebD+aF5h9CG2tK9kNodtcWQwfsBgf6OfiXJYDfWkG+7dX8C8AV8RZyZmAhStNhkwN4aewaJzwLhF4eimaszR4gtjNsyX9faHhmbGaLbtYWMopTZnVbSIyR1GLt33PRQwXimrLClwaimCbA7eUUniuJFu3mBlK1GY0+mOSN2+23lfepaKwZemS00dqF25CCOLxOPF4nIMHDxIEAWfPnmV1dZXLly8zPDzc0Ct2F7cfe8lP5fN5pJQ8+uijfPrTn+b06dNtz1nSNG3LAwE3266UMvQYBhKKUwd8/uJZiw8/7DI52GiLb0dWo4xUWmDoDheuRLg6V+TMKYP+AWtTSdyXL3r4snPb7QeQSzsk+nd2oritl0rFytCFxNRbe/GWpVSaTmBskA2pWzWlTwKFFDqaCrH3ShHoVf5clIJIgnXC0qRiopMS3IbAlFJ4xmZkY/enum8Fu5XZ2Eto+uQppd4EuHHjBl/96lc5d+4ct27dIh6Pc9999/Hxj3+cD37wg7t3pDuA6gc0rG72nnvu6RoxcH26IKHaHoRmhMrObhW6rrOUb5UaVetD1rpzfleWYhwZzLZZVyvIyxjRLvRuTKWGaHUOkZ4EN/MmufwSB/ZtTX9dCCiqOGv5at1xmzl3kFFT0BOsNRCOPPFKw5xrJyuN4iWt9PohTBrueoZDCh2EiSULeHUzOAIJvtIJlI5UGlJpKAQSURrwJ3WkEutN2eGShmHQNEWwjaoPpRTS88i6tcc7EA9YzmmUf5++mGRqFlr9XrqmsK3SdfN8xbGRcM36agghME2Te++9t/L3Xdy5uJP9VBAEfOpTn+JHP/oRxWKRL37xix1L8O5UGdVm/YrvOO5zccbgL9cJx/6h2mNY2sFhfi2hAmbmPaSE4/f6ZAoG33/e58hEwMl7LNKES+KurPhML249SHR1xueB/m0e+yZwvFpb07KEqgwh8IWJ2ayUyrQr8zGk2Vh+pISOChkM6IcJjFSTkiZ2saPMBqKmZ8MXZss1UiwauaN7r6qx25mNvYCWT5+UkgMHDvDpT3+66Wdud5NKecLqVlikUgrf93n11VfJ5/OMjY2F1s12AzlXYyXybp67JuiPSZLR0r/IFqX7WkEYMbq18AfIeq3VpUxdI1Xs3vAjx9fxpIEu2ovoZT2L2DZ7N1zRy+omze8Apm2TCcZ4bWqek8c6O2dNCHJBnHRIA32gTObcIXxTZ0Au1TiPYp0M4FrvAYZSl3Gt8J4hhYajLGxcpGagS4nyfNZUf2XwkgKoRJZE5ZtKgSZKczekXE+WK0GgNHxZypiBwm5iOTRBrVPqEBFDYy5be77JqCRV0CqOqTcquTLXRDaxchwK2yq97weKfEEx3Lv5s1ZvS+5Gp+58dMNPrays8LGPfYxr165x6NAhvva1r9Hf37i6/MpXvsJ/+A//AYAnn3yST3ziEzXvP/bYY1y5coULFy6g6zqf/OQn+eIXv8gv/uIvMjEx0fG5NZsgvl1sRjYsA376pMe3zlv85XMWj51xOTi8cRzL2dvzXMhCnnJVmSkLQCkTfWVWMX3L4fRJHTVq0huRFNcX70EgeW6TmRqbYTkNftHFaFeqq0MoGbCcqTWqbZENSv7BbFJKpXSDwIpiuPmSElUdytmNejghA2ID9JDZHLWQHaw7SpmN9c8rRdFoPlQWIJNJYVs6iURnpcG3A3tNKWo30PJqtHOxbvePblkWrttZb0KxWOTq1aucO3cO3/fZv38/p0+fZv/+/TtWo90fk0TlPCs5g8uLFi/eiPC9N2J872KUF2/YXF40Wc5qeFssz5VSsri4yKuvv4EeEsHYKnTDIOc156SlSeFbGyTXCpcXIwR+uypQgpxsbahawfMDXl1oX99Q03VE3wTnL0pkm7ImuiZI+4lQogGlRbptGFzPjHDNGcdbjwP46Ph1A45W+44hhYZjhZd8QSli5SgLpRSBZmHqEiE9vEDHDQwc36QYGBR9k4JnkvMssm6ErBch50fI+xGK0saVNq6yCDAQmo6hQc4xyRSa2wYtJFLWDiIGzK3VOr5ERJJ3BcF6eUEiIpm+ReXvMAgUEVts9C4VSscztAWycRd3Prrhp77whS/wvve9j0uXLvG+972PL3zhCw2fWVlZ4Xd/93d59tlnee655/jd3/1dVldXK+//+Z//eYNoyOnTp9E0bcsDaDVN2xE1qnYyJvdOBOwbCAik4JvnLK4ulK6zVKUZG7cDufSGVPjUtSKWvnEOng8/ejngmReKLKfAWi9BunQ1INeuyGELLNzqXg9kPQInV1NCBa2VqKrhaPFmVU0ooeObpdIjaYb46WYlVCFD9RwRa1o+Vflqhz0bZXIihWgpd6vrGqahMzU1xd///d9z4cIFFhYWdqTEsBu43UH4OxF73qu2a8SllNy6dYsf//jHvPbaa5UhSZFIZNemsfbIGySsWgNS9DUW0gZvLlg8dy3Kd1+P8XeXorw8Y3F92SBV0GjlEwqFAleuXOHcuXOsra3RP9R59KwZBLBSaE0kHN/Al92/jRQ6Wad9EpPzLPSQyE07SKlxAtFpZkYQGRrjxSmDotPaaXueYtVJrEvDVm8BIib4vs7UrMXL122mly1upAZ5dmEST+nkSCC0soINeIFG1o+wao5UlKiaQQkdD6vUiKfpRA2PQmBTDEp9Ip408ZVBgF5yEmLzcimxPvY1QGcla4Tem1shG1EDZldrr0/UUniBwFuXgoxZkvllcP0WREMoohGxHhFW5PLlbI2ivw3xuLtk4ycT3/jGNypZik984hP85V/+ZcNnvv3tb/PII48wMDBAf38/jzzyCN/61rcAyGaz/MEf/AFPPvlk6PZt2+44KAa3r4yq9Bl47/0eAkUgBf/rBYsrCxprOa0l2d9JLC1usAbHVfTZjSxifgX++u9cXpvyyWY8Xr3aHbJ25aZC7VCzfn0JFWzeHF6BKE3obvZeYNpI3USFDbYTWkNvp9/MF2payzIp1UG5bfnzCrE+xK91sLC3J8Hk5CRvfetbede73sXExASpVIpz587x3HPPcfXqVTKZTFvEfDekpHfaj+yVuUHV2POjrTYz4rlcjtnZWVZWVhgcHOTYsWPE41uPgm8HmoCTYznO3ehpIdEnyDqCrKNxc638PUVvRJKMSZLRgF7bp5BZYm5uliAImJiY4PTp0xRdyUq6CyGcMnQbz2kewQrcPDOZoe7trw7zuR6S0WVoIYVXjYwfI0aH0UM9wrXFrd8P8YFBXruZ4fBghv5k4+N0a8kjRy+x+MZ1jJjgeBo3Fo0GRZeBeMBrVxWQZC1ncHTcYXUlysKazvyaAQj6ezWWet7KUWvzgYASHQ8wlUdUd+mzMqy5rUlKKwgB2TxEbLBMwWreJGF72FU/ka6VJBDbHbQVMWB2Tae6VM/QAgRaxQlHTMVKGgpuK2emiFiispDKFVQlEDfQUzquzXCXbPxkYmFhoaJUEuePjQAAIABJREFUNTY2Vpk8Xo2bN2+yf//+yt+Tk5PcvHkTgN/+7d/mN3/zN5tKJ99pmY12y7OGexUPHgp46ZpRIhzPWzx05PZMY1dKcmO29hremM4THYw2+FOp4MU3JIdS0K2S4kBCNuXQ0035Q0oBkkLQGAlpt4wKICcSWKyGvqd0EzfaF/5eyGyN5nMuFAUtQVymQ8tkOymhgo0yKgW1zeh1EEIQj20EHjVNY2BggIGBkkR9sVhkaWmJy5cvk81m6evrq0iZh80/242sw07vo377eyGLsufJRiQSaTDivu9XJGsNw2BiYoKjR4/e9kWEpmnELY9jIx6XbrVfriWVYK2gs1bQYT2CoREhmRhnICEQUYknfVLZ9qZRt3esm0ndKpZyUbrZG9IIwUImymiyvQhPzjPoidkEXvvlV5dWWzeFt4Nobw83MhbZ/BL7xzdW3bMLHr7VRyxisrLiML/oo9txRFg6G4jZisszQeV4rq/Eub7SSISW1xRr6X4iPSZjfY3qVfWoEA48+swsvtTI+lsnWKYB6ZwgakMsosh5Jo4f0BvdWIQkIoqV3Obbsg1YSOk1jYKCAEuX5NczQZahyOYUmUJrohG1BZq2QTSq11Lt9GvA3ca+f8j4uZ/7Oebn5xtef+qpp2r+Fm1k+Krx0ksvcfnyZf7wD/+Qa9euhX5mq2Rjp3o2OiEx77zH441ZnYJbEox48bJGb0ISsXfXnwrPqZRElrGaluw/4LCYC7epM4ulDGnrIEX7uDwd8GC/3PYsIaXKikWCqF7K5Nej3TIqAEeLooKV0PtWodXO16h7r16sXDYJ7glAajpKilCPqdAQHWS11TrZ8LTWFQnxWLTl2i0SiTA5Ocnk5CRSSlKpFIuLi1y9ehVd1xkaGmJ4eLjS67EbSlE7TTb2YlBsz5ONshFXSpFOp5mdnSWTyTAyMsL9998fOuzpdqF8ox8Z9riV0UkVtl73KjFYLRisrvMLgeSB0Tw0JEW3Bm8TqVtT1yjKnR+AtubEmdAzBG1KHKW9GPE2sxtF0UvK6U6PjhWxSftjXLwyz4kjJjdmXYqil4VpF0cK7EQvRqJ1Q3M264em08MQSMEPX+vhvfevMdS7+bWR6PgoTOEzZKcJlE4h2FqvTdQMkBJsQ2FqEl8KAqWznNXoj/loGuSyWRCt+2BMXbGcMWpKMjQUluaS90p1xoamcF3Jaq5F+l7VEo1CUVFf7dBOv0Z5W9VG/C7x+IeD7373u03fGx0dZW5ujvHxcebm5hgZGWn4zL59+/j+979f+XtmZob3vve9/OhHP+L555/n0KFDlUDXe9/73prP7sUyqjIiFrz7hMd3Xy7bSkE6Wzqm3SQcxUz4/KW15QJEwm2ZH8BIP1xvTFRtCWtZ8AseZmx76wqpyiGlZkRItZ3ZUKqUtfVlE1UqTUMpLZwgCFE5DgCvRTlx+W4panGiMtuQ3VAd2spSJkTgiGjLcF9PBxPDNU2jv7+/Iu7gOE4l65HL5Ugmk/T39+9KGdJdslGLvXW0IdB1nS996Ut89atfZWZmhrGxMc6cOcOhQ4faJhq7Vf9WYdUC3jLpbLmRNgwKjblsb1e2pesaqy3UpXRNcX0HmsKbYW7VImizfDXvGehNojjV0DSD1xfDU8tbhWboqN4Jfng+4I35JHOZXogNYicSbJY9SVgBt8Kz4C3x/QvJTSL+Gwgw8JSBEDASWcXSwhc/SqnKP5REINGERBcBhhYw1BtwaMRlvN+jPx7QF/PJ5QFKZVWOB5oZbzr4CUr3UCZv1PVfKPriiqIsEQ1NKISSLKZaE42ILdD10naKjsILuVfaJRt70Yjfxfbx2GOP8ZWvfAUoKU59+MMfbvjMo48+yt/8zd+wurrK6uoqf/M3f8Ojjz7Kr/3arzE7O8u1a9f44Q9/yD333FNDNGB7mY3bWUZVxqn9AaPJ2s+ns3LTfrVuYnUlPHN/Y86jP9o8w7ucgm4F4QDmbm2eTW4FpUDKUlmWLmAp3Wi/TU2ia+0ds6+0kqR6s74HBV6TMqWang2lQlWoNjazru6lmaFXsxSc7CyzAYDePOZt2xamufWYuG3b7Nu3j4ceeoif+qmfYt++fWQyGbLZLM8++yyXL18mnU7vuR6Ivein9tbRVuHb3/42H/nIR/je976HaZo88sgjnDp1iv7+/o4Y5U4Z82b7Khv4hK24d7S76haLuQhSbD+Tk/NbS916voG3A03hYdCFz7MXPL72fZ3nXxdt2bKUt3kkZLY4SKC6r6hSLAQ41jCR3mTbxmBhNs2b01u9BwXfeSmJ06b/CzDwlY4mYDy6gsBfn65RIhSGViphso3SP8tQmLrC0FSpF0M0luuaOiSiAUVX4PmQ90zW8ga9TTiflD6ZbEllqhpDPYqV9QyGQGHpktmVTYiGJTDWiYbjKtwm12EwcZds3EVzfOYzn+E73/kOx48f57vf/S6f+cxnAHj++ed54oknABgYGOC3f/u3OXPmDGfOnOF3fud3KnXjm+FOy2x02gsiBPzMAy71BjidlRSKu0M4bs41J2vSaV5CnMrBvi62Fl6bVbQd/QpBScBQAKVsQ5i56aRfQ677YkeLhYpFKQFFPR6qJFXq2djIbTQroQJQVbO7HNHoY0vzPNpDaXr45uiJd696QghBf38/hw8fpq+vj4ceeohIJMLVq1c5e/Ysr7zyCvPz83je9sjkbmAv+qkdOdqVlRUeeeQRjh8/ziOPPFIjD1iNr3zlKxw/fpzjx49Xokr5fJ4PfvCDnDhxglOnTlWMPsCf/umfMjw8zEMPPcQnP/lJTp48yS/90i/x4Q9/mMHBwS0d626Tjep9HRz0GYh3V93i4mIvqk051jDohkHWbW5wLAOmV3evNC29miW7nj2/dFPj//1bjTeutzZqBU9HN5tPGlV6lJnUzogELK9JLLv90qxMqoDUt5uS1/j2+T78Nm8lH3OdcCgSRhFT3yAUYWSiFZSColsaGqUo9RflCgJfasyv6Q1qVQIF0qQY1J5zr11gqTIdXGHrLtOLrYmGbQkMo3SwrqcoOuH3/U+fDEi0OXh2Lxrxu9g+BgcH+du//VsuXbrEd7/73QqJOH36NF/+8pcrn3v88ceZmppiamqKf/bP/lnDdg4dOsSFCxcaXt9Og/jtLqMqY6xPcWC48TuZ3M4TDhG4LK40N3CXrhWJtVigb2fQaD2kgtTq1gKF5axGGUUfknFJ/dK7I7JR/o8QocP4AnTQ9CbZiLqheu3sB/B1u4G8dCJ7S2UeOTVTzauhaRrRaPfXGuV+inLW48EHH+Td7343+/fvJ5PJ8MILL1SyHqlU6o7MeuxFad0d8arb1Sz/rd/6LS5evMj58+c5e/Ysf/3Xf1353sc+9jFeeuklZmZm+L3f+70tR4zK2Clj3mxf5Ru3NBfjFnrqPEJ1TytaKp3Lq1sfdbpabFWCpFjKWOxsU/gGLAq8caPeeApevFQiHTO3mpOONTd8dSmE4I2VnVHQWksF2NH2y8t8LyCd3X6zIYDra/zty71tz/3wMXClQcJsX71MqZKj9QKB42vkPZ2ljMGlOZu5VZNsHlyvFKnLFwWeFOQdrZJtUEoh0NaFDjbQH3VIO1W/l7PK9FJzwqaUwjIF5jrR8PxSn0YYTh8NePvR9p/vu2TjLnYCd1oZ1Vb93vvuWyFuN/qrTE6S30HC4ebC+zXKCAKI681t2ewiNQIW28XUdICUnQcKN7IaJSiloWklhcJqdEI2qlHU6lStlMITJZ9dP68J1suo1gexFvXWAbj6AJFTV0VR6v/Yyr0a7sUT8eiOLKjDbLwQgr6+Po4fP8473/lO3vrWtxKNRrl27Rpnz57l5ZdfZm5u7o7JetSfw14gHjviVbejWR6LxfiZn/kZoDSw721vexszMzNN97VVI17GTkkLhkEIQbFYZGpqinPnzpFOpzl5z0FO7etudqMoIyw1UedoBU23cIPmpUWWrrGa796k8FbQNcXrl5s/2EoJ/u5ljT/7vmBpDeojQ0VfRwvp3cipZMvMzVbhurLltQvD7M0Mutm9IZKZgsEPX0+0eT8LMn4UNzBCyW4Ysch5BnnPxAkMPKkjlYbj15qQogu5Yik74vkCxwNfauSKAkvXWMrUXqOhhGS1sHENkhGfxXxrsmwaAsusnQ4ehpP7Je852dkCYy8a8bu483GnlVF1QmKklMzPz3P+/HmuXrnEBx+YJRFt/G42J8kXdoZwZNY2V1q8cj2P3qQPUgG9se4dWyYPqaW1jr5Tn9Uow/MFyURtdiNitB+ArLZQjhatSTgoBEUtjgIcs3EoX7mMSgFKa94b4UudXBBDVqkGenrt9joa6Fdz1I3fU0qR6KAxvBO0kxWwLIuJiYlK1uPAgQPkcrk7JuuxF4NiO6JGtV3N8jLW1tb4q7/6K/71v/7Xlde+/vWv84Mf/IB77rmHP/zDP9x2ZmOnpAWrIaVkaWmJpaUl0uk0Bw8e5MiRI5WbJRr1WcjoLGa693PcyvfQF3Ew9PYeBk0IlgrNU5a6pri+0mYtShcgHYdUCwWiMlxf4zvPQzKu+McPKeJVTnDNidGrbUS7NN3g9fnuNoWXkc5pHT38s9MprFj3S7lupWxeuKw4fTS3aT2UGxgsuT30mAWkKilcSSUIlFh3KpsvtN0QnxgEJWcctcEyNbIFRSwCi+laQz8QD1jObdQM98cC3rzZmrDpmsK2Ste5ejp4PQ6NSN7/0BYij3vQiN/FnY9IJEI6ne74ezvln9ohG9UzqoaGhjhx4gTRaMkH/JOBIv/9f0fJ18X5svnSscai3X2G5hc2z8Bm84qjsSK3cuF+an5VQxeSQHXn2GYXfPobRcuaoj6rUYaipCaViCqy62IfnZANXVTdH0LgYBGhtCbylIGDjUEB6siEYmPRX8p+NEdBWigE+cAmYWz8Fp6wMCkFBTefs6GQCgqeha82jkVCg/BvoZDD0HdmSn2nJUjlrEdfXx/Hjh3DdV2Wlpa4fv066XSa3t7eylwPy+pe8LAV9qKf2vLqdqc0y8vwfZ9f/uVf5l/9q3/FkSNHAPiFX/gFfvmXfxnbtvmv//W/8olPfIJHHnlk25mNnSIb+Xye2dlZlpeXGRwcZGBggNHR0YamQiHg/gmXH07plWnJ24VEZybdw6H+9hycj41sYYQdV+D6u3Nzxy2fv3u5s9KyVE7wzbOCiSF41ymJZSqcQAczAkHJON7IDHRYV9oeMjma1p2GoegECGvniNu1WxHidsB9+4u0bPQPSkMCM2001DeD20Kqt+CUSpyiNhQcgakrLLPUxN0XU6zltYoiSV9Mculm63tf00p9GkDNdPB6jPYpPvzw1rKFe9GI38WdjzttqF8zvyelrMyoEkKwb9++0BlVUQv+yU8X+O8/iFKsi/Vl8xIFxLtFOGTAzfn2ylcW5guInnDb6niC0WSepWx3gjyL6SjK9xEhg+Pq0SyrAaU1kuMpeqKKfFHhBxJDtF+uo2u1G17yB5g0S2uztEywUogTiTvoBASaga7KtnG9QU/RdglV3o8Q04to66baEVFM6az7VbEe36rqxyidPb7UyHt2uP8VWkO3eC7TOTFvF9vtdyhnPSYmJiojFxYXFzl//jxKKYaGhgiCYEf7Kvain9oy2dgpzfIy/sW/+BccP36cf/Nv/k3lteom8CeeeIJ/+2//LR/60IcoFrc+NbvbNbGlXoxFZmdnEUIwMTFRyWJcvny56b4ipuLUhMNL092TlM36UVKFAskWsoAAOccgr6ymgXDpFbiZ3loDfqfQhOKNy1v/PWeX4Ov/W+Oe/Yq3HpeseTH6tCLL+SjzucYprduF5yscV7TdxhIEilQ62PHynFen48QjkgPDzX97R24vcqQUoTKz1fCDjYnjmhD4gSJqKzJFKin5ZFRyebY+tV4LTTSfDl6N3qjPR38q2HIfzF404ndx52M7PRs7gXq/1yqL0QzxCPxf7ynw//xdtEEFLpeXoCAe2/6zJIv5tsUv5pd87h9zWc6FR5gLbjeLOQSrKw4DI5tvs1lWoww/EEQsRW9ccfN6htix9o4gkKJGIlcpWPAHGTcW0JDMe0NcnLXoPWLRb3k4RoyYlykd07qNVEKA1twXeFKvZCIUGk5gEjXWf3BNw1dmicA03KsKx9cpBiatK/Zrv2cYOo6z9TXAZujmUD8hBMlkkmQyWZP1cF2Xs2fP7ljWYy/6qR052u1olgM8+eSTpFIp/vN//s8135mbm6v8/5vf/Cb33XffHdMgns/nK70Y2WyWe++9l4ceeoiRkZHKTbEZsRlPBoy3OS27PQhms0k26xnOeJHmTk0psn4Pu9UUrgfuluZN1OPNacHX/j+N169bXF2Kcy07vP2N1kEpRTorOspqrKx5NdOydxIXbsSJWOHHViqZ2h7ZKBGNzc9FUcpyFBxACHJFgVSgCUncllxfoKYWuB6C0iyNaqIR9shGzYC3j7zCM8/8iPPnzzMzM9NxIGIvGvG7uPMRiUS25ae6DSEEvu8zPz/Piy++yNTUFH19fZw5c4ajR49uSjTK6I3Bx99dJGwUQq4gyeaDbQfzcunN+zWqUcg0/3y6YDHa173g4tSNAKVarx9aZTUqEKXATDyiyGR99DZNUH1JWME3AZ2cjOIrnRurMS5d9Xj2zRgKDalbsH685SF8/iYx5/rGcFkXpCqKuqyIUuRck5QTW//uJidTZ/s7GeK3FUgpd4zEW5bF2NgY8Xicd7/73Rw8eJBCocD58+d55plnuHTpEmtra9t+JvZib+GO9Gx85jOf4aMf/Sh/8id/wsGDB/na174GlDTLv/SlL/HlL3+5RrMcqGiWz8zM8NRTT3HixAne9ra3AfCpT32KJ554gj/6oz/im9/8JoZhMDAwwJ/+6Z9y9uxZ1tY6a9SqxnbIRqssRhjaqb89Oe7gerCa1wmUaJptaBcBBrPpOJPJXOj7qwULwwxfdOYdwZs3NCZGd2fQfNT0+fsXXbpFbBSC67M+P5gzuf+kYrTLfCOTEx2VZaXSHkGXyuQ2g64pHn+/oifaw7X5NJ5fa9y8bWY1gLqBfJvD80sONWqDlIK8C7Ym8YLm11CgiEY2iEbYdHAA21R84mclEev+EiHJ5VhcXOSVV17B930GBgYYGhqiv7+/JZmQUmKauyOCcBc/OdhuUKybyOVyLC8vk8/nGR8f57777mubXIRhIKH4P99V5KtnbQJZaxNKPVWSeFTb8oJoabGzgMGVGw7HT/hkiuF+axsz4hqQdwSZlQy9g8mmn9ksqwHrpVSuxIgKTtwTBdoro6oP0uT9UvR8yRskwRqvXCxl096c1vnpEyYJ0yEQBjqy4rtcrbW8bFHWRuQt2yARtcmWG3Y0jSIb948jI/gdBLKU2JifJYQgHtvZ3tCdlo0tk5nqrMfRo0fxPI+lpSWmp6e5cOECPT09DA8PbynrsReDYjuyiixrltcjTLP88ccfr/nM5ORkU9b3+c9/ns9//vM1rz3//PPbbhDvlGVW92K0m3KG9upvLQMePlJ6iKWEmVWdm6sGqaKO3CL5WHMSDLhFYlbtKk0qcKSNUXfPur7g8k2NqzdLtZem7pFI7kyzVhkCxfVpp6tR//1DitevBIDgx69kGRu1OXFPtKJktB24nsLx2v89CoWAfGF3tLGVkvz00WmU1wvRXg6O9nB1Ll2jNd8NsrFZCVX4sUG+WHL4URtyXiuDuZHRgObTwQ1d8U//kU9k3V4LIUgkEiQSCQ4fPozv+6ysrDA/P8/FixeJxWKV1Hb9c7sXI0Z3cedju6qJ20W5F2N2dhZd14lGowwPDzM5OdmV7Y8kFe85Ms0Pr+wPJxxKEo91TjiUkkzPdnbdlAJTFoCe0PdnFksZhJyz/WdbKcUrF4u8+93hZKOtrMY6yqVi8R4bN9Cw9M2/WL+aKKyTjTmnj9cv1s45KqgeEji4RoSon19XolpfAzXZfnUJFZQy4mP9FqamkckVK79nUEVYtI4v68YX4rHIji+id4NshJ2DaZqMj48zPj6OUopMJlPp9ZBSVnxSX1/fpsd3l2zcBnRD+radzEanWYwwdEpsNA0ODAYcGCxZIV/CzIrO7JpJuqi1Tz6EYCad5J6hlZqXl/NRjCqmEUiYvqXzxjW1vjAtbfz6PLxjyCPr7VzE1xYuM7e6l95OxuHydC0JnV9wWF52eOD+HoYGtn7rK6VI50RbfQFSKlaWiyzOp5iY7CPvbj3C1+6xve9Bn4N9RkUtI5lMMjg4TA57Q30k2P6j36o5fDOUsxyxCPT1QDpLXbmfImoLNK31dHBNg4+/x6enRebdMAxGRkYYGRmpZD2Wlpa4cOECnucxMDDA8PAw/f39e9KI38Wdj9tFNqp7MYaHhytZjNnZWYKwFOE2MBQv8p6j8/zd1HhD6W6+qFBIEh0SDjebJtdEca4Vpq4WGTsQxw3JmgYShvsg1yiS2TFWF9O4js+rr2U4dbKR3LST1SjDMHXWUi59SYOMG2Ew2nq2SD2cwEAqHT+AVy4FrGY2CEA0Irj/UIS5WQ30CHi5CtloheoSKqU0Th8G29TwPIlTyBKJNZ6z1kR+uBmqP53o4sTwpvvbYbLRzvaFEPT29tLb21vJeiwvLzMzM8Orr75KIpGoZD1suzHztBf91J4nG9uthd2stGkrjXNb3ddmMDQ4NBRwaGidfARwY8Vgds0gU9RQNCcfrrJYyMQY7SkZMC8QKM1kfZ4PC6sar12FogNhxvHlSz4njuqhxnu7sI2Acy93c1iOopDN4HqND6nnw4svZdg3YXPPsWhlMFwnSOcEm9WhBoHi1q08V95cwXNc/tOnRxkeEEzfknz7BVhI7QzpePCI4p33GcBGBCWVSrG4uMjq2k36Rg+D0DqeCVILhS7A9etVRzrcioJcASwT+nshWwDHLRnraqLRbDq4EPBL7/IY6m1/n9VZj0OHDlWyHgsLC1y8eJEgCPA8j56enm2VltzFXVRjN8uo6rMYExMTDYpSmqbh+93sDyw9W6M9eT74dpf/9YLVIOBQKCpQAT2J9pcd6ZXUlo7FcRV9dpFb+fDF6621Uja9lSjFZvA8n2wqj2EaTF3OghCcum9DhKSTrEYZZWWvbJtkQ6taque90jWfuhGwulZLJO+ZXO99i0RxizmkEG2de0GWfKiha7ztIBWbrJQin011iWyUtmlbJpa18yWsO71Q38r2TdNkbGyMsbGxStZjaWmJH//4xwRBUMl6JJPJSoXMXbKxy+hGg3h9tiEIgkoWo5mx3gq6rXxl6HBk2OfIcMlpuD5cXzaYWRbkPR2h6TXkY7GYoC9axDYkK4UYuilYzWq8dkWQyrbeV8ERFPIeegjL3i5evpDB87t3K5bKp1of581Zh2uXV3jHu0YZGmjfwJUi7M1Jne9L5mZzXJ1aQUqQMuDX/2k/w+v72D8ieOL/gGxR8u3n4c2bnfV9tMJ4v+RDD9e+Vq0RDrCSyrGUcnFDy6gUmgBdgCYCCFySPREiFsQtRdyGhL1R8zw3LvnWS+a25ZpdD3y/lOX4/9l77/i26nv//3m0LVu2ZVveM4mzkwaSUEIYARIghSQkYVzuZfR2cG/77QVub4EUuuivlFVGF6UttORS2sQJhQQo4yaUBsoqFELIDvFeki1v7XPO7w8jRZIlW9MjOc/HI49HZB2dcyxLn8/n9XmPl04zHDFRq8d2B790sY+yvIhPxUxw1ANg3759AOzfvx+Px8PZZ5+t1HAoJM14RDaiRTEikQ5ncv85pxeLXLTIw6sf6kak5zhcMlq1D0NGbON9b7eTRPvYNLc4MORlRFxUDzigqng4pSpRbC3dqDXD46hzyE1zqx5ZHmT+3GHBEU9Uw48xU4MsDxdtO7xajNrRN+GC2946fDrabBJNbSNF5LLZIm63F4Nei8cFXvXYGyleSY1PUpNtVDOvbOTzsiSSmaFnyBn6uY43jcqfOp0uE7+R10t/ZCOZtWJw1GPatGmBqEdra2sg6gFgNo9ufjvZOCnERqrSqOIZrJO9VjrQaaC2yEexcYDm5maMWWY+7VQxJJQgqTLQaVW09GVTYhrEJWo4XK+moyv28x9qkPn8Ah+DKWwf2FDfx1AKz2fJgUP1Y0dJnINOvB6Z11/rYOasHObONaFRj5EnKcsMOCKnT3k8Ei1NAzQ1nGhWIMsyly7Xs3jeyB7mWQaBjWcP7369dUDk3UMCTm9injQARr3E9ReOfVxeTiYIWgZlMGhEZN8Q7iE7Q70dZOhVFFosWCwWHA4HVquVOZVzop6rxAzXnuPlxQ81WPuSE0ySP8qhkdFqhnOIR3MHX7nQx4yS1PsPqNVqSkpKMJvNiKKIJoYe+goKY5Hspli0BVKiG2PpmIuCI/dzykS8Pi+v7dOOOKa330txjGLDZk/8O27vk1hY4cY2FLmdvDeJYPpQv4OhfgeZuVkggyRKuJ1uWtpAkgaZPzcTUYy/xlKtViHKEhoBBjyGUcWGLIPms7a3XklFd7+a/UdHfsYKMgaoLMpBkiTUajWCoMKrNqBi9DQ6h6in1KyiqiDStYevm2MyjhQbqnj/ZgIqlQpjRura/o/GeBWIp4rwqMfg4CCHDx+mubmZtrY28vPzmTlzZsR0q8nElJ9JkxUbsizT29tLa2trSqMYkUjHblI4g4ODNDY2YrfbqTQaOedzpeh0EjvfcfHxpypyszU06bJo7kzsy3C43kdlhXpEEWAiOIY8dPcn33HLj1YNfX2eiP4Lwfg8PjwuH8JnWzBHDvfR0jLEsmUWzLnRvxL9AyPTp5xOkaaGPtpbB0YcP61E5PILc3G73QiCgEajGfG5EgRYPk9g+Tw43i6x60Ow9ceXYqUWZL58sRyIBoxFXraOvGz/m5T52b+KQAenTz75BJfLhUajoaenJxC6jYRWA5cv9fFRg4p/HNNELTSMFad72BlcqyFi6hTA8tki86vS8z2T8MfoAAAgAElEQVQKDoGr0+Rgq3Dqkcw85Z83gseEZDfG0hnZ8LOwyofXB28cDBUcsqCm1+4kN2/0+xVED32DyX0He+1O0EdexLZ1Q0GOTM9gfBOQJElYW7uHgxZBb6FzyI3BaKCtw4PT6WPG9Eyys+P3VvD5QKODIa8eSR6IGinwySq0n7mH9zl1vL8vsphdZPyAjGd/HnjsrvkXBorOQRojYlRsNlCYG/ni/s+jXqchw6DDGeTsmEgaVVZmxrg145jskY3REAQBk8lEbm4uJpOJ/Px8uru7p8RcdVKIjUR2jPyDdWdnJyaTKeVRjEikS2z4i9f9gik/Px9BEKiurg4cc/FikSabQHePRHcSaTu9g1AtehCF5FX0p/VDCELqUlQKs0UON47+/sqyzGC/Y8Rg4BjysXtXO3Pn5TJrZtaIhbvTJeP9bKdquMjYR8OnvXTZIufVZqq6ueM/alF99jeXJCkgOlQq1Wc7TELIfUwrEbixBAYcEi+/D8faY0mxkvm38yVMGckPnpmZmWRmZlJdXU1HRwft7e2B0O1YbfoWVUtUFnh44QMtriQKxyVpeLL1+SL/HU+bJrG0Nn3RQaUblUI6SIXYSGV673iIDYDF0314RYF3jpxYaqjVKuy9jjHFhncovgLpSDS1eZk710uPM/I8YzJCzxgpxOHYO/rweXzoMkLHQUmUcDlcGIwGbF1eXM5+zjorQlhgDERZQJJlVIIKh1dHli7y+kaUVWgZHguPtWpCug36UQsS50pPh2RzGe17GSg6J/BYjlB6p1KpyTUOf+bC56lwck3GELER74g5LDbGJ4UK0isGIL0+HsHXUKlUaDQaioqKFLExHsQziEcarI1G43BB6jgUg6Y6dO10Omlra6Orq4uCgoKAYBoaGqKvL7SwTq+FdWeKPPWaOun2sh8dkVi6QMThSfwD3tI8gFdKndDI1vs43Dh2d5WBnsFRB5oD+3tpbh6OcmSbhn8/SZIZcgqAQF+fh0+P9tDfO0rvd9HFvRu2Y2ocniyFqnNwaFcH/vaSJOH1epFlGbVajUqlCvwDMBkFrjx3+LpvfCLy9gEfUgRxJ8syqxdLlFtSP7CpVCqysrKora0d0aYPoKCgAIvFgslkCgyseVnDaVWv7tXQ1J2gg/co659ZpRLnzUttB50R15+CXT4UJj/JpFHJssyxY8fo7e1NWXpvutKoIgmYM2d68fjgn8dPLDf0GQY62vopLo3e3aG/Nz4zv2hIbgcQuTVta9ewR487xg0Sj9tLb9fw3KqJUMvlj27Iskxfv4+BAS8mU7zznIAoyqg0w4a70cSG/60WJYGDDZHPVJnViUkI7UJp6D+M4HMga4YX+DLCCIGQadQhy3JgnvJvkPnnqeDogEGvxaDX4nIPp3wJcUY2tBpNSFfMdJNuMTAec8hUnKdOCrEx1iDuj2L09PRQUFDA3LlzMRiGQ6vt7e0p78oRjVTsJsmyHCgWEkWR0tJSampqRuzGRrpOhUXmrDkSfz+QrAoWaGnzkJdvIJEcKJfLh9UuJ/TaSHg9XprtHtRj5Nc7Bh3Esu8y0O/l1VfaWPC5PGqnG+kdALvdw7EjdhxDoyf6ypLIrec/jyU7dFfOKL504j40q4HhAUOSJHw+X2Dw9u9WwPCC4LyFoB38B5aqZez+EOxDqsDvcPo0idNr0zNoBk8m4W36PB4PXV1dHD9+nMHBQXJzc7FYLOTn56PRaLjkNB+H21S8cVAzpnt9ONHWPxUFEqsXp1doDF9/6g3iCpOfeCMbwRtjTqeT0tJSamtrU/bZHK/Ihp9z53rxiAKfNA7PPVqdBltbH0UlpqgLv05ragrqjza4qZruw+kdOT94vFCU46WtJ7Z0J2tzV+B3jNRcxh/d8Nf11Tc4WLgguuFfNLzisH+Qw6tDlATUEesght+3XocWX5QmHZ/X7R5hxiHIEsbeAwwVLAGGRUv4q/Ob70cr9eGc/0jEecq/5vKPl7kmIx3uYREW6ydUq4bKfCjODX1FulPNJ0Pr22SZivPUSSE2Ig3ioihitVppb29HrVZTVlYWMeSc7qLtYJIZ4D0eD21tbVit1oAjpb8rQTzXOWe+xPEOgXZ7ch/U9m6B8iIvDjH+nNRPPx2AFKZPDfQ60BlGLy7zeXx4XWKgTiMW9u2101A/gNst4nHHYLAky6yZ/RZLprUAwxGNSAQLD4Rh8SFJUtR0K1mWqS0TqC2DviGJ//vn8GS0+oyJGTB1Oh2lpaWUlpYiSRK9vb3YbDaOHz+ORqMZNguzWPiX5ZnsfF/LoCu2+5RlOWK9jSVHZsOZ6RcaEDqIKylUCqlCp9PhjaEiOVItxtGjR8nLy0vp4mK8xQbAygUevD4dh1uHBYcp10RTfQ9V0yK0lJNEWtpT0ypYFMGk8UQUGwD2/tjGlv6eIZxDwxHt0X5Xt8uLIWM4Et3e7mTObBNabXx/OxkBr09Gp1Ux6NWTox8ZSfe3vW3pjjyXZmg8LJF2RtxfM9r3BsRGuKWf3teKVhoWDhmf3BL4uXP+I3i93kB6bXFxMR6PB5VKhU6rRq/V4Pb6xoxsqAUoy4PyPFBPwHp5okz9pto1Us1JJzYGBwdpa2ujt7d3RBQjEsl6X8RDvNfyeyS0trbidDopKSnh9NNPH7NDzmgDoVrlT6cSGIpxERiNj46ILJwt4RrVBTqUjvZB3GLqhEZfV/+YQkOWZQb7HKgSGNkG+mNvWVKbe4xrz3ofiC40IhES9QhKt/J4PDQ2NiLLMm63G7VajSlDxRXnpH+QiXVAVqlU5OXlkZc3vGBwOp3YbDYOHTqE2+1mUZ6ZZlctjfaxc3IjfWSzjTLXnO1LWROBsZiKg7jC5CfSLrifsWox0rEhNp5pVMGsPs2Dx6envlOFwaij+ZidiurcEd85ye0MOGqnguONDnKKMhAjpBAPeQyUF0J7d/TXS6JEV9uJA8LrNYIJ/l0kCVrbXFRXxVeT4HRJNNq8zJ2hZcBjiCw2BAlZhk8+jTw4zso8ilqI/CZm9O4HyQsqLXqPFU9GSeA5k/tA5Nd8cgsZQDZQvPh+dDpdIOrh9XrJMAyLDeTInysBKM4djmboRlnCTPXIw0RENqbCxthJITa8Xi9//vOfqa6uRqPRUFpayowZM2JaNIw2CaSaWK/l8/kCBbqZmZmUl5eTnZ0d8wdqrEE/Pxu+fqmXdw9K7G/S0D2oJhFzNq9PoLfXiyEztmJxr1ek3eoDITUfO4/Li1o7dmTFN9iNSp3etnqZKjs/WPtCXCIjEiFRDy1YNfNYvHgxwIh0q0hF5qki0QEzIyODyspKKisrEUURu90OtiPIOhUtnvmjdkAJT7ky6mWuO8/HeK79FbGhkC7Cx+RYO0pNlDBI1znXLnHzzLsGWroE8gpzOHa4i5lzCkOOcfQlXxwezMCQRLXBSZcz8qJ/rIC3rd2OGKR+NNroc5g6rP6guXkoTrEh09Dsxe0Z9iXBoMUjqtGpQ4WDWiXR79LgcEe++XM1z45IofKjktxk9B3BaZ6HOnjvT5bJ8hwc8w5zjtwW8tg5/xHUajWDTg+iGH5RmfwsqLEIjKLRgm4h/ZGHdHonKZGNyExpsbF//35+9atfceDAAd5//31Wr14dd+HcZEqjGhgYoLW1lf7+foqLi1m0aFFCX4pYBn2dVuCchWrOWShT3+bmlfckup16EOKr5/i0RcacOUBp+Ugn0RHHHutHTlH6lCzLOIfcqMd4fxz9Q/h8ae7fLbq576ZGtObkhEYkZpftB/YDoelWsizj8XjSJjxSMeCr1Wosn/l2zJFl7H29vPJxJoOeyOI0+Guo08hct8LHKHN6WpiKg7jC1MHtdtPT0xNXR6mJFAbxnjOWuVQQYOPnXWx9y0AHGRxtsDF9Zn5IR52urlEacCRIS0sfhvzIi/4Wq0x2Fgw4Ro55Lqeb/u7Q1uaCEHnjUBBGtsweHBTp6fFiNsc29w0OSbg/yyA7XO9j0Wwtgx49eUGO4j5JQKOSabZFHiDzDAPUyB+Oeh2jtgUn80LmfIOvFa3UH9N9BuNPtyrUzaEt9/rAz00GifJcL5k6CTVqRHHseWqqRx7Gw917Ks5Tablbu93OqlWrqK2tZdWqVfT09EQ8bvPmzdTW1lJbW8vmzZsDP1+xYgWzZs1i0aJFLFq0CKvVCgwP1FdffTUzZsxgwYIF3HzzzVx88cWUlZXx4x//OKEOHePhfTHatURRpL29nQ8++ID6+nosFgtLly6lsrIyYfUd7+9UVSzwpS/IfPNyF6dVDqLyueJ6vX1AwO0avci+y+bA4U3dbkJ/z9CYQsPr9uL1pDfXX5ZE/udfjmMxp7+mwCi+RJb8CiZeRaPRoNFoAjUdXq8Xt9uN2+3G6/UmLaBTPSALgkB+bhb/eq7AourI75X/llWCxLXn+WLaBUsHUyEkrTC18KcVLlu2DLvdzpw5c/jc5z6HxWKZEAO+iY6WCAJctcxFQbZMYXkeB/d1Bp6TZYnmttS7rff0q8nPjFwHIsmQnz3yey/LMtbm0PyqUdOUNZE365pbYozUyMNRDT8OJwwMyQx4QzfMRHn4M3OwIfJn53MZH0S/Rm0V1FZh9BwFWQppr57liZxCFStZnoNovA1kqbtYZHqeBeUy5qzhuSrWeWqqi43xaH0bLGimynyVln3De++9lwsvvJBNmzZx7733cu+993LfffeFHGO327nrrrt4//33EQSBxYsXs3bt2oAF+9NPP82SJUtCXvPEE09gNps5duwYW7Zs4dlnn2XNmjV8//vfT/hexzuy4b+Ww+Ggra2N7u5uLBYL8+bNG7W2JN7rjDXoBxckw/BujF6vYc05KtacA00dXv7yjkh7rxb1GNvLgkpDQ+Mgs2blRnxe9Ek0t3lSlj7lGnKjUo9xLlnE2deHoElfVEOWZdZ8vpkz5g6l7RrRiFZkLkkSoiji8/mSinqkc0A+Y4ZIVYHISx9p8fhOXEOSQECiRvcWXR0WBIuFrKysCRtMp8ogrjB56enpYePGjWRkZCBJEu+8805En5rRmGhhECvxpiSrVPAvy138Qcrkzdc68c7zodVqELxuBh3p2QB0DTpBiPz+d3TLqASQguo6ersGcIc5ZI9WrxFNbLS3O5k924RujELx3gFxRK3K4eM+lizQ4vRpydAMCxFZFnB41PQOjbyeIMicK2yJfIHaqhP3KjvQ+9rwqofXXLGmUEVDKJuNAJSL7zM9Y2i4zq7nsZBjHOb/HHOeOhnEhhLZGEla7nbHjh3ccMMNANxwww0899xzI4555ZVXWLVqFXl5eZjNZlatWsXLL78c83mvuOIKdu/enfSAOd5iw+l08tFHH3H48GGys7NZunQp06ZNS5nQ8F8n2vviL+YSRRFJklCr1eh0OnQ6XciHt7JYw39eruf71wssm+FCLY0e7fBhwNYZedH96fF+5BQJDUmUcLvFUQcLjdjHLWds5ukv/YovLX6BqsxPEcTU9GwPpra4m+u+0JXy8yaCP+qRLfwfWq12RNTD4/Hg8XgCf/exSPeAXJQ77MlRkjt8LypBRhBg7VIPlmwZnU7HsWPHeOuttzhw4ABWqxVRHJ+OVAqnJslG5D0eDzfeeCMzZ85k9uzZPPPMM+Tm5rJ582ZefPFFjEZj3EIDJocwiIVE7lOjhn8728W02gL2f9gOgGswtfUawXza6MZkiByFH3IR4lkk+kTsnSM/A5H8NfxEExuSBG1to6eGybJMU8vIe/P4oLtXYiAo/VRGoL0n8pxabrSSiy30h59FM8Ixeo4hM3zPBl8LWmlgxDGxIJTNDvzfoB6K2tDD2PMYWX2/IXvg8YjzlNvtxuUafp/StS5L90J9vArEp9pmWFoiG52dnZSUDHc3KC4uprOzc8Qxra2tVFRUBB6Xl5fT2toaePzv//7vqNVqNm7cyHe+8x0EQQh5jUajIScnh+7uUVpIxMB4pFG53W7a2tro6OhAEAQWLFhAZmZm2q4X6UMYvMiMZ7dbpRJY9Xkdqz4PbTYfL77lpaVHGzGFydotYc6XQgx6euxOBpzqlHUTGuh1RE2fkiSJ0/L/yX8ufynQUu+iBUe5aMFRAD5uLuaFvQs4aK3EI0Tv7x4LRs0Qd321IeHXp5PRPD2CTZqCjZrCGY+8U40a1izxsa9Jxd4GNefN8VFq9tHdoqG8vJzy8nIkSaKnpwebzcaxY8fQ6/WBOpDxMOJUOHVINiJ/9913U1hYyJEjR5AkCbvdjiAIIfNcIguRdEU2Jku0RKuB//kXHf/53XZczmLsXekTG7IMOskFRG4b73SfsNO2tnYjRbDljvZ7jjWfjlUo3t0jRvUlOtooUpCrpyBjEJUw3K72cHNkYXOG7rUTDyIIjGCMniP0GpcDYEoghSpYZMSLMSjq4TD/J263m6amJmw2G5WVlSEt4IO9p5JlPCIb6Xb0noqRjYTFxsqVK+no6Bjx87vvvjvksSAIcf9hn376acrKyhgYGGDjxo089dRTXH/99VGPT+aDk67IhizL9Pb20tLSgtvtprS0lPnz59PY2JhWoQEnBkP/AjPYhEij0ST8IS21qPnqOjWSJPO3D128c1DGLRtQ+Vt5qLU01A8wo3bYxEiSZBpbXAgpKgof6ndGFRpasYebl29jZuFIYetnYUUHCyuGP7MdfVk8+8FC/tkyjX5fHoIqjsFB8nD//ztClE2sSUWi6VbjsTvjZ0GlxLxyCZUKXK7Q66pUKvLz88nPzweG0w9tNhv79+/H4/GQn5+PxWIhN3dk+0wFhXjYsWMHr7/+OjAckV+xYsUIsREckQcCEflrrrmG3/3udxw6dAgY/twWFBSEvFar1eL1euOObkylAvFEzilJEhqVzNWrs9jzUQsuV3ozDY7VOymqNOJwSQz1D5GTf8J0r8MOxXkyja0uBntHRuoFdfz1Gn5GKxSXZJmW9uh1j5IEbTaZokw9WTo3PhFabSPHO61a4kw+yyQZQ2gAaKVeNGIvHrWBLHfsKVTJiIwR5M/FyB6MGWCeBcwCh1Q+qvdUMs1Qpnqalv8aU22+S1hs7Nq1K+pzRUVFtLe3U1JSQnt7O4WFhSOOKSsrCwzsAC0tLaxYsSLwHIDJZOJf//Vfee+997j++uspKyujubmZ8vJyfD4ffX19gUVIoqR60PV6vXR0dNDR0UFWVhZVVVVkZ2cDwx4E45Gy5d/B9ng8gTBltB3sRFCpBM5frOP8xWC1+3jhLR+NXRrUWi0uUYdzyENGpo6G4/1IpEZo+Lw+RFEmXBNIosjni97jK8t2xRU9Kc4Z5GsXvAW8hcuj5vm9c3nz2Cw6HMUI6uj3LEsS37rqUyy5UzOlJyA+goQHEBL1UKvViKKY1vaA4fg/mmPt2BiNRqqqqqiqqsLn82G322lvb+fAgQNkZWUFoh6JpKsonNokE5Hv7e0F4Lvf/S6vv/4606dP5xe/+AVFRUWBY/2eUJOhZmMy1IH4Nzz8XLg8lxd21eNTpXczzuWRMevdNByx4+h3kJ0X2lZer5WxtkTOmNDpo7d5H0tsADS1ODCbRzqKd9pGb7ACUN8iUV2kI0vnxj4QuV19FfvQzCwb81zBGD1HkTUuNPLgmMemVGQA5M+NfE+qPYEkf4d0bsR5KjgyP1nqEf33OJ5CYKqkU6UljWrt2rVs3ryZTZs2sXnzZtatWzfimIsvvpg77rgjkBf76quvcs899+Dz+QKGfF6vlxdeeIGVK1eGnHfZsmVs376dCy64IOk3OlWDbn9/P62trQwODkZtW5tOT4/wgu/CwkI+/vhjjEYjhYWFFBQUpOULUJin5kuXqZFleHOvi7f2y9Q3q6mugD5H6q431O8aEdUwSN3897l11OQnVzdh0IlcuXQfVy7dhyzDG4drePX4WdR3ZiMKobU0l32+iTPmjX9BeDqIlG7ldrtpaGigs7OTWbNm4XK5ko6IxUM8E4FGo6GwsJDCwsJh48bBQaxWKx9++CGyLFNQUIDFYonLo0bh5CZdEXmfz0dLSwtnnXUWDz30EA899BDf+ta3eOqppwLH6PV6PJ74XbEngzBI5TlFUUQUxcB7rNFoAjvVl63M560Phqh3pTds/Onxfuxt3ciyTL+9PyS6sXd/P153ZDPX0fw1NDGIjY62YUfx4EJxUZLptMW2eXW4SUPxPBUNHSPHYkHycv1p22I6TzBGzzEEcfRajfESGZEIFx5wIi043HsqlnSrdEcFpmLUYTxIi9jYtGkTV111FU888QRVVVXU1dUB8P777/PYY4/x+OOPk5eXx3e/+12WLl0KwPe+9z3y8vIYGhri4osvDhQxr1y5kq9+9asAfPnLX+a6665jxowZ5OXlsWXLiY4LiarVZAZyURTp7Oykra0Ng8FAWVkZubm5Ue8jHQO8f3fI//v7oxgzZ85ElmWGhoYCCzCNRhPY+U1lQToMtzI8Z5GOcxaBvU/iL28PIPlUCJrkd8cHekPb3EqiyNmlb3HDGa+n3FlaVX0O51XDeTQBcLhBzwtv5XGwKZdis4PrJ0lBeKrxCw+jBszVUF19UUg6XirD2KOR6EAtCAImkwmTycT06dPxeDx0dXXR0NDAwMAAOTk5WCwWCgoKAhNSpGuHn1Ph5CJdEfn8/HyMRiMbNmwA4Morr+SJJ54Iea0/shEv6RIGqWasxiTBKb3+iHv4d/2ic/LY+WoXGnUmPjF937/W5t7AvfR29ASiG16Pjx5rb/QXCkQ0ylOpVAhjOQMy3GK3LcxRvK0jsrCJRJtNpr1Hz9GWUGEjyzJXVm/HpIu/3kUvdqAR7VGfn0ihEY5RtWf4P2FRj3jmqZOh9e1UJC1iIz8/n927d4/4+ZIlS3j88ccDj7/0pS/xpS99KeSYzMxMPvggco9og8HAtm0jlXuiubCQ2EAe7PxaWFjIggUL0I8SXg2+Vqp2qIJD0NG+WIIgkJWVRVZWFtOmTcPpdGKz2fjkk08CO7+FhYUpryHJy1Fx7SXDxbvvH/Tw2gciPS5tQotIt8uDjCoQMDbKVr51fh3luZE7xSRKNOfvWdVuZlW3A+0pvd5kx8SrgUnVoY2ebuUPYadKeKRqoNbpdJSWllJaWhqon7LZbNTX1wdEd0FBAZmZmYHrTcWiO4XUkUxEXhAE1qxZw+uvv84FF1zA7t27mTs3dFGVqNgYz46JyRBpLvXvPgdHMaKNF16vl9bWVmorBmjtFujsT086ldftweM80Rmqv7uPgZ4BsvOy6WrvRo5Spa1Sq6I6coe7ho9GU9OJQnGvT6K7J76/7a53QQobIyszPuWskn/EdZ5g1IyMuKVcZEBSQiMSiaRbpVsMKPNIZKa0g7ifRHNhIfaBXJIkurq6aG1tRRAEysrKxnR+jXStZHao/AreH4KON70lIyODyspKKisr8Xg82Gw2jhw5gtvtDggPkym5Lk3hLJmjY8kccLgktu/q41CrFkEbvSNHMLIk43J4UWs0SKKPCyrf4F8Xv5mye4smMBRCGa27VSJh7NFIRwhaEATMZnPAw8flcgU++06nk7y8PCwWCyaTSZkkTmGSicgD3HfffVx33XXccsstWCwWfv/734ecX6fTJZxG5fONndM/0QRHQsM9nEabp/r7+2lubmZgYICysjK+et1C7rivgeGVfWoXhbIsM9Q3MmWov6sflVqNcyB6i3SdIX5/jUgMDYnYezyYc7U0t8b3d5VlGR/qkHdFLbv5+rzfxnWe0ZgKIiMSsaZb+UVIukTBeDZXmUqcNGIjkUEcxo5suFwu2trasNls5OfnM2vWLIzG2BbL8V4rGsGpUv4FXbIF3zqdjrKyMsrKyvD5fHR3d9PY2Mjg4CBms5nCwsKUdPjxer20t7fT1tbGf8zdCXPhiK2MnYdX0OGpQVJFT+fq7x1CrdFiooNvXbCVkpz+pO7FjyIyEidad6tUpVuNRwjaYDBQUVFBRUVFoEWpzWbj0KFDeDwempubsVgsae8apzC5SCYiD1BVVcWePXuint9gMEyaNKp04fP5ApGM0RqTSJJEZ2cnLS0t6HQ6KioqmDt3buC7v2ShicONIk3W1N6fx+XG5x65Vuiz9eD1jl43oY6Sfjn8XHw1Ji0tTlQqFf2D8UU1vJ5QjylZlrl2+lMYNLGnYkUjLSIDxkVohBOebjUknoPNZqOxsRGtVotWq8Xj8aQlLViJbETmpBEbiQziEDl3VZZl7HY7ra2teL1eSktLWbJkSdK9k+OZNMKjGIIgoNVq09K/WaPRUFRURFFRUcDXoLOzM2A8aLFYyM/Pj+vaQ0NDNDc3c5ruf8kGZgWlP8+0tPIty9P4JIFXjpzBPzpOp08uQRBOfEGdQy4E4KLKV7li0bsp+T0VkZF6QqIeUdKt4ukaMt67Qv4WpQUFBTidTvbt24coiuzbt4+SkhKmT58+bveicHJzsqZR+T2cBEHAYDDw0UcfkZeXR1FRETk5oZ2XXC4XLS0t2Gw2LBYL8+fPj+iXs+5iC3c90gCkzktHlmUcvZELoSVRwufxoh6lAFwQhn06wlGpVXGPWe1tTgSNjngjN5IoI6hPvGa2aR+LLPH7YwRzMomMaGSq3yCzGKqLwSENRyXj9Z6KlfForRvMVIminPJiIxj/LnxHRwfZ2dnU1NRgMplScIfDxCI2/F8A/+CdiihGPAT7GsiyTF9fH1arlePHj5ORkRHobBWpLaosy3R1ddHc3Mzy3O0UjJHVplHJXDr7XS6d/S6dA7nsOHQOh3rnIaoyyBWa+J+L/kxB1tjt+EZDERjjRzzpVtF2kyZyV0iWZbRaLdXV1VRXV6fdmEnh1GIydaNKlvCCb38UY+HChYiiGNisO3jwIDk5ORiNRvr6+vB4PJSVlXHGGWeM+v3Ky9UyoyoDTQe0J+fbG8A95EQcJR3N43Rh0GRGXLypNeqIQgNi60I1AkFAlOJbJMqyDEFF6DqG+Mqc/43/2sG3cQoIjXCCox7h6SGDrpUAACAASURBVFajeU/FysngUJ4OTgqxkWguLBCIHhw4cIChoSFKSko4/fTTo3asSRfBBd/+KEYqC28TQRAEcnNzyc3NjdrZqrCwELVaTVtbG21tbZxfuJPK3PivVWTq5calzyPLz9NgL0i6na0iMiaW0dKtZFnG4/FENROcqM98+CQxFQd0hcnLZOpGlSixFHyr1WosFgtms5m2tjaam5vp6elBlmVyc3Njnluv+IKFhx5vAcZuvjIWsiRFrNUIRvKJyJKEEEEEafWpqdfwk5PhBEb6bYxGcAqVLMt8dfYTaFSJidC0iQyY1EIjnGjdrSDxZijpnsOmaprWSSE2DAYDLpdr7AOD8Pl8dHZ20t7ejsfjobS0lJycnHFdYIQX0o2nn0G8ROps1drayrvvvsvF5S8zi9BUqcSvQ8JCQxEYk5fRoh7BYWyvN/nc40SZqoO4wtRgqqZRRSr49v+LxNDQEC0tLdjtdoqLi1m6dCk6nW7Y06K/P+ZIeUmRngKzBocIPUmW6zkHhpBjeA+9Lg/6zJGpW6MJCpU6/jHjsmmvYfXVsHfgzJjXHMEpVItz32FGbkPc11VExujEUmQ+VrqV0u0qMieF2IhnEB8cHKS1tZW+vj4KCwtZuHAhe/fuJTc3ge34BPC3vw12Tk21w3c6kWWZ7u5umpqaWJ67nQXlE31HisiYakQSHv62l62trdTU1ATMBNPp6RHOVB3EFaYGyYiNiYhsxDNPBafQwrCz+syZM0MWXYIgkJOTQ05ODjNmzAhEyv/5z3+i1WoDBp3BXSWv+IKFx7d00kPifk2SKOIciM2I1ef2oMswjPTMiOKvodao415YqgSJs8s/wKB5F/VxH//sO3vMcwSnUBmFXq6dFb95nyI04iNa1GOsdCslshGZk0ZsjJZGJUkSVquVtrY21Go1ZWVlIwbC8cD/Ae3v7yczMzNtBd/pwOfzhaRKVYyPNhsVRWRMfQLCQwU55VBaujLwnfBHPSC6l0wqmaqDuMLUIJluVOmKbETK/47Fw8mPx+Ohra2N9vZ2zGYzs2bNiqmLW3ik3OFwYLVa2bt3L4IgBITH9GojBj0YDeCIL3khgKN/MC6x5vN40BpOpG6pNepR/DXin7/NhkEMmuHakaunbUdb7+Xd3vNHXY/4U6hkWeL/zXssLjNbRWSkhlg8PdRqdaBzaLqYqum+J4XYiDaIO51O2tra6OrqoqCggDlz5kTsfJFOwgvpZsyYQUtLC06nk/z8fAoLC8nOzp60HxiHw0FzczOLtJtTliqVChShcXKSo9p1wkxwlHSrVHQNCUcRGwrpZLIViPtrQfxiJh4Pp3BvjKVLlyZV52g0GgONGdxuN1arlQMHDuDz+Tjzc2ac/5BpcsXvo+Xz+nANxueq7XG40Oh1gTlZl+J6jdr85pDHG2p2oGkU+bt9ZdR1gD+F6jzLa5RmxdYPOK0iA04poRFOpHSrvr4+GhoaAulW/uh8st5T4UzVeeqkEBvBg7g/zae1tRVRFCkrK6OmpmbcW26GF3z7B+7i4mKKi4sRRTGQjjQ4OBjSKnCihYe/9W9TUxNn5WwjP/EIdspRRMapw2h1HqnoGhLOVN0xUpgaGAwGBgfj766XrgJxQRAQRTFmD6exvDFShV6vD/jgeL1ebDYbf/mrDZVgRpLj+347xigKj4bo9aHRDU98Km3kyIYggDqBeo3zKkY6fa+tegGN4OVv3atHvJ/+FKpctZX10/8y5vkVkTG++NOtjGYoMZ+IeqTKeyocRWxMIHq9HpvNxltvvYVGoyE3N5fp06eTlZUV0+uDd3iSIZ6Cb7VaHQgVS5IUEEgHDx4kNzeXoqKilJjqxYPP56O9vZ3W1lbOL9xJeXzNMtKKIjJObaJ1t4LEu4aEM1UHcYWpgV6vp7s7/j6uqY5s+OcpQRCwWq1YLBZ0Ol3UlN5wb4wFCxZgMEQ3Y00lWq2W0tJSrl6XyQt/7aWhM/bXet0ePM7Ecq/cDhdqrQZBEMhUORgSRxr5JhLV0Kk9zC2oj/jcFypfQaPysdu2JmQt4vWICLLIN+b9aszzK0Jj4okl3Soe76lwpuo8NaXFhizLvPHGG2zbto2hoSHuvPNO1q5dG3c41z+YJ/oHTLbgW6VSYbFYsFgsEU31ioqKyMvLS9sHLDhVygTMnCSpUn4UoaEQTjyeHrF2eJuqg7jC1GCi06jCPZxmzpwZqJPIyMigqKiIgoICNBoNsizT09NDc3MzHo+H8vJypk2bNmHfj2WLs3lhdzfDq7ixNwVlWWaoN4kWVpKELElkG9wMiZEnxETERnFWz6jPX1S+G43Kxyud6wOCQxQl1lS8QH5GX9TXKSJjchJLd6t4ox5TdZ5Km9iw2+1cffXVNDQ0UF1dTV1dHWazecRxmzdv5kc/+hEA3/nOd7jhhhsYGBjgnHNOLDBbWlq49tpreeSRR3jyySe59dZbKSsro729nenTp3PhhRdSU1PDhg0bErrXRMPUfudUSF0Ba7ipXm9vL1arlaNHj2IymSgsLIzbzTsSkzlVyo8iMhRiYTRPj3jC2FN1EFeYGiQqNpJNowpP6dXpdAG37+DOUJ2dnTQ0NCDLMj6fj9zcXKqrq0e4gE8EgiCw4u2HeS1/NU3mhWMe73G68HmSa6PtdrhwagzoomQ9JCI2Pld4ZMxjLij9GxrBx0sdG5FkFaUZzays2BNyTNrFRTCK0EgJ0bpbjeU9Fc5UnafSJjbuvfdeLrzwQjZt2sS9997Lvffey3333RdyjN1u56677uL9999HEAQWL17M2rVrMZvNfPTRR4HjFi9eHCIkrr76an7xi18EVOHjjz/OwEBiuZkQ385RNOfUdPzxBUHAbDZjNptH9Cg3Go2BHuXxRHImc6qUH0VkKCRDSNRDG3u61VQdxBWmBuPpsxFPwbffoM//3cjPz0elUmG32/n0008pKioKpFqNN4a7zsf59nA04HzU/M1xFozcswxBluUxDfxiQfL6MGh8uD3eEUXigiqRTUWZC6veienIc0v+jkbw8krbav5r/m/GV1z4UURGWkk03Wqq1hamTWzs2LGD119/HYAbbriBFStWjBAbr7zyCqtWrSIvLw+AVatW8fLLL3PNNdcEjjly5AhWqzUk0uHH/ybr9Xq6uhJ3nI5lMI/FOTWdhPcoHxwcxGq10tDQgF6vD0wIkcyRYPKnSoEiMhRSTzzpVqIoJtVRR0FhNJKJbMSzGeYv+PbPU6N5Y9hsNlpaWoBhb4xZs2aFLF6CW9KqVKpAnaFen7yr92gY7jo/8P+MZSfUxZm9h3H7ymjTTIv6WtegA8knRn0+VjINEjec9R6/3HMOWp025H1JJLMgWz+EOSO2BgFC2WyWl/Xz+UVb0air475W0ihCY1yJJ93Kvw6daqRtZu3s7KSkpASA4uJiOjtHVna1trZSUVEReFxeXk5ra2vIMVu2bOHqq68OeXOfeeYZ9uzZw8yZM3n44YcTHsT9RAtTx+ucOl4IgoDJZMJkMjF9+vSAOdKHH36IRqMJTAharRa73U5zczPLsusmZaoUKCJDYXzwCw+HenXILpG/QUNbWxvTpk3D4/EEvuPpXlQpnDokE9kYC39XKf9m2GgeTvF4YwS3pHW5XFitVvbt2wcQmGdSWSweLDIicVn22/zDOh+iiA1JknD0x9/xKxI3X/IPVs8/xh/+8Xkcbh06w4noRiIpVFU5HVGfixa5SOAyyaGIjAnHn27l4NyQeQqgt7eXpqYmiouLA/OULMvj1rAhGZISGytXrqSjY+QX6O677w557B8AE2HLli089dRTgcdr1qzhmmuuQa/X8+tf/5obbriBm2++OSmxER7ZmGoO35mZmdTU1FBTU4PT6aSjo4P33nsPj8fDpdW7KM+e6DsciSIwFCYCh3p14P/+NtkNDQ1kZGQwb948MjMz2bNnDw8++CAbN27k61//+gTercLJRLKbYuGEp/SOFsWAE94Yg4ODlJaWxu2NYTAYqKyspLKyMuCFsX//fiRJwmKxUFRUlJCPVcaz65E/7o3tWJWHubpPsYs12NVFI553Dgwhp6CYvtLiYPWCYwDccOY7/Oyv56LVn4huaBJQAWeWfhL4/4SkRY2FIjQmDf7ohp++vj7q6+tRq9XMmjWLrKwsPvjgAx588EEWLVrED3/4wwm609hJSmzs2rUr6nNFRUW0t7dTUlJCe3s7hYUj83bKysoCqVYwXAi+YsWKwOO9e/fi8/lYvHhx4Gf5+fmB/3/lK1/htttuS3jHyI8/shGPc+pkxOl00tzczOc0TzK3dKLvZiSKwFCYKMJFhtVqpbGxkaysLObPn49er2f37t08+OCDlJaW8vDDD7NgwYIJvGOFk41k5yk/0TycYvXGMJvNSadhBHtheDwerFYrBw8exOfzYbFYKCwsHNNJPOPZ9YH/CwtzY772BvfHHPxwDnYxVGyIoohzYCi+XyQCggD/38a/Bh5/Yc4nbH7nzEB0Q6VSIajie//UgsiypS4EtSIyFKITLDL8TXzq6+vR6/XMnDmTzMxM3n33Xe6//35UKhV33HEHy5cvn8A7jp20pVGtXbuWzZs3s2nTJjZv3sy6detGHHPxxRdzxx130NMzXAD26quvcs899wSe/9Of/hRSvwEEBAzAzp07mTNnTlI7Rv42gA6HA4PBEFerzMmAv0VhU1MTy7LryJtkKeeKwFCYSIJFhn/h1dTURG5uLgsXLkSn0/GXv/yFhx9+mNraWn79618zZ86cCbxjhZOVZOYpf7F3LB5OML7eGDqdjvLycsrLywMmfEeOHMHj8VBQUEBRURGZmZkBgRMsMhIhR++gJrOVtoEZDMknwvaOvgFIgfnh8tkdTLOERlq+dObbPPzaeWj1WtSa+NcGhTlDaNSpN2ZMGEVgTCrCRYbNZqOxsRGj0cicOXPIyMjgzTff5P777ycnJ4cf/ehHLF68eErVbqRtabpp0yauuuoqnnjiCaqqqqirqwPg/fff57HHHuPxxx8nLy+P7373uyxduhSA733ve4FicYC6ujr+8pdQx8yf/exn7Ny5E41GQ15eHk8++SR2uz3uHaPg3SGLxUJTUxMtLS0UFhYmHA4eT0RRDHSVWmHZQdkkSpVSBIbCRPPBkWp6e3vJytpHXl4eoijS1tZGfn4+ixYtQqPR8Nxzz/Gzn/2M0047jT/84Q9Mnz59om9b4SQmkchGcMG3f34yGo1RC74n2hvDb8JXWlqKz+fDZrPx6aefcqb698MHHG1MyXU2TH+X+n0VHPUtAsDn9eIeciZ9Xq1W5ntr/zbi5xfP2c/v3z2TIbeOjMz4RdusElvS95YSFJExqQgXGZ2dnTQ2NpKdnR2IuO/atYsHH3yQ8vJyHnnkERYsWDClRIYfYYz+3ZNIikfngw8+4Je//CU//elPRz0uksN3cMG3x+PBZrPR2dmJz+eblMLDnyrV3d3NBUXPT/TtBFAEhsJkIDiS4fP5OH78OO3t7QiCwLFjx2hsbMRsNrN9+3aWL1/ObbfdFtKkYhIz9WaX8WNKzFMdHR3ccMMNbN++fcxjw6MY/oW7zTa8aPXPTXq9PqSduclkoqKiguzsybH7lPHJLaE/SJHYAHhk76W8NXQ+XllPn82O15V8itqXz/+EL569N+Jzrxyay8O7V2Apt8Qt4O7a8Ao1hfak7y9hFJExqdj9dy1ZWVlYLBbMZjPd3d00NTVhNpupqqpCp9Px4osv8vDDDzNr1iy+/e1vM3v2JEzBG0nUeWqSJd0khl6vx+VyRX0+vB1gtIJvnU5HWVkZZWVlAeHhz0OdSOERvGN1pmnrcKrUyNq4CUERGQqTgXCR0dLSQnt7O8XFxSxfvhxRFPn44495++23sVqt6PV6DAbDhHgHKJyajBXZGM3DSafTUVVVRVVVVaAr1EcffRQ4X2lpKaeffvqk+TyPEBl+aqtSJjjW1bxHy6FyDg7UpkRo5GV7owoNgItnH+DJd5bhcXkwGGOPbhi0nokTGorImFT4IxlnnikHir7379+PTqejubmZhQsX8sILL/Dzn/+c008/nT/+8Y9Mmxa9zfNU4qQQGwaDIWIubDIF38HCw+v1jiiAKyoqwmg0pvx3CWZEqpQprZeLC0VkKEwGgkWG1+ulubmZzs5OSktLOeOMM/B4PDz++OM8+eSTrFmzhmeffRaLxYLD4eC1115TWtsqjBsGgyGi2IjHw8lv7mqz2dBqtZSXlweiHr29vWlpRxsPUUVGMCkSHDXZNgo1HXzQm5qdtzvXvjnmMV9e9haP/G0V+gx9zKksFXl9yd5afCgCY9IRnC4liiKtra20trZSWFjI/PnzcbvdPPfcc9x///309fVxxRVXcO211540QgNOErERvGMUj3NqrGi12hHC49ChQ2kTHk6nk5aWFrq6urig6HlqLSk7ddIoIkNhMhAsMjweD01NTdhsNsrLyznjjDNwOp388pe/5Omnn+bKK6/kb3/7G2bzCXMwo9HIZZddNhG3rnCKElwgHsnDabR5yuPx0NraSkdHB3l5ecyePTuk21NwxOOTT4ZbrI6X8DD2PBb4f8z5bCkSHEsKjvD8kXlJn2duRS9n1LSNedzKWQf53Xvn4PP40OpjM65aVD32eVOCIjImHcEiIzjiXlJSwtKlSxFFkT/84Q/85je/YdWqVezZsweTycSuXbvYs2cPy5Ytm8C7Ty0nRc2G1Wrl2muvZcuWLQGL92Cb93ThFx7+Go9khIcsywHDljNNW9Nwt4mjCAyFyUCwwABwu900NjZit9uprKykuLiYgYEBfvOb37B9+3auvfZavva1r02a/PUkUWo2ojMl5imA0047jd27dwcej+Xh1NfXR3NzM0NDQ5SVlVFcXByTN4ZfeFitViA9wiNYZADIrYfiP0kKBMflO2+m2zF6m93RUKlg203PUmhyjHqc3xtj90cl/PT5ReRaYmnXK/PLL/4ZkyF1/iohKAJjUhIsMrxeL01NTVit1pA0/SeffJLNmzezdu1abrnlFgoKCibwjlNG1HnqpBAbra2tzJs3j//6r/9i48aNzJw5c9zvwd/yr6OjIy7hIYoiHR0dtLS0cN6xHyEsmzy9/RWRoTAZCBcZTqeTxsZG+vr6qKqqoqioiJ6eHh599FGef/55vvzlL3PjjTemPc1xnFHERnSmxDwlSRJms5mvfvWrXHHFFcybNy+iy7ckSXR0dNDa2hrws8jNzU24A43b7aazsxOr1YosyxQVFSUlPMJFhp+JEht1h8/g5x+uTPj1axY3cNsX/h71+UgGfNfcdy6qzOIxncRzjUP87PqdCd9bRBSBMSkJN+LzeDw0NjbS1dVFRUUFpaWlOBwOnnjiCf74xz9y1VVX8Y1vfCMk4n4ScHKLDQC73c7OnTupq6uju7ubNWvWsGHDBqqrq8f9XvzCo7OzE6/XG1F4uFwumpub6erq4vzjP454nokSHorIUJgMhIsMh8NBQ0MDg4ODVFdXY7FYsNls/PznP2fXrl187Wtf49///d9P1joMRWxEZ8rMUwMDA7z00kts3bqV+vp6Vq9ezYYNG5gzZw5Hjx7F4/HQ39+PxWKhvLw85SlQwcIDYo94RKrFCF+ET5TYkGW4fOct2J3xby4YDRJ/+Z8/oY4QWBrN5fuvH5fw878sISs3a9TzL6lp4qaLowuZmFEExqQlXGS4XC4aGxvp6ekJibj/+te/5plnnuG6667j61//OllZo392pignv9gIxm638+yzz1JXV0d/fz/r1q1j/fr1E9LiMlh4uN1usrOzcblcfP6eGwFQX14y5jnGQ3QoAkNhshAuMgYHB2loaMDpdFJTU0N+fj7t7e088sgj/P3vf+emm27i2muvRauNLYd6iqKIjehMyXlqcHCQHTt28Oijj9LQ0IDJZOJHP/oRl1xyybh4Y8QiPGIq+E6GFHWm+t8DZ/Hbj1fE/brb1/6Dyz53ZMTPRxMafq594BzILB31b3XzxXtYXNMa930BisCY5ISLDKfTSUNDA/39/YGIu91u59FHH+WFF17gK1/5CjfeeOOkslJIA6eW2Aimq6uLP//5z9TV1eF0Olm3bh0bNmygtLR03O7BnyrV3NzMOQ9+I+pxEyE8FJGhMFkIFxkDAwMcP34cn89HTU0NZrOZpqYmHnroIT766CP++7//m6uuuiqmHPaTAEVsRGdKzlOyLHPJJZdQUVHB9ddfT2NjI3V1dXR2dnLZZZexYcOGcetG43a7A/WHAOc03x394Nqq1F04RWJDlmHtjm/S64o9ElRe4OBPX3s25GexiAw/f9tXxC9eWUZGVuTFo1oQ+d2NdcSV/aYIjElPuMgYGhqioaEBh8NBdXU1BQUFWK1Wfv7zn7N7926+/vWv88UvfvFkjbiHc+qKjWCsVivbt29n27ZtiKLI5Zdfzrp16ygpGXuRnwgul4uWlhZsNhuFhYXM3nR1zK8dS3gkIzoUgaEwmQgXGb29vdTX1wNQU1NDbm4uR48e5aGHHuLIkSPceuutrFu3LmK++0mMIjaiM2XnKa/XOyIi19vby3PPPcfWrVvp6ekJpARXVaVwkR8FSZLI3LEx8RPEI0QSFBryx72B/wsLTxRpP/7JuWz+5OzYTiLA7776ErVFJ/wv4hEafr740LmIGaUR62lKcnq475qXxz6JIjCmBLv/rsVkMlFYWEh+fj5Op5P6+nrcbjc1NTXk5eXR3t7Oww8/zNtvv81NN93Ev/3bv53sEfdwFLERTnt7O9u3b2f79u0IgsD69etZt24dhYWFSZ1XlofNWpqamnC73ZSXl1NUVBQSatVeuSSuc6ZKeCgiQ2Ey8XF9LRaLJdAtqqenh/r6ejQaDTU1NZhMJg4ePMhPfvITWltb2bRpE6tXrx6XFJNJiCI2onPSzlN2uz0QmR8YGAhE5svLy1N6nfCNsfLycvR6PRnPrgeiL/DjIlyIJCk2wu9DkuCyHd9iwD22seGyWZ3cf9Wu4fMkIDL87PmkiEd3LUdnGLlrffH8A/xbsEmgIiqmJP5Iht/jprm5GZvNFvDFqampQZZlHnzwQfbu3cs3v/lNrrzyylMl4h6OIjZGo7W1lbq6Op555hn0ej3r169n7dq1cbUiE0WRzs5OWlpayMjIoLKykpycnDFfF4/wSFZ0KGJDYTLgUK/G5/PR3d1NZ2cnfX19SJKE0Whk2rRp5OXl8fHHHwcMju68807OP//8U1Vk+FHERnROiXnKZrMFhIfL5eLyyy9n/fr1SaUE9/b20tzcjNPppLy8nOLi4qjfM8Nd5495voSFSIr41ccX8scDnx/1GK1G5vlv1pGp9yUlNPzc+IvzcGnC/gayzH1fO0RJfppa3iqknfB0KX/E3S8wtFotv/3tb3nyySexWq1ceuml3H777cyfPz/hznEnAYrYiJWmpibq6ur485//TFZWFuvXr2fNmjXk5eVFPD7ajlAijJfwUESHwkQQnC4lyzI2m42GhgYyMzMxm80cOHCAW265BY1GQ0ZGBj/+8Y/5whe+cCoP3MEob0J0Trl5qrOzk2eeeSYkJfjyyy+nuLh4zNdKkhTYGNPr9YGNsXi+Z7EIj3DGQ4h4RRVrnr+VIVf0FMsvnneAr1zjTP5in0Uq/v5hBr/cMQ2N9sROtl7r5be3HUj+GgrjTrDIkGU5EHHXarXU1NSQlZXFwYMHeeCBB2hvb+f2229n6dKlvPzyy+zcuZMnnngipo3mkxRFbCRCfX09dXV1PPvss5jNZtavX89ll11GdnY2+/btQxAEXC4XFRUVI1KlkkURHgonC+Eio7Ozk8bGRnJycqiqqsJgMPDmm2/ywAMPYDQaueqqqzh+/DgvvfQSN910E9dcc80E3v2kQREb0Tml56n29na2bdvG9u3bUavVgch8eEqw2+2mpaUFq9Wa0ta6fuHhfLsn8LOMZfF5ByQlRMLSs372f0vZ9k5kry2zycvO+z+Or2gbxkyB+n/3VzHgPfE7zCzv5zs31Md5EYWJJFxkdHV10dDQQEZGBjU1NRiNRvbu3cv999/PwMAAd955JytWrDjVI+7hKGIjWY4dO8bTTz/N5s2b8Xq91NbW8thjj1FWVpb2nddUCQ9FdCiMJ8Eiw29U1tTURF5eHlVVVWi1Wv7617/yk5/8hMLCQr7zne+wcOHCkO+TLMtKZGMY5U2IjjJPfUZLSwvbtm3jmWeewWAwsH79eiwWC2+++Wag1qOoqChtzRXkSxaNeUwsQmRM8TFKIbrbp+KyB6/G5Rm5CHzgvz5l2bzeCK8KI876ir/vNfCrHbWoPjPsuHZVExed0TPGqxQmA+Eiw2q10tjYSFZWFtXV1WRkZPDee+9x//33A3DHHXdw9tlnK/NSZBSxkSxHjhzhiiuuYP369Vx44YW8/vrr7Ny5k9LSUjZu3Mjq1avHxaQlVuGhiA6FiSJcZLS2ttLS0oLFYqGyshKNRsPLL7/MQw89xPTp0/n2t7/NnDlzlMF7dJQ3JzrKPBWG1+vlV7/6FQ8//DAqlYra2lrWrVvH2rVrx82xOBbh4SeaABkhOmLsdvWTl5ex4x+hbYNn17h5/PZPRn9hEkXcNz1UQ68zG2SZ396+D71W+VhOZoJFhj+1sKmpiZycHKqrq9Hr9bzxxhs88MADmEwm7rzzTpYsWaLMU6OjiI1kEUURn883oh5j//791NXV8fzzz1NdXc2GDRu45JJLQtzC00UswiOa6FCKyRVSTbDIEEWRlpYW2traKCoqoqKiApVKxc6dO/npT3/KwoUL2bRpEzNmzJjAOw5l27Zt/OAHP+DgwYO89957LFkS+fv18ssvc/PNNyOKIl/5ylfYtGnTeNyeMsNFR5mnwvjwww/Ztm0bX/va16ioqKC+vp6tW7fy3HPPYTab2bBhA5dddtm45ZbLlyyieX9osXTFvMhdoyIJj3hFh6tgLpfeehqezy6pUsG221LSEQAAIABJREFUH+2jKC9CwXaKukS9s8/IL3fMIMfo4RffTMBNXWFcCBcZ7e3tNDc3k5+fT2VlJVqtltdee40HH3yQ4uJi7rjjjhERd4WoKGIj3ciyzCeffMKWLVt48cUXqa2tZePGjaxatWpcHCPHEh6K6FBIB+EeGT6fj+bmZjo6OigpKQm06Ny2bRuPPvooZ555JrfffjuVlZUTcbujcvDgQVQqFf/xH//BT37yk4hiQxRFZs6cyf/93/9RXl7O0qVL+dOf/sTcuWlva6nMdNFR5qk4OHr0KHV1dezYsYOioiLWr1/PpZdeislkSts1ZVmmt7eXpqYmzJeNrMFKWHgEiY7wzlI//sMM/vLmsJj6wvJe7rju05EXSHE72psfmcH0Mhc3XdmS0vMqJE+wyBBFkba2NlpaWigsLKSiogKNRsNLL73Eww8/zPTp07njjjuYM2fOBN5xKHa7nauvvpqGhgaqq6upq6uLGKW87bbbePHFF5EkiVWrVvHTn/50PIWSIjbGE1mW+eijj9i6dSsvvfQSc+bMYePGjaxcuXJcXCSjCY9EU6tAER0KoYSLDK/XS1NTE1arlbKyMsrKyhBFkT/96U/8+te/5oILLuBb3/pWUm06x4sVK1ZEFRtvv/02P/jBD3jllVcAuOeeewD49re/ne7bUsRGdJR5KkEOHTrE1q1b2blzJ+Xl5WzYsCGlKcH+9JTm5mYyMjKoqqoK+OoANFWMXOzHKjzGEh1DToFLb12M0SCy494P0QbbHqTJ8+L9g0Z0Olg43ZGW8yvET0vXfHJzc1GpVCER9+LiYsrLywMR90ceeYRFixZx++23T6qIu5/bbruNvLw8Nm3axL333ktPTw/33XdfyDFvvfUWt956K3v27AHg7LPP5p577mHFihXjdZuK2JgoZFnmgw8+YMuWLbz66qssXLiQDRs2cMEFF6DTjW0+FC8DAwM0NTUxODhIRUUFVTevHXFMMqIDFOFxKhMuMjweD42NjXR3d1NeXk5paSkej4f//d//5Xe/+x2XXnop3/zmN7FYLBN0x/EzmtjYvn07L7/8Mo8//jgATz31FO+++y6/+MUv0n1bitiIjjJPpYD9+/ezdetWnn/+eWpqati4cSMXX3xxQinBHo+H1tZWOjo6Yu58FUl4QGTxESw8RhMd3//9LE6f2cO65dYTxyjmeic9DulcJEmip6cHq9VKT08PKpUKr9dLeXk5lZWVSJIUiLifddZZ3HbbbZMy4u5n1qxZvP7665SUlNDe3s6KFSs4fPhwyDFvv/023/jGN3jzzTeRZZlzzz2Xp556ajwjNFHnqVPS4nA8EQSBJUuWsGTJEmRZ5r333mPLli18//vf5/TTT2fDhg2sWLEiKUt7f5u2pqYmVCoVVVVVmM1mBEHAu+39wHH+iIf4XHtUwSG/vW9MwSE3vjH8uymi45QhXGS4XC4aGxvp6emhqqqK6dOn43Q6efTRR3nqqafYuHEjr7/+elR/moli5cqVdHR0jPj53Xffzbp16ybgjhQUJgfz5s3jhz/8IXfddRf79u1j69atPPDAA8ycOZMNGzZw0UUXjSkYBgcHaWpqor+/n/Lycs4444yYO19VNp/wpQgWHv8/e3ceFmW5vwH8HhZZFVBxZT0ZKJsoIpqyeHCPTKCTmmvqz6VNO26opXY6lRWhlZ20smwxGTS3zN1ERe2Ya1qalsMmsgrINsDMPL8/ODOxDaAwzAD357q4lHeW95mk98v9PlvluR7q4KFeZtdisF3V3c3Vf3nUGeLODbwS+jtMndz/OgmDRqtWeaiUkZER2rdvj9zcXEgkEnTs2BFCCLz88svIzc1FRkYGwsLC8MMPP6B797q3DzAEGRkZmnZ269YNGRkZNZ4zePBgDBs2DN27d4cQAi+88ILBDAVj2GhGEokEAQEBCAgIgEqlwtmzZyGVSvHKK69g4MCBCA8PR1BQUIO3uVePO7xz5w5sbGzQu3dvWFlZaX1+5eBhvPUJrc8TZ69WtLeBoaMyBpDWpXrIKCkpgUwmQ0FBAVxcXODm5oaCggLExMRg+/btmDx5Mk6fPl1lqIQhOXr0aKNe37NnT6SkpGi+T01NRc+ePRvbLCKDIZFI4OPjAx8fH/z73//G5cuXERsbi7feegseHh6IjIxEaGioZkiwEAI5OTlITk4GADg5OTV6dbmGBA8AcETVvT3UwUN9ZhMAwrii40vSszeQ87/3ZehoVarv9l1aWoqkpCTcu3cPjo6OCAgIQGlpKb766iukp6cjKCgIdnZ2OH78OBYsWIC4uDg9tbyqum6GVSaRSGr9/+uPP/7A9evXkZpaMWdoxIgROHXqFAID9f97GYdRGQClUonTp08jNjYWp06dwuDBgxEeHo6hQ4fWeldILpcjJSUF2dnZmnGHjekZMa8jeAANG1ql9bUMHy1S/IVO6NSpE7p06QJra2sUFxdDJpOhpKQELi4u6Ny5M/Ly8vCf//wHe/fuxcyZMzF37txmWYVN1+oaRqVQKODm5oZjx46hZ8+e8Pf3x7fffgtPT09dN4vDqLRjnWoGQgicP38eUqkUhw8fhqenJ2xsbHD9+nVER0fD2dlZ58u/Jzt64Nd7f8179OxYqvm7utejoUOsNBg8WqzqIUMulyMxMRH5+flwcnJC165dUVJSgs8//xzffPMNnnrqKbz00ktVJlYXFBTodHGEptKQYVTvvvsu5HI5Xn31VQDAv/71L5ibm2Pp0qXN1UzO2WgplEolTpw4AalUijNnzmDIkCGIjIzE4MGDcfr0ac3dJF3sWg7UHzwqe9gQwgBiuNQ9GeXl5cjOzkZaWhru378PY2NjdOjQAZ6ensjLy8OGDRtw+PBhzJ07FzNnzmySnYj1bdeuXXjxxReRlZUFW1tb+Pr64tChQ0hLS8Ps2bOxf/9+AMD+/fuxcOFCKJVKzJw5EytXrmyO5jFsaGdQdUoulyMoKAilpaVQKBR46qmn8Nprr0Emk2HixInIycmBn58fvv76a53M29O1e/fuITo6GnFxcXB0dER+fj58fHwQHh7e6CHBD+KAVT/N35skdAAMHi1E9ZBRXFyMxMREFBYWwsXFBfb29igoKMAnn3yC7du3Y8qUKZg/f77B9rg3xJIlS9CpUyfNBPF79+5pNhpUk0ql+PTTT3Hw4EEIITB69GgsXLgQTzzR8N/rGolhoyVSKBQ4evQooqOjcfnyZTg6OmLJkiV44okndLYDbGUPEjy04YTzlqH6cKn8/HzIZDKoVCo4OztDpVLhjTfewIEDByCXyzFlyhT861//ahF3hFoJhg3tDKpOCSFQVFQEa2trlJeXY+jQoXj//fcRExODiIgITJw4EfPmzUPfvn0xf/58fTf3gWVlZeHgwYOYMGEC2rVrB5VKhTNnzkAqlSI+Ph4DBw5EREQEAgMDGzwkuLEOWPWrEjiAqhPL1cFDW+gA2NvRElQPGYWFhUhMTERJSQlcXV3RqVMn5Obm4uOPP8b333+PmTNnYs6cOa2ixz0nJwdPP/00kpOT4ezsjLi4OHTs2BHnz5/Hxo0b8dlnn0GpVOK5557DyZMnIZFIMHr0aMTExDRnMxk2WiIhBMaMGYPevXtj/vz5kMlkiI2NxcWLFzFs2DBERkbCz8+vWdZQNt/6BJS77zbouXWtdlVZbUGEwaN5VQ8Zubm5kMlkMDIygqurK2xsbJCSkoKYmBhcuHABL730EhwdHbF3715cuHAB8fHxTd67RrVi2NDOYOtUcXExhg4dio8//hiPP/440tPTYWJiUmMJ5dZCqVQiISEBUqkUp06dwmOPPYbw8HAMGTJEpzfICgoKkJSUhOLiYnSLnFHlsdpCB8DejpakesgoKCiATCZDeXk5XF1dYWdnh6ysLGzYsAFHjhzBvHnzMHPmzGbZaoCqYNhoqZRKZY2LdGlpKY4cOQKpVIorV65g+PDhiIyMhK+vb7MEj8r7ePx6Uq75u2dQw4bS1BZGGDyaV+WQIYTAvXv3IJPJ0K5dO7i6uqJ9+/b4888/8d577+HGjRtYvHgxwsPDm6VHjWrFsKGdwdUppVIJPz8//PHHH3j++eexZMkSDBo0CH/88QcAICUlBWPGjMG1a9f03FLdUSgUOHnyJGJjY3H27FkMHToUERERGDx4cJPcoFBft5KSkiCRSKqswqimnlxefflcho6WoXrIyM/Px+3btwEArq6usLW1xd27d7F+/XokJCTgxRdfxJQpUwxmeOL27duxZs0aXL9+HefOnat1HiAAHDx4EAsWLIBSqcTs2bMRFRXVzC1tMgwbrZVcLsfBgwchlUpx/fp1jBgxAhEREfDx8WmW4HGzq1eDn1tbGGlI8GDoaDrVQ0Z2djYSExNhYWEBV1dXWFpa4saNG4iOjkZycjKioqLw+OOPG0TvRUMu3CkpKZg2bRoyMjIgkUgwZ84cLFiwQA+tbXIMG9oZbJ3Ky8tDeHg4Xn/9dcyYMaNNhY3KysvLcfz4cUilUpw7dw7BwcGIjIyEv7//A19bVCoV0tPTkZKSAmtr6wZPTBejfWsca0joABg8mlv1kKHucTc2Noarqys6dOiA5ORkxMTE4OLFi3j55ZcxYcKEZhu211DXr1+HkZER5s6dq3XREaVSCTc3Nxw5cgQODg7w9/fHtm3b4OHRIn/GGDbagpKSEuzfvx9SqRS3bt3CqFGjEBkZCQ8Pj2YLHnsKa04OfNK6vMax6sGDvR26VT1kZGZmIikpCdbW1nB1dYW5uTmuXr2Kd999F/fu3cOKFSsQGhpqECFDrSEX7rt37+Lu3bvo378/CgoK4Ofnh927d7fUC3dlDBvaGXSd+te//gULCwu8/fbbrX4YVUOUlZXh2LFjkEqluHjxIv7+978jIiKi3iHB5eXlSE1N1WwU6Ojo+NDDZKoHD4YOw3D6QnuUlZWhc+fOsLe3R1lZGRITE2FmZgZXV1dYW1vjjz/+wHvvvYfff/8dS5Yswfjx4w2+x72uFQ6rXwveeustAMDy5cubtY1NhJv6tQUWFhaIjIxEZGQkioqKsG/fPqxduxaJiYkYM2YMIiIi0Lt37/rf6CEUFxdDdWIHAvPy4ODggG3uf00urxxA1MFDPfxKHToqzwdRBw/1fh/AX8Gj8t4eDB71qxwyVCoVMjIykJSUBDs7O/j4+MDMzAznz5/HO++8A4VCgRUrViAoKKhZwumDasjmRN27d9dsfNS+fXv06dMHd+7caQ1hg1qIrKwsmJqawtbWFiUlJThy5AiWLVuGYcOGYceOHZg4cSK+/PLLNruJZLt27TBmzBiMGTNGMyT4k08+wdWrVzF8+HBERERUGRJcXFyM5ORk5OXloWfPng+0UaA2koOXNX+vHjzEL3l/BY5bSVUCh7hzo+L16tCR89d+IAweD0/dk9GvX0WolMlkuHjxIoQQKC4u1gyLWrduHVJTUxEVFYWxY8ca1M2wh3Xnzh04OjpqvndwcMB///tfPbZIN9p02NA25OLevXuYMGECEhMT4eLigri4uCrrMrcEVlZWmDBhAiZMmICCggJ8//33eO2113Dnzh08/vjjiIiIwKOPPtro8+Tn5yMxMRHl5eVwcnKCu7s7JBIJlhRd0jzn3UpLFKqDh7bQAfwVPCr3dtS20SB3MteueshIS0tDamoqOnXqhH79+qFdu3Y4c+YM3n33XZiZmWHVqlUYOHCgQYaMh5WYmIhLly4hICBA302hNuTu3buYPn06lEolVCoVnn76aYSFhcHDwwMTJ07EK6+8gn79+mHWrFn6bqremZmZISwsDGFhYZDL5Thw4AA2bNiA69evo2/fvvjzzz8xevRoTJkyRVNbmprk4GWoZx6avzYMAP7aHNDHtiJwAHWHDoDB4yFUHi4lhNDcDOvQoQMCAgJgamqK2NhYfPbZZ7h58yaCgoKwatUqDBs2zGCCRl0b8bXVGwq1adPDqLQNudiyZQs6duyoWc84NzcXb7/9tr6b2yTy8/OxZ88exMXFITMzE2FhYYiIiMDf/va3Br+Heqx/UlISTE1N4ezsDFtb2/pfiKrBA2jaIVZA2w4e1VeWUiqVuHPnDu7cuYMuXbrAyckJxsbGOHHiBKKjo9G5c2esWLGi2RYWaIiGXLjr6pJWKywsRHBwMFauXImIiAidtbcZGcY/kGFq1XVKTdvNsTVr1uDTTz+Fvb09AODNN9/E2LFj9dzah3fgwAG8+eabUCqV6NatG2QyGUaPHo2IiIjm2DxTQx08qgyrAmoMrVKrMcRKjcGjhsohQz0HJzk5GXZ2dnB2dq7S465UKrFixQoMHjwYCQkJ2LlzJxYuXIhHHnlEj5/gwXAYVRsPG9U9+eSTeOGFF/DCCy/Uu1Nja5Cbm4vdu3cjLi4Oubm5GDduHMLDw+HsXPvFVKlU4u7du0hNTYWNjQ2cnZ0btX51fZPLH3ZCOdC2Qkf1kKFQKJCamoq7d++iW7ducHR0hJGREQ4dOoSYmBi4urpi+fLlzTaXp6nVFzbKy8sRFhaGUaNG4Z///Gczt05nWt4/VPNpE3VK282xuLg4WFtbY/HixfpuYpM4ePAg3N3d4erqCgAoKirC999/j7i4OCQlJWHMmDGIjIyEu7t7s7XJYld4zYNaQocaw0dV1Sd9q3vcU1JSYG9vDycnJ5iamuL06dOIjo6Gubk5XnnlFfj7+7fIOlVZXTVLoVDAzc0Nx44dQ8+ePeHv749vv/22WYN1E2LYqE9iYiKCgoJw7do1ODk5IS+vohtVCAE7OzvN961VTk4Odu3ahbi4OBQWFuLJJ59EeHg4HBwckJmZiezsbOTn56NLly5wdHRs8qXl6goejQkdQOsNHtVDRnl5OVJSUpCRkYEePXrAwcEBAPD9999j/fr18PLyQlRUFNzc3PTR3CZT14VbCIHp06ejY8eOWL9+vR5apzMtu9rqVpupU5Wpb46dPn26VYWNuhQUFGDv3r2Ii4tDWlqaZkhwr169dH7exMTEit3hU9+s+8l1hJC2OMG8esio3OPetWtXODo6wtjYGPHx8YiOjoa9vT1WrlyJvn37tviQsWvXLrz44ovIysqCra0tfH19cejQIaSlpWH27NnYv38/AGD//v1YuHAhlEolZs6ciZUrV+q55Q+NYaMu1Ydc2NraVgkXdnZ2yM3N1WMLm1dWVha+++47fPXVV8jIyIBKpcJbb72Fxx9/vFlWfdAWPBg6KvwiexRdu3aFtbU1JBIJysrKkJycjKysLDg4OKBHjx4QQuC7777DRx99BH9/fyxbtgwuLi76bnqjNOTCnZCQgMDAQHh7e2vG9Lb0YSX/07Krrm61iTpVWeWbYzExMdiyZQs6dOiAAQMG4L333mtxcwwfhnpIsFQqRXZ2tmZIsLpHpLGq7+Ph4uICW1vbKr8Aq3s8qszx0KaN7eFRPWQoFAqkpKQgPT0d3bt3h4ODA4yMjHDw4EHExMTgkUcewfLly9GnT58WHzLaMIYNbWobcuHu7t4mhlHV5YUXXsDVq1cxffp0FBUV4bvvvoNKpUJ4eDiefPJJdOvWrVnaUVvwaGzoAFpm8Cg2HgOFQoHs7GxkZmaisLAQxsbGUCgUcHFxQffu3aFQKBAbG4tNmzYhODgYS5YsQc+ePfXddGo8Vl/tWn2dqqz6zbGMjAx07twZEokEr776Ku7evYvPP/9c381sVrm5uZqe+fz8fDzxxBOIiIiAk5PTA7+XSqVCZmYmkpOTYWVl1eB9PNRzPKqra85HrUOtWnjwqB4yysvLkZycjMzMTPTs2VNTj/bu3Yv3338f3t7eiIqKapIFa0jvGDZqo23IxZIlS9CpUyfNBPF79+7hnXfe0WNLm9/du3c1S4iqpaWlYceOHdixYweMjY01wUM9MVHXKgcPbbuV1xY6gJbd21F9uFRJSQmSkpKQl5cHOzs7yOVyzJkzB926dcPt27cRGRmJxYsXo0uXLnpqMekAw4Z2rbpOVVbffKTExESEhYW1mc0Ca5OTk4OdO3di+/btKCwsxPjx4zF+/HjNsFJtlEol0tLScOfOHXTs2BFOTk4wN6+9ztSntuBR30Tzlt7bUT1klJWVISkpCTk5OZoed5VKhR07duCjjz5CQEAAli1bpnWOqL608c1jG4thozbahlwEBATg6aefRnJyMpydnREXF4eOHTvqubWGJTU1FXFxcdi5cyfMzc0RERGBJ554Ap06dWqW85v+Q/tKRMCDhw7A8IJH9ZBRXFwMmUyGoqIiuLi4wN7eHiUlJfjiiy/w9ddfo3///lCpVLh06RImT56MqKgoPbWcdIBhQ7tWXafUtN0cq3xjaN26dfjvf/+L2NhYfTXToKiHBG/fvh1lZWWauYiVb6SVlZUhJSUFmZmZ6NatGxwcHGBqWnNz2odVPXjUtWkg0PJ6O+6XPVZl5265XI6kpCTk5ubCyckJ3bp10/S4b9y4EcOGDcOSJUvQo0cPPbZauza+eWxjMWyQ7iQlJSEuLg67du1C+/btER4ejieeeKLZxg3XFTxaYuioHjIKCwshk8lQWloKFxcXdOrUCYWFhfj0008hlUoxadIkPP/887CxsQFQMTY2LS3toYYQkMFi2NCuTdQpbTfHtm3bhsuXL2vmFWzatKlGrzQB6enpmuAhhEBISAh+/fVX9O3bFxMnTkT37t11vneD+WvDap/X0QJDR0Z+P2RkZCA7Oxvm5uaws7NDYWEhCgsL4eLigi5duqC0tBRff/01Pv/8c4wdOxYvv/xyi+lxb8gS62rqxRpGjBjRDC0zaAwb1Dxu376tCR6dOnVCeHg4wsLCNL8I64JKpcLdu3eRkpKCwPdeqPG4tsABGFboqB4y7t+/D5lMBoVCAVdXV82qaBs3bsTu3bvx7LPPYu7cubCysmrWdpJeMGxoxzrVSK15g9vqrly5gjVr1uCXX36Bi4sLVCoVIiIiMG7cuGYbEgw0fDldQwsd1YdLFRUV4datWygoKIBEIsHx48dhaWmpGTIVGRmJl156qcWNDmlo2Ki8WEOHDh2aqXUGi2HDkMycORP79u1Dly5dNGNrW9vGTABw69YtSKVS7NmzB926dUNERATGjh2L9u3bN8n7KxQK3LlzB2lpabUuyVu5x+NhAweg+9BRPWTk5eVBJpMBAFxdXWFra4usrCx89NFHOHjwIObOnYtZs2Y99HhiXWnIWFc1pVKJAQMGoGfPnti3b18ztrLFYtjQjnWqkdrSBrdffvklHnnkEQwdOhRARdDavn07vvvuO1hYWDT7kGCgWvBoaOAAmjV0VA8ZBQUFkMlkKCsrg6urKzp27IiCggLExMTg5MmTuHv3Lh555BFMmDABM2bMgJmZWbO1tT7cPFZnGDYMycmTJ2FtbY1p06ZVCRutea30GzduQCqVYu/evXB0dERERATGjBnzUHflK4+x7dGjB3r27FllzGhtzLc+Ue/7NnfoqBwyhBDIzc2FTCaDiYkJXF1d0aFDB6Snp+P999/HiRMn8MILL2DatGlNvsdJU2nIWFe1mJgYnD9/Hvfv32fYaBiGDe1Yp5pYW9vgVi0xMVHTM29jY6Ppmdd1b456md3ExEQMTf63Qe1SXj1k5OfnQyaTQaVSaXrcc3Nz8fHHH2PPnj1VetyTkpKwd+9ePPfcc82ybH5TaqObxzYWw4ahqb5qSGsPG5X9+uuvkEql2LdvH1xdXREZGYmRI0fWuxt5SUkJkpOTkZubC0dHx4ceY1tf8NB16KgeMnJyciCTyWBhYQEXFxdYW1sjJSUF69atw88//4yFCxdi0qRJ9QYqQ1HfRTo1NRXTp0/HypUrERMTw7DRMAwb2rFONaG2vsGt2p9//om4uDjs3r0bnTt31gSPphwqI4RAZmYmkpKSYGVlBRcXlyo34CyuLazxGq2BA2jS0FE9ZOTl5eH27dswMjKCq6srbGxskJWVhQ0bNuDQoUMG2+P+sNro5rGNxbBhaGoLG21tYyYhBK5evYrY2Fjs378fbm5uiIyMxIgRI6pcsAoLC5GYmIji4mI4OzujS5cuTbbpj7bgUV/gAB48dFQPGVlZWUhMTIS1tTVcXFxgaWmJP//8E+vWrcOvv/6KRYsWITIystXdEXrqqaewfPlyFBQUIDo6mmGjYRg2tGOdaiLc4LZ2N2/eRFxcHPbs2YPu3bsjPDy8UUOCK88ztLOzg7Ozc72/pFcOHroMHJVDRuUeF1NTU7i6uqJ9+/ZIT0/H+vXrcfLkSbz44ouYOnWqwfa4P6g2vnlsYzFsGJrqYaOtb8wkhMClS5cglUpx8OBBeHp6wsvLC4cPH8bixYvh6+sLOzs7ne4sWlvwaEjoAP4KHiLpVJXjJX97s8r3QghkZGQgKSkJNjY2miLz+++/47333kNiYiKWLVuGsLAwna+M8jAaO9Z137592L9/P/7zn/8gPj6eYaPhGDa0Y51qAtzgtmGuX78OqVSK77//Ho6OjoiMjMTo0aMbNCRYqVQiNTVV6zzDhrK4trDJA0f1kJGdnY3ExERYWFjA1dUVVlZWSElJ0QyBffnllzFx4sQW0+NOzYJhw9DUtflSW96YSQiBQ4cO4dVXX0VRURFsbGzQq1cvREREYNiwYc1290QdPBoaNgBAnL2q+bt88vdVHlOpVEhPT0dycjI6duwIZ2dntGvXDteuXcM777yDnJwcLF++HCNGjDDIkPEg6goby5cvx9dffw0TExPI5XLcv38fERER+Oabb/TQ0haFYUM71qlG4ga3D+fatWuaIcGPPPIIIiIiMGrUKFhYWFR5XuVdtBs6z7ChLHM31v5AAwNH9ZChHtZVvcc9JiYGv/32GxYvXoyIiAiD6XHnwiQGhWHD0FQPFNyYqYJKpcKqVaswbdo0uLm5QaVS4dy5c4iNjcWxY8cwYMAAhIeHIzg4uEk3XqqLxe0VtR5Xhwvl7ruaY+Xbz1d5jnpX2tTUVNjb28P0s92pAAAgAElEQVTJyQmmpqa4ePEi3nnnHZSWlmLFihUIDg7Waa9Nc2rokoHs2XggreOHQzdYpxqJG9w2jhACv/zyC6RSKfbv3w93d3dERkaiV69e2LFjB4YPH67ZRVuXN5OqBI86wkb1+RgqlQoZGRlITk6Gra0tnJ2dYWZmhhs3buC9995DUlKSwfa4c2ESg8KwYUgmTZqE+Ph4ZGdno2vXrnjttdcQHx/PjZnqoVKpcObMGUilUsTHxyMgIADh4eEIDAxstq5cdfBQLDuMX0/KNcfLjsWioKAAHTt2RNeuXWFtba1Zlrdr165wdHSEiYkJzp49i3fffRempqZYsWIFBg8e3GpCRkPGulbGsPFAWscPiW6wThm4trLcO1ARPPbs2YPVq1cjPT0dAQEBmDZtGkJDQ5t1+VdLo5M1juUU+Vfpdak8d6RTp05wcnKq0uN+7949rFixAqGhoQYXMqrjwiQGgWGDWhelUomEhATExsYiISEBjz32GCIiIvDYY4/prXtXpVIhMzMTiYmJKCoqQocOHVBSUoLBgwfj9OnTiI6Ohp2dHVasWIH+/fu3mpBBzYI/LNqxThm4trTce3JyMqZPn46lS5di1KhROH/+PKRSKY4cOQJfX1+Eh4fj73//e7P1zAMVy8VnZmYiMzMTCoUCnTt31gyZ6tKlC5ycnGBiYoILFy7gnXfeQVlZWYvrcefCJAZB6w8LZ/ZQi2RsbIzg4GAEBwdDoVDgxIkTkEqlWLx4MQIDAxEREYFBgwY1292YymNyHRwc0K1bN6Snp+O5557DtWvXYGtri5UrV2Ly5MkGM9aViKg5BAUFITExUd/NaBZOTk44fvy45vuBAwdi4MCBUKlUmuHRq1atwoABAxAREYHg4GCd98y3a9dOU5eSkpKQmpoKY2NjpKam4uDBg/D29sbWrVvRrl07rFy5EoMGDTKokNGQhUnqou5V8/PzQ3x8vA5aSPVhz0YbUltX9r179zBhwgQkJibCxcUFcXFxLXrJ3fLychw/fhxSqRQ///wzgoODERkZCX9/f51cPEtLS5GcnIycnBzN3h9CCPzwww9Yv349evfujUWLFiE1NRXbt2+HnZ0d3nvvvSZvB7V6hlP5DQ/rVAvA5d7/olKpcPr0aUilUpw4cQKDBg3SDAnWxc2o8vJypKSkICMjAz169ICDgwMkEgn27duHTz75BLdu3UKnTp3wzDPPYMqUKejRo0eTt0HXuDCJQeAwKqq9K3vp0qXo2LGjZrWR3NxcvP3223puadMoKyvDsWPHEBsbi0uXLuHvf/87IiMjm2QIk1wuR2JiIvLy8uDs7IyuXbtCpVJh586d2LBhAwYMGIBly5bB1dW1iT4NtXEMG9qxTrUAXO69dkqlEqdOnYJUKkVCQgKGDBmC8PDwJhkSXFZWhuTkZGRlZWkmqEskEhw+fBgxMTFwdnbG8uXL4enpiZycHOzevRve3t4ICAhook/XfLgwiUFg2KAK1S/4bWUd9dLSUhw+fBhSqRRXr17FiBEjEB4eDl9f3wcKHsXFxUhMTERhYaFmg0GFQoHY2Fhs3LgRQUFBWLJkCRwcHHT4aR5cQ5cHzMvLw+zZs3Ht2jVIJBJ8/vnnGDx4cDO3lmrBsKEd61QLwOXe66ceEhwbG4uffvoJgYGBiIyMREBAwAMNCS4tLUVSUhLu3bsHJycndOvWDUII7Nu3D+vXr4eHhweWL18ONzc3HX6a5sGFSQwKwwZVqH5Rr7xDrBACdnZ2VXaMbY3kcjkOHDgAqVSKGzduYOTIkYiIiIC3t7fW4FFUVASZTIaSkhK4uLigc+fOKCsrwzfffIPPPvsMo0aNwqJFi9C1a9dm/jQN09DlAadPn47AwEDMnj0bZWVlKC4uhq2tbTO3lmrBsKEd61QLwOXeH0x5eTl+/PFHSKVSnD9/HiEhIYiIiKhzSHBJSQmSkpKQn5+v6XFXKpWaHnd/f38sXbqUPe6kKwwbVKGusAEAdnZ2yM3N1Vfzml1xcTH2798PqVSKP/74A6NHj0ZkZCQ8PCrWKL979y6ysrJQXl4OFxcXdOzYESUlJdiyZQu++uorjB8/HgsWLECnTp30/Ekapq6u5vz8fPj6+uL27ds6mxzo4uKC2bNn49ixY/j555/h6uqKrVu34tdff8Wrr76KrKws/OMf/8DGjRu5M21VDBvasU4ZOC733jhlZWU4evQopFIpLl26hNDQUERERGiGBGdmZiI7OxuFhYVwcXGBvb09ysvLIZVKsXHjRgQHB2PJkiXo2bOnvj9Ki8A69dC01ykhRF1f1MrIZDLh6emp+d7NzU2kpaUJIYRIS0sTbm5u+mqa3hUWFopt27aJ8PBw4e7uLvr27SvGjBkjUlNTRVFRkUhPTxdvvPGG8PLyEq+//rrIzc3Vd5MfWHBwsPj5559rfezSpUvC399fTJ8+Xfj6+opZs2aJwsLCJj2/s7Oz6NWrl/jtt99EWVmZmDx5svjb3/4m/u///k8UFhaKpKQkYW9vL7755psmPW8rUN+1ui1/URv37LPPCnt7+yq1LScnRwwfPlz06tVLDB8+XNy7d0+PLWw6crlc7N27VzzzzDPCzc1NDBgwQAQEBAiZTCYKCwtFTk6OeP/994WPj49YunSpSE9P13eTq4iLixMeHh5CIpForUVCCJGbmysiIyOFu7u76N27tzhz5kyztZF16qFpvU4b9i4tpHPjxo3Dl19+CQD48ssvG7SMXGtlZWUFf39/FBcX49FHH0VkZCRMTEwwfvx4jBs3DqGhoWjXrh3Onj2LV155xeCGFw0fPhxeXl41vvbs2dOg1ysUCly8eBHz58/HpUuXYGVlhbVr1zZ5O+fMmYM+ffrA1NQUzzzzDG7fvo033ngDVlZWcHJyQkhICM6fP1//GxERAZgxYwYOHjxY5djatWsRGhqKW7duITQ0VCfXMn0wMzNDQEAASktL4eDggLCwMDg7OyM8PByRkZEIDAxEdnY2fvzxR7z99tsGN7TXy8sLO3fuRFBQUJ3PW7BgAUaPHo0bN27gypUr6NOnTzO1sALrVNNi/08bUrkr28HBAa+99hqioqLw9NNPY/PmzXB2dkZcXJy+m6lXdnZ2ePfdd+Ht7a05lp+fj/Xr12Pp0qVVdl81NEePHm3U6x0cHODg4KBZieSpp57SSYGuPFTC0tISxsbGml2E1ccKCgqa/LxE1DrVto/Hnj17NHsqTJ8+HSEhIa1mpUUbGxssWrSoyuIdxcXFiI6OxksvvWRwN8Iqa0hoyM/Px8mTJ7FlyxYAFfuEtGvXTsctq4p1qmkxbBgwFxcX/Pvf/8aUKVOa5P22bdtW6/Fjx441yfu3Bh07dkTHjh2rHLOxscHq1av11KLm061bNzg6OuL333+Hu7s7jh07ppm7QkRUm6auU00lIyND8wtjt27dkJGRoecWNR0zM7MaqwRaWlpi1apVempR05LJZLC3t8ezzz6LK1euwM/PD++//z6srKz03TR6SBxGRdQG7Nq1Cw4ODjh79iwef/xxjBo1CgCQlpaGsWPHap734YcfYvLkyfDx8cHly5exYsUKfTWZiKhJSCQSg9oRu7VrKUN6qfmwZ4OoDQgPD0d4eHiN4z169KiyDrmvry/HoRJRi9e1a1fN8rp3795Fly5d9N2kNqOlDOml5sOw0UIUFxdj0qRJUCgUkEqlsLa21neTGsXFxQXt27eHsbExTExM+AtuG1F9XHVISAgUCkWVY+pxukTUshhSnVIvfhIVFdXmFz9pafQ9pJd1qulxGFULkJ6ejuDgYPTo0QN79+5t8UFD7fjx47h8+TKDBhFRC6fPOjVp0iQMHjwYv//+OxwcHLB582ZERUXhyJEjePTRR3H06FFERUU1W3tIOw7pbZu4qZ8Bc3FxwaRJkxAbG4v58+dj6dKl+m5Sk3FxccH58+fRuXNnfTeFqCXggHPtWKf0qDXXqbqwd56oBu4g3hK5uLhALpejc+fO+Omnn1pNjwYAuLq6ws7ODhKJBHPnzsWcOXP03SQiQ8awoR3rlB615jpVF94wI6pBa53iMCoDt3btWnh7e2PEiBHIzc3Vd3OaTEJCAi5evIgDBw7go48+wsmTJ/XdJL3Yvn07PD09YWRkVOedsXXr1sHT0xNeXl6YNGkS5HJ5M7aSiEi71lqniKhpMGwYOBMTE2zduhVeXl4ICQlBZmamvpvUJHr27AkA6NKlC8LDw3Hu3Dk9t0g/GrKb6p07d/DBBx/g/PnzuHbtGpRKJWJjY5uxlURE2rXWOlUXiUSCkSNHws/PD5988om+m6MzvCFGTYFhowUwMjLCp59+itDQUAQGBiI5OVnfTWqUoqIizc6bRUVFOHz4MLy8vPTcKv3o06cP3N3d632eQqFASUkJFAoFiouL0aNHj2ZoHRFRw7S2OlWfttI7zxti1BS49K0Bq778WkxMDGJiYvTTmCaUkZGh2fNBoVDgmWeewejRo/XcKsPVs2dPLF68GE5OTrCwsMDIkSMxcuRIfTeLiKjV1qn61NY7X9cv5C1Vnz59GvQ89Q0xU1NT3hCjGhg2qNn97W9/w5UrV/TdjGYzfPhwpKen1zj+xhtvNGjt99zcXOzZswcymQy2trb4xz/+gW+++QZTpkzRRXOJiKgORUVFUKlUaN++vaZ3ftWqVfpult7whhjVh2GDSMcau5vq0aNH4erqCnt7ewBAREQEzpw5w7BBRKQHra13njfESNcYNogMnJOTE3766ScUFxfDwsICx44dw4ABA/TdLCKiNqm19c7zhhjpGieIU5tz8OBBuLu7o1evXli7dq1e29KQ3VQDAgLw1FNPoX///vD29oZKpeK+JERErZgh1an6VL4hJoTAsWPHGjzXg9oGbupHbYpSqYSbmxuOHDkCBwcH+Pv7Y9u2bfDw8NB304jqwk39tGOdolbFkOrUrl278OKLLyIrKwu2trbw9fXFoUOHkJaWhtmzZ2P//v0AgNWrV0MqlcLExAT9+vXDZ599BjMzs2ZvL+kVdxAnAoCzZ89izZo1OHToEADgrbfeAgAsX75cn80iqg/DhnasU9SqsE5RC8UdxImAivXAHR0dNd87ODjgzp07emwRERHRX1inqLVh2CAiIiIiIp1g2KA2pWfPnkhJSdF8n5qaqtmciYiISN9Yp6i1YdigNsXf3x+3bt2CTCZDWVkZYmNjMW7cOH03i4iICADrFLU+3GeD2hQTExNs2LABo0aNglKpxMyZM+Hp6anvZhEREQFgnaLWh6tREREZPq5GpR3rFBGR/nE1KiIiIiIial4MG0REREREpBMMG0REREREpBMMG0REREREpBMMG0REREREpBMMG0REREREpBMMG0REREREpBMMG0REREREpBP17SDOjaSIiMiQsU4RERkw9mwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOMGwQEREREZFOmNTzuGiWVhARUV0k+m6AAWOdIiLSP611ij0bRERERESkEwwbRERERESkEwwbRERERESkEwwbRERERESkE/VNECeiWpSXlyM1NRVyuVzfTaFWxNzcHA4ODjA1NdV3U4iohWOdIl14mDolEaLOhTy4ygdRLWQyGdq3b49OnTpBIuFCQdR4Qgjk5OSgoKAArq6u1R/mD5l2rFNEtWCdoqb2sHWKw6iIHoJcLucFnJqURCJBp06deBeSiJoE6xQ1tYetUwwbRA+JF3BqavyZIqKmxGsKNbWH+Zli2CBqoYyNjeHr6wsvLy/84x//QHFx8QO/x+zZs/Hbb78BAN58880qjz322GNN0k4iImqbWKcIYNggah5btwIuLoCRUcWfW7c2+i0tLCxw+fJlXLt2De3atcPGjRsf+D0+++wzeHh4AKh5ET9z5kyj26grCoWi2c8hhIBKpWrQa5VKpS6aRESkO6xTTYp16i8MG0S6tnUrMGcOkJQECFHx55w5TXIhVwsMDMQff/wBAIiJiYGXlxe8vLywfv16AEBRUREef/xx9O3bF15eXpBKpQCAkJAQnD9/HlFRUSgpKYGvry8mT54MALC2tgYATJw4ET/88IPmXDNmzMCOHTugVCqxZMkS+Pv7w8fHB5s2baq1bePHj4efnx88PT3xySefaI5bW1vj5ZdfhqenJ0JDQ5GVlaVp04IFCzR3w86dOwcAWLNmDaZOnYohQ4Zg6tSpkMvlePbZZ+Ht7Y1+/frh+PHjAIB169Zh5syZAICrV6/Cy8urxt00bW2Pj49HYGAgxo0bBw8PDyQmJsLd3R3Tpk2Dl5cXUlJSsG3bNnh7e8PLywvLli2r8nkWLVqEvn374uzZs4iKioKHhwd8fHywePHiB/43JSJqNqxTrFO6rFNCiLq+iAyWg4ODuHDhQq2PRUVFiXXr1jXoffz9/cW1a9ce6Ny//fZbw5/s7CxExeW76pez8wOdszorKyshhBDl5eVi3Lhx4j//+Y84f/688PLyEoWFhaKgoEB4eHiIixcvih07dojZs2drXpuXlyeEECI4OFj8/PPPVd6v+vvv3LlTTJs2TQghRGlpqXBwcBDFxcVi06ZN4vXXXxdCCCGXy4Wfn5+4fft2jXbm5OQIIYQoLi4Wnp6eIjs7WwghBADxzTffCCGEeO2118Tzzz+vaZO6rSdOnBCenp5CCCFWr14t+vfvL4qLi4UQQkRHR4tnn31WCCHE9evXhaOjoygpKRFKpVIEBgaKnTt3Cj8/P5GQkFCjTdrafvz4cWFpaan5HDKZTEgkEnH27FkhhBB37twRjo6OIjMzU5SXl4thw4aJXbt2aT6PVCoVQgiRnZ0t3NzchEqlEkIIkZubW/s/Yi20/GzVd61uy19EBot1inWKdUqwZ4NaptzcXKSlpaFPnz41HsvKysJXX32FuXPnAgBKS0sxa9YsODs7o3379vD19cWBAwc0z1+8eDFWrVqlu8YmJz/Y8QZS3+EZMGAAnJycMGvWLCQkJCA8PBxWVlawtrZGREQETp06BW9vbxw5cgTLli3DqVOnYGNj0+DzjBkzBsePH0dpaSkOHDiAoKAgWFhY4PDhw/jqq6/g6+uLgIAA5OTk4NatWzVe/8EHH6Bv374YNGgQUlJSNM8xMjLChAkTAABTpkxBQkKC5jWTJk0CAAQFBeH+/fvIy8sDAIwbNw4WFhYAgISEBEyZMgUA0Lt3bzg7O+PmzZswMjLCli1bMHXqVAQHB2PIkCE12lRX2wcOHFhlST9nZ2cMGjQIAPDzzz8jJCQE9vb2MDExweTJk3Hy5EkAFWOTIyMjAQA2NjYwNzfHrFmzsHPnTlhaWjb4vzcRtQ4PUqeAiutg9+7d0aFDB7i5ueGzzz7TPMY6VTfWKcOuU9zUj1qkq1evwsXFRfM/dGVbtmzB2LFjNY8pFAo4OjrixIkTcHJywv79+/H0009r3mPcuHGYN28e0tPT0a1bt6ZvrJNTRZd0bccbQT0WtiHc3Nxw8eJF7N+/H6+88gpCQ0MbXLjMzc0REhKCQ4cOQSqVYuLEiQAqekU//PBDjBo1Sutr4+PjcfToUZw9exaWlpYICQnRumRe5RUuqq92of7eysqqQW2+desWrK2tkZaWVuvj2toeHx9f4xwNPae5uTmMjY0BACYmJjh37hyOHTuGHTt2YMOGDfjxxx8b9D5E1Do8SJ0CgOXLl2Pz5s0wMzPDjRs3EBISgn79+sHPz491qh6sU4Zdp9izQS3SL7/8gl69euGll16Cvb09evTogSNHjgAADhw4gODgYM1zrayssGbNGri4uMDIyAhhYWFwdXXFhQsXAFT8z+fn54dDhw7pprFvvAFUv2NgaVlxvIkFBgZi9+7dKC4uRlFREXbt2oXAwECkpaXB0tISU6ZMwZIlS3Dx4sUarzU1NUV5eXmt7zthwgR88cUXOHXqFEaPHg0AGDVqFD7++GPNa27evImioqIqr8vPz4ednR0sLS1x48YN/PTTT5rHVCoVduzYAQD49ttvMXToUM1j6rG6CQkJsLGxqfUOV2BgILb+bzzxzZs3kZycDHd3d+Tn5+Oll17CyZMnkZOTozlHZQ1pe20GDhyIEydOIDs7G0qlEtu2bavys6ZWWFiI/Px8jB07FuvWrcOVK1fqfW8ial0epE4BgKenJ8zMzABU/OIqkUjw559/AmCdUmOdapl1ij0b1CJdvXoV58+fxz//+U+sX78er7/+Ot5++22MGDECV69ehbu7u9bXZmRk4ObNm/D09NQc69Onj+7+R/vfRDasXFnRJe3kVHEBVx9vQv3798eMGTMwcOBAABVLBvbr1w+HDh3CkiVLYGRkBFNTU3z88cc1Xjtnzhz4+Pigf//+mouj2siRIzF16lQ8+eSTaNeunea9ExMT0b9/fwghYG9vj927d1d53ejRo7Fx40b06dMH7u7umm5eoCIEnjt3Dv/+97/RpUsXzYUbqCis/fr1Q3l5OT7//PNaP+tzzz2H+fPnw9vbGyYmJtiyZQvMzMwwf/58PP/883Bzc8PmzZsxbNgwBAUFoUuXLprXNqTttenevTvWrl2LYcOGQQiBxx9/HE8++WSN5xUUFODJJ5+EXC6HEAIxMTH1vjcRtS4PU6eee+45bNmyBSUlJejXrx/Gjh2reYx1inWqxdapuiZ0NHimCFEzGzRokHjvvfc038fFxYnQ0FAhhBAmJibi+vXrtb6urKxMhIaGijlz5lQ5vmLFCs0kroZ4oIl3VKvqE/3UKk8GbIs4QZwTxKl1eNg6pVAoxKlTp8Trr78uysrKNMdZp5of61TtOEGcWj0hBK5du4YnnnhCc+zatWuadbjt7OxQUFBQ43UqlQpTp05Fu3btsGHDhiqPFRQUwNbWVrcNJyKiNuFh6xRQMYl36NChSE1NrXJ3n3WKWiqGDWpxZDIZAKBXr16aY5cuXYKvry8AwMfHBzdv3qzyGiEEZs2ahYyMDHz33XcwNTWt8vj169fRt29fHbecKissLKz1eHx8PAYMGNDMrSEiajoPU6eqUygUmjkbAOuUPrBONQ2GDWpxfvnlF3h7e1dZCeLSpUuai/DYsWNx4sSJKq+ZP38+rl+/ju+//77GyiByuRwXLlzAiBEjdN94IiJq9R60TmVmZiI2NhaFhYVQKpU4dOgQtm3bhtDQUACsU9SycYI4tThXr16tcncnOzsb6enp8PLyAgBMmzYNvr6+KCkpgYWFBZKSkrBp0yaYmZlVWTJw06ZNmDx5Mr7//nuEhISgR48ezf5ZiIio9XnQOiWRSPDxxx9j3rx5UKlUcHZ2xvr16zFu3DgAYJ2iFk0ihKjr8TofJDJUK1asQJcuXbBw4cJ6nxsQEIDNmzdrikBDXL9+vdaNmogaS8vPlqS25xIA1ilqoVinqKV60DrFsEH0EHgRJ11h2HhgrFNEtWCdIl150DrFORtELZREIsGiRYs030dHR2PNmjVNfp4333yzyvePPfZYk5+DiIhaH9YpAhg2iJrF1q2AiwtgZFTxZ7W9iB6KmZkZdu7ciezs7Ma/WR2qX8TPnDmj0/M1hkKhaPZzCCGgUqka9FqlUqmLJhERNRrrVPNoi3WKYYNIx7ZuBebMAZKSACEq/pwzp/EXchMTE8yZMwfr1q2r8VhWVhYiIyPh7+8Pf39/nD59WnN8xIgR8PT0xOzZs+Hs7KwpAuPHj4efnx88PT3xySefAACioqJQUlICX19fTP7fTrLW1tYAgIkTJ+KHH37QnHPGjBnYsWMHlEollixZAn9/f/j4+GDTpk21tr+286nf/+WXX4anpydCQ0ORlZUFAAgJCcGCBQvg6+sLLy8vnDt3DgCwZs0aTJ06FUOGDMHUqVMhl8vx7LPPwtvbG/369cPx48cBAOvWrcPMmTMBVEze9PLyQnFxcZU2aWt7fHw8AgMDMW7cOHh4eCAxMRHu7u6YNm0avLy8kJKSgm3btsHb2xteXl5YtmxZlc+zaNEi9O3bF2fPnkVUVBQ8PDzg4+ODxYsX1/8PTUSkY6xTrFM6rVN17finy90HiVqyB9mZ1dlZiIrLd9UvZ+fGtcHKykrk5+cLZ2dnkZeXJ959912xevVqIYQQkyZNEqdOnRJCCJGUlCR69+4thBDi+eefF2+++aYQQogDBw4IACIrK0sIIUROTo4QQoji4mLh6ekpsrOzNeepfl4hhNi5c6eYNm2aEEKI0pW8rvAAACAASURBVNJS4eDgIIqLi8WmTZvE66+/LoQQQi6XCz8/P3H79u0a7dd2PgDim2++EUII8dprr4nnn39eCFGxY+vs2bOFEEKcOHFCeHp6CiGEWL16tejfv78oLi4WQggRHR2t2WX3+vXrwtHRUZSUlAilUikCAwPFzp07hZ+fn0hISKjRJm1tP378uLC0tNR8DplMJiQSiTh79qwQQog7d+4IR0dHkZmZKcrLy8WwYcPErl27NJ9HKpUKIYTIzs4Wbm5uQqVSCSGEyM3NrdEG7iDOHcSJmgLrFOuUodQpLn1LpGPJyQ92/EF06NAB06ZNwwcffFBl/5CjR4/it99+03x///59FBYWIiEhAbt27QIAjB49GnZ2dprnfPDBB5rHUlJScOvWLXTq1EnruceMGYMFCxagtLQUBw8eRFBQECwsLHD48GH88ssv2LFjBwAgPz8ft27dgqura5XXazufkZERJkyYAACYMmUKIiIiNK+ZNGkSACAoKAj3799HXl4eAGDcuHGaz5+QkIAXX3wRANC7d284Ozvj5s2b8PHxwZYtW+Dj44O5c+diyJAhNT6Ttra3a9cOAwcOrPIZnJ2dMWjQIADAzz//jJCQENjb2wMAJk+ejJMnT2L8+PEwNjZGZGQkAMDGxgbm5uaYNWsWwsLCEBYWpvW/LxFRc2GdYp3SZZ1i2CDSMSenii7p2o43hYULF6J///549tlnNcdUKhV++uknmJubN+g94uPjcfToUZw9exaWlpYICQmBXC6v8zXm5uYICQnBoUOHIJVKMXHiRAAVvaUffvghRo0a1STnq7wpVuW/V/7eysqqQZ/z1q1bsLa2RlpaWq2Pa2t7fHx8jXM09Jzm5uYwNjYGUDGk4Ny5czh27Bh27NiBDRs24Mcff2zQ+xAR6QrrVOPOxzpVN87ZINKxN94ALC2rHrO0rDjeFDp27Iinn34amzdv1hwbOXIkPvzwQ833ly9fBgAMGTIEcXFxACrujuTm5gKouDNiZ2cHS0tL3LhxAz/99JPmtaampigvL6/13BMmTMAXX3yBU6dOYfTo0QCAUaNG4eOPP9a85ubNmygqKqryurrOp1KpNHdsvv32WwwdOlTzmFQqBVBxV8jGxgY2NjY12hQYGIit/xtofPPmTSQnJ8Pd3R35+fl46aWXcPLkSeTk5GjOUVlD2l6bgQMH4sSJE8jOzoZSqcS2bdsQHBxc43mFhYXIz8/H2LFjsW7dOly5cqXe9yYi0jXWKdYpNV3UKYYNIh2bPBn45BPA2RmQSCr+/OSTiuNNZdGiRVVW+/jggw9w/vx5+Pj4wMPDAxs3bgQArF69GocPH4aXlxe2b9+Obt26oX379hg9ejQUCgX69OmDqKgoTbcrAMyZMwc+Pj6aiXeVjRw5EidOnMDw4cPRrl07AMDs2bPh4eGB/v37w8vLC3Pnzq2xMkZd57OyssK5c+fg5eWFH3/8EatWrdI8Zm5ujn79+mHevHlVilZlzz33HFQqFby9vTFhwgRs2bIFZmZmePnll/H888/Dzc0NmzdvRlRUFDIzM6u8tiFtr0337t2xdu1aDBs2DH379oWfnx+efPLJGs8rKChAWFgYfHx8MHToUMTExNT73kREusY6xTqlpos6xU39iB5CS90sqbS0FMbGxjAxMcHZs2cxf/58zd0kQ2FtbY3CwsIax0NCQhAdHY0BAwbooVXNh5v6PTDWKaJasE7pDuvUg9UpztkgakOSk5Px9NNPQ6VSoV27dvj000/13SQiIiIN1qnWh2GDqA159NFHcenSJX03o0613S0CKia/ERFR68Y61fpwzga1eCqVCvUMByQiItIb1ilqy9izQS2SEAIKhQLl5eUoLS2FmZkZTExMYGRkBCMjI0gkkhrLzxERETUXIQSUSiXKyspQVlYGU1NTmJqask5Rm8OwQS2GOmCUlZUhOzsbZWVlsLe3h0qlgkqlglKphFKprPEaMzMzzYWdiIhIVyrfCCsvL9f0ZiiVShgbG2tqlZo6eKi/gJr7NBC1dAwbZNBUKpXmol15abfy8nLI5XLNxVsikWgu1EDFBT8vLw9paWmaFRMkEgmMjY01z+VdJSIiaix1gCgrK6uy10PlAKFSqQCgSt0RQmjCiZpEIkFpaSmsrKxYp6jV4JwNMjgqlQqlpaUoKCjA/fv3UVxcDKVSCSMjIxgbG2sCQ13Uj1cOFgCqDL1Sf5WXl7fI8bTW1tZaH0tLS8NTTz1V73u8+eabDTrXli1b8MILLwAANm7ciK+++krrc+Pj43HmzBmtj+/duxdr164FAMyYMaPWTYvqUr3Njz322AO9noiosdR1qrCwEPfv30dRURHKy8sfuE6pa1TlOnXhwgVNnZLL5Zo6pVQqWafqwDpluBg2SO/U41rlcrkmYJSUlEClUsHIyEgzF6OuC3d9F+DqF3X13Sb1XSX1RV0ul1e5qDfZhV22FdjtAnxrVPGnbGvTvK8WPXr0aNDFsaEX8crmzZuHadOmaX28rou4QqHAuHHjEBUV9cDnVave5roKBhFRU1DXqcbeCKuLuk5VDyCVhxBXr1NNeqOMdQoA65QuMGyQXqgvnuqAUVBQUCVgGBsb63yehba7SpUv6qWlpSgrK4NCoXj4i7psK3BuDlCcBEBU/HluTpNcyIUQWLJkCby8vODt7Q2pVAoASExMhJeXF4CKuz0REREYPXo0Hn30USxduhQAEBUVhZKSEvj6+ta66+oXX3wBNzc3DBw4EKdPn9YcX7NmDaKjowFU7ADr4eEBHx8fTJw4EYmJidi4cSPWrVsHX19fnDp1CjNmzMC8efMQEBCApUuXVrn7BABHjx7FgAED4Obmhn379mnaXPk5YWFhiI+Pr7XN6jtn2v5bxMfHIyQkBE899RR69+6NyZMnt7i7g0TU/JriRlhj1VenmqyXnnWKdUqHOGeDmk3llTnkcjmSkpLg6upaY6hTU3nQ96s89Kp6m6tP6Kvc61Hvea6sBJTFVY8piyuOu9a8eD6InTt34vLly7hy5Qqys7Ph7++PoKCgGs+7fPkyLl26BDMzM7i7u+PFF1/E2rVrsWHDhlp3Zr179y5Wr16NCxcuwMbGBsOGDUO/fv1qPG/t2rWQyWQwMzNDXl4ebG1tMW/ePFhbW2Px4sUAgM2bNyM1NRVnzpyBsbExtmzZUuU9EhMTce7cOfz5558YNmwY/vjjD62ft6421/Xf4tKlS/j111/x/+y9eZQcV3n3/63qfZveprtnRjOaVRpJ1mppLMsG4+V1DCJx4OAE8+KwL29iSEJCMMnrOCYJB+LjsG/5JbwBYrAdJwcEODGGgG1sWYstLMvSSCPNaBbN0vsyvdf2+2Ncpd7X6u6qmfqcw8Ga7r51u7r7Pvd7n62vrw833ngjXnjhBbzhDW+oeG8VFBQ2Hvyan0gkhDWe4zjhEKzT+RPl7BR/gMfnfxTaqcLXFKHYKcVOtRDFs6HQUjiOA0VRSCaTiMViiMfjyGazIAgC0Wi0LR6MZigMveJd2vxpF8dxQjUsfmEvOo1IzpcevNzf6+D555/Hu971LqhUKng8HrzpTW/CyZMni5532223wWq1Qq/XY8eOHZibm6s47vHjx3HzzTfD5XJBq9Xine98Z8nn7d69G+9+97vxyCOPQK0uf3bxe7/3e1CpVCUf+/3f/32QJIktW7ZgZGQE58+frzi3clS6F9dddx36+/tBkiT27t2L2dnZhq6hoKCw/uA36rl2anJyEslkEiqVqi0ejGYoF3oFIM8u5YZdKXaqGMVOtQ5FbCiIDi8wEokEotFonsAQI6419zrtprCSSOF8+P/lCRDjQOnBjJtbPV0BnU4n/LdKpcqrftIMTz75JO69916cOnUKExMTZcc1mUxlxyj8LhAEAbVaLVRvAYB0Ot3UPFv1/hUUFORJpYMw3kZJVVxUo9zcM5kMaJpW7JRip9qOIjYURCG3Mkc0Gs2rzKFWq0V3P9c6VqdjHjmOA7f7s+BUxvy/q4zAns82Pf4b3/hGPP7442AYBn6/H8899xyuu+66ml+v0WjySjXyHDx4EM8++yyCwSAoisITTzxR9ByWZbGwsIBbbrkF//AP/yAIS4vFgtXV1Zrn8MQTT4BlWUxPT2NmZgbj4+MYGhrCK6+8IlzjxIkTVefc7L1QUFBY3/Cl1PmDsEQiUfEgrNP2o10odqo6ip1qDiVnQ6FhGIYRktT46k2FSWzlyHXzNovkDcLQ/wYHAK/+XyC5ABgH1hb2wXeBqDWetgCapqHT6fD2t78dL774Ivbs2QOCIPDQQw+hp6enZvfrRz7yEezevRvXXnstvv/9q4mAvb29ePDBB3Ho0CHYbDbs3bu36LUMw+Cee+5BNBoFx3H44z/+Y9hsNvzO7/wO7rrrLhw5cgRf/epXq85h8+bNuO666xCLxfCtb30Ler0eN954I4aHh7Fjxw5s374d1157bdU5l7sXjbq7FRQU5E+5Xk3V7JRcvRoNU8FO4fVeVoBipxQ71RhElY2axHdxCu2Ed7vy9b/n5+cxMDCQV66vVliWxalTp3DgwIGG5uL3+xGPxzE8PIxsNgudTgeNRpP3nEgkgsXFRVxzzTUNXaMSU1NTGB0dhV6vF33s3PtY7p6ePn0aH/7wh/NOUhTWB5OTk0Ijyhw22M6nLhQ7pSDA26ncgzAAddupyclJ9Pf3w2KxiDIviqKg0Wig1WqrPvfo0aOi9GWYnp7Gtm3bhH+n02mo1eqKeQu1otipjU29dkrxbChUhE8q4xfu3OSyYDCIoaGhpsZWKIbjOGQyGeh0OnAcV7Sof+tb38JXvvIVfOlLX+rgLBUUFBSkQe5BGC8wcpOmG/FSiOl9X4/k3hvFTilUQxEbCkXklqjl63YXVrrILQXbCBvORV0nFEUJyWKFi/pHPvIRfPSjH+3U1BQUFBQ6Tu5BWCaTwczMDHp7e2EwGCRRonajEY/HYTQaBZGm2CmFXBSxoQDgauk/PraV3+DyCd6FSOHUp9PX7yS5ZQ2B2lzaCgoKCnKm8CAs106l02nhUEwMpGDj5EShd4P/W+7/K3Zq46KIjQ0My7J5CzcP78GoRLMLcbMLjbJo5VPo/QBKN39SkDbK5kZBIZ9KB2FiCYv1TCkR0CkUO7U+aMROKWJjg8HHtUajUWSzWZjNZgDVK3Mo5KNSqRCJRODxeCR735RTJXnB50G1ouiAgoKcaOQgTPFEFKPT6RAKheBwOCS75it2Sl40aqcUsbEBKFWiNhqNIp1Ow2q1NjTmRl8ETCYTAoEAIpFIS8bnE8RbRe5p10b/LKWEXq9Hf39/p6ehoNB2Gi1RyyO22FgP4qWnpwcrKyvw+/0A1nIBVSpVSzxCrbZZip2SDo3YKUVsrEMKK3PwHS5zK3OQJCn7hZSHYZi8Lp48rVyc+BO20dHRlowvVunDUjAMg+PHj+P6668X/sa/n1r7pCgoKCg0Q6UStcoaJA5qtTpvU3ju3Dl4PB7Y7XbRryW2zcr1eMzOzqKnpwdGozHPTgGKCJELithYJ5QrUVtu8yj3HyjDMFhdXcWrr74qJAZqNBrY7XbYbLaWLKa5SCkOthFyF2u+lHHuaSLfUTfX8Mv5/SooKHQeXmCkUinE43HodLqmS9TyKJ6N9UWu5z0Wi8Htdpe0U4WHZIqdkiaK2JAxtZSoLYccF1KKohAIBODz+ZBKpaDVajE+Pg6tVgutVguO4xAOhxEMBjE9PQ2KokAQBJaXl2G325VY+NcpVRM99//570Xuog5cFSi8CFEWdQUFhWrwdor3tHMch3g8jitXrmDHjh2irSNytGkKtcEfnObaHf6zZhgmrxS/4qWXJorYkBm8so/H43mhUOVK1JZDLgtzNpsVBAZN0+ju7saWLVuQSqUQiURgMpmEBEKtVguPxwOPxwMACIVCmJmZQTKZxOLiIjKZDCwWi+D5MJvNG3IhquaVKZXLkev94EUI7/3INQIb8X4qKCjkU6lEba6t2gjrBS+uVlZWQBAEnE4n7HZ7TZ3EWzEXOd7zUvOuZqdyn6d46TuPIjZkQKnKHKdOncLExERTiV5SFRvZbBZ+vx8+nw8sy8LlcmF8fBwGg0F4TiqVqjqOSqWCXq8X8io4jsPq6irC4TCmp6cRj8dhMBhgt9tht9thtVo3RCnFRgxOLd4PxaWtoLBxqadEbSsOu6QURsXbGp/Ph1AoBKPRCKfTCa1Wi1gshrm5OdA0ja6uLsH+GAwGZb0sQ602q1Y7xdsqxUvfPhSxIVGqVeZo9gcitR9XJpOB3++H3+8XBMa2bdvyBEYlajEKBEGgq6sLXV1dGBwcBMdxgodkaWkJk5OTUKlUgufDbrdDo9GUvZ7U7mE7KXeqxAvjmZkZDAwMQKfTKQl9CgrrFD7Bm7dVPNV6YMhBbNRLocAwmUxwu90YHh6GSqUCRVHQaDTo7e0FsHbvYrEYwuEwzp8/j2QyCbPZDLvdLlSNVNbKNRq9F7V46VmWRSQSgdvtVuxUC1HEhoQoVaK2VXGHnV6YgbXFdmFhQSjL53K5sH379rblVhAEAaPRCKPRiL6+PgBrXpVIJIJwOIzLly+DYRhYrVZBfOj1etkvQq0yYrkLeyQSQX9/v5LQp6CwzsitdFgYK1/rb1kK9qcatcyR4zjEYjH4fD6Ew+EigVEJkiRhs9lgs9kwPDwMjuOQSCQQDoeRzWZx9OhRaLVaOBwO4XnVxqyGXAWMmPMuFCA0TWNmZgYOhyPvOYqdEhdFbHSQwhK1DMOIVpmjGp1a7NPptODB4KtIXXPNNQ3V527F/LVaLdxuN9xuN4A1AcifPp07dw6pVApmsxkqlQoqlUqWi3c75sxxXJGLulpCn+LSVlCQHq0oUStnz0ahwDCbzXC73RgdHW0qDJcgCJjNZpjNZiwsLOCGG25AOp1GOByG1+vF1NQUCILI87x3Iu+jU7TKLuQe6ub+TUk8FxdFbLSZSiVq+c1WO2in2EilUoLAIEkSbrcbO3fuxOnTpxtuYNau+6RSqYSFHbia7Dc3N4dwOIyjR49Cp9Pl5X00e/rUatohNvjKaLkoiecKCvIg9yAsHo8jEAigp6dHtIMwuYkNvhGuz+dDJBKBxWKBy+VqWmBUQ6/Xo7e3Vwi9oihK8LxvpLyPUvZELMolnyuJ5+KiiI02UFiZ48KFC9i6dasgMNYjyWRSEBhqtRputxu7du2S/UkMQRCwWCzo7u6GwWDA6OiocPq0srKCCxcuSP70qV2eDTET+hSXtoJCaylVopbjOFAUhXA4jE2bNol2LTmEUQHA6uoqgsEgwuEwurq64Ha7MTY21rFCIhqNBi6XCy6XC0DlvA+73Q6LxaKslVWoxVbVYqcAxUtfCUVstIhKlTni8XjHRUYrFvtkMgmfz4dAIACNRgO32409e/aUTbKWM7kLVLnTp0gkIsnTJymJjUKqJZ7zkCQJhmGEBPTC1ygoKFSnWolaAEK4qJhI1bPBcRwikQh8Ph98Ph8sFgs2b97cUYFRiUp5H7Ozs1hdXRU87zabTQjVlhuttFl8ZEk9KF76+lHEhoiUKlELVK/M0QnEWuwTiYQgMHQ6HVwu17oVGLVSy+mTyWTKO31q5/dDymKjFKVOlTiOw7Fjx3Do0CHhMcX7oaBQnXpK1AKtEwatoJF58tWI/H4/IpEIrFYr3G431Go1urq68hKHpU5u3sfAwAAA5OV9BAIBxONxodeHFD3vpWilzRIrRKuc9yObzeKVV17Btddeu6HtlCI2mqRaidpydDqxuFEDwp+c+Hw+JBIJzMzMwOVyYd++fXU1FdxIlDt9ikQimJ+fRywWg1arFRb/rq6uls6nXd+9Vl0jd1HPbWypJPQpKJSm0RK1QOtCnjopYHiB4fP5EI1GYbPZ4Ha7sXXrVmGcaDQq6vyAzvS2yvW8MwyDTZs2gWEYWeV9tNqz0crqjMCabeJtVSkv/UYoD6/sDuukVGWOekvU8ot3s1+oZseodeHjk6J9Ph+CwSAMBgPcbjeMRiN27drV8PWbpdM11Ru997mnT3yCfCaTQTgchs/nw9TUFBKJBCYnJwUD0Ei1ro1CNZd27vOUhD6FjYIYJWoB6YY81Tsmy7LCGhuLxWC32+HxeDA+Pt6WdUAKOSocx0Gj0cDhcOR53qPRKCKRiKTzPuQmNkqNX85LvxFyFBWxUQO8wOA3hEajUfgCNHJiKsZC26xgqfa6Uh1Q3W43hoaGhHyT+fn5hq4tBuvhx5eLTqdDT08Penp6wLIsjh07BrfbjXA4jIWFBWSzWeH0yWazwWQyNXwPGolRlRu1Jp7zv2EloU9B7rSiRC2APO+hWLQrQbyUwOjt7cW2bdtqSgoWc45SXVtIkhSERbW8D7lUXKyHVosNlmXL2ttydmo9lodXxEYZSpWozWazmJ6ext69e5v6sAmCAMuyTf1gm10IS72erx/u9/tLdkCVG3L8QfKQJAmn0wmn0wlgbcFaXV1FOBzGxYsXkUgkYDQa80KvahUQUjhha5Z630MjCX3r2aWtsD4o7NV0+fJleDweGAwG0UIHeXslJq0KW+HvRygUgs/nw+rqKhwOB/r6+moSGAqV8z4KKy7yDQflkPdRjkpiQAzqETPrOfFcERs5VKrMoVKpoNFoRFHBYpwUiSU2yjUokpPAWA+b51xKvR+SJGG1WmG1WjE0NASO45BKpRAOh3HlyhXEYjGo1WrB82G328vm0HQ6X0gMxHgPtXg/otEoLBaLUPVKDou6wvqmXIlakiSRTqdF91y2ywvRDAzDIJlMIhgMYnZ2Fna7HZs2bUJXV1dbQo3XO5X6fVy+fBkMwyCVSmFxcVGyeR/l6KRnoxZqsVPxeBw6nQ5Go1GydmrDi416KnOIdcIjZhhVI/AhUqurqzhx4oRQP7zVDYoUaqPWut9GoxFGo1Gof5/NZhEOhxEMBjE9PQ2O42C1WgXvh16vr3l8qdOK06hSp0rz8/MYGRkRrqXT6WR/7xTkRy0lagH55FeIAcMwggcjkUhArVbD4XBgaGhI1OpC6wmx1v5SFReff/55ZLNZTE5OIpVKSTLvoxTtEBtijl/KTi0vL8PhcAhVQKVopzak2Gi0RK1YsaudEBt8EhjfAdVsNkOj0WD//v3rWmC0ykhKccOu1Wrh8Xjg8XgArBnjaDSKcDiMxcVFZDIZWCwWaLVaYcMitfdQK+3KO+HDHZWQKoV2U2+JWv4xOYQ8NQrDMAgGg/D5fEgmk3A4HBgYGIDFYsHCwgK0Wq2o85WiyJIiJElCrVZjeHhYdnkfrexODrTHVsnBTm0YsdFoidpcxPoAxfCQ1CI2SpX3c7lc2LJlCyiKwvnz52UrNKR62iYGYokAlUoFh8Mh1InnPVp82NXRo0eh1+vzDIBcvg+tNhA8chZkCvIjN8G70E7V8ttcj+tikcCw26F95Am49l8P94EDIHI2WBshoVuKFN73WvM+eNvTybyPVouBVueEAPIo+rJuxUZhZY65uTl0d3eLmjjXKK3M2ai1vJ8YC7NUjNp6NAqtSqDkQ+ZUKhW2bt0qGIClpSVMTk5CpVIJOR92u12yzRnb6dkoDFNRUBCTUiVqG60g1QrPRiegaVoQGKlUCk6nE4ODgzCbzYi9eBzTv/wlMpeWsPDFf8Xgpz+K7t+5FYB0bJJUaeXhSbVxa8n76ES/j3aWvm0V7RA0zbKuxEZhZQ6GYYREmUQiAafTKYkPROwwqtzqG/F4HDabrWp5P7FOgTbqyW8rjVqrDSb/mREEAYPBAIPBgL6+PgBreR+FBqAw70MKn3e7PBtyWMQV5EWunaIoSrQStfwYct1w0zSNQCAAv9+PdDoNp9OJ4eHhojLfwZ/8FwCACcyDTWsw+b77YN49DtOH74LlloOizUfO97LdNHKfSuV98GG/k5OTSKfTMJlMsNvtQj+zVqz5Uk8Ql8o1mkX2YqNUiVr+1JOvSQxI68RHrEUsGAwiFoshHo/XXX1DLMGzkWnl++/USYtWq4Xb7Ybb7QawFsIQi8UQDodx7ty5PAPQycS/di2ucljEFaRPO0rUAtKyc7XACwyfz4dMJoPu7m6MjIzAZDKVfn40huivXwAAsMkkTGNDyCwGEH/1AuIf/ywiu7fC8Dd/DPst17fzbdREKpUCQRCi55VIBTGqdPJ2BUBe3kc2m8XRo0dbkvfRai+54tlYQ5Zio1qJ2lK0ojFRozS60c+tvhEOh6FWq9Hf39/whq/TjQUVSiOV7ymwlvdRaADi8XjJxD+bzQabzdaWRa9d37vcRVz5nivUQ7tL1ALyOI2nKAqBQADJZBK/+c1v0N3djdHR0bICI5fw078Al80K/87MzqL7LdfC/+MTa/9+dQpn3vExWG+4FoN/9X9gu+Halr2PWkin0/D5fPD5fFCpVNBqtTh//jz0er2QT2exWNq2UWyld0BscvM+FhYWcMMNN7Qk76PVXnLFs7GG7MTGlStX8KUvfQn3338/gPoS56Ry4lOP8ClMjnM6ndi8eTMAYGBgAGazuaE5yH3jJAej2ihSjiElCAIWiwUWi0X4HvIGwOv1YmpqSvib1+uF3W5vSeJfOxdXuf9WFNpPNpvFRz7yEXzpS18CUGynWuWBkKpngxcYPp8PFEWhu7sber0eExMTdY3Dh1Dlkpp8DV3X70Ds2Dnhb9Gjp/Dqb38Etjddh6H/+0foOrCz7jk3amMymYwgMEiShNvtxu7du0EQBNRqNbRardAjaX5+HrFYDFqtVhAfVqu17mt2mnYd/rQi70PK9rZWCgWTFG2W7MQGy7K4cOFC3e4zKS3C1YRPbnIcH7s6NDSUF7varKem070+xCKVSmF5eRkqlQoul6uuTtobFbEXv0IDkE6n8dJLLyEWi2Fubg40TaOrq0tIPDcajU1fXw7VNxQ2Lmq1Gq+++mpFT3sr7JEU1mQeiqLg9/vh8/lA0zS6u7uxZcsWGI1Gp+GpQAAAIABJREFUAIDf769rvNSlaSTPXyh+gONAMhGouq1gAtG8hyLPnsCrx09j74+/DvOBvQ2/l2pks1nhvXIcB7fbjZ07d0Kn0wnP4cvsl+qRVFioI5FI4OLFi4L4KNegVUp0YoNbT95HubBfJWejPUj/G1yATqdDJpOp+3VSWoRLzaVU7Gqp5LhKYzQ7B7mQyWTg9Xrh9/uFSiVqtRoLCwt5p0Q6nU6WfTbkftLCn95t2bIFwNpCyOd9XLhwAclkssgA1LtQtitBXEGhEap9n1u1/nbag5/NZgU7RtM0XC4XxsfHYTAYmh47+NP/LvsYHQ7Dsm8EkV/EgIL7yqYzOP9Hn8G+Z74P1etCpxaqfUa5YophGLhcLmzfvl1onloPhQc2zz//PCwWC3w+H6ampoRwIYfDAZvNJrkqgVLZS5TK+ygX9svnfShioz3IUmxkc2I2a0Vqng2O44pcy06ns+bYVSmIhXbOgT858nq9AICuri44HA7s2LEDFEVBo9FgcHAQwNopEZ/bEgwGceLEibyFWirNhCohZ7FRKARIkhTyOXIbPkUikbwwAt7zYbPZqp7kKZ4NBTmznsKock/1WZYVVWDwcDSN0FNPV3wOfXkGllt3YfV/Xi16LHlpAZf/5H6M/fMXmppH7qFgNptFd3e36O8VWPsce3p60NPTA2BN2ITDYYRCIUxPT4PjONhsNjgcjrpCVeVa0alRKoX98nkf2WwWJpMJOp2uJf0+2p0gLsXPAZCp2GjEsyHmItzMl4eiKMTjcYRCIRAEUeRarhUxPBti0EqxUXhy5Ha7cc0110Cn0yEajWJlZaXk6/R6Pfr6+mCxWIR+EnzvkampqbzTD7vdLjkXdbtK33Zq/NzEv/7+fgBr3qpwOAy/34+LFy8CQJ74KDwt7IRnQ6qLuIL8kHsYVTabhc/ng9/vFwTGtm3bRN9080RfeBF0KFz1edziZZh3jSB+ZqbosaUf/hqON/8Ijne8ra5r83mTXq8XqVQKLper5kNBsdBoNHlVAmmaFnIVZmdnhRLlvPhoxLsiVcT+Phd6kS5duiTcz9y8D/5wstl+H+3yOkjdPklrl1UDjXo2xFqEeSNRz+k4f/Lj9/vBMAxIkkRvb6+w0WoEqXg2xKbWk6N6rq3T6UqeEgWDQUxPTwNA3imRFFzUcvZsNDJ+4WdE07QQe7uwsIBsNguLxSIIRDm4jRUUytGq9buVno10Oi2szQCaChviqXWtqBRClQdNQ9vNQWUxgFlN5T/Gcpj61Bew/403QPP6pr0cLMsiHo8jFothcXGxbM+PehHrM1er1eju7kZ3dzeANUHEr5dXrlxBNpsVNsztaJAn57BikiRhtVoF8dFI3kclpOr1aTeyExuNLqZiLcK1GolSJz/8pnlubq7pDa1UxIYYc8ituJVKpWouhVjLtUs9p9QpUTgcFipasCwrnKo7HI6SbtX10NSvleM3KwTUajWcTiecTieANQOwurqKcDiMixcvIhqNQqVSCaV5lcIAClKj0u9YLp4NvrJSMpnE2bNn87zLzVJr6XQqHEbshRdrHje7sgLHzdfA/5OXih8LJ3Hx/Z/Ajie/X/QYy7JCVb3c2P7R0VHJbxZVKpVQzWp0dDQvT47fMFssFiSTSaRSqZY0Z5Wr2Cgcv5G8j0qHzyzLSuIAs9PITmw0CkmSoGm66XEqLeaZTEYI+wHKn/ysl0pQzcCyLGiaxrlz5xCPx+F0OjE4OAiz2dz2hV2tVudVtGAYBpFIBKFQCPPz86BpWuii7XA4hM9TzotrK2lFiBN/+mS1WjE0NITFxUXE43FotVpcuXIFsVgsryeIGEmUcv59KUgbKedspNNpwY4RBAG32w2j0Yi9e/eKmu9Wqw0L/ffT4Oq03anJs3Dctg+h//lN0WOBFy9g5avfQs/H/w84jkMkEoHX60U0GoXdbkdfXx+sViuWl5fBMIzkhUYpSuXJra6u4vTp05iZmUE6nYbZbBZsWrN2t9WHb51M4C6V95FKpRCJRGrq96HkF66xYcSG2GFUPPzC7Pf7QRAEXC5X1ZOf9SI26p0Dx3FC7kQ0GgVN03V1PW8XKpWq6FQ9Go0iFArhzJkzyGazUKlUMJlMcDqddefbSAG5utRz0el02LRpk1A+MpvN5oXH5XqoGoljVkK1FJql3G9Bap4Nvvmc3+8HSZJwuVx5pVt5D73YYqMWQrWGUBVA++ehH+lDemap6LHpz30HsW1jCHdZYLVa4fF4MD4+LvleBY1CEAS6urpgNBqF/UkikUAoFMLMzAzi8TgMBoMgPuqtECjnUKFG5m4wGGAwGCr2++APKDOZTFtzaKT6OchSbDRyM8UMo0omk0LpVb5pz86dO2uuYrBexEYtcByHaDQqdD232WzCwv7SSy9JTmiUItetyruop6amkEql8lzU/ELdbB8JOYQ5VRu/ndU3eLRaLTweDzweD4D8OObFxUVkMhnhNM9ut1c9zVPEhkIzaDQa0DRd0sMmhZyNVColHJTxdmzXrl0l7Vir5lttzOT5C0hdmm5obDaZhGnYhcyCDxyV7xlh0jQS938R+1/4EdTqjRfiklukY/PmzeA4DqlUSvDmx2IxIVSI7/VRSznnViAHe1Wp34fP54PX6xWa3DaS97EekKXYaIRmxUYqlYLP50MkEgFFUejp6Sm7MFdDjFroUhAb5ebAu2x9Ph9CoRAsFgvcbjfGxsbyFo1aY3bL0an3T5IkDAaDUE2Jf7+hUAhTU1NIJBIwm81Ccl4nQsMqIXcxU+s1cuOY+dfweR/T09N5p3l83kfuyW2h2JDSZ6ggffjKiaXERqc8G7wd8/v9UKvVFQVGPeO2Yq5A6Y7h9ZCZm4Pp5h2I/7y4HO7qRS8W7/8sBj//YMPzkxvl1v7cRoN84Rq+hDzfaFCtVpctIa/kMOaTe0BJ07RQ1Yr3fMTj8bryPtYDG0ZsNLJwJJNJ+Hw+BAIBQbna7XaMjIw0VfZOrPyRTi+Ehfc0Ho8L98tkMsHj8WBkZKQlG08pbfx4F3VXVxeGhobyEsr4TS1fzYJ3UVeavxSrRUlpfAANhXTkfk6Dg4PgOK6oc69KpRJCr/R6veLZUGgYXmyYzeaix1qZs1FoF3LtGC8w9uzZU1dOUyfEBktRCD39P81faPYSuq7fgdixc0UPzX/7STh++w5Y3nCo+eusM/gS8n19fQCulicv1WhQp9PJNoex1R5s3lZVy/vg82xK5X1UotP7wFqRrdio9wtY6+KeSCSEhVmn08HlcuUtzNFoVBJeiVJGpROkUimsrKwgEAhAr9fD4/FgcHBw3av0ShQmlHEch2QyiVAoJFSz0Ov1guejVCUlOYuBdvTAEMN7QhCEEHvLG9RsNivE3gaDQaEKj91uh8vlKrlxVFAoRaUy7WJ4tyuNW3hQ1ojAKBxXTHuTnXwVBE1XHDP63PNgotHmL8ZxIOkwNB4HKG8o/yGGw/mP3o9rT/wYqhIHiFKwsVKhXAn5UCiEYDAohBXX22iwGnI/HCtnq+rJ++APv0rNUy7hvrIUG2q1umwsbDnKiQ2+mzHfbVqn08HtdmPfvn0lm71JJd+iky5ePpkwHA6Doij09fWVvV+VWI9u6lIQBAGTyQSTyYSBgQEAa6eNfA8JvoN2bshPK5H74g20TtBotVqhLPLq6ipmZmbQ19cnlEZWxIZCrVRqQNuKw6JEIoGlpSX4/X5QFFV0UNYMYs838+yT6EpQoNwu6DYPlnxO8KfNhVDlQkcisF67FYGnwkDB+0gtRzFz733Y8p2v5f1dSt5zsRBzbc4tIR+Px3Hx4kW4XK68RoPNFOhoxZw7MX6ttqpS3se5c+fy+n3kVhBTxEYLqRQLW47cjS0f5uL3+xEMBqHX6+F2u7F58+aqG2YxFl055mzw9dZ9Ph9IkoTH44HNZsPY2JgsqzE1S7P3no+P5Ssp5cbHhkIh0DSNixcvloyPbZb1kLPRjgWWd3/zxnIje+sU6qeSZ0OsMCrejvGeeL7c6e7du5seOxexPTHsygJsmQxi//JVkG9/F4z79uc9TgUCiB07Kdr1ACB9cQrdhycQePJE0WPLPzkG538WdxffCIdhYkGSZNVGg7kl5GvtNi93e9Xo+JX6ffAVxHQ6HSwWCxiGAcMwUKlUkhXJshYb9ZwyEgSBTCaD6elpBINBGI1GuN3uukN+xFh0xRIsrV4IKYoSBAbHcULVLb4cYjgcbnoOcl7MxfxR58bHRiIRzM/Pw2KxwOv1YmpqKm/hsdvtdXuRcpHKSU4zdKrilYJCrVTzbDRiRwo98Xq9Hi6XS/Asp1IpRMUIPSpATHvDRkLgUkkQAIwaCv6vfwFdb/4d2N7xTuE3HfyvnwEMI8r1cslMT8K8ZxTx0wUVrjhg6pMPY/8bDkHzejU7qW7apEip9bhco8FQKCSc1NdSxXGjeDaqUa7fh8/nQzabxYkTJ0CSJAYHBzE4WNpb2ElkKzbKnRjlklsVKRAIgGEY9PX1YWhoqOFTSqmEQLUq5pemaaGhE0VRcLvdJRsT8jTzPpr9AcpZqFSC4zio1eq8+Fg+lyAYDOLSpUsgCAI2m02Ij63Hyyf3xRton2djvdbdV2g9lcRGPes3f6LJCwyDwVDWEy+Wx4Rl2bVch9ftpJhig1lZuPqPeBS2HSMIPP4IMtMX4f74n4M0GhFssLdGNTiahsbJQGUxgllN5j2WjaYx9f5P4Jr/+kFLrr3eqbY+5jYaBK7uzwqrOBaGCclFDFQav1W2ymAwoLu7G+FwGHv37gVFUZLdF8lWbJRbxDmOQywWE3IKzGYz3G43ent7MTMzI8TDNYoYi7lUBAsPwzAIBALw+XxIp9NwuVzYsmVL1fCoTm6+NtrGLzeXAFgThXweweXLl/Ma2DkcjorJeXIsJVjqGushVEth/VItjKrS77BQYPCe+GoHZWLZBcrrReh730bvffeLOi4AMCtX8v7NeRfgfMNBBJ97EbOf/BjiB28CPTsnyrVKQXm9cNx8Dfw/eanoseCxKax85Zvo+eM/FD16gOO4lhwQ1juHVqzNjdynUlUccxsNrq6uCuHGFEW1bD2W++Fb7n3RaDSSDfeVrdjIXcQLG8d1dXXB5XJhdHRU+BAymYxoTf2kIhSaGYNlWdA0jbNnzyKRSKC7uxvDw8N1h6Y169lo5cZXrgtILWOr1eq8ZDKGYRCJRISmTDRNw2q1Cp6PQs+UXO8Nj1wqXilsXOoNoyrsT1SrwKg2biNkF+YR/uWzMB88BMvNt4krNpYXiv+4NA1ifAzchUsw/+opJJx2UMGwKNcrRWryLBy370fo5y8XPTb9+e/CevstQLez6evwotHr9SIQCAgV8PjwolKVCOWIGGt+uUaDi4uLSCaTePHFF+tuNNiuuVeiHaV15fAdkq3YSCQSWF5eRiwWQyQSQVdXV8nGcTxidhCXa84Gy7IIh8Pwer2Ix+OgaRqbN2+WXNM5hfpRqVRwOp1wOtcMJF/JIhQKCcl5XV1dcDgcoChK1gl37bqGXBZxBWlSi9goFBgmkwlutxvDw8MNnVCKJQoyc3MAx2HxHx/G6K49ovYFKfRs8DjINBKDm5Gem4d12zACx2ItydvgoVcuwzDWh9Slpfz5pWlceP8n0Xfk2w2PnUql4PV64ff7odfr0dPTg4GBAWg0GnAch1AoJFQi1Ol0cDgccDqdsFgsyprzOnyjQbfbjUwmg507dyKVSiEcDmNxcbFqo8FakWqCeK3IxU7JSmzQNI1nn30Wp06dwjPPPIP77rsPhw8fxpYtW6rebLEWYalUo6p1HhzHIRKJCN3P7XY7Nm3ahK6uLrz00ktNCY2NUrq23Yhx0pKbUJ6bnMf3jwgGg8LjlZLzOjX/arTDsyGXRVxBmpQLo+IFRjwex8mTJ5sWGLmIlcuXnV8LY2IzFK7c9+cgPv3XTXvSI5EIvMtL6PEuoeQvl8rC3O0AFTSDmr8M5xsPIPjM8YavWXVOqRSMm11Iz2vAZam8x2JTKzB9/ktQffzDNY+XzWbh8/ng9XqhUqng8XjySsJT1No1DAYDNm3aJFQiTKVSgkc6FotBr9cjk8kgGo2iq6tL1HWulWFUrVyP+bEL+yLxjQabKaTSjpyNdo4v1YNj0cTGU089hT/5kz8BwzD40Ic+hE9/+tN5j2cyGbznPe/Byy+/DKfTiccffxxDQ0MAgFdffRUf/ehHEYvFQJIkTp48WTIh+R3veAcGBgYwNjaGD37wg7jppptqnp+UPButDsXKzVsJhUKwWq3weDzYunVr0ZeyHRvDcrRDrMhVDIn9meQm56VSKfT09ECtVhcl5/FhV82I0PUiBAqvIdVFXEGa5Ho2+FBfv9+PUCgEs9kMtVqNAwcOiPo9Fus7mpm7mjORml+E9j8eBfehP6xrjMKDLpvNhl4NCXDl7ScXDcG+bzsCL7wMemYSXfuuQew3Zxt+H9XIzM+j+8374P9xcTnclUd+gd4bJ4CRkbKv5wuqeL1eMAxTVLGxFgrFRzKZxIkTJzA3N5fXAJYPu5LqOtSqeVWyJ+UaDQaDQUxPT4PjuDzxUSqXUe5hVHIJ9xVFbDAMg3vvvRc///nP0d/fj4mJCdx5553YsWOH8Jxvf/vbsNvtuHTpEh577DHcd999ePzxx0HTNO655x7827/9G/bs2YNgMFi2ss6RI0cAAJ/61KfA1OlelZpnQ+wxcmNDeWPm8Xjy8lZKIeWcC6leu9W0Q4CRJFmUnMfX8J6enkY8Hs9rIGSxWGpekNuVIL4eBI3C+kWj0WBqagpPPPEENm/eLIT6jo6OAgBOnTol+vermd9EZvIMdNt3rf33/HzeY9lfPYfsdYeAW/9XxTH4gy6v1yvkT3o8HiH6IPubF5GsOALALc/BceN1CP36GFTpMLRuF7I+f8Pvqxqpyddgu3EnIi+8lj8PhkPoga+Becsded3FWZZFMBiE1+tFMpmEy+XC1q1bRes3ZTQaodVqsXv3biFvIRQKYW5uDrFYTMj54MOupCA+pGKLcxsNAmtikM9lLGw06HA4oNPp1lWCuJQRRWycOHECY2NjGHn9BODuu+/GkSNH8sTGkSNH8OCDDwIA7rrrLnzsYx8Dx3F4+umnsXv3buzZswcAhJjzSuh0OqTT6brmKNaHLTXPRiKRgNfrFUoiejyemt3xYiR0dfL1naTTCeJij19Yw5vjOCSTSWGRzj1hs9vtFRMb10s+BcuyonRfVthYHD9+HI888gh++MMfYmxsDIcOHcLExETe95XjOMls0Hgi//p1OD7x14BGBzZZIAk4Dqtf+RKY6w9BZTQVPFR80FUufzKv7G0FiJXLMO/ZhfjpM+gaG0QgGGpd/gbHgUsHoO11ILscynsovRLDzB99CmPf+dpaGJjXi2g0CofDgcHBwZbnO/J5C0ajEf39/XniY3Z2FrFYDEajUfB8dEp8SNUeqtXqko0Gc3MZ0+k0VlZW4HQ6a240WA+K2FhDFLGxuLiIgYEB4d/9/f04fvx42eeo1WpYrVYEg0FMTU2BIAjccccd8Pv9uPvuu/GpT32q4vVq7bPRCkiSBE3TTY3RrGDhG7kEg0EkEgl4PJ6aup+Xmkenm/JJzeBuBGpZ/AiCgMlkgslkEn63yWQS4XBYSGzUarWCkcutDMJ33m71e1gPgkah9VQL8X3uuefwp3/6p3j11Vfx2GOP4a677hIe++53v4u///u/BwDcf//9eO973wsAePnll/G+970PqVQKhw8fxpe//GXhNzU7O4vf/d3fxd69exEIBHDrrbcWzUmKBy0Ek4Xvcw+g6z2lw6WYZBpX/vIvMPjlbwCAUJo3EAjAaDTWdNBVLjm8CI6Djo6C2tSHzMIcum86gMCvWpe/wUSj6NqzBQFvBCiwzcs/OY74F74C/eHfQk9PD8bHxzsaelwoPvhDocuXLwvlYsuJj06GTTeKmHPObTQIrK3xzz//PLLZbF6jQf5QTaxcRkVsSCBBnKZpPP/88zh58iSMRiNuu+027N+/H7fddlvZ11Sq8tFqxNigNxKKlclkhOoWKpUKZrMZLpcL4+PjDc9D6qVrayWVSiGTycBut8tuIS2FVN26vJHjY4vT6TRCoRCWlpbyKoNks1lYrVaxp53HeskLUWgttYT4bt68Gd/5znfw8MMP5702FArhM5/5DF566SUQBIH9+/fjzjvvhN1uxx/+4R/in//5n3Hw4EEcPnwYTz31FN7ylrcAAN75zncCAB577DEsLeVXOpIyar0GiStLiP/4MRBqNbgSh2qJySlMf/3LCF13A/R6PdxuNwYHB2s+XGCWaxQbAJBJw7LJhWwoDOrSOVj370L05TO1v75O0pcuwnV4Av6fFouazNeewM577obGbm/Z9Ruh8FColPjgw2GdTmfL7LVUPRvVIEkSarVaiMop12hQjFzGViGX3EJRxMamTZuwsHDVPXrlyhVhQ1L4nP7+ftA0jWg0CqfTif7+ftx0002Cm+vw4cM4depUVbHRSc9Gu8Ko+OoWPp8PBEHA7XZj165d0Gq1CIfD8Pubj2PtpFhoRqxQFIVkMomXX34ZJElCo9Hg3LlzwsLQyoVVYQ29Xo++vr6iyiCzs7MIBAJYXFysuzJIrSieDYVaqCXEly9UUvhZ/+xnP8Ptt98unILefvvteOqpp3DzzTcjFovh+uuvBwC85z3vwY9+9CNBbPB00k41AkGurcnpqQswbxvD6mvnSz4v+9MnseO222HatqPk4+VgUwlw0VD1J+bAhfxwTuxC4LkTION+6Po8yCx56xqjHpJTr0E93g/6Qr4oykbTmHrfn+Ka/360ZdcWg0riY3p6Gqurqzh9+jScTmdel26xrt0K2umNqdRokM9l5D1HfDhxpzf3crFTolj/iYkJXLx4EZcvX8amTZvw2GOP4Qc/+EHec+68805897vfxaFDh/Af//EfuPXWW4XwqYceegjJZBJarRbPPvssPvGJT1S8nl6vR7IwprRNtLqSFEVR8Pv98Pl8QnWLa665pqi6hRhfLjFyLtq5oadpGoFAACsrK4IR37VrFwiCgEajgUajQTweRzAYxIULFxCPx8GyLK5cuSKUdxULuZ7ktHJ8vjJIIpGAyWSCw+FAJBJBMBjEpUuXQBAEbDabsFA3kw/RCc9Gp42KQv3UEuJbz2sXFxexuLiI/v7+or8X0kkPfCMQLAOVTg06TYH1XYFq8wCY+eIcC45hsfTA/Rh95DGQJar7lIOtNYSq8HpLs7DfeBDhF47DvHkAWX8IHEVVf2EDECwLi1uFmNUEJprIeyx4/CKWv/xN9P5JHVW5GApIxUGkV0EQGsDuEXnGlSkUH0ePHsWWLVuKCoHwoUWNio9W7gM6XTGzsNEgH07MVwvj+6TY7XbRGg3Wg1xyC0URG2q1Gl/72tdwxx13gGEYfOADH8A111yDBx54AAcOHMCdd96JD37wg/iDP/gDjI2NweFw4LHHHgMA2O12/Nmf/RkmJiZAEAQOHz6Mt771rRWvp9PpEA63rrtoJcRKEM8dg99E+3w+ZLNZITyqUrJSq0VPrbT69XwjwpWVFSQSCaHyBwDMzMxAq9UK9ctzk5yHhoawurqKCxcugKIoTE5OIp1OC43tHA5HS5LBxEKOYiN3fJIkodVqiyqDhMNhwcXPsmxeZZBSZQkrXUMJo1KQMnLzbIBhoLcaEE9TYCkGRm0ccRUJjim2d1QkhqXP3I/+zz5U+/ClOofXCOmfg2nndiRem4TzDfsQ+FVxqVqxoHw+7Lz/9zH3g2cQ+c1c3mMz//Bd2H7rFhi2b7v6R5YFMnEQqTiI1CqQXgWRev1/1NVCNiq1FundtwMlyvq3C1585BYCKTy556MDHA4HTCZTTeusnA/f6iFXvPEHDqUaDfLiw2aztXxOcrFTosU1HD58GIcPH87729/+7d8K/63X6/HEE0+UfO0999yDe+65p+ZrdTqMSqymfnwDoHQ6je7uboyOjsJkMlUfAOKJnmZzNpq9fikKyyfa7XYMDAzkJbvV4tniPR7Dw8MYHh4WGtuFQiGcPXsWmUwGVqtVWFhL9XbpBO0ofduJJkZqtRoulwsulwvAWjw9X5Zwfn4eNE0Ln4fdbq/6eShiQ6EatYT4VnrtM888k/fam2++GZs2bcKVK1fy/l5qTDl4NnhPund5GcMcB41BA12XHplYGnQiDUO3CUnvasnXxk6eQuSnR2D77d+t6Vo1J4eXgmWhJ1LIetygLk3Cet1eRE+80vh4FTBvHUIX68f4R96E1ZfPYuoHp0En1/JXmDSNC+/9BPY++lmosok1cZFJgKhhzSboLNSXXwH23NKSeTdCqZN7XnxcvHgxL2ehHvEhJlISG6Wo1GjwwoULSCQSmJqaakk4MSAfO9XxBPFGaEZsNPvFbWaDzrIsQqEQvF4v4vE4VldXMTw83NAPWAqeDbHDqJLJJFZWVhAIBGAymdDT01OyfGKj5Da2GxkZAcuyQhm8M2fOgKIoWK1WIZ61npN2sZG7Z6OW8VUqFZxOp1Dumu8yHA6HhbKEVqtV8Hy02xMll0VcoTy1hPiW44477sBf/dVfCV70p59+Gp/73OeE5mrHjh3DwYMH8b3vfQ8f//jHi17fSbFR6TfIN6Lz+XygKGrNkz42Ct6iGuxG0CkKDMWAYFlozVpk46Xt7co3vwHT/gPQ9FYXcE2JDQBIJWEd7kEwEgXCS9AP9CK9sNzcmCXou+VagA1Bk4rBPOjE/j+7AYu/nsOVZ9e8HLFLfsx//psYff/tdY+tCS8iuzwLbe+QuJOukWprcynxEY/Hq4qPjeLZqIXCRoMvvPACbDZbUaNBh8MBm83W9D5DLnZKtmKjkUVcjB9FvQni/AbK6/VidXUVdrsd/f39SCQSQnOnRhBro9/pBPFMJiOEkKnVang8Hlx77bWiq/9SkCQpnDaMjo4Kn1UwGMTc3BwYhhHh4VzWAAAgAElEQVQWBYfDkRcX2erFtZVIRWwUQpKkcK/5z4P3RBWWJWQYRrLvQ0E61BLie/LkSbz97W9HOBzGT37yE/zN3/wNzp49C4fDgb/+67/GxMQEAOCBBx4QksW/8Y1vCKVv3/KWtxQlhwO1HYq1YqPAe71zK0QxDCOss7wnfcuWLUIeG5tMCGKDIAiYXGbElqJgaRY6sw50hgZLFds9lmIwf9+fY+S7j1b8rXAcB2alOK+lXrjACpwH9yHw3DGYevqQ8QXBZcSLciA0Ghg1aSADEFQGnM0JMrCMgZuH4d7XiwuPv4bEchxzP3oF3ddthfWawbqvoZ45CdreA7VEPOmVyA1NHhwcLCk+LBaLYBtbsWbKfR3mi/sUhhOHw2EhnDjXo19P93mguFiKVO+VbMVGI54NXig0s7jXssnnOA7RaBRerxeRSAQ2mw29vb3Ytm2bqF8EMTwbzb6+kTkwDAO/349IJIJkMone3l6hypbY1DO/3M0uP09efMzOzoJlWUF8NBvCVgm5b6LFqhSV64nix+XLEmYyGbzwwgstdfHL5cRIoTLVQnwnJibywqJy+cAHPoAPfOADRX8/cOAAXnvttRKvuEq1QzExQnIrjcswjOBJTyaT6O7uxvDwMMxmc/GLmPwytyqtGga7EalwEnSGhsFuQMKXKH4dgMxKACsPfRa9991fdk5sOABkUk29Lx5uaQa2Q9ch8uIJOG/Yg8CvTooyLgD0vuVGEJmrIbr6ZATspiGwi7PQWfXY9eH9CJ334+J/TuLsPz6Jg9/8EFS6+uwWQWXATJ2AevdNos27XZQTH7Ozs4hEInjhhReEAyG+KEuza7KcxUap33e5cGK+hxVFUUJuqd1ur+rRl4udkq3YaNSz0ewmsZxng98I8XkGFosFHo8HW7ZsKflFkEIlqHZWkypM9O7u7obZbMbWrVsbrhJVbe7N3uPCMJ/cBOfl5WVBTPKLQju8MWIgVzGTW5ZwaWkJhw4dQjweRzgcxqVLl4TKKrwgbLabrlKNSqEZqh2K8bZEzAaYLMuCpmmcP38eyWSy9k7XJXpq6Lr0oFIU6DQFjuWgt+qRjqZLvBgI//IZmG98AyxvuLn0vJoNoSpAFZyHdnQY2Uvnod61FfSZKVHGdY52A/FA3t/IaACs0QQkEyAIAs7tbtg+5cDMkxcx9dUnsf2Tb6/7OtrQPJJLczD21e8ZaRYx1zFefLhcLhiNRoyMjAgHQhcuXEAymSwqylLv9eVcwr4WW1gqnDgajSIcDgu5pZUaDSpio4U0KjbESu7mx+CTqbxeL4LBIEwmEzweD0ZHR6t++J32SvBjtDJngxdgKysrJRO9z5492/D1O7Hxyz2RUKlUMBqNUKvVCAQCuHTpkhCW5XQ6YbPZGt5EtOMkpxMJ4mKTe8qWW5YwFAphdnYWq6ur0Ov1eTXR61mU5bKIK0iTWjwbYnhIOY4TElJjsRgYhkFPTw+cTmfNv0OOKRYbBEHA1G1CbCkKjuGgNmigztCg08XPBQcsPfQQRq/ZDbXdUfQws9J4JaqSsCxMBg6MwwFzIoTM8ABSl5u7hnl8GOp4sMS1GKgsDtDJBPi7qdKqMfa2bUh64wifugD7tfU319VdPgna4ZFFOFWtlOpTwYsPXgA3Ij7kug43YkNyw7tHRkaEgjnhcFgQcLmNBhmGkcX9ka3YaCaMqhkIggBFUbh8+TICgQD0ej08Hg+GhoZEPaGqhlgu+FacGiSTSaHbeSsSvaUAx3FQq9V5sZjZbDavCgVfAo9PBJPK+2+1GOiU27tUQyu+LOHCwgJisRh0Op3g+ahWE10RGwrNUM1ONXPYUypUt6enB9u2bcOZM2fq75dQQmwAAKlWweg0IeGPg8nQ0Nv0SPgS4NjieTPpLBY+/UkM/9P/K36sns7hNUKmk7CPb0bg5GswOvTIrOjBpkp7Xmqh75Z9AFO66SCxGoJ6YATMwszVvxEETD0WcHQUDEVBVWevA5JKI3XhJNR73tjwnKVCuTW/lPjg8/AmJyeRSqXyxEepKAc5ezbEsCEEQcBqtcJqtQr3kPfoT09PIxAIIJPJwOVywW63o7u7W5JeeFmKDb1e3/YwqlQqJXTzTqfT6O3txb59+xoOnZFKGJVYc8hms/B6vXUneou1kEhhQdJqtfB4PPB41ho3ZTIZIeTq/Pnz0Gg0wqJaaaMr5xhVQDrzJwgCRqMRRqNRKE2aTqcRCoWwtLQk1ETPrQySe2CQayik8H4U5IXYno3cUN1QKISurq6SobqNHKqV8mzwaE06UEkK2UQGbJaF3qZHKlQ6/yJ1eR7+f/oaXB/9WN7fm65EVQbOvwTnjQcQfPYYHAd3IvDMSw2NQ+q0MKpTAFP+OURwBUSXDVwskv93AsjOzMAwXr93wxCeQ3JpEMa+zXW/VmrUskbmbpyHh4fLig++IqTBYFCKsRRQ6NE/deoUBgcHkUqlMD8/L4RjSQ1Zio1mPBv1fLkymYwgMFQqldDNe2pqSihr1imk0GeD4zhEIhGsrKyApmm43e66Er3F+hFKQWiUQqfTobe3F729vQCubnQXFxdx7tw5ofOo0+lEV1dX3v2Qs+eh2QRxyruE7IXXkJ2bBr18BRqHHV3v+jBIk1UYv1H0ej36+vpK1kSfmprKc2G3KxxMYX0ihtjgTzF9Pl/Nobr1ruscx5bM2cjF6DSCzlBgaRYqrQpakxbZRGkb7P/hEZgOvQHG3XvXxqcpsAFvzfOpm8UZWK/bj+iJl6HZsxXU6frzN3rffCOIqgnsHFQaNWiCBLj8z42LRUHFYtB0ddV9bc3MSTDdPVB1sNR6szQTDp0rPliWFcKu+AqEJEnCbDaju7tb9PLn7eg51WrvOMdxMJlMcDqd6O/vb2uETT3IVmyk0/W7S2tZ3LPZrFCDnOO4og00TdMtrURUK51KEM9N9A6Hw+jq6soroahQHr1eD4svAt3qKna+8UakUimEQiHMzc0hFovBYDDA6XQim83WXf6uHjrV1C8XJrGKzIXXQM1MgVqaBxPwgovHwFEZFL6SiNtBP/qPUN30dqi27hN1/oU10bPZrFCBLJlM4vjx47DZbOju7q65GZyCArCW48Uw5Y/KK62/fC5gIBCA0WiE2+2uOVS3bs9GYAVcyFfxKQRJwthtRnwlBibLQNf1ejlcusR1WA5XHnwAY488BtJoBONdAtgKLgMRUMeWYRgbATc7D83oIJLTc9VflINjxFmUGF6SVAKqTYNgrlwueoiamYZq126QdW721HQKsTMvwrDrUEd7OzWDWGsySZJF4uPChQvIZDJCsjQfduV0OptuxCvXYim5yCXcV7ZiQ0zPBt/kyOv1Cif027dvL/lFbmcFp0q0U2yUSvTu7+8X6ms3KjSaeQ+5r+3E6XOji8jc174LOuqH842HYDAYsGnTJmzatEnIL+BDfPgQLD7squ4Y7BbMvd7xWZoCNXMRmUvnQC9cBu1fARsNg0snAa60ICk1K4IACHBgnv1PsJfPAbf+fssWV61WK+ThhEIhHDhwAOFwGKmUOGU7FRR4CkVBMpmEz+eD3++HTqeDx+PB5s2b6w7VrXtdtdjAnPh21adp9BrorQakoynQ6dfL4fpLl8Ol40lcuf8+bP7CV0WvRFX6gjRMFjWSeh30BjXSJhPYROm5FWLZNgJ1LULjdYjQCkiHG2yhQKMoZOcXoB8eqn3er9O1uoCpV7QI0msN3yiKAk3Tsqlw2CpIkoRer4fVakVfX19e76XXXnsNmUxG6FHhcDjqFh/rwbOhiI0WIkbpW77JkdfrFZJrainDKlYFETFotdiolugdi8UkIbw6RSPvPfzr4yC4FOhECmrTVZdwbn4B//1yOBwIhUKYnp5GPB5vqqcEHQlBbbtaJabVYoPzLyPwzc+CjceLHifWJlDPgGuvIwhg4Ty4Rx+GfvigOJOtAl+BTAmnUhAbkiSRTqcRjUbh8/mg0WjgdrubygXkx61nbSL0RiBZ28ZcbzOASmXBZBmAQMVyuPHXJhF67BEYLO3ZZnCrEZi2DCD56kU4J7bDX2P+Rt8tewG6dGJ4OUgmA1alLkqsZwM+UHY7NDZrXeMBwHB2GcMH3oJYPIHl5WW89NJL0CfCGKDCwIHfgs1ul2yITLs6iOf2XhoZGRHERzAYxJkzZ5DNZusSH63eqIvVc6oScskt3HBiIxwOY3FxUWhyNDo6CpPJVNcYUqBV+Q7ZbBY+nw9erxcqlQo9PT1lE72lci86ReiJp+C+9z01P3/11UnQoQjUXSrMf/lbGPmrT5R8HsdxUKlUMJvNMJvNQlnXUp1beXdypRKCLJXF4gOfRM+9n4BufFdD77UeOJYF9fjXQXDiiHIC+eMQ2SS2n/8VGJsBql2HRLlG1Tls8O+6QmOU+t7k5gKGw2Fs2rQJe/bsEbowi3HNug/ETBYAKzWNbeq2ILYcAUux0Jg0oMuVwwXg/e73YH3jPrQryNawGoLhxgkEnzsGxxsPIPTryoKD1OtgIBvwWlJZsI5uEL6VonMTanYGqp27QNYpGDV0Eomp36B79w3Q6/W4bngT6Kd/BdKgB/3rx/GyczugNwvJ09Wq6bWbdoiNQnLFx+joqNCjIhQK1SQ+pBBS3CxSKchSDVmKDZVKVfPJDZ9jwPfCsNlsGBoaEjUspVGa+ZKIWY2K7+idG0ZWS6J3q/t0SBmO4xD8tyNAHWJj9ouvhypwgO+HPy0rNkpRqnMrn0g3OTmJdDoNi8WSV8WDZ+WLnwcdjSD6va/D/dlv1XzNRnGdfhZIxcFViFevixKihSAA5tiTYOcmoXrLe0CqZLmUKWwQSuUCulwuOBwOdHd3i3qthtZVo6Xmp6q0KhjsJqRCCdApGnqrHols6XK4HMMi8fI56Ld0g2zXWr80g679+xA7fQam8VEkLkyXfWrfm28EkW0sRFKbiIJx9YALFIg0ikJ2fh66kZGSYaGVMIZmkFwZhDEeAvWjJ6F2uQGVCjoVgf2hs6B334qg3iAUGeH7CDmdzqabmDZDK+14Pfuk3AIf5cSHzWYTxMd6CKMC5HEYti4tNF8lyev1IhqNwm63Y9OmTTAYDDCZTLBYal9YW0mzYqMZWJZFOp3G3NwcKIpCd3e3rBK9O/3jYlNppCanQUVXobHW9n0KP3sMwFpUEB2NInL8ZdgO7i96Xi3fi8L65XwVj2AwKCTSWa1WWBOrSL/0Iki9Hlw8ivjPfwyYxN3c5EItTMO6eBEgIKLYKP1ngiCAlcugH/k81G/9AMjuPnGup6AgAuFwGBzH4fbbb8df/uVfYmxsLC8XcHZ2tiUhuQ2F+hrMdT1dZ9GBSmVBpyiwDFexHC61mkIixsJiad+abd+3HaouM2KnJ5HusoCJrZZ8nmPYUVtieBlUqVXQWh2QzY+0YIMB0HYHNHZbXeMRAMjTv8C2mQsgNGqQRuNaWGomDVKthua1X8HTN46+A7cDgNDE9PLly1hdXRWqEjUSatsM7Qqjqpdy4iMYDGJhYQHZbBYMw2B5eRkOh0P0wixy8Tq0g3UjNvh6zV6vF+FwGFarFR6PB+Pj48KHHY1GJZNvIVZTvnooTPQmSRJutxubN29u6AfRbPndZn+EnfSKsNE4wLCY+/J3MPbAx6s+f/W1C6Bj8TWXO8eBIEjMfeEbsD1eOjGz3nuTW8WDj2WNRqPw/8UfgQTAvr7xj//Xv4N924fqGrtWWIZB8gdfFcIKCIIopxPqotSJaS4ElQH9o2+C3Hcr1PtvEeGK0i2nrCB9wuEw3vve9wodvR999FFs37696Hmtyv9rxLOheeObkXnpeXBMbfMhCAImpxmxpQg4hoVar65YDnf10iIMb9gLdaLxjX09aCxGuPdtAclxIG1eBJ77jZD7xWPZMQZVE0IDAMCxUFntoP0rRV4ManYGKvMukHWExzHxOIiFORAEAbJrLe+DIAiwDANCrQZBkiBWLoL+2RJUt/5vIc+vv78fHMchkUggGAwKoba5lZvELhtbiBTFRiG54gMAVldXMTk5iUQigYWFBdA0LXg+7HZ70+JDLsnb7UDWd4HfPF+6dAknT57E4uIinE4nJiYmsG3bNtjt9qLeBVIRG+0MIUomk7h8+TJOnjyJhYUFOBwOTExMwOl0wmg0dlR5t/IetNQ9urqWUOn9j6dqev7cF799Nbb39bccP30GTImOt2LcE5IkwT3zNMjXG1DxP3SCZeA8+jMcPXoU586dg9frbaiyWylS//nPQF6t+vb1USEAsKf+B9QPvwmWav79KCdS64unnnoK4+PjGBsbw+c///mixzOZDN75zndibGwMBw8exOzsLADg+9//Pvbu3Sv8jyRJvPLKKwCAm2++GePj48JjPt9adSKbzYaHH34Yx48fh91ux9atW0vOqVVio5FxVa4+6G+7s77rqEkYnWseETpNQ9elA6kuv6UIHD8DwtUe7yMJBkQmAed1u9A11APHTQeKntP3pj2iXItIRKFyl3hfNI3M3FzNBy5MPA7mdaHBkSTInAgMrmAtUlEJsP/1/4ELXg3hIggCZrMZg4OD2LdvH2688UYMDg6CoiicPXsWL7zwAlKpFJaXlxvKea1Eq8OoWgVBENDpdBgbG8N1112HgwcPoqenB/F4HKdPnxbs5MrKSkN2sh0J4rlI2WbJ0rNx7tw5ZDIZTExM4Otf/zpGRkYwPDxctVJDJ7wJ5Wi12Kgl0VsKXczlChtdq7KUunwFdCoFdZVTo9Cvjl79B3/LOA7zX/sXDP/Fx4qe3+xnwyQSCD3+bznXvLr5sK/MYmR0CHG1DqFQSAjnsNlscDqdsNvtdSerUpfOgpr8TX6ypFjrXo2J5gRBAMEl0I98Hqo7/gCqvuGGL6mcSK0fGIbBvffei5///Ofo7+/HxMQE7rzzTuzYsUN4zre//W3Y7XZcunQJjz32GO677z48/vjjePe73413v/vdAIAzZ87gbW97G/bu3Su87vvf/z4OHMjfyBIEIQgMvphJqfDUVq2fjY6ru+1toM68BHq59lK1WpMWVEqHbDwDJsvAYNMjEUiWfC5LMQi+NAnn7mFw0fqqP9WFTi/kYZCJEBz7toNQq5Hxb0Hi3MW1v+t1MJCl59kIZDwMzmAEl8ofkwuHQAVD0DodZV65Rq7QAADSnJ9/QWp14ArKhZMqAtzzT4Advx6qbRNFY5ZqmPf888/nneLb7faG1/y899niw5lWrcWF81apVEI+B7C2dkQiEaEfFsMweTkf1fJalcawV5GV2PD5fHjrW9+K/v5+MAyDX/7yl+iqo2MnSZKgq3RKbRetMDS5id4URcHj8WDnzp1lXYGdFgudvn4zsLHXS7pyHBb/6XEM/un7yj539ezFqyFUyE9B8P3nkSKxIcY9WX7oM3mNtLick04CwOq/fhndn/4HOJ1OAGu9ZsLhMEKhEGZmZgAAdrtdcCdXKsXJUhSS//6toqosoq2xdd4OgqHAPPn/wO08BPWhww1dslBsKAZDvpw4cQJjY2MYGRkBANx99904cuRIntg4cuQIHnzwQQDAXXfdhY997GNFG5FHH30Ud999d13XriQ2WmWPmvGYGD/wSaw+9BfgKKr21zhMoNNr3cVJnbpiOdx0aBWpcAJ6fXGeg1hoB4eQG8BJZmKwjfSA1GpxedkLOhyD57cONZwYXg5SrwedShadsdBzl6GymMt2CC8UGhwAtTW/dC6hVoNNJUEUCAJCpQIungDtnYPqpndUXKdIkoRarcbY2NjadRkG4XAYwWBQWPP5TbRdQmV2WxmNUs3zoFKp4HQ6BTtZSnzk2slC8dFuz4aUEfUuNOqq5pmfn4fZbMbDDz9ccnyXy4Vf/OIX+OEPfwij0ViX0ACktbkVay4syyIYDOLcuXN4+eWXkUwmsWXLFhw4cAADAwMVYw43QjWpVs2P92wAwNIPflLxuXNf/Jf8jXfOlKhQBLFXzuQ9v9lTouSZ00idyx+zMF6ZXppD+szVspB8P4nx8XFcf/312L9/P+x2O4LBIE6ePIljx45hamoKwWCwqCty8tGvAXQpF3P7wqiKrkwA3NkXQf37l8Gm699UKJ6N9cPi4iIGBgaEf/f392NxcbHsc9RqNaxWK4LBYN5zHn/8cbzrXe/K+9v73/9+7N27F3/3d39X8ntaqQGtlHI2gLXfWYJQIXHo9vquRxIwda+FUzEZGhqTBmp9+cOJ0KvTID39EM/1mY/W05M/P46DSq+FxaqC5+3/P3tvHibHdZ73/k5tvS/T07MPBoMdBCmKJABBlK2YomnxCpYYKXK8xaR8tdg3V1YY6bmJlMd6dPXIfhxZTuT4seIodpQbLZRBi7ZJbaZJS4pELQRJiBtEEARA7JiefXrfqurcP3q6p/eunukBZiS8/5CYqjp1qur0d853vu99vzchhSAw1h0h3glEPovWLJ3KsiicO4fd5JvUOxoAwucvORF1aMWnEUKgJmewv/GXyHRzInwzqKpKNBqtsfnhcJjZ2VmOHj3KU089xenTp1lcXOw4Ttc7srFebXcbeSg7H7t27eLQoUMcOnSIwcFBEokEzz77LD/60Y84ceJEJT15vSMbG339VY2eRTbWEqou40Mf+hBvectbWt6jHBaEkqG2LKsr73sjFeRbS1/KXJVcLsfTTz9NOBxmbGyMYDDY1cDuhbNwra6vfs5isYhlWT1XkmgHO7nibKRfPoNtWSgtxmJNChW1zywEnPuTP+fmB/6yJ/2SUhL70z9qjCrUvWYhBIkv/3dcf7S/6ZgpFxgbHBwESml5i4uLzMzMcPLkyUq4uX/+MsrZE83HXa+MbAeCeFsk5jAf+GPUu34ddete57e87mxcRxWOHj2K1+vlpptuqvztgQceYGxsjGQyyTvf+U6++MUvct99tVLY7WpCrReHUAjRsCHQDtXFW71eL8OH7kS/dJriq684bkOrqy7uCrmwClZzcQcJU//4A0b/xS9hvfxi4/E1Qgv6wawtUiiKOQqRKMP58/ju/1f40utT1VykFhG+QMOiX8aXMOfnMapkjq1UEuvihRrbKaVsiGpU2u6UJo6Fdfy7aIfeuqq+19v8fD7PwsJCRWbX5XJVdvmvpszuRlW6gsbIh2malcjHuXPnyOVyFRXUtaaqNcNm4hb2zNlYa6j64YcfZtu2bY4L7LULT7dCL52NtX7k1Sz06yt6a5rGwYMH17QoWmtkYy1Yy/W2bZPP53nxxRfJ5XJomlbJp4xGoxW1ifVCJY2q1BmmvvgwY7/9zobzkidOY8aTLSMbAMkfP49dLFZUS9Yytma/8D+wU4nmfa5bQMtMitQ/PETg8L/s2K5hGAwNDTE0NASUnI+FqSnE4w+27GuvbOCa68lIC+uxL2HvuhXtF/6Fo3d73dn46cHY2BgXL16s/PvSpUuMjY01PWd8fBzTNInH45UFBMCRI0caohrlNgKBAL/5m7/JU0891ZWzsV4cQifpWdWFBTVNY2hoqIbTZ7/r32L9xw9h55qnQzVDqbp4EatgIqTAHXKTXWweVbTyJizMYI5Pol065/geTqCoApo8vs/OY227gcDZE9jRUZi70tP7Vu6vCEyhrBQ1dbkgn8c8fw41EEB1ubCSSaxLFxpskfB4UFqkWwmXC2kWEW3skkjMYeWzqK61K0+5XC5GRkYYGRkBIJvNMj8/z7lz50gkEjUyu+u5g7+RnY16aJpGNBqt1M45f/486XSapaUlXn31VaSUNWlXa3U+NtM81TNno1mo+ujRoy3PqQ5Vu91u/viP/5jHH3+8ZQpVPdxud9fORq/SfsrtXA1no57oXT0pPP3002saaJuRIF6W7i2n85R16zVNQ9M0lpaWKjmotm1jWRbz8/P09fX19EdpJ2p3zi7/f3/b1Nk4/+n/0XnRbdtc/G//k63/5ncrf1rNtykuzJP45t+3PqHJp8o8/gi+X3wrSpeTk2EY+B7/MnY78nbvvI01NyGEgNPPUbxyFu2f/w6Kr30K5mYy4tfRHgcPHuTUqVOcPXuWsbExjhw5wpe//OWac+655x4+//nPc/vtt/PQQw9x5513Vn6Dtm3zN3/zNzzxxBOV88s7mNFolGKxyNe//nXuuuuuhntfizSqVu2aplnh9FmWxdDQUMvirYrbi/fX3kfq83/u+L5CCHwDJTlcq2ihe3SsgtVSDtc/WCIvLxUL2NO9WfhLBOTTLY8rmUXskUmUhRlsl6dOPa83EFYRrX8AczaG3h9BHd1C8ewZ7HSawtlz6KMj2JcvNrXxarB5VANAKApW0UR1tSYli3yG1PRFghPNFdDWAo/Hw/j4eI3M7sLCAqdOnWJxcRGfz4dlWT2X2d1MzkYzBINBxsfHgUZu5Fqdj83ELdwQBPGPf/zjfPCDH8Tvd55H2c6It0KvjHu5nbUu9Fst1Lsleq9HH5zianA+CoUCsViM6elp3G43IyMjbNmyhZdffplAIEBxmcxYH9KMx+MVedeTJ09iGEbPwsDVnA2A5PMvNzVcC9/+fuPFTR45duTvK87Gat/p1B9/vO3CvGm7tkX8f32Gvt/9d13dK/f0d7Eun23/DjdIZKMaIhPH/Ov/hPrmf4U6saftPTeLEb+O9tA0jc985jPcfffdWJbFu9/9bm688UY+9rGPceDAAe655x7e8573cO+997Jz504ikQhHjhypXP+9732PLVu2VKL2UIoM3H333ZUUzrvuuov3ve99Dfe+VmlU5d9MmdMXi8XI5XIVXpaTxaC2bz+u19xG/sUfO763qqt4Iz4y82mK2SJGwMDMm9hm43MqQkAmTWjvdpZSCWQ61aTF7mAPDKK2UZQQgCIsLN1AGRzFvti6uvhaIDIJ9MEh9P4+7EAIY+skuZMvI9MJ7Mtmc3ui6yidNk872EIBiPkr2GPbUdT1W96VZXb9fj8TExOcPn0aRVEqMrvlorLlyMda1i7r6RCs96ZS/TxS5kYODAwAre5X2+EAACAASURBVJ2PskJYO2GWq9H/XqJno3EtoeqjR4/y0EMP8e///b9naWkJRVFwu9383u81SoKW4XK5yHUR4oXeORu9WKTXTzS2bbO4uMj09DSpVIpoNMrOnTsdp5Wttg/XMo2qHcoRiampKUzTZGhoiFtuuaXi+TtxNHVdx+12V1L5crlcTRjY7/dXnI9uK6eX62yUIU2TmUf+iaG3r5Ar0yfPYMZTjRv8TV55cW6e5PETBG4qFf/q9t0mf/QE+VdPtb+uxafOv/RjilOX0EfGHd3LTifJPXqkYx97Nj56HD0TSOS5E9DG2bguWfjThcOHD3P4cK0y2Sc+8YnK/7vdbr7yla80vfaOO+7gySefrPmbz+fj2LFjHe97LdKooJRye+LECZLJJJFIpJKi3O2Ydv36/03x7AexU86Jx4bfRTFToJgtIi2JO+QiM18bQdA8OkIRYNsomQSBA/tJfP/70AXXpBnEwGDnc6wianQA68KriC07kRdPr+meTe9hGGiRMBKB0A3QNFzbJrFisZbfQAl0FrwRHaRWAYzkLJnZKfzDWzqe20v4fD6GhoYqMrvVlbrXIrO7mSMbneaRds7H6dOnEUK0VYX8mXQ21hKqrg5Pf/zjH8fv97d1NGB1kY1epf30YpIoOz7lqucLCwtdE7178SwbSY2qXAU+FotVHNF2Dlf1vZ30w+12MzY2xtjYWCUMPDc3x4kTJ8jlcpWdmP7+/s762YnGXbhL//2va5yNc05SqJYhBJz/kz/nps//Rdfv1LYsZv7i0w7GTPN2BbD0uU8z8NFPO7pf6vP/eSUf+SpgXRZkWmd99M1ixK9j46KTs9FLDmEymaxw+jRNY/fu3ezdu3dNiylF0/Dd9wGS/+0/Onb6hRB4o34Sl5ewTbtEHg9KcomVzUFPpKpgXaGA7srgPfh6Mk/+YNV9BVDDrdOQavqYTaJs24P96svIvkFYnFnTfWugaWiDgwhFQSrLSyxVRw0EkbkcMh5vuKS+iF8rKIaBzGYReuulm5ZZYilfwHuVbVhNDZC6St1lmd16afWy89FO6GczOxvdSt/WOx/FYrEiT9zM+dhM81TPnI21hqq7RTsj3gq9jGyspZ1sNksymWRhYYFAIMDw8DA7duxY1aBZy4/lWkvXlu+fy+WIxWLMzMzg8/kYGRlh9+7dbZ+rF3yTchh4cnKyYSemrJ/dKpxZn0YFEH/6hZp/L3yrSQoVrRfP8ad/jG1ZXX/Tmc/+F2S+c5SvnX9gzVwh++Mf4bnt9rZt5L7/KPbsFUf926iRDaBBr74em8mIX8fGRZlb2Ay9mI/KoiFzc3N4vV6GhoaIRCKVzateQN26G/ehXyD35P92fI2iKviiflIzScxcEd2nY+ZNzHyJue0K124gyVQKb18f1k23kD/+3Kr7anjcgLPoiJJaQI7vQJmfxtYNKHZfIboBQqAPDq4oR6nLoh9uD+RSqP1RzGwW6jZKlYBzJUmp6zV1ROqhmEXUXJrc4jTe/pHVPUeX6LSOKMvslonT5UX07Owsr7zyCpqmldQN+/sJhUK1Qiab2NlYa4S8XiGs3vmwbRspJXNzc4TD4Q1TG6UZeprUt5ZQdRlltapOWI2z0asc2dW0U0/01jSNycnJyo9vtf1YC65lnQ3LskilUszPz6NpWtMK51cT9Tsx5XBm+UetKAqRUJDo0DChUKghjQrAzhVY+N9HidxxiPSpsxSXks0jG61emWVx+a++AG96g+N+F65cIfndf3JGj2jzrYQQJB78H7hvfX3LcWUtzZP79sPOx13PnI3eNFODDkb5urNxHb1Auwj8auejZkpSt956a8V2xuPxnnNBXG9/F9mf/BiRbK501wy618AVcJFPlqqLGyEDa64kh+sK1aWtSomdTOIfDFEc24p9+fyq+tmmvEdTKMU0lsuNGNmKvHBqVfeshjY0VLORIQ2jxKPQDexMyaZowyOYFy9U7LEEtG5qhnl8kG3Pb/Ekpki7vXj7257WU3SzHnEqs7veSlfrbed73X79eyuL4czOznLq1CkmJyeZnJzs2f16iQ1BEF8NVksQv5ppVJZlMTc3RywWayB6lxewa8FaVbGudmRDSsnS0hKxWIxkMomqqoyPjzM62qQQ0jVGfTizUCgw9fcPcvnGW/nJi8eRTZwNgPN//gUidxzi3Kf/alVr7akHvkLwjtsdf9Mrn/yYYx52x2+dy5B8+AGC7/itpofTn//PbXfUGtDD+cG2bBS1h5NCh3dx3dm4jl6gV5yNspJULBZDSsng4GBLJan1sOtCCC684a1MPn4E2YUj4+nzUcyWqot7on1IU5JdyGIEmnDkikXI5/DvGCOeiCOSS131UemLYDiMapQhpI0a8GPFrsDoNuSVs11dXw1tcBClngjtclfdrGRPhGGgDg5iTU+X+t2iiF/LPptFJO3Nq5GaIzG8l3x8Hldo/T0OJZfG/fLLMPC2VV3fSmb3/PnzLCws8NJLLzEwMEB/f/+quEetcDXSqNazfUVR8Pl83HBDiev5MxPZuJq41mlUrYy5U6J3L6Ism0VNKpPJEIvFmJubIxAIMDIywt69ezl79mxHbsRGga7r8KPvceOv/hbmYoLvtjhv8fulqtwL/9Q8hQpou1NfmJnFPH8Jlp2cdog//g8Upy45N2YOvlX2u/+A//94B4qndrxm/+nvkPH5Flc1R0+NbK+d4g5E1M0kKXgdGxdr4WxYlsXCwkKNktTevXs7Kkmtl6RuIdCH+657yD72sONrhCLwDQRITsXxTIzg3zFO7LvPoQeaP4NMpzHCOn37X8vSD5+EgvM53tiy1fG5NX3MZ1DGt2GfP40M9kFises21EgEpe672AhElSKU1N1gltJdlUAQO5PFTsRRWxTxa9lfaWPrLkSx9btRM0mwLdLxxavibAxceBbP0hTWVy8h3nwvinttFdqrZXafeeYZtm/fTiqV4tSpU6TTaQKBQIVfuRaZ3auRRnU1IycbeZ667mysAvXtVJPznBK9exFluZZpUJ1gmiYzMzPEYjEURWF4eJj9+/dvaM+7HWQmjTlzheQPn0Abnmx5np3O8r0/+a+Yi4nWkY02r1wA5mc+Q35niCldQ6gaQtNB1xGajtANhOFCGC4Wf/ijroyLo08tbZY+91+I/N7vV/5kzlwh/8N/7D5Q0UPD1/NxanfnbFzHdawG3dbZkFJWNquqlaS6kYVfT7vu+sW3U3zxGcwp5xW4NZeGO+zBNiX9N20l+cq51lHK5XQqLRTC/7pDpH7whOONBj0apWk1PwdQ0ovILTtR5mLY6SRYzttRQyHUZuRupW6uc3uwE1kUpWQX1YEBpG23LOLXFpoObZwNIW3cqVlygSGK2RS6Z22L/3aw0wm88enSuFuaQ/7dn8PP/XOUrfs6X+wAQgi8Xi+RSISJiYnKemt+fp6XXnqpRtylW5ndjUYQ7xabaZ7a1M7GtVKjKreTzWaJxWLMzs7i9Xq7Inr3Sj73WlcAr1eEKu/EZTIZBgcHufHGG9v++FfbfyfP3ksjYi7Oo+oqC19/hL5f+T/bnqt8/utrWmeLQhZyOjLXga7QZrJpBqfvunDqOMWLZ9G3bCupdn3hT1eVEdVTG97rndoOiwnbtjetY3wdGwdut5tUqnl+fTm63Wyzqhz9XY0NW+/0WO+7/x+Sn/p3yOX6Rk7gDnlQdA2h64y++XasVLr1Qs80kdksHq8X89aD5H78lKN7aD4PFJxL9NZDyS5h+/wIYzvywiuOrhE+H0qryIRWK0IhhKgx6EJRUJY5gt1COFFfjE+TCw6Tnp8hPL5+zgZHv1FSJyx/S9PE/u7fIrefRPm5t695Hm5W8ygYDBIMBmtkdhcWFmpkdiORCJFIpK3M7nrb+fWWUL/ubFwFXKvIRqFQIJ1O8/LLL+NyuWoqeneDjeJs9CINK51OMzU1xfz8POFwmC1btjgqmreRQ371sBbmEUKQPX2K4kL7XGJzsVHasB7tjIRTXoSgS+60w5MFsPQ//wsD/++fkf36lyDjnBRa29DG/b5LiwvYs7MtCyfZtt11NdfruI56tNsUy+Vy5HI5nn766UqNgtWqElZjvdKoYHnhFwzjfdtvkP67Lzi+TgiB7neDpqMZGlp/H5kLl3E1424AMpNBGga+sI618waKp090vIfCGtOSkSiagkzGkcNbIdaepC7cbrT+/pbzmNRdjZs0mg5yZaND8XhXp4JlFpBCaStBrqcXAMjagkAhj2q03/G3Lp9BHdvRVTfsdAIZO9f0mHz1ONb0RZQ3/xZKINJVuzXtdFijVIu77NixA8uyWFpaYn5+nrNnzyKlrChd1cvs/rSlUW1k/Ew5G6tdXNcTvRVFYXJykqGhoa7bqu7LteZsrOX6YrHI7OwsMzMz5HI5hoeH2b59+6YZ+N3CXCzxFWQuS/Jo+502aToIwbd77Y6/SXfuRjff2pqfJvnNr2A9+/1V87x7acR7vU/r1jUuVmmXl3fBypPRZjLi17FxUe9slJWkpqen0XUdIUTPVfjWK7JRLUiiH7oT/fknKZ5xFgEAUHQVyhwGaeMaHUYmWqeb2skkSjhMcCLKYmIEe2aqdeMuN6KQ6eJpmkOYeZShEbh8AcsXQKRbREp0HW1goKWNs6WsJYcvQ7o8kFtpU0gbqWqILtK2oGT5bcOFyGdbnqPkMyhmDltzk56LERztwGl55lFszztRIsPOO3L0GyvzlZSlDabqsZeOYz/yWXjd3Si79ztvtwrdOgSqqlb4HNAos1t9fL0jD1fbmdnIG7ib2tlYTRqVU5RzZ2OxWAPR+8yZM2ueHDYjZ8O2bebn54nFYuTzeYLBIJFIpFKhe73vvxr0qn1r2dlQXSpL3/9h23PtYrEhXbcbCKdOqFi/dyeEIPu9f8Tl3SCpRHZvn9Wl6+zduxcoTUYLCws1k1EZlmVdT6e6jlXD5XKRSqX4zne+QzgcrihJ3XzzzRiGwdNPP91zue/1imzU22vPff8W648+iO10009RwXCBooBto6oKpssNhRY1giwLmcmg+HyEbtzFUiqJzDRPSTO2TnanlNeum5k4cnwbduwySjrV2K6qog8NIdptRgjRVGFK6Dq29KHkV9QMV+NslDra3i4JwLMUIx2dJFO08LcRxbAzScimkE9+Aw6/x9Ht7XQcOXWurk9Kg/iGtEzMJ76KcvIYyi+/B6XLyXGtC/ZWMrtXrlxhZmYGwzDI5/NEIhHHBZWd4npkYwWb1tlolwu7WkgpSaVSxGKxtkTvXhjzjZBGBZ0X4+V3MjU1xeLiYg1hMR6PE4vF1nT/zQJrYQ4AzdDIXbjc9lxpWdBJprXNa5eW07HVZSJV1wv2Hkzemgpmd3KUZQhVRfW40DxuhNHjlKYqgriu6wwNDVUilYVCgePHj7O0tMTRo0fRNI0DBw6sSfXkOn62kMvl+OY3v8mf/umfcvHiRfr6+vid3/mdqzKG1juyUUbOhvw/O4z++N87ul51G6Wd70AY4guARPN5sBWQueYOh8xmkYaBpusEDuwn8f3vNxV3MAa72I130tf0ApbHSzY8hH+pao4TouRodNqAUFovrUR4AKZXnI3Vrm1FB5ELAFdyhnR0EqmoXHrleCUN3Ov11i6qz78EUiIXYtgLMUfRDXn0m9TPEUI0cfkkYFlYF1/F/l9/gPKW30YbmezYfuXyHkcHqmV23W43hmGgKArnz58nkUjg8/kqaVdrldm9GpGNzbIZtqmdjW4jG9B8cV0mes/NzeHxeDoSvXvlKFxrh6XdjyCfzzM9Pc309HTlnezcubMhZHctK5BfTZhLpfxXRVew8h12oRypPrU+ZDt0Cur4hg5u2eW3Mous1UQomobdwdkQhoHa348a7kMNh1H7Sv/VhII4dwKsEhFVmmZXpNS2aLOTaBgGHo+HwcFB+vv7yefzXSmcXMfGw6OPPsr999+PZVm8973v5SMf+UjN8Xw+z3333cexY8fo7+/nwQcfZHJyknPnznHDDTewZ88eAF7/+tfz2c9+FoBjx47x27/922SzWQ4fPsyf/dmfVWzqF7/4Rc6cOcO73/1unnnmGe6///6r9qzrFdlQFIVCoVCZFxRFYXjfQQInn+q4AQOgeZfTinSjlGKUz4G0EW43slBoKQJhp1Io4TAu3cJ78PVkjv6gse2gH6zmtY86QUqJjUCtmw59hopQgYExmC09X33RvpbQWitMSSlrU1Mts2PdjGYQlonUdITZ2iZqmRX+oNvrAxY4efIkmUyGUChENBolEomgTZ8rnaQoTaMb5tmfIIYnUZcl0e3UEkyda9Kp2qeQUiKX+yd0HZnNYj3835Gv/Xn0N/yyo+dczwW7bdu4XC4GBwcZGxtDSkkmk2F+fr5BZjcSieD1NucYtWt/vSMbm4VbuGmdjdWkUVWjWCxWjKaqqg1VWNvhpyWyUX99NTfFNE2Gh4e55ZZbWg7ma8kZudqOTjmyIYTonL3U6bgCKK2Np7ScMrm7NMDdvi4HO2edUJ1qoLgMNL8P1e9D83nR/D40vxcxvBXT16iWYgPsvQ115jLMXkJoWmkyLBTWXnejw7NVK6C4XK4NnQt7He1hWRbvf//7efzxxxkfH+fgwYPcc889Nemfn/vc5+jr6+P06dMcOXKED3/4wzz44IMA7Nixg+eee66h3X/9r/81f/VXf8WhQ4c4fPgwjz76KG95y1sAeN/73gfA0aNH+cEPGhfH64le28Zy+mwqleL48eMMDw/XqAyK23+O4uxXsbIt0qGWUVPwzheEQqHkbAhQfD7sZAt+hGUh02mE34/PL7BuvIX8T2q/h6opdFnPD1jefMxlITIE2dr7C9tE7etDSSaw3V60gK+xaF+rdo0m5PAysimkZiDM0vpFAFI3VkUUL7XT2tlQzAJ6Jk7RG8JSdEbCAfbcchu2bZOYjTGXTHPhwgVeO3sFneWxM3elIbphP/cEaEdR3/bu0n2faoxqlA7U/U2yElGXcplsYmM/+z0KZ15Eu/tfoQxuaf+M6+hs1LcthMDn8+Hz+Rpkdk+cOFGR2S1HPjptQl1Po1rBpnY2uiWIW5aFaZq88MILFAoFBgcHKxW9u8FGcBR60UY5uhKPx5mamiKRSNDf39+0COHPOqzFhcr/6wGd7Eybsdfhkyi6ArK1EXWaRiW63AvrdqysebkiBL6dk6geD5rfh6I3Nze2u81ukQLW0ChK3wDi3AlEIQcuVynC0aEwX1t0WdTvOjYvnnrqKXbu3Mn27dsB+PVf/3UeeeSRGmfjkUce4eMf/zgAv/Irv8Lv/d7vtf29lO3l61//egDuu+8+Hn744YqzUcZaN8VWg14szKSUJBIJYrEYS0tL9Pf34/V62bdvH253LfFZcfvw3/464t/pUBOjmjCtKKV0qsQCSInQNITLhWwxp8tcDmkYCMPAP+THSmzDvLhc7VsIRLE1UbrlM9o2ZDNg20iPDzubpP4X75ZF7OExhJAoLmc7yK3I4VV3htAAzFdFg1Zraxx8a3diiqK3JM8bCqxs6gRf/t8EQoNsf+0bkK9+p6a99Le/wsyth+nv7ycUCiHnppD5HNalM4hwP0y1UOqqk8KvF0sRulHaLAJkYpHiV/4rys4bUX/x11DaRIOulrPR7L6tZHaff/55TNMkHA5XIh/1G7ObXe2ql9jUzoYTI15P9LZtmx07dqxpMd2LyMa1jo7kcrlKjRDLshgeHmbPnj1d/TA2gvTu1YCdz5cKPS0jOBkgcWb1fCE9pGHlbFrFzqVTbkWXNkx1OFlWml/jp3GNjeIaGux4nlQ1aCdbKQS2y4BdN6MuzCKmziIMA2lZlYmra1wv6vczg8uXL7Nly8ru6fj4OEePHm15jqZphEIh5udLohBnz57l1ltvJRgM8od/+Ie88Y1v5PLly4yPj9e0eflyYyrRajbFriWqa0f5/X6Gh4fZvXs3QghefPHFpvba2v16tGwCz85tZE+92rLtBkK1YZQI44V8KcLh8ZQ2ETqkUymKQnDXFhYTcWR8AW10HGF3R7CWllVyNORycqmqIj2BhugGgMjEu+OMCaU9eZzliET1v7veOlq+lVnomILlSs1TfippeMgnF9GmX0Up5mHuIvKJr1R1pPR9PZklAmaGixcznHrmSV6TKzlzhceOoE90IOOramkzR9L4LVUV4fUiM2XlMIl9+jj2+VfQfv6tqPsOdfH0a0e3dr4bmd1wOLzu80izyMxGxaZ2NnKtSGVtiN7PPPPMmnftNwLfYjVtWJZVqeoNEAwGiUaj3HDDDWvqx087ykpUZXj63GheFTPT/c66ETQIbgmyeGoJaYNoYodsx2lUzu+r6CrB8Si5+QSiTQpXNbrmeFRB9fsxBgccnWs7vY8isKKDZHU3vpmLiFwKOuR7t77pdWfjOjpjZGSECxcu0N/fz7Fjx3j729/OT37yE8fXO3E21nv3sxOKxWJFjldRlJa1o1rON5oLa8/P47EsirEZzGSLjZhmz+gPweJsKbrRKZ3KtkvpVIEAqrAJ3fYaln7wJMboePPzW0CaRchWRUI0rfRsbm9TZ0O6/SitFLOaQXWwrCpka5wExSoiFdUR6bsaQkpsw12K+LbqTiZRso/L9iy9ME3o8snS9QCppVoLvKwW1n/6KIO//F6KmSuVDDWRjmOdO4nmaR25EUJgS9mUX6dGB1A8HuxsBmt2dkUYoFjA/M7fYb3wA5S3vAst1O/8JawBa/3t1cvsmqZZUTYscz7OnDlDJBIhvOwo9xKbaZ7a1M5GvRHPZrNMT08zOzvblui91gG2EVKgnLYhpWRpaYmpqSlSqRQDAwPs3bsXj8dDIpHgypUr63r/nwbUOxsA3mEPiVe7j274R3z4h33Ez8axTdl0LDrlbHQzhgOj/ai6hh7wUExlHV276l+IouCZnHDWP4+fbhO2PKEgdnAfanwJcfGV0g6paTqrb7IM6aCC+GbRL7+O9hgbG+PixYuVf1+6dImxsbGm54yPj2OaJvF4nP7lgm3lNNv9+/ezY8cOXnnlFcbGxrh06VLbNqEkZNLO2ShHuK+2oky9jPng4GAND6MZ2m6y+fuwt92K/w1J4o9/p5SiVH99s00ORYFACBJLztKp8vlSOpXLha5Y+F93CMXnB5ylUcliAeo3KcvF7loRbdUuCbht0oEqMAsQiEByJT3X9gZRU4vd3Qs6OjdC2rhTM+SCJQ6GsXClFNVYhqzjfAhFQdo2cn4KeyGGvHiqtsEOxQFLjdKYUmcYKMEQspBH8XhRJrZipVLYczPIwrIIyPw01re/gvaO/6vzPXqAXjv6mqbVyOx+//vfx+/3MzU1xcsvv4xhGJXIRy9kdn+qnI1sNouu6z3XAl8rymlU2WyWhYUFpqenEUIwPDzcluhdXZhotbjWKVBO2shkMhWFrUAgwOjoKKFQqCHktt7Sue2wWZwVs4mzEdwaWJWz4Rv1oSgK3kEvqSvpUnSjbp3RbKJuCodD2Ah4cIdL0TzD58FMO9ulW+2XcW8Zc06k9IVWdxMhsMJ9iMBBlAunIbVYSoVwSh6/Htn4qUK7eergwYOcOnWKs2fPMjY2xpEjR/jyl79cc84999zD5z//eW6//XYeeugh7rzzToQQzM7OEolEUFWVV199lVOnTrF9+/aKJv+TTz7JoUOH+MIXvsAHPvCBhnt3Sve9mjawGQ+jLGPuBB1rQw1sRdl+M959V0gfb1Lxu9Wca7hLClXFgvN0Kl1HKAoel00+Oghz7at9SylL6VrNvoW+4hzYQqA0kJy7m+ul1+/MNHuDNc6Gqq9O8a5dFfEy3IlpcsFhFDOPJ7Ei5SttG+o3aaq+k/zR17Hnp1cOaRqq14vMZVpXTm8T1QBK33XZuVb9flS/Hyu+hDU/X+rLlbOYZ15E2/Gajs+1Vqx3VFFRFIaHhxkeLjl65fVqWWbX6/VWIiOrkdndTPNUSw9CCGH8zd/8DceOHUPXdW677TZ+6Zd+ybFhWk9ks1m+9a1vcezYMe69914+/elPd9yVKaPsKKzlA21U6dtyODwWi6GqKsPDw+zfv7/lrtl6SueuN5z0vVf9KytRVcPT70b1qFjZ5ovWZmPMCBoY/tIuWXA8QOpKuilR3GkalaPnE4LgeG1I2ggHyC8mOl+/iqGhBYMY0ajj86WxtroDUlWwJnehptKI8yfAJZyRx7sgiF+PamxcOJmnNE3jM5/5DHfffTeWZfHud7+bG2+8kY997GMcOHCAe+65h/e85z3ce++97Ny5k0gkwpEjRwD43ve+x8c+9jF0XUdRFD772c8SiUQA+Iu/+IuK9O1b3vKWBnI4dE6jWi+Z2mpU8zB8Pl8ND6MbOLG59uRrcWfiFK5MUVxYqm+g9YWBMCzMAg7SqaTETqVQg0FsIB0aws6m8KQbN4VKp8tSNKOVatPyIl8A0huEdLzmcLsq3fWwJaUIiYN5Vdp2rVOSiSOF4sh5qIHZOQVLX46YBJYuIKqiGjSTEq+ei2Yvl5y08iGPG2EYWKkkagvBD2lazaMa/kDL/qmhMCIQxE5nsBYXMX/wDcTWG1DXeZP7ai/WPR4PY2NjDTK7p0+fJpVKdS2z+1PhbADv/d3f/V0OHDiAaZp86Utf4s1vfjOf+tSnCIVWuRvZA3zjG9/g93//93njG9/IxMQEX/nKVzpfVIVeLPI3UmSjHA6fmpoim812rbD10xDZSKfTWJbF0NDQuvzwqpWoquEddpM861zb3T+6whXSPTqusEF+qdBAFHdMEHdyz6Ewah25UTM0irqKNDuN4e76IVQV9+REV9fYmtr1zmHjjQVWwI/YdwDlyjnE4kyJPF4stp70r0c2flrgaJ46fPgwhw8frrnwE5/4ROX/3W5307nkne98J+985zub3vjAgQMcP368beeulbMhpeTy5csdeRjdwKm9tvb+PP74HEtf/0Zp4Qko/dH2zo2igD8IqbijdCoKhFWAuQAAIABJREFUBexcDmtwHFVVKYzsxHW5gFLHuZC2XZK2bbO5ILUV+yjd3hpnw5Kg5Fo4Pc0QGkA4ndNyKaTuWln8W0WEvw+SzZ2mVhCArbsQ+UzLc5R8Gj27iCs5W+vgNJPbrVaUqlNGVMq775qOlHZjCrCUTUU71IGhqpNa9FFRUAJ+FL8fM5vHfv57qPvvbPlMvcC15Eu1ktldWFhwLLO7mdJ9G2ZTsdLbDz700EM89thjfPvb3+bJJ5/kueee4y//8i8x2+RGP/roo+zZs4edO3fyyU9+suF4Pp/n137t19i5cyeHDh3i3LlzADz++OPs37+f17zmNezfv59vf/vbTdu/6667ePbZZ/kP/+E/rOrFdgwFO0AvFslr7UcqlWJpaYmTJ08yPz/PxMQEBw4cYOvWrY4djbUOzGs5sE3TpFAocOzHz3L+UozZxQw/Pv4qz718hVfOxkgmUz1LTzAXGyMbAKGtrXdqmhlU30itMEFgrLT7Wr/W7hVnQ3XpeAebbwy4woHO76fL1+ee2ILSRYEhCdhrka+tb08RWGOTyB03IwwXYmIPYmRb85OvS99uaqx1nrpa0DQNq81Y66WzYds2s7OzvPjii2QyGUzT5MYbb+SWW25hZGRkzanQjuc9RUHufwu+W15b+ZPrhn2dZVrdHigv/KVEeL1tJWFlOo3tDlTumRndg1WluCHtZcWpTjam+r3U8R9yQnfuPADS12ZOaIZgnYiG0U4ytxaWx4/t8ZfMdAdbJYDA0uUGInnLIqnL7dXw4AQInx8EaOFw0wi8tOyGDR7hcqFUi/J0kDlUBBheFyRjWLnVFWp0imstzlCNsszu5OQk+/fv5/bbb2d8fJxsNssLL7zAD3/4Q1566SWmp6cpLn+3zTRPNbM+gtI6IFyumlooFBgaGuKrX/0qb3rTm7j33nsrOWjVWEvxpGg0yte+9jVGR0c5fvw4d999d0spwfJ/V6Nf3quoxLWIbFRXbzUMA5fLxcTERIWMdDX60MvrncKWkqIJeVOSTGVJpHLYKITHb0GtIuOVXaw8cHY6R2rhPIVMgqmpKfr7+zEMB8S9JmgV2XD3u1HdSknGtgOqU6jK8PZ7UV1xrLzVYPQs0y4VqmqHDkYyuG20pSFVFAXd66aYyfXE2Gp9YfRIX1fXSN3lWBnLMYTA9npKxQB9EZSdt2A//wT2sW/XenUOVF82yiR0HU2x6nnqqnaywxhaqw2t52FEIhEmJyfJ5XJMTDgUaXCIrvpqeNDu/k2MixcpTM+WIhSKSscdjGAfLMxQFoPtlE6lvXocImOgKFiqTnxgB30zp0q5/7msM/5WjYMhsVUVZdlBMZvJBbaDZoDpXOrYVjVqkpwd1guRgO0LoRVzpTojudZRDQDb5UXLJ2vldm27ZWqZUNUSSby44mwIw1jeJLURml5yKqvmrVZRDaV+feJUfNC2yMTOOTt5ldhIzkY9FEUhHA4TDocbZHbPnTuHbdsUCgUWFxc3JK+6Hu1+SRfOnDkDgGEYmKbJ0NAQCwsLJBKJphdUF08yDKNSPKkajzzyCO9617uAUvGkb33rW0gpufXWWxkdHQXgxhtvJJvNtg0/r1a/vFdpVFcrOlLerXrhhRd44YUXALj55pu5+eab8fv911xVay2ovr+UkoIpSeVsFlI2sSWLi3MWZ6ZNTk2ZnJu1mFq0SRVdKK4QmitQ42g0tK25CQzuITS8l0Qqz7PPPsuTTz7JqVOnWFhY6GoMWC0iG0IIvMPNOQf136U6har6HP/WUv53Q3TDQf/apkDv2U7g9W9oe70R8HYcP07ek9A03BPtq8A2bdu1Nr5GWygCMTiOEArqLb+A+rb3QqDKGepBdfTr2BDoep7aSFjt5lc2m+Xs2bM8/fTTXLp0iWg0ysGDB9m1axeBQGBdbHvXfQ1G8bzzXSiGjhVfclSADkUpVReHEll8OZ2qJXJZ3GeerfxTDUcxdW+lhkYrWMsRBCmUZSeohApvo/zvbt9hl/U+zEyypsKQzCQcRTfMQGSl1oVugDeAbKMSZQf7GrknVuvK47A8B1U9v1LmECw7YGogUPOKm3E1hNuN4qmb+xyuWYRtUrQgpJpkMu2dqdVCSrlpIgNlmd3du3dz6NAhDh48iKqqLC0t8fTTT3Ps2LENLbjT4ApJWVn2fDoej38pn8/jcrnQNI1MJkMgEGhpcNZSPClaRSr927/9W2677ba26UCrdTY2ShpVO6ennLsXi8VYXFykv7+f7du3N5Dzf1rUpGwbTsR0DNXEMq0mtmj1DpXmDlIgyNiOUfp8kEosMjU1xYkTJ/B4PESj0Up13KbVvE2zNFG2QGDCT/JcY6hXMbRKvjI0dzYA+m/bQ/z0bENNO0epVK2iFi6D/jfciuIWFEcmsKcutGzCCPnJLyXX5LS6t06grGJXRXaRMrAaCO9KCpkyuAXxjvdj/fBryNPPX3c2NjnWMk9tJHSzgK+uh1FWXmzFw1gPZ2M1bYqtN+G7481kXvixsygDgMcLuQxYZiWdqp06lTZ1HnVwAisYxXv2ebTF6abnLT8EeH0UwiO4YmeR0eFlQnZV+pXhAUqEar0L58HyhpDFQlezlS5kjQSuAFJotJPhkYAZHsKVquJ2qCr4w8hUvCFVyvYFUAq5xn4V2jgbQtTMX8AKwXt5HCgeD3ay5NC3UqBSqrkaXUJICdJmNOTh5ZdeIpvPEwqFiEajRCKRVWcqVMO2G3knmwWapqFpGnv27EFVVSzL2tDP0nSFIIQISykfAL5U/fdLly7xgQ98gIEBZ8W6VoOf/OQnfPjDH+axxx5re16nXNhW2CjVv5s5Pfl8nlgsxszMTKVOyM6dO1t63tdaTapXA/viokoqrwAGblVBI9PzkOBiyiSehkggwp69g2iqIJPJMDc3x8mTJ8lms4TD4Yoh05e5B1Z8se0k6R3woLpVrFztWDQCbvKLJSfECBrovuZcBlc0jH8kQOpyEqGICjnciSJVq/fff/utqMtFl9yTE2RmrpQm7ibQXDrF5ZB5U3Toht4fQQ+vTjBC6q41uJEdoLsRdVKSwnCh3fEr2OO7sH7wNaRtIZTO9Q02sgH/Wca1nKd6hU5zSXU9jFwu56geBqxsZvWyfsdq5xv1F/8l+oVX6YoEFupbUafqlE4FuE88hQj3o9QpSdVA08DrK9WRMDyY0VGEx1dSv6rpcOmdSSFw2c5StS1FYyq6i6CZxF/sUhLdUyuB6/F6odC6DTMQQSpq44aJEOAPIXM6IlN6V6V0qzBqopF0LpuRwysHZS1fQ1FQ3O7l2yyPAyEQHi8yly1VZK+Pavh8KJ4m0esu7KliFbENH/sGFYzx24jH48zNzXH+/Hls2yYSiRCNRgmHw6sa6+uZRnU1UrSqIzMbPY2qoXdCiF8C/oUQ4ohpmqRSKXw+H5qmsXv3bnbv3t2ysbUUTyqf/453vIMvfOEL7Nixo23HV/sRN1JkQ0qJZVnMzc0Ri8UwTZPh4WFuueWWymJ3PfuxESIjGdNgPrFi6nOWBgQI6RZCds/JaQdbwlyiyFLaJBrU6Q94mJiYYGJiAtu2K4bs7NmzCCHo7+8nlGxfZEkIgXcsQPLMcvRjOZPcCHoqzkarqAaAYuiM3LqFU5dfQnVpmNnl4kYOnFmlifSge3iAwA0rvx1FsTH23ULhxWdatmP0+cnNxVv8plp/X2EYuLd0V723ptV1NI5KVSpEw7Gdr0UMdaeadR0bC2uZpzYSmtng+sh2mYfRTdpsL+Y5J311CuM3/g3y+D8hnEYKFBW8Acgka9KpWqlTiWKhvaPhdoPLvcIvEApFXwTVpSMsC8VaiQYIJLZugOZGKXZWhrIRZMb34YmOIPI+mDnt7BnLqMuhVXOpeoHClVMpRTUUM998o0YI8PiQmgaJRWQwgijmVlKuKg3JxvoaNYdrnY2yo1HBckXyknJUBvLNFKhacEm74MEoVgHb8JDPZdGzSfr6+ujrK6XDlqt1z8zMcPLkSXRdr9SscFowr0F23rZZnJ7BLuQoCB3pjTIeXV39k6sRNdnInJN6VGZ7IYQqpbSADwL/CPzGK6+8wre+9S3uuusu9u7dWxqAbXLc1lI8aWlpiV/+5V/mk5/8JD/3cz/nqPOreckbQfq2PJkkEgmOHTtGf38/u3btcqSrXI1r7Wys5XopJalMjkVGQK3/joJ4TkNTVILuIpZpdrMZ0hGmJYktFlhMCQZDBmG/iqIoFUO2a9cuCoUCCwsLzD//NJ2YBYFtwRVnQwEsSpIay2jnbKgKBLdEMAIGukdHKFBMFx2lUYn696YIor/wuobfhR7QMfsHsednmvdBVTGiUYrzTSZW2frFeyYnEF3sJkkhMF1+soEh4qFJPEqeYG6me115B6hOoWp6PNAdmf06NgZ6MU9tJFTPJc3qYbSLbHdqd6M4G/l8numZWSxC7GLeeTTT64P8sppUp3SqVhsXQoDPVyI0V0EqKkV3AJdaStXRU5maRbAZiKAVnTlGxa03IdxeLEUjo/vx0mXiby6F1N2I4rLDYxbIKC58dqNjZQYiSM1AS3dQadJd2KFoiRiebOQcStNsG7EvHV/5t+KrW5ssXyuEQLjc2NlszTMrfj+iFYdElLUdOkMs80os3U3+ymm8u/ZXjtVX687lcszPz1cK5vn9/orz0WptVW0nzEKBxJVzaFaerOIjr+pkUkUURWU0olE0TfQuNsg2iw26WqiVYSjhJeBvgd8PBAK88sor3HDDDezduxeg7ctbS/Gkz3zmM5w+fZpPfOITFf3zxx57bNVKS61wLWtkVE8mHo8Hl8vF/v37V+2ZXmtnYzUop4pNT09DcA+0SQcwbcFCxsCra7jVPHYP608A5IuSi3N5FpIKA2GDgGdlbBuGwfDwMF6/j7axDU3DG5IoLgU7b1dmmfI39W3pb5lCBaDI0oTWv2eQ1JU4/j4fi2eWsE0HRaHqNNDDN+/F1R9uOE8IgWvnbrLzpbSEZvCODRFv4mwIt9F0B8wYGEALtJd5LEUvDAr+fpaCE6SCW2okGgtAwogSzVzAXUz0NKWqk7NxHZsWa56nrjbKG1zN+iSlZG5ujgsXLnTkYazmnr1EN23ats3c3BxTU1OViP3Ia2/HPvUj1FQXdSQCfbA0R6d0qqabHlVpUw39EwoFdwCXmQRRSiMVatV5hgeKnQUGiqO7KLp9SARFywahId0BRDe1OQCCUZi/VPmnqepQ52yUoxoAiukg6q9pK4pR9ccK7fmuss7REvUF+crCLopaIopnMjVtKtE26zZFLFc/BFvVUdoQ1ZWquaeAhjZ9DmNosum5bre7pmBeKpVifn6+UrMiHA5XCuaV+R7l6EM2lSQ3cxFVWizJEJbqRiilavJz8QJC2CTSBWzc7BlVHNmXzcwHWQ80s2gLwG8AO5977rmulXtWWzzpox/9KB/96Ecd32e1uNo1MkzTZHZ2llgsBlCZTKSUHD9+/JqrSTW7fikDQXdH6e6uFLXKhQeLxSLDw8NM7j3I2Xln4clMUSFTdBPyWAiHObTdIJ23SU/nCHpVBsMGHmPlm3RK6TddXjQzgXfITepiBlTQqvJUBw9sxRgLUzh/tun1olBSCBm6ZZxcPI1v0MfS2bijNCqW82aREi3go+/gzS1PVTUbfe9rKL78QuNBRUVmkriHImRj87VjUtUanA3hduMaH23eJUXBdvswfX3kg4Ogu5g3RrBbCN9JRWfWvwOjmCKaOY/Wi+8rBMLTpd59dZ82sKLHdVSwpnnqasIwDAqFAu7lVBTbtllYWCAWixGPxwmFQuzbt69yvBdYLzWqdm2WI/ZTU1MsLS0RjUbZuXMnvqoaC9buN6A89w/O06k0DTw+yKbbp1PVz6NuD6LF+5QIpGZgax7ILYCmY4taCVrFNskXCm2j2lb/OHl/SU3Qdq1QuovePlxdOhtS02scAq1eMQQw/aWoBuDo/UlFBd3ADkRQkrXy7S3ra5SPV5HHha43EQBZdjbcHpSMheL3Y82XZNSVQADRjry97EhZLh/ScKO0KWKo2Cv9sDWD4nwMLTKKorcnhwshCAQCBAIBJicnsW27IhtbzfcoFosk52eQiVkUIKv6sDVP5VsISg7DUiILQiFdlDx/3uKGMXAb7RdIV7sGxkZ3bJpFNp4ADgMLn/rUp3jd617HjTfeCKy8vI3yUKsxplejzoaUksXFRWKxGKlUioGBAfbu3YunahFqmua6Klo5vb4elg3n5wWqAhMRSbBDDlG7Z0in00xNTTE/P08kEqkoauVNePGy8+Jvy70lnl2/1CqARMYimc3S59cYCOoYusDjVxG60ZJM54lEKKYTBLb4SU9lCUz24R4ZpjhTMu6DB7dDIMRcE2dD6AaimAUhUHWNodeMApLAaArhMK9V0VTsokn0jQeacjiqYfT5yXsDKJnaiVAE+xB2DlfAR34uXiL7VQ7WvWQh8GzfVtktlJRUpUxviGJwAMsTrLlGqHpLR6MaBd3PleA+AvkZQrkYyhpSq4Qn0HQ30yk2U1XWn0FsunnK5XJVpNynp6crPIytW7dWNPJ76WjA+lQmb+XAlJ9renoaj8fDyMgIu3fvbvr+haZhTr4W7dVjziOZvgDkc8uE6BbpVOXfq6KUohktIkNS1bCD/aWFuCgJcgjANjyo5oo0rEAiAxFIN6+xZAf6SfaPoS0/o6W7K4qCGVeQrrP885nl2E3p/XplYblmR2nekYDZV4pqCLPgSJJXqqU5Vrp9WGYBNbtMOpeyZX2N0uE6vkYzkncZmoFEoPj9pYiTWWwf1WD53QoF2x+CDrXSRF3Uo+Dyo134Ca4dt7a9rh6KohCJRPAFQmj+UZbSGmH/Eh5ZhESpqnpGDZDTalOei7aKptggBPbyO7dsOH7RYtsg9AfaFJ28nkZVg8ovUi5bESnlE5QMOcCvVp9cz/bfTOSUMtYzjSqTyVQW18FgkLGxsZZEpV6SzHt5/XwKbCmwLTgzCyGPZLK/eZSj2XOZpsnMzAyxWAxVVRkZGWH79u2VH52U8OqshmWvbtxUp1ZRiGM4rJbuFFLCQtIknjaJBAwGswnce/aSPd4kIgAIV2mR4B3yoPsNhl6/A/fwIBe/uoB/oh/PQAApLUR0EDlXy5lQ+yI179A3VOIRDN2ikrjoLNVAaCrerVvwTXYmagsFtD03YD/7VM3fFV8AkqV8Ye/uHaRfPlWVz1vH/5jcViIFuv0UA/0UAwMV9Zam0AznIjRCkHQPkTSiRLMX8BSWVpValSiCFo87JgnWYzNVZf1Zw2abp86dO8fS0hJ33nknf/AHf8Dtt99ew8NIJBLrEpFZL+nbcl+bpUk5FTYhOoE9ew61zY52A4LL6VTL31LxerFTK4pNAkDXweNtutEgAenyIP2lNFO5vJlT1AwMwHL50AvpmolOuJrvnku3j+TIbpJ6lD5zFgBTaJS9jZziQmoGwkmqUxlWEYL9kJhbeZ5AHyzL+Jr+vkpUQyl2lvy3pVypxk5JkUqYRZRivnSsXYSqLuoh/M05h1JRS+9LCAQCNRxC5vMIB2PA8vhKY7SDndVzCfRsnKIrUEoJU1SKuRRaYh412N/xPmUULZsrczkK+RxCQDZnoShxQrpEAhktSF5t5HWUIhvN23x1xiKRtdk22NyxvZ5GVYtW0reibNRnZ2eZmZlhaWmJQqGA1+tlYmKCkZGRa/4i2+XCdrpmLah2WMra57FYDE3TSilCk5MdZdg2orMhJcylqr+pIJ4VvHhZsj0qCTTZ4CiTMROJBFeuXCGZTDI4ONgyLWA6oZDIrX0hlykqIMPoyvqkVlk2zC7lGUgncI0MkT3e/LxyrrCiKAT3jTJ6x2tQsLn8TYX+m0v1ZIQQuHZtI1fvbASClOqd18I/EiI756wgmeo2GHjTz9NQqKMFvF6NpbFtaJdXIi3lkLcUAteePUiPl8yPn1s+uHKt4g+g7LuFVLh5ClUz2IpeIsx3A0VlzrcN3ZUmmrmAbuU6X1MF6fJXSII+n6+GJOjEZl13NjYHNvI8Zds2hw8fxrIsNE3jgQceYN++fQ3nKYqC2UYVaLVYj8gGlHiHJ0+ebJkm5RTWjtehvPAYwmm9G00D93L9DWkjdH0lnUqIkpPRYuNJAnagr7ZY3vKYyBshDDO+zG2wEVVRWNU2S2my+ZWCclLVSY/tI+OJkLV8+FlEw8Ss5hMKgemLoMdjjt8HgPQEEIkqMndVDq/ZN7zyZycVyjWtNiotBFawHxZiSI8HIeZaOhw1fA1FQXiakasF0uVGKmrp9yUppa45HAvS8JTSd0X7sS8sE29iCimmyYTGMF0+Cq4g2sWXUfa9oeNv27JtYgt50pkcipAIAZqdZ69nClXaSCCthSiojYsbyxZoaitdsNJf55OSVM5k37iCWjdnXI9s1KKpsyGllEKI8MMPP8yjjz7KSy+9xMLCAqqqEo1GGR0dZe/evbz1rW/l5ptb54mvN8qF/Tztwnx16IVKh5SSQqHA8ePHyWazDA4OctNNN3XUPq/GRnQ2kjnIm40/LFsKTs9C2CPZWhXlKBaL5PN5nnnmGXw+HyMjI+zdu7elAcgU4MJi73TfEaXUKlVRCbkKy0Vtete8auVR7CI+n2TR5WmswAo1Bnv3b/0ztHAE0ov4JyIMHNhWORbt9zAV6ivV7VhGqlgk0GLIhHcOYBcspG1TyOTBMtH7IyX986KJbZrIool/2xY0l9qy4FUz+MeHyc1PlyZuWCG1j02geDx4du+icP4C5nxVCoEQuG5+Ldlgd0WaimL1RNei5mMqeAP+/Czh7BSKdLYw6R/fRtRwI6UknU4zPz9fqaUSCoWIRCL09/e3/L1edzY2B7qdpx599FHuv/9+LMvive99Lx/5yEdq2svn89x3330VhcAHH3yQyclJHn/8cT7ykY9QKBQwDIM/+ZM/4c477wTgjjvuYGpqqjIHlUVNFEXhr//6r+nr6+NXf/VXWxYgWy+Rjl62Wxb1uHz5MpqmsWPHjpZpUo77Z7gxx25Av9hiF6cZfAEo5JZtnUR4PEhVRYlEWyraSUXFDkVAqbVD5ciGrbshNweajqV70Ko4AopVJNc/jv/KK8vXCLITN5L2RbGlgolOWg0SUHMN7zrnCqLTnbPRUH08W0p3LVZFNcr96oRyClUNFBUrPIhSzJWqjrcgiVfzBRW3u7ktFKWNNqkopQWBbZXOU5zZe0VaWELtLINbVr2SpW9e+oegoLlQLp/CGG8ucW3bNjNLBRKpHIqwK+KQbiuF34qXFepJaWGKauOmqG1DEZ1qR6PVaM8XJc+ds9g7Cj73yvOsd2Rjs3ELW0U29gB/+JGPfIQ3velNvP/972fLli2oqsr09DTHjh3jqaee4sknn+RDH/oQd9xxx9Xt9TJW62ysdicplUoxNTXFwsICpmkyMTFBIBBYtQTvWtHriWo22a5PgqWsIHlZEjEWWZi+QGE53/LWW2/tqJ5iSzgzqyHbSKmuFpYtWMi68BkSt5rHsnqzo2cUSsZeVQRi+x448VzDOWUinT46jidolHbP0jB6xx48AyskZaGA/+abiD/xROVvfdFIaaJrAt3jBg/g9mKdu4i+cy+h7SNNTnSD3XnyqYamCVz7Xkv+xz8q/aFYQHr9aEOl3TMhBL7bbiX++Lcq0rfGjp0UR7Z3ZszXoWCvvZZGyjVASo/Qn72Et7DQPrVKM2C5mJ8QAr/fj9/vZ+vWrdi2TSKRYH5+nueffx7LsiqFHPv6+ipj+LqzsTnQzTx1//338/73v5/HH3+c8fFxDh48yD333FMTbfjc5z5HX18fp0+f5siRI3z4wx/mwQcfJBqN8rWvfY3R0VGOHz/O3XffzeXLlyvXPfDAAxw4cKChf+V6AC6Xq2Ir67FeEYi1tmtZVkXUo5wmtW3bNvL5fKUu1poxsovc1BncZpNNnGYQAgJhiC+UFqHeAIpPNHU0ylwy6Q838s5YcTaACm/DcvnQsksrtwOka2VXv7hlHynfIJaiI+1SGznhw6crDamiGd2PXwhH3IoKcmmk4amIhlDIgjdUE9XAth1Fg5o6G5Q4K0gdXO7mzoauo4+OUjh3HqREaRWpEOVnFrXv1yHXTkhZiop0srPL7cnyPZdh6l7shStYAxOorlpnYS6eZyGRRWHFyUDa+K04Hjuz3J4gqfdhKo2bAM0cDShrsTRPyZQSXrpsMR6RjPQtF4Vc58jGZuMW1qwEhBCKlNIG7gBeOHHixK80u+htb3sbAF/+8pevqQJIOyPeCt0u0AuFQoX8ZhgGIyMj7Nixg2PHjhEMti4c5gQbIbJRRr4ICQfZKpYUzOb6CAz6uWlQ5dixZxzJNF5eVMkU1ncBly4I0gUXfV4b7Hy7tNSOkFATPtcHhyieaDzPzpb0zj2TW0t/WE6r8o00StD6wxoJjxeZzSyf2tk4FDUXqBq+8RYTvMcPRYeTdRU0N5hjk1iXzyEzSfRdu2sMlx7txzW5FcwCSiiEsnsfBXf34z23Sm5OAxSVed9W4u5BounzGFbzZxbe0P/P3pvGSnaddb+/tfZYc5156ONut33aUzt2YscOUT5ckRslwYAFSoAowkjEIBCEDyChCMWKokgIhBBiCBLjK0EEcpQEZMSrBLhc6SL84tgJiWfHbqfd7u5zTp+5Ts17WOt+2FV1atg1naHbh/RfstWnqvbaa+/atZ71DP//03fRlVKSz+fJ5/PcfvvtBEHAzs4O29vbXLhwASnlWOVWN3FjcBA79corr7C8vMxtt90GwCc+8QmeeuqpDmfjqaee4vOf/zwAH//4x/n0pz+N1pr3vGefjHr+/PkW2XvUTHYzKBaH43I2DmIbhqlJbWxsUKuNV9I4DG9ac9wTvj16rx3LjjbK9RrasmMlYKOu2bmo7KoP2p0Nz07h4KMbZOf2BnimCqg7WYyJaYqZOQKzoSimo+M94eLh0+1tKCG+5yWcAAAgAElEQVRRyTxGeXBT2B5kp2FzvzFykMl3ZjWC2mg8trb1S+n2tk+Rc6ATKUSxgHYTiCDYJ4w7LsKysZaW8C9f7u9sGPt8jfZzBaYbq6TVA61Q0kCOmNlAGp17fyGoJfKIt57HuPN9AOwUPTZ3qwjCDkkSoQNywTaWjq5RIShak4Qy3iELMNEjiJp0QwBXthRXrxWYS1WwLPPYMxsnKSjWsUtsLOBorf8coFQqfSGdTvcc1FwcP/nJTzaPuyHGedAi3g+jLO7tUq2e5zE3N8d9993XNxV+o3CUmY2NkqB/orDnxBQ9m5dWNDqGVNWNYk2wUrhePwrBTsXANBJkbZ8g8JFy8HVpDQoDP7SoeCZ7NYO9quDuyus0XAhmJ0LecrNYtU4uRW2ngAkkJtNA2NIPj4MUmvT991N8JsooNHtsDJybaWPedhtmn2dPGSZyvMQG0OCRnF6isrOFmJyKNSqpd99H+Tvfxbn3fmrZ+ZhRhpzDtA60aA9CYCRYy95FLtwhW7rSIwEpx+ivYZomMzMzzMzMALQaOTazl81ymsXFRTJDeorcxPXDQezUV7/6VZaW9kUUlpaW+OY3v9nx+atXr3LLLRHPyjRNcrkcW1tbTE9Ptz7zta99jQceeKDD0fj5n/95DMPgYx/7GE888USPLRwUFDuOfhjNcUe1Dc0yqfX1dZLJJPPz87FlUsdR8uVLm9rUaRKbb41+UCobReUNs6XW1ISWEpWZ6t/kr4m2TW7dzuDUt6JNuDQ61hQjrFNavBvh2NTblIrChlCuFpJqaGLFLMJeIk9iTGdDG2aHFfadznXZGIUc3pV9Dq0EspE9al1bOgfrK5CdhEQStjeguNtqfigTCczFxYFk72b3Di1ka86h5WIGlb7H7E9SR9/BKM5GMwvS9ewp00GVChSvrXDNT6NVr0Klqerkgm1kwwFSSIrWRF9Hw1cGiv7Ze9G3t3vjfQFYGda9BN6Vl6hWi2itmZ6eZnJycjQBhRFx0jLwPb/IJulOCPF//9qv/Ro/9EM/xPz8PPl8nomJCaanp0mn061FUghxw6KAR+lsNKM6a2tr7OzsMDU11ZJqPS4c9r4d1eKvFGwPaUgah1AJzKl7ubSlOT0Zm60mVFH51MiOzBEhCAXbVRupAibSsqO0SmlJoCyqvkmxblCoSPywd35pVWj925AC7/Q9WK8/s38OaWF6ZWrOBLYZAALCwZm2zEyComlB4O+nywfAchM4uf5OrqpXDrydl1Jh3nkXZi7+GZeJBKn3vJtgarFvWn4QtOGMrkQ17tjZeazTtxJc/R56Z60VjRSpg2cbm40cXdfFtm3Onj3L1tYWtVrtprPxDsO4duooAmIvv/wyn/nMZ/jXf/3X1mt/93d/x6lTpygWi3zsYx/jS1/6Ej/3cz/XcdywzMZx1F4PC6rFlUkNU5M6LoWr2swyTnEdWR9hkwpRRD2TR5tWS19jWNlUN7TY31C2r22haSO9NtlXFVBPpNFWomPcUJstk+ZpE4teZ6NiZwb26YhFvYxulF8FyVzPutstBRsH3a5CBZHjFTQi4U3em2lG2QK30VBrei7iv7QF5kQm18sjab5HxGGJ/tg/xhixpFc3jhumRhVxKyJnI04EpZacJLH6Kkzc31WSpUmEJVLhfrPYEIOiPYHqwyMMlWg5kX3nI0YzaRoTa/Z+0v4qC7MZtra2uHgxEmVpcgYnJiYO5SyceGeD5vcL/vPPP8/Xv/71VnR/YmKCXC7Hz/7sz/Loo49GH76B5QZHUUbVHtVpaoS3SxMeJ250GVUT2xUOLEUrhGC7LNiram6b0aS6KgsubRmxpPPrBSWTbFU0SRO2KyaFqkHVG20+6bDQ8ffEqTze6/t/e24es7JBZe5c63dQK5YZpJhvCEXqvvuovPhiq8dGP4j8NDKbxDD7qIYIQSV/iuzO2yNdTxysXCa++24T6SxecvJAYyvDgqMX2gHAtgykNLBvuQc1ewb/0suIWgmROFxpI+wv4slkkmQyeehuzjdxLBjLTi0tLXH58n55ypUrVzh16lTHgKdOneLy5cssLS0RBAGFQqHFT7hy5Qo/+ZM/yd/+7d9y++23dxwDkMlk+OQnP8mzzz47trNxXJmN7nFHabo3bMzjcDY0ENz+ENYr/9FRwjQQttNQMxKg9dCyqW4os3MTHwqJgSY0HSxv3+kRQMIrUzaTHUu137Z1CrUF9AaOPGmj7CTSG9GJAgiDqJSqsIGf6+1XMQo5XMn9BoFamCCiuH537YJOpqLsUBNOAvyoTE5rjcpNIHc3+p6n9U21OxuhjxYjhBZbjsoI+yytI1WsmGdPS5PQSjBZvcx2qlGHoBWZcBdX7X8ngTApWhMdTmY7+vE0YiYzfL4tCGrmImUlOXduknPnzuH7Ptvb21y7do3vfe972LbdUkscl/974p2NthT1fzRf297e5lvf+hZf+tKX+Md//Eceeuih5mev20TjcNDMRhAErK2tsba2hlKKubm50TXC30E47OKvlCIIAi5v1CFG+m0cBErw+jWYSmtumYjWku2yYKN0hOpTB4TWgufeMJmZEFT90X/MadVZMjWXD3gzNYVdjvThRTINlQ2yi3lAEWgDVw83LJnFPPWrU71lCukccu4UJWmQm55EGEC5GBEFY1DLzrM3tYxb28WujiaV24MBi5UGapnZkSKFcQji9SeOBLa1/1xJJ4Vzx8OE5QJiTAJ7HE7aIv6DiHHt1EMPPcQbb7zBxYsXOXXqFE8++SR///d/3zHmo48+yt/8zd/w/ve/n69+9at88IMfRAjB7u4uP/qjP8rv/u7v8oEPfKD1+SAIWpt23/f553/+Zz70oQ/1zPVGEcSbtmHUMqlhODZnQ2tEZpJw+hbMzdEDJ8pyEE4C7aSGl021QSN6Nrm+dDBUDQyLUFodEXrHL1FkDqOx0VRKd0THFZJAyaj5WxeC1AT2OM4GoN00oe91cDUACP2RCOe+4WDr6HlTzcZ+hoXokszVqez+1lqpznVeSrSbBvo7Gy3lqfZ7qRXaSSK8/tweDfipyda8lGkhBzQZRDUzG/HXXk/kSBeusufMogyLbBs/A5qOxmQHT6dj+JEdjf0Ix6gQAtZ2FcWa5q5FiWVZzM3NMTcXqTpWq9VW1qNYLJLJZFrOxzDho5PWx2OkX+jk5CQf/vCH+eAHP8gP//APt0oKbrRBdl135MyG1ppCocDly5fZ3d3FsizuuOMOksnRoyFHjRtVRtVsPri5uYkyUod2NNpmxFZJUKhozkxpLm6+MyLCGzsGNV/geWq0SAqR1F6qy9kQQhCcvgv71acB8GQCy3Q5PRMZmT2dZlIMr9G1ZEj6/HkorCDnlpATk0jXBhmVenRQy/toqmsh2Vl4FwCbc+9i/u1nkKNq1ndeVN+3QieDsg7+bNTV8TmajtU7tpEana8xCDedjZOJQXZKCMEXv/hFPvKRjxCGIZ/61Kc4f/48n/vc53jve9/Lo48+yuOPP85jjz3G8vIyk5OTPPnkkwB88Ytf5MKFC3zhC1/gC1/4AhBJ3KZSKT7ykY/g+z5hGPKhD32IX/zFX+yZ16Cg2HFxNpr2bn19vVUmNYpq4CAcd1dydet7ULvXRusjAVFm4wC/eV/aLb5BE3UzhVuPMs2+lcCo729WzaBT2lbr3rXB1zYmvRvsqpPF5mrP6wMRBrFZDcMfTs4PhIGl2/ZEzWa60kSqLqcnmUE0S5PCABHup6FVIguGgYLYMl0tZdREEbrUqPRQ+VtlJ9CNnidaSoKZWxD1CkZpF+H1EuC1jux2X0dLSOqJPLPFC9RT0y1+BoAvLIrWxEC7PxYhXOixS4OFgEpd8/ylkLtOQcLeP1cikWBpaYmlpaVW5nFra4uXX36Zer3OxMQEU1NTsXyPE61G1Q4hRPK1114jn8+TSqVIp9OthepP/uRP+Kmf+qlWw6IbhVEyG9VqlbW1NTY2Nkin060vralKcpIxjrMRhiHr6+usrq4ipWRhYYGzZ8/ynQuF4QePiUAJ3twQ0SKB5HrzNdqxu1Pm8ma0fb+2KzkzpyiNoIqVVKWORauJqVMZqg1VKi0NgqU7W0G1mnZGvtTU2UW07G441nmwBow+xr2aP4VqSLwq02Fvapn8xvdGO3n3SWLmrKVBLT3d+8YYqIXyWL56rUIM4/pJCt7EOxfj2KlHHnmERx55pOP4pvMAUfDqK1/5Ss85nnjiCZ544onY83/7298eOsfrxdloL5Pa3NwklUpx7ty5AzXdi8NxZjYAhJSEtz2AeP2/Rls2DjiXQLr7fIMmDJNAmJiErWxAa446xApr6EY/BiWsng1nvyxuxUiQQSDH2KEqVG9WA2KVt3o+k8qj2rIyWkbk7Yhg3WVLDCMiVQKoMHI2dNRRW2WjzIMyXWQQ4+RYbfXS7fdSqSi7ISUijhsL+JleZUXtJAmcJAQ+ZnEHUS+3nAshmv/rfw99O43pVzH9StQ7haiMrWRODAyoDSOEd+Mw5ixU8PLlkFtnYDrba1+EEGSzWbLZLGfPnkUpxc7OTgffo5n1yOfzJ85O9SWIAw9/6EMf4od/+IeZnZ3lzJkzzM7O8k//9E8txY4b7Un1W8SDIGh19RZCMD8/zwMPPIBpmpRKJfb2Dlhy8g7DsMW/aXxWVlbY29tjZmamo7O3H4BwJo5tfoaMCGm+ksTHR44XWmne3uo0tKWKRhsKMSTDkVa7sa9PZwLeyC3gFlYRWmHPzdNcBP0xyoaEZcOQzudxizVEhmN74XzHa6XcKZKla9jV+Hn3G6ffT7iemh67p0Y7hGkfuPxqKEZs7ndQnLSI0Q8iTpqdOs4yqu4yqYWFBTKZDGEYHpmjAcfvbACQn8fPzGAXB5TvNI8dRWI1Bp50YiPdgWFjhtWe9wSQ9HbYMxcxpEaZCbr54KE2UCqmKlVI6k6WRH30oJ5OxGdrBpYaNWG7UGv/nGzNozeu1HbfwxDRcBK0MFoX4tkZzBhnQzltGe+OMqooZ6TsBEatV3VGOUm0tc9qFNCZPTEtgolZUApZ3sWoFFuzHFhCJgTV1AxWrRA1XTYSlM3c4Mz9CITwntOg8QOB1YdHOQouboTs1RS3zQ7eLzSl2Ju8sSbfY3V1lVdffRUpJVJKisXisQoZHRXiOBvNu/itv/iLv+D555/nu9/9Lv/+7//Oyy+/zOLiIl//+tcBMAYRS68D2hdxrTU7Ozusrq5SLpeZnZ3t2Fg3cZSp4Bsl+dtEv8Xf8zzW1ta4du1ay/jceeedPXPdLB2/kpgQYElFqPRYEYSjwMaOxO9qKrdVFNw6rykOydS3K1G1QwiBPn0OXlxFKJ+5mbb3xjB+FV+QGHY7+jynlclbIEYdanP+XuYvPYPsoyDSDW3EL3aenSJwD6e+pE07TjzkaDDi9R14+BMWMfpBxEmyU67rsrsbHwQ4qD0Kw5DNzU3W1tZiy6TW1tbw/QNoYg/AcTobzQDh6uoqtpHi3XJn4DrWJDwfBKpPoCmQFoRVpBQEhoMZ7hsJxyujk1F0PVBxxwt8LJwYVSqVm4H10Z2N3apHKtGltKIUQgUEDK59120ORNShuzk9gZImRvs9bfsug1Qey1sDaRJmJggQmOhW9rzzHCJyapp/d5dRsc9t6CCkA15stjwmayElKjOJSk9EpPnWCAMgBL6bw8ekMsTRGIen0XGcFtRCE8s8+G9LANtFTbkWcPeSxBzR1nTzPa5evcrq6ipvvvkmpVKJ8+fPMz8/vkT99ULf51ZrXQL4kR/5kdZrTz/9NN/4xjeONFpyGDiOw9tvv82//uu/ks/nyWazLC0tkc1m+26ij2rBbBHbDrlZP8wY7deitW5JGdbr9aFShkrDZunA0x5znmAaGqWDxkJ9/Bu5MBBc3op/vLcK4LhqYJ1mtxJVO2YXkxRfBJHOkrD2nyU7xtD0QxCEDPW9YoytlgY7s3fHf9yw2Z05x+S1mO6DcYgxuloIChO3klDjCS/0zEUcn9jCQSOao+Kms3FycFLs1FH02YhTkzp37lws7/A4JHWP2tnQWlOv13n77bfxPI/Z2VnuvfdeHMch3JhEXPzvvlvBfmTfUWArL3alDqTd2iAHptvhbBhhHaFDtBZ4fb4uX8c7GxUrTZLRt7WZXA7VRbAWgYcA/OQkZmU79jgN6DYxEdXVs0MbVsumaK07yqr87DTKTmJvX4VkBmXYqLCOiinnwrI6UjjKsFumTKCjsQFtOh2kdOWkoqaM3fNuKIrFQghoKIeN1I1diIYC12BHw8PmIO6qryRKC7Q+fOK+7muefyvkzgVIJ8Z/ni3LIpfLce7cuRPR4G8swsUHPvABlFL80i/9El//+tdvmFHe2dnhySef5C/+4i/I5/N85jOf4cEHHxwpgnVUmY2jWHgP67AIIfB9nzfffJOtrS3y+Ty33nrrSD0BdisRt+J6QjayHEGo+0rQHQUC3+e1t/v3pihWBdM5zd6A/XS/zAbARCqkND3LqalOZyAlRlMd8bVJxvYZutjFpM1L02c75Qq7UMkskCpew+ljkNqhpeiZQSm9iGO7UDucszFOSdn4OH5n46Qp093EPt4pdqqJw3I24sqkhqlJHTeZ+zDwPI/V1VXW19dbapBnzpzpvJ6ZM6iNtzBKfdaxQ9gPU/dx/KRBKExMHXT08ACQWmH5FUI3H3ssRBK43aVUWumoz4ubQdSKQ+emAeX3zs9olDIFbgb6re2JLLSJhGjZ5WzIfYleLQ1Ew75orcG0CdM2YUPVUBk2WgXomD2VtjurRQLTpWO1bO7ETaslcKIBL9uPAzjqPmS0Z29YMCri14y/9/ECg5LnAIJ6KHEMdWiHQ2t4bSXk1KRmYWK8Z7p9XRNC3PAM7jD0XYGFEBN//ud/zn/913/x/PPP8+abb1IoFLh06RKvvfYa0F/69hvf+AZ33nkny8vL/O7v/m7P+/V6nZ/5mZ9heXmZ973vfbz11lut937nd36H5eVl7rzzTv7lX/4ldvw/+ZM/oVKp8Iu/+It86lOf4pFHHhn5Rh/VInwU4xx08Q7DkLW1NV599dVWvd6DDz7IHXfcMXLzsY3ijSn/EgLWCwbFyvHJJu+WbOrh4M3u2nbU1K4fBjkbAJlbF0lO7d/rEElajNYZcY/MaGGRrm6xyrAozNw59LCNufOoIYogAOjOOYSmw272TEtr/TCo6+Na+DRSHK/k9kmIEt1EhMPYqeuFg3A2wjDk2rVrPP/887z00kuYpsl73vMezp8/z+Tk5NAA1XGVPB3U5iml2Nzc5IUXXuDFF19sXc/MzEyr+WI3wuWHG5KnvThoZiMwbEwRoPrcmkA2glSit+NHytuhHg7gSCIIu4IshvJJBkXqydG4kTqZg24iNxBIB9+wIoWotnvi28nW1lo5nRmu7nukTLvjmlp3vO1zzYaASppRuVnbuULTibInbqdCoZZmZ0luY/7t51duOrb0Nzr/aHuRkTIbgCTsKCdrx7iE8CaUhu1qguaW2VeSSmAeuD9ZN65sq7F/ryeNWzhoR5L97Gc/i23bLC0tcebMGZLJJK+88gof//jHgXjp2zAM+dVf/VX+7d/+jaWlJR566CEeffRR7rnnntZn/vqv/5qJiQkuXLjAk08+yWc+8xm+/OUv88orr/Dkk0/y8ssvs7Kywoc+9CFef/31Hkfic5/7HAD/63/9L7a3h0dw23HUZVTXa4z2FHqhUGBmZobl5WUuX77cquEbFRUPKiM2tjtqKA3rBQvXVlhmiNs/AXEghIHg0sbwjXbVE8wZimJs/e3gMiqAzC0zLd11INJ590fjElRliqGmR2tkl2Evzdw2sC9GC4bF7uydTKy9PKAMQTQaaEWf0MDmxB3R6+EhS6i0oBbIUVWGx4JAI495Ue3WL3+nL+I/4DiQnbqeGCZ928Q4ZVLDcBzOxkFKs9pl1icnJ7nttts6yKyD5insBMGpu7Auv9zznhZyrNi0FoLQdBEC9hIzhBhIeoUmAmlDWIlkzk0Xq40cbfsVhA4HBsV9bWE1O5mqEIMQicY3LFyGx9N1IgNh17wsl7IzgRFMYRIQ2CnsWpSBqGVnMUMPAp96uUK6bT8vup97KaOMhgo7kgTtjkLdyeESRIpcQiIaHcW97AzaMLH31nucBqlDVDKDUWzIvjdLu5uyuwi8GAWqtpkOuSutmeILq6OHRhyk1pgEhJ35lgMRwpso1BKoxnZZa6jWBdmEohoYmFIdOsshgCAEa4yCgBudsR0XgzgblyBKeT733HO88MILXLhwgccee4zHH38ciDfCzz77LMvLyy1p2U984hM89dRTHc7GU089xec//3kAPv7xj/PpT38arTVPPfUUn/jEJ3Ach7Nnz7K8vMyzzz7L+9///tg5HqSD+FFmNo7C2Rg2F8/zuHbtGmtrayQSCRYXF1sp9FqtdqA53KisBsBOySAIBaWq5Mom3DoXYBpHMx+t4Y2roy8mb68rJvMeWnR6PIb2SQxpzidtG7z9DYRwXDbkLLP14U2pQmkztLV21/eqTJu9qeWhYzdRSc+RTK3hNhoQ9gxvWB0RpVpyGs/JRhnwkc8Sj7q2W0bqqCE4/gX2pC3iP8g4qJ26nnBdd6BEu1KKS5cujVUmNQw3soyqW2Z9cXGRs2fPxv6mho65cAfh5mWMrqal42Q2QmmhDRMhoOxMUnfzfSsxA2m1QjCh6XQ4G0boYQ4r0dGNDanSWDporeOGDqlmF0jurQ48XhlWj7NRS0yj/VrkFIQBvpPGqu2hhIkybETooU0LN2mAH9mtfrMMpI2tqh3Zk3apX8/J4vjbKBllUISIPudPzGMUNtAxhHGJQqVyyOJuFMBqjt3IigzMakCvDHEfCKAs0mT17hApYY1BiEKiG87FQQnhAGXPohrs7xEqdUm5bmDIgFwyQOtI5t2SGlMefE8YKBinePek2alYZ0NEuqC3rayskEql+MAHPtDROXUQrl692pIcBFhaWuKb3/xm38+Ypkkul2Nra4urV6/yQz/0Qx3HXr3avyHOQTqIH2Vm47CLeT+HRWvdkjir1Wp9yd4HuZYghJ3xGpoeKdZ3m49c9KO/dM3ktoXgSDYEO3sG5fro4yhMHOlT67qFw7IaEbpEBB0X13VQ9eGa6tI0GO5sdD5bxdlzo2U12rA5d57FS/+FDHsjQVoarStQ0mQzHzkyo6aqByEi3x0PhA5vOhs3ARzOTl1PxNmppppUc41vlhUdVd+q6yJT2watNXt7e6yursbKrB9mnuHyw8iX/r1jbepXXtUxJyAwEyA0UkDVzlFJRP0jpIi33UJKAmFi6QAlu+wtGjfYo2jm+nIOFQahElg6QADtFZ/KdvCcNHa9vzKL6r4Xpo0nHCS1lqxs4CSpLNyNLG7StEMC8ITV2tApw4EwQAZ1DL+OkiZhIkMgLSxd6biX7SRw0xSEoRmVVgmJJMDLzUaOgzTQbowYgQ7R0kG7SUStHPXqECLKpAiBlxnWr2l0my1RVI00ybDY96jovmss4VNHopU4MCE8UIJCLdEKzCkNm3uChCPYrdiESjCZ9rEM0FpQDwWWVBwk1haEfZpe9YFS6ob2uRsX/WaaBv73r/zKrzA9Pc1f/uVfEoZha0P4TjHEB8lsHFWU6yicje6FtlqttlLOuVyOM2fODORgHMSgbJaiH8WNQLkmKNWMtr8lrqW5ummwNBNymJh64GsuXhv/uVzdMVmYVtSC/WOH8TUgojuI1r8lnpkmLX02ndPM1i8NPNa1R/jO2qJbynIpTp4dfkw3pMnO7N1Mrr7Qe2fbIoM7+bOtKNSosrmD4B+jEpVWIeKIMmH9cNPZODE4UXaquSFfW1trlUndcccdvPzyy5w6depIz3m9MhujyqyPM2bPZxIZwrnbMNfeBKJIfJjOYVb7r9NKGmxnb0WgSdW28KRFMTGz/2wIUErE8r8CaWOFAUIKNM1y0wjJegHbnKVO/9I2pUWrRKudrCwALzON4VcxVG8Jl5YGuoscXnFmMPFQWrfGklpTkQ5GehoDjUIiiUo/lZBIrVCGhdAhhlfDCOoYgAw9QjcXlUe1BaBCa5+DYZuSIGg0ixUSE4Wfn0MLETl4Mi6QpAm0gHQe2XQ2pBGVriUyUfPAARg1sxFde4BvJPF1HXtAn6pmibCFT5kEB9lbaA3blWSHrdwtN9Q0G89ssWahtGAq7SEEGAJCLQgVmFKPVVoVjPlzPWncwtiZaq33gAduu+021tbWgEirvNlEZBBOnTrF5cuXW39fuXKlZyFt/0wQBBQKBaampkY6th0HcTaOCkdVRhUEAWtra3znO9/htddeI5lM8uCDD3LnnXcOJXuP62xoDVulG1dSsF7o3oQKpNRsFU02Cwf/0WitubByMA9faYEOO3/lozgboo1hGKRyrCUiZyCRsVBDFra0M8KGvk2Jqrhw14F19vzcPNVMDKenMZznZKgkZ1svm/rwzoYSxxht0cHNzMZNAIezU9cThUKBK1eu8Nhjj3HlyhVmZmZ4+OGHuf322w/ExxgFx0kQb8qsv/jii7zwwgtIKXn3u989Mnn9IPNUt7wLZUf3Ktqc9j+HZ6VZm7yHup2jZuepOJPspeZ7SjvDPvo4TZK4EALf7MzMWEEFSwziDChcavvtLbqy3AKo5k/FljnpZB7QKBqUCsNiPcyg/Xorq9H4ZNR3ozF22MzACEBGhV5NTkvopggbmQsZBpjKj8qmmiRuaEnLQrT2KaNJkpcIoRBSooWMOnPH/K5qZoayOw22GxHMlYqCV0LipwdxNSJ4RgLPTKBGkMSXjXlXZGawnW08UxKFPmAQc6/mEOj9e+OHUKxEjpMmykQoDcWaybU9pyU6IIRASIGv5FgE8m6qzjD8jyGIa61Hk9bpwkMPPcQbb7zBxYsXOXXqFE8++SR///d/3/GZRx99lL/5m7/h/e9/P1/96lf54Ac/iBCCRx99lE9+8pP8xsxGh44AACAASURBVG/8BisrK7zxxhs8/PDDfc91kDKqo8JhFvMmEbBUKvHSSy8xOzvLXXfdRSKRGH7wIeZQqIIX3pgHUmvNTqk3wlGpGyTskKtbJq7lkz6A7d0pCMr1g28uVrYFp2cVZT8aY6QyqrbIVC07jzGZpbSSIm2V2XTPMFt7K/awkk6SNEcIYTSUqEI7wV526cA5HyklGzP3slTdxWiTIYyiT5KNyU51K0sfXIlKISjpNHs6SVb7R95AXAqBUuGxL6o3nY2Tg4PaqeuB//7v/+aJJ55gc3MT13X54z/+YyYnJ6/LuY8js1Gv16nVajz77LNMTEyMLLM+CKPaMCEEwe0PYb36/zU6aceUHyMophYopjobm1WdHEafsqk4hF28DYL93hUy9DGUhxbxkvVZsddRRitjlKWElNSy8yT31jpeV24KAp8tMYNykqSlT8IAHSgkqnXJApAE+2WwhgGq4X4JAYYd3Y2GgxK6aTwEykoidIioBfsbvy7uS93zoOGcRNmM5mZbEFhJCHtrsOt2htByoQ4qmUVWio3MhtGhZhUHJQxK9jSGETlQpqrh+BWsoIoRej3OWovgLSQVI02qTzmVaOhRRXdgfHuxVzUoek7Lt9IatgptylxKU61DJhWNXfUM1nYd5nJ1jMYxUkZlV2EoseRwAvlGUTPVJlQZhGAOuH0nzU4NnKlSinBMd8s0Tb74xS/ykY98hLvvvpuf/umf5vz583zuc5/jn/7pnwB4/PHH2draYnl5mT/4gz9oyeOeP3+en/7pn+aee+7hox/9KH/6p386UNL2RjobB1nMfd/n8uXLfPvb3+btt9/GcRzOnz/PbbfdNrajAeM7GzeUGF7QA7z8aEF4a92iPmaiKgw0F9cPE0mPetHW6vvf5SiZjWYYQgOlqTNIKdlM3wpAMm32jdKURDr29Q5oIsUQYHP67kNurgVIg83Z861lOyJLCoqZpai2t/m6BquPBv2AqVLWSS75p3i29C5eKJ9jrTLJ9wtTFOpu315NB4FlCvR1WGBPWsToBx3j2KnjkGXvN+bMzAx/+Id/yD/8wz+QyWSum6MBR5fZaMqsf+c73+F73/seUkre+973jiWzflTzFJlJwqlbEGhEl+0NDZuNiTt7HI3GkbHjSaFi16eItxFtsnVXTyMBJL09rJjmfRZ1bDr3IyLG2QBQtovnZjte0zLSNyzIaQLpsEsKQ9Ujx0erjo23DPcz0FoYDSvWCq03gmFt2XcnE+1+hUS1XZPuIm67jt1B5m4S8bUQ+FZvk0wNaNvBQFEXDiqZBdFQohph2SxZ+ci50xqkJDCTlBPT7GZuYSt7lr3kPDvWLBWZihyHtu8rkC6+7CWs03YvojK48RCEsFVJdtiAmieot5VaV2u9fQjrgcHqrkvQFtAVQiAl+FoM7Wu2XYb/91XB1541+fIzNl97bjD38aQ5GwN3ae0X0lwQRjG8jzzyCI888kjHa1/4whda/3Zdl6985Suxx372s5/ls5/97NBzNMe5UWVUoy6S7WTvarXK/Pw8999/P5Zl8eqrI3Z6PuQcAGo+lMYgTx8llNJc3en/w6l6kqQTUqkbXLxmcm7RxxihLj8MQy6sHIz41UTKVnw4/022vAx1d5pLtZmhzoaWBqKx2CsnSWhHi7CdS7BVnmTK2mbDPcNc7WLPsZ5MMLw5UTMilWI7cQuZQzSxK5Q1thWpTZWzi6T3VkCaBKZLIXNLx2c1o/d2r+GwFUyw4k1RV70LfqBM1so5NqspphNlsnbt0JmOwu42G6tXKJfLmKZJPp8/lsX2pC3iP+gY1U4dhyw70HfMpgjKxsbGUDt1mOaucThMmW+3zPr09HQr8/7cc88dafOwcbmP6uwDqMK1FsFZA2F6mmvu0gABjfiOz1JEZbRGzHoc8Tb8iLEhZIfT4Pp72K5H0CGEociIvR5LFCevG80IvPQkhlfFUJHjolTIFjMN8nvEYaxri0BkybNBu90wtEI3V2shUNLskEpvj+drIaMsgw7QQnas8d29OcIwRJvO/ihN+VohYxXAakYaKQVVXHx7Gqd+NeoU3hAgCRWtSH83POFQtaMmiTrUUZKl/UuSEs9Ogw0VspSVoh46HZvWikxjKj/K/LSj9XxIhtvbTqwWEjj2/jOuND1l3koTSwL3Q8nqrsN8ro5ltjmHjevyQoHZh0AuogljWxB6w/d2J81ODXQ2isUiqVQKKWXsQthcJG7UBb+TMxvdZO/Tp0+TyWR69PsPE30a5/j1vRsXnfUDSS0Y5qULQFP3JZfWTc7OD1eoWlmrUqoNVjsZBtdRVIwM78pcAi7xQEpQ2htiTKWkaUO87Mz+60JQzC0xVdgmnTEIawZGl7GJIklDHGQVooG9+bsIteQwHbP9MHI2ALam78Gt7iCkYGvyXK/1HfIseVjshHlWvCnK4Wj1bkfpdNyytIhUNWzbbjW1dF2X6elppqenSSaTR7JhO2mL+A86RrVTxyHLDgwdc5idaq7jR+lsHETAxPf9Ftnbdd0jkeAdhnFtoJCS8OyDiI2LEY9g4Q7k5CmmvICt3f4duruJ3k0oZM8aDVHzP8JyRBJ304g26V0rqGJ2ZTbSooTRtU5rPZgtEPE3FklsX0KaDjrwWeUUadmlXNbqBaXbjlWtvwSRnLrRUeK0f2YlzYjnoonWfK1b90O5XZn2Nq+sSdxuuO+x11K3M9H5hUnNzkL9KiqVbZVnBVr23JcmivZU65w1FfWxMAkw8DFFGJVCCdF6/oSUWGiqgYVrNu6/kFSMDKmw0DG/7szGqE/YVsnqkcLfKsieSgUhRKfUWBsCJVnZdZnP1bBNjR8KvEC2/gtCmMl6JK2wwxYKwAskVc9o3hZeeltw7+n485w0OzXQ2fjt3/7tViOe06dPY9s2+XyemZmZ1uJ+I3EjCeJxi2QYhmxsbLC6GmlpLyws8OCDD/aNBB2FszEKNouSF6+4LOTriGPuvhyHXmJ4L2q+JOWElOsGxarB6rZmcaq/sQwDzXo5f+i5JSxFkJmEaqQgJYXGS01A4Vr/g9qe+3K+Mztgpx1Wi4ssmCtsJM4wV/1+x/szmRGe1zAkTGTx0tMMafcxEEpD0okS7M15b87dS7K8gWdnez4fp5ASaIOrlSzf382TmcjgaQN1ADWzo3A6bMtACMHU1BQTE1FbxEqlwubmJq+//jqVSoVcLsf09DRTU1M9UtGjon0Rv1lC9c7HqHbquGTZh405zNloBq6O0p6O6mxordnZ2WFlZaUn8/6OxcQ8eBVUfgHhROXHQmgCv4ZhOn1+s+NsOZsiF9ExynSR7DsbQgUYqo4QIVoYGPi4VHvGiHNuuqGloJC9lZysssMEnrYxZOdYdoNw3nNVSu1nHqTZkX1pP7eSVkdWQkiJNi1E4EVdwttlcNvEPfb5DpEDEieLrm0n2ogLgRI2FSNNkn1p31BJMHqfQ086BA0VLD+U0bmEJMAmaC9G01HPErRGCoWBouS5GCLEaowbSBtfO9iqre+V3nc2RkXNl+zWEiSc/WPKNah442XyROO8awW3YeN653BtL4FrBcym65hGMxurO0qwtBZ4Qf/n9n+Us1GtVvna177Gzs4Oly9f5uGHH2ZhYYFcLsfU1BSLi4tMT0/z8Y9//Ibo/R4ms3HYSFK7o1AsFllZWelJOY8yxlGT+LpR9wVPX0hQ9SSltQS3THm4ln/dNlFKwdruaD/WaE4RNW+jYOLaPpOZmB+a1nx/tWkMDgfXVpgJh/W9PLPWLgDZtCYs9GYlWqdvnFkbJrXMbM/7/uQcwcYambQgrHaOY5uKocVKoU9h8V4AjEM1CTJ6ytHq7gR1N75/udHozBoi2aileWltgpdX0/vp+rc1KTfgXbdLhCXxDyA2cBinw7HMnu7eyWSS06dPc/r0aZRSFAoFNjc3uXTpElprpqammJ6eJpfLjbwwn7RF/Acdo9qpG+U42raN7/dXMDoOMvewMqpRMu/XAwcNuMm52zocpUqlwvz8PEJbENMDo98ZmryNnsuWEm0lEH6FUMiOjZIAkvVdKm6WOglyohBviUYps0ZSTs2QDq6x4i9BoxVdO2xd7xkv1p1qORsaqfdtTiA7OYQagTJtyuk5AunihvvOjW5f95oZBb2/GW5HzUghDUkVN1L6UlCypkiGbc5GP+6iPdm6pKpO4rQpePVclxAgBBpJQJTn36lnmHYLrbhfbzlVNFeFHM3p0w0HwBYdr20XB+9dwlBhmvvd7Dufo8G/pZpv8vaOZCrlk3V9BOC38To0YI1BEH+nB8YGegh/9Ed/BMAbb7zBj/3Yj2EYBvfffz8XLlzgmWeewfd9tNZ89KMfJZ8/fJR5XBw0s3EUaWutNRsbG7z11ls4jtPR2XvceRwGg47XGv7PBZeq14h+IHh7yyHjmtcty7FbGt0pKNUkc9mAa3vRY3llw8QxPVKJzuMLJcFe9Wg2gwkrWpS3rTlmiZwN29Bs24vMeJfjD2rccz89GWOlwHRMVq0z3CIuUkovkC1diQ5rSwkPQmAlCZKRQ+CYh3A2tIytR+6Hsmey4t3C//NiBl/Fr3LlGjzzssIwFPfdLkilDWr+wZyOnXqWW+ccNgs1pKoPdDpMQyKlGKgtLqVkYmKilfXwfb8VlX7llVdIJBIdJVf9cNPZOFkY1U597nOfG1mWfWlpaWRZ9mFjDvvNH4WMejf6Zd6bjQS11kMz79cDB7GB7b090uk0S0tLZLNZhBBsbGziBQHS6N7a9COJg+pT6hMaNqZfQaswyhy09SBy/RJWwsMUqm9QKmRf1aofQmGClKwbM9Q8F1sGPetgU468V0Y34m3ss1faOAINNSYQhNKOMhgN+IaFl5yJ+pVo0eFstH+um6PRff56IzseSrOVA6nYWcKqbKl/Kd27jm75GYKG7KSnLEDiKws7JgMSB4Gg6NlIkWXS3Yv4D0JSNjOkg0Krqd84T9X6noMWZsdvdackYuffhOdrwkAzNXEYWyHZKjvsVU2m015HZgMN5gD7f9Ls1NB0xDPPPMMv/MIv8NGPfpSnn36aD3/4w/zWb/0WEJUwvPTSS8emFT4MB81sHDRt3R5JKRQKTExMcN9992HbB+uYfBxGph0vXbVbG/d2FGsG5WsJTk962Kbfoz9+VNBac3lrvHR8qS4bsnVRQ6W3rtksn/JwrGiOKtS8uXp0KX7XjhY4kc3i7xpYsmE40mnYjj+mqYRSzS/2H3hqkmDjKqmUQpcjgqG23KEbDy0Exdmom3egBI55mGaHo5cOFOsOz1w5RVIG+Gr4MWEI33ldAwF3nRHMTkkq/ui/Jyk07z0bkk8aTGVSFKsuK1v9nY4mYa87szEIlmUxPz/P/Pw8WutWydWrr75KrVYjn8+3Sq7aM7NHXT9/E8ePUezUvffeyy//8i8fqSy71nqo1PswHEeGuzvzvrq6ys7OTquR4I2y2d0YR2ilaXtrtRrz8/OxHdelFNQqBVKZyZ6teWwGgyZvI6bUBzPaIIkGb6Oy23rPCOrIzoKf/fF0tFGvG0lMfNwYudjWZxtZGCVNJCGW0eu4GASECCSys0lg24a6KXMbh9CwaLchXnJmP/4vJL60sNQ+B2If0TFBQ7FQNnqAND8R2g4ao23/oEGaXFNTLBobjXvRO6uyM4lDxE+oKTuikcjRuYmGVITaoFh3MESKvFNGCAiFjSddHFVr7SFGsZ3Fhsxt0t3/bBBCaUhAs1JR5EYQlxwFvjJY3Uu0kle6MQdrQGXD/yhn4+mnn+ZTn/oUjz32GE888QTf+MY3+PSnP82f/dmfcf/995NMJgf2wThuHNTZGHdxr1arrK2tsbGx0Uo57+7uYlnWgR2N5jyOojFgHFZ3DV652n9uSgve2nJwZcAtMwOEPA6BSt1s1ByOjnJdMp8PWNuNHs1ACS5ds7h90UcK+P6qeeAmPXFINJwNwzRYCWc5IyO+TS4ZUt9xceL6ToRB5BRMnO47ro/FpYkHOLvzHGF2ErOwibbsvjNX0qSemKCSnkU1dM4rvkXiUPz30e6T1vDyWp4ggAsbbRyPEfHaJc1rl0KWZkNmsmXMRG7od/SupZB8cv/ZzyQM7lzq73TY1r6zcZAFVghBKpUilUpx5swZlFLs7u6yubnJxYsXW1yQ6enpm87GCcM4dqopyx6GIZ/61Kdasuzvfe97efTRR3n88cd57LHHWF5eZnJykieffBLolGU3TbNDlj1uzHFwHGVUQRDgeR7f+ta3cByHhYUFlpeX33Gbk2E20PM8VldXuXbtGtlsdmi5V3O8yYkMWzvdhPF+wZf480dypRpTCpTldBQECR1i9mkrV9KRJLAlIvERU3l9m6U2nQ0hYNot4ulOmy0b2RQlTJQMOxSnOuet2l4VbZtt8IXZ8dkQA9HMxghBzUxjeTuNZoBtZTxCgOiU/1XCQmqfupHEMCQV9pUIm3e3aE2BipwN3eVsbPo5nIlovELNxXFk29Gjodn1PdAGu/UkhlRk7Sg7U5UpTO0hdUPWWDTKv/o8YqGCnWonTwNga0/ulw/3QeS8jmcnBBrLVJhSY0qNYWgqdUnF692KhwreungBy7NiBVBOGrcw1tkQQhha6/DXf/3X+eVf/mV+/dd/HaUUH/3oR3nmmWf4vd/7Pf7gD/6Aubm5G+pdWZY1sBa2H0bJKHSnnOfn5ztSznt7e0fiKBxHZqNcF/zXm+5Im/KaSvHmuubMtIdlHC2XY2XrYDye3bLEEJqwsVBVPcnqpknGDShUjpBEicax9hfpSnIa/MjZkBK2EwvMVjrla7UQCKUIktkOPfJueKGJMhNcyj/A6fBb6L1ttNnpbGggsFNUU9PU3XyPx+eHBok+KfqRrm/EMrm1UppizaJUCjgMD+bKOlxZTzGRCbjnrCQQ8R1Uz86ELE3Gz62f0+FY0bN0VI6AlJLJyclW7wPP89ja2uLy5cuUy2W++93vMjU1xezs7KECCjdxfDiInToOWfa4MbsxaJ0/KmdDa83u7m6LwwAcKvN+PRBnA5ty8SsrK9TrdRYWFnjggQfG4oXalkk2k2SvuJ9V6KdIZYgwvtxJSupaYAIhooe3Ifusr3XtYImgoVYkKJl5cv5m7Mqq2vgljuE3VBnb5qYiLkMgTIRQPSpYzXRNOzlcN7b9Wgg0RiNbIVvX6GNgN+yK0JpANiNa3ZkAAWanrHkoTczQj5SniIJkrbyG1tEhKYfyrkvKqPXkKipuHgeoBxLL6uSRjIr2+x4og+1qGq1Ccq7XUKfKkg52W2Nu7kI+Fz/Wtb0EIQZmm02p1KHqiZG4hN1b3/bMRPMf3Rk2V2im0l5r/HI9vlcIwG1nlxD+Ot/73veoVqsdAiiDSorfiYj99WodsYv+6q/+qiXhJ6UkDEM+//nP8xM/8ROsra0xNzd3rGVAw3DQTcegxb2Zct7d3WVqaqpvyvkoDMRxOBtKwf95I4EXjP4QKi24uOGQTwgmU1Us6/Bkfz8Q7FYOVgtc8zuzGwBbJWOsLrCjwLE69a6tdJLCRpKcWWn87UJ3BtwwQXnUs3MDx/aVgWFA1cpzZfI9LNUqiMbCoKRByZ1l3bmVXLJ/x+7wkBkcQw6/X34oeXU1g1KaSvUwJVv72CnC0y8oXFtx37LEdGQrwzWdVty1MHxe7U7H5m7t0JmNYbBtm4WFBRYWFtjb22N5eZnNzU0uX748dqT6Jq4PToqdGobDltPWajVWV1fZ2Nggk8m0OAzf+ta33tGOBnTawHq9zsrKSquC4NZbx+9Q3j5eKuHg+wHVWsTr7HeHc5kMhWJlP9rfBl/agIdWKgoWBdFYSsRLwfpY+NokJffXdSWtRrfrUs/n24nbQoArO/t3qAbrTgmTQNLRwVvQ3OB3ORtCgo4y1KpFDm86EpoQE3S9UVOm0ULiSRvZ/QwKQSBtPKWwG26DkhaEVZTlEmJ2lGAHSmBIMAzJBtOkuAJtmY11P4+Tjmz6Xi2B43Qey4jbBdm1D/CVwRsrLksTIfMTIaGwqEu39f30o/Vulyyqgd2R1dAadooGIqanSDe0hmo1JJUab79UqpuEWjCbrSMFhAOEVjJJl5lcrwDKW2+9RbFY5OLFi0xPT98QzvS4GHiX7rvvPrTWrWhiM6r/+7//+8zPR506byS57KDodhR83+fatWusra21Us7nzp0b6MwchaMwzGFRurFEjLH/+87bDlvlg30nu1WbYs2MshzmwSPqAJuFwzks2yUDy9AdikeluoEhB3UiHw9NvkYLQrAu5sgRZTMyTkDZyJEKCx2fAShOnhk4dkQ6jFC2p1iZe5jp4hvsZU6z5dwCUmIoD+jvbIzf+3QfgZJ9mym1482tHKE2KJcPl9WIQ82DZ19RCKF4120Rr+OBM2FsQ6N+yCQMMon9zrXXI5ojhCCdTpNOp09U5OgHFSfdTh2Es6GUamXewzA8UPT/IDgOp61SqfDCCy/g+/6hSevddjmfTeH7AUEYUx6qNclkEjeRoFjxIyJaF7SZgLDhrDiplrMRGr3RaIWgQgqNRNHZLLBuZnBUraecSncpZwkAHUBTglYLPCyUMHp6PTTPCkaHsxFKC7PRdDaUJjQ0mpqZnbBtV9+8I3UzjRuWe0avK0kobOyGBrsSBp5MIE1JVXTeg2LdImtEAbxaIg/eldYd0BrqiTw2UPMNLLvzuxiLNhrzDOYmkmyUJbZRYDKrqclUK4sVt4TXfEElcHG6ihMKZUGgRnM0lNKsrdeYnu6fmeiHqmewtuswl6sP6CyuMdsej3YBlHPnzvH000+TTqe5evUqly5d4n3ve9/Y87ie6LsyCSEma7UajtN7I5eXl1v//o//+A/OnTvHwsLC8czwGNBc3JudvSuVCnNzc2OlnI+KbzFojLUdwb9/1+T/elfI6ZnhxujtLZM3rh0ukhVqyfc3XGYyARPp+oG2n1rB1Z3DGT0vECzkA1bbshsVL8p4XN0+mo1Dwuq9p2F2AlW62FC4EJQSs6RKnc6GslyCRJ+8bAOBErSvY8XEPMXEfMdnLDm4BNAcUZ0jDqE2MIeQw4uezeWdJFprypWjyWrEQWt44U3NxxZCDps0G4cgfhP/83FS7NQgBcRxsuSlUonV1VW2t7eZmppieXmZVCo1/MAjwlE1IKzVaqysrLC2toZpmtxzzz2k00fEtu3C1ESG9a0CWneSxB3XbZ3TcWzqld7Aj7QctIoEPpRht7b7yui1s1VSUdkSUA0d0mbneHvmJBP+escqq7oVnwSYaHzdrL/R1EQCMECKmFKwaLR2Z8O3XJRhYvsVlGE1mCUCLQRKR9mSfTSUFaWDpTr7ewjDgjAkEFYrLaSlQbVZQtXVjbzoJUF65BMeTsJkq5JtzXU9mMDONErQPbdDYhYiZ6Pm6Z7X47C358dmQdyUy2pR49oFku6+gHCia2nQGrYrSbxQdpwvDBmpTFtrTbWmUEqjDxGTrQcGq7sug/RYBjlhQohWNv4k2MQe0y+EkFprBfzG7//+7/PjP/7jTE1NkcvlSCQSrcZ1Fy5c4Jvf/Cb/+Z//yW//9m+fGGejVqtRLpd56aWXmJiY6JDNGwfXo4xqbUewtiv5yn8Kzp9RPHSHYird26UUYK8qefb7h+um3Y6NokmhYnB6qo4Ro5AxCHsVs4cYdrA5GLimotZWErZbMbBMjT8m8TwOPZkNwHRs1nanWLS2gEiUqq1HERrwMlNDxx7l+u0hncRd6+ArWajFQGdDa3hlNQ8IqtWwp1b4qJFNwt39+fQj46TVqd7E8eCk2SnbtvE8L9YpGmZLgiBoZd4ty2JhYYHbb7/9hvwODuNsKKXY2tpiZWWllY2566672NzcPDJHI86mSimZnsiwsV2kWUpkmia57H5j01TCplaJCbcIgTJdDL+Cr1VrwxRlDPbhYeO3kaXrysVVHmZ7Kas02JWTTKh9mcM4ErIUGpRCaNXgWNg0xBgJhdGZHWl+D22XrIRJzc1jBVcIZKRE1Wya52NG6lrNqonWODIif7d3KTdstKhHZVfN6xZGFGzD6nj+yp6JlhZ7dUE+4SGEYMeYJhUUUBq8ZA6bqEFeU1myHaVyyPdXHGxTsTgryDo+WppIoQlDBSrEdQVSCBxbUO5jGu1kggvrmntO7bWyAt1brK2yQ803cbtJ4cWI2zIMdU9HYzZIMFr3nkO3/tf2d9s/mn/7gYzhdURZk2pdjyzccyKdjcYCDvC33/72tz/7d3/3d5w/f56lpSVM06RWq7VUXIQQPP7449x1113XedrjQSnV6uytlMI0TW6//fYWOfQguB6ZjWs7jRp/LXjxLYO3rknuv13xrjMhaaeNJBXC02+4A9JxB4MXCi6su8zlfHKJ+kgPdCR3ezSp/CAUTOdD1nb3f3H1QDCfC7mydfjsRqLPZr5gz7KoI2fDsQXadhFeFKUSSlHJLw0d2xkSodEabKN/ZqMWGJiHUPgdRri7VkqxV4uic+V+K/cR4uE7GamsaxiOO7PxTq7tv4l9nDQ71VROjHM2+pGkd3d3WV1dpVwuMzs7y7333ht7/PXEQexetVplZWWFzc1NJicnO7IxhULhSH9z/eZnmib5TJJCsYghBPlcZ2baMCQaE0GvalQ1hDRRWWto2Bihh2oTB1EIqkTXEyjZWnsLQZpJa6+jDFpbDsV6mgyliJPXZy2zZAiBj8LE1xamjjbwobA6nQ0dKUZV3QnM8hqCBq9CCIqJabQwW704tDQIW/2TWrTutqE61bqUMFEiiHqBtG6UhTQN/K4SqkI9CWhqoU2oorU+TGSguMd6MNXKapR9N9Y2XlwRGIkEibTFjg87MaZRlRSB5+PVfPygxvx8fHA1kUnyvdWQe05FkrjtyaOtAtSxsLpsa82DSn04KVxpTa3e4Bh5IVqDN75GUQ9kY55SRGVfO7uKmjdmedk7HH13hVrr1wHefvttvvzlL/Pcc8+xvr5OKpXi7rvv5hOfQXL47gAAIABJREFU+AQ/+qM/ev1megCUSiVWVlZ69MXfeOONQ29YjiqzEcbUiTaxtts5x2JV8J8vGVxeF9xxi+CeU9EC9NxFl0L1+GqSrxUsChWDWybryCGk45pntJoIHgU29gwStuoYc7Nk4Fr6QM3k2uH0cTbMbIbatoXbcAZUKoPRcDa0hkpuQH8NIoJ7d+fubmgGc3F8ZYzKl4vF4LElr65FUT3PU/jxqoxHBsuEB5aHf24UHLcsbff4JyFi9IOMk2KnBjWgbbcl9Xqd1dVV1tfXSafTLC4uksvl3jHP4ajORpNTsrKygtaaxcVFzp4925ONuZ7XlUg4hCokmXBjs0LSNCHoXQxDwwEVcRZEIgOlrWhD30CFdLSh17DjuSRajdgk5TBByqh2rMd1K43r1wZeuxRgElDVFj42KWpoDAJhtvInClAi6kgVGA5VN0+yttvIZkAoGz0smsRwYRCK6L1Y9a0WsTyCryUhZgfHQ7pJQhWgxX7XbK0jBa6o8lhSqDpMpuo4rqRUzmAlXSyi7Icdk9UoFEOMVA4Vc+877omU2K6DYVpsrgv8KxVuWYrvF2OnMry1oTg7W21lOIJAUw6SFCuaiXxXVmNPjkQKrzccDd9XeJ7COMDzG/HLoiCx5ytUAK4Lti0QRpR1aj1B/4OS+AND0EopTp8+zW/+5m/2/cw7QZO+fQ5xZO9uffF3ipLUoDG8ALaK8ff10rpkbUdzddNBhMvsHVEmYRBqvuSNawkW8z5pt3+WY23n6BruAYRKkEuEHc5GEArmciFXD5HdCEOFG8PZABCGZFXPcZZG5+9kBr2zCWhCNz10Bah6BmJYAHLIs1MOk4ig3mjqNz4GKXdd3MoRNCJcpdLxZzXefXtv3exhcJzrzUlrlHQTJ8NODeoJJYSgUCiwtrZGEARHSvY+6useZvcqlQorKytsbW0NVHNsx1H2GBk2v3Sq/1xsy8ILYgQ7DAtCE1TQaL4HgYyywh4OATZaw57vIpDQpmpVVw6O9LDE/mtSCnblFEndq071/7P35kGSnOd55+/LzMq6+76758RcuMHBjC7KMkQAJFciKdOUyIUPwqYohaXQ5bBWAStIhRlhBqGwLe46JAetIHeJ0DJCQZ2kJJOEqBVXssQlMSMAnAEwg8Hc3V19VF915/V9+0dW1l3V1d1VAwzZTwQw3dVZWVl5fN/3vO/7PG8tNCQeul/6VK57crQweH7/EFeY5PVBYjLr98sID6G7Nmjl3h2ahlKBra8gb3loZjQ4U1RreiQIDaHp4FUX/LbScFVNXytNI2cModnZOheqzVIYoekEjrRZ2ycbAHlzmPGwT7ILTrhlVuPWik4ooeN22cZAaAJN0yiqGNduFDh6uPU1dUMDLG95DET8z09thMhbguGh+jVKJi8q82EnSKmwbP9L5vP+sXb7aEml8DyfpFhWc9lUrgAUgtqsKr4nMhtAV5Pum000wuEwxWKxYv/Xjdi7V0Rhr4NkJ8vDlU3Rse7fcgSv3oSkmeTQnIPlQsnrv83h4maIqKkzN9yc5fA8QTrb+wzLckYnEZHkStX7cWVLwxAlXLVznYqUEseRRMPtr58VHwPLJxvoBioaQ9glikPb13xbns52R9Vo31cLT2ksl4ZYLTicHF3dkRsZ+C5mfq1wi9pY2+Tmuj84e54vdOv3M/wDb+0qyzrsk427D3fLPNVINvL5PKlUiqWlJeLxOCdOnOip2LtXYu5atArUBWXKi4uLCCGYmZnh6NGjb8p12c3cns/nWVxcZH19gyOHDzePt2VTEM3K4eJX9bu6H8kv4I+lttSxpYnWwj4348abyqn0kMa6PUy8TR8lTwr0MtkArbIW8ESokpWwRdhvBiviFaVBPj5Rc9waSK9icWtEE5VFdaXhXe2HagZ4VvU7Kx1XiMp38sLDWK7ACA2iqeq9nHWidVIHW5r+8WuKaFjheJAvGYTN5vshXxLo8bJep8vLppVX4EJoeEaC16/mOH402uJeEmTkEGF3nc0tgWZGiTVUHHjS7+vVDYqWf9+7jsQq/9wuGaKUwpPgOD5B2e1S8XuGbLzVETTg+qEf+iH+83/+z5w9e7YrsXcvMht79UaHzgNjrU6hE7J2nFeuSWZHSoyOwXreF4T1E0Vb48pyhLkRh2jIqkwqa9n+3E5KCcKGQ67Gf1yhEQtZZOydkQ0pJbatCOmKUAdeZMQjWJZJuCziVvEBhOuQG+5seQvgettfO71Ds751O4nEwEXn1tYgh4a22m7b8vOlQSvnSKXg1SVfFA5+VqPfi7CTczCyM6v8NxX7ZGMf/UBQRuW6LisrK6RSKQzDYHp6mmg0ilKq565SwTzXy/u5ds6qLtLXGRsb49SpU0Sj0W320H5/dxJBmdfCwgJCCGZnZ7nnnntYXdtEtOj27WqmP/soxe3kg8SFTZ4BQEMqyDj+927dSFUj50VJNJRTFWSciMyia83vcZSBhue7VSlft6kBaH5na4HEFmE8pROuubxaKILn+ERAKVURoCt8F6pAf6FENQPjdxuHQskiUZ43NN1E13VcRydCHhUKs+GYmGETT2ng+p/hSoEsp/FDOhRd/L4fWgiwCZuwnjHwlEmiRSx0actEE4E2tfv7QEpVJR3hBJev5jl+JIzeJAzUWHeGsZRHMaMYHqqfGDO56jnqBM9TBImXXL6agQmKyZTyCYXrKixHtarG2xW+m6aiu5JseJ7H+9//frLZLJqm8Sd/8iccOXKk6/f3qoyqn6VYSxvdLwIVGvPrMXKW4sCkg6N08na/feUF8+smppAcGvcQmmK+j+Vca7kQCdMhZ9fUysoBBqKSTLG7JzIgGgDRFk5UjXgj9gjTKsVAKYURieOULKS5/WTaTTfUkGhdvy2VYKk4DJTT7U6SeN5iLN6+H0cjPKWhN/VuhZV8nK2iP+L3solfJ/zAvX3dfc+xTzb20WsopSgWi3zsYx/jne98J+985zvrxN5LS0tt9Rx7Qb8W8isrK6TTaXRdZ2ZmZk/OWL0+xu32VytWHx0d5eTJk3VlXroeQrVYKVpSwwQyDOCaMWw0XEIoBZtWjCC036pDOYAtw7jlcqpAQC2VQdaJMRRu7m/hKR0Nr5LRcJRBuDyme5qOJiUOZp1TFFB2iXJQ5bWJb61b1mwgqgtrLUSjb2s4GgM7A0C2UKQUKoGIogE5MYAZMtgsasTDoqLiCJkRKASZhuq+Cm6YhOHf07ZnMJhsXo9kiwJbmVVDrZ04WAapmTL0SJzXrxe556CBadZ/lqd0DFMj0eCqmct7bBbMrhb0xVI5q+FWsxrgk7hcwWvbOHCvaGeq0miU8mZnbrtB32fV9fV1nnzySY4fP86TTz7JxsZGy+2ee+45jh8/zvHjx3nuuecAv/7zx3/8xzl16hT3338/zzzzDOA3aPrMZz7DX//1X3Pw4MEdd0ntdwlUL45jeQdkI8BmXnDhmqJUcBhL2HSdl9wDbBXhjZUYq+t6z5rttYYgGm6OWsS7DKTVEg3ojmxETEkuNsHiyMMsDZ5iY6q7lbPWhe2SSev61E0niauqE4iuayzkRynugDzKFgTClRqvLlWtHgsFb2eD+y4wNaw4NLH9dm8lvNm1/fv47sJv//Zvc+bMGV599VWeeOIJPvrRj3L48OE6V6leBL9aoRdzVIBcLsfrr7/O5uYmxWKR++67j4cffpjx8fE9kfM7QTaUUqTTaV5++WVee+014vE4Z8+e5dixY016kkik9VpCar4D02ZoAiEExXL5VMGtX/B3GjkybhylIO+GkdJfnNvKbN1ErvIdArJhEhiwecLvnaGEhiwL0wOUXL3ueqjKsZWJRnlsE6Hq9xRlElPbSDGaGEKU7X1dKSh5gq2S7m9d83lbVnUCrj3rSxkTrfxZ7XorLW2F60TZ2jamKrVo6TgWiXL1lqRQaJVWEE2uUSsboqt713UVbpmnFPLVfXueRCrVN6LROkvm4260gO/70T777LM8/vjjXLlyhccff5xnn322aZv19XU+8YlP8K1vfYtvf/vbfOITn6iQkl/91V/l0qVLvPjii/zd3/0dX/nKVwCYmfEdgToJ79qh3yVQez0Oy4H13G4XPIIbyxqXbygGTJu42X8BMHaRb3yrgKH119ZoNaszHK//PquZ5tcaoZTCcVTd79uRDduFkF59T04bwBmY7Oo4Qy0cN+qPB8wWDf2UglRxqOl13dC5tjWK1+V6pNWnX18fwPWqk0m+0PvFTSNGjSu8/PJLzM/PUyp1n5l5M9GY2dgnHt+96EcgDODzn/884+PjPPLII3z605/mX/2rf8W73/1uHnjggZYLhF4Ev1phr/v1PI/FxUXOnz/PtWvXGBkZYWRkhLm5OSKR3vR06mcZlWVZ3LhxgxdeeIH19XXuueceTp8+zdTUVNuFWiwSahuiW9Fn0LWgN4bfabrg7eQ8aGTdGELAhu3/K4Qg59ZHzJSCkHDwqIqzLWVW9Ba2iOCIsN+PA0FOxSuEI+8YUNuVvG78qv5cG2iqdZUK4AqjXAoFi8VxLBWtNCwsFKzyrgXrxZrS5rq+EhqUGx+24m9beQ2vwUXFMPZejWFEwtxcgq2tZgZgu9UD3Njy0M3tXUuUUhSKZZLnSYql6lqjWLArJWD9gNnhdNyNGfi+H+2XvvQlnn76aQCefvpp/vRP/7Rpm6997Ws8+eSTjIyMMDw8zJNPPslXv/pVYrEYP/qjPwr4TZFOnz7N/Px83Xs7WQq2Q680G/0qo7q+WGKv5S0FS/DKdcHWlsNorH9ZDmkXuXCpgOWAmy/05TNq0WrtZ25jgOU4sn4gVHQUhwNYDfa9ltvdo+J6tLT3q0U729stN44tW0fWlB7mxuZwV8fQGBHJOyFurFXrwYtFjw6Oyz1BIgr/5PHj3HPPPTiOw4ULF/jmN7/J5cuXWV9f78viqhe4GwfxfewO/QqEAXzoQx/ipZde4urVq/ziL/5ix6BYLzMQjfvdzXOWzWa5fPky58+fx7ZtHnjgAR566CHGxsZ6noXpB9mwLIsLFy5w4cIFTNPk0Ucf5cSJE101DtQ0DSVah+I9qhONb3Pb2WWrFRxlUnBMLBmpe02vGXMcpWMIDxuzkqVW6JWMt61FsEW4srr3CFF0fa2mVIL6ZV0Nwaj9LqqWeKhyf5Ca45QatqcwdEHJM1F69Xj1kL9I3yo0XzdRU767UfC3C4eat1vJtu430zU63DOGaZLaMEin65+3YM7zPMVmobuSb8elksmp7UelpKKYtXZs3rIddE1xbFryY496/My72k/Sd+M81XfNxvLycqVr69TUFMvLy03bLCwscODAgcrvc3NzLCws1G2zubnJn/3Zn/HLv/zLda/vJrNxJxry7XQfQZfYVCrFYuEgMNj5zV0ita6xuqU4NmuhdIOC3cNLriTX3tikUG4Od+Wmy/33Odiyt/a3tVjP6YwmPdZqXK/WcgZjSbelE5Zte3VOEIHHdWwbsuE0POeu192okrcM2KahX7uBcqnYqcmkIC/jrJcsRiLtSZ1SYGhVLYZS8NrSMLWTTu4ONfEzDEEymSSZTHLkyBFc12VtbY1UKsVrr71GLBZjbGyM8fHxnkVK94q7cRDfx+7wpS99iW984xuAHwh77LHH+M3f/M26bWoDYUAlEPbUU09tGwirRbd9NnqJncxRtfNPOBxmZmaGEydONC0Ae02MekU2HMchlUqRSqVwXZfjx48zUNMhfCfQDRPVovmQpxno2CgFWSdCY6xWqe6sUFcLCUZiVs32Ak9EAV+74UoDExuLSJ142VM64CA0ga1F67p957w4Rtgf14PSqmqPDf8zarWEtaVQDiYuBrIsjBeahhJ+yZSmBe5V9e9VCkpivOm7qbKNLsBKLsTImEATCqfm9t7IaXiiOag2kgSry6lJbRM81Q2DtbyG4xSZnvYzR8EjllqVaPr2pfdKqYpWw/MUxWL1nrBK5cqEHpANXVMcmVScmFEcnVKYXSzR+t3cth/oycrziSeeYGlpqen1T37yk3W/CyF2dYJc1+Wpp57il37plzh69Gjd33ZbRvVW0WxYlsWlS5fIZDJMTEzw4IMPsvhSb11JXE9w6ZZgJOkxNyHZKPXGsSqbzrCeqz60noRipoCe6A1RaoeWUfkW95XjyLaWc7Fw52vX+D4pu7vWJVcjvM041sr2NutGKXqd07qappHKDxM1bKJG65I1V+kVlw6A1UKMjUL1gBxH0qWd+a5h6PDo8RavGwaTk5NMTk6ilCKfz5NOp7lw4QKO4zA6OsrY2BjDw8MtF/x3wrVmn2x876CfgbA/+qM/4m/+5m84ceIEn/70p7fNbLwZZEMpRTabZXFxkUwms61lfDf77PUxdoJSikwmw8LCAvl8nunpae6//36uXbu2a6IBEA2HKLQYIxUGrnTwlIbVIgMtVTt5eON+BEXHIBF2K7NwumAyGSv6OgA0NDwyXgxZM695quwspcAVYXRVJa9SCmxigP9+r5wdqSVEAj9SbxhUSnItZWIJP9ATzKtCD1XGQJ/ggOMKDFGdZvVQhEK+ObgnRC0F0sgV3bLWsiroXs2FWy4/TKN7stHNidZ0jYwTxb5d4NCBGGbIF3o7mF2V9Fi2qsQFa3UgSinyGb8sWOxyHbUbglGLu3Ge6gnZ+PrXv972b5OTk6RSKaanp0mlUkxMNCtGZ2dnKxEmgPn5eR577LHK7z/7sz/L8ePH+ZVf+ZWm975ZZGMv9bBBFGl+fh7P8zh16hQnT56sELFubW93ivWsYCOrODptYYQNCs7uL78q5njtevPI8MZtj4fut1sOxr3CZkFnfMBjNaPXvTY17LG04Z8715V4XvOIFFyy7TIbTdOG6u5aO57OdpWgOs1EIXCg2g5FW+OVpVFOz66Ua3br4SmtEvFypcZrqXril8/3uV048PBRiG1zEoQQJBIJEokEhw8fxnVd1tfXWV5e5tKlS0Sj0UrWI7DTvBPi7btxEN9He7wZgbD3vve9PPXUU4TDYf77f//vPP300/zYj/1YW91SvzQb7eY513VZWlpiaWmJSCTCzMxM3fzTCW8FslGbhYlGo8zOzla6rFuWtefji0ZC5HPN62GpBLZnUvJaZ+6FUFiegaFvP8YWnBBx062U1CoEDlE08mUpt6KgIug1mgCpfJtdqTRfHK6q85+s0WnYnl9KZeDhKh3KpU0KKMkwCSykgoIYpCSiKOUBCq8cUFNaqJIFKZXLh/O2xmDNmJ5zW5eQ2Y4gFKrdboBoOI+SLkILsZbVkC2yGv4J3Pa0VdDtNdY0DZsEV6/nGEwItnIhNGP78V0qRancLVxKVUc2HMerBB93MmTommIiniOpLTJsrjE5MsJYYoyQPshOg793o7aw72VU73vf+3juued45plneO655/iJn/iJpm3e9a538eu//uuVWtjnn3+eT33qUwB87GMfY2tri89+9rMt93+3lFG1iiKdOHGCVCpVSdEDlGzYyvfvxlEIrqYEiYjHoSlJxtl5lkPzLF54pfU5Vwoy6wXCQ/1tMOhnMevt7yzHJwmup3Dd5msTXC9NKCJm52tn6PV/N1p4obeC7CJmEhL1YbO8G2k7eNftWyreuKUYGopyY2uYo0Przduoqn/6jY0BbE9HoBhNKh486PH6Ky+xGHuYhTXol+3tbpr4GYbBxMQEExMTviivUGB1dZVXXnkF27Yreq47TTbuhkF8H+3xZgTCRkdHKz9/9KMf5dd+7dd4//vf37GMqh9Zu9o5qjEL0E0WY7t99voYt0M2m2VhYaFjFqYXz6uv2wghVP04rYBSh+yzYGfKyLxdn91YK5iMR0roQuEqHVvqRGvmHU8ZeErz+yhp9a6DSgtVzmPJ1QgpDYXAxcAo94qSSsMti7w1XSOvBghpLp7rl90qFfTjMCpZjlzJXyIWHY2BckIiZBhsldqUSjec/k0rxtRAkbBpYLtVHcfesbN7UIYSzC9nGZ/q7vMtq7r/RnerQrYmaLDN7WboisMTtRmMKHAPjnOQdDrN7du3uXjxIolEgvHxccbGxuqc6tp+n7swKNZ3svHMM8/wwQ9+kM997nMcOnSIL37xiwCcO3eOz3zmM3z2s59lZGSEj3/845w9exaA3/iN32BkZIT5+Xk++clPcurUKU6fPg3AL/zCL/DRj360sv+3ukC8NgrTGEXK5/NNA+1O+mvsBbmS4JUbMCjmGZkax9W6q5nXlMsrr+U6OiNdX5SciGygRbqL1u8G2aJOMpQl61Q7xmVLOkNxl9Ra6/cEp3o7cTiAqddvEwl1lxEwuoiahBvIxrLV7EDVCKUUV+clkYg/Wby+nGAgbDEWrfdoD+6mghNioxDhh0643DsrK17iK9cK/NjjglxJ8T++pbg0v0N/820wM5hjbHB7IWYnCCGIx+PE43EOHz6M53mVrEc2m+X8+fOVgbnRunKvuBsH8X3sDv0KhAUEBuDLX/4y9957L9FolEwm0/I4+ml9a9s2t2/fZmlpiVgsxszMDENDQ7telPcjC9OJbHiex8rKCouLi5im2VUWphdkyDBCyIZ6U7nNONnNKfVPnb9hwTHqshsgKHoRNOHiyBBKaVDbL0kIPKmVsw6qLrCllIYsB5lKjo6u63hKw1MGRrmnk5TVPhu6piG8amkWlcPScJQvDteEIG+XrXddn4wYoRCvr0SZHHBpteBvdeo9ZaILhWlouLQnt45tAd2tQxLxENYOk/SyjfC/EZ70u36DH+CrrQbwXIlTU+vVqozK0BVzwzZJLcU/PjvXskQqFAoxPT3N9PQ0SilyuRyrq6u8/PLLeJ5XKSkeGhpqOR/djfNU38nG6Ogof/VXf9X0+pkzZ+oG6Y985CN85CMfqdtmbm5u24HjzSyjandsQRZjYWGBbDbbMQrTTDbu3A0kkPzDywWm1zPcd8LDM2N0pOpKMn8zS7YL06lCDhJ91vxKLV7ufuofs5SS1Fp9tiNAIAyH7fUa0NyHI9qlhXB4G3G4UhCqsb0teSZb9vYanfSmIpeHsdFg/xovLYzw9sM2UaO2o6n/PSdGEtx3pP09nogIPviPwfUU/89LinOvg9OlCL4TTk5tAHsjG43QdZ3x8XEGBgYolUrce++9rK6u8tprr2FZFsPDw4yNjTEyMlLnFb8b3I2D+D52h34Fwv7rf/2vfPnLX8YwDEZGRvj85z/PCy+80Hae6vUCXinF1tYWa2trpNNp5ubmeOSRRwiF9m7c0Y/MRivUdiefmJioa4J4J44vEjEpOPUTXTeyPb1DbwSgodWqaMpulFxBzABbtbbgldSUTlX6V5c7eUuBLqAkddAVrjJwpU64/JZsUQdTlHt86Ch8PWcsFGItFyYaKeApHUf5PbNMQyPQfHhKUHRC3NoMOqVvfy4CrOYjTMaL3N7svCDIFATCUISMLprimmLHZKNblErVq9SY1SjmG57hQMNS1mCcnPX/LeSyLCxsYRpz236eEFUjlaNHj3ZlpHI3zlN3ZQfxWryZZVSNaKyFnZ2d5dSpU20H1JZkY/POlW2EhU2+KNCcLN++EOLQlMP0bByH1pNSYSPL/Ep3k+L8iuKR8RIl1T/Gkbc0poc8Ups6UkoKBUk7slR7mrfTa9guJGrs+oq2qKtXbQepqBNntzwO6gfqpdIQ2+ViS5bi5qIiGdfq7iXX03lxYYzvP7hUmeR0TSK1KGOD3S0sDF3wzkfhnY/C+SuK//c7kCnsrhxhNOEwNdi/fhpBI6NYLMahQ4c4dOgQnuexsbHB6uoqV65cwTTNStYjHt+50cLdOIjvY3foVyDsU5/6VCX7EeA73/lO38uobNtmaWmJ5eVl4vE4AwMDjI+PMz7e7Bq0W/TTjUpKSTqdZmFhASEEs7Ozu+pO3ovji4YN8llRp93rJgPcqRFbq300ZjcM4Vs9lWQY0eLzIqaG43go5ZONgHp4UuApga6DZ2sIfP2IqwT+RgpLmZjKz2ZIBG45y3Jrwx8nLTOMphxE4EBVQ2xQgrV8F30pWry2WQwxFHVJ5zrPSVvFMMPJLq/dLi5xN3Oa6ykCI7JGrYaUimK+/hnWNIEQ8C8e8xitFlmQ28M8sp2RysjICKFQ6K4r8f2uIBtvRhlVgKAWdnFxkWw2y9TUVNe1sK2O405mNnB8knb1psfR4y43lwxS6RwPngihIrG67p5YeS6+sbNQwtJSkaHJ/qY3NvICpVwKHbIttVkN2J5sWI5GIlT/+7aqb/D1EdsNADUHYnkG/3AtypEOwQ9PKl67JhECotHAjUQRDsGhMcmjRwWZ3DDSWve90wXMTezOzezR44JHj8ONJcVXzymWN9hRCOvBQwX0Lrqn7xat7P50XWdsbIyxsTHAb7aWTqe5fPkyxWKR4eFhxsfHu8563I2dWffx1ke/rG+VUmxsbLC4uEipVGJqaqqSxbh+/fpeDrkl+pHZ8DyPq1evkk6nGR0d5dSpUxVTiN3srxcI+m3U6ja2y2xIBVNJ2Cy0HzabS7FqsxsKQ/NQiLYGK5brZy8KjommSfRyrsSVfpbCNEUldiXRcWRNcAqDkPIrAUqOjmVDpCYTX3Cj6JoNnj/+2V79eCm6EKW041pr+e3XAVJ2zyH64UuolKrLapSKXt26wSrWP7+GoRFPmERMGGlI5vfKzKTRSCUoKb516xaZTIZcLsf4+Dhzc3O7fmbuFO56shGNRtna2trRe3pBNhzHwbZtzp07V6mF7ZTFaIXGgTtfgmzxzrHVICXoScAqAAPYLpx/1WF2PMPsXAxPM9E9i3Ov7DxivbSmmJosUqJ/D0HJ0RDSAbovn9mObDRa67pd2vFJpW8rGKu1vb20MshGRiO6Kpkab1GXqRTX5iWehFhUYBowO+rx6JH6KEoyGuHaQgLplYjEkoRDe1ssH54S/Jv3wGZO8RffUlxN1fq1t0Y8AvdMFLCt/t2/3WQdYrEYBw8e5ODBg0gp67IeoVCoLutapmekAAAgAElEQVTR6lmVUmIYd/2wuI+3GHptfWvbNqlUiuXlZQYGBjh48CDJZLLunu5Hw7xe7VMpxdraGgsLC5RKJeLxOEeOHNkz0e/ldzZCIaTdPdmYTMKRCcGF26Li7NSIVocWZDdQCk0ovyQKrWWWxBeHu7hKx6wpylL4OotElWv4rlVlcqPKeg2pPCSCXEmn6FJXslRSUSKqANIAXPJObSdyulLAqwbTlp1AVf7XxbZ9yGy4XnWuV0qRy1evfa3dbYCRsRiaJhhLyiZy2a8MeVBS7Hkew8PDTE1Nsbq6im3b+2Sj37iTZVS1jh65XA6Ahx9+eMeOHu2OY7lPlrftsLpWZepXbljcf5/DWt4P6S+sKpbX89x7pMAbNy0cd3e3yvxiibGZMP1qVm9okkKp874b5/HtyIbZoLvo9l7xuiA8ge2tI3VevO6HQxZWJJEwDA3Uf4/0umIrC6C4b+gVThwIMz4+TjKRpHFAPzSV5OaSxsxo+wjSTu/5oYTgnz8OtqN4/rzi5Wt+bXArnD3hu3z1M7W702iRpmmMjo5W3IGKxSLpdJorV65QKBQYGhqqZD0CgtGYPbnbUtX7eGuiU2ajW82GUor19XUWFxexLIvp6WlOnz7dlhz3Q3i+18W8ZVksLi6yurrK0NAQx44d49VXX2VqaqqHR9kbRCMmebuaMu9ENgwNjk365/q+WcFLN/06/kZoLccTP7sxGPEXs27Z0jbc4rJqmu53+1bV7ElQuVtyatypEChNC4wJsZXuv6Z8bUfBMXCkwHJ9DYem+eXClhpkJuI3ws0UG+Yz1ebnGnht5odu+UdfOyl1kqMqRbFYfVaKDVkN2/aaCM7IuG9OMj7Y2vmynxnygMwEJcW90GP1G98VZKPfZVSO41S0GLWOHufOnds10YDmgftOOVEBaMJjOV1fFpXZyINZdUZyPbjwhmJsQJDbZSl+elMxN12iJHrrGhRgKCrZzOzMmSS6jUC8scdGq34WrSCVtu2gGtjevpEeqDiBCCG4viA5aQpiEX8H2bzk1pL/uffOwePff4x0Os3169fJ5XIMDg4yPj7O6OgohmGg64Kjs52F2btN7ZohwXt+AH78++HvX1X83Sv+xBRA1+DMCdhc6y/Z2Gu0KBqNcuDAAQ4cOICUks3NTVZXV7l69SqGYTA2NkaxWOy5w9U+9rFdZqMTLMsilUqxsrLC4OAghw8fJplMdnwP9C+zsVMCE5R6LSwsYNs209PTPProo3s2c2h3fL36ztFwiKzyF/P+LluPbVLBIweq50TXBGHDw/FEU8TbdfK0clzysxvlBnrS/3cgKihYzSTHkXqdE1XwGVJp5WCJ70QVdPKWZbtcEEgFjjTwlB84ylkanlRoml+GZXthpCpi6KJso17zPWt/aXEqHE/gekEDv3p01e5wB5dtN1e409TkuNXz7LtDNWY1inXbDw5FMMvXa2yg+Wj63eH7e7aD+JuJfpGNVr7kvXL0CNA4cN9JshHBamLqt1MOJ49l2HLqu6/qeohGH42d4MbtEtMHIijRe6a/meuctm2edxQxs/N3aSQXtgwB27MtV2psJ1kICwdXaZy7Xr9YUEpw9ZbHqaM6ngdv3PKPwdAU/+QHwTB828eZmRmUUpWF8rVr1wiFQoyNjTExMdFxobzXOlIh4O33C95+P1y6rfjL87Ceg4eOCuIR2Ohz071eRos0TWNkZKTS46ZUKpFOp1lfXyedTpNOpxkfH2d2drYvi6J9fG9hp/NUUGa0uLiI4zi7WqD3I7OxE4G44zikUimWlpZIJpMcOnRoT529u0EvyIaUEqUUnuf5TVKFpEV/WMCfX05MKkINK6mpQbiyohMN1dfgxmNxNltWfQuE8LMWbplsjCcV845A1n64AFvqSFUrXq/PaGgisLQt29Yqo0Jg/MaEBlJpeFJQcvzO4IaufMGFErhSI9ziPpOev8j3h+DmmqqCpVWIWeM00N2soPoq2mjX7VspRbGDVsNzJZ5T/xyNTlTn2fEWt3S/jUbuRiOT70my0WlAasxi1HYn3QtcT2Ho9ftoymzcwTIqr02UbWXFIjqi6oRsyxt+Hf+Npd0N4ps5OOjlsY3to3E7wVDUZX61c1aj8TJHQortnlG9RlfhSsGWN8DWxiaDw50neonoWEgV2N5e2xjCdpu3dFzBtdserldNR7/zNDRWSQghGB4eZnjY72NSLBbrrGBHRkYYHx9neHi4bkDqZQfuUwcEpw7AyqYqNzwUd2SA7ReZiUQizM3NkcvlGBsbQ9d1VldXmZmZ6cvn7eN7C92W+5ZKJRYXF0mn0wwNDXH06FESid1ZSfejJ8Z2+2wM0m1X6vVWgpQSz/Mq46RhGESiUTwrj6daj+xDMZ8U1MLzPIpby6CmcD3R0CC2/fil66LsIOV3Ch+KKZYzAqeB6QRZiupQWCMCVzZg+NqOkMDzfNfCklMuE0XgyMBoRKCUhockWL0rIJ3VmRxouMbKJypKlclGw7yqFGSLGprmuzmGdxGTLZYUW0DRrSljrd2gRo/iuAohtnd/rIVhGniebDIxsezqOqFRqwFQyNU/t5GoQSIZLh+S3zC3Eb2ca1tBSnlXlE7V4q0/AmyDXvTZCHzJFxcXyefzdY4evUKuAH/7HY/H3qaRjPk3Ye3NmC1CvnTnMhuZrdaR+o0MHDxQYjlXLzbKlwSaUF15jbfCtdsOB4/KptTsXuBHfHaS1dherwEQ0qvRqJwdRtN0bjszJOVyR6Kid2F7qxC8cK096crmqYSFhhOKR49ve7hEo9GKKLq2Ad6lS5eIxWIV+0tN03o+AE4M1UTV+lyneiecoqSU6LpeIXP7WY199AKdgmJSShzHqTT06lWZkaZpuG5vmxG0C9QFtu+pVKqnQbrdHN9OEZCM4P2BraimaSRiis1SnsX8aNNqybFtCpsvc9PxeyAIIbh9+zZra2tMTk4SNxUbRZOBiF0hBm6H6UcARS8M+I5TQsBARFGoWd7UNgUMVvxCVO8ru0wqbKmRNIoUnCFipkfRDaHrvmbDq6i9fXiewPGq2YjNokEiUn+gnvTJiacURos5t+hofsNApcgXIDzY4st1gGUrcnlJPKI1GbS0glKwulpkdDRCqEtDFF0XrKw6TNd0EZdSUarpFl4qeXUaT+lJrGI9+Rgdr7o9DifAaPGY3unMxt1QUvU9STaCAbM2ixGPx5mZmdnxANktgx1KCqZGBM/+3y6Pn9H4Rw9phIzqwH1HLW+B+VSBdg5Ob1zPMzoVxvaqx7SehcMzGtcWdhcpyxUh5Baw9N40fAtpDqmN9n9vldWALpyopCBSI+zL2WXCaYRZWIADB1q/z/G07aMsSrGQSZC3Wj92StV6eSg+9COdd9cKgVvF+Ph4xaN7ZWWlspCxbZutrS0GBgZ6PkDdiWhOvwfVuzE9vY+3PlrNU8VikcXFRdbW1vA8j2PHju2qN0w73Ak3qqB5bSaTYXJyck+GKXcStaVSAbEwDKPp2Y+EQ5Q8k007ypBR03NBwdljBsK7nxs3bvDCCy/geR4jIyOcPHmS4eFhbq95rBc1io5OrNwUNtfBzESIqutfrHwKRxKKpZqyq2pWAgxdIisdxX24UkcTUHJNksrGK5dNKQyCycULHKrwxeFSCRxPYOgCXfPnwLxdDbQqRaWMLLj063nfHTFUXkLkiuXjwg+YDQ80llJ1HrfXNgKy13GzmnMlcF3F8nKRkZEw0WgXtvMEbllVBJ3CIdBq1JPzUgPR0DQYrjFhaaXXCPa1X0ZVj+8KsrHTWtjNzU2KxSIvvfTSnrIYwcDb7QLozCnBKzcE/+Obkm9elLznh/TKw7t8B/UaBjb5RqeJGuQLiqNmkdVi/cS3vuWz+G6tYBvx+g2bo/d4Xbk2bYeIZqE6NAycHlYsrDWf0+3E4a4UiJrDW9kKE1QxbEbnGCwsMhBrJiyWZ2xruCWQfPtaY8inYZvyvXRqDsY7b7otaj26jx49Sj6f58UXX+TmzZtkMpmKyHxsbKwnZQ79Jht3KrNxt0WM9vHWR0A2gsZ1i4uLKKWYmZnhyJEjnD9/vqdEA/rnRuW6LqlUisXFRUzT15KdPHnyrnhWWpVK6bredlwRQjR1tFYKDg67rKbmWVxcZGBggNOnTxOLxSqdny9fvkxyaAwh7iUSEkjlVwYUne7O0VD5VoiE6isKPOkAIYSQPHj4FhduHKwrefakjqaDqwws10BK8GSNBS4CKbWKg5VUCqVEudyruti33WrYy/9sDV34NuwA2ZLGcEz6nco9geXqlW09CbYD4S45Z8lSFMpOUDu5g4JruL5uEYsZDA2ZXQT8qj96UtWRDcuSSFlPPhrtboXQGE+4rBX8LzfehmwEGfJ+YZ9svAnoNrMRiNWC7qqmaXLmzJk9CmZ3FjkSQvCTj+ncTLlsZOH3vuYxljjOzBFJauPO1d+ZbE/OLr1R4NDRCDmr+sBki3BkWuON+d1NYEULCusrhEemd/X+AJqQbBY6d9mrHSxrsV1mo3bgLrk6DiabWZehpEBoGqnNOMlottllROrbko2NvMFmof11lmVhnSYk7//B3k/chmEQiUR46KGH6kTm169fxzCMSkZkt4ueOxHN2c9s7ONuxMLCAuvr67z73e/mv/23/8aJEyf67nrW68xGPp9naWmJTCbD3NwcDzzwAOFwF91O3wLwPK+SxQhKpbpdDEYiJoZWM2+4eRauvFwpd6sNVE5MTDAxMVHRrmSWbKzVCxhmDDF4tK1IGags5JWCyaT/Syz6KoZ+D7brL27dcqO9iGmjafDg4VtcXqjOp7YrCOm+5tJyQ0hVdogqd+QTwi/lCjISgctWIyct2Rpm+WsF629dV9gFQbQc4wuGybxVO14KQJIviq7IhlKKtY29l/oVCi627TE2FsEw2o/ftU9DbQO/RgcqALvUeFwKXdc491KGBx4cIW9p+5mNHeC7gmy0y2wEC6rFxUUKhQLT09OVLMYLL7yw54XLbgbzRFTwU+/Q+T//wk8PpHMJ/o8/8IjHbaJxs68dmAPYxS7ImQuGVwDq9QWpNYiYUNqZJr+C+bTJqVEPp43grhsMxyQ3s+2vneltsboRb7n4345s1F7OTMmfSCUaxZIkGhE4kWGWCzAVz9a9z1Vax4dpq2Bw/kr7LWqzAg9NpzCM3guTaz+jUWReKpVYXV3l8uXLlEolhoeHmZiYaBKZd8KdsPu705mNfexjL3j99df5+Z//+Uqn7K985StEIq0zsr3ODPYisyGlZHV1lcXFRYQQJJNJEokER44c6dFR9g9Sykq5FLBtFqMdYhGTkOYBAtf1ODGcY+Lk93e8VkIIBgcHecCUDEQfplgscjlloVSIutR5DWr1HMMDr1Z+Ng0X2zVRync8NDQYivn9PzQNhhIFWPO3LdoGMdNBQdmGlorFehB+U2XTJ1E1oCqXYpVF4gosjwrZCEiQrtUbx2hCVYTh9VBkcqKulKrdOqlk+cLw6olruVlrNMQTXVextOSXVcVizXNt7eVyPYVTwyVsW+J5nbMaD9xjcPKgzTde1FmeXyExPgXWMq470lQZ8N0wF/Ya3xVko1Sqvyls265oMZLJJHNzc32pUd/tYH7fYY0fuE/x/71afW8+75IvuMTjJvFEqK836tZmdxqXy9dKnDoVYaMmGl+yfWeqqwu7i5g5rsArFiCye2eqXKG9MDyq21y+YnHvCYOM2xw5TES367FRxVaZbAihUbAgbPpOVmulQYbDBcJGtZ5MtUlrSAXXlsK8elNve8zBdkJARC9y31yRUqlUqSOG7b34u0GnxUwkEqn0oOgkMu8UybwTZVT9zmzURqTuhrKQfewe6+vrfOhDH+LGjRscPnyYL37xixXyXYvnnnuO//gf/yMAH/vYx3j66acBeOyxx0ilUpXOvc8//zwTExNYlsWHP/xhzp8/z9DQEL/1W7/FD//wD3PmzJm2RGOnJbndYC+ZjUBPkk6nGR0d5dSpU0SjUdbW1tjc3OzZMfYDgR4TqJRKaZq24zHUtm0WFhZYWlrCGP4+pArxyCFBMjLe9T4Gov5nRqNRHjqsSL9mYLdZMgSL+nCofn4eiBXIlWJlNygNkEwOVYUc4VBNl3O0sr6iSh68GoJQzm9UCYdW1QoGcDxQSkMpvzO25/nbGDW6coHff6QUCMNrYNtgmmA5fmASINfC/EYpRXq9QRPR+tS0hK63brS4vm5hWR6Dg81lVb6Ws97qtlVWw3Uknld/oZ78gTgPHjd559sVf/MPFul8Hqe0xQsvVPs0BZUB+9a3zfiuIBuWZbXMYrztbW/rqz3YXgbz975d48q8ZC1T86KCfM6mWHBIJE0iUaMPIl7J4oqz/Yb4EY5iJg/GUN3r86uCgThk8rv77pdvONx/n1PuX7EzDEQ8FtOtz0lIOFy/ukXJhoXFIsmJZrIR7bLHhlKwVarmgW0HVjYFY4MKQ9e4nRvh2NBq9Y0trlPB1jh/JcxGtvOgUF1kKN754Dqzs7MVImtZVkXAqOt65efdoNvFTCuR+erqKi+//DJSSkZHR5mYmGgi8HdCIL6f2dhHr/Dss8/y+OOP88wzz/Dss8/y7LPP8pu/+Zt126yvr/OJT3yCc+fOIYTg0Ucf5X3ve1+FlHzhC1/gzJkzde/53Oc+x/DwMG+88Qa///u/z+/8zu/wIz/yIx3niuB57+W9t5OeGFDt7bGwsIDneczOznLkyJEmDVOvRee9QJDBCM7hpUuXmJiYYHx8fMd6tEwmw61bt8jn88zOznL27FkuzOsMJyDZXia4LTRNcHTc49Jy62vsSb9B6mCsUPf6+ECGxfUxnHLDPKVgOF7dJmxU53O/TCrQZyg0VJmg1GTtlaiakZQ5iZICgazLXDguhAxfHK6U3xBQ1LANIVplNaoflC9WyUbJbp4XipbCaqiQaBRwd0KnmSafd7GsFmVVytec1jpeObbEdRt6hmTrA9iTozoPHPPXKyFD8Pj3RfA8ha4fB45X+jRduXKFQqFQKdcbHR3ti3bjbtQW3vVkI5fLkc/nee9738t/+k//qW9ZjFbYS5o6bAqeelLnd/7IrXOTAN+OLbNlUSg4JAfClU6VvUBEc+rTltvgxoLDA/dbpHPViLbrwdyERia/O6W4J6G4lUdP1pOY1ZUcQ8NRQqH231dD0mqY0XBZWdiqEKCVdcn42nlK9z5a2WZ4sNHzvBm68L9T3glVvNWlVNiuP1ksrcP4kIJQlHQhzlgs739+w/22sG7y4hWjbvBuB1VO1Nw3/gbfN/41yPmvv55/DyMjI0QikYo9ZhB5D8oBdrI42Q0ZqBWZHzlyBMdxWFtbayky/24RiN8NA/c+9o4vfelLfOMb3wDg6aef5rHHHmsiG1/72td48sknK80fn3zySb761a/y1FNPddzvf/gP/wGAn/zJn+QXfuEXtn023sxu35Zlsbi4yOrqKkNDQx1dsd5qZKPWthb8QMmZM2coFAqsrq7y4osv1gVPgixUq/0sLy8zPz+PaZocOHCA4eHhyjU7MSWJ74FoBDg64XF52Wia81V5QQ+KycFM3d+SUQshpN87Q1NomlcX2wqbDvU1Rf41l4H7lPTLrVSwlag6NWqav52UfpmU45SzHmWht1EWh/v9OkSd6MFxqQjDA3hS4bgKMwyZHIyUS6kk/jwaZBr8rEazVmMnI6/jSrQOC/lWZVWKqhg9QGNfDc+T2Fb9sb3jbKTp+dVr+qYFfZrm5uaQUvLyyy+Ty+X41re+hWmalfuvVzqtu3GeumvJxo0bN/j3//7f88Ybb2BZFn/2Z39WmRDuFPY68B6e0jg1vcxrqamWf3cdycZakXDEIJk00TsIn7qF5u3MJhhgdbmAFjfrBshbS4qRpGR9m6h9O7xx2+PEsSzC9MupMptFrryyTGIwxsn7xglHmm/NsCFJrbfYmfLIrW2xuuGnfWfm4pSKHpmbkiA3IQQku3jOgx4bgV4DoFCsRhGUEohbrzAYK7E8/TCD4SKaUARjnu0KLtwIs5DujiAGEaaQsPmpB/+y7m8n4n8OFmDBd9aeYHx8nEQigVIK13UrC5husx69IAOhUIipqSmmpqYq/WkCkbllWbiuSyQSIR6P93ww7LfDR/AZ+5mN7w0sLy8zPe2La6emplheXm7aZmFhgQM1ftdzc3MsLCxUfv/X//pfo+s6H/jAB/jYxz6GEKLuPYZhMDg4yNraWsdj6ZdzVLv5SSnFxsYGCwsL2LbddW+PtwrZqBV8txr/kskkyWSSo0ePVvRor732Gq7rVjKziUQCy7KYn59ndXWV8fFxHnzwwZalbvFI78aEBw/c5ju3Dza9HpCA8WS26W+G7uF4GobmEQ/Xz+GGFug6Qn4prulQdCK+VkOBKxVmTZYrIB1K+qVQAp9w1GU+yq8FZUqO5wcZg9iZAGyv+X7N5yWiTHaUqpZSCeGTk0A0XigpWkltdzJlKKnauffXISirGhoycR1Zp8u0bQ/Hqb+fi/n6Awubgh9+pHsjBE3TiEQiTE1NMTw8TKFQIJ1Ob9t0dye4E4G3XuOOkI291sYGeN/73se1a9e4ePEiyWSSf/tv/y1nzpzh7NmzuyIae1189WLgvX8mRVbOMr/cPktglVyskkssHiKe6MLerQNK+Z2TjeW0yyNTFkvZ6iAsFYR0B9idG4lC4BQ9TBMcx+PlF24hNJ3cVpE3Lm9w+Ngg8Xi9nUUy7LEmG7NAHiqXqZy/oZEoyYEIJ05G4JG345Usip6B56k6jUUrSAnhco+NoITKcRWqxkNEcwr8+MX/jaizwcun/g23H/oAU/EMCMFazuDc6yZWi5Rx2/Og/Pvofznxd4T09ouNh0a/7oeHykGvG84/rQxWSils296WePSjJnxoaIihoSGOHz/Od77zHQzD4PXXX6dYLFYG1ZGRkZ5pTvo9wN6Ng/g+2uOJJ55gaWmp6fVPfvKTdb8HZQ87wRe+8AVmZ2fJZrN84AMf4Pd+7/f48Ic/vKvj7AfZaLXPwJUx0DMeOnSIgYGBN/U4obuxabeC71o9muM4pNNpLl++TDabRdM0ZmZmOHv2bF+7nMeiVcH3iShcvD2DbLH80jSvZeNYQ3dxpYbAZTiRb/p7JORgu36ZT9R02MiD5/pThusJzJBvcavKVrdBxsMwypmHsvhc1JIJt1oJ4Lpge43ZmProv5S+s9RA1IJymC9fqJZSBbeNUor0WmsHqq0tt2qV1QApFa4rcRyJY3t4toMe6u6aBWVVxYLD8Ei07vXGzyg2dAz/oYfCRHdINmszD7FYrGPT3UDr0U7P1W7/d5u28I6QjV7Uxv7xH/8xiUS1Idzo6Cijo6O7PqZeCPJ6MfDquuBXflLj3OuCP/0br66bZSMKeYdS0d2TnmN9Y3c2Uteu50iOV0uLANLZKNNjkErv7hxcW5A8PGTxwrcWcSyXkCn8wcR2uPHGFgcODzAwGJAZyepW/fuVUhgv/z2XzJOV10YnEqBp2LaHaerokTDBXeMSLO5bH48rNSK639Qoa/kjZLFUfag912Pu8h8Tdfxugg9f+gxXNm9y6x/9EktbEa7Ma+wkERxkNUYim3z/wVe6fh/A4dAfQ/g+AC7fPMLIyAjRaHTbcqt+Dky6rjM5OcnQ0BBSStbX11ldXeX1118nEolUUsk7GVRrcadTx3fLIL6P9vj617/e9m+Tk5OkUimmp6dJpVJMTEw0bTM7O1sptQKYn5/nscceq/wN/Cj6P/tn/4xvf/vbfPjDH2Z2dpbbt28zNzeH67psbW1tO1f1M7MR2LAuLCyQz+eZnp7m9OnTu1pc97NRYLvnTUqJ67oVQrhbwbfneSwvL7OwsEA8HufYsWN4nsfq6iovvPACiUSCiYkJRkdHe0Y8aklGLe6fW+TCfH12QwiIma2DgcloCSEUCJgabu5mGw65UPRt04PsfMEOoWuy4kYlKWc0ylkMSf1cWCEDAAJc1+8YHpRUOa7/RymhUIK42aBzKCo8D6KmS86RGIZGJg8jg/WzYr5Y7wRVi/nFIuPTBq6rcGwPy3Kxih6lolPnFgVwaFJw6doaQ+NJItHtfXZdVxELV+8Zx5HYDWp9u9SsZ33H9+18vmoXtGqlh0yn01y4cKGSdRsfH2doaKjj/HM3ZuDvCNnYa21sLpfjt37rt/jd3/1dPvjBD/bkmHohyOvVwKuU4swJwSNHNb7wdZuL13Ta7bai58g7JAdMzHD3l1AgSXUpDm9EJq+Ync6y4dTrLPZa2fUP55bJbhbQjep3LuZLhMwQt29kmJlLMDwaJWEUWcrVZ1HCF/+eizVEY3A4UtG3bGw4TE421JMqg7WcyViyNeEKXDuytolCo1hTPuW6kpuvLvCzW5+te8/xpa+g/8USf3vof99ZDpgq8fkXj/zFjt4HwOh9lR9PHroOXK/8vrj+EMlksqncyrbtOhFlr1G7YNA0jbGxMcbGxgAqncyDQTWI5gwODna9qL8bB9h9vHXxvve9j+eee45nnnmG5557jp/4iZ9o2uZd73oXv/7rv87Ghr/Ae/755/nUpz6F67psbm4yNjaG4zj8+Z//OU888UTdfn/wB3+QP/zDP+Qd73jHtvd4PxbxUkry+Tznzp0jFosxOzu7o+etFe5EV3KoF3yDv0hr1eG7GxQKBW7fvs36+jpTU1O87W1vq+tyPjo6ilKKbDbLysoKN27cIBQKVQTmu+kl0o5kBDg5k+aVeT+7EXx1AYwkci23H0tm0MUkAImw0zTXBI5UmpCEyhl8Twrfrlb6WhAl/dIpqfCdpcoZe6kAJciXBEa5gWHwuutVS6lsxxewpzdpoTlRrNU4S+XzDoOD4UopVVCM0SmroQtJqWBx7fXu+26UCjZLN9eIxsMMjycxI51NZ0oObK4VGBqNNTlQtbK7vfdIiNmJnS+TuwmM1eohDx8+jOu6FYOGV199lUQiUdFD1t6vwf7vtos0KG8AACAASURBVLnwjpCNvdbGfvzjH+ff/bt/19MmSN2K5zqhF9EoIQSWZbG0tMTKygo/eHSIf/zgHH/4PyMspduX/biuZGO9RDiskxgId2xkEyBMadfdvwGu3XKZPeRRsKuL+MU1ODipcWt55+ehVLBYX/LrU3VDR5YjF47l4tguIdNgcT6H40qMBrfByOVv8x3teN1rYxPVzJdUWp0gLcBKLtKWbASDfqYURkqF4/kCOseRXHpljYflRSKq1PS+o4UXeefyb/P81C/SbWYjyGrcP36VyWRzpKotakhGO8yMfKfy8/XUSZLJJMvLy6RSKQ4ePIht2z1xt2pEpwEwHo9z5MgRjhw5guu6pNNpbt++zcWLFxkYGKgMqp3c4+6E9e0+vnfwzDPP8MEPfpDPfe5zHDp0iC9+8YsAnDt3js985jN89rOfZWRkhI9//OOcPXsWgN/4jd9gZGSEfD7Pu971LhzHwfM8nnjiCX7mZ34GgJ/+6Z/mX/7Lf8mxY8cYGRnh93//9yuf2e4e7mVmI5vNsrCwwNbWFkopHn744abFym7RL81GsM9Wgu/d9MYInLVu376NlJIDBw5w/Pjxjp3CBwYGGBgY4NixYxSLRVZWVrh48SJSyjpb03Zj0HYEoxH12Q3/2s+OtJ4LhmIFdN0jZLgtg1qRMtkw9GpmQyqBEKocRFN45ddqtRhQ7btRi+AjpNQqzk2uB4Ye7Ld++2JNtkIpyG7ZDJarEnIFGEj4n5ErqLZrkK0tC6QNtBbxN6HmGIp5i2LeIpaMMjSWaBuEVQoyWyUSg5GmrIZje3UdxAEe30VWA3ZHBgzDYHJyksnJyQr5DUwOlFKVe3BgYOB7m2z0qzb2pZde4urVq3z605/mxo0bez3MCnZqC9gKexl4A3FtsVjkwoULzM7O1gn0fvVD8K3XBF/+Ow+rQ2mVZXlYqwVi8RCJpNnx3Cpn53qNus+yFabMUWCw7nW7PP7t5FS4jsvmStWvXQgNqI5CfnbDJw8rqTzFQpjRcX/wCt+4wEV7rs6UOzkYrhOVa7rOxobF6Gj9YFFwTAqW1rK5X3Dmtoom+XJWw7Ylr11M4zouP+X8btvv8/a5v2TZe4CX1Tu6+v5K+WL0n3rw+a6274ZktMKR6csAjCVAiIOMjIyg63pTuVVtydVu0S0ZMAyjTmSeyWRYWVnh5s2baJpW18m8dn934wC7j7cuRkdH+au/+qum18+cOcNnP1vNYH7kIx/hIx/5SN028Xic8+fPt9xvJBLhD/7gD5peNwwD13VbEuq9kg3P81hZWWFxcRHTNJmdneXo0aO88sorPSMa0L/Mhud5lf3uJRDiOA6Li4ukUikGBwc5fvx4Xfl1t4hGoxw6dIhDhw5h2zbpdJqrV6/WadGCUpedkowAJ2fSXJyfARFkNxTxcEPlQZAp1iFiuCQjzcEu8DUdAKbuYWheeX/C12co32GKMtFQSpQX1QKpVLV7eM3QLQiuS5VEeF7VSarR8ry2C/jqZohs1ipbwwqyeRiIA20cqAB0zWVttUAi5OCoLslGCxSyRQrZIvHBKEOjSUItnDyFEE1ZDf+99ed2dFDjkZO7e3Z6oQcOyO8999yDbdt1LpCWZbG8vNwy6/FWRc/IRr9qY7/5zW9y7ty5SpppZWWFxx57rG7b3aAXkaTd7MN1XZaWlkilUsTjcSKRSNvI0/ffKzh9QueLfw0vve52XMwX8g6aUMQ7GIEX87sroarF5WsWBw/nsGR1AF/dhCMzGtcWujsXUko2ljfqzl2TkNFycB0XI+RrU7Jb/uA1yzKXMwNIvf7Wrc1qBGgt1BYsZ8IcGS82/UUTCtvT2Cz6Dfgs2+PVC2u4juRe8xpjVjOZBhAP+aVl/1T/NMvuIZa4Z5sz4OPdx/9nR1F4BbskGo247+gt4Fbl94W1B0kmfTcwz/MqddG7nex3a607ODhYWRhYlsXq6mrFr3x4eLgiMt8Xb+/jbkbQE6qXZCOfz7O4uMj6+joTExM88MADlbKfoISyl+gl2QgE35qmMT8/z+TkJMlkclfPeDab5fbt22QyGWZmZnj00Ud71mPLNE1mZmaYmZmpCHxTqRSzs0vdR9jajIv3z6W4OH8ATai2WYsAsbDNaAunKgCBTxoipkPJ0SvkJSAd/m8angyyHtW/+/8pNGUjVdDI1t+vKpdSgZ/hlzWah628PwdbtsKyq+ehZBtA2bo/6Xc+L1qglFfX36IWm+s2hWyJxA58fkpW+zKN/FaR/FaRxHCMoZEERll07pcuSyyr/lnzXF90XosfPRvZtRlPrwNjpmkyPT3N9PQ0Sin+9m//llwux82bN0kmk5w+fbpnn9Uv3JEyqr3Uxo6MjPBzP/dzgG93+573vGfPRAN6VwLV7cCby+Uqqe2pqSkeeeQRQqEQL730Usf3hXTBP38C3nHa4PeeV6ystX/AshkbM2wQMltf1pX07sThtVBKYHgSq+EZXN+S6Fq1C2onbKUzOHY1wiE04dvYNaCYL5Ec8kmEEIJi3sGyV7D1+sV8ImkSjTVPLLphsLVlMzhYT+Q2SlEOqSKN44guJFkrTMkC25a8ejGN5/pGgR+wPr/9FwN+RvtV/ov8vygw1HYbpRTD0S1+4ODFzjvrEcloh9nRC5Wfry2eYHh4GMMw6rIetQLz7QbPXpQ5hcPhOr/yjY0NVlZWuHLlSqX8KxqN7lpkvo99vFkIyEarSPtO5hIpJaurqywuLiKEYHZ2lnvuuafp+exFqXAjejFvNgq+T506xfr6OlevXsVxHMbGxirWtNuJZFdXV7l9+za6rnPw4EHuvffevptgHDqU5tChwLZpb591ajbNK/PTaEIw0NDMrxFDsQLTw1st/2YaLgqIhm1QqtyArzofe7LcO6N8iwX/ygrZAEN3WMuFMQ1f3K2UQopA8wG2rXC9spsVsLwRZnhAsd6QrQju4mzGJpn0595MHoaiBVqVSOnCJb3a7LC1Hea7KN3ObRTIbRQYGEkwOOL3jomGfLOYWhQaXDpDBvzI6d3PMf0MjAUmCcePH+f48eNvCSvqbnBHyMZeamO7xU4XOr2I0Gy3DyllJbVtGAazs7OcOHGi7ji7PY7pEcGv/a+Cb74i+PO/9+oiCZV9aRrr6TyTM4NNfzOEy2qbFOZOcfWWxQP3O/z/7J13fJvVvf/fjyRLlixbw0PeI3sSMhvCSgIhEC4JcSihlzAK/OgtLbSUFaD0llu4DaSMcimXEQoUKHGaXkaZBQIto2EkJIEkZNrx1vDWlh49vz8UyZIs2bJjx3bQ+/Xy62VJR4+O5fOccz7nu2z27g1+l0tGtrYTS2fvJmt7hwOXPdqqoEhTIMWZN7xuH36/PyoziF+uDrm3hskxJf5MpyuALubrCEhy2l0qjJrYfOUiLXYNHo/E3m9s4ewXJWkWxjniC4OQVaP7Gn5+xM94RNqAKMU/WRMIcOnJbybsMzDkQiOWMYX7w7/vrS4jJycnnN0qdEIqk8missHEMtgxFTKZLCrrXMh3tb+ZO5Il9j5MxYekGExUKhXeeMUFSG4T73K5aGxsxGazkZOTw6RJkxIWqQtdc6RYNuIFfId+VCoVGRkZlJSUhOO5qqurcTgcGI1G8vLyou5xr9dLfX09ZrOZ7Oxspk6d2uv3MBhoNHuH7NpTipvY11iAKSu+1SJEhclCmiL+d5+lcSMBmeke1Gme4Al+6PDvaGpbKXB06ZRCc50QDBpHiIhVEJALfiRJedS96mg7ScLrk/D7I2ptCOAXAzgTFAnu7PCQX5CBTCbg84E8I3671lYPbsexH4T2Rmernc5WOxk6NRNK02iN+LhAQOrx+d+bpkKrGbhYOJ6ZE4e69tRgcVzExrH4xkZSXl7ON9/03PT15gubiKF0o4pdFKZMmZLwJLa/p0+nTBWYM0HOSx9IfH0w5JvZTSAg0NHqCKv48Od47f2KqeiLVpsDVLqokx23mIVSIeL1x7/JPC4vnS2dPZ6XyWSICb4Dl8NDpq57mDbJyyj376dGqgBAk5GGJiOxz6IgU8QPFO9KjxIbAQlUsgBHLHJ277JGfVfL/JvjX/uk+NYL/cRMLnf8gWdqbyAqsITgJD8xp5r8zHjVCTnuIiMekyuOAEfCj+tt08jKygqPVY/HE9fdaqjdnORyOUVFRWRmZkZl7ti9e3fSQea9kQpATzGUhCwb8Ui0loQCnRsaGhBFkaKiIioqKpK6z4ZiLPdXbIQCvkP3Vl8B35HxXIFAgJaWFpqamti7dy8ajQa/348oihQXFzNv3rwh32gNpcgIMbnIRq3ViEkf32oRQpWW+HsPxXpkqV1olN6jlg0Jf0AWtHQEQtYMOR6fRLoyaP0QpZCrVPe1VUoR71GPa683aCIJDU1Ril7RXE4vsWtcCEkCl9NPhjbxfCwX/LRae7foJKb/GxpHh4tAzCGg29VT6Jz1vWMTrymX356M2grikfTmC5uIwQ4QH+ii0N/JOzhxi/xgkcDCkwRe2iLD2tbtWiWTy+hoc6LNSkeu6J6IPc6B3tDxaTT7OHmal+au7rSADjeMKZRzoK7noin6Rdos8TNt9Pbne11e5FoZorz75k9TCHB0MuzNqgFH3a/cEhma6IXX7knD5ZOjTgt+d35RRkBSsH17W1R/DIouTnFGV/ZOJDIAGF8GQHnGQZaZNvOm+ftERt4p5R5Wn5QgKHwECI14FOd0C/xDDeMxGo1x3a0igzyHgkg/2NjMHZ2dnVit1nCQeShzR1+uGImunyLFYNObZSP20Mnj8dDY2IjVakWv1zNu3DgyMjLivvd4kux6FZlVaqAxYDKZDKPRiM/nw263I4oiSqUSu91OS0sLcrmcnJycISnGdzxERiTfG1eN/BimHkEIWsx1GhdKRYBAIOha5fGlIQjBzIeBQLCdGJAhE0QCkhQ2U/gjnB7kgoTDLaGQC/j8kJYWrFEB4PdJpCmD7wmIElLACyR2N+rs8obFRrxZuLXFjdvZfU+4PeAVvSjThy7oecfOFsqnBO+leOluxxYrKCs4tjF1vGtCjQZOKLHRn6wTg5X61uv1cuTIEcxmMzqdjrFjx/arH8mInkSVUyuKZNxxGfxzp5w3PvXh9QVfV6rTaaprpbiiO1+sNzbIYhA4UudAY1CG61MA1FtBq4ZIT6lAIECruY1AgoCORM+HyO/8mgbDvPDjWvl4Cn01tKrL0Gb2nQPd4ZTQqGNPrQWsXWpKjcG85qIk0NwiQ5KiY2IWS93CoFeRAWGhEeJ7xn/S5C7hq45TgODEdva4rT2DwkeoyIjH2KID4d/3HC4lJycnfA9AUAy73W5kMll4EzBYG/hElofIIPNx48bh8Xiw2WwcPHiwR5B5byehKbGRYihJxrLR2tpKQ0MDXq+XgoKCqAyFI4HexEZojRJFMSwsBlobw+VyUV9fj81mIy8vjxkzZoQD3yVJwm63hzPYHWtNjBDHW2BEotcee/IWpcKPKi24tggEGJdv47P9+ajSBOSyAC5Rjj7Dg9OjRpBJKAQRTyCNgBR0j4qcWT1uCUWGEHZv9h3dWwiIdHV6sTuCj02G3vvU2eEhP18TnLdjpm4ZPmzWaJfqTjuoBq/CQVxEf/f49XrEHgaSY7VqQMpKHo8TSmz0h2NxowqdpDY1NeHz+SgrKxuSiqyRp0O9VU49Y4aMWeMFnnvLx8FGBQICfhE62xxkGYIKvq7RRSJz50Bp6xApLXJjdnTfnF5fyLrRvWnvbOnC54k/mSrS+v7O9nlKyRPbcMq7ZzadwomsD6tGCEkCjwdiPdlsDhVFegdyWTArx8EYi4xa7mWZ689ActaMeKycuw3L5xU0dOaTo2nhlMig8FEkMuIRmd2qOAfqrFNRKBR9ulsNlGTFgEqloqioiKKionCQeSjDlUqlCqfWjfXzTomNFEOJSqXC7e6ZutTn89HW1kZnZyfZ2dmUlZWRlZU1DD3sm3iHdJGuUiGBMdDaGG1tbdTV1eH1eikpKUkY+J6ZmUlmZiZjx47F6XRitVr5+uuvkSSJ3Nxc8vLykq7LNZwiYzDJVHdv3AVBojy3na37ChCEwNEA8jRMui6snWqU8uBzLm98bxCvT0IdCFUcl/B4u0WMX+yO8RCEOHuXiKcCooTbJaLW9FznW2xevDEuTIFA/0ojDIxubxRHV7TY0WkFZk0anG3xUP0dozW28DsrNgYS6Ob3+8PF0dRqNUajkbS0NIqKivp1nd76ERtI19fpUGTKvwvmFuKXF/Dn96EFNeb6NrQ6NekKEZdnaDZR+w85MBWrcPu7r3+kWcIot9Iq5uLsdOLsSuzCJZPL+7RseFBh7DqAU99t3WhQT6FYl3y2CLszgEoli67bIMlodarI1bqRJNh7yE/k8ct82VYUJ2UmvmgvIgNAKJoEwNVz/spjWy/h4mkR7lOjXGjEoyR3d/j3g/XBgmZKpbJHTY/IDFf9YSB+sLFB5k6nE4vFwu7du/H5fFFB5imxkWIoiXSjCh1YNTQ04HA40Gg0FBYWUlFRMcy97J3Q/BlrxRAEgbS0tAFZYfx+P01NTTQ0NJCZmUlFRUW/xJZGo4mqiWGxWNi3bx9er7fXzFYnisgIkZ3ZndFJm+4hSxMca3J5AI3Si01Sk51pB/LIVLvRZ7ixdmrorhnejdcXwC92p4vt1pfd2agA/IGeG11fjKtgV5e3h9gQJF8w7nMYCA0D0R9A9EXvPU6doUD0e/EEBqf21FAwWq0mJ4TYUCqVCX1hE9Efy0Zk2lqTycRJJ52EUqmkubm5358bS0hs9KdyaijLVX19PQqFgpKSkqiUf7+8HD74Ss5r/9BSf9jKpHFD5+vr8khkKly4/d2fIQYgO62dZoeOjjgB4ZEke8/sdxdjFDtxy4OLkC6nf4WaRDFYdT0tLXoxtHSlk6t1Y3dF1+VQyAJcVL4p8QV7ERohkdF9LYmfzn8JmYwTUmTEY1zxwfDvuw+VhC0JkiSFs1v11+oxGH6wGo2G8vLycN2eyCBztVqNz+fD6/WiVPZeIDNFiv6Snp5OW1sb33zzDS6XC41GQ1FRETqdDovFgsvVs/bPSCO0Vvl8vmOyYkCwRkhdXR1tbW0UFBQwa9asYy5QplQqw6mz42W2mjlzcFMBjySKDd0xkcXZoYK5EmkykawMF1mOdLIzg5a1TLWHirwW9tVn4xNkxIoNUQxmaULqjteAYNB5ZNtGs0SGNvJ9AXxOB6IoDwvPjnY3uXlq3BGu3C02N153fG+HZGtbSEcF70Dnaac9+oBaLoNFczUoFN3WumOtPTUUjNZDsRNCbKjV6kF3owrl8G5oaAhnwYlNWzsYGa0gmMpPFMU+B7TH46G+vh6LxUJOTk6vKf8WzZQxf0oG9zzuoLPNfsx97I29+zsZW9xJh6wg/NxB1xjaLLa+41HE5KxLLtTk2L+lXjcPRZoMbWb/F6X9BxxkaeWYTCqUqmDhPpcvDbtHgdkW/X88Sb8frTzO95akNSOW75LQiGXq2DqgLvy41jIlnMpSkiS8Xm9SE/pgZ/iIDTJvamqiurqar776CoBJkybFLUCaIkV/+eqrr9iyZQubN2/mrrvuYvny5VEb66FIUzuYRB6G6XQ6duzYgcFgwGQyodf3EcsWgSRJ2Gw26uqC80FJSQkTJ04cEmEfmdkqaMU4cYUGgCa9+7ByQqENCB50qRQ+crUOCvVdpMmDbTLTPRi1HtKVHtxeJSBDr/Vgs3db8kNnnyGxISCRniYiSd1zcMAfPWbtnW4QwOf2Ic+Qh9/v8Yi0BY5uNwPeXjJQRYuZqFckCb/Xj8ftxWV3Ycz00lLfRbpWg0qtQq5MS259EIJxoh5XtNiZN81Foam7cK/TOTm8vwsJbDi2CveDQUpsDCMDdaOKJxTcbjcNDQ3htLWTJ09OuKE/lpzjITO0Xq/n8OHDaDQaTCYTOTk5UaZoSZJob2+nrq4Ot9vdr5R/apXAf/44l/VP1AFDF2TolxQIXUdAFxQbgUAAa3M7or/3yV0m798Ce9BVSJbWQZYpr9+LUyAQwOnw43T4aTZ7UCigtESDwaDEaldzuK7bAiMIcLH++Z4X6Yc1owffUaERj9K8PeHfD9SNxWg0olKp+nS3GsoMH4IgoNFo0Ov1TJ06Fa/XO6ICc1MMDa2traxevZqamhrKy8vZtGkTBkPPqNfnnnuOe+65B4Bf/vKXXHHFFXR1dXH66aeH29TX17NmzRoefvhhnn32WW655RaKiopoaGigpKSEk046icWLF7Ny5coe1x+sg6vBJFHA99SpU8OxUGazmX379pGVlYXJZMJoNMbdCPl8PhoaGmhqasJgMDBx4sTjkl3rRHOVShbl0XocaQoRrdpDns6FRuVDkqDA0EmmOrhfylJ76LLLgDQWTGykLK+LXTU5QNATQC4jHK+Ro/OQke4DIuI8IqZjvz+AvdNDpgZcdhfpGd1uzvYuH9JR8WGzeaKK+vaG6Bfxeny4nW7c9uh4J0EIWjdcnXZcncGDwbR0FekZatJUSmQKefyEIoDb2dOqsuSUaAEUOXaczslA994tcp063u5WKbExjPSWUjARkSdJkiSFs4D4/f6w7+xgp62NrZyqUCgoKCigsLAwKruGUqkkNzcXURRpbm4mIyMjHDTY382WUilj4fwsbJ12zG1D5xbybXsZ07SHsMnH0tHS1SPwKx7yNAVSkpYNAAcZlHd9S5qm/zEyLbZoFwW/Hw5XOwkc7CJdKdHR2R2vMSaziQJlQ3fjAVozgJTI6IPxJYeAQwB8c7CY3NxcNBpNXHeryIxsQ0HkJK5UKockpWaKkcW6des466yzWLt2LevWrWPdunXcd999UW1aW1u5++67+fLLLxEEgdmzZ7N8+XIMBgM7duwIt5s9ezaVlZXhx6tXr+bRRx8NFyZdv359+HQ0lqGo9j1QYmtjxHOVioyFkiSJjo4OzGYzBw4cQKvVkpeXR05ODk6nk9raWrq6uigqKmLu3LlDfl99VwVGPJQKkcx0L2qlL5geV4BzZhwKx2Bo1X4EGWhUPjI1PiZpWkmTefnmYAU+n4Q8XQinxS0zOXB7E+8hOtqObtYFcDs9SAEJ4ahLVEe7h3R1Bn6vhzZb4hhOSQK/z4uzS8TlcCH6xIRt4/XE5/bgcweFlCCTka5Vo1Sno4iweggCOLqihUt5oY+J5YkzgsWOqZDVI5G71fFap0YTJ8RqOtBsVB6Ph9raWpqbm9HpdIwZM6bfaWv7WiB6q5waSSi7RkFBAdXV1Rw6dAhBEFCr1eh0OtLT0wd8qnvqXAOf77Rjjl/mYtDoanPgUDnpautCCkjI+kgcLhMExH4W5tnvMGFqbKOwPCfp9wTEAC5nz5MUIeDF45bwuCFy6qo0bAz+ciwiA1JCo59MG1cP1IcfHzFPRq/X4/F4wvUzfD4foigOiRl7tE7iKQbOq6++yocffgjAFVdcwcKFC3uIjXfeeYclS5ZgNBoBWLJkCW+//TY/+MEPwm3279+PxWKJsnSECG2u+6ogPlQblGT92kVRHFDAtyAI6PV69Hp9WHjU1NSwe/fu8IHaYMRj9EVKZPRElSaiVvmJnNaUiu49i0YZ3GCb9N0CoDTPgVwWwOuToVF3j8myPCcNNmXUOA395vP6w9YCIfycL1wvw+sV8XpEOjs8+HuxanhdbgIeOx5vcu5QvRG0ejhwdQYD0dPSVaRr1Oh1Pe+zWKtGX8RaPUL7O5/Px5EjRxAEAa/XG7Z8DOU6NVpiC79zYiOUBaSxsRG3231c0tZC7wHfoYKAdXV1BAIBiouLmTx5MjKZDJfLhcVi4euvvwYIp/VL5NqViJVLc7C02ThiHrqBecRRgKzLisfhRfSLZOh7yeRE78X84reXcIpK6g5ZMJUYkSdZBcmW4CTFHWfI5Gk6mabemRIaI4Ay09EJXQX18jymT5+OXC4fMjP2aJ3EUwwcs9lMQUHQ/TM/Px+z2dyjTcgNKkRxcTENDQ1RbTZu3Mjq1aujxsxf//pX/vnPfzJhwgQeeugh0tPTcSYorjpUblSh6yYSDYlqOA3kXoqNKZw4cSKiKGKxWNixYwcKhYK8vDzy8vIGVXikREZi1Eo/GlXizb1aFRIb3Zb/NLlEvsFNQ4sGldwDBGuXlJkctNvliDHeCJIk0d7ac1y7HO6o4nwdHR4EX+IMVFJAosPWRnZu7/uGEP2dnkNWj/Gl6bREfCVaTYAFMwaenCFy/L3/fjsFBQXMnDkzrnV+MGpPjdZDsRNGbPTlRiWKImazmcbGxnDaWgguHAMl3gLRn8qpPp+PxsZGmpqa0Ol0jB8/vodlRa1Wh9P6eTwerFYre/fuxe/3h4VHMr6v5SVqJpUraLD58YtDs4kyprXS0KbC4wjeuD6vl7QEi4ogCH2mvI1FkiSQJPwi2Jo6MBX3UVGIoFXD7eppig0EJESxZwzAson/guJjiM2AlNAYAmZNsQAWAL4+UEROTg5arTbsUz4YWUNG6ySeonfOPvtsmpubezx/7733Rj0OnegPhI0bN/L8891xXhdccAE/+MEPUKlUPPHEE1xxxRVcccUVtLXFNy8PldhIdCCWbA2nvghZMmpraxPGFGq1WsaMGRNOO71z504EQQgLj/TYAkhJkBIYyaFW+tAoE++NNEfdq/IN0WKhONdJQ4uG9DQ/oEKX4UWv9aFWiUfFRvd94vH4g8XxYnDZXWQZM8P3lNvlJ1PpJ1G9L2eXA0kMJO3rMNCzoNj3LZzjRBm/3Ei/OessPeAi5BocsnqEvFsGo/bUaF2nThixkciy4XA4aGhooL29nby8vHDaWpvNRmdn72lZ+yIybe1Aa2MUFhYye/Zs0tL6Hu0qlSqc1s/n82G1Wtm/f3+f+cRDVJ6XS01jE/vq4r58zOhkrRzo6D6V8NhdKAxpcfsjT1P0qNzZF1KEOKk71Exuob7PNHm2BFkv/D5/j35p0/0sLPo04bWSEhopweIg1gAAIABJREFUhpzp4xuA7pPlmuZJGAyGHlaPyADzZCbn0TqJp+id9957L+FrJpOJpqYmCgoKaGpqipt9rKioKOxqBcFA8IULF4Yf79y5E7/fz+zZs8PPheq6AFxzzTXceuutXHvttQkPxYYqZiNSxPR3neqNUDxhQ0MDarWa0tJSdDpdr2ItMu202+3GarWye/duAoFA0oX4UiKjf2Sk+9CoeolFOGr1iHSjAijJdfLZt92PS/OCr6uVAXK0XTS1ZqBIkyMA7TGeA6EhIAUk/D6RNGXf28xAIECXLSjEk/V4GOiRaeQQlckkzp7fPxeq/pDI3epYak+N1nXqhBEbkZN4IBDAZrPR0NCATCajqKiI8ePHR02EA80kFYvP58Pn80XFYgykNkZ/SUtLo7CwkMLCwqh84k6nk+zsbPLy8noElGdqFcydrqbe6sLhHlzrhiRJHG5WIkUsmKLPj8/jRZmu6tFeJpcR6CNbVez1Ixdjj0ei1dJJTr4u4XsCYgC3u+eJS9C8KSLETFdnlu9IeFqStNBIWTWOO+X53aviviMV5ObmhosJhszYMpks6gQ3HkOZ7SrFyGT58uU899xzrF27lueee44VK1b0aLN06VLuuOOOsFXi73//O7/97W/Dr7/00ktR8RtAWMAAvPbaa0yePHlYYjYEQUAUxajA9N7Wqb5wuVzU1dXR0tKCyWRixowZqFQ95/e+SE9Pp6SkhJKSErxeL1arNaoQn8lkIiMjI3w/pkTGwNCpPWSk9+JGpfSRniaSqYkWJCW50Rvwsryg+5NaJaKQSbRY7GTq1WhUPjp7eCh0j2Ovy5OU2HB1OiIS9vTZHBi4ZSNSpsyc5CHXcHwSM/SW3ao/7lYpsTGMpKen43A4cLlcNDU1YbPZMBqNTJo0KWFsw0DN1pHpANPS0pAkiV27doUnyFg3qFAq3WRqYwyUyHzioiiG4z+6urqicqELgsDShdnsPlDPVwcGtQt4HC7s9p7WGY/DFZUJIkSGzEEXyX8PUpz/Vf3BJrJNiTN0JbZq9BQaSkWAFRVvxm2fEhqjh4ll1UA1ALv2F5Kbmxu+J/syYw92HY8UI5+1a9dy8cUX8/TTT1NWVsamTcFCnl9++SWPP/44GzZswGg0ctdddzF37lwAfvWrX4XdcAE2bdrEm29Gzx2PPPIIr732GgqFAqPRyLPPPsvhw4cTWuCHwo0q5CZls9kwmUzhQpUDcZVqbW2lrq4Ov99PSUkJ48aNG7R7RalUUlRURFFRET6fD5vNxqFDhzjllKENKP8ukKd30FtoY7pSxJjZM17BkOlDm94tQMpM3WIjRFe7i8JyD7Ze1nFHl5MMXaSbd8+1OhAI0NnS7V441GIj8n3n9DMwfLBIlN0qGXer2HVqtByQjXqxEQgEOHjwIFu2bGH//v3ceOONzJ49u88sGv2d3OOlA1SpVMycORNRFKMsC0ajkYyMDFpaWvpdG+NYkcvlYV/YQCBAa2srTU1NfPvtt+h0OmYHnuTfikw01C/F4hqcgmV+nw97e0fc1wJ+EZ/bi0oT6Zcr4fX3008xTnyHwxmgzebAmNszg5goinGtGgB+f8/nzyj+HJWi5/MpoTF6OWlCI9AYflzdNLFXdytRFFPpbr9jZGdn8/777/d4fs6cOWzYsCH8+KqrruKqq66Ke43Dhw/3eO63v/1tlPUDgoHmvVk2BkNsxAZ8jxs3DrPZzM6dO1Gr1eFaTsmMc7/fT2NjI42NjWRlZTF27FgyM5ML3h0oaWlpjB3bztixKaExGGRp+q5nUWCIH7RdnOvC5ZGjVvrJ0wdFsloZvUbGrWMR8ZToExH9InKFvMdrIZwd9iiX6qEubRnqQ2Gen2nj+lcyYajoj7uV3+8flYdio3plffnll/nNb36DwWDglFNO6RHw1xvJulElE/Atl8vDk3hDQwN1dXWYzeZwEJxGoxmWwSGTycjJySEnJwf1Nz8PF1CdVW5m+uH9vF977GJDkiS6Wtp7nSG8Thdp6crwd1Csb6fZkfxnS5KU8H9Vd7AZQ87YHpOezRL/xEL0iz36OtO0h8umv9WjbUponFhUFOwD4OMvteTk5JCbmxsuJtjR0UFzczPFxcV4vV4UCsWIruicYvTRW2zhscZsxKvhJJPJSE9PJysri3HjxuFwODCbzRw5cgSVShVes2LjBe12O/X19bS3BzPrJBtTeKykXKWGh9KcrrjPF+c6OVCfSUmeM7xBj7RsQPy4Cb02gCBISFLwVa/bi1obtH54PNHB5QExENw/RCAleRsM+ED/6PvOmZ84M9ZwEroPnM7JUQVtA4EAdrudxsZG9Hp9uPDs8SwoeCwcF7FxLFVaAbxeLz/96U/58MMPkclk3HvvvaxatYrp06fzwQcf8MEHH7Bt27Z+9am3k6RElVMT/UOdTid1dXW0traSn5/P3LlzUSqViKJIa2sr9fX17N27F6PRSF5eXtil6Xig/ubncZ9fNesbDrcVU91VfkzXd7R3Ivp6Pz0JiAG8Tjfp2mDwn0Hjprkf93lATFzYx97lpb3FgSGn27oh+kU8nvj/W39MkaAp2Qf5+byqHu1SQuPExBk4g8mTXdhsNvbu3YvLFXQhkMvlVFRUkJ2dzYEDB3jggQf43ve+xw033DDMPU5xotCb2BhIzEa8Gk6J1ilBENBqtWi1WsaOHRsuIrt9+3aUSiV5eXkIgkBTUxOCIFBSUsLEiROHfJ1KCYzhR5vA+lFyVGyE4jUA0pXR62q8EXvJ0g7aLBIHjwTHjsvhDouNto7o8eRo7yl0hjobFQioVQFOn+Xuu+kwEYrpCOF2u6mursbhcFBWVkZ2djZ1dXU88sgjZGdn9+ugfbg4LmLjWKu03nvvveTl5bF///6waxAETcQQnMTd7v4NnN7S1vZWOTWEJEnYbDbq6uqQJImSkhLGjx/fw+KRm5tLbm5uuN+NjY18++23GAwG8vLyMBgMgz6hJxIYkRTo7cwr2Uft3mJEaWDDwONy47Yn5/PodblRqtODgeEJUt/FQ5KkPlPk1h22dosNScKaIFYj+P/tDgIeq6/ltgXPx22bFCmhMWpwBs4I/65Wq8nKysJms6FWqzEYDFRXV7N48WIyMjIQRZG7776biy66aBh7nOJEoy/LRrJioz81nBIREh7FxcVUV1dz8OBBIJgxqrCwsM/MUsdKSmSMfIqynQiCRKmpez0VBJAJARKlr5012cXkMV6+d7KMg0eCbTxOD4GAhEwmELmUi34Re1s89+vE41gmSEydIHHqHIlTZop0dqnZvS/A7n0ie/aJHKmX+oz5EAQ4fZaLdNXIs1zHigy73U51dTUej4fy8nKys7Oprq7mnnvuYdeuXdx0002jZp06LmLjWKu0/vGPf+Tbb4MZZ0KuQZEkU2cjlti0tclWTvX5fDQ0NNDc3Ixer2fChAlJVR2PdGkKBAK0tbXR3NzMvn370Ol0mEwmDAbDMZnDkhEZkaw4eS+7zSV809L/TbPoF7G3tvfd8ChSQMLjdKHOzMBqT5xBKt77+qKr3UVnu4ssvRq/X8SbwKrh84rhBbQks5G7Tns6brtULY0Th0iRAdDW1kZ1dTVyuTzsg/7VV1/x1FNPUVFRwUUXXURzczMPPvggXq+XNWvWDFPPU5xo9LZOJVvhOzJt7UDz9AN0dnZSV1eH3W6nqKiIU089FYVCEa6FsWPHjrB78GAW4UuJjNGDMk1Cr/VSlB0dQJ5IbCjkEpcsDYqHeTMkXny1+zWfx4dKHT2G4lk14t0GCrnEjMkSC+YEWDBLQp8Vbk1ejpxxFXJWnBt087M7JPbsE9mzv1uAdMR8jCAIwxYYnohYkdHZ2Ul1dTV+v5+Kigr0ej0HDhzg9ttvp7q6mltvvZWnnnpqVLhPhTguYuNYqrS2twc3tHfddRcffvghY8eO5dFHH8VkMoXb9qeCeCSiKIZPiPqqnNrV1UVtbS1dXV0UFRUxZ86cAQeTymQysrOzyc7ORpIk2trasFgs7N+/n6ysLEwmE0ajMemB1F+REX6f0s/i8Xs41F6OS+w9v3kkkiRhb21PSghE4nW5yTUGaHUZ+258lEAgsQtVJHWHrUyZWUyLpYt4E6EkSYj+oNjIz7DymzOfiDuxpYTGiUGkyAhl06murkalUjFhwgQyMjL47LPPuP/++xEEgTvuuIPTTjstvOn7r//6ryGpe5Diu0t6enq/16nYgO9jqY0RCAQwm83U19ejVCopKSnpYVmPrIXhcrnCRfhkMlk48Uh/U92mBMboZVJJJwp59DofFBs9OecUOyZjcL0eVyaRrZdoaT9a0M/pjhIbol/E0d6zzlloKCrTJGZPkzh1ToDvnSyh7btuMQDaDIF5sxTMm9X9XF1jgN3fiuzeF2DPfpGCXJGC3JGRwSlWZLS3t1NdHcymWFFRgU6nY/fu3fz85z/HarWydu1ali5dOqpERohBExtDVaXV7/dTX1/PggULePDBB3nwwQe5+eabo6q19mcSD5mgQ/+snTt3kpeXR35+fo8guFBtjLq6OtLS0igpKWHKlCmDal4WBAGj0YjRaESSJNrb27FYLBw4cIDMzExMJhPZ2dk9BtdABUYsiybVsK1uD580zEn6Pa4uOz7PALI4SIDfk/SokyQpaUHTbrOTZf+KS096k/u3/YQA0ZYpvy8oNLLTW7l34R9SQuMEJVZkWK1WampqyMjIYPLkyajVaj7++GPuv/9+srKyuOeee5g9e3bce3o0TugpRi79ORSLDfg+lkBQt9tNfX09VquV3Nxcpk+fnlTVbrVaTVlZGWVlZbjdbiwWC19//TVAUtW/UyJj9DOppKf1IVJshGZNnVZk+ZndbQUB5pwU4J1/BtdhV1ewmnhwEyAkcJ+CTC2svc7P3BkSccpzDYiSQhklhTLOXTw41xsMIkVG6MC5uroahULBuHHj0Gq1bN++nfXr1+N0OrnzzjtZuHDhqElzG49BExtDVaU1OzsbjUZDZWUlAN///vd5+ulo95e+3KjiVU5VKpXMnj0br9eLxWJh7969+P3+cAB3S0tLuDbGtGnTBr02RjwEQcBgMGAwGJAkiY6ODiwWCwcPHkSr1WIymShP+z9gcNPDXTB9F9+2VNDizu6zrc/jDaaqGwAKOVy/8COe+OJcOj1ZfbbvK1YjlsWmtyjXNXLjzCd58KtrkSIEh98nolN1cN/iR1HIor+9VDD46CdWZISy7mRlZTF9+nRUKhXvv/8+DzzwAIWFhTz88MNMnz59VE/eKUYXfa1TIXfeyIDv0E9/CR1c1dXV4fF4KC4uZsyYMQMW0Onp6ZSWllJaWorH48FisYSrf4eER2iNTImME4d4wyVSbIRW0sqzOlHHxEB8b0a32JAkCb/PD1Kw2G+iPcQlFyo5fd7Ii6UYLGJFRktLC9XV1ajVaiZOnEhGRgZbt27l/vvvRy6Xc8cdd3DqqaeeEOvUcXGjOpYqrYIgcMEFF/Dhhx+yePFi3n//faZMid70JZrEkwmkUyqVFBcXU1RUhNVq5fDhw1RXV4crdBcUFBwXoRGLIAjo9Xr0ej2SJJHR/kT068lukJNgUhGcamnite29i41AoGeauv5w8799wanj6/i6eS9vH/xer21jK4b3hT7Dw9T8BgDGG45w/Yw/8ujOqwggx+8X0abZuW/R/5AmD46Hfn9/KaExIml3zw/7kwcCAZqbm6mtrcVgMDBjxgyUSiVvvvkmDz30EOPHj+eJJ55g8uTJfVw1RYrBJ5FlIzIxSUNDQ3jjPhBhIIoiTU1NNDQ0kJGRQVlZGTpd8jFyyaBSqaKqf1ssFrKzawb1M1KMXM6Z72Tn/nQcruD4LM33csbMnjEQJ0+RUKZJeH3BjbLH5UUCulrjWzWMeoHvXzD0KZaHg1iREbK4a7Vapk6dSnp6Oh999BHr169Hp9Nx7733JrS4j1aOi9g41iqt9913H5dddhk///nPyc3N5Zlnnom6fuwkHorFiMw5niiQLnZynjx5MjqdLq7Fw2QyHVfhoWl7/Lh91oWn1rOrNo8aW/z6F8E4jY5e09D2xsI5dv7tPBkwifMXOfi0rrNX64YkScmXEgWWTIo+TZuSfYj/mP4cT3x9OSqhg3sXP4KmfGz/O54SGSMSS+csrFYrNttOJElCpVLhcDjIzc1l1qxZyOVyXn75ZR555BFmzZrFCy+8wNixA/j/p0gxSMSuU7E1nGbMmIHVamXPnj1RxVmTCc6OTb8+c+bMQQvq7g297iB6Hd1H3CfQ5ihFfMaV+rjzmhbueya4P7t0WUdcC0i6Ck6aJPHl18Ex4exyoslIw9UVP+/9mu+nkZ5+Yo2fPXvU5ObmkpER3NOEDsN0Ol3Y4v7ee+/xwAMPUFxcfEJb3IU+0u2NCnuW2Wzm8ssvZ+PGjeFAur58XB0OB/X19eHJuaioKOHkHBIeFosFURTJzc0dUuFxPEVGJH/7NJ8/fjALKU6AtdvuTOhn2Rem3ACb/+urqHXoqbfG8fq2iQnfI/r8SVs2ZDLYfNVjcSuA7zKPo2RCBkZt/xMIpITGyCPSXUoURRoaGqivr0ej0SBJEjfddBNGo5EDBw5wzjnnsHbt2qjEE6OYE2/1GTxGxToFMHPmTD744IM+azi5XC7MZjMWiwWFQoHJZCI3NzdqjQq5YdTV1REIBCgpKSEnJ+e4xBpp1Hviv3ACbpJSxKe5Rc5nX6ezYmHiolmvb5Hx2AvdZ9plBT6+3tUzDiQvR2DjkxqUaSfG+HE6J+PxeLDZbFgsFux2O4FAgMzMTCZMmIBGo+GNN97goYceYuLEidx+++1MmjR43irDSMJ/4KiuIB7C5/Oxbds2qqqqOP/88xOmkE2mNkY8Qq5WoerCIYvHYAuP4RIZIc77npkvDzawsy56c+b3+bC3D0xopKUJPP6Lb3qsQefPreejPSV0uHpmweqvC9XkAksPoRFyk5pRBNBPoZESGSOOSJERShoRigObN28eoijy5z//GY/Hg06nY8GCBXz00Ufk5ORw9913D2PPU6QIEggEqK6u5plnnmHFihXk5eUlXHvUanVUViiz2cyOHTtQKBTk5OTg9/uxWCzodDrGjx+fVPr1Y0Wj2du3tVmSUoLjO0J+ttir0ACYNyPAYy90P05XxveMuPxi5QkhNCLdpRQKBYFAALfbTX5+PlqtlrfffptLL70USZKYOHEijz/+OCeffPIw9vj4cUKIjaKiIt5++23eeustKisrKSgoYOXKlSxbtozMzExsNht2u52mpiYMBkPStTHikUh4DNTVargFRiQKucS/zavh2yYTHn/wBE2SpGCcxgDPDn91VTW5Bl+P5wtz3CycVsurX/RU81I/U45ecV7t4MWwpITGiCJSZPh8Pmpra7FYLBQVFTFv3jy8Xi9PPfUUzz77LMuXL+fvf/87ubm5QLdvbIoUIwGZTMY///lP3n33XS677LJw4pMLLrgg7DIcj5DwCBX0qqmpQRAE1Go1Op2u36lo+0tUwHdKSKToB3nZUFEcoLo+KKrjedIU5gv825LRvRWNFBmiKFJfX09jYyP5+fnMmRPM9FlVVcWGDRu44IILOPfcc/n888/50Y9+xOOPP87MmTOHq+vHjRPCjSqWvXv3UlVVxcaNG5HL5Xg8Hp555hlmzJgx4NoYfRHpapWM8FC/vDL4y/iyqOcHM/B7oDzyykS27B4PgL2tI+kq4bGcf3oHt196MOHrljYltzxzKu3OaOuG3+tLupquLlPkpVu3DKh/UaRExoghthCf1+vlyJEjtLS0UFxcTGFhIU6nk6effpoXX3yRiy++mOuvvx6DwTBMPT4upHZ5iRmV61RNTQ1VVVW8/PLL6PV6Vq5cyQUXXIBerw+3CQQCWK1W6uvrkclklJaWYjQaEQQBp9OJ2WzGarWSlpYWdrWKTeE+UFJZpVIMBs/9VU7VG8GsVBNKXXy5LbpI4J03qjhv8egMDI8UGX6/n7q6OpqbmyksLKSoqAhRFHnxxRd56qmnWLJkCTfffDP5+fnD2OMhJ+E6dUKKDa/Xy9KlSzEajZx77rkcOXKEN954g7Fjx1JZWcnSpUuHNNC7N+GRfvciAIST9D3fOEKER70lnbtenE9zi4IuW9uArlFaIPLir3b0eRD23Htj+b+t3X+nJEn4vT0tIYm46Iw6rlry7YD6CKRExggiVmS43W6OHDlCW1sbpaWl5Ofn09XVxRNPPMHmzZu57LLLuO6668jMzBymHh9XUmIjMaNynYrk4MGDbNq0iVdeeYXc3FyWLFnC/v37KSoq4vzzz6ekpKTXNcvhcGCxWMLCIz8/n9zc3H4frmnU3wCylAUjxaCx96DATf8dFBOxYqO8ROBPj2qQyUbXeIsUGZEW99BhmNfr5dlnn+XZZ59lxYoV/PznPycnJ2cYe3zc+G6JDQCbzRb1z5UkiV27drFx40befPNNJk2axKpVqzj77LOTKnA0UELCw2w2c+rrN8dtMxKFx4Y3y3nu9Zx+VwkHUKkENt+zA0Omv8+2LR1KfvF0t3VD9PuTrq8hk8Ff7ngftWoAlZ5TImPEECsyXC4XNTU1dHZ2Ul5eTl5eHq2trTz22GO8/vrrXH311Vx77bVoNMlXvT8BGF2r8fFl1K5TsXzxxRfcc889bN++nfLycvR6PatWrWLZsmVJu/46HI6wxUOlUoUtHskIj6DYiCQlPFIcG4EAXHpjGh1dQg+xcfetKs46fXRYNWKrfXs8Hmpra2lpaaGkpISCgoKwxf3Pf/4zF198MT/96U9PdIt7LN89sdEbkiSxfft2qqqqePvtt5k2bRqrVq1i8eLFg+7/6vP5aGxspLGxEZ1OR3p6OlOevzph+5EiPMytaaz+1Un4+9YLUQgCrP/pIeZPTb4ex4tbxrDp08n9tmpMKe/id1dvTb5zKYExovi2phyr1YooiuTk5KDVarFYLLhcLsrLy8nJycFisfDoo4/y3nvv8eMf/5gf/vCHQ+6jPkJJ7fgSc8KsU3/6058YM2ZMuJDXnj17qKqq4m9/+xtlZWWsWrWKpUuXkpGRkdT1UsIjxUjgwaflvPeJnAklLr7cHhQb48fI+OPD6hGf5jVWZLjdbmpqaujo6KC0tBSTyURXVxePP/44f/3rX7n88su57rrrjkvShhFISmwkQpIkvvjiCzZu3Mi7777LzJkzWblyJYsXLz4m31e73U5dXR0dHR0UFhZSWFgYNcGHLB7jnr4s4TWGW3j87qVyXvlH31XFI/n+klZ+tqq6X+9ptyv42ZOn02pPR/Qlr25+e9V2ZlS09N4oJTBGHLGWjLa2Ng4cOIDT6UQul/P6668zY8YMtm3bxtatW7nhhhtYs2bNoPmij1JG9oo8vHwn1qlvvvmGqqqqsEvwqlWrOOecc5J2Cbbb7ZjNZmw2G+np6ZhMJnJycuIKj8j6U1qtlrlzYr/ilPBIkTwffynw34+lMb7ExbajYmPdXemcNm/kBobHigyn00lNTQ12u52ysrKwxf0Pf/gDb7zxBtdccw3XXnvtsBSBHkGkxEYyBAIBPvvsMzZu3MiWLVuYM2cOlZWVnHnmmUmdBEWm1gUoLS0lOzu7T+U+UoWHKMIFt82i057cojKuzMezt+8a0Gdt/EcFz2+ZkHQmKp1W5KXbEgSGpwTGiCRWZHR2dnL48GECgQAVFRUYDAYOHz7Mb37zG77++mvsdjtnnHEGa9as4dxzzx2mXgdpbW1l9erV1NTUUF5ezqZNmxKaxzs7O5kyZQoXXnghjz766GB1IbWzS8x3ap2SJImdO3dSVVXFW2+9xaRJk6isrOyXS3BIeFitVtRqddji4fP5qKurw2azYTKZKC4u7lF/KmXxSNFfnC645IY0KgrdbNvuYspEGU/+bmS6we7bpyU3Nzd8LzkcDqqrq3tY3P/nf/6H999//7tucY8lJTb6SyAQ4JNPPqGqqop//OMfzJ8/n5UrV3L66acjl8uj2vp8PpqammhsbESv11NSUpK0mTuWwRAegyk63vhXDr99rqzPdho1vPzfX5GhHkD8BNDhkPPv9y1CkpJbtCpPr+eac45mS0mJixFNrMhob2/n8OHDyGQyKioq0Ol0HDhwgAceeID9+/dzyy23cOGFFyKTydi2bRuNjY0sX758mHof5NZbb8VoNLJ27VrWrVtHW1sb9913X9y2P/vZz7BarRiNxpTYOD58Z9epkEvwxo0beeedd5g+fXrYJTiZCuKSJGG328MBroIgUFBQwJgxY5KyJMYt7pcSHinicMfvFDi6PGzb7uKh/0pn7syRZdVwOifjcrmwWq1YrVa8Xi+SJCGXyxk7dizZ2dk0Njby8MMP8+mnn/Kzn/2MSy+99LtucY8lJTaOBVEU+eijj9i4cSOffPIJCxYsoLKykoyMDLZu3cqsWbMoKCigsLBwUAfeSBEel99zEofrE/9dggCP3nyAGWM7g08McPN/++9z2Lmv79MBmQz+8kAD6tRBwogmUmRIkkRbWxvV1dWkpaVRUVGBVqtl7969rF+/noaGBtauXcuyZcuOSwXk/jJx4kQ+/PBDCgoKaGpqYuHChezbt69Hu23btrF+/XrOPfdcvvzyy5TYOD6k1imC99jnn39OVVUV7733XtgleNGiRXHXJVEUMZvN1NfXo9FoKC4uRi6Xh12tNBpN2NUq9oAtHinhkaI3XnlXxgcf+RB9Hh797cixasS6S3V0dFBdXY0oiuh0Or799ltuv/128vLyaGtr46677mL16tVDVkZhlJMSG4OF1+vlgQce4LHHHkMul3Paaadx5ZVXMn/+/CHdJCUjPCBGfAySm9XBejU/vHdKwuKxV14scc333QO6diTmFoGrflXYZ5HayWNFHrip+Zg/L8XQECsybDYbNTU1qNVqKioq0Gg07Ny5k/Xr19PR0cGdd97JokWLRqTICKHX62lvDyY9kCQJg8EQfhwiEAhX/jgRAAAgAElEQVSwePFiXnjhBd57772U2Dh+pNapGAKBAFu3bmXjxo188MEHzJ07l5UrV3LmmWdSX19PU1MTAHl5eRQXF/dwAQlZPCKFR35+PtnZ2SnhkWJANFlg3aMiV62GGVP7HkNDTazICB2GyeVyKioqyMrKYv/+/TzwwAMcOnSIRYsWYbFY+Oyzz7j77rtZuXLlMPV8RJPwJk9Js37y97//ndraWt555x3Gjx/Pli1b+POf/8wvfvELFi5cSGVlJXPnzh30DAuhyuXu//ygV+Eh7QpugIST9HDgSPcL48uQGrrrUSQlPI5aKMZlw5mnCHz4ac81fdpkGdd839HPvyY+pmyJmZPcbN/bu9/x5Rckn+kqxfEjVmRYLBaOHDmCVqtl6tSpqNVqvvjiC+6//34kSeL222/n9NNPHzHZSM4++2yam3uK2HvvvTfqsSAIcfv82GOPsWzZMoqLi4esjylSJINMJmPBggUsWLCAQCDAxx9/zCOPPMIPf/hD9Ho9P/7xj7nmmmsSWuIFQSAzM5PMzEzGjh1LV1cXZrOZw4cPk5GRgclk6lV4OF3d1u2w8Ig8RRoh93yK40dBHsyZMfxCI1JkSJJEa2sr1dXVqFQqJkyYQEZGBnv27GH9+vU0Nzdz2223cd5554UPwwKBAG73sR+uftdIWTYGCa/Xy3vvvcfGjRvZsWMHZ511FqtWrWLmzJlDupnyer3U1tYy7cX/l7BND1er3tysErhAOd1wwf/LwOPujsnIzJTxyv86GMy4KGubwJW/TGzdyMqEjfc1DN4Hpjhmvvwmh7y8PIxGIwBms5na2lp0Oh3l5eWoVCo+/vhj1q9fT0ZGBnfeeeeQCPKhJBk3qksvvZSPPvoImUyG3W7H6/Vy3XXXsW7dusHowuj5so4/qXWqD2644QbMZjM/+clP8Pv9bNq0iU8++YRTTz2VyspKFixYkJRlUZKksPCw2Wxotdo+hUckKYvHdxtRlJDLh+f/HSsyQhZ3jUZDeXk5Go2GHTt2sH79erq6urjzzjtZuHDhiLa4j0BSblTHE4/HwzvvvENVVRXffPMN55xzDitXrmTGjBmDtsEK3Sy1tbXIZDJKS0vRarVYrdaErla9iQ7oW3i88Fo6jz8f7L9MLrDht24mVAwsILw3fv2/2Xz+dXzrxsqznfy/yoFVNU8xuDgDZyBJEu3t7ZjNZiwWC6IokpmZSVFRESaTiS1btvC73/0Ok8nEL3/5S0466aRRJTJC3HLLLWRnZ4cDxFtbW7n//vsTtn/22WdTblTHj9Q61Qd+v7+Hj7nf7+fDDz9k48aNfPbZZ5xxxhmsWrWKefPmJS08Ojs7sVgs2Gw2MjMzw8Kjr/d3dnaSb6rv+cIonBtSjGxiRYbFYqGmpoasrCzKy8tJT0/n888/D8/nd9xxB6eddtqoXKdGACmxMVy4XC7eeustqqqq2LdvH0uXLqWyspJp06YNaDCLokhjYyMNDQ3odDpKS0vjZr7qK8ZjoNaOyuu0WKwiP70ywCXne/rd/2Ro6xS47PZCYouXy2Twl/UNfLfTWA8/ke5SofFYX19Pbm4uRqORr776iptuugmXy0VhYSHr1q1j4cKFI2LyTjaFbW1tLddccw11dXUIgsCLL77IzTffTG1tLWVlZWzatAmj0ciXX37J448/zoYNG6LenxIbx5XUOnWM+Hw+3n//faqqqvjyyy9ZtGgRq1atYs6cOUndtyHhYTabaWlpiSs8JEnCarVSW1uLQqGgtLQUg8GAIAgpi0eKQSdSZAQCAZqbm6mtrcVgMFBWVoZKpeKjjz5i/fr1ZGZmcueddyY93lMkJCU2RgJOp5PXX3+dTZs2cfjwYc4991xWrVrF5MmT+3yv2+0O5z/Pz8+nuLg46cxX/RIefVg7tptP4q9vK7n3F86kPnug/OaJbP61M9q6MWmsyIOpwPBhI1Jk+P3+cKBpaDzKZDJee+01Hn74YWbMmMGaNWvYtWsXr776Ktdeey2rV68ext4HSTaF7cKFC7nzzjtZsmQJdrsdmUyGRjOsGVRSK2BiUuvUIOL1enn33XfZuHEjO3fu5Oyzz6aysjJpl+BY4ZGRkYFCoaCjowODwUBpaWmv91IP4ZHa/KXoB7EiI3QYlp2dTVlZGQqFImxxLygo4I477hiRFveHHnqIDRs2IAgC06dP55lnnkm6js4wkhIbIw273c7f/vY3Nm3aRF1dHcuWLaOyspIJEyZEtevo6ODIkSN4PB5KSkrIy8s7Jh/C3oRHv9yshri2RadD4N9vKySyxt+9N7Qwc1IqMOt4ElsjI1T0y2w2U1hYSHFxMYFAgM2bN/PYY48xf/58brvtNkpLS4epx72TTOzFnj17uPbaa/n444+HqZdxGVkr4cgitU4NEW63O+wSvHv3bs455xwqKyuT2py5XC5qa2uxWq2oVCp8Ph86nQ6TyYTRaExqHUsJjxTJEikyRFGkoaGBhoYG8vLyKC0tRS6X89Zbb/Hggw8ybtw47rjjjqQOeoeDhoYGTjvtNPbs2YNarebiiy9m2bJlXHnllcPdtb5IiY2RTGdnJ6+99hpVVVWYzWbOPfdcVCoVe/fu5ac//SmlpaXodLpBV95JC48BBJQPBv+9IZuPtweVvFEn8cJvG4fss1JEEysyQokIrFYrxcXFFBYWIooiL730Ek888QSLFy/m5ptvprCwcJh6nBzJpLB95ZVX2LBhA0qlkurqas4++2zWrVuXVADsEJLaZSUmtU4dB1wuF2+++SabNm1i//79YZfgqVOnRq1N7e3t1NbW9jggkySJjo4OzGYzra2tZGVlkZ+fj8FgSAmPFAOiqakQi8VCS0sL6enpZGdnh/c1BQUFFBcXIwgCr776Kr///e85+eSTue222xg3btxwd71XGhoamD9/Pjt37iQrK4sLL7yQG264gXPOOWe4u9YXJ4bYcLvdnHHGGXg8Hvx+PxdddBF333031dXVXHLJJbS0tDB79myef/75pKqnjjQ6Ojp45JFHeOqppzAajWi1Ws4991wqKyspLy8f0s9OJDySEh1DJDgcLlh9SxGBAFx4lpNrV6UCw4eaWJHh8Xg4cuQIra2tlJSUUFBQgMfj4U9/+hPPPPMM559/Pr/4xS/Izc0dph73pLcUtldccUWUuDAYDLS1RY+rzZs3c/XVV/PVV19RWlrK6tWrWbZsGVdfffWQ970XUjurxIyodeq7gMPh4I033qCqqorq6mqWLl2KWq1m9+7d3HjjjZSWlqLXxyk4e5TIxBJtbW1hi0dKeKRIhtgaGT6fj0OHDmE2m5HJZOzevRubzYbBYOCll15iwYIF3HrrrSPW4h6P3//+99x5552o1WrOOeccXnzxxeHuUjKcGHU2VCoVW7ZsQavV4vP5OO200zjvvPN48MEHufHGG7nkkkv4j//4D55++ml+/OMfD3d3+011dTV6vZ7du3eTmZlJa2srL7/8MjfeeCOdnZ2sWLGClStXUlJSMuifHa+Ox/gvro9udOBIWHBIDd92C46WoxP/IIuODDWcMdvFR9vTuXRZqrbGUOIQo+tduN1uampq6OjooLS0lPHjx+N0OvnDH/7A888/z0UXXcSHH34YTnc7knjvvfcSvmYymWhqagq7UeXl5fVoU1xczMknn8yYMWMAuPDCC9m6detwi40Uo4j+Hox5PB4uv/xytm3bRnZ2NlVVVUN+wHQsZGRkcPHFF7N06VIeffRRnnzySXJyclCpVLz55pusWrWqV7EhCAIGgwGDwRAlPPbv35+U8OhRxyNVw+M7QazICFncbTYbxcXFTJgwAZ/Px44dO/jHP/5BY2Mj2dnZFBQUxE2kM1Jpa2vj1VdfDe8Jv//97/PCCy+wZs2a4e7agBlVCYQFQUCr1QJBJevz+RAEgS1btnDRRRcBcMUVV/DKK68MZzcHzMknn8z1119PZmYmAEajkauvvpp33nmHv/3tbxiNRn7yk5+wZMkSHn30URobh8atKCQ8XCtfxnzWc3xc+svuFw8cCRcLlBq+jSoUSMuebuExSFz/762cNtNDhjp1eDkUHG6cwNYderZu3cr+/ftpbm5m9+7d7Nq1C6PRyLx588jIyOCBBx5g0aJFSJLEp59+yq9//esRKTT6Yvny5Tz33HMAPPfcc6xYsaJHm7lz59Le3o7VagVgy5YtTJkytDFKKU4sQgdjO3fuZMeOHbz99tts3bqV2267jRtvvJGDBw9iMBh4+umnAXj66acxGAwcPHiQG2+8kdtuu22Y/4LkqK+vR6fTsXv3brZt28bbb7/N+PHj+eUvf8nChQv53e9+x+HDh3u9Rkh4TJo0ifnz55Ofnx+u1Lxnzx5aW1vpzQPD6ZoS/kkJjRMTp3NylNBwu93s27eP7du3o9FomDdvHtnZ2Tz11FOceeaZdHR08Morr1BdXc3f//73UWXRgOCBWUVFBbm5uaSlpVFZWcmnn3463N06JkaVGxUEA39mz57NwYMH+clPfsItt9zC/PnzOXjwIAB1dXWcd955fPPNN8Pc06HDYrGwefNm/vKXvyCKIhdeeCErVqygoKBg0D4jMjC9tLSUvLw8fD5f0OLR/rse7eNWJB/iIPIUAyfWXaqzs5P9+/fjcDiQyWS8++67TJw4kUOHDvHGG29w1VVX8aMf/Wi4MzIdMy0tLVx88cV9prB99913uemmm5AkidmzZ/Pkk08Ot2tmaheVmBG3TkXidDo57bTT+N///V/OP/98mpubUSgU/Otf/+LXv/4177zzDkuXLuXXv/41p5xyCn6/n/z8fKxW64jLkNMf2tvbeeWVV6iqqqKtrY0LLriAyspKysrK+n4zQVertrY2zGYz7e3t6PX6sMUj0ffS0dFBTU0NPp+PM84YPSfZKeITa8lwuVzU1NTQ2dlJWVkZJpMJp9PJH//4R1544QUuuugibrjhhrjpzEcTn332GVdddRVffPEFarWaK6+8kjlz5nD99df3/ebh5cSI2Yikvb2dlStX8pvf/IYrr7zyOyU2ImlqamLz5s1s3rwZQRBYuXIlK1asiOse0heRedDT0tIoKytLaAoPu1pFCI+4ggNSomMEESsyurq6OHz4MH6/n4qKCgwGAxaLhXvvvZfPPvuM1tZWzjzzTC655BKWL18+TL2OJtlaGbfeeitvvPEGgUCAJUuW8Pvf/340b95GbcePAyNynerPwdi0adN4++23KS4uBmDs2LF89tln5OTkDOefMGi0trbyf//3f2zatImuri5WrFhBZWVl+O/ti0AgQHt7O83NzXR0dEQJDwCr1cqRI0dQqVSUlZWh0+mi3q/R7B30vynF0BErMhwOBzU1NTgcDsrLy8nNzaWzs5Mnn3ySv/zlL6xZs4brrruOrKysYerx4POf//mfVFVVoVAomDlzJhs2bEClUg13t/rixIjZiESv17No0SL+9a9/0d7eHq6QWl9fT1FR0XB377hRUFDA9ddfz/XXX09DQwObNm1izZo1qFQqVq5cyfLly/tcsCILBer1eqZMmdLnCXbY1ar44bDwmMDr8RtHulalhMewECsyOjo6wu4NFRUV6PV6mpqaWLt2LR999BHXX389jz32GGlpaWzbto2vv/56OLodl3Xr1nHWWWeFa2WsW7euR62MTz/9lE8++YRdu3YBcNppp/GPf/yDhQsXDkOPU3wXkcvl/7+9O4+Kut77AP4eNhMRBIRhGVYJF8CIRdASMAFRDGPwmoZpKso1LTTF9ejVuqUXBLX03rylovkkQ5ZiSRTyaJaVuKGiVyWdYWdAGHQIWWbm9/zhw1xABgZh+A3weZ3jObHOB7T5zvu7fZCbm6ucGLt9+3bnX9RPmZmZISYmBjExMaisrMQ333yDpUuXor6+Hq+99hoiIyM7vMlOR0cHZmZmMDMzg0KhgEQiQWlpKfLy8qBQKGBqago3NzeV41bLF68UPLRX25BRW1sLoVCIhoYGODk5wczMDBKJBB9++CG+/fZbLFq0CDk5OX1+xb09W7duxdatW9kuo8f0qbBRWVkJfX19DBs2DI8fP0ZWVhbWrl2LSZMm4dixY5g9e7bKfdgDga2tLVauXImVK1eisLAQaWlpmD17NoyMjBAZGYlXX3211T77xsZGFBUVoaKiAlZWVvD29la7UWBLzcGjDn/9b/AYQsFDG7QMGc3bEoRCIfT09DBixAgYGxujsLAQW7ZsweXLl7Fy5Urs3LkTenr/fWrw8fGBj48PG+W3Kz09HWfPngXw5IxWUFDQU2GDw+Ggvr4ejY2NYBgGTU1N4HK5LFRLBjp1JsZsbW1RVFQEHo8HmUyGhw8fwtzcnOXKNcPCwgKxsbGIjY2FWCzG119/jcWLFyu3BL/22muwsrJS+fVNTU2QSCSQSqXg8XgYMmQIqqqqcO3aNZiamsLKyqrDq+LbDR7NOzz67spnn9Y2ZDx69AhCoRByubzVivvf/vY3ZGVl4a9//SsuXrzYF5rckf/Xp7ZRXb9+HfPnz4dcLodCocCsWbOwefNm3L9/H7Nnz0Z1dTVefPFFHDlypC8sN/UaoVCItLQ0HD9+HKamppgwYQIuXryI0NBQhIWFwcrKqluNAlXpNHi0ReGjx7QNGVVVVRCJRBg0aBCcnJxgZGSEP/74A8nJybh9+zZWr16NyMhItntJqEWdXhkAsHr1anz++edgGAbLly/Hhx9+2Nul9iR6FaSaVo1TwNMTY6GhoVi7di0OHTqEqKgo5c2JY8eOxdtvv429e/fixo0b+PTTT5GamqrccjSQlJWV4auvvsKxY8egq6urXJlv3hIskUhQVlYGqVQKOzu7p8YthUKB6upqiMViPHr0CGZmZuByuWr3qKLrdHtf25BRU1MDoVAIAHB2doaJiQnKysqwc+dOnD9/Hu+88w7mzp3L9vk5lWpqahATE4O8vDxwOBwcOHAA48ePZ7us3tT/zmz0pKKiIsybNw9isRgcDgdLlixBXFwctmzZgs8++0zZQ+Cjjz7CtGnTWK722Z09exZbt25FSUkJrK2tYWRkBD6fj/DwcI3vdaTg0TvahozKykqIRCIMGTIEjo6OMDQ0xO3bt7Fjxw4UFhZi3bp1CA8P10jY7I7u9sr4448/EBcXB4FAAAAICQlBQkICJk6cqNnCNYde+aimdeNUVyfG6uvr8eabb+Lq1aswMzNDamqq8urlgai4uFgZPOrr68EwDDw8PPDBBx/A3Ny80/DQ48EDoPDRg3JzdWFpaak8W9O84q6vrw8nJycMHToUhYWFSEpKwtWrV7Fy5Uq8/vrrrVbctdH8+fMxceJExMTEoLGxEXV1dR1eAd0PUdjoSFlZGcrKyuDl5QWpVApvb2+cOHECaWlpMDIywurVq9kusUf861//wksvvYSxY8cCAO7cuQOBQICTJ0/CxsYGUVFRmDp1qvJ6YU2h4NHzyms8MXToUHA4HDAMA7FYjIKCAhgbG8PR0RHPPfccbty4gcTERFRXV2PDhg2YPHmy1oUMdYwcORJnz55V9soICgrCnTt3Wn1OYmIi6uvrsWnTJgDA+++/j+eeew5r1qxho+SeQK90VBsQ4xTwbBNj27Ztw/79+6Grq4uPP/4YU6ZMYfNHUNv58+cRHx8Pa2trODk54ddff1VuCY6IiFD7xiGFQoGqqiqIxWJIpVKYm5uDy+XC2NiYVjx6WV3daMjlcmUQlEgkYBgGhoaGGDFihPL656SkJNy5cwfx8fF47bXX+sSK+8OHD+Hp6Yn79+/35YtIuovCRlfMmDEDy5cvx/nz5/tV2OjIrVu3IBAI8O2338LR0RF8Ph9hYWEaP3j18OFD5OfnI2DEJfW/iMKHkrTpJTx48AAVFRWQSqXKWdLhw4fD0dERBgYGuHTpEhISEiCTybBhwwYEBAT06SfD+Ph4mJubKw+IV1dXIyEhodXnCAQCfPbZZ8jMzATDMAgLC8OKFSvw6quvslR1t/XdvzDNGzDjVFcnxm7duoU5c+YgJycHpaWlCA4Oxt27d/vEizehUAgDA4NWF74IhUIIBAKcOHECpqam4PP5mD59+lO3T6nSY8GjDz9/sqHldimGYVBRUYGCggIMGTIEpqamyMvLw8qVK2FsbIympiZ8+OGHmDFjRp+aDMvNzcWSJUswZswYXLt2Dd7e3ti9e3efaibYAyhsqEskEiEgIAB5eXlITk5GSkoKjI2N4ePjg6SkpD5/f3NnGIZBXl4eBAIBTp06BRcXF0RFRSEkJASDBw/uscd59OgRRCIRGhsb4eDggOHDh6OpqQlisRgjjU6p/40GYPBoe7OUQqFAaWkpioqKlE9s27dvV/4+uVwuNm/ejHHjxmlFyPjqq6+wZcsW/Oc//0FOTo7Kw+eZmZmIi4uDXC5HTEwM1q1bB0C9XhlyuRxvv/02zp07Bw6Hg7CwMCQnJ/fmj9nT2P+L014Dbpxq1tnE2LZt2wAA69evB4BW/Tz6uvz8fKSlpSE9PR1cLheRkZEIDw9XNsXtTNvgMXz4cFhaWrYbPFq+QDY0NPz/Hg/Fmvix+pWWIUOhUEAsFqOwsBAmJiZwdHTEoEGDcP36dSQmJqKmpgYREREoKCjA//7v/2Lt2rV44403WKy+ay5dugR/f3+cP38efn5+iIuLg7GxMT744AO2S+tNFDbUUVtbi8DAQGzcuBF8Ph9isRjDhw8Hh8PBpk2bUFZWhgMHDrBdZq9hGAbXrl1Damoqvv/+e4wePRpRUVEIDg5+pgP4zQeVCwoKoKurC0dHxw77eHQpeAyA0NE2ZMjlcpSUlKCkpARcLhd2dnbQ1dXF2bNnkZiYCGNjYwwePBi3b9+Gv78/9u3bpxVh4z//+Q90dHQQGxuLHTt2tBs25HI5XF1dkZWVBR6PB19fXxw9enQgd/Jm/y9Oew2ocaqZOhNjy5cvh7+/P+bOnQsAWLRoEaZOnYqZM2eyXH3Pun37NtLS0nDy5EnY2tqCz+d3aUuwXC5XBo/a2loMHz4cXC4XhoaGKC8vR3FxMUxNTWFvb9/upBtdp9ta25BRVlaGoqIimJmZwcHBAQYGBrh48SISExMhl8uxYcMGTJw4UTk+MQyD+vr6Hp3g1LTy8nL4+/tDJBIBAH7++Wds374dp051YfK07+t/fTZ6WlNTE6KiohAdHQ0+nw8Ara7KXLx4MaZPn85WeazgcDjw9PSEp6cntm3bhsuXL0MgEOCDDz7A2LFjwefz8corr3R6M0TLGY2hQ4di1KhRnS4tGhgYwM7OTnmdbqfBo+pWvw0cbUOGTCZDUVERysvLYW1tDV9fX+jo6OCHH35AcnIynJyc8M9//hNjxowBh8OBQqHAzZs3tSJoAMDo0aM7/ZycnBy4uLgoD8nOnj0b6enpAzlsEKJUW1uLqKgo7Nq1C8bGxli6dCk2bdqknBhbtWrVgJoYGzVqFDZv3ozNmzfj5s2bEAgECAsLg6OjI6KiojBlypQOtwTr6j45sGxpaQm5XA6xWIybN2+irq4OxsbGGDlyJIYNG9a163QHmLY3SzX37youLoaFhQW8vLygr6+P8+fPIzExEYMHD1a54s7hcPpU0AAAKysr2NnZ4c6dOxg5ciSys7NpvGqBwgaepOhFixZh9OjReO+995TvLysrg7W1NQDg+PHjcHd3Z6tE1nE4HGW/hYSEBOTk5CA1NRV/+9vf4OXlBT6fj6CgoFZ9OlrOvA8fPhyenp7PtCKidvDoZ4GjbchoampCYWEhKioqYGtri3HjxgEAvv32W+zatQvu7u44ePAgXF1dW32djo4OPDw8eq3unlBSUgI7Ozvl2zweDxcuXGCxIkK0Q1cmxpr7dzQbCE1v3dzc8P7772Pr1q24ceMGBAIBEhMT4erqCj6fj9DQUJX9GR4/fozCwkJIJBLY2dnB0tISEokERUVFuHPnjnLFw8jIiILH/2svZBQXF6O0tBRWVlbw8fFRrrjv2LEDFhYWSExMxAsvvKA1E2A95ZNPPkF0dDQaGxvh7OyMgwcPsl2S1qBtVAB++eUXTJw4ER4eHsoDSR999BGOHj2K3NxccDgcODo6Yt++fcrwQZ5QKBT4/fffkZqaijNnzmDcuHEICgrCzz//DC8vLwQEBIDH42nkyjqVwaOPB462IaOxsREFBQWoqqoCj8eDjY0NFAoFvv76a+zduxe+vr5Yu3YtHB0d2Sm4HR1dXdvcdDMoKEjlNqpjx44hMzMTn3/+OQDgiy++wIULF7Bnzx7NFq69+teo3LMGxDgFPJkYmz9/PszMzLBr1y7l+1tOjO3cuRMXLlxAamoqbt68iTfeeEN5QHzy5MnIz8/vEwfEexLDMMjNzYVAIMD333+PMWPGICoqCpMnT8agQYNw//591NXVoa6uDg4ODrC0tHzqhbBcLseDBw8gFotRV1enVvBoqb8Fj7Yho6mpCcXFxSgvL4eNjQ14PB44HA4yMzORnJyMESNGYP369Rg9enS/CxlEibZRdeTll19Ge6GrJ3pqqLqqsLq6Gq+//jpEIhEcHR2RlpbWJw+f6+joYMKECZgwYQLu3r2LNWvWID4+HiNGjIBCoYCDg0OrGeqepHLFo4+ucLQNGfX19SgoKIBEIoGDgwNGjBgBmUyGI0eOYN++fQgMDER6erpWzlSePn26W18/EGdkCenM+fPn8cUXX8DDwwOenp4AVE+MAU9m+WfNmoUxY8ZAT08Pe/fuHXBBA3iyMv/iiy/ixRdfVG4JPnr0KNasWQN9fX2YmZnhyy+/bDdkNNPV1QWXywWXy1UGj/v37+Px48ewsLBQBg9V+suKR9uQ0djYiMLCQlRWVoLH4ylX3E+ePIndu3fDw8MDhw4dwvPPP89GuZ2Sy+Xw8fGBra0tvvtOzev4SZfRyoaGqbqqMCUlBWZmZsrrOyUSCf7xj3+wXW63bNq0Cf7+/pg6dSoYhsG5c+eQmpqKX3/9FS+99BKiolty4boAABoBSURBVKIwfvx4jV9n19DQgIqKCox0EGr0cXrKxRtPrl80MzODjo4OHj9+DJFIBKlUqpxla2howBdffIH9+/dj2rRpeO+995SddfuqjlY2ZDIZXF1dkZ2dDVtbW/j6+uLLL7+Em5sbC5VqBZoKVI3GqW7q6qQYwzCIi4tDRkYGDA0NkZKSAi8vL7Z/DLWVlZXhL3/5C5ycnBAaGoorV64gOzsbXl5eiIyMfGpLcEdkMplyxUPd4NFSXwkebUNGQ0MDCgoKUF1dDTs7O1hbW0OhUODYsWPYu3cv/Pz8sHbtWjg4OLBUsXqSk5Nx6dIlPHr0iMJG99FtVNqi+arC5cuXd9qYrL+QyWQ4c+YMBAIBLly4gICAAERFRWHcuHG9EjwKCgrgOerpLT1sq1MEgGEY1NTUoKKiAg8ePADDMNDR0YGzszO4XC7q6uqQkpKCw4cPg8/nIy4uDmZmZmyX3i3Hjx/HO++8g8rKSgwbNgyenp744YcfUFpaipiYGGRkZAAAMjIysGLFCsjlcixcuBAbN25kuXJWUdhQjcapburqpFhGRgY++eQTZGRk4MKFC4iLi+tTZ6oUCgWKi4thb2/f6n2//fYbUlNTcfbsWYwbNw6RkZEICAhQextwfwweZ848goGBAbhcLiwsLCCXyyESifDw4UPY29uDy+VCJpMhNTUVn376KSZNmoT4+HjY2NiwXXqniouLMX/+fGzcuBHJyckUNrqPwoY2aHlVob29PWpqagA82U9qamqqfLs/a2pqQnZ2NgQCAS5fvoxJkyaBz+fDx8enR/dxMgyD6upqiEQi6OnpKbtoa8OKR9vtUlKpFEKhEI2NjbCwsMDjx48xf/58DBkyBKWlpVi0aBFWrFihduOq3qBOrwxVs6XkmVDYUI3GqR7W2aRYbGwsgoKCMGfOHADAyJEjlZ/XH8jlcvzyyy8QCAT4+eefMX78ePD5fLz00ktqb0OTyWSorKyEWCxGQ0ODMni0dxNjy1UCHo8Ha2trDB16t6d/rC5puZLx559/oqSkBKWlpWAYBkZGRrC3t4eJiQkOHz6MAwcO9MkV95kzZ2L9+vWQSqXYsWMHhY3uozMbbGt7VWFLHA5nwByY0tfXR1hYGMLCwtDY2IisrCx8/vnneOeddxAcHIyoqCh4eno+8++jbXfSttfs2tnZoU5hx8pWq7Yh4+HDhxAKhVAoFHBycoKpqSkkEgm+/PJLMAyDCRMmgMPh4Pjx46ivr1c26NIG7u7u+OabbxAbG6vyc/T09JCUlNRqtjQkJISuAyREi4lEIly9ehV+fn4Qi8XKAGFlZQWxWAyg/dviSkpK+k3Y0NXVRWBgIAIDAyGXy/HTTz9BIBAgPj4eL7/8Mvh8fqdbgvX09GBtbQ1ra2tl8MjPz28VPDgcjnLLrL29PVxcXJTfk60zHm23S9XW1kIkEuHx48dwc3ODoaEh0tPTsWTJEkgkEvj5+eHrr7/W2jMZqnz33XewtLSEt7c3zp49y3Y5/R6FjV6g6qrC5htEysrK+tRsQE8xMDBAeHg4wsPDUV9fj8zMTOzduxe3bt1CSEgI+Hw+xo4dq1bwaNk4yNTUFB4eHh3e0z1o0KBeCx5tQ4ZEIoFQKISOjg6cnJxgYmKCyspKbNmyBZmZmViyZAkuXbqkvJ6RYRg8ePBAI7U9K3V6ZTQPtAAwdOhQjB49GiUlJRQ2CNFSNCn2NF1dXbzyyit45ZVXlFuCU1NT8d577yEwMBBRUVHKXkeqtAweTU1NKCoqwuXLlyGTyWBpaQl3d/cOe0/1RvBoGzKaV9ybmpqUk2FSqRSffvop0tLS8NZbbyE8PBxZWVlYuHAhduzYAT8/P43Upgnnz5/HyZMnkZGRgfr6ejx69Ahz587FkSNH2C6tX6JtVBqm6qrC+Ph4mJubK/fCVldXIyEhgcVKtcfjx4+RkZEBgUCA/Px8TJkyBXw+H25ubu1eR9jcy8PCwgL29vadNhnsSE8Gj5Yho+W2Ln19fTg5OWHo0KEoLy/H7t278dNPP2H58uWYN29et+rvbR0d8m6p5RbCti9iiFoG3qs89dE41QOampowffp0TJkyRdlvquX2qIG0jUodTU1NOH36NAQCAa5cuYJJkyYhKioK3t7eKkNZTU0NhEIhGIaBk5MTjIyMlFutmpqalCseHTUgbKkngkfbkPHw4UPcv38fAODk5IRhw4ZBIpHgX//6F9LT07FgwQLExsZ22pi3L2nuAULbqLqNzmywRVUPDz8/P8yaNQuFhYVwcHBAWlpanz/4qwl//vknvvvuO6SlpUEoFGLq1KmIioqCubk5MjMz4eLiAmtra4308njW4NE2ZDx48AAikQiDBw+Gk5MThgwZgqKiIuzcuRMXL17EihUrMGfOHI30IumO7vbKaFZbW4vAwEBs3LhRubJHuozChmo0TnVTVyfFTp06hT179igPiL/77rvIyclh8SdgV0NDA7KyspCamoobN24gODgYfD4fnp6eYBgG+fn5kEqlMDAwgKOjY7sTLk1NTaisrER5eblyxYPL5ardSburwaNtyGhecdfV1YWTkxOMjY1RWVmJPXv24IcffkBsbCwWLVqksiFiX0Zho8dQ2OivFi5cqNx7mJeXBwDYsmULPvvsM1hYWAB4Em56omcI22pra3Ho0CHs2rULUqkUkydPxurVqzFy5EiNP7Y6waNtyGg+O2JkZAQnJycMHjwY9+7dw86dO3Hz5k2sWrUKUVFRffre+87CRnuzpeSZUNhQjcapburqpBjDMFi+fDkyMzNhaGiIgwcPdrq6OVA0bwk+evQofv/9d+jp6SEoKAjbt29XezWgqakJFRUVEIvFPR48WoaM5hV3oVCIQYMGKVdbysvLsWvXLpw7dw7vvPMO3nzzzT614k5YQ2Gjvzp37hyMjIwwb968VmHDyMgIq1evZrm6nnXx4kUsW7YM7733HkJDQ3Hq1CkIBAJUVFRg+vTp4PP5cHZ21ngdLYPHTxcGg8vlwtLSEs899xwUCgXEYjEKCgpgamoKBwcHDBo0CHfu3EFSUhJEIhHWrl2L6dOna/za397QUdhQNVtKngmFDdVonNJiXZ0Q27ZtG/bv3w9dXV18/PHHmDJlCmu1P6vz589j+fLlCAgIwJgxY5CdnY38/HyEhYUptwSrq7GxUbnVqjvBo23IqKysREFBAQwNDeHo6KhccW/uO7Fy5UrMnj1b61bciVajsNGfiUQiTJ8+vd+HDYVC0e4hxZqaGhw/fhxpaWmQSCR49dVXwefze6WZUHPwEIvFqK+vh0KhgIWFBZydnWFgYIC8vDwkJCSgqqoK69evR0hISL8IGer0ylA1W9ofVtlYQGFDNRqntFhXJsRu3bqFOXPmICcnB6WlpQgODsbdu3f73OpvRUUFdHV1YW5urnxf85ZggUCAgoICTJ06FXw+H6NGjVL7+zY2NirHG4VCoQwe6m5tYhhGORlmbGwMR0dH5Yp7cnIybt26hdWrV4PP52vl75yuU9d6FDb6s/bCRkpKCoyNjeHj44OkpCSYmpqyXGXvqKqqUgYPqVSK1157DZGRkeDxeBp5vJYH1M3MzDBo0CAcPnwY3333HZ577jkYGhri/fffR2BgoFbd5KJOn4xmcrkcPj4+sLW1pT2t7NGefzzah8YpLafuhFjz9d7r168HAEyZMgVbtmzB+PHje7dgDZNKpTh58iTS0tJQUlKC8PBwREVFwcXFRe3v0ZXgoVAoUF5ejsLCwlYr7rdv30ZSUhIKCgr6xIq7quaTdMOh1lA5TmnvvyryzJYuXYp79+4hNzcX1tbWWLVqFdsl9Rpzc3PExMTgxx9/xMmTJzFs2DC8/fbbCA0Nxd69e1FWVtYjjyOTySASiZCTkwO5XA5fX1+4urqitLQUV69ehZOTE4KCgqCvr4/Nmzfj3r17PfK4PaW5T0ZAQECnn7t79261rrolhBB17dmzB2PHjsXChQshkUgAqO7f0d8MHToU0dHRSE9PR3Z2NkaMGIENGzZg0qRJSEpKglDY+aUkBgYG4PF48Pb2xgsvvAAdHR3cvHkTFy9eRGFhoXKlvbi4GDk5Oairq4OXlxdcXV2Rn5+PefPmYdWqVXjrrbfw888/IyIiQquDBvDkOnUvLy8Ara9TJ9qPNuP1Q1wuV/nfixcvxvTp01mshj0WFhaIjY1FbGwsxGIxjh07hpiYGMjlckRGRmLGjBmwsrLq0vdsviNdLBbDxsYG48aNA4fDwblz57Bjxw6Ymprio48+gpeXl3Ilo7y8HMOGDdPEj/jM1A0PxcXFOHXqFDZu3Ijk5GQNV0UIGQiWLl2KTZs2gcPhYNOmTVi1ahUOHDjAdlmsMDExwbx58zBv3jxIJBKcOHECq1evRk1NjXJLsL29fYffw8DAAHZ2drCze9I3qry8HJcuXUJDQwOGDRum3Np7+fJlJCQkoLGxERs2bNC6FfeuaNl8kmg/Chv9UHOzQODJ3np3d3eWK2Ifl8vFsmXLsGzZMpSWluLYsWN46623oKuri8jISERERHTYWLGxsRGFhYWorKwEj8dThoysrCwkJSXB3t4eu3fvhru7+1NP3l0NNNpkxYoVSEhIgFQqZbsUQkg/oWpCzNbWFkVFRcqPFRcXw9bWttfrY4upqSkWLFiABQsWoKqqCt988w3effdd1NbWYsaMGZ1uCZbJZMqtRjweDxYWFsjPz0d0dDQePnwIY2NjbNu2DeHh4X02ZAAdN58k2km718xIp+bMmYPx48fjzp074PF42L9/P9asWQMPDw+MHTsWZ86cwc6dO9kuU6vY2Njg3Xffxblz5/DFF19AJpMhOjoaEREROHjwIKqqqpSfW1dXh7t37+LKlSswNDSEn58fbGxscOrUKQQHByM9PR379+/Hl19+CQ8PD616Ag8ODoa7u/tTf9LT09X6+uYbZLy9vXusJkdHR/z973/HpEmTYGRkBA8PD1y/fh1Hjx6Fi4sLTExMEBMTA5lM1mOPSQjRLi23s7acEIuIiEBqaioaGhogFAqRn5+PcePGsVUmq8zNzbF48WL8+OOPSE9Ph4mJCZYuXYrQ0FD885//bPU7rK+vx71793Dx4kXo6Ohg3LhxsLe3R05ODjZu3AhXV1fs27cPixcvxo4dO5CYmMjiT9Y9TU1NiIqKQnR0tMb6NtE4pQEMw3T0hwxwCxYsYCwsLBg3Nzfl+6qqqpjg4GDGxcWFCQ4OZqqrq1mssOeIRCImISGB8ff3ZyZOnMgEBwczwcHBzL1795ja2lrm4cOHTEpKCuPj48PExsYy9+/fZ7vkbgsMDGQuXrzY7sfWrVvH2NraMg4ODgyXy2UGDx7MREdHd+vxHBwcGBcXF+bWrVtMY2MjEx0dzTg7OzOLFy9mamtrmYKCAsbCwoI5cuRItx6nH+rsuXog/yFabPbs2YyVlRWjp6fH2NraMp9//jkzd+5cxt3dnfHw8GBeffVVprS0VPn5f//73xlnZ2fG1dWVycjIYLFy7VRWVsbs2bOHCQwMZPz9/Zlp06Yx3t7ezO3btxmpVMpIpVLm66+/Zl566SXmjTfeYG7cuMEoFIpW30Mmk7FUffcoFArmzTffZOLi4jT6ODROPTOVz9N0GxXpUHvXFq5ZswZmZmbKrrISiQT/+Mc/WK60Z5SWlmLz5s24cuUKXnjhBdy8eRPDhw+HlZUVrl69ioCAAMTHx2vsdqvepk4HcKDnOqw6Ojpi2bJliI+PBwBkZGQgPDwcFRUVyjv3Z82aBVtbW1qRa017lsy0D41TA1h7fTyqq6vx+uuvQyQSwdHREWlpaTA1NQXDMIiLi0NGRgYMDQ2RkpKiPHDcl0gkErz//vvIzs7Giy++CKFQCD09PYwYMQK5ublwd3fH+vXr4erqynapPaq3rlOnceqZ0W1U5NkEBATAzMys1fvS09Mxf/58AMD8+fNx4sQJNkrTmJkzZ+Ly5cs4ePAgcnJysHv3bshkMmRmZmL37t39ImgcP34cPB4Pv/32G8LDw5WNs0pLSzXeB6P5PBEAGBoaQldXV/kE3vw+OiNCCFHHW2+9hczMzFbv2759OyZPnoz8/HxMnjwZ27dvBwB8//33yM/PR35+Pv79739j6dKlbJTcbfr6+pgwYQKuXr2KQ4cO4dy5czh06BAaGxtx9OhRHDx4sN8FDQB4+eWXwTAMrl+/jtzcXOTm5mpsvKJxqmdR2NBijo6OOHLkCNtlPEUsFiv/R7SysoJYLGa5op5jY2ODsLCwVmcvnn/+eRw+fLjVoUa2ffXVV3Bzc4OOjg4uXbqk8vNqamowc+ZMjBo1CqNHj8Zvv/0GAIiMjERxcTEaGhogFovxww8/AHjy82dkZDz1fYKCgqjHBiHkKWyPU12ZEEtPT8e8efPA4XDg7++PmpqaHrsOvTcZGRnhL3/5S6vGe3Z2djh06FCXenUQ0lsobJBuaa+jN9E8dftkxMXFISwsDLdv38a1a9eoXwYhpN9TNSE2UPp4aJPMzEyMHDkSLi4uyhUmMvDQ1beky7hcrvJ63bKysg6vjCWaoU5oePjwIc6dO4eUlBQAT+5iNzAw0HBlhBCiPWhCjD1yuRzLli1DVlYWeDwefH19ERERQR2/ByAKG31EXV0d5syZA5lMBoFAACMjI9ZqiYiIwKFDh7Bu3TocOnQIM2bMYK0WoppQKISFhQUWLFiAa9euwdvbG7t378aQIUNYq0kkErV6Oygo6KnrA5vDESGkb9GWcUrVhNhA7+PR23JycuDi4gJnZ2cAwOzZs5Genq71YYPGqZ5H26j6gPLycgQGBsLGxgYnT57s1Sfw9vp4rFu3DllZWXj++edx+vRprFu3rtfqGUi62ydDJpPhypUrWLp0Ka5evYohQ4bQMjYhRCPYHKfaap4QA9BqQiwiIgKHDx8GwzD4/fffYWJi0uogMOlZtG2NNKOVDS138+ZNbNq0CUuXLsWaNWt6/fGPHj3a7vuzs7N7uZKB5/Tp0936eh6PBx6PBz8/PwBPbtmisEEI6WlsjlNz5szB2bNn8eDBA/B4PGzduhXr1q3DrFmzsH//fjg4OCAtLQ0AMG3aNGRkZMDFxQWGhoY4ePBgr9ZKyEBFYUPLHTx4EMOHD8fbb7/Ndim9wtHREUOHDoWuri709PQ6vGmJdMzKygp2dna4c+cORo4ciezsbK1fviaE9D1sjlNdmRDjcDjYu3fvMz9We+OTqp4ehLatkf+ibVRabvv27fDw8EBISAgkEgnb5fSKM2fOIDc3l4JGB9Ttk/HJJ58gOjoaY8eORW5uLjZs2MBWyYSQfmogjVNtxydVPT0I4Ovri/z8fAiFQjQ2NiI1NRURERFsl0VYQGFDy+np6eF//ud/4O7ujqCgIFRUVLBdEukmdXtk7Ny5E25ubnB3d8ecOXNQX1+v/Ji6fTI8PT1x6dIlXL9+HSdOnKAZN0JIjxvI41R/b3LbHXp6etizZw+mTJmC0aNHY9asWXBzc2O7LMICCht9gI6ODj777DNMnjwZEydORGFhIdslaQyHw0FoaCi8vb3x73//m+1yNEKdHhklJSX4+OOPcenSJeTl5UEulyM1NbUXqySEEPUNhHGqvfGpPze57QnTpk3D3bt3ce/ePWzcuJHtcghL6MyGFmt7/VpycjKSk5PZKaaX/PLLL7C1tUVFRQVCQkIwatSoThvX9TXqNtaTyWR4/Pgx9PX1UVdXBxsbGw1XRgghXTOQxqn2xqeWqKcHIe2jlQ2iVZoPj1laWiIyMhI5OTksV8QOW1tbrF69Gvb29rC2toaJiQlCQ0PZLosQQgas9san5p4eAPpNk9v4+HiMGjUKY8eORWRkJGpqatguifRxFDaI1vjzzz8hlUqV//3jjz/C3d2d5aqeTXd7ZEgkEqSnp0MoFKK0tBR//vknjhw5ouGqCSGEtEfV+KSqp0dfFhISgry8PFy/fh2urq7Ytm0b2yWRPo62URGtIRaLERkZCeDJFqI33ngDYWFhLFf1bLrbI+P06dNwcnKChYUFAIDP5+PXX3/F3Llze6I8QgghXaBqfPL19W23p0df1nIV3d/fH8eOHWOxGtIfUNggWsPZ2RnXrl3T+ONkZmYiLi4OcrkcMTExWtkB3d7eHr///jvq6uowePBgZGdnw8fHh+2yCCFkQFI1Ppmbm2ukya22jFMHDhzA66+/zspjk/6DtlGRAUUul2PZsmX4/vvvcevWLRw9ehS3bt3q1RrU6ZHh5+eHmTNnwsvLCx4eHlAoFFiyZEmv1kkIIaT39cY4pc5W3w8//BB6enqIjo7u0ccmAw+HYZiOPt7hBwnpa3777Tds2bJF2ZeieS/q+vXr2SyLkM7QFTeq0ThF+hVtGKdSUlKwb98+ZGdnw9DQsNcel/RpKscpWtkgA0pJSQns7OyUb/N4PJSUlLBYESGEEPJfbI9TmZmZSEhIwMmTJylokB5BYYMQQgghhAAAli9fDqlUipCQEHh6euKvf/0r2yWRPo4OiJMBxdbWFkVFRcq3i4uLlXenE0IIIWxje5z6448/eu2xyMBAKxtkQPH19UV+fj6EQiEaGxuRmpqKiIgItssihBBCANA4RfofWtkgA4qenh727NmDKVOmQC6XY+HChXBzc2O7LEIIIQQAjVOk/6HbqAghRPvRbVSq0ThFCCHso9uoCCGEEEIIIb2LwgYhhBBCCCFEIyhsEEIIIYQQQjSCwgYhhBBCCCFEIyhsEEIIIYQQQjSCwgYhhBBCCCFEIyhsEEIIIYQQQjSCwgYhhBBCCCFEIzrrIE6NpAghhGgzGqcIIUSL0coGIYQQQgghRCMobBBCCCGEEEI0gsIGIYQQQgghRCMobBBCCCGEEEI0gsIGIYQQQgghRCMobBBCCCGEEEI04v8AwE4CqZtdvQ4AAAAASUVORK5CYII=\n", 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\n", + "image/png": 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\n", 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" ] @@ -1184,7 +1182,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "code_folding": [ 0 @@ -1201,18 +1199,6 @@ "14 basis functions used.\n", "5 basis functions used.\n" ] - }, - { - "data": { - "image/png": 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\n", 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lZjebWYWZbTCzk8JzJwPPNSc0s77AZ4Hvu/sOd59H0IB9AcDd64FFwCc/0DsQkXyVcfsF4O6PuPv/AVvbFqT2SxSMSTYdAkwF5hL0dv0G+Pfw3GRgRVra/YGku7+ZduxVIH2exTJgSo/VVkSkVVfar0yo/cpjCsYkmyYD17n7k+6eAt5IOzcIqEl73w+obpO/Guif9r4mzCci0tO60n5lQu1XHlMwJllhZgYcDPwl7fDBtDZo29g10NoBDGhTzAB2bfD6A1XdW1MRkV3tQfuVCbVfeUzBmGTLuPDv8rRjhwGLw9dLCIYmm70JFJjZhLRjU4Clae8PIhi6FBHpSV1tvzKh9iuPKRiTbDkEeM3dPe3YYbQ2RnOBjzefcPda4BHgWjPra2ZHAzOBewHMrBg4AvjbB1B3EclvXWq/AMyswMxKgDgQN7MSMysIz6n9ynMKxiRbJpP2LdDMBgPDgNfDQ/cAp5hZn7Q8XwX6AJuB+4GvuHtzz9gZwLPuvqGnKy4ieW9P2q/vAXXAlcDnw9ffC8+p/cpztmtgL9J7mNlPgM3uflMGaecDF7n7652lFRHpaWq/pCsUjImIiIhkkYYpRURERLJIwZiIiIhIFikYExEREcmigmxXoCuGDBniY8eOzXY1ROQDsmjRoi3uXpbtenQHtV8i+SfTNiyngrGxY8eycOHCbFdDRD4gZrY223XoLmq/RPJPpm2YhilFREREskjBmIiIiEgWKRgTERERySIFYyIiIiJZpGBMREREJIsUjImIiIhkkYIxERERkSxSMCYiIiKSRZEMxl5dV8Vzb1ZkuxoiIl22pW4LT619iuqG6mxXRUQ+IJEMxu76x2p+8OfXs10NEZEuW165nG8++03WbF+T7aqIyAckksGYiIiISK5QMCYiIiKSRQrGRERERLJIwZiIRJqZzTCzFWZWbmZXtnO+2MweCM/PN7OxaeeuCo+vMLPp4bEDzGxx2p/tZvaND+6ORCRqMgrGursxC49/08yWmtnrZna/mZV0xw2JiDQzszhwK3AyMBE4x8wmtkl2EbDN3ccDNwLXh3knArOAScAM4DYzi7v7Cnc/1N0PBY4AdgJ/+kBuSEQiqdNgrCcaMzMbAXwNmOruBwPxMJ2ISHeaBpS7+yp3bwRmAzPbpJkJ3B2+fhg4wcwsPD7b3RvcfTVQHpaX7gTgLXdf22N3ICKRl0nPWE81ZgVAHzMrAEqBDe/vVkRE3mMEsC7t/frwWLtp3D0BVAODM8w7C7i/o4ub2SVmttDMFlZUaO1DEWlfJsFYtzdm7v4O8HPgbWAjUO3uf23v4mrMROR9sHaOeYZpdpvXzIqAM4CHOrq4u9/u7lPdfWpZWVkG1RWRfJRJMNbtjZmZ7UXQazYOGA70NbPPt3dxNWYi8j6sB0alvR/Je3vhW9KEPfUDgcoM8p4MvOzu73ZznQFwb9vMikhUZRKM9URjdiKw2t0r3L0JeAT46J7cQEfUjIkI8BIwwczGhT1Zs4A5bdLMAS4IX58JPONBJDQHmBU+oDQOmAAsSMt3DrsZotxT1u53WBGJskyCsZ5ozN4GPmJmpeHcshOAZe//dgJqykQEWqZNXA48SdDGPOjuS83sWjM7I0x2JzDYzMqBbwFXhnmXAg8CbwBPAJe5exLAzEqBkwi+SIqIvC8FnSVw94SZNTdmceCu5sYMWOjucwgas3vDxqyS8MnIMF1zY5agtTGbb2YPAy+Hx18Bbu/+2xORfOfuc4G5bY5dk/a6HvhcB3mvA65r5/hOgnmxIiLvW6fBGPRYY/YD4AddqayIiIhI1GgFfhEREZEsUjAmIiIikkUKxkRERESySMGYiIiISBYpGBMRERHJIgVjIiIiIlkU2WBMO4mISC7SCvwi+SeSwViwqL+ISG4qajI2L16a7WqIyAckksGYiEguO3ZxGa/deT/Vm3tkD3IR6WUUjImI9DL9dgaboyQaG7NcExH5ICgYExHpZdyCSa/uqSzXREQ+CArGRER6KdeTSCJ5QcGYiEgv4+EzSJ5Sz5hIPlAwJiLSS6lnTCQ/KBgTEellmueMacFEkfygYExEpJdpDsHUMyaSHyIZjL32TjWba+qzXQ0Rka5LJoi1dIxpzphIPohkMFaZStKQ7UqIiOyBgpeXMW5TuLRFSj1jIvkgksHYzhik4toSSURyU3PrpWFKkfwQyWBMRCSXGZrAL5JPFIyJiPRSmjMmkh8UjIlIpJnZDDNbYWblZnZlO+eLzeyB8Px8Mxubdu6q8PgKM5uednyQmT1sZsvNbJmZHdWtlfY2f4tIpCkYE5HIMrM4cCtwMjAROMfMJrZJdhGwzd3HAzcC14d5JwKzgEnADOC2sDyAm4En3P1AYAqwrBsr3TJnLKUV+EXygoIxEYmyaUC5u69y90ZgNjCzTZqZwN3h64eBE8zMwuOz3b3B3VcD5cA0MxsAHAvcCeDuje5e1Z2VNi1tIZJXFIyJSJSNANalvV8fHms3jbsngGpg8G7y7gdUAL8zs1fM7Ldm1re9i5vZJWa20MwWVlRUdKHavstfIhJtCsZEJMraW+OmbYjTUZqOjhcAhwO/cvfDgFrgPXPRANz9dnef6u5Ty8rKMq91S371jInkAwVjIhJl64FRae9HAhs6SmNmBcBAoHI3edcD6919fnj8YYLgrNu0RIFa2kIkLygYE5EoewmYYGbjzKyIYEL+nDZp5gAXhK/PBJ7xYLXVOcCs8GnLccAEYIG7bwLWmdkBYZ4TgDe6q8KGtY5SKhgTyQsF2a6AiEhPcfeEmV0OPAnEgbvcfamZXQssdPc5BBPx7zWzcoIesVlh3qVm9iBBoJUALnP3ZFj0FcAfwwBvFfDF7qx386KvCsZE8oOCMRGJNHefC8xtc+yatNf1wOc6yHsdcF07xxcDU7u3pq30NKVIftEwpYhIL6WNwkXyg4IxEZHeJG3RV9faFiJ5QcGYiEivEwZh6hkTyQsKxkREepmmgn6AtkMSyRcKxkREepmqQfsD4KlkJylFJAoUjImI9CZp6/5raQuR/KBgTESk1wkiMtcwpUheiGYwZmgbERHJTdbaNaY5YyL5IZrBWLv7+4qI5IgwICt4V0OVIvkgosGYiEgui7H/gKn0e6WYuiUV2a6MiPQwBWMiIr2KYRh9CwYCkNzRlOX6iEhPUzAmItILmQXNs8U07UIk6hSMiYj0OkasuXlWMCYSeQrGRER6HVPPmEgeUTAmItKbWPB/LduFKxgTibyMgjEzm2FmK8ys3MyubOd8sZk9EJ6fb2Zj085dFR5fYWbT044PMrOHzWy5mS0zs6O644ZERHKetfaMKRgTib5OgzEziwO3AicDE4FzzGxim2QXAdvcfTxwI3B9mHciMAuYBMwAbgvLA7gZeMLdDwSmAMve/+2IiERB65wxUywmEnmZ9IxNA8rdfZW7NwKzgZlt0swE7g5fPwycYGYWHp/t7g3uvhooB6aZ2QDgWOBOAHdvdPeq9387IiK5LRieVM+YSD7JJBgbAaxLe78+PNZuGndPANXA4N3k3Q+oAH5nZq+Y2W/NrG97FzezS8xsoZktrKjIbPFDNV0ikrMadrDLnDEtwC8SeZkEY+3FNm2bh47SdHS8ADgc+JW7HwbUAu+Ziwbg7re7+1R3n1pWVpZBdQG0n5uI5KgdG4HWdca0z65I9GUSjK0HRqW9Hwls6CiNmRUAA4HK3eRdD6x39/nh8YcJgrNuURTbTswUkIlI7mruGfOUgjGRqMskGHsJmGBm48ysiGBC/pw2aeYAF4SvzwSe8WB32znArPBpy3HABGCBu28C1pnZAWGeE4A33ue9iIhEhBNr7hnT90qRyOs0GAvngF0OPEnwxOOD7r7UzK41szPCZHcCg82sHPgW4ZCjuy8FHiQItJ4ALnP3ZJjnCuCPZrYEOBT4SffdlohIoIeW5lljZq+Z2WIzW9gj9UbDlCL5oiCTRO4+F5jb5tg1aa/rgc91kPc64Lp2ji8GpnalsiIiXZG2NM9JBNMjXjKzOe6e3hPfsjSPmc0iWJrn7DZL8wwHnjKz/dO+UB7n7lt6oNaAt8wZ0zClSPRFcgX+s+b/lf+Y9/tsV0NEsq/bl+b5gOrdujelesZEIi+SwdjQ7ZWMrd6U7WqISPb1xNI8EDwV/lczW2Rml3R08T1Zmqclb/PD6JozJhJ5kQzGRERCPbE0D8DR7n44wc4kl5nZse1dfM+W5gkuY6anKUXyRSSDMY9XYLGabFdDRLKvJ5bmwd2b/94M/IluHL60lr+be8YUjIlEXSSDMaweYols10JEsq/bl+Yxs75m1h8g3Dnkk8Dr3Vbjtv1xmjMmEnkZPU2Zi0ztl0jec/eEmTUvzRMH7mpemgdY6O5zCJbmuTdcmqeSIGAjTNe8NE+CcGkeMxsK/CkcRiwA7nP3J7q54i3joa45YyKRF9lgTEQEun9pHndfBUzp/poGtiSSBIMWYTimYUqRyIvmMKWISI5qSLTpCtMwpUjkKRgTEelF3CD9YU49TSkSfQrGRER6oZanKRWLiUReJIMxb291IBGRnOEtwZh6xkSiL5LBmIhIzgq/TDYv+qoJ/CLRp2BMRKRXsV3/VjAmEnkKxkREepFgeNI1Z0wkj0Q2GNOiryKSy7Q3pUj+iGQw5u3u7ysikjtae8YUjIlEXSSDMRGRXGfNzbO2QxKJPAVjIiK9iYHjoGFKkbyhYExEpBcyPU0pkjciG4xp1piI5KZdn6Z0zRkTibxoBmOKxEQkx2nRV5H8Ec1gDNDiPCKSk9psFK6mTCT6ohmMqfESkRynvSlF8kc0gzERkRynCfwi+SO6wZjaLxHJVe6tc8bUlolEXiSDMdcEfhHJcRqmFMkfkQzGRERyVnOPmIYpRfKGgjERkV7IrHk7JAVjIlGnYExEpNfRoq8i+SSywZimjYkIgJnNMLMVZlZuZle2c77YzB4Iz883s7Fp564Kj68ws+lt8sXN7BUze7Q761u0dUdQfsswZXeWLiK9USSDMX2PFBEIAibgVuBkYCJwjplNbJPsImCbu48HbgSuD/NOBGYBk4AZwG1hec2+Dizr7joXVtUCrU9TekrRmEjURTIYExEJTQPK3X2VuzcCs4GZbdLMBO4OXz8MnGBBJDQTmO3uDe6+GigPy8PMRgKnAr/t9hq3GZbU05Qi0adgTESibASwLu39+vBYu2ncPQFUA4M7yXsT8F06GUQ0s0vMbKGZLayoqMi40rtMs1AwJhJ5CsZEJMramz7aNrrpKE27x83sNGCzuy/q7OLufru7T3X3qWVlZZ3XNryopV1aw5Qi0RfRYMwwfZkUkaA3a1Ta+5HAho7SmFkBMBCo3E3eo4EzzGwNwbDn8Wb2h+6rsrUuNQZ4Uo2ZSNRFMxjTo5QiEngJmGBm48ysiGBC/pw2aeYAF4SvzwSe8WA9iTnArPBpy3HABGCBu1/l7iPdfWxY3jPu/vnurHR6zxgKxkQiryDbFRAR6SnunjCzy4EngThwl7svNbNrgYXuPge4E7jXzMoJesRmhXmXmtmDwBtAArjM3ZMf+D1ozphI5CkYE5FIc/e5wNw2x65Je10PfK6DvNcB1+2m7GeBZ7ujnmmFtukZ05wxkaiL5jClVhoTkRy2y5wxxWIikRfRYExEJEeZ7dozpmFKkciLZDDmmsEvIrnKW1uwZCqhYEwkD0QyGBMRyVWJ5A6aHwlPklQwJpIHFIyJiPQinkph4bzXlCc0Z0wkDygYExHpVVrnjKU82cmGSyISBZENxjRrTERyVcucMU9iaK0xkajLKBgzsxlmtsLMys3synbOF5vZA+H5+WY2Nu3cVeHxFWY2vU2+uJm9YmaPvt8bERGJDEvrGQOtwi8ScZ0GY2YWB24FTgYmAueY2cQ2yS4Ctrn7eOBG4Pow70SC1awnATOA28Lymn0dWPZ+b0JEJCrM2XWYEvWMiURdJj1j04Byd1/l7o0EG+PObJNmJnB3+Pph4AQzs/D4bHdvcPfVQHlYHmY2EjgV+O37vw0RkWio6T8GWibwN/eMaeKYSJRlEoyNANalvV8fHms3jbsngGpgcCd5bwK+SyfTU83sEjNbaGYLKyoqMqiuiEju2j5iWEvPWBL1jInkg0yCsfbmwrclNgEJAAAgAElEQVRtGTpK0+5xMzsN2Ozuizq7uLvf7u5T3X1qWVlZ57VtrpDaLhHJSa3r72vOmEh+yCQYWw+MSns/EtjQURozKwAGApW7yXs0cIaZrSEY9jzezP6wB/Vvl+tRShHJVUbL19hUuMiYesZEoi2TYOwlYIKZjTOzIoIJ+XPapJkDXBC+PhN4xt09PD4rfNpyHDABWODuV7n7SHcfG5b3jLt/vhvuJ6RoTERyV+sE/nAWh3rGRCKtoLME7p4ws8uBJ4E4cJe7LzWza4GF7j4HuBO418zKCXrEZoV5l5rZg8AbQAK4zL25311ERNpys7R1xoIgTD1jItHWaTAG4O5zgbltjl2T9roe+FwHea8DrttN2c8Cz2ZSj65R4yUiucfSxilTzc83KRgTibRIrsCvZktEclVj0lvnjIXHXMOUIpEWyWBMRCRX7az3tKcpw2FKrTMmEmkKxkREehHfZaPwsEdMw5QikaZgTESkNzFa5lo0L9OjYUqRaItsMKZFX0UkV1lzEGYtC45lrzIi0uOiGYxpmTERyVXW2oA1B2PqGROJtmgGYyIiITObYWYrzKzczK5s53yxmT0Qnp9vZmPTzl0VHl9hZtPDYyVmtsDMXjWzpWb2o26vc/M3yph6xkTygYIxEYksM4sDtwInAxOBc8xsYptkFwHb3H08cCNwfZh3IsEC1pOAGcBtYXkNwPHuPgU4FJhhZh/pvkq3BmMeU8+YSD5QMCYiUTYNKHf3Ve7eSLAX7sw2aWYCd4evHwZOMDMLj8929wZ3Xw2UA9M8sCNMXxj+6cZoKW2eRTxsolNa2kIkyiIbjGnamIgAI4B1ae/Xh8faTePuCaAaGLy7vGYWN7PFwGbgb+4+v72Lm9klZrbQzBZWVFRkWGXDwrliFgZjqYSCMZEoi2Qw1scbFIyJCLT/vaxtL1ZHaTrM6+5Jdz8UGAlMM7OD27u4u9/u7lPdfWpZWVlGFU4mk60XjgVNdLKxKaO8IpKbIhmM9aUe06ZIIhL0Zo1Kez8S2NBRGjMrAAYClZnkdfcqgr11Z3RXhZPJZOvrnbXB3wrGRCItksGYiEjoJWCCmY0zsyKCCflz2qSZA1wQvj4TeMbdPTw+K3zachwwAVhgZmVmNgjAzPoAJwLLu7PSLSvw1wbBWErBmEikFWS7AiIiPcXdE2Z2OfAkEAfucvelZnYtsNDd5wB3AveaWTlBj9isMO9SM3sQeANIAJe5e9LM9gXuDp+sjAEPuvuj3VnvlqXGwl6yZFOiO4sXkV4musGYRilFBHD3ucDcNseuSXtdD3yug7zXAde1ObYEOKz7a9p8AWieruZhMJZqVDAmEmWRHKZ0YOPAAbzx/DPZroqISJe1TOBPBkFYSj1jIpEWyWCsrs8JrNqnjMdvvSHbVRER6RJL2w6JRDBXLNmU7CC1iERBJIOxZHx4tqsgIrKHrHU7JA/mW3hCPWMiURbROWOaMCYiuau5cyyWcpKeJKmHKUUiLaLBmIhILgtX4MdxT5FSx5hIpEUyGHNXz5iI5CajdQJ/HEiRItWkNk0kyiIZjCULyuhXMIgU2s9NRHJP85yxeApSnsCb4lmukYj0pGgGY/F9OH3Ul7NdDRGRPRQEYzFzkp7ANWdMJNIi+TSle2O2qyAisseahynNnWQqgSc0TCkSZZEMxlAwJiI5rHmtsbinSHoC0zJjIpEWzWCM1kePNJlfRHJN6wr8TSQ9CeoZE4m0SAZj7q1fIxu37cxiTURE9kS4tEWyiaSnQD1jIpEWyWAsfbZr/arKLFZERKSrDGteuDoR9IyZHgwXibRoBmNpw5RNleoZE5EcknJaesYSjSTdsZTtPo+I5LRoBmOeIJEKescSOzWZX0Ryh6e8dbPwRANJHHMFYyJRFtFgrLVPP7lTC/SISG5pXvQ1lkqQdCcW0aZaRALR/BduKWIWrFidqlMwJiK5I9gOKewJSyVIAjG0Ar9IlEUzGANiFtya1+sxJBHJJUbzKKUlEzSQQAtbiERbJLdDSu/S9wYFYyKSW5p7xrYM7sPCIeuoT8QYm0gRK4js92eRvBbJf9np67x6o54JF5Hc0hyMvT2qDIB3YpUk6hqyWSUR6UGRDMZ2ee6oUR38IpI73K0lGEvGg7/rrJHGHVqmRySqIhmMYWm3leg4mYhIb2MYZoa7k4oFbVmtNVBbVZ3lmolIT4loMNbaG6aVq0Uk1xgxHCcZb22iKza9m8UaiUhPimQwlj5MqWBMJL+Z2QwzW2Fm5WZ2ZTvni83sgfD8fDMbm3buqvD4CjObHh4bZWZ/N7NlZrbUzL7e/XWmJRgrThQDsLVSW7uJRFUkgzHCB8ETqUYsFcNd88ZE8pGZxYFbgZOBicA5ZjaxTbKLgG3uPh64Ebg+zDsRmAVMAmYAt4XlJYBvu/tBwEeAy9op8/3Vm3CYMh6juKkEHCqrtnXnJUSkF4lkMNY8+TXhieB1UsGYSJ6aBpS7+yp3bwRmAzPbpJkJ3B2+fhg4wYL9iGYCs929wd1XA+XANHff6O4vA7h7DbAMGNGdlQ62CneS8TjmhfSjhOodNd15CRHpRSIZjDVLejB73xMaqxTJUyOAdWnv1/PewKkljbsngGpgcCZ5wyHNw4D57V3czC4xs4VmtrCioiKzGns4gT8cpjSPMyDVh+11tZnlF5GcE8lgzFqGKYMFX71JwZhInmpvh+22XeUdpdltXjPrB/wv8A13397exd39dnef6u5Ty8rKMquwO4aRIkWyoAC8gAFeSk1jXUb5RST3RDIYK6kP5la09IwpGBPJV+uBUWnvRwIbOkpjZgXAQKByd3nNrJAgEPujuz/SnRVOJZMYMRLN7ZcXMNBLaUol2blTa42JRFEkg7GqAQsASHgQhHmTtkQSyVMvARPMbJyZFRFMyJ/TJs0c4ILw9ZnAMx489TMHmBU+bTkOmAAsCOeT3Qksc/cbur3GyQQxc5oI2y2PM8D7AFCpJypFIimSwVj1QMdxyosq2EmDesZE8lQ4B+xy4EmCifYPuvtSM7vWzM4Ik90JDDazcuBbwJVh3qXAg8AbwBPAZe6eBI4GvgAcb2aLwz+ndFedrXZ70DMWrlhtxBngpYCCMZGoymijcDObAdwMxIHfuvvP2pwvBu4BjgC2Ame7+5rw3FUEj44nga+5+5NmNipMPwxIAbe7+83dckdAyowd1PNG6bus9Sq+1nRkdxUtIjnG3ecCc9scuybtdT3wuQ7yXgdc1+bYPNqfT9ZtDGvpGTNiLT1jW7du7cnLikiWdNozlovr9MQsRipchb/W1DMmIrnFzGiy5mFKI+ZQ4nH1jIlEVCbDlDm3Tk/zk0jNEnUN3VW0iEjPcsOIkQx7xgrMaPQm+qYKFYyJRFQmwVgOrtMDybSn1+tqtD6PiOSO9J6xOEYjTfRLFSkYE4moTIKx3Funh9guPWM1Ne0WLSLS+1jwfw00AVDoMZpoon+yhLq6OurqtN6YSNRkEozl3Do9wTBla7xYU6NtREQkVxiGUWeNABS70ehJBnpfQE9UikRRJsFYzq3TY0DKWnvGamt3dPclRER6jFmMnbEmChIJClNOIyn29oGAgjGRKOp0aQt3T5hZ8zo9ceCu5nV6gIXuPocgsLo3XKenkiBgI0zXvE5PgnCdHjP7GME6Pa+Z2eLwUleHj6B3g117xnaqW19EcoUT9ow10aexgYJUgkYKGB4bBCgYE4mijNYZy7V1egwjmTZnbGe9gjERyQ0eBmMN1kRxUxNxb6IxVUBBPE6/0tLdBmM1NctIJLYTj5cSj/elqGhvCgsHfYC1F5E9kVEwlovSe8bqG7W0hYjkBrcmzIyEpejjTjzZSH0qWPS1f0nfDoOxnTvXsOClMyDti6hZAZMm3cjQffZsg4BVOxt4Yks187bVMKV/KeePGMy+xUV7VJaIdCySwVjbdcbqmxqzWBsRkcw19d2K1QXBWJwU8WQDtclgem+/ghI2dBCMvbNhNmbG5Mm3A5BM1LJ+/T288cZ3KCnel4EDD+v02il3Fm/fyRNbqnl8SzUrdwZfZMf1KeLvlTXc8va7zBgykC+OGMLRg/oRTP8VkfcrmsGYx1p6xgo8Rn1SwZiI5AoPF31NEQNiyUbqEkHQ049Camtrqa+vp6SkpCVHMtnAxo0PM2TIiZQNOaHl+N57f4yFiz7Lq0su4cip/0ufPqPbveLaugZufXszT26p5t3GBHGDowb244IRQ5g+ZCCjSopYW9fA3e9s5f6NW3msopr9S0u4cMRgzhq2N/0K4j36iYhEXSQ3CgdatkMqTsVp8KYs10ZEpAvMSFqKuDmxpnrqk8H35r6pYIhw06ZNuyTfXPE4TU3bGDnivF2OFxXtzaFT7sI9yeJXL6apqfo9l3J3vvr6Sh7ctJUjB/bllweN5vWjD+bhw8Zz8cgyRpUE1xzTp5hrxg/n5Y9O4qYDR9Enbly98h2m/HMpV765nhW19T3xSYjkhUgGY7G0CfzFqRiNJLJcIxGRzJg3t2FOzCDWVEcjRbg7+6T6UVRUxCuvvLJLnnfeuY8+fcaw115H7XI8kUhQWjqOQyb/mrq6t1ny2ldIpRpJJuvZuvU53nzzP7n9n19h0Y4Es5K3c2n9/+OY2IsM3E1HV594jFn7DubJqQcw94gJnDxkIPdt2MrHFyzn/CWr2NbUeXtbUdPAxXe/xKm3vMBjSzaSSrVdR1wkv0RymJK0OWOFyRg7CtQzJiK5xEiZYzGINewECmhM1VFQD4cccgiLFy9m+vTplJaWsmPHCqqrFzF+/FWYBd+vE4kETz31FC+++CKlpaUMHTqUYfueS1XV3fzzXyfT1LSRVKqBWKyYh+P/RVm8kYvGTKPinbt5/fUrKCkZwciR5zNi+NkUFPTvsJaHD+jL4RP78sPxI/jDhi3csOZdTl20knsPGceHSkvazfP8mxV868FX2V7fxPCBJVx238vsP7QfVxw/gVMm70s8pnlokn8i2TNm3vo0ZWHKaCRBMpnMcq1ERDLTPM3C4xCr34GZ0ej1WANMnTqVRCLBq6++CsD6d+4jFiti+L6fBWDr1q3ceeedvPjii0yZMoX999+fhoYGXloQY/Wqw6msrGLAgNM4dMpdFB8yj9cSI/nauHFMGHMhRx31FIdM/hUlJSMoL/8p8/5xNCvevJadO9futr5Digr4xthhPHToh6hKJDh10Urmbdt155PGRIqfzF3G+XctYO++hcy5/Gie/vYnuOWcw3CHK+5/hek3Pc+fF79DUj1lkmci2TOWvh1SQTIGBnV1dfTr1y/LNRMR6VyyuXMoDlZfC0CNV1NWN4KhQ4cycuRIFi5cyBFHHMKmTf/HPvucQmHhXrz66qs89thjxONxZs2axYEHHthSZiqVYsuWLdx///2sWN7Il740hZvXbGVIYQHnDR8MgFmcsrJPUlb2SbbXvM66t3/HO+/8kfXr72G//b7J2DFf3e0TlB8e1I/Hj9if819bzaxX3+Kn+4/kC8OHsHpLLV+f/QpL1ldz3odH871TJ9KnKBgLPWPKcE6bvC9zX9/ILU+v5OuzF3PL0yu54vgJnD5luHrKJC9EsmcsfZgyHk5fqN2uLZFEpPcblhxG/6IhACQLnHhDEIxVxyopTBWR2FLHUUcdxdatW/nLn/+bZHIHfWuO5qGHHuJPf/oTw4YN49JLL90lEAOIxWLss88+nHvuuSQSCf7rkTk8t62Gr47eh9L4e/9TMKD/wUya9AuO/ujzDB16GqtW3UD5Wz8j2OmuY2P6FPPo4RM4dq/+/L8V6zlv7mucdssLrN26k19//giu+/TklkCstW7GaYcM54mvH8tt5x1OYTzGNx5YzEk3PMcjL68nkUx1cDWRaIhoMAYedvPHw9HJmsr3PkXUm7g7L7+9jU3VeiJJJJ/FkzGS4d66yXiwtAXAztJgza+G8iomTZrE9ONPYmfDfFLJAh56fCnLli3j+OOP54ILLmDgwIEdll9WVsZZZ53FUwP2oTSZ4PPDdr9Cf3HxUCZNvIGRI7/A22//luXLr8Z999M++hfEuXXCKA4qr+Ufz79NfGAxD1/2UWYcPGy3+WIx45TJ+zL3a8fw688fTnFhnG89+Con3fg8Dy9SUCbRFclhyoQFc8YMaJ67v23rtvdVZsqdhzZt4+drNpFyDyauDijliAGlTO5fSp92vllmatnG7Vz7lzf416qt9CmMc/nx47n4mHEUa+0ekTyUaunZTxQ48VS4TmK/EnbWbqfkrSpKDx/Kfkv7Ujt6M5XbhlFEH2aUHsakjxxNLN55u1FTti9vD65h2uo3mPfUZk45Zfcr9JvF2H/CDygoGMCaNbeSSO5g0sRfEIu1vxr/4nVVfO3+V1i/bSfHf3gkTw5K8aVV67mn7zhG9ynutH6xmDHj4H355MRh/G3Zu9zy9Eq+89Cr/M8zK7nsuPF8+rARFL6PNlekt4nkb/P2WBIHHCfekMQcqrbteTA2b1sN0xe+ydeXv83gwgKmDuzLKzW1/OitDZzxSjkTXljCJxeu4I51FTSmMv/mVlnbyPf+7zVOveUFlm3azvdOPYhj9x/Cfz+5guk3Ps/fl2/e4zqLSG5KubcszdNYkCIe9owV9duLd+vW0rByG+/euIgdW5djfaqYcugXuOjsC9hraxFVc97K6Bo3rd3EwII4/zZiCAsWLGDBggWd5jEzPrTftxg//ko2b57LktcuJZncdd/fVMr59XNvceav/kky5Tz45aO469NTuH/KeDY2NHHyopUsqMp8ykgsZkyfNIxHr/gYd5w/lf4lBXz34SWc8Ivn+PPidzIuR6S3i2TPGGZ4+L9kopG+lFC5ZWuXi1lX38j3Vq7nyS3bGVFcyK8mjmHmPoOIhRNYNzc08fL2nby8vZZ5VTv4fvk73PlOBf+x33BOKxvY4UTXpmSKP7y4lhv/9ia1jUnOP2os3zhxAoNKi7j4mP147s0KfjRnKV/8/UuceNA+fP+0iYwZ3Pd9fSQikjuaH0BqjKeIhT1jxaWD2FT3GuMaJmOD4jD9XdgGY8acRklxGclP1FLz93UU7F1C/+NGddj+LN1RxxNbtvOdscM4Y8wk6iq38vjjj7PXXnsxYcKETus2ZvSXKIj3Z/mK77F48ReZMuUOCgr6U1HTwDcfWMy88i2cMnkYP/30IQwsLQTg2L3789gRE/jCklWcufgtbjhwFGcO2zvjz8PMOGniUE48aB+eWb6Zm54KJvrPW7mF//zUwZQUahRBclske8b2airEw76xZFMd/byE6u1dnzN22RtrmbdtB/+x377M+/BBfHroXi2BGMA+xYXMKBvI1R8azmOHT+C+Q/ajJBbjS0vXcPrLK1lYXfueMp9/s4KTb36BH/3lDaaMGsTjXz+GH54xiUGlrd39H9+/jCe+cSxXnnwg/3xrKyfd+Dw3/HUFdY1ankMk6gxaesbqC5MtPWOFxYN4u3YZjR8vZOjXDqPKX6Rfv4mUFAfzsAacOIY+U8rY/te1VN6/nFQH7cWNazbRPx7j4pFDiMVifOYzn2Ho0KE8+OCDrFy5MqM6jhgxi4Mn3UT19ld4+ZXzaGjYwuX3vczCtZX87DOTufXcw1sCsWbjS0t47Ij9mTqwL5cve5ufrtpIqpOHAd7z2ZhxwkFD+b/LjuaK48fz0KL1fPq2f7Jmy3vbWpFcEslgbGRdH1I4bpBqqKVfqoTquq49TbmkZicLqmv593HDuGLM0E7nhJkZxw8ewNNHHsANB4zi7fpGTnt5JRe/vpo1dQ2s3lLLxXe/xPl3LSCRTHHH+VO559+msf/Q9hdULCqIcenHP8Qz3/4EJx88jFueKefEG57jidc3dvo0k4i0MrMZZrbCzMrN7Mp2zheb2QPh+flmNjbt3FXh8RVmNj3t+F1mttnMXu/u+jqt64zVxRItwVhBcTApf3u8kkSqhurqRQwZ/InW+4gbe886gAEzxlL32hYqbnuVROWuDwStqK3nsYpqLhpZxqDCYGCkuLiY8847j8GDB3P//fezZMmSjOo5dOhpHDL519TWlnPbo9cwf3Ul3zt1IrOmje6wV27vwgJmT9mP8/bdm5vXvsv5r63mnfqu7x0cjxnf/uQB/O6LR7Kxuo7T/2ceTy7d1HlGkV4qksFYnFjLMGVRwumTirOzqY5EIvNtke5cv4XScNuPLl3bjHOHD+ZfHz6I74wdxtNbazhmziuccMNz/GvVVq46+UCe/OaxnDRx6G7X62k2bGAJN886jAcu+Qj9Swq49A8v84U7F1C+uabTvCL5zsziwK3AycBE4Bwzm9gm2UXANncfD9wIXB/mnQjMAiYBM4DbwvIAfh8e6/46e2vPWE28gVgqeAopFutLLB6nZmsFlZXzcE8yZMhxu+Y1Y8AnRjHkwkkkqhrY/MtXaNzQ+kX0N+s2B733I8t2yde/f38uvPBCRo8ezSOPPMKLL76YUV2HDDmOAyf+jj+89lFGD9jMzIM7nzNbFIvx8wNG8eMJI/jHthqOWbCcX729maY9WOj1uAP24dErPsa4sr58+d5F/GTusq4/cZlKgb7gSpZFMhiD5jljUByPU5QIvm1WVVW1mzqVatqlt2lLY4L/27yNzw3diwF7+ERj34I43xk3jCcPGU/psmoS/QrY+bGhVI4qpW4P/uF/eL/BPHrFx/jh6RN5dX0VM256gesee4Pt9Xuw1VMqCSseh83Lu55XJLdMA8rdfZW7NwKzgZlt0swE7g5fPwycYME3pZnAbHdvcPfVQHlYHu7+PFDZExX2tKcpq9iJ4RQVpKjfmaTf3oOp2bqFbVULiMf7MWDAlHbLKDlgb4ZefihWGGPL714nUVlPbSLJnzdX8amhgxhc9N7pwiUlJZx33nkceOCBPPHEEzz99NMZ9cI/9NrebK3fi/MmzuWVV85i69bnO81jZlw8soxnpx3I0YP68aO3NvDJhSt4qZ2pHZ0ZuVcpD116FJ//yGhuf34V594xn83bd7NEUCoFG5fAP38J950N14+BmybDv26F+u1dvr5Id4hmMOYETZk5xQVGaVPQoKxfvx6AVCrBpk1zWLr0W/zzn8fx92cP5Jm/j+fvz07khXkf4eeLfkNDypnZZzkNDRXvqyp3PVNOojHJHbMO45PD9+Lmte8y7cU3uHHNJnYkujYHrCAe48Kjx/H373yCzx4+kjteWM3RP3uGG//2JtV1GQZllavg96fB/bPgtg/DvZ+GlU/pm6FE1QhgXdr79eGxdtO4ewKoBgZnmHe3zOwSM1toZgsrKjJvS5LhBP7KWNCrVVKYZGd1I/0HD6FmSwXV1QsZOPAwWjvq3qtgSB+G/NvBeJOz5Xev8+f1W6lNpjhnNxPnCwsLOeusszj88MN54YUX+Mtf/rLbreQ2VNVx27PlnDJ5GF/45H9RXFTG4le/SPlb/00q1flIxJg+xdwzeRy/O3gs1Ykkp7+8km8vf5vKDDYbT1dcEOfHn5rMTWcfymvvVHPKLfP411vhQ1vusHkZzL8dZp8H/70f/OYY+Ot/wNZyOPgzMGgMPHk13DgJ/vo9qF7fpeuLvF/RfJoybZiSwiQFjSmKvIC1a9cyevROlq/4PnV1aygqKmPgwMMZtu+nwVMkU/XUN1bx6OZJHMwS6sp/xD/eKmTfYZ9mzJgvU1o6tku1mL9qK7NfWseXj92P6fuVMZ0yvj6mjutXb+T61Zu4Y30FV4weyoUjhnRpnbIh/Yq5/sxD+MJRY7jl6ZXc/PRK7vrHar549DguOnrceybOAkGDtPBO+Os1ECuA02+GHRXw0h3wx8/CkAPgI1+BKbOgsE+X7lOkF2tvLkDbbx4dpckk7265++3A7QBTp07NOG9zz1ilBT1FfeKN1FY30H9wGe+ueY0dO1aw37iTOy2ncGhfhlwwkYo7X+MPSzew395FHDlw909mx2IxTj/9dPr168fzzz/Pzp07+exnP0th4XvblZ89vhx3uOrkgygtLWXq1Ed4c+W1rF37a6qqFnLwpJsoKdl3t9czM04uG8Sxe/Xn52s2cfv6Ch7fUs33PzScWcP2zmg6R7NPHTaCifv25z/vfZRH77qOIcPXMX7nYqw2XCZo0Gg44FQYdyyMOwYGDG/N/M7L8K9fwr9ugxd/BZM+Ax+9HPZtv/dRpDtFMhgrKkrSFE6AbYo3YKkEw1KDWLfuTV5f+mMKCvozefJtlA05CbP/z957h8dRn2v/n5nZ3ntRL5bc5SYDtmmhd0hCSQIJBE7ek0IIJ72c5KTnJDlp5FBSICFA6AFMJ3Rww91ykWX1tqtV2d53Z35/jCzbuIn35P2dJNZ9XXPtSpr57uxo59n7afdzKAl6JhJjbKSXn85fwHLDYoZDjxMKPcJw6DH8/kuoq/0kFsvs455DvlTma0+0Ue0y8rlzDrSLz7MYuXdhA1sSaX7cHeY7XcPcNRDh1roA1wZd6MTpk7IFlXZ++7FWdg3Hue2Vfdz2yj7+8HYPN6yq46ZT6w90aMYH4ambofs1aHgfXP7fYK9S/7bqFtj5F1h/OzxzK7zyXWj9OCz/BNiObURnMIN/AAwC1Qf9XAUMH2WfQUEQNIAdNQU5nWP/5lBgSoG/pFFAq0Uv5InGC/iqvYQGhwAFu2PZtNbT19uJX9nIlugI/xYqqWmD41RfCILAWWedhdls5vnnn+f+++/nmmuuwWQyTe2zsXeC1duHueWsWVS71N9LkoG5c36I03EK7Xv/nXc2Xsq8uT89rLbtSDBrJP5jViVXBVx8Ze8g/9Y+wEOhCf6zuYq5luM4iNE+6H0Let6kuect7ksPgxbCESfrLctYdMGlmGafCc66o69RuRSuvAfO+Tasvwu23Attj0DdabDyszDrXHgP9nkGM3gv+KckY5LIVM1YVkogyzJB2U3E9gLFYpTFi/+AzbrgiMf+fnCUaoOO87xuJMGDzdZCfd1n6B+4h6GhPzMyshqTqVvab8cAACAASURBVAGjoQqDsQajsQqjoRqjsRqzuRlRVL3HO17rons0zb03noTpCPUZS21mHl7cyNpoih/3hPhaxyC3949wS42fqwMuDO8hUja/ws5vPtrK7uEEv351H79+tZM/rOnlhhW1fMq5AfOr/67WiV3yC1j2cTjY09ToYfGH1YhY3xrVK3zr57DmNjV8f8qnoWLxe7r+M5jB3xE2Ak2CINQDQ6gF+R951z6rgeuBdcCVwKuKoiiCIKwG/iwIws+BCqAJOL466v8QGllAllRnUhZkMBnRy2nSiTxWlwejL4mAhP0o9WJHwlNWBSkK529LEC114Hz/LIRpaHOdfPLJmEwmnnjiCe644w4uvvhi5s6dS1lW+M7TuwjaDXzyzMbDjgsELsNmW0jbzs+yfce/UFPzCRobvjBlH4+FeRYjTy2dxUOhCb7XNcy5m/byr9U+Pl/nx7x/ukAuDh0vQs8b0PMWxPrU35s8asSr7jSU+tN5oV3iB8+3E3jTwJ3VThY4p3GxHDVwwQ/hzK/A5nthw13w56vV7MGKz0DLNaA1TGOhGcxg+vinJGOgiiYqCMS1UcyyTLO+iKaiHY3mzKMSsd2pLOvjab7VWIF0EGHR6300zfoqdbWfZGjoQRLJneSyA8QT2yiVDhR86nQegsEryWkv587Xu7hsUQVnNHuP9FJTWOm08KRjFq9PJPlxT5gvdwzy094wn6jycn2FG7t2+v+ieRU27rxuGe3hBPe++A6L1nwKs7SFAetirB/6HY7K5qMfLAhQd6q6jXfBht/A1vthx8NQu0olZbMvBHFGXHEG/zhQFKUkCMLNwIuo8aB7FEXZJQjCd4FNiqKsBu4G7hMEoRM1IvahyWN3CYLwCLAbKAGfUSaHMgqC8CBwJuARBGEQ+A9FUe7+W5yzFs1UmlJGRnE60WdjyHI1ZqcPSyCLVqxFkkzHWUlFSVZ4JBzlLLeN+tOcJF/pJ9+bwHl5I4bZxxdeXbhwIR6Ph6eeeoqHH36YefPmkQ0uZudQgl99aPERnU0Ak6me1mWPs6/zB/T3/454bBMLFtyGwVBxxP0PhjjZlX6+x873uoa5vT/CkyNRfthcxfmmMtxzPox1gMGh2qwVn1EjWL65U86mANxwKrTUOLn5gS184M61fOey+Xxo+dEFcQ+Bwa5mDk75FOx6Atb+Gp6+BV79npo5WP4vYH5v3fYzmMHRIPwjaVa1trYqmzZtOu5+q7/waSZM82nXhnANv4EtezHLztlNLLiBth0f5tOf/ioGw+Gezefb+3liJMqWlfNxTpMEFYtxsrkBMuluRiLPEhl9jZ9s/AzD6Roe/biOOXXnHnV+27uhKAprYilu74/w2kQSsyTy0Qo3/1rtJaif3hqAmnZ89vPIhQx/cd3ElwdXYtBq+diKOj5xWj1uy/FnwwGQjcHW+1RiFh9QQ/wnfwqWXAv6I+ujzWAGf0sIgrBZUZTW/+3z+Ftguvbr/q98E6emmY3aLp6sfZI/vlRHRruAraazuPJrC9nRsZLsYD2X3vDitEjFy+MJrtvRzT0L6rjI6yDXFSP2ZCel0SzGFg+OSxqRbMe3L+VymbVr1/LSa2/yWHY+dW4zz37+bMRppO5GRp5lT/vXEQSJeXN/gtd7znGPORgbYim+3DHIQCLKS7u/RGO8A+HqP0HTudNyECfSBT730Fbe2jfGB5ZW8oMrFmLUvUfHUlGg5021rmzfS6AxwKIPq0TQc/zJBTM4MTFdG/ZPmwBXk5QiE7osOmOMRHAdpokzSCTgueeeO0zmYqxQ4vGRKFcFXNMmYgBarR2bdQGBwGUsavkNYeNf2BebxUfmvUyo9xbeXrOKfZ3/SSbTc9y1BEHgVKeVBxc18nJrM+e5bfx2YJST1u3h1j39dKSP0a4NkJmARz8Oj30cXA2In3ybKz/zI1649UzOnuvnN292cdpPXuNHz+9hPJU//pszOtRaiVu2wVV/BLMPXvgK/HwevPgNtU5jBjOYwd8UCsrUOKSyUCZj06OZUEvV4rFdiJJMpD3HwK62aa33YGgct1bDOW4bAIZGB/7PLcV2bi3Z3eOEf7aJ1LphlOPofEmSxGmnnYYy7wKyiobGxFYeeOABotOY++v3X8xJy5/CaKxiR9u/0rHvB8jy9MVeT3ZYeHlJA6/2/Sf1E7v41Nxv8kv9YtLTjCW4zDr++PGT+NzZTTyxdYj337GG7tH3JgSOIEDDGXDto/DpDdByNWx7AP57OTz4YehdM9OVPoP/a/xzRsY+/xlGzXPp0I4TTz3KxXXVmJq6qd/wX+xdpmHDhg0AOJ1OnE4nFouF5/V2nja5+fpELzWS2uKt0+lwOBw0NTXhcDiO+7qjyTxn/+x15gRtPPSJ5USjaxgafoixsVdQlDIOx8lUBK/C57sASZpex2JfNs9vBkZ5MDROVlY432Pj5hr/4R1Re5+H1bdANgpnfhVW3QrSoaSyM5Lk1692snr7MAaNxNWtVXxsZR2NXsu0zgWAwU2w/g7Y9SSgwNxL4ZTPQPVJh9aizWAGfwOciJGxe7767/Qb1Hv3L/V/4ac75uNdM8a6xV/j5GvbiBdvo/upVhzeJq78xveOGR0bL5RYvHYXN1Z5+M6sw1U5imNZYk92ku+Moa2y4LisEX2N7ajrdY2mOP8Xb/KBpZVcWZPn5ZdfRlEUzjnnHJYvX37cKJks59nX+SMGB+/DZlvEgvm/xGisOe41QVHgyU/D9j8zcf5/8QXLOTw/Fsej1fDZWh8fq5h+R/obHaPc+tBWimWFn17ZwoUL/weNSqkIvPM72Ph7yE5AxRJYcTPMu+Iw+zuDExPTtWH/lGTsiS/cwripib3aGDHdg1yzrECxs5mFfV8j8KVW4mTYs2cP4XCYaDRKPJ3mrnkr8OUyfKR/F4VCgWKxSD6fR5bV2g2/38/s2bNpbm6moqLiiEbnsw9u5cWdYZ773GnM8h0gOPl8hFDocYZDj5DN9qPRWPH7L6ey4mqs1vnTeu9jhRJ/GBrlnsExoqUyJ9nN3Fzj4xyzgvji11UPzb8Q3n8nBBYec63OSIo7Xu/kme0hCmWZM5q93LCqjjOavIjiNAlVfBDe+S1s/qNaTOtugoVXwoIPzoTsZ/A3w4lIxn7z9W8SmkyhvTb7FT65p5IFj7fz5mk/p+XKR8CwHWP0S7xx/z3Utizh3E98BrsvcMS1fjcwyjc7h3ht+eyjdiQqikJ2+yix53qQEwVMS3zYL6hDsh9eznDjHzeysWeCV794Jl6rnlgsxjPPPENnZyfV1dVceuml+Hy+477HSOQF9rR/lXI5i9d7HlVVH8Nhbz06sXz52/D2L+DMr6nOJrApnubHPSHeiqYI6LR8rs4/7Y70oViWzzywhW0DMW46tZ6vXjgH7XtomjoMhQxsf1AVjp3oAnu1Wmu25KNgODq5ncE/P05oMvb4F29lwtjAXl2aYs29XFZdZOipcznLeC32C+uxnlF1yP5/GYny6d19PNDSwNnuAzeOoiiMjY3R0dHB3r17GRgYQFEUdDodgUCAYDA4te0cV7jpT5u59Zwmbj3nyIXyiqIQi21gePhRIqPPI8t5rNb5VASvxu+/DK32+DdtulzmwdAEd/ZHaAiv47aOn+DLjzFy0mcJnvt1BM0068FQI3l/3tDP/Rv6GE3mqfeYuX5FLVe2VmPRT9Ory6eg7VFoe0ztxkSBQItKzOZ/ABzVx11iBjM4Gk5EMnbX935AuKyKOO9s2cjpew2ce+9u3jrvDmZd8l3c/lm0LPwN2/76HG/9+V4URWbZRVew9KLLMNnsU+soisJZG/eiF0VeaD1G884k5HyZ5OsDJN8aRBAFrO+rxnpqFYJWJSmv7Y3w8T9s5BsXzeUTpzcc8jo7duzghRdeIJfL0dLSwumnn47bfezi9lxumIGBPzIcepRSKYHFMoeqyusIBC4/tDlh3R3w4teg9Ua4+OeHReDfjib5SU+Yd+JpqgxaPl8X4Gq/C81xHMtCSeaHz+3hj2t7qXWbuLSlgotbgswJWN+TttkhkGXoeF5V9+9fC3obLLteLfY/lqzGDP5pcUKTsce++AUmjDW06wto5v2e8+0ldt53DSvts6lwNBH4QutUwaqiKFy4eR/JUpm3Tp6DeIybMJPJ0NnZyeDgIKFQiHA4TLFYpKwIPFlYiEGn4b8vqWTBvDmYzccWViwW44RHVjM8/DCp1B5EUY/PdyHBwAdwOk85prI2QGnPM0iPfJSQpZZPNn+FdyxzqdRruczn4HKfk0VW47QNSqEk8/zOEH9Y08u2gRgWvYarWqu4fkUddZ5jv49DkBhWu47aHoPhLervqk9Ridm8K8By7M7SGczg3TghydgPfkC4qJKxkaV7sO9Lcv3vetj8/l8QOOezNNTfTEPD5wBIjI3yxv330LH+bTQ6HS1nX0Dzyavw1TewKy9zweYOftxcxfWVnmmfZ2k8S+y5HnK7xpGcehwXNyDOdnLhbW+BAi/cejo6zeFRpFQqxZo1a9i4cSPlcnnapKxczhIeWc3g4H2kUnvQaKwEg1dRVXktpq5N8PhNajnEVfcetVhfUZSpjvRtyQz1Rh1frAtwhd95SGf8kfDSrjB/WtfH2q4xZAUavWYubqng0pYgTf7/QaPS4GZY92vY/RQoMtSeCos/AvMuB/17KA2ZwT80Tmgy9ugXvsS4uZK9BoXAwjtpLYnsWn0z+sJ6Lq79V7QVFgyznAhakc3FHB/WZ/gP0cINHieSU49k1SGatAjSsW9iWZYZGxvj92/s47ebY1zhHMKRHUYQBOrq6pg3bx5z5szBaj36Da0oCsnkToZDjxIOP0W5nEKn8+H3X0LAfxlW64LDSVX/evjT5eBfANevJiEaeHEszpMjMd6IJigpUGfUcbnPyeU+B3PNhmkTs639Ue5d28uzbSFKssL7Zvu4YWUdpzV53pu3ONENOx+HtsdhdA8Iklr8uuCDMOcStTngGBjLjrE+tJ51w+vojnXT4m1hVeUqlgeWY9TMTAg4UXBCkrHv/4BwaXK82fJe9nbt41t3TND5f27BtPi/aGn5LV7P2YccMz44wMbVj7H7rddQZBlBFHnzgg+zpaqZp6xFFixajFb/3rSxcp1RYk93UxrJ8Lhb5BfjMe65vpWz5vqPeVwymWTt2rVTpGzRokWcdtppxyVliqIQj29mcPA+IqMv4JzIsGhnknJwHpqP/xVBe3zHUFEUXhpP8JOeELtSOZpNBr5UH+Bir/2YjjbAWCrPCzvDPLNjmA09EygKNPstXDIZMXtPtbUHYWJkJ/Luv+Bu+wvCRA9ozSohW/wRVTZoRkj2nxonNhm79YuM2SqJmApUttxDbUSi4+1vkUvez4Xn3owr6qYUzYOs8KP5Bp4NanjptRSmg0ewCSCatGg8RgzNTgxzXGiDZoR3hb4zhRKn/+R1GrxmHvrEyYTDYfbs2cPu3bsZH1dno9XU1DB37lzmzJmD03l01cFyOcfY+KuMhFczNv46ilLEZGrA77+MgP8STKZ6GN0Ld58HZg/c+NJhOjfRYonnR+M8FYnxdixJWYEmk34qYtZsnp5BjiRyPLChnwc29DOWytPoNXPDyjo+sLQK83RTmPsxsmuSmD2mijNKOmg6TxWVbb4QdCaypSxbRrawbngd60Lr6Ih2AGDX22m0N7J7fDe5cg6dqGOpfymnVp7KqopVNDoa/+9TCjP4u8eJSMZ++/3vMTw5t9bXOsD9Axu4+1dlxr5yIYXap2iZ8wreirojHpuJxwh17qWvcx83umbT1NfOBX99GI1OT23LYhpbT6Zx6UmY7MdvSAJQygqdb/RxxUu7WYjELz1uzMv9mJb5kSzHlsNIJpOsWbOGTZs2TZGy008/HZfr+Npmhd5X0dx/DVmDwKYWM1pLHZVVH6EieBVa7fHPXVYUnhmN89OeEPsyeRZYjHy5PsC5btu07EUkmeP5NpWYbexVu0XnBm1c0hLk4oXBaWUM9k7s5Z6d9/Bi74uUlTI6UYdfbydYLBKIjxAo5Ajq7ATrziQw/0qCFcsxaaenHTeDfxyc0GRsy39+gvWZeZiFfdhXvQxdelJrv0BM9w56Y56bfvVbBERKssziDXs41WHhjvpKyhM5SrEccqpIOVVEThUoDKUoDqot0KJVi6HZhb7ejq7KgsZr4q63uvnxC+08+skVLK87YGQURWF0dJTdu3eze/duIhF1Ntr+RoA5c+YQDAaPahiKxTiRyPOER1YTi70DKLi1s1mwfhcSWoR/efm4NQhjhRLPjsZ4KhJjXSyFAsw1G7h8kpjVm45fX5YvlXmuTU1h7hiMY9VruHp5NR9bUUut+z2kMNWLAkObYefjyDv/QnthnHUWG+tcQbbKGQpKCa2oZYlvCSsqVrAiuII5rjlIokS+nGfzyGbWDK1hzdAauuJd6vU0+VlVuYpVFas4peIUbLqZYtl/JpyIZOzu732Pgf3Dub0baHf38q0fa0h8uZq4LUylZTUL3lX3+m48Ep7glj39PN5SR9VQL52b1tO1aQPJcXVYua++kbpFS6lbtJSK5rlImiM7WKWyzId/t57dwwkeO3su7j0xCr0JkASM89yYTwqgb3Qc5qQejPdMysa7VIdTa0K+8VlGc20MDt5HLL4RUdQT8F9OVdV102p+KisKT4xE+a/eML3ZAkusJr7SEOAM5/TrwsLxHM+2hXh2xzBb+lVJpAWVNjVitjA4NQoKVLu/aWQT9+y8h7eH3sakMXFl85VUWioJp8OE02FC6RCh9DCjmdEpCZP9sEkGAtYqgpZKAuYAAXOAoDlI0BwkYA7gNXnRTmOKwQz+fnBCk7GJb1/Kg/L7sEntVJz2Cr1tZjx/vZzBilpy8Se5+JYvMWfVGbw1keSq7V3cvaCOi71H97bKyQK5jii5vRPkOmIouRIAaY3AVXKChTYTvz13Lvo6Gxr3kVNoExMTtLe3097ePtUIYLPZpohZbW0tmqMYxFwuRGTgUTyr/xNdJsPmRQ501WfgD1yKx30mOt3x60FG8kWeHo2xOhLjnbg6fHiu2cAFHjvne+zHrTFTFIWtAzH+uKaX59pClBWFs+f4uGFlPatmuadl2EKpEOtC61g3vI4NoQ1E86rHOatYZkUmzYqyhmUN52NaeI2qqn0MMcdwOqwSs+E1rB9eT7KYRBIkWrwtrKxYyamVpzLPPQ9RmEkB/CPjRCRjD3z32+xTm7hZuPAxHM4sun4zJX+OdHwJYvQbXPSplmOuccWWfUQKJdacPGfq3lQUhUhvNz1bNtK7YwvDHe0osozWYKRmQQv1i1uZtfwUzI4D0ftf/LWDX72yj19es5grlqjSGMVIhvQ7YTJbRpAzJSSXAXOrH3OrH8l2dAfvSKRs5cqVeL3eA/YjOQJ3nwuFlBr598w6cHyqncHB+wiHn0KWs9isLfj9l+DzXXhcVf+irPBoeIKf9YYZyhc5xW7mKw1BVjjeW+pxKJbluR0hnmkLsX1AJWaLqh1cvMCPy7ePJ3ruZ8foDlwGF9fOvZZrZl+DXW8/4lolucRoZpRQZDvh9tWE+tcQKsYZ0eoImV2ERIVEKXPIMaIg4jV6p0jawYRt/6ND75jJFvwd4YQmY+HvX8GjhVPx6NvwrXibde16Vj20jF1zb0CvfZh8Js6s1lN4sHEpb2it/Cnehc/nwxGowOb1YbRYEY6Sx1dkhdJYlsJgkl+t7eG3g+Pco7HQXFL3l1wGDLMc6JscGBodiKbDvZh0Ok1HRwft7e10dXVRKpXQ6/U0NTXR3NxMQ0MDFstBRqKYg/s/CAMbyF75K4b1YcIjT5PLDQBgMs3C6TwJh+MknI6T0euP3Vo+lCvwzGiM50fjvBNPIwMBnZbzPDYu8NhZ5bSgP0Ydw0gixwPr+3hgQz/j6QKzfBbOnuNjaa2TpTVOvFbVIKcKKd4Jv8O64XWsD62nN9ELgMfoYUVwBSsqVnBK8BS8Ors6xLztMWh/FoppsPhh/vthwZVQ1XpMDbOSXKJtrI23h95mzdAado/vRkHBqXdySsUpnFp5KisrVuIxTr+IeQZ/HzgRydjD3/0We2QRQYGVK+5H1Kk2WsgBsd/Q9Y6Wm352GuJRpBi6MjlWbWjnGw1BPlt79PqufCZN/87t9O3YSu/2LcQjIwiCSNXc+TSdsopYYD43PbSbK5ZU8vOrD59Pq5RksrvGSL8TJt8VBxEMzS6MCz0Y57kRjUd2Lg8mZaVSCbvdTmNjI821QZrX3IoY7Ybrn4GqIw9CLxbjhEKPEw4/STK1CwC7fSl+38X4fBei1x/jPcsyfw5N8MveMCOFEqc7LXylPsiyd+s2TgMDExlW7+jn0fbVRMQXkPSjaGQPp/uu5PMrrqXWNb1U8BQUBQbeUWWKdj0B+QQZew3h+RcRrjmJkAihdOiQCFs4HabwLvFcg2SYImnNzmYW+xazyLsIn+n4kiMz+NvjhCZj7/z+o6zrb8Fv2Yq3dQN/Dmu5+b+8rD3p28w+SUM+9Q6d27fwyw/eTN1gJ5e88uihCwgCBrMFg8WCzeMj2DSHitlzCDbNwWhRi/HHU3lO/8lrnDHby+0fXkopkiHfHSe3L0q+O46SL4MA2kq1WUA/y46uxob4rhEchUKB7u5u9u7dy969e8lkVE/I7/fT2NhIY0M99Zu/h7jnKfjg3WpnIvsL/9uYiK4jFl1PLL6ZclmNeJlM9VPEzOE4CYPh6KKG44USr0wkeHEszmsTSTJlGbMk8j6XlQs8ds522446kSBXLPPsjhAPbexn+0CcQrmIaBzE4+lFZ+0koXShIGOQDLQGWqcI2CzHrKN7boUM7HtRJWb7/grlvKrZU3861KyA2pXgajgmOZvITbBueN1U5GwiNwHAXNdcVlasZFXlKhb7Fs+E+/8BcCKSsce++212ymDW5Fi68lGejmm5QK7A/+AQppse4eXV48xq9fG+a+egOwLh+X7XMHcORNiyYj5+/fQ+44qiMD7Qx971a+hY/zZDoQgPVl6NUSvyq1UGWlauwOo6ujNTGsuS3hgms22UcjwPkoBhlgPjAg+GeW4k8+HnkUwm2bt3L52dnfR37+OqwsPUMMRfPTdhWHAJjY2NVFZWHlNINpPpYSTyHJHIc6RS7YCAw7Ecn+8ilZgdJWuQLcv8aXiM2/oijBdLLLGaOMlhptVmZrndTOA41y1dTPNYx2Pct/s+RjIj1NuaaNBeSntXPe2hNIIAy2tdXNwS5MKFAXzW9zhYvJhVHdNtD0DXa4ACNSvVov/5V0yNo1MUhYncxGEELZQOMZwapiPaMUXWKswVLPIuYpFvEYu9i2l2Nc/YwP8fcEKTsfX3Xce6fS1UuDfhWbiZu0b1fPvRJjqsFxGxzuaj31/J+kKGj7T1cvfcas5zmIlHwkRHQiRHI2RTSXKpJNlkkmhoiNG+HpRJ8VdnRRW+2nqeV2bx/IiBB69pYOm8+kM6lZSyQmEwSX5flFxnjEJ/EmQFRAHFbyTrNxF36xmxaZiQZWKZIrFMkXimgJiLoc+OIqUiCKkxLuBVTmEbLxsvpq3yw1h9lZjtbvRaCZ1GRCOKaCUBSZTRlPZBcStybiul3FYUWa110+qqMVlbsVqXY3echNlUhVYSMWgPJYa5sszbsRQvjsV5cSxOpFBCEuBku4ULPDbO99ipNR6ahsiX86zuWs0bA2/yTmgj2XIaEBAKVeQSjZTTTRhKDSyu8bCsxsnSWidLapzYjdMwArm4apD2PAP961SFa1CjZvuJWc0K8M8/akpTVmTaJ9pZO7yWt4feZntkOyWlhFlr5uTAyWq9WeUqKi2Hq5PP4H8fJyIZu/37dzJaGsHvHKZ54SvcOWJnhfYMLvr3Fwj84Id0m5ewYXUPBosWT6UZi8uA1WXA5jFidOm5MDTIIqOBO+uqkDQiBosGjXb6cxgVReH637zFmr4ENxXeRj+4EwBPTR1VcxdMbvMPSWcefGxxMEWmbYzszjHKEzkQQd+oEjPjfPfhhf9yGfnRjyPueYpdsz/HmmQVw8Pq+CeDwUBDQwONjY3MmjULu/3IKT+AdLqTkchzjIw8SybTCYg4HSfh81+Mz3s+Ot3h3ZzpUpk/DI3x1/EE25MZcpMjoSr1WpbbzbTaVYI232JEKwpM5CZ4YM8DPNT+EIlCguWB5dy44EZWVayacjA7IymeawvxzI5hOkZSiALMr7DTWudkeZ2L1lonPtt7IGfxIdjxEGz7M4x3gtYEcy9TiVndacfsxiyWi+yZ2MP20e1si2xj++h2RjIj6rWVDMz3zGeRVyVni3yLcBmO31wxg/eGE5qMbf39rbzaH8Dn30bF3I38ckTPp3euwPdCFxtO/haNS3ysXmbihVSKHSvmYRTFo4b8AYq5HOGuDoY72gl17qV7cITbDefSlOri3LFXAbB5fXhr6jFX1VN0VjJq8NGThO6xNMORFJ5YkRYkFiExFwkd6o3bQ5kdlGmXZPpMIuOiQEGWKZRkri//hc+LD/K0fCovcxpOUZ1NmVG0DMs2hss2RhULKUWPwqGRIgGZausQs52dzHZ10uzswqxVo26jWRcdE7MYSM0izRy0+kYqHGYq7AaCDiMVdgMBu4GIpPBaLMULY3H2Ts7FnDNZZ3ae20Zo/HVu23Ibw+lhKi2VnBI8hRUVKzg5cDJ2vZ3BaJbNfdGprT2cYP/4u2a/hWWTxGxZrZMGj/nYdQ6yDGN7oW+tSsz61kFiUP2b3g41Jx8gaBVL4Cjit6lCig3hDVONAMNp1ejX2eqmGgFaA60z8hl/JzgRydgd3/8NkVKIhfNfx2fp44s9c6kKGvjh9/uxXXYpwf/4D0JdcXa8OkBiPEdyIkc2oUY/9lZoeeQ0K9e8laR5uDi1plYvYbRqMVh0mGw6rJMEzuo+8Gi0ahEEgT+t6+VbT+3im5fM46ZT6xkfHKBz4zoGdrcxtHc3pbw619YZrKRqxP9SGQAAIABJREFU3gFyZvMcmgZTFIXicJps2xjZtlFK4zkQQF9vV1OZ8z1IVi089yXY+Ds47/vqLFxUTcfu7m46Ozvp6uoimUwC4PF4pohZbW0tOt2ROzpTqb2TEbNnyWR6EAQJp2MFPv9F+LznH7EjsyDL7Exl2RzPsDGRZnM8zVBevYam8hjB3Euko69SlvOcVnUWn2y5iRbvsWv3OkaSPNcWYn33ONsGYuSKqlNf6zbRWutieZ2T1joXjd7j2D/1gqrj6LY9ADv/Avm4mjVY9CF1YLm78djHTyKcDrNtdBvbI9vZPrqdPRN7KMlqHXS1tVolZt5FLPYtZpZjFtI0BrHP4Og4ocnYwFff4CH9WryBHVTPfoefDdhZkpjPdbdvIvTRn7J7wMQvLnNQO1rkg+vU1J7epMFk02GwaNHoJCSNiEYrYjBrsfuMOPwmHD4TVo+Bb63exSObBnjo6gZG+nrZ097J6EA/2tgwjsKBAeQJrY2svQKtvw5XbQP2ylpcLjsOnYQnVcI6lkcfyqAMptS0JiBadeiqLJi1r2Ps+DrK3A8gX/l7ijKMTcTo7u6mr6ebwf5e8rksAJJGg9XpxmL3YHK4Mdpc6K1O0OgolmVKZYViqYRS7kZT3IZe3o5eaUNDHIB82Uhfso4947V0xerpjteSLakdQm6zjgqHEZvLQMalI2QQGMrvwRx9AG2hG6upgWsX3sJV9afh1WmPaVBS+RI7BmIqOeuPsqUvSmKyGcJp0rJ0MnK2tMbJomo7Jt1xJDRi/Sop61+rPo7tVX+vMUDlsklytgKqT54K6x8MRVHoTfSyZmgNbw+/zabwJvLlPDpRxzL/MlZUrKDJ2USDvYGAOTDTDPC/gBORjP3uR7+iaN/M7DlrqO3P8G/jK+jzdLF6/QryW7Yx66UXkd4VISoVyiTGc/yf7gH2FAs8ZvIhyArlokw2VSSXLJJNFcimimTieZLjOQq58iFraPUS+YCe2xITLPZauf2Di3BXWJC0Bz735VKJSE8Xg3t2MrhnJ0Ptu8lnVBtq8/qonDOfiqY5BJtm462tR5TUL3JFUSiGM2TbRsnuHKMUyYIADscTWLJ3U6i9EeXMb6MNmhENh973iqIQiUTo6uqiq6uLvr4+SqUSoigSCASoqamhurqa6upqbDbbYcemUu1EIs8yEnmWbLYfQdDgcq3C57sIr+e8Y04+eTu8i7va7mbH8CsogkjetIq07SLK2grqjTqWTaY1W+1m5pgNxxSYLZRkdg3H2dQbZVPfBJt6o4ynVRLtNGlprTtAzhZU2I8orDuFqTTmn6HrVdQ05opJUdkr3tMIplwpx+7x3Wwf3T4VQRvPqbJMJo2JhZ6FLPItUlOc3kVHbUiYwZFxQpOx/q++wYP6NbiDu6hvXs99HR52m/Q88lIN+Y59RO95mCtGY3wub+RcWYcsQy5ZIJMskEsVKRVlSkVZNWTJAvlMaWrtuKTwe0uOuYqGprxElDIpUcHtNVFdY6M6oMVXHkcfGyI92EO4ex/JsdGp461uL97aOjzVtXhq6/HW1OHwVyCP5Sn0JigMJKH3VZyZb5CX5zNW/A6S24quanKrtqCtsIBGYGRkhHA4TDgcZmRkhJGREbLZ7NRr2e12/H4/gUAAv9+P3+/H5XIhiqI6jy7bSzy+hXh8K/HEVlKpDkAGBMpiLYnybELZWXTG6mgfdTGcGqLoeAatbSeybCejfT9Z3xkwWVOmKStYS+BSBPySRJVWS61eR71Jj9ukw27U4jBqsRu12IxaBKB7LHVI9KxrVDXskigwL2ibjJ45aPZbqfeYD0utHoL02IGoWf9aCO0ApQyCqI5o2p/WrFlxxGkAuVJOlc8YVqNm3fHuqb8ZNUbqbHXU2+tpsDfQ4Gig3lZPra0WrTRTd/H/CiciGfvhnbdy8uynAWhqK3BvZjaPVIxwb9N3Md70DVw33ID/K18+7LinIlE+tauPm2t8fL3x2N2FAPlMkeREjuRkdG1kKMU39vSRKclcn9BjUgREScAZNOOttuAKWnAGTDj8JmweA6IkIstlxvr7VHK2eydDe3eTiasOqUavJ9DQRLBZJWcVTXOmUpvFUILyiz/D0PtLMpzNRO5zwIEmKG3QjC5oRhu0oK0wIzn0U45esVikr6+P3t5eBgYGGBoaolRSbbTD4ZgiZjU1Nfh8vqmas/0C25HIc4xEniOXG0QQtLhdp+FynYrJ1IDJ1IBeH2BLZCv37LyHt4bewqQxcVXzVVw37zrsBh9tyQwbExk2x9NsTKQZLaivbZFElthMtNpUcrbMZsJxlHrb/efTM5ZmU2+Ujb0TbOqL0jM2GRzQiCyqdkyRs6XHKu2ID8GOhyfTmPtAY4R5l6nRspoVoH1v9WqKojCUGjoketYR7aCsqOS93l4/FT1b5F1Eg6NhxlE9Bk54MvaA/i0qqndT1bCBth1G7nYKPLX0LgrXfYZHr7iG28+8kFf7d+LLplCKJUSjAcFkQtQbDikOT8gibSkNO0YlOsYUdpYVxiWFD+QNVIkadHkZpXzoNbS6DKrRCphwBswYzUUK2RDJ8SHG+nsZ7e9lYmgAeVJLSNJocFXV4K2upcatMLfzp8i2WornPkoxIlIcSFIYTKmFsQAiaH1mtFUWtD4TGrcBjduI6NSTzmcOIWcjIyOMjY2x//+s1Wrx+Xz4/X58Ph9erxev14vVaqVcTpNIbJ8iZ/H4NkqlGOkyvJwy82YSNILEJVVnc7L3JqJpMwPxDDsyOcLlMhOCTEIjkNMLlA9uVJAVhEwJIV1SH1MlxHQJa0nBodVgnyRodqMWg1YkV5KJZQqMJPIMxbIUSmpoXxSgxmVils/KLJ+FJp+FWT4LjT7LkWdp5pMwuHGSnK1Tn5fUdCvuJpWc7SdojprDmgImchP0xHvojnfTHeumJ9FDT6xnKrUJIAkS1dZq6ux1KkmzN1Bvr6feXo9V9z8YpTID4MQkY9++4/OcNucpAIrtTVSMdnNtnZ7PLf0cFz3YQ/zpp7FdeAHG+fORCwXkdJpnbR6+UTefxakYt21bgzEWpZxKIqczCKKIoNWARoOg0SJIEooiq3Wssqw+VxR+YlvGs5pK7mou0lJXRVxwMhGF8cEUo4OpqVQogCgJ2H2mKXLmDJhw+s3Y/UbyqQlC+9oJ7dvL8L52Ij3dyGWVsNi8fubVW1hSfg1Tpg959sUIV/0ROa1QCKUphlIUQ2mKw2lK41n2y3AJBs0BglYxSdJ8JgStSKlUIhwOMzAwQH9/PwMDA6RSar2sXq+nqqpqiqBVVVWh1+tRFIVEcgeRkWcZiTxHPh9CVmBXTuKVpI7evIBNo+WSigV8oPF8Avb5mEwNh0XRFEWhP1dgUzzNpkSGTfE0u9NZ9n8lNJn0LLebmWcxMsukp9FkoFKvPeo0gNFkns19E2zsjbKpd4KdwwnKsoIgwGy/Va05m6w9q3C8q5Riv47jtgfUySf5OCCAvQpc9eBqVJuf9m/OOtBNT2Q2U8ywa3zXVN3Z9tHtxPIq6bbqrLR4WqaiZy2eFiy6mXFP+3HCk7H79G8yr3YPttoNzFsb5bzqaj7YdBWfTy7nukiGiEbLH777pff0+rtcdXzx9Ju5cnADn07uQLLbEW12ynYPeZOHrNFLWuMkWTYRTwnERvOUCvLU8RqdiM1jxO41YnFp0WjilAoRcskwidFBCsO7uNz5BiVZ5MG+xeQw4QxW4qqsxlVZjcddhV3yoM8aKYUyFIeSyOnSIecoWrRo3EY0LgOSy4DGbQC7lqiSYjQ5PkXQwuHwIVE0g8EwRcz2b3aXnaf67+fuXX8mXcqxym7iXPM4dkkBBMzmJuz2JdhtSzCa6jDoA+j1fkRRR7JUZnciw854hvZUlq5Mnr5CkXCpxMHJEYMMpoKMNicjpIqUEwWysTzFZJEjmStRAFEUKJeVQ+QSHSbtJFGzsKDSzoIKG81+Kw7TQTUlpTwMbzuQ1hxYrzYJANgqD6Q1a1eBZ/ZRC2MzxQx9iT6VpMW76Yn30BPvoTfRO1V7AeA1eqfIWYOjYSqq5jV6j18fMgPgxCRjP77nVlrr1MhYZPz9fHjX71gaPIWAR+LJM//E6I9+THrtWsoTakPLCyvO4CfX/R8Wd7bzw7t/hVmjQbJaEa1WRJNJJVyl0tRGuaQ2vIii+jkUBJ62z+bnvpVc0/kaN+x8dupcRKsVXV0duro6FF8VGUuAtNZFUjaTyGiIT5SIj2VRDpg5JJOExqFDsGkRbDoUs0IhF0Ia38PKiYdZoNlDsqjj9ZEG9ia9yEY7ZbOToslJ3uggb7ST0zsoau04ZDP+vEwwr1BZgMqign7SMsgoRCSI6AQmDCJxk4akVUPOokUn5dHmopAao5gYpZBUNQ0RBOwuD/5gJVVV1dTX1eJyWni++1H+uPs++lIhfHozF3q8tBrTyIVBFOWAxdJq3ZhM9ZhNDZhM9ZPRtHqMxhrEyc7EdKnM1mRmqvZsSyLNRPHAGkZRoN6oEjOVoB14btUcGvnPFEps64+p5Kxvgi19UdIFda1Kh5HWycjZ8jonzT4r4n7x3WIO9r0Ekd3qaLr9W2b80A+btWKSnNUfStRc9Ucs7dgPRVHoS/Spac1RlaB1RjsRZBlLTmC+toYWfQNzNFXU4cFZ0CInEpTjceRUCsluR/K40Xi8aDxuNB4PktuNxuVCOIre5j8qTmgy1va1F3hcv57l9bvQB7dzztoRznOtJOIY5fkPvsgpmwb5mN/Jt6tciHo9SBJDIzFe39bHm22DbB+MoSgwL2jlrDorS60K/lKGD21VSJdk7le2oE9EKcdjyPE45VicciyGUiwech6CyUypehZ5fxNZezVZvYuMYCFV0JNMQ7l04NoHdXs4z/lLtEKOt63fJiMZKRXGyKUiJMdDJMcjqucDCIKI3efHVVmFwxXAbvBh0TrQKyZ0RR1iGuRYkXIiz8GMRdCJkyTNiOTUU7BCVEgRLSUYT8cZHR9ldHSUdCbNsGmYNlcbaW2aGrmGy+yXsTCwELfbhNEYplTqIJHYSjyxjVIpccj71uk86PVBlZwZAhj0QfT6IHpDEI3OT0Rx0pWV6czk6czk6Mrk2ZfJHWKwdIJAjV5HlVaDV5QwlBSEXJlyukg2UWA8lmMolmUsmSdXkjkaNKKA3ajFZ9VTfRBZm+23ELDpMcc6JlObamOAEg9RSGrIpR0UlEoUrRVFYwaNEUVjAsmAIhlA0KLIZSiV1U7bchm5VCRbSJPOp8jkU+TyaXKFDPliFqVcQpRBVECLhBEdekGLXtCiQ0KLhKSIaqNCqTS1plIuq48H/SzodGi8XnXz+Q56rj5qJ38n2u3/8KTvRCRjf7jzJmpmvw5AX7qFGze/yof1l7OzYiu/PuvXnFl9JoqiUI5G+VM8x9d7I5zhtPKHhfWYjtGIdCTkimW+8/RuHnynn1Nnebjno0tgJEyht5dCby/53j6iezvJ9/aii44jyofWmZUFkXGDgxF7NVFrNWlzkILBA1o7etGETpAQKDPP+FdOsd6PVsjxau4SnsieT6EcQyNPYCgnMJYSmAtxDMXUIU6YLEoUDHYKRidlswPZ6MJq9uHRuHAoJmx5EU9exl2Ag7/Cw8j0ItNLmR5kBimQFhNYxBQ+IYVbTBA1hRkyDzFkGqYoFTDk3ThjrRiyCymJRkqiAVmrwWmM4TOP4jGEcRtGcOlCOHRhjJoDdk9WRDJlPxm5kqxcQZ4qClRRFKpBcqJoNWR0AkmNwIQgM6rIhEolQoXiIc6pT6eh0aRn1hRRUx+rDTokQaBUlmkPJ9W05mR6M5JUMyY2g4ZltfvJmYuWKvvhZR3ZGER7DiJoBz1PjRx4P2UoSz7KhipkXYCy5KaMlbJsolzUUE7nKSfiB77/4nFK8RhKKn3Uz5oiQNmkRzSbERNpyOUP30kQkJxONB4PGo8byeNB4/Yc+vPkJjkcCNLff3PBCU3G7vvmnXRJI7Q0vInFHeWktmHeUir5WrCMz3M+u80f4YGWBuZptDy5dZhndgyza1i9sWb5LFy2qILLFlUcMn/s9tc6+emLe7n7+lbOPsKg3P3GsTgcojg8RHF4mOLwMKVQiOLQMMVQiHI0emB/oKCzkXcGqFgwTpWnjYzs5u30LQwV55EtHuodCEIJgyWNVpdAFKKUCuPk0xHyqRiF3OE3gEavx+by4rZX4jAHsOpdmAQbetmAJidBUoZ3pVdFs4Z97mHuMj/ITvYSFPycK5yLO+thPD5BOn3gdXQ63WQEzY3LLWMyZtBokghiDIhSLo9SKIyQz4cplZKHnZ9K2CaJ2iRhy0iVDCt+BmUHvQU9XdkSnZk8fbn8u08ViyRSoddRadDi1WiwIWAsK4i5MsnxHCOjaULRHKPJHIlcifwRCJuxmGNWKsy8dJjZqTD1sSG8Y4NIpYNItaimCBDUx/3PEQQESVSNgaRRvTmNFkGjA60OQasHrQFBowVJoiTIZOU8GTlHRsmTLmdIlbNklDyyALKAOmJGZ8FisGEx2LAaHdiMDmwGJ1qdHkGUQBJR8gVKo6OUIhH1cXQUeTItc8hnZj9pO4Sw+d716FWN2t8paTsRydgDt99IYO4bKIpATnBxyroRtpYa+WJ1Cb/NyG/OvYt6ez2/7hvhB90hznPb+O38OgySSFkukyqmSBaSJAtJ4vkU2UKRbLFEtlgiUyyTLRRJF4qEYkX+ulVLNKWhLpikviKBSTIjYCSWlIjEBQbHIZMXAQGDBJXkqJFTVJXTVJSS+PNJXNk4llQUQ2wccSyCMKmVqABan0RwWRSLPcVYtp7NmY8RVuaQLh3eAa4zSBitElp9BklKAQnkUoxiboJceoJMfIxC5tDPuVZvwOb1YXf7cDuqsOt9WEQ7+qIBISWgTBRg0unNCwU2uTp4076NjbptZIUMWkVPZbGe6nQt3pQLoXSogCqAIoiUJQMlyUBR0lMU9OQFPbK2jF4XxWSYwKIbw6EfwW0I49ZH0EoHbEimaGA85yKWtxPL2Ynm7cTyDqI5O9GCgzHRS1ZvQ2s3IFi0lE0a8gaRknTg+kgKuAWRgEZDtVZDvVFPk8nAbIsBqQzt4QRb+mNs6p1gX2RS0kiEerNItaZIhVjELxTwyDmccg5bMYMln8GQS6PLpiCRoByLUo5OUE4kUQrFw67DFAQFSQ+SSYtkMapRLqcb0RNA8lUheQJIdgei3UpITNNeGmRHvpuN6V10J3unltEXFAI5A9VFKxUFE/6cHndGwpEGc6qEMZ5DG0sjROOQP/z/gigiuV0HyJrbjcbrQTqIvGk8HiSPB8luP6qQ+/9rnOBk7C66pDBL5r+AGQPm4TQroh1cp/sga+Y1k7WdjW/g3ygXZQSxhCjKaEQJrSShESXKikxZKSMrMqIgIpaCRNpvwOIYoHHeevSSAa2kQyvp0Yl6dJIeraTHqrPg0jtwG+x4DU78RidugwOb3qbWDxVL6hfoyAilkRHk/k1YRu5Fo4yRiNcyuttLMTyOks9TFjXk9U5yBjdZg5u8LUjeFiBndJOVbOSUg3TNlKKqKSanEaU0Gk0WQcwAaeRSklIhSTEXn6rb2A+DZMZhCeCyBSlZdTzj3cRacxuOspXrRi/mgugqJA54HgWTQtxaIKHPEhXSTJSSTGRipLJH9oZ0Oh0mkwmLRYPFWsJsymMwZNHp0khSHIQYijJBuTRKWT6cTOwnbBpdkKRUwYQYZAIPY4qDSNlMpKwnXJQIFRTGiuXDjndpJSr0Oip0GhqTMQL7urDs2Yu1qxPXYC/ueGRq37jORLe9km57BV32CrrtFQxafJRFCR1FfEIMH1FqtXGqtQkqNTECYgyfMoFLHsdeGscgH34dyhoTZbMfbBVItiCSPQjWIFgDYA2SMtjokXN0p4em6tN64j0MJAemCmYBgv8fe3ceZ0lVH/z/862qu/XtfZl9hhlgBhhABEbA3QhGIIloggESE5KQkEUTzfrI83se488naMyTaEjURCJGJRokuDAikUTRCIRdGIWBYYYBZp/pfbl7VX2fP6q6+05P9/Sd9Xbf/r5n7uvWcurcc/r2Pf2955yqyi6dGPJc0bKC1mRr9EhFz82BR9NwCbd/GL+3l8p4oHag96DALRw9NDCWRAK3p5tEz6K4d23Kcxy4ue3tJ71BW4jB2N23XUPzmicIApft2zfQlRjj3Tv+izfJnzB6yr+T8AIS3VfzYuptdJR+TMfAFygFOfywTIg/a/4AlZGzKe69GlAyy+7Ea3l+xrSqDhpk0KAJDbKon42eq5aTtNDktdGWameZk+KsUh/vLHyF9cF/Uwyz7BreQGFvM15/L/QeIKyEFDLdFNOdlJKtlJOtlFNtVJo6KWfaKSdaKTlN+JKcUpYSGozgecO4Xg7HyaE6il8eplIYpFI6+DMYJB1G12R5eVGO57K7KTllmsMmLhl7FW8YPp/zc2eS1ASScHCaEmjGoZD2KSR98k6JnJTIhUXG/Dxj5TxjxRyj+TGC4ND2JpvN0tLSQmtrM62tAdnmMVKpIVy3j1D78f1+fL+PMOiHKfekDNWjrB3kgw5GKx0Ml9rZU+rhZX8Ju8Iu+pw2CqlmgqYk2uRF8zUAJwzpOdDL8p17WLVvL6f0HWBl/z6WDBygZ6SPZDBzUFV0E4wlmhhNNpFPNVHONBM0tyCtrSTa20l2tJPp6qC5p5O2rmZ6sgU6EiMki3uQid61l2B458H1SbdNGfKcfAx7STYPPEdvoZf+Qj99hT76i/FzoZ/+Qv/EbfKq3nQyZVhayrCq0srSUhOLikm68g5tOWgerZAeKeINjiGDI1CZps6eh9fZGQdnXYf2tnV14/VEAd3xHlFY0MHYbf/7U+x0+3jdRXdS3nkKA3vfyhUtf0PrWIX1F30dvzRC60tfx5UyaZSUBrgEuOLjSkBCApIakCQAQp4d+AXGyktYt/xWSI7gS4gvIYEoQbzsi1KRmYfLABKSIiEp0pLiN4b6eE/fDgYTaf5pzZvZumg9Tal2mlMddFc8evI+XaNlOoaLtA7naRoaJTEwRNjbj9/bS6l3gKLTTDnZQjnRTCV+lJPxcrKVSro12uY2EeCCFlEdQ8OxieCt7AyxafVmnlm5A4CzX2rjnO0tJH2HtJsl67WT9VppSXfTlummOdlBxmkmGaZwNPrDXManIGUKlCl6FUqJgJLnU3R9ik6FopYpaIlCUKLglwjCQxsy162QyRRpbQ1pbi6TaSqSSuVJJMZwnBFExlAdBQ49FiBwOhhLrGZYV1DZn8HZ4ZN5ZZi2HQdY/Moumqt69Xb1LGHbylN4ccUp9K0+lbFVp6GZdhL5AH+0wvBgkd6BAkO5ab6NVfEciSfiKqFCMiywSAZZzBCLZYDFMjjxWCRDLGaQJTJAWg5tLPJuC2PJHoqpHsqZRRSaFrEv28SelMNer8LecIRd5f3sGH2FYlCcsUxJJzkRoLUkWw4K2FqSLbRrho4xaBsLaR4ukxkqkhzM4Q6OQO8Afl8UwIUjI4dm7nkzDolW97a5nZ3HLWhbiMHYP936Rzx3esj6/Bb6n3oLp736m1z51Ha2JdfwnuJvkVu6ldzad5MYe4GmHXdDkIEwBeqRclOkEs2kE80kki14iSzqOqjrEDoOvkD/jhS5vQmc5hA9I6DS5KEJDwiQsICEeZwwj4Q5JCzgBDkkyCHhGE4wihOO4ISjCGPIlKDCUeUXRsd4/+AwTWHI7dnFfKbpVHLaEgV0YRPqp2nLuywtCD0lpbMS0u6HdJQrtJcrtJbLNBcLNBXzJAoFwhJUNBkFbBOPlokgrpJqpZRsJXSSqJYpSR+vtG9ie/dz7OrcReAGpMsepxxoYdWeJEv7UzgqZNxmWhPddLWsoCXTRTrRRMrJkHTSeJrECz3c4NBhMEUp4ZPzShTTAflkhbxXJi8lclokFxQZK+cpVKb/nHqeQ1sbNDf7NGXLZDJFksk8iUQOxxlFGCLUAVTjOb0BuIPg9QpeXwa3N0PQm8I54JPpHcXzJ9vEUiLBnu7F7F60hD3dixhsbmcsnWUklWE40cSom2HMzTAqKfzAhXKIVELwFQkUghA06vyfSdIVMkmXbMqjJZ2gO62sTQ5wqruf1exnabCbrvJuWvI78UZ3ItUTCpPN0YkDmQ5ItUbz0tLxc7xeSTUz6Ah9hPSrT19Yoj/I018Zo784SF+xbyJ4GylPaadUyRZhWSXLqnILS0sZFhWTdBZc2sZCsqM+qeEC3tAYMjAM0wTVkkhE89fi3ja35+Ch0sSyZWTOO2/mH9DU/GpswxprplwsdcoDvGbZc7jpEqW+JCsGW/hG4pdYvfgpcuk2/u7lT/OLw/fVlNddwRt5uLKaD3lf5Jf6nkSR6KEQ4sTrxM8hZccn7ynDjsOI4zDsOgw5LsOOw5jjkAlDfn5sjJV+wPczaW7pbGNf6RlyuzdPXwAH6Igfa0AQPBK4JGitQEupQHOpTLY0THPFpaUotJSguahkiyGd+YBMwSdVFLxyEqkk0CBBMZHlwfWj3HfeK+TSZc7ZeRpv3PZG0sESKqkE5YwSaJ6RMMdwOIaOjaEj+1HNQZhDNUfGSZH12mjyWkk6maiH0EmTcFI0OSmSboak20bSSZFwUngkEREqBJSkEgVvUqEoZYqVCoVSmeJwhYJbZlAqFKlQpExloodI8bwyiUSRRKJEOhyjY2iI1sER2voKdB/YSvrAs0icXBNKZZlSebUytELJrUiwb3knvekOBqSJkpPCdwJyDNCvAb0tWQrNHixtBpqjYVw/aqxSfkg6gGSgJHxwfUX8EMohWgkJygGVcoK9pSwvl5ZO/eJbRelw8ix3h1giQyySQXoYoKcySE9lgEW5fhbJNk5hiHPl4IYiVKGXFnY5rQy6KQa9JMNughHPY8x1ybkOOVfIF4W8U2DMydHr7KIQ/xyLlNCpBWuKH8vBEZfmRDOtyS465RSWldIAdUnvAAAgAElEQVT05D26cg4do0rrWEDTUIn0cIHktudxHnsUGTm0RxPPi4YMppnP5i1aRPrMM0ksWTLrZ2+h2ptu5yvyDshC+pKA1/U2kzrzK1y5eTNfyH6WX1r9NzT7Zd61fy/NqTMR8niOT1J8klogHQ6RCYtkciWyYYGk+njq80JxCV8auYyc38zPZB7mxsy3yewokVAfTwNcorMqJR5AFB0fSBz/nYnaulCiFL44jIow5DgUREn5Fc4o5VjkV3gxmeFfOhaxJZlhqQySkwEKhJTEpyIBReCl+DEjFQiTaJjELftkixWa84M05x2aC0JrUWgtQltJSRV89nWX2Lq8xItLCgSu0ppPcNGLyzhz76ksHT0VP9FGKdFMqdkh1Dx+OEp/OELf8BA6tBe0hGoZtDSxLChJJ03SzZByMqTcJpJOhlTVetbL0uU1kXK6SDgpkhJdcDogjAI0SlEbJxVKVCgGFYr9FUqDPgNxO1cOSrijgzSPNdM82kLLWDstYyO0jI3SNFbAGb9aNj5hYgy/O4+/SCmcUyHoUfz4MdrustdJ0Ot10++uZox2cjRRIENZ01Q0SSVM4IcJKhz+C5PjhyRKAW4xxCkEUAqgGOAXfYbLIUPFCoyVIFAe0iSwMn5smPitSeKzXHpZLftZ4+xnTXiAU/YfoFX6aWEnWfI0k6dJ89HvH5AAFsWPqXy3CT/ZTJhoIUy2UEqtYjCZYSCVYiiRYMAVhlwYIKBfy2wOCjxQGaa/MsrYlJuui0K24LKy0sLKSgtLixl6igk680JrTsmOlEnt3o73zI9hcDiazwukzz6bNV+767A/u6NRUzAmIpcDtwAu8DlV/csp+1PAl4ALgX7gGlV9Od53E3ADUZfGH6jqfbXkeSzc5j7S6RwObXSvfw1bn93HOj2DT5xyGYmwTE+4m8e6TqWCR0HTFDRDiSS+JqiEKXzNUAmzjJbb+FzpNSxzRyk1nckX5My4kZK4kRJACFWo4FICCkAJpRKGBGF0rbIQhdDnrfoUV+qjFEhxW/hmnh07hdNGlXWqJCjhOgVS7hgZZ4ykm8Nz8nhOEXGK4JQInYC8I+TEIecIeceh6AjlJiGXFQZEKIpDQRxKjlAWoSQQTO1xjRtZFSFVVtbsVTL5rTzfuZVsEbJFJZsXmkpZmirNpIJmEmEzXpjFoYnA66aSaKaUSFNUhwF1CEQJ455CKIAOxw1bBSV6Rss4hCRF8MQh6TgkxJ0I1jJOmjYnRcJNk3Ra4sAuiSsJtDSCju6DkUEY3o0zvAc3P3mBXT+VIde+hIHTuxjq6GCwvZ2hlgx+oogk8yQSJRKFIokdRVoSJToTO0gktsaBXZFksoSXKFJ00vTRTT/dDDkdFJMZCsk0JTIUSFMiTYEMpbiBK0kmXk9TIEWIG51o4Wv0jbMSTnlWeist9PvdPFsJcfwQqShaCQnL4cRZaUJIJ6MH9a4tlkGWOkN06igZv8SpfokmRshQIkMxfi6RmKH3MARyIoy40ReFEcdh1HHodxL0O0kGHY8hp8KwM8KoIzwvHpvEo5RyqaRcpMvDXZXADV28sAlX20hWXFrzHq2FBG3FFC2lFM3lBJlyknTFJdnrkNijOEEfoTNE6LxE92te4C3/88bj9XGf1Xxrv7JDS2BpCI6wam8f9698PbmhDLmzv8JnM79D3kvx9U0fYMPIs4ccG4gQOA6BC6ETBU57w07+Jn8d91UuZpnTyyeyf88bkj8hDIQQh1AdQhwqVV8umeZ5/CunhEpCA5rCEp1hhXVhhUT8ixsgDCfSdAB/OLgPT0PcMMQLQ9x4FMYHRh1n4gtqwREKErVn0bNQECd+ntyeyzjkmxxyjsOAOOxwhNG4nZvu1OuxVJnNy3ews2MHLYUf0JpX2vJCW15oLWTIllrJVFpIBy04msEhCWQREqiTwncS+G6CwHXxQ8F3hVKoBAKBhAQSoAyA7ouDtxJoGbRMUiDhuKTEJeVG7VsCj0zZp7VYIFXMkSjk8AojOIUhpDB0UA+SeimCbCeVltWMLG2n0NzKWHMLIy1ZxjJJiuJTkjJFyiS8AslcnpSfJ9lfIJnKc2rqGc5MPkbC83FdH8f1cR0fx63gOFH7UCZBgSZyZMnHjxxNk8tulnxTlnxTM3lpmdiep4kcGcpUXffMD5FyCKUAKYdIKcApB/ilgJ3lJnaWVvLDUtTGMe0AkpKhRDMFWiVPMwVapBA/5yeeW/wCLeWqdfbRQoGVUmA9eZol6o0M1SHQBEH8dz3QJHmSDDhJ+iRDn9PMgJNlWDKMOSnykmSH47E141JOO9Dh4YbxQxN4gUe6kiDrp0g1hXyw5k9z7WYNxkTEBT4NvA3YBTwuIhtVtbor5wZgUFVPF5FrgY8D14jIeuBa4GxgGfBdEVkXHzNbnkft6eHLCF9sY+SKV3Hq6tPoaHmMh77+NM92nc8Fu4cob/sVSgIEARKGJIIQTxU0JFTY5SR4KtHMj5KtlFyPa/r20hmkQcf7FTQ+sVGj/6qMD59F87DLNLl5Ul6BtJsj5RZY3rSNruRedhTWs2n4UrJhmotEwIkmbxcdh6K4lB2XUFxyjlCJ1ysiVBB8NyThFkhJgSR52iSPJ0V8b5RCYoxCIs+YV2IsUWLYLTPghQSe4oWTLVWThizxfbr8gNcUipzllymLUGyBQlsUvBUEii7knVEGJQr4xsQl57jkcKiEDlIW3JLglZREGdwAPF9JlJVM2SNb9ib+IKcqLknfJeW7eIFDwnfwQvACwQ0VR6O5D9EjwNEAJ/QRDQhDH8cv48WXjFAgn24m19ROrmslxWwbpWwHmszgOQ6euCQdh+UFl1OKLp5kcZwl4Hio60YPcQhciRpVB3xHGcOnLBUqboEwOUpbYoy2ZB518uAMg1NB4obMcQMcJ4gaNyfAcaNncQJCRyg5Hr6TpOQk8RNJSskUZYkfmqGo6eiZFBVNx+vRoxSkKFdSlCsZfL+Nvf4p7K+EiB/iVgA/xPMVN1S8gIlnL1TcELwQkmFAOvRJa0AqDElqSBIlgZJQxdMognARHBUcHEQdOnHpVAdVD8WFWb45z8iFciZ6BFIhkIDAqaD4QIUt/mbecnQ5H7H52H5l9u7jo3I7g7tXMVZ+mWV7XsMPNpzHo+4FuBrwW72fZ097gm9kN1Dws1TKLYTlNsJyM06QwStkCMpNlAOPnDocUJcu4E94hUX4hGNX8EOuiH4+471eCtHFIhQVBSdEnRCR6BRgdUIExSHE0ZDxi0NHhzqE6qLqEh72d2b8K2z1Kd7RuEIIICEa7486bBWVyeKNH6VxVmG8JigJhYQqTggqcTsdB3+hRttCVTQBYZsStgqBhBQFcjolPlCi3rGwhKjixsN2TghJBSeMXkeItolK3IalEFLR+T0IEhI/FAlD3DBHRZWKwFgmi2aaCGUpoePiOy6+6+GLh++4BK6L60Rf9h2IP41CalTIjPl4ODg04TotiDio44I4qCMEEv2EfUcpSYgSRhNuJJpRqASETgXcgNDx47bNp8mp0ORUwAkQt4Tj5HGcfYgbzat2HB/xorbPEZ/AFSqOS8WL2rqyk6TsZCi6GYqZNKVMhiJpihp9WS0StX2lIIX6Lo7vIBUHJ3DwfI3atUDx/Kg9c3ylHMBwqORC6As0+rsRRvu9MDqxwVMOXo5/VrXIxI/pOJQRqYBTQcUndCr44uOX99WU95GqpWfsImCbqm4HEJE7gKuA6obnKuDD8fJdwKckmgF3FXCHqpaAl0RkW5wfNeR51PYuzfDA2vM4pX8HL/TtiJqHd57OGt1KbjH838tb4g/5wZ3wk8uCCjRRZiUVvs7SWV9TJ977Q/M8vg69p9rhTP1FU2Bv/HhmmvSTDWX1L/PBtZl+PsFMNZ7+QzF1rslsZirNQfnUkKXEzf2h2xNEP61W4NCzZWcr07Smm49ZNTH00J9BGYcyaUaZ7ZrZ1ccGzDST7nBlO9IDjp8Ldp/UOxbMu/Zr9yq47czraT97kA5dhasB69iK40eBx391voHvd74BlfG2ZnxgMeoJI16PhhOdqp4tDlqeamIKxsR1vJxD8p9qus/xkX62j9XUPrxDy3FwrafbPnuZD79/9j/9k8fL1G06zbbp0h3pNp0pXXX6QwNkqTpKdKbjJ/9OHO5nd1B7qwWisaPJr3nj3RgBMM2FLqZ5vdoc8fT7mrN36CmdmAva1hKMLQd2Vq3vAi6eKY2q+iIyDHTF2x+ZcuzyeHm2PAEQkRuBGwFWrVpVQ3FheW+Bpen9VDSFihASdWPrlD+EM/+iRd9mJrcfpgmr/pJXtVGq13X6AKD6wzDzC5w8MzW4E+Tgb6gH7Tii1zkyh/uoT5v+CH+OM9V76useFBAecraNTlmbfujkWMrTKCrOSQ3G5l37dfXFb+Xh/VuQMP5j5TiEkqAik62KaDgRRDhRweMeGUUIcVRBNXqO043/ih4cWh3cBkVtYlUIJ3FbJoA40WMyNeOjAxN/b2f48M38mYxOghEEmfLsiBPnOzkpJC7UZL46PlYRLY/3iIUT+6O9B4dfB2+f+GlUl7H68z3T8pQ8qzdo1YJWpRr/kzN129TW4+DZeuPh0aF/jSaOk+qjD904tYwHvx/C5JjP9H+Pam0jD3/8dF/0Z3fkof0R5n/QH+vZpZ0j/upbk1qCsZmiiFrSzLR9ur7s6ftbVG8FboXobKSZiznpYzfcVEsyY0xdvONkvti8a7/Ov+AN3F9LQmNMw6hlUsguolMkxq0A9syURkQ8oA0YOMyxteRpjDHHytovY8ycV0sw9jiwVkTWiEiSaELrxilpNgLXx8tXA/dr1H+8EbhWRFIisgZYCzxWY57GGHOsrP0yxsx5sw5TxnMo3gfcR3RSx+dV9VkR+QjwhKpuBG4Dbo8nuA4QNU7E6e4kmtjqA+/V+K6r0+V5/KtnjFnIrP0yxswHDXkFfmNMY1iIV+A3xjSOWtuw+tw50xhjjDHGABaMGWOMMcbUlQVjxhhjjDF1ZMGYMcYYY0wdWTBmjDHGGFNH8+psShHpBV6pMXk30HcCizNXLIR6LoQ6gtVzOqeoas+JLMzJYu3XtKyejWMh1BGOvJ41tWHzKhg7EiLyRKOcEn84C6GeC6GOYPU0kxbKz8jq2TgWQh3hxNXThimNMcYYY+rIgjFjjDHGmDpq5GDs1noX4CRZCPVcCHUEq6eZtFB+RlbPxrEQ6ggnqJ4NO2fMGGOMMWY+aOSeMWOMMcaYOc+CMWOMMcaYOmrIYExELheRLSKyTUQ+WO/yHC0RWSki3xeR50TkWRF5f7y9U0T+U0S2xs8d8XYRkb+L6/1jEbmgvjWonYi4IvKUiNwTr68RkUfjOn5VRJLx9lS8vi3ev7qe5T4SItIuIneJyPPxe/raBn0v/zD+fX1GRP5VRNKN+H6eKNZ+zb/febA2rFHez3q1Xw0XjImIC3wauAJYD1wnIuvrW6qj5gN/rKpnAZcA743r8kHge6q6FvhevA5RndfGjxuBfzj5RT5q7weeq1r/OPDJuI6DwA3x9huAQVU9HfhknG6+uAX4jqqeCZxHVN+Gei9FZDnwB8AGVT0HcIFracz387iz9mv+/c5XsTZsnr+fdW2/VLWhHsBrgfuq1m8Cbqp3uY5T3e4G3gZsAZbG25YCW+LlzwLXVaWfSDeXH8AKog/xW4F7ACG6wrE39T0F7gNeGy97cTqpdx1qqGMr8NLUsjbge7kc2Al0xu/PPcDbG+39PIE/P2u/JtPPi9/5uKzWhjXA+1nP9qvhesaY/GGO2xVvm9fi7s/zgUeBxaq6FyB+XhQnm691/1vgz4AwXu8ChlTVj9er6zFRx3j/cJx+rjsV6AX+OR7K+JyIZGmw91JVdwN/DewA9hK9P0/SeO/niTIv3/fZNHj7BdaGNcT7Wc/2qxGDMZlm27y+foeINANfAz6gqiOHSzrNtjlddxH5WeCAqj5ZvXmapFrDvrnMAy4A/kFVzwdyTHbnT2de1jOeL3IVsAZYBmSJhiummu/v54nScD+PRm6/wNqww6Sfd/WsZ/vViMHYLmBl1foKYE+dynLMRCRB1JB9WVW/Hm/eLyJL4/1LgQPx9vlY99cD7xCRl4E7iLr5/xZoFxEvTlNdj4k6xvvbgIGTWeCjtAvYpaqPxut3ETVsjfReAlwGvKSqvapaAb4OvI7Gez9PlPn6vk9rAbRfYG1YI72fdWu/GjEYexxYG5/9kCSafLexzmU6KiIiwG3Ac6r6iapdG4Hr4+XrieZijG//1fgslkuA4fHu47lKVW9S1RWquprovbpfVX8Z+D5wdZxsah3H6351nH5Of9sCUNV9wE4ROSPedCmwmQZ6L2M7gEtEpCn+/R2vZ0O9nyeQtV/z7Hfe2rCGej/r137Ve8LcCZqEdyXwAvAi8P/VuzzHUI83EHV5/hh4On5cSTQm/T1ga/zcGacXojOxXgR+QnRGSN3rcQT1fQtwT7x8KvAYsA34NyAVb0/H69vi/afWu9xHUL9XA0/E7+c3gY5GfC+B/x94HngGuB1INeL7eQJ/ftZ+zbPf+ao6Wxs2z9/PerVfdjskY4wxxpg6asRhSmOMMcaYecOCMWOMMcaYOrJgzBhjjDGmjiwYM8YYY4ypIwvGjDHGGGPqyIIxM2eJyMsi8p56l8MYY46UtV/mSFgwZowxxhhTRxaMGWOMMcbUkQVjZl6Ib09xt4h8W0Sy9S6PMcbUytovMxsLxsycJyJLgP8iujnrO1Q1V+ciGWNMTaz9MrWwYMzMdeuB/wbuUtXfVdWg3gUyxpgaWftlamL3pjRzloi8THQj1j7gElUdq2+JjDGmNtZ+mSNhPWNmrvsg8BPguyLSUe/CGGPMEbD2y9TEgjEz1/nALxM1aD8QkcV1Lo8xxtTK2i9TEwvGzJynqqGq/hbwPeCHIrKq3mUyxphaWPtlamFzxowxxhhj6sh6xowxxhhj6siCMWOMMcaYOrJgzNSViOwUkQtm2PcxEflAjfk8JiJnH9/SGWPMzKz9MseLBWOmbuJTvZcBz0+zrwf4VeCz8XpKRG4TkVdEZFREnhKRK6oO+WvgIyej3MYYcyTtV7ztX0Rkr4iMiMgLIvKbVYdY+7XAWTBm6ulc4CVVzU+z79eAe1W1EK97wE7gzUAb8L+BO0Vkdbx/I/BTIrL0RBbYGGNiR9J+AXwMWK2qrcA7gL8QkQvjfdZ+LXAWjJl6ehXwoojcIiK9IrJHRN4W77uC6H5uAKhqTlU/rKovx6eK3wO8BFwY7y8CTwI/fZLrYIxZmGpuvwBU9VlVLY2vxo/T4n3Wfi1wFoyZenoVsAG4F1hM1KX/P+J95wJbZjowvnjiOuDZqs3PAeedkJIaY8zBjrj9EpHPiEieaGhzb3zsOGu/FjALxkw9nQvcrKr3qWoIbK7a1w6MTneQiCSALwNfVNXq+Rqj8XHGGHOiHXH7paq/B7QAbwS+DpSqdlv7tYBZMGbqQkQEOAf4VtXmc5hs0AaJGq2pxznA7UAZeN+U3S3A0HEvrDHGVDna9gtAVQNVfRBYAfxu1S5rvxYwC8ZMvayJn7dVbTsfeDpe/jHRMOSEuAG8jWhI4BdUtTIlz7OATce/qMYYc5Ajbr+m4RHPGYtZ+7WAWTBm6uVVwE/04Ptxnc9kY3Qv0ZmT1f6BqMH6uSlnKSEiKaLJ/P95YoprjDETjqj9EpFFInKtiDSLiCsibweuA+6P91v7tcB59S6AWbDOpepboIh0AUuAZ+JNXwKeFpGMqhZE5BTgt4nmWOyLOskA+G1V/TLRqeI/UNU9J6sCxpgF64jaL6IzJ38X+EeiTpBXgA+o6t1xemu/Fji7UbiZs0Tko8ABVf3bGtI+Ctygqs/MltYYY040a7/MkbBgzBhjjDGmjmqaMyYil4vIFhHZJiIfnGZ/SkS+Gu9/tOqq6IjITfH2LfE4OSJyhog8XfUYqfUeXsYYY4wxjWTWnjERcYEXgLcBu4DHgetUdXNVmt8DXqWqvyMi1wLvUtVrRGQ98K/ARUT38PousE5Vgyn57wYuVtVXjmvtjDHGGGPmuFp6xi4CtqnqdlUtA3cAV01JcxXwxXj5LuDS+DIEVwF3qGpJVV8iOg34oinHXgq8aIGYMcYYYxaiWs6mXE50g+Zxu4CLZ0qjqr6IDANd8fZHphy7fMqx1xL1nk1LRG4EbgTIZrMXnnnmmTUU2RjTCJ588sk+Ve2pdzmOh+7ubl29enW9i2GMOYlqbcNqCcZkmm1TxzZnSnPYY0UkSXRK700zvbiq3grcCrBhwwZ94oknZiuvMaZBiEjD9JivXr0aa7+MWVhqbcNqGabcBaysWl8BTL0WykQaEfGANmCghmOvAH6kqvtrKawxxhhjTKOpJRh7HFgrImvinqxrgY1T0mwEro+Xrwbuj69MvBG4Nj7bcg2wFnis6rjrOMwQpTHGGGNMo5t1mDKeA/Y+4D7ABT6vqs+KyEeAJ1R1I9H9Am8XkW1EPWLXxsc+KyJ3Et081QfeO34mpYg0EZ2h+dsnoF7GGGOMMfNCTbdDUtV7ie61Vb3tQ1XLReDdMxx7M3DzNNvzRJP8jTHGGGMWLLtRuDHGGGNMHVkwZowxxhhTRxaMGWOMMcbUkQVjxhhjjDF11NDB2JZH9vLwN7bVuxjGGFOz0svD7L/lR5T35updFGPMSdLQwdh3v/AcP7pvR72LYYwxNdNSQGVvDq0E9S6KMeYkaehgzBhjjDFmrrNgzBhjjDGmjiwYM8YYY4ypowURjEW3yTTGGGOMmXsWRDAWhhaMGWOMMWZuWhDBWFAJ610EY4ypyYFXXgJgYPeuOpfEGHOyLIhgLPStZ8wYMz+EQXDQszGm8S2IYMy3njFjjDHGzFELIhgLfAvGjDHGGDM3WTBmjDHGGFNHCyMYs2FKY4wxxsxRCyMYs54xY4wxxsxRFowZY4wxxtTRwgjGbJjSGGOMMXPUwgjGrGfMGDPv2PURjVkoFkgwZo2aMWZ+kHoXwBhz0jVsMFZ9c/CgYleyNqZRiMjlIrJFRLaJyAen2Z8Ska/G+x8VkdVV+26Kt28RkbfPlqeIrInz2BrnmYy3rxKR74vIUyLyYxG58sTW2hjTyBo3GKu6Obj1jBnTGETEBT4NXAGsB64TkfVTkt0ADKrq6cAngY/Hx64HrgXOBi4HPiMi7ix5fhz4pKquBQbjvAH+F3Cnqp4f5/mZ415Za7aMWTAaNhgLg+pgzOaMGdMgLgK2qep2VS0DdwBXTUlzFfDFePku4FIRkXj7HapaUtWXgG1xftPmGR/z1jgP4jzfGS8r0BovtwF7jnM9jTELSE3B2AkaFmgXkbtE5HkReU5EXns8KjSuOhize1Ma0zCWAzur1nfF26ZNo6o+MAx0HebYmbZ3AUNxHlNf68PAe0RkF3Av8PvTFVZEbhSRJ0Tkid7e3tpraYxZUGYNxk7EsEB8zC3Ad1T1TOA84Lljr86k6mCsesjSGDOvTTe/feoHfKY0x2s7wHXAF1R1BXAlcLuIHNKequqtqrpBVTf09PRMk50xxtTWM3bchwVEpBV4E3AbgKqWVXXo2KszKQgme8OqJ/MbY+a1XcDKqvUVHDpEOJFGRDyiYcSBwxw70/Y+oD3OY+pr3QDcCaCqDwNpoPsY6mWMWcBqCcZOxLDAqUAv8M/x2UifE5HsdC9+tN38YaCoBhQHP8nerY/UfJwxZk57HFgbn+WYJOp53zglzUbg+nj5auB+jb6RbQSujadVrAHWAo/NlGd8zPfjPIjzvDte3gFcCiAiZxEFYzYOaYw5KrUEYydiWMADLgD+IT4bKQccMhcNjr6bX0MFLQDK9ifunjW9MWbui7/svQ+4j2hqw52q+qyIfERE3hEnuw3oEpFtwB8Rty2q+ixRb9Zm4DvAe1U1mCnPOK//AfxRnFdXnDfAHwO/JSKbgH8Ffk2tC94Yc5S82ZMc0bDAriMYFtilqo/G2+9ihmDsaEXNYtQ2RiOmxphGoKr3Ek2ar972oarlIvDuGY69Gbi5ljzj7duJpmpM3b4ZeP2Rlv1IWGhnzMJRS8/YcR8WUNV9wE4ROSM+5lKib6vHjaoycRLUofNqjTFmbrLvjsYsOLP2jKmqLyLjXfgu8PnxYQHgCVXdSNR1f3vclT9AFLARpxsfFvCJhwXirH8f+HIc4G0Hfv241kyBOBiznjFjjDHGzFW1DFOeqGGBp4ENR1LYIxGGClTiNQvGjDHGGDM3Nez4XfUwpfWMGWPmD2uvjFloGjYYqx6mtMbNGGOMMXNVwwZj0fkDNoHfGDO/lPwK+2SIsl+ZPbExpiE0bJSiIaBRY2b9YsaY+aJ3bJB7Uk8ylButd1GMMSdJ4wZjqqj1jBljjDFmjmvYKCXqGRsPxqxvzBgzz9hVX41ZMBo3GFOdvM6YDVQaY4wxZo5q3GAsVDS+zpjdMs4YM1+UC9GXyFLRnyWlMaZRNG4wVnVpCw2tUTPGzA+VUnSTEr8UzJLSGNMoGjgYmxymDAMLxowxxhgzNzVuMBZOnk0ZWs+YMWaesEkVxiw8jRuMKRCORcsWjBlj5hmb62rMwtHAwZii4TBgPWPGGGOMmbsaNhgLfB8NRwDQwLdvmcYYY4yZkxo2GCuMDgMKTgughIGdmWSMMcaYuadhg7GgUgZAJB2t2013jTHGGDMHNXAwFgdfkjx43Rhj5jCx27cZs+A0bjDmx7dCkhQAftxTZowxxhgzlzRuMHZIz5idUWmMMcaYuadhgzF/Ys5Y1DNmw5TGGGOMmYsaNhgL/fGesKhnzIYpjTHGGDMXNWwwFvgVHBw8JxOtWzBmjDHGmDmoYYOx0K9w6bJf4Z1LXgvYMKUxxhhj5qaGDcYCv0JnasnkugVjxph5xO4ZYszC0bjBWHDw2ZO+BWPGmPlg/DJjdgs3YxaMmoIxEblcRLaIyDYR+eA0+1Mi8tV4/6Misrpq303x9i0i8rS2Z50AACAASURBVPaq7S+LyE9E5GkReeJ4VKba1Cvu2xX4jTHGGDMXebMlEBEX+DTwNmAX8LiIbFTVzVXJbgAGVfV0EbkW+DhwjYisB64FzgaWAd8VkXWqOn6jyJ9S1b7jWJ8J4ZSeML9sE/iNMcYYM/fU0jN2EbBNVberahm4A7hqSpqrgC/Gy3cBl0p0T4+rgDtUtaSqLwHb4vxOuOphSlc8mzNmjDHGmDmplmBsObCzan1XvG3aNKrqA8NA1yzHKvAfIvKkiNw404uLyI0i8oSIPNHb21tDcSNh1bCkK57NGTPGGGPMnFRLMDbdXWunziydKc3hjn29ql4AXAG8V0TeNN2Lq+qtqrpBVTf09PTUUFzY9xc3M/b44xPrriTsOmPGmHlBpm02jTGNrJZgbBewsmp9BbBnpjQi4gFtwMDhjlXV8ecDwDc4jsOXweAgVIKJ9ahnzIIxY4wxxsw9tQRjjwNrRWSNiCSJJuRvnJJmI3B9vHw1cL+qarz92vhsyzXAWuAxEcmKSAuAiGSBnwaeOfbqREbDLEGqfWLdc5J2o3BjjGkAqsoz3/kPfnz3vfUuijHHzaxnU6qqLyLvA+4DXODzqvqsiHwEeEJVNwK3AbeLyDaiHrFr42OfFZE7gc2AD7xXVQMRWQx8I5rjjwd8RVW/c7wqtaW8llLmhYl110lZz5gxZp6x64xNVRgd5Ue33MmykVMI1eOF5gdYd+kb610sY47ZrMEYgKreC9w7ZduHqpaLwLtnOPZm4OYp27YD5x1pYY+EaDix7LkpmzNmjDHz2O6nnmH/l3/CKd46Sm1l3NEEY9/eQ/8Zu+hasaLexTPmmDTsFfgdmfxW6Tkpu7SFMWZ+sY4xAMIg4Mf/9C1KX9lLl7sEuaSZU296Ky0/s5Ku5DI2/909VErFehfTmGPSsMGYUNUz5iTt0hbGGDPPjO7u5Zk//wadL7ZTTpfpfu+5LH/n+YgjdL3xdMI1LqtlPY98+sv1Lqoxx6QxgzEBqfpa6TpJArsCvzHGzBuv3PM4vbc8TVuli9zpJdZ9+Aqyq7oPSrPi1y/GT/ss2bOMZ77zH3UqqTHHrjGDMab0jEkS3+5NaYyZB+ITmxasykiRLX/5HdwHixQYI33dCs74zctw3EP/XDlJl2U3biDlZSj++z72v/RiHUpszLFr4GBssmquk7A5Y8YYc5QqfQXC0om/PFD/I9vZ+dEHSA+m2d++h7V/fjmLX732sMeklrfS/PaVLMms4ZlPfYtSPn/Cy2nM8dawwZhT9eXSlYTdKNyYBiEil4vIFhHZJiIfnGZ/SkS+Gu9/VERWV+27Kd6+RUTePlue8fUVHxWRrXGeyap9vygim0XkWRH5yomrcX0Fo2X2f/JJ9t78KAN3vUB55yjRZSSPn7Dg88pnHqTwzd3kK6MU3wwXfvAaktlMTcd3vOVUOCXBWu/VPPj3tx338hlzojVsMFbdM+aIR2DDlMbMeyLiAp8muo3aeuA6EVk/JdkNwKCqng58Evh4fOx6omsgng1cDnxGRNxZ8vw48ElVXQsMxnkjImuBm4hu63Y28IHjXde5Ek6UXh6BQPFWZils6uXAp5/mwC1PMfbwHsLCsfeW5Tb38srNP0ReCXiZzSz7w4s448q3HFEeIsKy6y8kTMPyA6t5+p5vHXO5jDmZGjgYm+wac8S1CfzGNIaLgG2qul1Vy8AdwFVT0lwFfDFevgu4VKKJWFcBd6hqSVVfArbF+U2bZ3zMW+M8iPN8Z7z8W8CnVXUQJm7r1pByWw7ghxX+9Xsf5hH3O4yuKxBqwNDdL7L3o48ycOcWSi8P19wbpUFIkKvg9xfY9y9PM/il58nnh9m5+mUu+T+/QedRXjPMaUqw5NfPJ5too/gf+9m95bmjyseYeqjpoq/zUfUwpSOeXdrCmMawHNhZtb4LuHimNPEdRIaBrnj7I1OOXR4vT5dnFzCkqv406dcBiMhDRHcm+fB0dxERkRuBGwFWrVpVcyXnkvy2fgbK+zjrLT/Fnhee5977/g5EWHf6xZzRdTH6TB/5Hx3AW5QhvbYD9UPCYoCWAsKSjxYDwlJAWPTRkg+Ttw1GNWRb4WlW/vLFvP7idx1zWdOr28i+dRkr7xc2feabdP7FcjItrcecrzEnWsMGY4f0jNkwpTGNYLpTDad2ycyUZqbt040QHC49RG3nWuAtwArgARE5R1WHDkqseitwK8CGDRvmyshjzcJygDOkDFT2ctlvvh/X8+jftZOtjz7EC48+xLcevgVPEpy9+s2cmj+XyiMF1FUC8fG1QiUoUirnKZTGKPsFKmEZX0tUwjIBPskVrbzxT3+D1u6e41bmjstOo7Cln/U7L+YHt/wjl//PP0Gchh0EMg2igYOx6jljrk3gN6Yx7AJWVq2vAPbMkGaXiHhAG9E9cw937HTb+4B2EfHi3rHq9LuAR1S1ArwkIluIgrPHj616k+bCHPTyzlEEIewSXC/6c9G1YiVdK67lkl+4lsF9e9j66H+z9dGH2PST7x50bLa9g5aublqW9tDS1UNr96povTtaz7a1n5AgSRxhya+dz+6/ephVA6fz2Nfu4uJ3/+Jxfx1jjqeGDcacqmv1OFjPmDEN4nFgrYisAXYTTcj/pSlpNgLXAw8DVwP3q6qKyEbgKyLyCWAZUfD0GFEP2CF5xsd8P87jjjjPu+PX+CZwHfAFEekmGrbcflxqOIcuM1Z4sR+A5nWLp93fsWQZF111NRdddTUjfQcYPrCflq4emju78BKJk1nUg7gtSXrecy7OPz9L3w82seOsTaw654TeDtmYY9KwwVj1MKWIS1C2YMyY+S6eA/Y+4D6iuVqfV9VnReQjwBOquhG4DbhdRLYR9YhdGx/7rIjcCWwGfOC9qhoATJdn/JL/A7hDRP4CeCrOmzjtT4vIZqJZUH+qqv0nuv4n29jz+xkp97L07DNmTdvavYjW7kUnoVS1yZzRSdPrl3DaQ/DEP36Nzo+soLmzq97FMmZajRuMVfeMiYNfKaOqC/7q1sbMd6p6L3DvlG0fqlouAu+e4dibgZtryTPevp3obMup2xX4o/jRkDRUdH+FvtJuLl730/UuzlHpuPI0CtsGOC98E9/920/xjj//XziuW+9iGXOIxpzVKODEPWMhikP04QuDE38FaWOMaQT+gTxO4FBsKpLONte7OEdFXIfF15+Hl0yyZuRMHrzj9noXyZhpNWYwBohEVQuJesYAfBuqNMbME1Lny74Wt0cnhqZPba9rOY6V15mm65qz6Eovo/RALy8++Vi9i2TMIRo0GNODesbGz6wMKnZGpTHG1GL0ub0U/DF6zj693kU5RN+uMfZtH645fdO5PWRes4iz2i/hyX/6N4YP7DuBpTPmyDVoMDY5gV9RnLiaduFXY4ypTWVnjr7SbpafeVa9izIhCEIe+9Z27vzo43zjr3/EzucGaj628x2n43QlubD1Mu77xC3298DMKY0bjMl4z9jkMKX1jBljzOyC4RJu0WHUGaC1Z/rLWpxsA3tzfO3jT/L4t19m7WsW0bG0iX//7E/o3Tla0/GScOn51XNIJpo4rbCeH3zxcye4xMbUrnGDsfGeMdGJ5cC3CfzGmPmhnhd9Lb0yAoC7vKnuZ6BrqGz63k7u/OjjjPYXufzGc3jbr5/Nz77v1aQyHvf8/SZG+go15ZVYnKXznWtZnFlN+bF+nn/ov05w6Y2pTQMHY+MT+CeHKS0YM8bMffW//M7Y8/vxwzId61fOnvgEGh0ocvctT/Pgv21l5ZkdXPuhizjtguhaZs0dKX72988j8EO+9febKI7VNuzYtGEx6Vd1cW7nG3nin++if/fO2Q8y5gRryGCs+npi0Q3m4iFLu7SFMcbMqvDiAP2lvSw7a31dXl9Vef6RvdzxkUc58PIIP/UrZ3Ll772KbFvqoHRdy5q58nfPZaS/wLc/82P8cjBDjpNEhM6fX4fbnuKijiv490/8DZVi8URVxZiaNORFXwdLgyx1qq4zJjZMaYwxtQhLAc4wDPr7OX/1qSf99QujZX7wlS1sf6qXpae3cen162nrycyYftnaDt7262dz3+ee4T9ue5bLf/tcHOfwvYtO2qP7PWcTfOZpTh89l9s/+Af0rFpD25KltC9eQtuiJbQvXkpLdzeOYxeJNSdeQwZjEF1nTFXjOWPxkKUFY8aYOa7eg5TlnSMIgnbLSb9a/Uubevn+vzxPqeDz2p8/jVdftmrWwArg9AsXkRtey4N3buWBr77Am65dN+tct+SKFtqvWIN8G5LJJvbsepHnn/o+o+XJu1o5rkfbokW0LY6CtPbFSyeW2xYvIZFMHeYVjKldTcGYiFwO3EJ037bPqepfTtmfAr4EXAj0A9eo6svxvpuAG4ju3/YHqnpf1XEu8ASwW1V/9phrEyuV9yCZLhTloGuOWTBmjDGHVdjWj6qSPfPknUVZLvg8+G9bee6/99K1opmrPnA+XcuP7Kr/5711JbnBEk/95w6aO1JcePnqWY9pfv1yKntyLHoaFiVX8Orlb4akg7YJpVSRMR2kv7CXfQPbee6F5ynlcwcf39FJ20SQFj2PL2daWut+8oOZP2YNxuKA6dPA24BdwOMislFVN1cluwEYVNXTReRa4OPANSKynugmvWcDy4Dvisi68ZvzAu8HngNaj1uNgIrfj9AT9YwxeWukwOaMGWPMYY09f4DhSi/L1p+c64vt2TrId7/wHGMDRS64/BQu+pk1uImjm8782nedxthQiUe+uZ1se4ozL1l62PTiCJ3XnEH7u06nsi9HZe/kw9kH6dIiulnEGXIe3tkZnJ4klSafnDvKcPEA/YO7Ge7dxys/eZqx/zr4PvHJTFMUnC1ZyoU/cxXL1s2d67WZuaeWnrGLgG3xDXMRkTuAq4DqYOwq4MPx8l3ApyT6SnAVcIeqloCXRGRbnN/DIrIC+Bmim/Ye95vtOiIoIcrkZH6bM2aMMTPTUNEDFfqKu1m79goAwlD577u24aUclq1tZ8mpbSTTxz7DpVIOeGzjdp7+3k5auzO8608uZOlpbceUpzjCpdefRX6kzPe/9DxNrUlWre+a9Tgn6ZJa1Upq1WS/gIZKMFicCNLKcZCmA0WacGhiCcsyK0guy5K4MIvTk6KULDDi9zPUu5+h/XsZ3r+XXc89w8ubnuQXP/QxFp869+5mYOaGWj5Ry4Hqc393ARfPlEZVfREZBrri7Y9MOXZ5vPy3wJ8BLYd7cRG5EbgRYNWqVTUUNz4OB43/jX/HsmFKY8z8cfIvNFbZl8MJHUrNJVJNTQD07hhl0/3Rn4An//0VxBF6VjazbG07y9a2s/T0dtLZxLT5+ZWAkd4iw715hnsLDB0oMHwgWh4bKKIKZ79pOa/7+dNqDvAGBwd56KGHcF2Xt7/97TjOwb1orudwxe+cyzf++kd857PP8K4/voCeVYf9MzMtcQSvK4PXlSFzdvfE9rDoH9KLlntsH1oJAUg5sLx7BauXnkHitVn8nw74+j//BV/72J9z3Uf+io6ly2d6SbOA1fLbP92g99RWYqY0024XkZ8FDqjqkyLylsO9uKreCtwKsGHDhppbJxEh1GjOmF301Rgzb9RxmlHppfGbg3dMbNv1fHTLoff8n9cy0ltgz7Yh9mwd4ic/2M3T342CtK7lWZad3k5zZ5rhvgLDcdA1NlQ66K9FKuvR1tPEklPbaLtkCSvO6GD5usnXOpz+/n4eeOABNm3aBESXv0gkElx22WWHpE1lPH7u98/jrr96gns+tYlf+LMLae2e+YzMI+GkPVKr20itnuzF01Dx+wsHBWjll0cobOoF4Mozf5uNT9/CXTd/iOs+8lc0d87eW2cWllqCsV1A9ZX/VgB7ZkizS0Q8oA0YOMyx7wDeISJXAmmgVUT+RVXfc1S1mEZ0BuX4nLGIBWPGGDOzkc37yPujLDpn7cS2Xc8P0rW8mbaeDG09GVau7wSiXq8DL4+yZ+sQe7YN8dwj+/BLAZmWBG09GZav66BtUSY+rom2RZkZe9AO58CBAzzwwAM888wzuK7LRRddxOte9zp++MMf8uCDD9LZ2ckFF1xwyHHZ9hQ/9/uv5uv/90m+9feb+Pk/vYBMc/LofziHIY6Q6Gki0dMEr+qZ2B7mK+Q39TJ094v83EXv52sP/RVf+9ifc82H/5J09shOUDCNrZZg7HFgrYisAXYTTcj/pSlpNgLXAw8DVwP3q6qKyEbgKyL/j73zjo+qWP/wM2drNj3ZFEICIZBEIPQqvUiV3gVsiN6fvV+v9XoVe0Gu7SrgxYso0kFEReldOkgvgRBI2/S6dX5/7CaUBAiQIMTz+OHDcnZmzhwku995553vKz7EncAfC/wupdwEPA/giYw9U5VCzD2ugku6c8ZKfcbUbUoVFZUbnz8vNOZILsRSkkzCLUPcf7Y7OXMsh4CEIg4fPkyDBg3KtgW1Ok3ZViW4i3g7bS70XlXjmJSamsratWvZv38/Op2OW2+9lVtvvRVfX/eWY//+/cnOzmbp0qUEBAQQE1PeEy2oljf9H2rKko92seyzPQx6ogU6/fWz61BMOnxujUDanOT+dIIh3Z5m/q9vs+jd1xn+4muqNYZKGZc9siKldACPAL/gPvk4R0q5TwjxmhBikKfZdCDYk6D/FPAPT999wBzcyf4/Aw+fc5KyWlE8kbHSGLmCojrwq6io3DRc79qUjhwrGqtCvjYHP7O75FDqsVxsMp9DKVv59ttvmTJlCmvWrCEvL69cf41GqRIhdvr0ab777jv+85//cPToUTp37swTTzxB7969y4SY+34aRo0aRXBwMN9//z0ZGRkVjhfRIIBeExqRmpjHr9P34XJd/1w8ny6R+HSMQDlkZ3Dfpzl9aD8/TnkXl/O6fB2q3ARU6vywlHKZlDJOSllfSvmG59orUsolntclUsqRUsoGUsq2pScvPe+94ekXL6X8qYKxV1elx1gpAnfOmMSdVCmERt2mVFFRUbkItpO5AGhrm8qunTqYjd2YDcDAgQMJDg5m1apVTJ48mdmzZ3PkyBFcLleV3D8pKYlvvvmGqVOncvLkSbp168aTTz5Jz5498fb2rrCP0Whk7NixaLVaZs2aRWFhYYXt6rcMpfOoOBJ3W1g3+zDyOitdIQT+t8fg1dSMfj/07/cox7ZtYfmXH1/3uajcmNRoB/7zImNCUcWYioqKykXIP5CG3WUjqHHdsmvJB7ORfnmEhYTRqlUrWrVqRWZmJjt27GDnzp0cPHiQgIAAWrZsSYsWLc6LXFWWEydOsGbNGhITEzGZTPTs2ZM2bdpgNBor1T8wMJA77riDGTNmMHv2bO666y50uvK5aU27R1KYU8KOX5LwDjTQul/0Fc/1WhCKIGhUPJYiBxyC7r3uZdWv/8XkH0CXsfdc17mo3HjUXDFWFhkr3abUqNuUKioqKheh5Hg2mdYzRDXsAIC1yE5aUhZFoVk0b9ChrF1wcDC9evWie/fuHDx4kO3bt7Ny5UpWr15N7dq10ev1aDSacr+0Wm25a0ePHiUpKQlvb2969+5N69at0euvPMk+MjKSoUOHMnfuXBYvXszw4cMrdL9vP7g+BdlWtiw+jre/gYYdLm0KW9UIrULw+IZkfLGHsJPhtOs8nC2L52Hy86f1gKHXdS4qNxY1U4yJ8pExrVavRsZUVFRUKsBldaDkuYuDt6wTDcDpwznYdDlIJLGxseX6aLVaEhISSEhIwGKxsGPHDk6fPo3VasXhcOB0Oiv85XA4yrY2/fz86NevHy1btqwwmnUlNG7cmKysLFasWEFQUBA9evQo10Yogh53eUxhvzmIyV9P3cbX12ZCMWoxT0gg/fPdxGTdQlHr21gzczomP38adSk/Z5W/BjVTjOGOjEnPaUpQxZiKispNglJIQEAKUhRdt1vakvLdfoyh2rLi4MkHs3F45aDX64mMjLxkf7PZTO/evSt9PyklTqcTRVHKmbZeC506dSIrK4u1a9cSFBRE8+bNy7XRaBX6/a0JCz7Ywc9f/sHQp1oQWrdKK/JdFo2vHvOEBDI+30UTZweKGuXx8+cfYfTxJaZlm+s6F5Ubg6r7KbjBUITbgR/pXoFptTrV2kJFReWGRyrHaNL0NxCnr8/9pKRgZyou6cIn/mxx8FMHM3GYsomJiUGrrdp1uxACrVZbpUKsdNzbb7+d6OholixZwokTJypsp/fSMvCRZnh561j6yW7ST+ZhK3Fc12R6ndkL8z0JuIrstPfrT626cfww+W1OHzpw3eagcuNQgyNjSlmhcACtRq/mjKmoqNzwuIqL0QAua0m130s6XWQvPErJDguJ+XuIbtQZgMIcK5aMTGwhxTRocHPVU9RqtYwePZpp06bx/fffM3HiRIKDy29FegcYGPBoMxa8t525b21z99UpePnq8fLTY/LTY/LVlb328vVc87w2mLQV5qVdCfooX4LHN8IyYx/d647lp5IvWfTOvxj9r3cwR9W9/AAqNYaaK8aEgpQu8FhbaDTqNqWKispNRDUHaVwlDjJnHcB6JIeMgFS2n/iVW2PvA9wlkGwGdxmkm02MAXh5eTFu3DimTZvGrFmzmDhxIiaTqVy7oFrejHqhDWeO5lCUZ6M4z0ZRvvv3/MwS0k7kUZJvq9DzTdEKTL5nRZqXnx5T2Wud+z0/Pd5+Bow+F8+HM8YFEjgiluw5h+nX9G8s+n0y8998hTteew+/kNCq/GtRuYGpuWIM4SkT7hZjOjVnTEVF5WbgOhjwO3KsZM74A3t6MSmhyazdMotmvfqj93ILluSD2ThNOZjNZgICAqp/QtVAUFAQY8aM4euvvy6zvKhou9XP7HXJupUul6SkwE5xvo2iPPev0tel4q0w14rlVD7F+fYKTWXj24fT485bUDQVb8t6twzDlW8n96dEBnd+kvkr3mbeGy8z5rV3Mfn5V9hHpWZRQ8WYcEfGXNIdHROg0ag5YyoqKiq2MwVYZuxDWp0c9dvL9i0/0mbQcDp7vK6klCQdsmA15NC8Qds/d7LXSJ06dRgyZAjz589nyZIlDB069Iq3FhVFlG1PBte+dFvpkliLHG7R5omwpSbmsmdlMvYSJ73va4xGV7Eg8+lSG2e+jYL1pxnS71nmL36TBW+9yqhX3igTySo1lxqawC9RyiJjntOU6jaliorKXxxrYi4Z/9kDSHYpq9m+80e63nkfXcbdWyZSctKKyC5MR+K6KbcoL6RJkyZ069aNPXv2sHbt2mq9l1AERh8dQRHeRMYHEtsmjM6j4ug0KpbjuzJY9p892G0Vl0ASQuDfvx5ezUJgezGDhj5D+oljLP7gTRx2e7XOW+XPp2aKMelO4Ee6KC2FqVV0agK/iorKTUD17VPmLEtEeCmsy13I4UNb6PfI0+XMRpMPZmM3ZKHVaqlbt2YkkXft2pWmTZuyatUq9u7de93v36xHFN3vvIWk/Vks/Xg3tpKKv4uEIggaGYehQQC67U5uH/4ESXt38fOnHyKrqOyUyo1JzRRjuFcZ7mPKngR+RadGxlRUVG4aqjp/35qUh/1UPn9krCc15ShDnn2ZRp27l2uXfCgbhymH6OjoazZivVEQQjBo0CDq1q3LokWLSEpKuu5zaNQxgl4TGpFyLJclU3ZRUlhxtKvUpV8X7o3PbgM9B93PoU3rWDnjS7WOZQ2mhooxiXt1Kc+PjKliTEVF5QbH6YmAOKs4ElKw4QwO4eBY9k5GvvQG9Vq0LtfG5ZKcPHwGuyiqEVuU51JqeeHv78/s2bPJysq67nOIaxNO3wcSyDiVz6LJOynKs1XYTjFqMd+bgOKrJ/RYKLf2Hs2uX5ayecHs6zxjletFzRRj4tzTlO6VhBoZU1FRuRmw2t0izOaouiiIM9dK8d4MjuXspEmfvkTE3VJhO8upfApcGQAVlkC62TGZTIwdOxYpJd9++y3FxcXXfQ4xzUO4/aGm5KYVsejDHRRkWytsp/HVEzIhAYSgniWeZp36snHOLHb/uuw6z1jlelAzxZiU7tIe0nOaElWMqaio3GxUnRgr2JKCdEmOFuykWe9+F22XfDAbmyEbf/8AgoKCquz+NxJms5nRo0eTlZXFnDlzcPwJ3wt1GgUz8LHmFORYWfjBdvIsFYtCrdkL872NcRXaSXDeSmyL9vw2/XMOb15/nWesUt3UTDGGhLLjyx4xhlZN4FdRUbnhkVWcwC/tLgo2p5BakkjtVgn4Bpkv2jbpQAZ2Yw5xcbHX7C5/IxMdHc2gQYNITEzkxx9//FNysSJiAxj8eAusRQ4WvL+D7NTCCtvpI90u/Y70YtoFDKB2bGOWffw+SX/svs4zVqlOaqgYKy0ULkE6kVKiEVo1MqaionLjU8UaqGh3OrLIwcHsLbToN+ii7Rx2JydPnkLirHH5YhXRvHlzunTpws6dO1mwYAG7du0iLS0Np7Ni64nqIKyeH0OeaonL6WLhBzuwJBdU2M4YF0jgyDjsJ/Lp0WAcgbVqs+i9SaQdP3rd5qpSvdRI01d3+r47gV8gcQIaRaMm8KuoqPylkFJSsOEM+a4cRC3DRXPFAFKP51GiyURRFKKjo6/fJP9EunfvTklJCbt27SqzvNBqtdSqVYtatWoRERFBREQEZrO5youal2KO9GHo0y1Z/NEuFn24g4GPNScs2q9cO+8WobjybeQuS6Rf2wdZvPZD5r/1T+547V0Ca13GjVblhqdGirGyXAspQUpcEhQ0ONVtShUVlb8QtsQ87CmFHMzaTMu7Bl5y6zH5YBY2YzZ1oupgMBiu4yz/PIQQ9O/fn759+5KZmcmZM2dISUnhzJkz7Ny5k99//x0AnU5HeHh4mTirVatWlQq0wHBvhj3TksUf7WTxRzsZ8HAzImLLl6Hy7RKJM8/t0j+o99PMX/IG8954hTteexefoPLF0FVuHmqoGPP4jIFbjAGK0KqRMRUVlRueqtylLNhwGoewk6GcZuCtwXX2/AAAIABJREFUnS/ZNvHAGZzaQmLjOlThDG4OFEUhJCSEkJAQmjVrBoDL5cJisZSJszNnzrBjxw62bNkCuAVaafSs9Pfg4OCrFmh+Zi+GPt2KJVN28sO/d9HvwSbUaVReYPn3r4ezwEbxxgyGDH+OebMnMf+tfzL61bcxevtc/V+Cyp9KDRVjHp8x6ULgwlkaGVPFmIqKys2CuLakckdOCcX7MzmSvZ3Gt/VCewkDV2uxgzNpSeDPXyJfrDIoikJoaCihoaHlBFqpOEtJSWHbtm1lJzL1ev15EbSIiAiCgoIqLdB8Ag0MeaolS6bs4sfP9tBnYgIxzUPOayMUQdCIOCwFdqzrchg85jkWzJzEondfY/iLr6PT/zWimjWNGirGSnPGKIuMaVDU05QqKio3PFKWxsauLUZmTcwDCUnFBxnd675Ltj1zOBurIRtvLx9CQ0Ov6b6VQTqd5P34I86cHPwHDUITUH5L7kbkXIHWvHlzAJxOZ5lAK42iXSjQOnXqROfOnSt1QtXkp2fIUy344ePd/PzlH9x2b0Pi2oSf10ZoFYLvbEjGl3sRG4sYMO5plsx8l6UfvcPgp19E0Wiq/uFVqpUaKsZkWQI/UuKUoJVqZExFReWvgzU1DyldRLRqhE/gxT3DSgrsbP/5BHZ9No3imlarpYWUksL160l/732shw8DkD75I/yHDCbozjsxxMRU272rC41GQ1hYGGFhYbRo0QJwC7SMjAxSUlI4cOAAK1eupLCwkD59+lQqSmb01jH4ieb8+Okefv1qPw6bi0YdI85roxi0mO9pTPrnu/He7qD36IdYPvtTln/xMX0efLxGW5PURGqstQVCIKULISVOJAIFp8Oh1vZSUbnJEUL0FUIcEkIcFUL8o4L3DUKI7z3vbxFCRJ/z3vOe64eEEH0uN6YQop5njCOeMfUX3GuEEEIKIcrXFrra5yt7dW2fVXnHUyl05NHktj4XbZN1ppC5b2/lTMoZpOIkNq76tihLDhzg1H33cer+B3AVF1N78ofUW7wIv9v7kzt/Acf7307SAw9QsGHDTf85rdFoCA8Pp0WLFowZM4Z27dqxZcsWFi9eXGnrDL1Ry4BHm1GnYRCrZh5k98pT5e9T6tKvCMyHguk0aDz71vzGum9nVPETqVQ3NVKMSXl2m1Lgcp+mlIrbkb+K672pqKhcP4QQGuBToB/QCLhDCNHogmb3AdlSygbAZOAdT99GwBigMdAX+EwIobnMmO8Ak6WUsUC2Z+zSufgCjwFbquNZrxVXlpUCRw6h9SqONp3Ya2Heu9uw2Wz4NclFCEFMNUSm7CkpnHnuHyQOG07Jvv2EPf8PYn5cil+/fhjj44mYNIkGq1dhfuxRSvYf4NR9Ezk+cCDZc+bgKimp8vlcbxRFoW/fvnTv3p3du3czZ84c7PaKi4RfiE6vof+DTanXzMz6OUfY/vOJcm20Zi/M9zTGVWSnbkYsLXoOZOuS+Wz7YUEVP4lKdVIpMVbVK1EhhFEI8bsQYrcQYp8Q4l9V9UBl94UyawsnIDyPqtpbqKjc1LQFjkopj0spbcBsYPAFbQYDX3tezwN6CveezWBgtpTSKqVMBI56xqtwTE+fHp4x8Iw55Jz7vA68C9yQikEpVigRhei9TOddl1Kyc3kSP362B625kJzQ7ZxMTqRHjx54eXlV2f2d+fmkf/Ahx/r2I++nnwiacC/1f11O0N13o+jPCzCiDQoi5KGHaLByBbXefguh05P6yj852q076R99hD0tvcrm9WcghKBr167069ePQ4cOMWvWLEoqKTQ1OoU+DyQQ2yaMzYuOs3nRsXKRw3Nd+hNke+LbdWbNN1/xx6pfq+NxVKqBy4qx6liJAlagh5SyGdAc6CuEaF81j1Q2c4R0nfUZ8yTFqvYWKio3NbWBc/drkj3XKmwjpXQAuUDwJfpe7HowkOMZ47x7CSFaAFFSyqWXmqwQ4gEhxDYhxLaMjIzKPqOHq9+qcxba0bq0uLwvuG53sfLrA6xfeBBR7xRJjt8xGA3cd999dO58aeuLyiJtNrJmfsOx3n3InDoV3z69qf/TMsKefRaNX3kz03NR9HoChgyh3oL51J35P7xatyLziy852rMnp5/9O8V/7KuSOf5ZtGvXjmHDhnHy5Em+/vprCgsrLoF0IRqNwm33NqJRpwi2/3yS9XOPlBNkxrhAgkbGYUvMo13IAOo2acEvX/yb/etWVcejqFQxlUngL1s1AgghSlei+89pMxh41fN6HvDJhStRIFEIcRRoK6XcBJTWfdB5flVZkoBEuJMXpRMhXe7ImPRExlQxpqJyM1NRVvKFnx0Xa3Ox6xUtSi/aXgih4F503nPxaXoaS/kl8CVA69atr1silCPTXXhaE2w87/r6eUfYs2M/1sjjWIuL6dixI926dUN3CduLyiKlJP+X5aRP/hD7ySRM7dsT+uwzeDVufMVjCSEwtWmDqU0bbKdOkTVzJrnz5pP3ww94tWpF0F134XtbT8RNeGqwadOmGAwG5s6dy1dffcVdd92Fv7//ZfspiqDbuHi0eoU9K5NxWJ10HXcLinL2n6mpRShOj0t/97bjWely8vOnkxGKQsOOXavzsVSukcqIsYpWje0u1kZK6RBCnLsS3XxB39KVpQbYDjQAPpVSVph3IYR4AHgAoE6dOpWYLtT3aYhR4w3SBZ6cMeG69siY0+lk/vz5xMfHl/nOqKioXFeSgahz/hwJnLlIm2QhhBbwB7Iu07ei6xYgQAih9UTHSq/7AgnAas+JtXBgiRBikJRy2zU/oYdryWG3puQCYKx1NhJVkF3Ctu1byQ86itnfzPghY4mMjKzcXBwOHBkZONLSsKem4UhPw56WhiMt3X0t3f1alpRgiG1A1Bf/wbtLlyo50aePiiL8hRcIefRRcubPJ3vmN5x+/HG8O9xK1PTpN+Wpwfj4eMaPH893333H9OnTueuuuzCbL17AvRQhBJ1GxqIzaNj+00nsNhc972mIRnN2PeHbJRJnvo2Cdae5rdt9LJdT+enjD1AUDfG3dqrOx1K5BiojxqpjJYqU0gk0F0IEAAuFEAlSyj/KNb6KlWWwIcw9Kc82pROJ8GxTXktkbP369ezfvx+73a6KMRWVP4etQKwQoh5wGncaxNgL2iwB7gY2ASOAlVJKKYRYAnwrhPgQiABigd9xf06VG9PTZ5VnjNmeMRdLKXOBsm9OIcRq4JmqEmJVIS0KTlpwSRe+dc96hv3+0xEKTInUjYpm/F3jKh0Nc+bnc6xvP5yZmefPU6dDGxaGNiwMr8aN0XbvgbFxI/z69UNoq941SePrS/A99+DTpSunn3ySwo2bKPjtN3x79arye10PoqOjueeee5g5cyZfffUV48ePJyIi4rL9hBC0H1wfnUHD5kXHcdic9JmYgEZ3VpD596uHq8hB4eoz9O5xP7+4vuDHf7+LoijEtvvrVVi4GajMT0x1rUQBkFLmeD7M+gLlxNg14TqbM1YaGbtaMZaSksKaNWsASEtLq7IpqqioVB5P5P0R4BdAA3wlpdwnhHgN2CalXAJMB2Z60iKycIsrPO3m4E6xcAAPexaFVDSm55bPAbOFEJOAnZ6xb3isaflYHXkE104AoDDXyvbdvyO9nPQf0O+KtiUL1q7FmZmJ+bFH3aLLI8A0AQHXNSplT0vD8uln5Myfj/DM//RTT1P743/j263bdZtHVVKrVi0mTJjAzJkzmTFjBmPHjq10kfZWfaPRGTSs+/4Iyz7fQ9//a4JO7962FYogcHgsuCSFK8/Qp8ff+Mn1OUunvMPAJ5+nQZsqTtFWuWYqc5qybCXq8dgZg3vleS6lK1E4ZyXquT7Gc9qyHp6VqBAixBMRQwjhBdwGHLz2x7kQ19mcMY+jxdW48DscDhYuXIjJZKJDhw7k5eVRXFxctVNVUVGpFFLKZVLKOCllfSnlG55rr3iEGFLKEinlSCllAyll29J8V897b3j6xUspf7rUmJ7rxz1jNPCMaa1gPt2qcnuylACbN7nLT1xVX5ljp8CRTUB4LQA2/3SIQuNpbolrRFhY2BWNVbBiBRqzGfPf/oZP164Yb7kFbWDgdRNijuxs0t55l2O9+5CzcCGBY8bQ4NflBIwdi7TbSf6/B0l9fdJNa4NhNpuZMGECfn5+fPPNNxw6dKjSfZt2j6L7nbeQdCCLpR/vxlZy9vtNKILAkXF4NQ+hcOUZ+nV5kNB69flh8tsc37G1Oh5F5Rq4rBjz5EqUrhoPAHNKV6JCiEGeZtOBYM9K9CngH56++4DSlejPnF2J1gJWCSH24BZ7v17uVNKVULqXeczXyhqzk1wlF+H578LImHRK7BlFlxxvzZo1pKenM2jQoLJVS3r6zX3UWkVF5QbFI3L0Ukt+BUafl0NKiaZIwaopQWcwUlxgY/vuzQgh6d33tisay2WzUbBmLb7du133ZHlnQSEZn37Ksdt6kfX11/j160f9n5YR/vJLaENCCHnkYdDrMcTGkj1rFidGjqLE4+p/s+Hv78+9995LaGgos2fPZs+ePZXu26hjBL0nNCblWC6LP9pFSeFZDzOhCIJGxuPVLITCFWfo1/1hQupGs+SDNzixa3t1PIrKVVIpn7GqXolKKfdIKVtIKZtKKROklK9Vx8Oletsp1EKWLhsAjdCVS+Av2p5G2uQdOPNtFY6RnJzM+vXradGiBXFxcWWrSnWrUkVF5UbEVeRAI8/aWmxatp8ifQoJjZoRFHTxskgVUbRlC67CQnx69qyGmVaMy2olc8YMjvXqheXjT/Du0IGYJYuJePst9OccONAGBREwfBi2EyeIeP89HFlZnBgxkqxvZt2UDv7e3t7cdddd1K1blwULFrBlS+W9hGPbhNH3gQQsyfks+nAnRXlnv8+ERhA0Kh6vpmaKfjtD/56PEhRZh0XvT+Lknl3V8SgqV0GNdOAvjY2VaNx7kw7c5Sc0QoPTcb7zse1MAbgk9rTyfi92u52FCxfi6+tLnz7ukiJ+fn4YDAY1MqaionJDUmproQ0yYC2ys3XXJhRFoXe/KxdU+StWIEwmvG+9taqnWQ7pcJA9dy7H+vQl/e13MDZsSPTcOUR+/G8MDSou0xR8991IhwPr0aPELF6EqX070iZNIvnBh3BkZVX7nKsao9HIuHHjiI+P56effmL16tWVFpYxzUMY8FAzctOLWPjBDgqyz+6oC40gaPQteDVxC7IBvR8nqFZtFr37Gkl/VD4Kp1J91FAxBi5cFGvd/4gdwh0NU4QW1wV1wexp7i1KR0b5HLD169eTmZnJ4MGDMRrdfj1CCMLCwtTImIqKynVBuq4sylN8OgcAQ0QA63/cQ7E+jRZNW+Hr63uF93VRsGIlPp07oxgMV9T3Su+Tt2wZx28fQOrLr6ANC6XOjBnU+Wo6Xk2aXLKvPjoa39t6kv3dbBSTiagvviDsxRcp3LiR44MHU7B+Q7XNu7rQ6XSMGjWKZs2asXr1an7++WdclSzjF9UoiIGPNacw18rCD7aTe873mtAIgsbE45UQTNGvZxjQ90n8w8JZ+O6/SN5ftWfnVK6cminGJBRjw+NmgVO4BZhW0ZXLGXOkX1yMJSYmEhUVRf369c+7HhYWRnp6+k0ZCldRUbmxsTpLE9Hdny+u4is7dFSYlIFLujDVDmbrro1ohI7b+na/4nmU7N2LIyMD39uqZ4tSSknBmjUkDh/B6aeeRuj1RH72KdGzZ+Pd/kIry4sTNGECrtxccuYvQAhB0J3jiZ47B42/P6cmTiTt7Xdw2SpOQ7lR0Wg0DB48uKzA+KJFiypdYDwiNoDBT7TAWuRgwXvbyTiVX/ae0CgE3XELxsZuQTaw/1P4mUNZ8ParnD64/xKjqlQ3VW8Gc4PgEGdXEqXblHrFeJ4YcxbacXmSHStK4rdYLMTHx5e7HhoaitVqJTc3l4CAgKqeuoqKyl8YeYGNo6vYgca78lYUtrQCrI48UtJ9KdFl0rZZx6uqOZn/2wrQajG1a0fSxPvB5UJfrx766Gj09ephqBeNtlYthHLxNb10OnGkpWFLTsaefBp78qmy17ZTSTgzLOiiooh47138+ve/qkMCphYt8GrRgqyvvybwjjEIrRZjfDz15s0j/d33yJoxg8ItW6j9/nsYLlhY38iUFhg3mUysWrWKkpISRo4cWSlbkrBoP4Y904ofPt7Fwg920P//mhB5iztfUGgUgu+4hcxvD1L8awqDBjzN4iXvMf+tfzLixdeIiGtY3Y+mUgE1U4wJcHKOGBNOkG4x5jonZ6w0KqZ463Cknx8ZKy4upqioqEJX5HOT+FUxpqKiUp3Y84rRmSsvplw5dgocOaR7PFrbd259VffNX7EC77ZtKNryO4Xr12OIbUDx7t24zqmnKAwG9HXrlgk0xdsbe3Iy9uRkbKeTsZ9JAfs5ebqKgjY8DH1kFD6du+DVojkBgwcjLigcfjlO5xRTbHPSINQHgOD7JpD8yKPk//orfv36uW9lNBL+yst4d+pEygsvkDh8BGHPP0/AqJE3jWt/aYFxLy8vli1bxjfffMMdd9xRljZzKYIivBn+91b88PFufvh4N7fd24jY1h5DdK1C8NhbyJx1gOLlKQwe9AyLFr/L/Df/yYiXXqdWg/JBCJXqpWaKMc4XY05PzphB44XznA8Gu0eMeTUKpnBrKi6bE8VjmmexWAAIDg4uN3ZoqNvVOi0trcLImYqKikpVYcstxETlTkFKKdEUa7BqisnMdqFILYFBV75gtB4/ju34cQLHjSVn3jx0UVHUW7wYhMBpsWBNTMR24gS2xBPYTpzAevgw+StXgsOBJjAQXWQkXo0b49e7D7rISHSRtdFHRaELD79i4XUu+SV2Pl11jK/WJ+KUksd7xvJQt/r4dO+Ovm5dMr/6L759+54ntnx7dMe4eDEpz/+D1H/+k8L16wh/7TW0gYFXPY/rTdu2bTEajSxatIivv/6a8ePH4+3tfdl+PoFGhj7dkmWf72H59H0U5dlo1sPtwy60CsHjGpL5zQGKl6cyZMjfWbjwHea/+QojX3qDsJiKD02oVA81UowJzooxvdNVljOmV7ywn5M74EgvQugVDLEBFG5NxZFRjL62e6WV6Sn9UVFkzGg04u/vr56oVFFRqXasuQWVbusqcqCVWqSvIK8oG2+9/1VFgfJXrADAEB9P2uuTCHniibLtSG1ICNqQELzbtj2vj7TbkTYbSiVEwpXidEnmbjvF+8sPYSmwMbxlJA6Xiw9/PczqQ+lMHt2coHvvJfXVVynaurXc3HRhoURNm0bWjK9JnzyZ4iFDiXjnbbzbV68TvZSSnb8mERkfSGhdv8t3uARNmzbFaDQyZ86cKyowbvTWMeix5vz61X7WzzlCUa6V9kPqI4Q4R5Dtp/iXVIYMeY6FC95i3qSXGPnKm4RGx1zTnFUqT81M4EfgFKViTCKFE4lbjDnOEWP29CK0ISZ0oSYAHOfkjVksFhRFIfAiqyf1RKWKisr1wJ5/aVPqc3FYPKkXAQasrnwC/MpH9itDwYqVGBs3pnDtOlAU/IcOvWwfodNVixDbdCyTAR+v5x8L9hId7M2SRzrywahmTBnTgiljmnMkvYB+U9bxc502KEFBZH3134rnpygET7iX6NnfoZhMJN07gfQPPkBWY3L/0e3pbFpwjOXT9+F0Vu5E5KWIi4tj/PjxFBQUMH369LIdnMuh1Wvo80ACjTtHsOOXJFZ8faBsPkKnEDy+EYa4QEp+TWXo8H+gM3oxd9JLZCSduOY5q1SOGh8ZMzhdFGqdSL2CQWPEYTvrveJIL8IQE4A22AsE2M85UZmZmUlgYCCaiySUhoWFceTIERwOB9pKFMV1uWwoytWH51VUVP4ayAteOYoqLxaKkt0G11YfPTLXSWhY6GV6lMeenk7x7t2YH3mY7Nmz8enaFd1VjHOtnMws5M1lB/hlXxq1A7z4ZGwLbm9S67xI3+DmtWkTHcTTc3bz/JIDdOvzOH+b/y6hR49e1JvMq3Fj6s2fR9pbb5M5dRqFmzZjfvD/3MXNhfBUQPD8Ltx5W5e9Xvbe2et2m4udU//ALBQyU83sW3uGpt0jK5zTlXC1BcYVRdB1bDzeAQZ+/yGR4nw7fR9IQGfQIHQK5jsbYvnffkqWpzF0xD9YMPdN5r7+IqNeeRNzVN1rnrfKpamhYkzgKBNjTqTiRGqU8yJjrhIHzlwb2jATQqegCTSWi4xVtEVZSmhoKFJKLBYL4eHhl5xP8ulvOXbsXTrcuhqdTk34V1FRuRTnbys6i+0XaVeewlMWXNJFnnBvekRFX/5L+kIKVq0GKVH8/HBmWAgYOeKKx7gW8krsfLryKP/dcAKtRvBsn3ju61QPo67ihXFEgBezJrZj+vpE3v3lILt7PsNLXy1i+JvPXPQeislErddfcyf3v/IKyQ8/Ui3PUuqSZvML43DJGOLbT8Tgde1fu1dbYFwIQZvb62Hy07Pm20Ms+nAHAx5phpevHqHTYL6rEZav92P9NZ1ho55n/velguwtgiOjrnneKhenRooxhCiLjBkd7nwxux4MWhOFdrcYK03e14WYPL97lXmNuVwusrKyiI2Nvegtzj1ReSkx5nRaSUz8GIcjH0vmamqFD7nGh1NRUfmr4JROXCWV85cCt62FzZFPnqdPTNyVf4Hmr/gNXZ06FG3chDYkBJ8uXa54jKvB6ZJ8v/UUHyw/RFaRjREtI3m2TzyhfuefHEwtTGXa3mkUO4q5v8n9RPtHoyiC+7vE0CnWzCP//oWnXQ3Z8d1WXhreEi/9xe0y/Pr0xrtdW2xJSSAlSOn2j5QAsuzaedelPO+9iq7nphex/vvD1EkIolFLf9KmfELj36dw8O4dNPpkErrQa480lhYYnzlzJt988w0jR46s9IGyxp1r4+WrZ/n0fcx/bzuDHmuOn9kLodMQfFcjMmfsw7o8g2GjX2D+d5OY+/oLjPrnWwRFXHtkT6ViaqQYE+fkjBmc7pOUVq0Tg8ZEri0DoMzKQhvmFmPaEBMlx3KRLklOTg5Op/OSkbHg4GA0Gs1l88ZSUuZis6WjKHoslhWqGFNRUbkkpXGxk5oMcpUkYkouHXk/F5njoMCRTV6hA43U4xdwZa77zoICijZtxn/oUHLmzSN44kSEVovTWYyiGKvNEmLjUQuvLd3PwdR82kYHMWNAI5pEnp+cbim2MG3vNOYcmoOCC62iY9nxZYyIG8GDzR8kyBhEw1p+LJrYmn8+9zmz6MqmM+v4aHRzmkZefEdCExCAVxVaFEmX5Of3tlMY7U2LZ9pj9Nbh26cPWx9+C58N8zjW73ZCn3zC7Yl2jcXXSwuMz5o1i9mzZzNkyBCaNWtWqb4xzUMY/HhzfvxsD/Pf3c6AR5sREuWLotcQfE/js4JszItuQfbaC4x69W0Cw6882qpyeWpkAr/gnMiY3b1CtGlcGDRntynt6UWgEWgD3asubYgXOFw4c6xlJykrsrUoRaPRYDabL3mi0uWyceLkf/D3b0l42BAyM9fict1cTtAqKip/DqlKLlsMx5G2yiV+SynRlGiw6azkFefibbj8SbsLKVy3Dmm3u6M8LhcBI4Zjtaaxbn17NmzsxMFDr3g+x6yXH6wSJFoKuf9/2xg7bQsFVgefjWvJ939rf54Qy7Xm8tG293hkaS+yznzNi5EG3o208k4dhXvrd2Tu4bn0X9CfaXunUeIowbd+PZ6qVcI7O2dSbHUw7LONfLLyCM4rLCt1tRzYmEJaYh4dhzfA6DHrVfR6Gr3zHFtvfZnikPqkTZrEiVGjKf5j3zXfz9vbm7vvvpu6deuycOHCKyowXqtBAEOfaYmiESz8YAfJB7M883ULMn20H/bfLAy/40WcDgdzXnuBnLTUa56zSnlqqBg7J4HfY/JqVZzohKFMjDnSi9CFeCE07pVe6Xalw1JcdkLlUpExuPyJypTUhVitKdSLfgSzuSdOZwE5OVuv6dlUVFT+KnjEg71yYsyZa0MrtTh9wCrzCbyKk5T5v61ACQykcNMmTO3bo69Th1OnZuB0FuHrm0Bq6kJ27b6XtevasvePR0lNXYzdnnvF9zmYmseLC/fSe/IaNh618Pe+8fz2VFf6exL0nc4STqevYM6mccxd1Y6Guf/h/uA8+vg5iPIJITJyHHqdL42tv/BVu1G0DW/DlB1TGLBwAIuPLiZwwj00Pbmb72ql0q9JLd5ffpjRX2ziVFblT6ZeDcUFNjYuPEpEbABx7c6PaPoGGYkf2IqNde/H5/lJ2NPTODFqFKmvT8KZn3+RESuHwWC46gLjwRE+DP97K3yDjPzwyW6ObHN/pyl6DeZ7EtDX8cP+WybD7ngRh9XKnNeeJzdddRKoamqkGDs3Z8zLY/Jq1TjRCh0uq6f8UXoRWo+lBYA21KvsemZmJkajEZPJxKUICwsjPz+foqLyP+Aul4OTJ/6Dr28TgoK6EBTUEUUxkGFZUSWPqKKi8hehkvn7JYfdUY08XycIF6HhV5aXJG02CtauxSuhMfbkZAJGjMDhyCf59LeEhvajWdMv6NxpG82aTiMsbAA5OVvZt/8p1q1vw44d40g69V/y8/dTUpKC01lcTgxYHU4W7zrNqP9sou9H65i7PZkRrSJZ9Ww3/ta5NkX5mzl2/EN+3zaSVWuacvCPBwgs2kyg3kRg2HCaNZ1Kl87badtmCXGxL9Gm9SJCQ/uRfXo69wWXMP22jwnxCuGlDS9xT/JbHLotFsc3M/j3yCZMGdOcQ2n59P1oLXO2naq2usKbFhzDXuyk6x3xFW7ptupTFy8/Pdss9Yj58UcCx44l+9tvOda/P3nLll3TvK6lwHipOWxYtB/Lp+9jz6pTACgGDeZ7G6OP8sOxIpthY1/AVlzE3NdfIM+ScdVzVSlPDc4Zc29PGkvFGA7AADaJy+rAmVWCzlMaAtwlkYSXFkdGEZZc90nKy+VHlDrxp6enlzumcLkAAAAgAElEQVTJkpa2hOKSJJrGfoEQAo3Gi8DADlgsK4mLffmmKcehoqLy5+J0VC6BP39PCkWOfAr07q2xOtG1r+g+hVu34srPx1VcguLvj2+v2zh15n84nQXUrXM/ABqNAbO5O2Zzd6R8nby8PWRYfsNi+Y0jRyadN54QenQ6f1B8ySoykJSjJafESDN/f0b2rUXTqCi07Cfp8CT+yNuDlHYkCmfsWg4WC/TezRne9Dl6hbWtaLpotT4kNJ5CckAbjhx5A0PhIT7vPIVNWalM2TGFl9ucpnmQi6eWfc3gQffROjqIp+fs4u/z9rDyQDpvDmtCkHfV2Q2lHM3hwMYUWvapQ1BExX5rei8tbW6vx9rZhzmVaKXeSy/iP3gwqa++yumnnsZ7/gLCX3kZfd2rs5IoLTDu5eXF5s2byc/PZ8iQIegrUfWg1Bx2+fR9rPv+CIU5NtoPiUExaDHf2xjLV39gW5HLsHEvseCb0hyyt/ANuvQOkkrlqJGRMYHAhQvhkme3KXEn8gsb2NM8JynDzv7ACCHKTlRmZmZeMl+slHNPVJ6LlE5OnPwMH5+GmM09y66bzT0oKTlFYeGRa3tAFRWVvwy2SkQ3pEtiP5FPanEiVuGO6MfccmUnKQtWrACjkZLdu/EfNAh0glOnZhAY0B4/vybl2guh4O/fnAb1n6F9u5+5tf0KEhI+4Zb4N4iJeRbpPZr9WU3YeNKPpGwn4T75dIhKpmPEZoJcM0k++SYnk77A5XJQ4tOJufnh/CPZwErZjuG3fsurvebT5CJC7OwcBFGRd9K61RwAduy4gwRdBosHL+bpVk9xJErDPVkf8cqGV9Dp8/l2Ynte6H8LKw6m0eejtaw5XDXRHZfTxZrvDuETaKB1/3qXbNuocwQBYSY2LjiG0+nCq0kC0XO+J+yllyjetYvjAweR8dlnuK7SjFZRFPr06UOvXr3Yv38/X331FTk5OZXqq9Vr6Pu3Jh5z2JOs9JjDKkYt5gkJ6Gv74FqZy7BxL1KUl8Pc116gIDvrquapcj41WIxJFAl6+/liDBs4SsVY+PnbkNoQE4Xp+eTn5182XwzA19cXLy+vckn8aenLKCpKJDr64fMiYGZzDwAs6laliopKJbHLy0fGbMn5KA6FAkMueYVFaKURb59Lp1mci3S5yF+xEkPduki7nYARw0lL+wGrNZU6de+v1BgmUzQ67578cLQNd8+J574FbZi6ZzSaoDcZ1msBI/uuoGfX9XTrupvu3Q7RseMWSqLe4bVkG8/t30Kupg7/7vkl/+3zX1qFtar03AH8/JrSts0SgoM6c/jwvzh88BnG3zKC732eov/vLpYe+4EBCwfw2e5PGXdrOIsf7kSgScfdX/3OPxf/QbGt8vYhFbFnVTKZpwvpPDoOneHSJyQ1GoUOw+qTk1bEgfVnABAaDUHjxxGzbBm+t/XE8u+PSRw0mMLNm69qPkIIOnbsyNixY8nKymLq1KkkJSVVqm+pOWybAfU4uDmVZZ/txW51ugXZfQnoInxwrcpj6PiXKMjOZu5rL1CYk31V81Q5S43cpizNGVMk6Bw2kGCTblGmcWiwpxaWGb2eizbEi5wdOWC49EnKs7cRhIaGnhcZk9LFiROfYjI1IDSkz3ntjYZwfH0TsFhWEB39YBU8qIqKSk3HUYk2xQcsSCkxNQolPznnik9SluzbhyMtDQQYmzbFEBfHyd+fwNs7juCgrhftJ6XkUFo+G45msvGohXVHLdgcLtrVC+LZPvH0aRyOXnt2zZ9rzWVzymY2nN7AhtMbSC9OJzYwlindp9A9qvs1pW/odAE0bfoFJ5Omcvz4B+Tn7yeh94fc+3EAQ7WxzB0ewhd7vmDe4Xk81PwhFjw0mA+XH+OrDYl8syWJyEAvooO9qWd2/4o2e1Mv2JvagV5olIvPqyC7hN9/SCS6STD1mlVuyy66qZmI2AB+X5pIXNtw9B4jWF1YKLU//BD/ocNIff11ku65F79BAwn7+9/RViJAcCFxcXHcf//9fPfdd8yYMYMBAwbQsmXLy/YTQtB2QD28/T3msJN3MuCRpnj56AmZkEDG9L3YV+UzbPwLzJ85qcyp3+SvmppfLTVSjAnAhURIiYILIbVYceDChY8zAHtakdt5/4IfMH2EDznCHTWrTGQM3FuVu3btwuVyoSgKGRm/Ulh4hMaNPkSI8oFHs7kniYn/xmazoNere+0qKioXcoEDv3AhnS6E5uIbGfl7U8iypRDRNAHrqV+ICKh4i1LabBRu3oz9TAr21BQcKanYU1OxHT8OioIjNQ3zQw+RmbWGwsLDNGr4XjmBdCqriA1HLWw4lsmmYxYsBe7ttOhgE2Pb1mFsuzrEhbn9zVzSxT7LPtadXseG0xvYY9mDS7rw1fvSIaIDver2olfdXigVfFZeDUIoRNf9G/5+Lfhj3+Ns33sHEQ91xXfSaiY99gN3NbqL97e9z+ubX2fWgVk82epJ+ia0Z90RC4mWQhIthWw7kUXhOZEynUZQJ8jkFmjBHpHmEWu1/Iysn3sUl0vSeXRcpcWkEIKOIxow961t7PjlJO2H1D/vfZ/OnYhZspjML7/EMnUaBavXEPrUkwSMGlVWsL2yhISEMHHiRObNm8eSJUtIT0+nV69eFy31dy7nmsMueG8HAx9thp/ZyyPI/sC+upBh419gwcw3mTvpJUa+/AYmvyu3VFGpsWLMnTOmSAkuiZBabE4bxYZCApxm7GmFGOOCyvXTR/qQqxQhEAQFlX+/IsLCwrDZbOTm5hIQEMCJE5/i5VWX0NDbK2wfYu5JYuIULJmriah1fcuMqKio3HzYhNuFX+Nd8Zewq9gBFifp1iSi9C1BSMLCwypse+aFF8lbutT9B40GbWgouvBwTG1aY09No+TgQfz6387uww9gMIQTFjYAgHVHMvhxTwobjlk4leU2zA7xNdCpgZkODcx0bGCmdoD7RHpmcSY/HPuBDWc2sPH0RrKt2QgEjYMbc3+T++lUuxMJ5gS0SvV9/QQGtqVt2x/Yt+8Jkl3LMd2tI+PrqTR5/R1m9J3BqlOrmLx9Mo+ufJTWYa0ZEDOAevUEUkp3OakSO5kFVjIKirEU2MgqtHKwsIQNGTYcrlKhJtEqYHBKgm7Rs371RgJNOgJMWgJMOrz07v9fAkHnyM4kmBPOm2NoXT/i2oaxa8UpGnepjW/Q+Ts1itFIyGOP4TdgAKn/eo3UV/9FzsKF1Hr1VYwNG17R34fJZGLcuHEsX76czZs3k56ezsiRI/Hy8rps35jmIQx6vDnLPOawAx9rhjnSl5D7EsiYuhf72iKGjH+eRTPfZN6klxj5ypt4+VyZ2bBKTRVjQuAS7pwxpESRWhwOG8WmIoJLQnDl28vliwEoJh15hhJ8halSxb/h7InKtLQ0nM6d5Bfso+Et76Bc5IPGx6cRBkM4FssKVYypqKhcFit2nMU2NB4D0XLvH8txnyAPE5w+5U5Ij6pX/iRl8d4/yFu6lMC77iR4wgS0ISFlDvDOgkKOdOmCX7++FLqOkZ2zmQYNnkdR9BxNL+DO6b/ja9Rya0wwEzvF0KF+MPVDvMksySQxN5F1qRs4fug4uzN2sz9zPwBBxiA61u5Ix9od6RDRgSBj5Ra4VYVBb6ZF8685nvhvTshPOJYyH99TI/GPak2POj3oHNmZ+Yfn8/nuz3l106uXH9AEWlP5L00nkAFkFAKFFXf9bPdnDIsdxmMtHiPY62wKTLvBMRzbkcGWJce57Z5GFT9HTAx1ZvyXvKVLSXv7HRKHjyDozjsxP/ooGp+KT21WhEajoV+/foSFhbF06VKmTp3KHXfcQUhIyGX7RnjMYZd+vJuF7++g34NNiYwPxDyxCZZpe2FdMUPGP8/CbzyC7KU3MPr4VHpuKjVUjOFx4Bcut4u0IrXYnTZsvjZEtnu1cu5JynPJ1Rbjb6t84mupGEtNTSW/4BOMxkjCwwdffGZCYDb3IDV1EU6nFY3GcAXPpaKiUtM5b6NLgl04sOUWojdX/JmVtycFu8uKuXkMh86kgYSY+PNrCEopSf/gAzSBgYQ89hiac74opctF1n//iywqImDECI4lTUWj8aF2xGgAFu5MRqPN4/WxwWTZ9nA0N5Hl24+TmJNIvv2sWam3zpv4wHgeaf4InWp3omFwwyrbfrxahNBQP+ZJvK1RHCh8jh0Hx9NI/wFhYbejU3SMuWUMQ2OHklmciUAghEAgUIRStuWoCKXsmntMgYLC9l9OsvOnJG5/uBlR8UG4XJCSa+VkZhEnM4s4YSkiMbOIA6kZ5Bl/YiGLWX5iOY+0eITR8aPRKlr8gr1o1jOSHcuTaNYjipA6FUeUhBD4DxyIT5cupE+eTNb//kfezz8T9uIL+PbqdUW5di1btsRsNvP9998zbdo0hg8fTlxc3GX7BUf4MOzZVvzw8W5++HgXve5tTINWoW5BNnUPrC9h8PjnWDzzbea/+TIjXpqEwVR5sfhXp0aKsbOnKaUnMqbD7rRh93fiki4UoaALL/+PxOVykWPPJ9wegTPPisbv8kLJYDAQGBhIds4GnK7dxMe/jqJUvIItxRzcg9OnvyUnZzPBwRdPjlVRUflro0iBFQfWnEIqijNIKSk5nEVa8UkatLyNjbOWo8OEl+n8La/C9Rso2ryZsBdeKBNiUkoK1qwhY/JHWA8dwrtDB4gPIn3zz9StMxGt1heXS7Jg9z58GnzIy5tKADB7mYnxj6F/TH/q+dcjxj+GGP8YQk2hN6x/YnjDERTPWE7yLWv4Q/MYOblbiW3wPIpiwKAxEOFzZfUWc9KLOPBLBo1a1SG28dm+9UOM1A85P2eqxO7k7Z/q8b9trdHVWcbbv7/N/CPzeb7t87QJb0PLvtHsX5/ChvlHGPxEi0v+HWr8/an16qsEDBlCyqv/4vRjj+PTtSthL7+EPrLyRbzr1KnD/fffz+zZs/n222/p1asXHTp0uOz/P98gI8Oeacmyz/bwy7Q/KMqLpWn3KMwTm5AxdS9iQwmDxj/H4pnvMP/NVxj+wusYLmOeruKmUssWIURfIcQhIcRRIcQ/KnjfIIT43vP+FiFE9DnvPe+5fkgI0cdzLUoIsUoIcUAIsU8I8XhVPRCUJvC7UFwSKSUKWhxOGxqjjlxbOsJLi+JbXjDl5eXhcDnxlyZspwoqfb/Q0FA0ml8wGMKJqDX8su0DAzugKF5kWFZeyWOpqKj8BThhMjOJf2ExBqBIDXbhwJ5fcRkfR1oRmhKFbCWd4Mg6FJTk4G08XwxIl4v0Dz5AFxlJwBh3tKto505O3nknyf/3IK7iYiI+eJ+oaVM5deq/CKEhKuoeALaeyCJTswopbHzR6wvWj1nPqlGrmN5nOi+1f4lxDcdxa8SthHmH3bBCrJTwsQ8T/J4gJL89yckz2b59DMXFp694HCkl62YfRtG6k/Avh1Gn4dVBjflq3O2Q+gD2M3eRmp/LhF8m8Pc1fyfbZaHNgGhOH8rh5B+ZlZqDV/Pm1Js3l9B/PEfh1q0cHzAQy5dTkVfgTRYQEMCECRNo1KgRv/76KwsXLsRuv3y5B6O3jkGPN6deUzPrvj/CpkXHULx1hNzfBE2gEcNGF4PG/52040dZ8Par2EqKKz2nvzKXFWNCCA3wKdAPaATcIYS4cHP7PiBbStkAmAy84+nbCBgDNAb6Ap95xnMAT0spGwLtgYcrGPOqEShlPmM4nWiEDrvTjlavZ1/OJry71qrwg6O0QLg/JmzJla8VFhrmj7f3acLChqEol4+maTQGgoM6YbGsqLayHCoqKjcnRRoDB0QCNo0ORWqw4sBRUFJx270ZSCnRx/pjLbZhk4UEBZxvy5P3449YDx4k5IkncGZkcOqRRzh5x1hsJ04S9srL1F/6A/63347dkcOZlLmEhw/GYHAfAJi34zj6wN/pFtmdDhEd8L+K4uM3Cl7NmmFq0RrTlFSaNPqEwqLj/L51IBbLqisa59iODJL2Z9FuUAze/pVPM+kWH8ryJ7rQKaI7Z/54lDDnQFYkrWTQokFsCfoZ71AtG+cfxeWsXAkjodUSfM891P9xKT6dO5Px4YccHzaMoq2Vr3+s1+sZOXIk3bt3Z8+ePcyYMYO8vLzL9tPqNfR9IIFGnSPY8fNJVv7vwP+zd97xTZX7H38/2W2azjTde7D3RqaIbEEBWYIgQ73iFr3q1et1XcWFOFBZLgRkKFscIDjYmzJaWqB775V5fn8kbYEOUn+4uHm/XiXh5DzPeU6T5nzOd4KbwiHI1Gj2SIyaMo+spDN89cp/MFc3/Pl1UYczlrHuwDlJklIkSTIBq4Arg6JGA584nq8FBgm72hkNrJIkyShJ0nngHNBdkqQsSZIOA0iSVAacBprXu6MphMAqbAjJhmSzIUeBTbIgU6rIqExE1anhWig1Ykzv748pzXkx5utjV/6SLdzpMXr9IIzGLMrLTzs9xoULF/9byIUCk7BgrjA2+HrpoQzyjemEd+/I/p+OgYCYuOja120mE3kL3kbduhUeAweQds89VP66B/8HHyB2+zf4Tp6McLTKSc9Ygc1WTXj4LMDuXvvm4laEvIppbe74/U/2D8DvrhmYMzNRH7LRvdtG3DShHDs+i+Mn7iXl/EJycrdSXn4Wm63h37ep2sLPa5LQh3nQrn/zL1l+HmoWT+vCy2M6k3G+H5a0R4nWdmLh0YWsbPVfjlUf5NQvWc2aUxkUROg7Cwld9D5SZRUXp04j86mnsRQ5V4hVCEH//v2ZMGECubm5LF68mIyMq1sMZXIZAya3oNuISM7syWbbohPYVHL8Z7dH7q3GbZ9g5OTHyDhziq/mP4/Z6BJkTeGMGAsB0i75fzr1hVPtPpIkWYASwM+ZsQ6XZidgX0MHF0LMEUIcFEIczMtzrnVFbZ0xmwQWCzJhd0lKjvoslkZMuYWFhSiVSrzD9ZjSy5Fszlmt3Nzt7SDKypy/a/TTDwSEqxq/CxcuGkRCIJerMGHBWlH/O8uSX4UotpFlPE9kxy6cPJGAsCno1qeudVHxypWYMzIwPPooua+8gjHpHCELF6K/915k2rq4Wau1ivT0T/HzG4iHNg6A70/lYPX4iVBtbLMr4v9V8Rg4EFVkJIVLl+HmFk6XLmsIC5tBedkZzp9fyMmT97Nv/3B+3NWOPXtv4tjxuzmX/BpZWV9RWnqc/ZtPUVFipP+kFsiaqPvWFEIIJvcIZ8sDfYjwDGPv3lvoon4chVrO1lYf8vSxeSTnn2/2vLqBA4neshm/2bMp2biRlKHDKFqzBsnJZuGtWrVi5syZyGQyli1bxvHjx506l+6jouk/uQWpCQVsWHAEk8AuyDzVaA8oGDH5EdJPneTr117EbGpY5LpwTow1FAhwpUppbJ8mxwohPIB1wEOSJDVoG5Uk6SNJkrpKktTVmRTcmuXYsCGQkGw2FA4xZnUsp7EPRFFRET4+PqjDPJGqLVgKnPN1S1IaFouS3FxnamXbUav0eHp2JM8lxly4cNEISoUGI2ZsVfVjeSpP2m9OZTHuSJIgrywdf10YKo3j+66sjPxFH6Dt3QtrUTHFa9biN2cOHn1uuHyeyoskJj6P2VxIRPic2u2fHP0WuSaHOR3u/MvHgzmLkMnwnTGD6lOnqNy3H7lcTXzcv+jdeycD+p+ke7dNtGn9FhER96DVtqCq6iKpqUs4dfoxDhy8FaPvrbS89SmySh8kMelFMjJWUVx8ELO5+e2Aov09WHdvb+4bGMOuY76UJT/IKP+7SNMmMm7LWN498i5VlubFW8nc3DA8+gjRX61HFRdL9jPPcnHyFKrPnHFqfGBgIHPmzCE0NJT169fz3XffYXNCzLXtF8LQu9uRn1bO+tcOU2Gy4j+nHXJPFR6HVAyf+BCpJ4+x8fWXGjWG/K/jTDZlOnBpOedQILORfdKFEArACyhsaqwQQoldiK2QJGn9b1p9IwgEEtizKa1WFMLu1zc74rOasoz5+vqiCrOnF5vSy1H6Xz0TpLIiEas1kORzyc1ap7/+RpJT3sBozKmN0XDhwoULsN/JqtXulJlzEA1ETZQeTKfQmEXUDV05+MsJJGGlbbs2ta9X/Pwz1uJivMaOJfuZZ3Hr3Bn/B+4HwGIpJzd3G1lZ6yguOQAIgoLG4e3dDYCCciMJ5VvR6rwYET38DzjbPw6vMaPJW7iQguXL0PbsUbtdLteg07VGp7s8fNlmM1NZcZEdq77DZEkhvreZalMKGRmrsNnqxJJS6YdWG4tWG4PWPRatNhZ3bQxqVePJDUq5jHlDWtI/3sDDq4+y+ucWDPJ9jnzZOj48/iEbkzfyWNfHGBzRvPIV6rg4Ij77jJING8id/xrnbxuL79Q7HLXJmq7/pdVqmTp1Ktu2beOXX34hNzeXsWPHotFomhwX3dGfWx5yFId97RCj7u+A/+x25H10HN0RGDbhAbatepuNb7xEr/GT8Y+IRqFsuvLA/xLOiLEDQJwQIgrIwB6QP/mKfTYCdwJ7gHHADkmSJCHERuALIcSbQDAQB+x3xJMtBU5LkvTmtTmVOoSwuyllDsuYUmb/EBnNdstVQ2JMkiSKioqIjY1FYXBHqGSY08qgk6HJY0mSRHnFGbTaHhQUFFBQUOBUX0uwx40lp7xBfv5OQkImNvMsXbhwcb3jpnHHUmFDWSpHskoIuf2CbCkxQr6VzOpkbuw0ip2LViNsCrr3a1871picAkDB4iWgVBLy+mtUmdI4n/guubnbsdmqcHePJiZ6HoGBo9FogmrHfn7oEDLtGUZGTUclV/2xJ/07I1Or8ZkymfyF72BMSkIdF9f0/jIlaSfcSTvUghunjaZVB3spC0myUV2dSUXlOSorkqmoOEdF5TlycjZjsdQ5euRyDwIDRxMd9RAqVcOFb7tH+bLtob48+/VJvj6aSbD1dqZ6DWOfajWP7nqUHkE9eLL7k8R4xzQ4viGEEHiPGYNuwAByFyyg8NPPKN32DQFP/hPd0KFNijuFQsGoUaMIDAxk69atLFmyhEmTJl312hYc682tj3Zmk6M47PB72xMwpz15Hx3H66iWobc/wPY173L+6CHkCgWGyBgC4+IJim1BUGwLvAICrxsrbHO5qhiTJMkihJgLbAfkwDJJkhKEEM8DByVJ2ohdWH0mhDiH3SI20TE2QQjxJXAKewblfZIkWYUQfYCpwAkhxFHHoZ6SJGnrtTktgYQEkg0sFlQyN7BdKsbquynLysqwWCz4+PggZAJliIdTGZXV1ZlYLGWEhXUFMkhKSnJajGm18Wg0IeQX7HCJMRcuXDioiwLRuNlvJM02CVN6GeoITwCqT9uTjUSEGiFTkFOcjp97MBr3OuFkSklB5uGB8cwZQt9/D5ufnMMHp2KxlBEUOIagoLF4enZs8OK3JnEVQinjvq7Tfs8T/dPwmTSJgo8WU7D8Y4JffqnJfavLzfy6LpmgGC9a9qwTrELIcHMLxc0tFPwG1G6XJAmTKb9WpJWWHiMzcxU5OZuIjnqIkJApDXZo8dQoWTCxEwNbGnhyzXEWJ2r5Z9hLjOt+hHeOvsO4jeOY3Goy93a4Fw+V89Xt5d7e9tpkt91G9nP/IePhR9CuWUvAM/9CHRXV5Nhu3bqh1+v58ssvWbx4MbfffjvR0dFNjvEL8WDs413YtPAoGx3FYSNn2wWZ9wkP7nrmfXLLL5KVdJbsc4mc2PEtR7ZtAsBN50lgbI04iycwtsX/TCV/p4q+OkTS1iu2PXvJ82pgfCNjXwJeumLbzzQcT3ZNEIDkaBQu2WzIZUpkNjlVDotYQ5axIkfmiY+PDwCqUB3lezKRLDaEovHQuvIKuy/eYOiMXm8kMTGRnj17OrdOIdDrB5GZuRqrtQq5/Op9wly4cPG/g7u7/TvBJCxUJebXirHixAwsNhPhvTtxZF8CkrDQpk2by8ZWJSRgKy/H985puPXrweHDk7BYSunSeVU9V9ylnMjMplj+Cy09+qB30/9+J/cnovDxwfu22yhaswb/hx5EaWjcA7JnQzLGKgv9JrVAyK5+2RJCoFb7o1b74+vTC7iD8PDZJCW9SGLS82RkriQ+7hl8fW9ocPzojiG09dcx4+1feWFHMsPbxbNi+Fd8cnoRn536jC0pW3ik6yOMjB7ZrA4Hbu3bE7nmS4pWriJvwQLO3zIav9mz8JszB1kTLsioqChmz57NypUr+eyzzxg6dCjdu3dv0oKl89Vw27wubHnPXhy27+3xtHa4LCvWpOLfzkBIixhUN09E5qOiICOVrKSzZJ2zC7TzRw+BI6zIJyjELsziWhAc1xJ9eCRyJ9sV/p34c3tV/E6cVeZSIqu0W8esVuRyGUqZhipHrZOmxFhNg3BVmA4sEubsRpqNOSgvt4sxD20L4uPjuXDhAkaj8xkjev0gbDYjhUW/Oj3GhQsX1z8SoHX0Hiy1llB6Prv2terMYsrMRUR17sbRw8cRNjnd+3eoG2uzYU5PByHwu+9eEhIeoqz8NG3bvN2kEANYsG8FQm7iwW4zf5fz+qvgO/1OsFop+uzzRvfJTinh1M+ZdLgxFH3ob7fQeHjE07HjJ7RvtwirtZojR6dx/MQ/qKpKa3D/mBBPFtzcmn5VCrafzGHSopPcbJjLyhErCfEI4emfn+bObXfW9gF1FiGX43vHFGK2bUU3dCj57y8iZdQtlO/e3eQ4X19fZs2aRXx8PNu2bWPTpk1YLE0nrGm0SkY/1JHIdnp+Wp3IwV0Z6Ge3Qx3lReXJfIrWJZHz5iGyXz6AbGclUbI2DBh8J9NeXsjcZasZ968X6TNxGr4hoVw4foQdyz7g8ycf4t3pt7PymXn8+Olizvy6m5LcnOuiXuf1Jy+BArldQAlsSFYrMrlAadVQUWUXY1ZzfTFWWFho7//lZS9PoQqtCeIvq33eEOXlZ3DThKNQeBAfH8+vv/5KSkoKrVq1cmqtPt7dkcs9yM37gWkXwsg2mQlUKQlUO34ufb1jdQYAACAASURBVK5WEqhS4KmQ/8/61V24uN6p+csWkkCrsycQlUnlVOfVhU2IcqgSFcjVarILU/HRBKP1rLNumDMz7clLIcEkZy0gv2AHLeKfR68f2OSxLVYLBws34S6LpW9Ep2t+bn8lVOHh6AYPpmj1avzuvrte022b1caulWfReqnpNrJpd54zCCHw978ZX9/+pKYt4cKFRRQU7CQ8fDaREfcgl1+eLNZhQCiDd2XQXm5jq8rMlKX7mN03miU3f8w3Fzaz4PACJm6eyPj48dzf6X68NQ3Xz2wIhb8/Ia/Nx3vsWLKff560OXejGzyYgKeeRBkU1OAYtVrNhAkT2LlzJz/99BP5+fncfvvteDThRlSo5Ay7uy27ViZy6JuLVJSaGHhHK4QQWPKrMF0sxZRahjG1lOqzFx2/KFAGavEM16EP70+XvqOQ+aopL8gn69xZhwUtkWPfbuPQlg0AuHt5X+LebEFgbNzfri/mdSnGRM3XmWRvFC6EQCk0VFTav8was4x5eXmhcJg/5T5qZFqlvS1SE17H8vKzeHi0ACAsLAy1Wk1iYqLTYkwmU+Hn25df8lI5Yq2kv48OAaRUGdlTXE6xxVpvjJtMRqBaQYhaxVMxQXT2/Ht96Fy4cHF1hBDoHH/blaIamSNxT7LaUFlUWN2tHPz1GDZhoU3ry12UNaUMqm/1IDfjM8LDZhIaOuWqx1x2ZBs2RQEjwu6+tifzF8XvrhmUbd9Oybq1+N5552WvndiVQX5aOUNmt0WluXaXSrlcTVTkfQQF3sa55PlcuPAeWVnriI39JwGGkbU32nKFjF5jYti++CSvT4xjXVExH+1O4eekfN6eeBObbh3EoqOLWHlmJdsvbueBTg8wNm4scpnc6bVoe/Yg+uuvKFj+MfmLFlE+4hf877sP32lTEQ1kOspkMgYNGoTBYGDDhg0sXryYiRMnEtSIgANHcdgpLXD3UnFwywUqS0y0H2i3NLp3DUDbLRAAW5UFU1oZxoulmFJLqTyaR8U+uzVYplWgCvckKCKSyD7tUE7UIckk8lMv1Lo3s84lknJov/2gQuAbHGoXZ3H22DP/8Ehkcud/N38016UYq0HCXh9FyECBhsKKbNxoOIC/psZYDUIIVKFNB/FbrdVUVp4nwGBP/ZbL5cTGxpKUlITNZkMmc84LrNcP4te8BFQyWNo2Eg9F3Qem0moj12Qmy2gmx2h/zDbZn/9SXM59py6yo1tL3H5jAUIXLlz8xbjE4+Lpbbc6VMvNKCz2i6OlsBqBDLmPmr37DyGzqul90+VWrIpffsEUbiO/VQL+/kOIja3XUrhBVp5ZgWT25sFet16bc/mL49ahA+5du1KwbDneEyciU9vLIFWUGNm3MYXw1r7EdHa2vmXz0GiCaNvmLUJDppCY9DwJCQ+Rkb6C+Phn0Ons4jqmsz+B0Z4c23qRfz/fk4EtDDyx7jgj3/mZp4a34vFej3Nb3G38d/9/eWHvC6xNXMtTPZ6io6Gj0+sQKhX6u+fgOWIEOS+9RO5rr1Hy9VcE/vvfuHft2uCYdu3a4efnx8qVK1m2bBljxoypF7N42TGEoMcoe/uon1YlkppgT0DReCjRh3rgF+qB3vHjMzAMuUKGZJOw5FZiTC3FdLEMU2op1aftBdaRgTLIA1W4jviIbrTtNgi5jxpjRQXZyYm1sWcph/eTsOt7ABQqNQHRMQQ6rGdBcfHo/Pz/Ml6m61SM2X+5krB/q8lkAgUarFYryOWYG7CMFRYW0rJly8u2KUN1VCcWYTNakanrK+qKiiTAhodH3bi4uDgSEhLIzs4mODjYqdX6+vVnPzq6a/IuE2IA7nIZkW5qIt3q90D7qbCM8ceSWXgxhyeiG78zceHCxd8PgcDdZA+5MMptKCxKbEYLlWn2C5LFV0XhhSxCvVvi5nH590PVseNUd7SBkNGq5SsIJwK9E/LOkG9NIFZzO15uTdeUup7Qz51L6vTpFK9eje80e/boL2vPYbNI9J0Y/7tfrL29u9Kt61dkZq4hOeUN9h8YTUjwRKKjH0Gl8uWGcXGsm3+Io9+lctOoaLaF9eXxtcf598YEdp7NZf649iy9eSnbL27n9QOvM3XbVEZFj+LhLg/j7+68kFSFhhC26H3Kduwg+8UXuXjHVLzGjMEw7zEUDVQICA4OZs6cOaxevZo1a9aQm5tL//79mzRCtO0XQlxXA/np5eSnl1PgeDz5YwZWi914IpMLfAK1l4u0oX746lRYK8yY0soc7s1SKg/lULHH3j5KplOiCvfEL9xAUJcYVLdMAIWgJDfHLs4cFrSj32zikOUrALTePnZxFudwb8bEonK7em3R34PrUozV/OnUBPUJAXLsX1Y2haqem9JoNFJZWVkbvF+DKtQDJDBnlaOOrN/qqLz8LECtmxLsYgwgMTHRaTGWUKWmUOjpYl0HDHZqDEBfXx3jAnx4NzWXMQE+tND+73yBunBxvSIuSTTPuu0WxPjbMcolsNitYuUXcgFIKS8FAT37dK83hzk1FeMtAi+vDiiVnk4d9839y5BsSmZ3nXRtTuRvgrZnD9y7dyf/o8V4jx9PxoUqkg7k0G1kFN6GP+bCLISckJCJGAzDOX9hIenpn5KTu4XoqAcJiZxCTGcDR75LpU3fEAzeGpZP78Zney/y0pbTDFvwE6+Obc/Q1kPpF9KPJSeW8HHCx+xI28G9He5lcqvJKGXOF1fV3Xgj2p49yf/gQwqWL6ds504MDz+M9+3jEVcILZ1Ox/Tp09m0aRO7du0iNzeXMWPGoFY33kBd7a4kJN6HkPg6T5TNaqM4p4r8jLJagZZ2ppCz++qSVty9VLXWM79QD/Tt/AnUa7DmV2FyWM+MqaVUO6xuyAXKYA/U4ToiIloTN6YnCm81VouZvAvna12bWUlnSD64t+aNQB8a7hBo9hg0v7BwZM1w/f5WrksxVotwKG0BCsn+4ZBp3Ou5KQsL7Xeal7opAVQhdZX4GxRjFWeQydxwc6trEK7VagkNDSUxMZEBAwY4tczNeSUosNHGuIHq6nvRaJwTcQDPxYbwQ0Epj59N46tOscj+IiZXFy5c/DYEVscjyCQLSqsnRUq7hawiq5DqrFIkq5HUwjw0Vh9ad7s8uNxmNGKxlWEOs+Lr08epYxZVF3Eg/3vklV0Z0rLpOlLXI/4PPsDFKXeQ//kX7E5vg6e/G52HhF994DVGqfQkPu5fBAdPICnxRRKTXiAjcxVtBs/j/DEZ+zalcONUewD8tF6R9Ir244FVR5n16UEm9wjnXyNa8UDnBxgTO4ZX9r/C6wdf56ukr3iqx1N0D6ov2htD5u6O4ZGH8Rp9C9n/eZ7s556jeP16Av/9LG5XuCMVCgVjxowhICCA7777jmXLljFx4sR619MmjyeX4RusxTdYC93qtleVmepZ0dLPpGGz2g0tcqUM36BLrGidDPj5qCHPnhxgTC2lYn825b/YmwbJvVSowj3RhnvSunV/Ot40AqGQUVVWSva5xFqBdu7AHk7u/Nb+nqg1BMTE1iUHxMWj8732JV+uSzFWY1aWHMEXQgZym6OGl8atnmXsyrIWNcg9Vcg9VZgzyhs8TnnZaTw84hHictUcFxfHzp07KS8vbzLTBOzWu815xfT2UqItriAv/3vCQp0vtKhXKXgmNphHzqSxKquQycHOFZx14eLvihBiKPA29iLUSyRJeuWK19XAp0AXoACYIEnSBcdrTwIzASvwgCRJ25ua09F5ZBXgCxwGpkqSZBJCPALMwl7MOg+4S5Kki9fk/Gz2PpQat1JMYTb8PYLJMCZQIioRWYXYioxclDIwSdW0i+qC7IraV+U//4yxhQQCfP2uLsYsNgvz97+JhJmbQ8ah+B+MP3Xv0gXtDTdwePM5ioOiGHl/BxTKPy/Y20MbR8eOH5Of/z1JSS+TeH4OLUf1Jun7kXS4MQy/EPt1JS5Ax9f39eaNbxP5aHcKe1MKeHtCJ9qFhvPeoPfYlb6LV/a/wsxvZzI0ciiPdn2UQG2g0+tQx8QQ/snHlG7eTM6r87kw/nZ8Jk3C/6EHkevqqgwIIejduzf+/v6sXbuWxYsXM2HCBCIiIv5fvwc3nYqwVr6Etaq7NlstNoqyKy4TaeeP53P616y635+vGn2oDn2oDr/OgfioZKjKTLUuzqoT+fYdFQJViA5VhI7A8EjCh7RHPl6FJEkUZ2c6LGdnyT53lkNbNmCzWvALDWf6G+//v86rIa5LMVaDTdS5KWU2FSqVCpu6cTHWkJJvrBK/vQ3SWfz19d2K8fHx7Ny5k6SkJDp1ajo9/GR5FanVJh4ID0VnbUNq6lJCgicgkzVu5r2SSYG+fJlVyPPJmQzWe+KvcvX7cnF9Iux3Pu9h9+enAweEEBslSbq04NJMoEiSpFghxETgVWCCEKI19u4gbbC3Z/teCBHvGNPYnK8Cb0mStEoI8YFj7kXAEaCrJEmVQoh7gfnAhGt5rv4hZ8l/0kKrnBAyziaQKvJxy3NHXikjS1ECEvS5qX6AdfkPOzC2siGXNHjqOjQwcx25lbnM2zWPw7mHMRX0YfpNPZrc/3pGM+M+zn+SQ6hXGRFt/vybWnspjMH4+vYjLW0Z5y+8T9SQ/Rz4+RCDxz1XWwpDrZDz1PBWDIj355Evj3Hr+7/wyM3x3N0vhgFhA+gZ1JPlJ5ez9ORSdqXv4p4O9zC11VSUcueuE0IIvEaNwqN/f/IWvE3RF19Qun07AU88gefIEZfF1MXFxdUWiP3kk08YMWIEXbp0uaa/F7lCViu0apAkicpS02UWtPz0ci6eLECy2XWAQi3HL1iLPswT/04afOQyNJUmLBkVlP+aSfnuDPv83mpUEZ6ow3XERnWlVe/+CLkMi8lE7oUUTNXNa97uLNf1LZCNGjelBBLo9XqsKnWDbko3N7cGG6GqQnVY8quwVV9e4M5kysVsLsJDV7+ERWBgIDqdjsTExKuucUteCXIBw/y9iYmeR3V1OukZXzTnNBFCML9FGBVWG/85d2UP9/pUmiw88uVRfjid06zjuHDxF6A7cE6SpBRJkkzYrVajr9hnNPCJ4/laYJCjH+5oYJUkSUZJks4D5xzzNTinY8yNjjlwzDkGQJKknZIkVTq27wVCr9UJXhloEG8QCKuCfFGMtcCIyqahRG5EYXPHEFa/tlTlkUMYW9rw9ujaYNudGvZm7WX8pvEczztFdeYEOnlMo02wc/Fl1xuSJLH/mAwhF0T+/C7W0tKrD/qDkMvVREbeS69e36GRDUDpv46ffxpEdvbGy4qd9o7V881DfRnSJpD535xl0uK9pBdVolFouLfjvXw9+mt6BPXgrUNvMXbTWPZk7mneOjw9CXz2GSLXrEEZGEjmvHmkzrgLY0rKZfvp9XpmzZpFVFQUmzZtYuvWrfbkud8RIQRaLzURbfzoPCSCm2e2YfK/ezBnQT/GP9mVgVNb0rp3EHKFjHOHcvnxqxS+WnuOlVtT2Z5ZyclQT/I7+GPpZEAY3DGeL6F4Uwq57x4l87k95H54jIofMvCx+BMW3XjW6P+H61KMCcn+dSa7JIDfZpPw8/PDIldiNZsv27+oqKiei7IGZU0Qf+blrsq6yvst640RQhAXF0dycnKTVYprXJS9vDzwUynw9e2Dj09vLlx4D4vl6n0xLyVeq2FuuIG1OUXsLmx8rCRJzFt7nPWHM7jn80P8eDa3Wcdx4eJPJgS4tGx5umNbg/tIkmQBSgC/JsY2tt0PKHbM0dixwG4t29bQYoUQc4QQB4UQB/Py8q56cgDiimLiyupslLhTQjmaPHvvyTKZGa3aq16mn81opKoyFase/IIGNTi/TbLxwbEPmPPtHIxGN0rO/YObI4bz8YymW9xcz5w/ls/FEwV06eOLqiCdwo8/ufqgPxiNOpDe/RaRd/AZqkq1JJx6mEOHJ1JWllC7j7e7incnd+L18R1IyChhyFu7WbHvIpIkEaoL5Z0b3+G9Qe9hsVmY890cHvnxEbLKs5o4an3c2rYhcvUqAp/7N9WnTpEyegy5by3AVlVnMXJzc2PKlCn06tWL/fv38/nnn1NZWdnErL8PCpUcQ4QnrW8Ipu+EeG59tDMz3+jLtJd7M/wf7elxSxT+YR7kZ1bwy+5MtuzMYN3+XLbkGTnqoyEnwovqUB3mCgtlP2dQ8Okp8j468bus9boUYzW3ljViTCYkJKtkt4zJ5JiM9S1jjQUbqhy+eVN6I2LskkzKS4mPj8dkMpGamtroMs9WVnOu0shIg/3uVghBbMw8zOYiLqYuvspJ1ufBiACi3FQ8kZhGldXW4D4f7Ephy/EsHrgxljiDjrs/O8Se5IJmH8uFiz+JhtTClb1QGtvnWm2vO5AQdwBdgdca2BdJkj6SJKmrJEld/f2dLTNQE/Nqf6wqScFN7UERZeSYL2KSTBhlVny86rvSqo8fx9jCrh399H3rvV5UXcS939/Le0ffw9PSg+zTd3N/3xt4d1InNH9ijNSfidlo5afVifiFaOk8qQu6wYMp/OQTrMXFf/bS6iFXyuhy4zCSt/0TL8XjVFamsP/AaE6feQqTydE8XgjGdQnlm4f60THcm6e/OskdS/eRVmgXQ/1C+/HV6K+4v9P9/JT+E6M3jGbx8cWYrPVLPjWGkMvxmTiRmK1b8Bo+nIIPPyRlxEjKduys3UcmkzFkyBDGjBlDamoqixcvJjf3z7/5F0Kg89UQ1V5P1+FRDJ3Tjjue78Xst/ox9vEu9J/cgvhuARiFjENnivjmSD5fJ5awudDEEaWcNO3vEwZ0XYoxm+P7slLtKPrqsIzp9fYMiApTnWXMarVSUlLSqGVM7qFC7q3GlHGlGDuLWh2EUlk/yxLszVXlcjlJSUmNrnNzbgkCGK6vm8PTsz0GwwhSU5dhNDbvg6uRy5gfH8b5KhMLL9Z3Qe5KzGP+9jOM6hDMw4Pj+Wxmd8J93Zn5yQEOpxY161guXPxJpANhl/w/FLjSN1+7jxBCAXgBhU2MbWx7PuDtmKPesYQQNwFPA7dIkuR8Q9qrcKUCrC7PwFPrhVWh4NeCTXydswwEBAYG1BtbsX8/xpY2FBVuuLlFXvaaxWbhoZ0PcSD7IG6lE8k9fysLJ/bg4cG/fy2tvzL7N5+nvMhIv0ktkMtl6O+fi62ykoJly//spTVIbBcDAZHenNzShm6dvyUsbAZZWevYs3cQqWnLsTkSQMJ83fl8Zg9evrUdR1OLGbJgN5/tvYjNJqGWq5nTfg4bxmzghuAbWHhkIbdtvI2fM35u1loUej3Br75C+KefINzdSP/HP0j7x32YMzJq9+nYsSPTp0/HbDazZMkSzp49e01/H9cKlUZBYLQXbfuF0H9yC8Y+3oXZb/Xjjhd6MvTutnQaHok82INSN5cYazYVqsvdlDVirOqSFkMlJSVIktRkGq4qxAPzFUH85eVnLiv2eiVqtZrIyMgm48a25BXTw0uLQX35mxsT/QiSZOb8hXcaP7lGuLT22NmK6trtFwsquP+Lw7QI0PHq2HYIIfDzULNiVg/8dWqmL9tPQmZJs4/nwsUfzAEgTggRJYRQYQ/I33jFPhuBmt4244Adkj24ZiMwUQihdmRJxgH7G5vTMWanYw4cc24AEEJ0Aj7ELsSu7e3+FXY+ozEHbx9vEDD6Xy8T1M3eXzI8pn6YWsW+vRhbSHiaYuoJrA+OfcDh3MOYs8ZhK+nB6jm9uKWD82V0rkfOH8vj6HeptO4bTHCs3UOhiY/Hc9gwCj//HIuj7NFfCSEEvcfGUlFi4uSPxcTHPU2P7lvw9OxIUtKL7Ns/koLCn2v3ndwjnO0P96NLhA/PfH2SKUvqrGTBHsG8NfAtPrzpQwSCe7+/lwd2PEBGeUZTS6iHtnt3otevx/DYo1Ts2UPyiJHkf7QYyZEsFxYWxuzZs2ur9r/55pt88cUX7Ny5k9OnT1NcXPyXbPYtZAIvf3diOhnoMSqa4fe2Z9A051odNpfrUozVWMZqK/Bjd1PWWL+qbXVvemM1xi5FGarDUlCNrcpu/rfZTFRUJjcpxsDuqiwoKKCgoL4bMLmymtMV1Yzwrx+A6+4eSUjwJDIzV1NZeb7JYzTEc7EheMhlPH42DZskUWG0MOfTQ8hkgsXTuuKuqgvqNXhqWDGrBx5qBVOX7icpp3mxai5c/JE44rfmAtuB08CXkiQlCCGeF0Lc4thtKeAnhDgHPAL80zE2AfgSOAV8A9wnSZK1sTkdcz0BPOKYy88xN9jdkh7AGiHEUSHElYLw/3GSl4sok6UQvb/dJVlSYsas8AJJRmTc5ULKVlFBSfERJDfw0V5eU2p/1n4+Ov4Risru6GW92DD3BjqFO18H6nqkKLuC75efwj9cR9/b4y57TT93LlJ1NQWLl/xJq2ua4Fhvojv5c/jbVCpKjGi1sXTssJz27T9CkkwcPXonR47MorzsHAChPu58eld3/ntbO05klDBkwW4+3XMBm+Na2DukN+tuWceDnR9kb9ZeRn89mkXHFmG0Om/wFSoVfrNmEbNlMx59+5D35pukjLmVir37APDy8mLGjBkMHTqUiIgICgsL2b17N6tXr2bBggXMnz+fTz75hG+//Zbjx4+Tl5eHzdZwuM31yHVZ2qKmvphN1BRPlLDZJJRKJWqZjOpL3uBz5+wfVr8G2j3UoAp1xI1llKOJ9aaiMgVJsjQaL1ZDXFwc27ZtIykpqd78W/LsVqjh/g27OSOj5pKVvY7k5Ddo1+7dJo9zJZfWHluZWcAvP14kKbeMT+/qQZhv/YrSoT7urJjdk9s/3MOUJftYc08vIvxczcdd/DWRJGkrsPWKbc9e8rwaGN/I2JeAl5yZ07E9BXu25ZXbb2r2wp1EXGIaEyYJs1RMYIg/HICczDwKi/JRSVrcdZeXvyla/SXGGBPYwC+8ruROYXUhT/70JFpZENlpI1lydwdCvN1+r+X/LTBVW9j2wQlkChnD7mlXr6aYOjoKr1GjKPriC3xnTEdpMPxJK22cXmNiuHAsn/2bzzNwSkt7KQz9IPx8+3D6xJtkZC+joHAn3tqBtGr3FFptNJO6h9Mv3p9/rjvOsxsS2HI8i9fGdSDczx2VXMWsdrMYGT2S1w++zvtH32fjuY38s/s/6R/W3+l1KYODCX3nHcp+/JGcF18idfp0PG8ZRcC8eaj8/enZs2ftviaTiZycHLKyssjOziYrK4t9+/bVZl8qlUoCAgIICgoiMDCQoKAgDAYDCsX1J12uvzOirr6YraaStcNNCeDhpqbQqMRYXU1uXh779u2jc+fO6C4pYHclNUH85owyNLHelJedts91FcuYr68ver2exMTEyz6AAJvziuns6U6IRtXgWLVKT3jYLM5fWEhJyVG8vJxv/Ap1tcf+/e0ZLGdLeGp4S/rENV41OEqvZcWsHkz4cA+TF+/jy3t6/c9/Ybtw8WejqASzKCUo3B8kyM8rpLy6GE+3y8WBzWikYPkyTLMUKNNsaPu0B+zZ08/88gyF1cWUptzDnT3j6RLRfIvY6fIqFEIQ467+23f5kCSJHz45TXFOJbc82BGdb8Nt5PT3/YOSzZsp+Ggxgf96+g9e5dXxDnCnbf8QTvyYTvuBofgFO4wGVWb2LkvBauuMb6ssbDE/snfvj/j7Dycm5kFCvGP49K7ufHkwjRc3n2bIgt08MbQF03pFIpMJArWBvN7/dcbFj+O/+/7L3B1z6R/anye6PUGYZ9hVVlWHbsAAtD16kP/RRxQsWUr5zh/xf+hBfCZORMjt4lelUhEWFkZYWN28VquVvLy8ywTasWPHOHDgAGBPDPD3979MoAUGBjbZgunvwHUpxmruKy2iJoBfqi38Fh8Wyp7yStavW0dBURE6nY6bb765yflk7krkvprajEp7GyQV7m5RTY4Du6ty7969GI3G2g/LxSojx8uqeCam6XiN8PCZpGd8zrnk+XTutKJZQbZCCMbL3TlytoSQSC9m9716i5P4AB2fzezBpMV7uWPJPlbf3RODztXv0oWLP5a6bEpllYRFY8LDS43cpiEzNxULRvx8Ls/MLFm/HpMxD2OIGc893sgcNRM/P/05u9N3o6sYj7smhnlDmrbmN8SqrAIeOmOv/KGTy+igc6eTZ91PkLrhG8q/Koe3XyTlSB69x8YS2rLhxC0AVXg43rfdSvHq1fjNvAtlUNAfuErn6DoikjN7s9mzPpmRczsg2Wxse+9NSvNyGf/sy/iHR/LTqkXkFH+Jre028vK3EhAwkqjIuUzoFkvfOH+eXH+C5zadYuvJbOaPbU+k3u4V6RnUk7Wj1rLi9AoWHVvEmA1jmNF2BjPbzcRN4dyNuszNDcODD+I16hayX3ienBdepHjNWty7dEERGIAyIACFIQBFgAFlQAAyd3fkcjmBgYEEBtZ1CrDZbBQVFdWKs6ysLJKSkjh69GjtPr6+vpcJtKCgILTav4+H57oUY3WWMYcYc8SMAURHRXFo907OOoTNHXfc0WCx1ytRhXjUZlSWl59Fq41rsqBiDXFxcfz666+kpKTQqpU98G+rw0U5shEXZQ0KhQdRUfeTmPgfCgp3ofcbcNXj1XAhv4JXvzqJ3s+NlFh3fioqp59v49a/GtqGePHxjG5MXbqfqUv2s2pOT3y0f68vWxcu/tZcEsesNEqYdBK2khL8lJHkVp0BAcEhdZmUktlMweIlVE31ASkXnyL790xKSQpvHnqTcE03Ek535uMZbdGqm/eV/2NhKY+dTaOvjwe3BfhwtLSSI2WVLErLxeJYZ4BKYRdmOi0tPTT4KRX4KRX4KuV4KuS/OVOzwmol32Qh32Qhz2Qhz2yufZ5vtqBXKpgW4kdLrfMW/NSEAvZuSCGuq4GON13dyqO/5x6Kv95A/gcfEvSf537TefyeuHmo6DIsgj3rk0k7XUj6qe2kHNrPwOl3E9rSXpz0prseI+PMcL7/+A2U/ieQ2m8jJ2czAYYRnv2cFAAAIABJREFUREbN5eMZ3VhzKJ0XNp9i6Nu7eWJoS+50WMmUciXT205nePRw3jj4Bh8e/5BNyZt4vPvj3Bh2o9PvrTo6ivBlyyjdupWCxUso2bABW3n9NoMyT0+UAQa7QLtCrGkDA2kRFETr1q0RQiBJEmVlZZcJtIyMDBIS6uqu6XS6egLNy6t+fb6/AtenGHN8m1ll9oB7GTZsNglJktD6+KAqyCamZ18CwiOIjY11ak5VqAdVJ/KxVpgpLz+Dn28/p8aFh4ejVqtJTEysFWOb84pp5+FGhNvVzaohwRNJS11O8rn5+Pn2rdcHsyEqjBbmfHYQmUywenp3pian8kRiGju6tcTNib5zXSJ8WTKtK9M/PsC0ZftZMbsHnhpXiyUXLv4IpEuKW6iNVkoNcix5eYyfMYJvPteTVXiRtp3qQiRKNm2myphBWRsb2l+U6AztANicvBmbJJGYMIRbO4UyoEXz4p4SyquYdfICcW4qllbvwtPUikktegNQbbWRUF7FkbJKu0ArreSb/PpV6xUCfJQKfB3izM/xvOZRCBwCy0y+2SG0HIKrqpHgbU+FDL1SSabRxLKMfPr6eDAr1J+b/DyRN3GRLcmr4tulCfgFezDQ0Wz7aihDQvAZP46iL9fgN3sWqtBr1mjhmtF+YCgnf8zgh4+3UXBxFa36DKDT0JGX7RPSsjVTXviAAxvWcnDlCgI6FiHafkdO7hYMhuEMbzmXvnH9eGr9Cf6z6RTbTmTz6rj2RDmsZAZ3A6/2e5Vx8eN4ed/LPLTzIW4IuYEnuz9JhKdz/SeFEHiNGIHXiBEAWMsrsOTmYMnJwZyTgyUnF0tONuacXCw5ORgTE7Hk58MVWZZCqURhMKAIDEQZYMDLEIBfQACdAgNQxMdj9vKmwGohOz+/1tWZlJRUm62p0WjqCTQ/Pz9ksj83n/G6FGO1AfzU1RkD+3vq4eOHAFqHBtFuwACn51SG2K1KFampmEx5V40Xq0EulxMTE0NSUhJWq5Ucs5VDpZU8GeWcyVsmUxEd8wgJCQ+Rnb2RoKBbm9zfXmH/GOdyy/n0rh7E+nswXx7G+GPJLLyYwxPRzh23d6yeD+/owpzPDjJj+QE+m9n9sixMFy5c/D5ItY8CjU2J5A7GnHT8+7Xgjn8OorrCjJuH3VotWSwUfPQRlVM8gAo8Nktonrbf9P2Q+gNqcyxypS/PjGzdrDVkVJuYciwFHWZWHJiLZ/4x+wvhvaHfo2hiBtHFS0sXrzo3ULHZQkqVkSKzlUKzxfFjf15gsv//bEU1hWYrRWYLNVJLLsBPqUCvVOCvUhLlpUavqvm/Ar1KaX9UKtCrFKgdF80Ck4UvsgpYnpHPnSfOE65RcVeInklBvngpL/+uMpusbPvQXjl92D1tUaqdL3Drd/fdFK9dR/77iwh+uV7ux5+OQimn3UAdO5aux9MvhMFz5jYoNBVKJb3GTSK+Zx++W/wOxz85TuyNCvJlO8jN3YrBMIyF4+ey/Www/9mUwLC3dzNvSEum945E7mhG3y2wG1+O+pJVZ1bx/tH3uXXDrUxvM51Z7WbhrqyfHNYUcg8tco9o1NGNh9BIZjOW/HyHYMt1PGY7hFsOVQkJWH7YgWSsn/Wp9/MjMMCA0hCAFBBAqZ8vBRo3CgTklpSwPzW1XqLApQLtj04UuC6vrjWWMYvMIcYk+6NklXD3tgevVhQ1r35MTRB/adZxoPHK+w3Rvn17Tp06xffff09am84AjDA07aK8lADDCFJTF5OS8iYGw3Dk8sYtau//mMzWE9mXBexfWntsTIAPLbTOxYENbGlg4cRO3PfFYWZ/epCld3b7n63S7cLFH8Wl7ZA0Gl+giKrCZLwYhBCiVogBFK9bT4UxhfIWVvS5nZCXn8ajXz9SSlJIKUmhOv8W5o9shW8zQg1KzBYmH02iwljOxkP3EOymgGkbIfc0/LoQPh8LQR2h3zxoMRwc4shbqaCz0rlLik2SKLFYsUrgq5T/pqQAP5WC+yMCuDfMwLb8Epam5/Fcciavns9mfKAPM0P9aaHVIEkSOz87Q0FGOSPndsDLv3miQRkQgPfECRSt+AL9nNmoIiObvdbfE7OxmhPffYSQCeRuI0E07cXwCw1jwr9f4fgP29m9YjlCHk37cQYKCn4kN3cbLQzD2HTPHJ7/ppoXNp9i24ks5o9rT7S//RqolCmZ2noqw6KG8ebBN1l8YjGbUjYxr+s8BkcMvqYuQKFUogwKQhkURGPOaEmyu/HNOblYcnMwZ9eJNXPN/48eRV5cjAEwAK0AmxCUGQyUhoVR7O9PUXU1x9PTOeiYVyYEel9fgkJCCAoOJigoiICAAKfCmn4L17cYu6S0BdgzKpVqJRqdJ+XNFGMyNwUKvRtFxafB++qZlJfSsmVLunXrxp49e/jFPYCWWg2x7s6/oULIiI15giNHp5GRsYLw8Lsa3G/n2Vxe//Yst3QIrhew/1xsCD8UlPL42TS+6hTr9JffsHZBvD6+A4+uOcY/Vhzmgzu6oFJcl+XpXLj4S1DrprSBm2cQUER1af22atbyCvLeeYfKu3TIFWbc15Wj6tIFubc3O07Ye5trTO0Z1f6SRCGbFaxmUDb8/WO0Wrlr3wFSjAq+SHiaVl3GQb/HQKGG6P7QdQYcWwU/vwWrp4B/K+j7KLS5FeTOX05kQuDjpHC7GgqZYJTBm1EGb06WVbI0I5/V2YV8mllAPx8PbswH88Ecet0STUSbxksYNYV+9myKv1xD3nvvE/La/Guy7muBJEl8v/g98lIv0H/qo+zbZOPYD2l0HRbZ5Dghk9Fh8DBiunRnx/IPOfDxrwTE9qbdrXoKCjaTm7uVh7oM5ZY2t/PsljKGvf0T84a0YMYNUbVWMr2bnpf7vlzrunx016P0DOrJkz2eJNrr6glj1wohBHJvb+Te3tAivtH9bEYjllyHSMu2u0f1uQ6LW1aWXbjl5lGhUVPk42P/8fbhbEYGxy4RYCFKJbOfvvbZtdelGJMcBnBrTTal4/81GZUe3j5UFDe/srIyxIMK8zlUKn9Uqub9UQ8dOpTkgkJOmCXmeDZfzPj63oCvTx8uXHyf4ODxKBSXB+NfyK/gwZVHaBnoyatj29e7O7m09tiqrEImBzu//ts6h1JltvL0Vyd5ePVR3p7YEYUTsWcuXLhoPjbJ7HiU46YLAU5RmVu/k0fB0iVU+uZSEWkhwmsG5hMr8HtyIgDfX/weYQznvsA8FLtfhfyzkJcIBefAZoHAdhDRG8J7QlhP0AUglWTyyC87+cWtDe/kfEbf2xdAYNvLD6pQQ5c7oeMUSPgKfnod1s+CnS9Bn4ehwyRQ/HkJP2117rzVMpx/RQezIquApRdy2S234j/GF2Urd+LMlnouTGdQ+PvjM2UyhcuWo797DmonY41/b45u38ypn3bSe/wUuo4YQG7qcQ5/c5HWNwTj7nn198HD149bHn2KpAN72LF0ET+8foKOI2YS1qOazKwV6KzfsHT0YFaduokXt5xm64ksXhvfgRiHlQygc0BnVo1cxZdnv+TdI+8ydsNYpraeyt0d7kar/OtkM8rUalRhYajCGk/ckGw2rIWFdrF2STxbaU4uOSXF5JrMKD2vngj3W7hOxZj93w6ReRTNsCCXlQLa2lpjWh/fZlvGwB7EX11wHq2mcfXdGHK5HLe+N8KFPMy/7KQ8JhgPD4+rD7yEmNh5HDgwmosXPyIm5tHa7eWXBOx/NLULbqqGXYk1tceeT85ksN4Tf5XzQflTekRQZbLy4pbTqJUyXh/XAZnsr5eR4sLF351qmT3+xWaTodZGghkq0o5jKShA4Sgebc7OpuDT5ZQ9q8FN44vX0QDyAY9Bg8gqzyKhIIG2pVHcXf40ZAjwiQB9C4i9EeRqSNsHB5fD3vftB/WN5r++w1kXMp5/ys8zfsKrTVu65ApoPx7ajoWzW2D367DpAfjuWQjrDmE97D8hnUH1x1+Q/VQKZui8cNuSSFK4mtM9vXkuOZP5F7IZH2B3YcY7Ga5RO+esWRSvXEXeu+8RuuCt32nlzpNx5hQ/frqE6M7d6HnbBAB63RrDyuf3c2DzefpPdj6UJq5bL8LbtOenLz7hyOatJO8zMHDG2wifI6SlfczwoO8YHN6Pt/f1ZfjbpTx6czwz+0TXWskUMgWTW01mSOQQFhxewPKE5WxJ2cJj3R5jaOTQv2T2YkMImQyFXo9Crwfa1G43AL+3/HZKjAkhhgJvA3JgiSRJr1zxuhr4FOgCFAATJEm64HjtSWAmYAUekCRpu2P7MmAkkCtJ0hW3X/8/JCTc3MpoG1pKVSioC3YCI7E5ylt4+PhSmJHe7HkLPb/HaE4lkNG/aV3flVQRoZLjXpjH6tWrufPOO5sVIOipa0tAwChS05YRGjoVtdpgD9hfUxew31CF/RqEEMxvEcagA2f5z7lM3m3tXBZMDbP6RlNpsvLmd4m4KeW8OKbt3+aPzIWLvwsKleNvWJKh8IpEniVhDrBSsmEjfnfNAKB4/XrK+lRi0lnp0OJtKt5fijo+HlVoKDtOrwDgyeojmCP6o7xjNSgvj7gx2yTOl5eTmJHI2bw0TlaY2KaJZ6qvkgfbj6nLeroaMhm0GgUtR0LyD3BqA6Tug6Rv7a8Lud0KF9ajTqR5hTo/P4CpEioLHD/5UFlof67xhvgh4F6/VpjFbOWbD08gmWw8Oa4NPoFaTpRVsjQ9n1XZhXySWUB/Hx0zQ/UMukoWZg0KHx98pk2l4IMPqT5zN5qWzoeqXGvKiwrZ9NZ/8fQ3MGzuowhH3J5PoJa2fYM5+VMm7QaG4hvkvBBWu2u5adY/aNVnAN9+9A4b5r9Oyxv603fKJvJL1pOatpzHOu8mrbIbS38ayLaTbXltXAdiDXVGBT83P1644QXGxo3l5X0v8/jux1mbuJYnuz9JrM9fw5r4V+WqSkDYaym8BwwG0oEDQoiNkiSdumS3mUCRJEmxQoiJwKvABCFEa+xNd9sAwcD3Qoh4SZKswMfAu9hF3DVFQkKlrqj7vzLf/niJZayiuBDJZqv9EF+N4uKDJBe+gnt+W/wqRzR7TYVmC78Ul3NfmIFbxoxh7dq1bNu2jVGjRjVrnpjoR8jN/Ybz5xfSsuWLvP9jMttOZvP08FZNVtivIV6rYW64gbcu5jBU78VIQ/3emE1x/42xVJqsfLArGXeVnKeGO5ci7sKFC+eozaaUBOgCCTxeTUZXN8q+24sfdjFWcnIX5aNt+PsPwVvWgbzDh9Hfczdgz6I0mDW0tZQhG/ZyrRAz2mx8lJbHupwikiuNmCUJ+/11JOHeKqb7efJibMhv+3sWAmJvsv+AXTClH7Rb4NL2wZHPYP+H9td0wXXCzDPYIbIKrxBcl2wzVzZxXDlE9YVWt9gFoS4ASZLYvTKR3ItlDLunHT6BdkHSTufOglbh/CsmmBWZBXycmc+0E+eJ0KgY6u9FqFpFsEZJsFpFiFqJXqWoF1vrN2MGRSu+IO+ddwl7r3lt6q4VVouFzQtewVhVydinX0CjvdzD0m1kFGf2ZbPnq2RG/KN9s+cPadmaqa8u/D/2zjs+ijL/4+/Zvtma3hPSSCGQUKUISBUVRUU9e+XsZ7nT8xRPOc969/POXs/e0FNEkCqg9BY6CQHSSK+bbMv2md8fE4JIC6Bngc/r9bxmdjLzzLObnWc/z7d8vmyY/V82zP6Mqm2bGX3NTQwfvpza2vdQ1b7DjGEb2dFWwM1vT+KyYeOYdmbaQaErhTGFfHLeJ3yx9wue3/w8l869lCtzr+S2gtswao7PI3SqoCdmmSFAWVeNNgRBmAlMQS62ux9TgBld+58DLwnyEz0FmClJkg+o7Cq2OwRYK0nSCkEQev0Yb+KHkATQaDwACC4QVTIZ63ZTWiMQQyE8Lidh5gNZjcGgG5drFxbLwIMmJK+3nu07bkOnSyQ9MB33qkYUWg3m8Sk9nrgWttoJSTA5xkq+KYGGhgZWr15NfHw8gwYN6vF70+tTSEy8krq6D6n0Xsb/La5hSmEC00YeuxrAftydGssym4Obi6v4uz+Rm5Kij31RFwRB4IFJ2Xj8Qd5cWUmYRsW9E47fbXsap3EaR0A3G1OAKZ6UWg918XpawzbTCxC9XmymbUgq6J31MK4Fy0EUMY4dh81rY1PjJm502SmNu4C8rpivb9scPLy3jnKPj+FWI+OTzfQ26Mg26MgM02JQ/shZ0mER0Hui3ABCQWjaCTUbugjaBiiZffA1WrN8XVgkGGMhps+B12GRYIg6sB8WCR37oGQO7JoD8/4I8/4EKcNo1I6mZl06g84dRHrhoXNblEbF3b1iuT1FzsJ8u7aF9+pa8YoH61lpBIE4rZoErZpEnYYErZoEnQbjnfeg++B9lNt2EN/vf+8dWP7hW9SVlnDuXfcTndLrkL/rTRoGTkpl3ewK9hW3nVDSgkqtZvilV5I9bCSL33iRRa8+x66VBYz//R0kD7uBmtp3USjfpm/kM2xpmM+t717KA5MvICv2QDyVUqHksuzLmJA6gec3P88HJR8wv3I+9w68lzHJYzBpfprYq18rekLGEoGa772uBc440jmSJAUFQbADkV3H1/3g2sTjGaAgCDcDN4MsoNoTiEhou8iYplIgkC3Hh3W7KSNks7bL1tZNxlyuPezYeQednRVERY0jJ/sJtNpoQqFOtm2/BVH0U9DvDcKGpNGuKcO5tJqQzUv41CyEHmQXft3cQbJOQ1+jvEodN24cTU1NzJ8/n+joaFJTe+4yTOt1B9vLlzJjVgW5cZE8ffGhAftHg06pYFZhJrfv2sf0vXVUdPp4LCuxR6Z6kAnZo+f3odMf4vmlewnTKLlldEaP738ap3Eax4YkAaY4wrwiOncY3oh2Qk4n3p078fYOYhQy0OkSaF22FFVcHLo+eSwsm42IyCi3H83k6dR4/Ty6t475rXbS9Vo+7pfO2Ejz//7NKFWQUCi3M26WjznqZetXWKRMulTHWVswLAIS+sO4R2TZjV1zCGydRXzHU1wXA1LjQFh1AeRdABGHZvepFQIXxFi5IMaKJEm0BULU+/zUewPU+fzU+wLUe+XteruLRl9ArjqQkgfTnwZbCP2KHV0kTbaoHUzc1CRqNZhUPx7RLVn5LVsWzGXAuVPIHXHk4t0FY5PZva6RBa/uYPwNeWQOPLFC55FJyVw+42m2L13Iio/e5f377mTYpVcy8LzbSU66npqad5GEt+gvPcLX332GOeZWrht19kFWsnBdODOGz+CS3pfwxLonmL5KzkRMMiaRE5FDTkQOuZG5ZIdnExMWc8p6WnpCxg73yUg9PKcn1x4VkiS9AbwBMGjQoB5dKyGhUnsJimCsF/Dl2wARMSRnVRqsMhlzd7QD0Ng4h12lD6FSGUhNuZma2vdYt34S2dkzaGlZjMu1i4KC/2AwyIQj/JIsVBE6HN/sI2T3EXl1LoqwIwfD2wNBVra7mJYU1f1FUygUTJ06lTfffJPPPvuMm2++GYulZ9pjCmU475behSgGeWqyeMSA/aPBoFLydn4aj5XX83pNC/u8fl7LS8XYw4lDoRB4emo/vEGRpxaUUt/h4f5JORiPs9zKaZzGaRwMnUKeSxRBBWhNoDVj9BmwpXTiKSnBsXE5gTyJxMQJiG43rlWrsV50IYIgsGT3FyQGgmwWz2FgTBIT1u8CBB5Kj+eW5OhuwdRfBMwJcjtZCALE5uHWZfDZ/DMJ1zZw3llVqMu+hiWPyi22r0zKci+AmENjvQRBkIVmNSr6HcFgE5IkWvxB6r1+SuctoGzVGrxXX0Oz0US9L8Bym5Mmf+CQHzizSsFAs4FzoiycHWUhVnti1Uyaqyr45o2XSMrNZ9RVNxz1XJVGyUX3DWD+KztY9OZOHK0Z9J/Yc0/O9yHLYJxL+sAhLHv7dVZ+/C6lq5cz8Za7SM+4i5SUGygtf4sc8W000p28+3UhQwvuJ7/X0IP6yY/K56PzPmJd/TqK24optZVSaitlSfWS7nMidBFkh2eTE5lDbkQu2RHZpJpSUSp++/qWPfnlrAW+nwuaBNQf4ZxaQRBUgAWw9fDaHx0SEiqVH19IgdImgBBCpbfj98q6Y8bwLstYexuNjV9RXPJHrJbB5Oe/gFYbQ3z8JZTsup/i4nsAyMx44KC6kIIgYB6XgjJCR/vne2h+bRtR1+ejijh8ds7bda0EJInJ0QfHZ+n1eq644grefPNNZs6cyY033ohafewH9YWleylu0nHnoPm0VX9DpfJueqXe2qNSSd+HUhD4W2YiaXot0/fWMmXLXt7vm06irmep6UqFwL8u7UehVMqK9XP4087vuGFMPkOzk0FjlLOo1GHdopCncRqncWwYjXLNPvX+WKnkIVhdpbSmgmvvBmwNyyAfouLG0vHFLCSPB8uUKbQ461nbuo0p7hB1eTezpLIRvVLB4kHZJPXwmf61IhQUWfj6TvzeICPvPgd1ohHG3Q/t+2DXXNmV+e0TcovKPkDM4vr2OJlA2eW2jNOqKZxyDmUv/Rudt4OUt9/uPicgSjT6D1jU6rx+qr1+ltuc/HlPLX/eU8tAcxiToiycE23psd6k1+Vizr+eRGcwMPmeB1D2IPFLb9Qw5d5Clr63i7VfluNo9TDq8t4oTlCWyBQRxZT7prN3wxqWvv0aH0//EwPOPZ/hl11NfvY9ZKffyNJNLxMb+JimiqsorxjCyP4PEBFe2N2HQlAwPHE4wxOHdx9z+V3sad/DLtsudtt2U2or5YOSDwiKcjlDvUpPVnhWNznLjcgl05qJTvXTiK/+XOgJGdsIZAmCkAbUIQfkX/mDc+YA1wFrgUuAZZIkSYIgzAE+FgThX8gB/FnAhh9r8EeCUu3BYGjHGxJQtskPmtrQgt8j/3MNXSr8rvZWnJX/wWTqQ//+H6DoWpEaDBkMHPAZNTVvEwp1kpLy+8Pex9A/BqVZQ9sHu2h+ZStR1/dBk3Twsur5qiaeqWzkvGgLA8yHZjpGR0dz8cUXM3PmTObOnctFF1101NXLuoo2Xvy2jKkDkrjnwqcpLVVRUfEv2tvX0ifvWbTa2CNeeyRcnxhFqk7D74urOHfTHt7vl06BqQcq1ZKEeukj3LTnJW7SAAFgcVf7PtRhMjHTGGSSdtBrwxH+ZgRN174hGmLzjy8D6zRO41cL2bbyj4i/sLpoLTMzxmJZ+x2kWmndNhenpQpFSIPR0IfK9x9AP2AA+uxePP7pJECi0TaRwjEJvNLWxl8zEn7zRAxg1X/30lhhZ+K0PkQmfi9APDwVht8pN0cDlH4tZ3yufBZW/BPCe8mkLL4AFEoQFIc2hK59ofuYQlAQe80Y2j/6GO+id9Hl9QFBQC0IJAsKkvdfq1OAXoGUFEupFMbCVjsLWu08UdHAExUNZIVpZWIWZaHQHHZYMW5JFJn/4j9xtrbyuxlPdf9+9QQqtZKJN/bBHKln86J9OG1ezv59PhrdiXswsoYMJyW/gJUfv8emeV+xd8Naxk+7g7TCgUwa+iBN9puZ+d2/6KWdw5YtU9Eaz6Rvzh+xmAsO259RY2RA7AAGxA7oPhYIBaiwV3QTtF22XcyrmMenuz8FQCkoSbOkdbs59zeLtueVbX5pECTp2J4/QRDOBZ5DTr15W5KkJwRBeAwokiRpjiAIOuADoD+yRezy7wX8TwduBILAPZIkLeg6/glwFhAFNAGPSpL01tHGMWjQIKmoqOhopwDw6ScTiIqtoM6tpv8/oPlvARo2XsewCbeSMUD2nb980xVkT4hGiJlH3/xXiIk5+5j9HgmBJjet7xQjeoJE3ZiPNtWMJEk8WdHAi9XNXBIbznM5KaiOosu1fPlyvv32W0aMGMG4ceMOW7S0o9PPOc+vRKdWMvcPZ2LUqpAkiYaGz9m9528olXrycv9BVNSYE3ofu1wert5egS0Q4tW8VCZFH+WLLYZg7t1yltTg30PfSwl4XXyztZxvd1RiUfqZnGOhIFaF4HeD3y1nRfnd4Hd1bTu/t++GoOfI90sbDWc/Ia9kT+OUgSAImyRJ6nmGyy8YPZ2/Xpr5KI/HHqhBW5+nRXp9KN+NiML0jYbOgX7MMYPJ9NxA3T33kPTPv1Je+2+uCfMySuzD0orrKJiaxQ6Xh43D8n784PxfGHatqWfZ+6X0n5DC8Kk9lE9wt0LpPNliVrEcxMBPO0gAfQREZkJkJnUReSwKy2WhFMWaTghKEKtRcXaUhUlRFkaEG7tdyqs/+4h1X3zCuJtup3DiuSd8++KVdSz/ZA8RCQYm39EPY/jJW5bqSktY/MaL2OpqyBkxmjHX/Z4wi+wBmre1jG+3vMLIhG8wqjsJjxhNRtpdWCyFx+j18BAlkTpXXbd7c39r7mzuPifeEH8IQYs3xP+scWg9ncN6RMZ+KejpZPbJzHHExFRR7tRw5oMSDS+GaCs9m/zCh8gbIccovPun20gYuwFzVDRnDJmPIJycKy3Y4aP1ze2EnH4irsvj7yE3b9e1cm1CJE/3Tjpm+SFRFJk3bx6bNm0iJyeHiy66CK32QECrJEnc+uEmlpU2M+u2EfRNOpgoud1l7Cy+G5erlJTkm8jIuA+F4vhXxM2+ANftqGSrs5NHMxK4JTn60C9y0Aezfi+vMEc/AGc9eJDVqrzFxYOzdrCh0sbQ9AieurgfaVE90LsRQ98jbPtJWyfUbZKVvj0d0P8qGPMwmHtW8Pw0ft04FcnYKx8/zGPxl3S/Xtw/lX7/OYMN/cJwqb1IUoDs3o8R/NNclP4a4oc3c5VVRasxCn/tdKITo1gTr+Lh9HjuTD1+S/mvCc37HMz652biMy2c/4eCE3PBee3gbJQzJiTxMO1Ix0UcS5bQ8fGHxPzxXnS5uYc/TwzKyQptZdBWLjfngWidDpWRpYnnsjB6NMvCsnELaoyCyDizhv5eN7ZX/8GAYSM5+7a7T5pUVBe3sfDNnWh0Kibf2Y+opJPPaAzzaVVmAAAgAElEQVQGAmyY/V/Wf/kZGr2es66dRt6osQiCQJvLx2Nziwg4PuPctO/Qq1xEhI/AYhmATpeITpeIXp+EVht3Qr9XAG2eNtm92V5KaVspu2y72OfYh9RlYbZoLeSE55Adkd1N0NIsaagUPbQOSpJM3m3lYKsAhQr6Xdbj8Z3SZOyjmWOJi9lHqV3D2Aeh9XkzrY0ppCU+S+F4OSPz6zdvRp+xlLy8Z4mPu/BHGV/I4afhP9v5W5zE3Hg1tyVH80hGQo8fIEmSWL9+PYsWLSI6OporrriC8HDZJP3hun08PHsnD52bw82jDp+5GAr5KCt7itq6DzCZ8snv8zxhYb2O+310hkTu2rWPr1vsXJsQyRNZSaj3W/X8bvj0aihfBmc/BcNuP2wfoijxaVENT87fhS8ocve4LG4elY76RMsoedplle/1r4NSDSPuhuF/+FnUvU/jf4dTkox9+CCPJf6u+/WDSRJ3b/o/PO072Tf2Quz2IrKVf8R5/zSSRruYFR3HDANclfYgr823kH5uLxoVIhuH5mH4ETP5fmnodPj571MbEQSBSx8adFAB9f8VRL+f8olno46NJXXmJz0nSz6X/MPeTdDKwFaOt62KVfp0FkaOZGHUCFo1EajFAMP91Zyj7OBsi5r46BTZwmZKOKF43NZaF/Ne3oavM8jZv88nNf/E6nX+EG21NSx+40Xqd5eQ0reQCdPuwBonL5rn72jg8blF9I9cyrnJ69BpW0H4PvcQ0Gpj0emS0HeRNJ0+SSZrukR0ugQUip5n23YGOtnTvqfbxbnbtps97Xvwi34AtEotWdas7hi07IhsemsjCbPXHyBdbeVd+5XgcxzoPKYP3L6mx2M5pcnYx5+MIza2ih0dGs5+CJzP9qLF10mU+h3OOD8dSZL47pvReDqbGXf2RrT6H0fvxC+K3La9knntTm4p9/HA8AzC8o4txPpDlJeX89///hdBELjssssIhEVx/ourGJIWwXs3DDlmGaLmlkXs2vUgkhQkJ/vvxMUdf8UAUZJ4qsvNela4iTfye2EOOODj30HtRrjgJdlKdQw0O7zMmFvM/B2N5MSZeOrivvRP6XnMwyGwVcCSGbJVzhQPYx+W6+GdAtk2pyJOTTL2Zx5L7ArLlUTONtl4z7dTzgq8vwIMkXT843Ys7o9oi8vnYqtAL0sGVcXXETRpqMg2Mj09nj/8hq1iYkhkzvNbaax0MPX+gUSn/HyaVe0zP6VxxgySX38N4+gjy030GJ02gg3FrH7zSaoNRsoLxrFYm06FRg6x6e8o4ZzWVUzq2EhWmAYhMkMmZxEZ3W5QwiKOGmPr7vDx9cvbaKtzM+ry3uSPOi7FqSNCEsVuGQwxFGLYJVcwaPJFiB1+Kj/fzdOVTSwliFoIMlTnZITFRf+MABEpHvzqFrzeOrzeWny+RmRt+APQaGKOQNTkfaXy6G7XYChAZdNWSuvWUNqynVJHFaW+NhzI9xEkiV6BIDl+Pzn+ADmacHJMvYiIypY/24h0GgMG2jGT26fn4TKnOBkbT2xsJZvbNUyeDoFn+tOk34K240tGXZZDW9tytm67kerlcUy84k0Sc/JOemyekMi0nVUstTl4NCWWixc0Eqh3E3FFDmF9j5+QtbW18cknn9Dc1s536sF0ikrm3z2SGFMPs2+89ewsvhe7vYj4uIvp3XsGKtXxW5E+rm/jz3tqGKx08+n2+9DYymDqW3I20nHgm5Im/jp7J01OL9cP78V9E7MxnIwMxr61sHi67MKM6wsTn4D0H2EiPI1fFE5FMvbqB3/ib0nXAKAKNpGqCbE61QLvngtXfgZZE/E9lIaAl3vHTmFdYxFT4/6PN5Z2knNBOtVi8DdvFVszq4wti6sZd30uOUN/3pAFye+n/JxzUVqt9Pr8vyftSpQkifkv/h+la1Yw9cG/0atgAJIksdflYWF9HQta7Wzxy3NnetDGpPb1nFM3n4H2HSj2C2vorDIpsyaDOfGAjMj+fWMc/gAs/k8x+3a20X9iCsMuzED4keoNO22tLHv7dco3rmdgr7PJUPVDUCmxTOpFZVIYi3c28s22eko75DjhFBSM1OsYnxfLsJGpaGK0+HxN3eTM463r3vd66vD6GpCkg+P81OpI9PokdKpodJIevR90bh86ezu61npUbVWyS3o/BAWSJYmGiFRKjRGUqpWUSh5Kvc00eFq6T4vRx5AaloqqTYXUJJFmSOPB2x/s8f+5p3PYb1QUSv6QfF2vtL5wFCY/AX8DkpRNZdVLaNSxtO+x0lRZftJkrDMkcs32CtZ0uPhndhLXJEQhToum9Z1ibJ/sglA2YYXHJ7oXGRnJtGnTuO6F+VTbQtyWFyTyKFpmP4ROl8CA/h9RWfUiVVUvY3dsIb/PC5hMx/der0yIJCvQRPTMqwj629g35X2y8s45rj4AJuTFMjQ9gn8u2s27a6pYXNzE4xfmMybnxMQISR0GNy2B4lmw5G/w/gXQexJM+DtEn64IcBq/XggcsAgYQnYagpGyWKqghNqNSEodWm07b5sGsqJ+DXcW3MeLX/pJOSOOrT4ff81I+E0Tsdrd7Wz5ppo+IxN+diIGIGg0RN1+Ow3Tp+NauhTT+PEn1d/m+XMoXb2cMy+/ll4FcoahIAj0NoXROzuLu7Kh0RdgUaudha0m3lRH8kr0OUSrBM7WuJkUqOLMjs3o2vZAw3bYvfDQ5ChBgcYQw3nmBFozzTSsNbCnMpXMsYUow5NkwmaKB/WJBfmbIqKYdNkfaPKPQuGAWsceAoUKhvYfQF+djr7JVv50Tg617Z18s62BxZvr+LTZyUebKrFuqmKEXsf4nBjGjs4jPn7IIf1L7hZ8zZvxtm3F69iDx7MPr70ZL1twqiVadAqk/cTSIjd1ugGdIgmdNh69MQOdJQedIRWTLpFRukTGqg5YVzu8HZTaSllTvoZ15evYbd+NU+1EipVos7b9JAkBv1EyJsMryLqzYR1xEAVB9Sra27XY7Zvp3XsGO4wLaa4qP7l7hERu3FHJmg4XL+amcEmcrGGm0KmIujGftveKsX26G8kvEjY49rj+iSsr7Ky3aRmfrMBTsZ7332/hsssuw2DomYVLoVCRkX4v4eFDKSn+ExuLppKV+ReSkq7t+TiaSxn8+VRCUic3D3qBpe0JvNjcwfnHWdMSwKRT89iUfKYUJvCXL3Zww7sbOb8ggUcm5xFtOk71bZDjJfpeItekW/8qrHgWXhkKg26QkwoMx2+RPI3T+LkhfM89o/F4sGvCcaDCnDgAit5BKp5HWUjDy1HtjEwcSUVZIY4EO61WJZfGhXNbcs/Lm/3a4OsMsPTdEqwxYYy4JOvnHk43LFMuoO2NN2h54UWMY8f2uObxD1FTsoPlH75F5uChDJlyyRHPi9OquS4xiusSo3AEQyxrc7Cg1c7sNoEPQ3kYzPmM6WVibISZEVYDKZIbwdUoJxI46rq3gqOeqEA94eY6VE4nfPWDG4VFHmxR279vij9wTHtwrUnRG8S+qAr3ugbUJg2G3yXh2rSdbYsXULplZbcMBkBSeBg3nJXBDWdl4PAG+G5rA4s21rKivoOVW4r5aOtSxmjbGBbhICe8gzBvNdgqEDzt6ACZKgpgSYbI9G53ohSRht8ciVcn4Ak0y9Y0by1ebx1ubx1ttq8RW784aNwqlaU7qcDnM1JT5UTdGGC0Ip3+/W+mT8Fgajw1dB6tVupJ4DfqppxAbGwFC+1qrn/CQOSUi9hWsAqFyo0hXIUY8jFs2LfMfuYJ3PYOrn3mhRMaj1+UXZOL2xw8l5PM5fGHBkKK/hBtH5Tg29uBppcZ89gUtFnWY5KhRruXc55fQYJVz6zbh7O7pJivvvoKk8nE5ZdfTlxc3PGN1W+jZNefaWv7lqio8eTlPo1afYzYrbrN8OFUOWD+mi9pDc/mhh2VbHS4mZ4ez50pJ166wh8UeW15OS8tK0OvUTL9vFwuHZh0cisOVwt89xRselcO7B/5Jzjj1hNe3Z3Gz49T0U35wmvX8WT2vQBEVW6nNa0fL2UouSRMD++eB65GHiSWJRlmHun/EbevbCCQY+WiGCsv5aX2uKzZrxHfvF3M3qJmpt4/kNi0n6Gs01FgnzuX+vv/TOK//4X5nOP3HjhtrXz4l3vQhhm46sl/ow3rgdbjD+ATRda0u1jQamdRq50mv6ytmahVMyLcyHCrkRHhJpIPoz1XsaGCDR+tIdJsZ/g4PQahTSZuzoYDBK6z7dCbai3dRC0YisRTrSHos6LOySbsrEIUUcmgs1K7u4RvXn8RW30tiTl9GDT5IjL6ZCO0V0BbhRwPbJOzTSVbBYLH1n0LURKoJ5IW4hHMacT1zic2Kx8hMhOsqcc9x0uSRCDQJrs/PTJJ83hraWvdg8NZiVLZgVIZPOgapdKIXp+ExdyfnJzHe3yvUzpmbObMsUTH7GNOu5qbXokhsmAw2wfGoIl/A6XSSP/C97BYCln58bsUff0lf3jvc1Q9UL7/PoKixG0l+5jb0sHTvZO4PvHIVhgpKOLe2IjzuxpCdj+aZBOmcSnossMPSz5CosTV/1nP1poOvr7rTDKi5ZVHXV0dM2fOxOv1cvHFF5Obm3tcY5YkiZradykrewaNJpKszAeJjp6E4nApvpUr4ZMr5EDQa2d313bzhkTuLa3my+YOroiP4JneSWhOQmG/rNnFQ7N2sKHKxvCMSJ68qC+9eiKDcTS07IbFf4W9i8CaAuMehfypp0Vjf4U4FcnYUx/fyfPx09CILoS19fhG9GaidhfvDL0Mpa2S9pdv5ty4FgZlnM2C2otwpRqYFGnmP/lpR9Uy/LVjb1ETi/9TzJDz0xh8XtrPPZxDIIVCVEyZAhKkz/kK4Tj03ULBAJ/O+Aut1fu46sl/EZnUszrMRx2PJLG308fqDher252s6XBhC8hW1xSdhhHhRkZYjYwINxKvlclZY4Wd+a9uRwxJnHtbXxKyfrBgD3i6yFn9QU201RCqqUThbUIhtCP8sCiUOgzMCYimeOwOH/6mMkyCgzDVwYQHc1KXhavLyhWZgRSRTmmziQVrG1m2z0ZxUC45Fa9SMrZXJGcPS2FYTvQJZ+qLokhpaSkrVqygsbERq9XKiBEjyM9PJRDYH7cmkzWvtw6NJorcnCd73P8pTcY+++8oIiPr+K9NzS0fpREZHk/pmbfh073EqPPuxmKR/fC7167k6+ee4eqnniM2vYdigciZhnftqubzpnZmZCRwa0rP4p6koIh7UxPOb2sIdfhQJxoxj01Glxt5UODky9+W8c9Fu/nH1H5cNjj5oD4cDgeffvopdXV1nHXWWYwaNeqwArFHg8O5k5KS+3C796LXpZCSMo34+KkHslF2L4DProOINLjmy0Pqx0mSxD+rGvlXVRMjrEbeyu+FVX3iHm9RlPhkYzVPzy/FHxK5e3wWvx95EjIY+1HxHSx6GJp2QNJgOcg/5Yc17k/jl4xTkYw9+v5feT15KqpgO8qlbkITIiDQwJ9iGrh3wJ18dNc5PDWwgZDln7RbojnPauK1gvQD8jO/Qbjafcz8+3qssWFcfN+AEy7p81PDsXAhdffcS8I/nsFyQc+TnJa89SrbFs9j8j1/IXvYmT/J2ERJYrfb20XOXKztcNERlMlZul7bZTUz0jekYv3rxTjaPIy7NpfeQ47shZFECfe6BuyLqkCUMI9PwTgsBqGz+WCL2vfdo+5WJHMC9mAYFVU2ahpcdKqjSRtzMf0mXdQtGnvY+0kS9eXtLF65j6UVrWwI+PEDRoWCUUlWJg5JYkyfOCz6YxtXRFGkpKSEFStW0NzcTEREBCNHjqRfv34of0Sh5FOajH0xazhWaxMftmm44+s8IhwS1Zc+RfnmZm76v5Hd57U31PH2Pbcw8Za76Dt2Yo/GIEkS9++u5cOGNv6SFsc9vY7PXQgghUQ6Nzfj+K6GUJsXdVwYprEp6POj2FrbwSWvrWVSfhwvXdH/sJazQCDA3Llz2b59OxERERQWFlJQUNDjQuPy+wjR0rqEffvewOHYilodQXLSdSS3h6Gae59cHuTqL2TL2BHweaONP5bWkKLX8FB6PGMizOhPYpJscnh59KtiFhY3khtv5pmpfemXdPyxaQdBDMHWj2HZ4+BqhLwLYfwMmWiexi8epyIZe+y9h3gl5TLUgWaUywJcc1E2b3a6iHItYPOEu7j18aGs7n8J9ogpXKA38PoZmT+rwvhPDUmUmPPCVhor7Pxu+hCsscfvvvtfQRJFKi+6GNHrIWPePIQe1JAsXr6Uha/8m0HnX8zoq2/8H4xShihJlLg83eRsnd2FIygCkKHTkFjjI3qPi4sHJDL2nLRDvmP+Bjcds/bir3GizbISfmEmqkj9cY1BkiTqdhWz8etZVGzagEqtIW/UWAZOvpCIhKRjXuuotPPtyn0sLWthVcBPOxJKYHCcmYkDEpmQH0dyxMHfl1AoxM6dO1m5ciWtra1ERUUxatQo+vTp86OSsP04pcnY7K+GYDK18VarhrtWDiJyRy2td/+HrUuqufWls7q/VJIo8tKNvyN35FjG33Rbj8bw6N46Xq9t4Z7UWP6SfnKZPFJIonN7C85l1QRbPHijtFzfaUfSKJh/96ijsntJkti5cydFRUXs27cPgIyMDAoLC8nJyelRwfH9/XR0bGRf9Rvod8wnu8xNZ2wKiitnobMcO0B2XYeLW4qraPIHMSgVTIw0c36M9aSI2aLiRh75aictTh83jEjjjxN6n5wMBsgii2tehDUvyIrYQ26GUfeD/gDZ6wx0UumopKKjggp7BW2eNvpE9mFQ3CDSLem/6R+8XypORTI2/Z0HeKvXFWh9dahXwLSR6Szs3M0ecxjTQ+t4vWEurckvYXYrKZ3ym/hojopty2pY9dlezroqmz4jfxw9rJ8SzqVLqb3jTuKfeBzr1KlHPbepooyZj/yZ+N45XDL97yh+xtJVIUlih9PT7dZcb3fjDsnkLCkgMCE1gjMjTJwRpke9oh7nyjoUehXWyenoCw9TqeU40VZXw+Z5X1G8YimhQID0gUMYPPliEnP7HLNvSZTwVtrZsKaGJXuaWRnwU4U89t7hYUwoiGdcTjRiWxWrV62ivb2dmJgYRo0aRV5e3nF7l44HpzQZmz+3H1qDm9datNy97Uyi5m3A/cxs1s2u4JYXRqPSHPjCz3z0AUQxxJV//79j9vvepq084IChZdu4vH4XlqgYzNExmCKjMUfHEJeehc5oPGY/P4QkSri3t3DP7B186/XyssbE0CFJGIbGo4469krDZrOxbds2tm7dit1uR6vVkp+fT//+/UlMTDz2QyJJsrr9t4/jSEhlc4YXUQGxMeeTmnozRmP2US8PihJrOlzMbelgXksHtkAIg1LB2VEWzo+2MCbCjO44iZnDG+AfC0v5cF018RYdUwoTmZAXQ2FyOMqTccc46rEvnUFl6WwqDBbKew2lQm+gwl5JvftAiRKVoMKoMdLh6wAgXBvOwNiBDIobxKDYQWSFZ6E4yRJap3FsnIpk7O7X7+XT3tcR5t1H2goNickmCje8zz+uux1r42OENHk4Iy7hdpWRR0b2PLzi1whbvZvPntpIck44597e71exIJIkiapLLyPU3k7GgvkImsNXBvA4HXz44D1IosTVTz9HmPmXVeQ6IEpsc7j5dF0Nq20uamNU+JUCgiSR5RQZqtYypl88w2MtWE4iTOWH6LR3sHXxPLYumofH6SA2PYtB519E7zNG9IisSiEJX3kHpevrWLq7mZVBP9sJIiKgx0+2yc9FQzK5dFQ/wrTHFyt+IjilydiqeWn49ApebNZya9koUj9cTuDFeaz8oorrnxmBwXJARmHZu6+zY9libn75HfSmw2fnuDva+erj95neaxDhXjeP1m2ns6UZZ2sLLlsbkiQzcEGhICk3n4yBZ5Ax6AyssT1zYXoDIf742Vbm72jkT0NSudKrxLOzDUQJbZYV49AEdLkRxxTkE0WRqqoqtm7dSklJCcFgkKioKAoLC+nXrx9m82HenyTB4odh7UvQ73KY8jLeQDPVNe9QXz+TUKiTyMjRpKbcgtU65JiT4X5iNqe5g/mtMjEzKhVMjLJwQbSVsyJMx0XMiqpsPLdkL+sq2giKElFGDWNzYpiQF8eZmVHoNYd/OCVJos3bRkVHBeX2cio6Kqi0V1JuL6fV09p9nlYUSZOUpMcUkJ58JunWdDIsGSSbk1EJKmqdtRQ1FVHUVMSmpk3UueoAMGlMDIyRydnA2IHkROT0vNbZafQYpyIZu+WNe/gq63rMnWWcvdLMMmWQM2qKWHz1JGJttbRGpRKw+dl6Tv8ei0D/GhEKinz+TBGudh9XPHIGYeb/fbmjE4VrxQpqbr6FuBkzCL/8d4f8XRRDzHpqBrUlO7j8b/8gLvOXrY1Y+l0tLXPLsUer2ZyoYWuGkc0BH15RQgD6GvUM70oIGGo1YvoRdO4Cfh8ly5exad6XtDfUY46OYeC5U8gfMwGN/tiu6kAgwKaiTaxasZK2Ti+OUDytwRh2oKITCb1Kwcje0YzPi2VsTgxRxhOQV+oBTmkytnJ+Gn6dgrca9Uys6c8Z7xQhPTeLb2c3cOWMMwiPO5CtV7V9C188+QganY4B505h4HkXog0zIIZCiGKInd9+w6qZHzDzrKnUJGeyYEAmfawHSE0oGMRla8Pe3Ej1zm2UF62ntUZ2G0YmpZAx6AwyBw0lLiPrsNoz9s4Av/+giA2VNh4+L5dpI+WsxZDDj3tjI+71DYQcfpRWLYYz4jAMikNpOvak5PV6KSkpYcuWLdTU1CAIApmZmRQWFpKdnY1KpYJQEL6+G7Z8CENugUlPH1TrLBCwU1v3ITU17xII2DCbC0hNuYXo6PEIwrEftkA3MWtnfoud9qBMzM6OsnBBjJXR4T0nZnZPgOV7WvimpInvSptx+oLo1ApGZEYyJFNBUoyTVn+1TLg6yqmwV+DwH6gnZlQbSbekk25NJ92SToY1gzRzGgn1O1AueRRa90CvkTDxcVlg8whocDV0E7OipiL2OeT/tUFtoDCmkEGxsuWsT2Qf1MqfftX1W8epSMauefPPfJN5JZHOYp5ek8AfkHWNTMkatDnh1KLkjPoAX11zqBjmbwlrZ5ezeeE+zrm1L+mFvy7tNEmS2HfFlQQaG8lYtBCF9uAf+lUz32f9l58x4eY76Tdu0s80ymNDkiQ6NzVjn19ByBui3C+yTyFwzh0FmJOMbHZ0srrDyep2F5sdnfglCaUA/Yxh3dmaQyyGkxIhlkSR8k0bKPr6S+pKi9GGGeg34Rz6T5qMKeJQFQO/309RURGrV6/G7XaTkpLC6NGj6ZWUim93Ox1bmlm3u5lVYoDVQogmSUQABqSGMz43lgl5sWREG340K+wpTcZWLEwjoFEwt8FEREMKl/5nL4qn32PJQhdTHxhIXNrB5uDW6irWfv4Je9avPmx/NeMvYmbmQB7PSmRa0rEnhY6mRio2rad803pqSnYiiSJ6s4WE3jnEZ2aT0DuH2IwsWr1w3dsbqGpz8+xlhVxQkHBIX1JIwrurDde6BnxlHaAU0OdHYRwWjybV3KMvTFtbG1u3bmXbtm04HA50Oh0FfXIY1fI+huqlMPoBWST1CH2FQl4aGmdRXf0mHk81en0vUlOmERd3MUplz1YTAVFidYeTOc0dLPgeMZsUZeH8GNlipj2K3z4oBql11lJhr2CPrZyi+l3stpXTEagFhb/7PL3CQro1nbyoTDKsGd3EK1p/lJiGUEDWJvvuKei0QcHlMPavYDl2fEpzZzObmzbL1rPGIsrtsoiwTqmjILqAgXEDGRQ7iH7R/dD28LM6jQM4FcnYqLf+zZ70MVjte1i8Lo4/iO2om/dSFJfLkLQI1lfZ+PelBVw84OgBzr9m1Jd1MPvZzeQMj2fsNccn4fNLgXvtWqpvuJHY6dOJuObq7uNlG9fx1f89Tt+xE5l4y10/4wiPjkBLJx1fluGrsKNJNRN+cSbOEHz90jY8Lj8Tp+WT1u8AGfKERDY53Kxud7G6w8Vmh5ugBCoBCk1hjAg3McJqZJDFQNgJxhM3lO2m6OvZ7F23GkGhIGfEKAZNvojo1DS8Xi8bN25k7dq1dHZ2kpaWJpOwXr0O6Uf0BvEUt9G5rZmde22slAKsVoXYHZSlNtKiDIzPjWF8biwDU8NRnURi2ilNxr5bnEZIpWBLfQTLnALPPu9E8Ye/sWRHFOffVUBK3uGr1DdXVVC2cR0ACqUSQaHAk5DCDR49gy1hzCzIQHGcbNnrclG5bRP7tm2mfu9u2utrAWjTRDE34XyCSg1/HaRl0tA8wuOPHt8VaO7Evb4B96YmJG8IdZwBXW4EmmQTmhQTSuPRLWaiKFJRUcGeDUvI3fsyaVI1ixVjac28lIyMDDIzM4mIiDjiGCQpRHPLIvbtex2ncycaTRTJSdeTmHgVanXPBRgDosSqdidzWmRi1hEMYfq+xayLmDW4Gviy7EuWVi+l0l5JQDxQiyzOEEe6JZ00SxphJNLQamZbhZaSWjlVe//DNCEvjgEp1p49TF47rHwW1r0ql5454xbIHAdx/Q4K9D8abF7bQeRsT/seJCTUCjV9o/p2x5wVRBcQpv7lZoX9UnAqkrFL3/oXK9PHkt9Sy382GQmEOnF+8xCP3PQiVW2dXNg/gb9OzkP7Pyx55Av5UCvU/5M4Sb8nyKdPbADgdw8PQaP7dbr/JUmi+trr8FVVkrl4MQq9Hlt9LR89dC/h8Ulc/rdnUB0hnuznhBQUcS6vxfFtNYJKgeWcNAyD47rDZNx2H/Nf2U5LtZMzL8ui35jkw/bjDoXYaD9AzrY5OwlJoACsaiUWlRKrStW9b1EpsapVWFVKLGqlvFUpCVerus5VEqZUIAgC9uZGNs3/ip3LvsEfCBCWW0CHUos/ECAzM5NRo0aRktIzrbaQO4BnZyuebe72GE0AACAASURBVC1UV7SzhiBrtBJFfj8BSSI8TM2YnBgm5MYysnc0xuNMJjulydiyJelICoFAfSL3B218+oYRxfDzWOoawegrs3tcoT4oSkzZspeyTh/fDs4m4TCqxccLj8vJglU7mL7Chkbyc2HrIswOOQ5JZzSR0DuHhOw8EnPyiEvPOuzDKvpDeLa24N7YiL/OSVfSCMpwbRcxM8vbBCOC+nuTp88Fq/4Fa15CUihpHHAfm8UcysrKaG9vB8BqtZKZmUlmZiZpaWlotYdacyRJor19Lfuq38BmW4lSacBiGYDJlI/J1AezKR+drmdq+gFRYmW7bDFb2GqnI+DD4ttGnG8l7fbNAAyOG0x+VP4B96IlDYP68MKwDXYPS3Y1s6SkibXlbfhDYvfDNDEvlpFZ0cfOzGzfB0v/Bju/Vy4jvJcs9xFfeGBrODyp/z7sPjtbmrdQ1Ci7NnfZdhGSQqgEFXlRed1uzf4x/TFqjj/547eOU5GM3fHqa3yRM5SrK31ctamCaFMSe0ueY9gXnwGgU//vSJgoibxX/B4vbHmBCG0EY1LGMC5lHIPiBqFW/DRu+KXv72L32gYuum8g8Rm/rKD240Xnxo3su+ZaDMOHY7z8MuYsnE2nw87VTz+HOeoE6/L+hPDtc9D+xV6CzZ3o+0VhnZyB8jCxegFfiG/eLqZyWysFY5MZfkkmimPENLuCIdbb3WxyuGnzB7EHQ9iDIToCITqCwe598Sh9qASwqFSEq5WYFAIhux1vWwvqgJ8wt4PIkJ++eXnk5uYSodV2ETwlFpUKvUI45m9SyOGnc0cLnm0tdFQ7WE+QNWGwJuDHHgihUSqY2CeWl64c0JOPEzjFydgry87FiZnslhim+9byxqIYYtuhaPijBH0hrpxxRo9EA/9d1cgzlY28lpfKlBgrHU2dNJTb8boDIMmkRJKArq02TIXBqsVo1WGwagkzqw+5z5xt9fzps62kRRl494YhxJu12Oprqd9T2tV2YaurAUCpUhGb0ZvEnDwSs/NIyM5FbzQd1J/oDxGoc+Gvccqt2knI3lUiXSmgjjegSTIQJixDs/tfCJ1N0PcyWWvre244m81GWVkZ5eXlVFRUEAgEUCgUJCcnk5mZSUZGBnFxcYekADudu6ir+wi7Yxtu9x4kSTbzqlRmTKY+cjP2wWzui16finCElXW1o5rP9nzOrLKvcPpsSMoIOg2jUFnOYkJcJv3NYeQZ9PQx6nqcuePyBVnRFWe2rLQZuyeARqVgREYk4/NiGZ8bS6z5KAHQ7lZo2NbVtsrb9qoDfzcnycQsYT9BKwDT0ZM23AH3QeRsZ9tOgmIQhaAgJyKHQbFyQsDA2IFYtL/uH6IfA6ciGXvx0QfYbR3EPS1xtOz5luTMMWxt+orJ7xw74/vHRKO7kYdXPcz6xvWclXQWKoWK1fWr8QQ9mDVmRieNZlzqOIYnDEevOj59qSOhYksLC17fwcBzUhk6JeNH6fPnRuvrb9D23nsUmdQ0WgyMzx1A7o3T0KSm/txD64boCWJfWIl7fSNKixbrhRnoc4++2BRFiTWfl7FtWQ1pBVFMuKkP6iMkVPUUkiThCom0B35I1kJ0dB1r9Xgpb2qmweHCq1QihhkIqLU4RRE5neDw0AjCQRa5/da3wx5TKTF2htDu6UC9ow2xzsVOQqw1K9HE6nn0psE9fk+nNBm7aNmLrBVkcVdjsIYHFr3GmfMqUb+9gEXvlTPmmhzyRhwanwUQCoh4XH42tDi5pq6OM0UN1+0JyiTMFTjsNUeCIECYWYMhXIfBomFlwMMn9a30jTLyz0l9SIgzYLBoUaoPJiidDjv1e0qpKy2mbncJTeVliCGZ5EQmpZDYZTlLyM7DEnNo8fGQw4e/WiZnYvkaDC3PoRH24Bd7Y1feBslDZMtZohF1nAFluPagPoLBIDU1NZSXl1NWVkZjYyMABoOB9PT0bnJm/IGMhyj6cLn24HTuxOksxuksxuUuRRTlmC6l0oDJmNdN0jRhvVnbWsmXZbPZ0LgBpaBkZNJILsm6hMHxw1hj9zK3uYMlbQ7aAgfKZiTp1PQx6rvImdxS9ZqjupCDIZGNVe0s2dXENyVNVNvkoOiCJAsT8mIZnxdLdqzp2NY8Tzs07pCJWX0XQWsrg/3lP4yxP7CgFYAl6YjxeJ6gh20t2+SEgMYitrdsxy/6ERDICs+S5TS6CFqk/tiWuN8aTkUy9sY995DuyyDLlE/95ndIGjiNXfa1DHrkqmMKYf5YWFS1iMfWPkZADPDgkAe5MPNCBEHAE/Swpn4Ny6qX8V3Ndzj8DnRKHSMSRzAuZRyjk0dj1pxYzUi33cfMv2/AFKFj6p8HolT9dqRjNn75GStmvk8/nYWkjVtBFAkbPBjrJVMxTZyIQv/jkNnjhSRJeHa20jGnAtHlxzgiEfOEVBTanpOq7d/KOnDRKSbOu6PgJ8t6dTgcrFmzhqKiIoLBIPn5+YwaNYqYGNnCKEoS9kCQkh3bWb/yOyprqgkYzET2G4A1tx8erR57MEhHMIQ9IBO99mAQeyCEM3Q0exzoBAGzCCavSJ+AwBtTjpzk9UOc0mRs6rLnWS2M7n7de9/HvP70XGIfeYQlVZm0VDsJjwsjzKzB4wrgcwcI+EWC/hBiSMKvgjcnWggq4JZFDmIsWhIyrcRnWYnPsGAM1yEh4SvZhXPe1zgWLCBkayOoCsNvTYA+AxF75RKMTsGrNOG0+/hvs43VIS+9/QrO69Sg+h6D15vUGMN1GMO1mCP1WGL0WGPCsMToMUboCAX9NJXtpW53CXWlxdTvKcXX6QZApdFiiYnFGhePJSYOS0wc1rg4wsMkLNteRVEyC8kUT2jwg3i14/HXuPHXOAk2d3bzB0GnRB1nQB3f1eLkpuha5TidTioqKrotZ52dMpGJi4vrJmYxMTGEhYUdQmZEMYC7sxynYydOl0zS9rTtYrUzSFGnik5RIEqtZkJcFuf3mkiv6GEYDJkoFAceaEmSaPYHKXZ5Dmrlnb5uk7ZBqSDPoCfPqOsmaDlGHYbD6NJIksTeZhfflMjEbGuNrCWWHKHvzqYZ3Cui5+WYfE5o3HmwBa2lFLokT9BHHGpBC087LEHzhXzsaNnRna25rWUbnqAHgHRLOgNjB9I3qi+JxkTiDfHEGeJ+01mbpyIZe/W2O+mr7INWH8MG13eoInozwpNEa6GNs6696Scdozvg5sn1TzKnfA59o/ry1MinSDUf3oITEANsatrE0n1LWVa9jGZPMzqljimZU7g692p6WXr1+L6SJDHv5e3U7m7nsocGExF/kvVpf0Go3rmNzx//K1lDhjH53r8QbG7G/uVsOmbNIlBdjcJoxDz5PKxTL0GXf2yB0x8LwQ4vHbPL8ZbaUCcYCL84C02S6dgXHgaV21pY/FYxepOGyXcUEJHw4/3/Ojo6WL16NZs3b0YURfr168fIkSOJijpyPWiQY8A3ff0lpWtWIIkSWUNHMGjyhcRnHqqbGRQlHKHvuUy7yVoIe6CLwHVZ6eJVSp7M7Xnt0FOajF216FmWasYxxbGZrVH51DlrWPTaO+i8IWI/mcWeDc3U7+3A7wmiM6rRGdWotUpUGiVqjZJXw7zMl7y8ERPH2MRwwsxqxM5ORJeLUEcHru+WY58zB39FBYJajXHsWMyTz0Py+encsIHODRvwV1UBUB2XzkuDLmeHKoIrowPcm6XFpzLilfR4fBKdbj+dTj8uewCXQ8ThVBMMHngYFSoBS5QeS0wY1hh5a47SIgZb6WiooKOpno6mRuzNjXQ0NYC/k8GRtQyOlBMFtnuyqDKMxBCTjDU2DkusTNgsETEoXUqCTZ0EGtxya3Qj+eQAeARQReoPkLOurcKqobGxsZuY1dTUIIoy6VCr1Vit1sM2rVHLiqYVfFH2BdtbtqMSVIyIzWF0RBS9FK24XSWEQi751oIGo7F3lwUtH5MxF70+GbU68qCJyhMS2e32UuLysNPloaSLpO1f5QjINdfyjLJ7M6+LpCVo1Qf10+z0srQrzmxVWSu+oIhZp2JkVjS9osKIt+hJtOqJt+qIt+gx61THnjD9ndBcIpOz/Ra05l2wPwlBa4H4fgdb0SIzQHEweQyEApTYSthUvY69pWtoqtgJHi8eLXRqBTxaAb01EktkAnGWJOKMcSQYEkgwJhBviCfBmHDE+LpfA05FMvbkQ/cx3pXPOnMzNpW88OkXTMXTtpXLX3wWpUqFo9VD6doGtAY1xnAtib3D0RlOjpRvbd7KgysfpN5dz7S+07i14NYex4WJksjO1p18sfcL5pbPJSAGOCvpLK7tcy2DYgcd83nZuaKO5R/vZuTvjhwQ/muCu6Od8k3rKS9az77tW7DExnPVE88epI8liSKdG4uwz/oCx6LFSF4v2uxsrFMvxnz++ajCw49yhxOHJEr8f3tnHmTHcR7239dzvfu93QUWi4s4eIHQQYIHCB4u3SXKOmhSclmOLKsSxfIhH3GlLIt2yoldtstyJbEVS1akSHZZjmPaJimKFmVRJimJpCVRoniLAEkIBAlgce3x9t1vrs4fM+/t28UCXEAEF1j0r2qqe3p6evrrnvnmm+6e7sa3x6l9fS9oKL1tA4Xr1iLWj2cEHnmxxt2ffpIwiHnHL76WdVuOv5TeYpienubBBx/k8ccfB+Cyyy7j+uuvZ3j45NKtT07w2Nf+mSfv/RrdVpO1W17Dle++mfMvv2rB6abmE4YNWq0X0m0PlpVlw4ZfXPT1z2lj7KO3foLbV72dXz30ZQo7/gN/sneSX3/6C9z06XtZ+Zu/ychHfmHOkkjh0aOEhw4RHDrMPdUGvza6mZ9/5lF+8e7bCY8eJW40kslRB8heeQXl97yH0g03YKWTqbbDNlOdKSbbkxzeu4fb7j3KvdVV5MIOv/DkP/PWfY8ct0db2THK0ahMRFgo0SmvpV05n2ZxE01vLXU9RL1pEYV64ByFV3SwczaSUWzkG2xrf5ZCPMFzahvfDN/AVCtGN6dQzSnsbn3ONSPlEBeGcCsrKI+uYtWaNWweXceQNYTX9dATAcGhJuFkp3+OeNacFrR4xOagP8l0rUq1Ondrd9pU3SovFF9gX2EfoQqpxBUuty/nusp1rB1e2zfWyuUSIhM0Gj/sd3HW6j8kDKuz8iqPTGYNGW9t4mbWkMms7bueN4aIzb6OzzONzpxWtBc7s9NfDNlW3zDrtaRdlM/gKUXLD3nw+YnkB4A9kxyc6RDFc+s+71qsqWRZXcmyppxJ/Knb8y84yDrsJgbZYAvaoach6hIFQtAtELibCWSMwC8QNIRgok5w8CDRxMSx6c0jsIWmp2l50EyNtVYGgqyDVShil8tkyyMUhkYpDq9ieGQdI6MbGB5Zh1UsYhWLi1pL79XkXDTGPvGxW3hXtIPbCo+xZecz7F9/HfXiNK9rjnDpT1/J6ObL+NL/eJTGdBcFlC2hrWDdpSvZet0a1l08RBiFvPjii+zbtw/f9wmCgDAMCYIA13XZtGkTmzdvplAoUPNrfP7Jz/PFZ77IWH6MP77+j9k2uo047hLHbaKoTRx38bwxLOv43WnNKKITabrBNHc+/0/cuutWprvTXDJ8CR/c+kFu2HjDgq241cMt/uGPvsfqCyq8+1cvfdnJrc9Upsb3s/v732X3I9/l4PPPgtaUR1dx/pU7uOKdP0VpxfGnRYrqdWp33031ttvpPP108pH/1rdQee/7yF97zaKMhsXgH2gw/aXnCfY3yFw8ROXGC7CHX7mJg2uTbe7+9JNUD7V40we3sOWak18ycHJykgcffJAnnngCpRTbtm3j+uuvp1L58dYp9tstnrr/X/nBV++kPnGUodVrueKdP8XWN7wZ27Fpt/fTau3pG13N1O/7RwZSUQwN7eDybX+76Oue08bYe+78It8rv56/2PlHvOEDn2Hboy+RqX+Dr96zi/i+h8hc+nqsQpFwagr/hRfQnQ7VQpFdG87nTz70y4zOTPN/vvRF1Ngw7UqWTkbR9DQNVzPjBOxb53GwFFHr1qj5yVb36/3upLC+hc7hG9HBEE75+wyvvJtc1CL2hXwX8h3YHGTYHJc4T5cZ0xVGdAm7C+HRQ4SHDxFOVYkafr8rEUAjdL0KjdIY04U1TOfX0nKHGM5P8hMr7mCNt5sjwSYeqv1HDgZb55RJJBrfColUHU0NoYYfTBN0qkhQJRvWcbQ/55zYyWCXR6iMrGb9yEZW58co6CJ204apEO3P9rOrnI3KO6iCQysfcL/zHf45/Fd2hy/g4rLNu4xt6jKG/GFqjTrVapVOpzPnevNb1srlMqVSgOsdRckUWk/iB4fodA7Q6Yzj+0fn1bzC80YHjLS1ZLzEaAucNewNh9nVkaQlrd5mV7NNOzW0bIELc5nUQEu7OfMZKpbFdNNnfKbNeLXNwWpn1j/TYbzaYaLRPeYeHM67swZayeM8y2edP8Noa5pybZLM1BGigwcJDhwgOLCfuNGcc74ojZOPcPIae0UJZ+06nE0X4Vx8Oda6rcSdgKjeIK7XErdRJ6onW3t6gs7MJEFthqjRQBot7FYXxz/xuAiA0LWI8hko5LGLRdzSENnKCHapjCoWsIpFVKGIVSygiqXUHQgrFJBXcH29c9EY++vf+T226au50/s+1z30EG33CnZvW03NqbI5HKIcX0reV2w9v0w83oAgRgMHdJNd+ijT+RkaapooSlq5HcfBcRxs28ZxHJrNJp1OB9vuMLx6hm5+J4X8UcbyFhm7QDeOacUxAS5dXHxcfDzqlGm7m2k7G2hYq6nLMNNxjslQMRGEtAbG3TgiFCyF0h1a/iRBMENGxWwsrGJL5TyG3SwFW1FSir337Ecd7fD+D7+ODSM5SrZ1dix7FMcc3P1saoA93J+2aHTT+Vxw1Q4uuHIHK87beNKydHbtonr7HdTuuotoZgZ7zWoqN91M5eabcNae2tqcsR9Ru/dFGg8dQOUcKu8+n+zrV5yWcu62Q7722afYv2uaK9+5ke3vOnaR8YU4cuQIDz74IE8//TSWZXHFFVdw3XXXLbxyzCkSBFUa9d3sefrr7H/+W4QcJjsU4pV9kKgfz7Yr5HObyOU2J1s+9WfPQ6mTmy/ynDbGLvj6v9Fw8tz12Ee50m7z0eu/wJ3VgK0zn+FTL17EIweb/NNrruBAZRixhLZjM5nO+eToLhfMfIrp+lNEOjom7aydpWznKSmHkhZKUUQp6FLq1lEzMd+s/TRP+ZdznnOU39nyAm98/WvIbPgJdHGMw63D7Jzcya6pXeyc2snOqZ0cah7qp51TK7HiCr7v0ek6RL5LpQkbGw3Ob82wuVNnQ6vGimYTr6VxwpiVm9uU1nUIO4ojTxaZ3lsgsIv4ThHfLeK7JQK3iO+U8LMVgmwF3y3h2wW6KgtpW53WGnQHHdeIdZUwruHHVcK4jopqOHENS4dzyqJQWM2qymZGS2vJOwX2eIf4Ru4R/i3zOL4K2NxZxzuq1/HGmasoxANzailB5R3CHDQzPg27S0N1qesWtaBJrdug1qrT8Y81chzHIZ/Pk8/nyeUcCsWAXLaN6zVw7BpKTaOZJAyPEARH0HruTxe2Xe63pjneGiaszezVa9kTDvNsN8OuZsRBf+45JVtRSX+nHk7nwRlykv0hx6YYxdjjR5B9B5D94zA+jjp8CHviCPnpo1Tqk3jR3DRbtsdkcYRmeQX+ylWoVatx162heN56Vmxex7pyh6H6LqT/N+cT0K0NCJJN5j7LDs1umcpA2LHHtF0kChW16mGOHN7L5NEXqU6NU586TKs6Qbc2TVibQZptcl3IdSDX1eS7UPAV2Y7GCV9eX6h8HlXsGWdFVKmIVSj2jbnCm95M7vJtL5sOnJvG2Odv+RgXyXbud5/m7f/yNZwgy3e3/y7e8E4OuJNktctYXKGcyWOP1VCV/dS7e9DWBJ7XxMbCiTK4Ok+hMIRXLGAVc1iux0xc4LmO4tmZw0zoPEdYxVFGOaLHqEsRvYh5xBQxBV2jzAxlqpRVi5WOzWgmT8kbxlcVuqpIhxzNWFEPIw60ptjXnKQRRaCyKKtIwMKtsJ4SVro2o67DaOoutL/CdRY1XcErSej7vPT0E+x+5Lv86JGHac1UUZbFuq2v44KrdnD+FVefsAXsZIi7XRr33Uf1tttpfuc7AOSvuYbK+95L4S1vOWZW/+PReXaK6Tt3E013yW8fo3zDRlTu9I4zjcKYb/6/Z9n17YNcfPUYb/q5Lcf8qNbj0KFDPPDAAzzzzDM4jsNVV13Ftddee8wPYosljn3a7ZfS1q0X0taupJUrCKb78UQcHLWK5oQw9WKLoJ5lzeZrufQNH2D0vNee0rUX4pw2xsbufwxE2P7AQ/yd/kP2rdnB+7b8V6aDDhCjVR4rmsbu7CSZgi7C9l9iSB9lo9Nlk5djvXisjzSrO02GWlUqjQkqMwfxWnO7i2Inx3P5y7grvpa/mriUWCzevn01269YTyyCH2safkit6TNZ7TA93WFqusNUtcN0tU03qKEy41jeOFZuHMdrYdldRLXR0ibSbTSJUbgyDNnW9bms0+XyTpeLfZ8Y+L/lIl8ol5EIhlsxlQ5UOjHltqbUFQqBSyFwyAUO2cAm41t4XYXbjpGOQ9S1CSSP75boeqXEWOtvZbpumdDyQLfR8Qw6rhHpaWrOIarZw0zlp/jR2ilmCj5OIGw6mOeifQVWzGTIWDk8lUtcK0fGKZJ1y2SdAlm7kIRLBkc7KD23RaVLQEM6NOwuHSeka4d0rJCuCmhrn7bu0o66tIMOkV6o1Scml4uoVEIKhS65XAcv00yMNqsKehJNe84ZIi5NazN71WvZz0aaFGgHGaypDpnJBrmJGoXJaSqTU4xMTrBqaoKV01PY8VzDfbpQYnJkJdWVK2msHKU9MkqrspJmaQUzuSGaZGi3QhoNn2qjy8RMF3/eHz2urZLWtXKWNWWXLZlptug9jIUHyEZ1MlEdL5jB8Wewu1VUdwbpVJGgdfyHQ9Q8o61nxM0abl2vwCEF44QcjNocDJuM+9McbB3mcPUA9anDeJ0wMdi6mlwXRqIsq+MSK6IsQ5FHKbDJdxXZToTTDpBGOxlzWa+z6uO/zfAHPnCCJ3iwPs49Y+xTt/wWE14yzu/qZx/iwrZH49c+w3MPH2J6+m9wN0xQGpugUBrHtpPWbK0tlBoln12P8oV2tUY7DHneXc9uex0/stazx9rApMz+kSs6YqUK2JwrMRJHOM06fqNOpzZD2GxixxF2HFNwbFaUS6zKZRnLZViVy1HICa57BGQ/UbiXTmc3jeZzxPHc58m2KwNDCtZQjx3+7fAuHtj/Q5ypzVy++yM0L+ow+tYhirkNtLTHET/kiB9wNHWP+OGcv6kHmT9dQc8/lM4tVelPX2AfM42Bs8ju0HajzguPfp/dj3yXvY8/StDt4GazbLzsSi648mo2bbuSTP70zhEYHDhA9Ut3MnPHHQTj41jlMqV3v5vK+95LZsuWBc+J6j7Vr+yh/cRR7JVZhm6+EG/TqzddjtaaH/zLizx81x7WXFjhHb/0ujnjGsfHx/nWt77Fs88+i+u6XH311ezYsYN8/uXHuGqt8f2JOYZWM/V3OvvRAw0prrsybeHaRC63iXzqz2TWo9L1hCcP7OMHd9/JMw/cTxQEnH/l1Vz5zptYe8mP/0PFOW2MrfvsN5FmhDvd5G3xD/hL55M8NXY9f/ra32JG1ykGz7NVv8TqMGRTt8X62gRjM0fI1w5gdWfmpNVyikxmxzicGWXcXcE+a4TdspIn2uvZ3ximW1dI2v2jPUWcsxENBDESxBDGyDw7QTsKXbCJ8zY676DzNnHBhozV/8NOdMzFrb1srz7JVbUn2T7zNBu6hwFoK4fH8uv5QX4dtw9v5SU3D7qLrbu4cYtCOEMuquNEDey4jcRdIh0QEtEV6IjQnjcGwQ00xTYUW1BsJ/58G5xYiC3Bd2zqWYdqHqYKEdP5gFjN3jtrplfxugOv54Kjr0G0R6Q0kdJofNBddNxNXD3f7fT3LWI8yyOj8onh1jPgrByO8nBUBkd5uCoz4HcRUQREdMSnQ0BbfDoS0MGnLcnWEp82AV3dQftNnKCL6/tkwhZ5XScbNclELbygi+t3cf0ApxVhVzV2bU5RoUUTVSAahu6wRXPYoz6SY3q4yNHKMONDq5i0R6hTpK5LNKRAS/K0JEdHZQnVAr9+aw1BjGqFWM0A1Y6QdoR0YqQbof0YHbx8NyNAVoWMWC1GrBYrVJNh1WRYtRhSTYakSUUalGhSokExrpOPG+SiGpmojnB8faCdPJKtEGUrTGRLHPSyjNsO45ZwSGLG425iuAUztKK5rZoZK8NYfow1+dW8/6Kf4U0b37IoWc5JY+z3/4AXXI+yrjJ86R1ckg1x3RVYKke78xIAft2hO7mCnPc6VoxdS3noIiwvxwHL5WFf81Ar5JF2SG/gwVgj5KKGz+tnYrbWFGvbMas6GnugusVRqJyDytn4mZgp1WRK15gIakx2qzS6TVp+h3iBDx8RIZfLUCrFFAo+2WwLz2viOHWgijCFqClEdY45txsL7Rg6GmLxcJ0yeW8FQ9nVjOTX4TplsIrUqTCti0zrAlNxhmrkUott6pFiJhJmQn1S0xXkLdU30AqWhasEVxQZSxC/S2dygvbRQ3QmjmKFAVnHYXT1GsbWrWd09RpyjpOco4SMUrgy4FeCqxSZ9Lgrqh/XETnplVy01mg/Iqr7NL/7KI1vfJvOM7vByuKs2Yh3/iVYI2PEXYibAXEjQPsRWELpTespvnE9skRThTz3vUPc+8VnKK1wecu/v5iubvLQQw+xe/duMpkMO3bs4Oqrrya7wPQeUdSm1dq7gNH1Qv+HL0jGE+f63YqJ2zO6bHvxf4i2Zqo8ds/dPP71u+nUa4ydfyFXvvtmLtx+LeoUh1+8osaYiNwAfBKwgM9rrf9k3nEPvwuYeQAAEklJREFU+CJwBTAJ/IzWem967Bbgw0AE/LrW+p7FpLkQi+6m/LOvEh6KQYSVuUnWtSf5C+9TrOPYQdBdcfiRs5b99ioOWCs4IKO8xCgvxqs4EA3TCr30RQjEJ5hQzuqSd9tknA45u03WbpOzO+TsFnmrTdFuMJqdYCx3lLydfD1qLahY44Yxnh/jhSHlls9Qs81Is42XjvloOQ7jxQr7S0O8VFrB/sIwXStDgEMgDj5eOq7DJeiP8XDnhHXx8LVDMegw1pliVesohbCKF84QRFO09AwNWtSky4xEzKiYuhLiAaVhaY2rNbbWaASJNZYv5FtCrgteqPEC+pvrQyZ08AIXL8rghh5u5OFEHnbsYWkPK/ZQeCjtgbhoXLRYhJYiUEKkIFKaWGkiNFpiYh1ixW3soIUTtvBCn0wY4MUhXhTiRhFuFOKEAXYUYYUBVuijAh+J/AXrr0dku4RuhsB16Xou7VyeVi5Hq5ChVXRplSy6ZQvlRFhWiGUHWFaQ+Oe4c8PtXlwVEtuapuRpUEy3wjx31l+nSDPdD7UN3Sgx/iONRBoiDVHc98+GaSSKIUxcCeefo2FeL7zomDIthqTFkG5TkRZl3aIkXYq6Q1E6FPAp0CUvPjkdkCUkS4ClIcYm0jYRNi0cqsplRlxqymVGMjTEpSU2Gzdk+eUPf/xln2NYWJG9mvpIRDYBtwLDwKPAB7XW/omucTwWq79+768+wec2vZ0/0L/NJr2b7zRttg5dxPnlDVRKl7Bi5K3sfuoIX338SZ5p+4wXhzk6vIrpygoiK/nSH54+wqaXnmfTvudZfWQ/GT8xggRF3i7hqgyulcVRGbz048azsnhWDs/O4aosrpXBlQyuuP3JmjWaLmHywTPwsTO7H9AlwJew7wYDY3Esy8fzmniZJhmvieN2kufD9tNnxE+eG9vHtgIsO3l2FoPWgpDoESFDLDlaqkxLDdGSCm0p05IiLVWgKQWaZNMtQ1vbdIKIViekHYT4MYTKIbJcYtslVBY+r5wxYwu4onAEXARHg6PBjcGJNU6ksaNkWIATxthBEubE4OjUjcGNNXYUY/sdnNDHcy2y5SKZoTKZrEV2fQm35GEJWCLpBhYD/jRcoZE4Jg5D4jAgDiPiMCAKgr4b+QFR4Cf+wCf0E3/o+wTpjyK+7/e33n4Yzq3DbDbLNddcw/bt2/E8l273UL9lq9XaQ6uZdC92uuNzzvO81amRtXnA+NpMJrP6uBOKnwpBt8MPH7ifR+6+k+lDBymuHOXyd7yHrW94K142t+hWVXgFjTERsYDngLcB+4HvAz+rtX5mIM6vAK/XWv+SiLwfuElr/TMishX4e2A7sAa4F7goPe2EaS7EYpXZJ//yFp48r8CmAyE6BgmZ/RtSA5KMlNJKkmARtPRkAUSj08LW6X56xsDcUPPKTSSNodG9OKKTWC9Xb4Nfpy8r3SvHgFQD1019Op3LWM8/ppPiGEwEjmlRmbsnx4boE0vai92LJb2VDnr+3jE9t8z0oFDpkV7eerMza0D1QvVA3fevOes7Xt1Ieu4xB7XMCdADpSvp3qx8Or0X9UAYQDyb0/4l0jgiiSGMJCn25BxwdfrS6B/rXb+XtznJCmjhmNo47j15nHVLFwydi6SyqbbPx/7LLYs441hF9mrrIxH5R+AOrfWtIvK/gSe01p853jVOJMuijbG/+T0+d97NAGxqv0g3mmQ6PoKKm1gyROBtpO1tAEm+1F1/inzrAIXWQQrNg1Rqu8l0JgHwJSYCNnY9LmkMI+3NBMEQWiu0pr8l900EOgRC0GG6mkbit0WwRaWbhS0Kq++3sJWdui6W2NjiYCkHW2yU2MSWhRYhUr0NQtFE/S0mJCIiJiQmkpiIKPWHaLuNtrpgd8BOXLG6YAUoK0RZIZaa51ohSqUfTXPCeu7iWpp7aCDCxschxCHAJsCd4w9wCHt+7eBrj0D3wh1C7RBK6mIT4BANuL2wMPWHYqVhs/4Qm6jnF5tIzow/oEXHKDRKx3P9xGkYKC0oDaJiUDEiMajEWJ/VzwodW8RaobWV3qsKrWU2zqwq67+T+zpoQG0n8QQ9Lzw5JnPSGEzrRIxNHeKxm9++6O7LxRpji6nF7cBurfWeNOFbgRuBQcPpRuC/pf7bgE9JktMbgVu11l3gBRHZnabHItI8Zb523mt5LP+aWTVrMBjOGG46fP+Pc/qrpo9EZCfwZuDfpXH+Jk33M8e7hn4Fxn1crJIltVZ2q5S6AZPeaqLsVtpWBidqUuq+yLrJLzHc2kmlswcvbibmd2pkWzlBZZMmvmwM76qFbFQaXdqPLj2JRohJXmw9f6yFGItIJ0ZDhEWctnJG2iZGobWNxkKjkhclyYsyxkJri1gLIj5Z6yCeqkOcXIE4+YDTfesPdJi4s29JBdpCY6O0hcYCbNA2iEJrh6pyqCqPmuVSF4cQF4ssCgtLWyhtoUShsBBtI6KwtAKx0s8XhaDSD+fki1ysEKyQ2I6InJDIiUESI0EkKZ2e0dAL6x1HJYaGIzFOz7CQ3vkRogKQBiIxWmK0ROj0eOJGyZelxIBODBR0el2NEg0Dfunvg5K433AAmliEUGxisYiwCMQiEjsxgBOzKKlnEqM4RiXhShGj0v20blN/Lzwe8EeogfD5x1K/KGI59hw9EAcNEtho3wbfRXcddOChuy5EDnM/jWf9gx+iyS0/eDxtGZPe8dmt9+HOQMqDH6XH/8AfaHyI68TBOKXGNPLeG07hyT4xizHG1gL7Bvb3A1cfL47WOhSRGWAkDf/uvHN7/+a+XJoAiMhHgI8Ai16F/ZfsHI8+cztq1RB0fHS3C2GI1r3WhIEWK5VWmFKISsY+uI6N67lkPRfLsbFtF7FsLFGIZeFaFsoSHEuwZXacl2XZKNtBWQ7KchHbxbaSc0U50J9AcVZf6+P4F4PWGlsl+VKSKKIFSvDlUum3y/R9qcKE3tqbcXJUg9YxaE2cfk5rHfdbo0TUbHnOk3Nw7/jvK71gGfS/3nvHtB7ITyqBjok1EKcpxBpNnK4fqtFx6pKcpOM0jRggJo7Tl0TveLr1r9rPc+/YXKl0r3lhUD7dk6j34hnIj07z18uXjiFOyjfWMck7LE7kSNOO4zhJoxeWCJo4kR4ov/T6ca+OknQH77SBQu2fp9P0Yt1r443pX6afajx4hd5tMisjOl0VYbYMeluvNDdaA3/Wnjyvpj4aAapa938jHox/vGvMGQtxKvrrAx/8FTbddwfbrr0ey8txZM/THN75I6aPzBB2OsRRiFBGyTVY9k8gSiGWjRKFKAsRhbIkCRdByoq9yQMKVvJ8KqUQAa00qndMx+lYptlKUxpU+nyxiK6Z3i0VDdS5nnOvgQzM2xf3nsnk7IH7be49qrXGI2IMWJ08KOlzEaXPc5TewzFoQRMT64hIUh1FDNon1jFx+gyHOkITE6YKLCYGhIjE6OnNL5iWRGLAakl0n44HSkn3dUQM6F6PSO/5T983Wga17aC+n/dM6TklMuvKbDH2rymz5Rf3DdtkjcZMw8f1g3kGh0YLc3o3FBFKR9gSIJK0cGk92EiU5K7fQcSsUQgDYcy28ksqa7/HZKAHAISgY6Pj2ffVsX0nswG6n/agHCd4X84P7u+rdJPE1WogbCC859ezcWQgPD5NDZGLSXahJ3C+uMeLc7zwhayGBUtWa/054HOQNPMfP5uz3HjDTdzITYuJajAYzi5eTX10omstJh+npL8Arn3LzX3/ukt2sO6SHYs91WAwnIUsZsTbfmBwfYp1wPjx4oiIDZSBqROcu5g0DQaDYT6vpj6aACppGvOvdbxrGAwGw0mzGGPs+8CFIrJJRFzg/cBd8+LcBXwo9b8PuD8dO3EX8H4R8dK/ki4EvrfINA0Gg2E+r5o+Ss/5RpoGaZpffplrGAwGw0nzst2U6XiIXwXuIRkT+lda6x+KyB8Aj2it7wK+APxtOiB2ikSZkcb7R5LBtSHwUZ3OxrZQmq+8eAaDYTmxBProt4FbReQPgcfStDneNQwGg+FUWJaTvhoMhuXBuTjpq8FgWD4sVoctzZS8BoPBYDAYDAbAGGMGg8FgMBgMS4oxxgwGg8FgMBiWEGOMGQwGg8FgMCwhxhgzGAwGg8FgWELOqr8pReQo8OIio69g3tIky5RzQc5zQUYwci7EBq31ytOZmVcLo78WxMi5fDgXZISTl3NROuysMsZOBhF5ZLn8En8izgU5zwUZwchpmOVcKSMj5/LhXJARTp+cppvSYDAYDAaDYQkxxpjBYDAYDAbDErKcjbHPLXUGXiXOBTnPBRnByGmY5VwpIyPn8uFckBFOk5zLdsyYwWAwGAwGw9nAcm4ZMxgMBoPBYDjjMcaYwWAwGAwGwxKyLI0xEblBRJ4Vkd0i8vGlzs+pIiLrReQbIrJTRH4oIr+Rhg+LyL+KyPOpO5SGi4j8r1TuJ0Xk8qWVYPGIiCUij4nIV9L9TSLycCrjP4iIm4Z76f7u9PjGpcz3ySAiFRG5TUR2pXV6zTKty99M79enReTvRSSzHOvzdGH019l3z4PRYculPpdKfy07Y0xELODTwDuArcDPisjWpc3VKRMC/1lrfQmwA/hoKsvHgfu01hcC96X7kMh8Ybp9BPjMq5/lU+Y3gJ0D+58A/iyVcRr4cBr+YWBaa30B8GdpvLOFTwJf01pvAS4lkXdZ1aWIrAV+HbhSa/1awALez/Ksz1cco7/Ovnt+AKPDzvL6XFL9pbVeVhtwDXDPwP4twC1Lna9XSLYvA28DngVWp2GrgWdT/2eBnx2I3493Jm/AOpKH+M3AVwAhmeHYnl+nwD3ANanfTuPJUsuwCBlLwAvz87oM63ItsA8YTuvnK8Dbl1t9nsbyM/prNv5Zcc+neTU6bBnU51Lqr2XXMsZsYfbYn4ad1aTNn9uAh4FVWuuDAKk7mkY7W2X/c+BjQJzujwBVrXWY7g/K0ZcxPT6Txj/T2QwcBf467cr4vIjkWWZ1qbU+APx34CXgIEn9/IDlV5+ni7Oy3l+OZa6/wOiwZVGfS6m/lqMxJguEndXzd4hIAbgd+E9a69qJoi4QdkbLLiLvAo5orX8wGLxAVL2IY2cyNnA58Bmt9TagyWxz/kKclXKm40VuBDYBa4A8SXfFfM72+jxdLLvyWM76C4wOO0H8s07OpdRfy9EY2w+sH9hfB4wvUV5+bETEIVFkf6e1viMNPiwiq9Pjq4EjafjZKPt1wHtEZC9wK0kz/58DFRGx0ziDcvRlTI+XgalXM8OnyH5gv9b64XT/NhLFtpzqEuCtwAta66Na6wC4A7iW5Vefp4uztd4X5BzQX2B02HKqzyXTX8vRGPs+cGH694NLMvjuriXO0ykhIgJ8Adiptf6fA4fuAj6U+j9EMhajF/7z6V8sO4CZXvPxmYrW+hat9Tqt9UaSurpfa/0B4BvA+9Jo82Xsyf6+NP4Z/bUFoLU+BOwTkYvToLcAz7CM6jLlJWCHiOTS+7cn57Kqz9OI0V9n2T1vdNiyqs+l019LPWDuNA3C+0ngOeBHwO8udX5+DDmuJ2nyfBJ4PN1+kqRP+j7g+dQdTuMLyZ9YPwKeIvkjZMnlOAl53wh8JfVvBr4H7Ab+CfDS8Ey6vzs9vnmp830S8l0GPJLW553A0HKsS+D3gV3A08DfAt5yrM/TWH5Gf51l9/yAzEaHneX1uVT6yyyHZDAYDAaDwbCELMduSoPBYDAYDIazBmOMGQwGg8FgMCwhxhgzGAwGg8FgWEKMMWYwGAwGg8GwhBhjzGAwGAwGg2EJMcaY4YxFRPaKyM8tdT4MBoPhZDH6y3AyGGPMYDAYDAaDYQkxxpjBYDAYDAbDEmKMMcNZQbo8xZdF5G4RyS91fgwGg2GxGP1leDmMMWY44xGRMeBbJIuzvkdr3VziLBkMBsOiMPrLsBiMMWY409kKfBu4TWv9y1rraKkzZDAYDIvE6C/DojBrUxrOWERkL8lCrBPADq11Y2lzZDAYDIvD6C/DyWBaxgxnOh8HngLuFZGhpc6MwWAwnARGfxkWhTHGDGc6IfABEoX2TRFZtcT5MRgMhsVi9JdhURhjzHDGo7WOtda/ANwHPCAi5y11ngwGg2ExGP1lWAxmzJjBYDAYDAbDEmJaxgwGg8FgMBiWEGOMGQwGg8FgMCwhxhgzGAwGg8FgWEKMMWYwGAwGg8GwhBhjzGAwGAwGg2EJMcaYwWAwGAwGwxJijDGDwWAwGAyGJcQYYwaDwWAwGAxLyP8HUqFQ7M/j7VIAAAAASUVORK5CYII=\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "## Plot joint distribution of k and m in 3d graph\n", "#for only 90 percent of the distributions \n", @@ -1444,26 +1404,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "code_folding": [ 0 ] }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "## Plot the copula \n", "# same plot as above for only 90 percent of the distributions \n", @@ -1679,7 +1626,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.7.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/examples/BayerLuetticke/notebooks/DCT-Copula-Illustration.py b/examples/BayerLuetticke/notebooks/DCT-Copula-Illustration.py index 72d09a72e..5ae84baf7 100644 --- a/examples/BayerLuetticke/notebooks/DCT-Copula-Illustration.py +++ b/examples/BayerLuetticke/notebooks/DCT-Copula-Illustration.py @@ -6,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.2' -# jupytext_version: 1.1.3 +# jupytext_version: 1.2.4 # kernelspec: # display_name: Python 3 # language: python @@ -38,7 +38,7 @@ # $[\frac{d v}{d m} = \frac{d u}{d c}]$. # In practice, the authors solve their problem using the marginal value of money $\texttt{Vm} = dv/dm$, but because the marginal utility function is invertible it is trivial to recover $\texttt{c}$ from $(u^{\prime})^{-1}(\texttt{Vm} )$. The consumption function is therefore computed from the $\texttt{Vm}$ function -# %% {"code_folding": [0]} +# %% {"code_folding": []} # Setup stuff # This is a jupytext paired notebook that autogenerates a corresponding .py file @@ -171,14 +171,14 @@ def in_ipynb(): '\n state variables are below the corresponding point.') -# %% {"code_folding": [0]} +# %% {"code_folding": []} ## Import BL codes import sys # Relative directory for BL codes sys.path.insert(0,'../../../..') # comment by TW: this is not the same as in TwoAsset.ipynb. -from HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset import FluctuationsTwoAsset +from FluctuationsTwoAsset import FluctuationsTwoAsset # %% {"code_folding": [0]} ## Import other necessary libraries @@ -215,14 +215,14 @@ def in_ipynb(): -# %% {"code_folding": []} +# %% {"code_folding": [0]} ## Choose an accuracy of approximation with DCT ### Determines number of basis functions chosen -- enough to match this accuracy ### EX3SS is precomputed steady-state pulled in above EX3SS['par']['accuracy'] = 0.99999 -# %% {"code_folding": []} +# %% {"code_folding": [0]} ## Implement state reduction and DCT ### Do state reduction on steady state EX3SR=FluctuationsTwoAsset(**EX3SS) # Takes StE result as input and get ready to invoke state reduction operation @@ -315,7 +315,7 @@ def DCTApprox(fullgrids,dct_index): # %% [markdown] # Depending on the accuracy level, the DCT operation choses the necessary number of basis functions used to approximate consumption function at the full grids. This is illustrated in the p31-p34 in this [slides](https://www.dropbox.com/s/46fdxh0aphazm71/presentation_method.pdf?dl=0). We show this for both 1-dimensional (m or k) or 2-dimenstional grids (m and k) in the following. -# %% {"code_folding": []} +# %% {"code_folding": [0]} ## 2D graph of consumption function: c(m) fixing k and h diff --git a/examples/BayerLuetticke/notebooks/OneAsset-HANK.ipynb b/examples/BayerLuetticke/notebooks/OneAsset-HANK.ipynb index b24247bfb..d40463430 100644 --- a/examples/BayerLuetticke/notebooks/OneAsset-HANK.ipynb +++ b/examples/BayerLuetticke/notebooks/OneAsset-HANK.ipynb @@ -62,9 +62,7 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "code_folding": [ - 0 - ], + "code_folding": [], "scrolled": false }, "outputs": [], @@ -88,7 +86,7 @@ "metadata": {}, "outputs": [], "source": [ - "from HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond import FluctuationsOneAssetIOUs, SGU_solver, plot_IRF" + "from FluctuationsOneAssetIOUsBond import FluctuationsOneAssetIOUs, SGU_solver, plot_IRF" ] }, { @@ -102,7 +100,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Elapsed time is 0.05101699999999987 seconds.\n", + "Elapsed time is 0.790651 seconds.\n", "Use Schmitt Grohe Uribe Algorithm\n", " A *E[xprime uprime] =B*[x u]\n", " A = (dF/dxprimek dF/duprime), B =-(dF/dx dF/du)\n", @@ -111,18 +109,18 @@ "Block number: 0 done.\n", "Block number: 1 done.\n", "Block number: 2 done.\n", - "Elapsed time is 25.685692000000003 seconds.\n", + "Elapsed time is 521.55736 seconds.\n", "Computing Jacobian F2 - DF/DYprime\n", "Total number of parallel blocks: 3 .\n", "Block number: 0 done.\n", "Block number: 1 done.\n", "Block number: 2 done.\n", - "Elapsed time is 0.9454619999999991 seconds.\n" + "Elapsed time is 19.462265000000002 seconds.\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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sYKzbtqcjMB5d28Ce/a0UDxGvmVbOJ95yMufOmMBpk0f7PIbZEUIRx85pgdra2qirq8u6jGNGRLB8w05+/eRm7l+xmbVb9wAwddwIzp2Z7GWcdeJ4RnkmWrOCJmlpRNR2bj8iTqLbkaO1Lahbt51fr9jMb1a8yIaX9lI0RLz+hHG89/XTOO/kiVRXlGVdppn1AweIHbZ9La38YW0D9z+5mQdWvkjDnv0MLR7CuTMmcO2FM7nglImeytzsKOQAsbw07m/hd09v5dcrNvPgqi28vK+FkcOKedMpE5l32nGcd/IEyob528vsaOafcOuznY3NLF71Ir9esZmHn9nKvpY2xpUN5W2vqmTe6cdx9knjfZmt2THEAWI92rKrid+sfJH7V2zm0bUNtLQFlWNKuWLOVN5y2nG8trqc4qIhWZdpZhnoU4BIWhIRF/TWZkeHjS/t5d7lm7jvyc386fkdRMAJFWX893NPYN5px3FG1Rh/AtzMeg4QSaXACKAi/fBe+2+N0SRzWdlR4sVdTdy7fBP3PLGJpet3AHBq5Wg+Nncm804/jpMmjnRomNkBetsD+SBwDTAZWMorAbIL+NoA1mWDYNvufdyXhsZ/rttOBJxy3Cg+8ZaTefurKn25rZn1qMcAiYivAl+V9HcR8a+DVJMNoB179vPrFZu554mNPLq2gbaAkyaO5KMXzOCiMyZz0sSRWZdoZkeIPp0DiYh/lXQ2UJ27TkT82wDVZf1o595m7l+xmV89sYnfr9lGS1swvaKMj7zpJC46YzIzJ/nwlJkdur6eRP8+cCKwDGhNmwNwgBSol5uSS27v+csmHl69lebWoKp8OB845wQuOqOS0yaPdmiY2WHp62W8tcCpcSxNnHUEatzfwuJVW/jVExv57dNb2d/SxuQxpVx1djUXnTHZV0+ZWb/qa4A8CRwHbBrAWiwP2/fs56Gnt7Bk1RaWPPUiTc1tTBw1jL+eM5V3vLqSM6eUe3ZbMxsQfQ2QCmClpP8Ecm/qdPGAVGXdigie2vwyDz61hSWrXuTPL7xEBFSMHMals6u46IzJvLZ6HEUODTMbYH0NkM8NZBHWs737W3n02W0sWbWFB5/awqadTQCcUTWGj14wg/NPmcjpk8d4T8PMBlVfr8L6naRpwIyIWCxpBMlNoGyAbHhpLw8+tYXfPrWF36/Zxr6WNkYMLeKcGRVcO3cm5508gYmjfbc+M8tOX6/C+u/AAmAcydVYxwPfAjyVST9pbQuWvbCjYy/jqc0vA8nNl66YM5ULZk1kzvRxnqzQzApGXw9hfQSYA/wRICJWS5o4YFUdI3bubebhZ7by4FNbeOjpLexobKZoiKidVs6n33YK558yiRMnlPnKKTMrSH0NkH0Rsb/9F5mkYpLPgRwWSfOAr5IcDrs9Im7u1D+M5LMms4EG4LKIWJf2XQ+8n+RzKX8fEfcfbj2D5aGnt/DNh9ZSt34HrW3B2BElvOnkiZx/ykTOnTGBMSN8i1czK3x9DZDfSfo0MFzShcCHgV8ezhtLKgK+DlwI1AOPS1oUEStzhr0f2BERJ0m6HPgycJmkU4HLgdNI5ulaLGlmRLRyBGjc38rOvc188NwTuGDWRGqmlPuqKTM74vQ1QD5F8st8OckEi/cCtx/me88B1kTEswCSfgzMB3IDZD6vXAF2N/A1JbtB84EfR8Q+4DlJa9LtPXqYNXXrvPPOO6jt3e9+Nx/+8IdpbGzkbW9720H9V111FVdddRXbtm3j0ksvPah/2oc+xOxpl/HCCy/w3ve+96D+j3/847zjHe/g6aef5oMf/OBB/f/wD//A3LlzWbZsGddcc81B/V/84hc5++yz+cMf/sCnP/3pg/pvu+02ampqWLx4MV/4whcO6v/2t7/NySefzC9/+Uu+8pWvHNT//e9/nylTpvCTn/yEb37zmwf133333VRUVHDnnXdy5513HtR/7733MmLECL7xjW9w1113HdT/0EMPAXDLLbdwzz33HNA3fPhw7rvvPgA+//nPs2TJkgP6x48fz89+9jMArr/+eh599MBvjaqqKn7wgx8AcM0117Bs2bID+mfOnMnChQsBWLBgAc8888wB/TU1Ndx2220AvOc976G+vv6A/rPOOosvfelLALzzne+koaHhgP4LLriAz3zmMwC89a1vZe/evQf0X3TRRVx33XXAwHzvfehDH+Kyy/y9V+jfe4WurwEyHLgjIv4PdOw9DAcaD+O9jwdeyFmuB17X3ZiIaJG0Exiftj/Wad0up5eXtIDkAgCmTp16GOWamVku9WV2EkmPAXMjYne6PBL4TUScnfcbS5cC8yLiA+nye4HXRcTVOWOeTMfUp8trSULmc8BjEfGDtP07wH0RcXdP71lbWxt1dXX5lmxmdkyStDQiaju39/VepKXt4QGQvh5xmDVtAKbkLFelbV2OSU/cjyE5md6Xdc3MbAD1NUD2SHpN+4Kk2cDeHsb3xePADEnTJQ0lOSm+qNOYRcCV6etLgQfTCR0XAZdLGiZpOjAD+M/DrMfMzA5BX8+BfBT4qaSNJHclPA647HDeOD2ncTVwP8llvHdExApJNwJ1EbEI+A7w/fQk+XaSkCEddxfJCfcW4CNHyhVYZmZHi17PgUgaAryeZI/h5LT56YhoHuDa+p3PgZiZHbruzoH0ugcSEW2Svh4RZ5JM625mZtbncyBLJL1TnlPDzMxSfQ2QDwI/BfZL2iXpZUm7BrAuMzMrcH2dzn3UQBdiZmZHlj7tgSjxHkmfSZenSJozsKWZmVkh6+shrG8AZwF/nS7vJpkI0czMjlF9/RzI6yLiNZL+DBARO9IP/5mZ2TGqr3sgzekEigEgaQLQNmBVmZlZwetrgPwL8HNgoqSbgP8HfHHAqjIzs4LX16uwfihpKck90AVcEhGrBrQyMzMraD0GiKRS4H8AJ5HcTOrbEdEyGIWZmVlh6+0Q1veAWpLweCtwy4BXZGZmR4TeDmGdGhGvgo6bNnnKdDMzA3rfA+mYcdeHrszMLFdveyCvzpnzSsDwdFlARMToAa3OzMwKVo8BEhFFg1WImZkdWfr6ORAzM7MDOEDMzCwvmQSIpHGSHpC0On0u72JMjaRHJa2Q9ISky3L67pT0nKRl6aNmcL8CMzPLag/kU8CSiJgBLEmXO2sE3hcRpwHzgNskjc3p/0RE1KSPZQNfspmZ5coqQOaTfEiR9PmSzgMi4pmIWJ2+3ghsASYMWoVmZtajrAJkUkRsSl9vBib1NDi9edVQYG1O803poa1bJQ3rYd0Fkuok1W3duvWwCzczs8SABYikxZKe7OIxP3dcRATpNPHdbKcS+D7wNxHRPoX89cApwGuBccAnu1s/IhZGRG1E1E6Y4B0YM7P+0tcbSh2yiJjbXZ+kFyVVRsSmNCC2dDNuNPAr4IaIeCxn2+17L/skfRe4rh9LNzOzPsjqENYi4Mr09ZXALzoPSO94+HPg3yLi7k59lemzSM6fPDmg1ZqZ2UGyCpCbgQslrQbmpstIqpV0ezrm3cC5wFVdXK77Q0nLSWYJrgC+MLjlm5mZklMQx4ba2tqoq6vLugwzsyOKpKURUdu53Z9ENzOzvDhAzMwsLw4QMzPLiwPEzMzy4gAxM7O8OEDMzCwvDhAzM8uLA8TMzPLiADEzs7w4QMzMLC8OEDMzy4sDxMzM8uIAMTOzvDhAzMwsLw4QMzPLiwPEzMzy4gAxM7O8OEDMzCwvmQSIpHGSHpC0On0u72Zca8790BfltE+X9EdJayT9RNLQwavezMwguz2QTwFLImIGsCRd7sreiKhJHxfntH8ZuDUiTgJ2AO8f2HLNzKyzrAJkPvC99PX3gEv6uqIkAecDd+ezvpmZ9Y+sAmRSRGxKX28GJnUzrlRSnaTHJLWHxHjgpYhoSZfrgeO7eyNJC9Jt1G3durVfijczMygeqA1LWgwc10XXDbkLERGSopvNTIuIDZJOAB6UtBzYeSh1RMRCYCFAbW1td+9jZmaHaMACJCLmdtcn6UVJlRGxSVIlsKWbbWxIn5+V9BBwJvAzYKyk4nQvpArY0O9fgJmZ9SirQ1iLgCvT11cCv+g8QFK5pGHp6wrgDcDKiAjgt8ClPa1vZmYDK6sAuRm4UNJqYG66jKRaSbenY2YBdZL+QhIYN0fEyrTvk8DHJK0hOSfynUGt3szMUPIH/bGhtrY26urqsi7DzOyIImlpRNR2bvcn0c3MLC8OEDMzy4sDxMzM8uIAMTOzvDhAzMwsLw4QMzPLiwPEzMzy4gAxM7O8OEDMzCwvDhAzM8uLA8TMzPLiADEzs7w4QMzMLC8OEDMzy4sDxMzM8uIAMTOzvDhAzMwsLw4QMzPLSyYBImmcpAckrU6fy7sY8yZJy3IeTZIuSfvulPRcTl/N4H8VZmbHtqz2QD4FLImIGcCSdPkAEfHbiKiJiBrgfKAR+E3OkE+090fEskGp2szMOmQVIPOB76Wvvwdc0sv4S4H7IqJxQKsyM7M+yypAJkXEpvT1ZmBSL+MvB37Uqe0mSU9IulXSsH6v0MzMelQ8UBuWtBg4rouuG3IXIiIkRQ/bqQReBdyf03w9SfAMBRYCnwRu7Gb9BcACgKlTpx7CV2BmZj0ZsACJiLnd9Ul6UVJlRGxKA2JLD5t6N/DziGjO2Xb73ss+Sd8FruuhjoUkIUNtbW23QWVmZocmq0NYi4Ar09dXAr/oYewVdDp8lYYOkkRy/uTJAajRzMx6kFWA3AxcKGk1MDddRlKtpNvbB0mqBqYAv+u0/g8lLQeWAxXAFwahZjMzyzFgh7B6EhENwAVdtNcBH8hZXgcc38W48weyPjMz650/iW5mZnlxgJiZWV4cIGZmlhcHiJmZ5cUBYmZmeXGAmJlZXhwgZmaWFweImZnlxQFiZmZ5cYCYmVleHCBmZpYXB4iZmeXFAWJmZnlxgJiZWV4cIGZmlhcHiJmZ5cUBYmZmeXGAmJlZXhwgZmaWl0wCRNK7JK2Q1Captodx8yQ9LWmNpE/ltE+X9Me0/SeShg5O5WZm1i6rPZAngf8KPNzdAElFwNeBtwKnAldIOjXt/jJwa0ScBOwA3j+w5ZqZWWeZBEhErIqIp3sZNgdYExHPRsR+4MfAfEkCzgfuTsd9D7hk4Ko1M7OuFGddQA+OB17IWa4HXgeMB16KiJac9uO724ikBcCCdHG3pN6CqzsVwLY81x0shV5jodcHhV9jodcHrrE/FFp907pqHLAAkbQYOK6Lrhsi4hcD9b6dRcRCYOHhbkdSXUR0e76mEBR6jYVeHxR+jYVeH7jG/lDo9bUbsACJiLmHuYkNwJSc5aq0rQEYK6k43Qtpbzczs0FUyJfxPg7MSK+4GgpcDiyKiAB+C1yajrsSGLQ9GjMzS2R1Ge9fSaoHzgJ+Jen+tH2ypHsB0r2Lq4H7gVXAXRGxIt3EJ4GPSVpDck7kO4NQ9mEfBhsEhV5jodcHhV9jodcHrrE/FHp9ACj5g97MzOzQFPIhLDMzK2AOEDMzy4sDpA+6m1KlEEiaIum3klam08N8NOuauiOpSNKfJd2TdS2dSRor6W5JT0laJemsrGvqTNK16f/xk5J+JKm0AGq6Q9IWSU/mtI2T9ICk1elzeYHV98/p//MTkn4uaWxW9XVXY07fxyWFpIosauuNA6QXvUypUghagI9HxKnA64GPFFh9uT5KckFEIfoq8OuIOAV4NQVWp6Tjgb8HaiPidKCI5MrErN0JzOvU9ilgSUTMAJaky1m5k4PrewA4PSLOAJ4Brh/sojq5k4NrRNIU4M3A84NdUF85QHrX5ZQqGdfUISI2RcSf0tcvk/zi6/aT+VmRVAW8Hbg961o6kzQGOJf0ar6I2B8RL2VbVZeKgeGSioERwMaM6yEiHga2d2qeTzLFEGQ81VBX9UXEb3JmsniM5LNkmenm3xDgVuB/AgV7pZMDpHddTalScL+gASRVA2cCf8y2ki7dRvLD0JZ1IV2YDmwFvpseYrtdUlnWReWKiA3ALSR/jW4CdkbEb7KtqluTImJT+nozMCnLYnrxt8B9WRfRmaT5wIaI+EvWtfTEAXKUkDQS+BlwTUTsyrqeXJIuArZExNKsa+lGMfAa4JsRcSawh2wPuxwkPY8wnyTsJgNlkt4oW5wAAAN6SURBVN6TbVW9Sz/4W5B/QUu6geQQ8A+zriWXpBHAp4H/lXUtvXGA9K67KVUKhqQSkvD4YUT8e9b1dOENwMWS1pEcAjxf0g+yLekA9UB9RLTvud1NEiiFZC7wXERsjYhm4N+BszOuqTsvSqoESJ+3ZFzPQSRdBVwE/LcovA/DnUjyh8Jf0p+ZKuBPkrqaWzBTDpDedTmlSsY1dUint/8OsCoi/nfW9XQlIq6PiKqIqCb593swIgrmr+eI2Ay8IOnktOkCYGWGJXXleeD1kkak/+cXUGAn+nMsIpliCApwqiFJ80gOp14cEY1Z19NZRCyPiIkRUZ3+zNQDr0m/TwuKA6QXvUypUgjeALyX5K/6ZenjbVkXdQT6O+CHkp4AaoAvZlzPAdK9o7uBPwHLSX52M5/uQtKPgEeBkyXVS3o/cDNwoaTVJHtONxdYfV8DRgEPpD8v38qqvh5qPCJ4KhMzM8uL90DMzCwvDhAzM8uLA8TMzPLiADEzs7w4QMzMLC8OELPDIKlK0i/SmWeflfQ1ScP6adtXSZrcH9syGwgOELM8pR/o+3fgP9KZZ2cAw4F/6odtFwFXkUxbcijrFR/ue5v1lQPELH/nA00R8V2AiGgFrgXeJ+lqSV9rHyjpHknnpa+/KakuvbfHP+aMWSfpy5L+BFwB1JJ8uHGZpOGSZkv6naSlku7PmS7kIUm3SaojmTLfbFD4rxWz/J0GHDBBZETsSucv6uln64aI2J7uZSyRdEZEPJH2NUTEawAkfQC4LiLq0vnO/hWYHxFbJV0G3EQymyzA0Iio7b8vzax3DhCzwfduSQtIfv4qSW5U1h4gP+lmnZOB00mm34DkhlKbcvq7W89swDhAzPK3Erg0t0HSaOA4oAGYmdNVmvZPB64DXhsROyTd2d6X2tPNewlYERHd3Wq3u/XMBozPgZjlbwkwQtL7oOPE91dIJut7DqiRNCS9NemcdJ3RJL/sd0qaRHKr5O68TDLpH8DTwASl92qXVCLptP7+gswOhQPELE/pfST+Crg0nXm2AWiLiJuA35OEyErgX0hm0SW9w9yfgaeA/5uO686dwLckLSM5ZHUp8GVJfwGWUbj3A7FjhGfjNesnks4GfgT8Vft96s2OZg4QMzPLiw9hmZlZXhwgZmaWFweImZnlxQFiZmZ5cYCYmVleHCBmZpaX/w9uIo6NGeXMMgAAAABJRU5ErkJggg==\n", 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/FxGvobLst40Rz6Y2szyrdQzi25J+T16Xekx1tnk2tZnlV61jEO8HJgMDkn5JpZspIsL9IoegtXkCHZNbWe07CDPLoVqX+25Pu5DxqlwqstYBYWY5VOty35L0B5L+Knk/Q9L8dEsbH8qlAmu2OCDMLH9qHYP4LPAa4PeT91uBm1KpaJyprMfkMQgzy59aA+LVEfEeoA8gIjZSefTVDlG5VGTd1h30D+zOuhQzs33UGhD9yQJ9ASCpE/B3tDFQLhWJgHVbfRdhZvlSa0D8H+BfgC5J1wL/CXw8tarGEW8cZGZ5VetTTF+R9AhwJpVHXM+PiOWpVjZOeLKcmeXVfgNCUhH4E+B4KpsFfT4idtWjsPHCAWFmeXWgLqbbgHlUwuFc4JMH8+GSFkhaIWmlpCurtBck3ZG0PyRp5rD2YyVtlfTBg/m6jaRjcitNE+SAMLPcOVAX05yIOAVA0i3Aj2r94GRQ+ybgbKAHeFjS4oj4yZDT3glsjIjjJV0IXM++q8TeCHyr1q/ZiCZMEF3tBY9BmFnuHOgOYs+KrS+ha2k+sDIinomIncDtwMJh5yykcpcCcBdw5uB6T5LOB54Blh3k1204Xd6b2sxy6EABcZqkzcmPLcCpg68lbT7AtdOA54a870mOVT0nCaBNQIekycBfAh/b3xeQdJmkJZKW9Pb2HqCc/Or23tRmlkP7DYiIaIqIUvKjPSKah7w+0EJ91VZ+Hb635mjnfAy4ceg2p6PUd3NEzIuIeZ2dnQcoJ788m9rM8qjW1Vxfih5gxpD304FVo5zTI6kZmAJsAF4NLJL0t8ARwG5JfRHxmRTrzUy5VGTTL/vp6x+g2NKUdTlmZkC6AfEwMFvSLOB54EL2ruU0aDFwCfAgsAh4ICICeP3gCZI+Cmw9XMMB9n3U9biOyRlXY2ZWUetM6oOWjClcDtwHLAfujIhlkq6RdF5y2i1UxhxWUtlzYsSjsOOBZ1ObWR6leQdBRNwD3DPs2NVDXvcBFxzgMz6aSnE54slyZpZHqd1BWO3K7Q4IM8sfB0QOlCY2U2yZ4IAws1xxQOSAJD/qama544DIiXK7Z1ObWb44IHKiPMUBYWb54oDIiXKyYF9lGoiZWfYcEDlRLhX5Zf8AW3Z4uw0zywcHRE50JZPl1rqbycxywgGRE93JZLnVm/wkk5nlgwMiJzyb2szyxgGRE4NdTGu2OCDMLB8cEDkxqbWZ9mIzazY5IMwsHxwQOdLt2dRmliMOiBwpl4ruYjKz3HBA5EhXqcBa30GYWU44IHKk0sXUx+7dnk1tZtlzQORIuVRk1+5gw/adWZdiZuaAyJO9W496HMLMsueAyJGuZLKcxyHMLA8cEDmyZ7kN30GYWQ44IHKks91dTGaWHw6IHGlpmsDRba2eLGdmueCAyJlyyTvLmVk+OCByxgFhZnnhgMiZcqngLiYzywUHRM50tRdZv20H/QO7sy7FzMY5B0TOdE8pEgG9W3wXYWbZckDkjGdTm1leOCBypqt9cOtR30GYWbYcEDkzuDf1Wu8LYWYZSzUgJC2QtELSSklXVmkvSLojaX9I0szk+NmSHpH0RPLzGWnWmScdk1tpniBWe+tRM8tYagEhqQm4CTgXmANcJGnOsNPeCWyMiOOBG4Hrk+PrgDdHxCnAJcCX0qozbyZMEF3tftTVzLKX5h3EfGBlRDwTETuB24GFw85ZCNyWvL4LOFOSIuLRiFiVHF8GFCUVUqw1V7pKRXcxmVnm0gyIacBzQ973JMeqnhMRu4BNQMewc34PeDQiRvyXWtJlkpZIWtLb2ztmhWetu1R0F5OZZS7NgFCVY8P30tzvOZJOotLt9K5qXyAibo6IeRExr7Oz8yUXmjeV2dQOCDPLVpoB0QPMGPJ+OrBqtHMkNQNTgA3J++nAvwAXR8TTKdaZO12lIpv7dvHLnQNZl2Jm41iaAfEwMFvSLEmtwIXA4mHnLKYyCA2wCHggIkLSEcC/AR+KiB+kWGMu+VFXM8uD1AIiGVO4HLgPWA7cGRHLJF0j6bzktFuADkkrgfcDg4/CXg4cD/yVpMeSH11p1Zo3e3aW8ziEmWWoOc0Pj4h7gHuGHbt6yOs+4IIq1/018Ndp1pZne5bb8HpMZpYhz6TOoa7BLiYPVJtZhhwQOVQqNjOxpcldTGaWKQdEDkmqPOrqLiYzy5ADIqe6vPWomWXMAZFT5VLRYxBmlikHRE51lwqs3txHxPDJ52Zm9eGAyKlyqUhf/2429+3KuhQzG6ccEDnlR13NLGsOiJwqtw/uTe0nmcwsGw6InOqekiy34TsIM8uIAyKnutorAeFHXc0sKw6InJrY2kSp2OwxCDPLjAMix7qnFN3FZGaZcUDkWLlU9CC1mWXGAZFjXe2eTW1m2XFA5Fi5VGDtlh3s3u3Z1GZWfw6IHOueUmTX7mD9tp1Zl2Jm45ADIsf8qKuZZckBkWODW4+u3eKAMLP6c0Dk2OBsaj/JZGZZcEDk2NFtBSS89aiZZcIBkWMtTRPomFxwF5OZZcIBkXPlUsFdTGaWCQdEznWXiu5iMrNMOCByrqtUdBeTmWXCAZFz5VKBdVt30j+wO+tSzGyccUDkXDnZerR3i8chzKy+HBA5113yznJmlg0HRM51Dc6mdkCYWZ05IHJusIvJj7qaWb05IHLuqEmttDTJXUxmVnepBoSkBZJWSFop6coq7QVJdyTtD0maOaTtQ8nxFZLOSbPOPJswQXS1F72iq5nVXWoBIakJuAk4F5gDXCRpzrDT3glsjIjjgRuB65Nr5wAXAicBC4DPJp83LnWVCqx1F5OZ1Vlzip89H1gZEc8ASLodWAj8ZMg5C4GPJq/vAj4jScnx2yNiB/AzSSuTz3swrWJPP/30Ecfe8pa38O53v5vt27fzxje+cUT7pZdeyqWXXsq6detYtGjRiPY//dM/5a1vfSvPPfccb3/720e0f+ADH+DNb34zK1as4F3veteI9g9/+MOcddZZNG14lq9/4TqOvG7fPD/+3D/miFkn8+LPnmTlt74w4voTFl5O+7TZrP/vJfzs3780ov3ERR9gctex9C77L5793h0j2k++6CqKR3ax+tEH6Hnw7hHtp178MVrbjmDVj77FqiX3jmh/xR9dT1Nrked+8A3WLP2PEe3z3v0pAJ79j9vpXb7vH21TS4FX/PHfAvDMd/6JDU89sk97y6QpnHbpNQCs/LebefHZZfu0F6d0cvLbPgzAim98mi2rVu7TPrlzBide8EEAln/tk2zrfW6f9vapx3PC+e8F4Mmv/DV9m3r3aT/iuJM4/k2XAbD01qvp375pn/ajZr+Kl519MQCPfuEvGOjfN+A7T3wNx/3WhQAs+eyfMVz5tN9ixuvOZ2BnH4/+w1+OaJ86bwFT55/Lzq0v8vg/fWRE+/TXLKT7FWfQt3EtT3712hHtx73hrXSe9Fq2rf0Fy+/6uxHts856Ox2/Mo8tzz/Firs/M6Ldf/fG5u/eWDn9hE6uetPw/38fujQDYhow9F9dD/Dq0c6JiF2SNgEdyfEfDrt22vAvIOky4DKAY489dswKz5s3nXIMD05qGXF8xlET6S63sXrTRFa1jLzBOrZjEkeX25jYO5E1VdqP65jEkeU2Wp4v0lulfVbnJNo62tARRTZUaX9ZVxsT29sYmFLkxSrtL+9qo6VQZEd7gS1V2meX2wDY1l5g27D2ptbmPe2bJrfSN6y9UGza075+cis7h7VPLrbsaV8zqZWBYe2liXvbn5/YQgxrP2JS6572Z4stTNi+b/uRk/e2P11sYkf/vu0dQ9pXtDYzwK592o9uL+xp/0mV35uupL1/RzMrqrSXpxSZXW7jl5N2sbJK+zFHFDm+3MbW5q08U6V96hFFZpbb2DgwiWertE8/ciLTy22s65vEc1Xa/XdvbP7ujZXBh1nGmiLS2e9Y0gXAORHxR8n7twPzI+K9Q85ZlpzTk7x/msqdwjXAgxHx5eT4LcA9EfHPo329efPmxZIlS1L5tZiZHa4kPRIR86q1pTlI3QPMGPJ+OrBqtHMkNQNTgA01XmtmZilKMyAeBmZLmiWplcqg8+Jh5ywGLkleLwIeiMotzWLgwuQpp1nAbOBHKdZqZmbDpDYGkYwpXA7cBzQBX4yIZZKuAZZExGLgFuBLySD0BiohQnLenVQGtHcB74mIgbRqNTOzkVIbg6g3j0GYmR28rMYgzMysgTkgzMysKgeEmZlV5YAwM7OqDptBakm9wLOH8BFHA+vGqJw05L0+yH+Nea8P8l9j3usD13iwjouIzmoNh01AHCpJS0Ybyc+DvNcH+a8x7/VB/mvMe33gGseSu5jMzKwqB4SZmVXlgNjr5qwLOIC81wf5rzHv9UH+a8x7feAax4zHIMzMrCrfQZiZWVUOCDMzq2rcB4SkBZJWSFop6cqs6xlO0gxJ/yFpuaRlkkbuT5kDkpokPSrpm1nXUo2kIyTdJemnye/la7KuaShJVyR/vk9K+qqkdLYIO7iavihpraQnhxw7StJ3JD2V/HxkDmv8RPLn/Likf5F0RJ7qG9L2QUkh6egsaqvFuA4ISU3ATcC5wBzgIkljv7HrodkFfCAiTgR+HXhPDmsE+DNgedZF7MengHsj4leB08hRrZKmAe8D5kXEyVSWx78w26oAuBVYMOzYlcD9ETEbuD95n6VbGVnjd4CTI+JU4L+BD9W7qCFuZWR9SJoBnA38ot4FHYxxHRBUtjddGRHPRMRO4HZgYcY17SMiXoiIHyevt1D5xjZif+4sSZoOvAn4h6xrqUZSCfhNKvuPEBE7I+LFbKsaoRmYmOysOIkc7KAYEf+Pyj4tQy0Ebkte3wacX9eihqlWY0R8OyIGNwH/IZUdKTMxyu8hwI3AXwC5fkpovAfENOC5Ie97yNk336EkzQReATyUbSUj/G8qf9l3Z13IKF4G9AL/mHSD/YOkyVkXNSgingc+SeV/ky8AmyLi29lWNapyRLwAlf+8AF0Z13Mg7wC+lXURQ0k6D3g+IpZmXcuBjPeAUJVjuUx0SW3APwN/HhGbs65nkKTfAdZGxCNZ17IfzcArgc9FxCuAbWTfNbJH0o+/EJgFTAUmS/qDbKtqfJKuotJF+5WsaxkkaRJwFXB11rXUYrwHRA8wY8j76eTg1n44SS1UwuErEfH1rOsZ5nXAeZJ+TqWL7gxJX862pBF6gJ6IGLzzuotKYOTFWcDPIqI3IvqBrwOvzbim0ayRdAxA8vPajOupStIlwO8Ab4t8TfZ6OZX/CCxN/s1MB34sqTvTqkYx3gPiYWC2pFmSWqkMDC7OuKZ9SBKVvvPlEXFD1vUMFxEfiojpETGTyu/fAxGRq//9RsRq4DlJJySHzqSy33le/AL4dUmTkj/vM8nRIPowi4FLkteXAHdnWEtVkhYAfwmcFxHbs65nqIh4IiK6ImJm8m+mB3hl8nc0d8Z1QCQDWZcD91H5B3lnRCzLtqoRXge8ncr/zB9Lfrwx66Ia0HuBr0h6HJgLfDzjevZI7mzuAn4MPEHl32XmSzFI+irwIHCCpB5J7wSuA86W9BSVp3Cuy2GNnwHage8k/17+Pmf1NQwvtWFmZlWN6zsIMzMbnQPCzMyqckCYmVlVDggzM6vKAWFmZlU5IMz2Q9J0SXcnq5c+I+kzkgpj9NmXSpo6Fp9llgYHhNkokklrXwe+kaxeOhuYCPztGHx2E3AplaU1Dua65kP92ma1ckCYje4MoC8i/hEgIgaAK4CLJV0u6TODJ0r6pqTTk9efk7Qk2d/hY0PO+bmkqyX9J3ARMI/K5L3HJE2U9CpJ35P0iKT7hixp8V1JH5f0PSrLqpvVhf83Yja6k4B9FiGMiM3JGjr7+7dzVURsSO4S7pd0akQ8nrT1RcRvAEj6I+CDEbEkWW/r08DCiOiV9FbgWiqrkQIcERFvGLtfmtmBOSDMRieqr+5bbRXgod4i6TIq/76OobIZ1WBA3DHKNScAJ1NZHgIqmwa9MKR9tOvMUuOAMBvdMuD3hh5INh8qA+uBXxnSVEzaZwEfBH4tIjZKunWwLbFtlK8lYFlEjLYV6mjXmaXGYxBmo7sfmCTpYtgzsPx3VBaD+xkwV9KEZPvI+ck1JSrfzDdJKlPZznY0W6gsKj6BFjIAAACUSURBVAewAuhUsle2pBZJJ431L8jsYDggzEaR7CPwu8CiZPXS9cDuiLgW+AGVkHiCym5wg9vCLgUepXL38cXkvNHcCvy9pMeodCktAq6XtBR4jPzuCWHjhFdzNauRpNcCXwX+R8530DMbEw4IMzOryl1MZmZWlQPCzMyqckCYmVlVDggzM6vKAWFmZlU5IMzMrKr/Dy4xVxJrCfusAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -249,13 +247,17 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "code_folding": [ + 0 + ] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Elapsed time is 0.04381399999999758 seconds.\n", + "Elapsed time is 0.1938239999999496 seconds.\n", "Use Schmitt Grohe Uribe Algorithm\n", " A *E[xprime uprime] =B*[x u]\n", " A = (dF/dxprimek dF/duprime), B =-(dF/dx dF/du)\n", @@ -264,18 +266,18 @@ "Block number: 0 done.\n", "Block number: 1 done.\n", "Block number: 2 done.\n", - "Elapsed time is 26.346356 seconds.\n", + "Elapsed time is 66.92355999999995 seconds.\n", "Computing Jacobian F2 - DF/DYprime\n", "Total number of parallel blocks: 3 .\n", "Block number: 0 done.\n", "Block number: 1 done.\n", "Block number: 2 done.\n", - "Elapsed time is 0.9628140000000016 seconds.\n" + "Elapsed time is 2.615786999999955 seconds.\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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KKg2QVySd1LEg6WTg1X6+94PADEnTJY0mf1K8oWhMA7Aweb0AuDeZ0LEBOD+5Sms6MAP4NWZmNmgqPQfyUeB2SR1/5U8E3tefN07OaVwC3EX+U+0rImK9pCuBxohoAK4DvpucJN9OPmRIxt1G/oT7XuBDEdH9QKaZmVVNr+dAJL0OeAv5PYZjyN/W9ncRsaf65Q0snwMxM+u7cudAet0DiYjXJH05It5Kflp3MzOzis+B3C3pvclnMMzMzCo+B/Ix4ABgn6RXyR/Giog4uGqVmZlZTat0OveDql2ImZkNLZVO5y5JF0r6l2R5iqRZ1S3NzMxqWaXnQP4TeCvwP5PlVvITIZqZ2TBV6TmQUyLiJEm/BYiIHcmH/8zMbJiqdA9kTzKBYgBImgAM/MxcZmY2ZFQaIF8FfgAcLmkp8N9A9zmezcxs2Kj0KqybJK0FziB/Ce/8iHi0qpWZmVlN6zFAJNUBfw+8kfzNpL4VEXsHozAzM6ttvR3CugHIkQ+Ps4Crql6RmZkNCb0dwpoZEX8OnTdt8pTpZmYG9L4H0jnjrg9dmZlZod72QN4saWfyWsCYZNlzYZmZDXM9BkhEjBisQszMbGip9HMgZmZmXThAzMwslUwCRNKhklZK2pg8jysxpl7SryStl/SwpPcV9F0v6UlJTcmjfnC/AjMzy2oP5HJgVUTMAFYly8XagIsi4nhgLvAfksYW9H8iIuqTR1P1SzYzs0JZBcg88h9SJHmeXzwgIn4fERuT15uBF4AJg1ahmZn1KKsAOSIitgAkz4f3NDi5edVo4PGC5qXJoa2rJe3Xw7qLJTVKamxpaRmI2s3MjCoGiKR7JD1S4jGvj9uZCHwX+NuI6JhC/lPAscBfAIcCnyy3fkQsj4hcROQmTPAOjJnZQKn0hlJ9FhGzy/VJel7SxIjYkgTEC2XGHQz8BPi/EbG6YNtbkpe7JH0buGwASzczswpkdQirAViYvF4I3Fk8ILnj4Q+A70TE7UV9E5NnkT9/8khVqzUzs26yCpBlwBxJG4E5yTKScpKuTcacB7wdWFTict2bJK0jP0vweODfBrd8MzNTRGRdw6DJ5XLR2NiYdRlmZkOKpLURkStu9yfRzcwsFQeImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZmlkkmASDpU0kpJG5PncWXG7Su4H3pDQft0SWuS9W+VNHrwqjczM8huD+RyYFVEzABWJculvBoR9cnjnIL2LwBXJ+vvAC6ubrlmZlYsqwCZB9yQvL4BmF/pipIEnA7ckWZ9MzMbGFkFyBERsQUgeT68zLg6SY2SVkvqCInDgBcjYm+y3AxMKvdGkhYn22hsaWkZqPrNzIa9kdXasKR7gCNLdC3pw2amRsRmSW8A7pW0DthZYlyU20BELAeWA+RyubLjzMysb6oWIBExu1yfpOclTYyILZImAi+U2cbm5PkJSfcBJwLfA8ZKGpnshUwGNg/4F2BmZj3K6hBWA7Aweb0QuLN4gKRxkvZLXo8HTgU2REQAPwcW9LS+mZlVV1YBsgyYI2kjMCdZRlJO0rXJmOOARkkPkQ+MZRGxIen7JPAxSZvInxO5blCrNzMzlP+DfnjI5XLR2NiYdRlmZkOKpLURkStu9yfRzcwsFQeImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZmlkkmASDpU0kpJG5PncSXG/KWkpoJHu6T5Sd/1kp4s6Ksf/K/CzGx4y2oP5HJgVUTMAFYly11ExM8joj4i6oHTgTbg7oIhn+joj4imQanazMw6ZRUg84Abktc3APN7Gb8A+FlEtFW1KjMzq1hWAXJERGwBSJ4P72X8+cDNRW1LJT0s6WpJ+1WjSDMzK29ktTYs6R7gyBJdS/q4nYnAnwN3FTR/CngOGA0sBz4JXFlm/cXAYoCpU6f25a3NzKwHVQuQiJhdrk/S85ImRsSWJCBe6GFT5wE/iIg9BdvekrzcJenbwGU91LGcfMiQy+WiL1+DmZmVl9UhrAZgYfJ6IXBnD2MvoOjwVRI6SBL58yePVKFGMzPrQVYBsgyYI2kjMCdZRlJO0rUdgyRNA6YA9xetf5OkdcA6YDzwb4NQs5mZFajaIayeRMQ24IwS7Y3ABwqWnwImlRh3ejXrMzOz3vmT6GZmlooDxMzMUnGAmJlZKg4QMzNLxQFiZmapOEDMzCwVB4iZmaXiADEzs1QcIGZmlooDxMzMUnGAmJlZKg4QMzNLxQFiZmapOEDMzCwVB4iZmaXiADEzs1QcIGZmlooDxMzMUnGAmJlZKpkEiKRzJa2X9JqkXA/j5kp6TNImSZcXtE+XtEbSRkm3Sho9OJWbmVmHrPZAHgH+BvhFuQGSRgBfB84CZgIXSJqZdH8BuDoiZgA7gIurW66ZmRXLJEAi4tGIeKyXYbOATRHxRETsBm4B5kkScDpwRzLuBmB+9ao1M7NSRmZdQA8mAc8ULDcDpwCHAS9GxN6C9knlNiJpMbA4WWyV1FtwlTMe2Jpy3cFS6zXWen1Q+zXWen3gGgdCrdV3VKnGqgWIpHuAI0t0LYmIOyvZRIm26KG9pIhYDiyv4P16LkZqjIiy52tqQa3XWOv1Qe3XWOv1gWscCLVeX4eqBUhEzO7nJpqBKQXLk4HN5FN5rKSRyV5IR7uZmQ2iWr6M90FgRnLF1WjgfKAhIgL4ObAgGbcQqGSPxszMBlBWl/H+taRm4K3ATyTdlbS/XtJPAZK9i0uAu4BHgdsiYn2yiU8CH5O0ifw5kesGoex+HwYbBLVeY63XB7VfY63XB65xINR6fQAo/we9mZlZ39TyISwzM6thDhAzM0vFAVKBclOq1AJJUyT9XNKjyfQwH826pnIkjZD0W0k/zrqWYpLGSrpD0u+Sf8u3Zl1TMUmXJv/Hj0i6WVJdDdS0QtILkh4paDtU0spkqqGVksbVWH1fSv6fH5b0A0ljs6qvXI0FfZdJCknjs6itNw6QXvQypUot2At8PCKOA94CfKjG6iv0UfIXRNSirwD/LyKOBd5MjdUpaRLwESAXEW8CRpC/MjFr1wNzi9ouB1YlUw2tSpazcj3d61sJvCkiTgB+D3xqsIsqcj3da0TSFGAO8IfBLqhSDpDelZxSJeOaOkXEloj4TfL6ZfK/+Mp+Mj8rkiYD7wauzbqWYpIOBt5OcjVfROyOiBezraqkkcAYSSOB/amBzz9FxC+A7UXN88hPMQQZTzVUqr6IuLtgJovV5D9Llpky/4YAVwP/RA8flM6aA6R3paZUqblf0ACSpgEnAmuyraSk/yD/w/Ba1oWU8AagBfh2cojtWkkHZF1UoYh4FriK/F+jW4CXIuLubKsq64iI2AL5P3CAwzOupyd/B/ws6yKKSToHeDYiHsq6lp44QHrXp6lTsiLpQOB7wD9GxM6s6ykk6a+AFyJibda1lDESOAn4RkScCLxCtodduknOI8wDpgOvBw6QdGG2VQ1tkpaQPwR8U9a1FJK0P7AE+HTWtfTGAdK7clOq1AxJo8iHx00R8f2s6ynhVOAcSU+RPwR4uqQbsy2pi2agOSI69tzuIB8otWQ28GREtETEHuD7wNsyrqmc5yVNBEieX8i4nm4kLQT+CvhfUXsfhjua/B8KDyU/M5OB30gqNbdgphwgvSs5pUrGNXVKpre/Dng0Iv4963pKiYhPRcTkiJhG/t/v3oiomb+eI+I54BlJxyRNZwAbMiyplD8Ab5G0f/J/fgY1dqK/QAP5KYagBqcakjSX/GwW50REW9b1FIuIdRFxeERMS35mmoGTku/TmuIA6UUvU6rUglOB95P/q74peZyddVFD0IeBmyQ9DNQDn8u4ni6SvaM7gN8A68j/7GY+3YWkm4FfAcdIapZ0MbAMmCNpI/mriJbVWH3XAAcBK5Ofl29mVV8PNQ4JnsrEzMxS8R6ImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOELN+kDRZ0p3JzLNPSLpG0n4DtO1Fkl4/ENsyqwYHiFlKyQf6vg/8MJl5dgYwBvjiAGx7BLCI/LQlfVlvZH/f26xSDhCz9E4H2iPi2wARsQ+4FLhI0iWSrukYKOnHkt6ZvP6GpMbk3h7/WjDmKUmflvTfwAVAjvyHG5skjZF0sqT7Ja2VdFfBdCH3SfqcpPvJT5lvNij814pZescDXSaIjIidyfxFPf1sLYmI7clexipJJ0TEw0lfe0T8DwBJHwAui4jGZL6zrwHzIqJF0vuApeRnkwUYGxHvGLgvzax3DhCz9ETpmZlLzeBc6DxJi8n//E0kf6OyjgC5tcw6xwBvIj/9BuRvKLWloL/cemZV4wAxS2898N7ChuTmVEcA24A/K+iqS/qnA5cBfxEROyRd39GXeKXMewlYHxHlbrVbbj2zqvE5ELP0VgH7S7oIOk98f5n8ZH1PAvWSXpfcmnRWss7B5H/ZvyTpCPK3Si7nZfKT/gE8BkxQcq92SaMkHT/QX5BZXzhAzFJK7iPx18CCZObZbcBrEbEU+CX5EFlH/k6CHbcdfgj4Lfm9lxXJuHKuB74pqYn8IasFwBckPQQ0Ubv3A7FhwrPxmg0QSW8Dbgb+pobvvmg2YBwgZmaWig9hmZlZKg4QMzNLxQFiZmapOEDMzCwVB4iZmaXiADEzs1T+P/tjzEQKKbk0AAAAAElFTkSuQmCC\n", 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OioirC8asTMY0JcvryYfM54GlEfH9pP024BcR0f1PkQK5XC4aGhrSlmxmNihJWh4Rua7t5Z4DqWsPD4Dk+cF9rGkDcFzB8rikreiY5MT94cDWMtc1M7MKKjdAXpF0RvuCpDOBV/v42k8AkyRNlDSc/EnxRV3GLAJmJc9nAg8nEzouAi5LrtKaCEwCfoOZmVVNuedAPg7cJ+lF8nclHAO8vy8vnJzTuBp4gPyn2m+PiFWSrgcaImIRcBvwveQk+TbyIUMy7gfkT7jvAz4SEd0PZJqZWcX0eg5E0huAt5HfYzgpaV4TEXsrXFu/8zkQM7MDV+ocSK97IBHxmqRbIuJ08tO6m5mZlX0OZImkv0k+g2FmZlZ2gPw9+U+f75G0S9LLknZVsC4zM6tx5U7nfmilCzEzs4Gl3OncJekKSf+cLB8naUplSzMzs1pW7iGs/wTeDvz3ZLmF/ESIZmY2SJX7OZCzIuIMSb8DiIjtyYf/zMxskCp3D2RvMoFiAEgaDfT/zFxmZjZglBsgNwE/At4kaR7w/4DuczybmdmgUe5VWHdJWg6cR34qkxkR8XRFKzMzs5rWY4BIqgP+ATiR/M2kvh0R+6pRmJmZ1bbeDmHdCeTIh8d7gBsqXpGZmQ0IvR3CmhwRfw4dN23ylOlmZgb0vgfSMeOuD12ZmVmh3vZA3low55WAEcmygIiIwypanZmZ1aweAyQihlSrEDMzG1jK/RyImZlZJw4QMzNLJZMAkXSkpMWS1iZfRxYZUy/p15JWSXpK0vsL+u6Q9KykxuRRX93vwMzMstoDuQ5YEhGTgCXJcletwJURcSowDfiapCMK+j8VEfXJo7HyJZuZWaGsAmQ6+Q8pknyd0XVARPwhItYmz18ENgOjq1ahmZn1KKsAOToiNibPNwFH9zQ4uXnVcGB9QfO85NDWjZIO6mHdOZIaJDU0Nzf3uXAzM8urWIBIekjSyiKP6YXjIiJIpokvsZ2xwPeAv42I9inkPwOcDPwFcCTw6VLrR8SCiMhFRG70aO/AmJn1l3JvKHXAImJqqT5JL0kaGxEbk4DYXGLcYcDPgLkRsbRg2+17L7slfQe4th9LNzOzMmR1CGsRMCt5Pgu4v+uA5I6HPwK+GxELu/SNTb6K/PmTlRWt1szMuskqQOYD50taC0xNlpGUk3RrMuZS4J3A7CKX694laQX5WYJHAf9W3fLNzEz5UxCDQy6Xi4aGhqzLMDMbUCQtj4hc13Z/Et3MzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSqZBIikIyUtlrQ2+TqyxLj9BfdDX1TQPlHSMknrJN0raXj1qjczM8huD+Q6YElETAKWJMvFvBoR9cnj4oL2LwE3RsSJwHbgqsqWa2ZmXWUVINOBO5PndwIzyl1RkoBzgYVp1jczs/6RVYAcHREbk+ebgKNLjKuT1CBpqaT2kDgK2BER+5LlJuDYUi8kaU6yjYbm5uZ+Kd7MzGBopTYs6SFgTJGuuYULERGSosRmxkfEBklvBh6WtALYeSB1RMQCYAFALpcr9TpmZnaAKhYgETG1VJ+klySNjYiNksYCm0tsY0Py9RlJjwCnA/8FHCFpaLIXMg7Y0O/fgJmZ9SirQ1iLgFnJ81nA/V0HSBop6aDk+SjgbGB1RATwS2BmT+ubmVllZRUg84HzJa0FpibLSMpJujUZcwrQIOlJ8oExPyJWJ32fBj4haR35cyK3VbV6MzND+T/oB4dcLhcNDQ1Zl2FmNqBIWh4Rua7t/iS6mZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUMgkQSUdKWixpbfJ1ZJExfyWpseDRJmlG0neHpGcL+uqr/12YmQ1uWe2BXAcsiYhJwJJkuZOI+GVE1EdEPXAu0Ao8WDDkU+39EdFYlarNzKxDVgEyHbgzeX4nMKOX8TOBX0REa0WrMjOzsmUVIEdHxMbk+Sbg6F7GXwbc3aVtnqSnJN0o6aB+r9DMzHo0tFIblvQQMKZI19zChYgISdHDdsYCfw48UND8GfLBMxxYAHwauL7E+nOAOQDHH3/8AXwHZmbWk4oFSERMLdUn6SVJYyNiYxIQm3vY1KXAjyJib8G22/dedkv6DnBtD3UsIB8y5HK5kkFlZmYHJqtDWIuAWcnzWcD9PYy9nC6Hr5LQQZLInz9ZWYEazcysB1kFyHzgfElrganJMpJykm5tHyRpAnAc8GiX9e+StAJYAYwC/q0KNZuZWYGKHcLqSURsBc4r0t4AfLBg+Tng2CLjzq1kfWZm1jt/Et3MzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzSyWTAJF0iaRVkl6TlOth3DRJayStk3RdQftEScuS9nslDa9O5WZm1i6rPZCVwH8DHis1QNIQ4BbgPcBk4HJJk5PuLwE3RsSJwHbgqsqWa2ZmXWUSIBHxdESs6WXYFGBdRDwTEXuAe4DpkgScCyxMxt0JzKhctWZmVszQrAvowbHACwXLTcBZwFHAjojYV9B+bKmNSJoDzEkWWyT1FlyljAK2pFy3Wmq9xlqvD2q/xlqvD1xjf6i1+sYXa6xYgEh6CBhTpGtuRNxfqdftKiIWAAv6uh1JDRFR8nxNLaj1Gmu9Pqj9Gmu9PnCN/aHW62tXsQCJiKl93MQG4LiC5XFJ21bgCElDk72Q9nYzM6uiWr6M9wlgUnLF1XDgMmBRRATwS2BmMm4WULU9GjMzy8vqMt73SWoC3g78TNIDSfsxkn4OkOxdXA08ADwN/CAiViWb+DTwCUnryJ8Tua0KZff5MFgV1HqNtV4f1H6NtV4fuMb+UOv1AaD8H/RmZmYHppYPYZmZWQ1zgJiZWSoOkDKUmlKlFkg6TtIvJa1Opof5eNY1lSJpiKTfSfpp1rV0JekISQsl/V7S05LennVNXUm6Jvk/Xinpbkl1NVDT7ZI2S1pZ0HakpMWS1iZfR9ZYfV9J/p+fkvQjSUdkVV+pGgv6PikpJI3KorbeOEB60cuUKrVgH/DJiJgMvA34SI3VV+jj5C+IqEVfB/5vRJwMvJUaq1PSscDHgFxEvAUYQv7KxKzdAUzr0nYdsCQiJgFLkuWs3EH3+hYDb4mI04A/AJ+pdlFd3EH3GpF0HHAB8MdqF1QuB0jvik6pknFNHSJiY0T8Nnn+MvlffCU/mZ8VSeOAi4Bbs66lK0mHA+8kuZovIvZExI5sqypqKDBC0lDgYODFjOshIh4DtnVpnk5+iiHIeKqhYvVFxIMFM1ksJf9ZssyU+DcEuBH4J6Bmr3RygPSu2JQqNfcLGkDSBOB0YFm2lRT1NfI/DK9lXUgRE4Fm4DvJIbZbJb0x66IKRcQG4Abyf41uBHZGxIPZVlXS0RGxMXm+CTg6y2J68XfAL7IuoitJ04ENEfFk1rX0xAHyOiHpEOC/gH+MiF1Z11NI0l8DmyNieda1lDAUOAP4ZkScDrxCtodduknOI0wnH3bHAG+UdEW2VfUu+eBvTf4FLWku+UPAd2VdSyFJBwOfBf4l61p64wDpXakpVWqGpGHkw+OuiPhh1vUUcTZwsaTnyB8CPFfS97MtqZMmoCki2vfcFpIPlFoyFXg2IpojYi/wQ+AdGddUykuSxgIkXzdnXE83kmYDfw38j6i9D8OdQP4PhSeTn5lxwG8lFZtbMFMOkN4VnVIl45o6JNPb3wY8HRH/kXU9xUTEZyJiXERMIP/v93BE1MxfzxGxCXhB0klJ03nA6gxLKuaPwNskHZz8n59HjZ3oL7CI/BRDUINTDUmaRv5w6sUR0Zp1PV1FxIqIeFNETEh+ZpqAM5L3aU1xgPSilylVasHZwAfI/1XfmDwuzLqoAeijwF2SngLqgX/PuJ5Okr2jhcBvgRXkf3Yzn+5C0t3Ar4GTJDVJugqYD5wvaS35Paf5NVbfzcChwOLk5+VbWdXXQ40DgqcyMTOzVLwHYmZmqThAzMwsFQeImZml4gAxM7NUHCBmZpaKA8SsDySNk3R/MvPsM5JulnRQP217tqRj+mNbZpXgADFLKflA3w+BHyczz04CRgBf7odtDwFmk5+25EDWG9rX1zYrlwPELL1zgbaI+A5AROwHrgGulHS1pJvbB0r6qaR3Jc+/KakhubfHvxaMeU7SlyT9FrgcyJH/cGOjpBGSzpT0qKTlkh4omC7kEUlfk9RAfsp8s6rwXytm6Z0KdJogMiJ2JfMX9fSzNTcitiV7GUsknRYRTyV9WyPiDABJHwSujYiGZL6zbwDTI6JZ0vuBeeRnkwUYHhG5/vvWzHrnADGrvkslzSH/8zeW/I3K2gPk3hLrnAS8hfz0G5C/odTGgv5S65lVjAPELL3VwMzCBkmHAWOArcCfFXTVJf0TgWuBv4iI7ZLuaO9LvFLitQSsiohSt9ottZ5ZxfgciFl6S4CDJV0JHSe+v0p+sr5ngXpJb0huTTolWecw8r/sd0o6mvytkkt5mfykfwBrgNFK7tUuaZikU/v7GzI7EA4Qs5SS+0i8D5iZzDy7FXgtIuYBvyIfIquBm8jPoktyh7nfAb8H/k8yrpQ7gG9JaiR/yGom8CVJTwKN1O79QGyQ8Gy8Zv1E0juAu4H3td+n3uz1zAFiZmap+BCWmZml4gAxM7NUHCBmZpaKA8TMzFJxgJiZWSoOEDMzS+X/A3bAyIlwmCVeAAAAAElFTkSuQmCC\n", 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\n", 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5LiZJvyXPSz2uutr8NLWZ5VetAXEJlaend0raImmrpC0p1jUhtDZPonNqK2t9BWFmOVTrdN/taRcyUZVLRdY7IMwsh2qd7luSfkfS/062Z0ian25pE0O5VGDdVgeEmeVPrV1MnwXeBPx2sr0NuCmViiaYynxMHoMws/ypNSDeGBHvB/oBImITlVtf7RUql4ps2LaDgcHdWZdiZraPWgNiIJmgLwAkdQH+iTYOyqUiEbBhm68izCxfag2IvwX+BeiWdA3w/4C/SK2qCcQLB5lZXtV6F9OXJS0DTqUy1cY5EbEi1comCD8sZ2Z5td+AkFQE/gA4lspiQZ+LiF31KGyicECYWV4dqIvpNmAelXA4E/jUwby5pIWSVkpaJemKKu0FSXck7Q9Kmjmi/WhJ2yR96GA+biPpnNpK0yQ5IMwsdw7UxTQnIk4EkHQL8MNa3zgZ1L4JOB3oBR6StCQinhx22HuBTRFxrKTzgOvYd5bY64Fv1voxG9GkSaK7veAxCDPLnQNdQeyZsfVldC3NB1ZFxNMRsRO4HVg04phFVK5SAO4CTh2a70nSOcAzwPKD/LgNp9trU5tZDh0oIE5O5l7aImkrcNJBzMV0FLB62HZvsq/qMUkAbQY6JbUBfwZ8fH8fQNLFkpZKWtrX13eAcvKrx2tTm1kO7TcgIqIpIkrJn/aIaB72Os0FDD4G3DB8mdMx6rs5IuZFxLyurq4Uy0mXn6Y2szyqdUW5l2MNMGPY9vRkX7VjeiU1Ax3ARuCNwGJJfwUcBuyW1B8RN6ZYb2bKpSKbfz5A/8AgxZamrMsxMwPSDYiHgNmSZlEJgvPYO5fTkCXAhcAPgMXAAxERwFuHDpD0MWDboRoOsO+trsd0Ts24GjOzilqfpD5oyZjCpcB9wArgzohYLulqSWcnh91CZcxhFfBBYNStsBOBn6Y2szxK8wqCiLgHuGfEvquGve4Hzj3Ae3wsleJyxA/LmVkepXYFYbUrtzsgzCx/HBA5UJrcTLFlkgPCzHLFAZEDknyrq5nljgMiJ8rtfprazPLFAZET5Q4HhJnliwMiJ8rJhH2Vx0DMzLLngMiJcqnIzwcG2brDy22YWT44IHKiO3lYbr27mcwsJxwQOdGTPCy3drPvZDKzfHBA5ISfpjazvHFA5MRQF9O6rQ4IM8sHB0ROTGltpr3YzLrNDggzywcHRI70+GlqM8sRB0SOlEtFdzGZWW44IHKku1Rgva8gzCwnHBA5Uuli6mf3bj9NbWbZc0DkSLlUZNfu4IXtO7MuxczMAZEne5ce9TiEmWXPAZEj3cnDch6HMLM8cEDkyJ7pNnwFYWY54IDIka52dzGZWX44IHKkpWkS09pa/bCcmeWCAyJnyiWvLGdm+eCAyBkHhJnlhQMiZ8qlgruYzCwXHBA5091eZONLOxgY3J11KWY2wTkgcqano0gE9G31VYSZZcsBkTN+mtrM8sIBkTPd7UNLj/oKwsyy5YDImaG1qdd7XQgzy1iqASFpoaSVklZJuqJKe0HSHUn7g5JmJvtPl7RM0uPJ36ekWWeedE5tpXmSWOulR80sY6kFhKQm4CbgTGAOcL6kOSMOey+wKSKOBW4Arkv2bwDeHhEnAhcCX0yrzryZNEl0t/tWVzPLXppXEPOBVRHxdETsBG4HFo04ZhFwW/L6LuBUSYqIH0XEc8n+5cBkSYUUa82V7lLRXUxmlrk0A+IoYPWw7d5kX9VjImIXsBnoHHHMbwEPR8SoX6klXSxpqaSlfX1941Z41npKRXcxmVnmcj1ILel4Kt1Ol1Rrj4ibI2JeRMzr6uqqb3EpqjxN7YAws2ylGRBrgBnDtqcn+6oeI6kZ6AA2JtvTgX8BLoiIp1KsM3e6S0W29O/i5zsHsy7FzCawNAPiIWC2pFmSWoHzgCUjjllCZRAaYDHwQESEpMOAfwOuiIjvp1hjLvlWVzPLg9QCIhlTuBS4D1gB3BkRyyVdLens5LBbgE5Jq4APAkO3wl4KHAtcJemR5E93WrXmzZ6V5TwOYWYZak7zzSPiHuCeEfuuGva6Hzi3ynmfAD6RZm15tme6Dc/HZGYZyvUg9UTVPdTF5IFqM8uQAyKHSsVmJrc0uYvJzDLlgMghSZVbXd3FZGYZckDkVLeXHjWzjDkgcqpcKnoMwswy5YDIqZ5SgbVb+omIrEsxswnKAZFT5VKR/oHdbOnflXUpZjZBOSByyre6mlnWHBA5VW4fWpvadzKZWTYcEDnV05FMt+ErCDPLiAMip7rbKwHhW13NLCsOiJya3NpEqdjsMQgzy4wDIsd6OoruYjKzzDggcqxcKnqQ2swy44DIse52P01tZtlxQORYuVRg/dYd7N7tp6nNrP4cEDnW01Fk1+5g40s7sy7FzCYgB0SO+VZXM8uSAyLHhpYeXb/VAWFm9eeAyLGhp6l9J5OZZcEBkWPT2gpIeOlRM8uEAyLHWpom0Tm14C4mM8uEAyLnyqWCu5jMLBMOiJzrKRXdxWRmmXBA5Fx3qeguJjPLhAMi58qlAhu27WRgcHfWpZjZBOOAyLlysvRo31aPQ5hZfTkgcq6n5JXlzCwbDoic6x56mtoBYWZ15oDIuaEuJt/qamb15oDIuSOmtNLSJHcxmVndpRoQkhZKWilplaQrqrQXJN2RtD8oaeawtg8n+1dKOiPNOvNs0iTR3V70jK5mVnepBYSkJuAm4ExgDnC+pDkjDnsvsCkijgVuAK5Lzp0DnAccDywEPpu834TUXSqw3l1MZlZnzSm+93xgVUQ8DSDpdmAR8OSwYxYBH0te3wXcKEnJ/tsjYgfwjKRVyfv9IK1iFyxYMGrfO97xDt73vvexfft23va2t41qv+iii7jooovYsGEDixcvHtX+h3/4h7zzne9k9erVvPvd7x7Vfvnll/P2t7+dlStXcskll4xq/8hHPsJpp51G0wvP8rXPX8vh1+6b58ee+fscNusEXnzmCVZ98/Ojzj9u0aW0HzWbjf+zlGf+/Yuj2l+7+HKmdh9N3/L/4tnv3jGq/YTzr6R4eDdrf/QAvT+4e1T7SRd8nNa2w3juh9/kuaX3jmp/3e9dR1NrkdXf/zrrHv2PUe3z3vdpAJ79j9vpW7Hvf21TS4HX/f5fAfD0t/+JF36ybJ/2likdnHzR1QCs+rebefHZ5fu0Fzu6OOFdHwFg5dc/w9bnVu3TPrVrBq8990MArPjqp3ipb/U+7e1HHstx5/wRAE98+RP0b+7bp/2wY47n2LMuBuDRW69iYPvmfdqPmP0GXn36BQD86PN/yuDAvgHf9do3ccyvnQfA0s/+MSOVT/41ZrzlHAZ39vOjf/izUe1HzlvIkfPPZOe2F3nsnz46qn36mxbR87pT6N+0nie+cs2o9mN+9Z10Hf9mXlr/M1bc9dej2med9m46f2EeW9f8hJV33ziq3V974/O1N14WHNfFlWeN/P37lUszII4Chn/X9QJvHOuYiNglaTPQmez/7xHnHjXyA0i6GLgY4Oijjx63wvPmrBNfxQ+mtIzaP+OIyfSU21i7eTLPtYy+wDq6cwrTym1M7pvMuirtx3RO4fByGy1rivRVaZ/VNYW2zjZ0WJEXqrS/uruNye1tDHYUebFK+2u622gpFNnRXmBrlfbZ5TYAXmov8NKI9qbW5j3tm6e20j+ivVBs2tO+cWorO0e0Ty227GlfN6WVwRHtpcl729dMbiFGtB82pXVP+7PFFiZt37f98Kl7258qNrFjYN/2zmHtK1ubGWTXPu3T2gt72p+s8m/TnbQP7GhmZZX2ckeR2eU2fj5lF6uqtL/qsCLHltvY1ryNp6u0H3lYkZnlNjYNTuHZKu3TD5/M9HIbG/qnsLpKu7/2xudrb7wM3cwy3hSRznrHkhYDCyPi95LtdwNvjIhLhx3zRHJMb7L9FJUQ+Rjw3xHxpWT/LcA3I+KusT7evHnzYunSpal8LmZmhypJyyJiXrW2NAep1wAzhm1PT/ZVPUZSM9ABbKzxXDMzS1GaAfEQMFvSLEmtVAadl4w4ZglwYfJ6MfBAVC5plgDnJXc5zQJmAz9MsVYzMxshtTGIZEzhUuA+oAn4QkQsl3Q1sDQilgC3AF9MBqFfoBIiJMfdSWVAexfw/ogYTKtWMzMbLbUxiHrzGISZ2cHLagzCzMwamAPCzMyqckCYmVlVDggzM6vqkBmkltQHPPsK3mIasGGcyklD3uuD/NeY9/og/zXmvT5wjQfrmIjoqtZwyATEKyVp6Vgj+XmQ9/og/zXmvT7If415rw9c43hyF5OZmVXlgDAzs6ocEHvdnHUBB5D3+iD/Nea9Psh/jXmvD1zjuPEYhJmZVeUrCDMzq8oBYWZmVU34gJC0UNJKSaskXZF1PSNJmiHpPyQ9KWm5pNHrU+aApCZJP5L0jaxrqUbSYZLukvRjSSskvSnrmoaTdFny//uEpK9ISmeJsIOr6QuS1icLew3tO0LStyX9JPn78BzW+Mnk//kxSf8i6bA81Tes7XJJIWlaFrXVYkIHhKQm4CbgTGAOcL6k8V/Y9ZXZBVweEXOAXwben8MaAf4YWJF1EfvxaeDeiPhF4GRyVKuko4APAPMi4gQq0+Ofl21VANwKLByx7wrg/oiYDdyfbGfpVkbX+G3ghIg4Cfgf4MP1LmqYWxldH5JmAL8O/KzeBR2MCR0QwHxgVUQ8HRE7gduBRRnXtI+IeD4iHk5eb6Xyg23U+txZkjQdOAv4h6xrqUZSB/ArVNYfISJ2RsSL2VY1SjMwOVlZcQrwXMb1EBH/SWWdluEWAbclr28DzqlrUSNUqzEivhURQ4uA/zeVFSkzMca/IcANwJ8Cub5LaKIHxFHA6mHbveTsh+9wkmYCrwMezLaSUf6Gyhf77qwLGcMsoA/4x6Qb7B8kTc26qCERsQb4FJXfJp8HNkfEt7KtakzliHg+eb0WKGdZTA3eA3wz6yKGk7QIWBMRj2Zdy4FM9IBoGJLagP8L/ElEbMm6niGSfgNYHxHLsq5lP5qB1wN/FxGvA14i+66RPZJ+/EVUguxIYKqk38m2qgNLlgfO7W/Akq6k0kX75axrGSJpCvDnwFVZ11KLiR4Qa4AZw7anJ/tyRVILlXD4ckR8Let6RngLcLakn1LpojtF0peyLWmUXqA3IoauvO6iEhh5cRrwTET0RcQA8DXgzRnXNJZ1kl4FkPy9PuN6qpJ0EfAbwLsiXw97vYbKLwKPJt8z04GHJfVkWtUYJnpAPATMljRLUiuVgcElGde0D0mi0ne+IiKuz7qekSLiwxExPSJmUvn3eyAicvXbb0SsBVZLOi7ZdSqV9c7z4mfAL0uakvx/n0qOBtFHWAJcmLy+ELg7w1qqkrSQSpfn2RGxPet6houIxyOiOyJmJt8zvcDrk6/R3JnQAZEMZF0K3EflG/LOiFiebVWjvAV4N5XfzB9J/rwt66Ia0B8BX5b0GDAX+IuM69kjubK5C3gYeJzK92XmUzFI+grwA+A4Sb2S3gtcC5wu6SdUrnyuzWGNNwLtwLeT75e/z1l9DcNTbZiZWVUT+grCzMzG5oAwM7OqHBBmZlaVA8LMzKpyQJiZWVUOCLP9kDRd0t3J7KVPS7pRUmGc3vsiSUeOx3uZpcEBYTaG5KG1rwFfT2YvnQ1MBv5qHN67CbiIytQaB3Ne8yv92Ga1ckCYje0UoD8i/hEgIgaBy4ALJF0q6cahAyV9Q9KC5PXfSVqarO/w8WHH/FTSdZIeBs4H5lF5eO8RSZMlvUHSdyUtk3TfsCktviPpbyQtpTKtulld+LcRs7EdD+wzCWFEbEnm0Nnf986VEfFCcpVwv6STIuKxpG1jRLweQNLvAR+KiKXJfFufARZFRJ+kdwLXUJmNFKA1IuaN36dmdmAOCLPx9w5JF1P5/noVlcWohgLijjHOOQ44gcr0EFBZNOj5Ye1jnWeWGgeE2dieBBYP3yGpBPQAG4FfGNZUTNpnAR8CfikiNkm6dagt8dIYH0vA8ogYaynUsc4zS43HIMzGdj8wRdIFsGdg+a+pTAb3DDBX0qRk+cj5yTklKj/MN0sqU1nOdixbqUwqB7AS6FKyVrakFknHj/cnZHYwHBBmY0jWEfhNYHEye+lGYHdEXAN8n0pIPAn8LZWZWAENvewAAABjSURBVElWCfsR8GPgn5PjxnIr8PeSHqHSpbQYuE7So8Aj5HdNCJsgPJurWY0kvRn4CvCbQ+uEmx3KHBBmZlaVu5jMzKwqB4SZmVXlgDAzs6ocEGZmVpUDwszMqnJAmJlZVf8fGSdLihrG++cAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -402,13 +404,17 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "code_folding": [ + 0 + ] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Elapsed time is 0.04532199999999875 seconds.\n", + "Elapsed time is 0.13286500000003798 seconds.\n", "Use Schmitt Grohe Uribe Algorithm\n", " A *E[xprime uprime] =B*[x u]\n", " A = (dF/dxprimek dF/duprime), B =-(dF/dx dF/du)\n", @@ -417,18 +423,18 @@ "Block number: 0 done.\n", "Block number: 1 done.\n", "Block number: 2 done.\n", - "Elapsed time is 24.163849 seconds.\n", + "Elapsed time is 66.06500900000003 seconds.\n", "Computing Jacobian F2 - DF/DYprime\n", "Total number of parallel blocks: 3 .\n", "Block number: 0 done.\n", "Block number: 1 done.\n", "Block number: 2 done.\n", - "Elapsed time is 0.8783719999999988 seconds.\n" + "Elapsed time is 2.7910440000000563 seconds.\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -603,7 +609,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/examples/BayerLuetticke/notebooks/OneAsset-HANK.py b/examples/BayerLuetticke/notebooks/OneAsset-HANK.py index 9b0ec72d9..e8bd3cb45 100644 --- a/examples/BayerLuetticke/notebooks/OneAsset-HANK.py +++ b/examples/BayerLuetticke/notebooks/OneAsset-HANK.py @@ -6,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.2' -# jupytext_version: 1.1.3 +# jupytext_version: 1.2.4 # kernelspec: # display_name: Python 3 # language: python @@ -19,7 +19,7 @@ # # This notebook solves a New Keynesian model in which there is only a single liquid asset. This is the second model described in . For a detailed description of their solution method, see the companion two-asset HANK model notebook. -# %% {"code_folding": [0]} +# %% {"code_folding": []} # Setup from __future__ import print_function @@ -59,7 +59,7 @@ def in_ipynb(): sys.path.insert(0, code_dir) sys.path.insert(0, my_file_path) -# %% {"code_folding": [0]} +# %% {"code_folding": []} # Ignore system warnings while running the notebook import warnings warnings.filterwarnings('ignore') @@ -73,7 +73,7 @@ def in_ipynb(): EX2SS=pickle.load(open("EX2SS.p", "rb")) # %% -from HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond import FluctuationsOneAssetIOUs, SGU_solver, plot_IRF +from FluctuationsOneAssetIOUsBond import FluctuationsOneAssetIOUs, SGU_solver, plot_IRF # %% {"code_folding": []} # Uncertainty Shock diff --git a/examples/BayerLuetticke/notebooks/TwoAsset.ipynb b/examples/BayerLuetticke/notebooks/TwoAsset.ipynb index 1d50ba6cb..53d3b4360 100644 --- a/examples/BayerLuetticke/notebooks/TwoAsset.ipynb +++ b/examples/BayerLuetticke/notebooks/TwoAsset.ipynb @@ -676,7 +676,7 @@ ], "source": [ "import time\n", - "from HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset import FluctuationsTwoAsset, SGU_solver, plot_IRF\n", + "from FluctuationsTwoAsset import FluctuationsTwoAsset, SGU_solver, plot_IRF\n", "\n", "start_time = time.perf_counter() \n", "\n", @@ -874,7 +874,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/examples/BayerLuetticke/notebooks/TwoAsset.py b/examples/BayerLuetticke/notebooks/TwoAsset.py index 0589e20ec..2cd0f6b03 100644 --- a/examples/BayerLuetticke/notebooks/TwoAsset.py +++ b/examples/BayerLuetticke/notebooks/TwoAsset.py @@ -1,18 +1,19 @@ # --- # jupyter: # jupytext: -# formats: ipynb,py:light +# formats: ipynb,py:percent # text_representation: # extension: .py -# format_name: light -# format_version: '1.4' -# jupytext_version: 1.1.3 +# format_name: percent +# format_version: '1.2' +# jupytext_version: 1.2.4 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- +# %% [markdown] # # Two Asset HANK Model [](https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13071) # # [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/HARK/BayerLuetticke/notebooks?filepath=HARK%2FBayerLuetticke%2FTwoAsset.ipynb) @@ -21,28 +22,30 @@ # - Jupyter notebook originally by Seungcheol Lee # - Further edits by Chris Carroll, Tao Wang, Edmund Crawley +# %% [markdown] # ### Overview # # BL propose a method for solving Heterogeneous Agent DSGE models that uses fast tools originally employed for image and video compression to speed up a variant of the solution methods proposed by Michael Reiter. # # The Bayer-Luetticke method has the following broad features: -# * The model is formulated and solved in discrete time (in contrast with some other recent approaches ) like that in "When Inequality Matters for Macro and Macro Matters for Inequality" -- henceforth, the '5guys' paper +# * The model is formulated and solved in discrete time (in contrast with some other recent approaches ) # * Solution begins by calculation of the steady-state equilibrium (StE) with no aggregate shocks # * Both the representation of the consumer's problem and the desciption of the distribution are subjected to a form of "dimensionality reduction" # * This means finding a way to represent them efficiently using fewer points -# * In contrast with 5guys, "Dimensionality reduction" of the consumer's decision problem is performed before any further analysis is done +# * "Dimensionality reduction" of the consumer's decision problem is performed before any further analysis is done # * This involves finding a representation of the policy functions using some class of basis functions # * Dimensionality reduction of the joint distribution is accomplished using a "copula" # * See the companion notebook for description of the copula -# * The method approximates the business-cycle-induced _deviations_ of the individual policy functions and distributions from those that characterize the riskless StE +# * The method approximates the business-cycle-induced _deviations_ of the individual policy functions from those that characterize the riskless StE # * This is done using the same basis functions originally optimized to match the StE individual policy function # * The method of capturing dynamic deviations from a reference frame is akin to video compression +# %% [markdown] # ### Setup # # #### The Recursive Dynamic Planning Problem # -# BL describe their problem in a generic way; here, we will illustrate the meaning of their derivations and notation using the familiar example of the Krusell-Smith model, henceforth KS. +# BL describe their problem a generic way; here, we will illustrate the meaning of their derivations and notation using the familiar example of the Krusell-Smith model, henceforth KS. # # Consider a household problem in presence of aggregate and idiosyncratic risk # * $S_t$ is an (exogenous) aggregate state (e.g., levels of productivity and unemployment) @@ -66,6 +69,7 @@ # \end{equation} # +# %% [markdown] # #### Solving for the StE # # The steady-state equilibrium is the one that will come about if there are no aggregate risks (and consumers know this) @@ -75,21 +79,22 @@ # * Representing the nodes of the discretization in a set of vectors # * Such vectors will be represented by an overbar # * e.g. $\bar{m}$ is the nodes of cash-on-hand $m$ -# * The optimal policy $\newcommand{\policy}{c}\newcommand{\Policy}{C}\policy(s_{i};P)$ induces flow utility $u_{\policy}$ whose discretization is a vector $\bar{u}_{\bar{\policy}}$ +# * The optimal policy $\newcommand{\policy}{c}\newcommand{\Policy}{C}\policy(s_{it};P)$ induces flow utility $u_{\policy}$ whose discretization is a vector $\bar{u}_{\bar{\policy}}$ # * Idiosyncratic dynamics are captured by a transition probability matrix $\Pi_{\bar{\policy}}$ # * $\Pi$ is like an expectations operator -# * It depends on the vectorization of the policy function $\bar{u}_{\bar{\policy}}$ +# * It depends on the vectorization of the policy function $\bar{\policy}$ # * $P$ is constant because in StE aggregate prices are constant # * e.g., in the KS problem, $P$ would contain the (constant) wage and interest rates # * In StE, the discretized Bellman equation implies # \begin{equation} -# \bar{v} = \bar{u}_{\bar{\policy}} + \beta \Pi_{\bar{\policy}}\bar{v} +# \bar{v} = \bar{u} + \beta \Pi_{\bar{\policy}}\bar{v} # \end{equation} # holds for the optimal policy # * A linear interpolator is used to represent the value function # * For the distribution, which (by the definition of steady state) is constant: # # \begin{eqnarray} +# \bar{\mu} & = & \bar{\mu} \Pi_{\bar{\policy}} \\ # d\bar{\mu} & = & d\bar{\mu} \Pi_{\bar{\policy}} # \end{eqnarray} # where we differentiate in the second line because we will be representing the distribution as a histogram, which counts the _extra_ population obtained by moving up @@ -107,10 +112,11 @@ # 1. Given $P$, # 1. Finding $d\bar{\mu}$ as the unit-eigenvalue of $\Pi_{\bar{\policy}}$ # 2. Using standard solution techniques to solve the micro decision problem -# * For the constant $P$ (e.g., wage and interest rate) +# * Like wage and interest rate # 2. Using a root-finder to solve for $P$ # * This basically iterates the other two steps until it finds values where they are consistent +# %% [markdown] # #### Introducing aggregate risk # # With aggregate risk @@ -120,6 +126,7 @@ # * Only prices and continuation values matter # * The distribution does not influence decisions directly +# %% [markdown] # #### Redefining equilibrium (Reiter, 2002) # A sequential equilibrium with recursive individual planning is: # * A sequence of discretized Bellman equations, such that @@ -134,8 +141,7 @@ # holds given the policy $h_{t}$, that is optimal given $P_t$, $v_{t+1}$ # * Prices, distribution, and policies lead to market clearing -# + {"code_folding": []} -# Do some generic setup stuff +# %% {"code_folding": [0, 6, 17]} from __future__ import print_function # This is a jupytext paired notebook that autogenerates a corresponding .py file @@ -174,9 +180,7 @@ def in_ipynb(): sys.path.insert(0, code_dir) sys.path.insert(0, my_file_path) -import time - -# + {"code_folding": [0]} +# %% {"code_folding": [0]} ## Load Stationary equilibrium (StE) object EX3SS_20 import pickle @@ -187,9 +191,7 @@ def in_ipynb(): ## WangTao: Find the code that generates this - -# - - +# %% [markdown] # #### Compact notation # # It will be convenient to rewrite the problem using a compact notation proposed by Schmidt-Grohe and Uribe (2004) @@ -212,7 +214,6 @@ def in_ipynb(): # \begin{equation} # \policy_t(s_{t}) = \arg \max\limits_{x \in \Gamma(s,P_t)} u(s,x) + \beta \mathop{\mathbb{E}_{t}} v_{t+1}(s_{t+1}) # \end{equation} -# where $C$ is the transition process for $S$ # * The solution is a function-valued difference equation: # \begin{equation} # \mathop{\mathbb{E}_{t}}F(X_t,X_{t+1},Y_t,Y_{t+1},\epsilon_{t+1}) = 0 @@ -221,12 +222,14 @@ def in_ipynb(): # * It becomes real-valued when we replace the functions by their discretized counterparts # * Standard techniques can solve the discretized version +# %% [markdown] # #### So, is all solved? # The dimensionality of the system F is a big problem # * With high dimensional idiosyncratic states, discretized value functions and distributions become large objects # * For example: -# * 4 income states $\times$ 100 illiquid capital states $\times$ 100 liquid asset states $\rightarrow$ $\geq$ 40,000 values in $F$ +# * 4 income states $\times$ 100 illiquid capital states $\times$ 100 liquid capital states $\rightarrow$ $\geq$ 40,000 values in $F$ +# %% [markdown] # ### Bayer-Luetticke method # #### Idea: # 1. Use compression techniques as in video encoding @@ -246,6 +249,7 @@ def in_ipynb(): # # The approach follows the insight of KS in that it uses the fact that some moments of the distribution do not matter for aggregate dynamics +# %% [markdown] # #### Details # 1) Compression techniques from video encoding # * Let $\bar{\Theta} = dct(\bar{v})$ be the coefficients obtained from the DCT of the value function in StE @@ -259,8 +263,9 @@ def in_ipynb(): # \bar{\Theta}(i), & \text{else} # \end{array}\right. # \end{equation} -# * This assumes that the basis functions with least contribution to representation of the StE policy function in levels, make no contribution at all to its changes over time +# * This assumes that the basis functions with least contribution to representation of the function in levels, make no contribution at all to its changes over time +# %% [markdown] # 2) Decoding # * Now we reconstruct $\tilde{v}(\theta_t)=dct^{-1}(\tilde{\Theta}(\theta_{t}))$ # * idct=$dct^{-1}$ is the inverse dct that goes from the $\theta$ vector to the corresponding values @@ -280,18 +285,17 @@ def in_ipynb(): # # 4) The large system above is now transformed into a much smaller system: # \begin{align} -# F(\{d\mu_t^1,...,d\mu_t^n\}, S_t, \{d\mu_{t+1}^1,...,d\mu_{t+1}^n\}, S_{t+1}, \theta_t, P_t, \theta_{t+1}, P_{t+1}) &= \phantom{~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} \notag -# \end{align} -# \begin{align} -# \phantom{F~~~~~~~~~} & \begin{bmatrix} +# F(\{d\mu_t^1,...,d\mu_t^n\}, S_t, \{d\mu_{t+1}^1,...,d\mu_{t+1}^n\}, S_{t+1}, \theta_t, P_t, \theta_{t+1}, P_{t+1}) +# &= \begin{bmatrix} # d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n) - d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n)\Pi_{\policy_t} \\ # dct\left[idct\left(\tilde{\Theta}(\theta_t) - (\bar{u}_{\policy_t} + \beta \Pi_{\policy_t}idct(\tilde{\Theta}(\theta_{t+1}))\right)\right] \\ # S_{t+1} - \Policy(S_t,d\mu_t) \\ # \Phi(\policy_t,d\mu_t,P_t,S_t) \\ # \end{bmatrix} # \end{align} -# 5) NOW we use standard techniques to solve this (much reduced system) +# +# %% [markdown] # ### The two-asset HANK model # # We illustrate the algorithm in a two-asset HANK model described as below @@ -301,7 +305,7 @@ def in_ipynb(): # - Maximizing discounted felicity # - Consumption $c$ # - CRRA coefficent: $\xi$ -# - Elasticity of substitution in CES consumption bundle: $\eta$ +# - EOS of CES consumption bundle: $\eta$ # - Disutility from work in GHH form: # - Frisch elasticity $\gamma$ # - Two assets: @@ -327,13 +331,11 @@ def in_ipynb(): # - Investment subject to Tobin's q adjustment cost $\phi$ # - Aggregate risks $\Omega$ include # - TFP $Z$, AR(1) process with persistence of $\rho^Z$ and shock $\epsilon^Z$ -# - Uncertainty -# * The $\sigma^{2}$ of the idiosyncratic policy shocks follows an AR(1) -# * A "shock" is a temporary (mean-reverting) increase in the magnitude of the idiosyncratic shocks +# - Uncertainty # - Monetary policy # - Central bank # - Taylor rule on nominal saving rate $R^B$: reacts to deviation of inflation from target by $\theta_R$ -# - $\rho_R$: policy inertia +# - $\rho_R$: policy innertia # - $\epsilon^R$: monetary policy shocks # - Government (fiscal rule) # - Government spending $G$ @@ -347,8 +349,9 @@ def in_ipynb(): # - Optimal policy for adjusters and nonadjusters are $c^*_a$, $n^*_a$ $k^*_a$ and $\liquid^*_a$ and $c^*_n$, $n^*_n$ and $\liquid^*_n$, respectively # -# + -from HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset import FluctuationsTwoAsset, SGU_solver, plot_IRF +# %% +import time +from FluctuationsTwoAsset import FluctuationsTwoAsset, SGU_solver, plot_IRF start_time = time.perf_counter() @@ -370,7 +373,7 @@ def in_ipynb(): ## Choose an accuracy of approximation with DCT ### Determines number of basis functions chosen -- enough to match this accuracy ### EX3SS is precomputed steady-state pulled in above -EX3SS['par']['accuracy'] = 0.9999999 +EX3SS['par']['accuracy'] = 0.99999 ## Implement state reduction and DCT ### Do state reduction on steady state @@ -386,6 +389,5 @@ def in_ipynb(): end_time = time.perf_counter() print('Elapsed time is ', (end_time-start_time), ' seconds.') -# - -#
+# %% From 9312acf0bba0149a35affe18c34dd48c82fc16c6 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 10 Dec 2019 10:15:59 -0500 Subject: [PATCH 144/242] fixing relative path holdover in BayerLuetticke/Assets/One/FluctuationsOneAssetIOUsBond.py --- .../BayerLuetticke/Assets/One/FluctuationsOneAssetIOUsBond.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUsBond.py b/examples/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUsBond.py index 4d6089358..7a98c93e9 100644 --- a/examples/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUsBond.py +++ b/examples/BayerLuetticke/Assets/One/FluctuationsOneAssetIOUsBond.py @@ -19,7 +19,7 @@ from scipy import linalg from math import log, cos, pi import time -from .SharedFunc2 import Transition, ExTransitions, GenWeight, MakeGrid2, Tauchen +from SharedFunc2 import Transition, ExTransitions, GenWeight, MakeGrid2, Tauchen import matplotlib.pyplot as plt import matplotlib.patches as mpatches import scipy.io From 8008c31ae130d9737da16b15e64037b03eeb11bb Mon Sep 17 00:00:00 2001 From: llorracc <@users.noreply.github.com> Date: Tue, 10 Dec 2019 10:16:38 -0500 Subject: [PATCH 145/242] Tiny NARK edits --- Documentation/NARK/NARK.tex | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/Documentation/NARK/NARK.tex b/Documentation/NARK/NARK.tex index 045356f3b..bba6f9e8e 100644 --- a/Documentation/NARK/NARK.tex +++ b/Documentation/NARK/NARK.tex @@ -126,7 +126,7 @@ \subsection{Strings} \\ \texttt{Dgd} & - & Stock of durable good \\ \texttt{Dvd} & - & Dividends \\ \texttt{Hse} & - & Quantity of housing (\textit{not} value, which is quantity $\times$ price) - \\ \texttt{Ilq} & - & Quantity of illiquid asset (\textit{not} value, which is quantity $\times$ price) + \\ \texttt{Nlq} & - & Quantity that is \textbf{N}ot \textbf{l}i\textbf{q}uid (e.g., $\mathtt{aNlq}$ is illiquid financial) \\ \texttt{Lbr} & - & Quantity of labor \\ \texttt{Pop} & - & Size of population \\ \texttt{Tax} & - & Tax -- should be modified by \texttt{Rte} or \texttt{Amt} articulated below @@ -220,14 +220,18 @@ \section{Parameters} \\ \verb|\tranShkInd| & $\tranShkInd$ & Transitory shock (individual) & $\Ex_{t}[\tranShkInd_{t+n}]=1$ if $\tranShkInd$ iid \\ \verb|\PermShkAgg| & $\PermShkAgg$ & Permanent shock (aggregate) & $\Ex_{t}[\PermShkAgg_{t+n}]=1$ if $\PermShkAgg$ iid \\ \verb|\permShkInd| & $\permShkInd$ & Permanent shock (individual) & $\Ex_{t}[\permShkInd_{t+n}]=1$ if $\permShkInd$ iid - \\ \verb|\PopGro| & $\PopGro$ & Population Growth Factor & $\texttt{Pop}_{t+1} = \PopGro \texttt{Pop}_{t}$ + \\ \verb|\PopGro| & $\PopGro$ & Population Growth Factor & $\mathtt{Pop}_{t+1} = \PopGro \mathtt{Pop}_{t}$ \\ \verb|\popGro| & $\popGro$ & Population Growth rate & $\PopGro = 1 + \popGro$ \\ \verb|\PtyGro| & $\PtyGro$ & Productivity Growth Factor & $\GLev = \PtyGro \PopGro$ \\ \verb|\ptyGro| & $\ptyGro$ & Productivity Growth rate & $\PtyGro = (1+\ptyGro)$ \\ \verb|\leiShare| & $\leiShare$ & Leisure share, Cobb-Douglas utility & $\uFunc(c,z)=(1-\CRRA)^{-1}(c^{1-\leiShare}z^{\leiShare})^{1-\CRRA}$ \\ \verb|\MPC| & $\MPC$ & Marginal Propensity to Consume & $\cFunc^{\prime}(\mRat)=\partial c/\partial m$ \\ \verb|\Pat| & $\Pat$ & Absolute Patience Factor (\texttt{Thorn}) & $\Pat = (\Rfree \DiscFac)^{1/\CRRA} $ + \\ \verb|\PatPGro| & $\PatPGro$ & Growth Patience Factor (\texttt{Thorn}) & $\Pat = (\Rfree \DiscFac)^{1/\CRRA}/\PtyGro $ + \\ \verb|\PatR| & $\PatR$ & Return Patience Factor (\texttt{Thorn}) & $\Pat = (\Rfree \DiscFac)^{1/\CRRA}/\Rfree $ \\ \verb|\pat| & $\pat$ & Absolute Patience rate (\texttt{thorn}) & $\pat = (\Rfree \DiscFac)^{1/\CRRA}-1 \approx \CRRA^{-1}(\rfree-\discRte) $ + \\ \verb|\patpGro| & $\patpGro$ & Growth Patience rate (\texttt{thorn}) & $\patpGro = \pat -\ptyGro $ + \\ \verb|\patr| & $\patr$ & Return Patience rate (\texttt{thorn}) & $\patr =\pat -\rfree $ \\ \verb|\riskyshare| & $\riskyshare$ & Portfolio share in risky assets & $\Rport_{t+1}=(1-\riskyshare)\Rfree+\riskyshare\Risky_{t+1}$ \\ \hline \end{tabular} From e9275aa5d71f96110a2104852ae39244c47b40a4 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 10 Dec 2019 10:26:23 -0500 Subject: [PATCH 146/242] Putting SolvingMicroDSOPs parametes in Calibration/ dir --- HARK/SolvingMicroDSOPs/{ => Calibration}/EstimationParameters.py | 0 HARK/SolvingMicroDSOPs/Calibration/__init__.py | 0 HARK/SolvingMicroDSOPs/__init__.py | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename HARK/SolvingMicroDSOPs/{ => Calibration}/EstimationParameters.py (100%) create mode 100644 HARK/SolvingMicroDSOPs/Calibration/__init__.py create mode 100644 HARK/SolvingMicroDSOPs/__init__.py diff --git a/HARK/SolvingMicroDSOPs/EstimationParameters.py b/HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py similarity index 100% rename from HARK/SolvingMicroDSOPs/EstimationParameters.py rename to HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py diff --git a/HARK/SolvingMicroDSOPs/Calibration/__init__.py b/HARK/SolvingMicroDSOPs/Calibration/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/HARK/SolvingMicroDSOPs/__init__.py b/HARK/SolvingMicroDSOPs/__init__.py new file mode 100644 index 000000000..e69de29bb From 2c2116193d829e7dafc471fdb41e435b0f602801 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 10 Dec 2019 11:24:18 -0500 Subject: [PATCH 147/242] adding seaborn as extra 'dev' dependency (since examples are used in docs). Fixes #451 --- setup.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 89b669f49..d8ab6f377 100644 --- a/setup.py +++ b/setup.py @@ -159,7 +159,8 @@ 'Sphinx<=1.6.7', 'nbsphinx', 'numpydoc', - 'recommonmark' + 'recommonmark', + 'seaborn' ]}, python_requires='>=2.7', From f8304af8a48bc8630348892ad33ba1f15649dc43 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Tue, 10 Dec 2019 17:39:47 -0500 Subject: [PATCH 148/242] add seaborn to requirements --- requirements.txt | 1 + setup.py | 1 + 2 files changed, 2 insertions(+) diff --git a/requirements.txt b/requirements.txt index 4b938908c..9fba034b0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,5 @@ funcsigs jupyter sphinx-rtd-theme nbsphinx==0.4.3 +seaborn recommonmark diff --git a/setup.py b/setup.py index 89b669f49..384378f56 100644 --- a/setup.py +++ b/setup.py @@ -151,6 +151,7 @@ 'dill', 'joblib', 'funcsigs', + 'seaborn', 'jupyter' ], extras_requires={ From f4bf2dbbc7403112a8b8e55b64a779b877d59b0a Mon Sep 17 00:00:00 2001 From: sb Date: Wed, 11 Dec 2019 11:52:03 -0500 Subject: [PATCH 149/242] Revert "adding seaborn as extra 'dev' dependency (since examples are used in docs). Fixes #451" This reverts commit 2c2116193d829e7dafc471fdb41e435b0f602801. --- setup.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/setup.py b/setup.py index d8ab6f377..89b669f49 100644 --- a/setup.py +++ b/setup.py @@ -159,8 +159,7 @@ 'Sphinx<=1.6.7', 'nbsphinx', 'numpydoc', - 'recommonmark', - 'seaborn' + 'recommonmark' ]}, python_requires='>=2.7', From 4b616b93a76014a0169e2095311d8b9d4e035889 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 12 Dec 2019 08:51:13 -0500 Subject: [PATCH 150/242] breakup example/test and code --- HARK/ConsumptionSaving/ConsIndShockModel.py | 176 +----------------- HARK/ConsumptionSaving/__init__.py | 1 + .../ConsumptionSaving/example_ConsIndShock.py | 148 +++++++++++++++ 3 files changed, 159 insertions(+), 166 deletions(-) create mode 100644 HARK/ConsumptionSaving/example_ConsIndShock.py diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 86aa387df..c3e9b83b6 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -24,6 +24,11 @@ CRRAutilityPP, CRRAutilityP_inv, CRRAutility_invP, CRRAutility_inv, \ CRRAutilityP_invP +__all__ = ['ConsumerSolution', 'ValueFunc', 'MargValueFunc', 'MargMargValueFunc', +'ConsPerfForesightSolver', 'ConsIndShockSetup', 'ConsIndShockSolverBasic', +'ConsIndShockSolver', 'ConsKinkedRsolver', 'PerfForesightConsumerType', +'IndShockConsumerType', 'KinkedRconsumerType'] + utility = CRRAutility utilityP = CRRAutilityP utilityPP = CRRAutilityPP @@ -85,18 +90,11 @@ def __init__(self, cFunc=None, vFunc=None, None ''' # Change any missing function inputs to NullFunc - if cFunc is None: - cFunc = NullFunc() - if vFunc is None: - vFunc = NullFunc() - if vPfunc is None: - vPfunc = NullFunc() - if vPPfunc is None: - vPPfunc = NullFunc() - self.cFunc = cFunc - self.vFunc = vFunc - self.vPfunc = vPfunc - self.vPPfunc = vPPfunc + self.cFunc = cFunc if cFunc is not None else NullFunc() + self.vFunc = vFunc if vFunc is not None else NullFunc() + self.vPfunc = vPfunc if vPfunc is not None else NullFunc() + # vPFunc = NullFunc() if vPfunc is None else vPfunc + self.vPPfunc = vPPfunc if vPPfunc is not None else NullFunc() self.mNrmMin = mNrmMin self.hNrm = hNrm self.MPCmin = MPCmin @@ -2659,157 +2657,3 @@ def constructAssetsGrid(parameters): aXtraGrid = np.insert(aXtraGrid, j, a) return aXtraGrid - -#################################################################################################### - -def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params - from HARK.utilities import plotFuncsDer, plotFuncs - from time import time - mystr = lambda number : "{:.4f}".format(number) - - do_simulation = True - - # Make and solve an example perfect foresight consumer - PFexample = PerfForesightConsumerType(**Params.init_perfect_foresight) - PFexample.cycles = 0 # Make this type have an infinite horizon - - start_time = time() - PFexample.solve() - end_time = time() - print('Solving a perfect foresight consumer took ' + mystr(end_time-start_time) + ' seconds.') - PFexample.unpackcFunc() - PFexample.timeFwd() - - # Plot the perfect foresight consumption function - print('Perfect foresight consumption function:') - mMin = PFexample.solution[0].mNrmMin - plotFuncs(PFexample.cFunc[0],mMin,mMin+10) - - if do_simulation: - PFexample.T_sim = 120 # Set number of simulation periods - PFexample.track_vars = ['mNrmNow'] - PFexample.initializeSim() - PFexample.simulate() - -############################################################################### - - # Make and solve an example consumer with idiosyncratic income shocks - IndShockExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks) - IndShockExample.cycles = 0 # Make this type have an infinite horizon - - start_time = time() - IndShockExample.solve() - end_time = time() - print('Solving a consumer with idiosyncratic shocks took ' + mystr(end_time-start_time) + ' seconds.') - IndShockExample.unpackcFunc() - IndShockExample.timeFwd() - - # Plot the consumption function and MPC for the infinite horizon consumer - print('Concave consumption function:') - plotFuncs(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5) - print('Marginal consumption function:') - plotFuncsDer(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5) - - # Compare the consumption functions for the perfect foresight and idiosyncratic - # shock types. Risky income cFunc asymptotically approaches perfect foresight cFunc. - print('Consumption functions for perfect foresight vs idiosyncratic shocks:') - plotFuncs([PFexample.cFunc[0],IndShockExample.cFunc[0]],IndShockExample.solution[0].mNrmMin,100) - - # Compare the value functions for the two types - if IndShockExample.vFuncBool: - print('Value functions for perfect foresight vs idiosyncratic shocks:') - plotFuncs([PFexample.solution[0].vFunc,IndShockExample.solution[0].vFunc], - IndShockExample.solution[0].mNrmMin+0.5,10) - - # Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, and pLvlNow_hist - if do_simulation: - IndShockExample.T_sim = 120 - IndShockExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow'] - IndShockExample.makeShockHistory() # This is optional, simulation will draw shocks on the fly if it isn't run. - IndShockExample.initializeSim() - IndShockExample.simulate() - - ########################################################################### - - # Make and solve an idiosyncratic shocks consumer with a finite lifecycle - LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) - LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once - - start_time = time() - LifecycleExample.solve() - end_time = time() - print('Solving a lifecycle consumer took ' + mystr(end_time-start_time) + ' seconds.') - LifecycleExample.unpackcFunc() - LifecycleExample.timeFwd() - - # Plot the consumption functions during working life - print('Consumption functions while working:') - mMin = min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]) - plotFuncs(LifecycleExample.cFunc[:LifecycleExample.T_retire],mMin,5) - - # Plot the consumption functions during retirement - print('Consumption functions while retired:') - plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire:],0,5) - LifecycleExample.timeRev() - - # Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, pLvlNow_hist, and t_age_hist - if do_simulation: - LifecycleExample.T_sim = 120 - LifecycleExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_age'] - LifecycleExample.initializeSim() - LifecycleExample.simulate() - -############################################################################### - - # Make and solve a "cyclical" consumer type who lives the same four quarters repeatedly. - # The consumer has income that greatly fluctuates throughout the year. - CyclicalExample = IndShockConsumerType(**Params.init_cyclical) - CyclicalExample.cycles = 0 - - start_time = time() - CyclicalExample.solve() - end_time = time() - print('Solving a cyclical consumer took ' + mystr(end_time-start_time) + ' seconds.') - CyclicalExample.unpackcFunc() - CyclicalExample.timeFwd() - - # Plot the consumption functions for the cyclical consumer type - print('Quarterly consumption functions:') - mMin = min([X.mNrmMin for X in CyclicalExample.solution]) - plotFuncs(CyclicalExample.cFunc,mMin,5) - - # Simulate some data; results stored in cHist, mHist, bHist, aHist, MPChist, and pHist - if do_simulation: - CyclicalExample.T_sim = 480 - CyclicalExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_cycle'] - CyclicalExample.initializeSim() - CyclicalExample.simulate() - -############################################################################### - - # Make and solve an agent with a kinky interest rate - KinkyExample = KinkedRconsumerType(**Params.init_kinked_R) - KinkyExample.cycles = 0 # Make the Example infinite horizon - - start_time = time() - KinkyExample.solve() - end_time = time() - print('Solving a kinky consumer took ' + mystr(end_time-start_time) + ' seconds.') - KinkyExample.unpackcFunc() - print('Kinky consumption function:') - KinkyExample.timeFwd() - plotFuncs(KinkyExample.cFunc[0],KinkyExample.solution[0].mNrmMin,5) - - if do_simulation: - KinkyExample.T_sim = 120 - KinkyExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow'] - KinkyExample.initializeSim() - KinkyExample.simulate() - - return PFexample - -if __name__ == '__main__': - PFexample = main() - - diff --git a/HARK/ConsumptionSaving/__init__.py b/HARK/ConsumptionSaving/__init__.py index e69de29bb..43c3e7b4d 100644 --- a/HARK/ConsumptionSaving/__init__.py +++ b/HARK/ConsumptionSaving/__init__.py @@ -0,0 +1 @@ +from .ConsIndShockModel import * \ No newline at end of file diff --git a/HARK/ConsumptionSaving/example_ConsIndShock.py b/HARK/ConsumptionSaving/example_ConsIndShock.py new file mode 100644 index 000000000..1f1dc31ff --- /dev/null +++ b/HARK/ConsumptionSaving/example_ConsIndShock.py @@ -0,0 +1,148 @@ +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType, KinkedRconsumerType +from HARK.utilities import plotFuncsDer, plotFuncs +from time import time + + +mystr = lambda number : "{:.4f}".format(number) + +do_simulation = True + +# Make and solve an example perfect foresight consumer +PFexample = PerfForesightConsumerType(**Params.init_perfect_foresight) +PFexample.cycles = 0 # Make this type have an infinite horizon + +start_time = time() +PFexample.solve() +end_time = time() +print('Solving a perfect foresight consumer took ' + mystr(end_time-start_time) + ' seconds.') +PFexample.unpackcFunc() +PFexample.timeFwd() + +# Plot the perfect foresight consumption function +print('Perfect foresight consumption function:') +mMin = PFexample.solution[0].mNrmMin +plotFuncs(PFexample.cFunc[0],mMin,mMin+10) + +if do_simulation: + PFexample.T_sim = 120 # Set number of simulation periods + PFexample.track_vars = ['mNrmNow'] + PFexample.initializeSim() + PFexample.simulate() + +"" +# Make and solve an example consumer with idiosyncratic income shocks +IndShockExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks) +IndShockExample.cycles = 0 # Make this type have an infinite horizon + +start_time = time() +IndShockExample.solve() +end_time = time() +print('Solving a consumer with idiosyncratic shocks took ' + mystr(end_time-start_time) + ' seconds.') +IndShockExample.unpackcFunc() +IndShockExample.timeFwd() + +# Plot the consumption function and MPC for the infinite horizon consumer +print('Concave consumption function:') +plotFuncs(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5) +print('Marginal consumption function:') +plotFuncsDer(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5) + +# Compare the consumption functions for the perfect foresight and idiosyncratic +# shock types. Risky income cFunc asymptotically approaches perfect foresight cFunc. +print('Consumption functions for perfect foresight vs idiosyncratic shocks:') +plotFuncs([PFexample.cFunc[0],IndShockExample.cFunc[0]],IndShockExample.solution[0].mNrmMin,100) + +# Compare the value functions for the two types +if IndShockExample.vFuncBool: + print('Value functions for perfect foresight vs idiosyncratic shocks:') + plotFuncs([PFexample.solution[0].vFunc,IndShockExample.solution[0].vFunc], + IndShockExample.solution[0].mNrmMin+0.5,10) + +# Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, and pLvlNow_hist +if do_simulation: + IndShockExample.T_sim = 120 + IndShockExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow'] + IndShockExample.makeShockHistory() # This is optional, simulation will draw shocks on the fly if it isn't run. + IndShockExample.initializeSim() + IndShockExample.simulate() + +########################################################################### + +# Make and solve an idiosyncratic shocks consumer with a finite lifecycle +LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) +LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once + +start_time = time() +LifecycleExample.solve() +end_time = time() +print('Solving a lifecycle consumer took ' + mystr(end_time-start_time) + ' seconds.') +LifecycleExample.unpackcFunc() +LifecycleExample.timeFwd() + +# Plot the consumption functions during working life +print('Consumption functions while working:') +mMin = min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]) +plotFuncs(LifecycleExample.cFunc[:LifecycleExample.T_retire],mMin,5) + +# Plot the consumption functions during retirement +print('Consumption functions while retired:') +plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire:],0,5) +LifecycleExample.timeRev() + +# Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, pLvlNow_hist, and t_age_hist +if do_simulation: + LifecycleExample.T_sim = 120 + LifecycleExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_age'] + LifecycleExample.initializeSim() + LifecycleExample.simulate() + +"" +# Make and solve a "cyclical" consumer type who lives the same four quarters repeatedly. +# The consumer has income that greatly fluctuates throughout the year. +CyclicalExample = IndShockConsumerType(**Params.init_cyclical) +CyclicalExample.cycles = 0 + +start_time = time() +CyclicalExample.solve() +end_time = time() +print('Solving a cyclical consumer took ' + mystr(end_time-start_time) + ' seconds.') +CyclicalExample.unpackcFunc() +CyclicalExample.timeFwd() + +# Plot the consumption functions for the cyclical consumer type +print('Quarterly consumption functions:') +mMin = min([X.mNrmMin for X in CyclicalExample.solution]) +plotFuncs(CyclicalExample.cFunc,mMin,5) + +# Simulate some data; results stored in cHist, mHist, bHist, aHist, MPChist, and pHist +if do_simulation: + CyclicalExample.T_sim = 480 + CyclicalExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_cycle'] + CyclicalExample.initializeSim() + CyclicalExample.simulate() + +"" +# Make and solve an agent with a kinky interest rate +KinkyExample = KinkedRconsumerType(**Params.init_kinked_R) +KinkyExample.cycles = 0 # Make the Example infinite horizon + +start_time = time() +KinkyExample.solve() +end_time = time() +print('Solving a kinky consumer took ' + mystr(end_time-start_time) + ' seconds.') +KinkyExample.unpackcFunc() +print('Kinky consumption function:') +KinkyExample.timeFwd() +plotFuncs(KinkyExample.cFunc[0],KinkyExample.solution[0].mNrmMin,5) + +if do_simulation: + KinkyExample.T_sim = 120 + KinkyExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow'] + KinkyExample.initializeSim() + KinkyExample.simulate() + + + +"" + From 778302d8606d48c0c8c5755a58dff5675e2cd63d Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Thu, 12 Dec 2019 09:51:45 -0500 Subject: [PATCH 151/242] prepare release 0.10.3 --- CHANGELOG.md | 23 +++++++++++++++++++++-- HARK/__init__.py | 2 +- setup.py | 2 +- 3 files changed, 23 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4af200686..cdb8b20fe 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,15 +1,34 @@ HARK -Version 0.10.2 +Version 0.10.3 Release Notes # Introduction -This document contains the release notes for the 0.10.1 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. +This document contains the release notes for the 0.10.3 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. For more information on HARK, see [our Github organization](https://github.com/econ-ark). ## Changes +### 0.10.3 + +Release Date: 12-12-2019 + +#### Major Changes + +- Added constrained perfect foresight model solution. ([#299](https://github.com/econ-ark/HARK/pull/299) + +#### Minor Changes + +- Fixed matplotlib GUI error. ([#444](https://github.com/econ-ark/HARK/pull/444)) +- Pinned sphinx dependency. ([#436](https://github.com/econ-ark/HARK/pull/436)) +- Fixed bug in ConsPortfolioModel in which the same risky rate of return would be drawn over and over. ([#433](https://github.com/econ-ark/HARK/pull/433)) +- Fixed sphinx dependency errors. ([#411](https://github.com/econ-ark/HARK/pull/411)) +- Refactored simultation.py. ([#408](https://github.com/econ-ark/HARK/pull/408)) +- AgentType.simulate() now throws informative errors if +attributes required for simulation do not exist, or initializeSim() has +never been called. ([#320](https://github.com/econ-ark/HARK/pull/320)) + ### 0.10.2 Release Date: 10-03-2019 diff --git a/HARK/__init__.py b/HARK/__init__.py index 625114886..bbfa2eb63 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,4 +1,4 @@ from __future__ import absolute_import from .core import * -__version__ = '0.10.2' +__version__ = '0.10.3' diff --git a/setup.py b/setup.py index 384378f56..23c220af9 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ # For a discussion on single-sourcing the version across setup.py and the # project code, see # https://packaging.python.org/en/latest/single_source_version.html - version='0.10.2', # Required + version='0.10.3', # Required # This is a one-line description or tagline of what your project does. This # corresponds to the "Summary" metadata field: From da832973cc59492350e8f8734440cf3ceab201b6 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 12 Dec 2019 10:39:48 -0500 Subject: [PATCH 152/242] fix slicing error in minimizeNelderMead (#460) --- HARK/estimation.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/HARK/estimation.py b/HARK/estimation.py index 62bc6ba66..5171cc96a 100644 --- a/HARK/estimation.py +++ b/HARK/estimation.py @@ -46,7 +46,8 @@ def objectiveFunctionMod(params): params_full[which_vars] = params out = objectiveFunction(params_full) return out - parameter_guess_mod = parameter_guess[which_vars] + # convert parameter guess to np array to slice it with boolean array + parameter_guess_mod = np.array(parameter_guess)[which_vars] # Execute the minimization, starting from the given parameter guess t0 = time() # Time the process From 83d597f1fcf812aa7308c41ba213f46334ba1c24 Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Thu, 12 Dec 2019 11:03:21 -0500 Subject: [PATCH 153/242] Update changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index cdb8b20fe..9c0a55404 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -20,6 +20,7 @@ Release Date: 12-12-2019 #### Minor Changes +- Fixed slicing error in minimizeNelderMead. ([#460](https://github.com/econ-ark/HARK/pull/460)) - Fixed matplotlib GUI error. ([#444](https://github.com/econ-ark/HARK/pull/444)) - Pinned sphinx dependency. ([#436](https://github.com/econ-ark/HARK/pull/436)) - Fixed bug in ConsPortfolioModel in which the same risky rate of return would be drawn over and over. ([#433](https://github.com/econ-ark/HARK/pull/433)) From 199a1346f7bd092654475114b61cfad2fa56a33a Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 12 Dec 2019 13:50:08 -0500 Subject: [PATCH 154/242] Adding ConsPortfolioModelDoc to examples/ --- .../ConsPortfolioModelDoc.ipynb | 406 ++++++++++++++++++ .../ConsPortfolioModelDoc.py | 199 +++++++++ 2 files changed, 605 insertions(+) create mode 100644 examples/ConsPortfolioModelDoc/ConsPortfolioModelDoc.ipynb create mode 100644 examples/ConsPortfolioModelDoc/ConsPortfolioModelDoc.py diff --git a/examples/ConsPortfolioModelDoc/ConsPortfolioModelDoc.ipynb b/examples/ConsPortfolioModelDoc/ConsPortfolioModelDoc.ipynb new file mode 100644 index 000000000..1837968e4 --- /dev/null +++ b/examples/ConsPortfolioModelDoc/ConsPortfolioModelDoc.ipynb @@ -0,0 +1,406 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ConsPortfolioModel Documentation\n", + "\n", + "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/DemARK/master?filepath=notebooks%2FConsPortfolioModelDoc.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "# Setup stuff\n", + "\n", + "import HARK.ConsumptionSaving.ConsPortfolioModel as cpm\n", + "import HARK.ConsumptionSaving.ConsumerParameters as param\n", + "import copy\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "from HARK.ConsumptionSaving.ConsPortfolioModel import PortfolioSolution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We implement three different ways to allow portfolio choice.\n", + "\n", + "The agent can choose \n", + " * any portfolio share ('continuous choice')\n", + " * only a specified set of portfolio shares ('discrete choice')\n", + " * With probability 1 (agent always gets to choose)\n", + " * With probability 0 < p < 1 (stochastic chance to choose)\n", + " \n", + "We allow two choices for the description of the \n", + "distribution of the stochastic variable:\n", + " 1. A generic discrete probability distribution\n", + " * Nodes and their probabilities are specified\n", + " 2. A true lognormal distribution\n", + " * The mean return and the standard deviation are specified\n", + " \n", + "In the discrete portfolio shares case, the user also must\n", + "input a function that *draws* from the distribution in drawRiskyFunc\n", + "\n", + "Other assumptions: \n", + " * distributions are time constant\n", + " * probability of being allowed to reoptimize is time constant\n", + " * If p < 1, you must specify the PortfolioSet discretely\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "# Set up the model\n", + "# Parameters from Mehra and Prescott (1985):\n", + "Avg = 1.08 # equity premium \n", + "Std = 0.20 # standard deviation of rate-of-return shocks \n", + "\n", + "RiskyDstnFunc = cpm.RiskyDstnFactory(RiskyAvg=Avg, RiskyStd=Std) # Generates nodes for integration\n", + "RiskyDrawFunc = cpm.LogNormalRiskyDstnDraw(RiskyAvg=Avg, RiskyStd=Std) # Generates draws from a lognormal distribution\n", + "\n", + "init_portfolio = copy.copy(param.init_idiosyncratic_shocks) # Default parameter values for inf horiz model\n", + "init_portfolio['approxRiskyDstn'] = RiskyDstnFunc\n", + "init_portfolio['drawRiskyFunc'] = RiskyDrawFunc\n", + "init_portfolio['RiskyCount'] = 2 # Number of points in the approximation; 2 points is minimum\n", + "init_portfolio['RiskyShareCount'] = 25 # How many discrete points to allow in the share approximation\n", + "init_portfolio['Rfree'] = 1.0 # Riskfree return factor is 1 (interest rate is zero)\n", + "init_portfolio['CRRA'] = 6.0 # Relative risk aversion\n", + "\n", + "# Uninteresting technical parameters:\n", + "init_portfolio['aXtraMax'] = 100 \n", + "init_portfolio['aXtraCount'] = 50\n", + "init_portfolio['BoroCnstArt'] = 0.0 # important for theoretical reasons\n", + "# init_portfolio['vFuncBool'] = True # We do not need value function for purposes here\n", + "\n", + "init_portfolio['DiscFac'] = 0.90\n", + "# Create portfolio choice consumer type\n", + "pcct = cpm.PortfolioConsumerType(**init_portfolio)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "code_folding": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Solve the model under the given parameters\n", + "\n", + "pcct.solve()\n", + "aMin = 0 # Minimum ratio of assets to income to plot\n", + "aMax = 10 # Maximum ratio of assets to income to plot\n", + "aPts = 100 # Number of points to plot \n", + "\n", + "# Campbell-Viceira (2002) approximation to optimal portfolio share in Merton-Samuelson (1969) model\n", + "pcct.MertSamCampVicShare = pcct.RiskyShareLimitFunc(RiskyDstnFunc(init_portfolio['RiskyCount']))\n", + "eevalgrid = np.linspace(0,aMax,aPts) # range of values of assets for the plot\n", + "plt.plot(eevalgrid, pcct.solution[0].RiskyShareFunc[0][0](eevalgrid))\n", + "plt.axhline(pcct.MertSamCampVicShare, c='r') # The Campbell-Viceira approximation\n", + "plt.ylim(0,1.05)\n", + "plt.text((aMax-aMin)/4,0.45,r'$\\uparrow $ limit as $m \\uparrow \\infty$',fontsize = 22,fontweight='bold')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Simulate 20 years of behavior for a set of consumers \n", + "SimPer = 20\n", + "\n", + "pcct.track_vars = ['aNrmNow', 't_age', 'RiskyShareNow']\n", + "pcct.T_sim = SimPer\n", + "pcct.initializeSim()\n", + "pcct.simulate()\n", + "pcct.RiskyShareNow_hist\n", + "\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.set_zlim(pcct.MertSamCampVicShare,1.0)\n", + "ax.scatter(pcct.aNrmNow_hist, pcct.t_age_hist, pcct.RiskyShareNow_hist)\n", + "plt.show()\n", + "\n", + "# The consumers are very impatient and so even if they start rich they end up as buffer stock savers\n", + "# But with all of their buffer stock savings in the stock market" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Solve the specialized / simple version for which there is a good approximation\n", + "# This is the version for which Campbell and Viceira provide an approximate formula\n", + "# assuming log normally distributed shocks\n", + "\n", + "init_lognormportfolio = copy.deepcopy(init_portfolio) # Use same parameter values\n", + "init_lognormportfolio['RiskyAvg'] = Avg\n", + "init_lognormportfolio['RiskyStd'] = Std\n", + "init_lognormportfolio['RiskyCount'] = 11 # Eleven is enough points to do justice to the distribution\n", + "lnpcct = cpm.LogNormalPortfolioConsumerType(**init_lognormportfolio)\n", + "\n", + "lnpcct.solve()\n", + "lnpcct.MertSamCampVicShare = lnpcct.RiskyShareLimitFunc(RiskyDstnFunc(init_portfolio['RiskyCount']))\n", + "\n", + "plt.plot(eevalgrid, lnpcct.solution[0].RiskyShareFunc[0][0](eevalgrid))\n", + "plt.axhline(lnpcct.MertSamCampVicShare, c='r')\n", + "plt.ylim(0,1.05)\n", + "plt.text((aMax-aMin)/4,lnpcct.MertSamCampVicShare-0.1,r'$\\uparrow $ limit as $m \\uparrow \\infty$',fontsize = 22,fontweight='bold')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Again simulate a few periods \n", + "lnpcct.track_vars = ['aNrmNow', 't_age', 'RiskyShareNow']\n", + "lnpcct.T_sim = SimPer\n", + "lnpcct.initializeSim()\n", + "lnpcct.simulate()\n", + "lnpcct.RiskyShareNow_hist\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.scatter(lnpcct.aNrmNow_hist, lnpcct.t_age_hist, lnpcct.RiskyShareNow_hist)\n", + "ax.set_zlim(lnpcct.MertSamCampVicShare,1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Version where only discrete values of portfolio share of risky assets are allowed\n", + "\n", + "init_portfolio_prb = copy.deepcopy(init_portfolio)\n", + "\n", + "init_portfolio_prb['AdjustPrb'] = 1.0\n", + "init_portfolio_prb['PortfolioDomain'] = cpm.DiscreteDomain([0.0, 0.5, 0.6, 1.0])\n", + "pcct_prb = cpm.PortfolioConsumerType(**init_portfolio_prb)\n", + "pcct_prb.solve()\n", + "\n", + "eevalgrid = np.linspace(0,100,100)\n", + "plt.plot(eevalgrid, pcct_prb.solution[0].RiskyShareFunc[0][0](eevalgrid))\n", + "plt.axhline(pcct_prb.RiskyShareLimitFunc(RiskyDstnFunc(init_portfolio['RiskyCount'])), c='r')\n", + "plt.ylim(0,1.05)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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2CSMdGvx/7zuOj75xFh994yzMG0F8/IkbWNmIIsUBerUCv35gAA/eFKUh50Ycf7u7u9He3o65ubmKPx/x6KlarUZ3d3deEUg8OCAeqRVr4hoMhoYJlO8UNDPSrZQIVSoVOjo68oiNYRjhuxOnmGiahl6vRyKRQCgUgsFgyCLjeiMUCuU9KHYaZNKtEaR9x+PxoLe3tyKyZVkWTqcTTqcT/f39WaO6xYpOFCjwHA+KAjh+e6qMVlAIJ4sPNLhCKTzwzUv41h+dAgDMDHbi2x84XfC1JI3gcDjQ39+PkydPgud5LC0tlfXe8s65RCGt2OBArvKXWBNXHBnvlkLdToh0K4VSqSxKxoFAAIFAAE6nE6lUCjRNZ7W11YuMQ6EQxsfHa9pHoyGTbpUQDzRwHAeLxYL+/v6ytmUYBmtra1hfX8fQ0BBOnjxZUcW4q02Fo2OdsPnjCCUzYDgeKgUFtVKBTLp4rtbmjxf9PXkQOBwODA4OZp0bieKrRTXbSil/ia17iI9aPB7Hyy+/nLXENRgM0Gg0LVXhbhbpsizb8OMQMiaazQRkAIhoU+SSsThnXMn3J0e6uxCFBhqUSmVZhJJOp2G32+HxeDAyMoIzZ84UXN7FUgyesWfwlPca+o0avP34KAbaNdvi2wyHNrUS77ljHP1GDX543YPhdi3SLAfwfJageCHoNYW/ciKQ43Q6MTQ0hNOnT+cVPnKlHStBvUlPLFBOHnbnz5/H4uKicDOHQiGsr68jmUxmRVaFco47Cc1KL1Q6kVZPKJVKtLe353UZsCwrrGrEZCz+vknuWKvV5n1O4XBYJt3dglIDDcWQTCaxurqKQCCAsbExSQtzYPuG++rP7bjiZzGr42H2xvDZH5txaqIDn/rPZSRyil+9bTSe/NBpaFUqbMUzOGcL4FPfu15Q9UurBD7+pux8LMuyWFtbg8vlwvDwcEGyJail17ZZfbo0TcNoNOZ1e5DIKhqNSra1EUJuRJ9qJeA4rindHM06TiXfO03TkmRMHqbBYBAulwvJZFIQpNdoNLhw4QKCwWDed7/TIJNuEVQy0FAI8XgcNpsN4XAYExMTZTnyxtMsrrkj6NFS0KpoaFU0rN4YPvWfm3mECwC+OIs7PvMCvvuBk7i+EUU4yeBr9x/H4p4OxJIpPHlpAz+9sobx3g78zslJzA9tX8wMwwjSjyMjI0XJlqAVSFcKUpGVuDXK4/EgGo0KE1y3qq1tJxfSqkE90iVSD1NCxl6vFy+++CKuXr2Kt7/97dDpdHj961+Phx56qKz9v/e978X3vvc99Pf348qVK3m/53keDzzwAJ566im0tbXh0UcfxdGjRwvsqTRk0i2AagYaxIhGo7BarYjH45iamsLCwkLZ26ro7Skbhr8pN8PzCCYySEmNkN3EBx97FbP9RqhoCs+b/Ljv9B7ctbcPv3dqAsc6U+ju7kZfX3tWPnl0dFQyxVEItRDBrSZdKUi1RqXT6YJtbYlEAmazWYiMG9HW1oqFtGKotUe3GMRk/NnPfhZ33XUXXnzxRcEppVzcf//9+PCHP4z77ruv4O+///3vw2QywWQy4aWXXsIHP/hBvPTSS1Wds0y6IpC2L5ZlKx5ooCgKwWAQNpsNDMMIo7qVEpVaqcBbDw/hyz8JQBFMgOOBfYMGLG9Gi24XSbIYuik+k8iweOLSBu7au114UigUYBgGFosFbrcbe/bsqYhsb0dItbWdO3cOXV1diMVicDgcBdvaanUX3k2FNHKcZl1rZOCDoihMTEyUvd3rXvc6rK6uSv7+iSeewH333QeKonD69GkEg0G43W4MDQ1VfI4y6aK2gQZg28I8Ho/DbDZjZmam7ER+huXwqiMEx1YcXToVpvoM2NOlxRvn+xBc10HX24eBTj2OjBgRiKXxrEnafaFH5KSroChk+O3IOJPJYGtrCy6XC1NTU7eMbHdqpFsJSDtgT09P1W1tOp2uLAnM3VRIaxbpNvL6crlc2LNnj/D/0dFRuFwumXQrRS0DDcTC3Gq1Qq1Ww2Aw4MCBA9BqtWVtz7AcHn7GjJ8se7EZThbM12rpG3j7iRF065T44u8dwTdfduKvnlpBriXZmYlOGHQqeCMpaJQKhJMMfvNgH0wmEzweD9ra2jAzM4PR0dGyzq0R2A2kK3X+lbS1JRIJUBSVFRXntrXttkiXYZimku5O7EgR47Yk3VzR8ErJVmxhvn//fuj1eiwtLeXJ1BXDlfUIzq9uIZTIICVh7Jhkga//woWv/8IFvZrGo/cdweffeRi+aArHxrow0dMGluehohXYCCXxxJIbW7EUjvdy6Inboe0bx5kzZ+BwOMo+r2pRjp7ubiVdKRRqawOyK/Hitjby+ng8Dq1Wi7a2toa2tTUz0m1GETKZTEpOcdaKkZGRrPvI6XRiZGSkqn3dVqRLyNbtdoPneQwMDJR9QZPE/OrqKjo7O/MszAvJMRZDPM2A5bZ1FLgy7uVYmsXbv3wBOjUNUICSovDnd8/ibUe3v/hunQJ3DaTh8/kxPj6O4eFDwoOkmD6vjMpQDwIs1dZmNpsRDofh9/vz2trE0pm1opkRdTPIPRgMNkxd7OzZs/j85z+Pd7zjHXjppZfQ0dFRVWoBuE1IN3eggWVZJJPJsm4gsfYAUdUqJE9YjtuDGNN9eujUipIyi1nnAkCjpKBS0kimWXz+WSvunGhHyONCIBDA+Pg4Zmdn824khUKRZ6PSbOyWSLeRS1fS1qbVajE2NgaDwQAgX/GLFGtrbWtrFhk26zi1TKO9853vxLPPPgufz4fR0VF86lOfEu6ZP/7jP8Y999yDp556CjMzM2hra8PXvva1qs9zV5Ou1ECDSqVCNFq8G4BhGDidTrhcLgwMDJQc1aVpOiu9EE0xeHl1C+5wEno1jcluHfaPdAiEONCuxSfu2YeHnl7BxbUtpMoMROmb2ysVFBKpDH728hLuPDCFvXv3ShJCMyLdUoQkk271x6m0rU3sLFysra2ZkW4z0gu1KIw99thjRX9PURS+8IUvVLXvXOw60i1noEGpVErmXwtZmJdzwYjTC+FEBn/+b1dwZT0CbzSd91olBRwd68CxsU588fcPg6YoPG/24Z//y4rL67GC+6cAqGgKDMuBYjkk0hz6jGrc/cun0K4rvtRsNOnG43E4HA5oNBoh8io0QrwbSLcZKJcMy1FrK9bW1sw+3XILzLUgGAzu+BFgYBeRbiUDDYVIN5VKYXV1FT6fryILc/E+BSHlax7Y/HEE4vmECwAMD5yzh3DOHsI/P2/HifFO3HNwEI//4Skob+rYmkwmuOIK/MyZQZrlcPfeLvxwyY4nl6PgKQr9HTr87W/vL0m4QONIl5hjRqNRDA8PI5PJZEVcWq1WiLbS6XTLky7QnMp4KY+0Yiim1pZMJoXImLS1nT9/vqq2tkrQCumFZqLlSbeagQYx6SYSCdhsNgSDQcmcaDkgViEAEExkwHFAOTzHA7joCCGYYJBmOLz7zBiA7SXlTI8Sp+aGt4caNmx47x2T+J+/1YdwgkGXXp0nNC6FepMuGW+ORCKYnp7G/v37kclkspbF5CaPxWKIRqMIBoMIBAKw2+1ZwjOtpALWzP7Zeh+HoijodDrodDqhre3cuXM4duxYwbY2omkgjowLCcyUg2aRbiuI3QAtTLq1DDQolUqkUilcuXIF0WgUExMTmJ+fz9s2zXC46AgixXBYGDKi1yDt70XTNJLJJADg2Fgn/u2VdVBAUX1bAobj0a5T4pkbHoF0WZbFxsYGrlvW8B27Co4wha2XVtBnsOOO6W784Z0TWQMRxVAv0k0mk0gmk3j11VcxPT1ddLxZfJMTnWGtViv4cUWjUYRCIbhcLkHST0zEBoNhx1n4NJN0m7HsJ62ShdraOI7L+p5y29oqUWtrVp9uKBSquqOgmdhZV3UZqGWgAdg247NYLIhGo5iZmZG0ME9lWHzsu9dg9kRBUYBGReNvf2s/Rju1eN7ih8UbQzzJYLZfjzct9GWlF46OdeIjvzqNv/uBCY5gsuQ5qWgKHA/oVDTi8TgsFgu2trbQ1dWF73uVcISDCMbTiKYYRFMsomkGK5tRfOGdh6FVlb6YayXdVCoFq9WKra0tqFQqnDhxouKWJZLTVSgUAqmKQcSuo9GoYFnEMEyWhQ8pCt0q14hbVUhr5HGkUOx7Ij3GUmptuW1tzSykyZFuA8CyLDKZTMVkGwwGYbVaBQvzeDyeNT2Ui2dueLG8GUGPfnuOO5TI4B9/YgHD8Ti3uoVwUpQT/rftQle3FuhtD+DsoX783qkxvGlhAO5gHJ/63jX81BSUPNZghwaxZBp371Hh8uXLmJqaQm9vL+LxOG5sBNCmVsAVZKGmaWRYDioFBW80DYs3hv3DpfsSqyXddDoNm80Gv9+PyclJ7Nu3D+fPn694PwTFbvJCzgPElSMajSIajQoWPjzPQ6fTCaRAlr6Nxm6KdKt9L+WoteW2tREVsPb29oaqtcmk2yAQv/tyUIuFeSC2XQQjF6ZWRW9Htxk2m3DJsQD4k4A/GcVnnoniH56xok1DQ6mgcMd0Nx599yKOjHZCp6YRT2Xw/auboCgFtiIxbHh9mDLweOPRSfT09ICiKHi9XrDstoiN2RsV3g+obSUynt+26CkHlZJuOp0WiopEklJsj12tA0Q122g0Gmg0mqyiEBmvjUajCIfDwtI3kUjgxo0bWWmKemrjNrMQ2GhyrzexF2tre+WVV8DzfFVtbZVAJt1bCJ7nhaetTqfD/Px83jKpFBaG27eFY1gOtIJCJMlgoqcNV9YjZW3PAYimttMNT13x4KfLPhwcacfn7j2Ebr0ad891wmw2I61N4613zeUpkpEWtP/7V6bxP//9KvRqGqEkA72aBsPxODzagZk+fVnnUi7pZjIZrK6uwuPxYHx8HKdPny44aFFN1FzPljFxHlKMc+fOYWhoKC/aItoY9bjBW6HgVw6apbugVqtB0zTGxsaEn4lXMPVUawuHw3lW8jsRu4p0eZ4XRnXb29tx8OBByVlsYj0j9YUeG+vEH/3SBL76oh0Zlsed0z1465EhfPRfL1d1bimGg3MrgS89Z8bdw2lkMhlMT09n9ViKQchtoqcN/+f3F2H2RHHRGUIonsGebh3OHhoS2stKoRRRMgyD1dVVbG5ulnS2qCXSbXSkSFGUZIqC5IvJDU5SFJW0SjUrvdAM3EqrHqkVTG7HS6VqbXKk2yAUuujFFubd3d1YXFwsmeMjbWPEfXd7Pzxs/jjCiTRGu9rwm0eG8BuHBsHxvEBwf/Eb8/hfT1yDq4wCWS6YTAbLTi/+6Myhkk9k8bCFUavE4lgnFsequ6CkSJcImos1dktFFQqFomryvBV9uuIbPHeIgKQoclulxDc3cakl2+wm0t1pNva5HS8EYrW2aDSKjY0NoVNIr9cjnU5jZWUFHMdl3c/l4umnn8YDDzwAlmXxh3/4h/j4xz+e9fu1tTW8+93vRjAYBMuyeOihh3DPPfdU/T5bjnTFKGZhXgq5pJthOXz8u1fxo+tewaVBTQOnJrrxy7O92DdowInxLpye6sZPPvJL2Agm8Lc/NGHJGUI8zSCUYFFs0a0EoFIq8SuHJ8taAlUqoFMMuaQr9kUbHR2taBBEypyyHIv1nTQcIZZjzFUAIze33++H3W5HJpOBSqWCWq1GIpFAOByGXq9vaRH4VtK4lUonkbY2k8mEF198EZubmzh27Bh0Oh3e/e534wMf+EDJfbMsiw996EP40Y9+hNHRUZw4cQJnz57FwsKC8Jq/+qu/wr333osPfvCDuHbtGu65556iguel0HKkS1FUluVMJaO6YuROpf3HpQ38dNmXZYuTZoHnLQH83BZAr16DPoMay5sRZDhAq6TwloOD+H/fcxyjXdtqY889/zz0Y/vBc8B/vOrAf171Islst4R1GzS4e6Ef9x4vTw6u3Nwpw3J45KdWfPviOhiWx+vnevHJX9+HNvVrNxTZl9jxt5gbcTHs5PRCPSBljJhOp+H1epFIJOByuRCLxbJykCQybmtra4louJmWQI36PEhb2+LiIo4cOYKXX34ZFy9eFAY9ysG5c+cwMzODqakpAMA73vEOPPHEE1mkS1EUwuFtl+1QKITh4eGazpxSi68AACAASURBVLvlSJfneVy8eBH9/f01uSDkkq7VF5P0IaNAIZbKYDOSEn6WZHh856Ib333VDb16u0tByTPobb+O0TYWb5zQ4E//+wl0dHQgmdmOWMvpqSUoN9L9wnNWfOXndrA39SGfuOSGO5zE1+47CsXN7ga7P4afOZJ41fMCfvVAeSaUUqg2vdAqpCsFtVoNo9GISCSCffv2Ach2jIhGo/B4PIJIee7U3U6ze2+mwliztHSJ+l+hqFgKhRwhcr3PHnzwQbzpTW/CI488glgshmeeeaamc2050qUoCidOnKh5P7mku7ffAFpBgZEQt41nChMyxwORFCFHHt5kAtcB/Gg1jbdHXPjEPQa8bA8ilmZweKQDgx3l9ZOWS7rfu7QJjnutfYzleFxdD2MjnMJguxpPvWzCX//YCY7jodYAS7EwHpmoPsKRSi+Ug1YmXSA/pyvlGEFEyqPRKLa2tuBwOIQBAjERN9NhOBe7TUu3FoWxUnjsscdw//3340//9E/x4osv4l3veheuXLlS9efXcqQL1CdqyiXdew4O4ufWAP59yZ0lKk4BUFDEmbfkmWX97zsX13FtI4qNUBIKatu77B/fcQiHRkpfHOW+R40q/4unKAobG27Yrq7jC+e3jfp4NgOtUoFr7ih+uuLF3QsDJfddy3nVa7udhHILaVIi5ZlMpqAUo1gYSK/XN+Vz2o2kW03nQjmOEF/5ylfw9NNPAwDOnDmDZDIJn8+XVQuoBDLp3gStoPDpty7g/b80jidedWM1EEMyw8PqjyOeZqGmKayHUkX2mA+GA6zeGDp0SlAUhWSGxV89tYxvvf9kyW3LXYp+5A3T+Mi3ryDNcgC//YCYbWehp9I4cOIEEi+dg5oGUuzNKJXnsBWrXtC8mcMROw21di+oVCp0dXVlFVJz26RIiuL8+fNCioKQcT2FgXZbeiEcDlcV6Z44cQImkwk2mw0jIyN4/PHH8S//8i9ZrxkbG8OPf/xj3H///bh+/TqSyWTRadZSaEnSrQeUSiUSiUTWzyiKwlSfAR9542zWz9mby/f/vOTGJ568jtjNVAOFbTHxjERKggyMkRtFRSvgjxWWe6wWvzLfj//z+0fwj8/cgD+cwB0TBvzpPYdhaNtOYxwf68SLVj8ont8e9KAoHBqt3tJkJwxH7CbktkkxDINLly7hyJEjWSkKp9OJVColaByI0xTVkFoztXSbZdVTDekqlUp8/vOfx9133w2WZfHe974X+/fvxyc/+UkcP34cZ8+exT/8wz/g/e9/Pz772c+Coig8+uijNT38WpJ06/G0VyqVZVvYKCjA6/WiN7GGR8/2YmpqCnq9HgzHY8kZxvMmL55ccmM9/Bqh0grgrtleLLnCYDgeNLWtWnZioqfIkSoDz/PY3NwEt2HFJ1/XjcnJyTwroU++ZS8++R/X8YLJA61CgY/dPYOFoepJV04vNGc8V0pwhmgcEGEgi8UijNXmTt0VI1WWZavqaa0UOz29AAD33HNPXt/tX/zFXwj/XlhYwAsvvFDT+YnRkqRbD5TjacbzPHw+HywWC/R6PQ4cOJBVFVXRFI6Pd+L4eCc+8quzuHTpEgy9w/CmFBhq12CsR49vv+LC535iQYLhsbinA5+4Z2/N504cia1WKzo7O3H06FHJYZB2nQr/+95D+NkLL+CX7ryz5mMXIs9QKASz2QwAAlEYDIY8/VWZdMs7RjGyLKRxIHaLIP3F4kmuXGGgUtOY9UQzTSlbYRoNaFHSbXSkS7QbrFYrDAYDDh06VJa1s1KpRHebElPDr335bzs6gt8+MgyG46FWVn6R5wqDe71eWCwWdHR0lDV5R0DfTAvUeqOJ0wvRaBQmkwksy2JychIKhULokXS73Ugmk4JOLgDB9n6n6eSWi2ZFupUeQ8otQiwMJNbEJZ0x7e3tgrllPYWBxGhWeiEcDmNmZqbhx6kHWvPqrwNUKlWeZY84gmxvb8+zWS8FqTYvhYKCukxFsOztXiNKEnEbDAYcOXKkovPK3RdBIs1iyRUCTVE4PNpR1kOBoiikUilcvnwZ8Xgcs7Oz6O7uFgxAc8WwiU7u5uYmYrEYlpaWhIq9OCqut0VMI9BqXQXiSa6Bgde6VRiGwY0bN0DTdMOEgQhI6qPRaBXdBeA2Jl1x9wLJjdpsNnR0dFRFakC+I3CtUCgU8Hq9gsVNuRG31L7EBTBfNIXf+8rLCCYy4HlguFOLb7znOIxa6UsilUrB4/EgnU5jfn5ecIQgKBQJEp1cEsHNzs5m+XWR3CTRPRATsV6vb1gEVi2akV5o9DGUSiVUKhX6+/uF4lMx5S9i21ONh5ps1ZOPliTdelyUhCDdbjdsNhs6OzsrWq5L7bNeegmBQEBYpufmkqtBLul+5odmeCIpIUe75o/jn56z4mN3zwmvcQTi+PTTK3CHktjXReHNowy6O9oxMjJSVcsMiRQL+XUB2e4RHo8H0WhUcJLNdY+4FVFxMwtpjUYuGRZT/iJOEeUKAxU7TqPQyOGIeqMlSbdWiAWVg8Fg0UJUJShm7V4utra2YDaboVKp0NHRgbm5uZoJF8gnXZs/Bo4HaOqmngXHw+qLC78PxNK490vnEUpmAA6w+SgkFX346Fh1F3a53nWFpBlTqZQQFXu9XsTj8aybvlCVvxHYCYW0eqFccqcoqqCHWjFhIDERZzKZpln1tIKWLtCipFvthc9xHNxuN+x2O7q7u9HW1ob5+fm6nRdN00ilKhugIAgGgzCbzaBpGvv27YPRaMSlS5fq5uKbO757dE8nzJ4YuJvRp0pBYXHPNtmxLIsnX1pGIp2BAhRAb5tnPnPDiwdOdYHOGc1LMxw+/f0V/PiGD0atEh9/0zROT2bfALW0mpEikVjuT3zTk7wkyRlX0jpVCXZqIa0a1BqBFhMGIikKl8sFv9+PSCSS5RLRiBy+TLo7DERv1263o7e3F8eOHYNGo8HPf/7zut5ISqVSaNUpF6TdiqIozM3NZV3EjZR3/JM3TMPkieIVRwg8D/zybA/ec2YPnE4n7HY7KBi3UzD8a9tQ2J7c43LO6cHv3cB/Xt5AkuHgiabx3791Fd+4/wj2DrwWfda7T7fQTX/u3Dns27dPiIrFrVO5UXE1Paq7Kb3QqOOo1Wp0d3cL2sWpVArz8/NZD0lxDj936q5aYaBUKlVVHeZWYFeTrphs+/r6cOLEiaybjRBRvXJOlZBkOByG2WwGz/OYmZkpmI9qJOm2qWl8+V2L8EXTUFBAOhLAhfPn0Nvbi5MnT+IwR+HrS7+AL5oGw3LQqmj81pFtt4oUk02eP7i2iaRIoS3DcnjeHGgo6RZCMU+1QkthUq3fCU7DBM1KLzTLroeMAWs0mqLCQLnOwmIiLiUM1Gr93y1JuqWehBzHweVyYW1tDf39/XlkS0BysPUk3VI53UgkArPZDJZlMTMzU7TiWqt1ejn74hMhrFgs6OzsFFYAAKAC8O0/OokvPmfDeiiJO6a68c4To/B4NvMucrVSsS3ucBO0gqpIxrLRUCgUBQVoSK44FothbW0NsVgMALKiL7Es425KL+yE3HEpYaBoNCopDERSFOJ97/SWQ4KWJF2gcOTEcRycTiccDgcGBgZw8uTJoi1HhHTr1UdYLDKNRqOwWCxIp9OYmZm55e4Rfr8fZrMZer1eskWuW6/G/5MzQVdI2vHPfnUGf/HUMpIZDkoFhXatEr9+oD9vu50WkUhFxYVkGcl1pNFoEIlE6tLDWgjNSi8003qo0uMUEwYiD0kiKH/u3Dk899xzSKfTePrpp3HgwAGMjIyUfcxSVj0A8K1vfQsPPvggKIrC4cOH8wRxKkXLkq4YYtuewcHBkmRLUI9ug9z95ZJkLBaDxWJBMpnE9PR01g1eCjRN1z3SDYVCWFlZgUqlqqoVrRB5/s7REfQb1HjmhgddbSq849gwOttUJbfbiZDSPCC29Ol0OsvcUtzDajAYalYCaxYZtkpUSCDVZnjw4EHMzc3hL//yL/Hss8/ikUcewR/8wR/gne98Z8l9lmPVYzKZ8Dd/8zd44YUX0NXVBY/HU/N7aVnSJbY9xH5maGioYtueepOuOL0Qj8dhsVgQj8cFsq30QlcoFHWLdDOZDCwWC9RqNfbu3ZtXdS4XUuR5x3Q3TowZ6+4ivFOgVqsFHQNi15I7ZutyuQQlsNwhj3Kj4p1oGLmTodVqMTk5iampKTz00EMVbVuOVc+XvvQlfOhDHxKi7mo1dMVoWdJ1OByw2WwYHh6u2n6mEaSbyWRw5coVRKNRTE9P501tVbO/WhCPx2E2m7G1tYXh4WHMzs6W3qgI6plnrieaReji71JqzFackxT7qeVW6nMFgcj7aGXDSzGalZ+udjCiHKuelZUVAMCdd94JlmXx4IMP4td+7ddqOt+WJd2Ojo6avL6A+pJuMpmExWJBLBbDzMwM9u/fX/MFR9O0YDVd7flEIhHMzMzAaDTWZaRWlnYs/Z1K5SRJVBwOh7PEZ8RRcTMEgXieb8p3sRsUxhiGgclkwrPPPgun04nXve51uHz5ck3Ha2nSrXXpXQ/STaVSsFqtCAaDmJqaQjAYrMsSBKguqkyn07BarQgEApiensbCwgIoihLm6GuFTLrVPUilLN8Zhsmq1Pv9flAUBa/XW1QmsxbsNlnHagcjyrHqGR0dxalTp6BSqTA5OYm5uTmYTKaafBpblnTrAaVSWfUEmZjcpqamsG/fPlAUBYvFUrfzq6R7gWEYrK6uYnNzExMTE9i7d2/eUrgeUb3Ug6AUIdzupCsFpVKZpY9rs9mEwlyu3gGJisWFu2pdI5pl1dMssZtqahTlWPW89a1vxWOPPYb3vOc98Pl8WFlZEXLA1aJlSbdemrqVElE6nYbNZoPf7y9IbvVEOZEuy7JYW1vD+vo69uzZgzNnzhSMYqq1Ts+FFHkmEgnh4i+Uxmi1ankhNKtPl6bpgi7DYkEgsWtEpTKZzbTqaZbuwtjYWMXblWPVc/fdd+OHP/whFhYWQNM0PvOZz1TUgVTwuDVt3eKohHQzmQxWV1fh8XgwMTGB2dlZyQu3XjdnsUhX3JNMionFoop6FcBySTedTsNisSAYDMJgMMButwvKYGIioGm65SPdZqDY0IKUIJCUTKZer4fRaBQiY/IwbKYpZbNyuocOHapq21JWPRRF4eGHH8bDDz9c0zmK0bKk26xIN5PJwG63Y3NzE+Pj45KRJAEhyno84Qu1jBGFNJvNhv7+/rJ7kutFumQ/DMPAbrdjY2NDyHVlMhkhos4lgng8jlQqheXlZRgMBoEMWqlSvxMn0qT6VwsJApFBILVaLdj75E511ROylm5htCzp1gPFSFdMKsWW7YX2WS/SFQ9HEFcLi8WC7u5uydFmKdSLdHmeRywWw0svvYTR0VHhcxHvuxARMAyDpaUlDAwMSLZSGY3GrLHbnYZWErwpJAhEZDI9Hg9isRhsNhsSiYQg3yhemdSj06VZVj2t5BoByKSbR7pk4GJ9fT2LVMoFGZCox2gx2ZfP54PZbIbRaKxa+7dW0iXuGmazGRzH4Y477qjowULIKtdUkQwYRCKRvLFbQsI7SYymlZ0jiEym0WhEKpUSerbFwjM+nw+rq6t1EQSqV/BRCq0k6wi0MOnWO2fKsiwcDgdcLhdGRkZK5kjL2WetiEQiQk9nLVY9QG2k6/f7YTKZBN+45eXlut1M4gEDMcTC5USiMTciMxqNTTW5bDWPtHKPUUh4RmzfU61MJsuyTbFbCofDMum2CkhRyG63w+l0Ynh4uOJR4lyUY+1eCpFIBCsrK4JUYbVFAjGqId1wOIyVlRUolUocPHgQer0e6XRaknyKPQgrbRkrJEaTm6e0Wq1Z1ftMJoNEIlHXntZc7AbniHJyreXIZAYCgTzHCPHEXbNyuvF4vGW0dIEWJt1aL35S/Y/FYmAYpmayJajFnDIWi8FsNiOdTmN2dhadnZ34+c9/XvM5AZWRbjweh8lkQiaTwezsbFa1vNrWs3r06UrlKZPJJCKRCDiOg8lkEia9xOmJehTtdmIhrdpjVEvsUjKZ4qjY4XAgFoshmUwiFAohFovlyWTWC+SautWpp0rQsqQLVHcji7V2BwYGoNfrMTU1VbcLoZr0QiKRyBohrrUPsBDKId1UKgWLxYJwOIyZmZksexyCQtKOBMVIqZGRp06ng1arhd1uF1YFhfQPiCqYOD1RSTGylQppxdCICDTXMQIArl27hp6eHvA8j62tLTidTqRSqSwfNaPRWBeZzJ1YeJVCS5NuJSD+aKurq1mtVn6/v64J/0pIV9zjOj09jb6+vlsyaEGm2TweDyYnJzE/P1+UPKtJLzQbhfQPcrVy19bW8gpGhAQKvZdWL6QRcBzXlFw4x3HC5ylGOp0WUhRktcnzvKDiVolMZrM6JOqJXU+6pK91dXUVvb29ea1WpIOhXhdhub2/q6ur8Hq9mJiYEEaIG4lCpMtxnCCNuWfPHpw+fbpklCVFujuJcKVQSCtXXDCKRCLw+XxZbsPiFMVuKaSxLFuVR1w1xylEiGq1Gmq1Ou+BWI1MZrUjwLcSLU26xaIunuexsbEBm82G7u5uHD9+vOCFVksOthBomkY6nS74O5ZlYbfb4Xa7MTY2VhbJ1Qti0hUPWAwMDFSUz24Fcq0EUgUjUrSLRCLCyC3JU3Z2djZEiAZoTiGtWdoLlUShpWQyY7EY1tfXEY1GwXEcdDodVldX4XA40NbWVtUKoRzXCAD4zne+g7e97W04f/48jh8/XtExCqGlSbcQSD+pzWbL8/0qBJVKVXfSzU0viCPKStvRSA611huRTLf5fD6YTCZ0dnZWPGBxO6FQ0e7q1asYGBgAx3GIRCJwu91IJpNZ0RiZtKv2+9oteWNynFrJvZhMptfrxaVLl3Dt2jUsLi7CaDTikUcewZEjR0rutxzXCGC7k+hzn/scTp06VdP7EKOlSVd8cfI8D6/XC4vFgo6ODiwuLpY1RNAIyx6yP57nsb6+jtXVVQwODlbVIUEi1FpvknA4LEQLhw8frqnn93aGTqeDXq/PkmcUF+1I5R7YNrgUpyd2ygOuWa1cQGNWRkQm8w1veAMUCgV6e3vxuc99DuFwuOy+4HJcIwDgE5/4BD72sY/hM5/5TN3Ov6VJF9gmNp/PB4vFAqPRKGmyKIVGWfZsbGzAarWip6enpoiyVi2HWCwGk8kk9LPWo+f3doVUFCpVtCtk+67RaPKmvJqdstlNlkDiEeBKcrvluEa88sorcDgceMtb3iKTLkEoFMLVq1eh1+urntiqJ+nyPI9wOAyPxwOapqse2RWj2gm3VCoFs9mMSCSC2dlZ9PT01K3n93ZFJYW0Qv2sRPuARMVer1co2hESZlm24ZKILMvuStKtJziOw0c/+lE8+uijdd93S5MuTdM4ePBgTUtlpVJZsw8ZAGxtbcFkMkGtVsNoNOYtU6pFpY7ADMPAZrPB6/ViampKcI6QUR/U8lkS7QOtVpvVAy3Wyc1kMlhaWgLLstDpdFnpiVqdhgmaUUhrloxnMBjMc3soB6VcIyKRCK5cuYK77roLALCxsYGzZ8/iySefrLmY1tKkazQaa45SlUolEolE1duHQiGYzWYoFAosLCxAo9Hg4sWLNZ2TGOU6AnMch7W1NbhcrqZ3RgCvRXH1IoadiEYVucQ6uevr6zh27JhQLIpEIlktVIXGbSv9npsR6TZT1nH//v0Vb1fKNaKjowM+n0/4/1133YW///u/l7sXbpV7BABEo1GYzWYwDJM1KstxXN0Eb4DS6YXcYl21Qj21IBgMYnl5GTzPC0tjsrTeKQph9UAzOgsIxJ5q4haqQuO2wGsiNOQzL1ZQakak20zSrcYJuBzXiEahpUm3HqiUdBOJBMxmM+LxOGZnZ7PGHoH62eKI91covUAKiGazGV1dXbek/Ssej2NlZQUsy2JhYUGYqxcPG4gVwpLJJJxOp0AMrTZJ1EzSlUKhcVtx0S5XsFxMxMTGpxmFtFbQ0i3lGiHGs88+W9UxCqGlSbeZkS7RJQiFQoIuQTNuwEKRbjAYxMrKCnQ6XcXdGvUgDo7jcP36dYRCIaFIRya7gMLEwLIszp8/D4qi4Ha7EY1Gs7QQxALmOxWNzlNWu39x0W5oaEjYVyqVQiQSybLxoWkaiUQC6+vrWVZK9YaspSuNlibdeqAU6WYyGVitVvj9fkxNTRXVJWgExKQbjUZhMpnAcRzm5+fzlJ5KgUQ51d5kxAQzHo9jcnKyovFlmqZB03RWsUKshSBuqyJSjYSIGynVWCkaeR71jEDFRbtcc8vz588LU4lkwqve7h073X79VkImXQnSFdv1jI+PFzWibCQUCgVSqRSuXLmCWCyGubm5qi8ykqqo9GYQjw0PDQ3BYDBgaGioZgISt0oNDg4KxyL+arlTX4QQiIhKs7+PRqcXmpG+UCqVUCqVGB0dFX4m1j3Ide8QE3Eln3mzSDcajWZpabQCWpp063GB5uZMxQ4S1dj1kPOqR9SSyWTg9XoRjUYxPz+P/fv31/SeqxEyJ64RHR0dQt7Y6/UWfH/1KNBIGS1mMhlhqWy324U8sbiS34zlf6tEusWQ+zlJ6R6k02nhMxc7R4g/c6miXTPs11tRSxdocdIFahfHJjcRx3FYX1+H3W7H0NBQTaLmJCVQ7cVAlvHr6+vo6OhAd3d31s1QLSoh3Wg0iuXlZdA0nTd4kvuZ8zwPjuPA83xWz7NCoQBFUXW5KVQqVcE8Mankb2xsIB6P49y5c8L4bb3zxM2IdHcSgajVavT09BR17yBFO5ISIpFxMyUXd0rqqVy0POnWCkIUv/jFL9Db21u2pXkxEMueSvfD8zxcLhfsdjuGh4dx+vRp+Hw+RCKRms6HoFwhc5PJVDSVQUiXkC3Zp0qlyiJg8m+WZcHzvDBtRYilVoKhaVrob+V5HpFIBMeOHRP6W3PHb8XpiWryxI2OpHe6awRQ3L2DpIQ2NjYQDodB0zSi0ajwudfDvUOMZnxejUDLk261ka5YIIdlWZw6darmkV2CSuUiybmYzWb09PRkEX89jS7LFTKfmpoqmspQKBQCeZLoT/xa8Y3FcRwCgQAsFgt6e3sF8gUg/E22rzQq5nge/2UKwB5IYE+XFvqbkSJZKovzxOJK/sbGRtV54lZPLzTiGIVSQjabDTqdDhqNJsu9g+O4PGPLap2zI5FIxcXknYCWJ91qEAgEYDKZoNfrceTIEVy6dKmu+adKiHJrawsrKyvQ6/UFtRoqHQMuhkKky/M8nE4n1tbWMDo6WnSSjTzclEolrl+/jo6ODrS3t0va3sRiMaysrAgpCnFrG4mGxX8D5RMxz/N4+MdW/OC6DyzHgVYocKCTwckT+SkAqUq+VJ6YiJcTmUbxtdHqhbRmFbgYhhGEyku5d6TT6SwhIKPRCJ1OV/LhEAwGG6K70Gi0POlWcpGGQiGYTCYolUocOHBAsP2ut3tEOZFuNBoVHH/3798vWYEtdwy4HOQKmRNt3dzouhDEqYT5+XnhxvH5fLDZbMhkMoJWgE6nQyAQQDwex9zcXMEbg9xQuVGx+DjFiNgTTeOH131oUyugoGiwHI+LHg7roRRGOstbsUjliYl4+cbGhmB42dbWhlQqBb/fX7G3Wrlo1Ui3EKT6dGt17xDvs1FiN41Gy5NuOYhEIjCbzeA4DnNzc3kScI3Q1JUiSjLRlkgkMDs7W7L9qxHphVAohJWVFWg0GiwuLhYdrsjN25LIs1Azfjweh9VqhcPhEJaMVqtVeG0x/zFyfuQ9E0hFxKFYCgoKoADwPKCgKCgoHvF0bZ+VVM4yHo9jaWkpy1tNPPFVbZ5YjGbZrzeLdCsR6i/XvYMIAV29ehWrq6ugabriFUIpx4iHH34YX/7yl6FUKtHX14evfvWrGB8fL3v/pbCrSTcej8NsNiOZTBYluEZo6uYSZTqdhs1mQyAQqMiEspo2LylwHAeLxQKKorB3796i+qOFyFbqfHmeh8fjEex/FhYWhBsulUohHA4LN048HodSqRTSEqWcFqSIeKpfiV6DGhvhFLRKBWKpDIxqBYaMSqTTaSgUiroV7EjKQaVSYWZmRnjPRKaRRMViFwlx8ajc4zerkNaM9EI90hhSD8BEIgGn04nl5WVcvnwZi4uL6OzsxOOPPy7k8YudVynHiMXFRbz88stoa2vDF7/4Rfz5n/85vvnNb9b0XsRoedItdJEmk0lYLBZEIhHB0rzYxVwveUfx/sTFIuKLNjExgbm5uYpurHpEumSqzuPxYHR0FDMzM0UJVNx9UIxsAQhRs8FgwNGjR/OW3RqNBn19fQVzqeFwGKurq4jFYsKyk5BxsfFUhUIBrVqB//27B/GX37uGlY0wZrvV+ORv7IdeqxbOn3RNkPOvtmBXCFIyjcRFIhKJCII04jxxoWUywW5KLzSqT5cIAb35zW9GIBDA6dOn8Wd/9mcIBAJliZiX4xjx+te/Xvj36dOn8Y1vfKOu76HlSVcMEk36/X5MT0+XrSVbLB1QDYg5pcPhwNraWsW+aLn7qvbcxHKP4+Pj2LNnDwwGg6S9OIAsoip2c5I0CcMwmJ+fr2gqSCqXGolEEIlE4HQ6BW0G8fLdaDQKeedkMgnvmhnvmwfmfvO4kJ8v9BnUUrCrFIVcJKSWyeLRW6PRuKsKac04TjAYFB7mucJTUijHMUKMr3zlK3jzm99c24nmoOVJl6Iood1pc3OzqmiynpEu6Rd1u90YGRmpue+3mvQCMee0Wq3o7+8XBj1sNpukYhlJJYhJqBCISHogEBBWEfUATdPo7OzMKowQ9SzixmGxWMAwjNBbPTo6irm5uaJFrVoLdvUg4mJ54kgkIuSJE4mE0AKZqwxWLzTTqqfRD5BwOCykexqBb3zjG3j55Zfx3HPP1XW/LU+6kUgEFy5cwJ49e6oa2QW2o5NUKlXzuZBWNJqm0d/fj7m5uZr3WalU4YR19wAAIABJREFUJGlBMxgMeU7IuQReSd6WTOw5HA7s2bMHJ0+ebPhNJS7Ykbyx1WpFX18f9Ho9otEoLl++jHQ6Da1Wm5UnLlbUqqRgJ15liH9erzyxOEInU3Xt7e1CVJxIJGrKE+eimaaUjUY1YjelHCMInnnmGfz1X/81nnvuuar7iKXQ8qSr1+trFu5WKpWCGHQ1iEQiWFlZgUKhwIEDB5BOp+F2u6veXzUgPbE8z0u2oBHSrYRsAQjGn8RksxmSfWKQz7etrQ3Hjh3Li2zJRBRJT7hcLiSTScE6yWg0or29varOCWD7s7VYLNDr9Vm5etJtUK+CHc/zUKlU6O3tLZknBpDV1yqVJ87FbjWlLBelHCMA4OLFi/jABz6Ap59+Osv1uV5oedIlkoG17qOa7oVEIgGTyYRUKoXZ2VnhAmBZtq454mJIp9Mwm80Ih8OYm5srmtuiKAqZTCYrb1uMbEkvsUqlyhtuaAbIe0skEpibm5OcPhJPRIlvknQ6LXROEBNImqaziLhY1MjzPOx2Ozwej6AbTIhY/PCq24SdBCEWyxMTrdxCeeJC017NIN1m+aNVE+mW4xjxP/7H/0A0GsXv/u7vAgDGxsbw5JNP1u28W5506wGVSlUR6abTaVgsFgSDwYKC5vUuzBWCuCticnKypM4vz/PQ6XSwWq1CpZcQT66vGRFsJ+4Y1dih1AJSANzY2MDk5CT6+/urSmWo1eqCUSOJiO12O6LRqKBWJk5PkNHlwcFBnDx5Mi+aLaSwJpWeIBFxKSKupE+3WJ44V6JR3E+cTCYbLoXYrGi6WqueUo4RzzzzTM3nVgwtT7rNdI8Qa+wWE/GuNnIuB2JPtOHh4ZJ5bHEqob29HWfOnBF0CMLhsLAUJzcm+d2tEGwnU3IWiwUDAwM4ceJE3fOPxdTKIpEI1tbW4Pf7AbxWEQ8Gg1mdE4VQTcGObEeImOO4mlI34jwxUaUj017kQePz+eDxeOByubJ0J2rJE+eiWXnjSCTS9ICgHmh50gVql3csRbocx8HpdMLhcJSlsVvPKTICnucFbdtyPNGK5W1zl+Lk/dntduj1euh0OthsNrhcrqyIuN6VdDFIKkOtVuPIkSN1Ex8qBzRNw2AwwOv1IplM4siRI+jo6BDavIgwEsMwWbKRZJUghXIKduTfDMMIRTOWZYXPuR4FOzLt1dvbC5Zl0d3dDaPRKGlwKZbFrOYh0AwtXaB5gx71xq4g3VohRbri1qu+vr6yNXYbQbovv/wyVCoVDh8+nKVtm4tKi2TBYBAmkwlGoxGnTp3KInJxREymyVQqlUA4pYpT5SCTyQiDLHNzc02PXMh3bLPZhAkl8n4I+Yhfm0gkEA6HEQwG4XA4kEql4E0r8aM1DpxCid9ZHMbr5wcrKtiJhYGI31yh9EQ9CnZk6S+VJyZtbLljt+KouFQ1vxmRbrPyxo3AriDdWiPdQm1ZJKpsb2/Pa70q53zqgWQyKTgPHzlyJCs3mYtKJ8nIiDTHcVhYWCg4XCCOkAjIUjUcDmcVp8QRcTkSiRzHweVywel0YmJiAnv37m26NioRatfpdAW7InJBpqHa2tqEcVOTJ4o/+eorSGRY8Dzw89Uw3rdiwelRrfB5kOV77vvjOA6rq6vwer0FtYvJw7OeBbti2gviIiMBedBEIhGEQiE4nU6k02mhM0SsCkbeXzNJV9bT3QUIh8NYWVmBUqnEwYMHJSedGgkygOD1ejE9PY1UKlX0PHJHXovdgJlMBjabTSgCljvJQ1DITUA81muz2YSxXjERi3OGfr8fZrNZEI1v9hKRjEWHw+GSGhSl8Nh5FxIZFgpqW30nw/H4yaYGf3TPISGPSh5OuX3HTqcTQ0NDOHHiRMHvTBzdEtRasKt0SS5+0BTKE0ejUXg8HsFpmBA2wzANLagRcfRWhEy6N8FxHJaWlpDJZG5JxZ6cA9G2HRsbE7Rt3W53wXRFpcMNJLIkRpv1ihIKFacYhsnrEuB5HgzDQKVSYWpqCr29vU3tGSVFyLW1NYyPj1c8uVgIGTZ/hcVwXMGHE8Mw8Pv9ghSmUqnE5uYmYrFY1qhzsRRWtYMdhIzrQYS5eWLx+yPTmNFoFBcuXACALN2JUu+vXIRCoZoelrcSu4J0a7lxSHtUIpHAvn37ii7hK0W5s/Ri54je3l6cPn0668LMFTKvVAGMdAT09fU1LbJUKpVCzlA8OjwxMQEAgr8WAKFdq729vajQTS0gwjzt7e04fvx4zZZMBL+9OISnrnqQYrYJTqtU4J3H8yecyEPP7XYLbYbk59FoFOFwGBsbGzCZTIJ+r7iFrZZRZ5JyIu1kJFKlabpuBTvgte88kUigvb0do6Ojwig3ifitVmvBPHGllu+hUKglOxeAXUK61UBsTzM5OVn39pNy7c5DoZCQVyzkHAG8VpirtEhGJrm0Wm3TOwKA7MhybGysoLqZuF3L5XIhGo2C47g8Iq42OhIPWFQqzFMOFvd04JF7D+ALz9mQYji87egw3n5sOOs1wWAQy8vL6O3tzWuDI2kYcdRG3BVIi1euSDwh49z+ajHERCzue56fn4dOpysqAFRrwU4sYC5OqRAUyxOLBYCKdcu0qmsEsEtIt5InJMdxcDgccDqd2LNnT9YSnix96wFClFKkG4/HYTKZkMlkMD8/XzQ/pVAoKpokS6VSWTrCt2IZtrW1BZPJhM7OzqKjw2JzSQIS/ZGlKnFv0Ov1WdFfse9KnE6ZmpqqesCiHNwx3Y07pvNz4+l0WphYPHjwYNGuEzHE7gpikXgxUZHOCWK4KdXWFwqFcOPGDSF/LpXnFUfEtRbsGIYpWngulCcGkKVPLM4TiyfsDAYDFApFy7pGALuEdMsBz/PY2NiAzWbLUt4iaIR7BPGJEoO0SG1tbWF2drZkOoPneWg0GoHAiC9ZoVYtMqVGzCXLFUqvJ8hoNMdx2L9/f1WFSHH0R8RIxMtUojhGxl7FBTuVSiWI/hAbomYX6nh+29XZ4XDUjfCLEVUhkXi9Xo9EIgGGYYraQQH1L9hJWfWUQiH3CIZh8lZCX/7yl+FyudDR0YHnnnsOhw8fLpuAS7lGpFIp3Hfffbhw4QJ6enrwzW9+U0iJ1QtUiVarlmiGI9beUiBeYJ2dnZieni6YH7t27RqGhoYqnuWWwqVLlzA5OSlEsGJt24mJCQwPD5cc2xWnEsSFqXA4LNxchGySySTcbjdGR0cxOjradFETlmWxuroKn89XV8nHYuB5XiDicDiMUCgkdE4MDg6ip6cH7e3tDfEzk0I4HMby8jI6OjowNTXVdHEgktKxWq3o6OgARVEVi8RLQYqIxVAoFDCZTBgaGmpYzjWZTOLv/u7v4PF40NHRgcuXL+MHP/hBye+ZZVnMzc1luUY89thjWQLm//RP/4RLly7hn//5n/H444/ju9/9brWuEZI3966IdKXIS+wFVmqooFE+aSTCtlqtGBwcLKmIJpW3lWrVcrlcMJvNQsRBlmX1Gl4oBfL+VldXMTIyItn+1AgQzQRiGimOriORCAKBAOx2O9LpdFY+tNQkWTUgK5hoNNqQ3HE5SCQSuHHjBtRqdd6gSymReELGtXZOkMGRkZERZDIZ8Dxf94KdVquFRqPBW97yFtx7771lb1eOa8QTTzyBBx98EADwtre9DR/+8IfrLi6/K0g3F7FYDCaTCSzLlt2H2QiftK2tLdy4cUOomBe70SstkpH3SFEUjh07JjxQxMMLHo9HmCIjZFPKHLISkIea0Wgsa7igEfD5fDCbzRgYGMjKWer1emGAgUg/5k6SiTV4Cwn/lAPxQ2d8fPyWDHmIC2VSSnOlROI3NzdhNpuFlI04T1xO5wQAWCwWhEIhHDp0CHq9Pm+4A6ifUHw1Od1yXCPEr1Eqlejo6IDf769rV9OuIF1ykZP2r3A4LEjxlYt6ki5pGFcqlcIFKIVCk2QcD7AcDxWdf/OSxv5QKFTQbLNQRCwmYvE4LyHiSnUVSKEulUrd0qhueXkZCoWiZGeGWPpR3OBP8qG5wj9iIi4mhk4m2tra2urahlYJSPcL0TquJGUg1VkQj8cRDoezVgrFROIDgQBWVlYwMjKS1f9dSZ4YqIyIw+GwXEi7lWBZFiaTCV6vt2p1LKVSWbN7BCGjSCSC7u5udHV1VTRJRlEUrrojuOwKgwcw0qnFSKcOSgWFAaMaHrcL6+vrmKjQkkiKiHMLMGS0U4qIOY6D3W7H5ubmLSvUiXPHs7OzFU/UEVDUa8aSYg1eMRG73W4kEgmo1eoswtFoNLDZbNja2sLevXtvSb8owzAwm82IxWJVFywLQaxUJu6cICLx4geUSqUSnJfJd1HseqindVKjXCPIa0ZHR8EwDEKhUN3rE7uCdBUKBfR6Paanp6vOGdXiHkGIYGNjA1NTU1hYWIDT6ZSMnKVSCY5AHBcdIQy0axBPM3ji0gaGjBroFQwSYT/efGCwbtX4QlqzhIhJoz4hHBIJ+Xw+DA0NSbYeNRJkgMRqtWJ4eLhhueNC7sXiz2VtbQ2RSERwASYFzUbnzgnI52CxWDA2NtaUdEYhkXiSjujr64NSqYTL5RKsqsoViQeqn7Db3NxsiGvE2bNn8fWvfx1nzpzBt7/9bbzhDW+o++e7a0h3eHi49AuLoJr0AmkNstvtGBkZyZJ8pGk6L3Iulbf1xdLQqhSgFRQcW0koeRZezyZ6B9rQNjgKtHc3tP2pEBEHAgEsLy8LkeHGxoYggk7+FFuC1wOxWAzLy8vQaDRYXFysexGsFNRqNdra2uBwOKDX63HkyBFQFCWsFIjgOyEccRGzng8GklJRKpW3LIeeSqVw48YNKBSKgvKiUiLx4jFno9FY9DouZp2UTCbx8MMPw+FwVHwdlOMa8b73vQ/vete7BF2Sxx9/vKJjlINd0TIGbEcjtSiNhcNhrK6u4tChQ2W9noztdnd3Y2pqKi+ft7m5iUgkgpmZmbKLZCZPFC/ZttDbRuO5q2sIxDJYGO/D/tEe+GNpzPXrsbinOXks4o4Ri8UwNzeXVYwU94aGw+GsJXg9iZhhGFitVgSDwVu2jCe9z1JKYGKICSccDgutWmIirkYsnAz0uN3ukpZMjYJ4urCc/nIxxFOH4XBYmDqsZNgFAF599VU88MADOHv2LD7+8Y/fkhx6BZC8+GXSvYl4PI7l5WUsLi4WfR1RIVOpVJidnS3YhpZhOfzsmgPmdf//396ZR0dV3/3/PWtmksm+7yuTCWHLBtIHVOyjPCpaH0UpaLECP462Kv5QKhgfFfWwVSqpWAW1bhW31sfjT1us1YbFkkBYhSQzCUnIvk4ms2/33t8f6fd6s0zWmbkhua9zOEeUk/t1mPu+n/tZ3h/MzkzBvMRgKKRij2JLsDtd+Oxf1ajvNEAcGAaDU4xF6RGQSkQwWF34r9wYxAT7NsojpjskdxwbGzsm8eTmQo1GI1uU4grOWIWY2xGQnJyMxMREXiz8iL1nXFwcUlJSJhS1Du6vNpvN4+qZNRqNqK6uRkREBNLT03kx7bZaraiqqkJQUBCysrK80nvMHXYhn81gk3hSsHM4HNizZw9KS0tx4MCBMQdGPDP9Rdflcg3brD1WnE4nzp8/j6KiomH/u91uR01NDWw226hR13fVXTh/pQu2Pj0CQ8OREBGMny2Ih5RYG1qcsDgoBEhFiAiSQyoWsVFEQmIilGH9ebNOsxO1nRZIJCIsSAxBcsTYxkgnCtcYJzU1ddI3uCch5ravDRZik8kErVYLlUqFzMxMXqIZu90OnU4HAFCr1V73rOD2zBIhBjBAiJVKJRoaGmAymaDRaHjpEOG2omk0Gp93C3BN4k0mE8rLy7Fz5044nU6o1Wo8+OCDuP766wdM5E1hpvdwhDfwlNMlr7hk0mq0ir3N6UZNhwkpUSr0SSnYbBac13Yj2NyEpKgQNNtluNDpxsUOG9r6HAgJECEv3IU758cOaTuKCVFgToLvfRPI5gKpVIr58+d7TWSGK0oNVwUPCAhAUFAQLBYL3G43NBoNL34RXJEZb8vhePDUM0uEmCw9lcvlCA8Ph16vh8vl8pot4lgwmUyoqqpiR6n9UTjljjqHh4fj0KFDiI+Px/bt22G323HmzBlERkZeLaLrkWkjupN9/Ry8PYLk0ZqampCamsoa43iCzdtSFGgwAMTsTSsOtmP+3Bg4bBacOdmM+m4LrvQ4oZQARhdwjgnAXKsC2X4eGeWaeXNXyPsS0qZFhJi0oTU3N7OicunSpQER8UQHF8YD8WuIjo72yULM0RCLxWwrWkBAAJYuXcp21Ay2fST+tCQq9ubbAEVRbDvc7NmzeYmwT58+jcceewx33303jh49yj5obrvtNr+fxRdMG9H1FgzDsIYq0dHRQ7xth/vz3CJZgEyC+YkhONtkhFIugd1FITUyENEhCnSLxQgJCUZvswkKKRAWHASbi4ZMwuCUrhXRjlbWT4EY2/hiGSTXkMVbZt4TwWAwQKfTITw8HIsXL2Y/58GDC83NzewE2eBR3smemziBOZ3OcTmBeROGYdDU1ITW1tYhETbJbXKNf8jwwmjGP+Olt7cXWq0W8fHxKCws9Pt3wm63Y+fOnThx4gTee+895Obm+vX6/mLa5HTdbvekl0EePXoUCoUCgYGByMrKGvE1m7vJdbDdIsMwqO2yoMvsRKhSCnWMChIRUFPXgEOnmnHFFgC9HVDKJZCI+4X6Jk00VhclweVyDciDkqEFb3UG6PV6tusiLS3N74YswMCJtuzs7DE19g8WYqPROGCUl5sjHgtkXU5zczMyMzN5GfQAvFMo406RkXyoy+UaIsSeWszcbjdbryB+u/7m1KlT2Lx5M1atWoXNmzfz8r30MtO/kDaa09hIWK1W6HQ6dHd3o6ioaNTWpOEmySxON6wOCoFyCYICpAP+bGdnJ+rr6xEXFwdFWAw+v9CJv13qgItmEBEkQ0ZkIB7/z0yEKoe/KYYrSHHFZiwGLjabjS0Oeeq68DUkZdPa2uoVoRs8KTWcEA/32RChCw8PR0ZGBi8dAW63m92C7ItC2eCilNFoHGL8ExwcDJPJhNraWqSmpiI+Pp4XK9AdO3bg1KlTOHDgAHJycvx6fR8iiO5wkF5Ug8EAtVqNuro6zJ8/32NE4KnftqrdhD+faUVVuxldJgcYhkGESo7r04IxL6gXwaoQ5GTPGvBzDTYn6rqtEEOE7NggKOVjf7J7ivqUSiWbliArUMiGjJ6enkmNzU4WvV6PmpoaREVFIS0tzWdCxzW3IWJDhFilUsFkMoGiKN48IwCwaQF/t8MNNv5pb28HTdMICQlhvzf+yJ8TysvL8cQTT2DNmjXYtGnTdIhuuUx/0aVpGi6Xa0x/lqIoNDY2orW1Fenp6ewT/uzZs8jOzh4SBY403GCyu/Ha0XpUtZvRYXSg2WCDmwYkAGgAcgkQFCBDZpQSu/97NuJCffPqxo1syC9iYh0REYGkpCSEhob6/YtN2q8YhoFarebl1ZV0JTQ1NUGlUoGmab/YPQ7GbrezJj1qtdrvk3VA//ekra0NV65cYd82Bg+7TKbHeizYbDa8+OKLOHPmDA4ePIjs7Gyv/NwphiC6wI9fuPr6esTHxw/pRf3hhx+QmprKtiuNZZKstc+ON45fQW2XGfXdVhhsbogAiAG4mP5PPjVCAaOdQk6cCgfunQ+xSARdhwk1XVYEyiUoTAlDsMJ7YmgwGFBTU4Pg4GDExcWx+T4S5XH3j402kjlRuFssfNl+NRrECYx4c5ACEzfqI7+IEHNTE94YtR2pUOZPbDYbqqqqoFAoMGvWrBGLbZ6mDrmpiYn4TZw4cQJbtmzBL37xCzz66KO8pHb8xPQXXbLh1BNkuig0NNTj9oiqqirExsYiPDzcY5FsMGaHGyXfXcbphh40GxywuACpWASKZkADEIuA1AglaAA0xeDTjYWo67bg/13oRKBcDKebQbBCigd+koygcaQYhsNut6O2thYulwtqtXrYAhXXQ5XcVMTMmivEk+nLJIYsk5nkmiykv7qvr2/MnsrcPWTeEmKTycTmj/maKOOK/mTGiLnOdKTIK5PJBghxUFDQsPeK1WrF888/jwsXLuDgwYNQq9WT/d+a6szc4QgS6YjFYsydO3fESrlUKh2yAHI0wbCbDMiR96BRKUKvXQ6rywmaZiAWAzQNBEhFkElEMNoohAZKESAV43htL6JUMihl/Tdgi8GOhm4bchM8L6ccCW5UyV3vPRxcD1VuGxK5kchWAWJSMh7PADJKLZPJeDGmAX4sXNbV1SE5OXmAv+toDLeHjJu26e3tZf1lybiqJyEmom80GnnNH5vNZlRVVbELQicj+sMZIpFuG7JinWv8Q4ZdHA4Htm3bhgceeAAvv/zydI5ux8S0FV2Hw4GamhrWsGU0702GYSCTyVBfXw+TyQSZUoVD53pxvE4PB8VgbkIw7lwQj8LUcMilYpjNZuh0OsjlctzyH/m4VSrD5S4LjtX24OtLXbC53DDa3WAgQrfZCZlEjAeXpiJAKukXdM6DkAHATOClgiswCQkJE54cEovFQzbyklFVYgRksVggkUgGRHzk9ZI01Ov1+mGN1f0FEX25XO41Fy6uEHM3URAh1uv1aGhoGNCiRdM0Ojo6kJKSMi7R9yY0TaO+vh49PT0+nfCTyWRDvJrdbjeMRiNKS0vxyiuvsN0R1dXVqK6unrb9t2Nl2qQXgH6hJdV6shF3NMMWbt6W9DuaTCa8XNqIsiYbHBTgoAGGASICJZBJJLA5XVBKgHX/kYJVC9NgdzNQyMQQD7qOw03hH9VdMNndyE8JgzqmP9o52dCLw5c6EaKUwulmIJeKsf4nKePK6xLjHZKr9IfNH3HRIq/eFouFTetER0cjPT3dJ8Mco8E1Ns/OzuZlowDDMOywh9vthkwmGzC0QH75w0vCYDBAq9UiNjaWl/QOwzA4fvw4nnzySWzYsAEPPfQQbDYbzp8/j7S0tCHG4ZNl3bp1+PLLLxETE4OLFy8C6O+WWbVqFRoaGpCWloZPPvnE38HAzMjp1tXV4cqVK0hKSkJycvLYxnaHKZLZXRTWv38O7UY7zI7+VIOLouGiABo0gqRiiESARCxGXlIw5HIZWg12RKnkyIwKxOKMCKjjghEZKBtWgBiGwQ+tJmjbTQgKkGJxRjjCA8cmmk6nE7W1tbDZbFCr1QNWrfgTkraRy+WIjo5mi3U2m82vI7xkR1p8fPyof+e+ggxatLS0DEjvcIcWyMOKGxF7W4i52yRycnJ46cU2m8149tlnodPpcPDgQWRmZvr8mkePHoVKpcLatWtZ0f3Nb36DiIgIbN26Fbt27UJvby92797t87NwmP6iC/QvxouNjR3xSzzSJBmBohn8nw/Ooa7LCovTDTEAm4sGxQASERAeJINUBHRbXAj/d9rS5ALcVP8HJpOIkBIZiF8sTMIdC+KHRMATgWvGwteqHGCgX4OnAhUZWOjr6xsysED6QScbmZP2K5FI5BMnsLFCCmVhYWFjGrTwND3G9VOYiBB3d3ejpqaGNytMhmFw7NgxbN26FRs3bsSDDz7o1wdgQ0MDVqxYwYpudnY2SktLER8fj7a2Nlx//fXQarV+Ow9miuiOZu843CSZJ/6p68KLX1Wjx+wGBUAuFcNJ0ZAAiAwOAMMw6DG7EBcSALPTDZpmYHFSIF+zMDkQFSTBhqJoFGXGTDiiYRiGtVwkr4t8VcCJ/eR4p5c8tWdNJOLzlxPYaFAUxW6/1Wg0k3rjGCzERqNxSGrCk7GN0+mETqdjN1/z8fAxmUx45plnUFdXhzfeeANpaWl+P8Ng0Q0LC4PBYADQ//mGh4ezv/cTM7d7ARj/enOr1YoIWysezlOg2hqE6k47JGKgz+5Co94Ok61/8k0hFSMuRI6aLjcoBgBEEEsAMURQBQVAoZTASEnQ3d2Nuro6UBSFoKCgAVNjIwkoKdbxtaaGQFatk1Xy4314eNrGS4Rm8OfDFRruMAfXCYyPPW0EsjUkKSnJK4UyT8sgick3acEbbGxjt9vR2NjI1i78DcMwOHLkCLZt24aHHnoIr732Gm9/JyMx2v3ub6a16I5XbF0uF+rr62EwGJCVlYX58wf2M9IMg2+qOvGvul6EKKTQxKpwpKYHV3rtsFldEIEBRYugDJBAhP5JNHVSDLL/vWKH2yPb2toKk8kEAANcxQIDA3Gm0YB/VTZARjnw34s1iI/mZ3SXmz/2dtvTSEJDVsUTK8PAwEDYbDaIRCLMmTPHa5tvx4vD4WBfUfPz8336EBSJRFCpVFCpVEM+n56eHlRWVoKmacjlcnR0dMBms7EPK39MHZpMJjz99NNobGzEF198gdTUVJ9fczzExsaira2NTS9wNz7zzbRKLxCnsfGKLVlR09LSMu5XZ73FidouC/5+qRNnW/rQaXJCKZMgKUyB/8qNwV15CZCIPf8sbmtWX18f/qnrwYk2CiGBckjkSiRHBOH/3jgLCpn/no80TaOlpQXNzc3IyMhATEwML5ECaepvbGxEZGQkGIYZ8KAiIqNSqXwaYXkqlPkb7jlIaoX7oOJOHQ7OEXtLiBmGQWlpKZ566ik8/PDDWL9+/ZSIbgenF7Zs2YLIyEi2kKbX67Fnzx5/Hmnm5HTdbveYJsmAgfnS6Ohorxix2F0UeixOyKViRAXJxyxWZGLuPZ0ICZEhAE3B4XSgpdeGm5JFUEcrBxSifBVl9fb2oqamhtedXEB/SkOr1Q7rBMZ9UHH3jnHzw54mo8bLeAtlvsJisaCqqgohISHIzMwc8Rxcz93BQuwpdTMWjEYjnn76abS0tODAgQNISUmZ7P+WV1i9ejVKS0vR3d2N2NhYbN++HXfccQfuuecetgbxySef+NvsaWaI7pYtW6BSqVBYWIiCggIEBwd7vPFMJhN0Oh0UCgUyMzN5q34TW0mxWIzMrCw8+7d6xAQHsNFxa58N6xanIjNCxkaTnZMzAAAT+0lEQVTD3EIU1x1qMtEM2QHndruHNf3xFy6XC7W1tbBarcjOzh5zSoMsgCSfEZmMGm6YYyxQFMVuIp5soWwykM0anZ2d0Gg0E96IzF0EScSYu5F3JCFmGAbfffcdiouLsWnTJjzwwANTIrqd4swM0dVqtSgrK0N5eTnOnDkDp9OJOXPmoKCgAEVFRcjNzUVzczPq6+sRGhrKa58rd704d4rriwtt+FbbjTClDFYnhRCFFJv/M3OILwO3EEWEmKZpqFSqAYW60W4ObjcAcZ3iA677VVpaGuLi4iYdqU7UEJ70/iYmJiIpKYm3IgyJ9okdpreFjltjIGJMvkMqlQo1NTXQaDTYtWsXOjs78frrryM5OdmrZ5jGzAzRHYzdbse5c+dQVlaGEydO4MiRI5BIJLj55puxdOlSFBYWIjU11a9PbW7rVUpKChISEgbc1BTN4FhtD6razYgIkuGmnOgxD07QNA2z2cwKMddDgQgx97WbpDRiY2P9/jlwMZvNqK6u9ssG4JE2FAcGBqKjowMSiYTX3l/SjkZ8G/xZOCRC3NXVhS1btuDChQuQy+VYsmQJli9fjvvuu89vZ7nKmZmiS2AYBrfffjtuvPFG3HPPPawQnzp1ip1gKyoqQkFBAQoLCxEeHu6T6IbkS0l+0B9VZoqiWIEhr91isRhOpxNyuRxqtRqhoaG8RHMTcQLzNqSHuKGhAR0dHVAoFGAYxicWj2NBr9dDp9PxGmUbDAZs27YNer0er7/+OmJiYlBZWQm9Xo9ly5b5/Povv/wy3nzzTYhEIsydOxdvv/02bw/ASTCzRRcAW1gbDE3TaGhoQHl5OcrLy1FRUQGTyYScnBxWhCe7ltxms7HtT2q1mrd8KfEo6OzsRFxcHGiaHrL+x1sTYyMx2AmMjwkqAnHhIpafEolkWEN4MjXGFWJvPjRdLhd0Oh2cTidycnJ4ERmGYfD111/jueeewxNPPIH77rvP728/LS0tWLJkCSorK6FUKnHPPffglltuwS9/+Uu/nsMLzOzhCMDzinaxWIyMjAxkZGRg9erVAPpvgB9++AHl5eV49913ceHCBUilUuTn5yM/Px+FhYWYNWvWqJVsrhFLVlYWb9NTDMOgq6uLdSNbtGjRgJuJOzHW29vLumZxBzlCQkK8Urm3WCzQarUICAjwmhPYRBipUObJWWy4LbzjGXbxREdHB+rq6ryWy54Ivb292Lp1K4xGIw4fPoyEhAS/n4Hgdrths9kgk8lgtVp5PYsvmDGR7mQg/aEVFRUoLy/HyZMnUVtbi5iYGDYaLiwsZB3NuH2uSUlJSExM5C1fyhW5rKysMbeacfs/+/r6BpidE5EZT38seQD19PRArVbz4gRG8FahbDhDeJqmB/THjlTMdDgcqK6uZnPIfDyAGIbB3/72N2zfvh1PPvkk1qxZw3tnQklJCYqLi6FUKnHTTTfhgw8+4PU8E0RIL3gbUhAjaYmTJ0+iu7sbUVFRaGtrw5o1a7Bhwwbe86UGgwHZ2dkTbjXiwjU7J4U6YljNnajz1A2QkJCApKQk3m5qh8MBnU4HmqZ95lPA/YxID/FgQ/jAwEC0t7ejsbERs2bN4m3YQq/X48knn4TNZsOrr77KTr7xSW9vL+666y58/PHHCAsLw913342VK1dejQU8QXR9DcMw2LhxI6qqqrB8+XK0tLTgzJkzoCgK8+bNY6PhnJwcnxbQGIZBe3s7Ghoa/JIvJYbV3EKdXC5HaGgoFAoFOjs7IZPJMGvWLN6KIQzDoKWlBU1NTcjKyvJ7Wxx3mEOv10Ov10MikSAmJoY1j5/IvrGJwjAMvvrqK7zwwgt46qmn8POf/3zKeBN8+umnOHz4MN566y0AwHvvvYeysjL84Q9/4Plk40bI6foakUiEzZs3Q6PRsF9gUpA5ffo0Tp48ib1796K6uhqhoaFs73BhYaHX0g8mkwlarZYdEPGHYbZUKkVERMSAaR+bzYbLly+jpaUFCoWCfY3m5of9cTbgx3a0kJAQFBUV8bLmmwxpGAwGOBwO5OfnIygoiBXiy5cvw2KxQCaTDSjU+cIQvqenB1u2bIHb7cY//vEPXoxyRiIlJQVlZWWwWq1QKpX49ttvUVhYyPexvIoQ6foZMnrMTUu0tLQgLS2NjYbz8/PHlZYgU1wWiwXZ2dm8DXwA/a+sNTU1iImJYXt/ud0AZJDDW0UoT3BXCPlyXc1YMJlMqKqqQmRkJNLT0z0+YMniR/LLm4bwDMPgiy++wI4dO1BcXIxVq1ZNmeh2MM8++yw+/vhjSKVS5OXl4c033+TNYW8SCOmFqQxN06itrWVF+PTp07BarcjNzWWFeM6cOUO+eNzXZj4r38CPO+nIGLFSqRzxz3OLUKRQB2DIIMdE3gDI0EdCQgKSk5N5+0yI8Pf29k7YpY07zNHX1zfAEJ60+I1WgOvu7sbjjz8OkUiE/fv3TynHrWmMILpXG06nE+fOnWOF+OLFi1AoFMjLy0NhYSFEIhHOnj2L9evXIz09nZfXZuBH56vm5mZkZmZO6oYe7LjGXYZJhHikV26n0wmtVuvTQtlY6e3thVar9brwj8cQnmEYfP7559i1axf+53/+B3ffffeUjW6nIYLoXu2QxYd///vfsXv3bnR3dyMuLg7h4eHIz89nJ+qioqL8dmMRbwBfOpJx/RP6+vrYV25uflgul7MR/2SFf7K43W7U1NSwHsSjRfzeYPDmCa1Wi+LiYgQEBEAul2P79u244YYbeE07zUAE0Z0ufPDBBwgKCsLPfvYz1m+2rKwMJ0+exKlTp9gWMVKomz9/vtcLMi6XixUWjUbjd1NxbqSn1+thNpshl8sRHx+P8PBwvxl5D4ZslBivJ7M3YRgGn332Gfbs2YP169cjLCwMp0+fRnR0NJ555hmfX99gMGDDhg24ePEiRCIR/vjHP2Lx4sU+v+4URBDdmYLb7calS5dYb4lz585BJBJhwYIF7CBHdnb2hKJSrllPenr6qOvtfQm3UJadnQ2pVDogP0xR1Lgd1yaK0+lEdXU1gP6FiHwVfTo6OvD4449DqVSipKSEl/7f+++/H0uXLsWGDRvgdDphtVp5HYThkZkhuocPH8amTZtAURQ2bNiArVu38n0k3mEYBmazGadPn0Z5eTlOnToFnU6HyMhIFBQUoKCgAAsXLhy1CEfa0YKDg5GZmclbDhkYWCjzNGwxXse1icC1o+QzrUHTNP7yl7/gpZdewvPPP4877riDl4dhX18fFixYgLq6OiF3PBNEl6IoqNVqfPPNN6xr2IcffojZs2fzfbQpBxmgOHnyJBsRt7e3Iysri01L5OXlQaVSwWAwoK6uDjRN82rmDfy4+dbtdkOj0Yy7UDac45pUKh2QHx7OX3c4bDYbqqqqoFQqkZWV5be+48G0t7dj8+bNCA4Oxr59+3jz9wCAc+fOYePGjZg9ezbOnz+PgoIClJSU8LbTjmemv+ieOHECzz33HL7++msAwM6dOwEA27Zt4/NYVw0URUGn07H54TNnzqCzsxNutxv33XcfbrvtNuTm5vIiLty0hrcjStIbS/qHR3NcI3n01tZWZGdns+bz/oamaXzyySd4+eWX8eKLL+L222/nPbqsqKjANddcg++//x6LFi3Cpk2bEBISghdeeIHXc/HE9J9Ia2lpGeBqn5SUhPLych5PdHUhkUiQk5ODnJwcPPDAA1izZg3mzJmDlStXorq6Gvv378elS5egUqkGmPykpKT41EvBYrGwBue+mCiTy+WIiopi858jOa4pFAr09PQgIiICRUVFvO1La29vx6ZNmxAREYEjR474e/eXR5KSkpCUlIRFixYBAFauXIldu3bxfKqpx7QRXQHvsn//fvZmvvnmmwH0C1JPTw9OnTqFsrIyfPTRR+wGDLKXrqCgwCsm8FxXMm8Z9owFkUgEpVIJpVLJjsiSt4DOzk6EhISgr68PFRUVE3Zcmyg0TeOjjz7C73//e+zYsQO33nor79Etl7i4OCQnJ0Or1SI7OxvffvutkN4bhmkjuomJiWhqamJ/39zcjMTERB5PdHUzXPQkEokQFRWFm2++mRVimqZRX1+P8vJyfPfdd9izZw/MZjNmz57NRsTz5s0bV/6VbE+Ij49HYWEhr1aDBoMBWq0WsbGx+MlPfsKehesm1tjYOGbHtYnS1taGTZs2ITo6GkeOHOEtrTEar7zyCu699144nU5kZGTg7bff5vtIU45pk9N1u91Qq9X49ttvkZiYiKKiIhw6dAi5ubleu8a6devw5ZdfIiYmBhcvXvTaz51uOJ1O1gS+vLwcP/zwA2QyGfLy8thCXVZW1hAx5RbKxjJK7EvcbjfrZ5GTkzOmbR8jOa5xC3XjgaZpHDp0CPv378fOnTtxyy23TKnoVsAj07+QBgB//etf8dhjj4GiKKxbtw7FxcVe/flHjx6FSqXC2rVrBdEdBwzDwGg0DjCBv3z5MmJjY9mURGVlJRQKBVatWoWYmBhehaW7uxs1NTVescYk3gmkUOdwOKBUKsfkuNba2opHH30U8fHx2Lt370ztd71amRmi6w8aGhqwYsUKQXQnCTHr+eyzz7B37152iiwjI4PND+fl5fnVZ5ZE2hRFQaPR+GTIYTTHtc7OTqjVanz++ed47bXXsHv3bixfvlyIbq8+pn/3gsDVhUgkQlJSEvr6+vDpp59i4cKFoCgKlZWVKC8vx5///GcUFxeDYZgBJvAajcbrHQwMw6CjowP19fXIyMjwqcfscPvXuI5rb7zxBo4dOwar1Ypbb70VLS0tcDqdV6O1oYAHhEh3nAiRrv8gRi7EBL68vBxarRbh4eFsWqKoqGhSKQC73Y6qqip2HT1fQw40TeO9997DgQMH8Nvf/hZLlizB+fPnUVFRgV/96ld+a0+jKIo11v/yyy/9cs1pihDpXi00NTVh7dq16OjogEgkwsaNG7Fp0ya+j8ULIpEIQUFBuPbaa3HttdcC+HGzMSnSvfvuu2htbUV6evoAE/iQkJARhZhYUra0tECtVvPa69rU1IRHHnkEGRkZOHbsGGu4vnjxYr+bxZSUlCAnJwdGo9Gv151JCJHuOPF1pNvW1oa2tjbk5+fDZDKhoKAAn3/+udDvOALEBJ5M050+fRp2u32ICTyZLNPr9airq0NISAgyMzN5G3KgaRrvvPMO3njjDezduxc//elPec3dNjc34/7770dxcTF+97vfCZHu5BAiXW+wevVqlJaWoru7G0lJSdi+fTvWr1/v1WvEx8ezW1mDg4ORk5ODlpYWQXRHQCwWQ61WQ61WY+3atQD6uwaICfyBAwdw8eJFyOVyBAUFwWg04vXXXx+2bc1fNDY24uGHH4Zarcb3338/oa0S3uaxxx7Dnj172C0eAr5BEN1x8OGHH/r1eg0NDTh79iw7VikwdgICArBo0SL2s6upqcHq1auRnJyMtLQ0bN++HfX19UhMTGSj4YKCAkRGRvo02qRpGm+99Rbefvtt7N27FzfccMOU6Ewg/ecFBQUoLS3l+zjTGiG9MEUxm8247rrrUFxcjDvvvJPv41z19PX1oa2tDRqNhv13NE2jsbFxgAl8X18fNBrNEBN4b9DQ0ICHH34Ys2fPxq5du6ZEdEvYtm0b3n//fUilUtZ74s4778Sf/vQnvo92tSL06V5NuFwurFixAsuXL8fmzZv5Ps6MwuVyDTGBF4vF7DRdYWEh1Gr1uPLAFEXhrbfewjvvvIN9+/bhuuuumxLRrSdKS0vx0ksvCTndySHkdK8WGIbB+vXrkZOT41PBtdvtuPbaa+FwOOB2u7Fy5Ups377dZ9e7WpDJZFiwYAEWLFiABx98cIAJfFlZGXbs2AGdTofo6GhWhIuKijxu0aivr8cjjzyCuXPn4vvvv5+p3rICHIRId4px/PhxLF26FHPnzmWLPDt27MAtt9zi1eswDAOLxQKVSgWXy4UlS5agpKQE11xzjVevMx0hGyO4JvCdnZ2sCXxhYSHmz5+PDz/8EO+//z5KSkqwdOnSKR3dCngdIb0g4Bmr1YolS5bgtddeE4p2E4SiKGi1WrZ/+PDhw1i4cCHeeeedMZnlCEw7BNEVGApFUSgoKEBtbS1+/etfY/fu3XwfadrAMIwQ2c5sPP7l82dUKsA7EokE586dQ3NzM06ePCmMNnsRfwpuU1MTli1bhtmzZyM3NxclJSV+u7bA+BFEVwBhYWFYtmwZDh8+7LNrUBSFvLw8rFixwmfXmKlIpVLs3bsXlZWVKCsrw6uvvorKykq+jyXgAUF0ZyhdXV0wGAwA+jfbfvPNNwN6WL0NmekX8D7x8fHIz88HMHCKUWBqIojuDKWtrQ3Lli3DvHnzUFRUhBtvvNFnUWhzczO++uorbNiwwSc/X+BHhCnGqY/QpztDmTdvHs6ePeuXawkz/f7BbDbjrrvuwr59+1inMoGphxDpCvgU7ky/gO9wuVy46667cO+99wpj41McoWVMwKcIM/2+h2EY3H///YiIiMC+ffv4Po5AP0KfrgD/+GOmPy0tDcHBwZBIJJBKpaioqPDZtaYK/ppiFBgXgveCwMzhn//8J6Kiovg+ht9YsmQJRgmeBKYQQqQrMK1IS0tDRUXFjBJdgSmJMJEmMDMQiUS46aabUFBQgIMHD/J9HAGBIQjpBYFpxfHjx5GYmIjOzk7ceOON0Gg07FJLAYGpwGjpBQGBqxaRSPQcADPDMC/xfRYBAYKQXhCYNohEoiCRSBRM/hnATQAEFx+BKYWQXhCYTsQC+N9/O3xJARxiGMZ3Lj4CAhNASC8ICAgI+BEhvSAgICDgRwTRFRAQEPAjgugKCAgI+JH/DwxdUQdps6/FAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Version where you can choose your portfolio share only with some 0 < p < 1\n", + "\n", + "init_portfolio_prb = copy.deepcopy(init_portfolio)\n", + "\n", + "init_portfolio_prb['AdjustPrb'] = 0.5\n", + "init_portfolio_prb['PortfolioDomain'] = cpm.DiscreteDomain([0.0, 0.6, 1.0])\n", + "pcct_prb = cpm.PortfolioConsumerType(**init_portfolio_prb)\n", + "\n", + "pcct_prb.solve()\n", + "\n", + "plt.plot(eevalgrid, pcct_prb.solution[0].RiskyShareFunc[0][0](eevalgrid))\n", + "plt.show()\n", + "\n", + "pcct_prb.track_vars = ['aNrmNow', 't_age', 'RiskyShareNow', 'CantAdjust']\n", + "pcct_prb.T_sim = 10\n", + "pcct_prb.AgentCount = 30\n", + "pcct_prb.initializeSim()\n", + "pcct_prb.simulate()\n", + "pcct_prb.RiskyShareNow_hist\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.scatter(pcct_prb.aNrmNow_hist, pcct_prb.t_age_hist, pcct_prb.RiskyShareNow_hist)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3.7 econ-ark", + "language": "python", + "name": "econ-ark" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/ConsPortfolioModelDoc/ConsPortfolioModelDoc.py b/examples/ConsPortfolioModelDoc/ConsPortfolioModelDoc.py new file mode 100644 index 000000000..753b691c8 --- /dev/null +++ b/examples/ConsPortfolioModelDoc/ConsPortfolioModelDoc.py @@ -0,0 +1,199 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.2' +# jupytext_version: 1.2.4 +# kernelspec: +# display_name: Python 3.7 econ-ark +# language: python +# name: econ-ark +# --- + +# %% [markdown] +# # ConsPortfolioModel Documentation +# +# [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/DemARK/master?filepath=notebooks%2FConsPortfolioModelDoc.ipynb) + +# %% {"code_folding": []} +# Setup stuff + +import HARK.ConsumptionSaving.ConsPortfolioModel as cpm +import HARK.ConsumptionSaving.ConsumerParameters as param +import copy +import matplotlib.pyplot as plt +import numpy as np +import warnings +warnings.filterwarnings('ignore') +from HARK.ConsumptionSaving.ConsPortfolioModel import PortfolioSolution + +# %% [markdown] +# We implement three different ways to allow portfolio choice. +# +# The agent can choose +# * any portfolio share ('continuous choice') +# * only a specified set of portfolio shares ('discrete choice') +# * With probability 1 (agent always gets to choose) +# * With probability 0 < p < 1 (stochastic chance to choose) +# +# We allow two choices for the description of the +# distribution of the stochastic variable: +# 1. A generic discrete probability distribution +# * Nodes and their probabilities are specified +# 2. A true lognormal distribution +# * The mean return and the standard deviation are specified +# +# In the discrete portfolio shares case, the user also must +# input a function that *draws* from the distribution in drawRiskyFunc +# +# Other assumptions: +# * distributions are time constant +# * probability of being allowed to reoptimize is time constant +# * If p < 1, you must specify the PortfolioSet discretely +# + +# %% {"code_folding": []} +# Set up the model +# Parameters from Mehra and Prescott (1985): +Avg = 1.08 # equity premium +Std = 0.20 # standard deviation of rate-of-return shocks + +RiskyDstnFunc = cpm.RiskyDstnFactory(RiskyAvg=Avg, RiskyStd=Std) # Generates nodes for integration +RiskyDrawFunc = cpm.LogNormalRiskyDstnDraw(RiskyAvg=Avg, RiskyStd=Std) # Generates draws from a lognormal distribution + +init_portfolio = copy.copy(param.init_idiosyncratic_shocks) # Default parameter values for inf horiz model +init_portfolio['approxRiskyDstn'] = RiskyDstnFunc +init_portfolio['drawRiskyFunc'] = RiskyDrawFunc +init_portfolio['RiskyCount'] = 2 # Number of points in the approximation; 2 points is minimum +init_portfolio['RiskyShareCount'] = 25 # How many discrete points to allow in the share approximation +init_portfolio['Rfree'] = 1.0 # Riskfree return factor is 1 (interest rate is zero) +init_portfolio['CRRA'] = 6.0 # Relative risk aversion + +# Uninteresting technical parameters: +init_portfolio['aXtraMax'] = 100 +init_portfolio['aXtraCount'] = 50 +init_portfolio['BoroCnstArt'] = 0.0 # important for theoretical reasons +# init_portfolio['vFuncBool'] = True # We do not need value function for purposes here + +init_portfolio['DiscFac'] = 0.90 +# Create portfolio choice consumer type +pcct = cpm.PortfolioConsumerType(**init_portfolio) + +# %% {"code_folding": []} +# Solve the model under the given parameters + +pcct.solve() +aMin = 0 # Minimum ratio of assets to income to plot +aMax = 10 # Maximum ratio of assets to income to plot +aPts = 100 # Number of points to plot + +# Campbell-Viceira (2002) approximation to optimal portfolio share in Merton-Samuelson (1969) model +pcct.MertSamCampVicShare = pcct.RiskyShareLimitFunc(RiskyDstnFunc(init_portfolio['RiskyCount'])) +eevalgrid = np.linspace(0,aMax,aPts) # range of values of assets for the plot +plt.plot(eevalgrid, pcct.solution[0].RiskyShareFunc[0][0](eevalgrid)) +plt.axhline(pcct.MertSamCampVicShare, c='r') # The Campbell-Viceira approximation +plt.ylim(0,1.05) +plt.text((aMax-aMin)/4,0.45,r'$\uparrow $ limit as $m \uparrow \infty$',fontsize = 22,fontweight='bold') +plt.show() + +# %% {"code_folding": [0]} +# Simulate 20 years of behavior for a set of consumers +SimPer = 20 + +pcct.track_vars = ['aNrmNow', 't_age', 'RiskyShareNow'] +pcct.T_sim = SimPer +pcct.initializeSim() +pcct.simulate() +pcct.RiskyShareNow_hist + +from mpl_toolkits.mplot3d import Axes3D +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.set_zlim(pcct.MertSamCampVicShare,1.0) +ax.scatter(pcct.aNrmNow_hist, pcct.t_age_hist, pcct.RiskyShareNow_hist) +plt.show() + +# The consumers are very impatient and so even if they start rich they end up as buffer stock savers +# But with all of their buffer stock savings in the stock market + +# %% +# Solve the specialized / simple version for which there is a good approximation +# This is the version for which Campbell and Viceira provide an approximate formula +# assuming log normally distributed shocks + +init_lognormportfolio = copy.deepcopy(init_portfolio) # Use same parameter values +init_lognormportfolio['RiskyAvg'] = Avg +init_lognormportfolio['RiskyStd'] = Std +init_lognormportfolio['RiskyCount'] = 11 # Eleven is enough points to do justice to the distribution +lnpcct = cpm.LogNormalPortfolioConsumerType(**init_lognormportfolio) + +lnpcct.solve() +lnpcct.MertSamCampVicShare = lnpcct.RiskyShareLimitFunc(RiskyDstnFunc(init_portfolio['RiskyCount'])) + +plt.plot(eevalgrid, lnpcct.solution[0].RiskyShareFunc[0][0](eevalgrid)) +plt.axhline(lnpcct.MertSamCampVicShare, c='r') +plt.ylim(0,1.05) +plt.text((aMax-aMin)/4,lnpcct.MertSamCampVicShare-0.1,r'$\uparrow $ limit as $m \uparrow \infty$',fontsize = 22,fontweight='bold') +plt.show() + +# %% +# Again simulate a few periods +lnpcct.track_vars = ['aNrmNow', 't_age', 'RiskyShareNow'] +lnpcct.T_sim = SimPer +lnpcct.initializeSim() +lnpcct.simulate() +lnpcct.RiskyShareNow_hist + +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(lnpcct.aNrmNow_hist, lnpcct.t_age_hist, lnpcct.RiskyShareNow_hist) +ax.set_zlim(lnpcct.MertSamCampVicShare,1.0) +plt.show() + +# %% +# Version where only discrete values of portfolio share of risky assets are allowed + +init_portfolio_prb = copy.deepcopy(init_portfolio) + +init_portfolio_prb['AdjustPrb'] = 1.0 +init_portfolio_prb['PortfolioDomain'] = cpm.DiscreteDomain([0.0, 0.5, 0.6, 1.0]) +pcct_prb = cpm.PortfolioConsumerType(**init_portfolio_prb) +pcct_prb.solve() + +eevalgrid = np.linspace(0,100,100) +plt.plot(eevalgrid, pcct_prb.solution[0].RiskyShareFunc[0][0](eevalgrid)) +plt.axhline(pcct_prb.RiskyShareLimitFunc(RiskyDstnFunc(init_portfolio['RiskyCount'])), c='r') +plt.ylim(0,1.05) +plt.show() + + +# %% +# Version where you can choose your portfolio share only with some 0 < p < 1 + +init_portfolio_prb = copy.deepcopy(init_portfolio) + +init_portfolio_prb['AdjustPrb'] = 0.5 +init_portfolio_prb['PortfolioDomain'] = cpm.DiscreteDomain([0.0, 0.6, 1.0]) +pcct_prb = cpm.PortfolioConsumerType(**init_portfolio_prb) + +pcct_prb.solve() + +plt.plot(eevalgrid, pcct_prb.solution[0].RiskyShareFunc[0][0](eevalgrid)) +plt.show() + +pcct_prb.track_vars = ['aNrmNow', 't_age', 'RiskyShareNow', 'CantAdjust'] +pcct_prb.T_sim = 10 +pcct_prb.AgentCount = 30 +pcct_prb.initializeSim() +pcct_prb.simulate() +pcct_prb.RiskyShareNow_hist + +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(pcct_prb.aNrmNow_hist, pcct_prb.t_age_hist, pcct_prb.RiskyShareNow_hist) +plt.show() + +# %% From f877b065d85a982e59dee1fd9427a00b96231818 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Wed, 1 Jan 2020 17:13:13 +0530 Subject: [PATCH 155/242] Linting parallel.py (#465) * linted parallel.py * use black to lint parallel * clean file handling Co-authored-by: ingridbgl <47956238+ingridbgl@users.noreply.github.com> Co-authored-by: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> --- HARK/parallel.py | 340 +++++++++++++++++++++++++++++------------------ 1 file changed, 214 insertions(+), 126 deletions(-) diff --git a/HARK/parallel.py b/HARK/parallel.py index 774a99cac..c45db667b 100644 --- a/HARK/parallel.py +++ b/HARK/parallel.py @@ -1,8 +1,8 @@ -''' +""" Early version of multithreading in HARK. To use most of this module, you should first install dill and joblib. Packages can be installed by typing "conda install dill" (etc) at a command prompt. -''' +""" from __future__ import division, print_function from builtins import next from builtins import zip @@ -19,29 +19,35 @@ # such that they will raise useful errors if called. def raiseImportError(moduleStr): def defineImportError(*args, **kwargs): - raise ImportError(moduleStr + ' could not be imported, and is required for this'+\ - ' function. See HARK documentation for more information on how to install the ' \ - + moduleStr + ' module.') + raise ImportError( + moduleStr + + " could not be imported, and is required for this" + + " function. See HARK documentation for more information on how to install the " + + moduleStr + + " module." + ) + return defineImportError + try: # Try to import joblib from joblib import Parallel, delayed -except: +except ImportError: print("Warning: Could not import joblib.") - Parallel = raiseImportError('joblib') - delayed = raiseImportError('joblib') + Parallel = raiseImportError("joblib") + delayed = raiseImportError("joblib") try: # Try to import dill import dill as pickle -except: +except ImportError: print("Warning: Could not import dill.") - pickle = raiseImportError('dill') + pickle = raiseImportError("dill") -def multiThreadCommandsFake(agent_list,command_list,num_jobs=None): - ''' +def multiThreadCommandsFake(agent_list, command_list, num_jobs=None): + """ Executes the list of commands in command_list for each AgentType in agent_list in an ordinary, single-threaded loop. Each command should be a method of that AgentType subclass. This function exists so as to easily disable @@ -59,13 +65,14 @@ def multiThreadCommandsFake(agent_list,command_list,num_jobs=None): Returns ------- none - ''' + """ for agent in agent_list: for command in command_list: - exec('agent.' + command) + exec("agent." + command) + -def multiThreadCommands(agent_list,command_list,num_jobs=None): - ''' +def multiThreadCommands(agent_list, command_list, num_jobs=None): + """ Executes the list of commands in command_list for each AgentType in agent_list using a multithreaded system. Each command should be a method of that AgentType subclass. @@ -79,25 +86,29 @@ def multiThreadCommands(agent_list,command_list,num_jobs=None): Returns ------- None - ''' + """ if len(agent_list) == 1: - multiThreadCommandsFake(agent_list,command_list) + multiThreadCommandsFake(agent_list, command_list) return None # Default number of parallel jobs is the smaller of number of AgentTypes in # the input and the number of available cores. if num_jobs is None: - num_jobs = min(len(agent_list),multiprocessing.cpu_count()) + num_jobs = min(len(agent_list), multiprocessing.cpu_count()) # Send each command in command_list to each of the types in agent_list to be run - agent_list_out = Parallel(n_jobs=num_jobs)(delayed(runCommands)(*args) for args in zip(agent_list, len(agent_list)*[command_list])) + agent_list_out = Parallel(n_jobs=num_jobs)( + delayed(runCommands)(*args) + for args in zip(agent_list, len(agent_list) * [command_list]) + ) # Replace the original types with the output from the parallel call for j in range(len(agent_list)): agent_list[j] = agent_list_out[j] -def runCommands(agent,command_list): - ''' + +def runCommands(agent, command_list): + """ Executes each command in command_list on a given AgentType. The commands should be methods of that AgentType's subclass. @@ -112,18 +123,37 @@ def runCommands(agent,command_list): ------- agent : AgentType The same AgentType instance passed as input, after running the commands. - ''' + """ for command in command_list: - exec('agent.' + command) + exec("agent." + command) return agent -#============================================================= -# ======== Define a parallel Nelder-Mead algorithm ========== -#============================================================= - -def parallelNelderMead(objFunc,guess,perturb=None,P=1,ftol=0.000001,xtol=0.00000001,maxiter=np.inf,maxeval=np.inf,r_param=1.0,e_param=1.0,c_param=0.5,s_param=0.5,maxcores=None,name=None,resume=False,savefreq=None,verbose=1): - ''' +"""============================================================= + ========= Define a parallel Nelder-Mead algorithm ========== + =============================================================""" + + +def parallelNelderMead( + objFunc, + guess, + perturb=None, + P=1, + ftol=0.000001, + xtol=0.00000001, + maxiter=np.inf, + maxeval=np.inf, + r_param=1.0, + e_param=1.0, + c_param=0.5, + s_param=0.5, + maxcores=None, + name=None, + resume=False, + savefreq=None, + verbose=1, +): + """ A parallel implementation of the Nelder-Mead minimization algorithm, as described in Lee and Wiswall. For long optimization procedures, it can save progress between iterations and resume later. @@ -189,58 +219,81 @@ def parallelNelderMead(objFunc,guess,perturb=None,P=1,ftol=0.000001,xtol=0.00000 The input that minimizes objFunc, as found by the minimization. fmin : float The minimum of objFunc; fmin = objFunc(min_point). - ''' + """ # If this is a resumed search, load the data if resume: simplex, fvals, iters, evals = loadNelderMeadData(name) dim_count = fvals.size - 1 - N = dim_count+1 # Number of points in simplex - K = simplex.shape[1] # Total number of parameters + N = dim_count + 1 # Number of points in simplex + K = simplex.shape[1] # Total number of parameters # Otherwise, construct the initial simplex and array of function values else: - if perturb is None: # Default: perturb each parameter by 10% - perturb = 0.1*guess + if perturb is None: # Default: perturb each parameter by 10% + perturb = 0.1 * guess guess[guess == 0] = 0.1 - params_to_opt = np.where(perturb != 0)[0] # Indices of which parameters to optimize - dim_count = params_to_opt.size # Number of parameters to search over - N = dim_count+1 # Number of points in simplex - K = guess.size # Total number of parameters - simplex = np.tile(guess,(N,1)) - for j in range(dim_count): # Perturb each parameter to optimize by the specified distance - simplex[j+1,params_to_opt[j]] = simplex[j+1,params_to_opt[j]] + perturb[params_to_opt[j]] + params_to_opt = np.where(perturb != 0)[ + 0 + ] # Indices of which parameters to optimize + dim_count = params_to_opt.size # Number of parameters to search over + N = dim_count + 1 # Number of points in simplex + K = guess.size # Total number of parameters + simplex = np.tile(guess, (N, 1)) + for j in range( + dim_count + ): # Perturb each parameter to optimize by the specified distance + simplex[j + 1, params_to_opt[j]] = ( + simplex[j + 1, params_to_opt[j]] + perturb[params_to_opt[j]] + ) # Initialize a few - fvals = np.zeros(dim_count+1) + np.nan + fvals = np.zeros(dim_count + 1) + np.nan iters = 0 evals = 0 # Create the pool of worker processes - cpu_cores = multiprocessing.cpu_count() # Total number of available CPU cores - cores_to_use = min(cpu_cores,dim_count) - if maxcores is not None: # Cap the number of cores if desired - cores_to_use = min(cores_to_use,maxcores) + cpu_cores = multiprocessing.cpu_count() # Total number of available CPU cores + cores_to_use = min(cpu_cores, dim_count) + if maxcores is not None: # Cap the number of cores if desired + cores_to_use = min(cores_to_use, maxcores) parallel = Parallel(n_jobs=cores_to_use) # Begin a new Nelder-Mead search if not resume: - temp_simplex = list(simplex) # Evaluate the initial simplex + temp_simplex = list(simplex) # Evaluate the initial simplex fvals = np.array(parallel(delayed(objFunc)(params) for params in temp_simplex)) evals += N # Reorder the initial simplex order = np.argsort(fvals) fvals = fvals[order] - simplex = simplex[order,:] + simplex = simplex[order, :] fmin = fvals[0] f_dist = np.abs(fmin - fvals[-1]) - x_dist = np.max(np.sqrt(np.sum(simplex**2.0 - np.tile(simplex[0,:],(N,1))**2.0,axis=1))) + x_dist = np.max( + np.sqrt( + np.sum(simplex ** 2.0 - np.tile(simplex[0, :], (N, 1)) ** 2.0, axis=1) + ) + ) if verbose > 0: - print('Evaluated the initial simplex: fmin=' + str(fmin) + ', f_dist=' + str(f_dist) + ', x_dist=' + str(x_dist)) - else: # Resume an existing search that was cut short + print( + "Evaluated the initial simplex: fmin=" + + str(fmin) + + ", f_dist=" + + str(f_dist) + + ", x_dist=" + + str(x_dist) + ) + else: # Resume an existing search that was cut short if verbose > 0: - print('Resuming search after ' + str(iters) + ' iterations and ' + str(evals) + ' function evaluations.') + print( + "Resuming search after " + + str(iters) + + " iterations and " + + str(evals) + + " function evaluations." + ) # Initialize some inputs for the multithreader - j_list = list(range(N-P,N)) - opt_params= [r_param,c_param,e_param] + j_list = list(range(N - P, N)) + opt_params = [r_param, c_param, e_param] # Run the Nelder-Mead algorithm until a terminal condition is met go = True @@ -248,78 +301,107 @@ def parallelNelderMead(objFunc,guess,perturb=None,P=1,ftol=0.000001,xtol=0.00000 t_start = clock() iters += 1 if verbose > 0: - print('Beginning iteration #' + str(iters) + ' now.') + print("Beginning iteration #" + str(iters) + " now.") # Update the P worst points of the simplex - output = parallel(delayed(parallelNelderMeadWorker)(objFunc,simplex,fvals,j,P,opt_params) for j in j_list) - new_subsimplex = np.zeros((P,K)) + np.nan + output = parallel( + delayed(parallelNelderMeadWorker)(objFunc, simplex, fvals, j, P, opt_params) + for j in j_list + ) + new_subsimplex = np.zeros((P, K)) + np.nan new_vals = np.zeros(P) + np.nan new_evals = 0 for i in range(P): - new_subsimplex[i,:] = output[i][0] + new_subsimplex[i, :] = output[i][0] new_vals[i] = output[i][1] new_evals += output[i][2] evals += new_evals # Check whether any updates actually happened - old_subsimplex = simplex[(N-P):N,:] + old_subsimplex = simplex[(N - P) : N, :] if np.max(np.abs(new_subsimplex - old_subsimplex)) == 0: if verbose > 0: - print('Updated the simplex, but must perform a shrink step.') + print("Updated the simplex, but must perform a shrink step.") # If every attempted update was unsuccessful, must shrink the simplex - simplex = s_param*np.tile(simplex[0,:],(N,1)) + (1.0-s_param)*simplex - temp_simplex = list(simplex[1:N,:]) - fvals = np.array([fvals[0]] + parallel(delayed(objFunc)(params) for params in temp_simplex)) - new_evals += N-1 - evals += N-1 + simplex = ( + s_param * np.tile(simplex[0, :], (N, 1)) + (1.0 - s_param) * simplex + ) + temp_simplex = list(simplex[1:N, :]) + fvals = np.array( + [fvals[0]] + + parallel(delayed(objFunc)(params) for params in temp_simplex) + ) + new_evals += N - 1 + evals += N - 1 else: if verbose > 0: - print('Updated the simplex successfully.') + print("Updated the simplex successfully.") # Otherwise, update the simplex with the new results - simplex[(N-P):N,:] = new_subsimplex - fvals[(N-P):N] = new_vals + simplex[(N - P) : N, :] = new_subsimplex + fvals[(N - P) : N] = new_vals # Reorder the simplex from best to worst order = np.argsort(fvals) fvals = fvals[order] - simplex = simplex[order,:] + simplex = simplex[order, :] fmin = fvals[0] f_dist = np.abs(fmin - fvals[-1]) - x_dist = np.max(np.sqrt(np.sum(simplex**2.0 - np.tile(simplex[0,:],(N,1))**2.0,axis=1))) + x_dist = np.max( + np.sqrt( + np.sum(simplex ** 2.0 - np.tile(simplex[0, :], (N, 1)) ** 2.0, axis=1) + ) + ) t_end = clock() if verbose > 0: t_iter = t_end - t_start - print('Finished iteration #' + str(iters) +' with ' + str(new_evals) + ' evaluations (' + str(evals) + ' cumulative) in ' + str(t_iter) + ' seconds.') - print('Simplex status: fmin=' + str(fmin) + ', f_dist=' + str(f_dist) + ', x_dist=' + str(x_dist)) + print( + "Finished iteration #" + + str(iters) + + " with " + + str(new_evals) + + " evaluations (" + + str(evals) + + " cumulative) in " + + str(t_iter) + + " seconds." + ) + print( + "Simplex status: fmin=" + + str(fmin) + + ", f_dist=" + + str(f_dist) + + ", x_dist=" + + str(x_dist) + ) # Check for terminal conditions if iters >= maxiter: go = False - print('Maximum iterations reached, terminating unsuccessfully.') + print("Maximum iterations reached, terminating unsuccessfully.") if evals >= maxeval: go = False - print('Maximum evaluations reached, terminating unsuccessfully.') + print("Maximum evaluations reached, terminating unsuccessfully.") if f_dist < ftol: go = False - print('Function tolerance reached, terminating successfully.') + print("Function tolerance reached, terminating successfully.") if x_dist < xtol: go = False - print('Parameter tolerance reached, terminating successfully.') + print("Parameter tolerance reached, terminating successfully.") # Save the progress of the estimation if desired if savefreq is not None: if (iters % savefreq) == 0: - saveNelderMeadData(name, simplex, fvals, iters, evals) - if verbose > 0: - print('Saved search progress in ' + name + '.txt') + saveNelderMeadData(name, simplex, fvals, iters, evals) + if verbose > 0: + print("Saved search progress in " + name + ".txt") # Return the results - xopt = simplex[0,:] + xopt = simplex[0, :] return xopt, fmin def saveNelderMeadData(name, simplex, fvals, iters, evals): - ''' + """ Stores the progress of a parallel Nelder-Mead search in a text file so that it can be resumed later (after manual termination or a crash). @@ -339,18 +421,17 @@ def saveNelderMeadData(name, simplex, fvals, iters, evals): Returns ------- none - ''' - f = open(name + '.txt','w') - my_writer = csv.writer(f,delimiter=' ') - my_writer.writerow(simplex.shape) - my_writer.writerow([iters, evals]) - my_writer.writerow(simplex.flatten()) - my_writer.writerow(fvals) - f.close() + """ + with open(name + ".txt", "w") as f: + my_writer = csv.writer(f, delimiter=" ") + my_writer.writerow(simplex.shape) + my_writer.writerow([iters, evals]) + my_writer.writerow(simplex.flatten()) + my_writer.writerow(fvals) def loadNelderMeadData(name): - ''' + """ Reads the progress of a parallel Nelder-Mead search from a text file, as created by saveNelderMeadData(). @@ -369,25 +450,24 @@ def loadNelderMeadData(name): The number of completed Nelder-Mead iterations. evals : int The cumulative number of function evaluations in the search process. - ''' - f = open(name + '.txt','rb') - my_reader = csv.reader(f,delimiter=' ') + """ + with open(name + ".txt", "rb") as f: + my_reader = csv.reader(f, delimiter=" ") my_shape_txt = next(my_reader) shape0 = int(my_shape_txt[0]) shape1 = int(my_shape_txt[1]) my_nums_txt = next(my_reader) iters = int(my_nums_txt[0]) evals = int(my_nums_txt[1]) - simplex_flat = np.array(next(my_reader),dtype=float) - simplex = np.reshape(simplex_flat,(shape0,shape1)) - fvals = np.array(next(my_reader),dtype=float) - f.close() + simplex_flat = np.array(next(my_reader), dtype=float) + simplex = np.reshape(simplex_flat, (shape0, shape1)) + fvals = np.array(next(my_reader), dtype=float) return simplex, fvals, iters, evals -def parallelNelderMeadWorker(objFunc,simplex,f_vals,j,P,opt_params): - ''' +def parallelNelderMeadWorker(objFunc, simplex, f_vals, j, P, opt_params): + """ A worker process for the parallel Nelder-Mead algorithm. Updates one point in the simplex, returning its function value as well. Should basically never be called directly, only by parallelNelderMead(). @@ -416,30 +496,30 @@ def parallelNelderMeadWorker(objFunc,simplex,f_vals,j,P,opt_params): The value of the objective function at the new point: objFunc(new_point). evals : int Number of evaluations of objFunc by this worker. - ''' + """ # Unpack the input parameters - alpha = opt_params[0] # reflection parameter - beta = opt_params[1] # contraction parameter - gamma = opt_params[2] # expansion parameter - my_point = simplex[j,:] # vertex to update - my_val = f_vals[j] # value at the vertex to update - best_val = f_vals[0] # best value in the vertex - next_val = f_vals[j-1] # next best point in the simplex + alpha = opt_params[0] # reflection parameter + beta = opt_params[1] # contraction parameter + gamma = opt_params[2] # expansion parameter + my_point = simplex[j, :] # vertex to update + my_val = f_vals[j] # value at the vertex to update + best_val = f_vals[0] # best value in the vertex + next_val = f_vals[j - 1] # next best point in the simplex evals = 0 # Calculate the centroid of the "good" simplex points - N = simplex.shape[0] # number of points in simplex - centroid = np.mean(simplex[0:(N-P),:],axis=0) + N = simplex.shape[0] # number of points in simplex + centroid = np.mean(simplex[0 : (N - P), :], axis=0) # Calculate the reflection point and its function value - r_point = centroid + alpha*(centroid - my_point) + r_point = centroid + alpha * (centroid - my_point) r_val = objFunc(r_point) evals += 1 # Case 1: the reflection point is better than best point if r_val < best_val: - e_point = r_point + gamma*(r_point - centroid) - e_val = objFunc(e_point) # Calculate expansion point + e_point = r_point + gamma * (r_point - centroid) + e_val = objFunc(e_point) # Calculate expansion point evals += 1 if e_val < r_val: new_point = e_point @@ -449,22 +529,22 @@ def parallelNelderMeadWorker(objFunc,simplex,f_vals,j,P,opt_params): new_val = r_val # Case 2: the reflection point is better than the next best point elif r_val < next_val: - new_point = r_point # Report reflection point + new_point = r_point # Report reflection point new_val = r_val # Case 3: the reflection point is worse than the next best point else: if r_val < my_val: - temp_point = r_point # Check whether reflection or original point - temp_val = r_val # is better and use it temporarily + temp_point = r_point # Check whether reflection or original point + temp_val = r_val # is better and use it temporarily else: temp_point = my_point temp_val = my_val - c_point = beta*(centroid + temp_point) - c_val = objFunc(c_point) # Calculate contraction point + c_point = beta * (centroid + temp_point) + c_val = objFunc(c_point) # Calculate contraction point evals += 1 if c_val < temp_val: new_point = c_point - new_val = c_val # Check whether the contraction point is better + new_val = c_val # Check whether the contraction point is better else: new_point = temp_point new_val = temp_val @@ -472,24 +552,32 @@ def parallelNelderMeadWorker(objFunc,simplex,f_vals,j,P,opt_params): # Return the outputs return new_point, new_val, evals -#============================================================================= -#============================================================================= def main(): print("Sorry, HARKparallel doesn't actually do much on its own.") print("To see an example of multithreading in HARK, see /Testing/MultithreadDemo.") print('To ensure full compatibility "out of the box", multithreading is not') - print('used in our models and applications; users can turn it on by modifying') - print('the source code slightly.') + print("used in our models and applications; users can turn it on by modifying") + print("the source code slightly.") K = 36 P = 24 my_guess = np.random.rand(K) - 0.5 + def testFunc1(x): - return np.sum(x**2.0)/x.size + return np.sum(x ** 2.0) / x.size + + xopt, fmin = parallelNelderMead( + testFunc1, + my_guess, + P=P, + maxiter=300, + savefreq=100, + name="testfile", + resume=False, + ) + xopt2, fmin2 = parallelNelderMead(testFunc1, xopt, P=P) - xopt, fmin = parallelNelderMead(testFunc1,my_guess,P=P,maxiter=300,savefreq=100,name='testfile',resume=False) - xopt2, fmin2 = parallelNelderMead(testFunc1,xopt,P=P) if __name__ == "__main__": main() From c5d8978e3c99cac62bbbfd178200afe198f08267 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 2 Jan 2020 14:56:13 +0530 Subject: [PATCH 156/242] Governance doc shouldn't be in source code (#422) * Governance doc shouldnt be in source code * remove GOVERNANCE.md --- GOVERNANCE.md | 3 --- HARK/GOVERNANCE.md | 3 --- 2 files changed, 6 deletions(-) delete mode 100644 GOVERNANCE.md delete mode 100644 HARK/GOVERNANCE.md diff --git a/GOVERNANCE.md b/GOVERNANCE.md deleted file mode 100644 index ee018ce74..000000000 --- a/GOVERNANCE.md +++ /dev/null @@ -1,3 +0,0 @@ -# HARK Project Governance - -This document is not yet drafted. Please contact the [TSC](./README.md#tsc-technical-steering-committee) if you have any questions. \ No newline at end of file diff --git a/HARK/GOVERNANCE.md b/HARK/GOVERNANCE.md deleted file mode 100644 index 03baf25bb..000000000 --- a/HARK/GOVERNANCE.md +++ /dev/null @@ -1,3 +0,0 @@ -# Econ-ARK Project Governance - -Econ-ARK project governance is still being drafted. Please refer to the [**Technical Steering Commitee**]() for questions. \ No newline at end of file From 5663528d02545f69cb238849bf72688751cd1ae8 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 2 Jan 2020 04:41:31 -0500 Subject: [PATCH 157/242] Improved parallelNelderMead (#300) * Improved parallelNelderMead Made some fixes to HARK.parallel.parallelNelderMead: 1) Renamed maxcores to maxthreads, to be more correct 2) Fixed various small comments 3) Applied fix if degree of parallelization is illegal 4) Fixed computation of x_dist (whoops) 5) Allowed initial simplex to be saved to disk 6) Adjusted save/load procedure to use comma as delimiter rather than space (and use one line per simplex point), to make it easier to copy-paste points from the simplex file while estimation is in progress. Also deleted superfluous test of parallelNelderMead in the main block, and trimmed down the message that's printed if HARK.parallel is ever executed. * use with open() instead of open/close for file handling * fix linting changes Co-authored-by: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> Co-authored-by: Mridul Seth --- HARK/parallel.py | 302 +++++++++++++++++++---------------------------- 1 file changed, 124 insertions(+), 178 deletions(-) diff --git a/HARK/parallel.py b/HARK/parallel.py index c45db667b..97c8b856f 100644 --- a/HARK/parallel.py +++ b/HARK/parallel.py @@ -129,35 +129,20 @@ def runCommands(agent, command_list): return agent -"""============================================================= - ========= Define a parallel Nelder-Mead algorithm ========== - =============================================================""" - - -def parallelNelderMead( - objFunc, - guess, - perturb=None, - P=1, - ftol=0.000001, - xtol=0.00000001, - maxiter=np.inf, - maxeval=np.inf, - r_param=1.0, - e_param=1.0, - c_param=0.5, - s_param=0.5, - maxcores=None, - name=None, - resume=False, - savefreq=None, - verbose=1, -): +#============================================================= +# ======== Define a parallel Nelder-Mead algorithm ========== +#============================================================= + +def parallelNelderMead(objFunc,guess,perturb=None,P=1,ftol=0.000001,xtol=0.00000001, + maxiter=np.inf,maxeval=np.inf,r_param=1.0,e_param=1.0, + c_param=0.5,s_param=0.5,maxthreads=None,name=None,resume=False, + savefreq=None,verbose=1): """ + A parallel implementation of the Nelder-Mead minimization algorithm, as described in Lee and Wiswall. For long optimization procedures, it can save progress between iterations and resume later. - + Parameters ---------- objFunc : function @@ -169,7 +154,7 @@ def parallelNelderMead( objFunc. If perturb[j] is non-zero, a simplex point will be created that perturbs the j-th element of guess by perturb[j]; if it is zero, then the j-th parameter of objFunc will not be optimized over. By - default, guess=None, indicating that all parameters should be optimized, + default, perturb=None, indicating that all parameters should be optimized, with an initial perturbation of 0.1*guess. P : int Degree of parallelization: the number of vertices of the simplex to try @@ -196,7 +181,7 @@ def parallelNelderMead( Parameter indicating magnitude of the contraction point calculation. s_param: float Parameter indicating magnitude of the shrink calculation. - maxcores : int + maxthreads : int The maximum number of CPU cores that the optimization should use, regardless of the size of the problem. name : string @@ -212,7 +197,7 @@ def parallelNelderMead( verbose : int Indicator for the verbosity of the optimization routine. Higher values generate more text output; verbose=0 produces no text output. - + Returns ------- min_point : np.array @@ -224,31 +209,35 @@ def parallelNelderMead( if resume: simplex, fvals, iters, evals = loadNelderMeadData(name) dim_count = fvals.size - 1 - N = dim_count + 1 # Number of points in simplex - K = simplex.shape[1] # Total number of parameters - # Otherwise, construct the initial simplex and array of function values - else: - if perturb is None: # Default: perturb each parameter by 10% - perturb = 0.1 * guess + N = dim_count+1 # Number of points in simplex + K = simplex.shape[1] # Total number of parameters + + # Otherwise, construct the initial simplex and array of function values + else: + if perturb is None: # Default: perturb each parameter by 10% + perturb = 0.1*guess guess[guess == 0] = 0.1 - params_to_opt = np.where(perturb != 0)[ - 0 - ] # Indices of which parameters to optimize - dim_count = params_to_opt.size # Number of parameters to search over - N = dim_count + 1 # Number of points in simplex - K = guess.size # Total number of parameters - simplex = np.tile(guess, (N, 1)) - for j in range( - dim_count - ): # Perturb each parameter to optimize by the specified distance - simplex[j + 1, params_to_opt[j]] = ( - simplex[j + 1, params_to_opt[j]] + perturb[params_to_opt[j]] - ) - # Initialize a few - fvals = np.zeros(dim_count + 1) + np.nan + + params_to_opt = np.where(perturb != 0)[0] # Indices of which parameters to optimize + dim_count = params_to_opt.size # Number of parameters to search over + N = dim_count+1 # Number of points in simplex + K = guess.size # Total number of parameters + simplex = np.tile(guess,(N,1)) + for j in range(dim_count): # Perturb each parameter to optimize by the specified distance + simplex[j+1,params_to_opt[j]] = simplex[j+1,params_to_opt[j]] + perturb[params_to_opt[j]] + + # Initialize iteration and evaluation counts, plus a 1D array of function values + fvals = np.zeros(dim_count+1) + np.nan + iters = 0 evals = 0 - + + # Make sure degree of parallelization is not illegal + if P > N - 1: + print('Requested degree of simplex parallelization is ' + str(P) + ', but dimension of optimization problem is only ' + str(N-1) + '.') + print('Degree of parallelization has been reduced to ' + str(N-1) + '.') + P = N-1 + # Create the pool of worker processes cpu_cores = multiprocessing.cpu_count() # Total number of available CPU cores cores_to_use = min(cpu_cores, dim_count) @@ -267,48 +256,32 @@ def parallelNelderMead( simplex = simplex[order, :] fmin = fvals[0] f_dist = np.abs(fmin - fvals[-1]) - x_dist = np.max( - np.sqrt( - np.sum(simplex ** 2.0 - np.tile(simplex[0, :], (N, 1)) ** 2.0, axis=1) - ) - ) + x_dist = np.max(np.sqrt(np.sum((simplex - np.tile(simplex[0,:],(N,1)))**2.0,axis=1))) if verbose > 0: - print( - "Evaluated the initial simplex: fmin=" - + str(fmin) - + ", f_dist=" - + str(f_dist) - + ", x_dist=" - + str(x_dist) - ) - else: # Resume an existing search that was cut short + print('Evaluated the initial simplex: fmin=' + str(fmin) + ', f_dist=' + str(f_dist) + ', x_dist=' + str(x_dist)) + if savefreq is not None: + saveNelderMeadData(name, simplex, fvals, iters, evals) + if verbose > 0: + print('Saved search progress in ' + name + '.txt') + else: # Resume an existing search that was cut short if verbose > 0: - print( - "Resuming search after " - + str(iters) - + " iterations and " - + str(evals) - + " function evaluations." - ) - + print('Resuming search after ' + str(iters) + ' iterations and ' + str(evals) + ' function evaluations.') + # Initialize some inputs for the multithreader - j_list = list(range(N - P, N)) - opt_params = [r_param, c_param, e_param] - - # Run the Nelder-Mead algorithm until a terminal condition is met + j_list = range(N-P,N) + opt_params= [r_param,c_param,e_param] + + # Run the Nelder-Mead algorithm until a terminal condition is met go = True while go: t_start = clock() iters += 1 if verbose > 0: - print("Beginning iteration #" + str(iters) + " now.") - - # Update the P worst points of the simplex - output = parallel( - delayed(parallelNelderMeadWorker)(objFunc, simplex, fvals, j, P, opt_params) - for j in j_list - ) - new_subsimplex = np.zeros((P, K)) + np.nan + print('Beginning iteration #' + str(iters) + ' now.') + + # Update the P worst points of the simplex + output = parallel(delayed(parallelNelderMeadWorker)(objFunc,simplex,fvals,j,P,opt_params) for j in j_list) + new_subsimplex = np.zeros((P,K)) + np.nan new_vals = np.zeros(P) + np.nan new_evals = 0 for i in range(P): @@ -316,7 +289,7 @@ def parallelNelderMead( new_vals[i] = output[i][1] new_evals += output[i][2] evals += new_evals - + # Check whether any updates actually happened old_subsimplex = simplex[(N - P) : N, :] if np.max(np.abs(new_subsimplex - old_subsimplex)) == 0: @@ -337,42 +310,22 @@ def parallelNelderMead( if verbose > 0: print("Updated the simplex successfully.") # Otherwise, update the simplex with the new results - simplex[(N - P) : N, :] = new_subsimplex - fvals[(N - P) : N] = new_vals - + simplex[(N-P):N,:] = new_subsimplex + fvals[(N-P):N] = new_vals + # Reorder the simplex from best to worst order = np.argsort(fvals) fvals = fvals[order] simplex = simplex[order, :] fmin = fvals[0] f_dist = np.abs(fmin - fvals[-1]) - x_dist = np.max( - np.sqrt( - np.sum(simplex ** 2.0 - np.tile(simplex[0, :], (N, 1)) ** 2.0, axis=1) - ) - ) + x_dist = np.max(np.sqrt(np.sum((simplex - np.tile(simplex[0,:],(N,1)))**2.0,axis=1))) t_end = clock() if verbose > 0: t_iter = t_end - t_start - print( - "Finished iteration #" - + str(iters) - + " with " - + str(new_evals) - + " evaluations (" - + str(evals) - + " cumulative) in " - + str(t_iter) - + " seconds." - ) - print( - "Simplex status: fmin=" - + str(fmin) - + ", f_dist=" - + str(f_dist) - + ", x_dist=" - + str(x_dist) - ) + print('Finished iteration #' + str(iters) +' with ' + str(new_evals) + ' evaluations (' + str(evals) + ' cumulative) in ' + str(t_iter) + ' seconds.') + print('Simplex status: fmin=' + str(fmin) + ', f_dist=' + str(f_dist) + ', x_dist=' + str(x_dist)) + # Check for terminal conditions if iters >= maxiter: @@ -386,25 +339,25 @@ def parallelNelderMead( print("Function tolerance reached, terminating successfully.") if x_dist < xtol: go = False - print("Parameter tolerance reached, terminating successfully.") - + print('Parameter tolerance reached, terminating successfully.') + # Save the progress of the estimation if desired if savefreq is not None: if (iters % savefreq) == 0: - saveNelderMeadData(name, simplex, fvals, iters, evals) - if verbose > 0: - print("Saved search progress in " + name + ".txt") - + saveNelderMeadData(name, simplex, fvals, iters, evals) + if verbose > 0: + print('Saved search progress in ' + name + '.txt') + # Return the results xopt = simplex[0, :] return xopt, fmin - - + + def saveNelderMeadData(name, simplex, fvals, iters, evals): """ Stores the progress of a parallel Nelder-Mead search in a text file so that it can be resumed later (after manual termination or a crash). - + Parameters ---------- name : string @@ -417,29 +370,33 @@ def saveNelderMeadData(name, simplex, fvals, iters, evals): The number of completed Nelder-Mead iterations. evals : int The cumulative number of function evaluations in the search process. - + Returns ------- - none + None """ - with open(name + ".txt", "w") as f: - my_writer = csv.writer(f, delimiter=" ") + N = simplex.shape[0] # Number of points in simplex + K = simplex.shape[1] # Total number of parameters + + with open(name + '.txt','wb') as f: + my_writer = csv.writer(f, delimiter=',') my_writer.writerow(simplex.shape) my_writer.writerow([iters, evals]) - my_writer.writerow(simplex.flatten()) + for n in range(N): + my_writer.writerow(simplex[n,:]) my_writer.writerow(fvals) - - + + def loadNelderMeadData(name): """ Reads the progress of a parallel Nelder-Mead search from a text file, as created by saveNelderMeadData(). - + Parameters ---------- name : string Name of the txt file from which to read search progress. - + Returns ------- simplex : np.array @@ -451,27 +408,37 @@ def loadNelderMeadData(name): evals : int The cumulative number of function evaluations in the search process. """ - with open(name + ".txt", "rb") as f: - my_reader = csv.reader(f, delimiter=" ") - my_shape_txt = next(my_reader) - shape0 = int(my_shape_txt[0]) - shape1 = int(my_shape_txt[1]) - my_nums_txt = next(my_reader) - iters = int(my_nums_txt[0]) - evals = int(my_nums_txt[1]) - simplex_flat = np.array(next(my_reader), dtype=float) - simplex = np.reshape(simplex_flat, (shape0, shape1)) - fvals = np.array(next(my_reader), dtype=float) + # Open the Nelder-Mead progress file + with open(name + '.txt','rb') as f: + my_reader = csv.reader(f, delimiter=',') - return simplex, fvals, iters, evals + # Get the shape of the simplex and initialize it + my_shape_txt = my_reader.next() + N = int(my_shape_txt[0]) + K = int(my_shape_txt[1]) + simplex = np.zeros((N, K)) + np.nan + + # Get number of iterations and cumulative evaluations from the next line + my_nums_txt = my_reader.next() + iters = int(my_nums_txt[0]) + evals = int(my_nums_txt[1]) + + # Read one line per point of the simplex + for n in range(N): + simplex[n, :] = np.array(my_reader.next(), dtype=float) + # Read the final line to get function values + fvals = np.array(my_reader.next(), dtype=float) -def parallelNelderMeadWorker(objFunc, simplex, f_vals, j, P, opt_params): + return simplex, fvals, iters, evals + + +def parallelNelderMeadWorker(objFunc,simplex,f_vals,j,P,opt_params): """ A worker process for the parallel Nelder-Mead algorithm. Updates one point in the simplex, returning its function value as well. Should basically never be called directly, only by parallelNelderMead(). - + Parameters ---------- objFunc : function @@ -487,7 +454,7 @@ def parallelNelderMeadWorker(objFunc, simplex, f_vals, j, P, opt_params): Degree of parallelization of the algorithm. opt_params : numpy.array Three element array with parameters for reflection, contraction, expansion. - + Returns ------- new_point : numpy.array @@ -506,20 +473,20 @@ def parallelNelderMeadWorker(objFunc, simplex, f_vals, j, P, opt_params): best_val = f_vals[0] # best value in the vertex next_val = f_vals[j - 1] # next best point in the simplex evals = 0 - + # Calculate the centroid of the "good" simplex points - N = simplex.shape[0] # number of points in simplex - centroid = np.mean(simplex[0 : (N - P), :], axis=0) - + N = simplex.shape[0] # number of points in simplex + centroid = np.mean(simplex[0:(N-P),:],axis=0) + # Calculate the reflection point and its function value r_point = centroid + alpha * (centroid - my_point) r_val = objFunc(r_point) evals += 1 - + # Case 1: the reflection point is better than best point - if r_val < best_val: - e_point = r_point + gamma * (r_point - centroid) - e_val = objFunc(e_point) # Calculate expansion point + if r_val < best_val: + e_point = r_point + gamma*(r_point - centroid) + e_val = objFunc(e_point) # Calculate expansion point evals += 1 if e_val < r_val: new_point = e_point @@ -539,8 +506,8 @@ def parallelNelderMeadWorker(objFunc, simplex, f_vals, j, P, opt_params): else: temp_point = my_point temp_val = my_val - c_point = beta * (centroid + temp_point) - c_val = objFunc(c_point) # Calculate contraction point + c_point = temp_point + beta*(centroid - temp_point) + c_val = objFunc(c_point) # Calculate contraction point evals += 1 if c_val < temp_val: new_point = c_point @@ -548,35 +515,14 @@ def parallelNelderMeadWorker(objFunc, simplex, f_vals, j, P, opt_params): else: new_point = temp_point new_val = temp_val - + # Return the outputs return new_point, new_val, evals def main(): print("Sorry, HARKparallel doesn't actually do much on its own.") - print("To see an example of multithreading in HARK, see /Testing/MultithreadDemo.") - print('To ensure full compatibility "out of the box", multithreading is not') - print("used in our models and applications; users can turn it on by modifying") - print("the source code slightly.") - - K = 36 - P = 24 - my_guess = np.random.rand(K) - 0.5 - - def testFunc1(x): - return np.sum(x ** 2.0) / x.size - - xopt, fmin = parallelNelderMead( - testFunc1, - my_guess, - P=P, - maxiter=300, - savefreq=100, - name="testfile", - resume=False, - ) - xopt2, fmin2 = parallelNelderMead(testFunc1, xopt, P=P) + print("You must import functions from it into your workspace.") if __name__ == "__main__": From 2bee725ec5313db71076a33a32e45f4004ec97e9 Mon Sep 17 00:00:00 2001 From: Sebastian Benthall Date: Mon, 6 Jan 2020 17:56:29 -0500 Subject: [PATCH 158/242] Fixing path to SetupParamsCSTW (#467) --- HARK/cstwMPC/cstwMPC.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/cstwMPC/cstwMPC.py b/HARK/cstwMPC/cstwMPC.py index 2490223b8..89fb19731 100644 --- a/HARK/cstwMPC/cstwMPC.py +++ b/HARK/cstwMPC/cstwMPC.py @@ -16,7 +16,7 @@ getPercentiles, getLorenzShares, calcSubpopAvg, approxLognormal from HARK.simulation import drawDiscrete from HARK import Market -import SetupParamsCSTW as Params +import HARK.cstwMPC.SetupParamsCSTW as Params import HARK.ConsumptionSaving.ConsIndShockModel as Model from HARK.ConsumptionSaving.ConsAggShockModel import CobbDouglasEconomy, AggShockConsumerType from scipy.optimize import golden, brentq From ad833152145529dc0f0f47d6c575a95a542967cd Mon Sep 17 00:00:00 2001 From: Sebastian Benthall Date: Thu, 9 Jan 2020 05:04:40 -0500 Subject: [PATCH 159/242] Loading default parameters into class during initialization (#466) * loading init_perfect_foresight parameters by default. See #446 * loading init_idiosyncratic_shock into IndShochConsumerType on initialization #446 * using default ConsIndShock parameters when configuring RAexample * loading kinked_R parameters into class by default * Using default params in initializer for classes in ConsPrefShockModel * removing act_T from default Cobb-Douglas parameters because it's already in __init__ * loading default params into initialization, last cases. see #446 * Fixing issue with multiple cycles arguments in IndShockExplicitPermIncConsumerType() and MedShockConsumerType() Co-authored-by: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> --- HARK/ConsumptionSaving/ConsAggShockModel.py | 39 +++++++++++---- .../ConsGenIncProcessModel.py | 10 ++-- HARK/ConsumptionSaving/ConsIndShockModel.py | 49 +++++++++++++++---- HARK/ConsumptionSaving/ConsMedModel.py | 10 ++-- HARK/ConsumptionSaving/ConsPrefShockModel.py | 23 +++++++-- HARK/ConsumptionSaving/ConsRepAgentModel.py | 3 +- HARK/ConsumptionSaving/ConsumerParameters.py | 3 -- 7 files changed, 103 insertions(+), 34 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index fd1c99702..f0a9fa831 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -20,6 +20,8 @@ from HARK import HARKobject, Market, AgentType from copy import deepcopy import matplotlib.pyplot as plt +import HARK.ConsumptionSaving.ConsumerParameters as Params + utility = CRRAutility utilityP = CRRAutilityP @@ -79,6 +81,9 @@ def __init__(self, time_flow=True, **kwds): Make a new instance of AggShockConsumerType, an extension of IndShockConsumerType. Sets appropriate solver and input lists. ''' + params = Params.init_agg_shocks.copy() + params.update(kwds) + kwds = params AgentType.__init__(self, solution_terminal=deepcopy(IndShockConsumerType.solution_terminal_), time_flow=time_flow, pseudo_terminal=False, **kwds) @@ -375,6 +380,9 @@ class AggShockMarkovConsumerType(AggShockConsumerType): state is subject to Markov-style discrete state evolution. ''' def __init__(self, **kwds): + params = Params.init_agg_mrkv_shocks.copy() + params.update(kwds) + kwds = params AggShockConsumerType.__init__(self, **kwds) self.addToTimeInv('MrkvArray') self.solveOnePeriod = solveConsAggMarkov @@ -886,7 +894,11 @@ class CobbDouglasEconomy(Market): Note: The current implementation assumes a constant labor supply, but this will be generalized in the future. ''' - def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds): + def __init__(self, + agents=[], + tolerance=0.0001, + act_T=1200, + **kwds): ''' Make a new instance of CobbDouglasEconomy by filling in attributes specific to this kind of market. @@ -906,6 +918,9 @@ def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds): ------- None ''' + params = Params.init_cobb_douglas.copy() + params.update(kwds) + kwds = params Market.__init__(self, agents=agents, sow_vars=['MaggNow', 'AaggNow', 'RfreeNow', 'wRteNow', 'PermShkAggNow', 'TranShkAggNow', 'KtoLnow'], @@ -1330,7 +1345,11 @@ class CobbDouglasMarkovEconomy(CobbDouglasEconomy): productivity growth factor can vary over time. ''' - def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds): + def __init__(self, + agents=[], + tolerance=0.0001, + act_T=1200, + **kwds): ''' Make a new instance of CobbDouglasMarkovEconomy by filling in attributes specific to this kind of market. @@ -1350,6 +1369,9 @@ def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds): ------- None ''' + params = Params.init_mrkv_cobb_douglas.copy() + params.update(kwds) + kwds = params CobbDouglasEconomy.__init__(self, agents=agents, tolerance=tolerance, act_T=act_T, **kwds) self.sow_vars.append('MrkvNow') @@ -1773,7 +1795,6 @@ def __init__(self, AFunc): ############################################################################### def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params from time import clock from HARK.utilities import plotFuncs @@ -1791,11 +1812,11 @@ def mystr(number): return "{:.4f}".format(number) if solve_agg_shocks_micro or solve_agg_shocks_market: # Make an aggregate shocks consumer type - AggShockExample = AggShockConsumerType(**Params.init_agg_shocks) + AggShockExample = AggShockConsumerType() AggShockExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - EconomyExample = CobbDouglasEconomy(agents=[AggShockExample], **Params.init_cobb_douglas) + EconomyExample = CobbDouglasEconomy(agents=[AggShockExample]) EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks # Have the consumers inherit relevant objects from the economy @@ -1842,12 +1863,12 @@ def mystr(number): return "{:.4f}".format(number) if solve_markov_micro or solve_markov_market or solve_krusell_smith: # Make a Markov aggregate shocks consumer type - AggShockMrkvExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks) + AggShockMrkvExample = AggShockMarkovConsumerType() AggShockMrkvExample.IncomeDstn[0] = 2*[AggShockMrkvExample.IncomeDstn[0]] AggShockMrkvExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample], **Params.init_mrkv_cobb_douglas) + MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample]) MrkvEconomyExample.DampingFac = 0.2 # Turn down damping MrkvEconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks AggShockMrkvExample.getEconomyData( @@ -1934,14 +1955,14 @@ def mystr(number): return "{:.4f}".format(number) PolyMrkvArray[StateCount-1, StateCount-1] += 0.5*(1.0 - Persistence) # Make a consumer type to inhabit the economy - PolyStateExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks) + PolyStateExample = AggShockMarkovConsumerType() PolyStateExample.MrkvArray = PolyMrkvArray PolyStateExample.PermGroFacAgg = PermGroFacAgg PolyStateExample.IncomeDstn[0] = StateCount*[PolyStateExample.IncomeDstn[0]] PolyStateExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample], **Params.init_mrkv_cobb_douglas) + PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample]) PolyStateEconomy.MrkvArray = PolyMrkvArray PolyStateEconomy.PermGroFacAgg = PermGroFacAgg PolyStateEconomy.PermShkAggStd = StateCount*[0.006] diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index 748e0b1fd..a76b4a0a5 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -18,6 +18,7 @@ getPercentiles from HARK.simulation import drawLognormal, drawDiscrete, drawUniform from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType +import HARK.ConsumptionSaving.ConsumerParameters as Params utility = CRRAutility utilityP = CRRAutilityP @@ -965,7 +966,7 @@ class GenIncProcessConsumerType(IndShockConsumerType): solution_terminal_ = ConsumerSolution(cFunc=cFunc_terminal_, mNrmMin=0.0, hNrm=0.0, MPCmin=1.0, MPCmax=1.0) poststate_vars_ = ['aLvlNow', 'pLvlNow'] - def __init__(self, cycles=1, time_flow=True, **kwds): + def __init__(self, cycles=0, time_flow=True, **kwds): ''' Instantiate a new ConsumerType with given data. See ConsumerParameters.init_explicit_perm_inc for a dictionary of the @@ -982,6 +983,10 @@ def __init__(self, cycles=1, time_flow=True, **kwds): ------- None ''' + params = Params.init_explicit_perm_inc.copy() + params.update(kwds) + kwds = params + # Initialize a basic ConsumerType IndShockConsumerType.__init__(self, cycles=cycles, time_flow=time_flow, **kwds) self.solveOnePeriod = solveConsGenIncProcess # idiosyncratic shocks solver with explicit persistent income @@ -1310,7 +1315,6 @@ def updatepLvlNextFunc(self): ############################################################################### def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import plotFuncs from time import clock import matplotlib.pyplot as plt @@ -1326,7 +1330,7 @@ def mystr(number): return "{:.4f}".format(number) print('percentile is ' + str(Params.init_explicit_perm_inc['pLvlPctiles'][-1]*100) + '.\n') # Make and solve an example "explicit permanent income" consumer with idiosyncratic shocks - ExplicitExample = IndShockExplicitPermIncConsumerType(**Params.init_explicit_perm_inc) + ExplicitExample = IndShockExplicitPermIncConsumerType() t_start = clock() ExplicitExample.solve() t_end = clock() diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 01e0e2f7a..db3784b43 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -22,6 +22,7 @@ import numpy as np from scipy.optimize import newton from HARK import AgentType, Solution, NullFunc, HARKobject +import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import warnings # Because of "patch" to warnings modules from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp from HARK.simulation import drawDiscrete, drawLognormal, drawUniform @@ -1596,7 +1597,12 @@ class PerfForesightConsumerType(AgentType): poststate_vars_ = ['aNrmNow','pLvlNow'] shock_vars_ = [] - def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds): + def __init__(self, + cycles=1, + time_flow=True, + verbose=False, + quiet=False, + **kwds): ''' Instantiate a new consumer type with given data. See ConsumerParameters.init_perfect_foresight for a dictionary of @@ -1613,6 +1619,11 @@ def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds): ------- None ''' + + params = Params.init_perfect_foresight.copy() + params.update(kwds) + kwds = params + # Initialize a basic AgentType AgentType.__init__(self,solution_terminal=deepcopy(self.solution_terminal_), cycles=cycles,time_flow=time_flow,pseudo_terminal=False,**kwds) @@ -1969,7 +1980,12 @@ class IndShockConsumerType(PerfForesightConsumerType): time_inv_.remove('MaxKinks') # This is in the PerfForesight model but not ConsIndShock shock_vars_ = ['PermShkNow','TranShkNow'] - def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): + def __init__(self, + cycles=1, + time_flow=True, + verbose=False, + quiet=False, + **kwds): ''' Instantiate a new ConsumerType with given data. See ConsumerParameters.init_idiosyncratic_shocks for a dictionary of @@ -1986,9 +2002,18 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): ------- None ''' + + params = Params.init_idiosyncratic_shocks.copy() + params.update(kwds) + kwds = params + # Initialize a basic AgentType - PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow, - verbose=verbose,quiet=quiet, **kwds) + PerfForesightConsumerType.__init__(self, + cycles=cycles, + time_flow=time_flow, + verbose=verbose, + quiet=quiet, + **kwds) # Add consumer-type specific objects, copying to create independent versions self.solveOnePeriod = solveConsIndShock # idiosyncratic shocks solver @@ -2324,7 +2349,10 @@ class KinkedRconsumerType(IndShockConsumerType): time_inv_.remove('Rfree') time_inv_ += ['Rboro', 'Rsave'] - def __init__(self,cycles=1,time_flow=True,**kwds): + def __init__(self, + cycles=1, + time_flow=True, + **kwds): ''' Instantiate a new ConsumerType with given data. See ConsumerParameters.init_kinked_R for a dictionary of @@ -2341,6 +2369,10 @@ def __init__(self,cycles=1,time_flow=True,**kwds): ------- None ''' + params = Params.init_kinked_R.copy() + params.update(kwds) + kwds = params + # Initialize a basic AgentType PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds) @@ -2669,7 +2701,6 @@ def constructAssetsGrid(parameters): #################################################################################################### def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import plotFuncsDer, plotFuncs from time import time mystr = lambda number : "{:.4f}".format(number) @@ -2677,7 +2708,7 @@ def main(): do_simulation = True # Make and solve an example perfect foresight consumer - PFexample = PerfForesightConsumerType(**Params.init_perfect_foresight) + PFexample = PerfForesightConsumerType() PFexample.cycles = 0 # Make this type have an infinite horizon start_time = time() @@ -2701,7 +2732,7 @@ def main(): ############################################################################### # Make and solve an example consumer with idiosyncratic income shocks - IndShockExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks) + IndShockExample = IndShockConsumerType() IndShockExample.cycles = 0 # Make this type have an infinite horizon start_time = time() @@ -2795,7 +2826,7 @@ def main(): ############################################################################### # Make and solve an agent with a kinky interest rate - KinkyExample = KinkedRconsumerType(**Params.init_kinked_R) + KinkyExample = KinkedRconsumerType() KinkyExample.cycles = 0 # Make the Example infinite horizon start_time = time() diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py index 75b2502ad..39dc2e45e 100644 --- a/HARK/ConsumptionSaving/ConsMedModel.py +++ b/HARK/ConsumptionSaving/ConsMedModel.py @@ -18,6 +18,7 @@ from HARK.ConsumptionSaving.ConsGenIncProcessModel import ConsGenIncProcessSolver,\ PersistentShockConsumerType, ValueFunc2D, MargValueFunc2D,\ MargMargValueFunc2D, VariableLowerBoundFunc2D +import HARK.ConsumptionSaving.ConsumerParameters as Params from copy import deepcopy utility_inv = CRRAutility_inv @@ -509,7 +510,7 @@ class MedShockConsumerType(PersistentShockConsumerType): ''' shock_vars_ = PersistentShockConsumerType.shock_vars_ + ['MedShkNow'] - def __init__(self,cycles=1,time_flow=True,**kwds): + def __init__(self,cycles=0,time_flow=True,**kwds): ''' Instantiate a new ConsumerType with given data, and construct objects to be used during solution (income distribution, assets grid, etc). @@ -527,6 +528,10 @@ def __init__(self,cycles=1,time_flow=True,**kwds): ------- None ''' + params = Params.init_medical_shocks.copy() + params.update(kwds) + kwds = params + PersistentShockConsumerType.__init__(self,cycles=cycles,**kwds) self.solveOnePeriod = solveConsMedShock # Choose correct solver self.addToTimeInv('CRRAmed') @@ -1361,7 +1366,6 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR ############################################################################### def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import CRRAutility_inv from time import clock import matplotlib.pyplot as plt @@ -1370,7 +1374,7 @@ def main(): do_simulation = True # Make and solve an example medical shocks consumer type - MedicalExample = MedShockConsumerType(**Params.init_medical_shocks) + MedicalExample = MedShockConsumerType() t_start = clock() MedicalExample.solve() t_end = clock() diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index 636c8c3dd..b1d0c4d6f 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -15,6 +15,7 @@ from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, ConsIndShockSolver, \ ValueFunc, MargValueFunc, KinkedRconsumerType, ConsKinkedRsolver from HARK.interpolation import LinearInterpOnInterp1D, LinearInterp, CubicInterp, LowerEnvelope +import HARK.ConsumptionSaving.ConsumerParameters as Params class PrefShockConsumerType(IndShockConsumerType): ''' @@ -23,7 +24,10 @@ class PrefShockConsumerType(IndShockConsumerType): ''' shock_vars_ = IndShockConsumerType.shock_vars_ + ['PrefShkNow'] - def __init__(self,cycles=1,time_flow=True,**kwds): + def __init__(self, + cycles=1, + time_flow=True, + **kwds): ''' Instantiate a new ConsumerType with given data, and construct objects to be used during solution (income distribution, assets grid, etc). @@ -41,7 +45,14 @@ def __init__(self,cycles=1,time_flow=True,**kwds): ------- None ''' - IndShockConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds) + params = Params.init_preference_shocks.copy() + params.update(kwds) + kwds = params + + IndShockConsumerType.__init__(self, + cycles=cycles, + time_flow=time_flow, + **kwds) self.solveOnePeriod = solveConsPrefShock # Choose correct solver def preSolve(self): @@ -206,6 +217,9 @@ def __init__(self,cycles=1,time_flow=True,**kwds): ------- None ''' + params = Params.init_kinky_pref.copy() + params.update(kwds) + kwds = params IndShockConsumerType.__init__(self,**kwds) self.solveOnePeriod = solveConsKinkyPref # Choose correct solver self.addToTimeInv('Rboro','Rsave') @@ -600,7 +614,6 @@ def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn, ############################################################################### def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params import matplotlib.pyplot as plt from HARK.utilities import plotFuncs from time import clock @@ -609,7 +622,7 @@ def main(): do_simulation = True # Make and solve a preference shock consumer - PrefShockExample = PrefShockConsumerType(**Params.init_preference_shocks) + PrefShockExample = PrefShockConsumerType() PrefShockExample.cycles = 0 # Infinite horizon t_start = clock() @@ -652,7 +665,7 @@ def main(): ########################################################################### # Make and solve a "kinky preferece" consumer, whose model combines KinkedR and PrefShock - KinkyPrefExample = KinkyPrefConsumerType(**Params.init_kinky_pref) + KinkyPrefExample = KinkyPrefConsumerType() KinkyPrefExample.cycles = 0 # Infinite horizon t_start = clock() diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py index 1f58c2c3b..6ae221511 100644 --- a/HARK/ConsumptionSaving/ConsRepAgentModel.py +++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py @@ -337,10 +337,9 @@ def main(): from copy import deepcopy from time import clock from HARK.utilities import plotFuncs - import HARK.ConsumptionSaving.ConsumerParameters as Params # Make a quick example dictionary - RA_params = deepcopy(Params.init_idiosyncratic_shocks) + RA_params = {} RA_params['DeprFac'] = 0.05 RA_params['CapShare'] = 0.36 RA_params['UnempPrb'] = 0.0 diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 83d6f827b..c11920e0a 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -211,7 +211,6 @@ 'CRRA': CRRAPF, 'PermGroFacAgg': PermGroFacAgg, 'AggregateL':1.0, - 'act_T':1200, 'intercept_prev': intercept_prev, 'slope_prev': slope_prev, 'verbose': verbose_cobb_douglas, @@ -250,13 +249,11 @@ pLvlPctiles = np.concatenate(([0.001, 0.005, 0.01, 0.03], np.linspace(0.05, 0.95, num=19),[0.97, 0.99, 0.995, 0.999])) PrstIncCorr = 0.98 # Serial correlation coefficient for permanent income -cycles = 0 # Make a dictionary for the "explicit permanent income" idiosyncratic shocks model init_explicit_perm_inc = copy(init_idiosyncratic_shocks) init_explicit_perm_inc['pLvlPctiles'] = pLvlPctiles init_explicit_perm_inc['PermGroFac'] = [1.0] # long run permanent income growth doesn't work yet -init_explicit_perm_inc['cycles'] = cycles init_explicit_perm_inc['aXtraMax'] = 30 init_explicit_perm_inc['aXtraExtra'] = [0.005,0.01] From 0bff841e9564fb2498aff078813abf24ca48ea0e Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 14:50:19 +0530 Subject: [PATCH 160/242] breakup ConsAggShockModel --- HARK/ConsumptionSaving/ConsAggShockModel.py | 199 +------------ .../example_ConsAggShockModel.py | 264 ++++++++++++++++++ 2 files changed, 266 insertions(+), 197 deletions(-) create mode 100644 HARK/ConsumptionSaving/example_ConsAggShockModel.py diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index 52a2bbd28..08c43a3df 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -15,7 +15,7 @@ from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution, IndShockConsumerType from HARK import HARKobject, Market, AgentType from copy import deepcopy -import matplotlib.pyplot as plt +# import matplotlib.pyplot as plt # All plotting is commented out utility = CRRAutility utilityP = CRRAutilityP @@ -25,6 +25,7 @@ utility_inv = CRRAutility_inv + class MargValueFunc2D(HARKobject): ''' A class for representing a marginal value function in models where the @@ -1764,199 +1765,3 @@ def __init__(self, AFunc): ''' self.AFunc = AFunc self.distance_criteria = ['AFunc'] - - -############################################################################### - -def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params - from time import clock - from HARK.utilities import plotFuncs - - def mystr(number): return "{:.4f}".format(number) - - solve_agg_shocks_micro = False # Solve an AggShockConsumerType's microeconomic problem - solve_agg_shocks_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy - - solve_markov_micro = False # Solve an AggShockMarkovConsumerType's microeconomic problem - solve_markov_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy - solve_krusell_smith = True # Solve a simple Krusell-Smith-style two state, two shock model - solve_poly_state = False # Solve a CobbDouglasEconomy with many states, potentially utilizing the "state jumper" - - # EXAMPLE IMPLEMENTATIONS OF AggShockConsumerType ### - - if solve_agg_shocks_micro or solve_agg_shocks_market: - # Make an aggregate shocks consumer type - AggShockExample = AggShockConsumerType(**Params.init_agg_shocks) - AggShockExample.cycles = 0 - - # Make a Cobb-Douglas economy for the agents - EconomyExample = CobbDouglasEconomy(agents=[AggShockExample], **Params.init_cobb_douglas) - EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks - - # Have the consumers inherit relevant objects from the economy - AggShockExample.getEconomyData(EconomyExample) - - if solve_agg_shocks_micro: - # Solve the microeconomic model for the aggregate shocks example type (and display results) - t_start = clock() - AggShockExample.solve() - t_end = clock() - print('Solving an aggregate shocks consumer took ' + mystr(t_end-t_start) + ' seconds.') - print('Consumption function at each aggregate market resources-to-labor ratio gridpoint:') - m_grid = np.linspace(0, 10, 200) - AggShockExample.unpackcFunc() - for M in AggShockExample.Mgrid.tolist(): - mMin = AggShockExample.solution[0].mNrmMin(M) - c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin, M*np.ones_like(m_grid)) - plt.plot(m_grid+mMin, c_at_this_M) - plt.ylim(0., None) - plt.show() - - if solve_agg_shocks_market: - # Solve the "macroeconomic" model by searching for a "fixed point dynamic rule" - t_start = clock() - print('Now solving for the equilibrium of a Cobb-Douglas economy. This might take a few minutes...') - EconomyExample.solve() - t_end = clock() - print('Solving the "macroeconomic" aggregate shocks model took ' + str(t_end - t_start) + ' seconds.') - - print('Aggregate savings as a function of aggregate market resources:') - plotFuncs(EconomyExample.AFunc, 0, 2*EconomyExample.kSS) - print('Consumption function at each aggregate market resources gridpoint (in general equilibrium):') - AggShockExample.unpackcFunc() - m_grid = np.linspace(0, 10, 200) - AggShockExample.unpackcFunc() - for M in AggShockExample.Mgrid.tolist(): - mMin = AggShockExample.solution[0].mNrmMin(M) - c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin, M*np.ones_like(m_grid)) - plt.plot(m_grid+mMin, c_at_this_M) - plt.ylim(0., None) - plt.show() - - # EXAMPLE IMPLEMENTATIONS OF AggShockMarkovConsumerType # - - if solve_markov_micro or solve_markov_market or solve_krusell_smith: - # Make a Markov aggregate shocks consumer type - AggShockMrkvExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks) - AggShockMrkvExample.IncomeDstn[0] = 2*[AggShockMrkvExample.IncomeDstn[0]] - AggShockMrkvExample.cycles = 0 - - # Make a Cobb-Douglas economy for the agents - MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample], **Params.init_mrkv_cobb_douglas) - MrkvEconomyExample.DampingFac = 0.2 # Turn down damping - MrkvEconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks - AggShockMrkvExample.getEconomyData( - MrkvEconomyExample) # Have the consumers inherit relevant objects from the economy - - if solve_markov_micro: - # Solve the microeconomic model for the Markov aggregate shocks example type (and display results) - t_start = clock() - AggShockMrkvExample.solve() - t_end = clock() - print('Solving an aggregate shocks Markov consumer took ' + mystr(t_end-t_start) + ' seconds.') - - print('Consumption function at each aggregate market \ - resources-to-labor ratio gridpoint (for each macro state):') - m_grid = np.linspace(0, 10, 200) - AggShockMrkvExample.unpackcFunc() - for i in range(2): - for M in AggShockMrkvExample.Mgrid.tolist(): - mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M) - c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin, M*np.ones_like(m_grid)) - plt.plot(m_grid+mMin, c_at_this_M) - plt.ylim(0., None) - plt.show() - - if solve_markov_market: - # Solve the "macroeconomic" model by searching for a "fixed point dynamic rule" - t_start = clock() - print('Now solving a two-state Markov economy. This should take a few minutes...') - MrkvEconomyExample.solve() - t_end = clock() - print('Solving the "macroeconomic" aggregate shocks model took ' + str(t_end - t_start) + ' seconds.') - - print('Consumption function at each aggregate market \ - resources-to-labor ratio gridpoint (for each macro state):') - m_grid = np.linspace(0, 10, 200) - AggShockMrkvExample.unpackcFunc() - for i in range(2): - for M in AggShockMrkvExample.Mgrid.tolist(): - mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M) - c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin, M*np.ones_like(m_grid)) - plt.plot(m_grid+mMin, c_at_this_M) - plt.ylim(0., None) - plt.show() - - if solve_krusell_smith: - # Make a Krusell-Smith agent type - # NOTE: These agents aren't exactly like KS, as they don't have serially correlated unemployment - KSexampleType = deepcopy(AggShockMrkvExample) - KSexampleType.IncomeDstn[0] = [[np.array([0.96, 0.04]), np.array([1.0, 1.0]), np.array([1.0/0.96, 0.0])], - [np.array([0.90, 0.10]), np.array([1.0, 1.0]), np.array([1.0/0.90, 0.0])]] - - # Make a KS economy - KSeconomy = deepcopy(MrkvEconomyExample) - KSeconomy.agents = [KSexampleType] - KSeconomy.AggShkDstn = [[np.array([1.0]), np.array([1.0]), np.array([1.05])], - [np.array([1.0]), np.array([1.0]), np.array([0.95])]] - KSeconomy.PermGroFacAgg = [1.0, 1.0] - KSexampleType.getEconomyData(KSeconomy) - KSeconomy.makeAggShkHist() - - # Solve the K-S model - t_start = clock() - print('Now solving a Krusell-Smith-style economy. This should take about a minute...') - KSeconomy.solve() - t_end = clock() - print('Solving the Krusell-Smith model took ' + str(t_end - t_start) + ' seconds.') - - if solve_poly_state: - StateCount = 15 # Number of Markov states - GrowthAvg = 1.01 # Average permanent income growth factor - GrowthWidth = 0.02 # PermGroFacAgg deviates from PermGroFacAgg in this range - Persistence = 0.90 # Probability of staying in the same Markov state - PermGroFacAgg = np.linspace(GrowthAvg-GrowthWidth, GrowthAvg+GrowthWidth, num=StateCount) - - # Make the Markov array with chosen states and persistence - PolyMrkvArray = np.zeros((StateCount, StateCount)) - for i in range(StateCount): - for j in range(StateCount): - if i == j: - PolyMrkvArray[i, j] = Persistence - elif (i == (j-1)) or (i == (j+1)): - PolyMrkvArray[i, j] = 0.5*(1.0 - Persistence) - PolyMrkvArray[0, 0] += 0.5*(1.0 - Persistence) - PolyMrkvArray[StateCount-1, StateCount-1] += 0.5*(1.0 - Persistence) - - # Make a consumer type to inhabit the economy - PolyStateExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks) - PolyStateExample.MrkvArray = PolyMrkvArray - PolyStateExample.PermGroFacAgg = PermGroFacAgg - PolyStateExample.IncomeDstn[0] = StateCount*[PolyStateExample.IncomeDstn[0]] - PolyStateExample.cycles = 0 - - # Make a Cobb-Douglas economy for the agents - PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample], **Params.init_mrkv_cobb_douglas) - PolyStateEconomy.MrkvArray = PolyMrkvArray - PolyStateEconomy.PermGroFacAgg = PermGroFacAgg - PolyStateEconomy.PermShkAggStd = StateCount*[0.006] - PolyStateEconomy.TranShkAggStd = StateCount*[0.003] - PolyStateEconomy.slope_prev = StateCount*[1.0] - PolyStateEconomy.intercept_prev = StateCount*[0.0] - PolyStateEconomy.update() - PolyStateEconomy.makeAggShkDstn() - PolyStateEconomy.makeAggShkHist() # Simulate a history of aggregate shocks - PolyStateExample.getEconomyData( - PolyStateEconomy) # Have the consumers inherit relevant objects from the economy - - # Solve the many state model - t_start = clock() - print('Now solving an economy with ' + str(StateCount) + ' Markov states. This might take a while...') - PolyStateEconomy.solve() - t_end = clock() - print('Solving a model with ' + str(StateCount) + ' states took ' + str(t_end - t_start) + ' seconds.') - - -if __name__ == '__main__': - main() diff --git a/HARK/ConsumptionSaving/example_ConsAggShockModel.py b/HARK/ConsumptionSaving/example_ConsAggShockModel.py new file mode 100644 index 000000000..c8d462a70 --- /dev/null +++ b/HARK/ConsumptionSaving/example_ConsAggShockModel.py @@ -0,0 +1,264 @@ +import HARK.ConsumptionSaving.ConsumerParameters as Params +from time import process_time +import numpy as np +import matplotlib.pyplot as plt +from HARK.utilities import plotFuncs +from HARK.ConsumptionSaving.ConsAggShockModel import ( + AggShockConsumerType, + CobbDouglasEconomy, + AggShockMarkovConsumerType, + CobbDouglasMarkovEconomy, +) + + +def mystr(number): + return "{:.4f}".format(number) + + +solve_agg_shocks_micro = False # Solve an AggShockConsumerType's microeconomic problem +solve_agg_shocks_market = ( + True +) # Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy + +solve_markov_micro = ( + False +) # Solve an AggShockMarkovConsumerType's microeconomic problem +solve_markov_market = ( + True +) # Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy +solve_krusell_smith = ( + True +) # Solve a simple Krusell-Smith-style two state, two shock model +solve_poly_state = ( + False +) # Solve a CobbDouglasEconomy with many states, potentially utilizing the "state jumper" + +# EXAMPLE IMPLEMENTATIONS OF AggShockConsumerType ### + +if solve_agg_shocks_micro or solve_agg_shocks_market: + # Make an aggregate shocks consumer type + AggShockExample = AggShockConsumerType(**Params.init_agg_shocks) + AggShockExample.cycles = 0 + + # Make a Cobb-Douglas economy for the agents + EconomyExample = CobbDouglasEconomy( + agents=[AggShockExample], **Params.init_cobb_douglas + ) + EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks + + # Have the consumers inherit relevant objects from the economy + AggShockExample.getEconomyData(EconomyExample) + +if solve_agg_shocks_micro: + # Solve the microeconomic model for the aggregate shocks example type (and display results) + t_start = process_time() + AggShockExample.solve() + t_end = process_time() + print( + "Solving an aggregate shocks consumer took " + + mystr(t_end - t_start) + + " seconds." + ) + print( + "Consumption function at each aggregate market resources-to-labor ratio gridpoint:" + ) + m_grid = np.linspace(0, 10, 200) + AggShockExample.unpackcFunc() + for M in AggShockExample.Mgrid.tolist(): + mMin = AggShockExample.solution[0].mNrmMin(M) + c_at_this_M = AggShockExample.cFunc[0](m_grid + mMin, M * np.ones_like(m_grid)) + plt.plot(m_grid + mMin, c_at_this_M) + plt.ylim(0.0, None) + plt.show() + +if solve_agg_shocks_market: + # Solve the "macroeconomic" model by searching for a "fixed point dynamic rule" + t_start = process_time() + print( + "Now solving for the equilibrium of a Cobb-Douglas economy. This might take a few minutes..." + ) + EconomyExample.solve() + t_end = process_time() + print( + 'Solving the "macroeconomic" aggregate shocks model took ' + + str(t_end - t_start) + + " seconds." + ) + + print("Aggregate savings as a function of aggregate market resources:") + plotFuncs(EconomyExample.AFunc, 0, 2 * EconomyExample.kSS) + print( + "Consumption function at each aggregate market resources gridpoint (in general equilibrium):" + ) + AggShockExample.unpackcFunc() + m_grid = np.linspace(0, 10, 200) + AggShockExample.unpackcFunc() + for M in AggShockExample.Mgrid.tolist(): + mMin = AggShockExample.solution[0].mNrmMin(M) + c_at_this_M = AggShockExample.cFunc[0](m_grid + mMin, M * np.ones_like(m_grid)) + plt.plot(m_grid + mMin, c_at_this_M) + plt.ylim(0.0, None) + plt.show() + +# EXAMPLE IMPLEMENTATIONS OF AggShockMarkovConsumerType # + +if solve_markov_micro or solve_markov_market or solve_krusell_smith: + # Make a Markov aggregate shocks consumer type + AggShockMrkvExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks) + AggShockMrkvExample.IncomeDstn[0] = 2 * [AggShockMrkvExample.IncomeDstn[0]] + AggShockMrkvExample.cycles = 0 + + # Make a Cobb-Douglas economy for the agents + MrkvEconomyExample = CobbDouglasMarkovEconomy( + agents=[AggShockMrkvExample], **Params.init_mrkv_cobb_douglas + ) + MrkvEconomyExample.DampingFac = 0.2 # Turn down damping + MrkvEconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks + AggShockMrkvExample.getEconomyData( + MrkvEconomyExample + ) # Have the consumers inherit relevant objects from the economy + +if solve_markov_micro: + # Solve the microeconomic model for the Markov aggregate shocks example type (and display results) + t_start = process_time() + AggShockMrkvExample.solve() + t_end = process_time() + print( + "Solving an aggregate shocks Markov consumer took " + + mystr(t_end - t_start) + + " seconds." + ) + + print( + "Consumption function at each aggregate market \ + resources-to-labor ratio gridpoint (for each macro state):" + ) + m_grid = np.linspace(0, 10, 200) + AggShockMrkvExample.unpackcFunc() + for i in range(2): + for M in AggShockMrkvExample.Mgrid.tolist(): + mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M) + c_at_this_M = AggShockMrkvExample.cFunc[0][i]( + m_grid + mMin, M * np.ones_like(m_grid) + ) + plt.plot(m_grid + mMin, c_at_this_M) + plt.ylim(0.0, None) + plt.show() + +if solve_markov_market: + # Solve the "macroeconomic" model by searching for a "fixed point dynamic rule" + t_start = process_time() + print("Now solving a two-state Markov economy. This should take a few minutes...") + MrkvEconomyExample.solve() + t_end = process_time() + print( + 'Solving the "macroeconomic" aggregate shocks model took ' + + str(t_end - t_start) + + " seconds." + ) + + print( + "Consumption function at each aggregate market \ + resources-to-labor ratio gridpoint (for each macro state):" + ) + m_grid = np.linspace(0, 10, 200) + AggShockMrkvExample.unpackcFunc() + for i in range(2): + for M in AggShockMrkvExample.Mgrid.tolist(): + mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M) + c_at_this_M = AggShockMrkvExample.cFunc[0][i]( + m_grid + mMin, M * np.ones_like(m_grid) + ) + plt.plot(m_grid + mMin, c_at_this_M) + plt.ylim(0.0, None) + plt.show() + +if solve_krusell_smith: + # Make a Krusell-Smith agent type + # NOTE: These agents aren't exactly like KS, as they don't have serially correlated unemployment + KSexampleType = deepcopy(AggShockMrkvExample) + KSexampleType.IncomeDstn[0] = [ + [np.array([0.96, 0.04]), np.array([1.0, 1.0]), np.array([1.0 / 0.96, 0.0])], + [np.array([0.90, 0.10]), np.array([1.0, 1.0]), np.array([1.0 / 0.90, 0.0])], + ] + + # Make a KS economy + KSeconomy = deepcopy(MrkvEconomyExample) + KSeconomy.agents = [KSexampleType] + KSeconomy.AggShkDstn = [ + [np.array([1.0]), np.array([1.0]), np.array([1.05])], + [np.array([1.0]), np.array([1.0]), np.array([0.95])], + ] + KSeconomy.PermGroFacAgg = [1.0, 1.0] + KSexampleType.getEconomyData(KSeconomy) + KSeconomy.makeAggShkHist() + + # Solve the K-S model + t_start = process_time() + print( + "Now solving a Krusell-Smith-style economy. This should take about a minute..." + ) + KSeconomy.solve() + t_end = process_time() + print("Solving the Krusell-Smith model took " + str(t_end - t_start) + " seconds.") + +if solve_poly_state: + StateCount = 15 # Number of Markov states + GrowthAvg = 1.01 # Average permanent income growth factor + GrowthWidth = 0.02 # PermGroFacAgg deviates from PermGroFacAgg in this range + Persistence = 0.90 # Probability of staying in the same Markov state + PermGroFacAgg = np.linspace( + GrowthAvg - GrowthWidth, GrowthAvg + GrowthWidth, num=StateCount + ) + + # Make the Markov array with chosen states and persistence + PolyMrkvArray = np.zeros((StateCount, StateCount)) + for i in range(StateCount): + for j in range(StateCount): + if i == j: + PolyMrkvArray[i, j] = Persistence + elif (i == (j - 1)) or (i == (j + 1)): + PolyMrkvArray[i, j] = 0.5 * (1.0 - Persistence) + PolyMrkvArray[0, 0] += 0.5 * (1.0 - Persistence) + PolyMrkvArray[StateCount - 1, StateCount - 1] += 0.5 * (1.0 - Persistence) + + # Make a consumer type to inhabit the economy + PolyStateExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks) + PolyStateExample.MrkvArray = PolyMrkvArray + PolyStateExample.PermGroFacAgg = PermGroFacAgg + PolyStateExample.IncomeDstn[0] = StateCount * [PolyStateExample.IncomeDstn[0]] + PolyStateExample.cycles = 0 + + # Make a Cobb-Douglas economy for the agents + PolyStateEconomy = CobbDouglasMarkovEconomy( + agents=[PolyStateExample], **Params.init_mrkv_cobb_douglas + ) + PolyStateEconomy.MrkvArray = PolyMrkvArray + PolyStateEconomy.PermGroFacAgg = PermGroFacAgg + PolyStateEconomy.PermShkAggStd = StateCount * [0.006] + PolyStateEconomy.TranShkAggStd = StateCount * [0.003] + PolyStateEconomy.slope_prev = StateCount * [1.0] + PolyStateEconomy.intercept_prev = StateCount * [0.0] + PolyStateEconomy.update() + PolyStateEconomy.makeAggShkDstn() + PolyStateEconomy.makeAggShkHist() # Simulate a history of aggregate shocks + PolyStateExample.getEconomyData( + PolyStateEconomy + ) # Have the consumers inherit relevant objects from the economy + + # Solve the many state model + t_start = process_time() + print( + "Now solving an economy with " + + str(StateCount) + + " Markov states. This might take a while..." + ) + PolyStateEconomy.solve() + t_end = process_time() + print( + "Solving a model with " + + str(StateCount) + + " states took " + + str(t_end - t_start) + + " seconds." + ) From 520fa1d2a8afdef97a07d709f6c07062d5c0ebb0 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 14:56:57 +0530 Subject: [PATCH 161/242] add __all__ var to ConsAggShockModel --- HARK/ConsumptionSaving/ConsAggShockModel.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index 08c43a3df..26f05d513 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -17,6 +17,10 @@ from copy import deepcopy # import matplotlib.pyplot as plt # All plotting is commented out +__all__ = ['MargValueFunc2D', 'AggShockConsumerType', 'AggShockMarkovConsumerType', +'CobbDouglasEconomy', 'SmallOpenEconomy', 'CobbDouglasMarkovEconomy', +'SmallOpenMarkovEconomy', 'CobbDouglasAggVars', 'AggregateSavingRule', 'AggShocksDynamicRule'] + utility = CRRAutility utilityP = CRRAutilityP utilityPP = CRRAutilityPP @@ -25,7 +29,6 @@ utility_inv = CRRAutility_inv - class MargValueFunc2D(HARKobject): ''' A class for representing a marginal value function in models where the From d5b6e55b51f4e7a2ad2290c1ec083d64d52a9251 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 14:59:35 +0530 Subject: [PATCH 162/242] update ConsPortfolioModel.py --- HARK/ConsumptionSaving/ConsPortfolioModel.py | 833 ++++++++++++------- 1 file changed, 529 insertions(+), 304 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py index f88f25717..87cecd7ce 100644 --- a/HARK/ConsumptionSaving/ConsPortfolioModel.py +++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py @@ -2,40 +2,50 @@ # Rfree and Risky-parameters. This should be possible by creating a list of # functions instead. -import math # we're using math for log and exp, might want to just use numpy? -import scipy.optimize as sciopt # we're using scipy optimize to optimize and fsolve -import scipy.integrate # used to calculate the expectation over returns -import scipy.stats as stats # for densities related to the returns distributions -from copy import deepcopy # it's convenient to copy things some times instead of re-creating them +import math # we're using math for log and exp, might want to just use numpy? +import scipy.optimize as sciopt # we're using scipy optimize to optimize and fsolve +import scipy.integrate # used to calculate the expectation over returns +import scipy.stats as stats # for densities related to the returns distributions +from copy import ( + deepcopy, +) # it's convenient to copy things some times instead of re-creating them # Solution is inherited from in the PortfolioSolution class, NullFunc is used # throughout HARK when no input is given and AgentType is used for .preSolve from HARK import Solution, NullFunc, AgentType from HARK.ConsumptionSaving.ConsIndShockModel import ( - PerfForesightConsumerType, # for .__init__ - IndShockConsumerType, # PortfolioConsumerType inherits from it - ConsIndShockSolver, # ConsIndShockPortfolioSolver inherits from it - ValueFunc, # to do the re-curving of value functions for interpolation - MargValueFunc, # same as above, but for marginal value functions - utility_inv, # inverse CRRA - ) + PerfForesightConsumerType, # for .__init__ + IndShockConsumerType, # PortfolioConsumerType inherits from it + ConsIndShockSolver, # ConsIndShockPortfolioSolver inherits from it + ValueFunc, # to do the re-curving of value functions for interpolation + MargValueFunc, # same as above, but for marginal value functions + utility_inv, # inverse CRRA +) from HARK.utilities import ( - approxLognormal, # for approximating the lognormal returns factor - combineIndepDstns, # for combining the existing - ) + approxLognormal, # for approximating the lognormal returns factor + combineIndepDstns, # for combining the existing +) -from HARK.simulation import drawLognormal # random draws for simulating agents +from HARK.simulation import drawLognormal # random draws for simulating agents from HARK.interpolation import ( LinearInterp, # piece-wise linear interpolation - LowerEnvelope, # lower envelope for consumption function around borrowing constraint - ) + LowerEnvelope, # lower envelope for consumption function around borrowing constraint +) -import numpy as np # for array operations +import numpy as np # for array operations +__all__ = [ + "PortfolioSolution", + "ContinuousDomain", + "DiscreteDomain", + "PortfolioConsumerType", + "ConsIndShockPortfolioSolver", + "LogNormalPortfolioConsumerType", +] # REMARK: The Campbell and Viceira (2002) approximation can be calculated from # the code below. TODO clean up # def CambellVicApprox() @@ -53,7 +63,7 @@ def _PerfForesightLogNormalPortfolioShare(Rfree, RiskyAvg, RiskyStd, CRRA): - ''' + """ Calculate the optimal portfolio share in the perfect foresight model. This does not depend on resources today or the time period. @@ -72,17 +82,18 @@ def _PerfForesightLogNormalPortfolioShare(Rfree, RiskyAvg, RiskyStd, CRRA): ------- optShare : Number The optimal portfolio share in the perfect foresight portofolio model. - ''' - PortfolioObjective = lambda share: _PerfForesightLogNormalPortfolioObjective(share, - Rfree, - RiskyAvg, - RiskyStd, - CRRA) - optShare = sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x + """ + PortfolioObjective = lambda share: _PerfForesightLogNormalPortfolioObjective( + share, Rfree, RiskyAvg, RiskyStd, CRRA + ) + optShare = sciopt.minimize_scalar( + PortfolioObjective, bounds=(0.0, 1.0), method="bounded" + ).x return optShare + def _PerfForesightDiscretePortfolioShare(Rfree, RiskyDstn, CRRA): - ''' + """ Calculate the optimal portfolio share in the perfect foresight model. This Does not depend on resources today or the time period. This version assumes that the return factor distribution is characterized by a discrete distribution @@ -103,12 +114,13 @@ def _PerfForesightDiscretePortfolioShare(Rfree, RiskyDstn, CRRA): ------- optShare : Number The optimal portfolio share in the perfect foresight portofolio model. - ''' - PortfolioObjective = lambda share: _PerfForesightDiscretePortfolioObjective(share, - Rfree, - RiskyDstn, - CRRA) - optShare = sciopt.minimize_scalar(PortfolioObjective, bounds=(0.0, 1.0), method='bounded').x + """ + PortfolioObjective = lambda share: _PerfForesightDiscretePortfolioObjective( + share, Rfree, RiskyDstn, CRRA + ) + optShare = sciopt.minimize_scalar( + PortfolioObjective, bounds=(0.0, 1.0), method="bounded" + ).x return optShare @@ -116,7 +128,7 @@ def _PerfForesightDiscretePortfolioShare(Rfree, RiskyDstn, CRRA): # It can either be "discrete" in which case it is only the number of draws that # are used, or it can be continuous in which case bounds and a pdf has to be supplied. def _PerfForesightLogNormalPortfolioIntegrand(share, Rfree, RiskyAvg, RiskyStd, CRRA): - ''' + """ Returns a function to evaluate the integrand for calculating the expectation in the perfect foresight porfolio problem with lognormal return factors. @@ -135,17 +147,18 @@ def _PerfForesightLogNormalPortfolioIntegrand(share, Rfree, RiskyAvg, RiskyStd, ------- integrand : function (lambda) Can be used to evaluate the integrand and the sent to a quadrature procedure. - ''' - muNorm = np.log(RiskyAvg/np.sqrt(1+RiskyStd**2/RiskyAvg**2)) - sigmaNorm = np.sqrt(np.log(1+RiskyStd**2/RiskyAvg**2)) - sharedobjective = lambda r: (Rfree+share*(r-Rfree))**(1-CRRA) + """ + muNorm = np.log(RiskyAvg / np.sqrt(1 + RiskyStd ** 2 / RiskyAvg ** 2)) + sigmaNorm = np.sqrt(np.log(1 + RiskyStd ** 2 / RiskyAvg ** 2)) + sharedobjective = lambda r: (Rfree + share * (r - Rfree)) ** (1 - CRRA) pdf = lambda r: stats.lognorm.pdf(r, s=sigmaNorm, scale=np.exp(muNorm)) - integrand = lambda r: sharedobjective(r)*pdf(r) + integrand = lambda r: sharedobjective(r) * pdf(r) return integrand + def _PerfForesightLogNormalPortfolioObjective(share, Rfree, RiskyAvg, RiskyStd, CRRA): - ''' + """ Returns the integral used in the perfect foresight portoflio choice problem with lognormal return factors evaluated at share. @@ -164,15 +177,17 @@ def _PerfForesightLogNormalPortfolioObjective(share, Rfree, RiskyAvg, RiskyStd, ------- integrand : function (lambda) Can be used to evaluate the integrand and the sent to a quadrature procedure. - ''' - integrand = _PerfForesightLogNormalPortfolioIntegrand(share, Rfree, RiskyAvg, RiskyStd, CRRA) - a = 0.0 # Cannot be negative - b = 5.0 # This is just an upper bound. pdf should be 0 here. - return -((1-CRRA)**-1)*scipy.integrate.quad(integrand, a, b)[0] + """ + integrand = _PerfForesightLogNormalPortfolioIntegrand( + share, Rfree, RiskyAvg, RiskyStd, CRRA + ) + a = 0.0 # Cannot be negative + b = 5.0 # This is just an upper bound. pdf should be 0 here. + return -((1 - CRRA) ** -1) * scipy.integrate.quad(integrand, a, b)[0] def _PerfForesightDiscretePortfolioObjective(share, Rfree, RiskyDstn, CRRA): - ''' + """ Returns the integral used in the perfect foresight portoflio choice problem with discretely distributed return factors evaluated at share. @@ -191,14 +206,15 @@ def _PerfForesightDiscretePortfolioObjective(share, Rfree, RiskyDstn, CRRA): ------- integrand : function (lambda) Can be used to evaluate the integrand and the sent to a quadrature procedure. - ''' - vals = (Rfree+share*(RiskyDstn[1]-Rfree))**(1-CRRA) + """ + vals = (Rfree + share * (RiskyDstn[1] - Rfree)) ** (1 - CRRA) weights = RiskyDstn[0] - return -((1-CRRA)**-1)*np.dot(vals, weights) + return -((1 - CRRA) ** -1) * np.dot(vals, weights) + def _calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc_adj, CRRA): - ''' + """ Set up the value function at the point of consumption, but before portofolio choice. Depends on the probability of getting the porfolio choice this period, the possible shares today (if AdjustPrb is not 1) and the value function @@ -225,11 +241,11 @@ def _calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc_adj, CRRA): ------- integrand : function (lambda) Can be used to evaluate the integrand and the sent to a quadrature procedure. - ''' + """ # AdjustCount could in principle just be inferred from AdjustPrb instead of # cartying it arround FIXME / TODO - wFunc = [] + wFunc = [] if AdjustCount == 1: # Just take the adjuster for ShareIndex in range(ShareNowCount[0]): @@ -239,9 +255,13 @@ def _calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc_adj, CRRA): for ShareIndex in range(ShareNowCount[1]): # TODO FIXME better grid evalgrid = np.linspace(0, 100, 200) - evVals = AdjustPrb*vFunc_adj[0][ShareIndex](evalgrid) + (1-AdjustPrb)*vFunc_adj[1][ShareIndex](evalgrid) - with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): - evValsNvrs = utility_inv(evVals,gam=CRRA) + evVals = AdjustPrb * vFunc_adj[0][ShareIndex](evalgrid) + ( + 1 - AdjustPrb + ) * vFunc_adj[1][ShareIndex](evalgrid) + with np.errstate( + divide="ignore", over="ignore", under="ignore", invalid="ignore" + ): + evValsNvrs = utility_inv(evVals, gam=CRRA) wFunc.append(ValueFunc(LinearInterp(evVals, evValsNvrs), CRRA)) return wFunc @@ -255,35 +275,46 @@ def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0): a list of lists where the first list contains the weights (probabilities) and the second list contains the values. """ - RiskyAvgSqrd = RiskyAvg**2 - RiskyVar = RiskyStd**2 + RiskyAvgSqrd = RiskyAvg ** 2 + RiskyVar = RiskyStd ** 2 - mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) - sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) + mu = math.log(RiskyAvg / (math.sqrt(1 + RiskyVar / RiskyAvgSqrd))) + sigma = math.sqrt(math.log(1 + RiskyVar / RiskyAvgSqrd)) return lambda RiskyCount: approxLognormal(RiskyCount, mu=mu, sigma=sigma) + def LogNormalRiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0): """ A class for generating functions that draw random values from a log-normal distribution as parameterized by the input `RiskyAvg` and `RiskyStd` values. The returned function takes no argument and returns a value. """ - RiskyAvgSqrd = RiskyAvg**2 - RiskyVar = RiskyStd**2 + RiskyAvgSqrd = RiskyAvg ** 2 + RiskyVar = RiskyStd ** 2 - mu = math.log(RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd))) - sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd)) + mu = math.log(RiskyAvg / (math.sqrt(1 + RiskyVar / RiskyAvgSqrd))) + sigma = math.sqrt(math.log(1 + RiskyVar / RiskyAvgSqrd)) return lambda rngSeed: drawLognormal(1, mu=mu, sigma=sigma, seed=rngSeed) class PortfolioSolution(Solution): - distance_criteria = ['cFunc'] - - def __init__(self, cFunc=None, vFunc=None, wFunc=None, - vPfunc=None, RiskyShareFunc=None, vPPfunc=None, - mNrmMin=None, hNrm=None, MPCmin=None, MPCmax=None): + distance_criteria = ["cFunc"] + + def __init__( + self, + cFunc=None, + vFunc=None, + wFunc=None, + vPfunc=None, + RiskyShareFunc=None, + vPPfunc=None, + mNrmMin=None, + hNrm=None, + MPCmin=None, + MPCmax=None, + ): """We implement three different ways to allow portfolio choice. The agent can choose: @@ -318,11 +349,11 @@ def __init__(self, cFunc=None, vFunc=None, wFunc=None, vPfunc = NullFunc() if vPPfunc is None: vPPfunc = NullFunc() - self.cFunc = cFunc - self.vFunc = vFunc - self.vPfunc = vPfunc + self.cFunc = cFunc + self.vFunc = vFunc + self.vPfunc = vPfunc self.RiskyShareFunc = RiskyShareFunc - self.wFunc = wFunc + self.wFunc = wFunc # self.vPPfunc = vPPfunc # self.mNrmMin = mNrmMin # self.hNrm = hNrm @@ -333,9 +364,9 @@ def __init__(self, cFunc=None, vFunc=None, wFunc=None, # These domains are convenient for switching to relavent code paths internally. # It might be simpler to just pass in vectors instead of DiscreteDomain. class ContinuousDomain(object): - def __init__(self, lower, upper, points = [np.nan]): + def __init__(self, lower, upper, points=[np.nan]): if lower > upper: - raise Exception('lower bounds is larger than upper bound') + raise Exception("lower bounds is larger than upper bound") else: self.lower = lower self.upper = upper @@ -347,6 +378,7 @@ def getPoints(self): def len(self): return len(self.points) + class DiscreteDomain(object): def __init__(self, points): self.points = points @@ -359,25 +391,32 @@ def len(self): def getPoints(self): return self.points + class PortfolioConsumerType(IndShockConsumerType): # We add CantAdjust to the standard set of poststate_vars_ here. We call it # CantAdjust over CanAdjust, because this allows us to index into the # "CanAdjust = 1- CantAdjust" at all times (it's the 0th offset). - poststate_vars_ = ['aNrmNow', 'pLvlNow', 'RiskyShareNow', 'CantAdjust'] + poststate_vars_ = ["aNrmNow", "pLvlNow", "RiskyShareNow", "CantAdjust"] time_inv_ = deepcopy(IndShockConsumerType.time_inv_) - time_inv_ = time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount'] - time_inv_ = time_inv_ + ['RiskyShareLimitFunc', 'PortfolioDomain'] - time_inv_ = time_inv_ + ['AdjustPrb', 'PortfolioGrid', 'AdjustCount'] + time_inv_ = time_inv_ + ["approxRiskyDstn", "RiskyCount", "RiskyShareCount"] + time_inv_ = time_inv_ + ["RiskyShareLimitFunc", "PortfolioDomain"] + time_inv_ = time_inv_ + ["AdjustPrb", "PortfolioGrid", "AdjustCount"] - def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): + def __init__(self, cycles=1, time_flow=True, verbose=False, quiet=False, **kwds): # Initialize a basic AgentType - PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow, - verbose=verbose,quiet=quiet, **kwds) + PerfForesightConsumerType.__init__( + self, + cycles=cycles, + time_flow=time_flow, + verbose=verbose, + quiet=quiet, + **kwds + ) # Check that an adjustment probability is set. If not, default to always. - if not hasattr(self, 'AdjustPrb'): + if not hasattr(self, "AdjustPrb"): self.AdjustPrb = 1.0 self.AdjustCount = 1 elif self.AdjustPrb == 1.0: @@ -388,24 +427,29 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): # the consumer cannot adjust in a given period. self.AdjustCount = 2 - if not hasattr(self, 'PortfolioDomain'): + if not hasattr(self, "PortfolioDomain"): if self.AdjustPrb < 1.0: - raise Exception('Please supply a PortfolioDomain when setting AdjustPrb < 1.0.') + raise Exception( + "Please supply a PortfolioDomain when setting AdjustPrb < 1.0." + ) else: - self.PortfolioDomain = ContinuousDomain(0,1) - + self.PortfolioDomain = ContinuousDomain(0, 1) if isinstance(self.PortfolioDomain, DiscreteDomain): self.DiscreteCase = True if self.vFuncBool == False: if self.verbose: - print('Setting vFuncBool to True to accomodate dicrete portfolio optimization.') + print( + "Setting vFuncBool to True to accomodate dicrete portfolio optimization." + ) self.vFuncBool = True else: self.DiscreteCase = False if self.AdjustPrb < 1.0: - raise Exception('Occational inability to re-optimize portfolio (AdjustPrb < 1.0) is currently not possible with continuous choice of the portfolio share.') + raise Exception( + "Occational inability to re-optimize portfolio (AdjustPrb < 1.0) is currently not possible with continuous choice of the portfolio share." + ) # Now we can set up the PortfolioGrid! This is the portfolio values # you can enter the period with. It's exact for discrete , for continuous @@ -414,7 +458,9 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): if self.BoroCnstArt is not 0.0: if self.verbose: - print("Setting BoroCnstArt to 0.0 as this is required by PortfolioConsumerType.") + print( + "Setting BoroCnstArt to 0.0 as this is required by PortfolioConsumerType." + ) self.BoroCnstArt = 0.0 self.ShareNowCount = [1] @@ -427,14 +473,16 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): self.update() - self.RiskyShareLimitFunc = lambda RiskyDstn: _PerfForesightDiscretePortfolioShare(self.Rfree, RiskyDstn, self.CRRA) + self.RiskyShareLimitFunc = lambda RiskyDstn: _PerfForesightDiscretePortfolioShare( + self.Rfree, RiskyDstn, self.CRRA + ) def preSolve(self): AgentType.preSolve(self) self.updateSolutionTerminal() def updateSolutionTerminal(self): - ''' + """ Updates the terminal period solution for a portfolio shock consumer. Only fills in the consumption function and marginal value function. @@ -445,13 +493,15 @@ def updateSolutionTerminal(self): Returns ------- None - ''' + """ # repeat according to number of portfolio adjustment situations # TODO FIXME this is technically incorrect, too many in [0] - cFunc_terminal = LinearInterp([0.0, 1.0], [0.0,1.0]) # c=m in terminal period - vFunc_terminal = LinearInterp([0.0, 1.0], [0.0,0.0]) # This is overwritten - RiskyShareFunc_terminal = LinearInterp([0.0, 1.0], [0.0,0.0]) # c=m in terminal period + cFunc_terminal = LinearInterp([0.0, 1.0], [0.0, 1.0]) # c=m in terminal period + vFunc_terminal = LinearInterp([0.0, 1.0], [0.0, 0.0]) # This is overwritten + RiskyShareFunc_terminal = LinearInterp( + [0.0, 1.0], [0.0, 0.0] + ) # c=m in terminal period if isinstance(self.PortfolioDomain, DiscreteDomain): PortfolioGridCount = len(self.PortfolioDomain.points) @@ -459,31 +509,42 @@ def updateSolutionTerminal(self): # This should be "PortfolioGridCount" that was set earlier, PortfolioGridCount = 1 - vFunc_terminal = PortfolioGridCount*[ValueFunc(cFunc_terminal, self.CRRA)] - vFunc_terminal = self.AdjustCount*[vFunc_terminal] - - vPfunc_terminal = PortfolioGridCount*[MargValueFunc(cFunc_terminal, self.CRRA)] - vPfunc_terminal = self.AdjustCount*[vPfunc_terminal] - - cFunc_terminal = PortfolioGridCount*[cFunc_terminal] - cFunc_terminal = self.AdjustCount*[cFunc_terminal] - - RiskyShareFunc_terminal = PortfolioGridCount*[RiskyShareFunc_terminal] - RiskyShareFunc_terminal = self.AdjustCount*[RiskyShareFunc_terminal] - - wFunc_terminal = _calcwFunc(self.AdjustPrb, self.AdjustCount, self.ShareNowCount, vFunc_terminal, self.CRRA) - - self.solution_terminal = PortfolioSolution(cFunc = cFunc_terminal, - RiskyShareFunc = RiskyShareFunc_terminal, - vFunc = vFunc_terminal, - wFunc = wFunc_terminal, - vPfunc = vPfunc_terminal, - mNrmMin=0.0, hNrm=None, - MPCmin=None, MPCmax=None) - + vFunc_terminal = PortfolioGridCount * [ValueFunc(cFunc_terminal, self.CRRA)] + vFunc_terminal = self.AdjustCount * [vFunc_terminal] + + vPfunc_terminal = PortfolioGridCount * [ + MargValueFunc(cFunc_terminal, self.CRRA) + ] + vPfunc_terminal = self.AdjustCount * [vPfunc_terminal] + + cFunc_terminal = PortfolioGridCount * [cFunc_terminal] + cFunc_terminal = self.AdjustCount * [cFunc_terminal] + + RiskyShareFunc_terminal = PortfolioGridCount * [RiskyShareFunc_terminal] + RiskyShareFunc_terminal = self.AdjustCount * [RiskyShareFunc_terminal] + + wFunc_terminal = _calcwFunc( + self.AdjustPrb, + self.AdjustCount, + self.ShareNowCount, + vFunc_terminal, + self.CRRA, + ) + + self.solution_terminal = PortfolioSolution( + cFunc=cFunc_terminal, + RiskyShareFunc=RiskyShareFunc_terminal, + vFunc=vFunc_terminal, + wFunc=wFunc_terminal, + vPfunc=vPfunc_terminal, + mNrmMin=0.0, + hNrm=None, + MPCmin=None, + MPCmax=None, + ) def getPostStates(self): - ''' + """ Calculates end-of-period assets for each consumer of this type. Parameters @@ -493,10 +554,12 @@ def getPostStates(self): Returns ------- None - ''' + """ # Calculate post decision ressources self.aNrmNow = self.mNrmNow - self.cNrmNow - self.aLvlNow = self.aNrmNow*self.pLvlNow # Useful in some cases to precalculate asset level + self.aLvlNow = ( + self.aNrmNow * self.pLvlNow + ) # Useful in some cases to precalculate asset level # We calculate the risky share given post decision assets aNrmNow. We # do this for all agents that have self.CantAdjust == 0 and save the @@ -511,21 +574,23 @@ def getPostStates(self): # First take adjusters these = np.logical_and(these_adjust, these_t) - RiskyShareNow[these] = self.solution[t].RiskyShareFunc[0][0](self.aNrmNow[these]) # should be redefined on mNrm in solve and calculated in getControls + RiskyShareNow[these] = self.solution[t].RiskyShareFunc[0][0]( + self.aNrmNow[these] + ) # should be redefined on mNrm in solve and calculated in getControls these_cant_adjust = self.CantAdjust == 1 these = np.logical_and(these_cant_adjust, these_t) - RiskyShareNow[these] = self.RiskySharePrev[these] # should be redefined on mNrm in solve and calculated in getControls - + RiskyShareNow[these] = self.RiskySharePrev[ + these + ] # should be redefined on mNrm in solve and calculated in getControls # Store the result in self self.RiskyShareNow = RiskyShareNow return None - # Simulation methods def getStates(self): - ''' + """ Calculates updated values of normalized market resources and permanent income level for each agent. Uses pLvlNow, aNrmNow, PermShkNow, TranShkNow. @@ -536,9 +601,9 @@ def getStates(self): Returns ------- None - ''' + """ RiskySharePrev = self.RiskyShareNow - self.RiskySharePrev = RiskySharePrev # Save this for the non-adjusters! + self.RiskySharePrev = RiskySharePrev # Save this for the non-adjusters! pLvlPrev = self.pLvlNow aNrmPrev = self.aNrmNow @@ -549,25 +614,29 @@ def getStates(self): RiskyNow = self.getRisky() # Calculate the portfolio return from last period to current period. - RportNow = RfreeNow + RiskySharePrev*(RiskyNow-RfreeNow) + RportNow = RfreeNow + RiskySharePrev * (RiskyNow - RfreeNow) # Calculate new states: normalized market resources and permanent income level - self.pLvlNow = pLvlPrev*self.PermShkNow # Updated permanent income level - self.PlvlAggNow = self.PlvlAggNow*self.PermShkAggNow # Updated aggregate permanent productivity level - ReffNow = RportNow/self.PermShkNow # "Effective" interest factor on normalized assets - self.bNrmNow = ReffNow*aNrmPrev # Bank balances before labor income - self.mNrmNow = self.bNrmNow + self.TranShkNow # Market resources after income + self.pLvlNow = pLvlPrev * self.PermShkNow # Updated permanent income level + self.PlvlAggNow = ( + self.PlvlAggNow * self.PermShkAggNow + ) # Updated aggregate permanent productivity level + ReffNow = ( + RportNow / self.PermShkNow + ) # "Effective" interest factor on normalized assets + self.bNrmNow = ReffNow * aNrmPrev # Bank balances before labor income + self.mNrmNow = self.bNrmNow + self.TranShkNow # Market resources after income # Figure out who can adjust their portfolio this period. - self.CantAdjust = stats.bernoulli.rvs(1-self.AdjustPrb, size=self.AgentCount) + self.CantAdjust = stats.bernoulli.rvs(1 - self.AdjustPrb, size=self.AgentCount) # New agents are always allowed to optimize their portfolio, because they # have no past portfolio to "keep". self.CantAdjust[self.new_agents] = 0.0 return None - def simBirth(self,which_agents): - ''' + def simBirth(self, which_agents): + """ Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as well as time variables t_age and t_cycle. Normalized assets and permanent income levels are drawn from lognormal distributions given by aNrmInitMean and aNrmInitStd (etc). @@ -580,20 +649,36 @@ def simBirth(self,which_agents): Returns ------- None - ''' + """ # Get and store states for newly born agents - N = np.sum(which_agents) # Number of new consumers to make - self.aNrmNow[which_agents] = drawLognormal(N,mu=self.aNrmInitMean,sigma=self.aNrmInitStd,seed=self.RNG.randint(0,2**31-1)) - pLvlInitMeanNow = self.pLvlInitMean + np.log(self.PlvlAggNow) # Account for newer cohorts having higher permanent income - self.pLvlNow[which_agents] = drawLognormal(N,mu=pLvlInitMeanNow,sigma=self.pLvlInitStd,seed=self.RNG.randint(0,2**31-1)) - - self.t_age[which_agents] = 0 # How many periods since each agent was born - self.t_cycle[which_agents] = 0 # Which period of the cycle each agent is currently in - self.new_agents = which_agents # store for portfolio choice forced to be allowed in first period + N = np.sum(which_agents) # Number of new consumers to make + self.aNrmNow[which_agents] = drawLognormal( + N, + mu=self.aNrmInitMean, + sigma=self.aNrmInitStd, + seed=self.RNG.randint(0, 2 ** 31 - 1), + ) + pLvlInitMeanNow = self.pLvlInitMean + np.log( + self.PlvlAggNow + ) # Account for newer cohorts having higher permanent income + self.pLvlNow[which_agents] = drawLognormal( + N, + mu=pLvlInitMeanNow, + sigma=self.pLvlInitStd, + seed=self.RNG.randint(0, 2 ** 31 - 1), + ) + + self.t_age[which_agents] = 0 # How many periods since each agent was born + self.t_cycle[ + which_agents + ] = 0 # Which period of the cycle each agent is currently in + self.new_agents = ( + which_agents + ) # store for portfolio choice forced to be allowed in first period return None def getControls(self): - ''' + """ Calculates consumption for each consumer of this type using the consumption functions. Parameters @@ -603,9 +688,9 @@ def getControls(self): Returns ------- None - ''' + """ cNrmNow = np.zeros(self.AgentCount) + np.nan - MPCnow = np.zeros(self.AgentCount) + np.nan + MPCnow = np.zeros(self.AgentCount) + np.nan these_cant_adjust = self.CantAdjust == 1 these_can_adjust = self.CantAdjust == 0 @@ -613,35 +698,72 @@ def getControls(self): for t in range(self.T_cycle): these_t = t == self.t_cycle these = np.logical_and(these_t, these_can_adjust) - cNrmNow[these], MPCnow[these] = self.solution[t].cFunc[0][0].eval_with_derivative(self.mNrmNow[these]) + cNrmNow[these], MPCnow[these] = ( + self.solution[t].cFunc[0][0].eval_with_derivative(self.mNrmNow[these]) + ) if any(these_cant_adjust): for portfolio_index, portfolio_value in enumerate(self.ShareNow): these_portfolio = np.equal(portfolio_value, self.RiskySharePrev) these = np.logical_and(these_t, these_portfolio) - cNrmNow[these], MPCnow[these] = self.solution[t].cFunc[1][portfolio_index].eval_with_derivative(self.mNrmNow[these]) + cNrmNow[these], MPCnow[these] = ( + self.solution[t] + .cFunc[1][portfolio_index] + .eval_with_derivative(self.mNrmNow[these]) + ) self.cNrmNow = cNrmNow self.MPCnow = MPCnow return None def getRisky(self): - return self.drawRiskyFunc(self.RNG.randint(0,2**31-1)) + return self.drawRiskyFunc(self.RNG.randint(0, 2 ** 31 - 1)) + class ConsIndShockPortfolioSolver(ConsIndShockSolver): - ''' + """ A class for solving a one period consumption-saving problem with portfolio choice. An instance of this class is created by the function solveConsPortfolio in each period. - ''' - def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool, - approxRiskyDstn, RiskyCount, RiskyShareCount, RiskyShareLimitFunc, - AdjustPrb, PortfolioGrid, AdjustCount, PortfolioDomain): - - ConsIndShockSolver.__init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, - PermGroFac, BoroCnstArt, aXtraGrid, vFuncBool, CubicBool) + """ + def __init__( + self, + solution_next, + IncomeDstn, + LivPrb, + DiscFac, + CRRA, + Rfree, + PermGroFac, + BoroCnstArt, + aXtraGrid, + vFuncBool, + CubicBool, + approxRiskyDstn, + RiskyCount, + RiskyShareCount, + RiskyShareLimitFunc, + AdjustPrb, + PortfolioGrid, + AdjustCount, + PortfolioDomain, + ): + + ConsIndShockSolver.__init__( + self, + solution_next, + IncomeDstn, + LivPrb, + DiscFac, + CRRA, + Rfree, + PermGroFac, + BoroCnstArt, + aXtraGrid, + vFuncBool, + CubicBool, + ) self.PortfolioDomain = PortfolioDomain if isinstance(self.PortfolioDomain, DiscreteDomain): @@ -671,9 +793,8 @@ def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, self.updateShockDstn() self.makeRshareGrid() - def makeEndOfPrdvFunc(self, AdjustIndex, ShareIndex): - ''' + """ Construct the end-of-period value function for this period, storing it as an attribute of self for use by other methods. @@ -684,26 +805,33 @@ def makeEndOfPrdvFunc(self, AdjustIndex, ShareIndex): Returns ------- none - ''' + """ if not self.DiscreteCase: - raise Exception("vFuncBool == True is not supported for continuous portfolio choice.") + raise Exception( + "vFuncBool == True is not supported for continuous portfolio choice." + ) # We will need to index vFuncNext wrt the state next period given choices # today. - VLvlNext = (self.PermShkVals_temp**(1.0-self.CRRA)*\ - self.PermGroFac**(1.0-self.CRRA))*self.vFuncsNext[AdjustIndex][ShareIndex](self.mNrmNext[AdjustIndex][ShareIndex]) - EndOfPrdv = self.DiscFacEff*np.sum(VLvlNext*self.ShkPrbs_temp,axis=0) - EndOfPrdvNvrs = self.uinv(EndOfPrdv) # value transformed through inverse utility + VLvlNext = ( + self.PermShkVals_temp ** (1.0 - self.CRRA) + * self.PermGroFac ** (1.0 - self.CRRA) + ) * self.vFuncsNext[AdjustIndex][ShareIndex]( + self.mNrmNext[AdjustIndex][ShareIndex] + ) + EndOfPrdv = self.DiscFacEff * np.sum(VLvlNext * self.ShkPrbs_temp, axis=0) + EndOfPrdvNvrs = self.uinv( + EndOfPrdv + ) # value transformed through inverse utility # Manually input (0,0) pair - EndOfPrdvNvrs = np.insert(EndOfPrdvNvrs,0,0.0) - aNrm_temp = np.insert(self.aNrmNow,0,0.0) + EndOfPrdvNvrs = np.insert(EndOfPrdvNvrs, 0, 0.0) + aNrm_temp = np.insert(self.aNrmNow, 0, 0.0) - EndOfPrdvNvrsFunc = LinearInterp(aNrm_temp,EndOfPrdvNvrs) - self.EndOfPrdvFunc = ValueFunc(EndOfPrdvNvrsFunc,self.CRRA) + EndOfPrdvNvrsFunc = LinearInterp(aNrm_temp, EndOfPrdvNvrs) + self.EndOfPrdvFunc = ValueFunc(EndOfPrdvNvrsFunc, self.CRRA) - - def makevFunc(self,solution, AdjustIndex, ShareIndex): - ''' + def makevFunc(self, solution, AdjustIndex, ShareIndex): + """ Creates the value function for this period, defined over market resources m. self must have the attribute EndOfPrdvFunc in order to execute. @@ -718,24 +846,26 @@ def makevFunc(self,solution, AdjustIndex, ShareIndex): vFuncNow : ValueFunc A representation of the value function for this period, defined over normalized market resources m: v = vFuncNow(m). - ''' + """ # Compute expected value and marginal value on a grid of market resources - mNrm_temp = self.mNrmMinNow + self.aXtraGrid - cNrmNow = solution.cFunc[AdjustIndex][ShareIndex](mNrm_temp) - aNrmNow = mNrm_temp - cNrmNow - vNrmNow = self.u(cNrmNow) + self.EndOfPrdvFunc(aNrmNow) + mNrm_temp = self.mNrmMinNow + self.aXtraGrid + cNrmNow = solution.cFunc[AdjustIndex][ShareIndex](mNrm_temp) + aNrmNow = mNrm_temp - cNrmNow + vNrmNow = self.u(cNrmNow) + self.EndOfPrdvFunc(aNrmNow) # Construct the beginning-of-period value function - vNvrs = self.uinv(vNrmNow) # value transformed through inverse utility + vNvrs = self.uinv(vNrmNow) # value transformed through inverse utility # Manually insert (0,0) pair. - mNrm_temp = np.insert(mNrm_temp,0,0.0) # np.insert(mNrm_temp,0,self.mNrmMinNow) - vNvrs = np.insert(vNvrs,0,0.0) - vNvrsFuncNow = LinearInterp(mNrm_temp,vNvrs) - vFuncNow = ValueFunc(vNvrsFuncNow,self.CRRA) + mNrm_temp = np.insert( + mNrm_temp, 0, 0.0 + ) # np.insert(mNrm_temp,0,self.mNrmMinNow) + vNvrs = np.insert(vNvrs, 0, 0.0) + vNvrsFuncNow = LinearInterp(mNrm_temp, vNvrs) + vFuncNow = ValueFunc(vNvrsFuncNow, self.CRRA) return vFuncNow - def addvFunc(self,solution): - ''' + def addvFunc(self, solution): + """ Creates the value function for this period and adds it to the solution. Parameters @@ -749,21 +879,27 @@ def addvFunc(self,solution): solution : ConsumerSolution The single period solution passed as an input, but now with the value function (defined over market resources m) as an attribute. - ''' + """ if not self.DiscreteCase: - raise Exception('You\'re not supposed to be here. Continuous choice portfolio domain does not support vFuncBool == True or AdjustPrb < 1.0.') + raise Exception( + "You're not supposed to be here. Continuous choice portfolio domain does not support vFuncBool == True or AdjustPrb < 1.0." + ) - vFunc = self.AdjustCount*[[]] + vFunc = self.AdjustCount * [[]] - for AdjustIndex in range(self.AdjustCount): # nonadjuster possible! + for AdjustIndex in range(self.AdjustCount): # nonadjuster possible! # this is where we add to vFunc based on non-adjustment. # Basically repeat the above with the share updated to be the "prev" # share an. We need to keep mNrmNext at two major indeces: adjust and # non-adjust. Adjust will just have one element, but non-adjust will need # one for each of the possible current ("prev") values. - for ShareIndex in range(self.ShareNowCount[AdjustIndex]): # for all share level indeces in the adjuster (1) case + for ShareIndex in range( + self.ShareNowCount[AdjustIndex] + ): # for all share level indeces in the adjuster (1) case self.makeEndOfPrdvFunc(AdjustIndex, ShareIndex) - vFunc[AdjustIndex].append(self.makevFunc(solution, AdjustIndex, ShareIndex)) + vFunc[AdjustIndex].append( + self.makevFunc(solution, AdjustIndex, ShareIndex) + ) solution.vFunc = vFunc return solution @@ -809,9 +945,16 @@ def prepareToCalcRiskyShareContinuous(self): i_s = 0 for s in RshareGrid: Rtilde = self.RiskyShkValsNext - self.Rfree - Reff = self.Rfree + Rtilde*s - mNext = a*Reff/(self.PermGroFac*self.PermShkValsNext) + self.TranShkValsNext - vHatP_a_s = Rtilde*self.PermShkValsNext**(-self.CRRA)*self.vPfuncNext(mNext) + Reff = self.Rfree + Rtilde * s + mNext = ( + a * Reff / (self.PermGroFac * self.PermShkValsNext) + + self.TranShkValsNext + ) + vHatP_a_s = ( + Rtilde + * self.PermShkValsNext ** (-self.CRRA) + * self.vPfuncNext(mNext) + ) vHatP[i_a, i_s] = np.dot(vHatP_a_s, self.ShkPrbsNext) i_s += 1 i_a += 1 @@ -838,13 +981,17 @@ def prepareToCalcRiskyShareDiscrete(self): i_s = 0 for s in RshareGrid: Rtilde = self.RiskyShkValsNext - self.Rfree - Reff = self.Rfree + Rtilde*s - mNrmNext = a*Reff/(self.PermGroFac*self.PermShkValsNext) + self.TranShkValsNext - - - VLvlNext = (self.PermShkValsNext**(1.0-self.CRRA)*\ - self.PermGroFac**(1.0-self.CRRA))*self.vFuncNext(mNrmNext) - vHat_a_s = self.DiscFacEff*np.sum(VLvlNext*self.ShkPrbsNext,axis=0) + Reff = self.Rfree + Rtilde * s + mNrmNext = ( + a * Reff / (self.PermGroFac * self.PermShkValsNext) + + self.TranShkValsNext + ) + + VLvlNext = ( + self.PermShkValsNext ** (1.0 - self.CRRA) + * self.PermGroFac ** (1.0 - self.CRRA) + ) * self.vFuncNext(mNrmNext) + vHat_a_s = self.DiscFacEff * np.sum(VLvlNext * self.ShkPrbsNext, axis=0) vHat[i_a, i_s] = vHat_a_s i_s += 1 @@ -860,11 +1007,10 @@ def calcRiskyShare(self): return RiskyShareFunc - def calcRiskyShareContinuous(self): # This should be fixed by an insert 0 - aGrid = np.array([0.0,]) - Rshare = np.array([1.0,]) + aGrid = np.array([0.0]) + Rshare = np.array([1.0]) i_a = 0 for a in self.aNrmPort: @@ -875,10 +1021,12 @@ def calcRiskyShareContinuous(self): Rshare = np.append(Rshare, 0.0) else: residual = LinearInterp(self.RshareGrid, self.vHatP[i_a, :]) - zero = sciopt.fsolve(residual, Rshare[-1]) + zero = sciopt.fsolve(residual, Rshare[-1]) Rshare = np.append(Rshare, zero) i_a += 1 - RiskyShareFunc = LinearInterp(aGrid, Rshare,intercept_limit=self.RiskyShareLimit, slope_limit=0) # HAVE to specify the slope limit + RiskyShareFunc = LinearInterp( + aGrid, Rshare, intercept_limit=self.RiskyShareLimit, slope_limit=0 + ) # HAVE to specify the slope limit return RiskyShareFunc def calcRiskyShareDiscrete(self): @@ -886,8 +1034,8 @@ def calcRiskyShareDiscrete(self): # choice today for a range of a values (those given in aNrmPort). # Should just use insert below ( at 0) - aGrid = np.array([0.0,]) - Rshare = np.array([1.0,]) # is it true for AdjustPrb < 1? + aGrid = np.array([0.0]) + Rshare = np.array([1.0]) # is it true for AdjustPrb < 1? i_a = 0 # For all positive aNrms @@ -899,11 +1047,17 @@ def calcRiskyShareDiscrete(self): i_a += 1 # TODO FIXME find limiting share for perf foresight - RiskyShareFunc = scipy.interpolate.interp1d(np.insert(self.aNrmPort, 0, 0.0), Rshare, kind='zero',bounds_error=False, fill_value=Rshare[-1]) + RiskyShareFunc = scipy.interpolate.interp1d( + np.insert(self.aNrmPort, 0, 0.0), + Rshare, + kind="zero", + bounds_error=False, + fill_value=Rshare[-1], + ) return RiskyShareFunc def prepareToCalcEndOfPrdvP(self): - ''' + """ Prepare to calculate end-of-period marginal value by creating an array of market resources that the agent could have next period, considering the grid of end-of-period assets and the distribution of shocks he might @@ -919,23 +1073,23 @@ def prepareToCalcEndOfPrdvP(self): ------- aNrmNow : np.array A 1D array of end-of-period assets; also stored as attribute of self. - ''' + """ # We define aNrmNow all the way from BoroCnstNat up to max(self.aXtraGrid) # even if BoroCnstNat < BoroCnstArt, so we can construct the consumption # function as the lower envelope of the (by the artificial borrowing con- # straint) uconstrained consumption function, and the artificially con- # strained consumption function. - aNrmNow = np.asarray(self.aXtraGrid) - ShkCount = self.TranShkValsNext.size - aNrm_temp = np.tile(aNrmNow,(ShkCount,1)) + aNrmNow = np.asarray(self.aXtraGrid) + ShkCount = self.TranShkValsNext.size + aNrm_temp = np.tile(aNrmNow, (ShkCount, 1)) # Tile arrays of the income shocks and put them into useful shapes - aNrmCount = aNrmNow.shape[0] - PermShkVals_temp = (np.tile(self.PermShkValsNext,(aNrmCount,1))).transpose() - TranShkVals_temp = (np.tile(self.TranShkValsNext,(aNrmCount,1))).transpose() - RiskyShkVals_temp = (np.tile(self.RiskyShkValsNext,(aNrmCount,1))).transpose() - ShkPrbs_temp = (np.tile(self.ShkPrbsNext,(aNrmCount,1))).transpose() + aNrmCount = aNrmNow.shape[0] + PermShkVals_temp = (np.tile(self.PermShkValsNext, (aNrmCount, 1))).transpose() + TranShkVals_temp = (np.tile(self.TranShkValsNext, (aNrmCount, 1))).transpose() + RiskyShkVals_temp = (np.tile(self.RiskyShkValsNext, (aNrmCount, 1))).transpose() + ShkPrbs_temp = (np.tile(self.ShkPrbsNext, (aNrmCount, 1))).transpose() if self.AdjustCount == 1: mNrmNext = [[]] @@ -944,34 +1098,36 @@ def prepareToCalcEndOfPrdvP(self): for AdjustIndex in range(self.AdjustCount): for ShareIndex in range(self.ShareNowCount[AdjustIndex]): - # Calculate share at current aNrm. If non-adjusting, it's just - # self.RiskySharePrev, else we use the recently calculated RiskyShareFunc - # First generate mNrmNext for adjusters - if AdjustIndex == 0: # adjust + # Calculate share at current aNrm. If non-adjusting, it's just + # self.RiskySharePrev, else we use the recently calculated RiskyShareFunc + # First generate mNrmNext for adjusters + if AdjustIndex == 0: # adjust sAt_aNrm = self.RiskyShareFunc(aNrmNow) # Then generate for non-adjusters - else: # non-adjuster + else: # non-adjuster sAt_aNrm = self.ShareNow[ShareIndex] # Get cash on hand next period. # Compose possible return factors self.Rtilde = RiskyShkVals_temp - self.Rfree # Combine into effective returns factors, taking into account the share - self.Reff = (self.Rfree + self.Rtilde*sAt_aNrm) + self.Reff = self.Rfree + self.Rtilde * sAt_aNrm # Apply the permanent growth factor and possible permanent shocks - mNrmPreTran = self.Reff/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + mNrmPreTran = ( + self.Reff / (self.PermGroFac * PermShkVals_temp) * aNrm_temp + ) # Add transitory income mNrmNext[AdjustIndex].append(mNrmPreTran + TranShkVals_temp) # Store and report the results - self.PermShkVals_temp = PermShkVals_temp - self.ShkPrbs_temp = ShkPrbs_temp - self.mNrmNext = mNrmNext - self.aNrmNow = aNrmNow + self.PermShkVals_temp = PermShkVals_temp + self.ShkPrbs_temp = ShkPrbs_temp + self.mNrmNext = mNrmNext + self.aNrmNow = aNrmNow return aNrmNow def calcEndOfPrdvP(self): - ''' + """ Calculate end-of-period marginal value of assets at each point in aNrmNow. Does so by taking a weighted sum of next period marginal values across income shocks (in a preconstructed grid self.mNrmNext). @@ -984,22 +1140,28 @@ def calcEndOfPrdvP(self): ------- EndOfPrdvP : np.array A 1D array of end-of-period marginal value of assets - ''' + """ - EndOfPrdvP = self.AdjustCount*[[]] + EndOfPrdvP = self.AdjustCount * [[]] for AdjustIndex in range(self.AdjustCount): for ShareIndex in range(self.ShareNowCount[AdjustIndex]): mNrmNext = self.mNrmNext[AdjustIndex][ShareIndex] - EndOfPrdvP[AdjustIndex].append(np.sum(self.DiscFacEff*self.Reff*self.PermGroFac**(-self.CRRA)* - self.PermShkVals_temp**(-self.CRRA)* - self.vPfuncNext(mNrmNext)*self.ShkPrbs_temp,axis=0)) + EndOfPrdvP[AdjustIndex].append( + np.sum( + self.DiscFacEff + * self.Reff + * self.PermGroFac ** (-self.CRRA) + * self.PermShkVals_temp ** (-self.CRRA) + * self.vPfuncNext(mNrmNext) + * self.ShkPrbs_temp, + axis=0, + ) + ) return EndOfPrdvP - - - def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): - ''' + def setAndUpdateValues(self, solution_next, IncomeDstn, LivPrb, DiscFac): + """ Unpacks some of the inputs (and calculates simple objects based on them), storing the results in self for use by other methods. These include: income shocks and probabilities, next period's marginal value function @@ -1024,30 +1186,41 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): Returns ------- None - ''' + """ # TODO: this does not yet # calc sUnderbar -> mertonsammuelson # calc MPC kappaUnderbar # calc human wealth - self.DiscFacEff = DiscFac*LivPrb # "effective" discount factor - self.ShkPrbsNext = self.ShockDstn[0] # but ConsumtionSolver doesn't store the risky shocks - self.PermShkValsNext = self.ShockDstn[1] # but ConsumtionSolver doesn't store the risky shocks - self.TranShkValsNext = self.ShockDstn[2] # but ConsumtionSolver doesn't store the risky shocks - self.RiskyShkValsNext = self.ShockDstn[3] # but ConsumtionSolver doesn't store the risky shocks - self.PermShkMinNext = np.min(self.PermShkValsNext) - self.TranShkMinNext = np.min(self.TranShkValsNext) - self.vPfuncNext = solution_next.vPfunc - self.WorstIncPrb = np.sum(self.ShkPrbsNext[ - (self.PermShkValsNext*self.TranShkValsNext)== - (self.PermShkMinNext*self.TranShkMinNext)]) + self.DiscFacEff = DiscFac * LivPrb # "effective" discount factor + self.ShkPrbsNext = self.ShockDstn[ + 0 + ] # but ConsumtionSolver doesn't store the risky shocks + self.PermShkValsNext = self.ShockDstn[ + 1 + ] # but ConsumtionSolver doesn't store the risky shocks + self.TranShkValsNext = self.ShockDstn[ + 2 + ] # but ConsumtionSolver doesn't store the risky shocks + self.RiskyShkValsNext = self.ShockDstn[ + 3 + ] # but ConsumtionSolver doesn't store the risky shocks + self.PermShkMinNext = np.min(self.PermShkValsNext) + self.TranShkMinNext = np.min(self.TranShkValsNext) + self.vPfuncNext = solution_next.vPfunc + self.WorstIncPrb = np.sum( + self.ShkPrbsNext[ + (self.PermShkValsNext * self.TranShkValsNext) + == (self.PermShkMinNext * self.TranShkMinNext) + ] + ) if self.CubicBool: - self.vPPfuncNext = solution_next.vPPfunc + self.vPPfuncNext = solution_next.vPPfunc if self.vFuncBool: - self.vFuncNext = solution_next.wFunc + self.vFuncNext = solution_next.wFunc # Update the bounding MPCs and PDV of human wealth: # self.PatFac = ((self.Rfree*self.DiscFacEff)**(1.0/self.CRRA))/self.Rfree @@ -1057,11 +1230,8 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): # self.MPCmaxNow = 1.0/(1.0 + (self.WorstIncPrb**(1.0/self.CRRA))* # self.PatFac/solution_next.MPCmax) - - - - def defBoroCnst(self,BoroCnstArt): - ''' + def defBoroCnst(self, BoroCnstArt): + """ Defines the constrained portion of the consumption function as cFuncNowCnst, an attribute of self. Uses the artificial and natural borrowing constraints. @@ -1076,15 +1246,15 @@ def defBoroCnst(self,BoroCnstArt): Returns ------- none - ''' + """ # Calculate the minimum allowable value of money resources in this period - self.BoroCnstNat = 0.0 #(self.solution_next.mNrmMin - self.TranShkMinNext)*\ - #(self.PermGroFac*self.PermShkMinNext)/self.Rfree + self.BoroCnstNat = 0.0 # (self.solution_next.mNrmMin - self.TranShkMinNext)*\ + # (self.PermGroFac*self.PermShkMinNext)/self.Rfree if BoroCnstArt is None: self.mNrmMinNow = self.BoroCnstNat else: - self.mNrmMinNow = np.max([self.BoroCnstNat,BoroCnstArt]) + self.mNrmMinNow = np.max([self.BoroCnstNat, BoroCnstArt]) # Can we just put in *some value* for MPCmaxNow here? # if self.BoroCnstNat < self.mNrmMinNow: @@ -1093,12 +1263,10 @@ def defBoroCnst(self,BoroCnstArt): # self.MPCmaxEff = self.MPCmaxNow # Define the borrowing constraint (limiting consumption function) - self.cFuncNowCnst = LinearInterp(np.array([0.0, 1.0]), - np.array([0.0, 1.0])) - + self.cFuncNowCnst = LinearInterp(np.array([0.0, 1.0]), np.array([0.0, 1.0])) def solve(self): - ''' + """ Solves a one period consumption saving problem with risky income and a portfolio choice over a riskless and a risky asset. @@ -1110,7 +1278,7 @@ def solve(self): ------- solution : ConsumerSolution The solution to the one period problem. - ''' + """ # TODO FIXME # This code is a mix of looping over states ( for the Risky Share funcs) @@ -1126,7 +1294,6 @@ def solve(self): vPfuncs = [[], []] RiskyShareFuncs = [[], []] - for AdjustIndex in range(self.AdjustCount): for PortfolioGridIdx in range(self.ShareNowCount[AdjustIndex]): if self.DiscreteCase: @@ -1142,11 +1309,16 @@ def solve(self): self.RiskyShareFunc = self.calcRiskyShare() else: val = self.PortfolioGrid[PortfolioGridIdx] - self.RiskyShareFunc = scipy.interpolate.interp1d(np.array([0.0,1.0]), np.repeat(val, 2), kind='zero',bounds_error=False, fill_value=val) + self.RiskyShareFunc = scipy.interpolate.interp1d( + np.array([0.0, 1.0]), + np.repeat(val, 2), + kind="zero", + bounds_error=False, + fill_value=val, + ) RiskyShareFuncs[AdjustIndex].append(self.RiskyShareFunc) - # Then solve the consumption choice given optimal portfolio choice aNrm = self.prepareToCalcEndOfPrdvP() @@ -1159,43 +1331,84 @@ def solve(self): self.cFuncLimitSlope = None # Generate all the solutions - for AdjustIndex in range(self.AdjustCount): # stupid name, should be related to adjusting + for AdjustIndex in range( + self.AdjustCount + ): # stupid name, should be related to adjusting for PortfolioGridIdx in range(self.ShareNowCount[AdjustIndex]): - cs_solution = self.makeBasicSolution(EndOfPrdvP[AdjustIndex][PortfolioGridIdx],aNrm,self.makeLinearcFunc) + cs_solution = self.makeBasicSolution( + EndOfPrdvP[AdjustIndex][PortfolioGridIdx], + aNrm, + self.makeLinearcFunc, + ) cFuncs[AdjustIndex].append(cs_solution.cFunc) vPfuncs[AdjustIndex].append(cs_solution.vPfunc) # This is a good place to make it defined at m!!! # solution = self.addMPCandHumanWealth(solution) - solution = PortfolioSolution(cFunc=cFuncs, - vPfunc=vPfuncs, - RiskyShareFunc=RiskyShareFuncs) + solution = PortfolioSolution( + cFunc=cFuncs, vPfunc=vPfuncs, RiskyShareFunc=RiskyShareFuncs + ) if self.vFuncBool: solution = self.addvFunc(solution) - solution.wFunc = _calcwFunc(self.AdjustPrb, self.AdjustCount, self.ShareNowCount, solution.vFunc, self.CRRA) - + solution.wFunc = _calcwFunc( + self.AdjustPrb, + self.AdjustCount, + self.ShareNowCount, + solution.vFunc, + self.CRRA, + ) return solution # The solveOnePeriod function! -def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, - CRRA, Rfree, PermGroFac, BoroCnstArt, - aXtraGrid, vFuncBool, CubicBool, approxRiskyDstn, - RiskyCount, RiskyShareCount, RiskyShareLimitFunc, - AdjustPrb, PortfolioGrid, AdjustCount, PortfolioDomain): +def solveConsPortfolio( + solution_next, + IncomeDstn, + LivPrb, + DiscFac, + CRRA, + Rfree, + PermGroFac, + BoroCnstArt, + aXtraGrid, + vFuncBool, + CubicBool, + approxRiskyDstn, + RiskyCount, + RiskyShareCount, + RiskyShareLimitFunc, + AdjustPrb, + PortfolioGrid, + AdjustCount, + PortfolioDomain, +): # construct solver instance - solver = ConsIndShockPortfolioSolver(solution_next, IncomeDstn, LivPrb, - DiscFac, CRRA, Rfree, PermGroFac, - BoroCnstArt, aXtraGrid, vFuncBool, - CubicBool, approxRiskyDstn, RiskyCount, - RiskyShareCount, RiskyShareLimitFunc, - AdjustPrb, PortfolioGrid, AdjustCount, - PortfolioDomain) + solver = ConsIndShockPortfolioSolver( + solution_next, + IncomeDstn, + LivPrb, + DiscFac, + CRRA, + Rfree, + PermGroFac, + BoroCnstArt, + aXtraGrid, + vFuncBool, + CubicBool, + approxRiskyDstn, + RiskyCount, + RiskyShareCount, + RiskyShareLimitFunc, + AdjustPrb, + PortfolioGrid, + AdjustCount, + PortfolioDomain, + ) # Do some preparatory work solver.prepareToSolve() @@ -1205,27 +1418,39 @@ def solveConsPortfolio(solution_next, IncomeDstn, LivPrb, DiscFac, return portsolution + class LogNormalPortfolioConsumerType(PortfolioConsumerType): - ''' + """ A consumer type with a portfolio choice. This agent type has log-normal return factors. Their problem is defined by a coefficient of relative risk aversion, intertemporal discount factor, interest factor, and time sequences of the permanent income growth rate, survival probability, and return factor averages and standard deviations. - ''' -# time_inv_ = PortfolioConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'AdjustPrb', 'PortfolioGrid'] + """ + + # time_inv_ = PortfolioConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'AdjustPrb', 'PortfolioGrid'] - def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): + def __init__(self, cycles=1, time_flow=True, verbose=False, quiet=False, **kwds): - PortfolioConsumerType.__init__(self,cycles=cycles, time_flow=time_flow, - verbose=verbose, quiet=quiet, **kwds) + PortfolioConsumerType.__init__( + self, + cycles=cycles, + time_flow=time_flow, + verbose=verbose, + quiet=quiet, + **kwds + ) - self.approxRiskyDstn = RiskyDstnFactory(RiskyAvg=self.RiskyAvg, - RiskyStd=self.RiskyStd) + self.approxRiskyDstn = RiskyDstnFactory( + RiskyAvg=self.RiskyAvg, RiskyStd=self.RiskyStd + ) # Needed to simulate. Is a function that given 0 inputs it returns a draw # from the risky asset distribution. Only one is needed, because everyone # draws the same shock. - self.drawRiskyFunc = LogNormalRiskyDstnDraw(RiskyAvg=self.RiskyAvg, - RiskyStd=self.RiskyStd) + self.drawRiskyFunc = LogNormalRiskyDstnDraw( + RiskyAvg=self.RiskyAvg, RiskyStd=self.RiskyStd + ) - self.RiskyShareLimitFunc = lambda _: _PerfForesightLogNormalPortfolioShare(self.Rfree, self.RiskyAvg, self.RiskyStd, self.CRRA) + self.RiskyShareLimitFunc = lambda _: _PerfForesightLogNormalPortfolioShare( + self.Rfree, self.RiskyAvg, self.RiskyStd, self.CRRA + ) From 77aaaf3ad96964e9ed26483ad5adc48f6c22e673 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 15:06:10 +0530 Subject: [PATCH 163/242] breakup ConsGenIncProcessModel --- .../ConsGenIncProcessModel.py | 152 +-------------- .../example_ConsGenIncProcessModel.py | 182 ++++++++++++++++++ 2 files changed, 186 insertions(+), 148 deletions(-) create mode 100644 HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index 36234e1a8..66b9d6230 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -15,6 +15,10 @@ from HARK.simulation import drawLognormal, drawDiscrete, drawUniform from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType +__all__ = ['ValueFunc2D', 'MargValueFunc2D', 'MargMargValueFunc2D', 'pLvlFuncAR1', +'ConsGenIncProcessSolver', 'GenIncProcessConsumerType', +'IndShockExplicitPermIncConsumerType', 'PersistentShockConsumerType'] + utility = CRRAutility utilityP = CRRAutilityP utilityPP = CRRAutilityPP @@ -1301,151 +1305,3 @@ def updatepLvlNextFunc(self): self.addToTimeVary('pLvlNextFunc') if not orig_time: self.timeRev() - - -############################################################################### - -def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params - from HARK.utilities import plotFuncs - from time import clock - import matplotlib.pyplot as plt - def mystr(number): return "{:.4f}".format(number) - - do_simulation = False - - # Display information about the pLvlGrid used in these examples - print('The infinite horizon examples presented here use a grid of persistent income levels (pLvlGrid)') - print('based on percentiles of the long run distribution of pLvl for the given parameters. These percentiles') - print('are specified in the attribute pLvlPctiles. Here, the lowest percentile is ' + - str(Params.init_explicit_perm_inc['pLvlPctiles'][0]*100) + ' and the highest') - print('percentile is ' + str(Params.init_explicit_perm_inc['pLvlPctiles'][-1]*100) + '.\n') - - # Make and solve an example "explicit permanent income" consumer with idiosyncratic shocks - ExplicitExample = IndShockExplicitPermIncConsumerType(**Params.init_explicit_perm_inc) - t_start = clock() - ExplicitExample.solve() - t_end = clock() - print('Solving an explicit permanent income consumer took ' + mystr(t_end-t_start) + ' seconds.') - - # Plot the consumption function at various permanent income levels - print('Consumption function by pLvl for explicit permanent income consumer:') - pLvlGrid = ExplicitExample.pLvlGrid[0] - mLvlGrid = np.linspace(0, 20, 300) - for p in pLvlGrid: - M_temp = mLvlGrid + ExplicitExample.solution[0].mLvlMin(p) - C = ExplicitExample.solution[0].cFunc(M_temp, p*np.ones_like(M_temp)) - plt.plot(M_temp, C) - plt.xlim(0., 20.) - plt.ylim(0., None) - plt.xlabel('Market resource level mLvl') - plt.ylabel('Consumption level cLvl') - plt.show() - - # Now solve the *exact same* problem, but with the permanent income normalization - NormalizedExample = IndShockConsumerType(**Params.init_explicit_perm_inc) - t_start = clock() - NormalizedExample.solve() - t_end = clock() - print('Solving the equivalent problem with permanent income normalized out took ' + - mystr(t_end-t_start) + ' seconds.') - - # Show that the normalized consumption function for the "explicit permanent income" consumer - # is almost identical for every permanent income level (and the same as the normalized problem's - # cFunc), but is less accurate due to extrapolation outside the bounds of pLvlGrid. - print('Normalized consumption function by pLvl for explicit permanent income consumer:') - pLvlGrid = ExplicitExample.pLvlGrid[0] - mNrmGrid = np.linspace(0, 20, 300) - for p in pLvlGrid: - M_temp = mNrmGrid*p + ExplicitExample.solution[0].mLvlMin(p) - C = ExplicitExample.solution[0].cFunc(M_temp, p*np.ones_like(M_temp)) - plt.plot(M_temp/p, C/p) - plt.xlim(0., 20.) - plt.ylim(0., None) - plt.xlabel('Normalized market resources mNrm') - plt.ylabel('Normalized consumption cNrm') - plt.show() - print('Consumption function for normalized problem (without explicit permanent income):') - mNrmMin = NormalizedExample.solution[0].mNrmMin - plotFuncs(NormalizedExample.solution[0].cFunc, mNrmMin, mNrmMin+20) - - print('The "explicit permanent income" solution deviates from the solution to the normalized problem because') - print('of errors from extrapolating beyond the bounds of the pLvlGrid. The error is largest for pLvl values') - print('near the upper and lower bounds, and propagates toward the center of the distribution.\n') - - # Plot the value function at various permanent income levels - if ExplicitExample.vFuncBool: - pGrid = np.linspace(0.1, 3.0, 24) - M = np.linspace(0.001, 5, 300) - for p in pGrid: - M_temp = M+ExplicitExample.solution[0].mLvlMin(p) - C = ExplicitExample.solution[0].vFunc(M_temp, p*np.ones_like(M_temp)) - plt.plot(M_temp, C) - plt.ylim([-200, 0]) - plt.xlabel('Market resource level mLvl') - plt.ylabel('Value v') - plt.show() - - # Simulate some data - if do_simulation: - ExplicitExample.T_sim = 500 - ExplicitExample.track_vars = ['mLvlNow', 'cLvlNow', 'pLvlNow'] - ExplicitExample.makeShockHistory() # This is optional - ExplicitExample.initializeSim() - ExplicitExample.simulate() - plt.plot(np.mean(ExplicitExample.mLvlNow_hist, axis=1)) - plt.xlabel('Simulated time period') - plt.ylabel('Average market resources mLvl') - plt.show() - -############################################################################### - - # Make and solve an example "persistent idisyncratic shocks" consumer - PersistentExample = PersistentShockConsumerType(**Params.init_persistent_shocks) - t_start = clock() - PersistentExample.solve() - t_end = clock() - print('Solving a persistent income shocks consumer took ' + mystr(t_end-t_start) + ' seconds.') - - # Plot the consumption function at various levels of persistent income pLvl - print('Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of ' - + str(PersistentExample.PrstIncCorr) + ':') - pLvlGrid = PersistentExample.pLvlGrid[0] - mLvlGrid = np.linspace(0, 20, 300) - for p in pLvlGrid: - M_temp = mLvlGrid + PersistentExample.solution[0].mLvlMin(p) - C = PersistentExample.solution[0].cFunc(M_temp, p*np.ones_like(M_temp)) - plt.plot(M_temp, C) - plt.xlim(0., 20.) - plt.ylim(0., None) - plt.xlabel('Market resource level mLvl') - plt.ylabel('Consumption level cLvl') - plt.show() - - # Plot the value function at various persistent income levels - if PersistentExample.vFuncBool: - pGrid = PersistentExample.pLvlGrid[0] - M = np.linspace(0.001, 5, 300) - for p in pGrid: - M_temp = M+PersistentExample.solution[0].mLvlMin(p) - C = PersistentExample.solution[0].vFunc(M_temp, p*np.ones_like(M_temp)) - plt.plot(M_temp, C) - plt.ylim([-200, 0]) - plt.xlabel('Market resource level mLvl') - plt.ylabel('Value v') - plt.show() - - # Simulate some data - if do_simulation: - PersistentExample.T_sim = 500 - PersistentExample.track_vars = ['mLvlNow', 'cLvlNow', 'pLvlNow'] - PersistentExample.initializeSim() - PersistentExample.simulate() - plt.plot(np.mean(PersistentExample.mLvlNow_hist, axis=1)) - plt.xlabel('Simulated time period') - plt.ylabel('Average market resources mLvl') - plt.show() - - -if __name__ == '__main__': - main() diff --git a/HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py new file mode 100644 index 000000000..a41c00abc --- /dev/null +++ b/HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py @@ -0,0 +1,182 @@ +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.utilities import plotFuncs +from time import process_time +import matplotlib.pyplot as plt +import numpy as np +from HARK.ConsumptionSaving.ConsGenIncProcessModel import ( + IndShockExplicitPermIncConsumerType, + IndShockConsumerType, + PersistentShockConsumerType, +) + + +def mystr(number): + return "{:.4f}".format(number) + + +do_simulation = False + +# Display information about the pLvlGrid used in these examples +print( + "The infinite horizon examples presented here use a grid of persistent income levels (pLvlGrid)" +) +print( + "based on percentiles of the long run distribution of pLvl for the given parameters. These percentiles" +) +print( + "are specified in the attribute pLvlPctiles. Here, the lowest percentile is " + + str(Params.init_explicit_perm_inc["pLvlPctiles"][0] * 100) + + " and the highest" +) +print( + "percentile is " + + str(Params.init_explicit_perm_inc["pLvlPctiles"][-1] * 100) + + ".\n" +) + +# Make and solve an example "explicit permanent income" consumer with idiosyncratic shocks +ExplicitExample = IndShockExplicitPermIncConsumerType(**Params.init_explicit_perm_inc) +t_start = process_time() +ExplicitExample.solve() +t_end = process_time() +print( + "Solving an explicit permanent income consumer took " + + mystr(t_end - t_start) + + " seconds." +) + +# Plot the consumption function at various permanent income levels +print("Consumption function by pLvl for explicit permanent income consumer:") +pLvlGrid = ExplicitExample.pLvlGrid[0] +mLvlGrid = np.linspace(0, 20, 300) +for p in pLvlGrid: + M_temp = mLvlGrid + ExplicitExample.solution[0].mLvlMin(p) + C = ExplicitExample.solution[0].cFunc(M_temp, p * np.ones_like(M_temp)) + plt.plot(M_temp, C) +plt.xlim(0.0, 20.0) +plt.ylim(0.0, None) +plt.xlabel("Market resource level mLvl") +plt.ylabel("Consumption level cLvl") +plt.show() + +# Now solve the *exact same* problem, but with the permanent income normalization +NormalizedExample = IndShockConsumerType(**Params.init_explicit_perm_inc) +t_start = process_time() +NormalizedExample.solve() +t_end = process_time() +print( + "Solving the equivalent problem with permanent income normalized out took " + + mystr(t_end - t_start) + + " seconds." +) + +# Show that the normalized consumption function for the "explicit permanent income" consumer +# is almost identical for every permanent income level (and the same as the normalized problem's +# cFunc), but is less accurate due to extrapolation outside the bounds of pLvlGrid. +print("Normalized consumption function by pLvl for explicit permanent income consumer:") +pLvlGrid = ExplicitExample.pLvlGrid[0] +mNrmGrid = np.linspace(0, 20, 300) +for p in pLvlGrid: + M_temp = mNrmGrid * p + ExplicitExample.solution[0].mLvlMin(p) + C = ExplicitExample.solution[0].cFunc(M_temp, p * np.ones_like(M_temp)) + plt.plot(M_temp / p, C / p) +plt.xlim(0.0, 20.0) +plt.ylim(0.0, None) +plt.xlabel("Normalized market resources mNrm") +plt.ylabel("Normalized consumption cNrm") +plt.show() +print( + "Consumption function for normalized problem (without explicit permanent income):" +) +mNrmMin = NormalizedExample.solution[0].mNrmMin +plotFuncs(NormalizedExample.solution[0].cFunc, mNrmMin, mNrmMin + 20) + +print( + 'The "explicit permanent income" solution deviates from the solution to the normalized problem because' +) +print( + "of errors from extrapolating beyond the bounds of the pLvlGrid. The error is largest for pLvl values" +) +print( + "near the upper and lower bounds, and propagates toward the center of the distribution.\n" +) + +# Plot the value function at various permanent income levels +if ExplicitExample.vFuncBool: + pGrid = np.linspace(0.1, 3.0, 24) + M = np.linspace(0.001, 5, 300) + for p in pGrid: + M_temp = M + ExplicitExample.solution[0].mLvlMin(p) + C = ExplicitExample.solution[0].vFunc(M_temp, p * np.ones_like(M_temp)) + plt.plot(M_temp, C) + plt.ylim([-200, 0]) + plt.xlabel("Market resource level mLvl") + plt.ylabel("Value v") + plt.show() + +# Simulate some data +if do_simulation: + ExplicitExample.T_sim = 500 + ExplicitExample.track_vars = ["mLvlNow", "cLvlNow", "pLvlNow"] + ExplicitExample.makeShockHistory() # This is optional + ExplicitExample.initializeSim() + ExplicitExample.simulate() + plt.plot(np.mean(ExplicitExample.mLvlNow_hist, axis=1)) + plt.xlabel("Simulated time period") + plt.ylabel("Average market resources mLvl") + plt.show() + +############################################################################### + +# Make and solve an example "persistent idisyncratic shocks" consumer +PersistentExample = PersistentShockConsumerType(**Params.init_persistent_shocks) +t_start = process_time() +PersistentExample.solve() +t_end = process_time() +print( + "Solving a persistent income shocks consumer took " + + mystr(t_end - t_start) + + " seconds." +) + +# Plot the consumption function at various levels of persistent income pLvl +print( + "Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of " + + str(PersistentExample.PrstIncCorr) + + ":" +) +pLvlGrid = PersistentExample.pLvlGrid[0] +mLvlGrid = np.linspace(0, 20, 300) +for p in pLvlGrid: + M_temp = mLvlGrid + PersistentExample.solution[0].mLvlMin(p) + C = PersistentExample.solution[0].cFunc(M_temp, p * np.ones_like(M_temp)) + plt.plot(M_temp, C) +plt.xlim(0.0, 20.0) +plt.ylim(0.0, None) +plt.xlabel("Market resource level mLvl") +plt.ylabel("Consumption level cLvl") +plt.show() + +# Plot the value function at various persistent income levels +if PersistentExample.vFuncBool: + pGrid = PersistentExample.pLvlGrid[0] + M = np.linspace(0.001, 5, 300) + for p in pGrid: + M_temp = M + PersistentExample.solution[0].mLvlMin(p) + C = PersistentExample.solution[0].vFunc(M_temp, p * np.ones_like(M_temp)) + plt.plot(M_temp, C) + plt.ylim([-200, 0]) + plt.xlabel("Market resource level mLvl") + plt.ylabel("Value v") + plt.show() + +# Simulate some data +if do_simulation: + PersistentExample.T_sim = 500 + PersistentExample.track_vars = ["mLvlNow", "cLvlNow", "pLvlNow"] + PersistentExample.initializeSim() + PersistentExample.simulate() + plt.plot(np.mean(PersistentExample.mLvlNow_hist, axis=1)) + plt.xlabel("Simulated time period") + plt.ylabel("Average market resources mLvl") + plt.show() From 46739f07bd9cf849007b0c19c32aa0a2c7a33089 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 15:10:27 +0530 Subject: [PATCH 164/242] breakup ConsPrefShockModel --- HARK/ConsumptionSaving/ConsPrefShockModel.py | 98 +---------------- .../example_ConsPrefShockModel.py | 102 ++++++++++++++++++ 2 files changed, 104 insertions(+), 96 deletions(-) create mode 100644 HARK/ConsumptionSaving/example_ConsPrefShockModel.py diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index a0b6f98a4..ba314f6bb 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -12,6 +12,8 @@ ValueFunc, MargValueFunc, KinkedRconsumerType, ConsKinkedRsolver from HARK.interpolation import LinearInterpOnInterp1D, LinearInterp, CubicInterp, LowerEnvelope +__all__ = ['PrefShockConsumerType', 'KinkyPrefConsumerType', 'ConsPrefShockSolver', 'ConsKinkyPrefSolver'] + class PrefShockConsumerType(IndShockConsumerType): ''' A class for representing consumers who experience multiplicative shocks to @@ -592,99 +594,3 @@ def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn, solver.prepareToSolve() solution = solver.solve() return solution - -############################################################################### - -def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params - import matplotlib.pyplot as plt - from HARK.utilities import plotFuncs - from time import clock - mystr = lambda number : "{:.4f}".format(number) - - do_simulation = True - - # Make and solve a preference shock consumer - PrefShockExample = PrefShockConsumerType(**Params.init_preference_shocks) - PrefShockExample.cycles = 0 # Infinite horizon - - t_start = clock() - PrefShockExample.solve() - t_end = clock() - print('Solving a preference shock consumer took ' + str(t_end-t_start) + ' seconds.') - - # Plot the consumption function at each discrete shock - m = np.linspace(PrefShockExample.solution[0].mNrmMin,5,200) - print('Consumption functions at each discrete shock:') - for j in range(PrefShockExample.PrefShkDstn[0][1].size): - PrefShk = PrefShockExample.PrefShkDstn[0][1][j] - c = PrefShockExample.solution[0].cFunc(m,PrefShk*np.ones_like(m)) - plt.plot(m,c) - plt.xlim([0.,None]) - plt.ylim([0.,None]) - plt.show() - - print('Consumption function (and MPC) when shock=1:') - c = PrefShockExample.solution[0].cFunc(m,np.ones_like(m)) - k = PrefShockExample.solution[0].cFunc.derivativeX(m,np.ones_like(m)) - plt.plot(m,c) - plt.plot(m,k) - plt.xlim([0.,None]) - plt.ylim([0.,None]) - plt.show() - - if PrefShockExample.vFuncBool: - print('Value function (unconditional on shock):') - plotFuncs(PrefShockExample.solution[0].vFunc,PrefShockExample.solution[0].mNrmMin+0.5,5) - - # Test the simulator for the pref shock class - if do_simulation: - PrefShockExample.T_sim = 120 - PrefShockExample.track_vars = ['cNrmNow'] - PrefShockExample.makeShockHistory() # This is optional - PrefShockExample.initializeSim() - PrefShockExample.simulate() - - ########################################################################### - - # Make and solve a "kinky preferece" consumer, whose model combines KinkedR and PrefShock - KinkyPrefExample = KinkyPrefConsumerType(**Params.init_kinky_pref) - KinkyPrefExample.cycles = 0 # Infinite horizon - - t_start = clock() - KinkyPrefExample.solve() - t_end = clock() - print('Solving a kinky preference consumer took ' + str(t_end-t_start) + ' seconds.') - - # Plot the consumption function at each discrete shock - m = np.linspace(KinkyPrefExample.solution[0].mNrmMin,5,200) - print('Consumption functions at each discrete shock:') - for j in range(KinkyPrefExample.PrefShkDstn[0][1].size): - PrefShk = KinkyPrefExample.PrefShkDstn[0][1][j] - c = KinkyPrefExample.solution[0].cFunc(m,PrefShk*np.ones_like(m)) - plt.plot(m,c) - plt.ylim([0.,None]) - plt.show() - - print('Consumption function (and MPC) when shock=1:') - c = KinkyPrefExample.solution[0].cFunc(m,np.ones_like(m)) - k = KinkyPrefExample.solution[0].cFunc.derivativeX(m,np.ones_like(m)) - plt.plot(m,c) - plt.plot(m,k) - plt.ylim([0.,None]) - plt.show() - - if KinkyPrefExample.vFuncBool: - print('Value function (unconditional on shock):') - plotFuncs(KinkyPrefExample.solution[0].vFunc,KinkyPrefExample.solution[0].mNrmMin+0.5,5) - - # Test the simulator for the kinky preference class - if do_simulation: - KinkyPrefExample.T_sim = 120 - KinkyPrefExample.track_vars = ['cNrmNow','PrefShkNow'] - KinkyPrefExample.initializeSim() - KinkyPrefExample.simulate() - - -if __name__ == '__main__': - main() diff --git a/HARK/ConsumptionSaving/example_ConsPrefShockModel.py b/HARK/ConsumptionSaving/example_ConsPrefShockModel.py new file mode 100644 index 000000000..07811dd25 --- /dev/null +++ b/HARK/ConsumptionSaving/example_ConsPrefShockModel.py @@ -0,0 +1,102 @@ +import HARK.ConsumptionSaving.ConsumerParameters as Params +import matplotlib.pyplot as plt +from HARK.utilities import plotFuncs +from time import process_time +import numpy as np +from HARK.ConsumptionSaving.ConsPrefShockModel import ( + PrefShockConsumerType, + KinkyPrefConsumerType, +) + +mystr = lambda number: "{:.4f}".format(number) + +do_simulation = True + +# Make and solve a preference shock consumer +PrefShockExample = PrefShockConsumerType(**Params.init_preference_shocks) +PrefShockExample.cycles = 0 # Infinite horizon + +t_start = process_time() +PrefShockExample.solve() +t_end = process_time() +print("Solving a preference shock consumer took " + str(t_end - t_start) + " seconds.") + +# Plot the consumption function at each discrete shock +m = np.linspace(PrefShockExample.solution[0].mNrmMin, 5, 200) +print("Consumption functions at each discrete shock:") +for j in range(PrefShockExample.PrefShkDstn[0][1].size): + PrefShk = PrefShockExample.PrefShkDstn[0][1][j] + c = PrefShockExample.solution[0].cFunc(m, PrefShk * np.ones_like(m)) + plt.plot(m, c) +plt.xlim([0.0, None]) +plt.ylim([0.0, None]) +plt.show() + +print("Consumption function (and MPC) when shock=1:") +c = PrefShockExample.solution[0].cFunc(m, np.ones_like(m)) +k = PrefShockExample.solution[0].cFunc.derivativeX(m, np.ones_like(m)) +plt.plot(m, c) +plt.plot(m, k) +plt.xlim([0.0, None]) +plt.ylim([0.0, None]) +plt.show() + +if PrefShockExample.vFuncBool: + print("Value function (unconditional on shock):") + plotFuncs( + PrefShockExample.solution[0].vFunc, + PrefShockExample.solution[0].mNrmMin + 0.5, + 5, + ) + +# Test the simulator for the pref shock class +if do_simulation: + PrefShockExample.T_sim = 120 + PrefShockExample.track_vars = ["cNrmNow"] + PrefShockExample.makeShockHistory() # This is optional + PrefShockExample.initializeSim() + PrefShockExample.simulate() + +########################################################################### + +# Make and solve a "kinky preferece" consumer, whose model combines KinkedR and PrefShock +KinkyPrefExample = KinkyPrefConsumerType(**Params.init_kinky_pref) +KinkyPrefExample.cycles = 0 # Infinite horizon + +t_start = process_time() +KinkyPrefExample.solve() +t_end = process_time() +print("Solving a kinky preference consumer took " + str(t_end - t_start) + " seconds.") + +# Plot the consumption function at each discrete shock +m = np.linspace(KinkyPrefExample.solution[0].mNrmMin, 5, 200) +print("Consumption functions at each discrete shock:") +for j in range(KinkyPrefExample.PrefShkDstn[0][1].size): + PrefShk = KinkyPrefExample.PrefShkDstn[0][1][j] + c = KinkyPrefExample.solution[0].cFunc(m, PrefShk * np.ones_like(m)) + plt.plot(m, c) +plt.ylim([0.0, None]) +plt.show() + +print("Consumption function (and MPC) when shock=1:") +c = KinkyPrefExample.solution[0].cFunc(m, np.ones_like(m)) +k = KinkyPrefExample.solution[0].cFunc.derivativeX(m, np.ones_like(m)) +plt.plot(m, c) +plt.plot(m, k) +plt.ylim([0.0, None]) +plt.show() + +if KinkyPrefExample.vFuncBool: + print("Value function (unconditional on shock):") + plotFuncs( + KinkyPrefExample.solution[0].vFunc, + KinkyPrefExample.solution[0].mNrmMin + 0.5, + 5, + ) + +# Test the simulator for the kinky preference class +if do_simulation: + KinkyPrefExample.T_sim = 120 + KinkyPrefExample.track_vars = ["cNrmNow", "PrefShkNow"] + KinkyPrefExample.initializeSim() + KinkyPrefExample.simulate() From 8715cac27e4d0b5ea47bca1b018f63a06a00882c Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 15:14:15 +0530 Subject: [PATCH 165/242] breakup ConsRepAgentModel --- HARK/ConsumptionSaving/ConsRepAgentModel.py | 60 +-------------- .../example_ConsRepAgentModel.py | 73 +++++++++++++++++++ 2 files changed, 75 insertions(+), 58 deletions(-) create mode 100644 HARK/ConsumptionSaving/example_ConsRepAgentModel.py diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py index e0d985ce9..f4c5183ea 100644 --- a/HARK/ConsumptionSaving/ConsRepAgentModel.py +++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py @@ -9,6 +9,8 @@ from HARK.simulation import drawUniform, drawDiscrete from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, MargValueFunc +__all__ = ['RepAgentConsumerType', 'RepAgentMarkovConsumerType'] + def solveConsRepAgent(solution_next,DiscFac,CRRA,IncomeDstn,CapShare,DeprFac,PermGroFac,aXtraGrid): ''' Solve one period of the simple representative agent consumption-saving model. @@ -326,61 +328,3 @@ def getControls(self): t = self.t_cycle[0] i = self.MrkvNow[0] self.cNrmNow = self.solution[t].cFunc[i](self.mNrmNow) - - -############################################################################### -def main(): - from copy import deepcopy - from time import clock - from HARK.utilities import plotFuncs - import HARK.ConsumptionSaving.ConsumerParameters as Params - - # Make a quick example dictionary - RA_params = deepcopy(Params.init_idiosyncratic_shocks) - RA_params['DeprFac'] = 0.05 - RA_params['CapShare'] = 0.36 - RA_params['UnempPrb'] = 0.0 - RA_params['LivPrb'] = [1.0] - - # Make and solve a rep agent model - RAexample = RepAgentConsumerType(**RA_params) - t_start = clock() - RAexample.solve() - t_end = clock() - print('Solving a representative agent problem took ' + str(t_end-t_start) + ' seconds.') - plotFuncs(RAexample.solution[0].cFunc,0,20) - - # Simulate the representative agent model - RAexample.T_sim = 2000 - RAexample.track_vars = ['cNrmNow','mNrmNow','Rfree','wRte'] - RAexample.initializeSim() - t_start = clock() - RAexample.simulate() - t_end = clock() - print('Simulating a representative agent for ' + str(RAexample.T_sim) + ' periods took ' + str(t_end-t_start) + ' seconds.') - - # Make and solve a Markov representative agent - RA_markov_params = deepcopy(RA_params) - RA_markov_params['PermGroFac'] = [[0.97,1.03]] - RA_markov_params['MrkvArray'] = np.array([[0.99,0.01],[0.01,0.99]]) - RA_markov_params['MrkvNow'] = 0 - RAmarkovExample = RepAgentMarkovConsumerType(**RA_markov_params) - RAmarkovExample.IncomeDstn[0] = 2*[RAmarkovExample.IncomeDstn[0]] - t_start = clock() - RAmarkovExample.solve() - t_end = clock() - print('Solving a two state representative agent problem took ' + str(t_end-t_start) + ' seconds.') - plotFuncs(RAmarkovExample.solution[0].cFunc,0,10) - - # Simulate the two state representative agent model - RAmarkovExample.T_sim = 2000 - RAmarkovExample.track_vars = ['cNrmNow','mNrmNow','Rfree','wRte','MrkvNow'] - RAmarkovExample.initializeSim() - t_start = clock() - RAmarkovExample.simulate() - t_end = clock() - print('Simulating a two state representative agent for ' + str(RAexample.T_sim) + ' periods took ' + str(t_end-t_start) + ' seconds.') - -if __name__ == '__main__': - main() - diff --git a/HARK/ConsumptionSaving/example_ConsRepAgentModel.py b/HARK/ConsumptionSaving/example_ConsRepAgentModel.py new file mode 100644 index 000000000..c71d1639e --- /dev/null +++ b/HARK/ConsumptionSaving/example_ConsRepAgentModel.py @@ -0,0 +1,73 @@ +from copy import deepcopy +from time import process_time +import numpy as np +from HARK.utilities import plotFuncs +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.ConsumptionSaving.ConsRepAgentModel import ( + RepAgentConsumerType, + RepAgentMarkovConsumerType, +) + +# Make a quick example dictionary +RA_params = deepcopy(Params.init_idiosyncratic_shocks) +RA_params["DeprFac"] = 0.05 +RA_params["CapShare"] = 0.36 +RA_params["UnempPrb"] = 0.0 +RA_params["LivPrb"] = [1.0] + +# Make and solve a rep agent model +RAexample = RepAgentConsumerType(**RA_params) +t_start = process_time() +RAexample.solve() +t_end = process_time() +print( + "Solving a representative agent problem took " + str(t_end - t_start) + " seconds." +) +plotFuncs(RAexample.solution[0].cFunc, 0, 20) + +# Simulate the representative agent model +RAexample.T_sim = 2000 +RAexample.track_vars = ["cNrmNow", "mNrmNow", "Rfree", "wRte"] +RAexample.initializeSim() +t_start = process_time() +RAexample.simulate() +t_end = process_time() +print( + "Simulating a representative agent for " + + str(RAexample.T_sim) + + " periods took " + + str(t_end - t_start) + + " seconds." +) + +# Make and solve a Markov representative agent +RA_markov_params = deepcopy(RA_params) +RA_markov_params["PermGroFac"] = [[0.97, 1.03]] +RA_markov_params["MrkvArray"] = np.array([[0.99, 0.01], [0.01, 0.99]]) +RA_markov_params["MrkvNow"] = 0 +RAmarkovExample = RepAgentMarkovConsumerType(**RA_markov_params) +RAmarkovExample.IncomeDstn[0] = 2 * [RAmarkovExample.IncomeDstn[0]] +t_start = process_time() +RAmarkovExample.solve() +t_end = process_time() +print( + "Solving a two state representative agent problem took " + + str(t_end - t_start) + + " seconds." +) +plotFuncs(RAmarkovExample.solution[0].cFunc, 0, 10) + +# Simulate the two state representative agent model +RAmarkovExample.T_sim = 2000 +RAmarkovExample.track_vars = ["cNrmNow", "mNrmNow", "Rfree", "wRte", "MrkvNow"] +RAmarkovExample.initializeSim() +t_start = process_time() +RAmarkovExample.simulate() +t_end = process_time() +print( + "Simulating a two state representative agent for " + + str(RAexample.T_sim) + + " periods took " + + str(t_end - t_start) + + " seconds." +) From e1e606166853907b7bef03187f2939825adc78d9 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 15:20:13 +0530 Subject: [PATCH 166/242] breakup ConsMarkovModel --- HARK/ConsumptionSaving/ConsMarkovModel.py | 164 +----------- .../example_ConsMarkovModel.py | 243 ++++++++++++++++++ 2 files changed, 245 insertions(+), 162 deletions(-) create mode 100644 HARK/ConsumptionSaving/example_ConsMarkovModel.py diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py index 2d03ed80f..1394869c1 100644 --- a/HARK/ConsumptionSaving/ConsMarkovModel.py +++ b/HARK/ConsumptionSaving/ConsMarkovModel.py @@ -14,6 +14,8 @@ from HARK.utilities import CRRAutility, CRRAutilityP, CRRAutilityPP, CRRAutilityP_inv, \ CRRAutility_invP, CRRAutility_inv, CRRAutilityP_invP +__all__ = ['ConsMarkovSolver', 'MarkovConsumerType'] + utility = CRRAutility utilityP = CRRAutilityP utilityPP = CRRAutilityPP @@ -963,165 +965,3 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): None ''' raise NotImplementedError() - - - -############################################################################### - -def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params - from HARK.utilities import plotFuncs - from time import clock - from copy import copy - mystr = lambda number : "{:.4f}".format(number) - - do_simulation = True - - # Define the Markov transition matrix for serially correlated unemployment - unemp_length = 5 # Averange length of unemployment spell - urate_good = 0.05 # Unemployment rate when economy is in good state - urate_bad = 0.12 # Unemployment rate when economy is in bad state - bust_prob = 0.01 # Probability of economy switching from good to bad - recession_length = 20 # Averange length of bad state - p_reemploy =1.0/unemp_length - p_unemploy_good = p_reemploy*urate_good/(1-urate_good) - p_unemploy_bad = p_reemploy*urate_bad/(1-urate_bad) - boom_prob = 1.0/recession_length - MrkvArray = np.array([[(1-p_unemploy_good)*(1-bust_prob),p_unemploy_good*(1-bust_prob), - (1-p_unemploy_good)*bust_prob,p_unemploy_good*bust_prob], - [p_reemploy*(1-bust_prob),(1-p_reemploy)*(1-bust_prob), - p_reemploy*bust_prob,(1-p_reemploy)*bust_prob], - [(1-p_unemploy_bad)*boom_prob,p_unemploy_bad*boom_prob, - (1-p_unemploy_bad)*(1-boom_prob),p_unemploy_bad*(1-boom_prob)], - [p_reemploy*boom_prob,(1-p_reemploy)*boom_prob, - p_reemploy*(1-boom_prob),(1-p_reemploy)*(1-boom_prob)]]) - - # Make a consumer with serially correlated unemployment, subject to boom and bust cycles - init_serial_unemployment = copy(Params.init_idiosyncratic_shocks) - init_serial_unemployment['MrkvArray'] = [MrkvArray] - init_serial_unemployment['UnempPrb'] = 0 # to make income distribution when employed - init_serial_unemployment['global_markov'] = False - SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment) - SerialUnemploymentExample.cycles = 0 - SerialUnemploymentExample.vFuncBool = False # for easy toggling here - - # Replace the default (lognormal) income distribution with a custom one - employed_income_dist = [np.ones(1),np.ones(1),np.ones(1)] # Definitely get income - unemployed_income_dist = [np.ones(1),np.ones(1),np.zeros(1)] # Definitely don't - SerialUnemploymentExample.IncomeDstn = [[employed_income_dist,unemployed_income_dist,employed_income_dist, - unemployed_income_dist]] - - # Interest factor, permanent growth rates, and survival probabilities are constant arrays - SerialUnemploymentExample.Rfree = np.array(4*[SerialUnemploymentExample.Rfree]) - SerialUnemploymentExample.PermGroFac = [np.array(4*SerialUnemploymentExample.PermGroFac)] - SerialUnemploymentExample.LivPrb = [SerialUnemploymentExample.LivPrb*np.ones(4)] - - # Solve the serial unemployment consumer's problem and display solution - SerialUnemploymentExample.timeFwd() - start_time = clock() - SerialUnemploymentExample.solve() - end_time = clock() - print('Solving a Markov consumer with serially correlated unemployment took ' + mystr(end_time-start_time) + ' seconds.') - print('Consumption functions for each discrete state:') - plotFuncs(SerialUnemploymentExample.solution[0].cFunc,0,50) - if SerialUnemploymentExample.vFuncBool: - print('Value functions for each discrete state:') - plotFuncs(SerialUnemploymentExample.solution[0].vFunc,5,50) - - # Simulate some data; results stored in cHist, mNrmNow_hist, cNrmNow_hist, and MrkvNow_hist - if do_simulation: - SerialUnemploymentExample.T_sim = 120 - SerialUnemploymentExample.MrkvPrbsInit = [0.25,0.25,0.25,0.25] - SerialUnemploymentExample.track_vars = ['mNrmNow','cNrmNow'] - SerialUnemploymentExample.makeShockHistory() # This is optional - SerialUnemploymentExample.initializeSim() - SerialUnemploymentExample.simulate() - -############################################################################### - - # Make a consumer who occasionally gets "unemployment immunity" for a fixed period - UnempPrb = 0.05 # Probability of becoming unemployed each period - ImmunityPrb = 0.01 # Probability of becoming "immune" to unemployment - ImmunityT = 6 # Number of periods of immunity - - StateCount = ImmunityT+1 # Total number of Markov states - IncomeDstnReg = [np.array([1-UnempPrb,UnempPrb]), np.array([1.0,1.0]), np.array([1.0/(1.0-UnempPrb),0.0])] # Ordinary income distribution - IncomeDstnImm = [np.array([1.0]), np.array([1.0]), np.array([1.0])] # Income distribution when unemployed - IncomeDstn = [IncomeDstnReg] + ImmunityT*[IncomeDstnImm] # Income distribution for each Markov state, in a list - - # Make the Markov transition array. MrkvArray[i,j] is the probability of transitioning - # to state j in period t+1 from state i in period t. - MrkvArray = np.zeros((StateCount,StateCount)) - MrkvArray[0,0] = 1.0 - ImmunityPrb # Probability of not becoming immune in ordinary state: stay in ordinary state - MrkvArray[0,ImmunityT] = ImmunityPrb # Probability of becoming immune in ordinary state: begin immunity periods - for j in range(ImmunityT): - MrkvArray[j+1,j] = 1.0 # When immune, have 100% chance of transition to state with one fewer immunity periods remaining - - init_unemployment_immunity = copy(Params.init_idiosyncratic_shocks) - init_unemployment_immunity['MrkvArray'] = [MrkvArray] - ImmunityExample = MarkovConsumerType(**init_unemployment_immunity) - ImmunityExample.assignParameters(Rfree = np.array(np.array(StateCount*[1.03])), # Interest factor same in all states - PermGroFac = [np.array(StateCount*[1.01])], # Permanent growth factor same in all states - LivPrb = [np.array(StateCount*[0.98])], # Same survival probability in all states - BoroCnstArt = None, # No artificial borrowing constraint - cycles = 0) # Infinite horizon - ImmunityExample.IncomeDstn = [IncomeDstn] - - # Solve the unemployment immunity problem and display the consumption functions - start_time = clock() - ImmunityExample.solve() - end_time = clock() - print('Solving an "unemployment immunity" consumer took ' + mystr(end_time-start_time) + ' seconds.') - print('Consumption functions for each discrete state:') - mNrmMin = np.min([ImmunityExample.solution[0].mNrmMin[j] for j in range(StateCount)]) - plotFuncs(ImmunityExample.solution[0].cFunc,mNrmMin,10) - -############################################################################### - - # Make a consumer with serially correlated permanent income growth - UnempPrb = 0.05 # Unemployment probability - StateCount = 5 # Number of permanent income growth rates - Persistence = 0.5 # Probability of getting the same permanent income growth rate next period - - IncomeDstnReg = [np.array([1-UnempPrb,UnempPrb]), np.array([1.0,1.0]), np.array([1.0,0.0])] - IncomeDstn = StateCount*[IncomeDstnReg] # Same simple income distribution in each state - - # Make the state transition array for this type: Persistence probability of remaining in the same state, equiprobable otherwise - MrkvArray = Persistence*np.eye(StateCount) + (1.0/StateCount)*(1.0-Persistence)*np.ones((StateCount,StateCount)) - - init_serial_growth = copy(Params.init_idiosyncratic_shocks) - init_serial_growth['MrkvArray'] = [MrkvArray] - SerialGroExample = MarkovConsumerType(**init_serial_growth) - SerialGroExample.assignParameters(Rfree = np.array(np.array(StateCount*[1.03])), # Same interest factor in each Markov state - PermGroFac = [np.array([0.97,0.99,1.01,1.03,1.05])], # Different permanent growth factor in each Markov state - LivPrb = [np.array(StateCount*[0.98])], # Same survival probability in all states - cycles = 0) - SerialGroExample.IncomeDstn = [IncomeDstn] - - - # Solve the serially correlated permanent growth shock problem and display the consumption functions - start_time = clock() - SerialGroExample.solve() - end_time = clock() - print('Solving a serially correlated growth consumer took ' + mystr(end_time-start_time) + ' seconds.') - print('Consumption functions for each discrete state:') - plotFuncs(SerialGroExample.solution[0].cFunc,0,10) - -############################################################################### - - # Make a consumer with serially correlated interest factors - SerialRExample = deepcopy(SerialGroExample) # Same as the last problem... - SerialRExample.assignParameters(PermGroFac = [np.array(StateCount*[1.01])], # ...but now the permanent growth factor is constant... - Rfree = np.array([1.01,1.02,1.03,1.04,1.05])) # ...and the interest factor is what varies across states - - # Solve the serially correlated interest rate problem and display the consumption functions - start_time = clock() - SerialRExample.solve() - end_time = clock() - print('Solving a serially correlated interest consumer took ' + mystr(end_time-start_time) + ' seconds.') - print('Consumption functions for each discrete state:') - plotFuncs(SerialRExample.solution[0].cFunc,0,10) - - -if __name__ == '__main__': - main() diff --git a/HARK/ConsumptionSaving/example_ConsMarkovModel.py b/HARK/ConsumptionSaving/example_ConsMarkovModel.py new file mode 100644 index 000000000..75ecb3794 --- /dev/null +++ b/HARK/ConsumptionSaving/example_ConsMarkovModel.py @@ -0,0 +1,243 @@ +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.utilities import plotFuncs +from time import process_time +from copy import deepcopy, copy +import numpy as np +from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType + +mystr = lambda number: "{:.4f}".format(number) + +do_simulation = True + +# Define the Markov transition matrix for serially correlated unemployment +unemp_length = 5 # Averange length of unemployment spell +urate_good = 0.05 # Unemployment rate when economy is in good state +urate_bad = 0.12 # Unemployment rate when economy is in bad state +bust_prob = 0.01 # Probability of economy switching from good to bad +recession_length = 20 # Averange length of bad state +p_reemploy = 1.0 / unemp_length +p_unemploy_good = p_reemploy * urate_good / (1 - urate_good) +p_unemploy_bad = p_reemploy * urate_bad / (1 - urate_bad) +boom_prob = 1.0 / recession_length +MrkvArray = np.array( + [ + [ + (1 - p_unemploy_good) * (1 - bust_prob), + p_unemploy_good * (1 - bust_prob), + (1 - p_unemploy_good) * bust_prob, + p_unemploy_good * bust_prob, + ], + [ + p_reemploy * (1 - bust_prob), + (1 - p_reemploy) * (1 - bust_prob), + p_reemploy * bust_prob, + (1 - p_reemploy) * bust_prob, + ], + [ + (1 - p_unemploy_bad) * boom_prob, + p_unemploy_bad * boom_prob, + (1 - p_unemploy_bad) * (1 - boom_prob), + p_unemploy_bad * (1 - boom_prob), + ], + [ + p_reemploy * boom_prob, + (1 - p_reemploy) * boom_prob, + p_reemploy * (1 - boom_prob), + (1 - p_reemploy) * (1 - boom_prob), + ], + ] +) + +# Make a consumer with serially correlated unemployment, subject to boom and bust cycles +init_serial_unemployment = copy(Params.init_idiosyncratic_shocks) +init_serial_unemployment["MrkvArray"] = [MrkvArray] +init_serial_unemployment["UnempPrb"] = 0 # to make income distribution when employed +init_serial_unemployment["global_markov"] = False +SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment) +SerialUnemploymentExample.cycles = 0 +SerialUnemploymentExample.vFuncBool = False # for easy toggling here + +# Replace the default (lognormal) income distribution with a custom one +employed_income_dist = [np.ones(1), np.ones(1), np.ones(1)] # Definitely get income +unemployed_income_dist = [np.ones(1), np.ones(1), np.zeros(1)] # Definitely don't +SerialUnemploymentExample.IncomeDstn = [ + [ + employed_income_dist, + unemployed_income_dist, + employed_income_dist, + unemployed_income_dist, + ] +] + +# Interest factor, permanent growth rates, and survival probabilities are constant arrays +SerialUnemploymentExample.Rfree = np.array(4 * [SerialUnemploymentExample.Rfree]) +SerialUnemploymentExample.PermGroFac = [ + np.array(4 * SerialUnemploymentExample.PermGroFac) +] +SerialUnemploymentExample.LivPrb = [SerialUnemploymentExample.LivPrb * np.ones(4)] + +# Solve the serial unemployment consumer's problem and display solution +SerialUnemploymentExample.timeFwd() +start_time = process_time() +SerialUnemploymentExample.solve() +end_time = process_time() +print( + "Solving a Markov consumer with serially correlated unemployment took " + + mystr(end_time - start_time) + + " seconds." +) +print("Consumption functions for each discrete state:") +plotFuncs(SerialUnemploymentExample.solution[0].cFunc, 0, 50) +if SerialUnemploymentExample.vFuncBool: + print("Value functions for each discrete state:") + plotFuncs(SerialUnemploymentExample.solution[0].vFunc, 5, 50) + +# Simulate some data; results stored in cHist, mNrmNow_hist, cNrmNow_hist, and MrkvNow_hist +if do_simulation: + SerialUnemploymentExample.T_sim = 120 + SerialUnemploymentExample.MrkvPrbsInit = [0.25, 0.25, 0.25, 0.25] + SerialUnemploymentExample.track_vars = ["mNrmNow", "cNrmNow"] + SerialUnemploymentExample.makeShockHistory() # This is optional + SerialUnemploymentExample.initializeSim() + SerialUnemploymentExample.simulate() + +############################################################################### + +# Make a consumer who occasionally gets "unemployment immunity" for a fixed period +UnempPrb = 0.05 # Probability of becoming unemployed each period +ImmunityPrb = 0.01 # Probability of becoming "immune" to unemployment +ImmunityT = 6 # Number of periods of immunity + +StateCount = ImmunityT + 1 # Total number of Markov states +IncomeDstnReg = [ + np.array([1 - UnempPrb, UnempPrb]), + np.array([1.0, 1.0]), + np.array([1.0 / (1.0 - UnempPrb), 0.0]), +] # Ordinary income distribution +IncomeDstnImm = [ + np.array([1.0]), + np.array([1.0]), + np.array([1.0]), +] # Income distribution when unemployed +IncomeDstn = [IncomeDstnReg] + ImmunityT * [ + IncomeDstnImm +] # Income distribution for each Markov state, in a list + +# Make the Markov transition array. MrkvArray[i,j] is the probability of transitioning +# to state j in period t+1 from state i in period t. +MrkvArray = np.zeros((StateCount, StateCount)) +MrkvArray[0, 0] = ( + 1.0 - ImmunityPrb +) # Probability of not becoming immune in ordinary state: stay in ordinary state +MrkvArray[ + 0, ImmunityT +] = ( + ImmunityPrb +) # Probability of becoming immune in ordinary state: begin immunity periods +for j in range(ImmunityT): + MrkvArray[ + j + 1, j + ] = ( + 1.0 + ) # When immune, have 100% chance of transition to state with one fewer immunity periods remaining + +init_unemployment_immunity = copy(Params.init_idiosyncratic_shocks) +init_unemployment_immunity["MrkvArray"] = [MrkvArray] +ImmunityExample = MarkovConsumerType(**init_unemployment_immunity) +ImmunityExample.assignParameters( + Rfree=np.array(np.array(StateCount * [1.03])), # Interest factor same in all states + PermGroFac=[ + np.array(StateCount * [1.01]) + ], # Permanent growth factor same in all states + LivPrb=[np.array(StateCount * [0.98])], # Same survival probability in all states + BoroCnstArt=None, # No artificial borrowing constraint + cycles=0, +) # Infinite horizon +ImmunityExample.IncomeDstn = [IncomeDstn] + +# Solve the unemployment immunity problem and display the consumption functions +start_time = process_time() +ImmunityExample.solve() +end_time = process_time() +print( + 'Solving an "unemployment immunity" consumer took ' + + mystr(end_time - start_time) + + " seconds." +) +print("Consumption functions for each discrete state:") +mNrmMin = np.min([ImmunityExample.solution[0].mNrmMin[j] for j in range(StateCount)]) +plotFuncs(ImmunityExample.solution[0].cFunc, mNrmMin, 10) + +############################################################################### + +# Make a consumer with serially correlated permanent income growth +UnempPrb = 0.05 # Unemployment probability +StateCount = 5 # Number of permanent income growth rates +Persistence = ( + 0.5 +) # Probability of getting the same permanent income growth rate next period + +IncomeDstnReg = [ + np.array([1 - UnempPrb, UnempPrb]), + np.array([1.0, 1.0]), + np.array([1.0, 0.0]), +] +IncomeDstn = StateCount * [ + IncomeDstnReg +] # Same simple income distribution in each state + +# Make the state transition array for this type: Persistence probability of remaining in the same state, equiprobable otherwise +MrkvArray = Persistence * np.eye(StateCount) + (1.0 / StateCount) * ( + 1.0 - Persistence +) * np.ones((StateCount, StateCount)) + +init_serial_growth = copy(Params.init_idiosyncratic_shocks) +init_serial_growth["MrkvArray"] = [MrkvArray] +SerialGroExample = MarkovConsumerType(**init_serial_growth) +SerialGroExample.assignParameters( + Rfree=np.array( + np.array(StateCount * [1.03]) + ), # Same interest factor in each Markov state + PermGroFac=[ + np.array([0.97, 0.99, 1.01, 1.03, 1.05]) + ], # Different permanent growth factor in each Markov state + LivPrb=[np.array(StateCount * [0.98])], # Same survival probability in all states + cycles=0, +) +SerialGroExample.IncomeDstn = [IncomeDstn] + + +# Solve the serially correlated permanent growth shock problem and display the consumption functions +start_time = process_time() +SerialGroExample.solve() +end_time = process_time() +print( + "Solving a serially correlated growth consumer took " + + mystr(end_time - start_time) + + " seconds." +) +print("Consumption functions for each discrete state:") +plotFuncs(SerialGroExample.solution[0].cFunc, 0, 10) + +############################################################################### + +# Make a consumer with serially correlated interest factors +SerialRExample = deepcopy(SerialGroExample) # Same as the last problem... +SerialRExample.assignParameters( + PermGroFac=[ + np.array(StateCount * [1.01]) + ], # ...but now the permanent growth factor is constant... + Rfree=np.array([1.01, 1.02, 1.03, 1.04, 1.05]), +) # ...and the interest factor is what varies across states + +# Solve the serially correlated interest rate problem and display the consumption functions +start_time = process_time() +SerialRExample.solve() +end_time = process_time() +print( + "Solving a serially correlated interest consumer took " + + mystr(end_time - start_time) + + " seconds." +) +print("Consumption functions for each discrete state:") +plotFuncs(SerialRExample.solution[0].cFunc, 0, 10) From 4f4545b1d28fd65472331b282f445b8202db4b39 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 15:26:35 +0530 Subject: [PATCH 167/242] update ConsMedModel --- HARK/ConsumptionSaving/ConsMedModel.py | 89 +----------------- .../ConsumptionSaving/example_ConsMedModel.py | 94 +++++++++++++++++++ 2 files changed, 97 insertions(+), 86 deletions(-) create mode 100644 HARK/ConsumptionSaving/example_ConsMedModel.py diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py index cd4578815..09d95e2cc 100644 --- a/HARK/ConsumptionSaving/ConsMedModel.py +++ b/HARK/ConsumptionSaving/ConsMedModel.py @@ -16,6 +16,9 @@ MargMargValueFunc2D, VariableLowerBoundFunc2D from copy import deepcopy +__all__ = ['MedShockPolicyFunc', 'cThruXfunc', 'MedThruXfunc', +'MedShockConsumerType', 'ConsMedShockSolver'] + utility_inv = CRRAutility_inv utilityP_inv = CRRAutilityP_inv utility = CRRAutility @@ -1352,89 +1355,3 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR solver.prepareToSolve() # Do some preparatory work solution_now = solver.solve() # Solve the period return solution_now - - -############################################################################### - -def main(): - import HARK.ConsumptionSaving.ConsumerParameters as Params - from HARK.utilities import CRRAutility_inv - from time import clock - import matplotlib.pyplot as plt - mystr = lambda number : "{:.4f}".format(number) - - do_simulation = True - - # Make and solve an example medical shocks consumer type - MedicalExample = MedShockConsumerType(**Params.init_medical_shocks) - t_start = clock() - MedicalExample.solve() - t_end = clock() - print('Solving a medical shocks consumer took ' + mystr(t_end-t_start) + ' seconds.') - - # Plot the consumption function - M = np.linspace(0,30,300) - pLvl = 1.0 - P = pLvl*np.ones_like(M) - for j in range(MedicalExample.MedShkDstn[0][0].size): - MedShk = MedicalExample.MedShkDstn[0][1][j]*np.ones_like(M) - M_temp = M + MedicalExample.solution[0].mLvlMin(pLvl) - C = MedicalExample.solution[0].cFunc(M_temp,P,MedShk) - plt.plot(M_temp,C) - print('Consumption function by medical need shock (constant permanent income)') - plt.show() - - # Plot the medical care function - for j in range(MedicalExample.MedShkDstn[0][0].size): - MedShk = MedicalExample.MedShkDstn[0][1][j]*np.ones_like(M) - Med = MedicalExample.solution[0].MedFunc(M_temp,P,MedShk) - plt.plot(M_temp,Med) - print('Medical care function by medical need shock (constant permanent income)') - plt.ylim([0,20]) - plt.show() - - # Plot the savings function - for j in range(MedicalExample.MedShkDstn[0][0].size): - MedShk = MedicalExample.MedShkDstn[0][1][j]*np.ones_like(M) - Sav = M_temp - MedicalExample.solution[0].cFunc(M_temp,P,MedShk) - MedicalExample.MedPrice[0]*\ - MedicalExample.solution[0].MedFunc(M_temp,P,MedShk) - plt.plot(M_temp,Sav) - print('End of period savings by medical need shock (constant permanent income)') - plt.show() - - # Plot the marginal value function - M = np.linspace(0.0,30,300) - for p in range(MedicalExample.pLvlGrid[0].size): - pLvl = MedicalExample.pLvlGrid[0][p] - M_temp = pLvl*M + MedicalExample.solution[0].mLvlMin(pLvl) - P = pLvl*np.ones_like(M) - vP = MedicalExample.solution[0].vPfunc(M_temp,P)**(-1.0/MedicalExample.CRRA) - plt.plot(M_temp,vP) - print('Marginal value function (pseudo inverse)') - plt.show() - - if MedicalExample.vFuncBool: - # Plot the value function - M = np.linspace(0.0,1,300) - for p in range(MedicalExample.pLvlGrid[0].size): - pLvl = MedicalExample.pLvlGrid[0][p] - M_temp = pLvl*M + MedicalExample.solution[0].mLvlMin(pLvl) - P = pLvl*np.ones_like(M) - v = CRRAutility_inv(MedicalExample.solution[0].vFunc(M_temp,P),gam=MedicalExample.CRRA) - plt.plot(M_temp,v) - print('Value function (pseudo inverse)') - plt.show() - - if do_simulation: - t_start = clock() - MedicalExample.T_sim = 100 - MedicalExample.track_vars = ['mLvlNow','cLvlNow','MedNow'] - MedicalExample.makeShockHistory() - MedicalExample.initializeSim() - MedicalExample.simulate() - t_end = clock() - print('Simulating ' + str(MedicalExample.AgentCount) + ' agents for ' + str(MedicalExample.T_sim) + ' periods took ' + mystr(t_end-t_start) + ' seconds.') - -if __name__ == '__main__': - main() - diff --git a/HARK/ConsumptionSaving/example_ConsMedModel.py b/HARK/ConsumptionSaving/example_ConsMedModel.py new file mode 100644 index 000000000..c1e853c8e --- /dev/null +++ b/HARK/ConsumptionSaving/example_ConsMedModel.py @@ -0,0 +1,94 @@ +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.utilities import CRRAutility_inv +from time import process_time +import matplotlib.pyplot as plt +import numpy as np +from HARK.ConsumptionSaving.ConsMedModel import MedShockConsumerType + +mystr = lambda number: "{:.4f}".format(number) + +do_simulation = True + +# Make and solve an example medical shocks consumer type +MedicalExample = MedShockConsumerType(**Params.init_medical_shocks) +t_start = process_time() +MedicalExample.solve() +t_end = process_time() +print("Solving a medical shocks consumer took " + mystr(t_end - t_start) + " seconds.") + +# Plot the consumption function +M = np.linspace(0, 30, 300) +pLvl = 1.0 +P = pLvl * np.ones_like(M) +for j in range(MedicalExample.MedShkDstn[0][0].size): + MedShk = MedicalExample.MedShkDstn[0][1][j] * np.ones_like(M) + M_temp = M + MedicalExample.solution[0].mLvlMin(pLvl) + C = MedicalExample.solution[0].cFunc(M_temp, P, MedShk) + plt.plot(M_temp, C) +print("Consumption function by medical need shock (constant permanent income)") +plt.show() + +# Plot the medical care function +for j in range(MedicalExample.MedShkDstn[0][0].size): + MedShk = MedicalExample.MedShkDstn[0][1][j] * np.ones_like(M) + Med = MedicalExample.solution[0].MedFunc(M_temp, P, MedShk) + plt.plot(M_temp, Med) +print("Medical care function by medical need shock (constant permanent income)") +plt.ylim([0, 20]) +plt.show() + +# Plot the savings function +for j in range(MedicalExample.MedShkDstn[0][0].size): + MedShk = MedicalExample.MedShkDstn[0][1][j] * np.ones_like(M) + Sav = ( + M_temp + - MedicalExample.solution[0].cFunc(M_temp, P, MedShk) + - MedicalExample.MedPrice[0] + * MedicalExample.solution[0].MedFunc(M_temp, P, MedShk) + ) + plt.plot(M_temp, Sav) +print("End of period savings by medical need shock (constant permanent income)") +plt.show() + +# Plot the marginal value function +M = np.linspace(0.0, 30, 300) +for p in range(MedicalExample.pLvlGrid[0].size): + pLvl = MedicalExample.pLvlGrid[0][p] + M_temp = pLvl * M + MedicalExample.solution[0].mLvlMin(pLvl) + P = pLvl * np.ones_like(M) + vP = MedicalExample.solution[0].vPfunc(M_temp, P) ** (-1.0 / MedicalExample.CRRA) + plt.plot(M_temp, vP) +print("Marginal value function (pseudo inverse)") +plt.show() + +if MedicalExample.vFuncBool: + # Plot the value function + M = np.linspace(0.0, 1, 300) + for p in range(MedicalExample.pLvlGrid[0].size): + pLvl = MedicalExample.pLvlGrid[0][p] + M_temp = pLvl * M + MedicalExample.solution[0].mLvlMin(pLvl) + P = pLvl * np.ones_like(M) + v = CRRAutility_inv( + MedicalExample.solution[0].vFunc(M_temp, P), gam=MedicalExample.CRRA + ) + plt.plot(M_temp, v) + print("Value function (pseudo inverse)") + plt.show() + +if do_simulation: + t_start = process_time() + MedicalExample.T_sim = 100 + MedicalExample.track_vars = ["mLvlNow", "cLvlNow", "MedNow"] + MedicalExample.makeShockHistory() + MedicalExample.initializeSim() + MedicalExample.simulate() + t_end = process_time() + print( + "Simulating " + + str(MedicalExample.AgentCount) + + " agents for " + + str(MedicalExample.T_sim) + + " periods took " + + mystr(t_end - t_start) + + " seconds." + ) From b451567486e05f38d2c31098aa3318874f90553b Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 15:30:09 +0530 Subject: [PATCH 168/242] update tractablebufferstock --- .../TractableBufferStockModel.py | 103 +-------------- .../example_TractableBufferStockModel.py | 123 ++++++++++++++++++ 2 files changed, 125 insertions(+), 101 deletions(-) create mode 100644 HARK/ConsumptionSaving/example_TractableBufferStockModel.py diff --git a/HARK/ConsumptionSaving/TractableBufferStockModel.py b/HARK/ConsumptionSaving/TractableBufferStockModel.py index 1fb135f89..1fcdc615b 100644 --- a/HARK/ConsumptionSaving/TractableBufferStockModel.py +++ b/HARK/ConsumptionSaving/TractableBufferStockModel.py @@ -31,6 +31,8 @@ from copy import copy from scipy.optimize import newton, brentq +__all__ = ['TractableConsumerSolution', 'TractableConsumerType'] + # If you want to run the "tractable" version of cstwMPC, uncomment the line below # and have TractableConsumerType inherit from cstwMPCagent rather than AgentType #from HARK.cstwMPC.cstwMPC import cstwMPCagent @@ -462,104 +464,3 @@ def getPostStates(self): ''' self.aLvlNow = self.mLvlNow - self.cLvlNow return None - - -############################################################################### - -def main(): - # Import the HARK library. The assumption is that this code is in a folder - # contained in the HARK folder. Also import the ConsumptionSavingModel - import numpy as np # numeric Python - from HARK.utilities import plotFuncs # basic plotting tools - from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType # An alternative, much longer way to solve the TBS model - from time import clock # timing utility - - do_simulation = True - - # Define the model primitives - base_primitives = {'UnempPrb' : .00625, # Probability of becoming unemployed - 'DiscFac' : 0.975, # Intertemporal discount factor - 'Rfree' : 1.01, # Risk-free interest factor on assets - 'PermGroFac' : 1.0025, # Permanent income growth factor (uncompensated) - 'CRRA' : 1.0} # Coefficient of relative risk aversion - - # Define a dictionary to be used in case of simulation - simulation_values = {'aLvlInitMean' : 0.0, # Mean of log initial assets for new agents - 'aLvlInitStd' : 1.0, # Stdev of log initial assets for new agents - 'AgentCount' : 10000, # Number of agents to simulate - 'T_sim' : 120, # Number of periods to simulate - 'T_cycle' : 1} # Number of periods in the cycle - - # Make and solve a tractable consumer type - ExampleType = TractableConsumerType(**base_primitives) - t_start = clock() - ExampleType.solve() - t_end = clock() - print('Solving a tractable consumption-savings model took ' + str(t_end-t_start) + ' seconds.') - - # Plot the consumption function and whatnot - m_upper = 1.5*ExampleType.mTarg - conFunc_PF = lambda m: ExampleType.h*ExampleType.PFMPC + ExampleType.PFMPC*m - #plotFuncs([ExampleType.solution[0].cFunc,ExampleType.mSSfunc,ExampleType.cSSfunc],0,m_upper) - plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper) - - if do_simulation: - ExampleType(**simulation_values) # Set attributes needed for simulation - ExampleType.track_vars = ['mLvlNow'] - ExampleType.makeShockHistory() - ExampleType.initializeSim() - ExampleType.simulate() - - - # Now solve the same model using backward induction rather than the analytic method of TBS. - # The TBS model is equivalent to a Markov model with two states, one of them absorbing (permanent unemployment). - MrkvArray = np.array([[1.0-base_primitives['UnempPrb'],base_primitives['UnempPrb']],[0.0,1.0]]) # Define the two state, absorbing unemployment Markov array - init_consumer_objects = {"CRRA":base_primitives['CRRA'], - "Rfree":np.array(2*[base_primitives['Rfree']]), # Interest factor (same in both states) - "PermGroFac":[np.array(2*[base_primitives['PermGroFac']/(1.0-base_primitives['UnempPrb'])])], # Unemployment-compensated permanent growth factor - "BoroCnstArt":None, # Artificial borrowing constraint - "PermShkStd":[0.0], # Permanent shock standard deviation - "PermShkCount":1, # Number of shocks in discrete permanent shock distribution - "TranShkStd":[0.0], # Transitory shock standard deviation - "TranShkCount":1, # Number of shocks in discrete permanent shock distribution - "T_cycle":1, # Number of periods in cycle - "UnempPrb":0.0, # Unemployment probability (not used, as the unemployment here is *permanent*, not transitory) - "UnempPrbRet":0.0, # Unemployment probability when retired (irrelevant here) - "T_retire":0, # Age at retirement (turned off) - "IncUnemp":0.0, # Income when unemployed (irrelevant) - "IncUnempRet":0.0, # Income when unemployed and retired (irrelevant) - "aXtraMin":0.001, # Minimum value of assets above minimum in grid - "aXtraMax":ExampleType.mUpperBnd, # Maximum value of assets above minimum in grid - "aXtraCount":48, # Number of points in assets grid - "aXtraExtra":[None], # Additional points to include in assets grid - "aXtraNestFac":3, # Degree of exponential nesting when constructing assets grid - "LivPrb":[np.array([1.0,1.0])], # Survival probability - "DiscFac":base_primitives['DiscFac'], # Intertemporal discount factor - 'AgentCount':1, # Number of agents in a simulation (irrelevant) - 'tax_rate':0.0, # Tax rate on labor income (irrelevant) - 'vFuncBool':False, # Whether to calculate the value function - 'CubicBool':True, # Whether to use cubic splines (False --> linear splines) - 'MrkvArray':[MrkvArray] # State transition probabilities - } - MarkovType = MarkovConsumerType(**init_consumer_objects) # Make a basic consumer type - employed_income_dist = [np.ones(1),np.ones(1),np.ones(1)] # Income distribution when employed - unemployed_income_dist = [np.ones(1),np.ones(1),np.zeros(1)] # Income distribution when permanently unemployed - MarkovType.IncomeDstn = [[employed_income_dist,unemployed_income_dist]] # set the income distribution in each state - MarkovType.cycles = 0 - - # Solve the "Markov TBS" model - t_start = clock() - MarkovType.solve() - t_end = clock() - MarkovType.unpackcFunc() - - print('Solving the same model "the long way" took ' + str(t_end-t_start) + ' seconds.') - #plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper) - plotFuncs(MarkovType.cFunc[0],0,m_upper) - diffFunc = lambda m : ExampleType.solution[0].cFunc(m) - MarkovType.cFunc[0][0](m) - print('Difference between the (employed) consumption functions:') - plotFuncs(diffFunc,0,m_upper) - -if __name__ == '__main__': - main() - diff --git a/HARK/ConsumptionSaving/example_TractableBufferStockModel.py b/HARK/ConsumptionSaving/example_TractableBufferStockModel.py new file mode 100644 index 000000000..c6641ab33 --- /dev/null +++ b/HARK/ConsumptionSaving/example_TractableBufferStockModel.py @@ -0,0 +1,123 @@ +import numpy as np # numeric Python +from HARK.utilities import plotFuncs # basic plotting tools +from HARK.ConsumptionSaving.ConsMarkovModel import ( + MarkovConsumerType, +) # An alternative, much longer way to solve the TBS model +from time import process_time # timing utility +from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType +import numpy as np + +do_simulation = True + +# Define the model primitives +base_primitives = { + "UnempPrb": 0.00625, # Probability of becoming unemployed + "DiscFac": 0.975, # Intertemporal discount factor + "Rfree": 1.01, # Risk-free interest factor on assets + "PermGroFac": 1.0025, # Permanent income growth factor (uncompensated) + "CRRA": 1.0, +} # Coefficient of relative risk aversion + +# Define a dictionary to be used in case of simulation +simulation_values = { + "aLvlInitMean": 0.0, # Mean of log initial assets for new agents + "aLvlInitStd": 1.0, # Stdev of log initial assets for new agents + "AgentCount": 10000, # Number of agents to simulate + "T_sim": 120, # Number of periods to simulate + "T_cycle": 1, +} # Number of periods in the cycle + +# Make and solve a tractable consumer type +ExampleType = TractableConsumerType(**base_primitives) +t_start = process_time() +ExampleType.solve() +t_end = process_time() +print( + "Solving a tractable consumption-savings model took " + + str(t_end - t_start) + + " seconds." +) + +# Plot the consumption function and whatnot +m_upper = 1.5 * ExampleType.mTarg +conFunc_PF = lambda m: ExampleType.h * ExampleType.PFMPC + ExampleType.PFMPC * m +# plotFuncs([ExampleType.solution[0].cFunc,ExampleType.mSSfunc,ExampleType.cSSfunc],0,m_upper) +plotFuncs([ExampleType.solution[0].cFunc, ExampleType.solution[0].cFunc_U], 0, m_upper) + +if do_simulation: + ExampleType(**simulation_values) # Set attributes needed for simulation + ExampleType.track_vars = ["mLvlNow"] + ExampleType.makeShockHistory() + ExampleType.initializeSim() + ExampleType.simulate() + + +# Now solve the same model using backward induction rather than the analytic method of TBS. +# The TBS model is equivalent to a Markov model with two states, one of them absorbing (permanent unemployment). +MrkvArray = np.array( + [[1.0 - base_primitives["UnempPrb"], base_primitives["UnempPrb"]], [0.0, 1.0]] +) # Define the two state, absorbing unemployment Markov array +init_consumer_objects = { + "CRRA": base_primitives["CRRA"], + "Rfree": np.array( + 2 * [base_primitives["Rfree"]] + ), # Interest factor (same in both states) + "PermGroFac": [ + np.array( + 2 * [base_primitives["PermGroFac"] / (1.0 - base_primitives["UnempPrb"])] + ) + ], # Unemployment-compensated permanent growth factor + "BoroCnstArt": None, # Artificial borrowing constraint + "PermShkStd": [0.0], # Permanent shock standard deviation + "PermShkCount": 1, # Number of shocks in discrete permanent shock distribution + "TranShkStd": [0.0], # Transitory shock standard deviation + "TranShkCount": 1, # Number of shocks in discrete permanent shock distribution + "T_cycle": 1, # Number of periods in cycle + "UnempPrb": 0.0, # Unemployment probability (not used, as the unemployment here is *permanent*, not transitory) + "UnempPrbRet": 0.0, # Unemployment probability when retired (irrelevant here) + "T_retire": 0, # Age at retirement (turned off) + "IncUnemp": 0.0, # Income when unemployed (irrelevant) + "IncUnempRet": 0.0, # Income when unemployed and retired (irrelevant) + "aXtraMin": 0.001, # Minimum value of assets above minimum in grid + "aXtraMax": ExampleType.mUpperBnd, # Maximum value of assets above minimum in grid + "aXtraCount": 48, # Number of points in assets grid + "aXtraExtra": [None], # Additional points to include in assets grid + "aXtraNestFac": 3, # Degree of exponential nesting when constructing assets grid + "LivPrb": [np.array([1.0, 1.0])], # Survival probability + "DiscFac": base_primitives["DiscFac"], # Intertemporal discount factor + "AgentCount": 1, # Number of agents in a simulation (irrelevant) + "tax_rate": 0.0, # Tax rate on labor income (irrelevant) + "vFuncBool": False, # Whether to calculate the value function + "CubicBool": True, # Whether to use cubic splines (False --> linear splines) + "MrkvArray": [MrkvArray], # State transition probabilities +} +MarkovType = MarkovConsumerType(**init_consumer_objects) # Make a basic consumer type +employed_income_dist = [ + np.ones(1), + np.ones(1), + np.ones(1), +] # Income distribution when employed +unemployed_income_dist = [ + np.ones(1), + np.ones(1), + np.zeros(1), +] # Income distribution when permanently unemployed +MarkovType.IncomeDstn = [ + [employed_income_dist, unemployed_income_dist] +] # set the income distribution in each state +MarkovType.cycles = 0 + +# Solve the "Markov TBS" model +t_start = process_time() +MarkovType.solve() +t_end = process_time() +MarkovType.unpackcFunc() + +print( + 'Solving the same model "the long way" took ' + str(t_end - t_start) + " seconds." +) +# plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper) +plotFuncs(MarkovType.cFunc[0], 0, m_upper) +diffFunc = lambda m: ExampleType.solution[0].cFunc(m) - MarkovType.cFunc[0][0](m) +print("Difference between the (employed) consumption functions:") +plotFuncs(diffFunc, 0, m_upper) From 0f836272bfe2759e4866afd0489702c922abf6b2 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 16:07:04 +0530 Subject: [PATCH 169/242] use github actions for testing --- .github/workflows/hark.yml | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 .github/workflows/hark.yml diff --git a/.github/workflows/hark.yml b/.github/workflows/hark.yml new file mode 100644 index 000000000..cb8111512 --- /dev/null +++ b/.github/workflows/hark.yml @@ -0,0 +1,35 @@ +name: HARK build on MacOS, Ubuntu and Windows + +on: [push] + +jobs: + build: + + runs-on: ${{ matrix.os }} + strategy: + max-parallel: 4 + matrix: + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: [3.6, 3.7] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt +# - name: Lint with flake8 +# run: | +# pip install flake8 +# # stop the build if there are Python syntax errors or undefined names +# flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics +# # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide +# flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pip install pytest + pytest \ No newline at end of file From 01a440671fa2c793c35c39bf33ad6aed19dd8c04 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 16 Jan 2020 17:28:28 +0530 Subject: [PATCH 170/242] fix examples for parameter loading --- .../example_ConsAggShockModel.py | 18 ++++++------------ .../example_ConsGenIncProcessModel.py | 2 +- HARK/ConsumptionSaving/example_ConsIndShock.py | 6 +++--- HARK/ConsumptionSaving/example_ConsMedModel.py | 2 +- .../example_ConsPrefShockModel.py | 4 ++-- 5 files changed, 13 insertions(+), 19 deletions(-) diff --git a/HARK/ConsumptionSaving/example_ConsAggShockModel.py b/HARK/ConsumptionSaving/example_ConsAggShockModel.py index c8d462a70..11f978408 100644 --- a/HARK/ConsumptionSaving/example_ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/example_ConsAggShockModel.py @@ -37,13 +37,11 @@ def mystr(number): if solve_agg_shocks_micro or solve_agg_shocks_market: # Make an aggregate shocks consumer type - AggShockExample = AggShockConsumerType(**Params.init_agg_shocks) + AggShockExample = AggShockConsumerType() AggShockExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - EconomyExample = CobbDouglasEconomy( - agents=[AggShockExample], **Params.init_cobb_douglas - ) + EconomyExample = CobbDouglasEconomy(agents=[AggShockExample]) EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks # Have the consumers inherit relevant objects from the economy @@ -104,14 +102,12 @@ def mystr(number): if solve_markov_micro or solve_markov_market or solve_krusell_smith: # Make a Markov aggregate shocks consumer type - AggShockMrkvExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks) + AggShockMrkvExample = AggShockMarkovConsumerType() AggShockMrkvExample.IncomeDstn[0] = 2 * [AggShockMrkvExample.IncomeDstn[0]] AggShockMrkvExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - MrkvEconomyExample = CobbDouglasMarkovEconomy( - agents=[AggShockMrkvExample], **Params.init_mrkv_cobb_douglas - ) + MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample]) MrkvEconomyExample.DampingFac = 0.2 # Turn down damping MrkvEconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks AggShockMrkvExample.getEconomyData( @@ -223,16 +219,14 @@ def mystr(number): PolyMrkvArray[StateCount - 1, StateCount - 1] += 0.5 * (1.0 - Persistence) # Make a consumer type to inhabit the economy - PolyStateExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks) + PolyStateExample = AggShockMarkovConsumerType() PolyStateExample.MrkvArray = PolyMrkvArray PolyStateExample.PermGroFacAgg = PermGroFacAgg PolyStateExample.IncomeDstn[0] = StateCount * [PolyStateExample.IncomeDstn[0]] PolyStateExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - PolyStateEconomy = CobbDouglasMarkovEconomy( - agents=[PolyStateExample], **Params.init_mrkv_cobb_douglas - ) + PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample]) PolyStateEconomy.MrkvArray = PolyMrkvArray PolyStateEconomy.PermGroFacAgg = PermGroFacAgg PolyStateEconomy.PermShkAggStd = StateCount * [0.006] diff --git a/HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py index a41c00abc..77fedcb8f 100644 --- a/HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py @@ -35,7 +35,7 @@ def mystr(number): ) # Make and solve an example "explicit permanent income" consumer with idiosyncratic shocks -ExplicitExample = IndShockExplicitPermIncConsumerType(**Params.init_explicit_perm_inc) +ExplicitExample = IndShockExplicitPermIncConsumerType() t_start = process_time() ExplicitExample.solve() t_end = process_time() diff --git a/HARK/ConsumptionSaving/example_ConsIndShock.py b/HARK/ConsumptionSaving/example_ConsIndShock.py index 1f1dc31ff..2544edc54 100644 --- a/HARK/ConsumptionSaving/example_ConsIndShock.py +++ b/HARK/ConsumptionSaving/example_ConsIndShock.py @@ -9,7 +9,7 @@ do_simulation = True # Make and solve an example perfect foresight consumer -PFexample = PerfForesightConsumerType(**Params.init_perfect_foresight) +PFexample = PerfForesightConsumerType() PFexample.cycles = 0 # Make this type have an infinite horizon start_time = time() @@ -32,7 +32,7 @@ "" # Make and solve an example consumer with idiosyncratic income shocks -IndShockExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks) +IndShockExample = IndShockConsumerType() IndShockExample.cycles = 0 # Make this type have an infinite horizon start_time = time() @@ -124,7 +124,7 @@ "" # Make and solve an agent with a kinky interest rate -KinkyExample = KinkedRconsumerType(**Params.init_kinked_R) +KinkyExample = KinkedRconsumerType() KinkyExample.cycles = 0 # Make the Example infinite horizon start_time = time() diff --git a/HARK/ConsumptionSaving/example_ConsMedModel.py b/HARK/ConsumptionSaving/example_ConsMedModel.py index c1e853c8e..23a295f32 100644 --- a/HARK/ConsumptionSaving/example_ConsMedModel.py +++ b/HARK/ConsumptionSaving/example_ConsMedModel.py @@ -10,7 +10,7 @@ do_simulation = True # Make and solve an example medical shocks consumer type -MedicalExample = MedShockConsumerType(**Params.init_medical_shocks) +MedicalExample = MedShockConsumerType() t_start = process_time() MedicalExample.solve() t_end = process_time() diff --git a/HARK/ConsumptionSaving/example_ConsPrefShockModel.py b/HARK/ConsumptionSaving/example_ConsPrefShockModel.py index 07811dd25..aa0c48e38 100644 --- a/HARK/ConsumptionSaving/example_ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/example_ConsPrefShockModel.py @@ -13,7 +13,7 @@ do_simulation = True # Make and solve a preference shock consumer -PrefShockExample = PrefShockConsumerType(**Params.init_preference_shocks) +PrefShockExample = PrefShockConsumerType() PrefShockExample.cycles = 0 # Infinite horizon t_start = process_time() @@ -60,7 +60,7 @@ ########################################################################### # Make and solve a "kinky preferece" consumer, whose model combines KinkedR and PrefShock -KinkyPrefExample = KinkyPrefConsumerType(**Params.init_kinky_pref) +KinkyPrefExample = KinkyPrefConsumerType() KinkyPrefExample.cycles = 0 # Infinite horizon t_start = process_time() From ea56ea6b8c241fa267b56c83158c9b5a23f93f1e Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Sun, 19 Jan 2020 22:50:24 +0530 Subject: [PATCH 171/242] move examples to ipython notebooks with jupytext --- HARK/ConsumptionSaving/__init__.py | 11 +- HARK/__init__.py | 5 +- .../example_ConsAggShockModel.ipynb | 479 +++++++++++++++ .../example_ConsAggShockModel.py | 39 +- .../example_ConsGenIncProcessModel.ipynb | 458 ++++++++++++++ .../example_ConsGenIncProcessModel.py | 6 +- .../example_ConsIndShock.ipynb | 566 ++++++++++++++++++ .../ConsumptionSaving/example_ConsIndShock.py | 112 ++-- .../example_ConsMarkovModel.ipynb | 492 +++++++++++++++ .../example_ConsMarkovModel.py | 8 - .../example_ConsMedModel.ipynb | 313 ++++++++++ .../ConsumptionSaving/example_ConsMedModel.py | 10 +- .../example_ConsPrefShockModel.ipynb | 316 ++++++++++ .../example_ConsPrefShockModel.py | 4 - .../example_ConsRepAgentModel.ipynb | 202 +++++++ .../example_ConsRepAgentModel.py | 18 +- .../example_TractableBufferStockModel.ipynb | 277 +++++++++ .../example_TractableBufferStockModel.py | 1 - 18 files changed, 3210 insertions(+), 107 deletions(-) create mode 100644 examples/ConsumptionSaving/example_ConsAggShockModel.ipynb rename {HARK => examples}/ConsumptionSaving/example_ConsAggShockModel.py (91%) create mode 100644 examples/ConsumptionSaving/example_ConsGenIncProcessModel.ipynb rename {HARK => examples}/ConsumptionSaving/example_ConsGenIncProcessModel.py (98%) create mode 100644 examples/ConsumptionSaving/example_ConsIndShock.ipynb rename {HARK => examples}/ConsumptionSaving/example_ConsIndShock.py (50%) create mode 100644 examples/ConsumptionSaving/example_ConsMarkovModel.ipynb rename {HARK => examples}/ConsumptionSaving/example_ConsMarkovModel.py (97%) create mode 100644 examples/ConsumptionSaving/example_ConsMedModel.ipynb rename {HARK => examples}/ConsumptionSaving/example_ConsMedModel.py (95%) create mode 100644 examples/ConsumptionSaving/example_ConsPrefShockModel.ipynb rename {HARK => examples}/ConsumptionSaving/example_ConsPrefShockModel.py (97%) create mode 100644 examples/ConsumptionSaving/example_ConsRepAgentModel.ipynb rename {HARK => examples}/ConsumptionSaving/example_ConsRepAgentModel.py (89%) create mode 100644 examples/ConsumptionSaving/example_TractableBufferStockModel.ipynb rename {HARK => examples}/ConsumptionSaving/example_TractableBufferStockModel.py (99%) diff --git a/HARK/ConsumptionSaving/__init__.py b/HARK/ConsumptionSaving/__init__.py index 43c3e7b4d..99245567a 100644 --- a/HARK/ConsumptionSaving/__init__.py +++ b/HARK/ConsumptionSaving/__init__.py @@ -1 +1,10 @@ -from .ConsIndShockModel import * \ No newline at end of file +from HARK.ConsumptionSaving.ConsAggShockModel import * +from HARK.ConsumptionSaving.ConsGenIncProcessModel import * +from HARK.ConsumptionSaving.ConsIndShockModel import * +from HARK.ConsumptionSaving.ConsMarkovModel import * +from HARK.ConsumptionSaving.ConsMedModel import * +from HARK.ConsumptionSaving.ConsPortfolioModel import * +from HARK.ConsumptionSaving.ConsPrefShockModel import * +from HARK.ConsumptionSaving.ConsRepAgentModel import * +from HARK.ConsumptionSaving.TractableBufferStockModel import * +from HARK.ConsumptionSaving.ConsumerParameters import * diff --git a/HARK/__init__.py b/HARK/__init__.py index bbfa2eb63..44f72023a 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,4 +1,3 @@ -from __future__ import absolute_import -from .core import * - +from HARK.core import * +from HARK.ConsumptionSaving import * __version__ = '0.10.3' diff --git a/examples/ConsumptionSaving/example_ConsAggShockModel.ipynb b/examples/ConsumptionSaving/example_ConsAggShockModel.ipynb new file mode 100644 index 000000000..30257be30 --- /dev/null +++ b/examples/ConsumptionSaving/example_ConsAggShockModel.ipynb @@ -0,0 +1,479 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import HARK.ConsumptionSaving.ConsumerParameters as Params\n", + "from time import process_time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from HARK.utilities import plotFuncs\n", + "from HARK.ConsumptionSaving.ConsAggShockModel import (\n", + " AggShockConsumerType,\n", + " CobbDouglasEconomy,\n", + " AggShockMarkovConsumerType,\n", + " CobbDouglasMarkovEconomy,\n", + ")\n", + "from copy import deepcopy\n", + "def mystr(number):\n", + " return \"{:.4f}\".format(number)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve an AggShockConsumerType's microeconomic problem\n", + "solve_agg_shocks_micro = False\n", + "# Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy\n", + "solve_agg_shocks_market = True\n", + "# Solve an AggShockMarkovConsumerType's microeconomic problem\n", + "solve_markov_micro = False\n", + "# Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy\n", + "solve_markov_market = True\n", + "# Solve a simple Krusell-Smith-style two state, two shock model\n", + "solve_krusell_smith = True\n", + "# Solve a CobbDouglasEconomy with many states, potentially utilizing the \"state jumper\"\n", + "solve_poly_state = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example impelementation of AggShockConsumerType" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "if solve_agg_shocks_micro or solve_agg_shocks_market:\n", + " # Make an aggregate shocks consumer type\n", + " AggShockExample = AggShockConsumerType()\n", + " AggShockExample.cycles = 0\n", + "\n", + " # Make a Cobb-Douglas economy for the agents\n", + " EconomyExample = CobbDouglasEconomy(agents=[AggShockExample])\n", + " EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks\n", + "\n", + " # Have the consumers inherit relevant objects from the economy\n", + " AggShockExample.getEconomyData(EconomyExample)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "if solve_agg_shocks_micro:\n", + " # Solve the microeconomic model for the aggregate shocks example type (and display results)\n", + " t_start = process_time()\n", + " AggShockExample.solve()\n", + " t_end = process_time()\n", + " print(\n", + " \"Solving an aggregate shocks consumer took \"\n", + " + mystr(t_end - t_start)\n", + " + \" seconds.\"\n", + " )\n", + " print(\n", + " \"Consumption function at each aggregate market resources-to-labor ratio gridpoint:\"\n", + " )\n", + " m_grid = np.linspace(0, 10, 200)\n", + " AggShockExample.unpackcFunc()\n", + " for M in AggShockExample.Mgrid.tolist():\n", + " mMin = AggShockExample.solution[0].mNrmMin(M)\n", + " c_at_this_M = AggShockExample.cFunc[0](m_grid + mMin, M * np.ones_like(m_grid))\n", + " plt.plot(m_grid + mMin, c_at_this_M)\n", + " plt.ylim(0.0, None)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now solving for the equilibrium of a Cobb-Douglas economy. This might take a few minutes...\n", + "intercept=-0.343892431186286, slope=1.104593288292029, r-sq=0.9968896217612445\n", + "intercept=-0.5036317603080784, slope=1.1516307652306579, r-sq=0.99558993623854\n", + "intercept=-0.45781284276113077, slope=1.1150500956007625, r-sq=0.9761544937568223\n", + "intercept=-0.3871208372015027, slope=1.0853000270407058, r-sq=0.9820100342330428\n", + "intercept=-0.3646746874098119, slope=1.0763760421084498, r-sq=0.9856903333255791\n", + "intercept=-0.3548104709407357, slope=1.07250161746339, r-sq=0.9860331018283293\n", + "intercept=-0.3504754703630661, slope=1.070813036044259, r-sq=0.9861654830885628\n", + "intercept=-0.3485780110442094, slope=1.0700786458110867, r-sq=0.9862204148294936\n", + "intercept=-0.3477516010972477, slope=1.0697604492756763, r-sq=0.9862437414876912\n", + "intercept=-0.34739343564383973, slope=1.0696231406893215, r-sq=0.986253720644644\n", + "intercept=-0.34723891990561373, slope=1.069564121912548, r-sq=0.9862579935066507\n", + "intercept=-0.34717253299479095, slope=1.069538844447615, r-sq=0.9862598203445164\n", + "Solving the \"macroeconomic\" aggregate shocks model took 472.744824 seconds.\n", + "Aggregate savings as a function of aggregate market resources:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ms/dev/HARK/HARK/ConsumptionSaving/ConsAggShockModel.py:1769: RuntimeWarning: divide by zero encountered in log\n", + " Aagg = np.exp(self.intercept + self.slope*np.log(Mnow))\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function at each aggregate market resources gridpoint (in general equilibrium):\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if solve_agg_shocks_market:\n", + " # Solve the \"macroeconomic\" model by searching for a \"fixed point dynamic rule\"\n", + " t_start = process_time()\n", + " print(\n", + " \"Now solving for the equilibrium of a Cobb-Douglas economy. This might take a few minutes...\"\n", + " )\n", + " EconomyExample.solve()\n", + " t_end = process_time()\n", + " print(\n", + " 'Solving the \"macroeconomic\" aggregate shocks model took '\n", + " + str(t_end - t_start)\n", + " + \" seconds.\"\n", + " )\n", + "\n", + " print(\"Aggregate savings as a function of aggregate market resources:\")\n", + " plotFuncs(EconomyExample.AFunc, 0, 2 * EconomyExample.kSS)\n", + " print(\n", + " \"Consumption function at each aggregate market resources gridpoint (in general equilibrium):\"\n", + " )\n", + " AggShockExample.unpackcFunc()\n", + " m_grid = np.linspace(0, 10, 200)\n", + " AggShockExample.unpackcFunc()\n", + " for M in AggShockExample.Mgrid.tolist():\n", + " mMin = AggShockExample.solution[0].mNrmMin(M)\n", + " c_at_this_M = AggShockExample.cFunc[0](m_grid + mMin, M * np.ones_like(m_grid))\n", + " plt.plot(m_grid + mMin, c_at_this_M)\n", + " plt.ylim(0.0, None)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example Implementations of AggShockMarkovConsumerType" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "if solve_markov_micro or solve_markov_market or solve_krusell_smith:\n", + " # Make a Markov aggregate shocks consumer type\n", + " AggShockMrkvExample = AggShockMarkovConsumerType()\n", + " AggShockMrkvExample.IncomeDstn[0] = 2 * [AggShockMrkvExample.IncomeDstn[0]]\n", + " AggShockMrkvExample.cycles = 0\n", + "\n", + " # Make a Cobb-Douglas economy for the agents\n", + " MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample])\n", + " MrkvEconomyExample.DampingFac = 0.2 # Turn down damping\n", + " MrkvEconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks\n", + " AggShockMrkvExample.getEconomyData(\n", + " MrkvEconomyExample\n", + " ) # Have the consumers inherit relevant objects from the economy" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "if solve_markov_micro:\n", + " # Solve the microeconomic model for the Markov aggregate shocks example type (and display results)\n", + " t_start = process_time()\n", + " AggShockMrkvExample.solve()\n", + " t_end = process_time()\n", + " print(\n", + " \"Solving an aggregate shocks Markov consumer took \"\n", + " + mystr(t_end - t_start)\n", + " + \" seconds.\"\n", + " )\n", + "\n", + " print(\n", + " \"Consumption function at each aggregate market \\\n", + " resources-to-labor ratio gridpoint (for each macro state):\"\n", + " )\n", + " m_grid = np.linspace(0, 10, 200)\n", + " AggShockMrkvExample.unpackcFunc()\n", + " for i in range(2):\n", + " for M in AggShockMrkvExample.Mgrid.tolist():\n", + " mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M)\n", + " c_at_this_M = AggShockMrkvExample.cFunc[0][i](\n", + " m_grid + mMin, M * np.ones_like(m_grid)\n", + " )\n", + " plt.plot(m_grid + mMin, c_at_this_M)\n", + " plt.ylim(0.0, None)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now solving a two-state Markov economy. This should take a few minutes...\n", + "intercept=[-0.45457028863479326, -0.5608165406907654], slope=[1.1215368972825372, 1.178334499213271], r-sq=[0.9981555551235348, 0.9936211956825644]\n", + "intercept=[-0.39063438982463117, -0.44292984675023017], slope=[1.0773896585972575, 1.0964491558602392], r-sq=[0.9998539446156635, 0.9996166998511072]\n", + "intercept=[-0.3375826098429694, -0.38469230352004224], slope=[1.0657220737945672, 1.0800826397775194], r-sq=[0.999923540564709, 0.999893702729503]\n", + "intercept=[-0.34438955842820446, -0.3938944194405977], slope=[1.0688335020439343, 1.0837845833652027], r-sq=[0.9999202360448058, 0.9998929405844074]\n", + "intercept=[-0.34513135214708335, -0.39438330618459266], slope=[1.0691336344455975, 1.0839898637364869], r-sq=[0.9999195924017966, 0.9998911821689769]\n", + "intercept=[-0.3452067391631015, -0.39440757767055756], slope=[1.0691611379897035, 1.084000818643343], r-sq=[0.9999195629192332, 0.9998910731648093]\n", + "Solving the \"macroeconomic\" aggregate shocks model took 392.50628099999994 seconds.\n", + "Consumption function at each aggregate market resources-to-labor ratio gridpoint (for each macro state):\n" + ] + }, + { + "data": { + "image/png": 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SuSInV8oVt5LtXomTLcjfr1Ev0FXv5J3dPnmjUvlw2/aePbSTaIJ+HfKjhR/xhy//IRvZDT7W/zF+ZeRXsBr2Vub3hiWfqh4+Va6+c9vOpfS0yaLd9/7KDJOadtDtPQtBkiRW4zmmlmPbIoIJ5jZSSIpl4rYa6Wt08Qu3tKnZ7oDPgcmwd4oTSZJYzOYZT2YYT2Q47LZzf93utP5r7BHWM+v80St/xDPzz9Bd082f3vun9Hv7d3tZGudDFCG28Ea7ZPMM6qDS8rmUgx+o2CX1+8Gy80JwNSiURGbXklV2yVQ4zla6oF7TVmejr9HF4wea1Yhgo9uyp5rYREnibCbHeCLDWCLDeDLNeCJDtKhsyArwG21+TdA1zo8kSTw98zT/9fX/Sq6Y4zcO/ga/2P+L2jTDa4V8Sj7ObHV827zuKcgnlAsEqO2QK+2hn6uMfPW07dmRr7FMgRPnbFSeXk2SL8mWidmgo7fBybv7G9R4YE+DE6dlb/3OFkWJ0+msUnnLwj2ezJBSvk+TILDfYeGReg+DDiuDThu9dgvWK9Q5qgn6HidVSPF7L/4eP178MQfrD/IHt/8BHe6O3V7WjYkkQTwkt8GvTFQEfHvVbXbJYj38ZMUu8fWC2bGrS79UJEliaSvzho3Kpa2Meo3XYWJ/o4uPv6Nd9bo7rmI7/E6RE0WmU1ml8k4znswwlcyQVeI0Vp2OAYeVn2uoZdBpZchpo9tmQU+JdHqWROJV4isTTCYm8PsfoWXfR3d8jZqg72EW4gv8+o9/nbn4HL99+Lf5SN9HtE3Pq0UxV/G6y1X3yjhko5VrygctDH1I6abceyNft5Mrlji9mlTFuxwVTGxrh+/02jnQWsOHb2lV/e56p2WXV37xpEsiU8mKcI8nMkynshQUY9+p1zHotPGLzV6GlMo7YDMjSEVSqdMkEj8jHppgNDFJMnkCUZSbl/R6Gw7Hfgz6KzM9UhP0PcpPQj/hd174HfSCni888AVubbx1t5d0/ZKMKMOnton32nQl1220QX0f9L9fFu2GQfn/e9TrBoilC2or/GRY3rCciSQpKtWozaSnt8HJoyNN9DW66Wty0eN3YjXtvY3ZeLHExDaveyyRYSadRckPUWvUM+SwcU+Lj0GnjSGnlVaLCaQCydQpEvEXSSxOcDQxSTJ1ElGUR+Tq9Q6czj6amz+M09GHKLawuWkkHF7Bbm6hsXHnvxdN0PcYkiTxxckv8vmjnyfoCfL5ez7PPue+3V7W9UGpCBunFctkm4CnIpVrXM2yaPe8RxHvIdn/3oMJE1COCYxlmQpXhPtcy6TeaaavycV9++vZ3+iiv8lNW61tT2a7N/JFJqoq7zRnM3n18QaTkUGnlYfr3Qw5bAw6rTSZjYhinlRqmnhigsT8BK8nJkgmTyFJ8oauweDE6ehnX/NHcDgHkMQWNjb0LIdXGD+yxHLkBLnCcQD0ko68d4P9A7swD13j2iFTzPDvX/r3fG/ue7y7/d187vbPYTPadntZe5PM1jarRPG7t08O1JuUbsoHFOFW/G5b7e6u+zIoH7owtRxjMlQZRhVVUiaCAB1eOyMtHjkiqGxW+px7swltLV/geEIW7THF9w7lKomaVouJQaeVJxvqGHBaGXRYqTcbKZWyJJMnSSQmiK1PsJSYIJU6jSTJ1pLB4Mbp7Ke15eM4nP1IUisb6wLLyyuMH11ieXWSbOEoADpJoFZy0CH6qClZMeXyFDKbuK5AdQ6aoO8ZQskQn332s0xvTvPZg5/lEwOf2FNRrl1DFOVzKNWKW9mo3D6v2+6TxfqWT8nRwIYBeXLgHpxhUmZ7Y86kUnWfXKkculBOmbxnoFEV7v2N1+YEwbdDkiRW8gVVtMu2yUq+It4Bq5mb3HY+4bQx7LTS77BSYzRQKqVJJE+QiE+wvjrJ2cQEqfQMkmKnGY01OJ0D1NXdjdPRB7Sxvg7L4WUmji4RXhknW3gdkMW7RnLQLtZRU7JhzOcppbdI6+IYGgrYg83Ud/Zide3D5fNekddi7/30bkAm1if4lR/+CkWxyJ/d92fcue/O3V7StUk5HrgyVhHw1SlQjslD0MvDptpu21Z1D4LTv7vrvkwiiWyVcJ8Ixzm7rTHHYzPu6qELO4kkSSzlClVV91giw3pB2ZgFgjYz76hxqEmTAYcVp0FPsZgikZwikZggvDzBdGKSVGoWFLfcaKzD5RrA670Pp3MAhDbW10SWw8ty5b0yRib/qvw8kkCtZKdNrKNWtGHKFSilt0gJUfQNeWwBE/WBbuw1raTjJtYWkkTm4ky9nKCQm+HAu/Lc/nhwx18fQSr/1K8yhw8fll5//fVdee69xOsrr/NrP/41PGYPX7j/C7S723d7SdcGqXVYPq5U3IqAb2+FL59NWbZKGgbAtx+Mey9xUUYUJeY2UspGZaX6Xk9WZpm01FrpVzYp+xpd9De7aHDtrcacMpIksZDNV9km48k0m4VKg063zcKQU/a6h502+hwW7Ppq8U7Ex4knJkinK/FRk6kep7Mfl3MAh7MfndDO2lqR5eVlQvNLLK8sk87J+wiCJFAj2fGKTupEB6ZcATGzRVLaQl9vxhlsxB8I4vS2kU1ZWVtIEJlPEJmPk0vJf2h0BgHvPif+Nif17S4agx7cvkvr3hYE4YgkSYfP+5gm6NcuxyLH+PQzn6bB3sBfPfBX+O17u5K8JCQJtuYqor08Jv87sVy5xt0qJ0sah+TbhkF5KNUeFLEy2UKJU6sJVbjLEcG0Mv7VoBPo8jvpV4S7PALWbd2bNlG5u3J71T2RzBBTuiuNgkCv3aJW3UMOK/sdVqx63duKt9nkx+kawOkcwOUcQBDaWV8vEA6HZfFeXiaVk4+REyTwSA5FvO2Y80XEdIykuInOZ8QZ9OPvDOJp7CCXtbO+kGR1ThbvdEzeXBV0ArVNdvxtTnxtLvztLmqb7OgNOgqRCNnxcYyNjVj6Lm1T9K0EXbNcrlHG1sb4zA8/g9/m52/e9Tf4bL7dXtKVp5hXpgduq7pXxiEXlx8X9PJhCx13yemSsnjv4Y1KgGg6X2WZTIZjzK6lKCkRwfL41w8dlo86629yEax3YDbszWRNSZKYSeeqbJOJZIZkuYtUJ7DfbuXReo9afffaLZh128Q7Ns6ZpYk3FW+//2FVvDc2ioRCISZGQyyHXyeZfQEoi7edJslFXakJc76ElI6RKK0j+DKYgvXUB4LUNb+TQsHF2mKSyFyC178fJ75+Rv1+PH4b+3prqG9zUd/mwtviwGjSU0omyU5MkPnXcZbHx8iMjVNcXQWg5qmnaLhEQX8rtAr9GmRyY5Jf/v4v47F4+OK7v3h9VubZuOxxL5eF+7icMimflGO0yVaJWnUPyXNM9tjBwtvZ3lWpVt7hWNWJOQ0ui1x1ly2TJveeHv9aFCVOpbNVm5UTyQwZURZvq06gz2Gtsk26bRaMOqEi3vFxEok3F+9y5a3TdbCxUZQr74UlwuEwyYyyfyKBR7LhlVzUlRxY8iXIxEkU1pC8BhwdPuqDAbwtASSphvWlFBGl8t5aTauNvs46iyLcsnXia3VithoQ83ly09NkxsbIjk+QGR8nf+YM5Y0MY1sr1sEhrEODWAYGsfTtR2e5NPtPs1z2ENOb03zyB5/EbrDzpQe/RKPjCuWbriaJVVgelavusoBvna08bvMqwq2Id+OwPLN7j2a7QR5ENRNJbrNMYlWHDOsE6PQ5qiyTvkYXdXv4xJy80hq/3TY5kaq0xtv0OgYdVoacVrVBJ2i1YNAJFItJEgnFNnlb8R5Er+9kY6NQJd6JdFJdi1uUxdtbcmApiLJ459eQanXY273UdwXwtQcQBC8boTSRuTiRhQSboRRieb0uE/XtFfGub3VidZqQRJH83DxZperOjI+TO3ECqSAXI/q6OqxDQ1gGB7AODmEZ6MdQs3NjjDVB3yPMbM3wie9/ApPexBcf/CItzpbdXtLFk9qA5WMQOgZh5SMRrjxe07Gt6h6Wb/fgqfDbSWQLnFxJMBmKqdnuUyuVQVQWo47eBpdaefc3ufdsV2WZbEnkRCpbsU2SaU4ms+TPaY0vV92DDiudNjN64eLF22AIsL6elzcsFfGOpxLqWlyiFZ/kok50YstLSJkYyfw6JbeEraOO+mCQ+o4ABlM9m8tZtfJeX0xSKsc4bQZVtGURd2H3mBAEgcJqZJt4yxW4mJT/eOhsNiz9/ViGBtUK3NDY+Jab0CVRolASL/nEJE3Q9wBnY2f5+L9+HJ2g44sPfpE2V9tuL+ntycYgPFoR7vBRiC5UHq/rgqYD8kfjsJw0sezdI+8kSSKSyFV1VE6G48xvpNVr6uwmudreZpl0ePfOIcPnY/tck3LSZDqVpViORRr0VVX3kMNGm9WE7hLE22gMVot3KEwsFVfX4hKteCUnXtGFLS9BNkEit0bRVcLeXocv2El9RxCLo5HN5Zxcec8nWFtIUMgpG8pmvSzcbU7ZPml34vJaEQSBUiIh+97bxLvse2MwYOnurhJvU2cngv7thVmSJKaW43xjNMw3RkN89LZ2fvWeS4stapui1zgbmQ0+/cynkZD463f99bUp5vmUbJeUhTt8TI4JlvG0QdNBuOmXKgK+h8W7fNzZZDhWGUYVjrORqrSJt9XZ6G9y8cFD++hvciuDqMx7MiJYJlUsKa3xctU9lshwOlU912TYaeP+Ordqn7RY5EpWFe/1CU68hXj7/Y/gcg5gNAZV22R6MkQ4NEo0+by6Fqci3t1iPfYCkEmQzEYoOFPo2nI4g534Awexu5vZWi0QmY+zeCrBkWfWyKXkFJTeoMPb4qD31ga18vY0yGMLxHye3MmTZH7wQ5YV6yR/prLZaWprw3bzzVgHB7EODWLu7b1o3zsUzfCN0RBfP7bI7NZZjPYFmpsj5M13AzufQ9cEfZcplAr85nO/yWZ2ky+/58t0ejp3e0lKVPAsLLwMCz+Dpdfl9Ek54+1qlkV7+EmlAj+4p5MmmXxJ7qpcrmS7T2477syk19Hd4OC+/fVKtttN7x6c3X0u8WJpW75brsBn07ny/h9+k4FBp433+uS5JkNOK41mY7V4r00wdQHibTIF1Q3LUydChJeOs5WoiLdDtOCVnHSJAewFASGTJJmLkLcn0bVmcAQ78HcO4apvJRopEZmLszKfYOyFLdIxuYIuxwUDIz5VvMtxQUkUyZ89S+bVV4go4p09eRLKvrfXi3VoCPf7HsEyMIh1oB+9x3NJr2ssU+DrozP808RLnI5NoLcuYKpZwl4r59rzZjc1rpsv7Yf2NmiWyy4iSRKfe/lzfO3U1/gvd/4XHup8aHcWUirKs0zKAr7wMiSVt5kWD+y7CZoPKeI9Invee5TNVP4Nlsn2E+JdFoN6Mnw5IrjXjjs7H1uFojrHe+w8Q6mazEbZNlGEe8hpw2+W/2C90TYZJ50+S7V4D6ppE7O5SxXv8FKI8FKYzfiW+lwOyUKd6MQnunAUdAjZJMlshJw1h6XFjberA39nkJrGduIbEpH5uOp7JzaURJAANX4bPsU28be7qNsnxwUBCqurcuKkLN4T5/jeAwNy4qTsezc0XPI7K1ESmd6c4Z/Gf8oL86+xnJtGMEUQBAkQaHd2crjxAMO+YYZ9w7S72i/rXZzmoV+j/MPJf+APX/lDPjnwST576LNX74kLWVh8uSLgi69V2uM9rdB6G7TeKt96e/bkqTmSJLEcyzIRijGpVN2T4RjL2yKC5RPiy8JdPiF+L1smAOv5YtVm5Vgiw2K2It4tFpPqdQ85rQw4rfhMly7em5ulKvHeiG2qz2WXzHhFF17RiaNoQJ9JksyukTGnMbc48QY7aOgMUtfSSSqmY3UuTmQ+ztp84vxxwXbF9251YrLKBkMpHt/me4+THR+nGFEmZBqNb/S9OzouyPd+MxL5BONr44xGRnlx8QjTWxMUUPZRSlYaLD28o+UQ7+q8hUHfIA7Tzh5eogn6Nciry6/y6Wc+zR3Nd/D5ez6P/kpG9CQJ1k/D7I9g5ocw9xIUM4Agb1SWBbzlVnA3X7l1XCHKLfET20bAToRi6lmVggABJSLYr6RM+hpd1NhNu7zyy2c1V6jarBxLZAhvmyjYYTXJm5Xbst41RlkIL1a8LT47cvwAACAASURBVJbuKvEOLYaqxNsmmVTxdhaN6DMpUrk1MoYkxhYn3mA7/kAQb1snuaRJqbzjrM4n2AynkN4sLtjmxOqQf1ZiLif73uMTavIkf7YSgTW1t1eJt7m3F5350qOgoiQyF5/jeOQ4x9fkj9noLBISSAKlXD1Crp3+ukEe77+DxwdGMBmurJOtCfo1xlJiiZ//zs9Ta6nlKw99Zcf/ggNyAuXM84qI/1g+kBigLgjB+yFwryzie2zjMl8UOR2ptMRPhGKcWI6TUlriy373QJNbiQm69+wUwe1IksRyrlBVdY8n0qzmq4dSlcV70GllwGHFfR7xjifkRp0q8TY34HRWmnSs1p6KeIfkrPdGdFMWMsBaFm/JibtoQpdJks6uk9InMDXbqe1qw9/ZRX17gGLeqop3ZCFx/rhgOXHS5sJRIwuw6nuPjavinZ2ervjePi/WoWGsg4Ny5ntgAL378n6fk/kk4+vjqniPrY0Rz8spG7POjpBrIxZtQsy0cWvzCE8c6OKBPj9289X7/dIE/RoiVUjx1HefYjW9yt+/9+93NtGyMgGnvgczP4LFV+UTdUxO6HynLODB++Rj0fYI6XyRE8sJpsIxJkJxJpdjVfluu0mv5rr7mlwMNLkJ1u99v1uSJBazeWWjshIX3FAmCuqALrulyjbpd1hxKKMALla8bbZeolFJbtIJhQgvhFjbXEdUNsEtkhGf6MIruXAXTegzadLZdZK6KMYmO7XBVvwBOestSQ51suC5cUGjWY+vHBdUNi1dXnlwmCRJFMu+9/i4LN4TE4gp2QrU2e1YBgexDg7It0NDGPz+y7LHJEmSq++1SvU9szWDhISAQIe7E7eui/X1BqbnaynlvAztq+H9I808PNx4eUfrieIlW5maoF9D/Pbzv80z88/w5/f9Obc33375X7CYhxPfhFf/EhZfke9rHFaq8Pug5eY9Mdc7li4wGY4xEa543ts3K2vtJrUxp1x9t9fZ92xLfJmyeB/fJtxjiTRbylAqgwA9dmWioENu0tnvsGJTRt9WxFseSvW24m3fTyIuyMKtdFlG1iOUlFZ8k2RQxbumZFHEe40kUQyNVmoCLfgDXTQEgugMHnmyoFJ5b58uWI4Lqm3y2+KCAKVYjMzEhCze4xNkx8Yorq3JL4rRiKWnp2rT0tTRgXCZezmpQkquvhX7ZGx9jFguBoDT6GTIN8SAdwhdro3xMx6ePSEnnVpqrTw20syjB5oJ+C7x3XR6E+ZelN81n30eDnwE3nFp+2ZaDv0aYS42x/fnvs+nhz59+WKeWIEjX4LX/1ZOpNR2wrv/Eww+AY76HVnvlaDcnFPZrJSr71C0cuRZk9tCX5Obh4ca6VfEu9G9N0fAbudCxHu/3cpDPrc8UdBpY7/dgqVKvCdZD721ePv978PlHMBu7yOZ1BEOhzl5IkR4YYaVtZcolmTRNUp6vKKTPmkftSUbhmyWbHadeGkZoWELuprwBrrwd74bi6OOtcUUkbk4M8cS/PTpmarpgnXN548LQsX3jv5IadYZGyc/N6e+LqaODuy336aKt7m3F53p8vY3JEliPj5fXX1HZ9R3HQF3gPta72PYN8yQd4hYvIZvji7zt99eZjOVx2NL8cShfTx2oJmDrTUX/7uXT8uBg7PPyyK+fFz+OZkc0HYH1AUu6/t7M7QK/SryJ0f+hC9Pfplnnnjm0qYnSpJspbz6lzD1NIhF6HoX3PwpuRq/xtIokiSxGs8xHooxvhSVb0OV+d2CAB11dvqb3VUblrXXwWblhYp3OSIoV97yREG4+Mrb4egnnTaotkloYYmV1RXyReXMS0lHnSRHBWtFO8Zsjnxmg1hxDcFnwhVoUCrvLhx1DWwspll9i7hgfZsLX5sTf7sL7z4HBiUuKJVKqu9dFu/sqVOq723w+bAMD1UGVfX3o3dd/mHa6UL6Dd53NBcF5Op70DeoxgYHfYO4TC7Orqd4+liIp0dDzG+kMRt03N/n57GRZu7q9l2cdVcqQOhoRcCXXoVSHnRG+V1yxztl67P50GW/Y74sy0UQhL8FHgYikiQNnOfxu4FvAOWt5n+RJOlzb7eoG03Qi2KRB772AAN1A/zpfX968V9gdQq+9zvy2zazGw48BTd98or9pb9Y3k68dQJ01TsZaHYz2OxioNlNb6MLx1XcTLpSXK54l0ppEokp2e+Ol9Mm25p0ziPe2axJ3rBUbJPllWWyeeW1lgTqJCde0YlXOZChkNkiVogg1upwdvhoCHYrWe8WtlZyVYmTrZXUeeOC/jZ5umA5LihJEsWVlepNy8nJiu/tcFQGVA0OYB0awui//MmhkiSxkFiQxVuxT05HT6vVd6e7UxXvYd8wnZ5OdIL8Wm8kc3x7bJmvHwsxuhhFEOC2zjref6CZBwcacF1os5gkQWSqYqHMvQT5BHJybFAW78675QSZyX7Z3/N2Ltdy+RLw/wB/9xbXvChJ0sOXsLYbhpdCL7GeWeexrscu7hOzMXjuP8MrfwEWF7zn/4KRXwDzFUjGXCAXKt7v7PYxtM/NQLMcE9zLw6jKlE/RGbtA2+SN4p0hmTxBJFw+jGG86hi0clSw3GHpdA6Qz1sIh8PMz5UnC75aOU0HgVrRQZtYh1d0YsmVKGWjRPOrlFwbFDq81ASCNARuxtvaTmKzKFfdc3GmX9tifWkRsaQkV5xG/O0uug7Xq9631Vl5t1SKxcgceYX4+BiZ8Qky42OU1tbldRiNmHt7cT/6qBwbHBrC1N5+2b43yNX3xPpEVfW9lZMblRxGB4PeQT419Cm5+vYO4jZXJ10y+RI/mArx9LEQL5xepyRK7G908XsP9fLIcBON7gscybw1X6nAzz4PKcXzrw3A0AflKrzjrl3tmn5bQZck6QVBENqv/FKub7515lvUmGsu/DxQSYKxf4Qf/L78i3PoY3Dfv7vqvywXI96DzS4G97npa3Rr4g2USjlSqWnWwuNK2mRcOT2+fABxHS7XEPW+B3G6BnE5BygWHYTDYRYXlKz30uvqTG8B8Ih2mkUPXrEFW15CzMSI5Vcp2DfIt9VQEwzgDwxT395JLl1u1EnwyrfirC28UkmcWPTUtzkZvq8Ff7uL+nY5Llj2isVsluyJSTaV2d7ZsTHy8/Pqa2Pq7MRx+x2qeJt7ei7b9y6/5ouJxSrv+9TWKbX67nB38M6Wd1aqb3fneXs4SqLET2fX+fqxEN+fWCGVL9HotvDLd3by/gNN9DZcgM2TWq8W8K05+X6HHzrvkavwjneC59qZirpT73dvEwThOBAGfluSpMnzXSQIwqeATwG0trbu0FNf+6QLaZ5ffJ5Hg49i1F3AW7rNM/Ctz8q/RM2H4MNfheaDV36hyKNgjy/GOLawxbHFKGNLsbcU7/2Nrj2f8YaLF+9zNyxFMU8ydYr15XHVOkmmppEkZQPSWIPLOYi37l5crkGczkFE0c3Kyopsm7waIrz4larJgm7Rhl9yMSA2YSsIkI4Tz0XIWDbIt3jwdHXiD9yGvzOIJJqJzMdZnYsz+qMEkfmjZJOyb60zCPhanPTe1ihbJ+0uPPU2BCVxUs57x54fJzN2XPa9p6ehKK/d4PdjHRrE/YEPVHxvp3NHXvd0Ic3kxqRqn4ytj7GZlZuV7EY7g95Bfnnwl+XNS9/QG6rvc3+Gk+E4Xz8W4lvHw0QSOZwWA48MN/H+A83c3F771qmoXBLmf1oR8dVx+X6zC9rvhFt/RRZwX881O+75gjZFlQr922/iobsAUZKkpCAIDwGflySp6+2+5o3koX/7zLf53Rd/ly89+CUO+Q+9+YViCX72Z/DsH8kbJ/f/ARz6+BXb7BRFidm1JEcXtji2EOXYQpRTkYR6Wnyw3sHQPjdDze4bUrzLnvcbxbtAKjWjbFjK4p1InkSS5NSHweCW7RLXIC6nLN6CUKeKdzgkd1luxaPqmpyiBa/kwie6cBb1kEmQyEZIGRKY9jnxdXXg75Q3LY1mpxwTVNrkV+fiJDcrG801jXbq2+X5JvVtTuqaHWriBFDPtcwcH3vjfO9tvrccGxzcEd+7/LovJZYYXRtVrZNTW6coKe9Y2l3tcuVdL1ffAXfggjqoFzfTfPN4mK8fCzETSWLUC9zTU89jB5q5p7f+zeeOF/MQel0W7zPPyf8Wi6A3Q+stykbm3dA4Avpr5/f+snPobyXo57l2DjgsSdL6W113Iwn6r/7oV5nenOYHT/xA3Zx5A+lN+OdPwuyPoee98N7/G1xNO7qOaDrPscUox+bl6nt0IUoiJ1dhbquRA60eDrTUcKDVw3CLZ88eOLydyxVvSSqRSs2oDTrx+ATJ5BSiKAuoXu+oEm+XaxC9voHV1dWqFvn16Ia6pvJ8E5/owlUyImSSpLJrJIQtjE0O6oKtNAS68QeC2Fx1bIRSqnBH5qqPRHN5LWpU0N8un2dpslTER0ylyExOkh1TDmgYG6O4siI/aDBg6elRbJPhHct7l6mqvhUBL1ffNoOtKnky7Bt+y+r7XKLpPN8dX+HpYyFenZO/5s3ttTx6oIn3DjbisZ3H/hFFueouWyjzP5NnGAk6WbTLFkrrrdf0UYdXNIcuCEIDsCpJkiQIws3IjWwbb/NpNwzRbJSfhn7KU31PvbmYL4/BV39BzpY/8j/g4Ed35C1dLF3g5bMb/GxW/phelU950QnQ0+DikZEmDrbKAt5xnTTpXI5tIkki6fRZttbG1Q3LRGIKUZQ3IPV6O05nP/uan5ITJ65BjMZm1tbWCYVCTE2ECS1+i7XNdcqFUrlF/qDYgbtklht1cmvExHnyDVbE7hb8wS4GAg/h8jUQXUkTmY8Tnk1w7EfzbIQmK5uWLhP+NiddN/nfMOMEQCoWyc3MsDU2pk4azM3MyEIGGFtasB06JFfeQ0NY9l/6uZbne+2XkktVyZNzq+93NL9DFe+gJ3jR84uyhRLPnozw9WMhnp2OUChJBHx2/vd39/C+4SZaam3nLkq2L1Uf/AXIKLNnvD1w4BdkAW9/B1gvbVTutcbbCrogCH8P3A14BUFYAv49YASQJOkLwBPAZwRBKAIZ4Elpt8Lt1yDPLDxDUSryUMebjMYd/xp849fAWgMf/x7sO+8f3gsini3w2tlNWcDPbDC1HEeS5CPQDrfV8shwIwfbahje57mqsyeuBDsh3pnMPNG18W0t8pOUSkrkTmfF6eyjuenn1OrbbG5hfX1TbtQ5GSK8+P2qLkuz0mU5LLVRU7Kiz2TIZNeIlRYp+kwUu5rwBrvp7byPmqZmUlsFteo++fIKkYXTFJVNS5NFj6/Nxcj9LWoFvn3TUpIkiuEw8Z9ss04mp5Ayyh8ftxvL8BDOBx7AOjyEZXBwR8+1zBQzTK5PVm1eVlXf3kE+MfAJRupHGPIO4bFcmmCKosSrc5s8fSzEd8aXSWSL+JxmPnpbO48daKa/yVXd9JNYlYX7zHOykMcW5ftdzdD9oFKF37Xj736vFbTGoivMx//146xn1vnm+79Z/YsnivDsf4QX/1jOqn7o7y66w7NQEnn5zAY/mVnn5dkNxkMxRAlMBh0HWz3c1unltkAdwy1uzIa9mzq5fNtEIptdJF6uuuPjJJKTFIvKOxadGYejD9e2o9Aslg62tmJV801W1laruizLLfK1JRuGXJZcZoNoIYJUp8PV2UhDoAt/oAtvSyu5tKTkvCvNOuVNS7VNvt2FX5lzsn3TEuQRseW0SXlMbGldiQyaTFj276807AwPYWxp2bHOWkmSCCVD1cmTzVMUlQ3fNldblXVyKdX3uUyvJPj6sRDfHA0RjmWxm/S8e6CBxw40c3vAWznSLxuTM+DlKnzthHy/xQMddyo++D1yv8Y1upF5sWizXHaJ1dQqD3ztAT4z/Bk+M/KZygO5JPzLp2D6O7K98tAfg+HCIl+FkshPZzf4zliYH0ytEk0XMOoFRlo83NZZx62BOg621lzyAbS7zc6Id7hqwzKeGKdYlGd2CIIJp6MXp2tA3bC02QJEo4mqRp3qLku9co6lk1rRgSmXo5DZZCu/SskNjg4fDcEu/J1d+No7kEr6yqalIuLbNy1rm+xKs47se29vkweQ8nmy09OKbXKeEbGdnfKp8sqYWEtPN8IORAbLZIvZquTJ8bXjbGRlF9VqsDLorXjfQ74haiw7U/mvxLJ883iIrx8Lc2I5jl4ncFeXl/cfaOaBPr+8IV/Iyl2YZR88dFQeQmewQtttlY7MhiG4kiOpdxFtlssu8a9z/4qExHs63lO5M5eErzwhD9J68L/ALZ9+28qhLOLfHVvm+1MrRNMFnGYD9/f5eWiwkXcEvXsy9y1JEqFcgeOJNMfjabXT8sJtE4lcboXE5k8IJcqNOhMUCvJbf0Ew4LD3UF//oCzerkHsti4SiQzhcJipqTChxVcILz9NTumy1KOjTnQQFP3UiQ4suRLF7CZbuRUK9mUyHV5cgQDtgYPUd3SiM5hZX0oSmUsw9dM4kf/vWPWmpc9KY6eb+ntl28TX6sRo1le9BoX5eZLbUie5qRNI5x6N9uijaupkpyKD5edfSa0wujbKaEROn0xvTqvVd6uzldubblfTJ0FPEINu52QjkS3wvYkVvjEa4qezG0gSjLR4+A/v6+e9Q414bQZ5DsorX5UFfOFlKGZB0MuR3jt/UxbxlpvBcOlzz68XtAr9CvLkt59ElET+8ZF/lO/IJeArH5TnsXzgr2Hg8bf8/PVkji88N8vXji4RTRdwmA08oIj4nV3ePVeFR3IFRhNpRhNpjsczHE+kWVdGwhoE6LVbGX6Tyhsgl1+XRTs+pjbq5POK7SDosdu7cDoraRO7vYdMplCZb7IUIhwKkc7KPrNO6bIsz/W25SWKmSjR3ApZUwpLey3+YBB/oAt/ZxCzzcHWcjlxIlfgG6GkumlZPpjB316Z7W1xVCeFipubVZV3ZnwcMaa8e7Basfb3V806MTQ27uhQskKpwInNE4xGRuX4YOQ4kYx8uo/VYGXAO1BVfddadr6RLV8UeeHUGl8fDfHDqVVyRZH2OhuPjjTz/pEmOggrFspz8qiLrPz6UN9XqcDb7pA7p29AtAp9F5iPzzO5MclvHfot+Y5cAv7XE7D0GjzxN9D/5iMAYpkCf/XCGf72pbNkCyUeGmzk0ZHmPSXim4WiUnUrAp7IsKycpFOe531fnYthp5URp40+h7VKvAuFGInYMVbiY8QTY8Tj4+Ryy8qjOuz2AHW1d6kblg7HfgoFCIfDzM2FCS2dILT0DPGk7JMLgFvpsvRJrTgLernLMrdKQgiTaHFhGeygKXCAg4Eu7DW1JDayrM7FCZ2Kc+yZ00QWEpVNS6uB+jYnI/e3Kp2WTuwec5X4ipkM6aPjVQJeWFpSvgUd5q4uXO96AMvQkNxtGQgg7PBpN+uZddU2GV0bZXJ9krwo5+WbHc0cbjjMSP0II74Rumq6drT63o4kSRxd2OLpY2G+PRZmK12g1m7iyZta+GC3nv7cMYSzX4W/ex4SYfmT3K2w/31yFrzjrmt6iui1giboV4jvnf0eAA92PHjBYp7OF/niS3P8xfOzxLNFHh5q5H97oPvSZzBfJeLFEmOJNKOKbXI8kWZh2xmWAauZ2zwORpzyPO8BhxX7tk3aUilNPHGEiFJ5x+NjZDKVNnOrtQ2P57BimwzhdPQhSSa5UScU5rWls4QWX2QjWjkOzSlZ8YlOekU/NSULpBPEcxFi4gxxvxljfxsNgS76Ag/haWgily5WEievLhGZmyKTqN607Lu9UY0LnrtpKZVK5Gdm1Kx3ZnyM3PQpKCn2UWMj1qEhan7+Sdn/7utDZ9/ZoU1FschMdEa1TkYjoywl5T8gRp2R/rp+fr735xmpH2HYN3xpEz8vktm1JN84FuLp0TALm2ksRh3v67HzVMMq/dlj6M++AMdOyxfb6mThLlfhNR3XzUbm1UKzXK4AkiTx6DcepdZSy5fu+3P4n4/JNssTfwv973/D9aIo8fevLfAnz5xiPZnnvt56fvNd3fQ3XXvHw6VKJSYU0T6eyDAaTzObyamPt1hMjDhtcuXtkq0T1zbxFsUcieTJbdbJWPVwKnMjLtcgLucQLtcQTucAOp2DtbU12TYJhQgvhoisr6nzPaySCZ/SqFMr2tBnM6SyETbzq0hePZ7OJhqCXTQEuvG2tCGWYH0pyerZSrNObE2Zxy5A7bZOS3+7i9pmO3p9dQ9B5VT5sTeeruNwVA5nGB7CMjCAsX7nq8tYLsbY2phqnYyvj5MuyocVe61eDtRXTprvq+vDpL86Y4nXEjm+PRbm6WMhji/FsAp5PrZvmfe7Zwgmj6BfHQNJBKMd2m6vTCas77/mRkBfi2gpl6vMyc2TfPBbH+T/vOX3+dDE92Hin+GJL57XMw9FM/zO147z0swGt3TU8jsP9nKobefywpdDXhSZTGZl31uxT06lsor0QqPZyLBSdY8ovnfdttEAolgklZ4hER9Tfe9kchpJkitfo7EWl2tIEW85cWIyednc3FRP1FlSEifluKAJA96SC5/kpK7kwJTLk8mus5VboegqYW/30djVjT/QTX17B3qjma0V+WAG1fdeSiIqRyE5aszqcCp/uzzje3unJUApmZJPlR+vCHhxdVV+0GDA0ttbJeA7NWVwO6IkMhebU62T0cgoZ2JnANALerprulXrZLh+mCZ701U9ECSdL/LM1CpfPxbip6dX6Zdmecwzw/3mEzQmxhDKs8H33VTpyGw+dMHpLo0Kmod+lfnu2e9iEAw8sDQli/n9f/AGMZckiX86ssTnvjWFJEn8p8cHefKmncsOXyySJDGfzXM0nuZoPMXReJqJRIa88ge/zmhgxGnjIZ9bqcBt+M3GbZ8vkk7Psbw5pqRNxpQuS/lgBIPBidM5SGvLJ2QRdw1hMjWQSMhxwdHREKGl77IcDqtzveXEiZMesRGf6MSal8hlZPHOmOeJttfiD3bREbiL2zq7MNvtpKI5VufiLEzFee27U0TmExSy1b73gXe1qgJu91QnI6RSSY4MHj8uV+DHj5ObmaU84MbY2ortppuwlqcM7t9/WafKvxnbD2woWyjlw4rdZjfDvmEe7nyYkfoR+uv6sRltb/MVdx51ouGRJWamXudQ6Ti/ZD7JX1inMJdScpuhexD2f6oyG3wXxz7fCGgV+g4jSiIP/vODBPR2/vz4j+XZ5Y/+WZUXGIln+d1/GedHJyPc2lnLf31i+I1ty1eYzUKRY/E0xxQBH02k2SzIwmfV6Rh2WjngsnHAZeeAy8Y+s7GqSzGbDcmirVonE5RKyoAnnQWns7+q+rZa28hkslWJk9BSiJQ6GlagVrSrzTqOgo5iOko0v0JSiGJuceELdtIQ6JZP1amtI5cuEJlPqLbJ6lxcPRZNZxDw7nMqtsn5m3UACqsRZcLgmBwbnJhASsu2hd7tVjcsr0S3ZZly40658h5bG2N6a7rquLSy7z1SP0K7q31X//BPhuM8+/LrRKd+yGB+lDv0U/iQB41JNR0IZQul/S6w1+3KOq9nNMvlKnIscoyPfu+j/NF6jEdqB+AjT1e9rfzO2DL/x9PjZPIl/u17evnF29qv+AyVnCgymchwNJHmaDzNsXiKsxnlPEig227hoMvGQZeNA04bvXYrhm1ryuc3icePyx9K4qSS9TbicPQq4j2IyzWEzRagUCixvLxcEfDFJaLxmPo13aINn1QeUGWCdIxYLkKstI6+wUJdVzsNypFonoYmxKLEeiipCvfq2TjR1bT69Tx+W5V14t3nQG+stj3ETIbs5KQs3Mq8k+KykpwpH0w8PIx1WBZxY1vbFRHOXCnHiY1t0cG146xn5PilzWBjyDekivf5DmzYDZaXlxh78VsUTj/LQG6Udp1sOeXMXgzBu9EH7patFM+NMxZ7t9Asl6vId098FbMkca+hBn7uf6liLkkS/+NHM/zJD08x0uLhjz80fMXSK/FiiddiKV6JJnklJlffOcUzbjAZOeiy8eHGOg64ZO/bUZU4yZJMHCNWFvDYcTLZBeVRHXZ7EK/3XrXydjh6EEU9q6urLC2FCIdPEFp4hvXNDSSlu8YuWfCJTrrEADWiDV06SSIXYaswQ9SrR+jcR0Owi0DgHrwtbQiCjmgkTWQuzvjzCVbnjrC+lEAsVvLe/g4XPbc2qCNizbbqvLckiuRmZxXxlu2T7akTY3MztgMHsH7sF+VBVX19V8Q6AYikI6p1Mro2yomNExREeR+hxdnCbY23qQK+E23zO0IuSer0iywe+R7mxZ/QUZylEUgLVjYbbiY98G+w9d6P2derJVGuIbQKfQcpikXu+583cSiT5r994Fvg6wbkTs/f//oEX319kccPNvOfHx+6uANo34aVXIGXo0lejaV4JZZkKplFQm7WGXTYuMVj57DLzkGXjSbLtul8ynTBeHxUFfBk8qR6KIPZ3IDLNYLbNYTLNaIkTqysr6+rm5ahxSVWI6vqgCqLZFRtk7qSA2M2Szq7xkZ+maKrhLPdX7VpaTRbSMVycuVdTp3MJ8hnlJkpZvlkHXW+9zkn66iv/ebmNt97TG7YSSizWsozvoeH1TGxBq93x17/7RTEAqe2TnE8clxNn4RTcq7apDPJjTv1w4z4RhjyDeG1Xpl1XDSlAiy9TnH2ORJTP8S5PoqBIjnJwJShl+y+O+m4+b009N52Tc0GvxHRKvSrxKsz32GTIu9tulMV80y+xK985QjPTq/xb+4N8psPdF/W23hJkpjN5HglmuLlWJJXoik1823T6zjssvFb7Q3c6pG9b7u+Uu3l8uusrY0Sj4+qkcHygCq93oHLNUhr6y/hdg3jcg1jNvuJx+MsLS1x6lSIpcWvshxeVmecGNHjLTnpk/bhK7kw54tks2ts5VZIm88gtHuVTct3cltnFxaHg3y2yJrie48/d1o+nGFLORFJJ1C3z0HXTX7V965peONYXzGfJ3vihCzg9YojQQAAIABJREFUin1SWFSm6ul0mLu7cb3nPap9Yurs3PHUSZloNqomT46vHWdifYJMUY5A1tvqGfGN8FTfU4z4Ruit7cV4mSe+7xjqIcfPIZ15HvHsT9AXU+gQWBA7OKZ/GH3wbkbueJCRtoZd8+w1Lg5N0HeQ7xz/KxyiyDvu+LeA3PH5yS+9xtGFLf7osUE+fMul+YupYomfRJP8aCPOs5sJFhUBrzMauMVt55P7vNzsdjDgsGJUxE8UiySTUyzGjhCNHSEeGyWbkytFQdDjsPfir38Yl2sEl3sYu62TfL7I8vIy09NLhJaeY2lxkURK2ehU2uSDoh+f6MJWQB5QlVshoZtjvcVD/XAnzYGbOKRsWpZKIpuhFKtzcV76l0Uic3E2l1PVc06CHnnjskP2vQ3nzKSRJIn8woIs3EoFnjtRmXUiH482RM2TPyc37PT3o7NdmQ1mURI5Ez1TNfdkLj4nr0Mw0FPbw+NdjzPiG2GkfoQGe8MVWcclE12U2+nPPCePmE3JLf+LQhPPFW7nNWEQR+/dvOvwfp4KejHotUz4XkOzXHaIXDbG3X9/B/cZavmPH3mBtUSOj/7tq8xEEnz+yQM8NNh4wV9LkiROprL8eDPBsxtxXomlKEgSdr2OO2sc3FPr4o4aBwFrxXooFhPEYqPEygIeH6VUkjcNzeZG3O4DuF0juFzDOJ39CIKZtbU1lpaWWFpaIrS4xNr6uup7OyUr9UqzTk3JipiOEsutEi1G0DVa8HV1Vm1aAsTXM7JlclauwNcWE5QKihXjMOLvqNgm/vPMOQFlTOzYOJnjo+ohDaUt+YT38qwT68iwnD4ZHt6x49HORzKfZHx9XLVOxtbGSBTkdzQ15hr1qLQR3wj93n6shmvslJv0pjwL5cxz8nTCzVn5blMdrwqDfDvZw8/EATqDPTx2oJl39Tfg2ONz8m8ENMvlKvDi6/8vSZ3AQ70fYi2R40N/8TNWYln+5hdv4q7uC2uxXssX+OryJl9Z3lBTKPvtFj7V4uOeWic3u+2YFOsgl4uwGnmFWFQW8GTyJHK3pQ6Ho5fGxg/gdh/C4z6ExdJEPB4nFAoxPb3E0uI/sBwOq9aJGQO+kosRqR1vyYExlyWZibCZO8uWW0QKNNHY1UPn/9/efce3XZ2LH/8ceclT3pYs7x1vOROSkIQkEEZDAoULlDLKr9wOSuHX0l5oC/2Vtq/2dtxy215aZoG2FG5IwijQQknCyI7t7OWR2Jb3XvKQdH5/fOWRQeIkluVx3q9XXomkr6VHGU+OH53nOelXEJmQhJe3N7buARoqOzm2u5PGE3tpONFJf49r0JaPjqjEYHKXmIe7LYMj9Gd82y4HB+k7dkzbMliqrb6Hx8QKgW9qCkHLlg2XTvzS0sZ91slwLK7T5oeSd2lTKcfbjiORCARpYWmsSl41/OFlQnDC5CtDDNq0aYRDhzvUlgIS6RtEQ9gcPgy7ipcbkzncZyYn1sDaRWYeLoglJmR8Ti1SPE+t0MfJ/315MXvsbbxxy3bueL6E8sYeXr53HnOSzj2tzikln7R183JtC+81dzAoJQsMgdxsDOfKiGBMftqHmHZ7N+3tO2lt/ZTWtk/p6dHmX3h5BWAIsWAwzMYQOhtDSAFCBNDQ0EB1dTVVVVVUn6waHlKllU6Ch1ffgYPQb2uhrb+Obq9OApLCiUlPx5SWiTE1A31QEI5BJ8013dRXdmgfXFZ20NmsNQxp872DhmveMckhhJsC0Z327bqUEntdnbZd0FU+6Tt0CNmnPY9XRMTwfm//ggL0ubnjOib2dEMzv4dKJ6NP3AnyCSI/Kl/ruowqIC8qj2Bf98Vy0ZwOqCsdKaNU7QBHP+h8kHFzqAqdxxudGTxXEUrHgCDWoOcGi5m1FjMZMZPw/Shjovahu1l3Vx1L161kjX8CR7ofZ1tFC8/eNYdlmZ89v2PA6eRP1maetzZzwjZAmLcXt5jCucMUQXqgHqdzkM7OvcMJvLNzL1La0en0hIbOJTzscsLCLiMoaBYDA3aqq6u1BH6yCqvVyqBr9R0o/YhxhhJ9Wumkzd6Il9FPK52kZ2JKy8QQrZUvulr6RiVvrXQytGUwMNQPY3II0ckhGJNDiEoIOWW+95Dhdvl9rm2De/fiaBp1wk529kjyzi/Ax+zeVvWhmd97G7Xtg0daj5xx4s5Q885YT5ufcFJCSzlUbDpztGxMLjJ5CVWh83i1MY51+9tp7OonWO/NdXkm1ljMzEsKn/Lnxiqq5OJ2m3b9ln6doLtnEZ+UNfPLmwvOmczfb+7g8bJaKmz9LDAE8p1kE9dGGvBhkJaWTeyreJPW1k9c51vqCAnJIzHhy4SFL8QQUkRv7wAnTpxg374TVJ34iKaWJmCo2zKIdGcMMU4D/v0Oem2NNPefoC3EjjMlFlN6Bslpi4hKSsbL24d+m53GE52U7emk4cR+Gio7hqcMDpVOCpbFa/Xv5BCCws789lw6HPSXlZ/ScTn6cGLfxEQCL7tM2zJYUDDuJ+ycbtAxyJHWI6fMPWno1Rph9F56ciJzuCvnLu28SzfN/B43XQ0js8ErNkOnVbt/1GjZ+oh5rD82wMYSK8cauvHxamRpZjQ3Wswsy4qeMiOXlUunEvo4eKf6XxjtkleP5/DdVVl8fnbcWa87aevn0WNW/tXaSVqAH3/NT2FZeCBtbdupOPYmjY3v4XB04+sbhdG4hvDwhYSFLsBu9+XEiRNs31ZJZdkzNLVqK10fvIlxhDDbmUKkMxBdbw/tfXW02Q/SaA7AmJtOfOZ85qVnERBiwOlw0lrXQ0NlJ4e2llFf2Ulb/ciukzBjAIm5EcQkG4hJCiHCfGbpBMDe0qKVTUpLtdLJ/v04Xe3yOoMB/3zX4cSFBfjn5eEV6t4T1VtsLcNlk9LGUg62HKTfoW2FNAWasERbhgdXZYRn4KObJFsHz6a/SzsjcyiBD52R6R+mjZZN+TakLKVDH8d7B+vZsM3Kjsr9SAmzE8P48ZpcrsszERaohl7NRKrkcolaW8u48s015LdFk5r2Wx67PvuM0kG/08lTVY385mQDXkLwrSQjd8X4UF/zHHV16+kfaMDLK4joqKuIMd5AUOBsqqutVFZWUlFWTn1DAxKJFzqMjlBineFE2P1w9LTR0m+lx6eTwNRozFmziM2YRXRyCl7ePvR1D1JX0UF9eQf1FR2nHNCgD/QZXnUP7T45vdsShs63PDacvG17947s+fb2PrVdvqDAbe3yQ4a2DpY0lWidl42lVHVpnazeOm+yw7O1+rerfDLptg6ezj4A1t0jCbxmt+uMTL02zCplqfbDmM+AE7Yca2JDSQ0fHG5kwO4kJTKQNRYzawrNJERM/IAuZeKpkosbvb3tNziEICRgNT+47sxkfqTHxn0HTnKst4/roww8nhKOo/Gv7N7xR+z2biIjl5Nu/D4BAQsoO36Sf757mPKKzdgddgSCaGcIhc4kouwBOLtbaeqroj2gAu9ZKcTn5JKbtZqQKK323d7QS115B4e3lVFX3jE860SnE0TGBzHrMtNwAjdE+Z818Q42NGg7ToZW3wcPIvu11a53dDT+hYWE3XqrtnUwJwed3r07JGx2GweaD1DaWEpJY8kpUwfD/MIojC7kpoybKIwqJDsiG733JN+x4XRC40FtG2HFZji5FQZ7QOggtggWPagl8Lh54KMfPulnw5sHeXtfHe29g0QE+nL7vATWWszkxxkm324bxWNUQr9E79R+ShySB1bfc8YHTuvqW3n4aA2BXjpeyk0gu/9dKot/y8BAE5GRy0mIf4DqaicfvLeP8srf4ZROAqUfGQ4jsY5QvHq6aLFV0+V7kvaseBJy88nLWYMhOgb7gIPGk12UFbdTX7Gf+vIO+npc2xADvTGlGMi6zIgp1UB0YsgZDTsAzv5++g4eOmX1ba+vB1wfXObkEHbbbVrppKAAH9PY99JfrKbeJkoaSyhpLDnjw8sUQworE1dSEFWAJdpCYoh7vxsYN20nR7YSVmyBXq1kRmQGFN7umky4CPxHSlMVTd1sLD3GxhLr8Ek/V2UbWWsxsyg9Eh/V9KOchUrol+BA2XYO+tpZNZBGhmlkIl6fw8kPyqy8XNvCAkMgT8SU01X2DY7aqjAY5mAy/pDS4i7efmMjgw47gdKPHEccpoFAujpP0OI4QHuWkfgr88nMuYowUyz2ASd15e0c+rQd67Fqmqq6hg8nDo0JILkgEmOqAVOqgdCYgDP3fEvJoNV66ur7yBFwdVz6mM0EzJ6tlU8KC/DLykLnxg8uARxOx/CRaUMlFGu39qGfn5cfuZG53JVz1/DJO6F699bix01v60jyrtgMba699UFGSFvuOiNzCRjMp3xZc3c/b++tZUNpLXur29EJWJgWyTeXp3N1rmr6Uc5P/Q25BK9vfRKAzy+4f/i+Kls/9x44wf5uG1+LC+WGvt/QePQtggIzSUr8Fbu293Lk+HZ8pTepjmjMAyHYOqtpFvvoKUwjY/4KEnLzkVJHQ0Unx3e3YT1WTENlJ06HRKcTRCeFULgiHmNqKMaUEPyDzky8cmCAvkOH6C0pxVZcTG9pyci2QX9//HNzibj7bm31nZ+Pd5T7z5fsHexlX/O+4dX3vqZ9dA9qowUi9BFYoi3clnUblmgLs8JnTZ65J+cz0AtV20Z2o9TtAyT4BkPyYpj/FS2JR2WeMZnQNuDg/cMNbCyxsuVYEw6nJNsUwveuncXqQtX0o1wYldAvQXH/AVIRzM9bAcDxnj5uLi3H5nTyVIqTaOtdtPTXEWv6KkcOJvHeu3vxljoK7YkEt3fS6l2BoyiN7PnXEpuRRXfrAJX7mvn77w9Qe7wDh92JEBCVEEzhinjMGWEYUw1nHJEGYG9rw1ZSiq2kmN7iEvoOHBiuffvExRF42WXauNjCQvzS093WcTlafU/9cPIuaSzhWNsxHNKBQJAamso1ydcM70CJC4qbGuUTAIfd1dCzSVuFV++AoSPW4ufDsu9ps8Fji846mdDhlGwrb2FDiZX3DtTRM+DAZNDz5cUprLWYyTSqph/l4qiEfpG27n2XCl+4RZcPwOFuGzeXarMyfh+9C13Fz8DPhI/Xo6x7rRqch8ixxxHWYaM3po6sb6zBmJ5FY2UnJ/a38PFru2mt1U7vCTMGkLvETFxmGKb0UPz8T/1jklIyUFGBraSE3uISbCUlIy3zPj7os2dpH1wWFeFvKXTLAcWnszvtHG87PpLAm0qo79Hq8f7e/uRF5nFv3r1Yoi3kR+UT4hvi9pjGjZTQfHykDl75MfS7GnqMeTD/3yF5KSReBr6Bn/EUksN1XWwstfJGqZWGzn6C/by5Pj+WNRYz85NV049y6VRCv0hvFf8RISSfX/gQ+7p6uXVvOb5C8IT/nxDWDRhCr6JkxyxqGk6Q4IgkqcOL7tBaMr62FkQsh7fX896zn9LXPYhOJzClh7Lo5lgS8yIIjT51+5l0Ouk/Xkbvzp307txB767dONq1I7+8DAb8LRYMa9cSYClEn5fn9p0noA2u2te0j5Im7QPM/U0jJ85HB0RjibZwd87dU2Pv99l01o1q6NkCXdqkSkITIGfNyEHHgeeeZ17bbuON0lo2llg52tCFt06wNDOax643s3yWavpRxpdK6BfB6XBQ4ihjltMHW0QWt5WWE+QFP/R+koD2zeh9v8Q7b9nxdnayoC+JPvsJ4u9YxeBgAh+9WkV7Qyl+Ad4k5kWQlBdJQnb4KXvApZT0Hz9O785dWhLftWt44qCP2UzQ0qUEzJmNf1ERvsnJbi9VSCmp7ak9pXwyNLhKJ3RkhGWwOnU1hdGFWKItmAJNU6d8MqSvE058MrIKbzqi3e8friXvlKVaAg9PPu9TdfYN8t7+ejaUWNle2TLc9POEq+knXDX9KG6iEvpFeH/nq1h9BJd5zeaWveVEeAu+J35CYE8pHc3/xseHBol1hJHQLolaGYZP8DK2bbTS1XqEyPggVtyTTWpRFN6jVmeDjY30fPwx3R9/Qu/OnThatUFR3rEmgpYsIWDePALmzcM3zvxZYY2bQecgR1uPDm8f3Nu4l0abNjs70CeQ/Mh8lhcs11rnI/MJ8p2CJ7m7TugZnosy3NDjD4mXa4d7pyyBmDwYw+EYA3YnHx1rYkOJlfcPNzBgd5IcGciDyzNYY4klMeLspRhFGU8qoV+Efx58CW9vyb+ibiJIJ3nU+SjB9pMcP3QNdY0+FA6Y8RGNRKy6ln0fddPbUU5McghX3JZBYm4EQgik04lt3z66N2+he8sW+g4eBLTmnaDFiwiYN5+A+fPwMZvdvtrt6O845czL0afuxAbGMts4G0u0BUu0hfTQ9Mk5uOp8pISmo64SyiZtNT7Q7WroscCih7RVePw88B7b2aJa0087G0usvL2vlrbeQcJdTT9rLGYKVNOPMsFUQr9AdvsgJaKazD49O/Qmfuj8IQZZz949K7F1RjC/Kxz/HD1tnVex861WjCkGVt6TjTkzDBwOuj/8kK73P6D7449xtLSATod/QQFRDz5I0NIl+GVmujUJSCmp6a45pXmnrL0MAC/hNXLqjmv2yaRvnT+XrnrXXnDXKryrTrs/PAXyb4GUZdq2Qv+wC3rayuYeNpZY2Vhq5WRLL37eOq7KMbLWEsvi9CjV9KN4jEroF+iNLc/Q5K0j3GHhft2zJDiq2b19GbreSPK7fPGfk82RHRIhelhyWwY5i804Oztofe45Wv/6V+y1degMBoIWLSJo6RICFy3CO+zCEsqFcDgdHGs7RnFjMcUNxZQ0ltBk06YzBvsEkx+dz6qkVViiLeRG5hLgM4XngQz0jBpstUk7MxNG1cGXaavwsMQLfuqW7n7e3lfHhhIrpdXtCAGXp0bwjSvTuTonhmD9FPvQV5mWVEK/QO+XvYafryQuKRGL/W8U71mOT280ab2Croh8Kj92kJATwdIvZOLX3Uj944/T8dZbyL4+AubPx/joowQtXeq2feBDs0+GkndpUyk9g9p2SFOgibnGuRRFF2GJsZAWmoZOTOHV5NB+8PJNWgKv3gnOQfDy07YQDq3CjfljqoOfzjbg4IPDDWwosfLRsSbsTsksUwiPXpvF6gIzRoNq+lEml/NmFSHE88D1QKOUMvcsjwvgSeBaoBe4W0pZPN6BTga9fT0c8Gkisy+Qa73/zIF9V+DXmUhSHzSIAuxN3qy4O4PUnCBa//g7rC++BDodhtWrCbvjDvSZGeMeU1tf23D5pLixmEMth7A7tdknaaFpXJ9yPZZoC0XRRZiC3D+Lxa2khNYKKP/QtRtl1H5wUwFc9jUtgScsAJ+LO9/T4ZRsrxhq+qmnu9+OMUTPvYuTWWsxk2WcQvvnlRlnLMvEPwG/A176jMevAdJdP+YDT7l+nnae+8ev6fDSEe4TS+WxdHyaZxE/CDW2XPyD/fncA/n4lmyi4tu/wNHcjGHtWqIeenBcG3tqu2vZ3bB7eAVe0VEBgI/Oh9zIXO7MvpPZMbMpiCrA4Gc4z7NNAT3N2jbCclcdvMM1uteQADk3uLYTLoXAiEt6mcN1nWwssfJGaS31nX0E+3lzbZ7R1fQTgZdq+lGmgPMmdCnlR0KIpHNccgPwktQGq28XQoQKIUxSyrpxinHS2F37d4L8nGR4+dJXVUjigOBEey7GlDBW3mik88cP07xlC/4FBcQ89RT+eWd8Q3PBrN1WdtXvYlf9LvY07BkeXhXsE0xhdCGfS/0cRdFF5ETm4Oc1tt0Zk9qgTZuLMpTA6/dp9+sN2gEPix7UVuHhKWfMRblQdR0jTT9H6oeafqL4/vWzWDErRjX9KFPOeBRyzUD1qNs1rvvOSOhCiPuA+wASEhLG4aUnTm1rLUf0XWTZgrE1FJLe5UVNby4Zc03Mjqig/ravIh0OYh59lLAv3I7wurhkUN1Vze763exu2M2u+l3U9Wi/jaF+ocyOmc0Xs7/InJg5pIelT+369xCnE+r3asm7fJN2ar3roGPi58OV34eUKyG2EMZhu2RX3yDvHqhnY4mVbRVa009RQihP3JDDdfmxqulHmdLGI6GfbZl01mOQpJRPA0+DdmLROLz2hHnq3R/Rq9MRak8ls9Of6t5ZWFYkknJ0HY2/fBn/ObOJ/elP8b3A/6i6BrrYWbeTrbVb2Vq7lZruGgDC9eHMjpnN3Tl3M9c4l9TQ1OmRwME1H3zTSFu9TWuiIjoH5n1ZK6MkXv6Zc1Eu1KDDyZajTWwotfLBoQb67U6SIgL45vJ01hSaSYpUTT/K9DAeCb0GiB91Ow6oHYfnnVQqOncR5utkvjWf6p40sheYML/1E9p37iT8rjuJfvjhMe1ccTgdHGg5wNbarWyr3ca+pn04pIMA7wDmmeZxZ86dzDPOI8WQMn2aUmxtUPnRyCp8aD54sAkyVkHqMq2tPjhm3F5SSklJtdb089bekaafW+fGs8ZipjA+dPr8/iqKy3gk9DeB+4UQf0P7MLRjutXP9x4v5oi+n4LeMNrbkknJNZHw2iP01dUS+/OfYbjhhnN+fb+jn+212/mg6gM2V2+mvb8dgSAnIocv5X6JheaF5EflT70BVp/F3q9tIazYpCXwulKQTvANgqTFsOCr2io8MuOS6+CnO1vTz8rsGNZazFyRoZp+lOltLNsWXwGWApFCiBrgccAHQEr5B+AdtC2LZWjbFu9xV7Ce8vw/f8JAkCDaNovYtETS//44jqYGEl54noCiorN+zaBzkI9rPubdynf5qOYjeu29BPsEsyR+CUvilrDAtGDqnMBzPlJCw8GRMsrJrTDYC8IL4ubCFd/RVuHm2eCGQytaewZ4e18tG0qslFRpTT+XpURw/7I0VuUaVdOPMmOMZZfLbed5XAJfH7eIJqE6r3JiBp3kdd3OrB0/wdncQPyzzxJQZDnj2urOal49+ipvVbxFa18r4fpwrkm+hhWJK5hvnD91TuE5nw7rSEdmxWbo0bpPicwAyxe1BJ64EPTu2bfdN6g1/WwssbL5qNb0k2UM5pFrslhdGIvJcHH70BVlKlOdoufx/idvc0xvZ25PFEU7/xuaaol/5pkzknmzrZn/Kf0fNhzfAMCS+CWsSVvDQvPC6VFKGT1etmITNB/T7g+MHmmpT1l6xjmZ48nplOyobGVDSQ3v7q+na1TTz5pCM7NMqulHmdlUQj+PjaV/wBEsSGxPR9dYQvyzzxBgGUnmNruNFw++yPMHnmfQMchNGTdxX/59RAe4/5QgtxoeL7tZS+BD42V9ArQdKEV3aavw6Oxxr4Ofrqyxmw0lNWwsqcXabiPQ14tVuSZuLDKzIEU1/SjKEJXQz6POp4r4Acma10uJf+7UZL7VupXHtj5GQ28DyxOW89Dsh0gMufDBT5OClNBSrrXVl3/oGi/bdUnjZS9Fc3c/b+3V6uL7ajrQCVicHsV3VmVyVbYRf1/V9KMop1MJ/Rz+svE5jusll3cZSfjJt4aTeZ+9j98U/4a/HP4LKYYUXrj6BeYY53g42otga9P2gZd/qO1G6ajS7g9LhvybL3q87MXqG3Tw/iFtGNaWY004nJKc2BC+f90sVhfGEh2shmEpyrmohH4OH9e8BsFQ0DOLkGuuAaCqs4oHPnyA8o5yvjDrCzxY9CB67ymSaIbKKEOr8NpibTuhX8hIW32qq61+gjidkp0nWllfPFIXNxn0fHlxCjcWmcmICZ6wWBRlqlMJ/RxqfGtJ7Rfc96XHANjftJ/7P7wfKSV/WPEHFpoXejjC8xg9nbB8k9bcM1RGMc9xbSe80rWdcGL/Kqi6uKKMP5XQP8NvX/gRJ/1gcacZ76goPqr5iG9v+Tbh+nD+sOIPJBmSPB3i2dnatcQ9tApvP6ndH5oAeZ/XEnjyFeA/8XvgVV1cUdxLJfTPsK9nM7ogyZVxN7D++Hp+tO1HZIZn8vvlvyfSP9LT4Y1w2ME6qoxi3ePqygzWEvfl39CS+DhMJ7wYqi6uKBNHJfSz6LPZOKlvIKPPh/bZ3jy59XEWxi7k10t/PTmOaDu9jNLf6dqNUgSLv60l8Lg5bunKHAtVF1cUz1AJ/Sx+/cL3qQvUkWFL4MniJ7ku5TqeWPiE5xqE+jpOLaO0ndDuNyRA7o2jyigTsxvls6i6uKJ4lkroZ1Eud+EtJa1OOwtMCyY+mTvs2g6UoQQ+1NTjG6Ql7svu92gZZTRVF1eUyUMl9NN0tLdTrm8hy+ZHb6Ivf1z664lJ5h1WKHsfyj6Aio9cZ2UKMBfB4v/rKqPM9VgZZbShOSobiq1sVnVxRZk0VEI/zS9e/g4tITrye5L47orfEuzrpnqvfQCqt8NxVxJvPKTdHxKnnZWZeqU2Izwg3D2vf4GG6uIbiq28s79O1cUVZRJSCf00NV770Tslc7NWYg4a50FTHTUjCbxiMwx0a0etJV4GK5+A9JUQleXxMspoqi6uKFOHSuijVJ8o47i+kyxbALevuO/Sn1BK7XCHI+/A0XehYb92vyEe8m7WEnjyFeA3uVa356qLr8yOIcBX/bVRlMlI/csc5ck3/x+dBh3RjlS8LvZAYns/VH4MR11JvKtW21IYv8C1Cr8KojIn1Soczl4XzzapuriiTCUqoY/S4H2UYIeTr133gwv7Qnu/Vko5sA6Of6C11/sEaHXwrB9A+tUQGOGeoC/B2erixhA9/2dxMjda4sg0Tq7vHBRFOTeV0F2K927jqH8P2b0hpKbknP8LnE6o2gb7X4ODG6GvHQIitX3hWddpH2j6TM5VraqLK8r0pBK6y0tbfo3NoMPkzDz3hZ21sOtZ2PcadFRrK/Gs6yH/Fm1e+CTYVng2LaPq4ntVXVxRpiX1r9ilwaeCCLuTh2796dkvaK2AT5+E0r+C06GVU5Y/BpnXgl/QxAY7Rv12B5uONLJuj5XNRxuxq7q4okxrKqEDH3y4kaP+/Vh6woiOij31wb4O2PRT2PkM6Ly1A5AXfhPCJufJRFKZ3AK2AAANg0lEQVRKSqvbeb24hrf21tFhGyQ62I97FyWztshMllGdu6ko05VK6MAbB15g0CCIJ+/UByo/gvX/Dl11MOceWPJdCDZ6JsjzsLbb2Fhi5fXiGiqaetD76Lg6x8iNRXEsSotUdXFFmQFUQgfqfU9iGpR85+5faHfYB2DTj+HT/4aINPjyv7RDICaZnn477x6o5/U9NWyvbEFKmJ8czleuSOWaPCPB+slZz1cUxT1mfEL/2+tPc0xvZ153FAGBQdDdBK/cqs0Yn30PXP0T8A30dJjDHE7JtvIWbTTtgXpsgw6SIgJ4aEUGay1m4sMnwXhfRVE8YsYn9I9qN+IMEaT7ztE++Hz5Ruiqh1teguwbPB3esLLGLl4vtrKxxEpdRx8hem/WFpm5qchMUUIYYpI1KimKMvFmfEKv860hsR8evvZWeHaldtrPXW9B/FxPh0ZrzwBv7a1lfXENe2s68NIJlmZE8f3rslk+Kxq9jxpNqyjKiBmd0J968aeU6SWXdxkRL6/Rztm8Yz1EpnsspgG7kw+PNLK+uIZNRxsZdGhbDX9wfTarC2KJCvbzWGyKokxuMzqhl3ZughCw9IRqDUL3vAuGuAmPQ0rJ3poO1hfX8ObeWtp7B4kK9uPuy5O4sSiOWSa11VBRlPOb0Qm9Rl9HWp+Or9g/hTsnPpnXttvYUGJlfXEN5U09+HnruCrHyE1FZhalReLtpZvQeBRFmdpmbEL/zz9+lyq9YHGnGW7+BZgKJuR1e/rtvHegnvUlNWwt17YazksK574rUrgmz0SI2mqoKMpFmrEJ/fjgTnR+kit8MyHjare+ltMp2VbRwuvFNbx3oJ7eAQcJ4QF8c3k6N1riSIhQWw0VRbl0MzKh9/b0cELfSEafD7d+6TNmt4yDky09rNtTw+t7aqjt6CPYz5sbCmO5qSiO2Ylqq6GiKONrRib0nz/3LeoNOrJsieA7vqvj3gE77+6v57Xd1eyobB2eavjItbNYmR2jthoqiuI2MzKhV3sdwEdKVqffNC7PJ6WkuKqN13bV8Pf9dXT320mKCODhqzO5sciMyeA/Lq+jKIpyLjMuoTc11lGubyPLpmflqjsv6bkaOvtYX2zlf/dUU9HUQ4CvF9flmbh5Tjxzk1RJRVGUiTWmhC6EWAU8CXgBz0opf3ba43cDvwCsrrt+J6V8dhzjHDe/euURWkN1WHqSL+rrBx1OPjjUwGu7q9lyrAmna5fKV5akcl2eiUC/Gfd/pKIok8R5s48Qwgv4PbASqAF2CSHelFIeOu3SV6WU97shxnFV732UAKeTOy/7+gV9XV2HjVd2VPG3XdU0dvVjDNHz1aWpfH52PMmRk2d4l6IoM9dYlpPzgDIpZQWAEOJvwA3A6Ql90qssO8gx/04ybIEUzV123uudTsmn5c38eftJPjjciFNKlmVGc8eCBJZkRKsZ44qiTCpjSehmoHrU7Rpg/lmuu0kIcQVwDHhISll9+gVCiPuA+wASEhIuPNpL9Lu//5iuUB0x9tRzXtc36GDdnhqe+6SSyuYewgN9ue+KFG6fl6DG0yqKMmmNJaGfbRkqT7v9FvCKlLJfCPEV4EXgyjO+SMqngacB5syZc/pzuF2jTxkGh5MHPvfYWR/v6hvkz9ureO6TSpq7+ymID+XJWwtZlWvEz1ttN1QUZXIbS0KvAeJH3Y4DakdfIKVsGXXzGeDnlx7a+Nq18wOO+NvIsYWQkDzrlMe6++38cUs5f9p6gq4+O4vTI/naUgsLUsLVThVFUaaMsST0XUC6ECIZbRfLrcDtoy8QQpiklHWum6uBw+Ma5Tj48/an6DMITPaM4fucTsn6Eis/f+8ITV39XJNr5GtL08iLM3gwUkVRlItz3oQupbQLIe4H/oG2bfF5KeVBIcSPgN1SyjeBB4QQqwE70Arc7caYL0qjTyWRdieP3PkrQNu18vW/FFNc1U5hfCh//OJsihLCPByloijKxRvTpmkp5TvAO6fd99ioXz8CPDK+oY2fd998iSP+AxT1RBASGsmek238+8t76Bt08KubC1hrMaNTO1YURZniZkQXzN9PrsMeIkiQs1i3p4ZH1+/HFKrnlS/PJz0m2NPhKYqijIsZkdAbfKowDTpJy3uIb//vXi5PjeD3txcRFujr6dAURVHGzbQ/Eucvf/svjurtJPbF8Ng/KliUFsmf7pmnkrmiKNPOtE/onzT9EykEwZ2ZZBmDeeqOIny9p/3bVhRlBpr2JZc6PytJ/bDL+2Zev2cuweqIN0VRpqlpvVT93dPfo9xPYh4w8cLd84gO1ns6JEVRFLeZ1gl9/8AOABKZS1p0kIejURRFca9pndCr/epJ7xP8x9d/7OlQFEVR3G7aJvQf//cDVPsKjAPxah6LoigzwrRN6Ce99uElJQujr/Z0KIqiKBNiWib0nq4uKv2ayOzz4Qv/9oCnw1EURZkQ0zKh//S5h2jw0RE9kOjpUBRFUSbMtEzodT5H8HVKPjfrC54ORVEUZcJMu4ReU11Jmb6NzD4/rlp5s6fDURRFmTDTLqE/+fpjtHnriB5I9nQoiqIoE2raJfRG3+MEOJ3csVB9GKooyswyrRL6oYN7OKbvItMWxJw5V3g6HEVRlAk1rRL6Mx/8J91eOqIHUz0diqIoyoSbVgm9ybccg8PJ1z732PkvVhRFmWamTULf8vE7HNH3kW4zkJKS5elwFEVRJty0SeivlzxLv05gtGd4OhRFURSPmDYJvdHnBJF2J9+985eeDkVRFMUjpkVCX7fhWY76D5DaF0FoWKSnw1EURfGIaZHQN9e8gV0I4u05ng5FURTFY6ZFQq/zrcI8IPn2l/7T06EoiqJ4zJRP6M+8+DOO+zlI6I8hMDjY0+EoiqJ4zJRP6MWdm5FCkO5l8XQoiqIoHjXlE3qtn5Xkfnj4q2p3i6IoM9uUTui/+MN3qPADc3+sp0NRFEXxuCmd0I/b9wCQH7jQw5EoiqJ43pRN6D1dXVT5NZDRp+OrX1KzWxRFUaZsQv/l89/F6iswDsR7OhRFUZRJYcom9Gqvg3hLyRLT9Z4ORVEUZVKYkgm9va2FSv9mMvp8uOXzX/F0OIqiKJPClEzoP3/xYRq9dUQPJHk6FEVRlEljTAldCLFKCHFUCFEmhPiPszzuJ4R41fX4DiFE0ngHOlqDz1H8nJIbc+5058soiqJMKedN6EIIL+D3wDVANnCbECL7tMvuBdqklGnAfwE/H+9Ah9RUV1CmbyezT8+y5Wvd9TKKoihTzlhW6POAMillhZRyAPgbcMNp19wAvOj69TpguRBCjF+YI37z+mO0eeuIGkh2x9MriqJMWd5juMYMVI+6XQPM/6xrpJR2IUQHEAE0j75ICHEfcJ/rZrcQ4ujFBA1EHuBA85MPuuX/jMkqktN+P2cA9Z5nBvWeL0ziZz0wloR+tqwpL+IapJRPA0+P4TXPHZAQu6WUcy71eaYS9Z5nBvWeZwZ3veexlFxqgNHdO3FA7WddI4TwBgxA63gEqCiKoozNWBL6LiBdCJEshPAFbgXePO2aN4G7XL/+PPChlPKMFbqiKIriPuctubhq4vcD/wC8gOellAeFED8Cdksp3wSeA14WQpShrcxvdWfQjEPZZgpS73lmUO95ZnDLexZqIa0oijI9TMlOUUVRFOVMKqEriqJME1MuoZ9vDMF0I4SIF0JsEkIcFkIcFEJ809MxTQQhhJcQokQI8banY5koQohQIcQ6IcQR15/3ZZ6OyZ2EEA+5/k4fEEK8IoTQezomdxBCPC+EaBRCHBh1X7gQ4n0hxHHXz2Hj8VpTKqGPcQzBdGMHviWlnAUsAL4+A94zwDeBw54OYoI9CbwnpcwCCpjG718IYQYeAOZIKXPRNly4ezOFp/wJWHXaff8B/EtKmQ78y3X7kk2phM7YxhBMK1LKOillsevXXWj/yM2ejcq9hBBxwHXAs56OZaIIIUKAK9B2jCGlHJBStns2KrfzBvxdvSsBnNnfMi1IKT/izL6c0eNSXgTWjMdrTbWEfrYxBNM6uY3mmmJpAXZ4NhK3+w3wHcDp6UAmUArQBLzgKjU9K4QI9HRQ7iKltAK/BKqAOqBDSvlPz0Y1oWKklHWgLdqA6PF40qmW0Mc0YmA6EkIEAa8DD0opOz0dj7sIIa4HGqWUezwdywTzBoqAp6SUFqCHcfo2fDJy1YxvAJKBWCBQCHGHZ6Oa+qZaQh/LGIJpRwjhg5bM/yKlXO/peNxsIbBaCHECraR2pRDiz54NaULUADVSyqHvvtahJfjpagVQKaVsklIOAuuByz0c00RqEEKYAFw/N47Hk061hD6WMQTTimsM8XPAYSnlrz0dj7tJKR+RUsZJKZPQ/nw/lFJO+5WblLIeqBZCZLruWg4c8mBI7lYFLBBCBLj+ji9nGn8IfBajx6XcBbwxHk86lmmLk8ZnjSHwcFjuthD4IrBfCFHquu9RKeU7HoxJcY9vAH9xLVYqgHs8HI/bSCl3CCHWAcVoO7lKmKYjAIQQrwBLgUghRA3wOPAz4DUhxL1o/7ndPC6vpVr/FUVRpoepVnJRFEVRPoNK6IqiKNOESuiKoijThEroiqIo04RK6IqiKNOESuiKoijThEroiqIo08T/BwMfqTffnvUWAAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if solve_markov_market:\n", + " # Solve the \"macroeconomic\" model by searching for a \"fixed point dynamic rule\"\n", + " t_start = process_time()\n", + " print(\"Now solving a two-state Markov economy. This should take a few minutes...\")\n", + " MrkvEconomyExample.solve()\n", + " t_end = process_time()\n", + " print(\n", + " 'Solving the \"macroeconomic\" aggregate shocks model took '\n", + " + str(t_end - t_start)\n", + " + \" seconds.\"\n", + " )\n", + "\n", + " print(\n", + " \"Consumption function at each aggregate market \\\n", + " resources-to-labor ratio gridpoint (for each macro state):\"\n", + " )\n", + " m_grid = np.linspace(0, 10, 200)\n", + " AggShockMrkvExample.unpackcFunc()\n", + " for i in range(2):\n", + " for M in AggShockMrkvExample.Mgrid.tolist():\n", + " mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M)\n", + " c_at_this_M = AggShockMrkvExample.cFunc[0][i](\n", + " m_grid + mMin, M * np.ones_like(m_grid)\n", + " )\n", + " plt.plot(m_grid + mMin, c_at_this_M)\n", + " plt.ylim(0.0, None)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now solving a Krusell-Smith-style economy. This should take about a minute...\n", + "intercept=[-0.6947798030939067, -0.6496080139025368], slope=[1.2149258628023265, 1.1997576240565722], r-sq=[0.9956715905392765, 0.9984824428718454]\n", + "intercept=[-0.3135251476096542, -0.3116229188271141], slope=[1.0431538367667323, 1.045596516196599], r-sq=[0.999962275011029, 0.9998598754299896]\n", + "intercept=[-0.35426968141472726, -0.34868612541912086], slope=[1.0690330861718151, 1.0684800597781756], r-sq=[0.9999980358752275, 0.9999953386182767]\n", + "intercept=[-0.37865592267297, -0.37059636120492523], slope=[1.0786086418446077, 1.0771335410485183], r-sq=[0.9999974645062959, 0.9999922753180536]\n", + "intercept=[-0.37949767811432283, -0.3713660319920014], slope=[1.0789208379791293, 1.0774420381883525], r-sq=[0.9999974136333588, 0.999991775874947]\n", + "intercept=[-0.37949650721764533, -0.3713757146425346], slope=[1.0789185711548974, 1.077446724524536], r-sq=[0.9999974136627449, 0.9999917639921949]\n", + "Solving the Krusell-Smith model took 105.49309900000003 seconds.\n" + ] + } + ], + "source": [ + "if solve_krusell_smith:\n", + " # Make a Krusell-Smith agent type\n", + " # NOTE: These agents aren't exactly like KS, as they don't have serially correlated unemployment\n", + " KSexampleType = deepcopy(AggShockMrkvExample)\n", + " KSexampleType.IncomeDstn[0] = [\n", + " [np.array([0.96, 0.04]), np.array([1.0, 1.0]), np.array([1.0 / 0.96, 0.0])],\n", + " [np.array([0.90, 0.10]), np.array([1.0, 1.0]), np.array([1.0 / 0.90, 0.0])],\n", + " ]\n", + "\n", + " # Make a KS economy\n", + " KSeconomy = deepcopy(MrkvEconomyExample)\n", + " KSeconomy.agents = [KSexampleType]\n", + " KSeconomy.AggShkDstn = [\n", + " [np.array([1.0]), np.array([1.0]), np.array([1.05])],\n", + " [np.array([1.0]), np.array([1.0]), np.array([0.95])],\n", + " ]\n", + " KSeconomy.PermGroFacAgg = [1.0, 1.0]\n", + " KSexampleType.getEconomyData(KSeconomy)\n", + " KSeconomy.makeAggShkHist()\n", + "\n", + " # Solve the K-S model\n", + " t_start = process_time()\n", + " print(\n", + " \"Now solving a Krusell-Smith-style economy. This should take about a minute...\"\n", + " )\n", + " KSeconomy.solve()\n", + " t_end = process_time()\n", + " print(\"Solving the Krusell-Smith model took \" + str(t_end - t_start) + \" seconds.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "if solve_poly_state:\n", + " StateCount = 15 # Number of Markov states\n", + " GrowthAvg = 1.01 # Average permanent income growth factor\n", + " GrowthWidth = 0.02 # PermGroFacAgg deviates from PermGroFacAgg in this range\n", + " Persistence = 0.90 # Probability of staying in the same Markov state\n", + " PermGroFacAgg = np.linspace(\n", + " GrowthAvg - GrowthWidth, GrowthAvg + GrowthWidth, num=StateCount\n", + " )\n", + "\n", + " # Make the Markov array with chosen states and persistence\n", + " PolyMrkvArray = np.zeros((StateCount, StateCount))\n", + " for i in range(StateCount):\n", + " for j in range(StateCount):\n", + " if i == j:\n", + " PolyMrkvArray[i, j] = Persistence\n", + " elif (i == (j - 1)) or (i == (j + 1)):\n", + " PolyMrkvArray[i, j] = 0.5 * (1.0 - Persistence)\n", + " PolyMrkvArray[0, 0] += 0.5 * (1.0 - Persistence)\n", + " PolyMrkvArray[StateCount - 1, StateCount - 1] += 0.5 * (1.0 - Persistence)\n", + "\n", + " # Make a consumer type to inhabit the economy\n", + " PolyStateExample = AggShockMarkovConsumerType()\n", + " PolyStateExample.MrkvArray = PolyMrkvArray\n", + " PolyStateExample.PermGroFacAgg = PermGroFacAgg\n", + " PolyStateExample.IncomeDstn[0] = StateCount * [PolyStateExample.IncomeDstn[0]]\n", + " PolyStateExample.cycles = 0\n", + "\n", + " # Make a Cobb-Douglas economy for the agents\n", + " # Use verbose=False to remove printing of intercept\n", + " PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample], verbose=False)\n", + " PolyStateEconomy.MrkvArray = PolyMrkvArray\n", + " PolyStateEconomy.PermGroFacAgg = PermGroFacAgg\n", + " PolyStateEconomy.PermShkAggStd = StateCount * [0.006]\n", + " PolyStateEconomy.TranShkAggStd = StateCount * [0.003]\n", + " PolyStateEconomy.slope_prev = StateCount * [1.0]\n", + " PolyStateEconomy.intercept_prev = StateCount * [0.0]\n", + " PolyStateEconomy.update()\n", + " PolyStateEconomy.makeAggShkDstn()\n", + " PolyStateEconomy.makeAggShkHist() # Simulate a history of aggregate shocks\n", + " PolyStateExample.getEconomyData(\n", + " PolyStateEconomy\n", + " ) # Have the consumers inherit relevant objects from the economy\n", + "\n", + " # Solve the many state model\n", + " t_start = process_time()\n", + " print(\n", + " \"Now solving an economy with \"\n", + " + str(StateCount)\n", + " + \" Markov states. This might take a while...\"\n", + " )\n", + " PolyStateEconomy.solve()\n", + " t_end = process_time()\n", + " print(\n", + " \"Solving a model with \"\n", + " + str(StateCount)\n", + " + \" states took \"\n", + " + str(t_end - t_start)\n", + " + \" seconds.\"\n", + " )" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/HARK/ConsumptionSaving/example_ConsAggShockModel.py b/examples/ConsumptionSaving/example_ConsAggShockModel.py similarity index 91% rename from HARK/ConsumptionSaving/example_ConsAggShockModel.py rename to examples/ConsumptionSaving/example_ConsAggShockModel.py index 11f978408..bf528a173 100644 --- a/HARK/ConsumptionSaving/example_ConsAggShockModel.py +++ b/examples/ConsumptionSaving/example_ConsAggShockModel.py @@ -9,31 +9,25 @@ AggShockMarkovConsumerType, CobbDouglasMarkovEconomy, ) - - +from copy import deepcopy def mystr(number): return "{:.4f}".format(number) -solve_agg_shocks_micro = False # Solve an AggShockConsumerType's microeconomic problem -solve_agg_shocks_market = ( - True -) # Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy - -solve_markov_micro = ( - False -) # Solve an AggShockMarkovConsumerType's microeconomic problem -solve_markov_market = ( - True -) # Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy -solve_krusell_smith = ( - True -) # Solve a simple Krusell-Smith-style two state, two shock model -solve_poly_state = ( - False -) # Solve a CobbDouglasEconomy with many states, potentially utilizing the "state jumper" +# Solve an AggShockConsumerType's microeconomic problem +solve_agg_shocks_micro = False +# Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy +solve_agg_shocks_market = True +# Solve an AggShockMarkovConsumerType's microeconomic problem +solve_markov_micro = False +# Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy +solve_markov_market = True +# Solve a simple Krusell-Smith-style two state, two shock model +solve_krusell_smith = True +# Solve a CobbDouglasEconomy with many states, potentially utilizing the "state jumper" +solve_poly_state = True -# EXAMPLE IMPLEMENTATIONS OF AggShockConsumerType ### +# ### Example impelementation of AggShockConsumerType if solve_agg_shocks_micro or solve_agg_shocks_market: # Make an aggregate shocks consumer type @@ -98,7 +92,7 @@ def mystr(number): plt.ylim(0.0, None) plt.show() -# EXAMPLE IMPLEMENTATIONS OF AggShockMarkovConsumerType # +# ### Example Implementations of AggShockMarkovConsumerType if solve_markov_micro or solve_markov_market or solve_krusell_smith: # Make a Markov aggregate shocks consumer type @@ -226,7 +220,8 @@ def mystr(number): PolyStateExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample]) + # Use verbose=False to remove printing of intercept + PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample], verbose=False) PolyStateEconomy.MrkvArray = PolyMrkvArray PolyStateEconomy.PermGroFacAgg = PermGroFacAgg PolyStateEconomy.PermShkAggStd = StateCount * [0.006] diff --git a/examples/ConsumptionSaving/example_ConsGenIncProcessModel.ipynb b/examples/ConsumptionSaving/example_ConsGenIncProcessModel.ipynb new file mode 100644 index 000000000..238f7e01f --- /dev/null +++ b/examples/ConsumptionSaving/example_ConsGenIncProcessModel.ipynb @@ -0,0 +1,458 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import HARK.ConsumptionSaving.ConsumerParameters as Params\n", + "from HARK.utilities import plotFuncs\n", + "from time import process_time\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from HARK.ConsumptionSaving.ConsGenIncProcessModel import (\n", + " IndShockExplicitPermIncConsumerType,\n", + " IndShockConsumerType,\n", + " PersistentShockConsumerType,\n", + ")\n", + "def mystr(number):\n", + " return \"{:.4f}\".format(number)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "do_simulation = True" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The infinite horizon examples presented here use a grid of persistent income levels (pLvlGrid)\n", + "based on percentiles of the long run distribution of pLvl for the given parameters. These percentiles\n", + "are specified in the attribute pLvlPctiles. Here, the lowest percentile is 0.1 and the highest\n", + "percentile is 99.9.\n", + "\n" + ] + } + ], + "source": [ + "# Display information about the pLvlGrid used in these examples\n", + "print(\n", + " \"The infinite horizon examples presented here use a grid of persistent income levels (pLvlGrid)\"\n", + ")\n", + "print(\n", + " \"based on percentiles of the long run distribution of pLvl for the given parameters. These percentiles\"\n", + ")\n", + "print(\n", + " \"are specified in the attribute pLvlPctiles. Here, the lowest percentile is \"\n", + " + str(Params.init_explicit_perm_inc[\"pLvlPctiles\"][0] * 100)\n", + " + \" and the highest\"\n", + ")\n", + "print(\n", + " \"percentile is \"\n", + " + str(Params.init_explicit_perm_inc[\"pLvlPctiles\"][-1] * 100)\n", + " + \".\\n\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving an explicit permanent income consumer took 24.5819 seconds.\n" + ] + } + ], + "source": [ + "# Make and solve an example \"explicit permanent income\" consumer with idiosyncratic shocks\n", + "ExplicitExample = IndShockExplicitPermIncConsumerType()\n", + "t_start = process_time()\n", + "ExplicitExample.solve()\n", + "t_end = process_time()\n", + "print(\n", + " \"Solving an explicit permanent income consumer took \"\n", + " + mystr(t_end - t_start)\n", + " + \" seconds.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function by pLvl for explicit permanent income consumer:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption function at various permanent income levels\n", + "print(\"Consumption function by pLvl for explicit permanent income consumer:\")\n", + "pLvlGrid = ExplicitExample.pLvlGrid[0]\n", + "mLvlGrid = np.linspace(0, 20, 300)\n", + "for p in pLvlGrid:\n", + " M_temp = mLvlGrid + ExplicitExample.solution[0].mLvlMin(p)\n", + " C = ExplicitExample.solution[0].cFunc(M_temp, p * np.ones_like(M_temp))\n", + " plt.plot(M_temp, C)\n", + "plt.xlim(0.0, 20.0)\n", + "plt.ylim(0.0, None)\n", + "plt.xlabel(\"Market resource level mLvl\")\n", + "plt.ylabel(\"Consumption level cLvl\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving the equivalent problem with permanent income normalized out took 0.0028 seconds.\n" + ] + } + ], + "source": [ + "# Now solve the *exact same* problem, but with the permanent income normalization\n", + "NormalizedExample = IndShockConsumerType(**Params.init_explicit_perm_inc)\n", + "t_start = process_time()\n", + "NormalizedExample.solve()\n", + "t_end = process_time()\n", + "print(\n", + " \"Solving the equivalent problem with permanent income normalized out took \"\n", + " + mystr(t_end - t_start)\n", + " + \" seconds.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized consumption function by pLvl for explicit permanent income consumer:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function for normalized problem (without explicit permanent income):\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Show that the normalized consumption function for the \"explicit permanent income\" consumer\n", + "# is almost identical for every permanent income level (and the same as the normalized problem's\n", + "# cFunc), but is less accurate due to extrapolation outside the bounds of pLvlGrid.\n", + "print(\"Normalized consumption function by pLvl for explicit permanent income consumer:\")\n", + "pLvlGrid = ExplicitExample.pLvlGrid[0]\n", + "mNrmGrid = np.linspace(0, 20, 300)\n", + "for p in pLvlGrid:\n", + " M_temp = mNrmGrid * p + ExplicitExample.solution[0].mLvlMin(p)\n", + " C = ExplicitExample.solution[0].cFunc(M_temp, p * np.ones_like(M_temp))\n", + " plt.plot(M_temp / p, C / p)\n", + "plt.xlim(0.0, 20.0)\n", + "plt.ylim(0.0, None)\n", + "plt.xlabel(\"Normalized market resources mNrm\")\n", + "plt.ylabel(\"Normalized consumption cNrm\")\n", + "plt.show()\n", + "print(\n", + " \"Consumption function for normalized problem (without explicit permanent income):\"\n", + ")\n", + "mNrmMin = NormalizedExample.solution[0].mNrmMin\n", + "plotFuncs(NormalizedExample.solution[0].cFunc, mNrmMin, mNrmMin + 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The \"explicit permanent income\" solution deviates from the solution to the normalized problem because\n", + "of errors from extrapolating beyond the bounds of the pLvlGrid. The error is largest for pLvl values\n", + "near the upper and lower bounds, and propagates toward the center of the distribution.\n", + "\n" + ] + } + ], + "source": [ + "print(\n", + " 'The \"explicit permanent income\" solution deviates from the solution to the normalized problem because'\n", + ")\n", + "print(\n", + " \"of errors from extrapolating beyond the bounds of the pLvlGrid. The error is largest for pLvl values\"\n", + ")\n", + "print(\n", + " \"near the upper and lower bounds, and propagates toward the center of the distribution.\\n\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the value function at various permanent income levels\n", + "if ExplicitExample.vFuncBool:\n", + " pGrid = np.linspace(0.1, 3.0, 24)\n", + " M = np.linspace(0.001, 5, 300)\n", + " for p in pGrid:\n", + " M_temp = M + ExplicitExample.solution[0].mLvlMin(p)\n", + " C = ExplicitExample.solution[0].vFunc(M_temp, p * np.ones_like(M_temp))\n", + " plt.plot(M_temp, C)\n", + " plt.ylim([-200, 0])\n", + " plt.xlabel(\"Market resource level mLvl\")\n", + " plt.ylabel(\"Value v\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kgjwRGYK7K5mIXATsCWpUITK6bzLDMhN5+MP1oQ7FGGM6jJdEcAPwBDBSRHbhbErzg6BGFSIxUZGcPa4vW/PLqbJy1MaYMOGl1tAuVT1VRBKACFUtEZG0YAcWKr2SnXGC3JKDVoXUGBMWvLQIXhWRKFUtc5NAb2BWsAMLlV7Jzlq5fcVWc8gYEx68JILXgZdFJNLdcvIDnFlE3VKm2yKwRGCMCRde9iP4q4hE4ySEbOB6VZ0f7MBCpb5FYFNIjTHhoaVaQz8OvAlkAUuBaSIyrbldx7q6tPhofJHCniJrERhjwkNLLYKkRrdfa+Z4txIRIfTvEc+O/eWhDsUYYzpES7WG7haRSOA+Vb21A2MKuQFp8WzLt0Vlxpjw0OJgsarWAEd3UCydRnZ6PNvyygnR9snGGNOhvKwjWCoibwL/Afwfk1X11aBFFWID0xMoOVhNXmklGUkxoQ7HGGOCysv00TQgHzgZZ1+Cc3D2Kei2jsl21su9umRniCMxxpjg8zJ99OqOCKQzGds/hamD0nhp0Q6uP2FIqMMxxpig8rJ5fayI3CAij4nI3+q+OiK4UDr9qN5szi3j5cU7ySmxqaTGmO7LS9fQs0Bv4HTgY6A/znaV3dopozIB+Ml/lnHLv5eFOBpjjAkeL4lgqKr+AihT1X8AXwfGBjes0BuYnsAL104F4NMNeRTYrmXGmG7KSyKocv8tFJExQApOqYlu79ghPXn9hhkAzNuYF+JojDEmOLwkgidFpAfwC+BNYDXwu6BG1YmM6ZtMUkwUn2/KD3UoxhgTFF5mDT3lfvsxMDi44XQ+UZERTMruwVfb94c6FGOMCYpWE4GIpAJX4nQH+c9X1RuDF1bnMqhnAl9sKUBVEZFQh2OMMe3Ky8rid4AFwAogLPdvHJAWT3llDQVllaQn2kpjY0z34iURxKrqj1s/rfvK6hEPwPaCcksExphux9M6AhG5VkT6iEha3VfQI+tEBqTXJwJjjOluvCSCSuD3wOfAYvdrUTCD6mwGpseTGu/jreV7Qh2KMca0Oy9dQz/GWVQWthPpY6IiuWzKAP4ydxOlB6tJjPHyYzPGmK7BS4tgFRD2fSJj+6UA2IY1xphux8tH2xqcPQnmAP4d3cNp+ig43UMA2/LLOapvSoijMcaY9uMlEbzufh0WEckC/olTsK4WeFJV/9jMucfgTFG9RFVfPtzX6ggD0xMAJxEYY0x34mVl8T/a+NzVwC2qukREkoDFIjJLVVcHnuTui3w/8H4bX6dDJMZE0TMxmo05paEOxRhj2pWXMYI2UdU9qrrE/b4EWAP0a+LUHwKvADnBiqW9TBuczpx1OVTVhOW6OmNMNxW0RBBIRLKBicDCRsf7AecDj3dEHEfqvAn9KCirtEqkxphuxXMiEJGEtryAiCTifOK/WVWLG939MPAzVa1p5TmuE5FFIrIoNze3LWG0i+OH9yQ5Nor/Lt0dshiMMaa9edmq8lgRWY3TtYOIjBeRx7w8uYj4cJLA86r6ahOnTAb+JSJbgYuAx0TkG41PUtUnVXWyqk7OyMjw8tJBERMVyddG92b2uhxUNWRxGGNMe/LSIvgDzjaV+QCqugw4vrUHiVOm82lgjao+1NQ5qjpIVbNVNRt4GfgfVT3sGUodaXJ2DwrLq2z2kDGm2/C0RFZVdzQqv9xiV45rBnAFsEJElrrH7gAGuM/ZJcYFGpuQlQrAku37ye7Zpt4yY4zpVLwkgh0iciygIhIN3IjbTdQSVZ0HeC7er6pXeT03lIb3SqJnYgzvrNjDBUf3D3U4xhhzxLx0DX0fuAFn6udOYIJ7OyxFRggXT+7P7LU5FJbbhvbGmK6v1USgqnmqermq9lLVTFX9tqqG9Qa+M4f2pFZh5a7Gk6CMMabr8bJV5SNNHC4CFqnqG+0fUud3VN9kAFbuLmLmsJ4hjqZrq6qppbiiyjb8MSaEvHQNxeJ0B21wv8YBacA1IvJwEGPrtFLjo+mXGseKnUWhDqVLO1BVw8kPzuW4380ht+Rg6w8wxgSFl0QwFDhZVf+kqn8CTgVG4awIPi2YwXVmk7N7sNDd0N60zdq9JewoqKC8soZj7v2QZTsKQx2SMWHJSyLoBwTOk0wA+rqrgcP2Y9z0wenklR5kU64VoWurtXucMZYkd6Off325PZThGBO2vCSC3+HsR/CMiPwd+Ap4wC058WEwg+vM6sYG3l+1L8SRdF1r95aQEB3J0l+exnkT+vL+qn3WwjImBLzMGnoaOJb6fQlmqupTqlqmqrcGO8DOqn+PeKZkp/HQrPV8tX1/qMPpktbvK2FYryQiI4Rjh6RTUFZ5xC2s5TsLOe/ReewrPtBOURrT/XktOncA2AMUAENFpNUSE+HgrnOPoqZWeXbBtlCH0iVtzStjsLs6e8qgdAC+3Oo9qZZXVvPPz7dSdrDaf+z3769j2c4i/vjRhnaN1ZjuzMv00e8BNwH9gaXANOBz4OTghtb5je6bzKmjMllus4cOW0VlDbuLDvjLdGSnxxMZIezaX+H5OX77zlqeXbCNPUUH+NkZI6mqqeWLLQUALNlmrTRjvPLSIrgJOAbYpqon4ewrELpa0J3M2H6pbMotpTTgU6mpN3ddDit3HZootxWUAfgTgYiQEuejsMLbau3K6lpeXbITgBe/2M7B6ho25ZZysLqW1Hgfe4qsa8gYr7wkggOqegBARGJUdS0wIrhhdR3j+qegCquaeLMLd7klB7nqmS85+0/zKCirf4PfmlfG/77wFQDDeyX6j6fG+Sgsr/L03Iu2FlBWWcMV0wZSWF7Fv7/cwRkPfwrAySMzKaqoorzSkrMxXnhJBDtFJBVnoHiWiLwB2M4srjH9UgBYsLnAag81smBzfSWSPUX1XT5PfrqZjTmlnDW2NyN7J/uPp8T7KKrwlgjmb8onMkK49YwRJERH8os3VvnvO3ZIT/c1rVVgjBdeZg2dr6qFqnoX8AucPQYO2TwmXGUkxZAUG8UfPlzPcb+bw9a8Ms+PVVV++84afvDcYnYXeu8b7yoWbqlPBPml9Uly9e5iRvRK4uFLJjY4/3BaBEu272dUnySSY30MyXRaFX1SYnnlB8fSv0ccAHstEZh2UFurHPe72Tz5ySYAdhdWcNFf5rN6d/epNdZiIhCRCBFZWXdbVT9W1TdV1T76BrjtzJGcP7EfpQer+dtnWzw/bt2+Ep74ZDPvrtzLQ7PWBzHC0Fi5q5jeybEA5Jc5aw9rapW1e4uZMbQn0VENf/1S46M9jRHU1CrLdhRy9IAeAGQmOa9x0aT+TBrYg36pTiKwQXzTHj7blMeOggp+885aAGavzWHRtv1866kFIY6s/bSYCFS1FlgmIgM6KJ4u6fKpA/nDJRMY2y+FLQEtgtySg+SXNr/4ekeB0wromRjN8p2FrNtbwoMfrKO21llUVV5ZzYEqL3sAdT51b/h1C+/qWgTb8ss4UFXLyD5JhzwmxUOLYPbafby5bBdllTX+RFC3CK3udlZaPDOH9uSvn272/yyNaYuiiipufNEZzxrRK4kl2/dz5+vOZ+PC8qpu0x3sZYygD7BKRD4SkTfrvoIdWFc0IC2eHQXlVFTW8PbyPRxz74dc/tTCZs/fud/Z7vKMMb3ZmFPKjS9+xZ9mb+TDNfvIKz3IjPtm852/fdElV9tuyXPe8KcOSiM6MoJcNyHWbfE5JOPQ3d1S432UHKimuqa2yedUVb7790X86KVlQP0b/8+/PorLpmQxY2h9JdjTj+pFQVklOVbMzhyBNXuK2V9eRXRkBOv2lXDHqysa3P/V9u5RH8tLIrgbOBu4B3gw4Ms0MjA9nq355Yz6v/e44YUlgFNGoeRAw0+5K3cV8YdZ6/lyawFxvkhOGJ5JrTpdRQDPLtjGK4t3sr+8ioVbCvh8U9fb/mFjjnMtI3onUaPKEx9v5pnPtrA132kxDUw/NBGkJ0QDcMdrKw4ZNK6qqeX376/z306IjiQrzekCGpyRyG8vGNegq2mA+/z3vbvmkMSydm8xn27ItdaCaVVdC//Msb0B5+8Z4LX/OZZYXwR/nrORqmY+uHQlXgaLPwa2Aj73+y+BJUGOq0vqER/d5PF3V+7l5AfnsmxHIarKz15Zzh8/2sA7K/bSr0ccM4am+88d1SeZ+Zvymbcxj/SEaOJ8kbyzck9HXUK72ez+AQ3qmcD4/s7Mqrv/u5q7/7saX6T43/QDTchyPuH/e9FOnp63hf1llRSWV1JdU8tPX17OY3OdwbrzJvTlySsn02gf7QYGpsUD8PrS3bzirjcAp6l/wWPzueLpL5i15tA6UTnFB3huwTZ/l94n63NZvrN7fOozh29LXhnRUREM71XflTlzaE8mDujB/ReOY9G2/fxtnvdxwc6q1UQgItcCLwNPuIf64UwlNY2cP7Gff9MagP85cQgAP315OZtzy7j+2cU889lWVu0u5qpjszllZCbfnjqA+OgoLpmcxdh+KfzuwnHU1CqfbshjQlYqxw/vyYerc7pc99CW3DJ3RpWPx6+YxJv/O8N/X3RkRJNv4qMDfnZLdxRy3O/mcMLv5/Leqr289tUu/30//trwBt1ATennzhwC+NkrK1joTmV9bsE2yiudcZfZaw7dbvTJTzZz5+srueLpL6isruXKv33BuY9+dhhXbrqTzbllZKfHM8D9YJGWEM3tZ40E4LwJ/RiflcpHa3IAeGzuRp74eFPIYj0SXjavvwGYAiwEUNUNIpIZ1Ki6qPTEGF6/YQb3vr2Gq47NJrtnAnPW5bJmTzERAnmlB7nnrdWM75/CL84eTWRE/Zvh/ReNQ1UJ7K0Y2SeJXsmxvL9qH7sKK+jfI77dYlVV/vbZVkb0Smr3XdbeW7mXL7YWMMhdNZyZFEtmUix/v/oYNueWMaL3oQPF4OwH/eDF47nnrdV8sr5+8fprS3YRHRnBtccP4s9zNvlnBbXEFxnBeRP6si2/nKU7CrnkyQXcdc5oXl68k2mD01CFlxbt4NWvdjL31pP8z/mVuyfC6j3FvLdqr//5VLXFFojpnrbklTIsM4mzx/VhZO8khvVq+Ls7PDORj9c73Yy/e8/purzu+MFd7nfFyxjBwcDpoiISBXStj6cdyBcZwV3nHuUvnXDr6cMZ2y+Fj289iSz3U8XZ4/o2SAJ1RITICGGE+8t28aQs/4Do4lZq52zOLT2sefNvLN3Nr95azbefXsgjH23gB88tblC8ra1yig/w/ecWsy2/nBlDGiaYE0dk8t2Zg1r8NH/hpP58+OMTuP6EwaTG+wD4aG0OQzMT+clpI9jy27OIivRWK/GPl07k9RtmkJ3u/Nzv+u9qtuSVccW0bH5w4hCOHpBKVY3y2cY83lu5l/X7Slixq4jLpgwgQuA/i3b4n2vEL97jhYW2X0I4qa6pZXtBOYMyEhCRQ5IAOOOCOSUHG3Tfbsr1vpaos/DSIvhYRO4A4kTka8D/AP8Nbljdx8kje3HyyF6AM/1sS14Zk7N7tPiYp6+aTH5pJdk9E6iuqaVnYjS/e28dWWnxTMxKPeTTRunBak5+8GP6pcbx2W3eagF+tDbH/33dGoYhGYn85PQjqx6y0S0jPTA9nutPGNym58hIiuH2M0dx+5mjuPft1fz10y2M7J3U5k9ZY/unstWdrXT+xH6cNbY3IsIJwzMY9vN3+enLywHwRQpVNcqxQ9JZuDmfTzfk+Z+jsrqWX7yxkm9NtZnU4WLn/gqqatTfsm1K3aSHupIp4BQ8HJqZ2NxDOiUvH61uwykytwK4HngHuDOYQXVXv7lgLL/6xhgmZKW2eF7/HvGMd8+JiozgmaumUHqwmgsem89db6465Pw/z9kIwK7CCp7zWBK7sLyS8VmpHBOQlAJXAjdWerCauetymr2/Tt2noRevnUasL9JTLC257cxRPP7to7nlCBLUveeP4aXrprH5N2fxh0sm+BOKiHDbmSOZMigNgKoap6E7cUAq1x4/mJNGZHDL14Zz+5lOn3BNrTaomWTaRlXZH8KfY02t8uAH6/zTt5tTN2NocAuJIDBJ/OobY4j1Rfhn/3UlXhLBecA/VfViVb1IVf+qXW3kspNIS4jmimkDD/uT7dj+Kbx383GcPa4P//h8G4/O3uAf5Cwqr+LJTzb7+7jvfB2Q7TgAAB5MSURBVH0lH69vvThscUUVqXE+xvevT0rLdhZRWd1wKlxRRRV3vLaCK59eyFXPfNnqJvObc0uJ80X6VxQfqcgI4YwxfTyNCzQnOdbH1MHpRDTRHfe94wbz7+un88dLJ/iP9UuN47IpA3jm6in88JRhXH/CEJ69ZgoA6/Z2vT/yzmJjTikz75/N/774FRN/NavJqrTtbWteGU99upkdBfVv+ou2FvCn2Ru56V9LW3zsDjdR1A0UN2VUn/oJDscP68mwzCTW7ythf1klP3t5OWc8/EmXmKbsJRGcC6wXkWdF5OvuGIHpYH1S4rhyejYAD3ywnmPvm83zC7excncRNbXKfReO5eXvT0cE5m/Ka/nJcN7gU+J89E5x3rB7JcdQWV3L2ysa1hN8ZfFOXli4nSXuwpmWdv6qrqll7rpcRvdNbvJNtzM7e1xfbj9zJA9ePL7JRD0kw2nq2x7Vh0dV/TPenluwjZ37K3h7udOf/ngQZ9hszSvjoQ/W8dt31/Drt9dw7qPzyClxfnfnu+tylu0obPFNenfhAaIjI+iZGNPsOZERwkh38sOAtHhG9E7i0w15TPzVLF5atIO1e0soqqjikY82sKETtxS8rCO4GhgK/Af4FrBJRJ4KdmDmUOPc+fgA5ZU1/Py1lbzl/lGN6ZvC5Ow0eibGUOyhgmehmwi+MdGZAvfCtdMY3iuR5xe0PCDaUotgweYCtuSV8d0ZgzxeUecRGSFcf8IQLpzUv8n7+6TEEh8dycYcSwSH46xH5nHiA3PZU1TBUndGVqwvgmGZiWwvaLlr5kj89JXlPDJ7I++v2sfI3kkUVVTx1KfOfP+6zYuqa9X/qb8puwsr6JMa2+qHmn9/fzqf3HoSIsK3pg7guGE9SYyJ8i94XLO3mIdmredH/15KTSdtHXiafqGqVcC7wL+AxTjdRaaDxfoiefaaKXz44+M5aUQG4GzK0i81jh7uAq3k2KhWSznX1irFbiLomRjDGzfMYEhGIscO6cmq3cUNfln3lTRsATTVIli9u5gnPt7Et592ymkc08pgeFckIgzLTGTB5nx2FVZQUdk1a0B1pIKyStbsKWZbfjnTfzubpTsK+c70gTx3zVSOHtCD3YUHgrI+prK6lhUBBQe/O2MQpx/Vm1eX7PTXwKrr0lmzp/lP6bsLK+ib0nqXZHKsjwHuzLSjB/Tg2WumsvyXp3HvN8YC9cUPV+4q5oUvGn7Q+mDVXrJve5vt+cFLil54WVB2hoj8HdgIXAQ8hVN/yITAccMyGJqZxDNXT/FPM502uH5lckpc6zX9SyurqVXn3EBj+qVQUVXDBrc8xLb8MrbmlRETFcErP5gO0GTtnrMe+ZTfvrvWfzsjqfmmdFd21Yxs1u4tYcZ9s/nJy8tCHU6nVzeecvGk/iTGRHHpMVn88pyjmJydRp/UWPJKD3LMvR+yLb9t0y1/++4a7n9vbYNjOSUH+OGLS6ioquHOr4/invOO4twJfTlnfF/ySit5a/lu9pdXcc74Pog45cybs7uwgr5tHJuKiBDS3A9n97l/GxlJMfz7yx0Nzvu3O0V5aYhXr3tpEVyFs5J4uKp+R1XfUVXb+qkTOM5dCHb0wPoBXy+JoMit8JkS3zAR1HU9Xfz45+woKOfEB+by/qp99E2NY9LANHrE+/z9rHW++/cvD3n+rraYxqvzxvfzf//28j0N1hmYQ63b69Trv/X0ESz/5Wncd+E4fzdLH3dsKq+0kr/MPfyxgtpap35V4GMXbS3gm49/zvurnNIhJ47I4Mrp2cT6IjlheAa+SOGxOc75E7JSmToojafnbfF3FQXamlfG7qIDjOjd9mmgaY3KqFw8qT8rdxexq7CC2WudGOsaRK1Nwgg2L2MEl6rq66p6EEBEZojIn4MfmmnNTacO40enDueCifX92l4Swa3up9nGLYLhvZI4dVQmJQeqeW7hNv8v6UC32dsrOZY9hfWJoKCsktlrW59S2l1ERAgvfG+q//atLy/vEl1EjWeCNaeliQCHI6/0IA/NWs9XOwpJT4gmIynmkH72uOj6OSdz1nkvoVJyoIrrn13Em8vqJzUUlFWyYHM+Fz3+uX+9CEB2QGHDhJgoJg7owbp9JaTG+5iY1YMnr5xM39TYJqdkP79wGxEC5wYk/8PVOBGcMioTVfjm45/z3b8vYvnOQva7s/82h3gSgqcxAhGZICK/E5GtwK+Bta08xHSApFgfN506jLjo+vn6KXE+/yf+phQfqGLBZucT0OiAqW91Lp6cBcA/52/jqL7JfPjjE3jg4vGA84cVuN/CCnf63x8uGe8/9qvzjjqCK+r8jh3ak09uPck/k2SOh7UVoVRZXcuYX77Pna+vaPG8hz9cz9TffHTYu26pKqt3F1N2sJo3lu5ib9EBfvP2Gh75aANvLN3NlEFpTbYQTxqRwTcm9OXOr49iX/FB/uJxBtGibft5f9U+bn6pfurnyl1F/rU04IwJfG/moENWoF80qT8pcT7+96ShxEVHkhzr4zvTs1m9p7jB9NLNuaU889lWzp/Y3z+rri0C19Hcfe5R/m1Zd7m7EX6xpcD/9/T8wu3MCeGHqmangorIcOBS4DIgH3gJEFU9qYNiM22QHOej5GA1tbVKRITw8fpcDlTVcPpRThndBe7UuX9dN81f8iJQllvPqKKqhqmD0huskByckcCHa5y6R68u3kmlW3735JG92Hrf14N9aZ3GgPR45t92MqP+7z3W7CnmrLGdd8gsp+QAlTW1PLdgOzeeMsy/m1ug9ftKePjDDQBszS9rUPyvNYu27efixz/33+6XGseogE2H6hbrNZYU6+PhSyeiqszflM9fP9nMtccNxtdK+ZDGUzBT4nz89OXl7Cs5wFljezMhK5Vrj2u61s83J2fxTfeDTp1TR/Xi12+vYe66HK5wp2c/OnsjUZHOYsP2cPzwDL5zrPPc/VLj/Ing12+vAZwu3k835DFvYx4njQxNGbeW1gSsBT4FzlHVjQAi8qMOicq0WUqcD1UoOVBNSryP7/ztCwA23HsmvsgIPlyzj8SYKCYOaHp1c92UN2g49gBO3f/qWuW2V5b7yy8cOyT9kC6mcBAdFUFWjzg2d/K6MvuK6/ue31y6m+8dd2jZjwc/qN/n4XD7qtc3emPeVVjhf6MDOLmVNzYR4fKpA5i9Nod5G/M4YVgGIk2PM6kqa/eWkJYQTb/UOC45JouYqAhudUuE3Hzq8Ablor0YmB5ParyPNe7AdkFZJW8t38NlU7LaZdLDxnvPbHAtw3slNvj5gFNNt7iiilW7Q7e1akvp90JgLzBHRP4qIqcA3XMUsBup++XdU9zwl+3zTfnM25DHvxft5NRRmcRENV3+ISnWR7/UOETguKEZDe6rm6UUWIPnl+d0766glgzJSOz06wpy3cH9qAjh12+v4fpnFzVYRFVQVskHq/dx/QmDiYyQw04EWwO6CpNio7hsSsNaTE1tQNTYjKE98UUKCzbnc8t/lvH95xYDMG9Dnr98eE2tMvP+Oby6ZBfj+qfw3x/O5NvTBnLO+L6AM2X5cJMA1E8LfmHhdu56cxW/eH0llTW1XNZONaWiIiMaFJisa22N7ZfiPz6qTzKj+6awendxs2MlX24t4LfvrDlkskZ7abZFoKqvAa+JSALwDeBHQC8R+Qvwmqp+EJSIzBGp2w9h1a5ihmfW/2Fc6bYMYn0RfN/dJ6E5b984k1hf5CG1gsb0S2bqoDQWurMsXrh2arMlpcPB0MxEPlqbw5OfbOK6452f6f6ySv+ajs6grkXw1o0zeXjWBt5btZe3V+zxv4Fuzi1F1ZmC/NqSXc2+0eSUHCA51nfI78SWPKdef2FFFXecNYoRvZJ40Z0r//nt3gogxvoiGdsvhYWbC9iUW0p0ZATvrdzrTwjv3HgcGUkx7CqsICE6kl9/Y0yDx35xxykkxba9VVpXY+rv87cCzj4idf357W1ydhqwyZloccsJrN9XSqwvkqGZiRQfqKawvKrJ358nP9nMrNX7qKiq4Z7zxhz6xEfIy6yhMlV9XlXPBvoDS3EK0bVIRLJEZI6IrBGRVSJyUxPnnCciy0VkqYgsEpGZbboK4zeoZyLx0ZGs3F1EXtmhn+5+d9H4Vn/JU+OjmywYJyL+N5Ds9HiOHdK++xh0NU5J7XTue3ct2/PLWbS1gIm/msWs1YfufBYq+4oPEBUhDM9M4rHLj2ZEryTufH2lv6x53ereAWnxZCbHNLlO5NMNuUy59yP/fPhAW/Kc/SWW/t9pfHNyFuP6p3DW2N789crJ9PGwGKvOpIE9WLqjkJID1eSXVfLIRxuI9TlvT++t3ONvqTxw8fhD9uXITI5tMGHicF0xbWCD22eP69vm52pNXVn54b0SGZiewNdGO5WJ+7sbKe3cX9Hk43YUlDO4ZwI3nTIsKHF5K+zuUtUCVX1CVb2k+mrgFlUdBUwDbhCR0Y3O+QgYr6oTgO/iLFYzRyAyQhjjfrqq25+grgn6wMXjOfsIBzbrSmjvbaephl1Zr+RYbj9zFLUK97y1iv8scrbEfOazzrF14Wtf7eSVJTvJdKdvRkQIvzh7NEUVVdz80lfMWr2PeRvyEHEGMTMSYw7Z02JjTglXPO20JgOnbILTXbOjoMK/9wY4HxYeu3yS/w3Oq1GNZrCt3lPMt6cOZGB6PJvyyshztw7tGYTFihdO6s97Nx/nvx3MVm5KnI/X/udYHr5kYoPj9YmgfvZSbslBNuwrYWNOKZvzyjh1dC/SW6h7dCSCVkBOVfcAe9zvS0RkDc42l6sDzgnsYE3ANrxpF6eNdmZCfOuvTsmH566ZSo8EX7s0d4dlJtE7OZYff234ET9Xd1A38+rDNfVT/+Zvyue1r3Zy/sSm6xZ1lB+95KwXOXtcffKfOawnD1w8np/8ZxnX/nMR4CzuivVFMmlgDx74YD1r9xYzsncy8zfm+Tdrnz44nQVb8ikqr/IvRNxdWEFlTS2DPIwDtKap383sngkMyUhkU06pv0WQEaQ3wpG9k3nrhzM5WF3b5KZR7WnigENLsNS1cgJbBN9+amGDktZDM4K3x8FhtQjaSkSygYm42102uu98EVkLvI3TKjBHqG6qaOnBav+eA+3V5xkZISy44xS+eUxW6yeHgcYzpq6ZOYjoyAi+3NryjnKtOdIaPIG7zU1tNIXz62P7cNbY3kS5ZRBOcz+9XzzZmYVzyRMLWLytgG89tZB73lqNL1K44aShqMIXW53xoZpa5e7/Op/psluo1+/VkEznOXyR9W/CA9PjGdwzgbV7S/jV285rBaNFUGdMvxQmDQxNnayUOB/JsVENJh803tegtQ2tjkTQS0qLSCLwCnCzqh6yWiVgUPp44FfAqU08x3XAdQADBtgOUa0JXB/wy3NGe97a0RyZtb86g1hfJF9uLWiwQOlwVNfUcv2zi9mcV8a7Nx3X5FjN3qIDLS50qqisYdzdzlyOmUN7cskxDf9m4qIjeezySYc8rldyLC9eN40LHpvPhX+pXxswuGei/03o2n8u4oufn8J/Fu3kwzXOWEhLO3h5FRMVyZ+/dTQj+yRxyoMfAzAwLYHpQ2p5at4WCt1FkglHMBbQ2Z08MpO3V+zhymMH8vmmfNIToskP2MBncFdtEYiIDycJPK+qr7Z0rqp+AgwRkUNGIFX1SVWdrKqTMzIymni0aaxuIdhRh7E4yLTNpIE9iJD6laRZPeKbHfRrzardxXy0NocteWX86KWlvLV8N+v2lvgrws7flMe0337Eeyv3Nvscq/cU+c+/9fQRREd5/zM/ekCPBpv0REdFcPUMp17PlGynZfGHWet5aNZ6hmUm8rMzRpLZTp/Svz6uD0MyEv3bgfZNjeXkkZl8Y4IzeOtMa+6+M9i/eUwWpQer+foj8/j122vIL6vk5lOH8dYPZ/Lhj08I6msHrUUgzv/Y08AaVX2omXOGAptUVUXkaCAaZxWzOUIvXTeNXYUVza4XMO3nX9dNa1C6u39aHLNW76PsYDUvfrGdZTuLOHd8Xz7dkMtd5xxFZU1ts9t47g5YbPTuyr28677hj89K5blrprDEne3zyYZczhjTu8nnWB1QWrmp1eOtOW9CP//uXavvPt3fonzqqsmMu+sDXl2yi5pa5ckrJ7dLa6CxX503htvPHOl/3YcvnciD35xAbTffGHFc/1RE6gvRgTN2MKZfSvMPaifB7BqaAVwBrBCRusIgdwADAFT1cZxFa1eKSBVQAVxi22C2j/TEmKDNMDAN+SIjCHxfH5qR6CxK+usCfy36/7ozbt5ZsYe80koe//bRnDHm0Blcu91ZO5MH9mCR+6Y/olcSy3YU8srinWxzi6o1nt0TaM0epwf29xeNO6TwmVePXX40K3YVNehWTI71Mb5/Cst2FhEh9TNd2ltkhByyLiAyQojs5utZE2OiGJSewOa8MqIjI6isqW2yHlgwBHPW0DxaWYmsqvcD9wcrBmNC4ZzxfXl0zkaW7yzizDG9/Z/qwSm7DDBnbW7TiaCwglhfBF8b3YtF2/bzt6smc/LIXpz9p0/55+fbqHZbHst3FqKqh3SVLNm+nxcWbufUUZn+AoJtcdbYPk3WUBqYnsCynUX0SYlrtS6QOXwnj8ykf04pz1x1DAera4iP7pidgW3/YWPaWawvknvOG8NVz3zBBUf3Z3xWKrklB+kR7+OBD9YT64s4ZEOU6ppazvzjp+zc7+yK9b3jBnPMoDT/AqSfnDaCq55x9n4Y2TuJtXtLWLGriIFpCf7pnKrKBY/NBzhkgLi9jOidBMugutZbaWtzeO48u36pVUclAbBEYExQnDA8g4V3nEJGYox/cZWqcuKITGavzeGhWespPVhNYozzJ7hkeyEbckqJihBG9UkmMkL8SQDgxBGZ3H/hWOauy+W7Mwdx8eOfc+6jnxEZIdx3wVgunpzFK0t2AXDW2N6cOio4VSy/OTmL37+/joFp7T82YELHEoExQdK45LOIs+q7rn9/3d5iJg1MI7/0IDf/6yuiIoT5t59McjN1cy45ZgCXHDOAyupaesT72F9eRU2tcuvLy9mYW8oTH28GnLUMwZpdk5EUwzs3HkfPpM5TT8kcOevkM6aDjerbcOP0B2etZ3fRAW49fQSZSbHNziiqEx0VwSc/PYlfBHQj1CUBgGFtqMJ5OEb3TW5yXwPTdVmLwJgO1jcllqTYKJbvLKSmdgD/XbabC4/uz/UntFwVNlBSrI/vTB/IvuIDPPmJkwSunD6QUX2Sm21RGNMcSwTGdDAR4WujevHW8j2cN6EfJQeqOX744VdyjYqM4I6zRiECxw/LYMbQ8K4Ga9rOuoaMCYHvzhxEeWUNd7zm7CU8fXB6m5/r9jNHWRIwR8QSgTEhMKZfCtMHp7Mtv5zBGQlkJlufuwkdSwTGhMj3jhsEOLuDGRNKNkZgTIicNCKTH548NKg7YhnjhSUCY0IkIkK45bQRoQ7DGOsaMsaYcGeJwBhjwpwlAmOMCXOWCIwxJsxZIjDGmDBnicAYY8KcJQJjjAlzlgiMMSbMSVfbK15EcoFtbXx4TyCvHcPpCuyaw4Ndc3g4kmseqKoZTd3R5RLBkRCRRao6OdRxdCS75vBg1xwegnXN1jVkjDFhzhKBMcaEuXBLBE+GOoAQsGsOD3bN4SEo1xxWYwTGGGMOFW4tAmOMMY1YIjDGmDAXNolARM4QkXUislFEbgt1PO1FRP4mIjkisjLgWJqIzBKRDe6/PdzjIiKPuD+D5SJydOgibzsRyRKROSKyRkRWichN7vFue90iEisiX4jIMvea73aPDxKRhe41vyQi0e7xGPf2Rvf+7FDG31YiEikiX4nIW+7tbn29ACKyVURWiMhSEVnkHgvq73ZYJAIRiQT+DJwJjAYuE5HRoY2q3fwdOKPRsduAj1R1GPCRexuc6x/mfl0H/KWDYmxv1cAtqjoKmAbc4P5/dufrPgicrKrjgQnAGSIyDbgf+IN7zfuBa9zzrwH2q+pQ4A/ueV3RTcCagNvd/XrrnKSqEwLWDAT3d1tVu/0XMB14P+D27cDtoY6rHa8vG1gZcHsd0Mf9vg+wzv3+CeCyps7ryl/AG8DXwuW6gXhgCTAVZ5VplHvc/3sOvA9Md7+Pcs+TUMd+mNfZ333TOxl4C5DufL0B170V6NnoWFB/t8OiRQD0A3YE3N7pHuuueqnqHgD330z3eLf7ObhdABOBhXTz63a7SZYCOcAsYBNQqKrV7imB1+W/Zvf+IiC9YyM+Yg8DPwVq3dvpdO/rraPAByKyWESuc48F9Xc7XDavlyaOheO82W71cxCRROAV4GZVLRZp6vKcU5s41uWuW1VrgAkikgq8Boxq6jT33y59zSJyNpCjqotF5MS6w02c2i2ut5EZqrpbRDKBWSKytoVz2+W6w6VFsBPICrjdH9gdolg6wj4R6QPg/pvjHu82PwcR8eEkgedV9VX3cLe/bgBVLQTm4oyPpIpI3Qe6wOvyX7N7fwpQ0LGRHpEZwLkishX4F0730MN03+v1U9Xd7r85OAl/CkH+3Q6XRPAlMMydcRANXAq8GeKYgulN4Dvu99/B6UOvO36lO9NgGlBU19zsSsT56P80sEZVHwq4q9tet4hkuC0BRCQOOBVnEHUOcJF7WuNrrvtZXATMVrcTuStQ1dtVtb+qZuP8vc5W1cvpptdbR0QSRCSp7nvgNGAlwf7dDvXASAcOwJwFrMfpV/15qONpx+t6EdgDVOF8OrgGp2/0I2CD+2+ae67gzJ7aBKwAJoc6/jZe80yc5u9yYKn7dVZ3vm5gHPCVe80rgf9zjw8GvgA2Av8BYtzjse7tje79g0N9DUdw7ScCb4XD9brXt8z9WlX3XhXs320rMWGMMWEuXLqGjDHGNMMSgTHGhDlLBMYYE+YsERhjTJizRGCMMWHOEoHpcCLyc7eC5nK3wuJU9/hT7VUM0K3g2LOVc+5ow/NeJSKPNnH8RBE5NuD290XkysN9/mATkcki8shhPqbVn6Xp2sKlxITpJERkOnA2cLSqHnTfYKIBVPV7HRzOHcBv2um5TgRKgfkAqvp4Oz1vuxGRKFVdBCwKdSymc7EWgelofYA8VT0IoKp56i6pF5G5IjLZ/b5URO53C299KCJT3Ps3i8i57jkNPp2LyFsBdWkIOP66+zyr6op4ich9QJzbInnePfZtcWr+LxWRJ9zy5YjI1SKyXkQ+xil90Pj5s4HvAz9yH3uciNwlIj8JuK4/iMgn4uyhcIyIvCpObflfBzxPk6/f6LW2uj+XL9yvoe7xDBF5RUS+dL9muMfvEpEnReQD4J9uy6Wutn+a+7NZLiILRGScezxdRD4QZx+AJ2i6no3pRiwRmI72AZDlvrE+JiInNHNeAjBXVScBJcCvcUpNnw/cc5iv+V33eSYDN4pIuqreBlSoU/P9chEZBVyCU/BrAlADXO7WdbkbJwF8DWc/iwZUdSvwOE6d/Amq+mkTMVSq6vHueW8ANwBjgKvcN94mX7+Z6ylW1SnAozj1dwD+6L7+McCFwFMB508CzlPVbzV6nruBr1R1HE7r6J/u8V8C81R1Ik4JgwHNxGG6CesaMh1KVUtFZBJwHHAS8JKI3Kaqf290aiXwnvv9CuCgqlaJyAqc/RcOx40icr77fRbOJh75jc45BecN80unlBFxOIW9puIkpFwAEXkJGH6Yrw/1ta1WAKvUrQcjIpvdmGY28/pNeTHg3z+4358KjJb6CqzJdTVrgDdVtaKJ55mJkzRQ1dluQkoBjgcucI+/LSL7D/NaTRdjicB0OHXKKc8F5rpv7N/B2WktUJXW1z+pxdmhC1Wtlfrqk9U0bNXGNn4tt6voVJxNS8pFZG5T5+F0f/xDVW9v9Phv0D7ljA+6/9YGfF93O6q512+GNvF9BM41NnjDdxNDWTPP01IJY6s9E0asa8h0KBEZISLDAg5NALa18em24tTnjxCRLJxyvY2l4GxhWC4iI3FKN9epEqecNTiFvC4SpwZ8Xf/5QJwNb050Py37gIubiaUESGrmPi+ae/2mXBLw7+fu9x8A/1t3gohM8PCan+B2P7kJM09VixsdPxPocVhXYrocaxGYjpYI/EmcksrVONUir2v5Ic36DNiC092yEmf7xsbeA74vIstxtvFbEHDfk8ByEVnijhPcibMzVARONdcbVHWBiNyF84a7x32NQwZxgf8CL4vIecAPD/dCVHV1U69P00kyRkQW4nyQu8w9diPwZ/c6o3DezL/fysveBTzjPqac+jLHdwMvisgS4GNg++Fej+larPqoMV2IOBu1TFbVvFDHYroP6xoyxpgwZy0CY4wJc9YiMMaYMGeJwBhjwpwlAmOMCXOWCIwxJsxZIjDGmDD3/3vOR3cX8S/iAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Simulate some data\n", + "if do_simulation:\n", + " ExplicitExample.T_sim = 500\n", + " ExplicitExample.track_vars = [\"mLvlNow\", \"cLvlNow\", \"pLvlNow\"]\n", + " ExplicitExample.makeShockHistory() # This is optional\n", + " ExplicitExample.initializeSim()\n", + " ExplicitExample.simulate()\n", + " plt.plot(np.mean(ExplicitExample.mLvlNow_hist, axis=1))\n", + " plt.xlabel(\"Simulated time period\")\n", + " plt.ylabel(\"Average market resources mLvl\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a persistent income shocks consumer took 21.1527 seconds.\n" + ] + } + ], + "source": [ + "# Make and solve an example \"persistent idisyncratic shocks\" consumer\n", + "PersistentExample = PersistentShockConsumerType(**Params.init_persistent_shocks)\n", + "t_start = process_time()\n", + "PersistentExample.solve()\n", + "t_end = process_time()\n", + "print(\n", + " \"Solving a persistent income shocks consumer took \"\n", + " + mystr(t_end - t_start)\n", + " + \" seconds.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of 0.98:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption function at various levels of persistent income pLvl\n", + "print(\n", + " \"Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of \"\n", + " + str(PersistentExample.PrstIncCorr)\n", + " + \":\"\n", + ")\n", + "pLvlGrid = PersistentExample.pLvlGrid[0]\n", + "mLvlGrid = np.linspace(0, 20, 300)\n", + "for p in pLvlGrid:\n", + " M_temp = mLvlGrid + PersistentExample.solution[0].mLvlMin(p)\n", + " C = PersistentExample.solution[0].cFunc(M_temp, p * np.ones_like(M_temp))\n", + " plt.plot(M_temp, C)\n", + "plt.xlim(0.0, 20.0)\n", + "plt.ylim(0.0, None)\n", + "plt.xlabel(\"Market resource level mLvl\")\n", + "plt.ylabel(\"Consumption level cLvl\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the value function at various persistent income levels\n", + "if PersistentExample.vFuncBool:\n", + " pGrid = PersistentExample.pLvlGrid[0]\n", + " M = np.linspace(0.001, 5, 300)\n", + " for p in pGrid:\n", + " M_temp = M + PersistentExample.solution[0].mLvlMin(p)\n", + " C = PersistentExample.solution[0].vFunc(M_temp, p * np.ones_like(M_temp))\n", + " plt.plot(M_temp, C)\n", + " plt.ylim([-200, 0])\n", + " plt.xlabel(\"Market resource level mLvl\")\n", + " plt.ylabel(\"Value v\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Simulate some data\n", + "if do_simulation:\n", + " PersistentExample.T_sim = 500\n", + " PersistentExample.track_vars = [\"mLvlNow\", \"cLvlNow\", \"pLvlNow\"]\n", + " PersistentExample.initializeSim()\n", + " PersistentExample.simulate()\n", + " plt.plot(np.mean(PersistentExample.mLvlNow_hist, axis=1))\n", + " plt.xlabel(\"Simulated time period\")\n", + " plt.ylabel(\"Average market resources mLvl\")\n", + " plt.show()" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py b/examples/ConsumptionSaving/example_ConsGenIncProcessModel.py similarity index 98% rename from HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py rename to examples/ConsumptionSaving/example_ConsGenIncProcessModel.py index 77fedcb8f..3763e7820 100644 --- a/HARK/ConsumptionSaving/example_ConsGenIncProcessModel.py +++ b/examples/ConsumptionSaving/example_ConsGenIncProcessModel.py @@ -8,13 +8,11 @@ IndShockConsumerType, PersistentShockConsumerType, ) - - def mystr(number): return "{:.4f}".format(number) -do_simulation = False +do_simulation = True # Display information about the pLvlGrid used in these examples print( @@ -126,8 +124,6 @@ def mystr(number): plt.ylabel("Average market resources mLvl") plt.show() -############################################################################### - # Make and solve an example "persistent idisyncratic shocks" consumer PersistentExample = PersistentShockConsumerType(**Params.init_persistent_shocks) t_start = process_time() diff --git a/examples/ConsumptionSaving/example_ConsIndShock.ipynb b/examples/ConsumptionSaving/example_ConsIndShock.ipynb new file mode 100644 index 000000000..63f6c9050 --- /dev/null +++ b/examples/ConsumptionSaving/example_ConsIndShock.ipynb @@ -0,0 +1,566 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import HARK.ConsumptionSaving.ConsumerParameters as Params\n", + "from HARK.ConsumptionSaving.ConsIndShockModel import (\n", + " PerfForesightConsumerType,\n", + " IndShockConsumerType,\n", + " KinkedRconsumerType,\n", + ")\n", + "from HARK.utilities import plotFuncsDer, plotFuncs\n", + "from time import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "mystr = lambda number: \"{:.4f}\".format(number)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "do_simulation = True" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Make and solve an example perfect foresight consumer\n", + "PFexample = PerfForesightConsumerType()\n", + "# Make this type have an infinite horizon\n", + "PFexample.cycles = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a perfect foresight consumer took 0.6234 seconds.\n" + ] + } + ], + "source": [ + "start_time = time()\n", + "PFexample.solve()\n", + "end_time = time()\n", + "print(\n", + " \"Solving a perfect foresight consumer took \"\n", + " + mystr(end_time - start_time)\n", + " + \" seconds.\"\n", + ")\n", + "PFexample.unpackcFunc()\n", + "PFexample.timeFwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Perfect foresight consumption function:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the perfect foresight consumption function\n", + "print(\"Perfect foresight consumption function:\")\n", + "mMin = PFexample.solution[0].mNrmMin\n", + "plotFuncs(PFexample.cFunc[0], mMin, mMin + 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "if do_simulation:\n", + " PFexample.T_sim = 120 # Set number of simulation periods\n", + " PFexample.track_vars = [\"mNrmNow\"]\n", + " PFexample.initializeSim()\n", + " PFexample.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Make and solve an example consumer with idiosyncratic income shocks\n", + "IndShockExample = IndShockConsumerType()\n", + "IndShockExample.cycles = 0 # Make this type have an infinite horizon" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "Solving a consumer with idiosyncratic shocks took 0.2175 seconds.\n" + ] + } + ], + "source": [ + "start_time = time()\n", + "IndShockExample.solve()\n", + "end_time = time()\n", + "print(\n", + " \"Solving a consumer with idiosyncratic shocks took \"\n", + " + mystr(end_time - start_time)\n", + " + \" seconds.\"\n", + ")\n", + "IndShockExample.unpackcFunc()\n", + "IndShockExample.timeFwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Concave consumption function:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Marginal consumption function:\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption function and MPC for the infinite horizon consumer\n", + "print(\"Concave consumption function:\")\n", + "plotFuncs(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)\n", + "print(\"Marginal consumption function:\")\n", + "plotFuncsDer(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions for perfect foresight vs idiosyncratic shocks:\n" + ] + }, + { + "data": { + "image/png": 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p8Xz5tnGsnJnD8JQAa3PXGt79m1kff+evEBFt2vGv+ywMn251dWIASaAL0U8dTjOHs8TuoOzkGaIjFbcVZLKmKJe5Y9MCoxX/Qs522LvBHJLVeAwGZcDNX4eZH4fBmVZXJ3xAAl2IPuyvaqHYXsnWPTW0drsYnT6IbyzOZ8WMHNISY60u7/3OnAT7b8zZ410tkDUN7noCJt4FUQFYrxgwEuhC9KKls4et3jmcB2vMHM7Fk7NYOyuXwhEBMIfzYudGuu147PzZ4wXLzSFZtlnSzRkmJNCF8NJaYz9xhmJ7JS/ur6Wrx0NBVhLfuWMiy6dlkxxv8RzO3vR0wYHNpi2/br8Z6Tb3ATPSTc4eDzsS6CLsnW4zcziL7Q6ON7STGBvF3TNyWFOUy+ScAO2MPFsLZU9B2e+goxEyCmDZL2DKKjl7PIxJoIuw5PFotlU0Umyv5NVDdfS4NYUjhnDfyjEsmZJFQkyA/mhU7zLLKge3mKag8YtME9CoG2VZRUigi/BS29LJRnsVG8scVDd3MiQhmnvnjGR1kY28zMFWl9c7twuOvgDbfw2OHRAzGGatg1mfhtTRVlcnAogEugh5PW4PfztST3FpJW8ea8Cj4Ya8NL6+OJ/bCjKJjQqgVvwLdbXArj+YbYctlZAywntI1ochLsDOgREBQQJdhKwTje2UlDnYXF5FQ2s3mUmxfP6WsawqtAXOHM7enH7XhPieP5luzhFzYeF3YfxiOSRLXJYEuggpXT1uXjl4iuJSB9uPnyYyQjEvP4M1RTZuGpdOVKC14p+jNZz4h3fb4UsQEQWTV5r18eHTrK5OBAkJdBESjpw6S3Gpg2d3V9PS2UNuagJfXWDmcGYmBcgczt64umH/ZhPkdfshYSjc+FUo+iQMHmZ1dSLISKCLoNXW7eL5vTUU2x3scTQTExnBgknDWFtk47rRQwNnDmdv2urNXE77k9DeYLYdLv8lTL5Hth2KqyaBLoKK1po9jmZK7A7+sreGdqebvIxEHl5awIrp2QwZFEBzOHtzar+5G9+/CdxOyFsA190Ho2+WbYfimkmgi6DQ3OHk2d3VlNgdHDnVSnx0JMumZrG6KJcZuQE0h7M3Hg8cexl2/Nqsk0cnwIyPmrb8tLFWVydCiAS6CFhamzmcJXYHLx04hdPlYWpOMt+9azLLpmYxOC4AW/Ev1N1qzh3f+Tg0HYekHLjt2ybM44dYXZ0IQRLoIuDUt3axubyKjXYHJ053kBQXxdoiG6uLcikYHgT7r8+chNL1Zg95dwvkFMG8h2HCMogM8L+ERFCTQBcBwe3RvHWsgQ2llbx+xMzhnDUqlQfm57FoUlZgzeHsjdbg2AnbH4UjzwMKJt7pPe2wyOrqRJiQQBeWqjrTwUa7g03lVdS2dJGWGMOnbhjF6kIbo9MTrS6vby4nHPpf2PEo1OyGuBS4/oumLV9OOxR+1u9AV0pFAmVAtdZ6qe9KEqHO6fLw2uE6NpRWsq2iEYCbxqXzyLIC5uVnEhMVoM0/F+poOr/tsLUWhubBkh/D1LUQE2ADoUXYuJI79AeAw0AQLGKKQFRR38bGMgfPlFdxut3J8OQ4Hrg1j3sKbWQH2hzOS6k/Ajsfg73F4OqCMfPM/vExt0JEEPxFJEJavwJdKZUDLAH+C/iyTysSIaXT6ebF/WYOZ+mJJqIiFPMnZLJmlo0b8tIDbw5nbzweM2R5x6/h3dchKg6mrDb7xzMmWF2dEP9ff+/QfwZ8Dbjk+aJKqXXAOoDc3Nxrr0wEtQPVLZTYHTy3p5rWLhej0gbx4KJ87p6RQ/rgIJlr6eyAfcWmEajxGCQOg3kPmSHLg9Ksrk6I9+kz0JVSS4F6rXW5UurmS32f1no9sB6gsLBQD1iFImi0dvXwv3tqKLE72F/dQmyUmcO5psjGrFGpgd38c6HWOjNk2f4UdDZB1lS4a713yHKAd6KKsNafO/S5wHKl1GIgDkhSSv1Ra/1h35YmgoHWmvKTZyi2O3hhXy2dPW7yhw3mW8sncue0bJITgmjf9akDZlll/yZw90D+ErjuczDiemnLF0Ghz0DXWn8d+DqA9w793yXMRVO7ky3eOZwV9W0MionkzunZrCmyMSUnOXjuxj0eqHgNtv8K3nvTtOXP/Jg5tnboGKurE+KKyD500W8ej+btdxsptjv468FT9Lg1M3JT+MHdU1gyJYtBsUF0OfV0mp0qO35t1scHZ8H8b5owl7Z8EaSu6CdQa/134O8+qUQErFMtXWwqc1BS5qDqTCcpCdF85Dozh3P8sACdw3kprXVm73jZU9Bx2qyPr/gNFNwp6+Mi6AXRLZXwJ5fbwxtHGygureSNo/V4NMwdO5SvLczn9oLMwG/Fv1jdQTNkef9Gsz4+fhHM+bwZ7xYsy0NC9EECXfyLk6fb2VjmYFNZFfWt3WQMjuW+m8ewqtDGiKFB1gGpNVS8btbHj78BUfFybK0IaRLogq4eN389VEeJvZK3K04ToWBefgari3K5ZXwAz+G8lJ4u2Fdi1scbjpj947f+X7N/PCHV6uqE8BkJ9DB2rK6V4lIHW3ZX0dzRQ86QeP799nGsnGljWHIAz+G8lLYGsz5ufxI6GmHYZLjrCZi4QtbHRViQQA8z7d0uXthXS7G9kl2VzURHKm6fOIy1RblcPybA53BeSv1hc2ztvo3g7oZxi2DO52DkDbI+LsKKBHoY0Fqzr6qFYu8czrZuF2MzEnloyQRWzMghNdDncPZGa3O+yvZHveerxMP0D5vzVdLyrK5OCEtIoIewlo4enttTTbHdweHas8RFR7B0ynDWzrIxI3dI8DT/XKiny3Rybn8UGg5DYqaZBlT4CVkfF2FPAj3EaK3Z+V4TJXYHL+6vpdvlYXJ2Mv955ySWTxtOUqDP4byU9kZztor9N9DeAJmT4M7HYdIKiAqSw76E8DEJ9BDR0NrNM7uqKLE7eK+xncFxUawqtLG6yMak7GSry7t69UfMbpW9xWZ9PG+B2T8+6kZZHxfiIhLoQczt0fzjnQaKSx28drgOl0dTNHIIX7hlLIsnZxEfE2TNP+doDcf/bpZVKl41549P+6A5KCt9nNXVCRGwJNCDUHVzp5nDWeagpqWLoYNi+MQHRrGq0MbYjCCYw3kprm7Yv9kEef1BGJQBtzxk1scHDbW6OiECngR6kOhxe3j9cB0bSh289U4DADfkpfPQ0gLmTwiSOZyX0nnGzOfc+QS01UHGRLjj1zB5payPC3EFJNAD3PGGNkq8czgb25xkJcdx/7w87pmZgy01weryrs2ZE2Ya0K4/QE+7mc9552Pmo6yPC3HFJNADUFePm5cO1FJc6mDne01ERihuzc9g7axcbhwXJHM4L6eqHP75Czi8FVQkTL7HvNE5bJLVlQkR1CTQA8ihmrOU2Ct5dnc1Z7tcjBiawNcWjmflzBwyBgdhK/6FPB449jL885dQ+U+ITYbrvwizPwNJw62uToiQIIFusbZuF1v31FBir2RvVQsxUREsmjSMNUW5zB6VGpyt+Bfq6YS9G8wbnacrIDkXFn7PdHXGBtlZ6kIEOAl0C2it2VXZTIm9kuf31dLhdDM+czCPLCvgrunZpCQEYSv+xdobzSFZpevNIInh02Hlb2HCHRApl50QviA/WX50pt3Jlt3VlNgrOVbXRkJMJMunDmd1kY1ptpTgbMW/WGOFOX987wZwdZmDsq6/XwYtC+EHEug+5vFoth8/TbHdwSsHTuF0e5hmS+F7KyazdOpwEoNpDuelaA2VO8z6+NEXITIGpq6BOV+QRiAh/CgE0iQw1Z/tYlO5acWvbOogOT6aD87OZXWRjQlZSVaXNzDcLjjyFxPk1eUQnwo3fQ2KPg2J6VZXJ0TYkUAfQC63hzePNbCh1MEbR+txezTXjU7lK7ePY8HEYcE3h/NSuttg9x/NGSvNJyF1NCz5MUz9IMQE+d54IYJYn4GulIoD3gJivd+/WWv9iK8LCyaOpg5K7A42lTuoO9tNWmIs624czapCG6PSgmwO5+W0njLdnGVPQVcL2K6DBd81A5cjQuQvKyGCWH/u0LuBeVrrNqVUNLBNKfWS1nqHj2sLaN0uN68eqqO41MG2ikYiFNw8PoNv32FjXn4G0cE2h/Ny6g55JwKVgHbDhGUw536wFVldmRDiAn0GutZaA23eT6O9v7QviwpkFfXn5nBW09TuJDslni/fNo6VM3MYnhJvdXkDR2t4702zPl7xGkQnQOHHzUSg1NFWVyeE6EW/1tCVUpFAOTAWeFRrvbOX71kHrAPIzc0dyBot1+E0czhL7A7KTp4hOlJxW0Ema4pymTs2Lfhb8S/k7oEDW2D7L+HUfnPi4byHoPCTMhFIiACnzA14P79ZqRTgWeB+rfWBS31fYWGhLisrG4DyrLW/qoVieyVb99TQ2u1idPog1hTZWDEjh7TEEDsFsKsFyp+GnY/D2WpIG2/2j0++B6KD/NgBIYKAUqpca114LY9xRbtctNbNSqm/AwuBSwZ6MGvp7GGrdw7nwRozh3Px5CzWzsqlcESQzuG8nLM15sTDst+Bs9VMAlr6Mxg7HyJC6H0AIcJAf3a5pAM93jCPB+YD3/d5ZX6ktabs5Bk2lFby4v5auno8FGQl8Z07JrJ8WjbJ8UE6h/Ny6g+b9fF9G80bnRPvMnfkw6dbXZkQ4ir15w49C3jau44eAWzUWj/v27L843SbmcNZbHdwvKGdxNgo7p6Rw5qiXCbnBPEczkvRGk7+0xxde+xliIo304DmfA6GjLS6OiHENerPLpd9QMjctnk8mm0VjRTbK3n1UB09bk3hiCHct3IMS6ZkkRATgr1WHjcceQHe/jlUl0HCULj5G1D0KRntJkQICcH06l1tSycb7VVsLHNQ3dzJkIRo7p0zktVFNvIyQ/QY13NH1/7zl9B0HIaMko5OIUJYSAd6j9vD347UU1xayZvHGvBo+MDYNL6+OJ/bCjKJjQrR7saOJrA/ZXasdDTC8Blwz9OmIUg6OoUIWSEZ6Cca2ykpc7C5vIqG1m4yk2L5/C1jWVVoC/45nJdz5qQ5X+XcjM6822HuAzBirhxdK0QYCJlA7+px88rBUxSXOth+/DSREYp5+RmsKbJx07h0okKpFf9itfvMG50HtpjgnrzK7FjJLLC6MiGEHwV9oB85dZbiUgfP7q6mpbOH3NQEvrrAzOHMTArhhhit4fgb8PYvzMeYwWa3yuz7IDnb6uqEEBYIykBv63bx/N4aiu0O9jiaiYmMYMGkYawtsnHd6KHBP4fzctwuOPQcvP0z05qfOAzmfxNmfhziU6yuTghhoaAJdK01exzNlNgd/GVvDe1ON3kZiTy8tIAV07MZMigE5nBejrPdrI1vfxRaKiFtHCz/FUxZBVEhdgyBEOKqBHygN3c4eXZ3NSV2B0dOtRIfHcmyqVmsLsplRm6IzOG8nLYGKH3CDFzuPAO5c2DxDyBvgbTmCyH+RUAGutaaHcebKLZX8tKBUzhdHqbmJPPduyazbGoWg+NCsBX/YqffNcOW9/wZXN2Qv8TsWLHNsroyIUSACqhAr2/t4pnyakrslZw43UFSXBRri2ysLsqlYHiIzOHsS1WZ6eg8/Jfzw5avvx/S8qyuTAgR4CwPdLdH89axBjaUVvL6ETOHc9aoVB6Yn8eiSVmhM4fzcrSGd141b3SefBvikuGGL8Osz8DgTKurE0IECcsCvepMBxvtDjaVV1Hb0kVaYgyfumEUqwttjE5PtKos/3K74OAW2PYzqD8ISTmw4L9hxkcgNkSPIxBC+IxfA93p8vDa4To2lFayraIRgBvz0nlkWQHz8jOJiQqTN/mcHbD7D/DPX5kdK+kT4M7HYfJKiAyD9weEED7hl0CvqG9jY5mDZ8qrON3uZHhyHA/cmsc9hTayQ2kOZ186msxulZ2PQ8dpsM2WHStCiAHjs0DvdLp5cb+Zw1l6oomoCMX8CZmsmWXjhrz00JrD2ZeWKrN/vPxpc8bKuIUw90swYo7VlQkhQohPAr2muZNZ332N1i4Xo9IG8eCifO6ekUP64DBrgKk/Ynas7N9o3vicfI/ZeihnrAghfMAngd7U7mT1hEzWFNmYNSo19Jt/LuYoNW90Hn3BTAUq+hTM+Tyk5JVsLhEAAAqaSURBVFpdmRAihPkk0CdkJfHT1dN88dCB6+Kth/FD4Kb/MFsPZSqQEMIPfBLoYbU+3tvWw4Xfg+kfgdgw2X4phAgIljcWBS1nB+z+I2z/JTRXQnq+bD0UQliqz0BXStmA/wGGAR5gvdb6574uLGD1tvVwkWw9FEJYrz936C7gK1rrXUqpwUC5UupVrfUhH9cWWFqqYPuvofz33vFuC+AD/yZbD4UQAaPPQNda1wK13t+3KqUOA9lAeAR603HY9lPYswG0xyypzH0AMidaXZkQQvyLK1pDV0qNBKYDO31RTECpPwLbfgL7N0FENMy8F67/IgwZYXVlQgjRq34HulIqEXgG+JLW+mwv/30dsA4gNzeI91vX7oN//AgObYXoeLjuc+b42sHDrK5MCCEuq1+BrpSKxoT5n7TWW3r7Hq31emA9QGFhoR6wCv2lqgze+iEcexlik+CGr5gwlz3kQogg0Z9dLgp4Cjistf6J70vysxPbTJAf/7tpBrrlIZj1aRm4LIQIOv25Q58LfATYr5Ta4/3aN7TWL/quLB/TGt59Hd76EVRuh0EZcNt3oPAT0gwkhAha/dnlsg0IjdZPrc2Syps/gJpdkJQNi35oBkpEh9ExvkKIkBQenaJaQ8Xr8MZ/mSBPGQHLfg5TPwhRMVZXJ4QQAyK0A11rszb+xnehqhSSc2H5r8zgZWnPF0KEmNAN9BPbTJCffNssrSz9KUz7sNyRCyFCVugFeuUOs7Ty3luQOAwW/whmfBSiwmy4hhAi7IROoNfug9e/BRWvmV0rC78HMz8mb3YKIcJG8Af6mRPwt/8yY97iUuC2b0PRpyEmwerKhBDCr4I30NsbTUOQ/SmIiDInH879kjQECSHCVvAFurMDtv/KDF/u6TR7yG96EJKyrK5MCCEsFTyB7vHAgc3w2jfhbDXkL4VbH4H0cVZXJoQQASE4At1hh5cfhOoyyJoGdz8lgyWEEOIigR3obfXw14dhX7HZgnjnYzBljYx6E0KIXgRmoHs8UP47sw3R2WGOsv3Al+XgLCGEuIzAC/TaffD8v5nllZE3wJKfyDq5EEL0Q+AEustptiH+48eQkAorfgOT7wEVGgc9CiGErwVGoNcdhGc/C6f2wdS1sPC/zbAJIYQQ/WZtoGsNO5+AVx82Y99W/wkmLLW0JCGECFbWBXp3G/zlAbO3fNxCc6xtYrpl5QghRLCzJtCbHfDnVdBwBOY9bHawyFZEIYS4Jv4P9Iaj8Ie7zB36h5+BMfP8XoIQQoQi/wZ6/RH43SIzLejjL8CwyX59eiGECGX+C/SztfDHu71h/hIMHeO3pxZCiHDQ58K1Uuq3Sql6pdSBq34Wjxue+SR0noEPbZIwF0IIH+jPO5G/BxZe07O8/TMz23PJjyBr6jU9lBBCiN71Geha67eApqt+hpYqePMHUHCHaRoSQgjhEwO2V1AptU4pVaaUKmtoaDj/H3Y8ZpZcbvuOtPELIYQPDViga63Xa60LtdaF6eneBiF3D+wthvGLYMiIgXoqIYQQvfBtN887r0JHI0z7kE+fRgghhK8D/cgLEJcCY2/16dMIIYTo37bFDcB2YLxSqkop9cl+PbLWcPwNGH2T2XsuhBDCp/psLNJaX93WlMZjZpjz6K9e1R8XQghxZXy35HL8TfNxzC0+ewohhBDn+S7QT78DsckwZKTPnkIIIcR5vgv0lipIzvHZwwshhPhXPgx0hwS6EEL4kdyhCyFEiPBNoGuPOVlRAl0IIfzGN4HudpqPEuhCCOE3Pgr0HvNRAl0IIfxG7tCFECJE+C7QVQQMzvLJwwshhHg/3y25JA6TM1yEEMKPfHeHLsstQgjhVxLoQggRIny35CKBLoQQfuW7xiIJdCGE8Cvftf5LoAshhF9JoAshRIjwYaDbfPbQQggh3s83ga4iIH6ITx5aCCFE73wT6JExoJRPHloIIUTvfBTo0iEqhBD+1q9AV0otVEodVUpVKKUe7PMPRMZcc2FCCCGuTJ+BrpSKBB4FFgEFwFqlVMFl/5AEuhBC+F1/7tBnARVa6+NaaydQDNxx2T8hgS6EEH7Xn0DPBhwXfF7l/dq/UEqtU0qVKaXKmtu6Bqo+IYQQ/dSfQO9tu4p+3xe0Xq+1LtRaF6ZkDL/2yoQQQlyR/gR6FXBhl1AOUOObcoQQQlyt/gS6HchTSo1SSsUAa4Ctvi1LCCHElYrq6xu01i6l1BeAV4BI4Lda64M+r0wIIcQV6TPQAbTWLwIv+rgWIYQQ18B3h3MJIYTwKwl0IYQIERLoQggRIiTQhRAiRCit39cjdO0PqlQrcHTAHzg4pQGNVhcRAOR1OE9ei/PktThvvNZ68LU8QL92uVyFo1rrQh89dlBRSpXJayGvw4XktThPXovzlFJl1/oYsuQihBAhQgJdCCFChK8Cfb2PHjcYyWthyOtwnrwW58lrcd41vxY+eVNUCCGE/8mSixBChAgJdCGECBEDGuhXPEw6hCilbEqpN5RSh5VSB5VSD3i/nqqUelUp9Y734xCra/UXpVSkUmq3Uup57+ejlFI7va9Fifc45pCnlEpRSm1WSh3xXh9zwvW6UEr9m/fn44BSaoNSKi5crgul1G+VUvVKqQMXfK3X60AZv/Bm6T6l1Iz+PMeABfpVDZMOLS7gK1rrCcB1wOe9//sfBF7XWucBr3s/DxcPAIcv+Pz7wE+9r8UZ4JOWVOV/Pwde1lrnA1Mxr0nYXRdKqWzgi0Ch1noS5jjuNYTPdfF7YOFFX7vUdbAIyPP+Wgc81p8nGMg79CsfJh1CtNa1Wutd3t+3Yn5oszGvwdPeb3sauNOaCv1LKZUDLAGe9H6ugHnAZu+3hMVroZRKAm4EngLQWju11s2E6XWBaWaMV0pFAQlALWFyXWit3wKaLvrypa6DO4D/0cYOIEUpldXXcwxkoPdrmHQ4UEqNBKYDO4FMrXUtmNAHMqyrzK9+BnwN8Hg/Hwo0a61d3s/D5foYDTQAv/MuPz2plBpEGF4XWutq4EdAJSbIW4BywvO6OOdS18FV5elABnq/hkmHOqVUIvAM8CWt9Vmr67GCUmopUK+1Lr/wy718azhcH1HADOAxrfV0oJ0wWF7pjXd9+A5gFDAcGIRZWrhYOFwXfbmqn5eBDPSwHyatlIrGhPmftNZbvF+uO/dPJe/Heqvq86O5wHKl1AnM0ts8zB17ivef2hA+10cVUKW13un9fDMm4MPxupgPvKe1btBa9wBbgOsJz+vinEtdB1eVpwMZ6GE9TNq7RvwUcFhr/ZML/tNW4F7v7+8F/tfftfmb1vrrWuscrfVIzHXwN631h4A3gJXebwuX1+IU4FBKjfd+6VbgEGF4XWCWWq5TSiV4f17OvRZhd11c4FLXwVbgo97dLtcBLeeWZi5Laz1gv4DFwDHgXeD/DORjB/ov4AOYfxLtA/Z4fy3GrB2/Drzj/Zhqda1+fl1uBp73/n40UApUAJuAWKvr89NrMA0o814bzwFDwvW6AL4FHAEOAH8AYsPlugA2YN476MHcgX/yUtcBZsnlUW+W7sfsDOrzOaT1XwghQoR0igohRIiQQBdCiBAhgS6EECFCAl0IIUKEBLoQQoQICXQhhAgREuhCCBEi/h+1BTM7EhfZqgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Compare the consumption functions for the perfect foresight and idiosyncratic\n", + "# shock types. Risky income cFunc asymptotically approaches perfect foresight cFunc.\n", + "print(\"Consumption functions for perfect foresight vs idiosyncratic shocks:\")\n", + "plotFuncs(\n", + " [PFexample.cFunc[0], IndShockExample.cFunc[0]],\n", + " IndShockExample.solution[0].mNrmMin,\n", + " 100,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare the value functions for the two types\n", + "if IndShockExample.vFuncBool:\n", + " print(\"Value functions for perfect foresight vs idiosyncratic shocks:\")\n", + " plotFuncs(\n", + " [PFexample.solution[0].vFunc, IndShockExample.solution[0].vFunc],\n", + " IndShockExample.solution[0].mNrmMin + 0.5,\n", + " 10,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, and pLvlNow_hist\n", + "if do_simulation:\n", + " IndShockExample.T_sim = 120\n", + " IndShockExample.track_vars = [\"mNrmNow\", \"cNrmNow\", \"pLvlNow\"]\n", + " IndShockExample.makeShockHistory() # This is optional, simulation will draw shocks on the fly if it isn't run.\n", + " IndShockExample.initializeSim()\n", + " IndShockExample.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Make and solve an idiosyncratic shocks consumer with a finite lifecycle\n", + "LifecycleExample = IndShockConsumerType(**Params.init_lifecycle)\n", + "LifecycleExample.cycles = (\n", + " 1\n", + ") # Make this consumer live a sequence of periods exactly once" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a lifecycle consumer took 0.0202 seconds.\n" + ] + } + ], + "source": [ + "start_time = time()\n", + "LifecycleExample.solve()\n", + "end_time = time()\n", + "print(\"Solving a lifecycle consumer took \" + mystr(end_time - start_time) + \" seconds.\")\n", + "LifecycleExample.unpackcFunc()\n", + "LifecycleExample.timeFwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions while working:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption functions during working life\n", + "print(\"Consumption functions while working:\")\n", + "mMin = min(\n", + " [LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]\n", + ")\n", + "plotFuncs(LifecycleExample.cFunc[: LifecycleExample.T_retire], mMin, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions while retired:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption functions during retirement\n", + "print(\"Consumption functions while retired:\")\n", + "plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire :], 0, 5)\n", + "LifecycleExample.timeRev()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, pLvlNow_hist, and t_age_hist\n", + "if do_simulation:\n", + " LifecycleExample.T_sim = 120\n", + " LifecycleExample.track_vars = [\"mNrmNow\", \"cNrmNow\", \"pLvlNow\", \"t_age\"]\n", + " LifecycleExample.initializeSim()\n", + " LifecycleExample.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Make and solve a \"cyclical\" consumer type who lives the same four quarters repeatedly.\n", + "# The consumer has income that greatly fluctuates throughout the year.\n", + "CyclicalExample = IndShockConsumerType(**Params.init_cyclical)\n", + "CyclicalExample.cycles = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a cyclical consumer took 0.3813 seconds.\n" + ] + } + ], + "source": [ + "start_time = time()\n", + "CyclicalExample.solve()\n", + "end_time = time()\n", + "print(\"Solving a cyclical consumer took \" + mystr(end_time - start_time) + \" seconds.\")\n", + "CyclicalExample.unpackcFunc()\n", + "CyclicalExample.timeFwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quarterly consumption functions:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption functions for the cyclical consumer type\n", + "print(\"Quarterly consumption functions:\")\n", + "mMin = min([X.mNrmMin for X in CyclicalExample.solution])\n", + "plotFuncs(CyclicalExample.cFunc, mMin, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate some data; results stored in cHist, mHist, bHist, aHist, MPChist, and pHist\n", + "if do_simulation:\n", + " CyclicalExample.T_sim = 480\n", + " CyclicalExample.track_vars = [\"mNrmNow\", \"cNrmNow\", \"pLvlNow\", \"t_cycle\"]\n", + " CyclicalExample.initializeSim()\n", + " CyclicalExample.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Make and solve an agent with a kinky interest rate\n", + "KinkyExample = KinkedRconsumerType()\n", + "KinkyExample.cycles = 0 # Make the Example infinite horizon" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a kinky consumer took 0.5089 seconds.\n", + "Kinky consumption function:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "start_time = time()\n", + "KinkyExample.solve()\n", + "end_time = time()\n", + "print(\"Solving a kinky consumer took \" + mystr(end_time - start_time) + \" seconds.\")\n", + "KinkyExample.unpackcFunc()\n", + "print(\"Kinky consumption function:\")\n", + "KinkyExample.timeFwd()\n", + "plotFuncs(KinkyExample.cFunc[0], KinkyExample.solution[0].mNrmMin, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "if do_simulation:\n", + " KinkyExample.T_sim = 120\n", + " KinkyExample.track_vars = [\"mNrmNow\", \"cNrmNow\", \"pLvlNow\"]\n", + " KinkyExample.initializeSim()\n", + " KinkyExample.simulate()" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/HARK/ConsumptionSaving/example_ConsIndShock.py b/examples/ConsumptionSaving/example_ConsIndShock.py similarity index 50% rename from HARK/ConsumptionSaving/example_ConsIndShock.py rename to examples/ConsumptionSaving/example_ConsIndShock.py index 2544edc54..6f47c197c 100644 --- a/HARK/ConsumptionSaving/example_ConsIndShock.py +++ b/examples/ConsumptionSaving/example_ConsIndShock.py @@ -1,103 +1,123 @@ import HARK.ConsumptionSaving.ConsumerParameters as Params -from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType, KinkedRconsumerType +from HARK.ConsumptionSaving.ConsIndShockModel import ( + PerfForesightConsumerType, + IndShockConsumerType, + KinkedRconsumerType, +) from HARK.utilities import plotFuncsDer, plotFuncs from time import time +mystr = lambda number: "{:.4f}".format(number) -mystr = lambda number : "{:.4f}".format(number) -do_simulation = True +do_simulation = True # Make and solve an example perfect foresight consumer PFexample = PerfForesightConsumerType() -PFexample.cycles = 0 # Make this type have an infinite horizon +# Make this type have an infinite horizon +PFexample.cycles = 0 start_time = time() PFexample.solve() end_time = time() -print('Solving a perfect foresight consumer took ' + mystr(end_time-start_time) + ' seconds.') +print( + "Solving a perfect foresight consumer took " + + mystr(end_time - start_time) + + " seconds." +) PFexample.unpackcFunc() PFexample.timeFwd() # Plot the perfect foresight consumption function -print('Perfect foresight consumption function:') +print("Perfect foresight consumption function:") mMin = PFexample.solution[0].mNrmMin -plotFuncs(PFexample.cFunc[0],mMin,mMin+10) +plotFuncs(PFexample.cFunc[0], mMin, mMin + 10) if do_simulation: - PFexample.T_sim = 120 # Set number of simulation periods - PFexample.track_vars = ['mNrmNow'] + PFexample.T_sim = 120 # Set number of simulation periods + PFexample.track_vars = ["mNrmNow"] PFexample.initializeSim() PFexample.simulate() -"" # Make and solve an example consumer with idiosyncratic income shocks IndShockExample = IndShockConsumerType() -IndShockExample.cycles = 0 # Make this type have an infinite horizon +IndShockExample.cycles = 0 # Make this type have an infinite horizon start_time = time() IndShockExample.solve() end_time = time() -print('Solving a consumer with idiosyncratic shocks took ' + mystr(end_time-start_time) + ' seconds.') +print( + "Solving a consumer with idiosyncratic shocks took " + + mystr(end_time - start_time) + + " seconds." +) IndShockExample.unpackcFunc() IndShockExample.timeFwd() # Plot the consumption function and MPC for the infinite horizon consumer -print('Concave consumption function:') -plotFuncs(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5) -print('Marginal consumption function:') -plotFuncsDer(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5) +print("Concave consumption function:") +plotFuncs(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5) +print("Marginal consumption function:") +plotFuncsDer(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5) # Compare the consumption functions for the perfect foresight and idiosyncratic # shock types. Risky income cFunc asymptotically approaches perfect foresight cFunc. -print('Consumption functions for perfect foresight vs idiosyncratic shocks:') -plotFuncs([PFexample.cFunc[0],IndShockExample.cFunc[0]],IndShockExample.solution[0].mNrmMin,100) +print("Consumption functions for perfect foresight vs idiosyncratic shocks:") +plotFuncs( + [PFexample.cFunc[0], IndShockExample.cFunc[0]], + IndShockExample.solution[0].mNrmMin, + 100, +) # Compare the value functions for the two types if IndShockExample.vFuncBool: - print('Value functions for perfect foresight vs idiosyncratic shocks:') - plotFuncs([PFexample.solution[0].vFunc,IndShockExample.solution[0].vFunc], - IndShockExample.solution[0].mNrmMin+0.5,10) + print("Value functions for perfect foresight vs idiosyncratic shocks:") + plotFuncs( + [PFexample.solution[0].vFunc, IndShockExample.solution[0].vFunc], + IndShockExample.solution[0].mNrmMin + 0.5, + 10, + ) # Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, and pLvlNow_hist if do_simulation: IndShockExample.T_sim = 120 - IndShockExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow'] - IndShockExample.makeShockHistory() # This is optional, simulation will draw shocks on the fly if it isn't run. + IndShockExample.track_vars = ["mNrmNow", "cNrmNow", "pLvlNow"] + IndShockExample.makeShockHistory() # This is optional, simulation will draw shocks on the fly if it isn't run. IndShockExample.initializeSim() IndShockExample.simulate() -########################################################################### - # Make and solve an idiosyncratic shocks consumer with a finite lifecycle LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) -LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once +LifecycleExample.cycles = ( + 1 +) # Make this consumer live a sequence of periods exactly once start_time = time() LifecycleExample.solve() end_time = time() -print('Solving a lifecycle consumer took ' + mystr(end_time-start_time) + ' seconds.') +print("Solving a lifecycle consumer took " + mystr(end_time - start_time) + " seconds.") LifecycleExample.unpackcFunc() LifecycleExample.timeFwd() # Plot the consumption functions during working life -print('Consumption functions while working:') -mMin = min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]) -plotFuncs(LifecycleExample.cFunc[:LifecycleExample.T_retire],mMin,5) +print("Consumption functions while working:") +mMin = min( + [LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)] +) +plotFuncs(LifecycleExample.cFunc[: LifecycleExample.T_retire], mMin, 5) # Plot the consumption functions during retirement -print('Consumption functions while retired:') -plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire:],0,5) +print("Consumption functions while retired:") +plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire :], 0, 5) LifecycleExample.timeRev() # Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, pLvlNow_hist, and t_age_hist if do_simulation: LifecycleExample.T_sim = 120 - LifecycleExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_age'] + LifecycleExample.track_vars = ["mNrmNow", "cNrmNow", "pLvlNow", "t_age"] LifecycleExample.initializeSim() LifecycleExample.simulate() -"" # Make and solve a "cyclical" consumer type who lives the same four quarters repeatedly. # The consumer has income that greatly fluctuates throughout the year. CyclicalExample = IndShockConsumerType(**Params.init_cyclical) @@ -106,43 +126,37 @@ start_time = time() CyclicalExample.solve() end_time = time() -print('Solving a cyclical consumer took ' + mystr(end_time-start_time) + ' seconds.') +print("Solving a cyclical consumer took " + mystr(end_time - start_time) + " seconds.") CyclicalExample.unpackcFunc() CyclicalExample.timeFwd() # Plot the consumption functions for the cyclical consumer type -print('Quarterly consumption functions:') +print("Quarterly consumption functions:") mMin = min([X.mNrmMin for X in CyclicalExample.solution]) -plotFuncs(CyclicalExample.cFunc,mMin,5) +plotFuncs(CyclicalExample.cFunc, mMin, 5) # Simulate some data; results stored in cHist, mHist, bHist, aHist, MPChist, and pHist if do_simulation: CyclicalExample.T_sim = 480 - CyclicalExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_cycle'] + CyclicalExample.track_vars = ["mNrmNow", "cNrmNow", "pLvlNow", "t_cycle"] CyclicalExample.initializeSim() CyclicalExample.simulate() -"" # Make and solve an agent with a kinky interest rate KinkyExample = KinkedRconsumerType() -KinkyExample.cycles = 0 # Make the Example infinite horizon +KinkyExample.cycles = 0 # Make the Example infinite horizon start_time = time() KinkyExample.solve() end_time = time() -print('Solving a kinky consumer took ' + mystr(end_time-start_time) + ' seconds.') +print("Solving a kinky consumer took " + mystr(end_time - start_time) + " seconds.") KinkyExample.unpackcFunc() -print('Kinky consumption function:') +print("Kinky consumption function:") KinkyExample.timeFwd() -plotFuncs(KinkyExample.cFunc[0],KinkyExample.solution[0].mNrmMin,5) +plotFuncs(KinkyExample.cFunc[0], KinkyExample.solution[0].mNrmMin, 5) if do_simulation: KinkyExample.T_sim = 120 - KinkyExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow'] + KinkyExample.track_vars = ["mNrmNow", "cNrmNow", "pLvlNow"] KinkyExample.initializeSim() KinkyExample.simulate() - - - -"" - diff --git a/examples/ConsumptionSaving/example_ConsMarkovModel.ipynb b/examples/ConsumptionSaving/example_ConsMarkovModel.ipynb new file mode 100644 index 000000000..e4a222e37 --- /dev/null +++ b/examples/ConsumptionSaving/example_ConsMarkovModel.ipynb @@ -0,0 +1,492 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import HARK.ConsumptionSaving.ConsumerParameters as Params\n", + "from HARK.utilities import plotFuncs\n", + "from time import process_time\n", + "from copy import deepcopy, copy\n", + "import numpy as np\n", + "from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType\n", + "mystr = lambda number: \"{:.4f}\".format(number)\n", + "do_simulation = True" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the Markov transition matrix for serially correlated unemployment\n", + "unemp_length = 5 # Averange length of unemployment spell\n", + "urate_good = 0.05 # Unemployment rate when economy is in good state\n", + "urate_bad = 0.12 # Unemployment rate when economy is in bad state\n", + "bust_prob = 0.01 # Probability of economy switching from good to bad\n", + "recession_length = 20 # Averange length of bad state\n", + "p_reemploy = 1.0 / unemp_length\n", + "p_unemploy_good = p_reemploy * urate_good / (1 - urate_good)\n", + "p_unemploy_bad = p_reemploy * urate_bad / (1 - urate_bad)\n", + "boom_prob = 1.0 / recession_length\n", + "MrkvArray = np.array(\n", + " [\n", + " [\n", + " (1 - p_unemploy_good) * (1 - bust_prob),\n", + " p_unemploy_good * (1 - bust_prob),\n", + " (1 - p_unemploy_good) * bust_prob,\n", + " p_unemploy_good * bust_prob,\n", + " ],\n", + " [\n", + " p_reemploy * (1 - bust_prob),\n", + " (1 - p_reemploy) * (1 - bust_prob),\n", + " p_reemploy * bust_prob,\n", + " (1 - p_reemploy) * bust_prob,\n", + " ],\n", + " [\n", + " (1 - p_unemploy_bad) * boom_prob,\n", + " p_unemploy_bad * boom_prob,\n", + " (1 - p_unemploy_bad) * (1 - boom_prob),\n", + " p_unemploy_bad * (1 - boom_prob),\n", + " ],\n", + " [\n", + " p_reemploy * boom_prob,\n", + " (1 - p_reemploy) * boom_prob,\n", + " p_reemploy * (1 - boom_prob),\n", + " (1 - p_reemploy) * (1 - boom_prob),\n", + " ],\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a consumer with serially correlated unemployment, subject to boom and bust cycles\n", + "init_serial_unemployment = copy(Params.init_idiosyncratic_shocks)\n", + "init_serial_unemployment[\"MrkvArray\"] = [MrkvArray]\n", + "init_serial_unemployment[\"UnempPrb\"] = 0 # to make income distribution when employed\n", + "init_serial_unemployment[\"global_markov\"] = False\n", + "SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment)\n", + "SerialUnemploymentExample.cycles = 0\n", + "SerialUnemploymentExample.vFuncBool = False # for easy toggling here" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Replace the default (lognormal) income distribution with a custom one\n", + "employed_income_dist = [np.ones(1), np.ones(1), np.ones(1)] # Definitely get income\n", + "unemployed_income_dist = [np.ones(1), np.ones(1), np.zeros(1)] # Definitely don't\n", + "SerialUnemploymentExample.IncomeDstn = [\n", + " [\n", + " employed_income_dist,\n", + " unemployed_income_dist,\n", + " employed_income_dist,\n", + " unemployed_income_dist,\n", + " ]\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Interest factor, permanent growth rates, and survival probabilities are constant arrays\n", + "SerialUnemploymentExample.Rfree = np.array(4 * [SerialUnemploymentExample.Rfree])\n", + "SerialUnemploymentExample.PermGroFac = [\n", + " np.array(4 * SerialUnemploymentExample.PermGroFac)\n", + "]\n", + "SerialUnemploymentExample.LivPrb = [SerialUnemploymentExample.LivPrb * np.ones(4)]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a Markov consumer with serially correlated unemployment took 0.6064 seconds.\n", + "Consumption functions for each discrete state:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Solve the serial unemployment consumer's problem and display solution\n", + "SerialUnemploymentExample.timeFwd()\n", + "start_time = process_time()\n", + "SerialUnemploymentExample.solve()\n", + "end_time = process_time()\n", + "print(\n", + " \"Solving a Markov consumer with serially correlated unemployment took \"\n", + " + mystr(end_time - start_time)\n", + " + \" seconds.\"\n", + ")\n", + "print(\"Consumption functions for each discrete state:\")\n", + "plotFuncs(SerialUnemploymentExample.solution[0].cFunc, 0, 50)\n", + "if SerialUnemploymentExample.vFuncBool:\n", + " print(\"Value functions for each discrete state:\")\n", + " plotFuncs(SerialUnemploymentExample.solution[0].vFunc, 5, 50)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate some data; results stored in cHist, mNrmNow_hist, cNrmNow_hist, and MrkvNow_hist\n", + "if do_simulation:\n", + " SerialUnemploymentExample.T_sim = 120\n", + " SerialUnemploymentExample.MrkvPrbsInit = [0.25, 0.25, 0.25, 0.25]\n", + " SerialUnemploymentExample.track_vars = [\"mNrmNow\", \"cNrmNow\"]\n", + " SerialUnemploymentExample.makeShockHistory() # This is optional\n", + " SerialUnemploymentExample.initializeSim()\n", + " SerialUnemploymentExample.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a consumer who occasionally gets \"unemployment immunity\" for a fixed period\n", + "UnempPrb = 0.05 # Probability of becoming unemployed each period\n", + "ImmunityPrb = 0.01 # Probability of becoming \"immune\" to unemployment\n", + "ImmunityT = 6 # Number of periods of immunity" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "StateCount = ImmunityT + 1 # Total number of Markov states\n", + "IncomeDstnReg = [\n", + " np.array([1 - UnempPrb, UnempPrb]),\n", + " np.array([1.0, 1.0]),\n", + " np.array([1.0 / (1.0 - UnempPrb), 0.0]),\n", + "] # Ordinary income distribution\n", + "IncomeDstnImm = [\n", + " np.array([1.0]),\n", + " np.array([1.0]),\n", + " np.array([1.0]),\n", + "] # Income distribution when unemployed\n", + "IncomeDstn = [IncomeDstnReg] + ImmunityT * [\n", + " IncomeDstnImm\n", + "] # Income distribution for each Markov state, in a list" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Make the Markov transition array. MrkvArray[i,j] is the probability of transitioning\n", + "# to state j in period t+1 from state i in period t.\n", + "MrkvArray = np.zeros((StateCount, StateCount))\n", + "MrkvArray[0, 0] = (\n", + " 1.0 - ImmunityPrb\n", + ") # Probability of not becoming immune in ordinary state: stay in ordinary state\n", + "MrkvArray[\n", + " 0, ImmunityT\n", + "] = (\n", + " ImmunityPrb\n", + ") # Probability of becoming immune in ordinary state: begin immunity periods\n", + "for j in range(ImmunityT):\n", + " MrkvArray[\n", + " j + 1, j\n", + " ] = (\n", + " 1.0\n", + " ) # When immune, have 100% chance of transition to state with one fewer immunity periods remaining" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "init_unemployment_immunity = copy(Params.init_idiosyncratic_shocks)\n", + "init_unemployment_immunity[\"MrkvArray\"] = [MrkvArray]\n", + "ImmunityExample = MarkovConsumerType(**init_unemployment_immunity)\n", + "ImmunityExample.assignParameters(\n", + " Rfree=np.array(np.array(StateCount * [1.03])), # Interest factor same in all states\n", + " PermGroFac=[\n", + " np.array(StateCount * [1.01])\n", + " ], # Permanent growth factor same in all states\n", + " LivPrb=[np.array(StateCount * [0.98])], # Same survival probability in all states\n", + " BoroCnstArt=None, # No artificial borrowing constraint\n", + " cycles=0,\n", + ") # Infinite horizon\n", + "ImmunityExample.IncomeDstn = [IncomeDstn]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving an \"unemployment immunity\" consumer took 0.5869 seconds.\n", + "Consumption functions for each discrete state:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ms/dev/HARK/HARK/interpolation.py:1710: RuntimeWarning: All-NaN slice encountered\n", + " y = np.nanmin(fx,axis=1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Solve the unemployment immunity problem and display the consumption functions\n", + "start_time = process_time()\n", + "ImmunityExample.solve()\n", + "end_time = process_time()\n", + "print(\n", + " 'Solving an \"unemployment immunity\" consumer took '\n", + " + mystr(end_time - start_time)\n", + " + \" seconds.\"\n", + ")\n", + "print(\"Consumption functions for each discrete state:\")\n", + "mNrmMin = np.min([ImmunityExample.solution[0].mNrmMin[j] for j in range(StateCount)])\n", + "plotFuncs(ImmunityExample.solution[0].cFunc, mNrmMin, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a consumer with serially correlated permanent income growth\n", + "UnempPrb = 0.05 # Unemployment probability\n", + "StateCount = 5 # Number of permanent income growth rates\n", + "Persistence = (\n", + " 0.5\n", + ") # Probability of getting the same permanent income growth rate next period" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "IncomeDstnReg = [\n", + " np.array([1 - UnempPrb, UnempPrb]),\n", + " np.array([1.0, 1.0]),\n", + " np.array([1.0, 0.0]),\n", + "]\n", + "IncomeDstn = StateCount * [\n", + " IncomeDstnReg\n", + "] # Same simple income distribution in each state" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Make the state transition array for this type: Persistence probability of remaining in the same state, equiprobable otherwise\n", + "MrkvArray = Persistence * np.eye(StateCount) + (1.0 / StateCount) * (\n", + " 1.0 - Persistence\n", + ") * np.ones((StateCount, StateCount))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "init_serial_growth = copy(Params.init_idiosyncratic_shocks)\n", + "init_serial_growth[\"MrkvArray\"] = [MrkvArray]\n", + "SerialGroExample = MarkovConsumerType(**init_serial_growth)\n", + "SerialGroExample.assignParameters(\n", + " Rfree=np.array(\n", + " np.array(StateCount * [1.03])\n", + " ), # Same interest factor in each Markov state\n", + " PermGroFac=[\n", + " np.array([0.97, 0.99, 1.01, 1.03, 1.05])\n", + " ], # Different permanent growth factor in each Markov state\n", + " LivPrb=[np.array(StateCount * [0.98])], # Same survival probability in all states\n", + " cycles=0,\n", + ")\n", + "SerialGroExample.IncomeDstn = [IncomeDstn]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a serially correlated growth consumer took 0.5156 seconds.\n", + "Consumption functions for each discrete state:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Solve the serially correlated permanent growth shock problem and display the consumption functions\n", + "start_time = process_time()\n", + "SerialGroExample.solve()\n", + "end_time = process_time()\n", + "print(\n", + " \"Solving a serially correlated growth consumer took \"\n", + " + mystr(end_time - start_time)\n", + " + \" seconds.\"\n", + ")\n", + "print(\"Consumption functions for each discrete state:\")\n", + "plotFuncs(SerialGroExample.solution[0].cFunc, 0, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a consumer with serially correlated interest factors\n", + "SerialRExample = deepcopy(SerialGroExample) # Same as the last problem...\n", + "SerialRExample.assignParameters(\n", + " PermGroFac=[\n", + " np.array(StateCount * [1.01])\n", + " ], # ...but now the permanent growth factor is constant...\n", + " Rfree=np.array([1.01, 1.02, 1.03, 1.04, 1.05]),\n", + ") # ...and the interest factor is what varies across states" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a serially correlated interest consumer took 0.5100 seconds.\n", + "Consumption functions for each discrete state:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Solve the serially correlated interest rate problem and display the consumption functions\n", + "start_time = process_time()\n", + "SerialRExample.solve()\n", + "end_time = process_time()\n", + "print(\n", + " \"Solving a serially correlated interest consumer took \"\n", + " + mystr(end_time - start_time)\n", + " + \" seconds.\"\n", + ")\n", + "print(\"Consumption functions for each discrete state:\")\n", + "plotFuncs(SerialRExample.solution[0].cFunc, 0, 10)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/HARK/ConsumptionSaving/example_ConsMarkovModel.py b/examples/ConsumptionSaving/example_ConsMarkovModel.py similarity index 97% rename from HARK/ConsumptionSaving/example_ConsMarkovModel.py rename to examples/ConsumptionSaving/example_ConsMarkovModel.py index 75ecb3794..bdae63fb2 100644 --- a/HARK/ConsumptionSaving/example_ConsMarkovModel.py +++ b/examples/ConsumptionSaving/example_ConsMarkovModel.py @@ -4,9 +4,7 @@ from copy import deepcopy, copy import numpy as np from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType - mystr = lambda number: "{:.4f}".format(number) - do_simulation = True # Define the Markov transition matrix for serially correlated unemployment @@ -101,8 +99,6 @@ SerialUnemploymentExample.initializeSim() SerialUnemploymentExample.simulate() -############################################################################### - # Make a consumer who occasionally gets "unemployment immunity" for a fixed period UnempPrb = 0.05 # Probability of becoming unemployed each period ImmunityPrb = 0.01 # Probability of becoming "immune" to unemployment @@ -168,8 +164,6 @@ mNrmMin = np.min([ImmunityExample.solution[0].mNrmMin[j] for j in range(StateCount)]) plotFuncs(ImmunityExample.solution[0].cFunc, mNrmMin, 10) -############################################################################### - # Make a consumer with serially correlated permanent income growth UnempPrb = 0.05 # Unemployment probability StateCount = 5 # Number of permanent income growth rates @@ -219,8 +213,6 @@ print("Consumption functions for each discrete state:") plotFuncs(SerialGroExample.solution[0].cFunc, 0, 10) -############################################################################### - # Make a consumer with serially correlated interest factors SerialRExample = deepcopy(SerialGroExample) # Same as the last problem... SerialRExample.assignParameters( diff --git a/examples/ConsumptionSaving/example_ConsMedModel.ipynb b/examples/ConsumptionSaving/example_ConsMedModel.ipynb new file mode 100644 index 000000000..c9768999c --- /dev/null +++ b/examples/ConsumptionSaving/example_ConsMedModel.ipynb @@ -0,0 +1,313 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import HARK.ConsumptionSaving.ConsumerParameters as Params\n", + "from HARK.utilities import CRRAutility_inv\n", + "from time import time\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from HARK.ConsumptionSaving.ConsMedModel import MedShockConsumerType" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mystr = lambda number: \"{:.4f}\".format(number)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "do_simulation = True" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ms/dev/HARK/HARK/ConsumptionSaving/ConsMedModel.py:635: RuntimeWarning: divide by zero encountered in power\n", + " vPgrid = cLvlGrid**(-self.CRRA)\n", + "/Users/ms/dev/HARK/HARK/utilities.py:141: RuntimeWarning: divide by zero encountered in reciprocal\n", + " return( c**(1.0 - gam) / (1.0 - gam) )\n", + "/Users/ms/dev/HARK/HARK/utilities.py:141: RuntimeWarning: divide by zero encountered in power\n", + " return( c**(1.0 - gam) / (1.0 - gam) )\n", + "/Users/ms/dev/HARK/HARK/ConsumptionSaving/ConsMedModel.py:649: RuntimeWarning: invalid value encountered in multiply\n", + " vGrid = utility(cLvlGrid,gam=self.CRRA) + MedShkGrid_tiled*utility(MedGrid,gam=self.CRRAmed)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a medical shocks consumer took 84.2861 seconds.\n" + ] + } + ], + "source": [ + "# Make and solve an example medical shocks consumer type\n", + "MedicalExample = MedShockConsumerType()\n", + "t_start = time()\n", + "MedicalExample.solve()\n", + "t_end = time()\n", + "print(\"Solving a medical shocks consumer took \" + mystr(t_end - t_start) + \" seconds.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function by medical need shock (constant permanent income)\n" + ] + }, + { + "data": { + "image/png": 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EODvTTnB8iDxTHr9a/ascLzqOZSGMq7mJ+eaXiMzMoDAYSDx6FHNjI4aa7aueddAbYeTaPINdc8zdcYMAWSXJbH0+j6LNaWj095a+WDTC2M3r2NoucOdqF9FIGHNaOrUvfY6y3ftJzc55oM8h6gzgv23Hf9NOdN4PAmjXJ6Gy3t/SeRTkQ1EZGZnHxhv/4y9ZvuFAyvbjzR1mMD2TdqGOcWcOqmkfSkeQjdIoXzN18pzYjm6l1HC4/BgtiUZenetiIbCASWPi+YLnqS+qp1KZi/u107iaV3JUFAoSdu3C3NhI4uFDKPRxcYxFRMZ7HAx2zTHRG2/6SVmXQGltBsXb00lM0d1zzZIoMj3Yj63tAkOd7QS9HvSJJkp37aG8bj+ZxWUPVKESc4fx37YTuGUnPOkBQJNnwrDJin6DBWXi/TtI14Kc5SIjI/PEGLjSzvX/9QbKJA3hgkEmslR0abdzw1MNM2E0c37Swg5+1tDJz2rasAQnQKXDWXqE19LyaXYPYlu0rbbgNxQ1sDdtJ6HWDlzNzXgvXoJIBG1pKebGxniOSloaAJIYTzQc7Jpj9PoCIX8Ug0lDcU06pbUZWLLv3/RjvzuOre0CA+0X8TjsqLRa1m/bQfme/eRt2IxStXYDQ/RHCPQ68d9aIHTHBRKoMxPQb7Ji2GhFdZ8vk4dBFnQZGZnHise9zKvf/iMIG5EKJljIcXLDXEV7aDf+uXjGuNbj4wXVVb5u6qTMfx0BiVBOLRcKttISW6RttouYFKMytZL6onqO5R/DMDjJclMT7tOvIbpcKK0WzMfrMTc2oCsrW73/0pyPwa45hrrjiYYqjYLCzVZKazPILku5b9OP22FnoP0itrYLOO6OIygU5G/cTHndfoq270CjW7sVIoZjBPud+G/ZCQ4tQUxClaqLi/gmK+r0tR+UPghy2aKMjMxj4bW//DPct5eRcjx4No0ykJZNq3SYKcdKxrjTx24G+Ya5i70J7WhiPiRNLjc3vkKzVskbsx14Jk+RZkjjy5VfpqGogRyvFldzM8tNL7MwMYGg05F46BDmE40k7NyJsLJT9rvDcV+8c46FCQ+CANnlKdQ2FFKwyXLfRMOA18NwZzu2tgtM2XoByCwp4+Arv0Tpzj0YzElrfn4pKhIcWoqLeL8TKSKiMGkw7lwXF/EP+I3gaSDv0GVkZD6Q/s42bv39mwgpGsL5NsaydHRqaulxVcFMCM18gKzoHL+gv8zn1G0khWZAY2Sy7AinUjJoWbzFpGcSvUrP4dzD1BfVszWhHN+bb+FqaiJw9RoAhpqauC9+9Mhqvng0HGPsdtwXv9u3iCRKWHKMq754gll7zzVHwiHuXLuCre0CYzeuIsaipKzLprxuP2V1+0lKz1jz80uiROjOMv6bdgK9TqRgFIVBhX6DBf1GK9oCM8J9fiN4EsiWi4yMzAPhXl7i9Le/B7EExIIJZnNc3DRV0RHYRXBWiWbWR4LPQ6O6m68YOykK3EZCwFtQx5s5lTQHZ7huv4mAQE1mDQ1FDRzK3IfYfQN3czOes+eQQiE0BQWYGxsx1x9HnZUFxAV0emiJwe55Rq8vEAnGMCZrKalJp6Qmg9SsewdbiWKMyd6eePt9dzvhQICE5BTKdu2lvG7/A7XfS5JEeNJD4KYdf48d0RNB0CjQV1rQb7KiK05CuE+545NGtlxkZGTWxKk/+QG+AR9i7jKuLePYrLm0ii8wZ09FPeRDuejhoKKXb5i62Gm4jEoMEjUUcWnTK7SoIpyf6yI0OUGBuYD/tOU/8WLBiySNL+L6lyamX/19Yk4nyqSk+NzNE43oqqpWRdY57WWoO+6Le5dCqHVKirakUVqbQVZx0j13wZIksTA2iq3tPAPtl/AtL6HRGyheab/PqdyAQnHv/JV78c5a8dhiEJQCutIUDNVWdGUpKDRrv9ZHgSzoMjKfcm53XKTvH84hWFQE8we4U2/ksrqWvqWXUPQH0SwEKI/18kpCB42mdhLDC4CZwY31NJvMvGq/hnP+LEnaJD5T/BkaihooCSbhPvUqrv/n6yyPjiKo1Rj378f80gmMdXUImnjpns8VYvhKvF7cMelFUAjkVqaw67Pryd9oQX0fAV2em8XWdgFb+0WWZqZQqlQUbN4Wb7/fsh215t5WzL24X6246WAO+koLivvUrD8osViMiYkJent7KSoqorKy8rFc953Igi4j8ynE5XRy+nf/GIEExII7TB/1cyNxI5f9v0TkLqhnfaQFJvispotfTLxMTsCGFFPiyN/HjzOLaPbeYWi5C5VHxf7s/dQX1bPLVE3wzDlcf/r7jF65AoB+61Yy/ut/xXTsKEqzGYBIKMadrjkGu+aYsi0iSZCWl0jd54sp3nb/ST9+1zIDHa0MtF1gdmQQgOyKKrYdP0FJbR0649ozxu9XK57UUPRYasXfRhRFJiYm6Ovrw2az4fP5UKvVpKY+WJfpWpE9dBmZTwmSJPHqn/wxvqEAUu4iS7mT9FkKaI3twTGfhGrGh3o5wAHFLb5h7mJrsBOFFCWYXsn5whqaJDeXF64hSiIbLRupL6rnaNZhVFd64vXi584hhcNo8vIwNTZgbmhAkx1vbxdFiamBRQa75rhz00E0FCMxRUdJbbxePDnj3iV+4WCAkSud2NouMHH7BpIoYs0roLxuP6W79mKyWNf8/B9YK77Jiir58dSKi6LI5OQkfX199Pf34/V6UalUlJSUUFVVRXFxMepHCPCSD0VlZD7F3Gw9y8A/t4JVIJA/yMg6E5dVuxhwFqOcCaBa8FMhTfAV42VeoA19ZAnRYOF62SFaDFreXLiKN+IlMyGT44XHOV54nMy7XlxNzbhPnya2tIQyORnTCy/E68U3bEAQBCRJwjHlZbBrjuEr8/hdYbQGFUVb0yitySCz6N7VIbFolInbN7C1XWDkaifRUAiTNY2y3fsor9uPJSdvzc/+gbXi1Wmo0+4/Mu5BkCSJqakp+vr66Ovrw+PxoFKpKC4uprKykpKSEjSax7Prlw9FZWQ+ZbgcDl79zg9QCglECkaZOhbiurGabu8vEx2LoZ71kxW6wxd0nfycqZ2M4CiSqOHu+gO0WNdxatnG9NJlDB4DR/KP0FDUwMZoBp6WU7h+61cYHx9H0GgwHjyIuaEB4546hJVdp3cpyFB33BdfnPGhUArkVaVSWptB3obUew6JkCSJmaGBePv95VYCHjc6YyKVew9SVrefrJLyNWeL36tWXPl2rXi1FXXW46kVlySJmZmZVRF3uVwolUrWr19PZWUlpaWlaLVr9/IfB/IOXUbmE0TLn/wA/6APKc+JM2+antQi2iJ7WJqLZ4xr3X6OqK7z9cROqoLXUEgxXFmbeSN3Iy0ROzedvSgEBTsyd1BfVM8+0zaiZy/ham4mcG2lXnz7dsyNDSQeOYLSZAIgFIhy58YCg13zTA8tgQQZhSZKazNYvzUdnfHeFoNz6m78cLPtIm77PCqNlqKtNZTv2U/+pi0oVWuzJj6oVtywyYom//HUikuSxNzcHL29vfT19bG8vIxCoVg95CwrK0One3xt/vfikSwXQRBygL8HMgAR+KEkST94z3sE4AfAC4Af+EVJkq5/0HVlQZeReTwMdLdz42/fQGFR4i8YYnBdEh3K3Yw4C1BO+1HZA2xmmK8mdnJYbEcb9RBJXEd7yR6aNXBh4SoRMcL6pPU0FDXwfPZzJFwbxNXUjPf8eaRIBE1hIeaGhnfVi8eiInf7nAx2zTN+20EsKmK26impzaC0Nh2z9d52hmfRsZotbh+/gyAoyN2wifK6/RTX7ESjX5sN8rRqxSVJYmFhYVXEFxcXEQSBwsJCKisrKS8vR69/MumJ9+JRLZco8C1Jkq4LgpAIXBME4S1Jkvrf8Z7ngeKV/2qBv1j5v4yMzBPA73HT/O0/hLCBWOEd7h4Lc81YzRXPN4ndiaKe9ZMXHuZlfQdfMrWTGrqLJOrpLzlAS1IKry32sLh4mRRdCl8o/QL1hcfJvxvG9e/NuE//DEsuF8qUFJK++EXMDQ3oqipXffHZURdDXXMMX5sn5IuiT1RTsWcdpTUZpOXfe0hE0OdluKsDW9sFJvt7QJLIKCrmwJe/TumuvSQkJa/52SNzPvw37fhvP9la8YWFhVU7xeFwIAgC+fn57N69m7KyMhISnkxWy6PwoYIuSdIsMLvyZ48gCDYgC3inoDcCfy/Ft/udgiAkCYKQufKzMjIyj4kzf/vXOLvnkPLcODZP0WsppDXyWZbnElD3xjPGX1Bd5euJnZQpriNIEgsZO/ibdfto8d9lxN2LOqTmQM4BGooa2C7m4jt1Gtf/95+ZmLiLoNXGc1QaG0jYtWvVF1+a860OT3Y7gqjUCgqqrZTUpN93SEQ0HGbsxtV4tviNK8QiEZIz17Hzs1+ivG4fyZlZa37uqDOw2vDzJGvFHQ7HqogvLCwAkJeXR21tLeXl5RgfoDTyo+CBPHRBEPKBS0CVJEnud/z7KeD3JElqW/n7WeA3JUm6+p6f/wbwDYDc3NytExMTj7p+GZlPPGO223T+6b8iJGkIFA4zmGXismonw87CVUtlK4N83dTJgWg8ECuQlMu59btoUQS47LiFKIlUW6upL6rnuZRdcL4DV9OKLy4I8RyVhoZ35aj43WGGr8RFfDUMqyyZktoMCqut9wzDEsUYU/292NouMtzVTsjvw2BOWm2/Ty8qXvOBZMwbJnAr3vDzJHLF32ZpaYne3l56e3uZn58HIDc3l8rKSioqKkhMTHws93lcPJYqF0EQjMC/A7/+TjF/++V7/Mj7vikkSfoh8EOIe+hrvbeMzKeNUDBA03d+H8mtIVZwl4kjAa4lbuKK5xvExmKoZ30UhAd5Wd/BF01tpISmkcQErpUfoCXRyBvOW/icHWQmZPK1DV+jPu8FUm/dxfWXzcyf/Xa8XrywEOtv/EbcF1+3DoBwMMpI1xxDXXNMDiythmHt+ux6Srank5D0/qoNSZKYvzPCQPsFBjpa8S0totbpKa7ZSXndfnKrNqFQrs0GEUMxAv1O/DcWCI0sgQjqjARMx/Ifa624x+Ohv7+fnp4epqamAMjKyuLo0aNUVFRgXmmC+rixJkEXBEFNXMz/UZKkH9/jLVPAO2czZQMzj748GZlPF23/9s9MnR2BXB+Oskl6rAW0RU/ELZU+Hwb3PC+qrvC1xE7KFDdAgsnMHfzvjH00e+8w7buNPqTnSN4RGosaqHQacDc34z71C0wtLsZzVD7/+Xi9+EqOihgTmeh1Mtg1x9gtO9GwSGKKji1HcimpySBl3b294sWZ6biIt19kaXZmtf2+bPd+CrdsQ61dm/hKMZHg8DL+Gws/LTNM0pK4NzteK36fpqMHJRAIYLPZ6OnpYXx8HEmSSE9P59ChQ1RVVZGcvHYf/1llLVUuAvB3wKIkSb9+n/e8CPwq8SqXWuC/S5JU80HXlatcZGTizIyPcOF7f4vSqCVQOMpAtpFO1Q6GnEUop/2o7X62M8hXEy9zINaBOubHk5zHm4U1NOPl+mIfAgK1mbU0FDWwT1NF+PUzuJubCQ2PxHNUDhzAfKJxNUdFkiQWxj0Mdc8xfHWegCeC1qBi/dY0Sj6g6cez6GCwo5WB9ovM3xkBQSC3cgNlu/dTXLNrze33kiQRvuvBf2OBQI8d0feOMsPqNDR5psdSZhgOhxkcHKS3t5fh4WFEUSQ5OZkNGzZQVVVF2srko6dJNOpBkqKo1Q/3BfKoZYt1QCvQQ7xsEeC3gFwASZL+ckX0/xQ4Rrxs8ZX3+ufvRRZ0mU8z0UiE5t//AyJ2gVj+FOP5AW6YNtDt2UZsRkQ96yMrMs/Lug6+qG4jOTxDTGPkcslemhP0nHPeJhQLkW/Kp3F9Iy9mHMLQcRtXUxO+y50gSeg3b46PbHv+2GqOisvuX236cS0EUKoU5G9MpaQmg7zKVJTq9x9uBr1ehrraGWi/uFqhkl5YTHndPkp21pGYYlnzc0cW/PhvLPw0zVClQF+RgqE6DV1JMoLq0csMo9EoIyMj9Pb2Mjg4SCQSITExkaqqKqqqqli3bt1TH0IRjXpwOM4xv/AqTmcreblfo6joWw91Lbn1X0bmGeHK66cYbbkK6yI4C+5yOy2Ptl4xCqoAACAASURBVGgdyyuNPwa3l+Oqbr5mvExJ8BYSAqMFO2hOy+WUZxh7wLE6QLkxv56CUW/cUnnrDJLfjzo7O14v3tiAJi/eIh/whBm5tsBg1xzzY24QIKskiZKaDIo2W9Ea3t+8EwkFuXP9Cra2i6sDIpIz11G2ex9lu/eTsm7tFSoxVyheoXJzgciMb7VCxVCdhr4yFcV9Jg09CKIoMjY2Rm9vLzabjWAwiF6vp6Kigg0bNpCbm4tijZ2mj4tIxI3TeYGFhdM4Fy8iimG02gzS0l4gM+MEiYkPl7YoC7qMzEeIY26Gt37vzxF0ekJFowxkGejU1DLoXL9qqdQKNr5qvMy+2GXUsQCLqQW8lr+FptgSNtfIuwYo7wxm4T/1Gq7mFqLz8ygSEzEdO4b5RCP6LVsQBIFIOMb4LQeD3XNM9i0iihKpWUZKatIp3p5O4j2GFouxGBM9Nxlou8DwlU4iwbcHROyhbPd+0gvXr3lnKwajBHoc+G++Iwgr24ihOg3DRivK+yQqPghv56f09PTQ19eHz+dDo9FQVlbGhg0bKCwsRLnGw9jHhd8/gcN5DofjLMvLV5CkKFpNOmlpz5OW/gJm02YE4dG+WGRBl5F5yoixGC0/+GMCY2Gkwlkm89xcN1fR6a0lOiuinvGRE5njZV0HX1C3khSeI6xJ5FJJHU16FW3OPqJSlPKUchqKGjhq3oHybAeuk00E+/tBqcS4Z0/cFz9wAIVWiyhKTA8sMdg9x50bdiKh+KSf4u3xST+W7Pf725IkMTs8gK3tIoOXWwm4XWgNCSsDIvaRXVG15gERYjhGcHCRwC07gYFFiEooU3VxEa+2or5P5+iDIEkS8/Pzq2WGy8vLKJXK1STDkpKSR0oyfPD1xHC5b+JwxEXc5xsGICGhGIvlEBbLwcci4u9EFnQZmafE7fbz9P3jRYSMGMuF4/Smr6M1thfHnBnVjA+j28NxVRdfM16mONiDiEBfwQ6aLet4zT2IK+zGorfEUw2zj5J5YzLegt/aCrEYuspKzI0NmF58EVVqajzRcPIdiYbuMBq9iqItVkprMlh3n0k/jskJBtov/jRDRa2hcGsNZXX7KKjehmqNoihFRIJDi/hvOwjanEhhEYVRjWGjFX21FU3OvTtHHxSn00lvby89PT2rXZtFRUVUVVU9lfyUdxKN+lhcasNhP4vDeZ5IZBFBUJGUtB2L5RBWyyH0+twndn9Z0GVkniAe1xKnfuf7CIKOSOEoQzkqurU19DrLUazE0+4U+uOWSjQ+tm3BUsSpvI00RRa445lEq9RyMOcg9YXHqZ7T4W0+hfv11xE9HlTp6Zgb6jE3NKAtLgbA7Qisdm4uzfnXlGjoti8w0HGJgbYL2O+OIygU5G2opmz3PtZv34nWsMYMlZU0w0CPg0C/EykUi1eoVK0MTS589CCst/NT+vv7sdls7+rarKqqoqKi4qm23geDs/FduPMsS0uXEcUwKpWJ1NT9WCwHSU3Zh1pteiprkQVdRuYJ8Ppf/QXLPYtQ4GAm38GNpHI6vLsIzQqoZ3zkRWZ4WdvO5zXtmMPzhLRmLpTs5qRWoGOxb7V7s3F9IwcV5cROn8PV3ExkagrBYMD03HOYGxsw1NYiKJUEvRFGri8w1D3H7IgLgMz1ZkprMyjakoYu4f27ar/bxdDlNgY6LjI9EE/ryCwpo3z3Pkp21K05Q0WKigRHl+N2Sr8TKRhD0KvQV6Zi2GhFW2R+5CAsSZKYnp7GZrNhs9lYXFwE4l2bFRUVlJeXP7WGH0mS8Hh6V60Uj7cPAL0+F4vlMBbLQZLM21Aonp698zayoMvIPCaGb17l6v9oQbBKeAtH6ctIp02sY2YuDdWMD5PbzXFVF181XmZ9sBdRUGAr2MlPUtN5zT2EO+wm3ZBOQ1EDDWkHMbf14WpqInDjBggCCTt3YG5sJPHwYRQJCUTDMcZ7nAx1zzHR60SMSSRnJlBaGz/cNKW+P+UvHPAzcrWLgbYLjK9M+UnNzl2d8pOUnrGmZ5ViK5G0t+wE+pxIgSiCTom+IjWeZliU9MhlhqIocvfu3VURd7vdKBQKCgoKKC8vp6ys7Knlp8RiIZaWOnA44lZKKDQHCJjNW1atFIOh6KmXPL4XWdBlZB6BgM9H87f/ACmsJlY4xmiuRLduGzedGxBmQqjtfnbRx1eMHeyNdqISQzisxbyat5GTETsj7vG4pZJ7kBO5x6kcCeNpbvlpNO36IpJOnMB0/DjqjAxEUWJmaImh7nlGry8QDsYwmDWUbE+npDZ+uPleUYlFI4zdvM5A2wVGr3UTDYdItFjjU35278OSm78mIYrnirsI9NgJ9DoQfVEE7YqIb7A8llrxaDTK+Pg4NpuNgYEBfD7f6mCI8vJySkpKMKzR/nlUwmEHDscFHI4zOBfbEMUASqWBlJQ9WC2HSE3dj0bzZOZ/PiyyoMvIPATn/+lHzLdPQN4SC/mz3EotpdW/G/+MGtWMn/zwNC9r2/i8uh1zZIGIzszF4j006RS0OnuISTE2WjfSWNjAQX8e0dNncb/6anxkW0oKpuMvYm5sRFdRAYBz2stg1zzDV+bxLYdQ65QUbbZSUptBVkkyivf40pIoMjXQx0DbRYY62wj6vOgSTZTuqKOsbt+ap/xIokR43B0fmtzrQPTGc8V15akYNlrQlaQg3KPh6EGIRCKMjo7S39/P0NAQwWAQjUZDcXEx5eXlFBcXP5XpPpIk4fMNx3fhjrO43DcBCa02A4vlMFbLQZKSdqBUPt1JQw+CLOgyMmtkdmyY89/7exRmNYGiIWzrUmgXdjE+n4Ny2odx2cNxZWe88SfUgyQoGCjcTZMlg1ddAyyHXFj1VuqL6mlI3I35wi1cTU2ER0fjI9sOHcTc2Ihx924EtRqfK8RQ1zwDnbPxcW0KgdyqVEpq0inYaEH1nmzveMv+HQbaLzLQfhHvohO1Vsf67Tsoq9tH3obNKFUf3qgjiRLhu24Ctx34exyInjCCWoGuLAX9Rgu60kfPFQ+FQgwNDWGz2RgeHiYSiaDT6SgtLaWiooLCwsKnUmIoihGWl7txOM5hd5wlGJwEIDFxw6qVYjSWf+RWylqRBV1G5gOIRaO0fO+PCE7HEIumGM8LctW4iavLWxGnI6jnfWwTB3kloZ3npI5444+liFfzN9MUdTDouoNaoeZg7kEas45R2efF29SC7/LleAv+1q2YTzRiOnoUpclENBJj7JaDgctzTPY7kSRILzBRtiODoq1p6I3vb7pZmpuJi3jbRRZnplAoleRXb6V89z6KttaiXkPZ3uqEn9sOAj12Yq4wqFaGQ2xcGQ6hfTQR9/v9DA4OYrPZGB0dJRaLkZCQQFlZGRUVFeTn5z+VZp9IxIXTeRG74wyLi5eIRj0oFBqSk3djXakP12rTn/g6ngSyoMvI3IP+y63c+tFZhMwIzqKVNvxwHcuzCainfWT4F/iCpp2f17VhCU8R1hhpL9nDSb2aS85eolKUytRKThQ1cnB5HdFX38Lz2uuIPh/qdeswnziB+UQjmtzceEPMmJuBzjlGrs4T8kcxJmspqc2gbEcGyfdIFPS7XQx2xEe1zQ4PApBdUUX57v0U1+5Cn/jhZXKSJBGZ9cVzxW/biS2F4hN+SpLjIl6RgkL7aK33Xq+XgYEB+vv7GRsbQ5IkzGYz5eXllJeXk5OT81Ta7gOBaRyOt7A7zqx2aarVqVgsB7FaDpGSshul8ul4808SWdBlZFYI+Hw0/c4fQFRNqGiMgVwdXZoaBuzFKKd9GBweDiuu8/XEdjaFbiAgMpRXQ1N6LqfcwyyGlkjRpVBfWE9Dwg6Sz9/CdbKJyORkvNTw6FHMJ05g2L4NQaHAuxRksGuOgctzLM/7UakVFG62UrYzk6zS9/vikVCQ0atd2NouMH7rOmIshjU3n/I9ByjdtReTxbqm54zY/XERv2Unag+AQkBXnIR+oxV9ReojT/jxeDzYbDb6+/uZmJhAkiRSUlIoLy+noqLiqQRgSZKE19uP3R4Xca/XBoDBsB6r9TBWy2FMpk2PtUvzWUAWdJlPPW3//r+ZOjeMlLfM3aJlbiSV0+mtIbqSbFgRGePL+lbqhQ70MTfL5mxOF27jpOTC5hpFpVCxP3s/L607RmWvB+/JZvxXrsSn/dTWxi2V555DkZBAJBzjzg07g52zTA4sgRSvFy/bmcn6LWlo3iOmohhjsrcHW9t5hro6iAQDGFNSKa/bT3ndfqx5BWt6xuhiMH6wectOZHYlBKvAjH6TFX2VBeU96tQfBJfLtSrid+/eBcBisVBRUUFFRQXp6elPXMTf9sPtjrdw2M8SDM0QLy3cuiriBsPaPq+PK7Kgy3wqsc9N89bv/QWKBDWu4gl61qXRJtVhn0tCNe0j1b3IS6p2XjG0kx2+Q0Sp5XJxHSeNBs4v9REV41kqjYUNHHKkI54+g/vNt5ACATR5eZhfOoG5oQH1unXxTJQRFwOds4xcWyASjJGYqqN0R9xSMb8nx0SSJOwTY/S3nmeg/SK+pUU0egMlO+oor9tPTkXVmipUYu4w/p64iIfvroxpy01Ev9GKYaMFpenRqjWWlpZWuzXfnuyTlpa2KuJPI088GvWs+uFO54UVP1xHSkodVstzWCwHnrnSwvsx5g9xdtFNpVHPzqSHq69/LCPoZGQ+Lpz679/HO+ojVjjL8IEY3cbN3Fp8DkV/EO2Cl6Oc55WEdnbpr6CUooxaN/C9zJdo8Y3hCA6TTDJfLP0ijbpaUs7fwvX9v2FpZhZFYiLm+nrMJ06g31yNIAi4HQFuvjrGQOccbnsAlVbJ+i1WynZk3jNHxe1YwNZ2EVvreZxTd1Eo41N+Kvbsp2DLdtSaDxfgmC9CoNdB4Jad0NhKkmHmypi2jVZU90hSfBCcTif9/f309/czOxuf856ZmcmhQ4coLy/HYll7/vnDEm+1P4vdcYalpU4kKYJanYLVehSr5TApKXUole9vqnrWCMZEOl1ezjrdnHV6uBMIAfDNHOtDC/oHIe/QZT4RrHZwZkRxFE5xMz2f1lAdvhktqmkfRaFJflbbyudV7SRGnbgSLLy+vpYmIUDPSjztnuw9nMg6xqZeP96fNOG/ehUUChJ27ybppRMYDx5EodMRDkYZvR63VKaHlgHIKk2mbOe9hycHfV6GOtuxtZ1nqr8XgHWlFVTs2U/Jjro1HW6KwSiBfieBW3aCw8sgSqgsevSbrBg2WVGnPdphn91uXxXxtwclZ2Vlrbbcp6SkPNL1P4x4ffjQqh/u8fQAoNfnr1gpz2E2b0YQnm4c7sMwGQxzzunmrNNN65KXgCiiUwjsSjJyMNXEoRQTBYaH/81JtlxkPpGEwyFOfvu7iD4l4fVjDOTo6NDUMrRQhGrah2lxmXplJ68kdFAS7icqKOlcv4smk5lzywOExTDFycWcKGzkOU8u0qkz8SoVvx91Xi5Jn/ks5hONqNPTkUSJ6aElBjrnGL2+QDQsYrbqKduZQUltxvta8KORCGM3r2JrPc+da93EolGSM7Oo2HOAsrr9a2q/F8MxggMrcbSDK3G0Sdr41PtNVtSZCQ/tWb8z/Kq/vx+73Q5ATk7OqognJSU91LXXiihGcbmuYnecwW4/s1ofbjJVY7Ucxmp97plotf8wwqJIt8u3ugsf8gcByNVpOJRq4lCqiV1JRgyPmHXzNrKgy3yi6Hq1iTunb6HIczNVYOd6cjkdnh1EZkAz66VW7OcXdG0cphO1GGIsrYSmrFJaglMsBJ2YtWZeKHiBE+Y9pF3sx/WTk4THx+NVKs8fI+kzn1kdFLG84Gewc46Bzlm8iyE0OiXrt6VTtjOTjELTu8RGEkWmB/uxtV1g6HK8c9NgTqJs117K9xxY04AIKSoSHF5aCcFaRArHUCSqMWyIi7gm9+HjaCVJYm5ublXEnU4nEE8wfFvETaYnmxgYjfpYXGyNH2o6LhCNLq/Uh+/CajmMxXIIrfbpz/l8UGZDYc45PZxbdHNx0YM3JqIWBHYmJayKeJFe+0S+jGRBl/nY43I4OP2dHyBoVfiL79C3LoVWqY7p2XRU0z6yvbN8Xt3Gy7o2LJFZ3DoTbxTtoEkd4ZZrFKWgZHfWbk7kvsjWYRHvyWZ8be0giui3bSXpM5/FdPQIioQEQoEoI1fnGeycY3bUhSBATnkKZTszKdj0/u5N59QktrYL2NouxLPFtVqKt++kfM8B8jZUo/iQRpp7hWCtxtFusqItePg42ncmGPb397O0tIQgCBQUFFBRUfFUwq9CIfs7/PD2lehZMxbLAayW50hJ2YNK9fSicB+GqChxzb2yC1900+eN78KztGoOpZo4mGJiT7KRBNWTt4RkQZf52PL6X/0FS31OKJxlPN/PFWM1V5xbYCaMYcHNUeEKryS0Ux25hYREV0ENJ5MtnHUPE4qFKDQXcqKokWPhEoTXLuJuaSHmcsUzxk+cIOmlE2jy8xFFiSnbIgOdc9y5aScWEUnOMFC2M5OSmgyMye/2PL1Li6tNP/N3RhAEBXmbNlNRt5+i7TvQ6D74wO7t1nv/LTuBnpX8FO07kgyLkx46jlYURaamplarU1wuFwqFgsLCQioqKigtLX2iWeKSJOH3j2K3n8HuOIN7JS9Fp8uO78Kth0kyb0eheLZrMuzhCOecHs6u7MJd0RgqAbabEziUEt+FlyXonrolJAu6zMeKu8N9tP/gXxCsIq6icXrSs7kQ3MvyTAKqGR+bwsO8rG2lQXkZXczL3ZRcmnKraA7PMxewk6hJ5IWCF2hM2kdmxzDun5wkNDyMoFZjPHyIpM98loRdOxGUShZnfQx2zjLYOYfPFUZrUFG8PW6ppOW9294IBwOMdF+mv/U8d3tuIUki6YXFVOyJx9J+WLa4JElEpr0rteIOYq5QfOp9eQqGTVZ0pckI9xhMsRbejqF9W8Q9Hg9KpZKioqJVEdfrn1xViCTFcLluYHe8hd1+hkBgHIDExKoVEX8OY0LpM+2Hi5LETY+fMysHmrc8AQDSNKq4jZJiYm9KIqansAv/IGRBl3nmkUSRlu9/D/9UiFjRJMP5Ip3aGnrnylBMB0hddvCSqp2v6FvJjozjU+t5c/0OTmrgunsUhaBg57qdnMg5zvY7Av6mUz8d27ZpYzye9vnnUSYlEfRFGL4yz0DnHAvjbgSFQF5lCqU7MinYaEH5jmRBUYwxcfsm/ZfOMXK1k2gohMmaTsWe/ZTV7Sc1K+dDny2y4Md/c4HAbQdRRyDeel+cHBfxR2i9f1vE35507/P5UKlUrF+/noqKCkpKSp7oaDZRDLO0dJmFhdexO86sjmJLTtqBxXoYq+UQOt26J3b/x4EnGuPiooe3VkTcEYmiALat7MIPpiZSZdQ/U19EsqDLPLOM9d6g8y9OImRGsBdNcdNaRKu3Dv+0EvWsl51iH1/RX+KA2IVCinArexM/tmbxuneUQDRAvimfxqIGng+VoHztUjye1uVClZaGubEB84kTaIuKEGMid/sWGeicZey2AzEqkZqVsGqpGN4zhd45NUnfpbPYLp3Du7SILsFI6a49lNcdYF3phyfzRV2heOv9zQUiMytdm0VJ8QqVylQUhofr2nzbTunr66Ovrw+v14taraa4uJjKykrWr1//RGNoYzE/Tmcrdvsb2B1nicW8KJUJpKbux2p9DkvqflSqxCd2/8fBmD/EGaebt5wuLi/7iEgSZpWSgymJPGcxsz8lkRT1s2sHyY1FMs8UsWiU5t//Q8LOGKH1d+l/UUuHqpbRuRdRdvvIcM/yS+qLfFnfiiUyi0ObzI+KDvBjyc2Ybwa9N8ix/GO8lLyfnI47uP+mCffwCIJWS+KhQ5hfemnVUnFMebnyb8MMdc8TcIfRGdVU7c2ibEcmlpx3D4oIer0MdFyi7+IZ5kaGEBQKCjZv4+C+wxRs2f6hg5NFfwR/r4PAzXc0/OQkYj5eiGGTFWXi+1MU14IkSczMzNDX10dvby9utxulUklxcfHqpHuN5uGuvRaiUQ8OxzkW7G/gdF5EFIOoVEmkpR0jzXqU5OTdz3R+eESU6HZ5ecvp5ozTzYg/3txTYtDxjRwrz6Wa2GZKQPWIc1CfBeQdusxTY+h6N9f+56uwLsR0sZ1rKaVcdtcSnRbRznk4IN3kK4ZWdkSvIiLSXrCNH5tTuOQeJirFqLZW85m849SN6Qi0nMbX2havUqmuxvzSS5ieP4bSZCLgCTPUHc8Yd0x6USgF8jdYKN2RQV5VKsp3TNwRYzHGb12n7+JZRq92EotGsebmU7n/MGW7932oL/52rbj/xgLBoSWIxRt+DNVWDNVpqCwP51tLksT8/PyqiC8tLaFQKFYn3ZeWlj5ROyUcdq7Uh7/B4mIHkhRBo0nDaj1CmvUoSUk1z/ShpjMc5dyim7ecbi4sunFHRTSCwO5kI4dSTTyXaiJP/+x+CX0QsuUi85ERjURo+t0/IOqR8JeM05Nj5pJiN7OzVlRTXvJ8M3xJfZGXNa2Yok4mTOmczNtIc9TOQnCRFF0KDYX1NMY2YHyzC9erpxFdLlQZGZgbGzE3NqItLCAWFZnocTLQOctEjxNRlLDmJlK2M4Pi7envyxh33B2n79I5bK3n8S0voU80UV63n8r9h0nLL/zAZ5JEidDIctwX73Wu1IprMGyyYqi2os56/4i4tWK32+nt7aWvrw+Hw4EgCBQWFlJZWUl5efkTPdgMBmew299kwf4my8tXABGdLoe0tKOkWY9iMlU/s8mFkiRh8wXjVorDzVW3D4n4gebhFQHfm5z4VMoKnzSyoMs8dWydbdz80VuQ42dy/SJXkirpXt6OOB3BMO/iCFf5akIr1ZFb+BVKzhRu5ycGHVc9d1AICvZk7eEz6UfYcH0Jz49PEhoYiFsqR46Q9NIJDLW1oFDgmPRiuzzLcPc8QV8EvUlD6UrGeGrWu+urAx43A+0X6bt4lvk7IyiUSgq3bKdy32EKNm9FqfpgSyU868N/fR7/TXt8wo9Oib7KgqE6DW3hw9eKLy4uror42233+fn5VFVVUV5e/kRLDP3+MRbsb2K3v4HbfQuAhIRirNajpFmPYTSWPVMHgu8kEBNpX/bylsPFGaeb6VAEgE2Jep5LNfOcxcQGox7FM7r+h0UWdJmnQjQS4Se/8x2iAYFg8cpuXNrD7P/P3nmHt1mfbfuULFm2PCTbkuW9Y8eOR5zE2SE7ZJCEhBBGW6AUKFDooKUDykfpeNu+paV96aC0UEoLZJK99162Y8crjh3vIUuyJcva6/n+cEhJQ0wcnEHQeRw5ciS2nucnRb5y615XpwpJm5VMezNfCdzPMslh5F4ztVFJrI7PYou9FYvHRlJYEkvS72ZeTwJs3kPfrl0ILhdBI0agXHYP4QsWEBAefrltm0REWoGarPExJOVEIv5Y/7bX46GxrITqA3s4X3ISn9dDdEo6I6bNZPikqcjDFQM+J6/Zia1Mj61Uh1tr7d8rnhWBfFQ0wcOjrtlr02QyXUynfLQAKzExkdzcXHJycggLuz6FRUEQsFhr0et2oNfvwGLtN84IC8sjWj0XtXoOISEDf0K5mXQ4XBcKmmYOG/uw+wTkAWKmRYQxS9XfWqiRXX9bu5uJX9D9XFfKD+yhetVhSOqlLd3ISWUeJ3vG4GtzE6Y3sUB8nMfkB8lyn8UaEMiOjHGsCYQKSzOB4kDmpMzhHsU0kg830Pvhh7jb2hArFCgWLkS57B6Chg/H4/bSdKabs8c6aanuQfAJ/bZtE2LJGB1N0H/t+tY1NVB9cA/Vh/ZjN/ciVyj7UypTZ37qfnGfy4u9qhtbaRfOetPF4mZIYTTB+SoCPsEi7mowm81UV1dTWVl5cRVtXFwcubm5jBgxAoVi4P9crhVB8GE2n0Gv34FOvwO7vRkQoVQWXcyJ36rthV5BoMxsu1jQrLT094YnBQUyOyqc2apwJihDkd0AR6RbBb+g+xlynA4763/2a/CKsWc0UJEYwQHvFDrbVUjaLRQ463lItp+F4mPIfDZqooexJi6dLdYmrB4b6Yp0lqctZUZrGK4N2y6O4cvHj0e5bBlhs2chCgykq8nM2WP/sW0LUcou7hj/b9s2m7mXs4f3U3lgD/qmBsQBEtLHjGXE1FmkFIwa0DxZ8Ak4z5uwndZhrzQguHwERMiQF0YjL4xGqr62bYYWi4WamhoqKytpbm4GQKPRXBTx67XF8KPFVzr9dvT6XTid2v4e8YgJRKvvRKWejSzw+q/BvRas3v7e8O2GXvZ099Ht9hAggqLwEGarFMyOCmeY/PrsSfk88JkEXSQSvQ3cBegEQcj9hK9PAzYAjRf+6kNBEH76aYfyC/rnk+qjhzjz3l5INtOWbuCkMo8ThrH42two9D3cHXCEx+QHSHE3YA2Usz1jAqslbqr6mpAFyLgz5U6WB04kZl8lvRs24u3p6R/DX7oE5dKlBCYmYjU5OXu88xLbttSRarInxBI//FLbNq/HTUPpKaoO7KXx9Cl8Xi+atGH9KZWJd3zqalq31oq1VIetTIfP3J8Xl+epkY+KJjA5/Jry4g6Hg5qaGioqKi56bKrVakaMGEFubu512yfu83kwmU6i021Fp9+B292DWCwjKvIO1NFzUUVNRyq9Pp8CPis6p5ud3Wa2G3o5ZOzD6RNQSgKYERXOnKhwpkWGobyFe8NvJJ9V0O8ALMC7Awj69wRBuGswh/IL+ucHr8fDhl//BrfRiz2rkcqkcPb7ptDZ3t+pkues56tB+1koOkKgz051bA5rYlPZ0lePzWMnQ5nB8sRFTDsnxbl+C/ayMpBICJs+HeWyewiZPBkfIprPdFN9tIOWym6ET7Ft07c0UblvFzWH9mHvMxOijCB7ynRGTJ2JKjF54OfT58JWpuvPi3d+LC9eGE1w9rXlxT0eD3V1dVRUVFBbW4vX6yUiIoLc3Fxyc3PRaK6Pw7wgeDEaT6DTCjDHggAAIABJREFUb0On247b3UNAgJyoqOlER89DFTX1ljRGFgSBczYnOwy9bDf0Umq2Af2plLkqBXNU4YxThCK9DXrDh5rPNFgkCMJBkUiUMtSH8nPr03Kuf6cK8U7aM/WcisjhuPFhfNVuwnQmvizewBPy/aRTh0UawoaMiayRuKnuayLIXMedyXO41zMS9e5y+l75IyabjcC0NKKffx7F4kVIVCqMWitH1zdSe7wTe58buSKQwjnJZE+MRam5VIgcFgtnjxygcv+uC10qEjLGjCN3+myS8wsH3Groc3lxVHdjLdXhrOv3+ZQmhKJcmEZwgfqa8uIfjd6fOXOG6upqHA4HISEhjB49mvz8fOLj469LWkAQvBhNJ9HpPhLxbsTiYFSqGWii5xMVNfWWdPPx+AROma1sN/Sy09BLo90FwMgwOT9IjWGuSnFTll3dTlxVDv2CoG8eIEJfC7QBHfRH61VXuM4TwBMASUlJoz/KKfq5tdj+lz9hqjXhzGrlTIqcQ6LJtHeqkbRaybE38LBsH0vEh5H5bFTFZLEmNp2tlvPYPHaGRQzj/pgFTDrjxrF+M67684iCgwmfNw/lsmUEF47E7fRSX6Kj5kgn2oZeRGIRKXlRZE+KI3nEpV0qgs9Hc2U5Vft3U3fyKF63G3VyKrnTZ39ql4ogCLha+7AVd2Er1yM4vf0GER/lxa/R5Uer1VJRUUFFRQVmsxmpVEp2djZ5eXmkpaUR8Cnrcq8FQfBiMp2iS7cVvX4HLpfhgohPJzp6PqqoabekiFs9XvYbP8qHm+lxey8O+HwUicfKrt+U6+3IZy6KfoqghwM+QRAsIpFoPvAHQRCGfdo1/SmXW4sevZbtP/8zAZFetJkdnFJlctg8EW+bl5AuE3eJjvO4fD9Z7lr6pMFsyxjPGombGksLwZJg5ibfyb2ufCK3naJvx47+dsOCfJTLlhE+bz7iEDldjWaqj3RQX6zD7fSi1MjJnhRL1rgYQhSXTu316rRU7t9D1YHd9Bn0BIWEMnzyNHKnz0aTmj7gc/FaXNhO67EWa/F02RBJxQTnqwkZHU1gyrX1i5tMposirtPpLk5t5ufnk5WVdV1G7/8j4tvQ67dfEPEgVKoZF9Ip027JdEqX082uT8iHz4oK506VgumRYYTeBgM+N4vrKuif8L1NwBhBEAwDfZ9f0G8NDq58n/Yj9QgZWmrS4JB0InXaFCStVjKtTTwUuJdlksMEeS1URWf0R+PWJuxeO5kRmdyXuIgpVQL21etx1tYiDglBsXgxyvvvIygzE5vZRe0JLTVHOzF2WpEEiskYoyFnYiwx6YpLPl67nQ7qTh6jct8uWqvOgEhESn4hudNnkz56HJIBRFPwCTjqjNhOabHX9IBXIDAxDHmRBnm+GnHQ4AtqNpuN6upqzpw5Q0tLC9DfK56Xl8eIESOuy8BPv4gX96dT9Nv+I+JR04nWzL8lRfzT8uF3qsIZ68+HDxnXdTmXSCSKAboEQRBEItFYQAx0f9br+rl+2C0W1r/yG0QyMGU1cXpREvssS7E1BhCiNXGvsIXH5fsZIaumTyJjU8Y41ki91FhaCbY1MS9tHstERah2lGD+yf9hstmQZWcT88orKO5aAMFyWqq6qflrBU3lBnwXesanf3k4GWOiLzFRFgQBbf05Kvft4uzRg7jsNhSaGCbd9xVy7phBuEo94HPxdNuxFndhK+nCa3YhDpEQOiGOkCINUs3gBdftdlNbW0tFRQV1dXX4fD5UKhUzZswgLy+PiIiBd7tcC4LguyDiW9Hpt+Ny6RGLg4iKmnYhJz7tlnP08QkCxb1Wthh62WHopelj+fAfpsZwpz8fflP4VEEXiUQfANMAlUgkagNeBqQAgiC8ASwDnhKJRB7ADtwv3Kzmdj8DUnF4P5UfHITUbpomWTgcUkS5dhbik3Yy+hr5inQvy4MOE+I1UxmRystxs9lmbcbubGJ4yHD+36gfckedFPvr67GfXkVvYCDh8+YR8cD9BBUUYDY4OLm7g7PHtFhNToJCpeTNSCB7YixRcZeO4dt6TVQf3Evl/t10t7UgkcnIHDeJ3OmzSRg+AtEAgyKC24utshvbKS3Ohl4QQVBmBMpF6QQNj0QkGVyXykfFzbKyMqqrq3G5XISGhjJu3Djy8vKIjY0dcmESBIE+SxVd2o106bbgdGr7Wwwvivj0W07E3T6BoyYLW/Qmtht60bk8BIpETI4I5anEaH8+/BbAP1j0BWDTa69hbbNiz2rmTLKCvZ5pdLeGEdjex3RvKU8G76HIW4Y9QMKO9AmskAlUXciNz0+dz7KQyah3nKZ33Tq8RiPS5CQi7rsfxZK7EYUqaCjTU3W4nfZaU7//Zk4UOZNiSclXXbrZ0Ne/2bBy7y7Ol5zA5/USmzmc3GmzyZowBZn8yqmEj9x+rMVd2Mp0CA4vAVFBhIzRIB+lQaIY/Oa8np4eysvLKS8vx2QyERgYSE5ODvn5+aSkpCC+DtOHNlsj2q5NdHVtwmZrQCSSEBV5BxrNQlSqmbeciNu9Pg709LHFYGKnwUyvx4s8QMzMyHAWqBXMjAonzJ8Pv6H4J0W/gPT1Gtn8k98hUojozGrjhDqbY8Zx+FrcROn13CfZz+NB+1B5tLQq4liVms86eyu9rj7SFGncP2w5s9qU2Fevx3r4MAQEEDZjOsr77ydkwgT6epxUHeqg5mgH9j434aogsifGMnxCLKERl6517dVpqdy3i8oDe7B0GwgOV5Bzxwzyps8hKmFgxx+v1d3fM36qq3+XikSMPE+FfIzmmsyTnU4nVVVVlJeXX5zcTEtLo6CggOzs7OtS3HQ4tei6tqDt2khfXyX9Y/djidEsJDp6LlLp0KdxPgt9Hi+7u81s0ZvY092H3edDKQlgjiqcBWold0SEEXyNfqd+Pjt+Qf8CUXn4IBUf7ENI7+ZchosDQRM515mGpMVCru08j8l2cZfoKGLByZGU0axQRnC4tw6xSMyMpBk8qJ5H8v46TKtX49FqkURHo1y+HOW9ywhQR9NS2U3lwXaaq7oRASn5KkbcEU9SduQl4upxuag7dYzKvTtpqSxHJBKTUlBI7ow5pI8eO+Bmw4/W01qLtdirusErIE0IJWRMDPKRgy9w+nw+GhsbKS8vp6amBrfbTVRUFAUFBRQUFFyXHSputwmdbjvaro2YTCcBgbCwXGI0i4jWLCBIFjPk9/wsGFwedhp62aLv70xxCQLRgRLmqRQsUCuZoPQXNW8V/IL+BWDT73+Ptc2CI6uRsqQIdrtmYGqVE9zeyzzhJE/K95DjqaFHFsL69HGswky7XYc6WM2yYfewyJQG63fQt2cPeDyETJyI8oH7CZs+HZvVS82RTqoOt2PpcSIPDyRnchw5k+MIi7w0Gtc1NVyc4HRYLYSrNeROn8WIqbM+vcDZ48BacqHAaXIilkv6e8bHxBAYO/hUhMFguJhSMZvNyGQycnNzGTlyJAkJCUOeF/d6bej1u+nSbaa7+yCC4EYuT0OjWUiMZiFy+cBLwW40HQ4XWw29bNX3ctxkwQckBgWyQN0v4qPD5bfd6tnbAb+g36ZYzCY2vfxbRAoBfVYLJ9TZHO6eiLfFTWy3li9L9/BI4D7CvUYq1KmsiEtnu6UJl8/FGM0YHohfRGGJEfPKNbgaGwlQKFAsXUrEfcuRJifTfs5E1cF2Gk7r8fkEEoZHMGJKPKkjVQR87CO302bl7JEDVOzdRVdDHQESCRljJ5I3fQ5JufmfUuD0Ya8yYC3uwnneBIBsWAQhYzQE50QNusBpt9upqqqirKyMtrY2RCIR6enpjBw5kqysLKSfYiM3WHw+Nz09h9B2bUKv34XPZ0cmi0GjuQuNZiFhoSNuqU6PdoeLzXoTG3UmSi60F2bKg1igVjBfrbjlDJH9XI5f0G8zzleUcvKNjZBmoiGjjwPyCVR1ZCFptpJrr+cbQdu5k6N4BR8708fxnlxCpaUFuUTOwvSFLBcVEbrxIOYtWxEcDoILClA+cD/hc+fi9gVw9piWqkPtGLU2ZHIJwyfEMmJK3CXbDQVBoOPcWc7s3sa540fwuJyoklLImzGH7MnTPnUplqvdgrVYi61Mj2D3EBAhI2S0BvkYDRLl4KzVBEGgqamJ0tJSampq8Hg8qNVqRo4cSV5eHuHhA59lsAiCQF9fBZ3adXR1bcbt7kEiUaKJnodGswilcswt5ezT6XSxWdfLRp2JU2YrAHmhwdylVjJfrWBYyPWzsvMz9PgF/Tbh8NpVtB04h2t4C2dSQ9gjzEDfFk5gax8zvSU8K99JvqcSY1AYa4aNZ8UFG7eU8BQezLyfmY2h2Fasxl5cgig4GMXChf0th9nZdLdbOLO3lXMnu/C4fUSnhJN7RzzDxkQjCfxPF4PLYafm0H7Kd25B39JEYHAwwydOJW/GHDTpwwaM7nw2N7ZyPdZTWtwdVpCICB6hIqRIgyxNOegCp9lspqysjNOnT2M0GpHJZOTn5zNy5Eji4uKGPNJ0ODrRajfQqV2HzVaPWByISjWL2JglREZORiy+dVr2upxuNulNbNKZONHbL+IjQoNYpI5gYbSSNPnn00/Tj1/QP/dsePVV7DobluwGihPi2W2bibMZwjqN3Cs+wNPBu9F42jkfkcB7SSPYZG3A4XUyIXYCD8XfTdaRVkwfrMTT2Yk0IYGIL30J5dIliMPCaanuoXxPC601RiRSMZljNeROTUCddKljTndbC2U7t1J9cA8uux11cioj5yxg+OSpBAZdeYfIR3vGrcVd2KsM4BGQxoUQUhSDvECNWD64FIjX6+XcuXOcPn2auro6BEEgJSWFwsJCcnJyhjyl4vFY0et3otWuo8d4FBBQKMYQG7OE6Oj5SKVDG/1/FvQuN5v1vWzUGTlu6vfUHB4SxKJoJQvVSn8kfpvgF/TPIU6HnQ9f+hViuYBheANH1bkc6p6E0OwkvqeDRwN38RXpPoK8fRxLzOfdSBVHzPUEigNZmL6QByUT+9MqmzYjOJ3IJ4wn8itfIXTqVDxeqD2u5czeVoxaG3JFIHnTEsidEk9Q6H8E0evxUH/qOOU7t9BaXUGARELmhCmMnDOf2GEDe016TA5sxV1YS7rwGp2IgiXIR6oJGRND4H95fV4N3d3dlJaWUlZWhtVqJTQ0lJEjR1JYWEhUVNQ1vcZXon8l7XE6tevQ63fg9doIDkoiJnYJMZrFyOUDr+e9kehdbrbq+9Mpxy4UNofJZSyO7o/Es/wifttxXUf//QwtJoOBra/8H+I4Bx0TO9kfOoHSzjlIjlnIs53hmaBtzAk+hlvkY3P6RP4d6OG8tQOVW8Iz+U+zsEOD+y/rsZ1aiTkoCMXddxPxpQcJyszEanJyYlMTVYc6cFjdqJPCmPXVHDJGR18yANTXbeDMnu1U7NmB1WQkXK1hyoOPkDt99sDbDb0+7NU9WE92XrRuk2UoUcxNIThHNeg94263m7Nnz1JSUkJTUxMikYjMzExGjRpFRkbGkG81tNtb6excS0fnGpzOTiSSMDSaRcTGLEGhGH3LFAuNbg9b9L1s0Bk5YuwX8Qy5jG+naFgUrWR4yK23ddHPjcEfod8itJ2v5dBr70NqL/WZFvYETqOuLRlps5mJnkq+K9/KaE8ZvbJwVmWO5z23lm6niezIbB5Ku49xpVZ6//Ue7pYWpHFx/WmVe5YSoFSib+mjbE8L9cU6fD6B1HwVI2clEpuhvChSgiDQUllO2Y4tnC85gSAIpBWOoWDOfFIKRiEWX1k8PSYn1pOdWE9p8fW5CVDIkI/REDJagyRy8BGiTqejtLSU8vJy7HY7SqWSUaNGMXLkyCEvcHq9TvT6HXR0rsZoPAqIiIycTFzsMlSqWQQE3BoRrt3rY2d3L+u6jOzp7sMtCKQFy1gcrbwg4v69KV8U/BH6LUxt8TFK39mFb1gXlfPF7BDNQtesRNZq5m5hJ98N2UaauI5OuYbfpM1jjbUBm+Usk+In8WjsUlJ21WD6+W8w9PYSXFBA9HPPETZ7FoJITNMZA+V7SumoMyGVBZB7Rzz5MxJQfMwf0+NyUXNkP6VbN2JoaSI4LJwxC5dSMGsuiugrD798tN3QerwTx9keAIKyIgkZF0NQVuSgC5wul4uqqipKS0tpbW1FLBaTnZ3NqFGjSE1NHfIx/L6+Kjo6VqPt2oDHYyYoKIHU1G8TF3vPLWOY7PEJHDb1sbbLyDZ9LxavD02ghEcTVNyjiSDP32Lo57/wR+g3ieKdW6nbXIw3u5WytFC2uudibgokpMPEvaIDfEu+A7W7gzp1Gu8kZLK1txYBmJs6l0dCZqFYd4DeDRsR3G7CZs0k8quPIh9ViMvhoeZoJ2f2tWHW2wmNlJE/PZGcSbHIPlaAtJqMlO/aSvmubdh6TaiSUhg9fzHDJ00dcE2t1+LCWtyF9UQnXqMTcaiUkKIYQsbGIIkYfDTb2dlJSUkJFRUVOJ1OoqKiGD16NAUFBUO+ntbt7kXbtYHOjjX0WaoQiwNRq+8kLvZeIiIm3BKthoIgcLrPxoddRjboTOhdHsIlYhaoldyjiWCCMpQAv4h/ofFH6LcQxzd+SPPeGhw55ym7S8lWx3JsNQEotN18W7qLJ+U7CPWYqNAU8FN1HvtNNQRbGrlv+P086B6N6F8fYtn/LL0yGYp7lhL18MMEpqTQ1+PgyNp6qg934LJ70KSGM35xGumF6kscgPTNjZRs2cDZI/vxejykjSpi9IK7SRyRf8VoTxAEXI1mLCc6sVcawCsgS1OgmJd6TcM/DoeDyspKSkpK6OzsRCKRkJOTw+jRo0lKShrSqFMQfBiNx+joXI1evwOfz0VY6AgyM39CjGYhUqlyyO71WThvc7C2y8i6LiONdheBIhGzVeEs1UQwMzKcIP/uFD9XgV/QbxDHN66lee9Z7CPOU7ogim22L+GoEhHRZeAbgTt5PGQHwV4zxckTeVMRxjFjDeE2L0/nP8VSUwaO1/+N9dQ/CVAqUT3zDBEPPoAkMhJDm4XTb1dRV6wDIL1QTcHMRGLS/lO8FHw+GstKKNmyjpbKM0hkMvJm3knh3EVExsVf8cyC24v1tA7LkY5+558gCaHjYwkZFzto+zZBEOjo6LgYjbvdbjQaDfPmzSM/P5/g4KEt5Nnt7XRq19LZuQaHox2JJJy42PuIi7uXsLARQ3qva8Xk9rBeZ2KVtodSsw0RMEkZyrPJGhaoFCj8Lvd+Bok/5XKdObZhDc37zmLPbeRUsoYd1tm4GnxE6fQ8KdvOVyU7CfRaOTpsCm+Gyig1nSMyKJJHsh/mrg4Nlrf+iaOiAkl0NFFfexTlvfciCg6mo85E6Y4WWqq6kcoCyJkcR8HMxEt2q3g9HmqPHuTkhjV0t7UQGqWi8M67yJ85l6DQK7cOenqdWI91Yj3Zic/mQRobQuikOILz1YgDB9dZ8lE0XlxcjFarRSqVkpuby+jRo4fcRNnrdaI37KSzYw09xiOAQGTEJGJjl6FWz7klCpwen8C+HjOrtEZ2GHpxCQLDQ4JYHhPJUk0EMbKh7aP3c/vh70O/CRzfvI6m3dU4R9RTnBLNVus8XOe9qPU6npZt5aGAXQT4HOzPuoM3g6Da3IhGruHR7EeYUy/H/NY/cNbVI01MJOrxx1DcfTciiZTGMwZKdzTT1WgmOExK/vREcqfGExTyHyFwOxxU7NtJ8eZ19Bn0qBKTGbt4GZkTphAguXLU52wxYznSgb3CAIJAcE4UoZPiCUwNH5TwXikaHzNmDHl5eQQFDZ2wfmQU0dm5Bq12Ix5PL0FB8cTGLiM2ZinBwQlDdq/PQo3FzkptD2u7jOhdHiKlASzVRLA8JtJf3PQzKPw59BvI2eNHKPv3fty5TZTND2Wj88s4KkVEdOl4Pmgbj4ZsR+JzcDBzOn8MdFHb10iiLJFXxr/MtHOBGJ9/A31jI7JhGcT95jeEz5uLTySm9oSW0ztbMGpthKuCuOP+TLInxl4ylm/vM3N6+2ZO79iMo89M/PARzHrsaVJHjrlyftwrYK/UYzncgau1D1FQAKGT4gidEDfolkOXy0VFRQWnTp267tG4y9XTX+DsXIPFcra/wKmaQ1zc8lumwKl3uVnf1Z9SqbDYkYhgdpSC5TERzIwKJ/A6GGj4+WLjj9CHiLaGWg69ugLvcB0VmQFs9N1F33kpYR09fE26i2cCtyDzmDmWNYM/Bouo6K0jKSyJJ/O/zpQGGT2v/wlnXR2yYRmonnmWsNmz8An9E53FW5vo63agSgxl1Jxk0kddWui0mXsp3ryOsu2bcTsdpI0ey9hFy4gfnnPF8/pcXmwlXfQdasfb40CiCiZ0UhzyURrEssGlVYxGI6dOnaK0tBSHw0F0dDRjxowhPz9/SKNxn89DT88hOjpXYzDsRRDchIXlERd7LxrNXUilQ7/XfLBYPV62GXpZ22XkoLEPrwD5YcEsj4lkSXQEUYH+GMrPZ8MfoV9HzD09bHrpNUSpFuoXmPkwYAmGxlBCWk08HrCP50I2EebppjhlMq+HBVNqqiVWGstPJ7zCjHYlPd//E53V1QSmpBD36quEz5uLgIizF4TcbHAQnRzGHfdnkpwbdUmUazP3UrJ5Hae3b8btcjJ84h2Mu/teVEkpVzyvz+bGcqwTy9F2fFYPgUlhKBekEpQdNajecUEQaGxs5OTJk9TW1gKQnZ3N2LFjSU5OHtJo3Go9T2fnGjq163G5dEilkSQmPERs7D2EhmYN2X2uFbdP4ICxjw8v9IvbfT4SgqR8IzGapTER/slNPzcMv6BfI4IgsPJHLyOSg352HetDFlDXnExgYx+L2MvPQlYT5e6gPn4s31eN43BPJWqnmhfHvcg8UxLGl/6PjvJypAkJxP7ylygW3oUgElN7sotTW5sw6+2ok8JY8I3LhdzeZ6b4IyF3OsiaMIUJ9zwwoJ2b1+yi72Ab1pOdCC4fQcMjCZuaQGDK4PLjHo+H8vJyjh8/jl6vJzg4mEmTJlFUVDSkzj8+nxOdfift7R9gMp1AJAogKmo6cbH3EBU1HbH45hYPP+oXX6s1sl5notvtIUISwL0xEdyjiaBIEeI3h/Bzw/EL+jWw662/0XNWj6mwnl2asRzvehZpmZki52leVawiw1GJISKbV9Km8KHuBCEWK98d/V2WysbT+9rrdOzdiyQmhphXXkG5dAlIJJwv1XN8w3l6dXZUiaHMfyqPlHzVJWLrsFoo3rSO0m0b+4V8/GQmLHuAqISkK57Va3bRd6AVywkt+HzIC6IJm5qANGZwQzt2u52SkhKOHz+OxWIhJiaGxYsXk5ubO6QbDu32Vto7VtDRsQq3u4fgoCTS079PbMxSZLKBHY9uBM12J2u0RtZ2GWmwO5GJRcyJUnCPJoIZUWH+vLifm4pf0AdB/ZlSit/cijO3gZI5KjZYn4JiO3HGRn4ZtppZ7McREM2b4x7krZ5SXLpTPDj8QR5LWI777/+mfeWriIOCUH/nO0Q+/BDioCC0Db0cWVOHtsFMVHwI857MI7XgUiH3uFyc3rGZk+tW4bBayJwwhQn33I8q8cpb/7xmJ33727Cc7ASfgLxQQ/iMRCRRg/v4bzabOX78OMXFxbhcLtLT01m6dCmpqalDllYRBB/dPQdpa3uX7u6DgAi1aibx8Q8SGTn5phc4e9weNupMrNUaOWW2IgImKkN5Jjmau9RKwv2u935uEfyCfhUIPh8rvvcyIpWX1nlNvCd5AENtKLI2I08E7+E7oWuQ+lwcHPMg/+M4T7vuMDOTZvLt3G8QtuEAhqeX47PbUd67DPUzzyBRqejV2zn+r0rqS3TIwwOZ/pXhDJ8Qi/hjeWyfz0v1wX0cXfUefd16UkaOZsoDDxOdknbFs/psbswH2rAc6eiPyEdpCJ8+eCE3GAwcPnyYM2fOIAgCI0aMYNKkScTGxl7z6/jfeDxWOrUf0tb2T2y2RgIDo0lNeZa4uOUEBQ3dfa4Fh9fHrm4za7p62HthGdbwkCB+nBbLEk0E8UG3jpmFn88HPVYXx853c7jewOQMFQvyh/497hf0T+HU9k007KzCUljHjvhxHNUtRFZjYpy7jD9H/AuN/TzapDv4tSaG3drDpCnSePvOt8lpcNP55WfQNbcQOnUq0c9/D1lGBm6Xl2Prz1O2uwWxSMSYBSkUzk4i8GNO9oIg0FB6ikPvv0N3Wwsx6cOY+/R3SMrNv+I5BbcXy9EOzPvaEJwe5COjCZ+VNGgh7+7u5sCBA1RUVBAQEMCYMWOYMGECERER1/wa/jcORwetre/Q0bkKj6eP8PACRuS8RnT03Jvu+lNjsfNeZzdrtEZMHi8xgVIeS1CxLCaSHP9GQz+DwOH2UtJs5FCdgcP1eqo6zAgChMokJEcNbtL6avEL+hXwuN2s+vYrCGk2mucY+KfvcexlAgpdB79UrGOxaDOCNJ5/Fz3F65378Om1fGvUt/hS9F30/Oa3tGzejDQ5icS//53QyZMAaKowcHDFOfq6HWSNi2H83emERlxqBdbd1sred/5KS0UZEbFxLPzODxk2btKAe1bs5Xp6tzXi7XURlBVB+NxUAmMHlyM3Go0cPHiQsrIyAgICmDBhApMmTRrSBVk2WyPNzW/SqV0H+FCr55KU+FUUisIhu8e1YPV42aAz8V5nNyVmG4EiEfPUCh6MjWJyhH8Zlp+rQxAEzmr7OFxn4FC9gZON3TjcPiRiEaOSI3huViaThqnIj1cguU67efyC/glUHDnI2dXHcIyvZXv8BA53LUNWbWKKUMOflW+jcLTRNvorvBRgprh1C5PjJ/PC2BcI23mClq8tRrDbUT39NFFffwKxTIbF6OTQqnM0nNYTESPn7ucKic+8NOJ12mwc/3AFpVs3IA0KYvojX6dg9rwBJztdHRZMG8/jajIjjQ8l8r4sZGmDWzZlsVg4cOAAJSUliEQixo4dy+TJkwkLC/v0B18lfX0cRU5bAAAgAElEQVTVNDX/BZ1uG2JxIPHx95OU+DjBwVfeI3MjqLHY+Ue7gbVdRqxeH8PkMl7JiGOZJtLfL+7nqugyOzhcZ+BwvYFDdQYMFicAGdGh3F+UxJRhKsalRREquzHvJ/+79r9Y9eOf4A3y0jW7hjelT9BXKSG4w8AvIzdzr20VyFNYO/VFft2whgBRAD+d+FPuUkxC+/2X6dy/H3lRETGvvIIsLRVBEKg9oeXQynN43T7G353GyFlJl7gDAZw7fpi9//gr1l4TudNmM+WBh5ArrizMPruH3u2NWE9qEcslRCwdhnyMZlB95G63mxMnTnDo0CFcLhejR49mypQpQ9p6aDSdorn5L3R3HyAgIJTk5CdJTHwEWaBqyO4xWDw+gZ3dvbzVZuCIyYJMLGJRtJKvxEZRpAjxp1T8DIjb66O4ycj+Wh37a/XUdvUBEBUSyKQMFZOHqZgyTEWs4ubMHvgF/QIet5vV3/w57pwuKrKlvG/7HrJSE8m2Flaq3yK+7wyWkffzU0UI2879i3Gx4/jZxJ8ReqKaxhcX47Pb0bzwIyK+/GVEYjH2Phf736+l4bSemDQFMx/JRvlfGwqtJiN73v4LdSeOEp2azuLnf0xsxsCDMvaqbozr6/FZXIROiCN8VtKgjJYFQaC6uppdu3ZhMpnIzMxk9uzZqNVD1xJoNp+h/vxvMBqPIpVGkp72XeLjv3xTDZXNHi//6ujm7TY97U438TIpL6bF8mBslD8a9zMguj4H+2v17K/VceicgT6nB2mAiKKUSH44ajiTM1TkxIZf0tBws/C/k4HOpvMc/M1qnOOq2Jh0B8f1RQRX9DBNVscbYb9D6vRydt4v+G77Ntpa2/hm4Td5NOcRuv/wOm1/+xtBOTnE/eZ/kaWnA6Bt6GX7m5XYLS4mLEln5Oyky/6xz504wq43/4jb6WDyAw9TtHAp4gE8Mr0WF6ZNDdjL9UhjQlA9nENgwuDSIj09PWzZsoXz58+j0Wh46KGHSEu7csfMYLHZGjnf8Dt0uq1IpREMy3iR+PgHCAi4eZOSXU43b7bpebfdQJ/Xx0RlKD8bFs+cKAWSW+AH0M+th88nUNZmYv9ZHftq9VS09wKgCZexID+WaVnRTB6mumFplMFw653oBlN2aA/1G8qwTT/AWxEP09gYT2BdD0+rinnO9jqisGT2zfgePyh7jbDAMN6+820KAtNof+LrWI8eQ3nffWhefAFxYCCCIFCxv40jq+sJjZSx7PtjUCddKroel4v97/6d8l1bL3SvPDfghCeAvbob49pz+BxewmcnEzY1YVCmEl6vlyNHjnDw4EHEYjHz5s2jqKhoyGzd3O5eGhp/T3v7+4hEUlJSniE56TEkkqHLww+WepuDv7ToWK014hEE7opW8o2kaArCrk93gZ/PNw63lyP1BnZVd7G7RofB4kQsglFJETx/ZxbTstTkxA5uqvpm8KmCLhKJ3gbuAnSCIOR+wtdFwB+A+YANeEQQhNKhPuj14NjGtbQdbcA8eQ//F/Ysplo50pZe3ojfydzuf0LKFD4YtYRflfyKnMgcXp/5OuFdFpq+fB+eri5if/ELlPcsBcDr9rHvvbPUHteSkq9i5sPZl6y0BTB2trPptV+hb25kzMKlTL7/KwRIrpwuETw+erc3YTncjjQ2BPXjWYOe8NTpdKxfv56Ojg5ycnKYO3fukBktC4KPjs7VnD//Km63ifi4+0hN/dZNneissdh5tUnLVn0vMrGIB2IjeSopmpRg2ac/2M8XCpPNxd6zOnZWdXGwTo/N5SVUJmFqlprZ2RqmZqqJCPl8zRtcTYT+DvBH4N0rfH0eMOzCr3HAXy78fktzbONaOo41YZm4m9+HfBtLtQxRu5UVyZsZ3/UBQsGX+H1CGm+f/j3TEqbx6zt+jaimnqavPwlA8r//RXB+f1+4w+pm2xsVdNSZGLswlTHzUi4rULZWV7Dxt/8DIhFLfvgyaYVFA57P022n+4OzuNsshEyIRTk/DZH06iNqn8/H8ePH2bNnDzKZjHvvvZcRI4bOqcdsPkNt7cuY+86gUIwmK/Plm+oEVG9z8Gqjlg06E6EBYr6VrOFrCSrUgX7DCD//od1kZ0ellp3VWk41GfH6BDThMpYUxjNnRAzj0yKRfY4nfz9V0AVBOCgSiVIG+JbFwLtC/x7e4yKRSCkSiWIFQegcojMOOZVHD9J+uJm+iTv4bchz2M7KENqtrEvbTGHHBwhFT/CaJoZ/VL3D8szlvDDuBZzlZ2j52mMEREaS9Pe/EZiSAoDZYGfzH8vpNdiZ/bUcMotiLr/f/t3sevOPKDUxLPnhT1BqLv+ej2Ov6qZnVS2IRER9OZvg3MF1hdhsNtatW0ddXR1ZWVksXLiQ0AEcigaD12unofH3tLS8TWBgFCNyfodGs+imfRRttjv5bZOWNVojQQFink2K5qmkaCL89m1+LtBusrOtopPNZzopazUBkKkJ5cmpaczOiSE/XnFLFDSHgqF418cDrR/7c9uFv7tM0EUi0RPAEwBJSVdeKHU96Wpr4tzKEhxTtvDX8Kex1AdBq5V3hh2hsPUDGPcUf4pJ4B9n/sp9Wffx4rgXcVRU0Pr4EwSoVSS/+y5SjQYAk87G+t+W4vH4WPytQuKGXd5qeGztBxxd9R5JuQUsfO5HBIVcWVgFn0Df3hbMu1uQJoQS9WD2oE0mWltbWb16NVarlfnz51NUVDRkYms0nqTm7A+w21uIi7ufYRk/vGl58l63h981d/F2m4EAETyeqOaZpGh/RO4HgA6Tna0VnWyp6OR0S7+Ij4gL5/k7s5ifF0uqaugG5m4lhkLQP0ktPtE1QxCEN4E3od/gYgjuPWgO/Px97ONPsSLyAdo6Y5A0mPj1sBqmtf4Z8u5lQ8Z4/nr0JZYOW8oL417A3dzcL+YREST/858XxbxXb2PDa6fxegWWPDeKqPhLhVoQBI6ufo/ja1eQc8cM5nz9mwMOCQkeH8Y157CV6ZGPiiZiybBBpVgATp8+zaZNmwgPD+fRRx8lPn5oBnd8PhcNjX+gufmvBAcnMqrwPSIixg/JtQeLxyfwboeBV5u0GN1eHoiN5PnUGGJln69cp5+h5yMR31rRSekFEc+J7RfxBXmxpNymIv5xhkLQ24CPt2kkAB1DcN0h59/P/BBXtoXqpCTKrAWE1hhYFG9iecevIWUK5ZOe5JVdTzAuZhw/Hv9jfKZeWr/+JIjFJL31d6Qx/akSh8XNxj+U4XH5uPu5wsvEHOD42hUcX7uC3OlzmPPEM4gG6CjxOb10/7saZ52J8DuTCZuWOKio2ufzsW/fPg4dOkRqairLly8nOHhoWgVttkYqq75DX18FcbHLycx8iYCAm9MpcsTYx4/OtXPO5mCSMpRXMuLI9XetfKExWJxsKu9gU3nHZSJ+O0fiV2IoBH0j8IxIJFpBfzG091bMnx9Y/R6SqEic2SWs9P4CZYUJpUzgV6LXEQUp6V38Os/tegyNXMOrU19FQgAt33kOd0cHSf98h8ALKSKv18f2v1VgMTk/MTIHqDqwh6Or32PE1JmfLuYuL4Z3qnA19RJxzzBCPiEHPxAej4f169dTWVnJqFGjWLBgAQED9LMPBoNhL5VV30EkkpCX+2eio+8ckusOFr3LzSv1HazpMpIUFMg/clOYq1Lc8i1kfq4PdpeXndVa1p9u52CdAa9PYHhM2BdWxD/O1bQtfgBMA1QikagNeBmQAgiC8Aawlf6WxXr62xa/er0Oe614PR4M+zrxTNnF3yRPITvvwG52sbNwH9KaGnhwFb+reYduRzfvL3gfZZCS7nfewXb8OLE//xnyUaMuXuvomnraa03MfCSbmLTLx+Rbq86w86+vk5RbwOwnnh1QzAW3j+5/VeNq6iXyvizkI6MH9bw8Hg8rV66krq6OWbNmMWnSlZd4DQZB8NHU9GcaGn9PWFgO+XlvEBQU95mvO/hzCKzSGvl/9e3YvD6+nazhm8ka5NdpsZGfWxevT+B4QzcflrazvbITq8tLnCKIJ+5IY0lhPJmamzfzcCtxNV0uD3zK1wXgG0N2ouvA6ldewZnq4pwmnWZ7EvJGHU/luEg8+xaMepjjYQo+PPYhX8v9GjlROTjPn0f/u9cInTEDxT33XLxO69kezuxrI296AsPHX77L2GoysvkP/4tSE8PC5340cM5cEOhZcw5nnYmIZZmDFnO3283KlSupr69nwYIFFBUN3AZ5tXg8Fqprvo9ev4MYzd0MH/4LAgKGzuj5atE63XyvtpXd3WbGKUJ4NSuRYSE3/hx+bi5ntWbWlbazoawDrdlBmEzCXflx3F0Yz7jUyNumO2WouO17uzxuN5LeCITCDazlJ0Q3WXEFiPm2aCUEhuKa/gKv7PwqKeEpPFnwJILPR8cLLyCWy4l95ScXI16Xw8O+d8+i1MiZuCT9svsIPh/b//J7XDYb9/745wN2swD07W3FXq4nfG4KIWM0g3pObrebFStWcP78eRYuXMjo0aMH9fgr4XTqKCv/KlZrHcMyXiQx8as3PK0hCAKru4y8VNeOy+fjpxlxPJag9vtzfoEw2VxsLO9gdXEbFe29SMQipmWpeemuHGZmRxMk/fz2iV9vbntBX/vzn+NIdFClzKPXEoqsVcfPxziQVW6D6S+yrv0AbZY23pj1BkGSIHo3bcZRfobYX/0SyccWVh398DwWo4Olz49GEnj5G6pk6waaykqY+bWnUSWlDHgm2xk95l3NyEf1+3sOBrfbzQcffEBDQwOLFi1i1MfSQZ8Fm62R02WP4HYbKch/i6ioKUNy3cGgd7l57mwru7rNjFWE8PvhSaTJ/ROeXwS8PoHD9QZWF7eys6oLl9dHTmw4Ly/MYVFBHFGh/vfB1XDbC7rUEI47+yhb+AGaNhuuwADuM78NchXOoq/x5pblFEYXMjFuIj6XC/1rryHLyUaxaNHFa+hb+qg61E7B9MRPzJt3NdRz6P1/klE0noLZ8wY8j6vdgnH1OQKTw4lYOmxQEbDX62X16tU0NDSwePFiCguHxhjCbK6grPxRAEYV/pvw8Cs7I10vdneb+VZNC1avl1cuROV+Y4nbnyaDldUlrXxY2k5nrwOlXMqD45JYNjqB3PihW+X8ReG2FvQ9K97BpfbRqoqm2xmBvFXLiwU2pGcPw53/w5rm7ehsOn45+ZeIRCJ63nu/v6vlFz+/WMwUBIFDq84RHCqlaGHqZffwuN1s/eNvkSsUzPn6NwcUaJ/DQ/f7NYjlUqK+kj2oBVs+n49169Zx7tw5FixYMGRi3tNzhDMVTyGVRlA48h3k8suf4/XE4fXxs/MdvNVuIDskiL+MSGd4yM3bzujn+mN1ethS0cma4jZONvUgFsEdmWp+vCCHWTnRn+vR+5vNbS3o3bubcE6sYL94AYpOG04B7he2QWAYroL7+fvmZRTFFDE2dixei5XuN94gZPJkQiZMuHiN+hIdnfW9TPtSFrLgy1+uk+tX0dPeytIf/oTgsCsvvRIEAdP6erw9DtRfzycg9OoHYQRBYOvWrVRWVjJr1qwhK4B26bZRVfUdQuRpjBz5D2SyweXyPys1FjtPVzdTY3XweIKKF9PiCPJ3sNyWCILAqSYjq4tb2VLRic3lJVUVwvfnZrG0MIEYhb/gPRTctoJu7jUiUUYSEKvnDCOJajcwKy2AkPpNMPqrbO88gsFu4BeTfwGAadUqvL29qL/57MVreN0+jq07T1RCKNmTLm/bM7Q2c2LdarInTyO1cMyA57GV6rCV6QmfnYwsZXAfJQ8cOEBxcTGTJ09m8uTJg3rslWhv/4CztS+hUIyiIP/vN9R8QhAE/tFu4JXzHYQFBPBefhozo26e+YWf64e+z8makjZWFbfSaLASEhjAXfmxLB+TyOjkCP8swRBz2wr6ll//Bne8leOyiYgMLixWN99UnoAOF0LRY7x34iXSFGlMiJ2Az+Wi5x//QD5+/MUNigBVh9vp63aw8JsFl7VHCYLArr/9iUC5nGkPPz7gWdx6G6YN9QSmKgibPvDu8/+mvLyc/fv3U1BQwMyZMwf12E9CEASam9/gfMOrREVNIy/3jzfUgKLX7eE7Z1vZauhlRmQYf8hO8u9fuc3w+gQO1elZcbKV3TVdeHwCY1Mi+cb0DOblxhByCxpD3C7ctq+stCccT24JB3ie6C4HspAAMltXQ/oMygUb1d3V/HjcjxGJRJjWrcej1xP3v7+++Hi300vx1ibiM5UkZkdedv3aowfpqK1m9hPPIg+/csQteAV6Vp1DJBETeX/WoHw/Gxsb2bBhA6mpqSxcuPAzRzOC4KO+/le0tL5FjGYx2dm/Riy+cWJ6ps/G45VNtDtdvJwex5OJan+EdhvR2WtndXEbK0+10m6yExkSyFcnpXBfURIZ0UOz7dPPwNyWgt6t68CjCKZbFUK3KwJ5h5Zf5nchqu2A+f/L+zXvEyYNY2H6QgSvl+633iIoLw/5+P8snCrf24q9z834p9IvEx23y8nB995BnZJG7vRZA56l71Ab7tY+Ih/IQqK4+tYrvV7PypUriYyMZPny5UgGGFK6Gnw+D2fPvkCndi0JCQ+ROewlRKIbk68WBIF/dXTzUn07UVIJ6wqHUaT44o5n3054vD721epZcbKFfbU6fAJMyojiR/OHMztH4y9w3mBuS0Hf8bs/4oy1UCYZRWCbHZ8Ac4XDEKSgO3EMu079Px7IfgC5VE7fvn24W1qIfu13lwwRle1qISUv6hPbFIs3fUhft575z3wXsfjKb1h3lxXzrmaCR0QRnH/1Lj5Wq5X33nuPgIAAvvSlL33mRVs+n5PKqu+g1+8gNfVbpKY8e8MiY6vHy/Pn2viwy8j0yDD+mJ3sN2W+DWjtsbHyVCurS1rpMjtRh8l4cmo69xUlkhzl/8/6ZnFb/mQFmkLx5RdTzEIiut3ERUkIb9oBuUvZ1rIHj+BhaUa/dZzxgw+QqNWEzfpPpF11qAOnzcOY+Ze38PX1GDi5YQ2Z4yaRkHOZI99FBK9Az+pziIMCUC7JuGoB9Xq9rFq1CovFwiOPPEJERMQgn/1/X8/OmYqn6Ok5xLBhPyYp8cat2jlrtfN4ZRPnbU5+kBrDt5I1/onPzzEuj4/dNV18cLKFw/UGAKZlqvnp4iRmDI9G6u9QuuncdoLudrnwhgVjVQVgcEci12l5MrcRUZ0F8u5lU9WfyI7MJiMiA1dLC9ZDh1E9/TQiaX8u2ev2Ub67hfisCDSpl3deHP7gXQSfjzu+PLAw9h1sw91mIfLB4YNqUdy5cyfNzc0sWbKEhITBTZH+Nx5PH2Xlj9HbW0r28F8RF3fvZ7re1SIIAiu0Pbxwrp1QiZhVI9OZHOFfnvR5pUFvYeWpVtaUtNFtdRGrCOKbM4axvCiReKV/ZuBW4rYT9J1//wvumHbKJKOQtDvwCTDduR/CYqlXaKjuruYHRT8AwPjBChCLUS5ffvHxtSe0WHtdzHw457JrG1qbqT60jzF3LUERfeU1t26tFfPuZoLzVMgHkWopKyvjxIkTjB8/noKCgqt/0p+Ay9VDWfkjWCy15I74PRrNgs90vaul1+3h+XNtbNSZmKgM5S85yWhk/i6Wzxsuj48dVVreO9HM8YYeAsQiZg6P5oGxSdyRqSbAvxTrluS2E3RzpR5P0XmKeYSIbhdxSh9hbfth3NfZ1LiVAFEA81Ln4XO5MH34IWGzZiHV9G86FASB07taUCeF8f/bO+/wOKszb99nZjQaSaPee7ctS1ZxNzamGTCYxXQIBDCwkM2GbNomm2R3syyb/ZJvs8sm35UsWUJIAoQeB2NjDKYZcGxjyyoukqzeRmXUZjS9ne+PEW6ybLlhjzj3denSlPd9zzk68/505jlPySqZbOrY8eoL6A0GFq+9bcr2pT+YRVFj0BG3dnISr6kwmUxs3LiRvLw8rr766tMe99G43QPU1N6P09lF+bxfk5R0xVldb7rssdj5m4Md9Lm9/CA/nUdzU1T4fojRZ3Hywq4uXvy0myGbm+yECL577WxuX5BFSowK/rnYmXGCLoQRb7yHbn82UYP9rJs7gGjxEii+mjerf8zyzOUkRiRi++gjAhYLsTffdPhc06ExxgYcrFpXMsnmPdDeyqFd21l665dOGhE6/vGEqeWekmmbWux2Oy+99BJGo5Hbb7/9rApUuN0DVFffhcc7TGXF74iPX3LG15ouUkqe7hniX1t7yQjXs7GqmPnKiyVkkFLyl9Zhnt3RwbsNgwSk5IrZKdy7LJfLipNVitoQYuYJeoSOUaMRYfHiD0gu0TeD0NJijGfAMcDXq4KRoNZ33kETFUXUJZccPvfAJyb0EToK50/OTb7jtRcwRBlZsGbtlG377V7GP+jGUJJA5LykafVXSsnGjRux2+089NBDREWduRAGbeYP4vEOU1X5LLGxlWd8reli8/n5dlM3bwyOcW1SDP9vTg6xYTPuYzUjsbq8/Km6h+d2dtJmthMfGcbDlxZwz5IcshNUab9QZEbdee1N+9AkjtCvyUBj8wKQZqmF9Arqx5oBqEqpQvr92N57H+Pll6PRB1fRHpePtlozJZekT0qPaxkcoLX6U5befMdJ85yPf9SD9PiJXZ037T4fOHCAxsZGVq1aRUbGmVcF8vvd1NV/Bbu9lYqKpz8XMW+0O/nr/R20Odz8U0E6X8tJUYFCIcBBk5Xndnbyek0vTq+fyuw4nrijguvnpatc4yHOjBL0T//0JzQxFkzkEeHwER0O+oFaWPgg9eZ64sLjyI7OxnXgIP7RUYyXX3743K4DI/i9AYpOsDrf9/47CATzrpq6pqbf6sb+FxORlSmEpU5vlW21Wtm0aROZmZksOyoh2OkipZ8DB7/N2NguSuf+N4kJ5ybfy8l4rX+E7zb1YNRpeLWykOXKi+Wixu3zs2V/P8/t6GRP5yjhOg1rKzO4d2ke87JUmtqZwowSdJ/JiS5tCBMrCHcEuCphCDHqgqxF1Lc8S3lyOUIIHLt2AhC5ZPHhc9tqzRiiwkgvOvbD7ff52P/BO+RXLSAmaeoycdb3u5F+ScyqnGn1NRAI8Prrr+P3+7n55pvP2G4upaTp0OOYzVsoLvpH0tJuPPVJZ4E7EOCfm3t51jTM0tgo/rc0T3mxXKQEApLdHSO8Xmti874+LE4veYmR/NOaEm5bkEVc5PTdaRWhwYwS9HBPJIHIYfrIQjNuZ0mmCUbBmlhIa3Ur1+UHi0/Yd+1CX1hIWEpQoP2+AJ37hiiYn4LmuOCItupPsY+NUr5q9ZTt+oad2D/tJ2pxGrrE6fnl7t69m7a2NtasWUNS0vTs7Seio+NX9PY+T07Ow+TkPHjG15kOXU43Dx/ooG7cyd9mp/DDgnR0asPsokJKSUPfOBvqetlYa8JkcRGp13LN3FRumZ/FiqIktck5g5lRgq4XUVgj/Yx7IzG4rJRouiAskv3+cQDKk8uRXi/OPdXE3nRkc7OnaRSPy09B5WSf8bp338KYmER+5dTpca3vdYFGEHPl9DIpms1mtm7dSnFxMQsXnjzt7snoNb1MW/t/k5Z2E0WF3zvj60yHd4etPHqwE7+U/K4sj+uS485re4rTo3vEwRt1JjbU9nJowIZOI7hsVjL/cF0wp0qkSrfwhWBGzbIM1zMWEY3G6gMgy90KKXOpH96PQFCWVIZz3z4CDgeRS44k4mqrNaML15J9nO/5WH8fnfU1LLvtbjRTmES8/XYcNYMYV2ahjTl18i2/38/69esJCwvjxhtvPONNRLP5XRob/4nEhJWUzPnpeUu05ZeS/2zv5787Byg1Gni6NJ98VefzomDY5mbzvj5erzVR3TkKwKK8eH58UxnXz0snIUqZVL5ozCxBj/IyqE1B2LyAJMbSBGU3UWeuozCukGh9NOYdO0AIoibs54GApL1uiNzSRHTH7fDXvfsWQqNh3lXXTNmmZUsHIlxHzDSLPW/bto2+vj7uuOMOoqPPbCNxbGwP+w/8HTHRZZSV/fK8pcAd8vj424MdfDRq4660BH4yK4sIla/jgmJ3+9h6cIANtb181DyEPyCZnRrN91bP5saKDLLilbvhF5kZI+jDA70EYiyYyERr81JssKBxjyFTSqlv+wOrcoPJt+w7dmAoLUUbFzQZ9LeO4bR6KKw61tzi83jY/+G7FC1cSnTCiW3c7g4LrsYRYlbnoYk8tah2d3fz8ccfU1lZydy5k1MLTIdxWyN19Y8QHp5ORcXT6HTnJ4Bnt8XOIwc6GPX6eGJONnenJ56XdhSnxusP8NEhMxtqTWw9OIDT6yczLoKHLy3gpqoM5qSpak+KIDNG0Pe+8xaB6BFMlBPh8HNlbD9YoDM2FavHSnlSOQG7HWddPYnr7j98XsteM9owDbnzjhWsQ7u24xq3UnH19SdsT0qJ5a0ONDF6jJec2n/c7Xazfv16YmJiWL166g3Wk2GzN1NTcy9abQRVlb9Hrz/3Iiul5Dc9Zh5vNZEZrmfT/GLKotWq7/MmEJBUd43yek0vm/f1MerwEhcZxi3zM7mpKpMFOfFqc1MxiRkj6MMHOhCzhugjk4DNx8LUHkBQjxsIbog6qqvB6z1cBFoGJG17B8mZm4DecOyfou6dzcSnZ5BTVn58UwC4GkbwdFqJu7kIjf7ULofvvPMOo6OjrFu3DoPh9HNiOBzt1NTcixBa5lc9T0TE6ZWymw7jPj/fbuxmo3mM1Ukx/EJFfX7uNPZb2VBr4o1aE71jTgxhGq6Zm8baygwuLU5Gr1MmL8XUzJi7VWMBf8Qog+5kwtxmimU7JBZSP3aIqLAoCmILGNrxZ4ReT8T8+QD0t1uxWzyTQv3Nne2YDjVw2b0PITSTbyAZkFje7kCXFEHUwqmzLn5GU1MT1dXVLF++nLy8vNMem8PRyd6aLyOln/nzXyAycnKe9rOlwRaM+uxwqajPz5ue0aCHyhu1Jhr7x9FqBB0+mGgAACAASURBVJcWJ/Hda2dz9dxUVYNTMW1mzCdFF4hgxKhH2iUAaY5myFtMvbmesqQytBot9h07iJg/H83ECrl17yAanSC//Fgbed3WzejC9JRefuLyco69g/gGHCTcMwehPbno2e123njjDVJTU7niitPPeuh09lJT82X8fhfz5/8RY1TxaV/jVHwW9Rmt0/BqRRGXxKv6j+ebEbuHzfv62FDby+6OoIfKgtx4Hl9bypp56SQalSeR4vSZMYIuwiIYNcQghr3EYMNg78GRch+HOl/kwbIH8Y2M4G5sJPmb3wSCtuLWvYPkzE1EH3Hkz+B2ODj48YfMvuRSIoyTvVCkN4B1aydhWUYiyk4eEPRZ4i2Xy8V999132nVBXa4+9tbcg89vo6rqOaKNc07r/FNe3x/gRy0q6vPzwuEJeqi8UWti2yEzvoCkOMXId68NeqiohFiKs2XGCDpGHwPaVITdxwJDLwAHjXH4pZ+K5AocO4Ph/lGXBO3nAx1WbKNulqwtOOYyB7a9i9flpPLaG07YjG2nCb/FTfztxac0SdTU1NDY2Mg111xDamrqaQ3H7R5kb82X8XpHqap6lpjoqcvdnQldTjd/faCD+nEnX8tJ4Qf5KurzfOD1B/ikZYgNNb28c3AAh8dPeqyBh1bks7Yyk5L0aGXaUpwzpiXoQojVwC8ALfC0lPKnx72/DvgZ0Dvx0i+llE+fw36eFBkIEIi10EcmeruHlcY+sHF4Q3Re8jzsO36OJjoaQ2kpAK17zWi0x5pbZCBA7dubSC+eTVrhZNNGwOHF+n434cVxGIpOXutzcHCQzZs3k5+fz9KlS0967PF4PEPsrbkXj2eQysrfExtzdtWLjmfrkIWvN3QRQEV9ng+klOztGuX1GhNv7utjxO4hNiKMtZWZrK3MYHFegvJQUZwXTinoQggt8CvgaqAH2C2EeENKefC4Q1+WUj56Hvp4Slr3VxMwjtIr5yFsfioSu8CYRr21jZzoHBIMCbTs2EHkksUIrfawuSW7JIHwo/zHO+prGO0zcf3X//6E7Vjf70a6fMReX3DC9z/D4/Hw6quvEh4ezi233ILmBBurU+H1jlJTcx8uVw+VFc8QF7tg2ueeCoc/wL+1mvhd7xClRgO/LcsnL0LZas8VzQPjvF7by4ZaEz2jTsJ1GlbNTeWmykxWzkoiXKdS0yrOL9NZoS8GWqSUbQBCiJeAtcDxgn7B2P/+NgIxI5g86QQ8NvK8rcjUMurMdSxNX4qnuxtvTw8J69YBYO4aZ3zYxaI1ecdcp2bLRqLi4pm1dPmkNrxDTmw7TEQtTEOffvJgns2bN2M2m7n33ntPKxrU67VQU3M/Dmc7FeVPn9NqQzVWB48e7KTV6eYrWcn8oCAdg4r6PGtMY86JHComGvqsaASsKE7mW6tmcW1ZGkbloaL4HJnOpy0T6D7qeQ9wIqW5VQixEjgEfEtK2X38AUKIR4BHAHJyppdmdjq4OsdwLHXhshuIZoR4exv9KasY6n+T8uRy7Dt2AEfs5617zWg0gvyKI9Gho/0m2murWXbrXWh1kzcGLW+1I7SCmGtyT9qXuro6amtrWblyJYWF068pGqw29AA2ezPl5U+SkDD5n8qZ4AtIftE5wBOd/aTqw3i1opBLE1Tu8rNhzOFh875+Xq/t5dP2EQAqs+N47K/msqY8g+Ro9a1HcWGYjqCfyNgnj3u+EXhRSukWQvwN8AfgykknSfkU8BTAwoULj7/GGaN16hiJikAMeSkWPQjppy4yuIouTy7HsfO36FJS0OfnHza3ZM6JxxB1RLhr334TjUZD+arrJl3f3TaG68AwMdfkoo2eOuGR2Wxm06ZN5Obmctlll027/z6fndq6BxkfP8C8sl+RlHj59Ad/Etocbh5t6GSv1cGtqfH8n+JMFSh0hjg9ft5tGGBDrYlthwbx+iUFyVF8++pZrK3MIDdR1VBVXHimc3f3AEeHJWYBpqMPkFIOH/X0N8D/PfuuTR+hjWA4PA5h87IgPPjFoF66CNeGUxxbRMeOnRhXrkQIwWCnFYvZyfxrj6y0PU4H+z/YyqylKzDGJxxzbRmQjL3ZjjZWj3FF5pR9+MxuHhYWxq233jrtghV+v5O6+oexWusoLf0Fyckn9n0/HaSUPGca5l9aTOg1gl/PzeWm1JNv4iom4/MH2N46zIaaXt4+0I/d4yc1Jpx1l+SxtjKT0owY5aGiuKiYjqDvBoqFEPkEvVjuAu4++gAhRLqUsm/i6Y1Awznt5amICjCoTUNr97I0wgT+aOptnZQmlhI41Ip/dJTIZUFPk+bdA2i0goKjknHte38rHqeDBddPLgDtqB3E22sj/s7ZJw3x37JlC4ODg9xzzz3ExEwvWZLPN05d3cOMWfZQOvcJUlMmfzs4XQbdXr7d1M27w1ZWxhv5+ZwcMgwqjep0kVJS0z3GG7UmNtWbGLJ5iDHo+KuKDG6szGBJfiJa5aGiuEg5paBLKX1CiEeBtwm6LT4jpTwghHgc2COlfAP4OyHEjYAPGAHWncc+TybOSq8sRGvzURLdgSetlIbhBu4uuRvbJ9sBMC5fjgxIWqoHySlNPGxuCfj97H1rA5lzSkkrmnXMZQNuP9YtHYRlGYmsmFz84jPq6+vZu3cvK1asoLh4epGcXu8oNbUPYLM1UFb6c1JTT+z3fjq8ZR7jO03dOPwBflycyYOZSWjUCnJatAza2DDhodI14kCv07CqJIW1lZlcPjtZeagoQoJpGVSllJuBzce99qOjHv8A+MG57dr0cLucED1KrycD6fWT6W6hIfFGPGMm5iXNw/7x84TPLUGXnIypeRTbqJtltxzZrGz+9C9YzYNccf8jk649/kE3fquHhHtKEFOsygYGBti4cSM5OTnTDu13uwepqb0Pp7OT8nlPkpQ0abvhtBj3+fnn5l5e6h9hnjGCX87NZXbU6ScA+6LRb3Gxsc7E67W9HDAFPVSWFyXx9SuLuLYsjRiDippVhBYhv0O29/0teKKsjNjimCUOEuZ3UmMIehlURM5ipKaGxAeDtTYPfTqATq8hvzy42pZSsmfjeuLTMyhcsPiY6/qGnIx/3ENkVQrhuSc2oTidTl566SXCw8O5/fbbp2U3dzp7qKm9F49niIqKZ0iIX3Y2w2fXmI1HG7rodXn4Zm4q385LRX8afu9fNPotLt5rHGBjnYld7SNICRVZsfzohrncUJ5OSoz6R6gIXUJe0Ht372d4rgB7gLmiE4C9gXGyo7OJrG9hxOcjasVy/P4ArXvN5FckExYeFN7exgP0tzZz1UN/Oymr4timNoRWQ+x1J85sGAgEWL9+PRaLhXXr1k3L39xub6Wm9j78fgdVlc8SG1t1xuP2BAL8rL2fX3YNkmPQ83pVEYvjVFKt45FScsBk5d2GAd5rGGRfrwWA/KQovnFVMTdWZFCQrP5uiplByAu63+xlODIS0e+lSt9FQKOj1tLGiqxLsW39BE1UFJGVlXQeHMFl91K86EhOlT2bXscQHUPpZceaPJyNI7gaR4i9Lh9tzIk3FLdt20ZzczPXX3/9tHzqx8cPUlN7PyCYP//Fs0q01Wh38ujBLvbbnNyTnsC/FmViVDbew7i8fna0DfPehIj3WVwIAVXZcXxv9WxWlaRSnGJUHiqKGUfICzpSz1B4AhqblwXhPXTFz2LEPUpVciX2j39N5LKlCL2e5j0DhEfqyJkbdEsc7eultXoXS2++g7DwI1+zpTeAZVMbuqQIjMtPXImoqamJbdu2UVlZyaJFi07ZRYtlL7V1D6LVGplf9dwZ5zP3BAI82WXmic5+jFotf5iXz7VJsWd0rZnGsM3N+42DvNswwMfNQzg8fiLCtKyclcS3rp7FlXNSSFIpaRUznJAXdBEl6RdpaGw+iiPa2BpfCa4WKp0peE0mEh95BK/HT3vtEMULU9BOVHypfnMDWq12UlbF8W3d+IacJD1YhjhBdZjh4WHWr19Peno6a9asOeUqb2RkO/X7/ga9Ppn5Vc9jMJy6XN2JqLbY+U5TN412F2uSY/nprCyS9V/cTTspJS2DNrZOrML3do0iJaTFGLi5KpNVc1NZVpCIIUx9c1F8cQh9QY8dp9s5h2T/KEbfCHv1OuJkHPG1HQwCUStW0F5rxuv2M2txsLqQw2rhwLb3KLn0CqLijgTceIecWD/sJqIiGcOsyYE4LpeLl156CY1Gw5133klY2MkFta/vzzQ0fp+oyEIqK/9AePjUro9TMe7z85O2Pn7XO0R6eNgXelXu9QfY3T7Cuw3BlXjXiAOAsswYvnFVMatKUlWwj+ILTcgLuowZY9CWzGWaTwCo9Y5QmVKJ/YVP0BcUoM/KpPFPNUQnGMgoDqaJ3bt5Az6vh4V/dcuR60jJ2IYWhFZD3JrJ2RT9fj+vvfYaw8PD3HvvvcTFTZ1yVkpJZ+eTtLb9F/Hxyyif9yQ63ennT9litvCD5h763V4ezEzi+wXpRH/BbOUWh5cPDw3ybsMgHzYNMu7yoddpWF6YyCMrC7iqJIX02IgL3U2F4qIgpAXdbOrGFu3BY9dSIdoY0mjpcA5ya+wdOHY/Sdydd2AbddHdOMrC6/IQGoHLbqNmyyZmLVlOYuaRjAbOejPu5jHibiw84Ubo1q1baWlp4YYbbiA/f2obeCDg49Chx+g1vUha6lpKSn6KRnN6kZr9bi//2NzDm2YLJVEGfluax/zYL06ukM5hO1sPBk0pn3aM4A9IEqP0rC5NY9XcVFYUJak6mwrFCQjpu2LvlrcYjNWj6fayQN9BXUo+4GF+TxjS7cZ46UqadvWDhDnLguaW2i2b8DgdLLn5jsPXCTi8jG1qIyzTSNTS9Ent7Nmzh507d7JkyRIWLlw4ZX/8fgf793+DoeH3yc39KoUF3zmtr/8BKXneNMyP20y4A5IfFqTz1ewUwmZ4qLk/IKnpGj1sSmkZtAEwK9XIV1YWcFVJKpXZcSrkXqE4BSEt6JYmE4OXRyPGPZSLNp6OnY3eN0RidTu2yEgiFi+i6Sc1pBfFEpscicflpPqtNyiYv4iUvCNmlbE32wnYvSStK5sUEdre3s7mzZspKirimmuumbIvbs8Q9XUPYx3fz+xZj5OVdc9pjeWQ3cV3m7rZZbGzIs7If8zOpiBy5npl2N0+Pm42s/XgIB80DTJi96DTCJYUJHD34hxWlaSSk6hqbCoUp0NIC3rAIRjUJZLhGCQufJQajZ+yhFIcH35E1KWXMmRyM9rv4IovB32+69/dgmvceszq3NU0gqN6gOgrstFnHhtgMjg4yMsvv0xCQgK33XbblJGgDkc7tbUP4vYMUj7vydPKmGj3+flF5wBPdpsxajX8fE42d6YlzMiNPdOYk/caBtjaMMjO1mE8/gAxBh1XzElhVUkqK2clExvxxfXcUSjOlpAWdBHhp9NZQLloxSkEB91D/J12Cb7B3URfeQW1O/vQhmkoXJCCz+Nhz6Y/k1NWTsasEgACLh+j61vQpUQQc+WxwUFWq5Xnn38enU7HPffcg8Fw4pDw4ZFP2L//6wihY37VH4mNrZxW36WUrB8Y5d9a++j3eLktNZ5/KcqYUa6IgYBkv8kSNKUcHOBgnxWAvMRI7luWy1UlqSzMiydMVU5SKM4JoS3ocXb6bancqdlKvSECn/Qzr9ENGg2GS1bQ/JMDFFQmEx6ho/rNDdhHR1hzVL1Qy1vt+K1ukr9agQg7Iioul4s//vGPuFwuHnjgAeLjJ7swSinp6fkDzS3/h8jIQirK/5eIiOlVYaofd/BPzb18arFTHh3B02V5LJwhm57dIw4+aRlie8sQf2kdZsTuQSNgQW48379uDqtKUilMjpqR30AUigtNSAu6K9aJ02agXNPG7sRMNMJL3O5DhM2fT2eHF7fDx5xlaXhcTna9/go5ZRVkl5YHz20dw76rH+OKTMJzjiTf8vl8vPzyy5jNZu6++27S0ydvkgYCbpqaHsPU9wpJSasonftf6HSnzgcy5PHx07Y+/tg3TEKYjifmZHNXWkJIp7gdsXv4S+sQ21uG2d4ydNg3PCU6nMtnJbO8KIkr5qSQEKVysisU55uQFXQZCDAYF4bo91ChaeeZiHyW+nPwNjUQ/73vsfcjEzFJBrLnJPDphldxWi0sv/NeIJjnfHR9M9pEwzE1QgOBABs2bKC9vZ2bbrqJoqKiSe16PEPU7/tbLJZq8vK+RkH+NxHi5CYDb0Dy+94hftbRh8Mf4JGsZL6dlxqS5eCcHj+fdozwl5YhPmkZ4oApaEaJDtexpCCRB5fnsaI4icJklStFofi8CT1FmeBQ9U4GjAZyx3sJF3bq/TZ+2DMbAO+8SzD9tpdlNxfidtrZvfFPFMxfRMas4Obo2MZW/CMukh8uP1yFSErJ5s2b2bdvH1dddRWVlZNt4RZLDfv2P4rXO0ZZ6S9OWZRCSskHI+M81mLikMPFZfHR/FtxJrNCKFe5zx+gvtdyWMD3do7h8QcI0wrm58Tznatnsbw4ifLMWHTKFq5QXFBCV9A/2El/aTwVnmbqYvR4pZ/iA2Po8/NpbhNodIKSS9KpfvMV3Hb74dW5c/8Qjj0DRF+eTXhBMIReSsk777zDnj17WLFiBStWrDimraC9/FmaW36CITydhQteITq69KT922u18++tfWwfs5Fr0PP7snyuTbr4w9KllLSabWxvGeaTliF2tg4z7vYBMDc9hnXL81helMSivHgi9SH78VEoZiQhe0fa+sfpLsrjS5q32RURhdGjQV93CMO962jc2U9hVQoy4KD6zQ3MWrqClLwC/BY3o+ubCcsyErPqyAbmtm3b2LFjB4sXL+aqq646RnR9PjuNjT9kYHATSUlXMbfkZ4SFTZ1LpcXh4idtfbxptpAYpuPHxZncl5F4URedGLC62D6xAt/eMsSA1Q1AVnwEN1Sks7woiWUFiSSqbIUKxUVNyAo6YV4GbMmUi3b+JzqOG3pTwdeJOfsSPO1OylZmsv2V5/F7PSy/88vIgGTk1UNIb4CEO2cfzqS4fft2PvzwQyorK1m9evUxYj4+foD9B76Jw9FBYcF3yc19ZEp7eb/by3919PNC3zAGjYbv5KXy1eyUizJP+YDVxaftI+zuGOEvrcOHIzPjI8O4pCiJ5YVJrChKUoE9CkWIEbKCHoh1YreGU6xtZ782hYcaQZeayv5OHfHpUejCRtj33jtUrb6BhIwsrO924m4ZI+6WIsKSg0K1fft2tm7dSmlpKTfeeCOaiVW0lAG6u39PS+vPCAuLo6rq2SlLxVm8Pn7VNchvesz4JKzLSOKbeakXjT950IRiZ3fHyOGf7hEnABFhWhbmxXP7giyWFyUxNz0GjQqvVyhClpAV9KEkDYVD3RyKkIS5A6TU9+K79WEGO8dZfnsR2577X8Kjolh625dwHRrF+l4XkVUpRC0K5nTZtm0bH3zwAWVlZdx8882HxdztNnOw4buMjHxMUtIqSub8BL0+YVL7I14fv+k283SPmXF/gFtS4/mH/DRyIy6sWcLrD3DAZGX3xAp8T+coI3YPAIlRehbmxXP/sjwW5SUwNyNGBfUoFDOIkBX0/lg987sa2J5iYGmLBuH10h5Vhd4QICKih679dVz5wFcI84Ux+NJ+dCmRxN0cdEN87733+Pjjj6moqGDt2rWHxXxo6AMONvwDfr+N2bMeJzPz7kmbmGaPl193m/l97xB2f4A1ybF8KzeVsugLY56wu33UdI3xaccIezpGqOkaw+n1A5CbGMmVc1JYlBfPorwE8pNUQI9CMZMJSUF32u10aBJYy0e8GBnFI61GvNmpdLR5Kb8ig+2vPEFCZjbzLr+Wkd8eRPoliV8uQWrhzU2bqK6uZsGCBaxZswaNRoPXO0Zz87/T178eY9RsSkufx2icdUyb/W4v/9M1yHOmIVwBydqUOL6Rm0qJ8fPLxR0IBD1QarrHqOkao7Z7jEMD4/gDEo2AkvQY7lyUzaK8BBblxasK9grFF4yQFPTqtzbT404mT99Ev19HXpOFnuu/jrAJAt49jPaZuOX7j2F5vQ1P93hQzGN1vPLKKzQ1NXHppZdy5ZVXIoRgcPBtmg79CK93lLzcr5KX93W02iNmk/pxB091m9kwOEYAyS2p8XwjN5WiyPMvlkM2N7UTwl3bPUZd99hhF8IYg46K7DiuLilkQV4C83PiiDZcHHZ7hUJxYQhJQe/b14g/tYCeKCvLGuLxE06HI5XsEsHeza8xa9mlJJiTGK/tJubaPPx5Bl547jm6u7u5/vrrWbx4MS5XH4eaf4zZvAWjcS6VFc8c9i33S8nbQxae6jaz02InSqvh/sxEHs5KPm828nGXlwMmK/t6LNT3WqjtHj28eanVCOakRXNjZQZVOfFUZsdRkBSlNjAVCsUxhKSge8QYpZZ2tsdGsLpeg6niDrxeyZjpTXTh4SxfcDvjb3YTuTAV22wtLz31FA6HgzvuuIM5cwrp6HiS9o5fAQEKC/6enJy/RqMJY9zn58W+YZ7uGaLL5SHLEMZjhRncnZFIzDl0PzxavPf1Wtjfa6FtyH74/fRYA5XZcdy7NJfK7HjmZcYSob/43B8VCsXFRUgK+miihvlDB3hfE0662cDOOQuJT2qmv6mB62/5Jo4tJsKL4ugtdrHhmVeIjIzkgQceIDz8EDt3PYrT2Uly8jUUF/0jBkMmuy12Xujr4w3zGA5/gCWxUfyoMIPVSbHozmIVHAhIukcdNPSN09BnpbHfSmP/OJ3DjsPHpMcaKMuM5aaqTOZlxTIvM5YkFcCjUCjOgJAU9E6jkfnjjSyrh4681Xi9Qwy2bKGqfDXR+wyQGs7OpHaq/7yXnJwcrruuCFPf3zM29imRkflUVvyOQPQynukf5aW+RpodbiK1Gm5KieO+jCQqY07fY8Xq8tLYN05jv5WGid9N/eM4PEGPEyEgPzGK0owYbp2fxbzMWMoyY0mOVuKtUCjODSEp6B3EkWAYY2VzGnVVS8H7GrkJpcxyVjIW5+EDahiuHeaSSzJJSv6Igw3/jl6fTFrh49TqVvHzbhufjB3AL2FRTBRPzElhbXIcUacwq0gpGbC6aTXbgj+DNlrNdlrNNvosrsPHxUaEUZIezR0LsylJj2ZOWgyzUqOV2UShUJxXQlLQ460WbJ0GerJvwWPfQq4hncroq6mO7qTO0UJ67BBXX9OHy/UcndY8OpP/k92BMj5qd+CVfeQa9HwtO4Xb0hImZT6UUmJxeukacdA94qR96Ihotw7asE+suCGYMrYgxciywkSKUoyUpMdQkhZDaky48vdWKBSfO9MSdCHEauAXgBZ4Wkr50+PeDweeBRYAw8CdUsqOc9vVIL0th8hy7mJeYzndKaOURxdAXCYbY98nMukglTlm2rUJvOBdzL7wb3HIEwVDkGXw8FBWEjcmx5Gn1TEw7qa3x8qu0QG6Rxx0DTvoHnXQNeJg3OU7ps3MuAgKkqO4fWE2hSlGCpOjKEo2khythFuhUFw8nFLQhRBa4FfA1UAPsFsI8YaU8uBRhz0EjEopi4QQdwH/F7jzfHT4va0bSWhxYstbRlqeYFdmF8PxXfTpk2n23EWvLQvpBY0nQI5Wx3J8GH0Sh93B+9ZBXrS6cPsCx1xTr9OQHR9BTkIkC3PjyU6IDP7ER5KXFKnSxCoUipBgOkq1GGiRUrYBCCFeAtYCRwv6WuCxicevAb8UQggppTyHfQXgA6eObbnfIeAXSIeAJgl+yWfr5DCGDx9rAoZ1GtJiDaTGBF0BP3ucFmMgLTacrPhIko3hyqdboVCEPNMR9Eyg+6jnPcCSqY6RUvqEEBYgERg6+iAhxCPAIwA5OdMrqHw8ET4Hxgg3euEnITqKjNgEMo3R5MVEkBilJz5ST1xkGPGRwccxETplFlEoFF8IpiPoJ1LD41fe0zkGKeVTwFMACxcuPKPV+/987wdncppCoVDMeKaTO7UHyD7qeRZBa8YJjxFC6IBYYORcdFChUCgU02M6gr4bKBZC5Ash9MBdwBvHHfMGcP/E49uA98+H/VyhUCgUU3NKk8uETfxR4G2CbovPSCkPCCEeB/ZIKd8Afgs8J4RoIbgyv+t8dlqhUCgUk5mWP56UcjOw+bjXfnTUYxdw+7ntmkKhUChOB1V/TKFQKGYIStAVCoVihqAEXaFQKGYIStAVCoVihiAulHehEMIMdJ7h6UkcF4UawqixXJzMlLHMlHGAGstn5Eopk0/0xgUT9LNBCLFHSrnwQvfjXKDGcnEyU8YyU8YBaizTQZlcFAqFYoagBF2hUChmCKEq6E9d6A6cQ9RYLk5mylhmyjhAjeWUhKQNXaFQKBSTCdUVukKhUCiOQwm6QqFQzBBCTtCFEKuFEE1CiBYhxPcvdH/OBiFEhxBinxCiVgix50L353QQQjwjhBgUQuw/6rUEIcRWIUTzxO/4C9nH6TDFOB4TQvROzEutEOL6C9nH6SKEyBZCfCCEaBBCHBBCfGPi9ZCal5OMI+TmRQhhEEJ8KoSomxjLv068ni+E2DUxJy9PpCY/+/ZCyYY+UbD6EEcVrAa+dFzB6pBBCNEBLJRShlywhBBiJWADnpVSlk289h/AiJTypxP/bOOllP9wIft5KqYYx2OATUr5nxeyb6eLECIdSJdS7hVCRAPVwE3AOkJoXk4yjjsIsXkRwfqXUVJKmxAiDPgE+AbwbWC9lPIlIcSvgTop5ZNn216ordAPF6yWUnqAzwpWKz5npJQfMbkq1VrgDxOP/0DwJryomWIcIYmUsk9KuXfi8TjQQLDeb0jNy0nGEXLIILaJp2ETPxK4Enht4vVzNiehJugnKlgdkhM9gQTeEUJUTxTQDnVSpZR9ELwpgZQL3J+z4VEhRP2ESeaiNlGcCCFEHlAF7CKE5+W4cUAIzosQQiuEqAUGga1AKzAmpfRNHHLOdCzUBH1axahDiOVSyvnAdcDXJr7+Ky48TwKFj4xiXAAAAaRJREFUQCXQB/zXhe3O6SGEMAJ/Ar4ppbRe6P6cKScYR0jOi5TSL6WsJFiPeTFQcqLDzkVboSbo0ylYHTJIKU0TvweBPxOc7FBmYML++ZkddPAC9+eMkFIOTNyEAeA3hNC8TNhp/wT8UUq5fuLlkJuXE40jlOcFQEo5BnwILAXihBCfVYw7ZzoWaoI+nYLVIYEQImpiwwchRBRwDbD/5Gdd9BxdLPx+YMMF7MsZ85n4TXAzITIvExtwvwUapJRPHPVWSM3LVOMIxXkRQiQLIeImHkcAqwjuCXwA3DZx2Dmbk5DycgGYcFX6OUcKVv/7Be7SGSGEKCC4KodgbdcXQmksQogXgcsJpgEdAP4FeB14BcgBuoDbpZQX9YbjFOO4nODXegl0AF/5zAZ9MSOEWAF8DOwDAhMv/5Cg/Tlk5uUk4/gSITYvQohygpueWoIL6FeklI9P3P8vAQlADfBlKaX7rNsLNUFXKBQKxYkJNZOLQqFQKKZACbpCoVDMEJSgKxQKxQxBCbpCoVDMEJSgKxQKxQxBCbpCoVDMEJSgKxQKxQzh/wN3rwvX457O3gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption function\n", + "M = np.linspace(0, 30, 300)\n", + "pLvl = 1.0\n", + "P = pLvl * np.ones_like(M)\n", + "for j in range(MedicalExample.MedShkDstn[0][0].size):\n", + " MedShk = MedicalExample.MedShkDstn[0][1][j] * np.ones_like(M)\n", + " M_temp = M + MedicalExample.solution[0].mLvlMin(pLvl)\n", + " C = MedicalExample.solution[0].cFunc(M_temp, P, MedShk)\n", + " plt.plot(M_temp, C)\n", + "print(\"Consumption function by medical need shock (constant permanent income)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Medical care function by medical need shock (constant permanent income)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the medical care function\n", + "for j in range(MedicalExample.MedShkDstn[0][0].size):\n", + " MedShk = MedicalExample.MedShkDstn[0][1][j] * np.ones_like(M)\n", + " Med = MedicalExample.solution[0].MedFunc(M_temp, P, MedShk)\n", + " plt.plot(M_temp, Med)\n", + "print(\"Medical care function by medical need shock (constant permanent income)\")\n", + "plt.ylim([0, 20])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "End of period savings by medical need shock (constant permanent income)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the savings function\n", + "for j in range(MedicalExample.MedShkDstn[0][0].size):\n", + " MedShk = MedicalExample.MedShkDstn[0][1][j] * np.ones_like(M)\n", + " Sav = (\n", + " M_temp\n", + " - MedicalExample.solution[0].cFunc(M_temp, P, MedShk)\n", + " - MedicalExample.MedPrice[0]\n", + " * MedicalExample.solution[0].MedFunc(M_temp, P, MedShk)\n", + " )\n", + " plt.plot(M_temp, Sav)\n", + "print(\"End of period savings by medical need shock (constant permanent income)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ms/dev/HARK/HARK/utilities.py:160: RuntimeWarning: divide by zero encountered in power\n", + " return( c**-gam )\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Marginal value function (pseudo inverse)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the marginal value function\n", + "M = np.linspace(0.0, 30, 300)\n", + "for p in range(MedicalExample.pLvlGrid[0].size):\n", + " pLvl = MedicalExample.pLvlGrid[0][p]\n", + " M_temp = pLvl * M + MedicalExample.solution[0].mLvlMin(pLvl)\n", + " P = pLvl * np.ones_like(M)\n", + " vP = MedicalExample.solution[0].vPfunc(M_temp, P) ** (-1.0 / MedicalExample.CRRA)\n", + " plt.plot(M_temp, vP)\n", + "print(\"Marginal value function (pseudo inverse)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "if MedicalExample.vFuncBool:\n", + " # Plot the value function\n", + " M = np.linspace(0.0, 1, 300)\n", + " for p in range(MedicalExample.pLvlGrid[0].size):\n", + " pLvl = MedicalExample.pLvlGrid[0][p]\n", + " M_temp = pLvl * M + MedicalExample.solution[0].mLvlMin(pLvl)\n", + " P = pLvl * np.ones_like(M)\n", + " v = CRRAutility_inv(\n", + " MedicalExample.solution[0].vFunc(M_temp, P), gam=MedicalExample.CRRA\n", + " )\n", + " plt.plot(M_temp, v)\n", + " print(\"Value function (pseudo inverse)\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulating 10000 agents for 100 periods took 36.5912 seconds.\n" + ] + } + ], + "source": [ + "if do_simulation:\n", + " t_start = time()\n", + " MedicalExample.T_sim = 100\n", + " MedicalExample.track_vars = [\"mLvlNow\", \"cLvlNow\", \"MedNow\"]\n", + " MedicalExample.makeShockHistory()\n", + " MedicalExample.initializeSim()\n", + " MedicalExample.simulate()\n", + " t_end = time()\n", + " print(\n", + " \"Simulating \"\n", + " + str(MedicalExample.AgentCount)\n", + " + \" agents for \"\n", + " + str(MedicalExample.T_sim)\n", + " + \" periods took \"\n", + " + mystr(t_end - t_start)\n", + " + \" seconds.\"\n", + " )" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/HARK/ConsumptionSaving/example_ConsMedModel.py b/examples/ConsumptionSaving/example_ConsMedModel.py similarity index 95% rename from HARK/ConsumptionSaving/example_ConsMedModel.py rename to examples/ConsumptionSaving/example_ConsMedModel.py index 23a295f32..5d255aefa 100644 --- a/HARK/ConsumptionSaving/example_ConsMedModel.py +++ b/examples/ConsumptionSaving/example_ConsMedModel.py @@ -1,6 +1,6 @@ import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import CRRAutility_inv -from time import process_time +from time import time import matplotlib.pyplot as plt import numpy as np from HARK.ConsumptionSaving.ConsMedModel import MedShockConsumerType @@ -11,9 +11,9 @@ # Make and solve an example medical shocks consumer type MedicalExample = MedShockConsumerType() -t_start = process_time() +t_start = time() MedicalExample.solve() -t_end = process_time() +t_end = time() print("Solving a medical shocks consumer took " + mystr(t_end - t_start) + " seconds.") # Plot the consumption function @@ -76,13 +76,13 @@ plt.show() if do_simulation: - t_start = process_time() + t_start = time() MedicalExample.T_sim = 100 MedicalExample.track_vars = ["mLvlNow", "cLvlNow", "MedNow"] MedicalExample.makeShockHistory() MedicalExample.initializeSim() MedicalExample.simulate() - t_end = process_time() + t_end = time() print( "Simulating " + str(MedicalExample.AgentCount) diff --git a/examples/ConsumptionSaving/example_ConsPrefShockModel.ipynb b/examples/ConsumptionSaving/example_ConsPrefShockModel.ipynb new file mode 100644 index 000000000..c6e4764af --- /dev/null +++ b/examples/ConsumptionSaving/example_ConsPrefShockModel.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import HARK.ConsumptionSaving.ConsumerParameters as Params\n", + "import matplotlib.pyplot as plt\n", + "from HARK.utilities import plotFuncs\n", + "from time import process_time\n", + "import numpy as np\n", + "from HARK.ConsumptionSaving.ConsPrefShockModel import (\n", + " PrefShockConsumerType,\n", + " KinkyPrefConsumerType,\n", + ")\n", + "mystr = lambda number: \"{:.4f}\".format(number)\n", + "do_simulation = True" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Make and solve a preference shock consumer\n", + "PrefShockExample = PrefShockConsumerType()\n", + "PrefShockExample.cycles = 0 # Infinite horizon" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a preference shock consumer took 0.8477220000000001 seconds.\n" + ] + } + ], + "source": [ + "t_start = process_time()\n", + "PrefShockExample.solve()\n", + "t_end = process_time()\n", + "print(\"Solving a preference shock consumer took \" + str(t_end - t_start) + \" seconds.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions at each discrete shock:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption function at each discrete shock\n", + "m = np.linspace(PrefShockExample.solution[0].mNrmMin, 5, 200)\n", + "print(\"Consumption functions at each discrete shock:\")\n", + "for j in range(PrefShockExample.PrefShkDstn[0][1].size):\n", + " PrefShk = PrefShockExample.PrefShkDstn[0][1][j]\n", + " c = PrefShockExample.solution[0].cFunc(m, PrefShk * np.ones_like(m))\n", + " plt.plot(m, c)\n", + "plt.xlim([0.0, None])\n", + "plt.ylim([0.0, None])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function (and MPC) when shock=1:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Consumption function (and MPC) when shock=1:\")\n", + "c = PrefShockExample.solution[0].cFunc(m, np.ones_like(m))\n", + "k = PrefShockExample.solution[0].cFunc.derivativeX(m, np.ones_like(m))\n", + "plt.plot(m, c)\n", + "plt.plot(m, k)\n", + "plt.xlim([0.0, None])\n", + "plt.ylim([0.0, None])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "if PrefShockExample.vFuncBool:\n", + " print(\"Value function (unconditional on shock):\")\n", + " plotFuncs(\n", + " PrefShockExample.solution[0].vFunc,\n", + " PrefShockExample.solution[0].mNrmMin + 0.5,\n", + " 5,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Test the simulator for the pref shock class\n", + "if do_simulation:\n", + " PrefShockExample.T_sim = 120\n", + " PrefShockExample.track_vars = [\"cNrmNow\"]\n", + " PrefShockExample.makeShockHistory() # This is optional\n", + " PrefShockExample.initializeSim()\n", + " PrefShockExample.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Make and solve a \"kinky preferece\" consumer, whose model combines KinkedR and PrefShock\n", + "KinkyPrefExample = KinkyPrefConsumerType()\n", + "KinkyPrefExample.cycles = 0 # Infinite horizon" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a kinky preference consumer took 1.006215 seconds.\n" + ] + } + ], + "source": [ + "t_start = process_time()\n", + "KinkyPrefExample.solve()\n", + "t_end = process_time()\n", + "print(\"Solving a kinky preference consumer took \" + str(t_end - t_start) + \" seconds.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions at each discrete shock:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption function at each discrete shock\n", + "m = np.linspace(KinkyPrefExample.solution[0].mNrmMin, 5, 200)\n", + "print(\"Consumption functions at each discrete shock:\")\n", + "for j in range(KinkyPrefExample.PrefShkDstn[0][1].size):\n", + " PrefShk = KinkyPrefExample.PrefShkDstn[0][1][j]\n", + " c = KinkyPrefExample.solution[0].cFunc(m, PrefShk * np.ones_like(m))\n", + " plt.plot(m, c)\n", + "plt.ylim([0.0, None])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function (and MPC) when shock=1:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Consumption function (and MPC) when shock=1:\")\n", + "c = KinkyPrefExample.solution[0].cFunc(m, np.ones_like(m))\n", + "k = KinkyPrefExample.solution[0].cFunc.derivativeX(m, np.ones_like(m))\n", + "plt.plot(m, c)\n", + "plt.plot(m, k)\n", + "plt.ylim([0.0, None])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "if KinkyPrefExample.vFuncBool:\n", + " print(\"Value function (unconditional on shock):\")\n", + " plotFuncs(\n", + " KinkyPrefExample.solution[0].vFunc,\n", + " KinkyPrefExample.solution[0].mNrmMin + 0.5,\n", + " 5,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Test the simulator for the kinky preference class\n", + "if do_simulation:\n", + " KinkyPrefExample.T_sim = 120\n", + " KinkyPrefExample.track_vars = [\"cNrmNow\", \"PrefShkNow\"]\n", + " KinkyPrefExample.initializeSim()\n", + " KinkyPrefExample.simulate()" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/HARK/ConsumptionSaving/example_ConsPrefShockModel.py b/examples/ConsumptionSaving/example_ConsPrefShockModel.py similarity index 97% rename from HARK/ConsumptionSaving/example_ConsPrefShockModel.py rename to examples/ConsumptionSaving/example_ConsPrefShockModel.py index aa0c48e38..89e332abf 100644 --- a/HARK/ConsumptionSaving/example_ConsPrefShockModel.py +++ b/examples/ConsumptionSaving/example_ConsPrefShockModel.py @@ -7,9 +7,7 @@ PrefShockConsumerType, KinkyPrefConsumerType, ) - mystr = lambda number: "{:.4f}".format(number) - do_simulation = True # Make and solve a preference shock consumer @@ -57,8 +55,6 @@ PrefShockExample.initializeSim() PrefShockExample.simulate() -########################################################################### - # Make and solve a "kinky preferece" consumer, whose model combines KinkedR and PrefShock KinkyPrefExample = KinkyPrefConsumerType() KinkyPrefExample.cycles = 0 # Infinite horizon diff --git a/examples/ConsumptionSaving/example_ConsRepAgentModel.ipynb b/examples/ConsumptionSaving/example_ConsRepAgentModel.ipynb new file mode 100644 index 000000000..336d8464f --- /dev/null +++ b/examples/ConsumptionSaving/example_ConsRepAgentModel.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "from time import time\n", + "import numpy as np\n", + "from HARK.utilities import plotFuncs\n", + "import HARK.ConsumptionSaving.ConsumerParameters as Params\n", + "from HARK.ConsumptionSaving.ConsRepAgentModel import (\n", + " RepAgentConsumerType,\n", + " RepAgentMarkovConsumerType,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a quick example dictionary\n", + "RA_params = deepcopy(Params.init_idiosyncratic_shocks)\n", + "RA_params[\"DeprFac\"] = 0.05\n", + "RA_params[\"CapShare\"] = 0.36\n", + "RA_params[\"UnempPrb\"] = 0.0\n", + "RA_params[\"LivPrb\"] = [1.0]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a representative agent problem took 0.2792696952819824 seconds.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make and solve a rep agent model\n", + "RAexample = RepAgentConsumerType(**RA_params)\n", + "t_start = time()\n", + "RAexample.solve()\n", + "t_end = time()\n", + "print(\n", + " \"Solving a representative agent problem took \" + str(t_end - t_start) + \" seconds.\"\n", + ")\n", + "plotFuncs(RAexample.solution[0].cFunc, 0, 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulating a representative agent for 2000 periods took 9.091149091720581 seconds.\n" + ] + } + ], + "source": [ + "# Simulate the representative agent model\n", + "RAexample.T_sim = 2000\n", + "RAexample.track_vars = [\"cNrmNow\", \"mNrmNow\", \"Rfree\", \"wRte\"]\n", + "RAexample.initializeSim()\n", + "t_start = time()\n", + "RAexample.simulate()\n", + "t_end = time()\n", + "print(\n", + " \"Simulating a representative agent for \"\n", + " + str(RAexample.T_sim)\n", + " + \" periods took \"\n", + " + str(t_end - t_start)\n", + " + \" seconds.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a two state representative agent problem took 0.7686238288879395 seconds.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make and solve a Markov representative agent\n", + "RA_markov_params = deepcopy(RA_params)\n", + "RA_markov_params[\"PermGroFac\"] = [[0.97, 1.03]]\n", + "RA_markov_params[\"MrkvArray\"] = np.array([[0.99, 0.01], [0.01, 0.99]])\n", + "RA_markov_params[\"MrkvNow\"] = 0\n", + "RAmarkovExample = RepAgentMarkovConsumerType(**RA_markov_params)\n", + "RAmarkovExample.IncomeDstn[0] = 2 * [RAmarkovExample.IncomeDstn[0]]\n", + "t_start = time()\n", + "RAmarkovExample.solve()\n", + "t_end = time()\n", + "print(\n", + " \"Solving a two state representative agent problem took \"\n", + " + str(t_end - t_start)\n", + " + \" seconds.\"\n", + ")\n", + "plotFuncs(RAmarkovExample.solution[0].cFunc, 0, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulating a two state representative agent for 2000 periods took 9.762195110321045 seconds.\n" + ] + } + ], + "source": [ + "# Simulate the two state representative agent model\n", + "RAmarkovExample.T_sim = 2000\n", + "RAmarkovExample.track_vars = [\"cNrmNow\", \"mNrmNow\", \"Rfree\", \"wRte\", \"MrkvNow\"]\n", + "RAmarkovExample.initializeSim()\n", + "t_start = time()\n", + "RAmarkovExample.simulate()\n", + "t_end = time()\n", + "print(\n", + " \"Simulating a two state representative agent for \"\n", + " + str(RAexample.T_sim)\n", + " + \" periods took \"\n", + " + str(t_end - t_start)\n", + " + \" seconds.\"\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/HARK/ConsumptionSaving/example_ConsRepAgentModel.py b/examples/ConsumptionSaving/example_ConsRepAgentModel.py similarity index 89% rename from HARK/ConsumptionSaving/example_ConsRepAgentModel.py rename to examples/ConsumptionSaving/example_ConsRepAgentModel.py index c71d1639e..121413911 100644 --- a/HARK/ConsumptionSaving/example_ConsRepAgentModel.py +++ b/examples/ConsumptionSaving/example_ConsRepAgentModel.py @@ -1,5 +1,5 @@ from copy import deepcopy -from time import process_time +from time import time import numpy as np from HARK.utilities import plotFuncs import HARK.ConsumptionSaving.ConsumerParameters as Params @@ -17,9 +17,9 @@ # Make and solve a rep agent model RAexample = RepAgentConsumerType(**RA_params) -t_start = process_time() +t_start = time() RAexample.solve() -t_end = process_time() +t_end = time() print( "Solving a representative agent problem took " + str(t_end - t_start) + " seconds." ) @@ -29,9 +29,9 @@ RAexample.T_sim = 2000 RAexample.track_vars = ["cNrmNow", "mNrmNow", "Rfree", "wRte"] RAexample.initializeSim() -t_start = process_time() +t_start = time() RAexample.simulate() -t_end = process_time() +t_end = time() print( "Simulating a representative agent for " + str(RAexample.T_sim) @@ -47,9 +47,9 @@ RA_markov_params["MrkvNow"] = 0 RAmarkovExample = RepAgentMarkovConsumerType(**RA_markov_params) RAmarkovExample.IncomeDstn[0] = 2 * [RAmarkovExample.IncomeDstn[0]] -t_start = process_time() +t_start = time() RAmarkovExample.solve() -t_end = process_time() +t_end = time() print( "Solving a two state representative agent problem took " + str(t_end - t_start) @@ -61,9 +61,9 @@ RAmarkovExample.T_sim = 2000 RAmarkovExample.track_vars = ["cNrmNow", "mNrmNow", "Rfree", "wRte", "MrkvNow"] RAmarkovExample.initializeSim() -t_start = process_time() +t_start = time() RAmarkovExample.simulate() -t_end = process_time() +t_end = time() print( "Simulating a two state representative agent for " + str(RAexample.T_sim) diff --git a/examples/ConsumptionSaving/example_TractableBufferStockModel.ipynb b/examples/ConsumptionSaving/example_TractableBufferStockModel.ipynb new file mode 100644 index 000000000..576dc599a --- /dev/null +++ b/examples/ConsumptionSaving/example_TractableBufferStockModel.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np # numeric Python\n", + "from HARK.utilities import plotFuncs # basic plotting tools\n", + "from HARK.ConsumptionSaving.ConsMarkovModel import (\n", + " MarkovConsumerType,\n", + ") # An alternative, much longer way to solve the TBS model\n", + "from time import process_time # timing utility\n", + "from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType\n", + "import numpy as np\n", + "do_simulation = True" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the model primitives\n", + "base_primitives = {\n", + " \"UnempPrb\": 0.00625, # Probability of becoming unemployed\n", + " \"DiscFac\": 0.975, # Intertemporal discount factor\n", + " \"Rfree\": 1.01, # Risk-free interest factor on assets\n", + " \"PermGroFac\": 1.0025, # Permanent income growth factor (uncompensated)\n", + " \"CRRA\": 1.0,\n", + "} # Coefficient of relative risk aversion" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Define a dictionary to be used in case of simulation\n", + "simulation_values = {\n", + " \"aLvlInitMean\": 0.0, # Mean of log initial assets for new agents\n", + " \"aLvlInitStd\": 1.0, # Stdev of log initial assets for new agents\n", + " \"AgentCount\": 10000, # Number of agents to simulate\n", + " \"T_sim\": 120, # Number of periods to simulate\n", + " \"T_cycle\": 1,\n", + "} # Number of periods in the cycle" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving a tractable consumption-savings model took 0.005015999999999909 seconds.\n" + ] + } + ], + "source": [ + "# Make and solve a tractable consumer type\n", + "ExampleType = TractableConsumerType(**base_primitives)\n", + "t_start = process_time()\n", + "ExampleType.solve()\n", + "t_end = process_time()\n", + "print(\n", + " \"Solving a tractable consumption-savings model took \"\n", + " + str(t_end - t_start)\n", + " + \" seconds.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the consumption function and whatnot\n", + "m_upper = 1.5 * ExampleType.mTarg\n", + "conFunc_PF = lambda m: ExampleType.h * ExampleType.PFMPC + ExampleType.PFMPC * m\n", + "# plotFuncs([ExampleType.solution[0].cFunc,ExampleType.mSSfunc,ExampleType.cSSfunc],0,m_upper)\n", + "plotFuncs([ExampleType.solution[0].cFunc, ExampleType.solution[0].cFunc_U], 0, m_upper)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "if do_simulation:\n", + " ExampleType(**simulation_values) # Set attributes needed for simulation\n", + " ExampleType.track_vars = [\"mLvlNow\"]\n", + " ExampleType.makeShockHistory()\n", + " ExampleType.initializeSim()\n", + " ExampleType.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Now solve the same model using backward induction rather than the analytic method of TBS.\n", + "# The TBS model is equivalent to a Markov model with two states, one of them absorbing (permanent unemployment).\n", + "MrkvArray = np.array(\n", + " [[1.0 - base_primitives[\"UnempPrb\"], base_primitives[\"UnempPrb\"]], [0.0, 1.0]]\n", + ") # Define the two state, absorbing unemployment Markov array\n", + "init_consumer_objects = {\n", + " \"CRRA\": base_primitives[\"CRRA\"],\n", + " \"Rfree\": np.array(\n", + " 2 * [base_primitives[\"Rfree\"]]\n", + " ), # Interest factor (same in both states)\n", + " \"PermGroFac\": [\n", + " np.array(\n", + " 2 * [base_primitives[\"PermGroFac\"] / (1.0 - base_primitives[\"UnempPrb\"])]\n", + " )\n", + " ], # Unemployment-compensated permanent growth factor\n", + " \"BoroCnstArt\": None, # Artificial borrowing constraint\n", + " \"PermShkStd\": [0.0], # Permanent shock standard deviation\n", + " \"PermShkCount\": 1, # Number of shocks in discrete permanent shock distribution\n", + " \"TranShkStd\": [0.0], # Transitory shock standard deviation\n", + " \"TranShkCount\": 1, # Number of shocks in discrete permanent shock distribution\n", + " \"T_cycle\": 1, # Number of periods in cycle\n", + " \"UnempPrb\": 0.0, # Unemployment probability (not used, as the unemployment here is *permanent*, not transitory)\n", + " \"UnempPrbRet\": 0.0, # Unemployment probability when retired (irrelevant here)\n", + " \"T_retire\": 0, # Age at retirement (turned off)\n", + " \"IncUnemp\": 0.0, # Income when unemployed (irrelevant)\n", + " \"IncUnempRet\": 0.0, # Income when unemployed and retired (irrelevant)\n", + " \"aXtraMin\": 0.001, # Minimum value of assets above minimum in grid\n", + " \"aXtraMax\": ExampleType.mUpperBnd, # Maximum value of assets above minimum in grid\n", + " \"aXtraCount\": 48, # Number of points in assets grid\n", + " \"aXtraExtra\": [None], # Additional points to include in assets grid\n", + " \"aXtraNestFac\": 3, # Degree of exponential nesting when constructing assets grid\n", + " \"LivPrb\": [np.array([1.0, 1.0])], # Survival probability\n", + " \"DiscFac\": base_primitives[\"DiscFac\"], # Intertemporal discount factor\n", + " \"AgentCount\": 1, # Number of agents in a simulation (irrelevant)\n", + " \"tax_rate\": 0.0, # Tax rate on labor income (irrelevant)\n", + " \"vFuncBool\": False, # Whether to calculate the value function\n", + " \"CubicBool\": True, # Whether to use cubic splines (False --> linear splines)\n", + " \"MrkvArray\": [MrkvArray], # State transition probabilities\n", + "}\n", + "MarkovType = MarkovConsumerType(**init_consumer_objects) # Make a basic consumer type\n", + "employed_income_dist = [\n", + " np.ones(1),\n", + " np.ones(1),\n", + " np.ones(1),\n", + "] # Income distribution when employed\n", + "unemployed_income_dist = [\n", + " np.ones(1),\n", + " np.ones(1),\n", + " np.zeros(1),\n", + "] # Income distribution when permanently unemployed\n", + "MarkovType.IncomeDstn = [\n", + " [employed_income_dist, unemployed_income_dist]\n", + "] # set the income distribution in each state\n", + "MarkovType.cycles = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the \"Markov TBS\" model\n", + "t_start = process_time()\n", + "MarkovType.solve()\n", + "t_end = process_time()\n", + "MarkovType.unpackcFunc()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving the same model \"the long way\" took 3.202354 seconds.\n" + ] + }, + { + "data": { + "image/png": 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cuwd4CW9L4mNmttU5dz+wzsyWA18DfuWc+yrecsynzUxLKhEsEDDe2VnJE+/t5dWCMgCuzEnmzovHc9nkBK2Ni/ggqH3oHXvKX+j2ue92+TgfuLRnS5NQVHuklWfWF/O/q/eyq/Iwo4cN4v8smMTtc9NJHaV94yJ+0u13EpT8g3X8dvUe/rLxIEda27kgPY6fL5vJkuljGTwwyu/yRAQFupxGe8B4paCMX7+9mzW7q4iJHsDSmSnccfF4pqWM9Ls8EelGgS4nqG9q5el1xTz+7m72Vx0hJW4I31qSw7K8dEYO1ck/IqFKgS7H7DvUyOPv7uHpdftpaG7jooxRfGtxLgunJDNQ53CKhDwFej9nZqzZXcWv397NywVlRDnH9TPG8tnLJjAjNc7v8kTkLCjQ+6mWtgDPbT7Ir9/ezdaDdYwaGs0XL5/MHRePJ1kHR4iEJQV6P9PQ3MZTa/bx67d3U1LbRFbycH5003RunJ1CTLR2q4iEMwV6P1Fe38Tj7+zht6v3Ut/UxryJ8fzrTdNZkJWoW/JFIoQCPcLtqmjgV2/t4pn1B2gNBFg8bQx3z5/ErDStj4tEGgV6hNq4r5pfvrGLl/JLiY4awMfzUvm7j0wkI2GY36WJSC9RoEcQM+PdnYf4z1XbWb2rihExA/ni5ZO565IMEmPVslYk0inQI4CZ8XpRBf+5ajsb9tWQPGIw37kul1vnpDN8sP6JRfoL/W8PY4GA8XJBGQ+t2sGHB2pJiRvCD26cxscvTNWOFZF+SIEehtoDxootJTy0ageFpfWMHz2UB26ewY2zUxg0UHd0ivRXCvQwEggYz31YwoOvbGNnxWEmJQ7j58tm8tEZ43Rrvogo0MOBmbEyv4yfrdxGUVk92cmxPHT7bBZPG0uUDpIQkQ4K9BBmZryxrYKfvbyNzcW1TEwYxn/cNpvrp4/ViUAicgIFeohavesQ/7ayiLV7qkkdNYSf3DKDj81O0dKKiJySAj3EbC6u4YEXi3h7RyXJIwbzgxunsSwvTRc7ReSMFOghYn9VIw+8VMTfPjhI/LBBfOe6XD41b7y2H4pI0BToPqtpbOGhVTt44r29DBgA91wxmb9fMJHYGJ0MJCJnR4Huk6bWdp54bw8PrdpBfXMbH78wlX9cmM2YkepFLiLnRoHex8yM5zaX8KMVhRyoOcKCrETuW5JDzpgRfpcmImFOgd6H8g/W8f2/bWXN7ipyx47gxzfP4LLMBL/LEpEIEVSgO+cWAQ8CUcCjZvajk4z5BPB9wIAPzOz2HqwzrFUfbuFnL2/jd+/vZeSQaP7fx6Zx60XpuilIRHrUGQPdORcFPAwsBIqBtc655WaW32VMJnAfcKmZVTvnknqr4HDSHjB+v2Yf/7ayiPqmNu68OIOvXp3FyKG64CkiPS+YGfocYIeZ7QJwzj0FLAXyu4z5O+BhM6sGMLPyni403GzaX8O3nv2Q/JI6Lp44mu/dMEXr5CLSq4IJ9BRgf5fHxcDcbmOyAJxz7+Aty3zfzF7s/kLOubuBuwHS09PPpd6QV9/Uyk9fKuKJ1XtJjo3h4dsvYMn0MTq3U0R6XTCBfrIkspO8TiZwOZAKvOWcm2ZmNcf9IbNHgEcA8vLyur9GWDMzXtpayveWb6W8vpm7Ls7ga9dkaT+5iPSZYAK9GEjr8jgVOHiSMavNrBXY7Zwrwgv4tT1SZYg7WHOE7/51C68UlDNl7Ah+eUeeDmEWkT4XTKCvBTKdcxOAA8CtQPcdLH8BbgMed84l4C3B7OrJQkORmfHHdcX84Ll82gLGt5fk8plLM9RAS0R8ccZAN7M259w9wEt46+OPmdlW59z9wDozW97x3DXOuXygHfiGmR3qzcL9VlrbxH3Pbua1ogrmTYznJ7fMJC1+qN9liUg/5sz8WcrOy8uzdevW+fLe58PM+PPGA3x/+VZa2gPcuyiHOy/OUH9yEekTzrn1ZpZ3sud0p+hZqDrcwr3PbGZlfhl540fx04/PJCNhmN9liYgACvSgvbuzkq/+YRPVh1v59pJcPnvZBN3pKSIhRYF+Bq3tAR58ZTsPv76DCaOH8eu7LmJayki/yxIROYEC/TSKqxv58pMb2bCvhk/kpfK9j05l2GD9lYlIaFI6ncKb2yr48lMbaW83Hrx1FktnpfhdkojIaSnQuzEz/uv1nfx0ZRFZSbH88o4LdeFTRMKCAr2LhuY2vv70B7y4tZTrZ4zlgVtmMHSQ/opEJDworTrsO9TIZ3+zlt2Vh/nOdbl87rIJaqglImFFgQ5s3FfN53+zjraA8dvPzuGSyTpFSETCT78P9BUflvAPf9hE8ogY/uczFzEpcbjfJYmInJN+Hei/fns3//J8PrPT4vjVnXmMHj7Y75JERM5Zvwx0M+Pnr2znP17dzuJpY/j5slnEREf5XZaIyHnpd4EeCBg/eD6f/3lnD5/IS+Vfb5qhW/hFJCL0q0BvDxj3PbuZp9cV85lLM/i/101Rl0QRiRj9JtDNjG//+UOeXlfMl66czD8uzNK2RBGJKP0i0M2Mf/5bPk+t3c8Xr5jE167J9rskEZEe1y/OSnvgpSIef3cPn7tsAl9XmItIhIr4QH/s7d384vWdfHJuOt+5LlfLLCISsSI60F/cUsoPns/n2qnJ3L90msJcRCJaxAb6xn3V/MMfNjIzNY5/XzZbWxNFJOJFZKCX1zVx92/Xkxg7mEfvymPIIN00JCKRL+ICvbU9wD2/30h9Uyu/ujOPBN3OLyL9RMRtW/zxikLW7Kni35fNImfMCL/LERHpMxE1Q39xSwmPvr2buy4ez42zdWSciPQvQQW6c26Rc67IObfDOXfvacbd4pwz51xez5UYnPK6Ju579kOmp4zk29dN6eu3FxHx3RkD3TkXBTwMLAamALc5505ITOdcLPBl4P2eLvJMzIxvPrOZxpZ2fr5sFoMGRtQPHiIiQQkm+eYAO8xsl5m1AE8BS08y7gfAA0BTD9YXlKfW7ue1ogruXZzD5CQdUCEi/VMwgZ4C7O/yuLjjc8c452YDaWb23OleyDl3t3NunXNuXUVFxVkXezLldU388PkCLpk0mrsuzuiR1xQRCUfBBPrJ7sixY086NwD4OfC1M72QmT1iZnlmlpeYmBh8lafxL88X0Nwe4Icfm65WuCLSrwUT6MVAWpfHqcDBLo9jgWnA6865PcA8YHlfXBh9Z0clyz84yBcWTCIjYVhvv52ISEgLJtDXApnOuQnOuUHArcDyo0+aWa2ZJZhZhpllAKuBG8xsXa9U3KGtPcD3lm9l/OihfOHySb35ViIiYeGMgW5mbcA9wEtAAfC0mW11zt3vnLuhtws8lWc2FLOjvIH7FufqPFAREYK8U9TMXgBe6Pa5755i7OXnX9bpHWlp5+cvb2d2ehzXTk3u7bcTEQkLYblh+/F391Ba18S9i3LUEldEpEPY9XI53NzGL9/cyRXZicydONrvckRE+kZzA1QUnXZI2AX6U2v3U9PYypeuyvS7FBGRntfSCJVFUF7g/aoohPJCqN13xj8aVoHe0hbg0bd2MWdCPBekj/K7HBGRc9faBJXbOkK7wAvtigKo3suxW32iBsHoTEi7CC64E5Jy4J9PvRclrAL9r5sOUFLbxA9vmu53KSIiwWlrhkM7us24C6B6N1jAGzNgIIyeDGNnwczbICkXEnMhfiJEBR/TYRPoZsav3tpFzphYLs/qmbtMRUR6THsrHNoJ5fmdoV1R6H3O2r0xLsoL6eQpMO1mL7iTciF+EgwcdN4lhE2gr91TzbayBh64eYZ2toiIf9rboGrX8csk5YXeLDzQ2jHIQfwEb5ade0PHjDsHEjJhYO+dohY2gf6/q/cSGzOQ62eO9bsUEekPAu1QvafbGneht+7d3tI5Lm68F9jZi7wAT8qBhCyIHtLnJYdFoFc2NLNiSwmfnDueoYPComQRCReBgLeDpPsad+U2aOvSDXxkmjfLnnRl54w7MRsGhU4fqbBIx2fWF9PabnxybrrfpYhIuDKD2v3HL5NUFHh7u1sbO8fFjvNm2RPme6GdlOsF9+BY/2oPUlgE+p83HmB2ehyZyaH/FyoiPjODuoMnrnFXFEFLfee44cleYF9wlxfgiR3BPSTOv9rPU8gHemFpHYWl9dy/dKrfpYhIKDGDhrLjl0mO3oTTXNs5bmiCN8uedVuXGXcODI33r/ZeEvKB/peNB4ka4Lhuui6GivRbDRUnmXEXwJHqzjFDRkHSFJh+S2doJ+XCsAT/6u5jIR3ogYCxfNMBFmQlMnp47231EZEQ0Vh14q6S8gJorOwcM3ikF9RTlnbuKknMheFJ0M+3NId0oG/YV83B2ia+uTjH71JEpCcdqem2TNLxe0NZ55hBsV5YZy8+fsYdO7bfB/ephHSgr8wvIzrKcWVOkt+liMi5aKrzLkZ2Xy6p73KKZfQw72Lk5KuPX+MemargPkshG+hmxktbS7lkUgKxMdF+lyMip9NyuPOCZNflktr9nWMGDoHELG874NFlkqQcGJkOA8LyaIaQE7KBvq2sgb2HGrl7/kS/SxGRo1qPdMy4uy6X5ENNl9auUYO9OyXT50Hipztn3KMyYICOi+xNIRvoK7eW4hwsnKIj5kT6XGsTHNp+4q6Sqt0ca+06INrrTZKSB7Pv6FwuGTXhrDoESs8J2b/1lwvKmJUWR1JsjN+liESuthavqVT3Ne6qnZ2tXV2U19p1zHSY/onO5ZLRkyBKy6GhJCQD/VBDM5uLa/n6NVl+lyISGdpbvQ6BXXeVlBd4wR1o88a4AV5r18QcmHpj54x79ORe7RAoPSckA/3tHd6e049kqu+5yFkJtHvLIsfNuAugcvvxrV1HZXhhnXNdZ0/u0ZkQrZ+Iw1lIBvob2yoYNTSaaSkj/S5FJDQFAlCz5/hlkqMdAtubO8fFpXvLI5kLO3aV5HoXLAcN9a106T1BBbpzbhHwIBAFPGpmP+r2/D8CnwfagArgs2a291wKMjPe2l7JpZMTiBqgPajSzwUC3ta/7rtKKrZB25HOcSNSvbXtiQs6Z9wJ2TB4uH+1S587Y6A756KAh4GFQDGw1jm33MzyuwzbCOSZWaNz7gvAA8CycymosLSeivpm5uuYOelPzKDuQLcZd35Ha9fDneNix3pr23mf6VjjnuLdlBMzwr/aJWQEM0OfA+wws10AzrmngKXAsUA3s9e6jF8NfOpcC3prewUAH8nsPw11pB8xg/rSE9e4K4qgua5z3LAkb8Y9+1Pe70eDe8go/2qXkBdMoKcAXW73ohiYe5rxnwNWnOwJ59zdwN0A6eknP6xi9a4qJiYMY+zIvj++SaTHmMHhihN3lVQUQFPX1q6jvbXtGZ/oPOk9KTciW7tK7wsm0E+2kG0nHejcp4A8YMHJnjezR4BHAPLy8k54jfaAsXZPFdfPUKtcCSOHD3WZaXcJ7yNVnWNi4rygnnqTN9s+1iFQS4vSc4IJ9GIgrcvjVOBg90HOuauBbwMLzKy5+/PBKCyto76pjbkTRp/LHxfpXUeqT7LGXejNxI8aPMJb2869vmOZpGMv9/BkNZqSXhdMoK8FMp1zE4ADwK3A7V0HOOdmA78EFplZ+bkWs2a3N6OZM0E/boqPmmq9Ne2uyyTlhdBQ2jlm0HBvTTvz2o5dJR0z7hHjFNzimzMGupm1OefuAV7C27b4mJltdc7dD6wzs+XAT4DhwB+d9828z8xuONti3t9VReqoIYyL0/q59IHmhi6tXbssmdQd6BwTPdTbtz3pii5r3DneNkF1CJQQE9Q+dDN7AXih2+e+2+Xjq8+3EDNjzZ4qrshW73PpYS2NUFl0/DJJeSHUdusQmJgFGZcd35M7bryCW8JGyNwpurOigarDLczVcoucq9Ym707J7rtKqvdy7Dp+1CDvFve0OXDhnZ27StTaVSJAyAT6+1o/l2C1NXsdAsu77Syp3t3ZIXDAQK+p1LjZMPP2zjXu+Ilq7SoRK2S+s9fvqSZh+GDGj1aPCenQ3gqHdp64xn1oJ1i7N8ZFeSGdPNU77f3ockn8JBg4yN/6RfpYyAT6pv01zEqLw2mHQP/T3ubNrrvvKjm04/gOgfETvK2AuTd0rnEnZKq1q0iHkAj02sZWdlUe5uYLU/0uRRTj8eYAAAkCSURBVHpToB2q93Q2mDp67mTlNmhv6RwXN94L7uxFnbtKErIgWrufRE4nJAJ9U3ENALPS4nyuRHpEIODtIDluV0lHa9e2ps5xI9O9sJ50ZeeMOzEbBg3zr3aRMBYagb6vBudgRqr6n4cVM6gt7jbjLvBau3btEDgixQvrCfO7bAnMhsGx/tUuEoFCI9D3VzM5cTixMTqfMCSZQX3J8aFdXujdlNNS3zlu+Bhvxn3BnZ27ShKzYYh+8hLpC74HupmxaX8NV+cm+12KmEFD+fHLJEdvwmnu0iFwWKI305512/E34ahDoIivfA/0fVWNVDe2Mitds7g+dbjyxF0lFQVeA6qjhsR7YT39ls7QTsqFYepVLxKKfA/0Tft1QbRXNVaduKukvAAaKzvHxIz0lkemLO3cVZKYC8OT1GhKJIz4Hugb99UwJDqK7GRdIDsvR2q6LZN0/N5Q1jlmUKwX1tmLu8y4p0DsGAW3SATwPdA37a9hespIBkapAVJQmuq6dAjscoGyvkuL+uhh3sXIyVd3LpMk5Xq7TRTcIhHL10BvbQ+QX1LHHfPG+1lGaGo53HlB8lh4F3onwB81cIjXIXDC/M5lkqRcGJmmDoEi/ZCvgb6zooGWtgDTUvrxieWtRzpm3N2WS2r2do6JGuzdKZk+DxI/3TnjjhuvDoEicoyvgb71gHfK+bRx/eCGorZm707J42bcBVC1m2OtXQdEe71JUi7sOO2940CFURnqECgiZ+RvoB+sIyZ6ABMTh/tZRs9qa/GaSnVf467adXyHwNGTYcx0mP6Jzhl3/ESI0s1VInJufA30LQdryR07gqgBYXihrr3VC+nuu0oO7YBAmzfGDfBCOjEHpt7YOeMePVmtXUWkx/ka6AUH61g6e5yfJZxZoN1bFuk+467cdnxr11EZXmBnL/G2AibleCfjRMf4Wb2I9CO+BXpLW4D65jamhsr6eSAANXtOXOOu2AbtzZ3j4tK9WXbm1V5wJ3a0dh2kgzlExF++BfqRVm89uc8viAYC3ta/7rtKKrdBa2PnuBGp3ix74uVdenJnw+AIWu8XkYjia6DHDHBkjemlgDSDugMnmXEXQUtD57jYsd4s+8LPHN8hMKYfb6UUkbDkW6A3tbQzPTmWwQPPcx+1GdSXnrjGXVEIzXWd44YleYE9+1PH9+QeMur83l9EJET4OkOfOu4sZsFmcLjixF0l5QXQVNM5buhob5Y9Y9nxd0+qtauIRLigAt05twh4EIgCHjWzH3V7fjDwBHAhcAhYZmZ7TveabQE7daAfPtR50vvR29/L8+FIVeeYmDgvqKfd1K1DYGIwX5KISMQ5Y6A756KAh4GFQDGw1jm33Mzyuwz7HFBtZpOdc7cCPwaWnem1ZyYAe9/rtlxS4M3Ejxo8wgvu3I8e35N7eLIaTYmIdBHMDH0OsMPMdgE4554ClgJdA30p8P2Oj/8EPOScc2Zmp3rRHLePC56c1fmJQcO9sM669vgZ94hxCm4RkSAEE+gpQJcWfxQDc081xszanHO1wGigsusg59zdwN0AWWNHwML7O8N7ZJqCW0TkPAQT6CdL2e4z72DGYGaPAI8A5OXlGZd+JYi3FxGRYATTNLsYSOvyOBU4eKoxzrmBwEigChER6TPBBPpaINM5N8E5Nwi4FVjebcxy4K6Oj28BVp1u/VxERHreGZdcOtbE7wFewtu2+JiZbXXO3Q+sM7PlwK+B3zrnduDNzG/tzaJFROREQe1DN7MXgBe6fe67XT5uAj7es6WJiMjZ0MGTIiIRQoEuIhIhFOgiIhFCgS4iEiGcX7sLnXP1QJEvb95zEuh2N2yYUf3+C/evQfX3vfFmdtIuhH6eKVpkZnk+vv95c86tC+evQfX7L9y/BtUfWrTkIiISIRToIiIRws9Af8TH9+4p4f41qH7/hfvXoPpDiG8XRUVEpGdpyUVEJEIo0EVEIoQvge6cW+ScK3LO7XDO3etHDefKOZfmnHvNOVfgnNvqnAvLUzqcc1HOuY3Ouef8ruVcOOfinHN/cs4VdvxbXOx3TWfDOffVju+fLc65J51zMX7XdCbOucecc+XOuS1dPhfvnHvZObe94/dRftZ4Oqeo/ycd30ObnXN/ds7F+Vnj+erzQO9y6PRiYApwm3NuSl/XcR7agK+ZWS4wD/himNV/1FeAAr+LOA8PAi+aWQ4wkzD6WpxzKcCXgTwzm4bXljocWk4/Dizq9rl7gVfNLBN4teNxqHqcE+t/GZhmZjOAbcB9fV1UT/Jjhn7s0GkzawGOHjodFsysxMw2dHxcjxckKf5WdXacc6nAdcCjftdyLpxzI4D5eH34MbMWM6vxt6qzNhAY0nHC11BOPAUs5JjZm5x4EtlS4DcdH/8GuLFPizoLJ6vfzFaaWVvHw9V4J7KFLT8C/WSHTodVIB7lnMsAZgPv+1vJWft34J+AgN+FnKOJQAXwPx3LRo8654b5XVSwzOwA8FNgH1AC1JrZSn+rOmfJZlYC3mQHSPK5nvPxWWCF30WcDz8CPagDpUOdc2448AzwD2ZW53c9wXLOXQ+Um9l6v2s5DwOBC4BfmNls4DCh/aP+cTrWmZcCE4BxwDDn3Kf8rap/c859G2859Xd+13I+/Aj0YA6dDmnOuWi8MP+dmT3rdz1n6VLgBufcHrzlriudc//rb0lnrRgoNrOjPxn9CS/gw8XVwG4zqzCzVuBZ4BKfazpXZc65sQAdv5f7XM9Zc87dBVwPfDLcz0L2I9CDOXQ6ZDnnHN7abYGZ/czves6Wmd1nZqlmloH3d7/KzMJqdmhmpcB+51x2x6euAvJ9LOls7QPmOeeGdnw/XUUYXdTtpusB8XcBf/WxlrPmnFsEfBO4wcwa/a7nfPV5oHdcgDh66HQB8LSZbe3rOs7DpcAdeDPbTR2/lvhdVD/0JeB3zrnNwCzghz7XE7SOnyz+BGwAPsT7fxjyt6A7554E3gOynXPFzrnPAT8CFjrntgMLOx6HpFPU/xAQC7zc8X/5v30t8jzp1n8RkQihO0VFRCKEAl1EJEIo0EVEIoQCXUQkQijQRUQihAJdRCRCKNBFRCLE/wexMJdNxxSTBwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Difference between the (employed) consumption functions:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\n", + " 'Solving the same model \"the long way\" took ' + str(t_end - t_start) + \" seconds.\"\n", + ")\n", + "# plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper)\n", + "plotFuncs(MarkovType.cFunc[0], 0, m_upper)\n", + "diffFunc = lambda m: ExampleType.solution[0].cFunc(m) - MarkovType.cFunc[0][0](m)\n", + "print(\"Difference between the (employed) consumption functions:\")\n", + "plotFuncs(diffFunc, 0, m_upper)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/HARK/ConsumptionSaving/example_TractableBufferStockModel.py b/examples/ConsumptionSaving/example_TractableBufferStockModel.py similarity index 99% rename from HARK/ConsumptionSaving/example_TractableBufferStockModel.py rename to examples/ConsumptionSaving/example_TractableBufferStockModel.py index c6641ab33..ec7ca7660 100644 --- a/HARK/ConsumptionSaving/example_TractableBufferStockModel.py +++ b/examples/ConsumptionSaving/example_TractableBufferStockModel.py @@ -6,7 +6,6 @@ from time import process_time # timing utility from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType import numpy as np - do_simulation = True # Define the model primitives From 13a486f46d0c28cb726e00b0f755f10c73e146df Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 23 Jan 2020 15:09:26 +0530 Subject: [PATCH 172/242] setup testing, fix bugs in code --- HARK/ConsumptionSaving/ConsMarkovModel.py | 22 +++--- HARK/ConsumptionSaving/tests/__init__.py | 0 .../tests/test_ConsMarkovModel.py | 70 +++++++++++++++++++ HARK/__init__.py | 3 +- 4 files changed, 86 insertions(+), 9 deletions(-) create mode 100644 HARK/ConsumptionSaving/tests/__init__.py create mode 100644 HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py index 1394869c1..85331e5a1 100644 --- a/HARK/ConsumptionSaving/ConsMarkovModel.py +++ b/HARK/ConsumptionSaving/ConsMarkovModel.py @@ -593,7 +593,7 @@ def makevFunc(self,solution): return vFuncNow -def solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, +def _solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): ''' Solves a single period consumption-saving problem with risky income and @@ -681,7 +681,7 @@ class MarkovConsumerType(IndShockConsumerType): def __init__(self,cycles=1,time_flow=True,**kwds): IndShockConsumerType.__init__(self,cycles=1,time_flow=True,**kwds) - self.solveOnePeriod = solveConsMarkov + self.solveOnePeriod = _solveConsMarkov self.poststate_vars += ['MrkvNow'] if not hasattr(self, 'global_markov'): self.global_markov = False @@ -702,15 +702,19 @@ def checkMarkovInputs(self): StateCount = self.MrkvArray[0].shape[0] # Check that arrays are the right shape - assert self.Rfree.shape == (StateCount,),'Rfree not the right shape!' + if not isinstance(self.Rfree, np.ndarray) or self.Rfree.shape != (StateCount, ): + raise ValueError('Rfree not the right shape, it should an array of Rfree of all the states.') # Check that arrays in lists are the right shape for MrkvArray_t in self.MrkvArray: - assert MrkvArray_t.shape == (StateCount,StateCount),'MrkvArray not the right shape!' + if not isinstance(MrkvArray_t, np.ndarray) or MrkvArray_t.shape != (StateCount, StateCount): + raise ValueError('MrkvArray not the right shape, it should be of the size states*statres.') for LivPrb_t in self.LivPrb: - assert LivPrb_t.shape == (StateCount,),'Array in LivPrb is not the right shape!' - for PermGroFac_t in self.LivPrb: - assert PermGroFac_t.shape == (StateCount,),'Array in PermGroFac is not the right shape!' + if not isinstance(LivPrb_t, np.ndarray) or LivPrb_t.shape != (StateCount, ): + raise ValueError('Array in LivPrb is not the right shape, it should be an array of length equal to number of states') + for PermGroFac_t in self.PermGroFac: + if not isinstance(PermGroFac_t, np.ndarray) or PermGroFac_t.shape != (StateCount, ): + raise ValueError('Array in PermGroFac is not the right shape, it should be an array of length equal to number of states') # Now check the income distribution. # Note IncomeDstn is (potentially) time-varying, so it is in time_vary. @@ -718,8 +722,10 @@ def checkMarkovInputs(self): # at a particular point in time. Each income distribution at a point in time should itself # be a list, with each element corresponding to the income distribution # conditional on a particular Markov state. + # TODO: should this be a numpy array too? for IncomeDstn_t in self.IncomeDstn: - assert len(IncomeDstn_t) == StateCount,'List in IncomeDstn is not the right length!' + if not isinstance(IncomeDstn_t, list) or len(IncomeDstn_t) != StateCount: + raise ValueError('List in IncomeDstn is not the right length, it should be length equal to number of states') def preSolve(self): """ diff --git a/HARK/ConsumptionSaving/tests/__init__.py b/HARK/ConsumptionSaving/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py b/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py new file mode 100644 index 000000000..7de568b28 --- /dev/null +++ b/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py @@ -0,0 +1,70 @@ +import numpy as np +from HARK import MarkovConsumerType +import HARK.ConsumptionSaving.ConsumerParameters as Params +from copy import copy +import unittest + +class test_ConsMarkovSolver(unittest.TestCase): + def setUp(self): + + # Define the Markov transition matrix for serially correlated unemployment + unemp_length = 5 # Averange length of unemployment spell + urate_good = 0.05 # Unemployment rate when economy is in good state + urate_bad = 0.12 # Unemployment rate when economy is in bad state + bust_prob = 0.01 # Probability of economy switching from good to bad + recession_length = 20 # Averange length of bad state + p_reemploy = 1.0 / unemp_length + p_unemploy_good = p_reemploy * urate_good / (1 - urate_good) + p_unemploy_bad = p_reemploy * urate_bad / (1 - urate_bad) + boom_prob = 1.0 / recession_length + self.MrkvArray = np.array( + [ + [ + (1 - p_unemploy_good) * (1 - bust_prob), + p_unemploy_good * (1 - bust_prob), + (1 - p_unemploy_good) * bust_prob, + p_unemploy_good * bust_prob, + ], + [ + p_reemploy * (1 - bust_prob), + (1 - p_reemploy) * (1 - bust_prob), + p_reemploy * bust_prob, + (1 - p_reemploy) * bust_prob, + ], + [ + (1 - p_unemploy_bad) * boom_prob, + p_unemploy_bad * boom_prob, + (1 - p_unemploy_bad) * (1 - boom_prob), + p_unemploy_bad * (1 - boom_prob), + ], + [ + p_reemploy * boom_prob, + (1 - p_reemploy) * boom_prob, + p_reemploy * (1 - boom_prob), + (1 - p_reemploy) * (1 - boom_prob), + ], + ] + ) + + init_serial_unemployment = copy(Params.init_idiosyncratic_shocks) + init_serial_unemployment["MrkvArray"] = [self.MrkvArray] + self.model = MarkovConsumerType(**init_serial_unemployment) + + def test_checkMarkovInputs(self): + # check Rfree + self.assertRaises(ValueError, self.model.checkMarkovInputs) + # fix Rfree + self.model.Rfree = np.array(4 * [self.model.Rfree]) + # check MrkvArray, first mess up the setup + self.model.MrkvArray = np.random.rand(3, 3) + self.assertRaises(ValueError, self.model.checkMarkovInputs) + # then fix it back + self.model.MrkvArray = self.MrkvArray + # check LivPrb + self.assertRaises(ValueError, self.model.checkMarkovInputs) + # fix LivPrb + self.model.LivPrb = [np.array(4 * self.model.LivPrb)] + # check PermGroFac + self.assertRaises(ValueError, self.model.checkMarkovInputs) + # fix PermGroFac + self.model.PermGroFac = [np.array(4 * self.model.PermGroFac)] diff --git a/HARK/__init__.py b/HARK/__init__.py index 44f72023a..c235d788e 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,3 +1,4 @@ +__version__ = '0.10.3' + from HARK.core import * from HARK.ConsumptionSaving import * -__version__ = '0.10.3' From eb8d1a683522a592ef8f1a29c4b4e4ec6d680b8a Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 23 Jan 2020 15:43:19 +0530 Subject: [PATCH 173/242] time.clock is removed from py3.8 (#471) * change time.clock to time.process_time * use time instead of process_time * update core.py --- HARK/core.py | 6 +++--- HARK/cstwMPC/cstwMPC.py | 10 +++++----- HARK/interpolation.py | 18 +++++++++--------- HARK/parallel.py | 6 +++--- HARK/tests/OpenCLtest.py | 10 +++++----- 5 files changed, 25 insertions(+), 25 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index e1129aafd..aeee94fea 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -18,7 +18,7 @@ from .utilities import getArgNames, NullFunc from copy import copy, deepcopy import numpy as np -from time import clock +from time import time from .parallel import multiThreadCommands, multiThreadCommandsFake @@ -794,7 +794,7 @@ def solveAgent(agent, verbose): completed_cycles = 0 # NOQA max_cycles = 5000 # NOQA - escape clause if verbose: - t_last = clock() + t_last = time() while go: # Solve a cycle of the model, recording it if horizon is finite solution_cycle = solveOneCycle(agent, solution_last) @@ -822,7 +822,7 @@ def solveAgent(agent, verbose): # Display progress if requested if verbose: - t_now = clock() + t_now = time() if infinite_horizon: print('Finished cycle #' + str(completed_cycles) + ' in ' + str(t_now-t_last) + ' seconds, solution distance = ' + str(solution_distance)) diff --git a/HARK/cstwMPC/cstwMPC.py b/HARK/cstwMPC/cstwMPC.py index 89fb19731..67c3c4ff8 100644 --- a/HARK/cstwMPC/cstwMPC.py +++ b/HARK/cstwMPC/cstwMPC.py @@ -11,7 +11,7 @@ import numpy as np from copy import copy, deepcopy -from time import clock +from time import time from HARK.utilities import approxMeanOneLognormal, combineIndepDstns, approxUniform, \ getPercentiles, getLorenzShares, calcSubpopAvg, approxLognormal from HARK.simulation import drawDiscrete @@ -589,10 +589,10 @@ def main(): center_range = param_range, spread = spread, dist_type = Params.dist_type) - t_start = clock() + t_start = time() spread_estimate = golden(paramDistObjective,brack=spread_range,tol=1e-4) center_estimate = EstimationEconomy.center_save - t_end = clock() + t_end = time() else: # Run the param-point estimation only paramPointObjective = lambda center : getKYratioDifference(Economy = EstimationEconomy, @@ -601,10 +601,10 @@ def main(): center = center, spread = 0.0, dist_type = Params.dist_type) - t_start = clock() + t_start = time() center_estimate = brentq(paramPointObjective,param_range[0],param_range[1],xtol=1e-6) spread_estimate = 0.0 - t_end = clock() + t_end = time() # Display statistics about the estimated model #center_estimate = 0.986609223266 diff --git a/HARK/interpolation.py b/HARK/interpolation.py index 144101c2b..23268829b 100644 --- a/HARK/interpolation.py +++ b/HARK/interpolation.py @@ -3510,7 +3510,7 @@ def main(): print("of the model modules in /ConsumptionSavingModel. In the future, running") print("this module will show examples of each interpolation class.") - from time import clock + from time import time import matplotlib.pyplot as plt RNG = np.random.RandomState(123) @@ -3616,13 +3616,13 @@ def main(): rand_x = RNG.rand(N)*5.0 rand_y = RNG.rand(N)*5.0 rand_z = RNG.rand(N)*5.0 - t_start = clock() + t_start = time() z = (f(rand_w,rand_x,rand_y,rand_z) - g(rand_w,rand_x,rand_y,rand_z))/f(rand_w,rand_x,rand_y,rand_z) q = (dfdw(rand_w,rand_x,rand_y,rand_z) - g.derivativeW(rand_w,rand_x,rand_y,rand_z))/dfdw(rand_w,rand_x,rand_y,rand_z) r = (dfdx(rand_w,rand_x,rand_y,rand_z) - g.derivativeX(rand_w,rand_x,rand_y,rand_z))/dfdx(rand_w,rand_x,rand_y,rand_z) p = (dfdy(rand_w,rand_x,rand_y,rand_z) - g.derivativeY(rand_w,rand_x,rand_y,rand_z))/dfdy(rand_w,rand_x,rand_y,rand_z) s = (dfdz(rand_w,rand_x,rand_y,rand_z) - g.derivativeZ(rand_w,rand_x,rand_y,rand_z))/dfdz(rand_w,rand_x,rand_y,rand_z) - t_end = clock() + t_end = time() z.sort() print(z) @@ -3689,9 +3689,9 @@ def main(): rand_x = RNG.rand(N)*5.0 rand_y = RNG.rand(N)*5.0 rand_z = RNG.rand(N)*5.0 - t_start = clock() + t_start = time() z = (f(rand_w,rand_x,rand_y,rand_z) - g(rand_w,rand_x,rand_y,rand_z))/f(rand_w,rand_x,rand_y,rand_z) - t_end = clock() + t_end = time() #print(z) print(t_end-t_start) @@ -3711,11 +3711,11 @@ def main(): rand_x = RNG.rand(1000)*5.0 rand_y = RNG.rand(1000)*5.0 - t_start = clock() + t_start = time() z = (f(rand_x,rand_y) - g(rand_x,rand_y))/f(rand_x,rand_y) q = (dfdx(rand_x,rand_y) - g.derivativeX(rand_x,rand_y))/dfdx(rand_x,rand_y) r = (dfdy(rand_x,rand_y) - g.derivativeY(rand_x,rand_y))/dfdy(rand_x,rand_y) - t_end = clock() + t_end = time() z.sort() q.sort() r.sort() @@ -3785,9 +3785,9 @@ def main(): rand_y = RNG.rand(N)*5.0 rand_z = RNG.rand(N)*5.0 - t_start = clock() + t_start = time() z = (f(rand_w,rand_x,rand_y,rand_z) - g(rand_w,rand_x,rand_y,rand_z))/f(rand_w,rand_x,rand_y,rand_z) - t_end = clock() + t_end = time() z.sort() print(z) print(t_end-t_start) diff --git a/HARK/parallel.py b/HARK/parallel.py index 97c8b856f..2de073a75 100644 --- a/HARK/parallel.py +++ b/HARK/parallel.py @@ -10,7 +10,7 @@ from builtins import range import multiprocessing import numpy as np -from time import clock +from time import time import csv @@ -274,7 +274,7 @@ def parallelNelderMead(objFunc,guess,perturb=None,P=1,ftol=0.000001,xtol=0.00000 # Run the Nelder-Mead algorithm until a terminal condition is met go = True while go: - t_start = clock() + t_start = time() iters += 1 if verbose > 0: print('Beginning iteration #' + str(iters) + ' now.') @@ -320,7 +320,7 @@ def parallelNelderMead(objFunc,guess,perturb=None,P=1,ftol=0.000001,xtol=0.00000 fmin = fvals[0] f_dist = np.abs(fmin - fvals[-1]) x_dist = np.max(np.sqrt(np.sum((simplex - np.tile(simplex[0,:],(N,1)))**2.0,axis=1))) - t_end = clock() + t_end = time() if verbose > 0: t_iter = t_end - t_start print('Finished iteration #' + str(iters) +' with ' + str(new_evals) + ' evaluations (' + str(evals) + ' cumulative) in ' + str(t_iter) + ' seconds.') diff --git a/HARK/tests/OpenCLtest.py b/HARK/tests/OpenCLtest.py index 3e16a4902..aa2d5ca25 100644 --- a/HARK/tests/OpenCLtest.py +++ b/HARK/tests/OpenCLtest.py @@ -9,7 +9,7 @@ # EVERY machine will have a device 0:0, which by default is the CPU # Other devices will have various numbers # Substitute her the device you want to compare to the CPU -from time import clock +from time import time if __name__ == "__main__": @@ -82,17 +82,17 @@ c_buf = ctx.create_buffer(cl.CL_MEM_WRITE_ONLY | cl.CL_MEM_ALLOC_HOST_PTR, size=c.nbytes) # Write only, allocate memory, use byte size of array c # Run the kernel and time it - t_start = clock() + t_start = time() krn.set_args(a_buf, b_buf, c_buf, k[0:1]) # Set kernel arguments as the three buffers and a float queue.execute_kernel(krn, [N], None) # Execute the simple kernel, specifying the global workspace dimensionality and local workspace dimensionality (None uses some default) queue.read_buffer(c_buf, c) # Read the memory buffer for c into the numpy array for c - t_end = clock() + t_end = time() print('OpenCL took ' + str(t_end-t_start) + ' seconds.') # Now do the equivalent work as the kernel, but in Python (and time it) - t_start = clock() + t_start = time() truth = (a + b) * k[0] - t_end = clock() + t_end = time() print('Python took ' + str(t_end-t_start) + ' seconds.') # Make sure that OpenCL and Python actually agree on their results From 205cf9a3f7add48bec2dea502b03db1a9810ce01 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 23 Jan 2020 15:45:40 +0530 Subject: [PATCH 174/242] add 3.8 and trigger the action on pull_request --- .github/workflows/hark.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/hark.yml b/.github/workflows/hark.yml index cb8111512..3e5fb2a94 100644 --- a/.github/workflows/hark.yml +++ b/.github/workflows/hark.yml @@ -1,6 +1,6 @@ name: HARK build on MacOS, Ubuntu and Windows -on: [push] +on: [push, pull_request] jobs: build: @@ -10,7 +10,7 @@ jobs: max-parallel: 4 matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: [3.6, 3.7] + python-version: [3.6, 3.7, 3.8] steps: - uses: actions/checkout@v1 @@ -32,4 +32,4 @@ jobs: - name: Test with pytest run: | pip install pytest - pytest \ No newline at end of file + pytest From d1852ee4c9f604b57458df5dda70c58b111717d7 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 23 Jan 2020 15:52:43 +0530 Subject: [PATCH 175/242] fix double build issue --- .github/workflows/hark.yml | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/.github/workflows/hark.yml b/.github/workflows/hark.yml index 3e5fb2a94..b0dffba51 100644 --- a/.github/workflows/hark.yml +++ b/.github/workflows/hark.yml @@ -1,6 +1,12 @@ name: HARK build on MacOS, Ubuntu and Windows -on: [push, pull_request] +on: + push: + branches: + - master + pull_request: + branches: + - master jobs: build: From 86b126541f3aa4c762ff8fb913ecc4be0811660d Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 23 Jan 2020 16:06:18 +0530 Subject: [PATCH 176/242] Add badge to GitHub Action build --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 1e0e8b8b9..3f46a9f6c 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,9 @@ [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org/) [![Donate](https://img.shields.io/badge/donate-$2-brightgreen.svg)](https://numfocus.salsalabs.org/donate-to-econ-ark/index.html) +[![Actions Status](https://github.com/econ-ark/hark/workflows/HARK%20build%20on%20MacOS%2C%20Ubuntu%20and%20Windows/badge.svg +)](https://github.com/econ-ark/hark/actions) + no retirement)\n", + " \"tax_rate\" : 0.0, # Flat income tax rate (legacy parameter, will be removed in future)\n", + " \n", + " # Parameters for constructing the \"assets above minimum\" grid\n", + " \"aXtraMin\" : 0.001, # Minimum end-of-period \"assets above minimum\" value\n", + " \"aXtraMax\" : 20, # Maximum end-of-period \"assets above minimum\" value\n", + " \"aXtraCount\" : 48, # Number of points in the base grid of \"assets above minimum\"\n", + " \"aXtraNestFac\" : 3, # Exponential nesting factor when constructing \"assets above minimum\" grid\n", + " \"aXtraExtra\" : [None], # Additional values to add to aXtraGrid\n", + " \n", + " # A few other paramaters\n", + " \"BoroCnstArt\" : 0.0, # Artificial borrowing constraint; imposed minimum level of end-of period assets\n", + " \"vFuncBool\" : True, # Whether to calculate the value function during solution \n", + " \"CubicBool\" : False, # Preference shocks currently only compatible with linear cFunc\n", + " \"T_cycle\" : 1, # Number of periods in the cycle for this agent type \n", + " \n", + " # Parameters only used in simulation\n", + " \"AgentCount\" : 10000, # Number of agents of this type\n", + " \"T_sim\" : 120, # Number of periods to simulate\n", + " \"aNrmInitMean\" : -6.0, # Mean of log initial assets\n", + " \"aNrmInitStd\" : 1.0, # Standard deviation of log initial assets\n", + " \"pLvlInitMean\" : 0.0, # Mean of log initial permanent income\n", + " \"pLvlInitStd\" : 0.0, # Standard deviation of log initial permanent income\n", + " \"PermGroFacAgg\" : 1.0, # Aggregate permanent income growth factor\n", + " \"T_age\" : None, # Age after which simulated agents are automatically killed\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The distribution of permanent income shocks is specified as mean one lognormal, with an age-varying (underlying) standard deviation. The distribution of transitory income shocks is also mean one lognormal, but with an additional point mass representing unemployment; the transitory shocks are adjusted so that the distribution is still mean one. The continuous distributions are discretized with an equiprobable distribution.\n", + "\n", + "Optionally, the user can specify the period when the individual retires and escapes essentially all income risk as $\\texttt{T_retire}$; this can be turned off by setting the parameter to $0$. In retirement, all permanent income shocks are turned off, and the only transitory shock is an \"unemployment\" shock, likely with small probability; this prevents the retired problem from degenerating into a perfect foresight model.\n", + "\n", + "The grid of assets above minimum $\\texttt{aXtraGrid}$ is specified by its minimum and maximum level, the number of gridpoints, and the extent of exponential nesting. The greater the (integer) value of $\\texttt{aXtraNestFac}$, the more dense the gridpoints will be at the bottom of the grid (and more sparse near the top); setting $\\texttt{aXtraNestFac}$ to $0$ will generate an evenly spaced grid of $a_t$.\n", + "\n", + "The artificial borrowing constraint $\\texttt{BoroCnstArt}$ can be set to $\\texttt{None}$ to turn it off.\n", + "\n", + "It is not necessary to compute the value function in this model, and it is not computationally free to do so. You can choose whether the value function should be calculated and returned as part of the solution of the model with $\\texttt{vFuncBool}$. The consumption function will be constructed as a piecewise linear interpolation when $\\texttt{CubicBool}$ is \\texttt{False}, and will be a piecewise cubic spline interpolator if $\\texttt{True}$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "## Solving and examining the solution of the idiosyncratic income shocks model\n", + "\n", + "The cell below creates an infinite horizon instance of $\\texttt{IndShockConsumerType}$ and solves its model by calling its $\\texttt{solve}$ method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true, + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "IndShockExample = IndShockConsumerType(**IdiosyncDict)\n", + "IndShockExample.cycles = 0 # Make this type have an infinite horizon\n", + "IndShockExample.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "After solving the model, we can examine an element of this type's $\\texttt{solution}$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "print(vars(IndShockExample.solution[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "The single-period solution to an idiosyncratic shocks consumer's problem has all of the same attributes as in the perfect foresight model, with a couple additions. The solution can include the marginal marginal value of market resources function $\\texttt{vPPfunc}$, but this is only constructed if $\\texttt{CubicBool}$ is $\\texttt{True}$, so that the MPC can be accurately computed; when it is $\\texttt{False}$, then $\\texttt{vPPfunc}$ merely returns $\\texttt{NaN}$ everywhere.\n", + "\n", + "The $\\texttt{solveConsIndShock}$ function calculates steady state market resources and stores it in the attribute $\\texttt{mNrmSS}$. This represents the steady state level of $m_t$ if *this period* were to occur indefinitely, but with income shocks turned off. This is relevant in a \"one period infinite horizon\" model like we've specified here, but is less useful in a lifecycle model.\n", + "\n", + "Let's take a look at the consumption function by plotting it, along with its derivative (the MPC):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "print('Consumption function for an idiosyncratic shocks consumer type:')\n", + "plotFuncs(IndShockExample.solution[0].cFunc,IndShockExample.solution[0].mNrmMin,5)\n", + "print('Marginal propensity to consume for an idiosyncratic shocks consumer type:')\n", + "plotFuncsDer(IndShockExample.solution[0].cFunc,IndShockExample.solution[0].mNrmMin,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "The lower part of the consumption function is linear with a slope of 1, representing the *constrained* part of the consumption function where the consumer *would like* to consume more by borrowing-- his marginal utility of consumption exceeds the marginal value of assets-- but he is prevented from doing so by the artificial borrowing constraint.\n", + "\n", + "The MPC is a step function, as the $\\texttt{cFunc}$ itself is a piecewise linear function; note the large jump in the MPC where the borrowing constraint begins to bind.\n", + "\n", + "If you want to look at the interpolation nodes for the consumption function, these can be found by \"digging into\" attributes of $\\texttt{cFunc}$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "print('mNrmGrid for unconstrained cFunc is ',IndShockExample.solution[0].cFunc.functions[0].x_list)\n", + "print('cNrmGrid for unconstrained cFunc is ',IndShockExample.solution[0].cFunc.functions[0].y_list)\n", + "print('mNrmGrid for borrowing constrained cFunc is ',IndShockExample.solution[0].cFunc.functions[1].x_list)\n", + "print('cNrmGrid for borrowing constrained cFunc is ',IndShockExample.solution[0].cFunc.functions[1].y_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "The consumption function in this model is an instance of $\\texttt{LowerEnvelope1D}$, a class that takes an arbitrary number of 1D interpolants as arguments to its initialization method. When called, a $\\texttt{LowerEnvelope1D}$ evaluates each of its component functions and returns the lowest value. Here, the two component functions are the *unconstrained* consumption function-- how the agent would consume if the artificial borrowing constraint did not exist for *just this period*-- and the *borrowing constrained* consumption function-- how much he would consume if the artificial borrowing constraint is binding. \n", + "\n", + "The *actual* consumption function is the lower of these two functions, pointwise. We can see this by plotting the component functions on the same figure:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "plotFuncs(IndShockExample.solution[0].cFunc.functions,-0.25,5.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating the idiosyncratic income shocks model\n", + "\n", + "In order to generate simulated data, an instance of $\\texttt{IndShockConsumerType}$ needs to know how many agents there are that share these particular parameters (and are thus *ex ante* homogeneous), the distribution of states for newly \"born\" agents, and how many periods to simulated. These simulation parameters are described in the table below, along with example values.\n", + "\n", + "| Description | Code | Example value |\n", + "| :---: | --- | --- |\n", + "| Number of consumers of this type | $\\texttt{AgentCount}$ | $10000$ |\n", + "| Number of periods to simulate | $\\texttt{T_sim}$ | $120$ |\n", + "| Mean of initial log (normalized) assets | $\\texttt{aNrmInitMean}$ | $-6.0$ |\n", + "| Stdev of initial log (normalized) assets | $\\texttt{aNrmInitStd}$ | $1.0$ |\n", + "| Mean of initial log permanent income | $\\texttt{pLvlInitMean}$ | $0.0$ |\n", + "| Stdev of initial log permanent income | $\\texttt{pLvlInitStd}$ | $0.0$ |\n", + "| Aggregrate productivity growth factor | $\\texttt{PermGroFacAgg}$ | $1.0$ |\n", + "| Age after which consumers are automatically killed | $\\texttt{T_age}$ | $None$ |\n", + "\n", + "Here, we will simulate 10,000 consumers for 120 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\texttt{pLvlInitMean})$, as $\\texttt{pLvlInitStd}$ has been set to zero; they will have essentially zero assets at birth, as $\\texttt{aNrmInitMean}$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n", + "\n", + "These example parameter values were already passed as part of the parameter dictionary that we used to create $\\texttt{IndShockExample}$, so it is ready to simulate. We need to set the $\\texttt{track_vars}$ attribute to indicate the variables for which we want to record a *history*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IndShockExample.track_vars = ['aNrmNow','mNrmNow','cNrmNow','pLvlNow']\n", + "IndShockExample.initializeSim()\n", + "IndShockExample.simulate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now look at the simulated data in aggregate or at the individual consumer level. Like in the perfect foresight model, we can plot average (normalized) market resources over time, as well as average consumption:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(np.mean(IndShockExample.mNrmNow_hist,axis=1))\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Mean market resources')\n", + "plt.show()\n", + "\n", + "plt.plot(np.mean(IndShockExample.cNrmNow_hist,axis=1))\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Mean consumption')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could also plot individual consumption paths for some of the consumers-- say, the first five:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(IndShockExample.cNrmNow_hist[:,0:5])\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Individual consumption paths')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other example specifications of idiosyncratic income shocks consumers\n", + "\n", + "$\\texttt{IndShockConsumerType}$-- and $\\texttt{HARK}$ in general-- can also represent models that are not infinite horizon. \n", + "\n", + "### Lifecycle example\n", + "\n", + "Suppose we wanted to represent consumers with a *lifecycle*-- parameter values that differ by age, with a finite end point beyond which the individual cannot surive. This can be done very easily by simply specifying the time-varying attributes $\\texttt{PermGroFac}$, $\\texttt{LivPrb}$, $\\texttt{PermShkStd}$, and $\\texttt{TranShkStd}$ as Python *lists* specifying the sequence of periods these agents will experience, from beginning to end.\n", + "\n", + "In the cell below, we define a parameter dictionary for a rather short ten period lifecycle, with arbitrarily chosen parameters. For a more realistically calibrated (and much longer) lifecycle model, see the [SolvingMicroDSOPs REMARK](https://github.com/econ-ark/REMARK/blob/master/REMARKs/SolvingMicroDSOPs.md)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "LifecycleDict={ # Click arrow to expand this fairly large parameter dictionary\n", + " # Parameters shared with the perfect foresight model\n", + " \"CRRA\": 2.0, # Coefficient of relative risk aversion\n", + " \"Rfree\": 1.03, # Interest factor on assets\n", + " \"DiscFac\": 0.96, # Intertemporal discount factor\n", + " \"LivPrb\" : [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1],\n", + " \"PermGroFac\" : [1.01,1.01,1.01,1.02,1.02,1.02,0.7,1.0,1.0,1.0],\n", + " \n", + " # Parameters that specify the income distribution over the lifecycle\n", + " \"PermShkStd\" : [0.1,0.2,0.1,0.2,0.1,0.2,0.1,0,0,0],\n", + " \"PermShkCount\" : 7, # Number of points in discrete approximation to permanent income shocks\n", + " \"TranShkStd\" : [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0,0,0],\n", + " \"TranShkCount\" : 7, # Number of points in discrete approximation to transitory income shocks\n", + " \"UnempPrb\" : 0.05, # Probability of unemployment while working\n", + " \"IncUnemp\" : 0.3, # Unemployment benefits replacement rate\n", + " \"UnempPrbRet\" : 0.0005, # Probability of \"unemployment\" while retired\n", + " \"IncUnempRet\" : 0.0, # \"Unemployment\" benefits when retired\n", + " \"T_retire\" : 7, # Period of retirement (0 --> no retirement)\n", + " \"tax_rate\" : 0.0, # Flat income tax rate (legacy parameter, will be removed in future)\n", + " \n", + " # Parameters for constructing the \"assets above minimum\" grid\n", + " \"aXtraMin\" : 0.001, # Minimum end-of-period \"assets above minimum\" value\n", + " \"aXtraMax\" : 20, # Maximum end-of-period \"assets above minimum\" value\n", + " \"aXtraCount\" : 48, # Number of points in the base grid of \"assets above minimum\"\n", + " \"aXtraNestFac\" : 3, # Exponential nesting factor when constructing \"assets above minimum\" grid\n", + " \"aXtraExtra\" : [None], # Additional values to add to aXtraGrid\n", + " \n", + " # A few other paramaters\n", + " \"BoroCnstArt\" : 0.0, # Artificial borrowing constraint; imposed minimum level of end-of period assets\n", + " \"vFuncBool\" : True, # Whether to calculate the value function during solution \n", + " \"CubicBool\" : False, # Preference shocks currently only compatible with linear cFunc\n", + " \"T_cycle\" : 10, # Number of periods in the cycle for this agent type \n", + " \n", + " # Parameters only used in simulation\n", + " \"AgentCount\" : 10000, # Number of agents of this type\n", + " \"T_sim\" : 120, # Number of periods to simulate\n", + " \"aNrmInitMean\" : -6.0, # Mean of log initial assets\n", + " \"aNrmInitStd\" : 1.0, # Standard deviation of log initial assets\n", + " \"pLvlInitMean\" : 0.0, # Mean of log initial permanent income\n", + " \"pLvlInitStd\" : 0.0, # Standard deviation of log initial permanent income\n", + " \"PermGroFacAgg\" : 1.0, # Aggregate permanent income growth factor\n", + " \"T_age\" : 11, # Age after which simulated agents are automatically killed \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, we have specified a ten period model in which retirement happens in period $t=7$. Agents in this model are more likely to die as they age, and their permanent income drops by 30\\% at retirement. Let's make and solve this lifecycle example, then look at the $\\texttt{solution}$ attribute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "LifecycleExample = IndShockConsumerType(**LifecycleDict)\n", + "LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods -- a lifetime -- exactly once\n", + "LifecycleExample.solve()\n", + "print('First element of solution is',LifecycleExample.solution[0])\n", + "print('Solution has', len(LifecycleExample.solution),'elements.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was supposed to be a *ten* period lifecycle model-- why does our consumer type have *eleven* elements in its $\\texttt{solution}$? It would be more precise to say that this specification has ten *non-terminal* periods. The solution to the 11th and final period in the model would be the same for every set of parameters: consume $c_t = m_t$, because there is no future. In a lifecycle model, the terminal period is assumed to exist; the $\\texttt{LivPrb}$ parameter does not need to end with a $0.0$ in order to guarantee that survivors die.\n", + "\n", + "We can quickly plot the consumption functions in each period of the model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Consumption functions across the lifecycle:')\n", + "mMin = np.min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)])\n", + "LifecycleExample.unpackcFunc() # This makes all of the cFuncs accessible in the attribute cFunc\n", + "plotFuncs(LifecycleExample.cFunc,mMin,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"Cyclical\" example\n", + "\n", + "We can also model consumers who face an infinite horizon, but who do *not* face the same problem in every period. Consider someone who works as a ski instructor: they make most of their income for the year in the winter, and make very little money in the other three seasons.\n", + "\n", + "We can represent this type of individual as a four period, infinite horizon model in which expected \"permanent\" income growth varies greatly across seasons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "CyclicalDict = { # Click the arrow to expand this parameter dictionary\n", + " # Parameters shared with the perfect foresight model\n", + " \"CRRA\": 2.0, # Coefficient of relative risk aversion\n", + " \"Rfree\": 1.03, # Interest factor on assets\n", + " \"DiscFac\": 0.96, # Intertemporal discount factor\n", + " \"LivPrb\" : 4*[0.98], # Survival probability\n", + " \"PermGroFac\" : [1.082251, 2.8, 0.3, 1.1],\n", + " \n", + " # Parameters that specify the income distribution over the lifecycle\n", + " \"PermShkStd\" : [0.1,0.1,0.1,0.1],\n", + " \"PermShkCount\" : 7, # Number of points in discrete approximation to permanent income shocks\n", + " \"TranShkStd\" : [0.2,0.2,0.2,0.2],\n", + " \"TranShkCount\" : 7, # Number of points in discrete approximation to transitory income shocks\n", + " \"UnempPrb\" : 0.05, # Probability of unemployment while working\n", + " \"IncUnemp\" : 0.3, # Unemployment benefits replacement rate\n", + " \"UnempPrbRet\" : 0.0005, # Probability of \"unemployment\" while retired\n", + " \"IncUnempRet\" : 0.0, # \"Unemployment\" benefits when retired\n", + " \"T_retire\" : 0, # Period of retirement (0 --> no retirement)\n", + " \"tax_rate\" : 0.0, # Flat income tax rate (legacy parameter, will be removed in future)\n", + " \n", + " # Parameters for constructing the \"assets above minimum\" grid\n", + " \"aXtraMin\" : 0.001, # Minimum end-of-period \"assets above minimum\" value\n", + " \"aXtraMax\" : 20, # Maximum end-of-period \"assets above minimum\" value\n", + " \"aXtraCount\" : 48, # Number of points in the base grid of \"assets above minimum\"\n", + " \"aXtraNestFac\" : 3, # Exponential nesting factor when constructing \"assets above minimum\" grid\n", + " \"aXtraExtra\" : [None], # Additional values to add to aXtraGrid\n", + " \n", + " # A few other paramaters\n", + " \"BoroCnstArt\" : 0.0, # Artificial borrowing constraint; imposed minimum level of end-of period assets\n", + " \"vFuncBool\" : True, # Whether to calculate the value function during solution \n", + " \"CubicBool\" : False, # Preference shocks currently only compatible with linear cFunc\n", + " \"T_cycle\" : 4, # Number of periods in the cycle for this agent type \n", + " \n", + " # Parameters only used in simulation\n", + " \"AgentCount\" : 10000, # Number of agents of this type\n", + " \"T_sim\" : 120, # Number of periods to simulate\n", + " \"aNrmInitMean\" : -6.0, # Mean of log initial assets\n", + " \"aNrmInitStd\" : 1.0, # Standard deviation of log initial assets\n", + " \"pLvlInitMean\" : 0.0, # Mean of log initial permanent income\n", + " \"pLvlInitStd\" : 0.0, # Standard deviation of log initial permanent income\n", + " \"PermGroFacAgg\" : 1.0, # Aggregate permanent income growth factor\n", + " \"T_age\" : None, # Age after which simulated agents are automatically killed \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This consumer type's parameter dictionary is nearly identical to the original infinite horizon type we made, except that each of the time-varying parameters now have *four* values, rather than just one. Most of these have the same value in each period *except* for $\\texttt{PermGroFac}$, which varies greatly over the four seasons. Note that the product of the four \"permanent\" income growth factors is almost exactly 1.0-- this type's income does not grow on average in the long run!\n", + "\n", + "Let's make and solve this consumer type, then plot his quarterly consumption functions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "CyclicalExample = IndShockConsumerType(**CyclicalDict)\n", + "CyclicalExample.cycles = 0 # Make this consumer type have an infinite horizon\n", + "CyclicalExample.solve()\n", + "\n", + "CyclicalExample.unpackcFunc()\n", + "print('Quarterly consumption functions:')\n", + "mMin = min([X.mNrmMin for X in CyclicalExample.solution])\n", + "plotFuncs(CyclicalExample.cFunc,mMin,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The very low green consumption function corresponds to the quarter in which the ski instructors make most of their income. They know that they are about to experience a 70% drop in \"permanent\" income, so they do not consume much *relative to their income this quarter*. In the other three quarters, *normalized* consumption is much higher, as current \"permanent\" income is low relative to future expectations. In *level*, the consumption chosen in each quarter is much more similar" + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": "779f6c5616b04b58baaaa0f6c348270c", + "lastKernelId": "a944b08f-0ae0-4c26-883f-9fab53a82ac3" + }, + "cite2c": { + "citations": { + "6202365/HQ6H9JEI": { + "DOI": "10.1016/j.econlet.2005.09.013", + "URL": "http://econ.jhu.edu/people/ccarroll/EndogenousArchive.zip", + "author": [ + { + "family": "Carroll", + "given": "Christopher D." + } + ], + "container-title": "Economics Letters", + "id": "6202365/HQ6H9JEI", + "issued": { + "month": 9, + "year": 2006 + }, + "page": "312–320", + "page-first": "312", + "title": "The Method of Endogenous Gridpoints for Solving Dynamic Stochastic Optimization Problems", + "type": "article-journal" + } + } + }, + "jupytext": { + "cell_metadata_filter": "collapsed,code_folding", + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ConsIndShockModel/IndShockConsumerType.py b/examples/ConsIndShockModel/IndShockConsumerType.py new file mode 100644 index 000000000..39965b594 --- /dev/null +++ b/examples/ConsIndShockModel/IndShockConsumerType.py @@ -0,0 +1,419 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: collapsed,code_folding +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.2' +# jupytext_version: 1.1.3 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # IndShockConsumerType Documentation +# ## Consumption-Saving model with Idiosyncratic Income Shocks + +# %% {"code_folding": [0]} +# Initial imports and notebook setup, click arrow to show +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType +from HARK.utilities import plotFuncsDer, plotFuncs +from time import clock +import matplotlib.pyplot as plt +import numpy as np +mystr = lambda number : "{:.4f}".format(number) + +# %% [markdown] +# The module $\texttt{HARK.ConsumptionSaving.ConsIndShockModel}$ concerns consumption-saving models with idiosyncratic shocks to (non-capital) income. All of the models assume CRRA utility with geometric discounting, no bequest motive, and income shocks are fully transitory or fully permanent. +# +# $\texttt{ConsIndShockModel}$ includes: +# 1. A very basic "perfect foresight" model with no uncertainty. +# 2. A model with risk over transitory and permanent income shocks. +# 3. The model described in (2), with an interest rate for debt that differs from the interest rate for savings. +# +# This notebook provides documentation for the second of these models. +# $\newcommand{\CRRA}{\rho}$ +# $\newcommand{\DiePrb}{\mathsf{D}}$ +# $\newcommand{\PermGroFac}{\Gamma}$ +# $\newcommand{\Rfree}{\mathsf{R}}$ +# $\newcommand{\DiscFac}{\beta}$ + +# %% [markdown] +# ## Statement of idiosyncratic income shocks model +# +# Suppose we want to solve a model like the one analyzed in [BufferStockTheory](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/), with all the same features as the perfect foresight consumer, plus idiosyncratic shocks to income each period. Agents with this kind of model are represented by the class $\texttt{IndShockConsumerType}$. +# +# Specifically, this type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\newcommand{\tShkEmp}{\theta}{\tShkEmp_t}$ and a completely permanent shock $\newcommand{\pShk}{\psi}{\pShk_t}$. Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\underline{a}$. As with the perfect foresight problem, this model can be framed in terms of *normalized* variables, dividing all real variables by $P_t$: +# +# \begin{eqnarray*} +# v_t(m_t) &=& \max_{c_t} {~} u(c_t) + \DiscFac (1-\DiePrb_{t+1}) \mathbb{E}_{t} \left[ (\PermGroFac_{t+1}\psi_{t+1})^{1-\CRRA} v_{t+1}(m_{t+1}) \right], \\ +# a_t &=& m_t - c_t, \\ +# a_t &\geq& \underline{a}, \\ +# m_{t+1} &=& \Rfree/(\PermGroFac_{t+1} \psi_{t+1}) a_t + \theta_{t+1}, \\ +# (\psi_{t+1},\theta_{t+1}) &\sim& F_{t+1}, \\ +# \mathbb{E}[\psi]=\mathbb{E}[\theta] &=& 1, \\ +# u(c) &=& \frac{c^{1-\rho}}{1-\rho}. +# \end{eqnarray*} + +# %% [markdown] +# ## Solution method for IndShockConsumerType +# +# With the introduction of (non-trivial) risk, the idiosyncratic income shocks model has no closed form solution and must be solved numerically. The function $\texttt{solveConsIndShock}$ solves the one period problem for the $\texttt{IndShockConsumerType}$ class. To do so, HARK uses the original version of the endogenous grid method (EGM) first described [here](http://www.econ2.jhu.edu/people/ccarroll/EndogenousGridpoints.pdf) ; see also the [SolvingMicroDSOPs](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/) lecture notes. +# +# Briefly, the transition equation for $m_{t+1}$ can be substituted into the problem definition; the second term of the reformulated maximand represents "end of period value of assets" $\mathfrak{v}_t(a_t)$ ("Gothic v"): +# +# \begin{eqnarray*} +# v_t(m_t) &=& \max_{c_t} {~} U(c_t) + \underbrace{\DiscFac (1-\DiePrb_{t+1}) \mathbb{E}_{t} \left[ (\PermGroFac_{t+1}\psi_{t+1})^{1-\CRRA} v_{t+1}(\Rfree/(\PermGroFac_{t+1} \psi_{t+1}) a_t + \theta_{t+1}) \right]}_{\equiv \mathfrak{v}_t(a_t)}. +# \end{eqnarray*} +# +# The first order condition with respect to $c_t$ is thus simply: +# +# \begin{eqnarray*} +# U'(c_t) - \mathfrak{v}'_t(a_t) = 0 \Longrightarrow c_t^{-\CRRA} = \mathfrak{v}'_t(a_t) \Longrightarrow c_t = \mathfrak{v}'_t(a_t)^{-1/\CRRA}. +# \end{eqnarray*} +# +# Where the marginal value of end-of-period assets can be computed as: +# +# \begin{eqnarray*} +# \mathfrak{v}'_t(a_t) = \DiscFac (1-\DiePrb_{t+1}) \mathbb{E}_{t} \left[ \Rfree (\PermGroFac_{t+1}\psi_{t+1})^{-\CRRA} v'_{t+1}(\Rfree/(\PermGroFac_{t+1} \psi_{t+1}) a_t + \theta_{t+1}) \right]. +# \end{eqnarray*} +# +# To solve the model, we choose an exogenous grid of $a_t$ values that spans the range of values that could plausibly be achieved, compute $\mathfrak{v}'_t(a_t)$ at each of these points, calculate the value of consumption $c_t$ whose marginal utility is consistent with the marginal value of assets, then find the endogenous $m_t$ gridpoint as $m_t = a_t + c_t$. The set of $(m_t,c_t)$ gridpoints is then interpolated to construct the consumption function. + +# %% [markdown] +# ## Example parameter values to construct an instance of IndShockConsumerType +# +# In order to create an instance of $\texttt{IndShockConsumerType}$, the user must specify parameters that characterize the (age-varying) distribution of income shocks $F_{t+1}$, the artificial borrowing constraint $\underline{a}$, and the exogenous grid of end-of-period assets-above-minimum for use by EGM, along with all of the parameters for the perfect foresight model. The table below presents the complete list of parameter values required to instantiate an $\texttt{IndShockConsumerType}$, along with example values. +# +# | Parameter | Description | Code | Example value | Time-varying? | +# | :---: | --- | --- | --- | --- | +# | $\DiscFac$ |Intertemporal discount factor | $\texttt{DiscFac}$ | $0.96$ | | +# | $\CRRA $ |Coefficient of relative risk aversion | $\texttt{CRRA}$ | $2.0$ | | +# | $\Rfree$ | Risk free interest factor | $\texttt{Rfree}$ | $1.03$ | | +# | $1 - \DiePrb_{t+1}$ |Survival probability | $\texttt{LivPrb}$ | $[0.98]$ | $\surd$ | +# |$\PermGroFac_{t+1}$|Permanent income growth factor|$\texttt{PermGroFac}$| $[1.01]$ | $\surd$ | +# | $\sigma_\psi $ | Standard deviation of log permanent income shocks | $\texttt{PermShkStd}$ | $[0.1]$ |$\surd$ | +# | $N_\psi $ | Number of discrete permanent income shocks | $\texttt{PermShkCount}$ | $7$ | | +# | $\sigma_\theta $ | Standard deviation of log transitory income shocks | $\texttt{TranShkStd}$ | $[0.2]$ | $\surd$ | +# | $N_\theta $ | Number of discrete transitory income shocks | $\texttt{TranShkCount}$ | $7$ | | +# | $\mho$ | Probability of being unemployed and getting $\theta=\underline{\theta}$ | $\texttt{UnempPrb}$ | $0.05$ | | +# | $\underline{\theta} $ | Transitory shock when unemployed | $\texttt{IncUnemp}$ | $0.3$ | | +# | $\mho^{Ret}$ | Probability of being "unemployed" when retired | $\texttt{UnempPrb}$ | $0.0005$ | | +# | $\underline{\theta}^{Ret} $ | Transitory shock when "unemployed" and retired | $\texttt{IncUnemp}$ | $0.0$ | | +# | $(none)$ | Period of the lifecycle model when retirement begins | $\texttt{T_retire}$ | $0$ | | +# | $(none)$ | Minimum value in assets-above-minimum grid | $\texttt{aXtraMin}$ | $0.001$ | | +# | $(none)$ | Maximum value in assets-above-minimum grid | $\texttt{aXtraMax}$ | $20.0$ | | +# | $(none)$ | Number of points in base assets-above-minimum grid | $\texttt{aXtraCount}$ | $48$ | | +# | $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\texttt{aXtraNestFac}$ | $3$ | | +# | $(none)$ | Additional values to add to assets-above-minimum grid | $\texttt{aXtraExtra}$ | $None$ | | +# | $\underline{a} $ | Artificial borrowing constraint (normalized) | $\texttt{BoroCnstArt}$ | $0.0$ | | +# | $(none) $ |Indicator for whether $\texttt{vFunc}$ should be computed | $\texttt{vFuncBool}$ | $True$ | | +# | $(none)$ |Indicator for whether $\texttt{cFunc}$ should use cubic splines | $\texttt{CubicBool}$ | $False$ | | +# |$T$| Number of periods in this type's "cycle" |$\texttt{T_cycle}$| $1$ | | +# |(none)| Number of times the "cycle" occurs |$\texttt{cycles}$| $0$ | | + +# %% {"code_folding": [0]} +IdiosyncDict={ + # Parameters shared with the perfect foresight model + "CRRA": 2.0, # Coefficient of relative risk aversion + "Rfree": 1.03, # Interest factor on assets + "DiscFac": 0.96, # Intertemporal discount factor + "LivPrb" : [0.98], # Survival probability + "PermGroFac" :[1.01], # Permanent income growth factor + + # Parameters that specify the income distribution over the lifecycle + "PermShkStd" : [0.1], # Standard deviation of log permanent shocks to income + "PermShkCount" : 7, # Number of points in discrete approximation to permanent income shocks + "TranShkStd" : [0.2], # Standard deviation of log transitory shocks to income + "TranShkCount" : 7, # Number of points in discrete approximation to transitory income shocks + "UnempPrb" : 0.05, # Probability of unemployment while working + "IncUnemp" : 0.3, # Unemployment benefits replacement rate + "UnempPrbRet" : 0.0005, # Probability of "unemployment" while retired + "IncUnempRet" : 0.0, # "Unemployment" benefits when retired + "T_retire" : 0, # Period of retirement (0 --> no retirement) + "tax_rate" : 0.0, # Flat income tax rate (legacy parameter, will be removed in future) + + # Parameters for constructing the "assets above minimum" grid + "aXtraMin" : 0.001, # Minimum end-of-period "assets above minimum" value + "aXtraMax" : 20, # Maximum end-of-period "assets above minimum" value + "aXtraCount" : 48, # Number of points in the base grid of "assets above minimum" + "aXtraNestFac" : 3, # Exponential nesting factor when constructing "assets above minimum" grid + "aXtraExtra" : [None], # Additional values to add to aXtraGrid + + # A few other paramaters + "BoroCnstArt" : 0.0, # Artificial borrowing constraint; imposed minimum level of end-of period assets + "vFuncBool" : True, # Whether to calculate the value function during solution + "CubicBool" : False, # Preference shocks currently only compatible with linear cFunc + "T_cycle" : 1, # Number of periods in the cycle for this agent type + + # Parameters only used in simulation + "AgentCount" : 10000, # Number of agents of this type + "T_sim" : 120, # Number of periods to simulate + "aNrmInitMean" : -6.0, # Mean of log initial assets + "aNrmInitStd" : 1.0, # Standard deviation of log initial assets + "pLvlInitMean" : 0.0, # Mean of log initial permanent income + "pLvlInitStd" : 0.0, # Standard deviation of log initial permanent income + "PermGroFacAgg" : 1.0, # Aggregate permanent income growth factor + "T_age" : None, # Age after which simulated agents are automatically killed +} + +# %% [markdown] +# The distribution of permanent income shocks is specified as mean one lognormal, with an age-varying (underlying) standard deviation. The distribution of transitory income shocks is also mean one lognormal, but with an additional point mass representing unemployment; the transitory shocks are adjusted so that the distribution is still mean one. The continuous distributions are discretized with an equiprobable distribution. +# +# Optionally, the user can specify the period when the individual retires and escapes essentially all income risk as $\texttt{T_retire}$; this can be turned off by setting the parameter to $0$. In retirement, all permanent income shocks are turned off, and the only transitory shock is an "unemployment" shock, likely with small probability; this prevents the retired problem from degenerating into a perfect foresight model. +# +# The grid of assets above minimum $\texttt{aXtraGrid}$ is specified by its minimum and maximum level, the number of gridpoints, and the extent of exponential nesting. The greater the (integer) value of $\texttt{aXtraNestFac}$, the more dense the gridpoints will be at the bottom of the grid (and more sparse near the top); setting $\texttt{aXtraNestFac}$ to $0$ will generate an evenly spaced grid of $a_t$. +# +# The artificial borrowing constraint $\texttt{BoroCnstArt}$ can be set to $\texttt{None}$ to turn it off. +# +# It is not necessary to compute the value function in this model, and it is not computationally free to do so. You can choose whether the value function should be calculated and returned as part of the solution of the model with $\texttt{vFuncBool}$. The consumption function will be constructed as a piecewise linear interpolation when $\texttt{CubicBool}$ is \texttt{False}, and will be a piecewise cubic spline interpolator if $\texttt{True}$. + +# %% [markdown] {"heading_collapsed": true} +# ## Solving and examining the solution of the idiosyncratic income shocks model +# +# The cell below creates an infinite horizon instance of $\texttt{IndShockConsumerType}$ and solves its model by calling its $\texttt{solve}$ method. + +# %% {"hidden": true} +IndShockExample = IndShockConsumerType(**IdiosyncDict) +IndShockExample.cycles = 0 # Make this type have an infinite horizon +IndShockExample.solve() + + +# %% [markdown] {"hidden": true} +# After solving the model, we can examine an element of this type's $\texttt{solution}$: + +# %% {"hidden": true} +print(vars(IndShockExample.solution[0])) + +# %% [markdown] {"hidden": true} +# The single-period solution to an idiosyncratic shocks consumer's problem has all of the same attributes as in the perfect foresight model, with a couple additions. The solution can include the marginal marginal value of market resources function $\texttt{vPPfunc}$, but this is only constructed if $\texttt{CubicBool}$ is $\texttt{True}$, so that the MPC can be accurately computed; when it is $\texttt{False}$, then $\texttt{vPPfunc}$ merely returns $\texttt{NaN}$ everywhere. +# +# The $\texttt{solveConsIndShock}$ function calculates steady state market resources and stores it in the attribute $\texttt{mNrmSS}$. This represents the steady state level of $m_t$ if *this period* were to occur indefinitely, but with income shocks turned off. This is relevant in a "one period infinite horizon" model like we've specified here, but is less useful in a lifecycle model. +# +# Let's take a look at the consumption function by plotting it, along with its derivative (the MPC): + +# %% {"hidden": true} +print('Consumption function for an idiosyncratic shocks consumer type:') +plotFuncs(IndShockExample.solution[0].cFunc,IndShockExample.solution[0].mNrmMin,5) +print('Marginal propensity to consume for an idiosyncratic shocks consumer type:') +plotFuncsDer(IndShockExample.solution[0].cFunc,IndShockExample.solution[0].mNrmMin,5) + +# %% [markdown] {"hidden": true} +# The lower part of the consumption function is linear with a slope of 1, representing the *constrained* part of the consumption function where the consumer *would like* to consume more by borrowing-- his marginal utility of consumption exceeds the marginal value of assets-- but he is prevented from doing so by the artificial borrowing constraint. +# +# The MPC is a step function, as the $\texttt{cFunc}$ itself is a piecewise linear function; note the large jump in the MPC where the borrowing constraint begins to bind. +# +# If you want to look at the interpolation nodes for the consumption function, these can be found by "digging into" attributes of $\texttt{cFunc}$: + +# %% {"hidden": true} +print('mNrmGrid for unconstrained cFunc is ',IndShockExample.solution[0].cFunc.functions[0].x_list) +print('cNrmGrid for unconstrained cFunc is ',IndShockExample.solution[0].cFunc.functions[0].y_list) +print('mNrmGrid for borrowing constrained cFunc is ',IndShockExample.solution[0].cFunc.functions[1].x_list) +print('cNrmGrid for borrowing constrained cFunc is ',IndShockExample.solution[0].cFunc.functions[1].y_list) + +# %% [markdown] {"hidden": true} +# The consumption function in this model is an instance of $\texttt{LowerEnvelope1D}$, a class that takes an arbitrary number of 1D interpolants as arguments to its initialization method. When called, a $\texttt{LowerEnvelope1D}$ evaluates each of its component functions and returns the lowest value. Here, the two component functions are the *unconstrained* consumption function-- how the agent would consume if the artificial borrowing constraint did not exist for *just this period*-- and the *borrowing constrained* consumption function-- how much he would consume if the artificial borrowing constraint is binding. +# +# The *actual* consumption function is the lower of these two functions, pointwise. We can see this by plotting the component functions on the same figure: + +# %% {"hidden": true} +plotFuncs(IndShockExample.solution[0].cFunc.functions,-0.25,5.) + +# %% [markdown] +# ## Simulating the idiosyncratic income shocks model +# +# In order to generate simulated data, an instance of $\texttt{IndShockConsumerType}$ needs to know how many agents there are that share these particular parameters (and are thus *ex ante* homogeneous), the distribution of states for newly "born" agents, and how many periods to simulated. These simulation parameters are described in the table below, along with example values. +# +# | Description | Code | Example value | +# | :---: | --- | --- | +# | Number of consumers of this type | $\texttt{AgentCount}$ | $10000$ | +# | Number of periods to simulate | $\texttt{T_sim}$ | $120$ | +# | Mean of initial log (normalized) assets | $\texttt{aNrmInitMean}$ | $-6.0$ | +# | Stdev of initial log (normalized) assets | $\texttt{aNrmInitStd}$ | $1.0$ | +# | Mean of initial log permanent income | $\texttt{pLvlInitMean}$ | $0.0$ | +# | Stdev of initial log permanent income | $\texttt{pLvlInitStd}$ | $0.0$ | +# | Aggregrate productivity growth factor | $\texttt{PermGroFacAgg}$ | $1.0$ | +# | Age after which consumers are automatically killed | $\texttt{T_age}$ | $None$ | +# +# Here, we will simulate 10,000 consumers for 120 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \exp(\texttt{pLvlInitMean})$, as $\texttt{pLvlInitStd}$ has been set to zero; they will have essentially zero assets at birth, as $\texttt{aNrmInitMean}$ is $-6.0$; assets will be less than $1\%$ of permanent income at birth. +# +# These example parameter values were already passed as part of the parameter dictionary that we used to create $\texttt{IndShockExample}$, so it is ready to simulate. We need to set the $\texttt{track_vars}$ attribute to indicate the variables for which we want to record a *history*. + +# %% +IndShockExample.track_vars = ['aNrmNow','mNrmNow','cNrmNow','pLvlNow'] +IndShockExample.initializeSim() +IndShockExample.simulate() + +# %% [markdown] +# We can now look at the simulated data in aggregate or at the individual consumer level. Like in the perfect foresight model, we can plot average (normalized) market resources over time, as well as average consumption: + +# %% +plt.plot(np.mean(IndShockExample.mNrmNow_hist,axis=1)) +plt.xlabel('Time') +plt.ylabel('Mean market resources') +plt.show() + +plt.plot(np.mean(IndShockExample.cNrmNow_hist,axis=1)) +plt.xlabel('Time') +plt.ylabel('Mean consumption') +plt.show() + +# %% [markdown] +# We could also plot individual consumption paths for some of the consumers-- say, the first five: + +# %% +plt.plot(IndShockExample.cNrmNow_hist[:,0:5]) +plt.xlabel('Time') +plt.ylabel('Individual consumption paths') +plt.show() + +# %% [markdown] +# ## Other example specifications of idiosyncratic income shocks consumers +# +# $\texttt{IndShockConsumerType}$-- and $\texttt{HARK}$ in general-- can also represent models that are not infinite horizon. +# +# ### Lifecycle example +# +# Suppose we wanted to represent consumers with a *lifecycle*-- parameter values that differ by age, with a finite end point beyond which the individual cannot surive. This can be done very easily by simply specifying the time-varying attributes $\texttt{PermGroFac}$, $\texttt{LivPrb}$, $\texttt{PermShkStd}$, and $\texttt{TranShkStd}$ as Python *lists* specifying the sequence of periods these agents will experience, from beginning to end. +# +# In the cell below, we define a parameter dictionary for a rather short ten period lifecycle, with arbitrarily chosen parameters. For a more realistically calibrated (and much longer) lifecycle model, see the [SolvingMicroDSOPs REMARK](https://github.com/econ-ark/REMARK/blob/master/REMARKs/SolvingMicroDSOPs.md). + +# %% {"code_folding": [0]} +LifecycleDict={ # Click arrow to expand this fairly large parameter dictionary + # Parameters shared with the perfect foresight model + "CRRA": 2.0, # Coefficient of relative risk aversion + "Rfree": 1.03, # Interest factor on assets + "DiscFac": 0.96, # Intertemporal discount factor + "LivPrb" : [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1], + "PermGroFac" : [1.01,1.01,1.01,1.02,1.02,1.02,0.7,1.0,1.0,1.0], + + # Parameters that specify the income distribution over the lifecycle + "PermShkStd" : [0.1,0.2,0.1,0.2,0.1,0.2,0.1,0,0,0], + "PermShkCount" : 7, # Number of points in discrete approximation to permanent income shocks + "TranShkStd" : [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0,0,0], + "TranShkCount" : 7, # Number of points in discrete approximation to transitory income shocks + "UnempPrb" : 0.05, # Probability of unemployment while working + "IncUnemp" : 0.3, # Unemployment benefits replacement rate + "UnempPrbRet" : 0.0005, # Probability of "unemployment" while retired + "IncUnempRet" : 0.0, # "Unemployment" benefits when retired + "T_retire" : 7, # Period of retirement (0 --> no retirement) + "tax_rate" : 0.0, # Flat income tax rate (legacy parameter, will be removed in future) + + # Parameters for constructing the "assets above minimum" grid + "aXtraMin" : 0.001, # Minimum end-of-period "assets above minimum" value + "aXtraMax" : 20, # Maximum end-of-period "assets above minimum" value + "aXtraCount" : 48, # Number of points in the base grid of "assets above minimum" + "aXtraNestFac" : 3, # Exponential nesting factor when constructing "assets above minimum" grid + "aXtraExtra" : [None], # Additional values to add to aXtraGrid + + # A few other paramaters + "BoroCnstArt" : 0.0, # Artificial borrowing constraint; imposed minimum level of end-of period assets + "vFuncBool" : True, # Whether to calculate the value function during solution + "CubicBool" : False, # Preference shocks currently only compatible with linear cFunc + "T_cycle" : 10, # Number of periods in the cycle for this agent type + + # Parameters only used in simulation + "AgentCount" : 10000, # Number of agents of this type + "T_sim" : 120, # Number of periods to simulate + "aNrmInitMean" : -6.0, # Mean of log initial assets + "aNrmInitStd" : 1.0, # Standard deviation of log initial assets + "pLvlInitMean" : 0.0, # Mean of log initial permanent income + "pLvlInitStd" : 0.0, # Standard deviation of log initial permanent income + "PermGroFacAgg" : 1.0, # Aggregate permanent income growth factor + "T_age" : 11, # Age after which simulated agents are automatically killed +} + +# %% [markdown] +# In this case, we have specified a ten period model in which retirement happens in period $t=7$. Agents in this model are more likely to die as they age, and their permanent income drops by 30\% at retirement. Let's make and solve this lifecycle example, then look at the $\texttt{solution}$ attribute. + +# %% +LifecycleExample = IndShockConsumerType(**LifecycleDict) +LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods -- a lifetime -- exactly once +LifecycleExample.solve() +print('First element of solution is',LifecycleExample.solution[0]) +print('Solution has', len(LifecycleExample.solution),'elements.') + +# %% [markdown] +# This was supposed to be a *ten* period lifecycle model-- why does our consumer type have *eleven* elements in its $\texttt{solution}$? It would be more precise to say that this specification has ten *non-terminal* periods. The solution to the 11th and final period in the model would be the same for every set of parameters: consume $c_t = m_t$, because there is no future. In a lifecycle model, the terminal period is assumed to exist; the $\texttt{LivPrb}$ parameter does not need to end with a $0.0$ in order to guarantee that survivors die. +# +# We can quickly plot the consumption functions in each period of the model: + +# %% +print('Consumption functions across the lifecycle:') +mMin = np.min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]) +LifecycleExample.unpackcFunc() # This makes all of the cFuncs accessible in the attribute cFunc +plotFuncs(LifecycleExample.cFunc,mMin,5) + +# %% [markdown] +# ### "Cyclical" example +# +# We can also model consumers who face an infinite horizon, but who do *not* face the same problem in every period. Consider someone who works as a ski instructor: they make most of their income for the year in the winter, and make very little money in the other three seasons. +# +# We can represent this type of individual as a four period, infinite horizon model in which expected "permanent" income growth varies greatly across seasons. + +# %% {"code_folding": [0]} +CyclicalDict = { # Click the arrow to expand this parameter dictionary + # Parameters shared with the perfect foresight model + "CRRA": 2.0, # Coefficient of relative risk aversion + "Rfree": 1.03, # Interest factor on assets + "DiscFac": 0.96, # Intertemporal discount factor + "LivPrb" : 4*[0.98], # Survival probability + "PermGroFac" : [1.082251, 2.8, 0.3, 1.1], + + # Parameters that specify the income distribution over the lifecycle + "PermShkStd" : [0.1,0.1,0.1,0.1], + "PermShkCount" : 7, # Number of points in discrete approximation to permanent income shocks + "TranShkStd" : [0.2,0.2,0.2,0.2], + "TranShkCount" : 7, # Number of points in discrete approximation to transitory income shocks + "UnempPrb" : 0.05, # Probability of unemployment while working + "IncUnemp" : 0.3, # Unemployment benefits replacement rate + "UnempPrbRet" : 0.0005, # Probability of "unemployment" while retired + "IncUnempRet" : 0.0, # "Unemployment" benefits when retired + "T_retire" : 0, # Period of retirement (0 --> no retirement) + "tax_rate" : 0.0, # Flat income tax rate (legacy parameter, will be removed in future) + + # Parameters for constructing the "assets above minimum" grid + "aXtraMin" : 0.001, # Minimum end-of-period "assets above minimum" value + "aXtraMax" : 20, # Maximum end-of-period "assets above minimum" value + "aXtraCount" : 48, # Number of points in the base grid of "assets above minimum" + "aXtraNestFac" : 3, # Exponential nesting factor when constructing "assets above minimum" grid + "aXtraExtra" : [None], # Additional values to add to aXtraGrid + + # A few other paramaters + "BoroCnstArt" : 0.0, # Artificial borrowing constraint; imposed minimum level of end-of period assets + "vFuncBool" : True, # Whether to calculate the value function during solution + "CubicBool" : False, # Preference shocks currently only compatible with linear cFunc + "T_cycle" : 4, # Number of periods in the cycle for this agent type + + # Parameters only used in simulation + "AgentCount" : 10000, # Number of agents of this type + "T_sim" : 120, # Number of periods to simulate + "aNrmInitMean" : -6.0, # Mean of log initial assets + "aNrmInitStd" : 1.0, # Standard deviation of log initial assets + "pLvlInitMean" : 0.0, # Mean of log initial permanent income + "pLvlInitStd" : 0.0, # Standard deviation of log initial permanent income + "PermGroFacAgg" : 1.0, # Aggregate permanent income growth factor + "T_age" : None, # Age after which simulated agents are automatically killed +} + +# %% [markdown] +# This consumer type's parameter dictionary is nearly identical to the original infinite horizon type we made, except that each of the time-varying parameters now have *four* values, rather than just one. Most of these have the same value in each period *except* for $\texttt{PermGroFac}$, which varies greatly over the four seasons. Note that the product of the four "permanent" income growth factors is almost exactly 1.0-- this type's income does not grow on average in the long run! +# +# Let's make and solve this consumer type, then plot his quarterly consumption functions: + +# %% +CyclicalExample = IndShockConsumerType(**CyclicalDict) +CyclicalExample.cycles = 0 # Make this consumer type have an infinite horizon +CyclicalExample.solve() + +CyclicalExample.unpackcFunc() +print('Quarterly consumption functions:') +mMin = min([X.mNrmMin for X in CyclicalExample.solution]) +plotFuncs(CyclicalExample.cFunc,mMin,5) + +# %% [markdown] +# The very low green consumption function corresponds to the quarter in which the ski instructors make most of their income. They know that they are about to experience a 70% drop in "permanent" income, so they do not consume much *relative to their income this quarter*. In the other three quarters, *normalized* consumption is much higher, as current "permanent" income is low relative to future expectations. In *level*, the consumption chosen in each quarter is much more similar diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb new file mode 100644 index 000000000..cc57b6c4a --- /dev/null +++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb @@ -0,0 +1,369 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# KinkedRconsumerType: Consumption-saving model with idiosyncratic income shocks and different interest rates on borrowing and saving" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "# Initial imports and notebook setup, click arrow to show\n", + "from HARK.ConsumptionSaving.ConsIndShockModel import KinkedRconsumerType\n", + "from HARK.utilities import plotFuncsDer, plotFuncs\n", + "from time import clock\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "mystr = lambda number : \"{:.4f}\".format(number)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The module $\\texttt{HARK.ConsumptionSaving.ConsIndShockModel}$ concerns consumption-saving models with idiosyncratic shocks to (non-capital) income. All of the models assume CRRA utility with geometric discounting, no bequest motive, and income shocks are fully transitory or fully permanent.\n", + "\n", + "$\\texttt{ConsIndShockModel}$ currently includes three models:\n", + "1. A very basic \"perfect foresight\" model with no uncertainty.\n", + "2. A model with risk over transitory and permanent income shocks.\n", + "3. The model described in (2), with an interest rate for debt that differs from the interest rate for savings.\n", + "\n", + "This notebook provides documentation for the third of these models.\n", + "$\\newcommand{\\CRRA}{\\rho}$\n", + "$\\newcommand{\\DiePrb}{\\mathsf{D}}$\n", + "$\\newcommand{\\PermGroFac}{\\Gamma}$\n", + "$\\newcommand{\\Rfree}{\\mathsf{R}}$\n", + "$\\newcommand{\\DiscFac}{\\beta}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Statement of \"kinked R\" model\n", + "\n", + "Consider a small extension to the model faced by $\\texttt{IndShockConsumerType}$s: that the interest rate on borrowing $a_t < 0$ is greater than the interest rate on saving $a_t > 0$. Consumers who face this kind of problem are represented by the $\\texttt{KinkedRconsumerType}$ class.\n", + "\n", + "For a full theoretical treatment, this model analyzed in [A Theory of the Consumption Function, With\n", + "and Without Liquidity Constraints](http://www.econ2.jhu.edu/people/ccarroll/ATheoryv3JEP.pdf)\n", + "and its [expanded edition](http://www.econ2.jhu.edu/people/ccarroll/ATheoryv3NBER.pdf).\n", + "\n", + "Continuing to work with *normalized* variables (e.g. $m_t$ represents the level of market resources divided by permanent income), the \"kinked R\" model can be stated as:\n", + "\n", + "\\begin{eqnarray*}\n", + "v_t(m_t) &=& \\max_{c_t} {~} U(c_t) + \\DiscFac (1-\\DiePrb_{t+1}) \\mathbb{E}_{t} \\left[ (\\PermGroFac_{t+1}\\psi_{t+1})^{1-\\CRRA} v_{t+1}(m_{t+1}) \\right], \\\\\n", + "a_t &=& m_t - c_t, \\\\\n", + "a_t &\\geq& \\underline{a}, \\\\\n", + "m_{t+1} &=& \\Rfree_t/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", + "\\Rfree_t &=& \\cases{\\Rfree_{boro} \\texttt{ if } a_t < 0 \\\\\n", + " \\Rfree_{save} \\texttt{ if } a_t \\geq 0},\\\\\n", + "\\Rfree_{boro} &>& \\Rfree_{save}, \\\\\n", + "(\\psi_{t+1},\\theta_{t+1}) &\\sim& F_{t+1}, \\\\\n", + "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &=& 1.\n", + "\\end{eqnarray*}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving the \"kinked R\" model\n", + "\n", + "The solution method for the \"kinked R\" model is nearly identical to that of the $\\texttt{IndShockConsumerType}$ on which it is based, using the endogenous grid method; see the notebook for that model for more information. The only significant difference is that the interest factor varies by $a_t$ across the exogenously chosen grid of end-of-period assets, with a discontinuity in $\\Rfree$ at $a_t=0$.\n", + "\n", + "To correctly handle this, the $\\texttt{solveConsKinkedR}$ function inserts *two* instances of $a_t=0$ into the grid of $a_t$ values: the first corresponding to $\\Rfree_{boro}$ ($a_t = -0$) and the other corresponding to $\\Rfree_{save}$ ($a_t = +0$). The two consumption levels (and corresponding endogenous $m_t$ gridpoints) represent points at which the agent's first order condition is satisfied at *exactly* $a_t=0$ at the two different interest factors. In between these two points, the first order condition *does not hold with equality*: the consumer will end the period with exactly $a_t=0$, consuming $c_t=m_t$, but his marginal utility of consumption exceeds the marginal value of saving and is less than the marginal value of borrowing. This generates a consumption function with *two* kinks: two concave portions (for borrowing and saving) with a linear segment of slope 1 in between." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example parameter values to construct an instance of KinkedRconsumerType\n", + "\n", + "The parameters required to create an instance of $\\texttt{KinkedRconsumerType}$ are nearly identical to those for $\\texttt{IndShockConsumerType}$. The only difference is that the parameter $\\texttt{Rfree}$ is replaced with $\\texttt{Rboro}$ and $\\texttt{Rsave}$.\n", + "\n", + "While the parameter $\\texttt{CubicBool}$ is required to create a valid $\\texttt{KinkedRconsumerType}$ instance, it must be set to $\\texttt{False}$; cubic spline interpolation has not yet been implemented for this model. In the future, this restriction will be lifted.\n", + "\n", + "| Parameter | Description | Code | Example value | Time-varying? |\n", + "| :---: | --- | --- | --- | --- |\n", + "| $\\DiscFac$ |Intertemporal discount factor | $\\texttt{DiscFac}$ | $0.96$ | |\n", + "| $\\CRRA $ |Coefficient of relative risk aversion | $\\texttt{CRRA}$ | $2.0$ | |\n", + "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | $\\texttt{Rboro}$ | $1.20$ | |\n", + "| $\\Rfree_{save}$ | Risk free interest factor for saving | $\\texttt{Rsave}$ | $1.01$ | |\n", + "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\texttt{LivPrb}$ | $[0.98]$ | $\\surd$ |\n", + "|$\\PermGroFac_{t+1}$|Permanent income growth factor|$\\texttt{PermGroFac}$| $[1.01]$ | $\\surd$ |\n", + "| $\\sigma_\\psi $ | Standard deviation of log permanent income shocks | $\\texttt{PermShkStd}$ | $[0.1]$ |$\\surd$ |\n", + "| $N_\\psi $ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | $7$ | |\n", + "| $\\sigma_\\theta $ | Standard deviation of log transitory income shocks | $\\texttt{TranShkStd}$ | $[0.2]$ | $\\surd$ |\n", + "| $N_\\theta $ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | $7$ | |\n", + "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\texttt{UnempPrb}$ | $0.05$ | |\n", + "| $\\underline{\\theta} $ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | $0.3$ | |\n", + "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\texttt{UnempPrb}$ | $0.0005$ | |\n", + "| $\\underline{\\theta}^{Ret} $ | Transitory shock when \"unemployed\" and retired | $\\texttt{IncUnemp}$ | $0.0$ | |\n", + "| $(none)$ | Period of the lifecycle model when retirement begins | $\\texttt{T_retire}$ | $0$ | |\n", + "| $(none)$ | Minimum value in assets-above-minimum grid | $\\texttt{aXtraMin}$ | $0.001$ | |\n", + "| $(none)$ | Maximum value in assets-above-minimum grid | $\\texttt{aXtraMax}$ | $20.0$ | |\n", + "| $(none)$ | Number of points in base assets-above-minimum grid | $\\texttt{aXtraCount}$ | $48$ | |\n", + "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\\texttt{aXtraNestFac}$ | $3$ | |\n", + "| $(none)$ | Additional values to add to assets-above-minimum grid | $\\texttt{aXtraExtra}$ | $None$ | |\n", + "| $\\underline{a} $ | Artificial borrowing constraint (normalized) | $\\texttt{BoroCnstArt}$ | $None$ | |\n", + "| $(none) $ |Indicator for whether $\\texttt{vFunc}$ should be computed | $\\texttt{vFuncBool}$ | $True$ | |\n", + "| $(none)$ |Indicator for whether $\\texttt{cFunc}$ should use cubic splines | $\\texttt{CubicBool}$ | $False$ | |\n", + "|$T$| Number of periods in this type's \"cycle\" |$\\texttt{T_cycle}$| $1$ | |\n", + "|(none)| Number of times the \"cycle\" occurs |$\\texttt{cycles}$| $0$ | |\n", + "\n", + "These example parameters are almostidentical to those used for $\\texttt{IndShockExample}$ in the prior notebook, except that the interest rate on borrowing is 20% (like a credit card), and the interest rate on saving is 1%. Moreover, the artificial borrowing constraint has been set to $\\texttt{None}$. The cell below defines a parameter dictionary with these example values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "KinkedRdict={ # Click the arrow to expand this parameter dictionary\n", + " # Parameters shared with the perfect foresight model\n", + " \"CRRA\" : 2.0, # Coefficient of relative risk aversion\n", + " \"DiscFac\": 0.96, # Intertemporal discount factor\n", + " \"LivPrb\" : [0.98], # Survival probability\n", + " \"PermGroFac\" :[1.01], # Permanent income growth factor\n", + " \n", + " # New parameters unique to the \"kinked R\" model\n", + " \"Rboro\" : 1.20, # Interest factor on borrowing (a < 0)\n", + " \"Rsave\" : 1.01, # Interest factor on saving (a > 0)\n", + " \n", + " # Parameters that specify the income distribution over the lifecycle\n", + " \"PermShkStd\" : [0.1], # Standard deviation of log permanent shocks to income\n", + " \"PermShkCount\" : 7, # Number of points in discrete approximation to permanent income shocks\n", + " \"TranShkStd\" : [0.2], # Standard deviation of log transitory shocks to income\n", + " \"TranShkCount\" : 7, # Number of points in discrete approximation to transitory income shocks\n", + " \"UnempPrb\" : 0.05, # Probability of unemployment while working\n", + " \"IncUnemp\" : 0.3, # Unemployment benefits replacement rate\n", + " \"UnempPrbRet\" : 0.0005, # Probability of \"unemployment\" while retired\n", + " \"IncUnempRet\" : 0.0, # \"Unemployment\" benefits when retired\n", + " \"T_retire\" : 0, # Period of retirement (0 --> no retirement)\n", + " \"tax_rate\" : 0.0, # Flat income tax rate (legacy parameter, will be removed in future)\n", + " \n", + " # Parameters for constructing the \"assets above minimum\" grid\n", + " \"aXtraMin\" : 0.001, # Minimum end-of-period \"assets above minimum\" value\n", + " \"aXtraMax\" : 20, # Maximum end-of-period \"assets above minimum\" value\n", + " \"aXtraCount\" : 48, # Number of points in the base grid of \"assets above minimum\"\n", + " \"aXtraNestFac\" : 3, # Exponential nesting factor when constructing \"assets above minimum\" grid\n", + " \"aXtraExtra\" : [None], # Additional values to add to aXtraGrid\n", + " \n", + " # A few other paramaters\n", + " \"BoroCnstArt\" : None, # Artificial borrowing constraint; imposed minimum level of end-of period assets\n", + " \"vFuncBool\" : True, # Whether to calculate the value function during solution \n", + " \"CubicBool\" : False, # Preference shocks currently only compatible with linear cFunc\n", + " \"T_cycle\" : 1, # Number of periods in the cycle for this agent type \n", + " \n", + " # Parameters only used in simulation\n", + " \"AgentCount\" : 10000, # Number of agents of this type\n", + " \"T_sim\" : 500, # Number of periods to simulate\n", + " \"aNrmInitMean\" : -6.0, # Mean of log initial assets\n", + " \"aNrmInitStd\" : 1.0, # Standard deviation of log initial assets\n", + " \"pLvlInitMean\" : 0.0, # Mean of log initial permanent income\n", + " \"pLvlInitStd\" : 0.0, # Standard deviation of log initial permanent income\n", + " \"PermGroFacAgg\" : 1.0, # Aggregate permanent income growth factor\n", + " \"T_age\" : None, # Age after which simulated agents are automatically killed\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving and examining the solution of the \"kinked R\" model\n", + "\n", + "The cell below creates an infinite horizon instance of $\\texttt{KinkedRconsumerType}$ and solves its model by calling its $\\texttt{solve}$ method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "KinkyExample = KinkedRconsumerType(**KinkedRdict)\n", + "KinkyExample.cycles = 0 # Make the example infinite horizon\n", + "KinkyExample.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An element of a $\\texttt{KinkedRconsumerType}$'s solution will have all the same attributes as that of a $\\texttt{IndShockConsumerType}$; see that notebook for details.\n", + "\n", + "We can plot the consumption function of our \"kinked R\" example, as well as the MPC:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Kinked R consumption function:')\n", + "plotFuncs(KinkyExample.solution[0].cFunc,KinkyExample.solution[0].mNrmMin,5)\n", + "\n", + "print('Kinked R marginal propensity to consume:')\n", + "plotFuncsDer(KinkyExample.solution[0].cFunc,KinkyExample.solution[0].mNrmMin,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating the \"kinked R\" model\n", + "\n", + "In order to generate simulated data, an instance of $\\texttt{KinkedRconsumerType}$ needs to know how many agents there are that share these particular parameters (and are thus *ex ante* homogeneous), the distribution of states for newly \"born\" agents, and how many periods to simulated. These simulation parameters are described in the table below, along with example values.\n", + "\n", + "| Description | Code | Example value |\n", + "| :---: | --- | --- |\n", + "| Number of consumers of this type | $\\texttt{AgentCount}$ | $10000$ |\n", + "| Number of periods to simulate | $\\texttt{T_sim}$ | $500$ |\n", + "| Mean of initial log (normalized) assets | $\\texttt{aNrmInitMean}$ | $-6.0$ |\n", + "| Stdev of initial log (normalized) assets | $\\texttt{aNrmInitStd}$ | $1.0$ |\n", + "| Mean of initial log permanent income | $\\texttt{pLvlInitMean}$ | $0.0$ |\n", + "| Stdev of initial log permanent income | $\\texttt{pLvlInitStd}$ | $0.0$ |\n", + "| Aggregrate productivity growth factor | $\\texttt{PermGroFacAgg}$ | $1.0$ |\n", + "| Age after which consumers are automatically killed | $\\texttt{T_age}$ | $None$ |\n", + "\n", + "Here, we will simulate 10,000 consumers for 500 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\texttt{pLvlInitMean})$, as $\\texttt{pLvlInitStd}$ has been set to zero; they will have essentially zero assets at birth, as $\\texttt{aNrmInitMean}$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n", + "\n", + "These example parameter values were already passed as part of the parameter dictionary that we used to create $\\texttt{KinkyExample}$, so it is ready to simulate. We need to set the $\\texttt{track_vars}$ attribute to indicate the variables for which we want to record a *history*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "KinkyExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow']\n", + "KinkyExample.initializeSim()\n", + "KinkyExample.simulate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the average (normalized) market resources in each simulated period:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(np.mean(KinkyExample.mNrmNow_hist,axis=1))\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Mean market resources')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's plot the distribution of (normalized) assets $a_t$ for the current population, after simulating for $500$ periods; this should be fairly close to the long run distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(np.sort(KinkyExample.aNrmNow),np.linspace(0.,1.,KinkyExample.AgentCount))\n", + "plt.xlabel('End-of-period assets')\n", + "plt.ylabel('Cumulative distribution')\n", + "plt.ylim(-0.01,1.01)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see there's a significant point mass of consumers with *exactly* $a_t=0$; these are consumers who do not find it worthwhile to give up a bit of consumption to begin saving (because $\\Rfree_{save}$ is too low), and also are not willing to finance additional consumption by borrowing (because $\\Rfree_{boro}$ is too high).\n", + "\n", + "The smaller point masses in this distribution are due to $\\texttt{HARK}$ drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks. For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$. You can verify this by changing $\\texttt{TranShkCount}$ to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye." + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": "779f6c5616b04b58baaaa0f6c348270c", + "lastKernelId": "a944b08f-0ae0-4c26-883f-9fab53a82ac3" + }, + "jupytext": { + "cell_metadata_filter": "collapsed,code_folding", + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb b/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb new file mode 100644 index 000000000..25bfddfa5 --- /dev/null +++ b/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb @@ -0,0 +1,676 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PerfForesightConsumerType" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "# Initial imports and notebook setup, click arrow to show\n", + "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType\n", + "from HARK.utilities import plotFuncs\n", + "from time import clock\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "mystr = lambda number : \"{:.4f}\".format(number)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The module $\\texttt{HARK.ConsumptionSaving.ConsIndShockModel}$ concerns consumption-saving models with idiosyncratic shocks to (non-capital) income. All of the models assume CRRA utility with geometric discounting, no bequest motive, and income shocks that are either fully transitory or fully permanent.\n", + "\n", + "$\\texttt{ConsIndShockModel}$ currently includes three models:\n", + "1. A very basic \"perfect foresight\" model with no uncertainty (shocks are zero).\n", + "2. A model with risk over transitory and permanent income shocks.\n", + "3. The model described in (2), with an interest rate for debt that differs from the interest rate for savings.\n", + "\n", + "This notebook provides documentation for the first of these three models.\n", + "$\\newcommand{\\CRRA}{\\rho}$\n", + "$\\newcommand{\\DiePrb}{\\mathsf{D}}$\n", + "$\\newcommand{\\PermGroFac}{\\Gamma}$\n", + "$\\newcommand{\\Rfree}{\\mathsf{R}}$\n", + "$\\newcommand{\\DiscFac}{\\beta}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Statement of the model\n", + "\n", + "The $\\texttt{PerfForesightConsumerType}$ class solves the problem of a consumer with Constant Relative Risk Aversion utility \n", + "${\\CRRA}$\n", + "\\begin{equation}\n", + "U(C) = \\frac{C^{1-\\CRRA}}{1-\\rho},\n", + "\\end{equation}\n", + "who has perfect foresight about everything except whether he will die between the end of period $t$ and the beginning of period $t+1$. Permanent labor income $P_t$ grows from period $t$ to period $t+1$ by factor $\\PermGroFac_{t+1}$. The consumer faces no artificial borrowing constraint: He is able to borrow against his entire future stream of income.\n", + "\n", + "At the beginning of period $t$, the consumer has market resources $M_t$ (which includes both market wealth and currrent income) and must choose how much to consume $C_t$ and how much to retain in a riskless asset $A_t$, which will earn return factor $\\Rfree$. The agent's flow of future utility $U(C_{t+n})$ from consumption is geometrically discounted by factor $\\DiscFac$ per period. The consumer only experiences future value if he survives, which occurs with probability $1-\\DiePrb_{t+1}$.\n", + "\n", + "For parallelism with the treatment of more complicated problems, we write the problem rather elaborately in Bellman form as:\n", + "\n", + "\\begin{eqnarray*}\n", + "V_t(M_t,P_t) &=& \\max_{C_t}~U(C_t) ~+ \\DiscFac (1 - \\DiePrb_{t+1}) V_{t+1}(M_{t+1},P_{t+1}), \\\\\n", + "& s.t. & \\\\\n", + "A_t &=& M_t - C_t, \\\\\n", + "M_{t+1} &=& \\Rfree A_t + Y_{t+1}, \\\\\n", + "Y_{t+1} &=& P_{t+1}, \\\\ \n", + "P_{t+1} &=& \\PermGroFac_{t+1} P_t.\n", + "\\end{eqnarray*}\n", + "\n", + "The parameters of the consumer's problem are the coefficient of relative risk aversion $\\CRRA$, the intertemporal discount factor $\\DiscFac$, an interest factor $\\Rfree$, and age-varying sequences of the permanent income growth factor $\\PermGroFac_t$ and survival probability $(1 - \\DiePrb_t)$. [These lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA) show that under these assumptions the problem can be transformed into an equivalent problem stated in terms of *normalized* variables (represented in lower case); all real variables are divided by permanent income $P_t$ and value is divided by $P_t^{1-\\CRRA}$. The Bellman form of the normalized model (see the lecture notes for details) is:\n", + "\n", + "\\begin{eqnarray*}\n", + "v_t(m_t) &=& \\max_{c_t}~U(c_t) ~+ \\DiscFac (1 - \\DiePrb_{t+1}) \\PermGroFac_{t+1}^{1-\\CRRA} v_{t+1}(m_{t+1}), \\\\\n", + "& s.t. & \\\\\n", + "a_t &=& m_t - c_t, \\\\\n", + "m_{t+1} &=& a_t (\\Rfree/\\PermGroFac_{t+1} )+ 1.\n", + "\\end{eqnarray*}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solution method for PerfForesightConsumerType\n", + "\n", + "Because of the assumptions of CRRA utility, no risk other than mortality, and no artificial borrowing constraint, the problem has a closed form solution in which consumption is a linear function of resources, and the utility-inverse of the value function is also linear (that is, $u^{-1}(v)$ is linear in $m$). Details of the mathematical solution of this model can be found in the lecture notes [PerfForesightCRRA](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA). \n", + "\n", + "The one period problem for this model is solved by the function $\\texttt{solveConsPerfForesight}$, which creates an instance of the class $\\texttt{ConsPerfForesightSolver}$. To construct an instance of the class $\\texttt{PerfForesightConsumerType}$, several parameters must be passed to this constructor. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example parameter values\n", + "\n", + "| Parameter | Description | Code | Example value | Time-varying? |\n", + "| :---: | --- | --- | --- | --- |\n", + "| $\\DiscFac$ |Intertemporal discount factor | $\\texttt{DiscFac}$ | $0.96$ | |\n", + "| $\\CRRA $ |Coefficient of relative risk aversion | $\\texttt{CRRA}$ | $2.0$ | |\n", + "| $\\Rfree$ | Risk free interest factor | $\\texttt{Rfree}$ | $1.03$ | |\n", + "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\texttt{LivPrb}$ | $[0.98]$ | $\\surd$ |\n", + "|$\\PermGroFac_{t+1}$|Permanent income growth factor|$\\texttt{PermGroFac}$| $[1.01]$ | $\\surd$ |\n", + "|$T$| Number of periods in this type's \"cycle\" |$\\texttt{T_cycle}$| $1$ | |\n", + "|(none)| Number of times the \"cycle\" occurs |$\\texttt{cycles}$| $0$ | |\n", + "\n", + "Note that the survival probability and income growth factor have time subscripts; likewise, the example values for these parameters are *lists* rather than simply single floats. This is because those parameters are in principle *time-varying*: their values can depend on which period of the problem the agent is in (for example, mortality probability depends on age). All time-varying parameters *must* be specified as lists, even when the model is being solved for an infinite horizon case where in practice the parameter takes the same value in every period.\n", + "\n", + "The last two parameters in the table specify the \"nature of time\" for this type: the number of (non-terminal) periods in this type's \"cycle\", and the number of times that the \"cycle\" occurs. *Every* subclass of $\\texttt{AgentType}$ uses these two code parameters to define the nature of time. Here, $\\texttt{T_cycle}$ has the value $1$, indicating that there is exactly one period in the cycle, while $\\texttt{cycles}$ is $0$, indicating that the cycle is repeated in *infinite* number of times-- it is an infinite horizon model, with the same \"kind\" of period repeated over and over.\n", + "\n", + "In contrast, we could instead specify a life-cycle model by setting $\\texttt{T_cycle}$ to $1$, and specifying age-varying sequences of income growth and survival probability. In all cases, the number of elements in each time-varying parameter should exactly equal $\\texttt{T_cycle}$.\n", + "\n", + "The parameter $\\texttt{AgentCount}$ specifies how many consumers there are of this *type*-- how many individuals have these exact parameter values and are *ex ante* homogeneous. This information is not relevant for solving the model, but is needed in order to simulate a population of agents, introducing *ex post* heterogeneity through idiosyncratic shocks. Of course, simulating a perfect foresight model is quite boring, as there are *no* idiosyncratic shocks other than death!\n", + "\n", + "The cell below defines a dictionary that can be passed to the constructor method for $\\texttt{PerfForesightConsumerType}$, with the values from the table here." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "PerfForesightDict = {\n", + " # Parameters actually used in the solution method\n", + " \"CRRA\" : 2.0, # Coefficient of relative risk aversion\n", + " \"Rfree\" : 1.03, # Interest factor on assets\n", + " \"DiscFac\" : 0.96, # Default intertemporal discount factor\n", + " \"LivPrb\" : [0.98], # Survival probability\n", + " \"PermGroFac\" :[1.01], # Permanent income growth factor\n", + " \n", + " # Parameters that characterize the nature of time\n", + " \"T_cycle\" : 1, # Number of periods in the cycle for this agent type\n", + " \"cycles\" : 0 # Number of times the cycle occurs (0 --> infinitely repeated)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspecting the solution\n", + "\n", + "With the dictionary we have just defined, we can create an instance of $\\texttt{PerfForesightConsumerType}$ by passing the dictionary to the class (as if the class were a function). This instance can then be solved by invoking its $\\texttt{solve}$ method." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "PFexample = PerfForesightConsumerType(**PerfForesightDict)\n", + "PFexample.cycles = 0\n", + "PFexample.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The $\\texttt{solve}$ method fills in the instance's attribute $\\texttt{solution}$ as a time-varying list of solutions to each period of the consumer's problem. In this case, $\\texttt{solution}$ will be a list with exactly one instance of the class $\\texttt{ConsumerSolution}$, representing the solution to the infinite horizon model we specified." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + } + ], + "source": [ + "print(PFexample.solution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each element of $\\texttt{solution}$ has a few attributes. To see all of them, we can use the $\\texttt{vars}$ built in function: the consumption functions are instantiated in the attribute $\\texttt{cFunc}$ of each element of $\\texttt{ConsumerType.solution}$. This method creates a (time varying) attribute $\\texttt{cFunc}$ that contains a list of consumption functions by age." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'cFunc': , 'vFunc': , 'vPfunc': , 'vPPfunc': , 'mNrmMin': -50.49994992551661, 'hNrm': 50.49994992551661, 'MPCmin': 0.04428139169919579, 'MPCmax': 0.04428139169919579}\n" + ] + } + ], + "source": [ + "print(vars(PFexample.solution[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two most important attributes of a single period solution are the (normalized) consumption function $\\texttt{cFunc}$ and the (normalized) value function $\\texttt{vFunc}$; the marginal value function $\\texttt{vPfunc}$ is also constructed. Let's plot those functions near the lower bound of the permissible state space (the attribute $\\texttt{mNrmMin}$ tells us the lower bound of $m_t$ where the consumption function is defined)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear perfect foresight consumption function:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('Linear perfect foresight consumption function:')\n", + "mMin = PFexample.solution[0].mNrmMin\n", + "plotFuncs(PFexample.solution[0].cFunc,mMin,mMin+10.)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Perfect foresight value function:\n" + ] + }, + { + "data": { + "image/png": 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fhd59tBG4Dfiiuw9krXoCuM7MasxsFdAKvALsAFqDO42qSV+MfiIIk58A1wbb3wg8Pt16uvrSdx4tbqjJ8x2JiFS2SWcKk/hboAZ4Nn2tmJfd/U/dfZeZPQq8Tfq00i3ungQws1uB7UAUeMDddwX7ug3YZmZ/DbwGfH+6xRzuHQYUCiIi+SooFNz97FOsuwe4J0f7U8BTOdr3kL47KW/dfXEAFs9VKIiI5CNUn2ju0kxBRKQgoQuFqqgxv66q1KWIiJSlUIVCd98wi+bWEFzfEBGRaQpVKHT1DuvUkYhIAcIXCrrILCKSt1CFwujpIxERyU9oQiGVco70x3X6SESkAKEJhRNDIyRTTtOcvH6GQURECFEo9AyMANBUr9tRRUTyFZ5QGEyHQqNCQUQkb+EJhYH0V1zMr9PpIxGRfIUmFI5rpiAiUrDQhMLoNYVGfcWFiEjeQhcK+t4jEZH8hScUBuM01MSIRUPzlkREZlxoRtDjAyPM1/UEEZGChCcUBkd0kVlEpEChCYWewREadTuqiEhBwhMKA3FdZBYRKVBoQuH4oK4piIgUKhSh4O70DIzoMwoiIgUKRSgMxJMkUs48hYKISEFCEQr9wwkA5tbESlyJiEh5C0Uo9CkURESKIhSh0D+cBGCOQkFEpCChCIXe4fT3Hs2piZa4EhGR8haKUBidKej0kYhIYUISCulrCjp9JCJSmFCEgi40i4gURyhCQbekiogUR2hCwQzqq3WhWUSkEAWFgpndbWZvmtnrZvZjM1setJuZfdvMOoL1n8na5kYzez943JjVfqGZvRVs820zs6nW0TecZE51jGlsIiIiORQ6U/imu1/g7uuAJ4G/DNo3Aa3BYwtwP4CZLQDuAi4G1gN3mVlTsM39Qd/R7TZOtYj+4YRuRxURKYKCQsHdT2S9nAN4sLwZeNjTXgYazWwZsAF41t2Puvsx4FlgY7Bunru/5O4OPAxcNdU6+uIJ3XkkIlIEBY+kZnYPcANwHPi9oHkFsD+rW2fQdqr2zhztE/3NLaRnFZx55pn0Dyd0kVlEpAgmnSmY2XNmtjPHYzOAu9/p7iuBR4BbRzfLsSvPoz0nd9/q7m3u3rZ48eL06aNqhYKISKEmHUnd/fIp7usfgH8mfc2gE1iZta4ZOBi0f3Zc+0+D9uYc/aekbzjJikb9FKeISKEKvfuoNevlF4F3g+UngBuCu5AuAY67+8fAduAKM2sKLjBfAWwP1vWa2SXBXUc3AI9PtY6+4RHm6kKziEjBCj3n8jUz+ySQAj4E/jRofwq4EugABoCbANz9qJndDewI+v2Vux8Nlv8MeBCoA54OHlPSP5zUhWYRkSIoaCR19387QbsDt0yw7gHggRzt7cB5+dTRpwvNIiJFUfafaHYgnkhppiAiUgRlHwqpVPomJX3FhYhI4co+FDy4cbW2SqEgIlKosg+FVJAKdQoFEZGChScUdPpIRKRgIQiF9HNtVdm/FRGRkiv7kdSDmYKuKYiIFK7sQ2F0pqBrCiIihQtBKGimICJSLGUfCq67j0REiqbsQyFz+kh3H4mIFCwEoRCcPoopFEREChWCUEg/11aX/VsRESm5sh9J3Z2IQXW07N+KiEjJlf1ImvL0nUfp3+YREZFChCAUXHceiYgUSdmHgrs+oyAiUixlHwopd33vkYhIkZT9aJoOBc0URESKoexDQaePRESKJwSh4LodVUSkSMp+NE05VMfK/m2IiMwKZT+aukJBRKRoyn40dVyhICJSJGU/mqYcahQKIiJFUfajqbsrFEREiqTsR1N3fRmeiEixlP1o6uhCs4hIsZT9aJpyXWgWESmWUIymNfrVNRGRoihKKJjZl83MzWxR8NrM7Ntm1mFmb5rZZ7L63mhm7wePG7PaLzSzt4Jtvm3T+IEEzRRERIqj4NHUzFYCnwc+ymreBLQGjy3A/UHfBcBdwMXAeuAuM2sKtrk/6Du63cap1qALzSIixVGM0fRe4C9IX/MdtRl42NNeBhrNbBmwAXjW3Y+6+zHgWWBjsG6eu7/k7g48DFw11QI0UxARKY6CRlMz+yJwwN3fGLdqBbA/63Vn0Haq9s4c7RP93S1m1m5m7aBQEBEplthkHczsOWBpjlV3Al8Brsi1WY42z6M9J3ffCmwFqFnW6vrwmohIcUwaCu5+ea52MzsfWAW8EVwTbgZ+aWbrSf9Lf2VW92bgYND+2XHtPw3am3P0nxKFgohIceQ9mrr7W+5+hru3uHsL6YH9M+5+CHgCuCG4C+kS4Li7fwxsB64ws6bgAvMVwPZgXa+ZXRLcdXQD8PhUa9HpIxGR4ph0ppCnp4ArgQ5gALgJwN2PmtndwI6g31+5+9Fg+c+AB4E64OngMSXVUX1OQUSkGIoWCsFsYXTZgVsm6PcA8ECO9nbgvHz+tmYKIiLFEYrRVNcURESKIxSjqWYKIiLFEYrRVKEgIlIcoRhN9TUXIiLFEYrRVDMFEZHiCMVoGotM+QtVRUTkFMIRCjp9JCJSFKEYTauimimIiBRDKEIhFgnF2xARKblQjKaaKYiIFEfZh4IB0/jlThEROYWyD4Wcv8QgIiJ5KftQMKWCiEjRlH8oKBNERIqm/EOh1AWIiIRI+YeCpgoiIkVT/qFQ6gJEREKk/ENBqSAiUjTlHwqaK4iIFE35h4IyQUSkaMo+FEREpHjKPhT0UwoiIsVT9qGgW1JFRIqn7ENBRESKp+xDQRMFEZHiKf9Q0C2pIiJFU/6hoEwQESma8g+FUhcgIhIi5R8KmiqIiBRNCEKh1BWIiIRHQaFgZl81swNm9nrwuDJr3R1m1mFmu81sQ1b7xqCtw8xuz2pfZWa/MLP3zeyHZlY9pRoKeQMiInKSYswU7nX3dcHjKQAzWwNcB6wFNgL3mVnUzKLAd4BNwBrg+qAvwNeDfbUCx4Cbp/LHdfpIRKR4Ttfpo83ANncfdve9QAewPnh0uPsed48D24DNlh7ZLwMeC7Z/CLhqKn9IkSAiUjzFCIVbzexNM3vAzJqCthXA/qw+nUHbRO0LgR53T4xrz8nMtphZu5m1Dw4OFOEtiIgITCEUzOw5M9uZ47EZuB84C1gHfAx8a3SzHLvyPNpzcvet7t7m7m319fWTvQUREZmi2GQd3P3yqezIzL4LPBm87ARWZq1uBg4Gy7nau4FGM4sFs4Xs/pP93al0ExGRKSj07qNlWS+vBnYGy08A15lZjZmtAlqBV4AdQGtwp1E16YvRT7i7Az8Brg22vxF4fEo1FPIGRETkJJPOFCbxDTNbR/pUzz7gTwDcfZeZPQq8DSSAW9w9CWBmtwLbgSjwgLvvCvZ1G7DNzP4aeA34/lQKqKkq+49aiIjMGpb+R3r5amtr8/b29lKXISJSVszsVXdvG9+uf2aLiEiGQkFERDIUCiIikqFQEBGRDIWCiIhkKBRERCRDoSAiIhkKBRERySj7D6+ZWRfw4TQ3W0T6+5ZExyKbjsUYHYsxYT0Wn3D3xeMbyz4U8mFm7bk+yVeJdCzG6FiM0bEYU2nHQqePREQkQ6EgIiIZlRoKW0tdwCyiYzFGx2KMjsWYijoWFXlNQUREcqvUmYKIiOSgUBARkYyKCgUz+6qZHTCz14PHlVnr7jCzDjPbbWYbSlnnTDKzL5uZm9mi4PV8M/u/ZvaGme0ys5tKXeNMGX8sgrbPBv+t7DKzn5WyvpmU61gE7ReZWdLMrp1o27DJ8f+RPzKzN4PHi2b2qVLXWEyF/hxnObrX3f9bdoOZrSH9e9FrgeXAc2Z2zuhPiIaVma0EPg98lNV8C/C2u/++mS0GdpvZI+4eL0mRMyTXsTCzRuA+YKO7f2RmZ5Sqvpk0wX8XmFkU+Drpn9OtCBMci73A77r7MTPbRPpC9MWlqO90qKiZwilsBra5+7C77wU6gPUlrmkm3Av8Benf2B7lQIOZGTAXOEr6d7bDLtex+EPgn9z9IwB3P1yKwkog17EA+HPgH4FKOQ6Q41i4+4vufix4+TLQXIrCTpdKDIVbg2nfA2bWFLStAPZn9ekM2kLLzL4IHHD3N8at+ltgNXAQeAv4T+6emun6ZtIpjsU5QJOZ/dTMXjWzG0pQ3oya6FiY2QrgauB/lqSwEjjFfxfZbgaenqGSZkToTh+Z2XPA0hyr7gTuB+4mnfp3A98CvgRYjv5lf6/uJMfiK8AVOdZtAF4HLgPOAp41sxfc/cRpK3QG5HksYsCFwOeAOuAlM3vZ3d87bYXOgDyPxX8HbnP3ZHoSGQ55HovRbX+PdCj89umprjRCFwrufvlU+pnZd4Eng5edwMqs1c2k/6Vc1iY6FmZ2PrAKeCP4P3gz8EszWw/cBHzN0x9g6TCzvcC5wCszU/Xpkeex6AS63b0f6Dez54FPAWUdCnkeizZgW9C+CLjSzBLu/n9mpurTI59j4e6HzOwC4HvAJnc/MmMFz4CK+vCamS1z94+D5f8MXOzu15nZWuAfSF9HWA78P6A17BeaR5nZPqDN3bvN7H7gV+7+VTNbAvwS+JS7h/FbIn/NuGOxmvTptA1ANelgvM7dd5awxBmTfSzGtT8IPOnuj5WirlIY99/FmcC/ADe4+4ulraz4QjdTmMQ3zGwd6VND+4A/AXD3XWb2KPA26Yuqt1RKIORwN/Cgmb1F+rTabZUSCOO5+ztm9gzwJpACvlcpgSCn9JfAQuC+YBaRCNO3qFbUTEFERE6tEu8+EhGRCSgUREQkQ6EgIiIZCgUREclQKIiISIZCQUREMhQKIiKS8f8BGzBx40bRGYsAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('Perfect foresight value function:')\n", + "plotFuncs(PFexample.solution[0].vFunc,mMin+0.1,mMin+10.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solution Method\n", + "\n", + "\n", + "### Recursive Formula for $\\kappa_{t}$\n", + "\n", + "The paper [BufferStockTheory](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) has a few other results that are used in the solution code. One is [the recursive formula for the MPC](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/#MPCnvrs). Starting with the last period, in which $\\kappa_{T}=1$, the inverse MPC's (and therefore the MPC's themselves) can be constructed using the recursive formula:\n", + "\n", + "\\begin{align}\n", + "\\kappa_{t}^{-1} & = & 1 + \\kappa_{t+1}^{-1}(\\Rfree \\beta)^{1/\\rho}/G \n", + "\\end{align}\n", + "\n", + "### Consumption Function\n", + "\n", + "For the perfect foresight problem, there is a well-known [analytical solution]( http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/#cFuncAnalytical) for the consumption function: Calling $o_{t}$ 'overall wealth' (including market wealth plus human wealth $h_{t}$) and designating the marginal propensity to consume in period $t$ by $\\kappa_{t}$:\n", + "\n", + "\\begin{align}\n", + "\\mathrm{c}_{t} & = o_{t}\\kappa_{t}\n", + "\\end{align}\n", + "\n", + "and in our normalized model $o_{t} = m_{t}-1+h_{t}$ (the '-1' term subtracts off the normalized current income of 1 from market resources $m$ which were market wealth plus current income).\n", + "\n", + "### Value Function\n", + "\n", + "A convenient feature of the perfect foresight problem is that the value function has a simple [analytical form](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/#vFuncAnalytical):\n", + "\n", + "\\begin{align}\n", + "\\mathrm{v}_{t} & = \\mathrm{u}(\\mathrm{c}_{t}(m))\\kappa_{t}^{-1}\\\\\n", + " &= \\mathrm{u}(o_{t} \\kappa_{t}) \\kappa_{t}^{-1} \\\\ \n", + " &= \\mathrm{u}(o_{t})\\kappa_{t}^{1-\\rho} \\kappa_{t}^{-1} \\\\\n", + " &= \\mathrm{u}(o_{t})\\kappa_{t}^{-\\rho}\n", + "\\end{align}\n", + "\n", + "This means that the utility-inverse of the value function, ${\\scriptsize \\Lambda} \\equiv \\mathrm{u}^{-1}(\\mathrm{v})$, is linear:\n", + "\n", + "\\begin{align}\n", + "\\scriptsize \\Lambda_{t} & = o_{t} \\kappa_{t}^{-\\rho/(1-\\rho)}\n", + "\\end{align}\n", + "\n", + "When uncertainty or liquidity constraints are added to the problem, the ${\\scriptsize \\Lambda}$ function is no longer linear. But even in these cases, the utility-inverse of the value function is much better behaved (e.g., closer to linear; bounded over any feasible finite range of $m$) than the uninverted function (which, for example, approaches $-\\infty$ as $m$ approaches its lower bound).\n", + "\n", + "Our procedure will therefore generically be to construct the inverse value function, and to obtain the value function from it by uninverting. That is, we construct an interpolating approximation of $\\scriptsize \\Lambda_{t}$ and compute value on-the-fly from\n", + "\n", + "\\begin{align}\n", + "\\mathrm{v}_{t}(m) & = \\mathrm{u}({\\scriptsize \\Lambda_{t}}(m))\n", + "\\end{align}\n", + "\n", + "In this case, the interpolation is exact, not an approximation: We need only two points to construct a line, so we choose the minimum possible value of normalized market resources, $\\texttt{mNrmMin}$, where $o_{t}=0$ so that $c_{t}=0$, and that minimum plus 1, where the inverted value function will have the value $\\kappa_{t}^{-\\rho/(1-\\rho)}$. From these we construct $vFuncNvrs$ as a linear interpolating function (which automatically extrapolates to the whole number line).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking Solution Conditions\n", + "\n", + "The code performs tests for whether the supplied parameter values meet various conditions that determine the properties of the solution. Some conditions (like the Finite Human Wealth Condition) are required for the model to have a sensible solution, and if these conditions are violated the code generates a warning message. Other conditions govern characteristics of the model like whether consumption is falling (whether the consumer is 'absolutely impatient'). All conditions can manually be performed using the syntax below. The function returns \"False\" if none of the key conditions has been violated." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The value of the absolute impatience factor for the supplied parameter values satisfies the Absolute Impatience Condition. Therefore, the absolute amount of consumption is expected to fall over time.\n", + "\n", + "The value of the growth impatience factor for the supplied parameter values satisfies the Growth Impatience Condition. Therefore, the ratio of individual wealth to permanent income will fall indefinitely.\n", + "\n", + "The return impatience factor value for the supplied parameter values satisfies the Return Impatience Condition. Therefore, the limiting consumption function is not c(m)=0\n", + "\n", + "The Finite Human wealth factor value for the supplied parameter values satisfies the Finite Human Wealth Condition. Therefore, the limiting consumption function is not c(m)=Infinity\n", + "and human wealth normalized by permanent income is 51.50000\n", + "and the PDV of future consumption growth is 22.58285\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PFexample.checkConditions(verbose=True,public_call=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An element of $\\texttt{solution}$ also includes the (normalized) marginal value function $\\texttt{vPfunc}$, and the lower and upper bounds of the marginal propensity to consume (MPC) $\\texttt{MPCmin}$ and $\\texttt{MPCmax}$. Note that with a linear consumption function, the MPC is constant, so its lower and upper bound are identical." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating the model\n", + "\n", + "Suppose we wanted to simulate many consumers who share the parameter values that we passed to $\\texttt{PerfForesightConsumerType}$-- an *ex ante* homogeneous *type* of consumers. To do this, our instance would have to know *how many* agents there are of this type, as well as their initial levels of assets $a_t$ and permanent income $P_t$.\n", + "\n", + "### Setting Parameters\n", + "\n", + "Let's fill in this information by passing another dictionary to $\\texttt{PFexample}$ with simulation parameters. The table below lists the parameters that an instance of $\\texttt{PerfForesightConsumerType}$ needs in order to successfully simulate its model using the $\\texttt{simulate}$ method.\n", + "\n", + "| Description | Code | Example value |\n", + "| :---: | --- | --- |\n", + "| Number of consumers of this type | $\\texttt{AgentCount}$ | $10000$ |\n", + "| Number of periods to simulate | $\\texttt{T_sim}$ | $120$ |\n", + "| Mean of initial log (normalized) assets | $\\texttt{aNrmInitMean}$ | $-6.0$ |\n", + "| Stdev of initial log (normalized) assets | $\\texttt{aNrmInitStd}$ | $1.0$ |\n", + "| Mean of initial log permanent income | $\\texttt{pLvlInitMean}$ | $0.0$ |\n", + "| Stdev of initial log permanent income | $\\texttt{pLvlInitStd}$ | $0.0$ |\n", + "| Aggregrate productivity growth factor | $\\texttt{PermGroFacAgg}$ | $1.0$ |\n", + "| Age after which consumers are automatically killed | $\\texttt{T_age}$ | $None$ |\n", + "\n", + "We have specified the model so that initial assets and permanent income are both distributed lognormally, with mean and standard deviation of the underlying normal distributions provided by the user.\n", + "\n", + "The parameter $\\texttt{PermGroFacAgg}$ exists for compatibility with more advanced models that employ aggregate productivity shocks; it can simply be set to 1.\n", + "\n", + "In infinite horizon models, it might be useful to prevent agents from living extraordinarily long lives through a fortuitous sequence of mortality shocks. We have thus provided the option of setting $\\texttt{T_age}$ to specify the maximum number of periods that a consumer can live before they are automatically killed (and replaced with a new consumer with initial state drawn from the specified distributions). This can be turned off by setting it to $\\texttt{None}$.\n", + "\n", + "The cell below puts these parameters into a dictionary, then gives them to $\\texttt{PFexample}$. Note that all of these parameters *could* have been passed as part of the original dictionary; we omitted them above for simplicity." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "# Create parameter values necessary for simulation\n", + "SimulationParams = {\n", + " \"AgentCount\" : 10000, # Number of agents of this type\n", + " \"T_sim\" : 120, # Number of periods to simulate\n", + " \"aNrmInitMean\" : -6.0, # Mean of log initial assets\n", + " \"aNrmInitStd\" : 1.0, # Standard deviation of log initial assets\n", + " \"pLvlInitMean\" : 0.0, # Mean of log initial permanent income\n", + " \"pLvlInitStd\" : 0.0, # Standard deviation of log initial permanent income\n", + " \"PermGroFacAgg\" : 1.0, # Aggregate permanent income growth factor\n", + " \"T_age\" : None, # Age after which simulated agents are automatically killed\n", + "}\n", + "\n", + "PFexample(**SimulationParams) # This implicitly uses the assignParameters method of AgentType" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To generate simulated data, we need to specify which variables we want to track the \"history\" of for this instance. To do so, we set the $\\texttt{track_vars}$ attribute of our $\\texttt{PerfForesightConsumerType}$ instance to be a list of strings with the simulation variables we want to track.\n", + "\n", + "In this model, valid elments of $\\texttt{track_vars}$ include $\\texttt{mNrmNow}$, $\\texttt{cNrmNow}$, $\\texttt{aNrmNow}$, and $\\texttt{pLvlNow}$. Because this model has no idiosyncratic shocks, our simulated data will be quite boring.\n", + "\n", + "### Generating simulated data\n", + "\n", + "Before simulating, the $\\texttt{initializeSim}$ method must be invoked. This resets our instance back to its initial state, drawing a set of initial $\\texttt{aNrmNow}$ and $\\texttt{pLvlNow}$ values from the specified distributions and storing them in the attributes $\\texttt{aNrmNow_init}$ and $\\texttt{pLvlNow_init}$. It also resets this instance's internal random number generator, so that the same initial states will be set every time $\\texttt{initializeSim}$ is called. In models with non-trivial shocks, this also ensures that the same sequence of shocks will be generated on every simulation run.\n", + "\n", + "Finally, the $\\texttt{simulate}$ method can be called." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "# Create PFexample object\n", + "PFexample.track_vars = ['mNrmNow']\n", + "PFexample.initializeSim()\n", + "PFexample.simulate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each simulation variable $\\texttt{X}$ named in $\\texttt{track_vars}$ will have the *history* of that variable for each agent stored in the attribute $\\texttt{X_hist}$ as an array of shape $(\\texttt{T_sim},\\texttt{AgentCount})$. To see that the simulation worked as intended, we can plot the mean of $m_t$ in each simulated period:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot market resources over time\n", + "plt.plot(np.mean(PFexample.mNrmNow_hist,axis=1))\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Mean normalized market resources')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A perfect foresight consumer can borrow against the PDV of his future income-- his human wealth-- and thus as time goes on, our simulated impatient agents approach the (very negative) steady state level of $m_t$ while being steadily replaced with consumers with roughly $m_t=1$.\n", + "\n", + "The slight wiggles in the plotted curve are due to consumers randomly dying and being replaced; their replacement will have an initial state drawn from the distributions specified by the user. To see the current distribution of ages, we can look at the attribute $\\texttt{t_age}$." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the CDF\n", + "N = PFexample.AgentCount\n", + "F = np.linspace(0.,1.,N)\n", + "plt.plot(np.sort(PFexample.t_age),F)\n", + "plt.xlabel('Current age of consumers')\n", + "plt.ylabel('Cumulative distribution')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The distribution is (discretely) exponential, with a point mass at 120 with consumers who have survived since the beginning of the simulation.\n", + "\n", + "One might wonder why HARK requires users to call $\\texttt{initializeSim}$ before calling $\\texttt{simulate}$: Why doesn't $\\texttt{simulate}$ just call $\\texttt{initializeSim}$ as its first step? We have broken up these two steps so that users can simulate some number of periods, change something in the environment, and then resume the simulation.\n", + "\n", + "When called with no argument, $\\texttt{simulate}$ will simulate the model for $\\texttt{T_sim}$ periods. The user can optionally pass an integer specifying the number of periods to simulate (which should not exceed $\\texttt{T_sim}$).\n", + "\n", + "In the cell below, we simulate our perfect foresight consumers for 80 periods, then seize a bunch of their assets (dragging their wealth even more negative), then simulate for the reamining 40 periods." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# The final resulting distribution is reasonably coherent\n", + "PFexample.initializeSim()\n", + "PFexample.simulate(80)\n", + "PFexample.aNrmNow += -5. # Adjust all simulated consumers' assets downward by 5\n", + "PFexample.simulate(40)\n", + "\n", + "plt.plot(np.mean(PFexample.mNrmNow_hist,axis=1))\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Mean normalized market resources')\n", + "plt.show()" + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": "779f6c5616b04b58baaaa0f6c348270c", + "lastKernelId": "a944b08f-0ae0-4c26-883f-9fab53a82ac3" + }, + "jupytext": { + "cell_metadata_filter": "collapsed,code_folding", + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ConsIndShockModel/PerfForesightConsumerType.py b/examples/ConsIndShockModel/PerfForesightConsumerType.py new file mode 100644 index 000000000..3b7eeea69 --- /dev/null +++ b/examples/ConsIndShockModel/PerfForesightConsumerType.py @@ -0,0 +1,319 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: collapsed,code_folding +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.2' +# jupytext_version: 1.2.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # PerfForesightConsumerType + +# %% {"code_folding": [0]} +# Initial imports and notebook setup, click arrow to show +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType +from HARK.utilities import plotFuncs +from time import clock +import matplotlib.pyplot as plt +import numpy as np +mystr = lambda number : "{:.4f}".format(number) + +# %% [markdown] +# The module $\texttt{HARK.ConsumptionSaving.ConsIndShockModel}$ concerns consumption-saving models with idiosyncratic shocks to (non-capital) income. All of the models assume CRRA utility with geometric discounting, no bequest motive, and income shocks that are either fully transitory or fully permanent. +# +# $\texttt{ConsIndShockModel}$ currently includes three models: +# 1. A very basic "perfect foresight" model with no uncertainty (shocks are zero). +# 2. A model with risk over transitory and permanent income shocks. +# 3. The model described in (2), with an interest rate for debt that differs from the interest rate for savings. +# +# This notebook provides documentation for the first of these three models. +# $\newcommand{\CRRA}{\rho}$ +# $\newcommand{\DiePrb}{\mathsf{D}}$ +# $\newcommand{\PermGroFac}{\Gamma}$ +# $\newcommand{\Rfree}{\mathsf{R}}$ +# $\newcommand{\DiscFac}{\beta}$ + +# %% [markdown] +# ## Statement of the model +# +# The $\texttt{PerfForesightConsumerType}$ class solves the problem of a consumer with Constant Relative Risk Aversion utility +# ${\CRRA}$ +# \begin{equation} +# U(C) = \frac{C^{1-\CRRA}}{1-\rho}, +# \end{equation} +# who has perfect foresight about everything except whether he will die between the end of period $t$ and the beginning of period $t+1$. Permanent labor income $P_t$ grows from period $t$ to period $t+1$ by factor $\PermGroFac_{t+1}$. The consumer faces no artificial borrowing constraint: He is able to borrow against his entire future stream of income. +# +# At the beginning of period $t$, the consumer has market resources $M_t$ (which includes both market wealth and currrent income) and must choose how much to consume $C_t$ and how much to retain in a riskless asset $A_t$, which will earn return factor $\Rfree$. The agent's flow of future utility $U(C_{t+n})$ from consumption is geometrically discounted by factor $\DiscFac$ per period. The consumer only experiences future value if he survives, which occurs with probability $1-\DiePrb_{t+1}$. +# +# For parallelism with the treatment of more complicated problems, we write the problem rather elaborately in Bellman form as: +# +# \begin{eqnarray*} +# V_t(M_t,P_t) &=& \max_{C_t}~U(C_t) ~+ \DiscFac (1 - \DiePrb_{t+1}) V_{t+1}(M_{t+1},P_{t+1}), \\ +# & s.t. & \\ +# A_t &=& M_t - C_t, \\ +# M_{t+1} &=& \Rfree A_t + Y_{t+1}, \\ +# Y_{t+1} &=& P_{t+1}, \\ +# P_{t+1} &=& \PermGroFac_{t+1} P_t. +# \end{eqnarray*} +# +# The parameters of the consumer's problem are the coefficient of relative risk aversion $\CRRA$, the intertemporal discount factor $\DiscFac$, an interest factor $\Rfree$, and age-varying sequences of the permanent income growth factor $\PermGroFac_t$ and survival probability $(1 - \DiePrb_t)$. [These lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA) show that under these assumptions the problem can be transformed into an equivalent problem stated in terms of *normalized* variables (represented in lower case); all real variables are divided by permanent income $P_t$ and value is divided by $P_t^{1-\CRRA}$. The Bellman form of the normalized model (see the lecture notes for details) is: +# +# \begin{eqnarray*} +# v_t(m_t) &=& \max_{c_t}~U(c_t) ~+ \DiscFac (1 - \DiePrb_{t+1}) \PermGroFac_{t+1}^{1-\CRRA} v_{t+1}(m_{t+1}), \\ +# & s.t. & \\ +# a_t &=& m_t - c_t, \\ +# m_{t+1} &=& a_t (\Rfree/\PermGroFac_{t+1} )+ 1. +# \end{eqnarray*} + +# %% [markdown] +# ## Solution method for PerfForesightConsumerType +# +# Because of the assumptions of CRRA utility, no risk other than mortality, and no artificial borrowing constraint, the problem has a closed form solution in which consumption is a linear function of resources, and the utility-inverse of the value function is also linear (that is, $u^{-1}(v)$ is linear in $m$). Details of the mathematical solution of this model can be found in the lecture notes [PerfForesightCRRA](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA). +# +# The one period problem for this model is solved by the function $\texttt{solveConsPerfForesight}$, which creates an instance of the class $\texttt{ConsPerfForesightSolver}$. To construct an instance of the class $\texttt{PerfForesightConsumerType}$, several parameters must be passed to this constructor. + +# %% [markdown] +# ## Example parameter values +# +# | Parameter | Description | Code | Example value | Time-varying? | +# | :---: | --- | --- | --- | --- | +# | $\DiscFac$ |Intertemporal discount factor | $\texttt{DiscFac}$ | $0.96$ | | +# | $\CRRA $ |Coefficient of relative risk aversion | $\texttt{CRRA}$ | $2.0$ | | +# | $\Rfree$ | Risk free interest factor | $\texttt{Rfree}$ | $1.03$ | | +# | $1 - \DiePrb_{t+1}$ |Survival probability | $\texttt{LivPrb}$ | $[0.98]$ | $\surd$ | +# |$\PermGroFac_{t+1}$|Permanent income growth factor|$\texttt{PermGroFac}$| $[1.01]$ | $\surd$ | +# |$T$| Number of periods in this type's "cycle" |$\texttt{T_cycle}$| $1$ | | +# |(none)| Number of times the "cycle" occurs |$\texttt{cycles}$| $0$ | | +# +# Note that the survival probability and income growth factor have time subscripts; likewise, the example values for these parameters are *lists* rather than simply single floats. This is because those parameters are in principle *time-varying*: their values can depend on which period of the problem the agent is in (for example, mortality probability depends on age). All time-varying parameters *must* be specified as lists, even when the model is being solved for an infinite horizon case where in practice the parameter takes the same value in every period. +# +# The last two parameters in the table specify the "nature of time" for this type: the number of (non-terminal) periods in this type's "cycle", and the number of times that the "cycle" occurs. *Every* subclass of $\texttt{AgentType}$ uses these two code parameters to define the nature of time. Here, $\texttt{T_cycle}$ has the value $1$, indicating that there is exactly one period in the cycle, while $\texttt{cycles}$ is $0$, indicating that the cycle is repeated in *infinite* number of times-- it is an infinite horizon model, with the same "kind" of period repeated over and over. +# +# In contrast, we could instead specify a life-cycle model by setting $\texttt{T_cycle}$ to $1$, and specifying age-varying sequences of income growth and survival probability. In all cases, the number of elements in each time-varying parameter should exactly equal $\texttt{T_cycle}$. +# +# The parameter $\texttt{AgentCount}$ specifies how many consumers there are of this *type*-- how many individuals have these exact parameter values and are *ex ante* homogeneous. This information is not relevant for solving the model, but is needed in order to simulate a population of agents, introducing *ex post* heterogeneity through idiosyncratic shocks. Of course, simulating a perfect foresight model is quite boring, as there are *no* idiosyncratic shocks other than death! +# +# The cell below defines a dictionary that can be passed to the constructor method for $\texttt{PerfForesightConsumerType}$, with the values from the table here. + +# %% {"code_folding": [0]} +PerfForesightDict = { + # Parameters actually used in the solution method + "CRRA" : 2.0, # Coefficient of relative risk aversion + "Rfree" : 1.03, # Interest factor on assets + "DiscFac" : 0.96, # Default intertemporal discount factor + "LivPrb" : [0.98], # Survival probability + "PermGroFac" :[1.01], # Permanent income growth factor + + # Parameters that characterize the nature of time + "T_cycle" : 1, # Number of periods in the cycle for this agent type + "cycles" : 0 # Number of times the cycle occurs (0 --> infinitely repeated) +} + +# %% [markdown] +# ## Inspecting the solution +# +# With the dictionary we have just defined, we can create an instance of $\texttt{PerfForesightConsumerType}$ by passing the dictionary to the class (as if the class were a function). This instance can then be solved by invoking its $\texttt{solve}$ method. + +# %% +PFexample = PerfForesightConsumerType(**PerfForesightDict) +PFexample.cycles = 0 +PFexample.solve() + +# %% [markdown] +# The $\texttt{solve}$ method fills in the instance's attribute $\texttt{solution}$ as a time-varying list of solutions to each period of the consumer's problem. In this case, $\texttt{solution}$ will be a list with exactly one instance of the class $\texttt{ConsumerSolution}$, representing the solution to the infinite horizon model we specified. + +# %% +print(PFexample.solution) + +# %% [markdown] +# Each element of $\texttt{solution}$ has a few attributes. To see all of them, we can use the $\texttt{vars}$ built in function: the consumption functions are instantiated in the attribute $\texttt{cFunc}$ of each element of $\texttt{ConsumerType.solution}$. This method creates a (time varying) attribute $\texttt{cFunc}$ that contains a list of consumption functions by age. + +# %% +print(vars(PFexample.solution[0])) + +# %% [markdown] +# The two most important attributes of a single period solution are the (normalized) consumption function $\texttt{cFunc}$ and the (normalized) value function $\texttt{vFunc}$; the marginal value function $\texttt{vPfunc}$ is also constructed. Let's plot those functions near the lower bound of the permissible state space (the attribute $\texttt{mNrmMin}$ tells us the lower bound of $m_t$ where the consumption function is defined). + +# %% +print('Linear perfect foresight consumption function:') +mMin = PFexample.solution[0].mNrmMin +plotFuncs(PFexample.solution[0].cFunc,mMin,mMin+10.) + +# %% +print('Perfect foresight value function:') +plotFuncs(PFexample.solution[0].vFunc,mMin+0.1,mMin+10.1) + +# %% [markdown] +# ## Solution Method +# +# +# ### Recursive Formula for $\kappa_{t}$ +# +# The paper [BufferStockTheory](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) has a few other results that are used in the solution code. One is [the recursive formula for the MPC](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/#MPCnvrs). Starting with the last period, in which $\kappa_{T}=1$, the inverse MPC's (and therefore the MPC's themselves) can be constructed using the recursive formula: +# +# \begin{align} +# \kappa_{t}^{-1} & = & 1 + \kappa_{t+1}^{-1}(\Rfree \beta)^{1/\rho}/G +# \end{align} +# +# ### Consumption Function +# +# For the perfect foresight problem, there is a well-known [analytical solution]( http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/#cFuncAnalytical) for the consumption function: Calling $o_{t}$ 'overall wealth' (including market wealth plus human wealth $h_{t}$) and designating the marginal propensity to consume in period $t$ by $\kappa_{t}$: +# +# \begin{align} +# \mathrm{c}_{t} & = o_{t}\kappa_{t} +# \end{align} +# +# and in our normalized model $o_{t} = m_{t}-1+h_{t}$ (the '-1' term subtracts off the normalized current income of 1 from market resources $m$ which were market wealth plus current income). +# +# ### Value Function +# +# A convenient feature of the perfect foresight problem is that the value function has a simple [analytical form](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/#vFuncAnalytical): +# +# \begin{align} +# \mathrm{v}_{t} & = \mathrm{u}(\mathrm{c}_{t}(m))\kappa_{t}^{-1}\\ +# &= \mathrm{u}(o_{t} \kappa_{t}) \kappa_{t}^{-1} \\ +# &= \mathrm{u}(o_{t})\kappa_{t}^{1-\rho} \kappa_{t}^{-1} \\ +# &= \mathrm{u}(o_{t})\kappa_{t}^{-\rho} +# \end{align} +# +# This means that the utility-inverse of the value function, ${\scriptsize \Lambda} \equiv \mathrm{u}^{-1}(\mathrm{v})$, is linear: +# +# \begin{align} +# \scriptsize \Lambda_{t} & = o_{t} \kappa_{t}^{-\rho/(1-\rho)} +# \end{align} +# +# When uncertainty or liquidity constraints are added to the problem, the ${\scriptsize \Lambda}$ function is no longer linear. But even in these cases, the utility-inverse of the value function is much better behaved (e.g., closer to linear; bounded over any feasible finite range of $m$) than the uninverted function (which, for example, approaches $-\infty$ as $m$ approaches its lower bound). +# +# Our procedure will therefore generically be to construct the inverse value function, and to obtain the value function from it by uninverting. That is, we construct an interpolating approximation of $\scriptsize \Lambda_{t}$ and compute value on-the-fly from +# +# \begin{align} +# \mathrm{v}_{t}(m) & = \mathrm{u}({\scriptsize \Lambda_{t}}(m)) +# \end{align} +# +# In this case, the interpolation is exact, not an approximation: We need only two points to construct a line, so we choose the minimum possible value of normalized market resources, $\texttt{mNrmMin}$, where $o_{t}=0$ so that $c_{t}=0$, and that minimum plus 1, where the inverted value function will have the value $\kappa_{t}^{-\rho/(1-\rho)}$. From these we construct $vFuncNvrs$ as a linear interpolating function (which automatically extrapolates to the whole number line). +# +# + +# %% [markdown] +# ## Checking Solution Conditions +# +# The code performs tests for whether the supplied parameter values meet various conditions that determine the properties of the solution. Some conditions (like the Finite Human Wealth Condition) are required for the model to have a sensible solution, and if these conditions are violated the code generates a warning message. Other conditions govern characteristics of the model like whether consumption is falling (whether the consumer is 'absolutely impatient'). All conditions can manually be performed using the syntax below. The function returns "False" if none of the key conditions has been violated. + +# %% +PFexample.checkConditions(verbose=True,public_call=True) + +# %% [markdown] +# An element of $\texttt{solution}$ also includes the (normalized) marginal value function $\texttt{vPfunc}$, and the lower and upper bounds of the marginal propensity to consume (MPC) $\texttt{MPCmin}$ and $\texttt{MPCmax}$. Note that with a linear consumption function, the MPC is constant, so its lower and upper bound are identical. + +# %% [markdown] +# ## Simulating the model +# +# Suppose we wanted to simulate many consumers who share the parameter values that we passed to $\texttt{PerfForesightConsumerType}$-- an *ex ante* homogeneous *type* of consumers. To do this, our instance would have to know *how many* agents there are of this type, as well as their initial levels of assets $a_t$ and permanent income $P_t$. +# +# ### Setting Parameters +# +# Let's fill in this information by passing another dictionary to $\texttt{PFexample}$ with simulation parameters. The table below lists the parameters that an instance of $\texttt{PerfForesightConsumerType}$ needs in order to successfully simulate its model using the $\texttt{simulate}$ method. +# +# | Description | Code | Example value | +# | :---: | --- | --- | +# | Number of consumers of this type | $\texttt{AgentCount}$ | $10000$ | +# | Number of periods to simulate | $\texttt{T_sim}$ | $120$ | +# | Mean of initial log (normalized) assets | $\texttt{aNrmInitMean}$ | $-6.0$ | +# | Stdev of initial log (normalized) assets | $\texttt{aNrmInitStd}$ | $1.0$ | +# | Mean of initial log permanent income | $\texttt{pLvlInitMean}$ | $0.0$ | +# | Stdev of initial log permanent income | $\texttt{pLvlInitStd}$ | $0.0$ | +# | Aggregrate productivity growth factor | $\texttt{PermGroFacAgg}$ | $1.0$ | +# | Age after which consumers are automatically killed | $\texttt{T_age}$ | $None$ | +# +# We have specified the model so that initial assets and permanent income are both distributed lognormally, with mean and standard deviation of the underlying normal distributions provided by the user. +# +# The parameter $\texttt{PermGroFacAgg}$ exists for compatibility with more advanced models that employ aggregate productivity shocks; it can simply be set to 1. +# +# In infinite horizon models, it might be useful to prevent agents from living extraordinarily long lives through a fortuitous sequence of mortality shocks. We have thus provided the option of setting $\texttt{T_age}$ to specify the maximum number of periods that a consumer can live before they are automatically killed (and replaced with a new consumer with initial state drawn from the specified distributions). This can be turned off by setting it to $\texttt{None}$. +# +# The cell below puts these parameters into a dictionary, then gives them to $\texttt{PFexample}$. Note that all of these parameters *could* have been passed as part of the original dictionary; we omitted them above for simplicity. + +# %% {"code_folding": [0]} +# Create parameter values necessary for simulation +SimulationParams = { + "AgentCount" : 10000, # Number of agents of this type + "T_sim" : 120, # Number of periods to simulate + "aNrmInitMean" : -6.0, # Mean of log initial assets + "aNrmInitStd" : 1.0, # Standard deviation of log initial assets + "pLvlInitMean" : 0.0, # Mean of log initial permanent income + "pLvlInitStd" : 0.0, # Standard deviation of log initial permanent income + "PermGroFacAgg" : 1.0, # Aggregate permanent income growth factor + "T_age" : None, # Age after which simulated agents are automatically killed +} + +PFexample(**SimulationParams) # This implicitly uses the assignParameters method of AgentType + +# %% [markdown] +# To generate simulated data, we need to specify which variables we want to track the "history" of for this instance. To do so, we set the $\texttt{track_vars}$ attribute of our $\texttt{PerfForesightConsumerType}$ instance to be a list of strings with the simulation variables we want to track. +# +# In this model, valid elments of $\texttt{track_vars}$ include $\texttt{mNrmNow}$, $\texttt{cNrmNow}$, $\texttt{aNrmNow}$, and $\texttt{pLvlNow}$. Because this model has no idiosyncratic shocks, our simulated data will be quite boring. +# +# ### Generating simulated data +# +# Before simulating, the $\texttt{initializeSim}$ method must be invoked. This resets our instance back to its initial state, drawing a set of initial $\texttt{aNrmNow}$ and $\texttt{pLvlNow}$ values from the specified distributions and storing them in the attributes $\texttt{aNrmNow_init}$ and $\texttt{pLvlNow_init}$. It also resets this instance's internal random number generator, so that the same initial states will be set every time $\texttt{initializeSim}$ is called. In models with non-trivial shocks, this also ensures that the same sequence of shocks will be generated on every simulation run. +# +# Finally, the $\texttt{simulate}$ method can be called. + +# %% {"code_folding": [0]} +# Create PFexample object +PFexample.track_vars = ['mNrmNow'] +PFexample.initializeSim() +PFexample.simulate() + +# %% [markdown] +# Each simulation variable $\texttt{X}$ named in $\texttt{track_vars}$ will have the *history* of that variable for each agent stored in the attribute $\texttt{X_hist}$ as an array of shape $(\texttt{T_sim},\texttt{AgentCount})$. To see that the simulation worked as intended, we can plot the mean of $m_t$ in each simulated period: + +# %% {"code_folding": [0]} +# Plot market resources over time +plt.plot(np.mean(PFexample.mNrmNow_hist,axis=1)) +plt.xlabel('Time') +plt.ylabel('Mean normalized market resources') +plt.show() + +# %% [markdown] +# A perfect foresight consumer can borrow against the PDV of his future income-- his human wealth-- and thus as time goes on, our simulated impatient agents approach the (very negative) steady state level of $m_t$ while being steadily replaced with consumers with roughly $m_t=1$. +# +# The slight wiggles in the plotted curve are due to consumers randomly dying and being replaced; their replacement will have an initial state drawn from the distributions specified by the user. To see the current distribution of ages, we can look at the attribute $\texttt{t_age}$. + +# %% {"code_folding": [0]} +# Plot the CDF +N = PFexample.AgentCount +F = np.linspace(0.,1.,N) +plt.plot(np.sort(PFexample.t_age),F) +plt.xlabel('Current age of consumers') +plt.ylabel('Cumulative distribution') +plt.show() + +# %% [markdown] +# The distribution is (discretely) exponential, with a point mass at 120 with consumers who have survived since the beginning of the simulation. +# +# One might wonder why HARK requires users to call $\texttt{initializeSim}$ before calling $\texttt{simulate}$: Why doesn't $\texttt{simulate}$ just call $\texttt{initializeSim}$ as its first step? We have broken up these two steps so that users can simulate some number of periods, change something in the environment, and then resume the simulation. +# +# When called with no argument, $\texttt{simulate}$ will simulate the model for $\texttt{T_sim}$ periods. The user can optionally pass an integer specifying the number of periods to simulate (which should not exceed $\texttt{T_sim}$). +# +# In the cell below, we simulate our perfect foresight consumers for 80 periods, then seize a bunch of their assets (dragging their wealth even more negative), then simulate for the reamining 40 periods. + +# %% {"code_folding": [0]} +# The final resulting distribution is reasonably coherent +PFexample.initializeSim() +PFexample.simulate(80) +PFexample.aNrmNow += -5. # Adjust all simulated consumers' assets downward by 5 +PFexample.simulate(40) + +plt.plot(np.mean(PFexample.mNrmNow_hist,axis=1)) +plt.xlabel('Time') +plt.ylabel('Mean normalized market resources') +plt.show() diff --git a/examples/GenIncProcessModel/GenIncProcessModel.ipynb b/examples/GenIncProcessModel/GenIncProcessModel.ipynb new file mode 100644 index 000000000..503dcddf6 --- /dev/null +++ b/examples/GenIncProcessModel/GenIncProcessModel.ipynb @@ -0,0 +1,539 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Permanent versus Persistent Income Shocks\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ], + "collapsed": true + }, + "outputs": [], + "source": [ + "# Initial imports and notebook setup\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import sys\n", + "import os\n", + "from copy import copy\n", + "from HARK.utilities import plotFuncs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from HARK.ConsumptionSaving.ConsGenIncProcessModel import *\n", + "import HARK.ConsumptionSaving.ConsumerParameters as Params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`ConsIndShockModel` assumes that income has a permanent component $p$ which is subject to \"permanent\" shocks:\n", + "\n", + " $\\log p_{t+1} = \\log p_{t} + \\log \\psi_{t+1}$\n", + "\n", + "Many papers in the literature instead examine models in which shocks merely have some persistence,\n", + "\n", + "$\\log p_{t+1} = \\gamma \\log p_{t} + \\log \\psi_{t+1}$\n", + "\n", + "where if $0 < \\gamma < 1$ then $\\lim_{n \\uparrow \\infty} \\mathbb{E}_{t}[\\log p_{t+n}]=0$ (which means that the level of $p$ reverts to its mean of $p=1$. The two models become identical as $\\gamma$ approaches 1.\n", + "\n", + "This notebook describes HARK's tools to solve models with persistent shocks.\n", + "\n", + "1. `ConsGenIncProcessModel` extends `ConsIndShockModel` by explicitly tracking persistent income $p_t$ as a state variable.\n", + "1. `IndShockExplicitPermIncConsumerType` is a type of consumer created for comparison and for whom we know for sure that their income process is one in which $\\gamma=1$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## General Income Process model\n", + "In `ConsGenIncProcessModel` the user can define a generic function $G$ that translates current $p_{t}$ into expected next period persistent income $p_{t+1}$ (subject to shocks). \n", + "\n", + "\n", + "The agent's problem can be written in Bellman form as:\n", + "\n", + "\\begin{eqnarray*}\n", + "v_t(M_t,p_t) &=& \\max_{c_t} U(c_t) + \\beta (1-\\mathsf{D}_{t+1}) \\mathbb{E}_{t} [v_{t+1}(M_{t+1}, p_{t+1}) ], \\\\\n", + "a_t &=& M_t - c_t, \\\\\n", + "a_t &\\geq& \\underline{a}, \\\\\n", + "M_{t+1} &=& R a_t + \\theta_{t+1}, \\\\\n", + "p_{t+1} &=& G_{t+1}(p_t)\\psi_{t+1}, \\\\\n", + "\\psi_t \\sim F_{\\psi t} &\\qquad& \\theta_t \\sim F_{\\theta t} \\\\\n", + " \\mathbb{E} [F_{\\psi t}] = 1 & & \\mathbb{E} [F_{\\psi t}] =1 \\\\\n", + "U(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n", + "\\end{eqnarray*}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The one-period problem for this model is solved by the function $\\texttt{solveConsGenIncProcess}$, which creates an instance of the class $\\texttt{ConsGenIncProcessSolver}$. The class $\\texttt{GenIncProcessConsumerType}$ extends $\\texttt{IndShockConsumerType}$ to represent agents in this model. To construct an instance of this class, several parameters must be passed to the constructor, as shown in the table below (parameters can be either \"primitive\" or \"constructed\" if they have already built-in capabilities from previous models).\n", + "\n", + "### Example parameter values to solve GenIncProcess model\n", + "\n", + "| Param | Description | Code | Value | Constructed |\n", + "| :---: | --- | --- | --- | :---: |\n", + "| $\\beta$ |Intertemporal discount factor | $\\texttt{DiscFac}$ | 0.96 | |\n", + "| $\\rho $ |Coefficient of relative risk aversion | $\\texttt{CRRA}$ | 2.0 | |\n", + "| $R $ | Risk free interest factor | $\\texttt{Rfree}$ | 1.03 | |\n", + "| $1 - \\mathsf{D}$ |Survival probability | $\\texttt{LivPrb}$ | [0.98] | |\n", + "| $\\underline{a} $ |Artificial borrowing constraint | $\\texttt{BoroCnstArt}$ | 0.0 | | \n", + "| $(none) $ |Indicator of whether $\\texttt{vFunc}$ should be computed | $\\texttt{vFuncBool}$ | 'True' | |\n", + "| $(none)$ |Indicator of whether $\\texttt{cFunc}$ should use cubic lines | $\\texttt{CubicBool}$ | 'False' | |\n", + "|$F $ |A list containing three arrays of floats, representing a discrete
approximation to the income process:
event probabilities, persistent shocks, transitory shocks | $\\texttt{IncomeDstn}$ | - |$\\surd$ |\n", + "| $G$ |Expected persistent income next period | $\\texttt{pLvlNextFunc}$ | - | $\\surd$ |\n", + "| $ (none) $ |Array of time-varying persistent income levels | $\\texttt{pLvlGrid}$ | - |$\\surd$ |\n", + "| $ (none) $ | Array of \"extra\" end-of-period asset values | $\\texttt{aXtraGrid}$ | - |$\\surd$ |\n", + "\n", + "### Constructed inputs to solve GenIncProcess\n", + "The \"constructed\" inputs above are using expected attributes and are drawn on various methods as explained below.\n", + "\n", + "\n", + "* The input $\\texttt{IncomeDstn}$ is created by the method $\\texttt{updateIncomeProcess}$ which inherits from $\\texttt{IndShockConsumerType}$. (*hyperlink to that noteboook*)\n", + "\n", + "* The input $\\texttt{pLvlNextFunc}$ is created by the method $\\texttt{updatepLvlNextFunc}$ which uses the initial sequence of $\\texttt{pLvlNextFunc}$, the mean and standard deviation of the (log) initial permanent income, $\\texttt{pLvlInitMean}$ and $\\texttt{pLvlInitStd}$. \n", + "In this model, the method creates a trivial $\\texttt{pLvlNextFunc}$ attribute with no persistent income dynamics. But we can overwrite it by subclasses in order to make an AR1 income process for example. \n", + "\n", + "\n", + "* The input $\\texttt{pLvlGrid}$ is created by the method $\\texttt{updatepLvlGrid}$ which updates the grid of persistent income levels for infinite horizon models (cycles=0) and lifecycle models (cycles=1). This method draws on the initial distribution of persistent income, the $\\texttt{pLvlNextFuncs}$, $\\texttt{pLvlInitMean}$, $\\texttt{pLvlInitStd}$ and the attribute $\\texttt{pLvlPctiles}$ (percentiles of the distribution of persistent income). It then uses a simulation approach to generate the $\\texttt{pLvlGrid}$ at each period of the cycle.\n", + "\n", + "\n", + "* The input $\\texttt{aXtraGrid}$ is created by $\\texttt{updateAssetsGrid}$ which updates the agent's end-of-period assets grid by constructing a multi-exponentially spaced grid of aXtra values, based on $\\texttt{aNrmInitMean}$ and $\\texttt{aNrmInitStd}$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Consumer with Explicit Permanent Income\n", + "\n", + "Let's make an example of our generic model above with an \"explicit permanent income\" consumer who experiences idiosyncratic shocks to permanent and transitory, and faces permanent income growth.\n", + "\n", + "The agent's problem can be written in Bellman form as:\n", + "\n", + "\\begin{eqnarray*}\n", + "v_t(M_t,p_t) &=& \\max_{c_t} U(c_t) + \\beta (1-\\mathsf{D}_{t+1}) \\mathbb{E} [v_{t+1}(M_{t+1}, p_{t+1}) ], \\\\\n", + "a_t &=& M_t - c_t, \\\\\n", + "a_t &\\geq& \\underline{a}, \\\\\n", + "M_{t+1} &=& R/(\\Gamma_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", + "p_{t+1} &=& G_{t+1}(p_t)\\psi_{t+1}, \\\\\n", + "\\psi \\sim F_{\\psi}, \\mathbb{E} [F_{\\psi t}] = 1 &\\qquad& \\theta_t \\sim F_{\\theta}, \\mathbb{E} [F_{\\psi}] = 1, \\\\\n", + "U(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n", + "\\end{eqnarray*}\n", + "\n", + "\n", + "This agent type is identical to an $\\texttt{IndShockConsumerType}$ but for explicitly tracking $\\texttt{pLvl}$ as a state variable during solution as shown in the mathematical representation of GenIncProcess model. \n", + "\n", + "To construct $\\texttt{IndShockExplicitPermIncConsumerType}$ as an instance of $\\texttt{GenIncProcessConsumerType}$, we need to pass additional parameters to the constructor as shown in the table below.\n", + "\n", + "### Additional parameters to solve ExplicitPermInc model\n", + "\n", + "| Param | Description | Code | Value | Constructed |\n", + "| :---: | --- | --- | --- | :---: |\n", + "|(none)|percentiles of the distribution of persistent income|$\\texttt{pLvlPctiles}$|||\n", + "| $G$ |Expected persistent income next period | $\\texttt{pLvlNextFunc}$ | - | $\\surd$ |\n", + "|$\\Gamma $|Permanent income growth factor|$\\texttt{PermGroFac}$|[1.0]| |\n", + "\n", + "\n", + "### Constructed inputs to solve ExplicitPermInc\n", + "\n", + "* In this \"explicit permanent income\" model, we overwrite the method $\\texttt{updatepLvlNextFunc}$ to create $\\texttt{pLvlNextFunc}$ as a sequence of linear functions, indicating constant expected permanent income growth across permanent income levels. This method uses the attribute $\\texttt{PermGroFac}$, and installs a special retirement function when it exists.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ], + "collapsed": true + }, + "outputs": [], + "source": [ + "# This cell defines a dictionary to make an instance of \"explicit permanent income\" consumer.\n", + "GenIncDictionary = { \n", + " \"CRRA\": 2.0, # Coefficient of relative risk aversion\n", + " \"Rfree\": 1.03, # Interest factor on assets\n", + " \"DiscFac\": 0.96, # Intertemporal discount factor\n", + " \"LivPrb\" : [0.98], # Survival probability\n", + " \"AgentCount\" : 10000, # Number of agents of this type (only matters for simulation)\n", + " \"aNrmInitMean\" : 0.0, # Mean of log initial assets (only matters for simulation)\n", + " \"aNrmInitStd\" : 1.0, # Standard deviation of log initial assets (only for simulation)\n", + " \"pLvlInitMean\" : 0.0, # Mean of log initial permanent income (only matters for simulation)\n", + " \"pLvlInitStd\" : 0.0, # Standard deviation of log initial permanent income (only matters for simulation)\n", + " \"PermGroFacAgg\" : 1.0, # Aggregate permanent income growth factor (only matters for simulation)\n", + " \"T_age\" : None, # Age after which simulated agents are automatically killed\n", + " \"T_cycle\" : 1, # Number of periods in the cycle for this agent type\n", + "# Parameters for constructing the \"assets above minimum\" grid\n", + " \"aXtraMin\" : 0.001, # Minimum end-of-period \"assets above minimum\" value\n", + " \"aXtraMax\" : 30, # Maximum end-of-period \"assets above minimum\" value \n", + " \"aXtraExtra\" : [0.005,0.01], # Some other value of \"assets above minimum\" to add to the grid\n", + " \"aXtraNestFac\" : 3, # Exponential nesting factor when constructing \"assets above minimum\" grid\n", + " \"aXtraCount\" : 48, # Number of points in the grid of \"assets above minimum\"\n", + "# Parameters describing the income process\n", + " \"PermShkCount\" : 7, # Number of points in discrete approximation to permanent income shocks\n", + " \"TranShkCount\" : 7, # Number of points in discrete approximation to transitory income shocks\n", + " \"PermShkStd\" : [0.1], # Standard deviation of log permanent income shocks\n", + " \"TranShkStd\" : [0.1], # Standard deviation of log transitory income shocks\n", + " \"UnempPrb\" : 0.05, # Probability of unemployment while working\n", + " \"UnempPrbRet\" : 0.005, # Probability of \"unemployment\" while retired\n", + " \"IncUnemp\" : 0.3, # Unemployment benefits replacement rate\n", + " \"IncUnempRet\" : 0.0, # \"Unemployment\" benefits when retired\n", + " \"tax_rate\" : 0.0, # Flat income tax rate\n", + " \"T_retire\" : 0, # Period of retirement (0 --> no retirement)\n", + " \"BoroCnstArt\" : 0.0, # Artificial borrowing constraint; imposed minimum level of end-of period assets\n", + " \"CubicBool\" : False, # Use cubic spline interpolation when True, linear interpolation when False\n", + " \"vFuncBool\" : True, # Whether to calculate the value function during solution \n", + "# More parameters specific to \"Explicit Permanent income\" shock model\n", + " \"cycles\": 0,\n", + " \"pLvlPctiles\" : np.concatenate(([0.001, 0.005, 0.01, 0.03], np.linspace(0.05, 0.95, num=19),[0.97, 0.99, 0.995, 0.999])),\n", + " \"PermGroFac\" : [1.0], # Permanent income growth factor - long run permanent income growth doesn't work yet \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now create an instance of the type of consumer we are interested in and solve this agent's problem with an infinite horizon (cycles=0)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Make and solve an example \"explicit permanent income\" consumer with idiosyncratic shocks\n", + "ExplicitExample = IndShockExplicitPermIncConsumerType(**GenIncDictionary)\n", + " \n", + "print('Here, the lowest percentile is ' + str(GenIncDictionary['pLvlPctiles'][0]*100))\n", + "print('and the highest percentile is ' + str(GenIncDictionary['pLvlPctiles'][-1]*100) + '.\\n')\n", + " \n", + "ExplicitExample.solve()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below, we generate a plot of the consumption function for explicit permanent income consumer at different income levels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ], + "collapsed": true + }, + "outputs": [], + "source": [ + "# Plot the consumption function at various permanent income levels.\n", + "print('Consumption function by pLvl for explicit permanent income consumer:')\n", + "pLvlGrid = ExplicitExample.pLvlGrid[0]\n", + "mLvlGrid = np.linspace(0,20,300)\n", + "for p in pLvlGrid:\n", + " M_temp = mLvlGrid + ExplicitExample.solution[0].mLvlMin(p)\n", + " C = ExplicitExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp))\n", + " plt.plot(M_temp,C)\n", + "plt.xlim(0.,20.)\n", + "plt.xlabel('Market resource level mLvl')\n", + "plt.ylabel('Consumption level cLvl')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Permanent income normalized\n", + "\n", + "An alternative model is to normalize it by dividing all variables by permanent income $p_t$ and solve the model again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Make and solve an example of normalized model\n", + "NormalizedExample = IndShockConsumerType(**GenIncDictionary)\n", + "NormalizedExample.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Compare the normalized problem with and without explicit permanent income and plot the consumption functions\n", + "print('Normalized consumption function by pLvl for explicit permanent income consumer:')\n", + "pLvlGrid = ExplicitExample.pLvlGrid[0]\n", + "mNrmGrid = np.linspace(0,20,300)\n", + "for p in pLvlGrid:\n", + " M_temp = mNrmGrid*p + ExplicitExample.solution[0].mLvlMin(p)\n", + " C = ExplicitExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp))\n", + " plt.plot(M_temp/p,C/p)\n", + "\n", + "plt.xlim(0.,20.)\n", + "plt.xlabel('Normalized market resources mNrm')\n", + "plt.ylabel('Normalized consumption cNrm')\n", + "plt.show()\n", + "\n", + "print('Consumption function for normalized problem (without explicit permanent income):')\n", + "mNrmMin = NormalizedExample.solution[0].mNrmMin\n", + "plotFuncs(NormalizedExample.solution[0].cFunc,mNrmMin,mNrmMin+20.)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The figures above show that the normalized consumption function for the \"explicit permanent income\" consumer is almost identical for every permanent income level (and the same as the normalized problem's $\\texttt{cFunc}$), but is less accurate due to extrapolation outside the bounds of $\\texttt{pLvlGrid}$. \n", + "\n", + "The \"explicit permanent income\" solution deviates from the solution to the normalized problem because of errors from extrapolating beyond the bounds of the $\\texttt{pLvlGrid}$. The error is largest for $\\texttt{pLvl}$ values near the upper and lower bounds, and propagates toward the center of the distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Plot the value function at various permanent income levels\n", + "if ExplicitExample.vFuncBool:\n", + " pGrid = np.linspace(0.1,3.0,24)\n", + " M = np.linspace(0.001,5,300)\n", + " for p in pGrid:\n", + " M_temp = M+ExplicitExample.solution[0].mLvlMin(p)\n", + " C = ExplicitExample.solution[0].vFunc(M_temp,p*np.ones_like(M_temp))\n", + " plt.plot(M_temp,C)\n", + " plt.ylim([-200,0])\n", + " plt.xlabel('Market resource level mLvl')\n", + " plt.ylabel('Value v')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Simulate many periods to get to the stationary distribution\n", + "ExplicitExample.T_sim = 500\n", + "ExplicitExample.track_vars = ['mLvlNow','cLvlNow','pLvlNow']\n", + "ExplicitExample.initializeSim()\n", + "ExplicitExample.simulate()\n", + "plt.plot(np.mean(ExplicitExample.mLvlNow_hist,axis=1))\n", + "plt.xlabel('Simulated time period')\n", + "plt.ylabel('Average market resources mLvl')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 2. Persistent income shock consumer\n", + "\n", + "\n", + "Class to solve consumption-saving models with idiosyncratic shocks to income in which shocks are persistent and transitory. This model extends $\\texttt{ConsGenIndShockModel}$ by allowing (log) persistent income to follow an AR(1) process.\n", + "\n", + "The agent's problem can be written in Bellman form as:\n", + "\n", + "\\begin{eqnarray*}\n", + "v_t(M_t,p_t) &=& \\max_{c_t} U(c_t) + \\beta (1-\\mathsf{D}_{t+1}) \\mathbb{E} [v_{t+1}(M_{t+1}, p_{t+1}) ], \\\\\n", + "a_t &=& M_t - c_t, \\\\\n", + "a_t &\\geq& \\underline{a}, \\\\\n", + "M_{t+1} &=& R a_t + \\theta_{t+1}, \\\\\n", + "log(p_{t+1}) &=& \\varphi log(p_t)+(1-\\varphi log(\\overline{p}_{t+1} ) +log(\\Gamma_{t+1})+log(\\psi_{t+1}), \\\\\n", + "\\\\\n", + "\\psi_t \\sim F_{\\psi t} &\\qquad& \\theta_t \\sim F_{\\theta t}, \\mathbb{E} [F_{\\psi t}] = 1 \\\\\n", + "\\end{eqnarray*}\n", + "\n", + "### Additional parameters to solve PersistentShock model\n", + "\n", + "| Param | Description | Code | Value | Constructed |\n", + "| :---: | --- | --- | --- | :---: |\n", + "|$\\varphi$|Serial correlation coefficient for permanent income|$\\texttt{PrstIncCorr}$|0.98||\n", + "||||||\n", + "\n", + "### Constructed inputs to solve PersistentShock\n", + "\n", + "* For this model, we overwrite the method $\\texttt{updatepLvlNextFunc}$ to create the input $\\texttt{pLvlNextFunc}$ as a sequence of AR1-style functions. The method uses now the attributes $\\texttt{PermGroFac}$ and $\\texttt{PrstIncCorr}$. If cycles=0, the product of $\\texttt{PermGroFac}$ across all periods must be 1.0, otherwise this method is invalid.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ], + "collapsed": true + }, + "outputs": [], + "source": [ + "# Make a dictionary for the \"persistent idiosyncratic shocks\" model\n", + "PrstIncCorr = 0.98 # Serial correlation coefficient for persistent income\n", + "\n", + "persistent_shocks = copy(GenIncDictionary)\n", + "persistent_shocks['PrstIncCorr'] = PrstIncCorr\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The $\\texttt{PersistentShockConsumerType}$ class solves the problem of a consumer facing idiosyncratic shocks to his persistent and transitory income, and for which the (log) persistent income follows an AR1 process rather than random walk." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Make and solve an example of \"persistent idisyncratic shocks\" consumer\n", + "PersistentExample = PersistentShockConsumerType(**persistent_shocks)\n", + "PersistentExample.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Plot the consumption function at various levels of persistent income pLvl\n", + "print('Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of ' + str(PersistentExample.PrstIncCorr) + ':')\n", + "pLvlGrid = PersistentExample.pLvlGrid[0]\n", + "mLvlGrid = np.linspace(0,20,300)\n", + "for p in pLvlGrid:\n", + " M_temp = mLvlGrid + PersistentExample.solution[0].mLvlMin(p)\n", + " C = PersistentExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp))\n", + " plt.plot(M_temp,C)\n", + "plt.xlim(0.,20.)\n", + "plt.xlabel('Market resource level mLvl')\n", + "plt.ylabel('Consumption level cLvl')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Plot the value function at various persistent income levels\n", + "if PersistentExample.vFuncBool:\n", + " pGrid = PersistentExample.pLvlGrid[0]\n", + " M = np.linspace(0.001,5,300)\n", + " for p in pGrid:\n", + " M_temp = M+PersistentExample.solution[0].mLvlMin(p)\n", + " C = PersistentExample.solution[0].vFunc(M_temp,p*np.ones_like(M_temp))\n", + " plt.plot(M_temp,C)\n", + " plt.ylim([-200,0])\n", + " plt.xlabel('Market resource level mLvl')\n", + " plt.ylabel('Value v')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Simulate some data\n", + "PersistentExample.T_sim = 500\n", + "PersistentExample.track_vars = ['mLvlNow','cLvlNow','pLvlNow']\n", + "PersistentExample.initializeSim()\n", + "PersistentExample.simulate()\n", + "plt.plot(np.mean(PersistentExample.mLvlNow_hist,axis=1))\n", + "plt.xlabel('Simulated time period')\n", + "plt.ylabel('Average market resources mLvl')\n", + "plt.show()" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py", + "metadata_filter": {"cells": "collapsed"} + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb new file mode 100644 index 000000000..4f90992c9 --- /dev/null +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -0,0 +1,576 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A Gentle Introduction to HARK\n", + "\n", + "This notebook provides a simple, hands-on tutorial for first time HARK users -- and potentially first time Python users. It does not go \"into the weeds\" - we have hidden some code cells that do boring things that you don't need to digest on your first experience with HARK. Our aim is to convey a feel for how the toolkit works.\n", + "\n", + "For readers for whom this is your very first experience with Python, we have put important Python concepts in **boldface**. For those for whom this is the first time they have used a Jupyter notebook, we have put Jupyter instructions in _italics_. Only cursory definitions (if any) are provided here. If you want to learn more, there are many online Python and Jupyter tutorials." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "# This cell has a bit of initial setup. You can click the triangle to the left to expand it.\n", + "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n", + "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n", + "# The most common problem beginners have is to execute a cell before all its predecessors\n", + "# If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm\n", + "import numpy as np\n", + "import HARK \n", + "from time import clock\n", + "from copy import deepcopy\n", + "mystr = lambda number : \"{:.4f}\".format(number)\n", + "from HARK.utilities import plotFuncs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Your First HARK Model: Perfect Foresight\n", + "\n", + "$$\\newcommand{\\CRRA}{\\rho}\\newcommand{\\DiscFac}{\\beta}$$\n", + "We start with almost the simplest possible consumption model: A consumer with CRRA utility \n", + "\n", + "\\begin{equation}\n", + "U(C) = \\frac{C^{1-\\CRRA}}{1-\\rho}\n", + "\\end{equation}\n", + "\n", + "has perfect foresight about everything except the (stochastic) date of death, which occurs with constant probability implying a \"survival probability\" $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb < 1$. Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and currrent income) and must choose how much of those resources to consume $C_t$ and how much to retain in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$. Between periods, the agent dies with probability $\\mathsf{D}_t$, ending his problem.\n", + "\n", + "The agent's problem can be written in Bellman form as:\n", + "\n", + "\\begin{eqnarray*}\n", + "V_t(M_t,P_t) &=& \\max_{C_t}~U(C_t) + \\beta \\aleph V_{t+1}(M_{t+1},P_{t+1}), \\\\\n", + "& s.t. & \\\\\n", + "%A_t &=& M_t - C_t, \\\\\n", + "M_{t+1} &=& R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n", + "P_{t+1} &=& \\Gamma_{t+1} P_t, \\\\\n", + "\\end{eqnarray*}\n", + "\n", + "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\mathsf{\\aleph}_t\\}$. To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an $\\textit{infinite horizon}$ consumer with constant income growth and survival probability.\n", + "\n", + "## Representing Agents in HARK\n", + "\n", + "HARK represents agents solving this type of problem as $\\textbf{instances}$ of the $\\textbf{class}$ $\\texttt{PerfForesightConsumerType}$, a $\\textbf{subclass}$ of $\\texttt{AgentType}$. To make agents of this class, we must import the class itself into our workspace. (Run the cell below in order to do this)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The $\\texttt{PerfForesightConsumerType}$ class contains within itself the python code that constructs the solution for the perfect foresight model we are studying here, as specifically articulated in [these lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/). \n", + "\n", + "To create an instance of $\\texttt{PerfForesightConsumerType}$, we simply call the class as if it were a function, passing as arguments the specific parameter values we want it to have. In the hidden cell below, we define a $\\textbf{dictionary}$ named $\\texttt{PF_dictionary}$ with these parameter values:\n", + "\n", + "| Param | Description | Code | Value |\n", + "| :---: | --- | --- | :---: |\n", + "| $\\rho$ | Relative risk aversion | $\\texttt{CRRA}$ | 2.5 |\n", + "| $\\beta$ | Discount factor | $\\texttt{DiscFac}$ | 0.96 |\n", + "| $R$ | Risk free interest factor | $\\texttt{Rfree}$ | 1.03 |\n", + "| $\\newcommand{\\LivFac}{\\aleph}\\LivFac$ | Survival probability | $\\texttt{LivPrb}$ | 0.98 |\n", + "| $\\Gamma$ | Income growth factor | $\\texttt{PermGroFac}$ | 1.01 |\n", + "\n", + "\n", + "For now, don't worry about the specifics of dictionaries. All you need to know is that a dictionary lets us pass many arguments wrapped up in one simple data structure." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "# This cell defines a parameter dictionary. You can expand it if you want to see what that looks like.\n", + "PF_dictionary = {\n", + " 'CRRA' : 2.5,\n", + " 'DiscFac' : 0.96,\n", + " 'Rfree' : 1.03,\n", + " 'LivPrb' : [0.98],\n", + " 'PermGroFac' : [1.01],\n", + " 'T_cycle' : 1,\n", + " 'cycles' : 0,\n", + " 'AgentCount' : 10000\n", + "}\n", + "\n", + "# To those curious enough to open this hidden cell, you might notice that we defined\n", + "# a few extra parameters in that dictionary: T_cycle, cycles, and AgentCount. Don't\n", + "# worry about these for now." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make an **object** named $\\texttt{PFexample}$ which is an **instance** of the $\\texttt{PerfForesightConsumerType}$ class. The object $\\texttt{PFexample}$ will bundle together the abstract mathematical description of the solution embodied in $\\texttt{PerfForesightConsumerType}$, and the specific set of parameter values defined in $\\texttt{PF_dictionary}$. Such a bundle is created passing $\\texttt{PF_dictionary}$ to the class $\\texttt{PerfForesightConsumerType}$:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "PFexample = PerfForesightConsumerType(**PF_dictionary) \n", + "# the asterisks ** basically say \"here come some arguments\" to PerfForesightConsumerType" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In $\\texttt{PFexample}$, we now have _defined_ the problem of a particular infinite horizon perfect foresight consumer who knows how to solve this problem. \n", + "\n", + "## Solving an Agent's Problem\n", + "\n", + "To tell the agent actually to solve the problem, we call the agent's $\\texttt{solve}$ **method**. (A *method** is essentially a function that an object runs that affects the object's own internal characteristics -- in this case, the method adds the consumption function to the contents of $\\texttt{PFexample}$.)\n", + "\n", + "The cell below calls the $\\texttt{solve}$ method for $\\texttt{PFexample}$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "PFexample.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Running the $\\texttt{solve}$ method creates the **attribute** of $\\texttt{PFexample}$ named $\\texttt{solution}$. In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (paramterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem. \n", + "\n", + "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem. The consumption function stored as the first element (element 0) of the solution list can be retrieved by:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PFexample.solution[0].cFunc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the results proven in the associated [the lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$. \n", + "\n", + "This is why $\\texttt{cFunc}$ can be represented by a linear interpolation. It can be plotted between an $m$ ratio of 0 and 10 using the command below." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mPlotTop=10\n", + "plotFuncs(PFexample.solution[0].cFunc,0.,mPlotTop)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure illustrates one of the surprising features of the perfect foresight model: A person with zero money should be spending at a rate more than double their income (that is, $\\texttt{cFunc}(0.) \\approx 2.08$ - the intersection on the vertical axis). How can this be?\n", + "\n", + "The answer is that we have not incorporated any constraint that would prevent the agent from borrowing against the entire PDV of future earnings-- human wealth. How much is that? What's the minimum value of $m_t$ where the consumption function is defined? We can check by retrieving the $\\texttt{hNrm}$ **attribute** of the solution, which calculates the value of human wealth normalized by permanent income:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This agent's human wealth is 50.49994992551661 times his current income level.\n", + "This agent's consumption function is defined (consumption is positive) down to m_t = -50.49994992551661\n" + ] + } + ], + "source": [ + "humanWealth = PFexample.solution[0].hNrm\n", + "mMinimum = PFexample.solution[0].mNrmMin\n", + "print(\"This agent's human wealth is \" + str(humanWealth) + ' times his current income level.')\n", + "print(\"This agent's consumption function is defined (consumption is positive) down to m_t = \" + str(mMinimum))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yikes! Let's take a look at the bottom of the consumption function. In the cell below, set the bounds of the $\\texttt{plotFuncs}$ function to display down to the lowest defined value of the consumption function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR FIRST HANDS-ON EXERCISE!\n", + "# Fill in the value for \"mPlotBottom\" to plot the consumption function from the point where it is zero.\n", + "plotFuncs(PFexample.solution[0].cFunc,mPlotBottom,mPlotTop)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Changing Agent Parameters\n", + "\n", + "Suppose you wanted to change one (or more) of the parameters of the agent's problem and see what that does. We want to compare consumption functions before and after we change parameters, so let's make a new instance of $\\texttt{PerfForesightConsumerType}$ by copying $\\texttt{PFexample}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NewExample = deepcopy(PFexample)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python, you can set an **attribute** of an object just like any other variable. For example, we could make the new agent less patient:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NewExample.DiscFac = 0.90\n", + "NewExample.solve()\n", + "mPlotBottom = mMinimum\n", + "plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],mPlotBottom,mPlotTop)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Note that you can pass a **list** of functions to $\\texttt{plotFuncs}$ as the first argument rather than just a single function. Lists are written inside of [square brackets].)\n", + "\n", + "Let's try to deal with the \"problem\" of massive human wealth by making another consumer who has essentially no future income. We can virtually eliminate human wealth by making the permanent income growth factor $\\textit{very}$ small.\n", + "\n", + "In $\\texttt{PFexample}$, the agent's income grew by 1 percent per period -- his $\\texttt{PermGroFac}$ took the value 1.01. What if our new agent had a growth factor of 0.01 -- his income __shrinks__ by 99 percent each period? In the cell below, set $\\texttt{NewExample}$'s discount factor back to its original value, then set its $\\texttt{PermGroFac}$ attribute so that the growth factor is 0.01 each period.\n", + "\n", + "Important: Recall that the model at the top of this document said that an agent's problem is characterized by a sequence of income growth factors, but we tabled that concept. Because $\\texttt{PerfForesightConsumerType}$ treats $\\texttt{PermGroFac}$ as a __time-varying__ attribute, it must be specified as a **list** (with a single element in this case)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Revert NewExample's discount factor and make his future income minuscule\n", + "# print(\"your lines here\")\n", + "\n", + "# Compare the old and new consumption functions\n", + "plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],0.,10.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now $\\texttt{NewExample}$'s consumption function has the same slope (MPC) as $\\texttt{PFexample}$, but it emanates from (almost) zero-- he has basically no future income to borrow against!\n", + "\n", + "If you'd like, use the cell above to alter $\\texttt{NewExample}$'s other attributes (relative risk aversion, etc) and see how the consumption function changes. However, keep in mind that \\textit{no solution exists} for some combinations of parameters. HARK should let you know if this is the case if you try to solve such a model.\n", + "\n", + "\n", + "## Your Second HARK Model: Adding Income Shocks\n", + "\n", + "Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines. Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints.\n", + "\n", + "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$. Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$. As with the perfect foresight problem, this model can be framed in terms of __normalized__ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory).\n", + "\n", + "\\begin{eqnarray*}\n", + "v_t(m_t) &=& \\max_{c_t} ~ U(c_t) ~ + \\phantom{\\LivFac} \\beta \\mathbb{E} [(\\Gamma_{t+1}\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ], \\\\\n", + "a_t &=& m_t - c_t, \\\\\n", + "a_t &\\geq& \\underset{\\bar{}}{a}, \\\\\n", + "m_{t+1} &=& R/(\\Gamma_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", + "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &=& 1, \\\\\n", + "u(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n", + "\\end{eqnarray*}\n", + "\n", + "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$. To create an $\\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shocks. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks? Can't the joint distribution of permanent and transitory shocks be just about anything?\n", + "\n", + "_Yes_, and HARK can handle whatever correlation structure a user might care to specify. However, the default behavior of $\\texttt{IndShockConsumerType}$ is that the distribution of permanent income shocks is mean one lognormal, and the distribution of transitory shocks is mean one lognormal augmented with a point mass representing unemployment. The distributions are independent of each other by default, and by default are approximated with $N$ point equiprobable distributions.\n", + "\n", + "Let's make an infinite horizon instance of $\\texttt{IndShockConsumerType}$ with the same parameters as our original perfect foresight agent, plus the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary:\n", + "\n", + "\n", + "| Param | Description | Code | Value |\n", + "| :---: | --- | --- | :---: |\n", + "| \\underline{a} | Artificial borrowing constraint | $\\texttt{BoroCnstArt}$ | 0.0 |\n", + "| $\\sigma_\\psi$ | Underlying stdev of permanent income shocks | $\\texttt{PermShkStd}$ | 0.1 |\n", + "| $\\sigma_\\theta$ | Underlying stdev of transitory income shocks | $\\texttt{TranShkStd}$ | 0.1 |\n", + "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | 7 |\n", + "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | 7 |\n", + "| $\\mho$ | Unemployment probability | $\\texttt{UnempPrb}$ | 0.05 |\n", + "| $\\underset{\\bar{}}{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | 0.3 |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n", + "\n", + "IndShockDictionary = {\n", + " 'CRRA': 2.5, # The dictionary includes our original parameters...\n", + " 'Rfree': 1.03,\n", + " 'DiscFac': 0.96,\n", + " 'LivPrb': [0.98],\n", + " 'PermGroFac': [1.01],\n", + " 'PermShkStd': [0.1], # ... and the new parameters for constructing the income process. \n", + " 'PermShkCount': 7,\n", + " 'TranShkStd': [0.1],\n", + " 'TranShkCount': 7,\n", + " 'UnempPrb': 0.05,\n", + " 'IncUnemp': 0.3,\n", + " 'BoroCnstArt': 0.0,\n", + " 'aXtraMin': 0.001, # aXtra parameters specify how to construct the grid of assets.\n", + " 'aXtraMax': 50., # Don't worry about these for now\n", + " 'aXtraNestFac': 3,\n", + " 'aXtraCount': 48,\n", + " 'aXtraExtra': [None],\n", + " 'vFuncBool': False, # These booleans indicate whether the value function should be calculated\n", + " 'CubicBool': False, # and whether to use cubic spline interpolation. You can ignore them.\n", + " 'aNrmInitMean' : -10.,\n", + " 'aNrmInitStd' : 0.0, # These parameters specify the (log) distribution of normalized assets\n", + " 'pLvlInitMean' : 0.0, # and permanent income for agents at \"birth\". They are only relevant in\n", + " 'pLvlInitStd' : 0.0, # simulation and you don't need to worry about them.\n", + " 'PermGroFacAgg' : 1.0,\n", + " 'T_retire': 0, # What's this about retirement? ConsIndShock is set up to be able to\n", + " 'UnempPrbRet': 0.0, # handle lifecycle models as well as infinite horizon problems. Swapping\n", + " 'IncUnempRet': 0.0, # out the structure of the income process is easy, but ignore for now.\n", + " 'T_age' : None,\n", + " 'T_cycle' : 1,\n", + " 'cycles' : 0,\n", + " 'AgentCount': 10000,\n", + " 'tax_rate':0.0,\n", + "}\n", + " \n", + "# Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to\n", + "# think about them for now." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we need to import the relevant subclass of $\\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", + "IndShockExample = IndShockConsumerType(**IndShockDictionary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can solve our new agent's problem just like before, using the $\\texttt{solve}$ method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IndShockExample.solve()\n", + "plotFuncs(IndShockExample.solution[0].cFunc,0.,10.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Changing Constructed Attributes\n", + "\n", + "In the parameter dictionary above, we chose values for HARK to use when constructing its numeric representation of $F_t$, the joint distribution of permanent and transitory income shocks. When $\\texttt{IndShockExample}$ was created, those parameters ($\\texttt{TranShkStd}$, etc) were used by the **constructor** or **initialization** method of $\\texttt{IndShockConsumerType}$ to construct an attribute called $\\texttt{IncomeDstn}$.\n", + "\n", + "Suppose you were interested in changing (say) the amount of permanent income risk. From the section above, you might think that you could simply change the attribute $\\texttt{TranShkStd}$, solve the model again, and it would work.\n", + "\n", + "That's _almost_ true-- there's one extra step. $\\texttt{TranShkStd}$ is a primitive input, but it's not the thing you _actually_ want to change. Changing $\\texttt{TranShkStd}$ doesn't actually update the income distribution... unless you tell it to (just like changing an agent's preferences does not change the consumption function that was stored for the old set of parameters -- until you invoke the $\\texttt{solve}$ method again). In the cell below, we invoke the method $\\texttt{updateIncomeProcess}$ so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "OtherExample = deepcopy(IndShockExample) # Make a copy so we can compare consumption functions\n", + "OtherExample.PermShkStd = [0.2] # Double permanent income risk (note that it's a one element list)\n", + "OtherExample.updateIncomeProcess() # Call the method to reconstruct the representation of F_t\n", + "OtherExample.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below, use your blossoming HARK skills to plot the consumption function for $\\texttt{IndShockExample}$ and $\\texttt{OtherExample}$ on the same figure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Use the line(s) below to plot the consumptions functions against each other" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "collapsed,code_folding", + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.py b/examples/Gentle-Intro/Gentle-Intro-To-HARK.py new file mode 100644 index 000000000..698543675 --- /dev/null +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.py @@ -0,0 +1,305 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: collapsed,code_folding +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.2' +# jupytext_version: 1.2.3 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # A Gentle Introduction to HARK +# +# This notebook provides a simple, hands-on tutorial for first time HARK users -- and potentially first time Python users. It does not go "into the weeds" - we have hidden some code cells that do boring things that you don't need to digest on your first experience with HARK. Our aim is to convey a feel for how the toolkit works. +# +# For readers for whom this is your very first experience with Python, we have put important Python concepts in **boldface**. For those for whom this is the first time they have used a Jupyter notebook, we have put Jupyter instructions in _italics_. Only cursory definitions (if any) are provided here. If you want to learn more, there are many online Python and Jupyter tutorials. + +# %% {"code_folding": []} +# This cell has a bit of initial setup. You can click the triangle to the left to expand it. +# Click the "Run" button immediately above the notebook in order to execute the contents of any cell +# WARNING: Each cell in the notebook relies upon results generated by previous cells +# The most common problem beginners have is to execute a cell before all its predecessors +# If you do this, you can restart the kernel (see the "Kernel" menu above) and start over +# %matplotlib inline +import matplotlib.pyplot as plt +from tqdm import tqdm +import numpy as np +import HARK +from time import clock +from copy import deepcopy +mystr = lambda number : "{:.4f}".format(number) +from HARK.utilities import plotFuncs + +# %% [markdown] +# ## Your First HARK Model: Perfect Foresight +# +# $$\newcommand{\CRRA}{\rho}\newcommand{\DiscFac}{\beta}$$ +# We start with almost the simplest possible consumption model: A consumer with CRRA utility +# +# \begin{equation} +# U(C) = \frac{C^{1-\CRRA}}{1-\rho} +# \end{equation} +# +# has perfect foresight about everything except the (stochastic) date of death, which occurs with constant probability implying a "survival probability" $\newcommand{\LivPrb}{\aleph}\LivPrb < 1$. Permanent labor income $P_t$ grows from period to period by a factor $\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and currrent income) and must choose how much of those resources to consume $C_t$ and how much to retain in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\beta$. Between periods, the agent dies with probability $\mathsf{D}_t$, ending his problem. +# +# The agent's problem can be written in Bellman form as: +# +# \begin{eqnarray*} +# V_t(M_t,P_t) &=& \max_{C_t}~U(C_t) + \beta \aleph V_{t+1}(M_{t+1},P_{t+1}), \\ +# & s.t. & \\ +# %A_t &=& M_t - C_t, \\ +# M_{t+1} &=& R (M_{t}-C_{t}) + Y_{t+1}, \\ +# P_{t+1} &=& \Gamma_{t+1} P_t, \\ +# \end{eqnarray*} +# +# A particular perfect foresight agent's problem can be characterized by values of risk aversion $\rho$, discount factor $\beta$, and return factor $R$, along with sequences of income growth factors $\{ \Gamma_t \}$ and survival probabilities $\{\mathsf{\aleph}_t\}$. To keep things simple, let's forget about "sequences" of income growth and mortality, and just think about an $\textit{infinite horizon}$ consumer with constant income growth and survival probability. +# +# ## Representing Agents in HARK +# +# HARK represents agents solving this type of problem as $\textbf{instances}$ of the $\textbf{class}$ $\texttt{PerfForesightConsumerType}$, a $\textbf{subclass}$ of $\texttt{AgentType}$. To make agents of this class, we must import the class itself into our workspace. (Run the cell below in order to do this). + +# %% +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType + +# %% [markdown] +# The $\texttt{PerfForesightConsumerType}$ class contains within itself the python code that constructs the solution for the perfect foresight model we are studying here, as specifically articulated in [these lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/). +# +# To create an instance of $\texttt{PerfForesightConsumerType}$, we simply call the class as if it were a function, passing as arguments the specific parameter values we want it to have. In the hidden cell below, we define a $\textbf{dictionary}$ named $\texttt{PF_dictionary}$ with these parameter values: +# +# | Param | Description | Code | Value | +# | :---: | --- | --- | :---: | +# | $\rho$ | Relative risk aversion | $\texttt{CRRA}$ | 2.5 | +# | $\beta$ | Discount factor | $\texttt{DiscFac}$ | 0.96 | +# | $R$ | Risk free interest factor | $\texttt{Rfree}$ | 1.03 | +# | $\newcommand{\LivFac}{\aleph}\LivFac$ | Survival probability | $\texttt{LivPrb}$ | 0.98 | +# | $\Gamma$ | Income growth factor | $\texttt{PermGroFac}$ | 1.01 | +# +# +# For now, don't worry about the specifics of dictionaries. All you need to know is that a dictionary lets us pass many arguments wrapped up in one simple data structure. + +# %% {"code_folding": []} +# This cell defines a parameter dictionary. You can expand it if you want to see what that looks like. +PF_dictionary = { + 'CRRA' : 2.5, + 'DiscFac' : 0.96, + 'Rfree' : 1.03, + 'LivPrb' : [0.98], + 'PermGroFac' : [1.01], + 'T_cycle' : 1, + 'cycles' : 0, + 'AgentCount' : 10000 +} + +# To those curious enough to open this hidden cell, you might notice that we defined +# a few extra parameters in that dictionary: T_cycle, cycles, and AgentCount. Don't +# worry about these for now. + +# %% [markdown] +# Let's make an **object** named $\texttt{PFexample}$ which is an **instance** of the $\texttt{PerfForesightConsumerType}$ class. The object $\texttt{PFexample}$ will bundle together the abstract mathematical description of the solution embodied in $\texttt{PerfForesightConsumerType}$, and the specific set of parameter values defined in $\texttt{PF_dictionary}$. Such a bundle is created passing $\texttt{PF_dictionary}$ to the class $\texttt{PerfForesightConsumerType}$: + +# %% +PFexample = PerfForesightConsumerType(**PF_dictionary) +# the asterisks ** basically say "here come some arguments" to PerfForesightConsumerType + +# %% [markdown] +# In $\texttt{PFexample}$, we now have _defined_ the problem of a particular infinite horizon perfect foresight consumer who knows how to solve this problem. +# +# ## Solving an Agent's Problem +# +# To tell the agent actually to solve the problem, we call the agent's $\texttt{solve}$ **method**. (A *method** is essentially a function that an object runs that affects the object's own internal characteristics -- in this case, the method adds the consumption function to the contents of $\texttt{PFexample}$.) +# +# The cell below calls the $\texttt{solve}$ method for $\texttt{PFexample}$ + +# %% +PFexample.solve() + +# %% [markdown] +# Running the $\texttt{solve}$ method creates the **attribute** of $\texttt{PFexample}$ named $\texttt{solution}$. In fact, every subclass of $\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (paramterization/configuration), that instance must be instructed to $\texttt{solve()}$ its problem. +# +# The $\texttt{solution}$ attribute is always a $\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem. The consumption function stored as the first element (element 0) of the solution list can be retrieved by: + +# %% +PFexample.solution[0].cFunc + +# %% [markdown] +# One of the results proven in the associated [the lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$. +# +# This is why $\texttt{cFunc}$ can be represented by a linear interpolation. It can be plotted between an $m$ ratio of 0 and 10 using the command below. + +# %% +mPlotTop=10 +plotFuncs(PFexample.solution[0].cFunc,0.,mPlotTop) + +# %% [markdown] +# The figure illustrates one of the surprising features of the perfect foresight model: A person with zero money should be spending at a rate more than double their income (that is, $\texttt{cFunc}(0.) \approx 2.08$ - the intersection on the vertical axis). How can this be? +# +# The answer is that we have not incorporated any constraint that would prevent the agent from borrowing against the entire PDV of future earnings-- human wealth. How much is that? What's the minimum value of $m_t$ where the consumption function is defined? We can check by retrieving the $\texttt{hNrm}$ **attribute** of the solution, which calculates the value of human wealth normalized by permanent income: + +# %% +humanWealth = PFexample.solution[0].hNrm +mMinimum = PFexample.solution[0].mNrmMin +print("This agent's human wealth is " + str(humanWealth) + ' times his current income level.') +print("This agent's consumption function is defined (consumption is positive) down to m_t = " + str(mMinimum)) + +# %% [markdown] +# Yikes! Let's take a look at the bottom of the consumption function. In the cell below, set the bounds of the $\texttt{plotFuncs}$ function to display down to the lowest defined value of the consumption function. + +# %% +# YOUR FIRST HANDS-ON EXERCISE! +# Fill in the value for "mPlotBottom" to plot the consumption function from the point where it is zero. +plotFuncs(PFexample.solution[0].cFunc,mPlotBottom,mPlotTop) + +# %% [markdown] +# ## Changing Agent Parameters +# +# Suppose you wanted to change one (or more) of the parameters of the agent's problem and see what that does. We want to compare consumption functions before and after we change parameters, so let's make a new instance of $\texttt{PerfForesightConsumerType}$ by copying $\texttt{PFexample}$. + +# %% +NewExample = deepcopy(PFexample) + +# %% [markdown] +# In Python, you can set an **attribute** of an object just like any other variable. For example, we could make the new agent less patient: + +# %% +NewExample.DiscFac = 0.90 +NewExample.solve() +mPlotBottom = mMinimum +plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],mPlotBottom,mPlotTop) + +# %% [markdown] +# (Note that you can pass a **list** of functions to $\texttt{plotFuncs}$ as the first argument rather than just a single function. Lists are written inside of [square brackets].) +# +# Let's try to deal with the "problem" of massive human wealth by making another consumer who has essentially no future income. We can virtually eliminate human wealth by making the permanent income growth factor $\textit{very}$ small. +# +# In $\texttt{PFexample}$, the agent's income grew by 1 percent per period -- his $\texttt{PermGroFac}$ took the value 1.01. What if our new agent had a growth factor of 0.01 -- his income __shrinks__ by 99 percent each period? In the cell below, set $\texttt{NewExample}$'s discount factor back to its original value, then set its $\texttt{PermGroFac}$ attribute so that the growth factor is 0.01 each period. +# +# Important: Recall that the model at the top of this document said that an agent's problem is characterized by a sequence of income growth factors, but we tabled that concept. Because $\texttt{PerfForesightConsumerType}$ treats $\texttt{PermGroFac}$ as a __time-varying__ attribute, it must be specified as a **list** (with a single element in this case). + +# %% +# Revert NewExample's discount factor and make his future income minuscule +# print("your lines here") + +# Compare the old and new consumption functions +plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],0.,10.) + +# %% [markdown] +# Now $\texttt{NewExample}$'s consumption function has the same slope (MPC) as $\texttt{PFexample}$, but it emanates from (almost) zero-- he has basically no future income to borrow against! +# +# If you'd like, use the cell above to alter $\texttt{NewExample}$'s other attributes (relative risk aversion, etc) and see how the consumption function changes. However, keep in mind that \textit{no solution exists} for some combinations of parameters. HARK should let you know if this is the case if you try to solve such a model. +# +# +# ## Your Second HARK Model: Adding Income Shocks +# +# Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines. Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints. +# +# Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\theta_t$ and a completely permanent shock $\psi_t$. Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\underline{a}$. As with the perfect foresight problem, this model can be framed in terms of __normalized__ variables, e.g. $m_t \equiv M_t/P_t$. (See [here](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). +# +# \begin{eqnarray*} +# v_t(m_t) &=& \max_{c_t} ~ U(c_t) ~ + \phantom{\LivFac} \beta \mathbb{E} [(\Gamma_{t+1}\psi_{t+1})^{1-\rho} v_{t+1}(m_{t+1}) ], \\ +# a_t &=& m_t - c_t, \\ +# a_t &\geq& \underset{\bar{}}{a}, \\ +# m_{t+1} &=& R/(\Gamma_{t+1} \psi_{t+1}) a_t + \theta_{t+1}, \\ +# \mathbb{E}[\psi]=\mathbb{E}[\theta] &=& 1, \\ +# u(c) &=& \frac{c^{1-\rho}}{1-\rho}. +# \end{eqnarray*} +# +# HARK represents agents with this kind of problem as instances of the class $\texttt{IndShockConsumerType}$. To create an $\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\underline{a}$ and a sequence of income shocks. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks? Can't the joint distribution of permanent and transitory shocks be just about anything? +# +# _Yes_, and HARK can handle whatever correlation structure a user might care to specify. However, the default behavior of $\texttt{IndShockConsumerType}$ is that the distribution of permanent income shocks is mean one lognormal, and the distribution of transitory shocks is mean one lognormal augmented with a point mass representing unemployment. The distributions are independent of each other by default, and by default are approximated with $N$ point equiprobable distributions. +# +# Let's make an infinite horizon instance of $\texttt{IndShockConsumerType}$ with the same parameters as our original perfect foresight agent, plus the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary: +# +# +# | Param | Description | Code | Value | +# | :---: | --- | --- | :---: | +# | \underline{a} | Artificial borrowing constraint | $\texttt{BoroCnstArt}$ | 0.0 | +# | $\sigma_\psi$ | Underlying stdev of permanent income shocks | $\texttt{PermShkStd}$ | 0.1 | +# | $\sigma_\theta$ | Underlying stdev of transitory income shocks | $\texttt{TranShkStd}$ | 0.1 | +# | $N_\psi$ | Number of discrete permanent income shocks | $\texttt{PermShkCount}$ | 7 | +# | $N_\theta$ | Number of discrete transitory income shocks | $\texttt{TranShkCount}$ | 7 | +# | $\mho$ | Unemployment probability | $\texttt{UnempPrb}$ | 0.05 | +# | $\underset{\bar{}}{\theta}$ | Transitory shock when unemployed | $\texttt{IncUnemp}$ | 0.3 | + +# %% {"code_folding": []} +# This cell defines a parameter dictionary for making an instance of IndShockConsumerType. + +IndShockDictionary = { + 'CRRA': 2.5, # The dictionary includes our original parameters... + 'Rfree': 1.03, + 'DiscFac': 0.96, + 'LivPrb': [0.98], + 'PermGroFac': [1.01], + 'PermShkStd': [0.1], # ... and the new parameters for constructing the income process. + 'PermShkCount': 7, + 'TranShkStd': [0.1], + 'TranShkCount': 7, + 'UnempPrb': 0.05, + 'IncUnemp': 0.3, + 'BoroCnstArt': 0.0, + 'aXtraMin': 0.001, # aXtra parameters specify how to construct the grid of assets. + 'aXtraMax': 50., # Don't worry about these for now + 'aXtraNestFac': 3, + 'aXtraCount': 48, + 'aXtraExtra': [None], + 'vFuncBool': False, # These booleans indicate whether the value function should be calculated + 'CubicBool': False, # and whether to use cubic spline interpolation. You can ignore them. + 'aNrmInitMean' : -10., + 'aNrmInitStd' : 0.0, # These parameters specify the (log) distribution of normalized assets + 'pLvlInitMean' : 0.0, # and permanent income for agents at "birth". They are only relevant in + 'pLvlInitStd' : 0.0, # simulation and you don't need to worry about them. + 'PermGroFacAgg' : 1.0, + 'T_retire': 0, # What's this about retirement? ConsIndShock is set up to be able to + 'UnempPrbRet': 0.0, # handle lifecycle models as well as infinite horizon problems. Swapping + 'IncUnempRet': 0.0, # out the structure of the income process is easy, but ignore for now. + 'T_age' : None, + 'T_cycle' : 1, + 'cycles' : 0, + 'AgentCount': 10000, + 'tax_rate':0.0, +} + +# Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to +# think about them for now. + +# %% [markdown] +# As before, we need to import the relevant subclass of $\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function. + +# %% +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType +IndShockExample = IndShockConsumerType(**IndShockDictionary) + +# %% [markdown] +# Now we can solve our new agent's problem just like before, using the $\texttt{solve}$ method. + +# %% +IndShockExample.solve() +plotFuncs(IndShockExample.solution[0].cFunc,0.,10.) + +# %% [markdown] +# ## Changing Constructed Attributes +# +# In the parameter dictionary above, we chose values for HARK to use when constructing its numeric representation of $F_t$, the joint distribution of permanent and transitory income shocks. When $\texttt{IndShockExample}$ was created, those parameters ($\texttt{TranShkStd}$, etc) were used by the **constructor** or **initialization** method of $\texttt{IndShockConsumerType}$ to construct an attribute called $\texttt{IncomeDstn}$. +# +# Suppose you were interested in changing (say) the amount of permanent income risk. From the section above, you might think that you could simply change the attribute $\texttt{TranShkStd}$, solve the model again, and it would work. +# +# That's _almost_ true-- there's one extra step. $\texttt{TranShkStd}$ is a primitive input, but it's not the thing you _actually_ want to change. Changing $\texttt{TranShkStd}$ doesn't actually update the income distribution... unless you tell it to (just like changing an agent's preferences does not change the consumption function that was stored for the old set of parameters -- until you invoke the $\texttt{solve}$ method again). In the cell below, we invoke the method $\texttt{updateIncomeProcess}$ so HARK knows to reconstruct the attribute $\texttt{IncomeDstn}$. + +# %% +OtherExample = deepcopy(IndShockExample) # Make a copy so we can compare consumption functions +OtherExample.PermShkStd = [0.2] # Double permanent income risk (note that it's a one element list) +OtherExample.updateIncomeProcess() # Call the method to reconstruct the representation of F_t +OtherExample.solve() + +# %% [markdown] +# In the cell below, use your blossoming HARK skills to plot the consumption function for $\texttt{IndShockExample}$ and $\texttt{OtherExample}$ on the same figure. + +# %% +# Use the line(s) below to plot the consumptions functions against each other + diff --git a/examples/HowWeSolveIndShockConsumerType/HoweWeSolveIndShockConsumerType.ipynb b/examples/HowWeSolveIndShockConsumerType/HoweWeSolveIndShockConsumerType.ipynb new file mode 100644 index 000000000..0ad325026 --- /dev/null +++ b/examples/HowWeSolveIndShockConsumerType/HoweWeSolveIndShockConsumerType.ipynb @@ -0,0 +1,466 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How we solve a model defined by the `IndShockConsumerType` class\n", + "The IndShockConsumerType reprents the work-horse consumption savings model with temporary and permanent shocks to income, finite or infinite horizons, CRRA utility and more. In this DemARK we take you through the steps involved in solving one period of such a model. The inheritance chains can be a little long, so figuring out where all the parameters and methods come from can be a bit confusing. Hence this map! The intention is to make it easier to know how to inheret from IndShockConsumerType in the sense that you know where to look for specific solver logic, but also so you know can figure out which methods to overwrite or supplement in your own `AgentType` and solver!\n", + "## The `solveConsIndShock` function\n", + "In HARK, a period's problem is always solved by the callable (function or callable object instance) stored in the field `solveOnePeriod`. In the case of `IndShockConsumerType`, this function is called `solveConsIndShock`. The function accepts a number of arguments, that it uses to construct an instance of either a `ConsIndShockSolverBasic` or a `ConsIndShockSolver`. These solvers both have the methods `prepareToSolve` and `solve`, that we will have a closer look at in this notebook. This means, that the logic of `solveConsIndShock` is basically:\n", + "\n", + " 1. Check if cubic interpolation (`CubicBool`) or construction of the value function interpolant (`vFuncBool`) are requested. Construct an instance of `ConsIndShockSolverBasic` if neither are requested, else construct a `ConsIndShockSolver`. Call this `solver`.\n", + " 1. Call `solver.prepareToSolve()`\n", + " 1. Call `solver.solve()` and return the output as the current solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Two types of solvers\n", + "As mentioned above, `solveOnePeriod` will construct an instance of the class `ConsIndShockSolverBasic`or `ConsIndShockSolver`. The main difference is whether it uses cubic interpolation or if it explicitly constructs a value function approximation. The choice and construction of a solver instance is bullet 1) from above.\n", + "\n", + "#### What happens in upon construction\n", + "Neither of the two solvers have their own `__init__`. `ConsIndShockSolver` inherits from `ConsIndShockSolverBasic` that in turn inherits from `ConsIndShockSetup`. `ConsIndShockSetup` inherits from `ConsPerfForesightSolver`, which itself is just an `Object`, so we get the inheritance structure\n", + "\n", + "- `ConsPerfForesightSolver` $\\leftarrow$ `ConsIndShockSetup` $\\leftarrow$ `ConsIndShockSolverBasic` $\\leftarrow$ `ConsIndShockSolver`\n", + "\n", + "When one of the two classes in the end of the inheritance chain is called, it will call `ConsIndShockSetup.__init__(args...)`. This takes a whole list of fixed inputs that then gets assigned to the object through a\n", + "```\n", + "ConsIndShockSetup.assignParameters(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool)\n", + "```\n", + "call, that then calls\n", + "```\n", + "ConsPerfForesightSolver.assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac)\n", + "```\n", + "We're getting kind of detailed here, but it is simply to help us understand the inheritance structure. The methods are quite straight forward, and simply assign the list of variables to self. The ones that do not get assigned by the `ConsPerfForesightSolver` method gets assign by the `ConsIndShockSetup` method instead.\n", + "\n", + "\n", + "After all the input parameters are set, we update the utility function definitions. Remember, that we restrict ourselves to CRRA utility functions, and these are parameterized with the scalar we call `CRRA` in HARK. We use the two-argument CRRA utility (and derivatives, inverses, etc) from `HARK.utilities`, so we need to create a `lambda` (an anonymous function) according to the fixed `CRRA` we have chosen. This gets done through a call to\n", + "\n", + "```\n", + "ConsIndShockSetup.defUtilityFuncs()\n", + "```\n", + "that itself calls\n", + "```\n", + "ConsPerfForesightSolver.defUtilityFuncs()\n", + "```\n", + "Again, we wish to emphasize the inheritance structure. The method in `ConsPerfForesightSolver` defines the most basic utility functions (utility, its marginal and its marginal marginal), and `ConsIndShockSolver` adds additional functions (marginal of inverse, inverse of marginal, marginal of inverse of marginal, and optionally inverse if `vFuncBool` is true).\n", + "\n", + "To sum up, the `__init__` method lives in `ConsIndShockSetup`, calls `assignParameters` and `defUtilityFuncs` from `ConsPerfForesightSolver` and defines its own methods with the same names that adds some methods used to solve the `IndShockConsumerType` using EGM. The main things controlled by the end-user are whether cubic interpolation should be used, `CubicBool`, and if the value function should be explicitly formed, `vFuncBool`.\n", + "### Prepare to solve\n", + "We are now in bullet 2) from the list above. The `prepareToSolve` method is all about grabbing relevant information from next period's solution, calculating some limiting solutions. It comes from `ConsIndShockSetup` and calls two methods:\n", + "\n", + "1. `ConsIndShockSetup.setAndUpdateValues(self.solution_next,self.IncomeDstn,self.LivPrb,self.DiscFac)`\n", + "2. `ConsIndShockSetup.defBoroCnst(self.BoroCnstArt)`\n", + "\n", + "First, we have `setAndUpdateValues`. The main purpose is to grab the relevant vectors that represent the shock distributions, the effective discount factor, and value function (marginal, level, marginal marginal depending on the options). It also calculates some limiting marginal propensities to consume and human wealth levels. Second, we have `defBoroCnst`. As the name indicates, it calculates the natural borrowing constraint, handles artificial borrowing constraints, and defines the consumption function where the constraint binds (`cFuncNowCnst`).\n", + "\n", + "To sum, `prepareToSolve` sets up the stochastic environment an borrowing constraints the consumer might face. It also grabs interpolants from \"next period\"'s solution.\n", + "\n", + "### Solve it!\n", + "The last method `solveConsIndShock` will call from the `solver` is `solve`. This method essentially has four steps:\n", + " 1. Pre-processing for EGM: solver.prepareToCalcEndOfPrdvP\n", + " 1. First step of EGM: solver.calcEndOfPrdvP\n", + " 1. Second step of EGM: solver.makeBasicSolution\n", + " 1. Add MPC and human wealth: solver.addMPCandHumanWealth\n", + "\n", + "#### Pre-processing for EGM `prepareToCalcEndOfPrdvP`\n", + "Find relevant values of end-of-period asset values (according to `aXtraGrid` and natural borrowing constraint) and next period values implied by current period end-of-period assets and stochastic elements. The method stores the following in `self`:\n", + "\n", + " 1. values of permanent shocks in `PermShkVals_temp`\n", + " 1. shock probabilities in `ShkPrbs_temp`\n", + " 1. next period resources in `mNrmNext`\n", + " 1. current grid of end-of-period assets in `aNrmNow`\n", + "\n", + "The method also returns `aNrmNow`. The definition is in `ConsIndShockSolverBasic` and is not overwritten in `ConsIndShockSolver`.\n", + "\n", + "#### First step of EGM `calcEndOfPrdvP`\n", + "Find the marginal value of having some level of end-of-period assets today. End-of-period assets as well as stochastics imply next-period resources at the beginning of the period, calculated above. Return the result as `EndOfPrdvP`.\n", + "\n", + "#### Second step of EGM `makeBasicSolution`\n", + "Apply inverse marginal utility function to nodes from about to find (m, c) pairs for the new consumption function in `getPointsForInterpolation` and create the interpolants in `usePointsForInterpolation`. The latter constructs the `ConsumerSolution` that contains the current consumption function `cFunc`, the current marginal value function `vPfunc`, and the smallest possible resource level `mNrmMinNow`.\n", + "\n", + "#### Add MPC and human wealth `addMPCandHumanWealth`\n", + "Add values calculated in `defBoroCnst` now that we have a solution object to put them in.\n", + "\n", + "#### Special to the non-Basic solver\n", + "We are now done, but in the `ConsIndShockSolver` (non-`Basic`!) solver there are a few extra steps. We add steady state m, and depending on the values of `vFuncBool` and `CubicBool` we also add the value function and the marginal marginal value function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Let's try it in action!\n", + "First, we define a standard lifecycle model, solve it and then" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import HARK.ConsumptionSaving.ConsumerParameters as Params\n", + "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "LifecycleExample = IndShockConsumerType(**Params.init_lifecycle)\n", + "LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once\n", + "LifecycleExample.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at the solution in time period second period. We should then be able to" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from HARK.utilities import plotFuncs\n", + "plotFuncs([LifecycleExample.solution[0].cFunc],LifecycleExample.solution[0].mNrmMin,10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us then create a solver for the first period." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSolverBasic\n", + "solver = ConsIndShockSolverBasic(LifecycleExample.solution[1],\n", + " LifecycleExample.IncomeDstn[0],\n", + " LifecycleExample.LivPrb[0],\n", + " LifecycleExample.DiscFac,\n", + " LifecycleExample.CRRA,\n", + " LifecycleExample.Rfree,\n", + " LifecycleExample.PermGroFac[0],\n", + " LifecycleExample.BoroCnstArt,\n", + " LifecycleExample.aXtraGrid,\n", + " LifecycleExample.vFuncBool,\n", + " LifecycleExample.CubicBool)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "solver.prepareToSolve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many important values are now calculated and stored in solver, such as the effective discount factor, the smallest permanent income shock, and more." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9503999999999999" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.DiscFacEff" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.850430160026919" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.PermShkMinNext" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These values were calculated in `setAndUpdateValues`. In `defBoroCnst` that was also called, several things were calculated, for example the consumption function defined by the borrowing constraint." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotFuncs([solver.cFuncNowCnst],solver.mNrmMinNow,10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we set up all the grids, grabs the discrete shock distributions, and state grids in `prepareToCalcEndOfPrdvP`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-2.49175086e-01, -2.30003713e-01, -2.09710489e-01, -1.88206151e-01,\n", + " -1.65392397e-01, -1.41160763e-01, -1.15391357e-01, -8.79513885e-02,\n", + " -5.86934916e-02, -2.74537788e-02, 5.95040319e-03, 4.17230788e-02,\n", + " 8.00926738e-02, 1.21315551e-01, 1.65680145e-01, 2.13511821e-01,\n", + " 2.65178592e-01, 3.21097879e-01, 3.81744527e-01, 4.47660342e-01,\n", + " 5.19465496e-01, 5.97872200e-01, 6.83701170e-01, 7.77901553e-01,\n", + " 8.81575133e-01, 9.96005867e-01, 1.12269613e+00, 1.26341136e+00,\n", + " 1.42023542e+00, 1.59563952e+00, 1.79256862e+00, 2.01455026e+00,\n", + " 2.26583269e+00, 2.55156109e+00, 2.87800413e+00, 3.25284719e+00,\n", + " 3.68557479e+00, 4.18797327e+00, 4.77479697e+00, 5.46465892e+00,\n", + " 6.28123238e+00, 7.25488755e+00, 8.42494379e+00, 9.84280193e+00,\n", + " 1.15763503e+01, 1.37162363e+01, 1.63849084e+01, 1.97498249e+01])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.prepareToCalcEndOfPrdvP()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we calculate the marginal utility of next period's resources given the stochastic environment and current grids." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "EndOfPrdvP = solver.calcEndOfPrdvP()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we essentially just have to construct the (resource, consumption) pairs by completing the EGM step, and constructing the interpolants by using the knowledge that the limiting solutions are those of the perfect foresight model. This is done with `makeBasicSolution` as discussed above." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "solution = solver.makeBasicSolution(EndOfPrdvP,solver.aNrmNow,solver.makeLinearcFunc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we add the MPC and human wealth quantities we calculated in the method that prepared the solution of this period." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.addMPCandHumanWealth(solution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All that is left is to verify that the solution in `solution` is identical to `LifecycleExample.solution[0]`. We can plot the against each other:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotFuncs([LifecycleExample.solution[0].cFunc, solution.cFunc],LifecycleExample.solution[0].mNrmMin,10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although, it's probably even clearer if we just subtract the function values from each other at some grid." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_grid = np.linspace(0, 20, 200)\n", + "LifecycleExample.solution[0].cFunc(eval_grid) - solution.cFunc(eval_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "hide_input": false, + "jupytext": { + "formats": "ipynb,py:light" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/HowWeSolveIndShockConsumerType/HoweWeSolveIndShockConsumerType.py b/examples/HowWeSolveIndShockConsumerType/HoweWeSolveIndShockConsumerType.py new file mode 100644 index 000000000..e12ee3bb7 --- /dev/null +++ b/examples/HowWeSolveIndShockConsumerType/HoweWeSolveIndShockConsumerType.py @@ -0,0 +1,165 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:light +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.4' +# jupytext_version: 1.2.4 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# # How we solve a model defined by the `IndShockConsumerType` class +# The IndShockConsumerType reprents the work-horse consumption savings model with temporary and permanent shocks to income, finite or infinite horizons, CRRA utility and more. In this DemARK we take you through the steps involved in solving one period of such a model. The inheritance chains can be a little long, so figuring out where all the parameters and methods come from can be a bit confusing. Hence this map! The intention is to make it easier to know how to inheret from IndShockConsumerType in the sense that you know where to look for specific solver logic, but also so you know can figure out which methods to overwrite or supplement in your own `AgentType` and solver! +# ## The `solveConsIndShock` function +# In HARK, a period's problem is always solved by the callable (function or callable object instance) stored in the field `solveOnePeriod`. In the case of `IndShockConsumerType`, this function is called `solveConsIndShock`. The function accepts a number of arguments, that it uses to construct an instance of either a `ConsIndShockSolverBasic` or a `ConsIndShockSolver`. These solvers both have the methods `prepareToSolve` and `solve`, that we will have a closer look at in this notebook. This means, that the logic of `solveConsIndShock` is basically: +# +# 1. Check if cubic interpolation (`CubicBool`) or construction of the value function interpolant (`vFuncBool`) are requested. Construct an instance of `ConsIndShockSolverBasic` if neither are requested, else construct a `ConsIndShockSolver`. Call this `solver`. +# 1. Call `solver.prepareToSolve()` +# 1. Call `solver.solve()` and return the output as the current solution. + +# ### Two types of solvers +# As mentioned above, `solveOnePeriod` will construct an instance of the class `ConsIndShockSolverBasic`or `ConsIndShockSolver`. The main difference is whether it uses cubic interpolation or if it explicitly constructs a value function approximation. The choice and construction of a solver instance is bullet 1) from above. +# +# #### What happens in upon construction +# Neither of the two solvers have their own `__init__`. `ConsIndShockSolver` inherits from `ConsIndShockSolverBasic` that in turn inherits from `ConsIndShockSetup`. `ConsIndShockSetup` inherits from `ConsPerfForesightSolver`, which itself is just an `Object`, so we get the inheritance structure +# +# - `ConsPerfForesightSolver` $\leftarrow$ `ConsIndShockSetup` $\leftarrow$ `ConsIndShockSolverBasic` $\leftarrow$ `ConsIndShockSolver` +# +# When one of the two classes in the end of the inheritance chain is called, it will call `ConsIndShockSetup.__init__(args...)`. This takes a whole list of fixed inputs that then gets assigned to the object through a +# ``` +# ConsIndShockSetup.assignParameters(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) +# ``` +# call, that then calls +# ``` +# ConsPerfForesightSolver.assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac) +# ``` +# We're getting kind of detailed here, but it is simply to help us understand the inheritance structure. The methods are quite straight forward, and simply assign the list of variables to self. The ones that do not get assigned by the `ConsPerfForesightSolver` method gets assign by the `ConsIndShockSetup` method instead. +# +# +# After all the input parameters are set, we update the utility function definitions. Remember, that we restrict ourselves to CRRA utility functions, and these are parameterized with the scalar we call `CRRA` in HARK. We use the two-argument CRRA utility (and derivatives, inverses, etc) from `HARK.utilities`, so we need to create a `lambda` (an anonymous function) according to the fixed `CRRA` we have chosen. This gets done through a call to +# +# ``` +# ConsIndShockSetup.defUtilityFuncs() +# ``` +# that itself calls +# ``` +# ConsPerfForesightSolver.defUtilityFuncs() +# ``` +# Again, we wish to emphasize the inheritance structure. The method in `ConsPerfForesightSolver` defines the most basic utility functions (utility, its marginal and its marginal marginal), and `ConsIndShockSolver` adds additional functions (marginal of inverse, inverse of marginal, marginal of inverse of marginal, and optionally inverse if `vFuncBool` is true). +# +# To sum up, the `__init__` method lives in `ConsIndShockSetup`, calls `assignParameters` and `defUtilityFuncs` from `ConsPerfForesightSolver` and defines its own methods with the same names that adds some methods used to solve the `IndShockConsumerType` using EGM. The main things controlled by the end-user are whether cubic interpolation should be used, `CubicBool`, and if the value function should be explicitly formed, `vFuncBool`. +# ### Prepare to solve +# We are now in bullet 2) from the list above. The `prepareToSolve` method is all about grabbing relevant information from next period's solution, calculating some limiting solutions. It comes from `ConsIndShockSetup` and calls two methods: +# +# 1. `ConsIndShockSetup.setAndUpdateValues(self.solution_next,self.IncomeDstn,self.LivPrb,self.DiscFac)` +# 2. `ConsIndShockSetup.defBoroCnst(self.BoroCnstArt)` +# +# First, we have `setAndUpdateValues`. The main purpose is to grab the relevant vectors that represent the shock distributions, the effective discount factor, and value function (marginal, level, marginal marginal depending on the options). It also calculates some limiting marginal propensities to consume and human wealth levels. Second, we have `defBoroCnst`. As the name indicates, it calculates the natural borrowing constraint, handles artificial borrowing constraints, and defines the consumption function where the constraint binds (`cFuncNowCnst`). +# +# To sum, `prepareToSolve` sets up the stochastic environment an borrowing constraints the consumer might face. It also grabs interpolants from "next period"'s solution. +# +# ### Solve it! +# The last method `solveConsIndShock` will call from the `solver` is `solve`. This method essentially has four steps: +# 1. Pre-processing for EGM: solver.prepareToCalcEndOfPrdvP +# 1. First step of EGM: solver.calcEndOfPrdvP +# 1. Second step of EGM: solver.makeBasicSolution +# 1. Add MPC and human wealth: solver.addMPCandHumanWealth +# +# #### Pre-processing for EGM `prepareToCalcEndOfPrdvP` +# Find relevant values of end-of-period asset values (according to `aXtraGrid` and natural borrowing constraint) and next period values implied by current period end-of-period assets and stochastic elements. The method stores the following in `self`: +# +# 1. values of permanent shocks in `PermShkVals_temp` +# 1. shock probabilities in `ShkPrbs_temp` +# 1. next period resources in `mNrmNext` +# 1. current grid of end-of-period assets in `aNrmNow` +# +# The method also returns `aNrmNow`. The definition is in `ConsIndShockSolverBasic` and is not overwritten in `ConsIndShockSolver`. +# +# #### First step of EGM `calcEndOfPrdvP` +# Find the marginal value of having some level of end-of-period assets today. End-of-period assets as well as stochastics imply next-period resources at the beginning of the period, calculated above. Return the result as `EndOfPrdvP`. +# +# #### Second step of EGM `makeBasicSolution` +# Apply inverse marginal utility function to nodes from about to find (m, c) pairs for the new consumption function in `getPointsForInterpolation` and create the interpolants in `usePointsForInterpolation`. The latter constructs the `ConsumerSolution` that contains the current consumption function `cFunc`, the current marginal value function `vPfunc`, and the smallest possible resource level `mNrmMinNow`. +# +# #### Add MPC and human wealth `addMPCandHumanWealth` +# Add values calculated in `defBoroCnst` now that we have a solution object to put them in. +# +# #### Special to the non-Basic solver +# We are now done, but in the `ConsIndShockSolver` (non-`Basic`!) solver there are a few extra steps. We add steady state m, and depending on the values of `vFuncBool` and `CubicBool` we also add the value function and the marginal marginal value function. + +# # Let's try it in action! +# First, we define a standard lifecycle model, solve it and then + +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType +import numpy as np +import matplotlib.pyplot as plt +LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) +LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once +LifecycleExample.solve() + +# Let's have a look at the solution in time period second period. We should then be able to + +from HARK.utilities import plotFuncs +plotFuncs([LifecycleExample.solution[0].cFunc],LifecycleExample.solution[0].mNrmMin,10) + +# Let us then create a solver for the first period. + +from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSolverBasic +solver = ConsIndShockSolverBasic(LifecycleExample.solution[1], + LifecycleExample.IncomeDstn[0], + LifecycleExample.LivPrb[0], + LifecycleExample.DiscFac, + LifecycleExample.CRRA, + LifecycleExample.Rfree, + LifecycleExample.PermGroFac[0], + LifecycleExample.BoroCnstArt, + LifecycleExample.aXtraGrid, + LifecycleExample.vFuncBool, + LifecycleExample.CubicBool) + +solver.prepareToSolve() + +# Many important values are now calculated and stored in solver, such as the effective discount factor, the smallest permanent income shock, and more. + +solver.DiscFacEff + +solver.PermShkMinNext + +# These values were calculated in `setAndUpdateValues`. In `defBoroCnst` that was also called, several things were calculated, for example the consumption function defined by the borrowing constraint. + +plotFuncs([solver.cFuncNowCnst],solver.mNrmMinNow,10) + +# Then, we set up all the grids, grabs the discrete shock distributions, and state grids in `prepareToCalcEndOfPrdvP`. + +solver.prepareToCalcEndOfPrdvP() + +# Then we calculate the marginal utility of next period's resources given the stochastic environment and current grids. + +EndOfPrdvP = solver.calcEndOfPrdvP() + +# Then, we essentially just have to construct the (resource, consumption) pairs by completing the EGM step, and constructing the interpolants by using the knowledge that the limiting solutions are those of the perfect foresight model. This is done with `makeBasicSolution` as discussed above. + +solution = solver.makeBasicSolution(EndOfPrdvP,solver.aNrmNow,solver.makeLinearcFunc) + +# Lastly, we add the MPC and human wealth quantities we calculated in the method that prepared the solution of this period. + +solver.addMPCandHumanWealth(solution) + +# All that is left is to verify that the solution in `solution` is identical to `LifecycleExample.solution[0]`. We can plot the against each other: + +plotFuncs([LifecycleExample.solution[0].cFunc, solution.cFunc],LifecycleExample.solution[0].mNrmMin,10) + +# Although, it's probably even clearer if we just subtract the function values from each other at some grid. + +eval_grid = np.linspace(0, 20, 200) +LifecycleExample.solution[0].cFunc(eval_grid) - solution.cFunc(eval_grid) + + + + From b64e4451394449832dc22895405a7b984a2ff0d8 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Wed, 5 Feb 2020 16:12:45 +0530 Subject: [PATCH 190/242] run notebook to get output plots in documentation (#490) --- .../IndShockConsumerType.ipynb | 330 +++++++++++++----- .../KinkedRconsumerType.ipynb | 108 +++++- .../GenIncProcessModel.ipynb | 276 +++++++++++---- 3 files changed, 549 insertions(+), 165 deletions(-) diff --git a/examples/ConsIndShockModel/IndShockConsumerType.ipynb b/examples/ConsIndShockModel/IndShockConsumerType.ipynb index 868c9f5b4..5db4fe881 100644 --- a/examples/ConsIndShockModel/IndShockConsumerType.ipynb +++ b/examples/ConsIndShockModel/IndShockConsumerType.ipynb @@ -10,11 +10,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "code_folding": [ - 0 - ] + "code_folding": [] }, "outputs": [], "source": [ @@ -134,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "code_folding": [ 0 @@ -215,12 +213,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "hidden": true, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], "source": [ "IndShockExample = IndShockConsumerType(**IdiosyncDict)\n", "IndShockExample.cycles = 0 # Make this type have an infinite horizon\n", @@ -238,11 +248,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "hidden": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'cFunc': , 'vFunc': , 'vPfunc': , 'vPPfunc': , 'mNrmMin': 0.0, 'hNrm': 44.991920196607595, 'MPCmin': 0.044536273404377116, 'MPCmax': 1.0, 'mNrmSS': 1.5488165705077026}\n" + ] + } + ], "source": [ "print(vars(IndShockExample.solution[0]))" ] @@ -262,11 +280,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "hidden": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function for an idiosyncratic shocks consumer type:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Marginal propensity to consume for an idiosyncratic shocks consumer type:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "print('Consumption function for an idiosyncratic shocks consumer type:')\n", "plotFuncs(IndShockExample.solution[0].cFunc,IndShockExample.solution[0].mNrmMin,5)\n", @@ -289,11 +346,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "hidden": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mNrmGrid for unconstrained cFunc is [-0.25017509 -0.23682357 -0.04309334 0.08570877 0.19249704 0.28773035\n", + " 0.37527975 0.4572262 0.53458817 0.60902763 0.68157147 0.75266421\n", + " 0.82159155 0.89091324 0.96108615 1.03297006 1.10702535 1.18386894\n", + " 1.26405846 1.34797683 1.43498517 1.52575439 1.62247992 1.7264799\n", + " 1.83886328 1.96091089 2.09399047 2.23965509 2.39978161 2.57666105\n", + " 2.77296758 2.99185309 3.23706867 3.51312387 3.82546834 4.18073875\n", + " 4.58704087 5.05438059 5.59517832 6.2249668 6.96332613 7.83514604\n", + " 8.87231638 10.11613869 11.62057399 13.45688075 15.72024305 18.53939481\n", + " 22.09098021]\n", + "cNrmGrid for unconstrained cFunc is [0. 0.01235151 0.18691037 0.29541926 0.38070319 0.45312275\n", + " 0.51644051 0.57261755 0.62253956 0.66772112 0.70902525 0.74671381\n", + " 0.77986847 0.81082056 0.8397706 0.86728992 0.89351353 0.91869035\n", + " 0.94296058 0.9662323 0.98732483 1.00628889 1.02460772 1.04277873\n", + " 1.06096172 1.07933575 1.0979846 1.11695897 1.13637025 1.15642563\n", + " 1.17732806 1.19928447 1.22251841 1.24729118 1.27390725 1.30273462\n", + " 1.33419368 1.3688058 1.40720505 1.45016983 1.49866721 1.55391366\n", + " 1.61742883 1.69119491 1.77777206 1.88053049 2.00400672 2.15448643\n", + " 2.3411553 ]\n", + "mNrmGrid for borrowing constrained cFunc is [0. 1.]\n", + "cNrmGrid for borrowing constrained cFunc is [0. 1.]\n" + ] + } + ], "source": [ "print('mNrmGrid for unconstrained cFunc is ',IndShockExample.solution[0].cFunc.functions[0].x_list)\n", "print('cNrmGrid for unconstrained cFunc is ',IndShockExample.solution[0].cFunc.functions[0].y_list)\n", @@ -314,11 +398,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "hidden": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plotFuncs(IndShockExample.solution[0].cFunc.functions,-0.25,5.)" ] @@ -349,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -367,9 +464,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.plot(np.mean(IndShockExample.mNrmNow_hist,axis=1))\n", "plt.xlabel('Time')\n", @@ -391,9 +513,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.plot(IndShockExample.cNrmNow_hist[:,0:5])\n", "plt.xlabel('Time')\n", @@ -418,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "code_folding": [ 0 @@ -480,9 +615,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First element of solution is \n", + "Solution has 11 elements.\n" + ] + } + ], "source": [ "LifecycleExample = IndShockConsumerType(**LifecycleDict)\n", "LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods -- a lifetime -- exactly once\n", @@ -502,9 +646,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions across the lifecycle:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "print('Consumption functions across the lifecycle:')\n", "mMin = np.min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)])\n", @@ -525,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "code_folding": [ 0 @@ -589,9 +753,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ms/dev/HARK/HARK/interpolation.py:1710: RuntimeWarning: All-NaN slice encountered\n", + " y = np.nanmin(fx,axis=1)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quarterly consumption functions:\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "CyclicalExample = IndShockConsumerType(**CyclicalDict)\n", "CyclicalExample.cycles = 0 # Make this consumer type have an infinite horizon\n", @@ -612,37 +804,10 @@ } ], "metadata": { - "@webio": { - "lastCommId": "779f6c5616b04b58baaaa0f6c348270c", - "lastKernelId": "a944b08f-0ae0-4c26-883f-9fab53a82ac3" - }, - "cite2c": { - "citations": { - "6202365/HQ6H9JEI": { - "DOI": "10.1016/j.econlet.2005.09.013", - "URL": "http://econ.jhu.edu/people/ccarroll/EndogenousArchive.zip", - "author": [ - { - "family": "Carroll", - "given": "Christopher D." - } - ], - "container-title": "Economics Letters", - "id": "6202365/HQ6H9JEI", - "issued": { - "month": 9, - "year": 2006 - }, - "page": "312–320", - "page-first": "312", - "title": "The Method of Endogenous Gridpoints for Solving Dynamic Stochastic Optimization Problems", - "type": "article-journal" - } - } - }, "jupytext": { - "cell_metadata_filter": "collapsed,code_folding", - "formats": "ipynb,py:percent" + "cell_metadata_filter": "collapsed,code_folding,heading_collapsed,hidden", + "formats": "ipynb,py:percent", + "notebook_metadata_filter": "all" }, "kernelspec": { "display_name": "Python 3", @@ -659,38 +824,27 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 + "version": "3.7.6" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb index cc57b6c4a..a1d509741 100644 --- a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb +++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "code_folding": [ 0 @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "code_folding": [ 0 @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -213,9 +213,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kinked R consumption function:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kinked R marginal propensity to consume:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "print('Kinked R consumption function:')\n", "plotFuncs(KinkyExample.solution[0].cFunc,KinkyExample.solution[0].mNrmMin,5)\n", @@ -250,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -268,9 +307,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.plot(np.mean(KinkyExample.mNrmNow_hist,axis=1))\n", "plt.xlabel('Time')\n", @@ -287,9 +339,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.plot(np.sort(KinkyExample.aNrmNow),np.linspace(0.,1.,KinkyExample.AgentCount))\n", "plt.xlabel('End-of-period assets')\n", @@ -315,7 +380,8 @@ }, "jupytext": { "cell_metadata_filter": "collapsed,code_folding", - "formats": "ipynb,py:percent" + "formats": "ipynb,py:percent", + "notebook_metadata_filter": "all" }, "kernelspec": { "display_name": "Python 3", @@ -332,7 +398,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.6" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { diff --git a/examples/GenIncProcessModel/GenIncProcessModel.ipynb b/examples/GenIncProcessModel/GenIncProcessModel.ipynb index 503dcddf6..2f3b0b3f4 100644 --- a/examples/GenIncProcessModel/GenIncProcessModel.ipynb +++ b/examples/GenIncProcessModel/GenIncProcessModel.ipynb @@ -9,12 +9,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "code_folding": [ - 0 - ], - "collapsed": true + "code_folding": [] }, "outputs": [], "source": [ @@ -30,10 +27,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "from HARK.ConsumptionSaving.ConsGenIncProcessModel import *\n", @@ -163,12 +158,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "code_folding": [ 0 - ], - "collapsed": true + ] }, "outputs": [], "source": [ @@ -222,11 +216,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here, the lowest percentile is 0.1\n", + "and the highest percentile is 99.9.\n", + "\n" + ] + } + ], "source": [ "# Make and solve an example \"explicit permanent income\" consumer with idiosyncratic shocks\n", "ExplicitExample = IndShockExplicitPermIncConsumerType(**GenIncDictionary)\n", @@ -246,14 +248,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "code_folding": [ 0 - ], - "collapsed": true + ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function by pLvl for explicit permanent income consumer:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot the consumption function at various permanent income levels.\n", "print('Consumption function by pLvl for explicit permanent income consumer:')\n", @@ -282,11 +303,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], "source": [ "# Make and solve an example of normalized model\n", "NormalizedExample = IndShockConsumerType(**GenIncDictionary)\n", @@ -295,11 +326,48 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized consumption function by pLvl for explicit permanent income consumer:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function for normalized problem (without explicit permanent income):\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Compare the normalized problem with and without explicit permanent income and plot the consumption functions\n", "print('Normalized consumption function by pLvl for explicit permanent income consumer:')\n", @@ -333,11 +401,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot the value function at various permanent income levels\n", "if ExplicitExample.vFuncBool:\n", @@ -355,11 +434,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Simulate many periods to get to the stationary distribution\n", "ExplicitExample.T_sim = 500\n", @@ -409,12 +499,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "code_folding": [ 0 - ], - "collapsed": true + ] }, "outputs": [], "source": [ @@ -436,10 +525,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "# Make and solve an example of \"persistent idisyncratic shocks\" consumer\n", @@ -449,11 +536,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of 0.98:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot the consumption function at various levels of persistent income pLvl\n", "print('Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of ' + str(PersistentExample.PrstIncCorr) + ':')\n", @@ -471,11 +576,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot the value function at various persistent income levels\n", "if PersistentExample.vFuncBool:\n", @@ -493,11 +609,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lLprNe4qDeUpjjDmmeEkEu0WkJ+6qZG4XzxwP7xsHXAUsE5HF7ra7ga4Aqvpc48Nteinx0dZGYIwJa14SwS3AJKCfiGwDNgA/behNqjoTkIb2q7L/tV73bUopcdEUWq8hY0wY85IItqnqRBFJBCLcrqCtAx1YsCTHRVFYWk5FpRIZ4TlvGWNMi+Glsfg9EYlS1f1uEmgPfBXowILFN81EkZUKjDFhyksi+AB4R0Qi3fEAU3B6EbUI/mkmrAupMSZMeVmP4AURicFJCJnAzao6K9CBBYuvRJB/oIwuIY7FGGNCob65hu6o+hToAiwGjheR4+vqEtrc+BensRKBMSZM1VciSK7x/P06tjdrvjUJrOeQMSZc1TfX0J/cmUH/qqq/DWJMQZXqW5PAxhIYY8JUvY3F7hxAw4MUS0gcqhqyEoExJjx5GUewWEQ+At4G9vs21jF3ULOTZMtVGmPCnJdE0BrIw5kq2keBFpEIIiOE5Ngoayw2xoQtL91HrwtGIKHkzDdkVUPGmPDUYCIQkTjgBpxlK/1LVKrq9QGMK6iS46JsuUpjTNjyMrL430B74EfADKAzznKVLYYtTmOMCWdeEkEvVf0DsF9VXwXOAQYFNqzgssVpjDHhzEsi8N0q7xOR44BUnKkmWoyUuGjyrdeQMSZMeek1NElEWgF/AD4CkoA/BjSqIMtIiSW3sBRVpcZKbMYY0+J56TX0ovtwBtAjsOGERvuUOA5WVLJn/0HaJMWGOhxjjAkqL72G0oCrcaqD/Pur6q2BCyu42qc4naF2FJRYIjDGhB0vVUOfAXOAZUBlYMMJjXapTiLYWVDCwI6pIY7GGGOCy0siiFPVOxrerfnylwjyS0MciTHGBJ+ncQQi8jMR6SAirX1fAY8siDKSYxFxqoaMMSbceCkRHAT+DtyDM8cQ7vcW03AcHRlBelIsO/MtERhjwo+XRHAHzqCy3YEOJpTap8RZicAYE5a8VA2tAIoDHUiotUuJY6clAmNMGPJSIqjAWZNgGuBvTW1J3UcB2qfGMn/TnlCHYYwxQeclEXzgfjWKiHQBXsOZsK4SmKSqj9fY5wLgz+7r5cDtqjqzsedqCu1T4thXXEZJWQVx0ZGhCMEYY0LCy8jiV4/w2OXAnaq6UESSgQUi8pWqrqyyzzfAR6qqIjIYeAvod4TnOyrtUg6NJejWJjEUIRhjTEh4aSM4Iqqao6oL3ceFQBbQqcY+Rarq64mUyKFeSUHnSwS7Cm0sgTEmvAQsEVQlIpnAMGBuLa9dJCKrgE+BWhe7EZGbRGS+iMzPzc0NSIxVSwTGGBNOPCcCETmi+hIRSQLexan/L6j5uqq+r6r9gAtx2gsOo6qTVHWkqo7MyMg4kjAa1C7FmWNoZ4GVCIwx4aXBRCAiJ4jISpyqHURkiIg84+XgIhKNkwQmq2q9i92r6rdATxFJ93LsppYaH01MVAS7rERgjAkzXkoEj+IsU5kHoKpLgJMaepM4E/u/BGSp6iN17NPL3Q8RGQ7E+M4TbCJCu5RYqxoyxoQdL91HUdUtNRZsqfDwtnHAVcAyEVnsbrsb6Ooe8zngx8DVIlIGHAAurdJ4HHTtkuOsasgYE3a8JIItInICoCISA9yKW01UH3c8QL3Lfanqw8DDXgINhnYpcWTtOKwZwxhjWjQvVUM/B27B6fq5FRjqPm9x2qbEsstKBMaYMONlQNlu4MogxBJy7VLiKCotp6i0nKRYT7VmxhjT7HlZqvKJWjbnA/NV9cOmDyl0fF1IdxWUkJSRFOJojDEmOLxUDcXhVAetdb8GA62BG0TksQDGFnTtkn2Dyqx6yBgTPrzUf/QCTlXVcgAReRaYApyOs45xi9HWP82EdSE1xoQPLyWCTjjzAPkkAh1VtYIq01K3BIdGF1siMMaEDy8lgr/hrEcwHac76EnAQ+6UE18HMLagS4qNIiEm0qqGjDFhxUuvoZdE5DNgNE4iuFtVt7sv/zaQwQWbM7rYViozxoQXr5POlQA5wB6gl4g0OMVEc9U22cYSGGPCi5fuozcCtwGdgcXA8cBs4NTAhhYa7VLiWLJ1X6jDMMaYoPFSIrgNGAVsUtVTcNYVCMyiAMeAzq3i2bb3AKXlXqZTMsaY5s9LIihR1RIAEYlV1VVA38CGFTrHdUqlvFJZvaMw1KEYY0xQeEkEW0UkDWcB+69E5ENgewPvabYGdUoFYOnW/BBHYowxweGl19BF7sP7RWQakAp8EdCoQqhzq3gSYyLJ3lUU6lCMMSYo6k0EIhIBLFXV4wBUdUZQogohESEjOZbdRdZzyBgTHuqtGlLVSmCJiHQNUjzHhPQkSwTGmPDhZWRxB2CFiPwA7PdtVNXzAxZViKUnxZKda1VDxpjw4CUR/CngURxj0pNjmLPBSgTGmPDgpbF4hoh0A3qr6tcikgBEBj600MlIimNfcRkHyyuJifI6+NoYY5qnBj/lRORnwDvA8+6mTjhdSVus9OQYAGsnMMaEBS+3u7cA44ACAFVdC7QNZFCh1iPdWZ3MBpUZY8KBl0RQqqoHfU9EJArQwIUUekO6pBIZISzYtDfUoRhjTMB5SQQzRORuIF5ETgfeBj4ObFihlRATxcCOKczftCfUoRhjTMB5SQS/x5lkbhlwM/AZcG8ggzoWDO/aiiVb8imrqAx1KMYYE1BeEsEFwGuq+j+qeomqvqCqLbpqCGBkZisOlFWQlVMQ6lCMMSagvCSC84E1IvJvETnHbSNokIh0EZFpIpIlIitE5LZa9rlSRJa6X7NEZEhjLyBQRnRrBWDtBMaYFq/BRKCq1wG9cNoGrgDWiciLHo5dDtypqv1xFrO5RUQG1NhnA3Cyqg4G/gxMakzwgdQhNZ5OafG8Pncz//PcLHbk2/KVxpiWydNoKVUtAz4H3gAW4FQXNfSeHFVd6D4uBLJwxiBU3WeWqvpuuefgrIJ2zBjerRVrdxUxb+NeJs/dFOpwjDEmILwMKDtTRF4BsoFLgBdx5h/yTEQycVY2m1vPbjfgJJva3n+TiMwXkfm5ucFbHO3G8d352YndGdujDR8ubrFLMBhjwpyXEsG1OCOJ+6jqNar6maqWez2BiCQB7wK3q2qtLa8icgpOIvjf2l5X1UmqOlJVR2ZkZHg99VEb0iWNe84ZwOkD2rF5TzHb9h0I2rmNMSZYvLQRXKaqH6hqKYCIjBORp70cXESicZLAZFV9r459BuOUMi5Q1TzvoQfP8T3aADBnnRNeVk4Bd7y1mOxdNvLYmHCwakcBu4tKWbE9nzfnbaaldZz02gNoKE5D8U9wGnhr/VCv8R4BXgKyVPWROvbp6h7rKlVd4zXoYOvXPpm0hGjmrM/jxyM6888pq/k6axcz1+7m89tOpE1SbKhDNMYEwEOfZbEpbz9frthJanw0+QfKABjapRV92yeHOLqmU2eJQET6iMgfRSQLeArYAoiqnqKqT3o49jjgKuBUEVnsfp0tIj8XkZ+7+/wRaAM8474+/yivJyAiIoQx3Vsze30eW/YU882qXUzom8GuwlI+sLYDY1qkuevzmPTter5csRPAnwQAfvTYtzz4ycpQhdbk6qsaWgWcBpynquPdD/8KrwdW1ZmqKqo6WFWHul+fqepzqvqcu8+Nqtqqyusjj+5yAufkPm3ZuvcAd761hAgRHrpoEH3aJfHp0u1UVLasYqJp/lSVm16bz/Mz1oU6lGaprKKSO95aQrc2CRzfo7V/+6BOqf7Hr87eSGUL+d+vLxH8GNgBTBORF0TkNECCE9ax58JhHUlLiOaHjXsY3yudjmnxXDG6Kws37+PlmRtCHZ4x1Uxfk8uUlTv5v89X8ZdPD925FpSU8ea8zcfUB9jG3fvZsqc41GH47Sooofc9n7Nt3wH+eO4AXrt+DP+6dhTtU+L4648H8dxPR/A/IzpTVqFsz28ZHUjqTASq+r6qXgr0A6YDvwbaicizInJGkOI7ZiTERHHOIKfX7Im90wG4dlx3+rVP5tu1wevSapq32evy+GhJYKsTVZUnv1nrf/7CdxvYureYe95fxuD7p/C/7y5j6qpdAY3Bq617i5nwj+mc+Ldpx0wD7OfLdwDQLiWWCX3bEhMVwSn92jLn7tMY2DGVM49rz09GdQFg7a6WsaStl15D+1V1sqqeizPgazHORHRh53c/6sdNJ/XgUvePAJxBZ4s37zum7rCCqbJSyQ7hP8Osdbt5ddbGkJ2/sa5+eS63/ncRU1ftDMjxiw+W86ePV7Jw8z6GdD5UjXHGo98yee5m//MvV+xAVZmxJpdVOwI7n1ZJWQU3/3s+y7flV9ueX1zG+Ien+Z+vPEbm9fpq5U56ZiQy9+6JREbUXgnSK8NZs2Rq1rGRUI9Wo9ZhVNU9qvq8qp4aqICOZakJ0dx9dn+S46L924Z3bUVhaXmLuTNorHs+WMbER2bwwMcr+WDRtqCf/4oX5nLfRys478mZFJV6Ht4SErmFpZRVODcMHwWok8HzM9bzipsY/3KRU41x3pCOFB+soG+7Q71cpq3O5cPF27nm5R+48oW5HCxvull2VZWtew9V9Xy0eDtfrtjJw1+s8m/bsqeYIQ9Mqfa+Oeudad9LyipCVjooKCljzvo8Jg5oV+9+rRJjuGJMV/49Z1NIb4Saii3Ie5R8k9Mt3Bw+k9Nt2VNM/oEydheV8t8ftgDw8vcbuP3NxewqaPo5mVS11rvWvCpLiS7bls/b851YCkvKKCgpO2z/UKqsVF5y25J6t01ixprcgHQyqLqqXr/2yZx5XHuevHwYq/58Jp/cOh6ADqlx7C4q5R9TVgOQt/8gj369htLyClZsz6ekzHOfkFo9/+16xj88jfW5RcxYk8vv3l0KUG1K98+X5wDQPT2RpfefQXpSLFk5BWzbd4B+f/iCdxfWfVOxI7+Ep6auZc3OIx/HU1JWQWl5BYXu38kjX63htdkb+XRpDuWVyun9608EALed1hsRAl7VFwyWCI5SZpsEWifGhM0speUVlZzx6LcM+dMURj74NQAXDz80hdScDQ0v5rNlTzFvzdvi+a7vX99v5MzHvmP+xkPHnrl2N6f8YzpREcLjlw2lf4cUnpm+jvwDZVz49PeM/svXzF6Xd8yMBn96WjbPzVjH+F7p/PLUXuwtLmPp1n1Ncuz8A2U8Mz2bWdm7mbvBGfR43bhMoiIP/XvHRUcSHRlB9l/O4uNfjadDahxb9x7g1xP7cErfDJ6dvo5Lnp3NOU/M5L4PV7A5r5hZ63Yzb+Oew6p0wPlQ/83bS1ix/dBrhSVl/N/nWfz1c+fO/4lv1vLQp1kA9EhPZM76PYx56GuWbNnHtFW59G2XzLTfTCAlLpoBHVNYub2Ab9c47W1vztt82Dl9HvlqNf+YsoY73lrM0q2Nr5Zdvi2fwfdPoe+9XzDywa95a/4WnvhmLX/8cAV3vbeMoV3SGN61VYPHaZcSx4iurZixpvm3EXoaUGbqJiKM65XOlBU7KL5gIAkxLftHmpNfwgH3jjE9KYaM5DiuHNOV99w7uG/X5HL+kI6UlFXw3sJtnDGwHW0SY3DGFzru/WA5M9bkkltUyi2n9KrzXMUHy7njzSV8scJpvJuxJpfcwlK+WLGD3MJSCkrKef1nYzihZzo90pM476mZ/P3LVazL3Q/A5S/M4bR+bXnp2lH+Y1ZUKle/PJeeGUk8cMFxTf7zqcu3a3NJiInkuatGUFZeSYQ41TPDPHzgqCqz1+excnsB2/eVkJEcS1lFJbee1huAjxZv429frPbv/8hPhnDx8Nrnb4yKjCA9KZbZd51GeUUlUZERlFVU8vjXa3lqWjYA7y/exrJt+dXq7Lu2TuDCoR25fWIf1u8u4oKnvmf/wQpWbi/gs9tOBOCxr9f6Sz2Af4zNwz8eRFx0JLe9sZidBaVc/8o88vYf5FenHvrd92+fzL/W5fGd2/Fi3sa9XPXSXAZ1SuV3Z/ardg1LtzrJZ/m2As5/6nse/vEgLh3V9bBr3ZS3n+mrc7lkRGcSYw/9X/79y9UcrKhkQIcU1uUW8bt3nBLLdeMyaZscx49HdCKijraBmkZmtualmespKasgLjrS03uORS37UytIrh7bjY+XbOfmfy/gzxccR2Z6YqhDCpite5077Mk3jmFcL6f3lCDdTOoAABqWSURBVKpy33kD+D47j8+W5XDfeQOY9O16npyazd3vL+O+8wZw3bjugPNBvMS9E378m7UM7pzKib1rnz/q37M3+ZMAwJNTs6u9funILpzQ04lhUOdUzhnUgf/Mce4kh3VNY9Hmfew7UL2K6JOl2/k+O4/vs/O4+eSeLNy0l3MGdfD/45eUVTBr3W4GdEilfWrcEf+c9u4/yNJt+RSWlDGxfzuWbMnnmhO6kRQbBbEwuHMaM9fmcsfpfRo81mfLdnDL6wsP2x4VKSTFRrFiWwFx0RGUlDlVL77fS0N8JYboyAjuPKMP2buK+GLFDg6WVx7WcLt5TzFPTM3m27W7WbzlUEmm0i3VFZSUMXnuJk7pm8E1J2Ty+DdrWbR5Hx/9chyDO6dRfPBQ+03efmcJ9AuHHSpJ9shI5GBFJZ8tO/T7/m7tbr5bu5vLR3elS+sEwPn9rN1VxK9O7cWADin8YvJCpq7aVS0RFJSUMTVrF7e/uRiAtbsKufecAfz5k5VcPTaTOevzuPaETO4/fyDLt+Uzf+MeRndvw4COKZ5+blUN75rGcxXKos37GNuzTaPff6ywRNAERmW25toTMnll1kYe/2Ytj146NNQhBYSqssVtBOzcKt6/XUS4blx3BnVK5eusnVz+whyWbzv0QfLp0hx/IliXW8S+4jLuPL0Pr87eyO/eWcqs359arcTg8+7CrYzp3pqrxnYjK6eAp6dVHxx11dhu1Z7fNrE3ny7LIT0phvd+cQK/fH3RYSvMzatSvTTur1MB+M3bS8hIjuWBCwaycNM+/51xu5RY7j67PxcMPfSBVVmp3PDqPM4Y2J7LRx9+Fzp9tTP1yHuLtrHH/cB78vJhHKyo9M9ZBTCuVxuem7GeotJyJznUYvWOQl6auZ75G51qx69+fRL3fbSC9bn72VFQUq0UcELPNvzxvAHMXb+HdimNT2AiwjNXDmf/wXIe/3otL87cwOs3jmFwlzT++MFyfnVab257YxGLt+yjf4cU/8911Y5Cnp6WTWyUk4hun9iHIV3S/Nfqu0tOiIni3V+MJT0plm/X7iazTQI93Z43AJltDt083XN2f7btO+Bv9P5g0TZ+5ZZ+Hvosi4pKZXT31pzYO4PLRnXhk6U57C8tJzE2ioWb93LxM7MAZ9LIrq0TeHv+Vsb1TGfy3M3+nlO+D+3jOqVyXJVBYo01tmcbUuKiePn7DRzfo3Wtf8fNgSWCJnL/+QPZlLefeRv38M6CrZzSN4Pt+0o4rlOKpz+OwpIyyiqU1okxRxzDzoKSI/oQ8Cktr+ChT7O4+eSedEyLr/ba8m353PTafLa7C/R0SI0/7P2+ao7l2woYldmKF68exdPTs3nl+43+f9SNu51qm5P6ZJCWEM0fPlzB1r0HSIiJrDZnU/6BMtbsLOLO0/tw7uCOnDu4I7+Y0Iuk2CiKSss5WF552M+qT7tkXrpmJH3aJSMitE+N45tVO1FV/+9gxXYnts17itlZUEpkhBATGcGuglIe+HglFVXaLXYWlPLcjPXVEsHM7N1MW53LtNW5ZLZJZNm2ffzsxB6ICFk5BVz/yjxqVlnf+8Fy4qMjq92pn9Q7g6enrePDxdu4coyT0BZu3kvP9CS27itmyZZ83lmwhYWbnbvv/zehJ73bJTP5xjEcKKtgwB+/ZHT31hw4WMGybfmM7NaKfu1T6Ne+8Xe1PhERQnJcNPeeO4BfTOjp/3084t7Y/OfGMewuLKV7eiI7C0p5fe4mnpiazd+/dBLSgA4pDHa7rNZWTTKimzNC96o2h5eYu2cc2jaqe2uu6ZDCsK5pTPp2Pf/8ag1ZOwq49bTe/PeHzVw2qgvj3Z/lT0Z14Y15W5g8dxM3ndSTL90xABP7t+Xus/uTlVPIx0u28/L3TpVV68QYTu3XlpPqKIU2VnJcNFce341np6+j+12fMe03E+gegBqBwpIyVm4vIDM98aj+x+tiiaAJjenRhmmrc/nN20v825776XDOPK7h5RsumzSHlTkFZD1wZqPqGjfs3s/8jXv4Pns3Hyzezus3juEEj1UDNc3KzuPV2ZvIzi1iZLfWdGoVT6+2SRzXMZWnp2X7k0C/9snERB3ezyAyQvjZid2ZtS6PZ64cQWpCNBP7t2PSt+v5dFkOPxnZhY15TiLIbJNIfIxznWc//h2FbtfPZ68czlmDOvCF26vE1ysL8N85+6pXanNald4eHVLjKCmrZF9xGa0SY6ioVFblFHLZ6C48feVwtuw5wODOqag6/ep/9d9FAPztx4M5uW8GHy/ZzoOfZrF93wE6psVTXlHp/9ADpw0CnAQ0oW9bPlqynQgRfnZSd56fsZ7Zd53K2Y9/x97iMs4e1L7a73V099YM75rGs9PXccXorpSWV3LxM7NonxLHjio9r2KjnDr9m0/qCTh37gkxUcy9+zTSk2LZmLefBRv3csGwjg3+fhujtokUU+KiSXG7TrdPjeOOM/py28Q+PPjpSt74YQs3n9zjiO+IM9zzdU9PZEjnVESEC4Z2YlNeMSu2F/DZsh3+aqOfHt/Nf57hXVsxrlcbXvhuA1ePzeS7tbsZ26MNL17jtAv5/sbmbdzLxP7tePGapp/F5pxBHXh2ulNa/eeU1Tx1xfAmP8fDX6ziP3M2c/NJPbjr7P5NfnxLBE3onEEdmLM+j9U7CslxPzSfnb6OHw1sX+8/SPauQlZsd4rar8/dzPXju3s+58Ofr6pWjz5rXd4RJ4K17rTavjp0n9aJMezZf5Drx3Xn4uGd6JFR9x3P3Wf3r3atozJb0TMjkQc+XsmWPcV8vnwHaQnRpCZEkxIfxb3n9Of1HzZTmOskghe+W8+GvP387YvVDOyYwvBuDTem1qWTW6rZtu8ArRJj2LC7iANlFQzsmErb5DjaJh+6szrzuPb+x+cO6UBCTJQ/CT329RoeumgQs9fnsWxbPo9eOoRNecX8Z85mdheV8sYPW9hbfJD/zN7E2J5tuOus/tx6am8SY6M4a1AHZ7nTkV2qxSYiXDaqK797dykrcwr8A5d21Oh++9QVwzm9lj7tvrvCnhlJ1apYgi0yQrjvvIHcd97AozqOiPDDPaeRFl+9Y8HNJ/dgQt8Mpq7axetzN3PD+O6HVeXcckovrnhhLq/P3cyanYXceGIP/2sdUuO5+eQepMZHc9Xx1asSm8rAjimcP6QjWTkFfLI0h/83oaDR7Q1lFc4NS0Zy7Xc48zfuJS0h+rC/o6ZiiaAJdWmdwCvXjWbVjgLOfOw7AJZszWfuhj3V6od3FTg9P3x/8NNX57rvj+evX6yiY1p8tQ8mny17isnKKeCMgc5rs9flMTN7N11ax/PGTWO5/l/zeGpaNpeM6OxvsC4+WM5v31lKwYEyXrxmJLFRdZc2fL0xfN66eSxrdhbynzmbOLF3OrdN7E1qfHQd73bUTHgiwgtXj+TUf87wN/YO75rmf+3GE3v4/3GfmZ7N375YzcLN+zhvSEf+fsngo+qJ4atuWJdbREJMJBMf+RZw/nFrio6M4O2fj6X4YIW/51f/Ds5+b83fyqDOaazbVURcdARnHdeBuOhIbp/Yh9+9s4S35m/lixU7GN41jb9cOAjA30vlD+cMYHyvdCb0Obwq4rT+bYmKEP7+5WrOGHDo9/1/Fw/irveWATCmyoRnLV3VxOwTGxXJ4M5pDO6cxu0Ta29YH9ujDSO6teIBdzbQ3m2rJ8a7zmr6O+iqRIQnLh9GfnEZJ/5tKg9+upJfTOjJ+F7pDZaQCkrKSIyJ4nfvLOX9RdtY9efDawSKD5azZmchvzylF73aBibpWyIIgD5tnRGcPTMS2bP/IK/N3uhPBN9n7+bKF+fy3E9H+D/sZ2Y7jWePXDqU299YzM//s4Cfndid4oMVXDisE6MynQ+Di5+dRW5hKWsePIv9peX+qonzhnSkU1o8J/VJZ/XOQh78NMtfBH5m2jo+XepUszw9NZs7zujrjzN7VxGT527ihJ7pjMpsxTc1hsuP7t6a0d1b89OjvJPqkZHEzSf34NVZG7nn7P51llhuGN+dvfsPkhgbxS2n9CI68uiGuXRPTyQyQli7s4jyikMV93X9M/l+zj5x0ZH+3kf/nLKaopJyJvZvV+0fdXzvDN6avxWAe84ZQNc2CdWOER8TydmDaq8abJMUyy8m9OTJqdn+m4F/XTuKCX0zKD5YweDOqf6qGFM3EeGXp/TiulfmAXX/fgMtNSGaW07pxf99vopZ6/K4++x+3ORW6flsyttPTn4JozNbU6HKaf+cQWxUhL833sJNew/7/5izPo9KhWFHUTpuiCWCAIiIEL7//akkxUbx1NS1vPDdBsb9dSp3n92f37/n9FleunUfZx7Xnv/+sJnpq3P56fFdGd61FVN+fRL9/vAFL3znNG5l7yrizZvHAs4UBeBUdXyTdWiumo5uN8e7zupPpTqjfHcWlBAXHcmny3I4qU8G6YkxPDE1m9P6t2NIlzRUlVv/u4iVOQX86/uN/mM9efkwf115U/rfH/XjztP71tq24BMbFck95wxosnPGRkXSPT2RNTsL/Q3Ll43q0qgE8/bNY/ls+Q7u+3A5Gcmx3HV29T7tE/oeutMf0KHxDbV3nN6HAR1SuPeD5fRqm8Qp/doCTlI03k3om0GntHi27TtAzxAlAoDrx3dn0eZ9fLFiBw99topdBaXce+6hv+kzH/uOA2UVjOzWihXbC/xjcnz+8OFy7j9/ION6pvPpshzmb9zD0m35pCVEM67nkVX5emGJIEB89dOn9W/HC99tYNu+A9zy+kJ841Ry8kuoqFSenpbNwI4p/N4tvsZFR/LE5cNYvHkfe/aX8v26w1fvXJVTwFvudArg3ImAk4B+PLwzL83c4G/oBDh/SEduOLE7nyzN4aMl2xnSJY2Pl+awMqeAif3bsWJ7PpWq/PmC4zhjYHuWb89nTPemrZKIiBBiPA7SaUr92iezaPM+umckEhMZwf9dPKhR74+KjOD8IR05f0jtjbEpcdEM6JBCTv4Bf8NkY4gIZw3qwFl1lBqMNyLC13eczLrcojq74wZDdGQEz101gnW5Rdz/0QpenLmBt+ZvYUiXNNbuLPJ/8M+vMhPBpSO7oCg9MpJ4elo2V730A2cMaMeUlYdu9u49p3+9N1FHyxJBgA3tkuZ/nBATya8n9uHbtbl8k7WTDxZtY+veA9x7zoBqf7y+D55np6/jg8Xbmbs+jzfmHfrg/8VkZ3BRv/bJrNpRSHqVHh792ifTJjHGnwTAqetOiYvmtP5tmTx3E2cMaMdfP8tiYMcUnr9qBBHiDPTyDTAKdJ1qMI3o1opPluawaPM+2qbEBqSf9/u3nEBl083ZZo5QfEzkUY0JaEo9M5J4+dpRXP/KPP/AuFP6ZnDWoPbkHygjLT6G8b3bIIi/FAhOUrjz7SX+JHBi73SuGZvZ4CR4R8sSQYDFRUdy66m96NQqnv8Z0QUR2F1Uyndrd3Pn20vo1z6ZM+r4Jft651w6yWkLqLpmKsCbN41l8dZ9nNT7UJExIkK4fWJvHvw0i/OHdGRTXrF/8MyfLzyOHz36LVe8OBdV5bHLhvl7q0RFNs+BMA3x1fv/sGFPta6oTam+BngTvqIjI/j3DWMY+eDX7C4q5fmrRjZ4V98qMYZnrhzO09OyGdAhJWglRUsEQVC1gRbg16f3Yc3OQqatzuW203rXOa/JsK5p1Z4vue8MFm3ey7yNe4iLjiQ1IZqTa+mNctXYTK4Y0+2wudTTk2L52Uk9eGTKGp64Yhijm7j651jUv0OKP4EezZQRxhypz24dT1FpueeqnbjoSO6s8ZkRaHKsrArk1ciRI3X+/GNyjftG8c1pc0rftvVWV1RUKle9NJdLR3WpNsL1SKkqRaXl1dZUaOkueuZ7Fm3e53lwnzEtkYgsqGtdeEsEpsVbvaOQj5Zs487T+3qeVdKYlqa+RGBVQ6bF69s+md+279fwjsaEKVuYxhhjwpwlAmOMCXOWCIwxJswFLBGISBcRmSYiWSKyQkRuq2WffiIyW0RKReQ3gYrFGGNM3QLZWFwO3KmqC0UkGVggIl+p6soq++wBbgUuDGAcxhhj6hGwEoGq5qjqQvdxIZAFdKqxzy5VnQeU1XIIY4wxQRCUNgIRyQSGAXOP8P03ich8EZmfm5vblKEZY0zYC3giEJEk4F3gdlUtaGj/2qjqJFUdqaojMzKaZq1RY4wxjoAOKBORaJwkMFlV32uKYy5YsGC3iGw6wrenA7ubIo5mxK45PNg1h4ejueY6V5gKWCIQZwKdl4AsVX2kqY6rqkdcJBCR+XUNsW6p7JrDg11zeAjUNQeyRDAOuApYJiKL3W13A10BVPU5EWkPzAdSgEoRuR0YcKRVSMYYYxovYIlAVWcC9c7wpao7gM6BisEYY0zDwm1k8aRQBxACds3hwa45PATkmpvdNNTGGGOaVriVCIwxxtRgicAYY8Jc2CQCETlTRFaLSLaI/D7U8TQVEXlZRHaJyPIq21qLyFcistb93srdLiLyhPszWCoiw0MX+ZGra0LDlnzdIhInIj+IyBL3mv/kbu8uInPda35TRGLc7bHu82z39cxQxn+kRCRSRBaJyCfu8xZ9vQAislFElonIYhGZ724L6N92WCQCEYkEngbOAgYAl4vIgNBG1WReAc6sse33wDeq2hv4xn0OzvX3dr9uAp4NUoxNzTehYX/geOAW9/fZkq+7FDhVVYcAQ4EzReR44GHgUfea9wI3uPvfAOxV1V7Ao+5+zdFtOPOU+bT06/U5RVWHVhkzENi/bVVt8V/AWODLKs/vAu4KdVxNeH2ZwPIqz1cDHdzHHYDV7uPngctr2685fwEfAqeHy3UDCcBCYAzOKNMod7v/7xz4EhjrPo5y95NQx97I6+zsfuidCnyC0x29xV5vleveCKTX2BbQv+2wKBHgzHq6pcrzrdSYCbWFaaeqOeDMAgu0dbe3uJ9DjQkNW/R1u9Uki4FdwFfAOmCfqpa7u1S9Lv81u6/nA22CG/FRewz4HVDpPm9Dy75eHwWmiMgCEbnJ3RbQv+1wWby+toFt4dhvtkX9HGpOaOjMalL7rrVsa3bXraoVwFARSQPeB/rXtpv7vVlfs4icC+xS1QUiMsG3uZZdW8T11jBOVbeLSFvgKxFZVc++TXLd4VIi2Ap0qfK8M7A9RLEEw04R6QDgft/lbm8xP4c6JjRs8dcNoKr7gOk47SNpIuK7oat6Xf5rdl9PxVkIqrkYB5wvIhuBN3Cqhx6j5V6vn6pud7/vwkn4ownw33a4JIJ5QG+3x0EMcBnwUYhjCqSPgGvcx9fg1KH7tl/t9jQ4Hsj3FTebE5E6JzRssdctIhluSQARiQcm4jSiTgMucXerec2+n8UlwFR1K5GbA1W9S1U7q2omzv/rVFW9khZ6vT4ikijOio6ISCJwBrCcQP9th7phJIgNMGcDa3DqVe8JdTxNeF3/BXJwVnnbitN7og1OI9ta93trd1/B6T21DlgGjAx1/Ed4zeNxir9LgcXu19kt+bqBwcAi95qXA390t/cAfgCygbeBWHd7nPs82329R6iv4SiufQLwSThcr3t9S9yvFb7PqkD/bdsUE8YYE+bCpWrIGGNMHSwRGGNMmLNEYIwxYc4SgTHGhDlLBMYYE+YsEZigE5F73Bk0l7ozLI5xt7/YVJMBujM4pjewz91HcNxrReSpWrZPEJETqjz/uYhc3djjB5qIjBSRJxr5ngZ/lqZ5C5cpJswxQkTGAucCw1W11P2AiQFQ1RuDHM7dwENNdKwJQBEwC0BVn2ui4zYZEYlS1fnA/FDHYo4tViIwwdYB2K2qpQCqulvdIfUiMl1ERrqPi0TkYXfira9FZLT7+noROd/dp9rduYh8UmVeGqps/8A9zgrfJF4i8lcg3i2RTHa3/VScOf8Xi8jz7vTliMh1IrJGRGbgTH1Q8/iZwM+BX7vvPVFE7heR31S5rkdF5Ftx1lAYJSLviTO3/INVjlPr+Wuca6P7c/nB/erlbs8QkXdFZJ77Nc7dfr+ITBKRKcBrbsnFN7d/a/dns1RE5ojIYHd7GxGZIs46AM9T+3w2pgWxRGCCbQrQxf1gfUZETq5jv0RguqqOAAqBB3Gmmr4IeKCR57zePc5I4FYRaaOqvwcOqDPn+5Ui0h+4FGfCr6FABXClO6/Ln3ASwOk461lUo6obgedw5skfqqrf1RLDQVU9yd3vQ+AW4DjgWveDt9bz13E9Bao6GngKZ/4dgMfd848Cfgy8WGX/EcAFqnpFjeP8CVikqoNxSkevudvvA2aq6jCcKQy61hGHaSGsasgElaoWicgI4ETgFOBNEfm9qr5SY9eDwBfu42VAqaqWicgynPUXGuNWEbnIfdwFZxGPvBr7nIbzgTnPmcqIeJyJvcbgJKRcABF5E+jTyPPDobmtlgEr1J0PRkTWuzGNr+P8tflvle+Puo8nAgPk0AysKb45a4CPVPVALccZj5M0UNWpbkJKBU4CLna3fyoiext5raaZsURggk6d6ZSnA9PdD/ZrcFZaq6pMD81/UomzQheqWimHZp8sp3qpNq7mudyqook4i5YUi8j02vbDqf54VVXvqvH+C2ma6YxL3e+VVR77nkfVdf46aC2PI3CusdoHvpsY9tdxnPqmMLa5Z8KIVQ2ZoBKRviLSu8qmocCmIzzcRpz5+SNEpAvOdL01peIsYVgsIv1wpm72KRNnOmtwJvK6RJw54H31591wFryZ4N4tRwP/U0cshUByHa95Udf5a3Nple+z3cdTgF/6dhCRoR7O+S1u9ZObMHerakGN7WcBrRp1JabZsRKBCbYk4ElxplQux5kt8qb631Kn74ENONUty3GWb6zpC+DnIrIUZxm/OVVemwQsFZGFbjvBvTgrQ0XgzOZ6i6rOEZH7cT5wc9xzHNaIC3wMvCMiFwC/auyFqOrK2s5P7UkyVkTm4tzIXe5uuxV42r3OKJwP8583cNr7gX+57ynm0DTHfwL+KyILgRnA5sZej2lebPZRY5oRcRZqGamqu0Mdi2k5rGrIGGPCnJUIjDEmzFmJwBhjwpwlAmOMCXOWCIwxJsxZIjDGmDBnicAYY8Lc/wd7uEUpjTueWQAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Simulate some data\n", "PersistentExample.T_sim = 500\n", @@ -513,8 +640,9 @@ ], "metadata": { "jupytext": { + "cell_metadata_filter": "collapsed", "formats": "ipynb,py", - "metadata_filter": {"cells": "collapsed"} + "notebook_metadata_filter": "all" }, "kernelspec": { "display_name": "Python 3", @@ -531,7 +659,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.7.6" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false } }, "nbformat": 4, From 0b75e7dfc61cc358ce3ce56c3327e1067dac512a Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 6 Feb 2020 14:35:53 -0500 Subject: [PATCH 191/242] cosmetic fixes to Journey 1; use new default parameter functionality --- Documentation/notebooks/Journey_1_PhD.ipynb | 62 +++++++++++++-------- 1 file changed, 40 insertions(+), 22 deletions(-) diff --git a/Documentation/notebooks/Journey_1_PhD.ipynb b/Documentation/notebooks/Journey_1_PhD.ipynb index 63aa8582a..dc4851a1e 100644 --- a/Documentation/notebooks/Journey_1_PhD.ipynb +++ b/Documentation/notebooks/Journey_1_PhD.ipynb @@ -33,9 +33,11 @@ "$$\n", "U(C)=\\frac{C^{1-\\rho}}{1-\\rho}\n", "$$\n", + "\n", "Now assume that every consumer faces some uncertainty on hers income (e.g. it follows AR (1) process), which is idiosyncratic - the realizations of each shock is (potentially) different for each agent. In this setting the bellman equation looks like:\n", + "\n", "\\begin{eqnarray*}\n", - "V(M_t, Y_t) &=& \\max_{C_t} U(C_t) + E\\[\\beta V(M_{t+1}, Y_{t+1})\\], \\\\\n", + "V(M_t, Y_t) &=& \\max_{C_t} U(C_t) + E[\\beta V(M_{t+1}, Y_{t+1})], \\\\\n", "& s.t. & \\\\\n", "A_t &=& M_t - C_t, \\\\\n", "M_{t+1} &=& R (M_{t}-C_{t}) + Y_t, \\\\\n", @@ -109,7 +111,8 @@ "metadata": {}, "source": [ "## 4 Agent-type class \n", - "Agent-type class enables you to build the macroeconomic models, such as presented in the introduction. It is also the essential part of the macroeconomic model in HARK. Therefore, to use HARK, you always need to use agent-type classes! \n", + "Agent-type class enables you to build the macroeconomic models, such as presented in the introduction. It is also the essential part of the macroeconomic model in HARK. Therefore, to use HARK, you always need to use agent-type classes!\n", + "\n", "### 4.1 Introductory example\n", "As an example, let's solve the stochastic model from the introduction. Assume the income process of the agent i in the period t: $Y_{i,t}$, is given by: \n", "\n", @@ -125,25 +128,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'<' not supported between instances of 'NoneType' and 'NoneType'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mHARK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutilities\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotFuncs\u001b[0m \u001b[1;31m#useful function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mExample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIndShockConsumerType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_idiosyncratic_shocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\Desktop\\HARK\\HARK\\ConsumptionSaving\\ConsIndShockModel.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, cycles, time_flow, verbose, quiet, **kwds)\u001b[0m\n\u001b[0;32m 1793\u001b[0m \u001b[1;31m# Add consumer-type specific objects, copying to create independent versions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1794\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolveOnePeriod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msolveConsIndShock\u001b[0m \u001b[1;31m# idiosyncratic shocks solver\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1795\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Make assets grid, income process, terminal solution\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1797\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Desktop\\HARK\\HARK\\ConsumptionSaving\\ConsIndShockModel.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1848\u001b[0m '''\n\u001b[0;32m 1849\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdateIncomeProcess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1850\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdateAssetsGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1851\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdateSolutionTerminal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1852\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Desktop\\HARK\\HARK\\ConsumptionSaving\\ConsIndShockModel.py\u001b[0m in \u001b[0;36mupdateAssetsGrid\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1831\u001b[0m \u001b[0mnone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1832\u001b[0m '''\n\u001b[1;32m-> 1833\u001b[1;33m \u001b[0maXtraGrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconstructAssetsGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1834\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maXtraGrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maXtraGrid\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1835\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddToTimeInv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'aXtraGrid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Desktop\\HARK\\HARK\\ConsumptionSaving\\ConsIndShockModel.py\u001b[0m in \u001b[0;36mconstructAssetsGrid\u001b[1;34m(parameters)\u001b[0m\n\u001b[0;32m 2441\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2442\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maXtraGrid\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2443\u001b[1;33m \u001b[0mj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maXtraGrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearchsorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2444\u001b[0m \u001b[0maXtraGrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maXtraGrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'NoneType' and 'NoneType'" - ] - } - ], + "outputs": [], "source": [ "import sys #set path of the notebook \n", "import os\n", @@ -153,7 +140,7 @@ "import Documentation.notebooks.Jounery_1_param as Params #imported paramters \n", "from HARK.utilities import plotFuncs #useful function\n", "\n", - "Example = IndShockConsumerType(**Params.init_idiosyncratic_shocks) \n" + "Example = IndShockConsumerType() \n" ] }, { @@ -427,7 +414,38 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.5" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, From c96acc58105902065645ed0730e84732e654cada Mon Sep 17 00:00:00 2001 From: sb Date: Sat, 8 Feb 2020 19:14:33 -0500 Subject: [PATCH 192/242] simplify checks for time varying solveOnePeriod, fixes #496 --- HARK/core.py | 21 ++++++++------------- 1 file changed, 8 insertions(+), 13 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index aeee94fea..e33ab7aec 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -165,15 +165,13 @@ class Solution(HARKobject): class AgentType(HARKobject): ''' - A superclass for economic agents in the HARK framework. Each model should - specify its own subclass of AgentType, inheriting its methods and overwriting + A superclass for economic agents in the HARK framework. Each model should + specify its own subclass of AgentType, inheriting its methods and overwriting as necessary. Critically, every subclass of AgentType should define class- specific static values of the attributes time_vary and time_inv as lists of strings. Each element of time_vary is the name of a field in AgentSubType that varies over time in the model. Each element of time_inv is the name of - a field in AgentSubType that is constant over time in the model. The string - 'solveOnePeriod' should appear in exactly one of these lists, depending on - whether the same solution method is used in all periods of the model. + a field in AgentSubType that is constant over time in the model. ''' def __init__(self, solution_terminal=None, cycles=1, time_flow=True, pseudo_terminal=True, tolerance=0.000001, seed=0, **kwds): @@ -871,12 +869,6 @@ def solveOneCycle(agent, solution_last): else: T = 1 - # Check whether the same solution method is used in all periods - always_same_solver = 'solveOnePeriod' not in agent.time_vary - if always_same_solver: - solveOnePeriod = agent.solveOnePeriod - these_args = getArgNames(solveOnePeriod) - # Construct a dictionary to be passed to the solver # time_inv_string = '' # for name in agent.time_inv: @@ -893,9 +885,12 @@ def solveOneCycle(agent, solution_last): solution_next = solution_last for t in range(T): # Update which single period solver to use (if it depends on time) - if not always_same_solver: + if hasattr(agent.solveOnePeriod, "__getitem__"): solveOnePeriod = agent.solveOnePeriod[t] - these_args = getArgNames(solveOnePeriod) + else: + solveOnePeriod = agent.solveOnePeriod + + these_args = getArgNames(solveOnePeriod) # Update time-varying single period inputs for name in agent.time_vary: From ede363ceb83b2b1ec6abfd62e1125131cf0d5ef7 Mon Sep 17 00:00:00 2001 From: Sebastian Benthall Date: Sun, 9 Feb 2020 05:32:54 -0500 Subject: [PATCH 193/242] changing version and author in sphinx (#489) * changing version and author in sphinx * adding Econ-ARK team to copyright notice * 2020 Co-authored-by: Mridul Seth --- Documentation/conf.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Documentation/conf.py b/Documentation/conf.py index f6d974c7b..a48157ac5 100644 --- a/Documentation/conf.py +++ b/Documentation/conf.py @@ -69,17 +69,17 @@ # General information about the project. project = u'HARK' -copyright = u'2016, TEMP' -author = u'TEMP' +copyright = u'2020, Econ-ARK team' +author = u'Econ-ARK team' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. -version = u'0.97' +version = u'latest' # The full version, including alpha/beta/rc tags. -release = u'0.97' +release = u'latest' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 17ada0805060286a6c30e725886ff6f40c8ef4bc Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Sun, 9 Feb 2020 22:03:28 +0530 Subject: [PATCH 194/242] fix setup.cfg order (#501) --- setup.cfg | 24 ++++++++++-------------- 1 file changed, 10 insertions(+), 14 deletions(-) diff --git a/setup.cfg b/setup.cfg index 1c2d5f15d..2674c4d06 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,16 +1,11 @@ [metadata] name = econ-ark version = attr: HARK.__version__ -description = Heterogenous Agents Resources & toolKit -long_description = file: README.md -long_description_content_type = text/markdown url = https://github.com/econ-ark/HARK -author = - Carroll, Christopher D - Palmer, Nathan - White, Matthew N. - Kazil, Jacqueline - Low, David C +project_urls = + Bug Reports = https://github.com/econ-ark/HARK/issues + Documentation = https://econ-ark.github.io/HARK +author = Econ-ARK team author_email = econ-ark@jhuecon.org classifiers = Development Status :: 3 - Alpha @@ -31,8 +26,13 @@ classifiers = Programming Language :: Python :: 3.6 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 -keywords = economics modelling modeling heterogeneity license = Apache Software License +description = Heterogenous Agents Resources & toolKit +long_description = file: README.md +long_description_content_type = text/markdown +keywords = economics modelling modeling heterogeneity + +[options] python_requires = >=2.7 install_requires = numpy matplotlib @@ -43,10 +43,6 @@ install_requires = numpy seaborn jupyter -project_urls = - Bug Reports = https://github.com/econ-ark/HARK/issues - Documentation = https://econ-ark.github.io/HARK - [options.extras_require] dev = future From 5952bc575d23c68b66e06dbd50bda86aac791241 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Sun, 9 Feb 2020 23:16:58 +0530 Subject: [PATCH 195/242] add packages = find: (#502) --- setup.cfg | 40 +++++++++++++++++++++++----------------- 1 file changed, 23 insertions(+), 17 deletions(-) diff --git a/setup.cfg b/setup.cfg index 2674c4d06..a9729ea32 100644 --- a/setup.cfg +++ b/setup.cfg @@ -33,15 +33,23 @@ long_description_content_type = text/markdown keywords = economics modelling modeling heterogeneity [options] +packages = find: +install_requires = + numpy + matplotlib + scipy + dill + joblib + funcsigs + seaborn + jupyter python_requires = >=2.7 -install_requires = numpy - matplotlib - scipy - dill - joblib - funcsigs - seaborn - jupyter + + +[options.packages.find] +exclude = + Testing + Documentation [options.extras_require] dev = @@ -50,17 +58,15 @@ dev = nbsphinx recommonmark + [options.package_data] -HARK.SolvingMicroDSOPs.Calibration = SCFdata.csv -HARK.cstwMPC = - EducMortAdj.txt - SCFwealthDataReduced.txt - USactuarial.txt +hark.solvingmicrodsops.calibration = + SCFdata.csv +hark.cstwmpc = + EducMortAdj.txt + SCFwealthDataReduced.txt + USactuarial.txt -[options.packages.find] -exclude = - Testing - Documentation [bdist_wheel] universal=1 From b2a89c134bd2b1ff53d9d1cdaf341bb1933b9dcf Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Sun, 9 Feb 2020 23:51:58 +0530 Subject: [PATCH 196/242] fix include package_data (#503) --- setup.cfg | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/setup.cfg b/setup.cfg index a9729ea32..24f9b7871 100644 --- a/setup.cfg +++ b/setup.cfg @@ -34,6 +34,7 @@ keywords = economics modelling modeling heterogeneity [options] packages = find: +include_package_data = True install_requires = numpy matplotlib @@ -45,12 +46,6 @@ install_requires = jupyter python_requires = >=2.7 - -[options.packages.find] -exclude = - Testing - Documentation - [options.extras_require] dev = future @@ -58,15 +53,18 @@ dev = nbsphinx recommonmark - [options.package_data] -hark.solvingmicrodsops.calibration = +HARK.SolvingMicroDSOPs.Calibration = SCFdata.csv -hark.cstwmpc = +HARK.cstwMPC = EducMortAdj.txt SCFwealthDataReduced.txt USactuarial.txt +[options.packages.find] +exclude = + Testing + Documentation [bdist_wheel] universal=1 From 87d9885a0cd5913ac0735e58a94275dca0692c1a Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Mon, 10 Feb 2020 03:03:03 +0530 Subject: [PATCH 197/242] move data files to manifest.in (#505) * include_package_data conflicts with package_data * fix data files --- MANIFEST.in | 6 ++++-- setup.cfg | 8 -------- 2 files changed, 4 insertions(+), 10 deletions(-) diff --git a/MANIFEST.in b/MANIFEST.in index 492b95d00..1294a8d4e 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -5,15 +5,17 @@ include *.md include LICENSE # include everything in the root -include HARK include requirements.txt include setup.py include MANIFEST.in include setup.cfg +# include data files +recursive-include HARK/cstwMPC *.txt + # Include most files # recursive-include ConsumptionSaving * -recursive-include Demos * +# recursive-include Demos * # recursive-include dist * # recursive-include FashionVictim * # recursive-include SolvingMicroDSOPs * diff --git a/setup.cfg b/setup.cfg index 24f9b7871..f6ab799e7 100644 --- a/setup.cfg +++ b/setup.cfg @@ -53,14 +53,6 @@ dev = nbsphinx recommonmark -[options.package_data] -HARK.SolvingMicroDSOPs.Calibration = - SCFdata.csv -HARK.cstwMPC = - EducMortAdj.txt - SCFwealthDataReduced.txt - USactuarial.txt - [options.packages.find] exclude = Testing From c8dffd8a8d7b38fef6d5681b51eeb4a381f0cc80 Mon Sep 17 00:00:00 2001 From: sb Date: Sun, 9 Feb 2020 17:03:00 -0500 Subject: [PATCH 198/242] adding test for default solution to PerfForesightConsumerType. See #504 --- HARK/tests/test_PerfForesightConsumerType.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 HARK/tests/test_PerfForesightConsumerType.py diff --git a/HARK/tests/test_PerfForesightConsumerType.py b/HARK/tests/test_PerfForesightConsumerType.py new file mode 100644 index 000000000..049c0fee0 --- /dev/null +++ b/HARK/tests/test_PerfForesightConsumerType.py @@ -0,0 +1,18 @@ +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType + +import unittest + +class testPerfForesightConsumerType(unittest.TestCase): + + def test_default_solution(self): + agent = PerfForesightConsumerType() + agent.solve() + c = agent.solution[0].cFunc + + self.assertEqual(c.x_list[0], -0.9805825242718447) + self.assertEqual(c.x_list[1], 0.01941747572815533) + self.assertEqual(c.y_list[0], 0) + self.assertEqual(c.y_list[1], 0.511321002804608) + self.assertEqual(c.decay_extrap, False) + + From 0f4aefd274b2c490beb10d3738bd61b0fba24d7d Mon Sep 17 00:00:00 2001 From: llorracc <@users.noreply.github.com> Date: Mon, 10 Feb 2020 12:07:27 +0100 Subject: [PATCH 199/242] Update Installation To Include retrieving submodules --- Installation_instruction.md | 28 ++++++++++++++++++++++++---- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/Installation_instruction.md b/Installation_instruction.md index dfa5b053a..0ae0516a9 100644 --- a/Installation_instruction.md +++ b/Installation_instruction.md @@ -5,7 +5,7 @@ HARK is an open source project written in Python. It's compatible with both Pyt ## Instructions for a new user In order to use HARK, you firstly need to download Python and add Python to the PATH. We recommend to install python 3, as eventually support for python 2 will end. If you are not confident about the installation process you can use this step-by-step guide https://realpython.com/installing-python/ . -Next, install text editor for Python. If you do not use Anaconda Python distribution (see below), we recommend [Atom](https://atom.io/). To use Atom for Python, firstly install it (you can use this [manual](https://flight-manual.atom.io/getting-started/sections/installing-atom/)). Next, install the [packages](https://flight-manual.atom.io/using-atom/sections/atom-packages/) for Python, we recommend to install at least [autocomplete-python](https://atom.io/packages/autocomplete-python) and [atom-ide-debugger-python](https://flight-manual.atom.io/using-atom/sections/atom-packages/). The last enables debugging the Python scripts, that is to set breakpoints and call variables in certain spot of your code (with increase in your codes' sophistication you will find this tools very helpful in finding bugs). +Next, install a text editor for Python. If you do not use the Anaconda Python distribution (see below), we recommend [Atom](https://atom.io/). To use Atom for Python, first install it (you can use this [manual](https://flight-manual.atom.io/getting-started/sections/installing-atom/)). Next, install the [packages](https://flight-manual.atom.io/using-atom/sections/atom-packages/) for Python, we recommend to install at least [autocomplete-python](https://atom.io/packages/autocomplete-python) and [atom-ide-debugger-python](https://flight-manual.atom.io/using-atom/sections/atom-packages/). The last enables debugging the Python scripts, that is to set breakpoints and call variables in certain spot of your code (with increase in your codes' sophistication you will find this tools very helpful in finding bugs). After installing Python and the text editor, you can install HARK package. The simplest way is to use [pip](https://pip.pypa.io/en/stable/installing/). @@ -13,8 +13,6 @@ To install HARK with pip, at a command line type `pip install econ-ark`. If you prefer to isolate the installation of `econ-ark` from the installations of any other python tools and packages, use a virtual environment such as [virtualenv](https://virtualenv.pypa.io/en/latest/). - - To install `virtualenv`, then to create an environment named `econ-ark`, and finally to activate that environment, at a command line type: ``` @@ -110,8 +108,13 @@ About the differences between conda and pip check https://www.anaconda.com/under ``` pip install -e . ``` +## Content Aside from the Toolkit + +If you don't already have one, you should designate a place on your computer where you will be putting all the Econ-ARK content. +For example, it is common to just create a folder `GitHub` in your home user directory. Inside `GitHub` you should create folders +corresponding to the GitHub ID's of users whose work you want to obtain; for example, `GitHub/econ-ark`. Inside the `econ-ark` directory you can obtain a number of different resources. -### HARK demonstrations +### Demonstrations And Illustrations Most of the modules in HARK are just collections of tools. To look at a demonstrations, check repository: * [DemARK](https://github.com/econ-ark/DemARK): Demonstrations of the use of HARK @@ -122,6 +125,23 @@ git clone https://github.com/econ-ark/DemARK.git ``` Once you have downloaded them, you will find that the repo contains a `notebooks` directory that contains a number of [jupyter notebooks](https://jupyter.org/). If you have the jupyter notebook tool installed (it is installed as part of Anaconda), you should be able to launch the jupyter notebook app from the command line with the command: +### [REMARK](https://github.com/econ-ark/REMARK/#readme): Replications and Examples Made Using the ARK + +From inside the `GitHub/econ-ark` directory, you will want to obtain your own local copy of the REMARK repo: +``` +git clone https://github.com/econ-ark/REMARK.git +``` + +Once the download finishes (it should have created `GitHub/econ-ark/REMARK`, change into that directory. +Its root level is mostly descriptive; the main content is in the `REMARKs` subdirectory, so `cd REMARKs` to +have a look at what is there. Each REMARK is contained in a directory with the handle of the REMARK; +for example, `BufferStockTheory` is the handle for the REMARK on 'The Theoretical Foundations of Buffer Stock Saving'. + +At the top level of the directory for each REMARK we have some meta-information (title, authors, etc) and an eponymous Jupyter notebook, e.g. `BufferStockTheory.ipynb.` In most cases there will also be an subdirectory, e.g. `BufferStockTheory` which is a placeholder for the substantive content of the project (like, the original paper). + * Until a REMARK is finalized and frozen, the original content is often kept somewhere else, e.g.\ in an author's GitHub repo. In this case, the REMARK repo uses a `submodule` version, which is sort of like a link to the original material. To save space, a regular 'clone' of the REMARK repo does not incorporate all the submodules; therefore you may find those folders empty when you first use them. In order to obtain the **real** content, in the root directory of the repo in question (e.g., in REMARKs/BufferStockTheory), you need execute the command `git submodule update --recursive --remote`. + +Once you have downloaded them, you will find that the repo contains a `notebooks` directory that contains a number of [jupyter notebooks](https://jupyter.org/). If you have the jupyter notebook tool installed (it is installed as part of Anaconda), you should be able to launch the jupyter notebook app from the command line with the command: + ``` jupyter notebook ``` From b229a2c4fb48d5f863a084b2ae329b7348de1e69 Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 10 Feb 2020 09:28:19 -0500 Subject: [PATCH 200/242] adding test for IndShockConsumerType.getShocks() --- HARK/tests/test_IndShockConsumerType.py | 31 +++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 HARK/tests/test_IndShockConsumerType.py diff --git a/HARK/tests/test_IndShockConsumerType.py b/HARK/tests/test_IndShockConsumerType.py new file mode 100644 index 000000000..41a3eda70 --- /dev/null +++ b/HARK/tests/test_IndShockConsumerType.py @@ -0,0 +1,31 @@ +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType + +import numpy as np +import unittest + +class testIndShockConsumerType(unittest.TestCase): + + def test_getShocks(self): + agent = IndShockConsumerType( + AgentCount = 2, + T_sim = 10 + ) + + agent.solve() + + agent.initializeSim() + agent.simBirth(np.array([True,False])) + agent.simOnePeriod() + agent.simBirth(np.array([False,True])) + + agent.getShocks() + + self.assertEqual(agent.PermShkNow[0], + 1.0050166461586711) + self.assertEqual(agent.PermShkNow[1], + 1.0050166461586711) + self.assertEqual(agent.TranShkNow[0], + 1.1176912196531754) + + + From 61a3e766c3553b126d4ae31cad826aa97b6d6086 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Tue, 11 Feb 2020 20:16:34 +0530 Subject: [PATCH 201/242] add util functions for notebooks (#512) * include_package_data conflicts with package_data * fix data files * add util functions for notebooks * fix fstrings to be 2.7 compatible --- HARK/utilities.py | 161 +++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 151 insertions(+), 10 deletions(-) diff --git a/HARK/utilities.py b/HARK/utilities.py index b90b7a035..7f4cd7230 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -1279,13 +1279,154 @@ def plotFuncsDer(functions,bottom,top,N=1000,legend_kwds = None): plt.show() -def main(): - print("Sorry, HARK.utilities doesn't actually do anything on its own.") - print("To see some examples of its functions in action, look at any") - print("of the model modules in /ConsumptionSavingModel. As these functions") - print("are the basic building blocks of HARK, you'll find them used") - print("everywhere! In the future, this module will show examples of each") - print("function in the module.") - -if __name__ == '__main__': - main() +def determine_platform(): + """ Untility function to return the platform currenlty in use. + + Returns + --------- + pf: str + 'darwin' (MacOS), 'debian'(debian Linux) or 'win' (windows) + """ + import platform + pform = platform.platform().lower() + if 'darwin' in pform: + pf = 'darwin' # MacOS + elif 'debian' in pform: + pf = 'debian' # Probably cloud (MyBinder, CoLab, ...) + elif 'ubuntu' in pform: + pf = 'debian' # Probably cloud (MyBinder, CoLab, ...) + elif 'win' in pform: + pf = 'win' + else: + raise ValueError('Not able to find out the platform.') + return pf + +def test_latex_installation(pf): + """ Test to check if latex is installed on the machine. + + Parameters + ----------- + pf: str (platform) + output of determine_platform() + + Returns + -------- + bool: Boolean + True if latex found, else installed in the case of debian + otherwise ImportError raised to direct user to install latex manually + """ + # Test whether latex is installed (some of the figures require it) + from distutils.spawn import find_executable + + latexExists = False + + if find_executable('latex'): + latexExists = True + return True + + if not latexExists: + print('Some of the figures below require a full installation of LaTeX') + # If running on Mac or Win, user can be assumed to be able to install + # any missing packages in response to error messages; but not on cloud + # so load LaTeX by hand (painfully slowly) + if 'debian' in pf: # CoLab and MyBinder are both ubuntu + print('Installing LaTeX now; please wait 3-5 minutes') + from IPython.utils import io + + with io.capture_output() as captured: # Hide hideously long output + os.system('apt-get update') + os.system('apt-get install texlive texlive-latex-extra texlive-xetex dvipng') + latexExists=True + else: + raise ImportError('Please install a full distribution of LaTeX on your computer then rerun. \n \ + A full distribution means textlive, texlive-latex-extras, texlive-xetex, dvipng, and ghostscript') + +def in_ipynb(): + """ If the ipython process contains 'terminal' assume not in a notebook. + + Returns + -------- + bool: Boolean + True if called from a jupyter notebook, else False + """ + try: + if 'terminal' in str(type(get_ipython())): + return False + else: + return True + except NameError: + return False + + +def setup_latex_env_notebook(pf): + """ This is needed for use of the latex_envs notebook extension + which allows the use of environments in Markdown. + + Parameters + ----------- + pf: str (platform) + output of determine_platform() + """ + if test_latex_installation(pf): + latex_preamble = r'\usepackage{amsmath}\usepackage{amsfonts}\usepackage[T1]{fontenc}' + import os + from os import path + import matplotlib.pyplot as plt + latexdefs_path = os.getcwd()+'/latexdefs.tex' + if path.isfile(latexdefs_path): + latex_preamble = latex_preamble+r'\input{'+latexdefs_path+r'}' + else: # the required latex_envs package needs this file to exist even if it is empty + from pathlib import Path + Path(latexdefs_path).touch() + plt.rcParams['text.latex.preamble'] = latex_preamble + + +def make_figs(figure_name, saveFigs, drawFigs, target_dir="Figures"): + """ Utility function to save figure in multiple formats and display the image. + + Parameters + ---------- + figure_name: str + name of the figure + saveFigs: bool + True if the figure needs to be written to disk else False + drawFigs: bool + True if the figure should be displayed using plt.draw() + target_dir: str, default = 'Figures/' + Name of folder to save figures to in the current directory + + """ + import matplotlib.pyplot as plt + if saveFigs: + import os + # Where to put any figures that the user wants to save + my_file_path = os.getcwd() # Find pathname to this file: + Figures_dir = os.path.join(my_file_path, "{}".format(figure_name)) # LaTeX document assumes figures will be here + if not os.path.exists(Figures_dir): + os.makedirs(Figures_dir) # If dir does not exist, create it + # Save the figures in several formats + print("Saving figure {} in {}".format(figure_name, target_dir)) + plt.savefig(os.path.join(target_dir, '{}.png'.format(figure_name))) # For web/html + plt.savefig(os.path.join(target_dir, '{}.pdf'.format(figure_name))) # For LaTeX + plt.savefig(os.path.join(target_dir, '{}.svg'.format(figure_name))) # For html5 + # Make sure it's possible to plot it by checking for GUI + if drawFigs and find_gui(): + plt.ion() # Counterintuitively, you want interactive mode on if you don't want to interact + plt.draw() # Change to false if you want to pause after the figure + plt.pause(2) + +def find_gui(): + """ Quick fix to check if matplotlib is running in a GUI environment. + + Returns + ------- + bool: Boolean + True if it's a GUI environment, False if not. + """ + try: + import matplotlib.pyplot as plt + except: + return False + if plt.get_backend() == 'Agg': + return False + return True From 68492c043df15b2c474cd18a386e5d84caf8bdee Mon Sep 17 00:00:00 2001 From: llorracc <@users.noreply.github.com> Date: Wed, 12 Feb 2020 00:06:19 +0100 Subject: [PATCH 202/242] Edit HARKmanual so it compiles to html --- Documentation/HARKmanual/HARKmanual.tex | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/Documentation/HARKmanual/HARKmanual.tex b/Documentation/HARKmanual/HARKmanual.tex index c7c02df20..50876f019 100644 --- a/Documentation/HARKmanual/HARKmanual.tex +++ b/Documentation/HARKmanual/HARKmanual.tex @@ -51,7 +51,11 @@ \maketitle \begin{center} -\includegraphics[scale=0.8]{UserGuidePic.pdf} + \ifdvi + \includegraphics[width=4in]{UserGuidePic.png} + \else + \includegraphics[width=2in]{UserGuidePic.pdf} + \fi \end{center} \thispagestyle{empty} From 8201f89ffe170d98dab9e9544e3e87e41eaef69f Mon Sep 17 00:00:00 2001 From: llorracc <@users.noreply.github.com> Date: Wed, 12 Feb 2020 00:07:41 +0100 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a/Documentation/HARKmanual.tex b/Documentation/HARKmanual.tex deleted file mode 100644 index ca1181ba6..000000000 --- a/Documentation/HARKmanual.tex +++ /dev/null @@ -1,666 +0,0 @@ -% !TeX spellcheck = en_GB - -\documentclass[12pt,titlepage,letterpaper]{econtex} -\usepackage{econtexSetup}\usepackage{econtexShortcuts} -\usepackage{graphicx} -\usepackage{hyperref} -\providecommand{\E}{\mathbb{E}} - -\newboolean{ContributingToHARKOnly} -\setboolean{ContributingToHARKOnly}{false} - -\begin{document} -\ifthenelse{\boolean{ContributingToHARKOnly}}{(This is an excerpt of the full manual, available at \url{http://econ-ark.org/HARK/Documentation/HARKmanual.pdf)}}{ % If ContributingToHARKOnly is true, do not include any text until that section -\title{\LARGE A User's Guide for HARK: \\ Heterogeneous Agents Resources and toolKit} - -\date{\normalsize{\today}} - -\author{ -{Christopher D Carroll\authNum} \\ {\small Johns Hopkins University} -\and -{Alexander M Kaufman\authNum} \\ {\small Consumer Financial Protection Bureau} -\and -{David C Low\authNum} \\ {\small Consumer Financial Protection Bureau} -\and -{Nathan M Palmer\authNum} \\ {\small U.S.\ Treasury Office of Financial Research} -\and -{Matthew N White\authNum} \\ {\small University of Delaware} -} - - -\begin{authorsinfo} -\name{ -Carroll: \url{http://econ.jhu.edu/people/ccarroll/}, email: \href{mailto:ccarroll@jhu.edu}{\texttt{ccarroll@jhu.edu}} -} -\name{ -Kaufman: \href{mailto:alexander.kaufman@cfpb.gov}{\texttt{alexander.kaufman@cfpb.gov}} -} -\name{ -Low: \href{mailto:david.c.low@cfpb.gov}{\texttt{david.c.low@cfpb.gov}} -} -\name{ -Palmer: \href{mailto:nathan.m.palmer@ofr.treasury.gov}{\texttt{nathan.m.palmer@ofr.treasury.gov}} -} -\name{ -White: \href{mailto:mnwecon@udel.edu}{\texttt{mnwecon@udel.edu}} -} -\end{authorsinfo} - -\thanks{The materials in this resource reflect work by the persons named herein and others, much of it accomplished at the CFPB. However, none of the materials here should be interpreted as reflecting official opinions of the CFPB or any employees of the CFPB. After appropriate technical review, these resources are expected to appear on the CFPB's official GitHub page. Views expressed in this presentation are those of the authors and not necessarily of the Office of Financial Research or other government organizations.} - -\maketitle - -\begin{center} -\includegraphics[scale=0.8]{UserGuidePic.pdf} -\end{center} - -\thispagestyle{empty} -\newpage - -\tableofcontents - -\newpage - -\section{Introduction}\label{sec:Introduction} - -If you are willing to risk some mild psychological trauma, conjure to mind your first experience of hand-coding a structural economic model. Your clunky effort probably built on legacy code provided by an adviser or colleague -- which itself came from who-knows-what apocryphal sources. Efforts to combine elements from one model with those from another were likely frustrated by the ``Tower of Babel'' problem: Code from one source could not ``speak'' to code from another without your own intermediation as a translator, possibly between two unfamiliar languages and aided only by oracular comments that, at best, made sense only in the context of other (now missing) code. - -After months of effort, you may have had the character-improving experience of proudly explaining to your adviser that not only had you grafted two ideas together, you also found a trick that speeded the solution by an order of magnitude, only to be told that your breathtaking insight had been understood for many years, as reflected in an appendix to a 2008 paper; or, worse, your discovery was something that ``everybody knows'' but did not exist at all in published form! - -Learning by doing has value, but only within limits. We do not require young drivers to design an internal combustion engine before driving a car, nor must graduate students write their own matrix inversion algorithms before running an OLS regression. - -In recent years, considerable progress has been made in addressing these kinds of problems in many areas of economic modeling. Macroeconomists using representative agent models can send Dynare model files to each other; reduced form econometricians can choose from a host of econometric packages. But modelers whose questions require explicit structural modeling involving nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply be aggregated away) are mostly still stuck in the bad old days. - -The ultimate goal of the HARK project is to fix these problems. Specifically, our aim is to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents.\footnote{By ``heterogeneous,'' we mean that agents might differ before anything in the model has ``happened'' (\textit{ex-ante} heterogeneity); and agents might experience different stochastic events during the model (\textit{ex-post} heterogeneity).} Further, we seek to establish (with input from the community) standards for the description and specification of objects like discrete approximations to continuous distributions and interpolated function approximations, so that numeric methods can be quickly swapped without ugly ``patching.'' - -We hope that HARK will make it much easier and faster for researchers to develop solution and estimation methods for new models. The open source nature of HARK will make it easier for other researchers to audit and verify new models and methods, and to collaborate on correcting deficiencies when found. As HARK expands to include more canonical models and more tools and utilities, we can all spend less time managing numerical minutiae and more time fretting about identification arguments and data accuracy. - -\subsection{Getting Started}\label{sec:GettingStarted} - -If you want to get started using HARK right away, this section provides a very easy quickstart guide for getting HARK up and running on your computer in just a few minutes. More information can be found in the \texttt{README.md} file in the HARK repository (step 4), but here is a quick version for those who want to jump right in: - -\begin{enumerate} -\item \textbf{Download Anaconda:} Go to \href{https://www.anaconda.com/download/}{https://www.anaconda.com/download/} and download Anaconda for your operating system; be sure to get the version for Python 2.7. - -\item \textbf{Install Anaconda:} Follow the easy instructions on that page to install Anaconda. - -\item \textbf{Add extra packages:} (optional) If you want to use HARK's multithreading feature, you need to add two packages that aren't part of the default Anaconda distribution. Simply open a command prompt and do \texttt{conda install joblib} and \texttt{conda install dill}, accepting defaults to install. - -\item \textbf{Download HARK:} Go to \href{https://github.com/econ-ark/HARK}{https://github.com/econ-ark/HARK}, the home of the HARK repository. Click on the lime green button labeled ``Clone or download'' toward the upper right of the main panel, and select ``Download ZIP''.\footnote{This is the fastest way to get HARK running on a computer. Later, if you want to make your own contributions to HARK by adding code (or fixing ours!), you'll need to clone the repository on your computer. See section \ref{sec:GitHub} for more.} - -\item \textbf{Unzip HARK:} Unpack the \texttt{HARK.zip} file using any archive utility, like \href{http://www.peazip.org/}{Peazip}. Put the files anywhere you'd like, maintaining the internal directory structure. - -\item \textbf{Run Spyder:} Open a command prompt and do \texttt{spyder}. Spyder is an interactive development environment (IDE) for iPython, a slightly prettier, more interactive flavor of Python. - -\item \textbf{Open a HARK module:} Go to the directory where you put HARK and open any file with the \texttt{.py} extension; we recommend \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}. - -\item \textbf{Run the module:} Click on the green arrow ``play'' button toward the right side of the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue box pops up). Congratulations! HARK is now up and running on your computer. -\end{enumerate} - -If you followed our recommendation to try \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}, you should see graphical representations of the solution to a few consumption-saving models with idiosyncratic shocks to permanent and transitory income (see \texttt{/Documentation/ConsumptionSavingModels.pdf} for details of these models). If you chose a different module, you might get some examples of some other model, results of a structural estimation, a polite notification that this particular module doesn't do much on its own, or no output at all. See the \texttt{README.md} for a full list of modules from which you can expect non-trivial output. - -\subsection{Structure of HARK}\label{sec:StructureOfHARK} - -HARK is written in Python, an object-oriented programming (OOP) language that has experienced increasing popularity in the scientific community in the past several years. A significant reason for the adoption of Python is the \texttt{numpy} and \texttt{scipy} packages, which offer a wide array of mathematical and statistical functions and tools; HARK makes liberal use of these libraries. Python's object-oriented nature allows models in HARK to be easily extended: more complex models can inherit functions and methods from more fundamental ``parent'' models, eliminating the need to reproduce or repurpose code. - -As implied in the previous section, we strongly encourage HARK users to use the Anaconda distribution of Python, which includes all commonly used mathematical and scientific packages, an interactive development environment for iPython (Spyder), and a package manager that allows users to quickly install or update packages not included in the default distribution (conda). - -For users unfamiliar with OOP, we strongly encourage you to review the background material on OOP provided by the good people at \href{http://quant-econ.net/}{QuantEcon} (for more on them, see below) at this link: \href{http://quant-econ.net/py/python_oop.html}{Object Oriented Programming}. Unlike non-OOP languages, OOP bundles together data and functions into \textit{objects}. These can be accessed via: \texttt{object\_name.data} and \texttt{object\_name.method\_name()}, respectively. For organizational purposes, definitions of multiple objects are stored in \textit{modules}, which are simply files with a \texttt{.py} extension. Modules can be accessed in Python via:\\ - -\texttt{import module\_name as import\_name} -\\ - -\noindent -This imports the module and gives it a local name of \texttt{import\_name}. We can access a function within this module by simply typing: \texttt{import\_name.function\_name()}. The following example will illustrate the usage of these commands. \texttt{CRRAutility} is the function object for calculating CRRA utility supplied by \texttt{HARK.utilities} module. \texttt{CRRAutility} is called \textit{attributes} of the module \texttt{HARK.utilities}. In order to calculate CRRA utility with a consumption of 1 and a coefficient of risk aversion of 2 we run:\\ - -\texttt{import HARK.utilities as Hutil} - -\texttt{Hutil.CRRAutility(1,2)}\\ - -Python modules in HARK can generally be categorized into three types: tools, models, and applications. \textbf{Tool modules} contain functions and classes with general purpose tools that have no inherent ``economic content'', but that can be used in many economic models as building blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include functions for data analysis (e.g.\ calculating Lorenz shares from data, or constructing a non-parametric kernel regression), functions to create and manipulate discrete approximations to continuous distributions, or classes for constructing interpolated approximations to non-parametric functions. Tool modules generally reside in HARK's root directory and have names like \texttt{HARK.simulation} and \texttt{HARK.interpolation}; they do not necessarily do anything when run. - -\textbf{Model modules} specify particular economic models, including classes to represent agents in the model (and the ``market structure'' in which they interact) and functions for solving the ``one period problem'' of those models. For example, \texttt{ConsIndShockModel.py} concerns consumption-saving models in which agents have CRRA utility over consumption and face idiosyncratic shocks to permanent and transitory income. The module includes classes for representing ``types'' of consumers, along with functions for solving (several flavors of) the one period consumption-saving problem. When run, model modules might demonstrate example specifications of their models, filling in the model parameters with arbitrary values. When \texttt{ConsIndShockModel.py} is run, it specifies an infinite horizon consumer with a particular discount factor, permanent income growth rate, coefficient of relative risk aversion (etc), who faces lognormal shocks to permanent and transitory income each period with a particular standard deviation; it then solves this consumer's problem and graphically displays the results.\footnote{Running \texttt{ConsIndShockModel.py} also demonstrates other variations of the consumption-saving problem, but their description is omitted here for brevity.} Model modules generally have \texttt{Model} in their name. - -\textbf{Application modules} use tool and model modules to solve, simulate, and/or estimate economic models \textit{for a particular purpose}. While tool modules have no particular economic content and model modules describe entire classes of economic models, applications are uses of a model for some research purpose. For example, \texttt{/Demos/SolvingMicroDSOPs/StructEstimation.py} uses a consumption-saving model from \texttt{ConsIndShockModel.py}, calibrating it with age-dependent sequences of permanent income growth, survival probabilities, and the standard deviation of income shocks (etc); it then estimates the coefficient of relative risk aversion and shifter for an age-varying sequence of discount factors that best fits simulated wealth profiles to empirical data from the Survey of Consumer Finance. A particular application might have multiple modules associated with it, all of which generally reside in one directory. - -\subsection{Other Resources}\label{sec:OtherResources} - -In the incredibly unlikely scenario in which this document does not fill all of the gaps in your knowledge and answer any questions you might have while reading it, here is a collection of potentially helpful other resources. - -\begin{itemize} - -\item \href{https://econ-ark.github.io/HARK/}{Online documentation} for HARK tool and model modules, produced using Sphinx. - -\item A \href{https://docs.python.org/2.7/tutorial/}{tutorial on Python 2.7}, straight from the source. - -\item \href{https://en.wikipedia.org/wiki/Object-oriented_programming}{Wikipedia article on object-oriented programming}, not the subject of an edit war. - -\item \href{http://quant-econ.net/}{QuantEcon}, a collection of lectures on quantitative economic modeling from a couple of economists, which definitely in no way influenced HARK whatsoever. - -\item A \href{https://www.atlassian.com/git/tutorials/}{tutorial on git}, the repository management and tracking system used by the HARK project, created by Linus Torvalds. - -\item A \href{https://guides.github.com/activities/hello-world/}{tutorial on GitHub}, a website that provides a useful framework for git that makes it significantly more user-friendly, despised by Linus Torvalds. - -\item \href{https://en.wikipedia.org/wiki/Linus_Torvalds}{Wikipedia article on Linus Torvalds}, just in case. - -\item The \href{http://www.numpy.org/}{homepage for NumPy}, a numerical package extensively used by HARK. - -\item The \href{http://www.sphinx-doc.org/}{homepage for Sphinx}, a tool for producing HTML documentation. - -\item A \href{http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/}{collection of lecture notes} by Chris Carroll, to catch you up on the basics of consumption-saving models. - -\item A \href{http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/}{particular set of lecture notes} by Chris Carroll, explaining many of the numeric techniques used in HARK and offering an example of a simple structural model that, in a stunning coincidence, is implemented in HARK as an example. - -\item A \href{http://www.econ2.jhu.edu/people/ccarroll/BufferStockTheory.pdf}{deep dive} into the theoretical foundations of buffer stock saving. Warning: dense. - -\end{itemize} - - -\section{General Purpose Tools}\label{sec:GeneralPurposeTools} - -HARK's root directory contains six tool modules,\footnote{The ``taxonomy'' of these modules is in flux; the functions described here could be combined into fewer modules or further divided by purpose.} each containing a variety of functions and classes that can be used in many economic models-- or even for mathematical purposes that have nothing to do with economics. Some of the tool modules are very sparely populated at this time, while others are quite large. We expect that all of these modules will grow considerably in the near future, as new tools are ``low hanging fruit'' for contribution to the project.\footnote{That is, as the foundational, building-block elements of HARK, new tools are not difficult to program and do not require extensive integration with many moving parts.} - -\subsection{HARKcore}\label{sec:HARKcore} - -A key goal of the project is to create modularity and interoperability between models, making them easy to combine, adapt, and extend. To this end, the \texttt{HARK.core} module specifies a framework for economic models in HARK, creating a common structure for them on two levels that can be called ``microeconomic'' and ``macroeconomic''. - -Microeconomic models in HARK use the \texttt{AgentType} class to represent agents with an intertemporal optimization problem. Each of these models specifies a subclass of \texttt{AgentType}; an instance of the subclass represents agents who are \textit{ex-ante} homogeneous-- they have common values for all parameters that describe the problem. For example, \texttt{ConsIndShockModel} specifies the \texttt{IndShockConsumerType} class, which has methods specific to consumption-saving models with idiosyncratic shocks to income; an instance of the class might represent all consumers who have a CRRA of 3, discount factor of 0.98, etc. The \texttt{AgentType} class has a \texttt{solve} method that acts as a ``universal microeconomic solver'' for any properly formatted model, making it easier to set up a new model and to combine elements from different models; the solver is intended to encompass any model that can be framed as a sequence of one period problems. For a complete description, see section \ref{sec:Microeconomics}. - -Macroeconomic models in HARK use the \texttt{Market} class to represent a market (or other aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual agents in the model into aggregate outcomes that are ``passed back'' to the agents. For example, the market in a consumption-saving model might combine the individual asset holdings of all agents in the market to generate aggregate capital in the economy, yielding the interest rate on assets (as the marginal product of capital); the individual agents then learn the aggregate capital level and interest rate, conditioning their next action on this information. Objects that microeconomic agents treat as exogenous when solving (or simulating) their model are thus endogenous at the macroeconomic level. Like \texttt{AgentType}, the \texttt{Market} class also has a \texttt{solve} method, which seeks out a dynamic general equilibrium: a ``rule'' governing the dynamic evolution of macroeconomic objects such that if agents believe this rule and act accordingly, then their collective actions generate a sequence of macroeconomic outcomes that justify the belief in that rule. For a more complete description, see section \ref{sec:Macroeconomics}. - -Beyond the model frameworks, \texttt{HARK.core} also defines a ``supersuperclass'' called \texttt{HARKobject}. When solving a dynamic microeconomic model with an infinite horizon (or searching for a dynamic general equilibrium), it is often required to consider whether two solutions are sufficiently close to each other to warrant stopping the process (i.e.\ approximate convergence). It is thus necessary to calculate the ``distance'' between two solutions, so HARK specifies that classes should have a \texttt{distance} method that takes a single input and returns a non-negative value representing the (generally dimensionless) distance between the object in question and the input to the method. As a convenient default, \texttt{HARKobject} provides a ``universal distance metric'' that should be useful in many contexts.\footnote{Roughly speaking, the universal distance metric is a recursive supnorm, returning the largest distance between two instances, among attributes named in \texttt{distance\_criteria}. Those attributes might be complex objects themselves rather than real numbers, generating a recursive call to the universal distance metric.} When defining a new subclass of \texttt{HARKobject}, the user simply defines the attribute \texttt{distance\_criteria} as a list of strings naming the attributes of the class that should be compared when calculating the distance between two instances of that class. For example, the class \texttt{ConsumerSolution} has \texttt{distance\_criteria = ['cFunc']}, indicating that only the consumption function attribute of the solution matters when comparing the distance between two instances of \texttt{ConsumerSolution}. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.core.html}{here} for online documentation. - -\subsection{HARK.utilities}\label{sec:HARKutilities} - -The \texttt{HARK.utilities} module carries a double meaning in its name, as it contains both utility functions (and their derivatives, inverses, and combinations thereof) in the economic modeling sense as well as utilities in the sense of general tools. Utility functions included at this time are constant relative risk aversion and constant absolute risk aversion. Other functions in \texttt{HARK.utilities} include some data manipulation tools (e.g.\ for calculating an average of data conditional on being within a percentile range of different data), functions for constructing discrete state space grids, convenience functions for retrieving information about functions, and basic plotting tools using \texttt{matplotlib.pyplot}. - -The module also includes functions for constructing discrete approximations to continuous distributions (e.g.\ \texttt{approxLognormal()} to approximate a log-normal distribution) as well as manipulating these representations (e.g.\ appending one outcome to an existing distribution, or combining independent univariate distributions into one multivariate distribution). As a convention in HARK, continuous distributions are approximated as finite discrete distributions when solving models; an $N$-dimensional random variable is formatted as a length $N+1$ list of 1D arrays, with the first element representing event probabilities and all other elements are realizations of the $N$ component RVs. This both simplifies solution methods (reducing numeric integrals to simple dot products) and allows users to easily test whether their chosen degree of discretization yields a sufficient approximation to the full distribution. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.utilities.html}{here} for online documentation. - -\subsection{HARK.interpolation}\label{sec:HARKinterpolation} - -The \texttt{HARK.interpolation} module defines classes for representing interpolated function approximations. Interpolation methods in HARK all inherit from a superclass such as \texttt{HARKinterpolator1D} or \texttt{HARKinterpolator2D}, wrapper classes that ensures interoperability across interpolation methods. For example, \texttt{HARKinterpolator1D} specifies the methods \texttt{\_\_call\_\_} and \texttt{derivative} to accept an arbitrary array as an input and return an identically shaped array with the interpolated function evaluated at the values in the array or its first derivative, respectively. However, these methods do little on their own, merely reshaping arrays and referring to the \texttt{\_evaluate} and \texttt{\_der} methods, which are \textit{not actually defined in} \texttt{HARKinterpolator1D}. Each subclass of \texttt{HARKinterpolator1D} specifies their own implementation of \texttt{\_evaluate} and \texttt{\_der} particular to that interpolation method, accepting and returning only 1D arrays. In this way, subclasses of \texttt{HARKinterpolator1D} are easily interchangeable with each other, as all methods that the user interacts with are identical, varying only by ``internal'' methods. - -When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to know the ``distance'' between functions generated by successive iterations of a solution procedure. To this end, each interpolator class in HARK must define a \texttt{distance} method that takes as an input another instance of the same class and returns a non-negative real number representing the ``distance'' between the two. As each of the \texttt{HARKinterpolatorXD} classes inherits from \texttt{HARKobject}, all interpolator classes have the default ``universal'' distance method; the user must simply list the names of the relevant attributes in the attribute \texttt{distance\_criteria} of the class. - -Interpolation methods currently implemented in HARK include (multi)linear interpolation up to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D ``lower envelope'' interpolator. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.interpolation.html}{here} for online documentation. - -\subsection{HARK.simulation}\label{sec:HARKsimulation} - -The \texttt{HARK.simulation} module provides tools for generating simulated data or shocks for post-solution use of models. Currently implemented distributions include normal, lognormal, Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use, these tools are used in the consumption-saving models of \texttt{ConsIndShockModel.py} to simulate permanent and transitory income shocks as well as unemployment events. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.simulation.html}{here} for online documentation. - -\subsection{HARK.estimation}\label{sec:HARKestimation} - -Methods for optimizing an objective function for the purposes of estimating a model can be found in \texttt{HARK.estimation}. As of this writing, the implementation includes only minimization by the Nelder-Mead simplex method, minimization by a derivative-free Powell method variant, and two small tools for resampling data (i.e.\ for a bootstrap); the minimizers are merely convenience wrappers (with result reporting) for optimizers included in \texttt{scipy.optimize}. Future functionality will include more robust global search methods, including genetic algorithms, simulated annealing, and differential evolution. See \href{https://hark.readthedocs.io/en/latest/generated/HARK.estimation.html}{here} for full documentation. - -\subsection{HARK.parallel}\label{sec:HARKparallel} - -By default, processes in Python are single-threaded, using only a single CPU core. The \texttt{HARK.parallel} module provides basic tools for using multiple CPU cores simultaneously, with minimal effort.\footnote{\texttt{HARK.parallel} uses two packages that aren't included in the default distribution of Anaconda: \texttt{joblib} and \texttt{dill}; see step 3 of the instructions in section \ref{sec:GettingStarted} for how to install them.} In particular, it provides the function \texttt{multiThreadCommands}, which takes two arguments: a list of \texttt{AgentType}s and a list of commands as strings; each command should be a method of the \texttt{AgentType}s. The function simply distributes the \texttt{AgentType}s across threads on different cores and executes each command in order, returning no output (the \texttt{AgentType}s themselves are changed by running the commands). Equivalent results would be achieved by simply looping over each type and running each method in the list. Indeed, \texttt{HARK.parallel} also has a function called \texttt{multiThreadCommandsFake} that does just that, with identical syntax to \texttt{multiThreadCommands}; multithreading in HARK can thus be easily turned on and off.\footnote{In the future, \texttt{HARK.parallel} might be absorbed into \texttt{HARK.core} and \texttt{HARK.estimation}, particularly if \texttt{joblib} and \texttt{dill} become part of the standard Anaconda distribution.} The module also has functions for a parallel implementation of the Nelder-Mead simplex algorithm, as described in Wiswall and Lee (2011). See \href{https://hark.readthedocs.io/en/latest/generated/HARK.parallel.html}{here} for full documentation. - -\section{Microeconomics: the AgentType Class}\label{sec:Microeconomics} - -The core of our microeconomic dynamic optimization framework is a flexible object-oriented representation of economic agents. The \texttt{HARK.core} module defines a superclass called \texttt{AgentType}; each model defines a subclass of \texttt{AgentType}, specifying additional model-specific features and methods while inheriting the methods of the superclass. Most importantly, the method \texttt{solve} acts as a ``universal solver'' applicable to any (properly formatted) discrete time model. This section describes the format of an instance of \texttt{AgentType} as it defines a dynamic microeconomic problem;\footnote{Each instance of \texttt{AgentType} represents an \textit{ex-ante} heterogeneous ``type'' of agent; \textit{ex-post} heterogeneity is achieved by simulating many agents of the same type, each of whom receives a unique sequence of shocks.} - -\subsection{Attributes of an AgentType}\label{sec:AttributesOfAnAgentType} - -A discrete time model in our framework is characterized by a sequence of ``periods'' that the agent will experience. A well-formed instance of \texttt{AgentType} includes the following attributes: -\begin{itemize} -\item \texttt{solveOnePeriod}: A function, or a list of functions, representing the solution method for a single period of the agent's problem. The inputs passed to a \texttt{solveOnePeriod} function include all data that characterize the agent's problem in that period, including the solution to the subsequent period's problem (designated as \texttt{solution\_next}). The output of these functions is a single \texttt{Solution} object, which can be passed to the solver for the previous period. - -\item \texttt{time\_inv}: A list of strings containing all of the variable names that are passed to at least one function in \texttt{solveOnePeriod} but do \textit{not} vary across periods. Each of these variables resides in a correspondingly named attribute of the \texttt{AgentType} instance. - -\item \texttt{time\_vary}: A list of strings naming the attributes of this instance that vary across periods. Each of these attributes is a list of period-specific values, which should be of the same length. If the solution method varies across periods, then \texttt{'solveOnePeriod'} is an element of \texttt{time\_vary}.\footnote{\texttt{time\_vary} may include attributes that are never used by a function in \texttt{solveOnePeriod}. Most saliently, the attribute \texttt{solution} is time-varying but is not used to solve individual periods.} - -\item \texttt{solution\_terminal}: An object representing the solution to the ``terminal'' period of the model. This might represent a known trivial solution that does not require numeric methods, the solution to some previously solved ``next phase'' of the model, a scrap value function, or an initial guess of the solution to an infinite horizon model. - -\item \texttt{pseudo\_terminal}: A Boolean flag indicating that \texttt{solution\_terminal} is not a proper terminal period solution (rather an initial guess, ``next phase'' solution, or scrap value) and should not be reported as part of the model's solution. - -\item \texttt{cycles}: A non-negative integer indicating the number of times the agent will experience the sequence of periods in the problem. For example, \texttt{cycles = 1} means that the sequence of periods is analogous to a lifecycle model, experienced once from beginning to end; \texttt{cycles = 2} means that the agent experiences the sequence twice, with the first period in the sequence following the last. An infinite horizon problem in which the sequence of periods repeats indefinitely is indicated with \texttt{cycles = 0}. - -\item \texttt{tolerance}: A positive real number indicating convergence tolerance, representing the maximum acceptable ``distance'' between successive cycle solutions in an infinite horizon model; it is irrelevant when \texttt{cycles > 0}. As the distance metric on the space of solutions is model-specific, the value of \texttt{tolerance} is generally dimensionless. - -\item \texttt{time\_flow}: A Boolean flag indicating the direction that time is ``flowing.'' When \texttt{True}, the variables listed in \texttt{time\_vary} are listed in ordinary chronological order, with index 0 being the first period; when \texttt{False}, these lists are in reverse chronological order, with index 0 holding the last period. -\end{itemize} - -An instance of \texttt{AgentType} also has the attributes named in \texttt{time\_vary} and \texttt{time\_inv}, and may have other attributes that are not included in either (e.g.\ values not used in the model solution, but instead to construct objects used in the solution). - -\subsection{A Universal Solver}\label{sec:UniversalSolver} - -When an instance of \texttt{AgentType} invokes its \texttt{solve} method, the solution to the agent's problem is stored in the attribute \texttt{solution}. The solution is computed by recursively solving the sequence of periods defined by the variables listed in \texttt{time\_vary} and \texttt{time\_inv} using the functions in \texttt{solveOnePeriod}. The time-varying inputs are updated each period, including the successive period's solution as \texttt{solution\_next}; the same values of time invariant inputs in \texttt{time\_inv} are passed to the solver in every period. The first call to \texttt{solveOnePeriod} uses \texttt{solution\_terminal} as \texttt{solution\_next}. In a finite horizon problem, the sequence of periods is solved \texttt{cycles} times over; in an infinite horizon problem, the sequence of periods is solved until the solutions of successive cycles have a ``distance'' of less than \texttt{tolerance}. - -The output from a function in \texttt{solveOnePeriod} is an instance of a model-specific solution class. The attributes of a solution to one period of a problem might include behavioral functions, (marginal) value functions, and other variables characterizing the result. Each solution class must have a method called \texttt{distance()}, which returns the ``distance'' between itself and another instance of the same solution class, so as to define convergence as a stopping criterion; for many models, this will be the ``distance'' between a policy or value function in the solutions. If the solution class is defined as a subclass of \texttt{HARKobject}, it automatically inherits the default \texttt{distance} method, so that the user must only list the relevant object attributes in \texttt{distance\_criteria}. - -The \texttt{AgentType} also has methods named \texttt{preSolve} and \texttt{postSolve}, both of which take no arguments and do absolutely nothing. A subclass of \texttt{AgentType} can overwrite these blank methods with its own model specific methods. \texttt{preSolve} is automatically called near the beginning of the \texttt{solve} method, before solving the sequence of periods. It is used for specifying tasks that should be done before solving the sequence of periods, such as pre-constructing some objects repeatedly used by the solution method or finding an analytical terminal period solution. For example, the \texttt{IndShockConsumerType} class in \texttt{ConsIndShockModel} has a \texttt{preSolve} method that calls its \texttt{updateSolutionTerminal} method to ensure that \texttt{solution\_terminal} is consistent with the model parameters. The \texttt{postSolve} method is called shortly after the sequence of periods is fully solved; it can be used for ``post-processing'' of the solution or performing a step that is only useful after solution convergence. For example, the \texttt{TractableConsumerType} in \texttt{TractableBufferStockModel} has a \texttt{postSolve} method that constructs an interpolated consumption function from the list of stable arm points found during solution. - -Our universal solver is written in a very general way that should be applicable to any discrete time optimization problem-- because Python is so flexible in defining objects, the time-varying inputs for each period can take any form. Indeed, the solver does no ``real work'' itself, but merely provides a structure for describing models in the HARK framework, allowing interoperability among current and future modules. - -\subsection{The Flow of Time and Other Methods}\label{sec:OtherMethods} - -Because dynamic optimization problems are solved recursively in our framework, it is natural to list time-varying values in reverse chronological order-- the \texttt{solve()} method loops over the values in each time-varying list in the same direction that a human would read them. When simulating agents after the solution has been obtained, however, it is much more convenient for time-varying parameters to be listed in ordinary chronological order-- the direction in which they will be experienced by simulated agents. To allow the user to set the order in which ``time is flowing'' for an instance of \texttt{AgentType}, the HARK framework includes functionality to easily change ordering of time-varying values. - -The attribute \texttt{time\_flow} is \texttt{True} if variables are listed in ordinary chronological order and \texttt{False} otherwise. \texttt{AgentType} has the following methods for manipulating time: -\begin{itemize} -\item \texttt{timeReport()}: Prints to screen a description of the direction that time is flowing, for interactive convenience and as a reminder of the functionality. - -\item \texttt{timeFlip()}: Flips the direction of time. Each attribute listed in \texttt{time\_vary} is reversed in place, and the value of \texttt{time\_flow} is toggled. - -\item \texttt{timeFwd()}: Sets the direction of time to ordinary chronological order. - -\item \texttt{timeRev()}: Sets the direction of time to reverse chronological order. -\end{itemize} - -These methods are invoked to more conveniently access time-varying objects. When a new time-varying attribute is added, its name should be appended to \texttt{time\_vary}, particularly if its values are used in the solution of the model (or is part of the solution itself). For example, the \texttt{solve()} method automatically adds the string \texttt{'solution'} to \texttt{time\_vary} if it is not already present. Note that attributes listed in \texttt{time\_vary} \textit{must} be lists if \texttt{solve()} or \texttt{timeFlip()} are used. Some values that could be considered ``time varying'' but are never used to solve the model are more conveniently represented as a \texttt{numpy.array} object (e.g.\ \ the history of a state or control variable from a simulation); because the \texttt{numpy.array} class does not have a \texttt{reverse()} method, these attributes should not be listed in \texttt{time\_vary}. - -The base \texttt{AgentType} is sparsely defined, as most ``real'' methods will be application-specific. Two final methods bear mentioning. First, the \texttt{\_\_call\_\_} method points to \texttt{assignParameters()}, a convenience method for adding or adjusting attributes (inherited from \texttt{HARKobject}). This method takes any number of keyword arguments, so that code can be parsimoniously written as, for example, \texttt{AgentInstance(attribute1 = value1, attribute2 = value2)}. Using Python's dictionary capabilities, many attributes can be conveniently set with minimal code. Second, the \texttt{resetRNG} method simply resets the \texttt{AgentType}'s random number generator (as the attribute \texttt{RNG}) using the value in the attribute \texttt{seed}.\footnote{Every instance of \texttt{AgentType} is created with a random number generator as an instance of the class \texttt{numpy.random.RandomState}, with a default \texttt{seed} of zero.} This method is useful for (\textit{inter alia}) ensuring that the same underlying sequence of shocks is used for every simulation run when a model is solved or estimated. - - -\subsection{Sample Model: Perfect Foresight Consumption-Saving}\label{sec:PerfectForesight} - -To provide a concrete example of how the \texttt{AgentType} class works, consider the very simple case of a perfect foresight consumption-saving model. The agent has time-separable, additive CRRA preferences over consumption, discounting future utility at a constant rate; he receives a particular stream of labor income each period, and knows the interest rate on assets that he holds from one period to the next. His decision about how much to consume in a particular period can be expressed in Bellman form as: - -\begin{eqnarray*} -V_t(M_t) &=& \max_{C_t} \utilFunc(C_t) + \beta \PLives_t \E [V_{t+1}(M_{t+1}) ], \\ -A_t &=& M_t - C_t, \\ -M_{t+1} &=& \Rfree A_t + Y_{t+1}, \\ -Y_{t+1} &=& \Gamma_{t+1} Y_t, \\ -\utilFunc(C) &=& \frac{C^{1-\CRRA}}{1-\CRRA}. -\end{eqnarray*} -An agent's problem is thus characterized by values of $\CRRA$, $\Rfree$, and $\beta$ plus sequences of survival probabilities $\PLives_t$ and income growth factors $\Gamma_t$ for $t=0,\cdots,T$. This problem has an analytical solution for both the value function and the consumption function. - -The \texttt{ConsIndShockModel} module defines the class \texttt{PerfForesightConsumerType} as a subclass of \texttt{AgentType} and provides solver functions for several variations of a consumption-saving model, including the perfect foresight problem.\footnote{As the name implies, the module is mostly concerned with models with idiosyncratic shocks to income.} A HARK user could specify and solve a ten period perfect foresight model with the following commands: - -\vspace{0.25cm} - -\noindent \texttt{MyConsumer = PerfForesightConsumerType(time\_flow=True, cycles=1,} - -\texttt{ CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98,} - -\texttt{ LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90],} - -\texttt{ PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02],} - -\texttt{ Nagents=1000)} - -\noindent \texttt{MyConsumer.solve()} - -\vspace{0.25cm} - -The first line makes a new instance of \texttt{ConsumerType}, specifying that time is currently ``flowing'' forward\footnote{This is relevant for the intepretation of the time-varying inputs \texttt{LivPrb} and \texttt{PermGroFac}.} and that the sequence of periods happens exactly once; the next three lines (all part of the same command) set the time invariant (\texttt{CRRA}, \texttt{Rfree}, \texttt{DiscFac}) and time varying parameters (\texttt{LivPrb}, \texttt{PermGroFac}). Note that \texttt{dir(MyConsumer)} shows all the attributes of \texttt{MyConsumer}. After running the \texttt{solve} method, \texttt{MyConsumer} will have an attribute called \texttt{solution}, which will be a list with eleven \texttt{ConsumerSolution} objects, representing the period-by-period solution to the model.\footnote{The last element of \texttt{solution} represents the terminal period solution, where the agent consumes all available resources because there is no future.} Each \texttt{ConsumerSolution} has several attributes: -\begin{itemize} -\item \texttt{cFunc}: Optimal consumption function for this period as a function of (normalized) market resources $m_t = M_t/Y_t$. Can be called like any other function: \texttt{MyConsumer.solution[0].cFunc(5)} returns consumption at $m_t = 5$. - -\item \texttt{vFunc}: Value function (over market resources $m_t$); can be evaluated like \texttt{cFunc}. - -\item \texttt{mNrmMin}: Minimum value of normalized market resources $m_t$ such that \texttt{cFunc} and \texttt{vFunc} are defined. - -\item \texttt{hNrm}: Normalized human wealth-- the PDV of future income (ignoring mortality) divided by current period income $Y_t$. - -\item \texttt{MPC}: The constant marginal propensity to consume (linear consumption function). - -\end{itemize} - -The following command generates the plot for the perfect foresight consumption function: - -\vspace{0.25cm} - -\noindent \texttt{mMin = MyConsumer.solution[0].mNrmMin} - -\noindent \texttt{plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10)} - -\vspace{0.25cm} - -To solve a version of this problem in which the sequence of ten periods happens three times (yielding a \texttt{solution} attribute with thirty-one elements), the user can do: - -\vspace{0.25cm} - -\noindent \texttt{MyConsumer(cycles = 3)} - -\noindent \texttt{MyConsumer.solve()} - -\vspace{0.25cm} - -To solve an infinite horizon problem in which the agent experiences the same one period problem indefinitely, the user can do: - -\vspace{0.25cm} - -\noindent \texttt{OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02,} - -\texttt{DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000)} - -\noindent \texttt{OtherConsumer.solve()} - -\vspace{0.25cm} - -This instance is specified as being infinite horizon by setting \texttt{cycles=0}. Note that the time-varying inputs are still specified as (one element) lists, even though they take on the same value in every period; this is because an infinite horizon model might consist of a multi-period sequence repeated indefinitely, rather than just one repeated period. The \texttt{solution} attribute of \texttt{OtherConsumer} will be a list containing one instance of \texttt{ConsumerSolution}, representing the solution to every period of the model. - -\section{Macroeconomics: the Market Class}\label{sec:Macroeconomics} - -The modeling framework of \texttt{AgentType} is deemed ``microeconomic'' because it pertains only to the dynamic optimization problem of agents, treating all inputs of the problem as exogenously fixed. In what we label as ``macroeconomic'' models, some of the inputs for the microeconomic models are endogenously determined by the collective states and controls of agents in the model. In a dynamic general equilibrium, there must be consistency between agents' beliefs about these macroeconomic objects, their individual behavior, and the realizations of the macroeconomic objects that result from individual choices. - -The \texttt{Market} class in \texttt{HARK.core} provides a framework for such macroeconomic models, with a \texttt{solve} method that searches for a dynamic general equilibrium. An instance of \texttt{Market} includes a list of \texttt{AgentType}s that compose the economy, a method for transforming microeconomic outcomes (states, controls, and/or shocks) into macroeconomic outcomes, and a method for interpreting a history or sequence of macroeconomic outcomes into a new ``dynamic rule'' for agents to believe. Agents treat the dynamic rule as an input to their microeconomic problem, conditioning their optimal policy functions on it. A dynamic general equilibrium is a fixed point dynamic rule: when agents act optimally while believing the equilibrium rule, their individual actions generate a macroeconomic history consistent with the equilibrium rule. - -\subsection{Down on the Farm}\label{sec:DownOnTheFarm} - -The \texttt{Market} class uses a farming metaphor to conceptualize the process for generating a history of macroeconomic outcomes in a model. Suppose all \texttt{AgentTypes} in the economy believe in some dynamic rule (i.e.\ the rule is stored as attributes of each \texttt{AgentType}, which directly or indirectly enters their dynamic optimization problem), and that they have each found the solution to their microeconomic model using their \texttt{solve} method. Further, the macroeconomic and microeconomic states have been reset to some initial orientation. - -To generate a history of macroeconomic outcomes, the \texttt{Market} repeatedly loops over the following steps a set number of times: -\begin{enumerate} -\item \texttt{sow}: Distribute the macroeconomic state variables to all \texttt{AgentType}s in the market. - -\item \texttt{cultivate}: Each \texttt{AgentType} executes their \texttt{marketAction} method, likely corresponding to simulating one period of the microeconomic model. - -\item \texttt{reap}: Microeconomic outcomes are gathered from each \texttt{AgentType} in the market. - -\item \texttt{mill}: Data gathered by \texttt{reap} is processed into new macroeconomic states according to some ``aggregate market process''. - -\item \texttt{store}: Relevant macroeconomic states are added to a running history of outcomes. -\end{enumerate} -This procedure is conducted by the \texttt{makeHistory} method of \texttt{Market} as a subroutine of its \texttt{solve} method. After making histories of the relevant macroeconomic variables, the market then executes its \texttt{calcDynamics} function with the macroeconomic history as inputs, generating a new dynamic rule to distribute to the \texttt{AgentType}s in the market. The process then begins again, with the agents solving their updated microeconomic models given the new dynamic rule; the \texttt{solve} loop continues until the ``distance'' between successive dynamic rules is sufficiently small. - -\subsection{Attributes of a Market}\label{sec:AttributesOfAMarket} - -To specify a complete instance of \texttt{Market}, the user should give it the following attributes:\footnote{For some purposes, it might be useful to specify a subclass of \texttt{Market}, defining \texttt{millRule} and/or \texttt{calcDynamics} as methods rather than functions.} -\begin{itemize} -\item \texttt{agents}: A list of \texttt{AgentType}s, representing the agents in the market. Each element in \texttt{agents} represents an \textit{ex-ante} heterogeneous type; each type could have many \textit{ex-post} heterogeneous agents. - -\item \texttt{sow\_vars}: A list of strings naming variables that are output from the aggregate market process, representing the macroeconomic outcomes. These variables will be distributed to the \texttt{agents} in the \texttt{sow} step. - -\item \texttt{reap\_vars}: A list of strings naming variables to be collected from the \texttt{agents} in the \texttt{reap} step, to be used as inputs for the aggregate market process. - -\item \texttt{const\_vars}: A list of strings naming variables used by the aggregate market process that \textit{do not} come from \texttt{agents}; they are constant or come from the \texttt{Market} itself. - -\item \texttt{track\_vars}: A list of strings naming variables generated by the aggregate market process that should be tracked as a history, to be used when calculating a new dynamic rule. Usually a subset of \texttt{sow\_vars}. - -\item \texttt{dyn\_vars}: A list of strings naming the variables that constitute a dynamic rule. These will be stored as attributes of the \texttt{agents} whenever a new rule is calculated. - -\item \texttt{millRule}: A function for the ``aggregate market process'', transforming microeconomic outcomes into macroeconomic outcomes. Its inputs are named in \texttt{reap\_vars} and \texttt{const\_vars}, and it returns a single object with attributes named in \texttt{sow\_vars} and/or \texttt{track\_vars}. Can be defined as a method of a subclass of \texttt{Market}. - -\item \texttt{calcDynamics}: A function that generates a new dynamic rule from a history of macroeconomic outcomes. Its inputs are named in \texttt{track\_vars}, and it returns a single object with attributes named in \texttt{dyn\_vars}. - -\item \texttt{act\_T}: The number of times that the \texttt{makeHistory} method should execute the ``farming loop'' when generating a new macroeconomic history. - -\item \texttt{tolerance}: The minimum acceptable ``distance'' between successive dynamic rules produced by \texttt{calcDynamics} to constitute a sufficiently converged solution. -\end{itemize} - -Further, each \texttt{AgentType} in \texttt{agents} must have two methods not necessary for microeconomic models; neither takes any input (except \texttt{self}): -\begin{itemize} -\item \texttt{marketAction}: The microeconomic process to be run in the \texttt{cultivate} step. Likely uses the new macroeconomic outcomes named in \texttt{sow\_vars}; should store new values of relevant microeconomic outcomes in the attributes (of \texttt{self}) named in \texttt{reap\_vars}. - -\item \texttt{reset}: Reset, initialize, or prepare for a new ``farming loop'' to generate a macroeconomic history. Might reset its internal random number generator, set initial state varibles, clear personal histories, etc. -\end{itemize} - -When solving macroeconomic models in HARK, the user should also define classes to represent the output from the aggregate market process in \texttt{millRule} and for the model-specific dynamic rule. The latter should have a \texttt{distance} method to test for solution convergence; if the class inherits from \texttt{HARKobject}, the user need only list relevant attributes in \texttt{distance\_criteria}. - -\subsection{Sample Model: FashionVictim}\label{sec:FashionVictim} - -To illustrate the \texttt{Market} class, consider a simple example in the emerging economic subfield of aesthemetrics, the \texttt{FashionVictimModel}.\footnote{This model is inspired by the paper ``The hipster effect: When anticonformists all look the same'' by Jonathan Touboul.}\footnote{For a more traditional macroeconomic model, the \texttt{ConsAggShocksModel} module includes a consumption-saving model with both idiosyncratic and aggregate shocks, in which individual asset holdings are aggregated into total productive capital, endogenizing the (dynamic) interest and wage rate.} This module defines a subclass of \texttt{AgentType} called \texttt{FashionVictimType}. Each period, fashion victims make a binary choice of style $s$: to dress as a jock (0) or punk (1). They receive utility directly from the outfit they wear and as a function of the proportion of the population who \textit{just wore} the same style; they also pay switching costs ($c_{pj}$,$c_{jp}$) if they change styles rather than keep the same as the previous period. Moreover, they receive an idiosyncratic type 1 extreme value (T1EV) preference shock to each style in each period. Defining the population punk proportion as $p$ and the conformity utility function as $f:[0,1]\rightarrow \mathbb{R}$, the current period utility function is thus: -\begin{equation*} -u(s_t;s_{t-1},p_t) = s_t f(p_t) + (1-s_t) f(1-p_t) + s_t U_p + (1-s_t) U_j - c_{pj} s_{t-1}(1-s_t) - c_{jp}(1-s_{t-1})s_t. -\end{equation*} - -Fashion victims are forward looking and discount future utility at a constant rate of $\beta$ per period. To simplify the analysis, we assume they believe that the population punk proportion in the next period is a linear function of the punk proportion in the current period, subject to a uniformly distributed shock. No restrictions are put on the function $f$; fashion victims might be conformists who like to dress the same as others ($f'(p) > 0$) or hipsters who like to style themselves in the minority ($f'(p) < 0$).\footnote{In practice, $f$ is parameterized as the beta distribution for convenience.} A fashion victim's problem can be written in Bellman form as: -\begin{equation*} -V(s_{t-1},p_t) = \E \left[ \max_{s_t \in \{0,1\}} u(s_t;s_{t-1},p_t) + \eta_{s_t} + \beta \E \left[ V(s_t,p_{t+1}) \right] \right], -\end{equation*} -\begin{equation*} -p_{t+1} = a p_t + b + \pi_{t+1}, \qquad \pi_{t+1} \sim U[-w,w], \qquad \eta_0,\eta_1 \sim T1EV. -\end{equation*} - -An instance of \texttt{FashionVictimType} is thus characterized by values of $U_p$, $U_j$, $c_{pj}$, $c_{jp}$ and a function $f$, as well as beliefs about $p_{t+1}$ as a function of $p_t$ (summarized by slope $a$, intercept $b$, and uniform shock width $w$). Given this information, a \texttt{FashionVictimType}'s infinite horizon microeconomic model can be solved by backward induction in a few lines; the ``one period solver'' is given by \texttt{solveFashion}. However, while individual agents treat the dynamics of $p_t$ as exogenous, they are in fact endogenously determined by the actions of all the fashion victims in the market.\footnote{The market might consist of agents all of the same type, or might have several types with \textit{ex-ante} heterogeneity.} A dynamic general equilibrium of the ``macroeconomic fashion model'' is thus characterized by a triple of $(a,b,w)$ such that when fashion victims believe in this ``punk evolution rule'' and act optimally, their collective fashion choices exhibit this same rule when the model is simulated. - -The search for a dynamic general equilibrium is implemented in HARK's \texttt{Market} class with the following definitions: - -\vspace{0.25cm} - -\noindent \texttt{sow\_vars = ['pNow']} (macroeconomic outcome is $p_t$) - -\noindent \texttt{reap\_vars = ['sNow']} (microeconomic outcomes are $s_t$ for many agents) - -\noindent \texttt{track\_vars = ['pNow']} (must track history of $p_t$) - -\noindent \texttt{dyn\_vars = ['pNextSlope','pNextIntercept','pNextWidth']} (dynamic rule $(a,b,w)$) - -\noindent \texttt{millRule = calcPunkProp} (aggregate process: average the style choices of all agents) - -\noindent \texttt{calcDynamics = calcFashionEvoFunc} (calculate new $(a,b,w)$ with autoregression of $p_t$) - -\noindent \texttt{act\_T = 1000} (simulate 1000 periods of the fashion market) - -\noindent \texttt{tolerance = 0.01} (terminate solution when $(a,b,w)$ changes by less than 0.01) - -\vspace{0.25cm} - -The \texttt{agents} attribute has a list of 22 \texttt{FashionVictimType}s, which vary in their values of $U_p$ and $U_j$, and their $f$ functions. The \texttt{marketAction} method of \texttt{FashionVictimType} simulates one period of the microeconomic model: each agent receives style preference shocks $\eta_0$ and $\eta_1$, sees the current proportion of punks $p_t$ (sown to them as \texttt{pNow}), and chooses which style to wear, storing it in the binary array \texttt{sNow}, an attribute of \texttt{self}. - -The \texttt{millRule} for this market is extremely simple: it flattens the list of binary arrays of individual style choices (gathered in the \texttt{reap} step) and averages them into a new value of $p_t$, to be tracked as a history and \texttt{sow}n back to the \texttt{agents} to begin the cycle again. Once a history of 1000 values of $p_t$ has been generated with the \texttt{makeHistory} method, we can calculate a new dynamic fashion rule with \texttt{calcFashionEvoFunc} by regressing $p_t$ on $p_{t-1}$, approximating $w$ as twice the standard deviation of prediction errors.\footnote{This is an arbitrary way to calculate $w$, but accuracy is not important in a silly example.} The new fashion rule is an instance of the simple \text{FashionEvoFunc} class, whose only methods are inherited from \texttt{HARKobject}. - -When the \texttt{solve} method is run, the solver successively solves each agent's microeconomic problem, runs the \texttt{makeHistory} method to generate a 1000 period history of $p_t$, and calculates a new punk evolution rule based on this history; the solver terminates when consecutive rules differ by less than 0.01 in any dimension. - -} -% \hypertarget{ContributingToHARK}{\section{Contributing to HARK}\label{sec:ContributingToHARK}} -\section{Contributing to HARK}\label{sec:ContributingToHARK} - -%After reading this far into the user guide, it is safe to assume that you are eager to contribute to HARK's development. -This section provides an overview of how users can contribute new code to HARK, what sort of contributions are most desired, and style conventions for the toolKit. Before attempting to extend HARK, we strongly recommend that you familiarize yourself with its features and models by looking at the source code. - -\subsection{What Does HARK Want?}\label{sec:WhatDoesHARKWant} - -The easiest and most straightforward way to contribute to HARK is by writing new general purpose tools for inclusion in one of the top-level modules. This might be a commonly known data analysis technique (e.g.\ a kernel density estimator) for \texttt{HARK.utilities}, an optimization method (local or global) for \texttt{HARK.estimation}, an interpolation method for \texttt{HARKinterpolation}, etc. If you've found a technique useful in your own research and believe it could be of use to others on entirely different projects, then it probably belongs in HARK. Likewise, if you read through a HARK module expecting to find a certain tool or function (because of similar functions that are present, or merely because \textit{it's very useful}) but don't, this is a good sign that HARK has a hole that you can fill.\footnote{Case in point: As of this moment, \texttt{HARK.utilities} has no function for making a discrete approximation to the normal distribution, even though there are several well known methods for doing so and it is an obviously useful function.} - -Secondly, the development team wants to expand HARK to include more canonical models. Most of the models available in the toolKit at the time of its public beta release concern agents with (geometrically discounted) CRRA utility over a unitary consumption good, who receive permanent and transitory shocks to income and have access to a single risk free asset. This is an important class of models, but that description has an awful lot of qualifiers. In short, there are many more common features of dynamic household models that are not yet in HARK, but would be very valuable for the toolKit. New model contributions should ``play nicely'' with existing models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent possible. For ideas of potential model extensions for HARK, see section \ref{sec:FutureModels}.\footnote{Although that list is limited to extensions and variations on consumption-saving models, HARK has no such restrictions. If there is an entirely different sort of dynamic model that fits in the HARK framework, it can be contributed.} - -Finally, contributions to HARK are not restricted to entirely new functionality. While we have done our best to provide flexible frameworks and useful tools, we have almost certainly overlooked an unexpected case that would not be (properly) handled by the toolKit as is. Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as an Issue on GitHub so that someone else might. The ``mistake'' you find might not even be in the code itself, but a deficiency in the documentation: a misnamed (or incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing) comment, even a grammatical or spelling error. No matter how niggling, we want your fix. - -For those who want to contribute to the HARK project without programming a new model or digging through the code for errors and omissions, consider reading through the Issues page on GitHub to see what other users have flagged for development. You might find several pieces of ``low hanging fruit'' for you to quickly fix. - -\subsection{Git and GitHub}\label{sec:GitHub} - -Git is a distributed version control system that has gained increasing popularity in the decade since its creation; it is designed to enable collaboration and ``non-linear workflow'' among a (potentially disperse) programming team. As HARK is to be developed by volunteer researchers all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds as a command line tool, others have since extended git with further functionality and a user interface more friendly to those more familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK project is hosted. This section provides a brief overview of how to use GitHub to interact with (and potentially contribute to) HARK. - -To prepare to contribute to HARK, follow these simple steps: -\begin{enumerate} -\item \textbf{Make a GitHub account:} Go to \href{https://www.github.com}{github.com}; the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.\footnote{There are non-free ways to sign up for GitHub, but you don't need to worry about them.} - -\item \textbf{Install GitHub Desktop}: Go to \href{https://desktop.github.com/}{desktop.github.com} and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account. - -\item \textbf{Fork the HARK repository:} Go to the \href{https://github.com/econ-ark/HARK}{HARK repository page}, click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead. - -\item \textbf{Add the repository to Desktop:} Open GitHub Desktop, click on the $+$ icon in the upper left, and select clone. Find the repository named \texttt{yourusername/HARK}, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine. -\end{enumerate} - -You now control a ``fork'' of the main HARK repository, hosted by GitHub, and have a clone of this fork on your local machine. You are free to edit your fork of HARK in any way you'd like; other GitHub users can view your fork (and fork it themselves), but they cannot make changes to your fork unless you give them permission by adding them as a contributor. Changes made to the local copy of your fork do not get automatically sent to the remote copy on GitHub for public viewing. To make changes to your fork on GitHub, follow these steps: -\begin{enumerate} -\item \textbf{Make local changes:} Edit your local copy of the repository in any way you'd like: add or delete lines from existing files; add, delete, or rename entire files or directories. - -\item \textbf{Commit local changes:} Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button. - -\item \textbf{Push the commit:} Click on the Sync button near the upper right to ``push'' your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced. -\end{enumerate} - -The left hand panel of the Changes tab has a summary of the files that have been changed, added, or removed since your last commit; the right hand panel shows the specific changes made to each changed file. After step 2, a record of your changes has been stored locally by the git system, but is not yet reflected in the public remote copy on GitHub.\footnote{You can make several local commits without syncing with the remote server. This is useful for working without an internet connection. Say, on an intercontinental flight.} The ``timeline'' summary of the repository at the top of the Desktop window shows the history of commits for the repository: each dot represents a commit that was pushed to the remote server, and each open ring represents a local commit that has not been pushed to the remote server; the large broken ring on the far right represents changes that have not been locally committed.\footnote{Clicking on a dot or ring will display the changes made in that commit (or uncommitted changes).} The History tab lists all previous commits, including the timestamp, contributor, and a record of changes made. - -Suppose you have mucked about in your fork for a while and have made changes to HARK that you think should be included in the main repository hosted by Econ-Ark. To make your dream a reality, follow these steps: -\begin{enumerate} -\item \textbf{Isolate the changes:} If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should \textit{isolate the desired changes in a new branch of your fork}. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,\footnote{Or since your last update from the main repository hosted by Econ-Ark.} then edit this branch to include \textit{only the changes you want to push to the main repository}. - -\item \textbf{Issue a pull request:} Go to \href{https://github.com/econ-ark/HARK}{HARK's GitHub page} and click the ``New pull request'' button. Click the text that says ``compare across forks'', and select \texttt{econ-ark/HARK} as the base fork, \texttt{master} as the base, your fork as the head fork, and the branch to be merged as the ``compare''. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you've done. Click the ``Create pull request'' button. - -\item \textbf{Be patient:} Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section \ref{sec:SubmissionApprovalProcess} for procedures followed by the HARK team when evaluating outside contributions. - -\end{enumerate} - -\subsection{Submission Approval Process}\label{sec:SubmissionApprovalProcess} - -Before submitting to HARK, the development team would like you to make sure that your intended contribution is listed as an Issue on the GitHub page. If you are going to fix or address an Issue that is already posted, reply to that post with a note indicating that you are about to work on it and an estimate of when you will make a pull request for it. If you want to do something that no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly) describing what you intend to do.\footnote{If your contribution is a tiny adjustment or to fix/add some documentation or comments, you shouldn't make an Issue.} This will both help prevent two people from working on the same thing (and one of them potentially wasting time on it), and lets the development team know what's coming. Moreover, if you propose a contribution that is (a) already in HARK, (b) not in the spirit of HARK, or (c) a fix for something that's not actually broken, this gives the team a chance to let you know not to spend effort on it. - -After creating a pull request to submit your contribution, it will be evaluated by one or more members of the HARK team. Criteria that the HARK team will consider when choosing whether to accept a submission include: -\begin{enumerate} -\item \textbf{Does the contribution break previous code?} If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility. - -\item \textbf{Is the code well documented?} Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section \ref{sec:DocumentationConventions}) and enough comments for a reader to follow the algorithm. - -\item \textbf{Is the contribution relevant?} The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn't belong in HARK. - -\item \textbf{Is the code demonstrated with an example?} If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you've contributed a new function, consider providing a snippet of code demonstrating its use.\footnote{In the future, HARK will make use of in-line testing.} - -\item \textbf{Is the code correct?} Your code should actually do what it claims to do. - -\item \textbf{Does the code use HARK conventions?} To the best of your ability, contributions to HARK should follow the style guidelines in section \ref{sec:NamingConventions}. We don't demand perfection, as these are guidelines rather than rules. - -\item \textbf{Is the code well integrated in HARK?} In the beta release of HARK, we have tried to present a ``taxonomy'' of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy. - -\item \textbf{Is this already in HARK?} If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK. -\end{enumerate} - -The HARK team intends to release more specific guidelines in the near future, so that contributors are better able to judge their submissions before issuing a pull request. We are in the process of recruiting ``czars'' to oversee particular aspects of HARK, providing their knowledge and expertise.\footnote{If you have knowledge and/or expertise and would like to contribute as a czar, please contact a HARK team member so that we can judge your credentials even though we have way less expertise.} While czars might directly evaluate submissions as a HARK team member, their more important role is to help establish these guidelines for their domain, more specifically nailing down what we mean by ``correct'' or ``well integrated''. - -\subsection{Naming Conventions}\label{sec:NamingConventions} - -Object naming conventions in HARK are fairly different than existing standards, and differ somewhat between tool modules vs model or application modules. The following conventions apply throughout HARK: -\begin{itemize} -\item Functions and methods are always in ``camel case'': no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. \texttt{approxLognormal}. - -\item Function and method names should accurately and concisely describe what the function does; the first word in the name \textit{must be a verb}.\footnote{HARK standards have specified several ``keyword verbs'' that have specific meaning.} - -\item Variable and class names \textit{should not} have a verb as their first word. - -\item Class names should use no underscores and capitalize the first letter of each word; moreover, a class name \textit{must include a noun}. E.g.\ \texttt{ConsPerfForesightSolver}. -\end{itemize} - -When naming variables in model modules, the HARK team strongly discourages using single letter names, like \texttt{c} for consumption. Instead, we encourage contributors to use longer, more descriptive variable names using additional words and common abbreviations to specify the variable more precisely. In \texttt{/Documentation/NARK.pdf}, we list standard single letter variable ``bases'' and an array of prefixes and suffixes to adjust them. Economic variables in model modules should (usually) not use underscores, instead using camel case to the greatest extent possible.\footnote{For ``non-economic'' variables that are only used temporarily, underscores are permissible.} The development team prefers this standard so that users can translate between Python code and LaTeX script with minimal work. - -Conventions for naming variables in HARK's tool modules are significantly closer to more commonly used standards. Variable names should be in all lower case, with underscores between words, e.g.\ \texttt{data\_to\_match}. The functions and classes in these modules are more general and almost surely do not have any inherent ``economic content''; they are numerical or algorithmic objects, not variables that might appear in an equation in an article for a (non-computational) economics journal. Variable names in application modules (e.g.\ the files that execute the \texttt{cstwMPC} estimations) are a mix of the conventions for tool and model files, as appropriate for each variable. That is, variables that are directly related to ``economic variables'' in model modules should follow those conventions, while objects created solely for data manipulation or reporting should use the style of tool modules. - -\subsection{Documentation Conventions}\label{sec:DocumentationConventions} - -The HARK team wants the toolKit to be as accessible to users as possible; our greatest fear\footnote{Other than spiders, of course.} is that a new user will open up our code, get hopelessly confused trying to read it, and then never look at HARK again. To prevent this tragic outcome, we have tried hard to provide comprehensive, accurate documentation and comments within the code describing our methods.\footnote{To the extent that documentation and comments are missing, we encourage users to rectify this in their contributions.} Moreover, HARK uses the Sphinx utility to generate a website with \href{https://econ-ark.github.io/HARK/}{online documentation} for all of our tool and model modules, so that users can learn about what's available in HARK without digging through the source code. When making contributions to HARK, the development team asks users to format their inline documentation to work with Sphinx by following a few simple rules. - -\begin{itemize} -\item The top of every module should begin with a ``docstring'' providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:\footnote{The triple quote to begin and end a docstring can use either the apostrophe \texttt{'} or quotations marks \texttt{"}.} - -\texttt{"""} - -\texttt{Specifies an economic model and provides methods for solving it. More specific description of the key features of the model and variations of it in this module. Maybe some comments about the solution method or limitations of the model. Your bank account routing number.} - -\texttt{"""} - -\item The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as: - -\texttt{def functionName(input1,input2):} - -\hspace{0.9cm}\texttt{"""} - -\hspace{0.9cm}\texttt{Concise description of the function. More details about what} - -\hspace{0.9cm}\texttt{the function does, options or modes available, and maybe mathematical} - -\hspace{0.9cm}\texttt{methods used. Credit to a source if you poached their algorithm.} - -\vspace{0.5cm} - -\hspace{0.9cm}\texttt{Parameters} - -\hspace{0.9cm}\texttt{--------------------} - -\hspace{0.9cm}\texttt{input1: type} - -\hspace{1.8cm}\texttt{Description of what input1 represents.} - -\hspace{0.9cm}\texttt{input2: type} - -\hspace{1.8cm}\texttt{Description of what input2 represents.} - -\vspace{0.5cm} - -\hspace{0.9cm}\texttt{Returns} - -\hspace{0.9cm}\texttt{--------------} - -\hspace{0.9cm}\texttt{output\_name: type} - -\hspace{1.8cm}\texttt{Description of the output(s) of the function. Might have} - -\hspace{1.8cm}\texttt{multiple entries. If no output, this is just "None".} - -\hspace{0.9cm}\texttt{"""} - -\item Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it. -\end{itemize} - -Finally, if you write a new model module, the HARK team asks that you also provide a short mathematical writeup of the model as a PDF. This document does not need to go into great detail about the solution method for the model or the functions and classes included in the module, merely specify the economic model and provide a summary of how it is solved. See \texttt{/Documentation/ConsumptionSavingModels.pdf} for an example of this. - -\ifthenelse{\boolean{ContributingToHARKOnly}}{}{ -\section{Future of HARK}\label{sec:FutureOfHARK} - -HARK is in a state of perpetual incompleteness, always expanding to include more tools and more models. As an open source project in the hands of many contributors around the world, the HARK team cannot fully predict future features of the toolKit. This section provides an overview of what we would like to see in the near future, as well as potential mechanisms to encourage researchers to contribute their code to the project. - -\subsection{Future Tools}\label{sec:FutureTools} - -The current frameworks in \texttt{HARK.core} concern agents who have ``isolated'' dynamic problems or interact with each other only through aggregate outcomes to which they contribute atomically. We hope to eventually provide a framework for models in which agents directly interact with each other; this may include a system for specifying the state of networks and how connections are formed and broken. \texttt{HARK.core} might also develop a framework more suited to industrial organization models and dynamic games.\footnote{It might already be possible to shoehorn some of these models into the \texttt{Market} framework.} - -The \texttt{HARK.estimation} module in particular is very sparsely populated, with its functions mostly serving as wrappers for ``off the shelf'' optimization methods from \texttt{scipy.optimize}. There are plenty of non-standard or more complex optimization techniques available, including global and/or stochastic search methods. We believe that additional optimizers are ``low hanging fruit'' as straightforward contributions to HARK and thus will be added in the near future. Moreover, there are plenty of distributions that have no approximator in \texttt{HARK.utilities} (or data generator in \texttt{HARK.simulation}), and these functions are among the easiest for new contributors to write. The data analysis tools in \texttt{HARK.utilities} only include functions that we happened to use for our current research projects, and we expect that this will be the mode of growth for future tools. There are some obvious holes that we believe will be filled quite quickly; for example, we provide a rudimentary non-parametric kernel regression function, but have not written a kernel density estimator to generate PDFs from data. - -The classes currently in \texttt{HARK.interpolation} are all variations on linear (or cubic) spline interpolation, and each is specifically written for a fixed number of dimensions. As these methods use full tensor grids, they are highly impractical for representing functions in higher dimensions, suffering greatly from the curse of dimensionality.\footnote{The HARK team only included interpolators for up to 4 dimensions, and some would argue this is already too many for tensor grid methods.} We are quite confident that \texttt{HARK.interpolation} will soon include techniques more applicable to high dimensional state spaces, including Smolnyak interpolation, other (adaptive) sparse grid methods, and regression-based interpolation. For lower-dimensional spaces, HARK will also soon provide Delaunay interpolation classes. - -The \texttt{HARK.parallel} module currently provides a simple interface for handling \textit{ex-ante} heterogeneity among agents. While useful for speeding up moderately-sized problems, the libraries used are only able to access the CPU of the local machine running the code. In the very near future, the HARK team will provide an interface (and example) for using Apache Spark, which bills itself as a ``fast and general engine for large-scale data processing''. Specifically, users will be able to easily scale up their computing resources and run HARK code on an Amazon EC2 session or other cluster. - -\subsection{Future Models}\label{sec:FutureModels} - -The economics models currently included in HARK mostly concern consumption-saving problems with constant relative risk aversion consumers who experience fully permanent and fully transitory shocks to income. This is obviously a very small subset of all consumption-saving models (which are themselves a small subset of all dynamic models economists might be interested in). Consider some examples of variants, features, and extensions that are not included in HARK, but could be in the future: -\begin{itemize} -\item Consumers with other utility functions, such as CARA or expo-power. - -\item Consumers with time-inconsistent preferences, e.g.\ hyperbolic discounting. - -\item Income shocks that are neither fully transitory nor fully permanent. - -\item Consumers with a bequest motive or terminal utility function. - -\item Utility from leisure and endogenous labor supply. - -\item Investment in a durable consumption good such as housing. - -\item Exogenous shocks to expenses. - -\item Multiple consumption goods, as with endogenous medical care. - -\item Portfolio allocation between a risky and riskless asset. - -\item Job search intensity as a control variable. - -\item Endogenous health evolution and health investment. -\end{itemize} - -The HARK team is currently developing a module for mixing discrete and continuous choice models, in the style of Jorgensen, Rust, Iskhakov, and Schjerning (2015). Among many other models, this framework will enable HARK users to easily develop features like: -\begin{itemize} -\item Labor supply on the extensive margin: the timing of retirement. - -\item Selection of job type or sector (by riskiness, specific human capital, etc). - -\item Choice of medical insurance contract. - -\item Decision to rent or own housing. - -\item Endogenous default on debt (with ``macroeconomic'' pricing of debt). - -\item Household formation, dissolution, and reproduction. -\end{itemize} - -Finally, we hope to provide a generic solver for any discrete choice model with T1EV shocks. The scope of HARK is limited only by what users choose to put in it. We hope that within a year of its debut, HARK will include a significant number of canonical models, providing more building blocks for future researchers to create novel combinations and new extensions. - -\subsection{Bounty Hunting}\label{sec:BountyHunting} - -While HARK users are free to write and contribute any tools and models that they believe could be helpful, building the toolKit in a ``bottom up'' style, the HARK team hopes to provide mechanisms for targeted ``top down'' development. The team is working to secure grant funding for HARK so as to offer ``bounties'' for particular models, tools, applications, or frameworks. Suppose the team decides that the model in Important Paper X should be included as part of HARK; we will issue a call for contributions to fill this hole, offering a modest prize of, say, \$20,000 to the programmer who submits the best implementation of the model, as judged by clearly stated criteria.\footnote{Actual bounties may be significantly smaller than this. But be honest: You're much more interested in contributing to HARK after reading that sentence.} The bounty would be announced as an Issue on HARK's GitHub page, as well as in a potential electronic digest of HARK news. - -Bounties might be placed on items as small as a single data analysis tool or as large as a complex estimation of a large general equilibrium model, with prizes scaling accordingly. We hasten to emphasize that we cannot guarantee funding for HARK, so the bounties (like everything else in this section) are purely speculative. As economists, the HARK team understands the incentive compatibility problems associated with asking researchers to contribute significant amounts of their time, effort, and unique skills in service of a tenuous concept like the Greater Good in exchange for even more tenuous Glory. We acknowledge that economic researchers are also optimizing agents with their own preferences over leisure and consumption, and agree that Greed is a valid mechanism to expand HARK. - -\subsection{All Aboard the Ark}\label{sec:AllAboardTheArk} - -As observant readers may have noticed, the HARK repository is hosted by a GitHub account named Econ-Ark, with no explanation as to what this might mean. Looking a few years into the future, the development team believes that HARK might be the first piece of a broader collection of computational economics resources. HARK is open source but somewhat akin to an exclusive club: contributions are required to adhere to standards and norms so as to foster modularity and interoperability among models and tools. Consistent with its namesake ship, the Econ-Ark has a significantly lower bar for admission, with a place on board for everything. - -On one level, the Ark might serve as a warehouse for any and all quantitative models and tools, for any economic purpose, in any subfield, and written in any language. Through nearly unanimous feedback, the HARK team understands that many economists are willing to share code that they have already written, but are reluctant to expend the considerable effort to translate and format it for HARK. In the future, we will set up a mechanism for code like this to be hosted on the Ark, with minimal requirements imposed on its authors (and correspondingly minimal warranty for potential users). Relevant projects on the Ark might become bounty targets for HARK, with users incentivized to port the original code into Python and to meet HARK standards; direct access to the original source code will significantly expedite the translation process, and provide a suitable benchmark for testing and verifying the contribution to HARK. - -On a different level, the Ark might also include cohesive toolkits like HARK, but geared toward purposes other than heterogeneous agents macroeconomics models. For example, the previously mentioned modules for networks among agents (and other agent-based modeling), industrial organization and dynamic games, and traditional discrete choice models might become their own toolkits, with some interoperability with HARK. In support of this, some of the tool modules currently in HARK might be ``promoted'' and become universally shared across all Econ-Ark toolkits. - -The dark secret of HARK is that it is named for heterogeneous agents macroeconomics, but designed and written by structural microeconomists. The development team believes these subfields to have considerable overlap in terms of methods and techniques, and that HARK might not be noticed by half of its potential audience due to its name. 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z+#S(_TROuKeRBk|1o7X>4bgW&teY1x3T9kum%A z)#LZ7iF7;?*m51>Yp4E~H6Z*0huLW&T!loZShxToU;s$_;ZGZQ5jFM?E0Es#Euz{G(@Pheyfe^$CCX#p&b|UX17Jh>-tp1o(9yf{;ZLQpYdig>VD8xsVP(Jh*%U2t|SdKz@G2H3Fo>i_zbTxWR}8{+yQw zF)uF|;TXjJgS^L!Xax|?LblfgM#}MBI|11u=f4gb(t!vY5RSce7B6DVwNri>hitDi z4LK7J@`4NLBBa^Kr6F4cUB?AE-A@AW{t>f3RsHNB4E(7Aj94*Z7%$Q)gyl$!kZJHM zT)$dG$N;|#xz@nLj}StXAf)-oJor;TQVPkuP9}sVqyp~si`YgLFhir*y= z$W~4WJJ1picN7AF{u;{Z;B3M2n}IAMCtq|j}K8d{>kGyfs zIWQL@AO9wYs5Xd4`yV_AkKljuz=+58A97$pnjpe~pDZ zL#=FK&ez~*IeT*lAQ+j-h_a&U;DCGruKQ|MUNA%=A(P7m4t0iKrxrIaH#awckx}}w H4B-C&>HZ~G From dc8178647f26ccefc8dbdacab791b74db2774c70 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Wed, 12 Feb 2020 10:53:01 +0530 Subject: [PATCH 204/242] force travis to build and test HARK (#517) --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index c9146b845..d00c9c07b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -7,6 +7,7 @@ python: cache: pip install: - pip install -r requirements.txt + - pip install . script: - pytest # - flake8 HARK From 149650fc9c98e07bce6dbb538f736691c5d265a1 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 13 Feb 2020 15:44:50 -0500 Subject: [PATCH 205/242] ConsLaborModel has default parameters Adopted new style of default parameters. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 1 - HARK/ConsumptionSaving/ConsLaborModel.py | 19 +++++++++++-------- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index db3784b43..2d9064c25 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1619,7 +1619,6 @@ def __init__(self, ------- None ''' - params = Params.init_perfect_foresight.copy() params.update(kwds) kwds = params diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 0d2ec2942..09eaf8a3e 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -270,7 +270,11 @@ def __init__(self,cycles=1,time_flow=True,**kwds): Returns ------- None - ''' + ''' + params = Params.init_labor_intensive.copy() + params.update(kwds) + kwds = params + IndShockConsumerType.__init__(self,cycles = cycles,time_flow=time_flow,**kwds) self.pseudo_terminal = False self.solveOnePeriod = solveConsLaborIntMarg @@ -510,9 +514,8 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): if __name__ == '__main__': import HARK.ConsumptionSaving.ConsumerParameters as Params # Parameters for a consumer type - from HARK.utilities import plotFuncsDer, plotFuncs import matplotlib.pyplot as plt - from time import clock + from time import process_time mystr = lambda number : "{:.4f}".format(number) # Format numbers as strings do_simulation = True @@ -521,12 +524,12 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): ############################################################################### # Make and solve a labor intensive margin consumer i.e. a consumer with utility for leisure - LaborIntMargExample = LaborIntMargConsumerType(**Params.init_labor_intensive) + LaborIntMargExample = LaborIntMargConsumerType() LaborIntMargExample.cycles = 0 - t_start = clock() + t_start = process_time() LaborIntMargExample.solve() - t_end = clock() + t_end = process_time() print('Solving a labor intensive margin consumer took ' + str(t_end-t_start) + ' seconds.') t = 0 @@ -597,9 +600,9 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): LifecycleExample = LaborIntMargConsumerType(**Params.init_labor_lifecycle) LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once - start_time = clock() + start_time = process_time() LifecycleExample.solve() - end_time = clock() + end_time = process_time() print('Solving a lifecycle labor intensive margin consumer took ' + str(end_time-start_time) + ' seconds.') LifecycleExample.unpackcFunc() LifecycleExample.timeFwd() From e1c681acc55c52fd9ad4cd21ec713b84f0dfdb01 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 13 Feb 2020 16:35:28 -0500 Subject: [PATCH 206/242] Moved ConsLaborModel main code to /Examples Following new style in Mridul's refactoring PR, moved all of the main block code for ConsLaborModel into a new example file in the appropriate directory. Also did a bunch of cleanup on simulation methods for the labor model, which were incorrect. --- HARK/ConsumptionSaving/ConsLaborModel.py | 276 +++++------------- .../example_ConsLaborModel.py | 226 ++++++++++++++ 2 files changed, 300 insertions(+), 202 deletions(-) create mode 100644 examples/ConsumptionSaving/example_ConsLaborModel.py diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 09eaf8a3e..1188e85bb 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -17,6 +17,8 @@ from HARK.interpolation import LinearInterp, LinearInterpOnInterp1D, VariableLowerBoundFunc2D, BilinearInterp, ConstantFunction from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, MargValueFunc from HARK.ConsumptionSaving.ConsGenIncProcessModel import ValueFunc2D, MargValueFunc2D +import HARK.ConsumptionSaving.ConsumerParameters as Params +import matplotlib.pyplot as plt class ConsumerLaborSolution(Solution): ''' @@ -342,6 +344,7 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): None ''' raise NotImplementedError() + def getControls(self): ''' @@ -357,13 +360,16 @@ def getControls(self): ''' cNrmNow = np.zeros(self.AgentCount) + np.nan MPCnow = np.zeros(self.AgentCount) + np.nan + LbrNow = np.zeros(self.AgentCount) + np.nan for t in range(self.T_cycle): these = t == self.t_cycle - cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # Assign consumtion values - MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # Assign Marginal propensity to consume values (derivative) + cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # Assign consumption values + MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # Assign marginal propensity to consume values (derivative) + LbrNow[these] = self.solution[t].LbrFunc(self.bNrmNow[these], self.TranShkNow[these]) # Assign labor supply self.cNrmNow = cNrmNow self.MPCnow = MPCnow - return None + self.LbrNow = LbrNow + def getPostStates(self): ''' @@ -377,8 +383,14 @@ def getPostStates(self): ------- None ''' - self.mNrmNow = self.bNrmNow + LaborIntMargExample.solution[t].LbrFunc(self.bNrmNow, self.TranShkNow)*self.TranShkNow - return None + mNrmNow = np.zeros(self.AgentCount) + np.nan + aNrmNow = np.zeros(self.AgentCount) + np.nan + for t in range(self.T_cycle): + these = t == self.t_cycle + mNrmNow[these] = self.bNrmNow[these] + self.LbrNow[these]*self.TranShkNow[these] # mNrm = bNrm + yNrm + aNrmNow[these] = mNrmNow[these] - self.cNrmNow[these] # aNrm = mNrm - cNrm + self.mNrmNow = mNrmNow + self.aNrmNow = aNrmNow def updateTranShkGrid(self): @@ -491,205 +503,65 @@ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None): ''' if ShkSet is None: ShkSet = self.TranShkGrid[t] - if bMin is None: - bMinSet = self.solution[0].bNrmMin(TranShkSet) - else: - bMinSet = bMin*np.ones_like(TranShkSet) - if bMax is None: - bMaxSet = bMinSet + 20. - else: - bMaxSet = bMax*np.ones_like(TranShkSet) - + for j in range(len(ShkSet)): - Shk = ShkSet[j] - B = np.linspace(bMinSet[j],bMaxSet[j],300) - C = LaborIntMargExample.solution[t].cFunc(B,Shk*np.ones_like(B)) + TranShk = ShkSet[j] + if bMin is None: + bMin_temp = self.solution[t].bNrmMin(TranShk) + else: + bMin_temp = bMin + if bMax is None: + bMax_temp = bMin_temp + 20. + else: + bMax_temp = bMax + + B = np.linspace(bMin_temp, bMax_temp, 300) + C = self.solution[t].cFunc(B, TranShk*np.ones_like(B)) plt.plot(B,C) plt.xlabel('Beginning of period bank balances') plt.ylabel('Normalized consumption level') plt.show() - - -############################################################################### - -if __name__ == '__main__': - import HARK.ConsumptionSaving.ConsumerParameters as Params # Parameters for a consumer type - import matplotlib.pyplot as plt - from time import process_time - mystr = lambda number : "{:.4f}".format(number) # Format numbers as strings - - do_simulation = True - - -############################################################################### - - # Make and solve a labor intensive margin consumer i.e. a consumer with utility for leisure - LaborIntMargExample = LaborIntMargConsumerType() - LaborIntMargExample.cycles = 0 - - t_start = process_time() - LaborIntMargExample.solve() - t_end = process_time() - print('Solving a labor intensive margin consumer took ' + str(t_end-t_start) + ' seconds.') - - t = 0 - bMax = 100. - - # Plot the consumption function at various transitory productivity shocks - TranShkSet = LaborIntMargExample.TranShkGrid[t] - B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: - B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) - C = LaborIntMargExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,C) - plt.xlabel('Beginning of period bank balances') - plt.ylabel('Normalized consumption level') - plt.show() - - # Plot the marginal consumption function at various transitory productivity shocks - TranShkSet = LaborIntMargExample.TranShkGrid[t] - B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: - B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) - C = LaborIntMargExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,C) - plt.xlabel('Beginning of period bank balances') - plt.ylabel('Marginal propensity to consume') - plt.show() - - # Plot the labor function at various transitory productivity shocks - TranShkSet = LaborIntMargExample.TranShkGrid[t] - B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: - B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) - Lbr = LaborIntMargExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,Lbr) - plt.xlabel('Beginning of period bank balances') - plt.ylabel('Labor supply') - plt.show() - - # Plot the marginal value function at various transitory productivity shocks - pseudo_inverse = True - TranShkSet = LaborIntMargExample.TranShkGrid[t] - B = np.linspace(0.,bMax,300) - for Shk in TranShkSet: - B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) - if pseudo_inverse: - vP = LaborIntMargExample.solution[t].vPfunc.cFunc(B_temp,Shk*np.ones_like(B_temp)) - else: - vP = LaborIntMargExample.solution[t].vPfunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,vP) - plt.xlabel('Beginning of period bank balances') - if pseudo_inverse: - plt.ylabel('Pseudo inverse marginal value') - else: - plt.ylabel('Marginal value') - plt.show() - - if do_simulation: - LaborIntMargExample.T_sim = 120 # Set number of simulation periods - LaborIntMargExample.track_vars = ['bNrmNow', 'cNrmNow'] - LaborIntMargExample.initializeSim() - LaborIntMargExample.simulate() -# plt.plot(np.linspace(0,100,10000), LaborIntMargExample.cNrmNow_hist[30]) -# plt.show() - -############################################################################### - - # Make and solve a labor intensive margin consumer with a finite lifecycle - LifecycleExample = LaborIntMargConsumerType(**Params.init_labor_lifecycle) - LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once - - start_time = process_time() - LifecycleExample.solve() - end_time = process_time() - print('Solving a lifecycle labor intensive margin consumer took ' + str(end_time-start_time) + ' seconds.') - LifecycleExample.unpackcFunc() - LifecycleExample.timeFwd() - - bMax = 20. - - # Plot the consumption function in each period of the lifecycle, using median shock - B = np.linspace(0.,bMax,300) - b_min = np.inf - b_max = -np.inf - for t in range(LifecycleExample.T_cycle): - TranShkSet = LifecycleExample.TranShkGrid[t] - Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less - B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - C = LifecycleExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp, C) - b_min = np.minimum(b_min, B_temp[0]) - b_max = np.maximum(b_min, B_temp[-1]) - plt.title('Consumption function across periods of the lifecycle') - plt.xlabel('Beginning of period bank balances') - plt.ylabel('Normalized consumption level') - plt.xlim(b_min, b_max) - plt.ylim(0., None) - plt.show() - - # Plot the marginal consumption function in each period of the lifecycle, using median shock - B = np.linspace(0.,bMax,300) - b_min = np.inf - b_max = -np.inf - for t in range(LifecycleExample.T_cycle): - TranShkSet = LifecycleExample.TranShkGrid[t] - Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less - B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - MPC = LifecycleExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp, MPC) - b_min = np.minimum(b_min, B_temp[0]) - b_max = np.maximum(b_min, B_temp[-1]) - plt.title('Marginal consumption function across periods of the lifecycle') - plt.xlabel('Beginning of period bank balances') - plt.ylabel('Marginal propensity to consume') - plt.xlim(b_min, b_max) - plt.ylim(0., 1.) - plt.show() - - # Plot the labor supply function in each period of the lifecycle, using median shock - B = np.linspace(0.,bMax,300) - b_min = np.inf - b_max = -np.inf - for t in range(LifecycleExample.T_cycle): - TranShkSet = LifecycleExample.TranShkGrid[t] - Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less - B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - L = LifecycleExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp, L) - b_min = np.minimum(b_min, B_temp[0]) - b_max = np.maximum(b_min, B_temp[-1]) - plt.title('Labor supply function across periods of the lifecycle') - plt.xlabel('Beginning of period bank balances') - plt.ylabel('Labor supply') - plt.xlim(b_min, b_max) - plt.ylim(0., 1.01) - plt.show() - - # Plot the marginal value function at various transitory productivity shocks - pseudo_inverse = True - TranShkSet = LifecycleExample.TranShkGrid[t] - B = np.linspace(0.,bMax,300) - b_min = np.inf - b_max = -np.inf - for t in range(LifecycleExample.T_cycle): - TranShkSet = LifecycleExample.TranShkGrid[t] - Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less - B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) - if pseudo_inverse: - vP = LifecycleExample.solution[t].vPfunc.cFunc(B_temp,Shk*np.ones_like(B_temp)) - else: - vP = LifecycleExample.solution[t].vPfunc(B_temp,Shk*np.ones_like(B_temp)) - plt.plot(B_temp,vP) - b_min = np.minimum(b_min, B_temp[0]) - b_max = np.maximum(b_min, B_temp[-1]) - plt.xlabel('Beginning of period bank balances') - if pseudo_inverse: - plt.ylabel('Pseudo inverse marginal value') - else: - plt.ylabel('Marginal value') - plt.title('Marginal value across periods of the lifecycle') - plt.xlim(b_min, b_max) - plt.ylim(0., None) - plt.show() - \ No newline at end of file + + + def plotLbrFunc(self,t,bMin=None,bMax=None,ShkSet=None): + ''' + Plot the labor supply function by bank balances at a given set of transitory shocks. + + Parameters + ---------- + t : int + Time index of the solution for which to plot the labor supply function. + bMin : float or None + Minimum value of bNrm at which to begin the plot. If None, defaults + to the minimum allowable value of bNrm for each transitory shock. + bMax : float or None + Maximum value of bNrm at which to end the plot. If None, defaults + to bMin + 20. + ShkSet : [float] or None + Array or list of transitory shocks at which to plot the labor supply + function. If None, defaults to the TranShkGrid for this time period. + + Returns + ------- + None + ''' + if ShkSet is None: + ShkSet = self.TranShkGrid[t] + + for j in range(len(ShkSet)): + TranShk = ShkSet[j] + if bMin is None: + bMin_temp = self.solution[t].bNrmMin(TranShk) + else: + bMin_temp = bMin + if bMax is None: + bMax_temp = bMin_temp + 20. + else: + bMax_temp = bMax + + B = np.linspace(bMin_temp, bMax_temp, 300) + L = self.solution[t].LbrFunc(B, TranShk*np.ones_like(B)) + plt.plot(B,L) + plt.xlabel('Beginning of period bank balances') + plt.ylabel('Labor supply') + plt.show() diff --git a/examples/ConsumptionSaving/example_ConsLaborModel.py b/examples/ConsumptionSaving/example_ConsLaborModel.py new file mode 100644 index 000000000..ded047a38 --- /dev/null +++ b/examples/ConsumptionSaving/example_ConsLaborModel.py @@ -0,0 +1,226 @@ +''' +Example implementations of the intensive margin labor supply model in ConsLaborModel.py. +''' + +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.ConsumptionSaving.ConsLaborModel import LaborIntMargConsumerType +import numpy as np +import matplotlib.pyplot as plt +from time import process_time +mystr = lambda number : "{:.4f}".format(number) # Format numbers as strings + +do_simulation = True + +# Make and solve a labor intensive margin consumer i.e. a consumer with utility for leisure +LaborIntMargExample = LaborIntMargConsumerType() +LaborIntMargExample.cycles = 0 + +t_start = process_time() +LaborIntMargExample.solve() +t_end = process_time() +print('Solving a labor intensive margin consumer took ' + str(t_end-t_start) + ' seconds.') + +t = 0 +bMin_orig = 0. +bMax = 100. + +# Plot the consumption function at various transitory productivity shocks +TranShkSet = LaborIntMargExample.TranShkGrid[t] +bMin = bMin_orig +B = np.linspace(bMin,bMax,300) +bMin = bMin_orig +for Shk in TranShkSet: + B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) + C = LaborIntMargExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,C) + bMin = np.minimum(bMin, B_temp[0]) +plt.xlabel('Beginning of period bank balances') +plt.ylabel('Normalized consumption level') +plt.xlim(bMin, bMax-bMin_orig + bMin) +plt.ylim(0., None) +plt.show() + +# Plot the marginal consumption function at various transitory productivity shocks +TranShkSet = LaborIntMargExample.TranShkGrid[t] +bMin = bMin_orig +B = np.linspace(bMin,bMax,300) +for Shk in TranShkSet: + B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) + C = LaborIntMargExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,C) + bMin = np.minimum(bMin, B_temp[0]) +plt.xlabel('Beginning of period bank balances') +plt.ylabel('Marginal propensity to consume') +plt.xlim(bMin, bMax-bMin_orig + bMin) +plt.ylim(0., 1.) +plt.show() + +# Plot the labor function at various transitory productivity shocks +TranShkSet = LaborIntMargExample.TranShkGrid[t] +bMin = bMin_orig +B = np.linspace(0.,bMax,300) +for Shk in TranShkSet: + B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) + Lbr = LaborIntMargExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) + bMin = np.minimum(bMin, B_temp[0]) + plt.plot(B_temp,Lbr) +plt.xlabel('Beginning of period bank balances') +plt.ylabel('Labor supply') +plt.xlim(bMin, bMax-bMin_orig + bMin) +plt.ylim(0., 1.) +plt.show() + +# Plot the marginal value function at various transitory productivity shocks +pseudo_inverse = True +TranShkSet = LaborIntMargExample.TranShkGrid[t] +bMin = bMin_orig +B = np.linspace(0.,bMax,300) +for Shk in TranShkSet: + B_temp = B + LaborIntMargExample.solution[t].bNrmMin(Shk) + if pseudo_inverse: + vP = LaborIntMargExample.solution[t].vPfunc.cFunc(B_temp,Shk*np.ones_like(B_temp)) + else: + vP = LaborIntMargExample.solution[t].vPfunc(B_temp,Shk*np.ones_like(B_temp)) + bMin = np.minimum(bMin, B_temp[0]) + plt.plot(B_temp,vP) +plt.xlabel('Beginning of period bank balances') +if pseudo_inverse: + plt.ylabel('Pseudo inverse marginal value') +else: + plt.ylabel('Marginal value') +plt.xlim(bMin, bMax-bMin_orig + bMin) +plt.ylim(0., None) +plt.show() + +if do_simulation: + t_start = process_time() + LaborIntMargExample.T_sim = 120 # Set number of simulation periods + LaborIntMargExample.track_vars = ['bNrmNow', 'cNrmNow'] + LaborIntMargExample.initializeSim() + LaborIntMargExample.simulate() + t_end = process_time() + print('Simulating ' + str(LaborIntMargExample.AgentCount) + ' intensive-margin labor supply consumers for ' + str(LaborIntMargExample.T_sim) + ' periods took ' + mystr(t_end-t_start) + ' seconds.') + + N = LaborIntMargExample.AgentCount + CDF = np.linspace(0.,1,N) + + plt.plot(np.sort(LaborIntMargExample.cNrmNow), CDF) + plt.xlabel('Consumption cNrm in ' + str(LaborIntMargExample.T_sim) + 'th simulated period') + plt.ylabel('Cumulative distribution') + plt.xlim(0.,None) + plt.ylim(0.,1.) + plt.show() + + plt.plot(np.sort(LaborIntMargExample.LbrNow), CDF) + plt.xlabel('Labor supply Lbr in ' + str(LaborIntMargExample.T_sim) + 'th simulated period') + plt.ylabel('Cumulative distribution') + plt.xlim(0.,1.) + plt.ylim(0.,1.) + plt.show() + + plt.plot(np.sort(LaborIntMargExample.aNrmNow), CDF) + plt.xlabel('End-of-period assets aNrm in ' + str(LaborIntMargExample.T_sim) + 'th simulated period') + plt.ylabel('Cumulative distribution') + plt.xlim(0.,20.) + plt.ylim(0.,1.) + plt.show() + +############################################################################### + +# Make and solve a labor intensive margin consumer with a finite lifecycle +LifecycleExample = LaborIntMargConsumerType(**Params.init_labor_lifecycle) +LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once + +start_time = process_time() +LifecycleExample.solve() +end_time = process_time() +print('Solving a lifecycle labor intensive margin consumer took ' + str(end_time-start_time) + ' seconds.') +LifecycleExample.unpackcFunc() +LifecycleExample.timeFwd() + +bMax = 20. + +# Plot the consumption function in each period of the lifecycle, using median shock +B = np.linspace(0.,bMax,300) +b_min = np.inf +b_max = -np.inf +for t in range(LifecycleExample.T_cycle): + TranShkSet = LifecycleExample.TranShkGrid[t] + Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + C = LifecycleExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp, C) + b_min = np.minimum(b_min, B_temp[0]) + b_max = np.maximum(b_min, B_temp[-1]) +plt.title('Consumption function across periods of the lifecycle') +plt.xlabel('Beginning of period bank balances') +plt.ylabel('Normalized consumption level') +plt.xlim(b_min, b_max) +plt.ylim(0., None) +plt.show() + +# Plot the marginal consumption function in each period of the lifecycle, using median shock +B = np.linspace(0.,bMax,300) +b_min = np.inf +b_max = -np.inf +for t in range(LifecycleExample.T_cycle): + TranShkSet = LifecycleExample.TranShkGrid[t] + Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + MPC = LifecycleExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp, MPC) + b_min = np.minimum(b_min, B_temp[0]) + b_max = np.maximum(b_min, B_temp[-1]) +plt.title('Marginal consumption function across periods of the lifecycle') +plt.xlabel('Beginning of period bank balances') +plt.ylabel('Marginal propensity to consume') +plt.xlim(b_min, b_max) +plt.ylim(0., 1.) +plt.show() + +# Plot the labor supply function in each period of the lifecycle, using median shock +B = np.linspace(0.,bMax,300) +b_min = np.inf +b_max = -np.inf +for t in range(LifecycleExample.T_cycle): + TranShkSet = LifecycleExample.TranShkGrid[t] + Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + L = LifecycleExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp, L) + b_min = np.minimum(b_min, B_temp[0]) + b_max = np.maximum(b_min, B_temp[-1]) +plt.title('Labor supply function across periods of the lifecycle') +plt.xlabel('Beginning of period bank balances') +plt.ylabel('Labor supply') +plt.xlim(b_min, b_max) +plt.ylim(0., 1.01) +plt.show() + +# Plot the marginal value function at various transitory productivity shocks +pseudo_inverse = True +TranShkSet = LifecycleExample.TranShkGrid[t] +B = np.linspace(0.,bMax,300) +b_min = np.inf +b_max = -np.inf +for t in range(LifecycleExample.T_cycle): + TranShkSet = LifecycleExample.TranShkGrid[t] + Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less + B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) + if pseudo_inverse: + vP = LifecycleExample.solution[t].vPfunc.cFunc(B_temp,Shk*np.ones_like(B_temp)) + else: + vP = LifecycleExample.solution[t].vPfunc(B_temp,Shk*np.ones_like(B_temp)) + plt.plot(B_temp,vP) + b_min = np.minimum(b_min, B_temp[0]) + b_max = np.maximum(b_min, B_temp[-1]) +plt.xlabel('Beginning of period bank balances') +if pseudo_inverse: + plt.ylabel('Pseudo inverse marginal value') +else: + plt.ylabel('Marginal value') +plt.title('Marginal value across periods of the lifecycle') +plt.xlim(b_min, b_max) +plt.ylim(0., None) +plt.show() + From 206bad01561074aeaa51838b6baf13a4a588bdec Mon Sep 17 00:00:00 2001 From: llorracc <@users.noreply.github.com> Date: Thu, 13 Feb 2020 23:16:55 +0100 Subject: [PATCH 207/242] Remove HARKmanual.html from master --- Documentation/HARKmanual.md | 1275 ++++++++++ Documentation/HARKmanual.pdf | Bin 543441 -> 543288 bytes .../ContributingToHARK-Add.bib | 0 .../ContributingToHARK/ContributingToHARK.4ct | 8 - .../ContributingToHARK/ContributingToHARK.bib | 2 - .../ContributingToHARK/ContributingToHARK.cfg | 6 - .../ContributingToHARK/ContributingToHARK.css | 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Resources and toolKit + +February 13, 2020 + +Christopher D Carroll1 +Johns Hopkins University +Alexander M Kaufman2 +Consumer Financial Protection Bureau +David C Low3 +Consumer Financial Protection Bureau +Nathan M Palmer4 +U.S. Treasury Office of Financial Research +Matthew N White5 +University of Delaware + + + +![PIC](UserGuidePic-.png) + +1 Carroll: , email: +[ccarroll@jhu.edu](mailto:ccarroll@jhu.edu) 2 Kaufman: +[alexander.kaufman@cfpb.gov](mailto:alexander.kaufman@cfpb.gov) 3 Low: +[david.c.low@cfpb.gov](mailto:david.c.low@cfpb.gov) 4 Palmer: +[nathan.m.palmer@ofr.treasury.gov](mailto:nathan.m.palmer@ofr.treasury.gov) 5 +White: [mnwecon@udel.edu](mailto:mnwecon@udel.edu) + +## Contents + +0.1 Introduction +0.1.1 Getting Started +0.1.2 Structure of HARK +0.1.3 Other Resources +0.2 General Purpose Tools +0.2.1 HARKcore +0.2.2 HARK.utilities +0.2.3 HARK.interpolation +0.2.4 HARK.simulation +0.2.5 HARK.estimation +0.2.6 HARK.parallel +0.3 Microeconomics: the AgentType Class +0.3.1 Attributes of an AgentType +0.3.2 A Universal Solver +0.3.3 The Flow of Time and Other Methods +0.3.4 Sample Model: Perfect Foresight Consumption-Saving +0.4 Macroeconomics: the Market Class +0.4.1 Down on the Farm +0.4.2 Attributes of a Market +0.4.3 Sample Model: FashionVictim +0.5 Contributing to HARK +0.5.1 What Does HARK Want? +0.5.2 Git and GitHub +0.5.3 Submission Approval Process +0.5.4 Naming Conventions +0.5.5 Documentation Conventions +0.6 Future of HARK +0.6.1 Future Tools +0.6.2 Future Models +0.6.3 Bounty Hunting +0.6.4 All Aboard the Ark + +### 0.1 Introduction + +If you are willing to risk some mild psychological trauma, conjure to mind +your first experience of hand-coding a structural economic model. Your clunky +effort probably built on legacy code provided by an adviser or colleague – +which itself came from who-knows-what apocryphal sources. Efforts to combine +elements from one model with those from another were likely frustrated by the +“Tower of Babel” problem: Code from one source could not “speak” to code from +another without your own intermediation as a translator, possibly between two +unfamiliar languages and aided only by oracular comments that, at best, made +sense only in the context of other (now missing) code. + +After months of effort, you may have had the character-improving experience of +proudly explaining to your adviser that not only had you grafted two ideas +together, you also found a trick that speeded the solution by an order of +magnitude, only to be told that your breathtaking insight had been understood +for many years, as reflected in an appendix to a 2008 paper; or, worse, your +discovery was something that “everybody knows” but did not exist at all in +published form! + +Learning by doing has value, but only within limits. We do not require young +drivers to design an internal combustion engine before driving a car, nor must +graduate students write their own matrix inversion algorithms before running +an OLS regression. + +In recent years, considerable progress has been made in addressing these kinds +of problems in many areas of economic modeling. Macroeconomists using +representative agent models can send Dynare model files to each other; reduced +form econometricians can choose from a host of econometric packages. But +modelers whose questions require explicit structural modeling involving +nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply +be aggregated away) are mostly still stuck in the bad old days. + +The ultimate goal of the HARK project is to fix these problems. Specifically, +our aim is to produce an open source repository of highly modular, easily +interoperable code for solving, simulating, and estimating dynamic economic +models with heterogeneous agents.[2](HARKmanual2.html#fn2x0) Further, we seek +to establish (with input from the community) standards for the description and +specification of objects like discrete approximations to continuous +distributions and interpolated function approximations, so that numeric +methods can be quickly swapped without ugly “patching.” + +We hope that HARK will make it much easier and faster for researchers to +develop solution and estimation methods for new models. The open source nature +of HARK will make it easier for other researchers to audit and verify new +models and methods, and to collaborate on correcting deficiencies when found. +As HARK expands to include more canonical models and more tools and utilities, +we can all spend less time managing numerical minutiae and more time fretting +about identification arguments and data accuracy. + +#### 0.1.1 Getting Started + +If you want to get started using HARK right away, this section provides a very +easy quickstart guide for getting HARK up and running on your computer in just +a few minutes. More information can be found in the README.md file in the HARK +repository (step 4), but here is a quick version for those who want to jump +right in: + + 1. Download Anaconda: Go to and download Anaconda for your operating system; be sure to get the version for Python 2.7. + 2. Install Anaconda: Follow the easy instructions on that page to install Anaconda. + 3. Add extra packages: (optional) If you want to use HARK’s multithreading feature, you need to add two packages that aren’t part of the default Anaconda distribution. Simply open a command prompt and do conda install joblib and conda install dill, accepting defaults to install. + 4. Download HARK: Go to , the home of the HARK repository. Click on the lime green button labeled “Clone or download” toward the upper right of the main panel, and select “Download ZIP”.[3](HARKmanual3.html#fn3x0) + 5. Unzip HARK: Unpack the HARK.zip file using any archive utility, like [Peazip](http://www.peazip.org/). Put the files anywhere you’d like, maintaining the internal directory structure. + 6. Run Spyder: Open a command prompt and do spyder. Spyder is an interactive development environment (IDE) for iPython, a slightly prettier, more interactive flavor of Python. + 7. Open a HARK module: Go to the directory where you put HARK and open any file with the .py extension; we recommend /Demos/ConsumptionSaving/ConsIndShockModel.py. + 8. Run the module: Click on the green arrow “play” button toward the right side of the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue box pops up). Congratulations! HARK is now up and running on your computer. + +If you followed our recommendation to try +/Demos/ConsumptionSaving/ConsIndShockModel.py, you should see graphical +representations of the solution to a few consumption-saving models with +idiosyncratic shocks to permanent and transitory income (see +/Documentation/ConsumptionSavingModels.pdf for details of these models). If +you chose a different module, you might get some examples of some other model, +results of a structural estimation, a polite notification that this particular +module doesn’t do much on its own, or no output at all. See the README.md for +a full list of modules from which you can expect non-trivial output. + +#### 0.1.2 Structure of HARK + +HARK is written in Python, an object-oriented programming (OOP) language that +has experienced increasing popularity in the scientific community in the past +several years. A significant reason for the adoption of Python is the numpy +and scipy packages, which offer a wide array of mathematical and statistical +functions and tools; HARK makes liberal use of these libraries. Python’s +object-oriented nature allows models in HARK to be easily extended: more +complex models can inherit functions and methods from more fundamental +“parent” models, eliminating the need to reproduce or repurpose code. + +As implied in the previous section, we strongly encourage HARK users to use +the Anaconda distribution of Python, which includes all commonly used +mathematical and scientific packages, an interactive development environment +for iPython (Spyder), and a package manager that allows users to quickly +install or update packages not included in the default distribution (conda). + +For users unfamiliar with OOP, we strongly encourage you to review the +background material on OOP provided by the good people at +[QuantEcon](http://quant-econ.net/) (for more on them, see below) at this +link: [Object Oriented Programming](http://quant-econ.net/py/python_oop.html). +Unlike non-OOP languages, OOP bundles together data and functions into +objects. These can be accessed via: object_name.data and +object_name.method_name(), respectively. For organizational purposes, +definitions of multiple objects are stored in modules, which are simply files +with a .py extension. Modules can be accessed in Python via: + +import module_name as import_name + +This imports the module and gives it a local name of import_name. We can +access a function within this module by simply typing: +import_name.function_name(). The following example will illustrate the usage +of these commands. CRRAutility is the function object for calculating CRRA +utility supplied by HARK.utilities module. CRRAutility is called attributes of +the module HARK.utilities. In order to calculate CRRA utility with a +consumption of 1 and a coefficient of risk aversion of 2 we run: + +import HARK.utilities as Hutil + +Hutil.CRRAutility(1,2) + +Python modules in HARK can generally be categorized into three types: tools, +models, and applications. Tool modules contain functions and classes with +general purpose tools that have no inherent “economic content”, but that can +be used in many economic models as building blocks or utilities; they could +plausibly be useful in non-economic settings. Tools might include functions +for data analysis (e.g. calculating Lorenz shares from data, or constructing a +non-parametric kernel regression), functions to create and manipulate discrete +approximations to continuous distributions, or classes for constructing +interpolated approximations to non-parametric functions. Tool modules +generally reside in HARK’s root directory and have names like HARK.simulation +and HARK.interpolation; they do not necessarily do anything when run. + +Model modules specify particular economic models, including classes to +represent agents in the model (and the “market structure” in which they +interact) and functions for solving the “one period problem” of those models. +For example, ConsIndShockModel.py concerns consumption-saving models in which +agents have CRRA utility over consumption and face idiosyncratic shocks to +permanent and transitory income. The module includes classes for representing +“types” of consumers, along with functions for solving (several flavors of) +the one period consumption-saving problem. When run, model modules might +demonstrate example specifications of their models, filling in the model +parameters with arbitrary values. When ConsIndShockModel.py is run, it +specifies an infinite horizon consumer with a particular discount factor, +permanent income growth rate, coefficient of relative risk aversion (etc), who +faces lognormal shocks to permanent and transitory income each period with a +particular standard deviation; it then solves this consumer’s problem and +graphically displays the results.[4](HARKmanual4.html#fn4x0) Model modules +generally have Model in their name. + +Application modules use tool and model modules to solve, simulate, and/or +estimate economic models for a particular purpose. While tool modules have no +particular economic content and model modules describe entire classes of +economic models, applications are uses of a model for some research purpose. +For example, /Demos/SolvingMicroDSOPs/StructEstimation.py uses a consumption- +saving model from ConsIndShockModel.py, calibrating it with age-dependent +sequences of permanent income growth, survival probabilities, and the standard +deviation of income shocks (etc); it then estimates the coefficient of +relative risk aversion and shifter for an age-varying sequence of discount +factors that best fits simulated wealth profiles to empirical data from the +Survey of Consumer Finance. A particular application might have multiple +modules associated with it, all of which generally reside in one directory. + +#### 0.1.3 Other Resources + +In the incredibly unlikely scenario in which this document does not fill all +of the gaps in your knowledge and answer any questions you might have while +reading it, here is a collection of potentially helpful other resources. + + * [Online documentation](https://econ-ark.github.io/HARK/) for HARK tool and model modules, produced using Sphinx. + * A [tutorial on Python 2.7](https://docs.python.org/2.7/tutorial/), straight from the source. + * [Wikipedia article on object-oriented programming](https://en.wikipedia.org/wiki/Object-oriented_programming), not the subject of an edit war. + * [QuantEcon](http://quant-econ.net/), a collection of lectures on quantitative economic modeling from a couple of economists, which definitely in no way influenced HARK whatsoever. + * A [tutorial on git](https://www.atlassian.com/git/tutorials/), the repository management and tracking system used by the HARK project, created by Linus Torvalds. + * A [tutorial on GitHub](https://guides.github.com/activities/hello-world/), a website that provides a useful framework for git that makes it significantly more user-friendly, despised by Linus Torvalds. + * [Wikipedia article on Linus Torvalds](https://en.wikipedia.org/wiki/Linus_Torvalds), just in case. + * The [homepage for NumPy](http://www.numpy.org/), a numerical package extensively used by HARK. + * The [homepage for Sphinx](http://www.sphinx-doc.org/), a tool for producing HTML documentation. + * A [collection of lecture notes](http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/) by Chris Carroll, to catch you up on the basics of consumption-saving models. + * A [particular set of lecture notes](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/) by Chris Carroll, explaining many of the numeric techniques used in HARK and offering an example of a simple structural model that, in a stunning coincidence, is implemented in HARK as an example. + * A [deep dive](http://www.econ2.jhu.edu/people/ccarroll/BufferStockTheory.pdf) into the theoretical foundations of buffer stock saving. Warning: dense. + +### 0.2 General Purpose Tools + +HARK’s root directory contains six tool modules,[5](HARKmanual5.html#fn5x0) +each containing a variety of functions and classes that can be used in many +economic models– or even for mathematical purposes that have nothing to do +with economics. Some of the tool modules are very sparely populated at this +time, while others are quite large. We expect that all of these modules will +grow considerably in the near future, as new tools are “low hanging fruit” for +contribution to the project.[6](HARKmanual6.html#fn6x0) + +#### 0.2.1 HARKcore + +A key goal of the project is to create modularity and interoperability between +models, making them easy to combine, adapt, and extend. To this end, the +HARK.core module specifies a framework for economic models in HARK, creating a +common structure for them on two levels that can be called “microeconomic” and +“macroeconomic”. + +Microeconomic models in HARK use the AgentType class to represent agents with +an intertemporal optimization problem. Each of these models specifies a +subclass of AgentType; an instance of the subclass represents agents who are +ex-ante homogeneous– they have common values for all parameters that describe +the problem. For example, ConsIndShockModel specifies the IndShockConsumerType +class, which has methods specific to consumption-saving models with +idiosyncratic shocks to income; an instance of the class might represent all +consumers who have a CRRA of 3, discount factor of 0.98, etc. The AgentType +class has a solve method that acts as a “universal microeconomic solver” for +any properly formatted model, making it easier to set up a new model and to +combine elements from different models; the solver is intended to encompass +any model that can be framed as a sequence of one period problems. For a +complete description, see section 0.3. + +Macroeconomic models in HARK use the Market class to represent a market (or +other aggregator) that combines the actions, states, and/or shocks (generally, +outcomes) of individual agents in the model into aggregate outcomes that are +“passed back” to the agents. For example, the market in a consumption-saving +model might combine the individual asset holdings of all agents in the market +to generate aggregate capital in the economy, yielding the interest rate on +assets (as the marginal product of capital); the individual agents then learn +the aggregate capital level and interest rate, conditioning their next action +on this information. Objects that microeconomic agents treat as exogenous when +solving (or simulating) their model are thus endogenous at the macroeconomic +level. Like AgentType, the Market class also has a solve method, which seeks +out a dynamic general equilibrium: a “rule” governing the dynamic evolution of +macroeconomic objects such that if agents believe this rule and act +accordingly, then their collective actions generate a sequence of +macroeconomic outcomes that justify the belief in that rule. For a more +complete description, see section 0.4. + +Beyond the model frameworks, HARK.core also defines a “supersuperclass” called +HARKobject. When solving a dynamic microeconomic model with an infinite +horizon (or searching for a dynamic general equilibrium), it is often required +to consider whether two solutions are sufficiently close to each other to +warrant stopping the process (i.e. approximate convergence). It is thus +necessary to calculate the “distance” between two solutions, so HARK specifies +that classes should have a distance method that takes a single input and +returns a non-negative value representing the (generally dimensionless) +distance between the object in question and the input to the method. As a +convenient default, HARKobject provides a “universal distance metric” that +should be useful in many contexts.[7](HARKmanual7.html#fn7x0) When defining a +new subclass of HARKobject, the user simply defines the attribute +distance_criteria as a list of strings naming the attributes of the class that +should be compared when calculating the distance between two instances of that +class. For example, the class ConsumerSolution has distance_criteria = +[’cFunc’], indicating that only the consumption function attribute of the +solution matters when comparing the distance between two instances of +ConsumerSolution. See +[here](https://hark.readthedocs.io/en/latest/generated/HARK.core.html) for +online documentation. + +#### 0.2.2 HARK.utilities + +The HARK.utilities module carries a double meaning in its name, as it contains +both utility functions (and their derivatives, inverses, and combinations +thereof) in the economic modeling sense as well as utilities in the sense of +general tools. Utility functions included at this time are constant relative +risk aversion and constant absolute risk aversion. Other functions in +HARK.utilities include some data manipulation tools (e.g. for calculating an +average of data conditional on being within a percentile range of different +data), functions for constructing discrete state space grids, convenience +functions for retrieving information about functions, and basic plotting tools +using matplotlib.pyplot. + +The module also includes functions for constructing discrete approximations to +continuous distributions (e.g. approxLognormal() to approximate a log-normal +distribution) as well as manipulating these representations (e.g. appending +one outcome to an existing distribution, or combining independent univariate +distributions into one multivariate distribution). As a convention in HARK, +continuous distributions are approximated as finite discrete distributions +when solving models; an ![N ](HARKmanual0x.png)-dimensional random variable +is formatted as a length ![N + 1 ](HARKmanual1x.png) list of 1D arrays, with +the first element representing event probabilities and all other elements are +realizations of the ![N ](HARKmanual2x.png) component RVs. This both +simplifies solution methods (reducing numeric integrals to simple dot +products) and allows users to easily test whether their chosen degree of +discretization yields a sufficient approximation to the full distribution. See +[here](https://hark.readthedocs.io/en/latest/generated/HARK.utilities.html) +for online documentation. + +#### 0.2.3 HARK.interpolation + +The HARK.interpolation module defines classes for representing interpolated +function approximations. Interpolation methods in HARK all inherit from a +superclass such as HARKinterpolator1D or HARKinterpolator2D, wrapper classes +that ensures interoperability across interpolation methods. For example, +HARKinterpolator1D specifies the methods __call__ and derivative to accept an +arbitrary array as an input and return an identically shaped array with the +interpolated function evaluated at the values in the array or its first +derivative, respectively. However, these methods do little on their own, +merely reshaping arrays and referring to the _evaluate and _der methods, which +are not actually defined in HARKinterpolator1D. Each subclass of +HARKinterpolator1D specifies their own implementation of _evaluate and _der +particular to that interpolation method, accepting and returning only 1D +arrays. In this way, subclasses of HARKinterpolator1D are easily +interchangeable with each other, as all methods that the user interacts with +are identical, varying only by “internal” methods. + +When evaluating a stopping criterion for an infinite horizon problem, it is +often necessary to know the “distance” between functions generated by +successive iterations of a solution procedure. To this end, each interpolator +class in HARK must define a distance method that takes as an input another +instance of the same class and returns a non-negative real number representing +the “distance” between the two. As each of the HARKinterpolatorXD classes +inherits from HARKobject, all interpolator classes have the default +“universal” distance method; the user must simply list the names of the +relevant attributes in the attribute distance_criteria of the class. + +Interpolation methods currently implemented in HARK include (multi)linear +interpolation up to 4D, 1D cubic spline interpolation, (multi)linear +interpolation over 1D interpolations (up to 4D total), (multi)linear +interpolation over 2D interpolations (up to 4D total), linear interpolation +over 3D interpolations, 2D curvilinear interpolation over irregular grids, and +a 1D “lower envelope” interpolator. See [here](https://hark.readthedocs.io/en/ +latest/generated/HARK.interpolation.html) for online documentation. + +#### 0.2.4 HARK.simulation + +The HARK.simulation module provides tools for generating simulated data or +shocks for post-solution use of models. Currently implemented distributions +include normal, lognormal, Weibull (including exponential), uniform, +Bernoulli, and discrete. As an example of their use, these tools are used in +the consumption-saving models of ConsIndShockModel.py to simulate permanent +and transitory income shocks as well as unemployment events. See +[here](https://hark.readthedocs.io/en/latest/generated/HARK.simulation.html) +for online documentation. + +#### 0.2.5 HARK.estimation + +Methods for optimizing an objective function for the purposes of estimating a +model can be found in HARK.estimation. As of this writing, the implementation +includes only minimization by the Nelder-Mead simplex method, minimization by +a derivative-free Powell method variant, and two small tools for resampling +data (i.e. for a bootstrap); the minimizers are merely convenience wrappers +(with result reporting) for optimizers included in scipy.optimize. Future +functionality will include more robust global search methods, including +genetic algorithms, simulated annealing, and differential evolution. See +[here](https://hark.readthedocs.io/en/latest/generated/HARK.estimation.html) +for full documentation. + +#### 0.2.6 HARK.parallel + +By default, processes in Python are single-threaded, using only a single CPU +core. The HARK.parallel module provides basic tools for using multiple CPU +cores simultaneously, with minimal effort.[8](HARKmanual8.html#fn8x0) In +particular, it provides the function multiThreadCommands, which takes two +arguments: a list of AgentTypes and a list of commands as strings; each +command should be a method of the AgentTypes. The function simply distributes +the AgentTypes across threads on different cores and executes each command in +order, returning no output (the AgentTypes themselves are changed by running +the commands). Equivalent results would be achieved by simply looping over +each type and running each method in the list. Indeed, HARK.parallel also has +a function called multiThreadCommandsFake that does just that, with identical +syntax to multiThreadCommands; multithreading in HARK can thus be easily +turned on and off.[9](HARKmanual9.html#fn9x0) The module also has functions +for a parallel implementation of the Nelder-Mead simplex algorithm, as +described in Wiswall and Lee (2011). See +[here](https://hark.readthedocs.io/en/latest/generated/HARK.parallel.html) for +full documentation. + +### 0.3 Microeconomics: the AgentType Class + +The core of our microeconomic dynamic optimization framework is a flexible +object-oriented representation of economic agents. The HARK.core module +defines a superclass called AgentType; each model defines a subclass of +AgentType, specifying additional model-specific features and methods while +inheriting the methods of the superclass. Most importantly, the method solve +acts as a “universal solver” applicable to any (properly formatted) discrete +time model. This section describes the format of an instance of AgentType as +it defines a dynamic microeconomic problem;[10](HARKmanual10.html#fn10x0) + +#### 0.3.1 Attributes of an AgentType + +A discrete time model in our framework is characterized by a sequence of +“periods” that the agent will experience. A well-formed instance of AgentType +includes the following attributes: + + * solveOnePeriod: A function, or a list of functions, representing the solution method for a single period of the agent’s problem. The inputs passed to a solveOnePeriod function include all data that characterize the agent’s problem in that period, including the solution to the subsequent period’s problem (designated as solution_next). The output of these functions is a single Solution object, which can be passed to the solver for the previous period. + * time_inv: A list of strings containing all of the variable names that are passed to at least one function in solveOnePeriod but do not vary across periods. Each of these variables resides in a correspondingly named attribute of the AgentType instance. + * time_vary: A list of strings naming the attributes of this instance that vary across periods. Each of these attributes is a list of period-specific values, which should be of the same length. If the solution method varies across periods, then ’solveOnePeriod’ is an element of time_vary.[11](HARKmanual11.html#fn11x0) + * solution_terminal: An object representing the solution to the “terminal” period of the model. This might represent a known trivial solution that does not require numeric methods, the solution to some previously solved “next phase” of the model, a scrap value function, or an initial guess of the solution to an infinite horizon model. + * pseudo_terminal: A Boolean flag indicating that solution_terminal is not a proper terminal period solution (rather an initial guess, “next phase” solution, or scrap value) and should not be reported as part of the model’s solution. + * cycles: A non-negative integer indicating the number of times the agent will experience the sequence of periods in the problem. For example, cycles = 1 means that the sequence of periods is analogous to a lifecycle model, experienced once from beginning to end; cycles = 2 means that the agent experiences the sequence twice, with the first period in the sequence following the last. An infinite horizon problem in which the sequence of periods repeats indefinitely is indicated with cycles = 0. + * tolerance: A positive real number indicating convergence tolerance, representing the maximum acceptable “distance” between successive cycle solutions in an infinite horizon model; it is irrelevant when cycles > 0. As the distance metric on the space of solutions is model-specific, the value of tolerance is generally dimensionless. + * time_flow: A Boolean flag indicating the direction that time is “flowing.” When True, the variables listed in time_vary are listed in ordinary chronological order, with index 0 being the first period; when False, these lists are in reverse chronological order, with index 0 holding the last period. + +An instance of AgentType also has the attributes named in time_vary and +time_inv, and may have other attributes that are not included in either (e.g. +values not used in the model solution, but instead to construct objects used +in the solution). + +#### 0.3.2 A Universal Solver + +When an instance of AgentType invokes its solve method, the solution to the +agent’s problem is stored in the attribute solution. The solution is computed +by recursively solving the sequence of periods defined by the variables listed +in time_vary and time_inv using the functions in solveOnePeriod. The time- +varying inputs are updated each period, including the successive period’s +solution as solution_next; the same values of time invariant inputs in +time_inv are passed to the solver in every period. The first call to +solveOnePeriod uses solution_terminal as solution_next. In a finite horizon +problem, the sequence of periods is solved cycles times over; in an infinite +horizon problem, the sequence of periods is solved until the solutions of +successive cycles have a “distance” of less than tolerance. + +The output from a function in solveOnePeriod is an instance of a model- +specific solution class. The attributes of a solution to one period of a +problem might include behavioral functions, (marginal) value functions, and +other variables characterizing the result. Each solution class must have a +method called distance(), which returns the “distance” between itself and +another instance of the same solution class, so as to define convergence as a +stopping criterion; for many models, this will be the “distance” between a +policy or value function in the solutions. If the solution class is defined as +a subclass of HARKobject, it automatically inherits the default distance +method, so that the user must only list the relevant object attributes in +distance_criteria. + +The AgentType also has methods named preSolve and postSolve, both of which +take no arguments and do absolutely nothing. A subclass of AgentType can +overwrite these blank methods with its own model specific methods. preSolve is +automatically called near the beginning of the solve method, before solving +the sequence of periods. It is used for specifying tasks that should be done +before solving the sequence of periods, such as pre-constructing some objects +repeatedly used by the solution method or finding an analytical terminal +period solution. For example, the IndShockConsumerType class in +ConsIndShockModel has a preSolve method that calls its updateSolutionTerminal +method to ensure that solution_terminal is consistent with the model +parameters. The postSolve method is called shortly after the sequence of +periods is fully solved; it can be used for “post-processing” of the solution +or performing a step that is only useful after solution convergence. For +example, the TractableConsumerType in TractableBufferStockModel has a +postSolve method that constructs an interpolated consumption function from the +list of stable arm points found during solution. + +Our universal solver is written in a very general way that should be +applicable to any discrete time optimization problem– because Python is so +flexible in defining objects, the time-varying inputs for each period can take +any form. Indeed, the solver does no “real work” itself, but merely provides a +structure for describing models in the HARK framework, allowing +interoperability among current and future modules. + +#### 0.3.3 The Flow of Time and Other Methods + +Because dynamic optimization problems are solved recursively in our framework, +it is natural to list time-varying values in reverse chronological order– the +solve() method loops over the values in each time-varying list in the same +direction that a human would read them. When simulating agents after the +solution has been obtained, however, it is much more convenient for time- +varying parameters to be listed in ordinary chronological order– the direction +in which they will be experienced by simulated agents. To allow the user to +set the order in which “time is flowing” for an instance of AgentType, the +HARK framework includes functionality to easily change ordering of time- +varying values. + +The attribute time_flow is True if variables are listed in ordinary +chronological order and False otherwise. AgentType has the following methods +for manipulating time: + + * timeReport(): Prints to screen a description of the direction that time is flowing, for interactive convenience and as a reminder of the functionality. + * timeFlip(): Flips the direction of time. Each attribute listed in time_vary is reversed in place, and the value of time_flow is toggled. + * timeFwd(): Sets the direction of time to ordinary chronological order. + * timeRev(): Sets the direction of time to reverse chronological order. + +These methods are invoked to more conveniently access time-varying objects. +When a new time-varying attribute is added, its name should be appended to +time_vary, particularly if its values are used in the solution of the model +(or is part of the solution itself). For example, the solve() method +automatically adds the string ’solution’ to time_vary if it is not already +present. Note that attributes listed in time_vary must be lists if solve() or +timeFlip() are used. Some values that could be considered “time varying” but +are never used to solve the model are more conveniently represented as a +numpy.array object (e.g. the history of a state or control variable from a +simulation); because the numpy.array class does not have a reverse() method, +these attributes should not be listed in time_vary. + +The base AgentType is sparsely defined, as most “real” methods will be +application-specific. Two final methods bear mentioning. First, the __call__ +method points to assignParameters(), a convenience method for adding or +adjusting attributes (inherited from HARKobject). This method takes any number +of keyword arguments, so that code can be parsimoniously written as, for +example, AgentInstance(attribute1 = value1, attribute2 = value2). Using +Python’s dictionary capabilities, many attributes can be conveniently set with +minimal code. Second, the resetRNG method simply resets the AgentType’s random +number generator (as the attribute RNG) using the value in the attribute +seed.[12](HARKmanual12.html#fn12x0) This method is useful for (inter alia) +ensuring that the same underlying sequence of shocks is used for every +simulation run when a model is solved or estimated. + +#### 0.3.4 Sample Model: Perfect Foresight Consumption-Saving + +To provide a concrete example of how the AgentType class works, consider the +very simple case of a perfect foresight consumption-saving model. The agent +has time-separable, additive CRRA preferences over consumption, discounting +future utility at a constant rate; he receives a particular stream of labor +income each period, and knows the interest rate on assets that he holds from +one period to the next. His decision about how much to consume in a particular +period can be expressed in Bellman form as: + +![Vt\(Mt \) = maCx u\(Ct\) + β//DtE \[Vt+1 \(Mt+1 \)\], + + t + At = Mt - Ct, + Mt+1 = RAt + Yt+1, + + Yt+1 = Γ t+1Yt, + + -C1-ρ- + u\(C \) = 1 - ρ . ](HARKmanual3x.png) + +An agent’s problem is thus characterized by values of ![ρ +](HARKmanual4x.png), ![R ](HARKmanual5x.png), and ![β ](HARKmanual6x.png) +plus sequences of survival probabilities ![D + +// t ](HARKmanual7x.png) and income growth factors ![Γ + + t ](HARKmanual8x.png) for ![t = 0,⋅⋅⋅,T ](HARKmanual9x.png). This problem +has an analytical solution for both the value function and the consumption +function. + +The ConsIndShockModel module defines the class PerfForesightConsumerType as a +subclass of AgentType and provides solver functions for several variations of +a consumption-saving model, including the perfect foresight +problem.[13](HARKmanual13.html#fn13x0) A HARK user could specify and solve a +ten period perfect foresight model with the following commands: + +MyConsumer = PerfForesightConsumerType(time_flow=True, cycles=1, + +CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98, + +LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90], + +PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02], + +Nagents=1000) + +MyConsumer.solve() + +The first line makes a new instance of ConsumerType, specifying that time is +currently “flowing” forward[14](HARKmanual14.html#fn14x0) and that the +sequence of periods happens exactly once; the next three lines (all part of +the same command) set the time invariant (CRRA, Rfree, DiscFac) and time +varying parameters (LivPrb, PermGroFac). Note that dir(MyConsumer) shows all +the attributes of MyConsumer. After running the solve method, MyConsumer will +have an attribute called solution, which will be a list with eleven +ConsumerSolution objects, representing the period-by-period solution to the +model.[15](HARKmanual15.html#fn15x0) Each ConsumerSolution has several +attributes: + + * cFunc: Optimal consumption function for this period as a function of (normalized) market resources ![m = M ∕Y + t t t ](HARKmanual10x.png). Can be called like any other function: +MyConsumer.solution[0].cFunc(5) returns consumption at ![mt = 5 +](HARKmanual11x.png). + + * vFunc: Value function (over market resources ![mt ](HARKmanual12x.png)); can be evaluated like cFunc. + * mNrmMin: Minimum value of normalized market resources ![mt ](HARKmanual13x.png) such that cFunc and vFunc are defined. + * hNrm: Normalized human wealth– the PDV of future income (ignoring mortality) divided by current period income ![Yt ](HARKmanual14x.png). + * MPC: The constant marginal propensity to consume (linear consumption function). + +The following command generates the plot for the perfect foresight consumption +function: + +mMin = MyConsumer.solution[0].mNrmMin + +plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10) + +To solve a version of this problem in which the sequence of ten periods +happens three times (yielding a solution attribute with thirty-one elements), +the user can do: + +MyConsumer(cycles = 3) + +MyConsumer.solve() + +To solve an infinite horizon problem in which the agent experiences the same +one period problem indefinitely, the user can do: + +OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02, + +DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000) + +OtherConsumer.solve() + +This instance is specified as being infinite horizon by setting cycles=0. Note +that the time-varying inputs are still specified as (one element) lists, even +though they take on the same value in every period; this is because an +infinite horizon model might consist of a multi-period sequence repeated +indefinitely, rather than just one repeated period. The solution attribute of +OtherConsumer will be a list containing one instance of ConsumerSolution, +representing the solution to every period of the model. + +### 0.4 Macroeconomics: the Market Class + +The modeling framework of AgentType is deemed “microeconomic” because it +pertains only to the dynamic optimization problem of agents, treating all +inputs of the problem as exogenously fixed. In what we label as +“macroeconomic” models, some of the inputs for the microeconomic models are +endogenously determined by the collective states and controls of agents in the +model. In a dynamic general equilibrium, there must be consistency between +agents’ beliefs about these macroeconomic objects, their individual behavior, +and the realizations of the macroeconomic objects that result from individual +choices. + +The Market class in HARK.core provides a framework for such macroeconomic +models, with a solve method that searches for a dynamic general equilibrium. +An instance of Market includes a list of AgentTypes that compose the economy, +a method for transforming microeconomic outcomes (states, controls, and/or +shocks) into macroeconomic outcomes, and a method for interpreting a history +or sequence of macroeconomic outcomes into a new “dynamic rule” for agents to +believe. Agents treat the dynamic rule as an input to their microeconomic +problem, conditioning their optimal policy functions on it. A dynamic general +equilibrium is a fixed point dynamic rule: when agents act optimally while +believing the equilibrium rule, their individual actions generate a +macroeconomic history consistent with the equilibrium rule. + +#### 0.4.1 Down on the Farm + +The Market class uses a farming metaphor to conceptualize the process for +generating a history of macroeconomic outcomes in a model. Suppose all +AgentTypes in the economy believe in some dynamic rule (i.e. the rule is +stored as attributes of each AgentType, which directly or indirectly enters +their dynamic optimization problem), and that they have each found the +solution to their microeconomic model using their solve method. Further, the +macroeconomic and microeconomic states have been reset to some initial +orientation. + +To generate a history of macroeconomic outcomes, the Market repeatedly loops +over the following steps a set number of times: + + 1. sow: Distribute the macroeconomic state variables to all AgentTypes in the market. + 2. cultivate: Each AgentType executes their marketAction method, likely corresponding to simulating one period of the microeconomic model. + 3. reap: Microeconomic outcomes are gathered from each AgentType in the market. + 4. mill: Data gathered by reap is processed into new macroeconomic states according to some “aggregate market process”. + 5. store: Relevant macroeconomic states are added to a running history of outcomes. + +This procedure is conducted by the makeHistory method of Market as a +subroutine of its solve method. After making histories of the relevant +macroeconomic variables, the market then executes its calcDynamics function +with the macroeconomic history as inputs, generating a new dynamic rule to +distribute to the AgentTypes in the market. The process then begins again, +with the agents solving their updated microeconomic models given the new +dynamic rule; the solve loop continues until the “distance” between successive +dynamic rules is sufficiently small. + +#### 0.4.2 Attributes of a Market + +To specify a complete instance of Market, the user should give it the +following attributes:[16](HARKmanual16.html#fn16x0) + + * agents: A list of AgentTypes, representing the agents in the market. Each element in agents represents an ex-ante heterogeneous type; each type could have many ex-post heterogeneous agents. + * sow_vars: A list of strings naming variables that are output from the aggregate market process, representing the macroeconomic outcomes. These variables will be distributed to the agents in the sow step. + * reap_vars: A list of strings naming variables to be collected from the agents in the reap step, to be used as inputs for the aggregate market process. + * const_vars: A list of strings naming variables used by the aggregate market process that do not come from agents; they are constant or come from the Market itself. + * track_vars: A list of strings naming variables generated by the aggregate market process that should be tracked as a history, to be used when calculating a new dynamic rule. Usually a subset of sow_vars. + * dyn_vars: A list of strings naming the variables that constitute a dynamic rule. These will be stored as attributes of the agents whenever a new rule is calculated. + * millRule: A function for the “aggregate market process”, transforming microeconomic outcomes into macroeconomic outcomes. Its inputs are named in reap_vars and const_vars, and it returns a single object with attributes named in sow_vars and/or track_vars. Can be defined as a method of a subclass of Market. + * calcDynamics: A function that generates a new dynamic rule from a history of macroeconomic outcomes. Its inputs are named in track_vars, and it returns a single object with attributes named in dyn_vars. + * act_T: The number of times that the makeHistory method should execute the “farming loop” when generating a new macroeconomic history. + * tolerance: The minimum acceptable “distance” between successive dynamic rules produced by calcDynamics to constitute a sufficiently converged solution. + +Further, each AgentType in agents must have two methods not necessary for +microeconomic models; neither takes any input (except self): + + * marketAction: The microeconomic process to be run in the cultivate step. Likely uses the new macroeconomic outcomes named in sow_vars; should store new values of relevant microeconomic outcomes in the attributes (of self) named in reap_vars. + * reset: Reset, initialize, or prepare for a new “farming loop” to generate a macroeconomic history. Might reset its internal random number generator, set initial state varibles, clear personal histories, etc. + +When solving macroeconomic models in HARK, the user should also define classes +to represent the output from the aggregate market process in millRule and for +the model-specific dynamic rule. The latter should have a distance method to +test for solution convergence; if the class inherits from HARKobject, the user +need only list relevant attributes in distance_criteria. + +#### 0.4.3 Sample Model: FashionVictim + +To illustrate the Market class, consider a simple example in the emerging +economic subfield of aesthemetrics, the +FashionVictimModel.[17](HARKmanual17.html#fn17x0) +[18](HARKmanual18.html#fn18x0) This module defines a subclass of AgentType +called FashionVictimType. Each period, fashion victims make a binary choice of +style ![s ](HARKmanual15x.png): to dress as a jock (0) or punk (1). They +receive utility directly from the outfit they wear and as a function of the +proportion of the population who just wore the same style; they also pay +switching costs (![cpj ](HARKmanual16x.png),![cjp ](HARKmanual17x.png)) if +they change styles rather than keep the same as the previous period. Moreover, +they receive an idiosyncratic type 1 extreme value (T1EV) preference shock to +each style in each period. Defining the population punk proportion as ![p +](HARKmanual18x.png) and the conformity utility function as ![f : \[0, 1\] → ℝ +](HARKmanual19x.png), the current period utility function is thus: + +![u\(st;st- 1,pt\) = stf\(pt\)+ \(1 - st\)f\(1- pt\)+stUp+ \(1- st\)Uj- +cpjst-1\(1- st\)- cjp\(1- st-1\)st. + +](HARKmanual20x.png) + +Fashion victims are forward looking and discount future utility at a constant +rate of ![β ](HARKmanual21x.png) per period. To simplify the analysis, we +assume they believe that the population punk proportion in the next period is +a linear function of the punk proportion in the current period, subject to a +uniformly distributed shock. No restrictions are put on the function ![f +](HARKmanual22x.png); fashion victims might be conformists who like to dress +the same as others (![ ′ + +f \(p\) > 0 ](HARKmanual23x.png)) or hipsters who like to style themselves in +the minority (![ ′ + +f \(p\) < 0 ](HARKmanual24x.png)).[19](HARKmanual19.html#fn19x0) A fashion +victim’s problem can be written in Bellman form as: + +![ \[ \] + +V \(st-1,pt\) = E max u\(st;st-1,pt\) + ηst + βE \[V\(st,pt+1\)\] , + + st∈{0,1} +](HARKmanual26x.png) + +![pt+1 = apt + b + πt+1, πt+1 ~ U \[- w, w \], η0,η1 ~ T 1EV. + +](HARKmanual27x.png) + +An instance of FashionVictimType is thus characterized by values of ![Up +](HARKmanual28x.png), ![U + + j ](HARKmanual29x.png), ![c + + pj ](HARKmanual30x.png), ![c + + jp ](HARKmanual31x.png) and a function ![f ](HARKmanual32x.png), as well as +beliefs about ![p + + t+1 ](HARKmanual33x.png) as a function of ![pt ](HARKmanual34x.png) +(summarized by slope ![a ](HARKmanual35x.png), intercept ![b +](HARKmanual36x.png), and uniform shock width ![w ](HARKmanual37x.png)). +Given this information, a FashionVictimType’s infinite horizon microeconomic +model can be solved by backward induction in a few lines; the “one period +solver” is given by solveFashion. However, while individual agents treat the +dynamics of ![pt ](HARKmanual38x.png) as exogenous, they are in fact +endogenously determined by the actions of all the fashion victims in the +market.[20](HARKmanual20.html#fn20x0) A dynamic general equilibrium of the +“macroeconomic fashion model” is thus characterized by a triple of ![\(a,b,w +\) ](HARKmanual39x.png) such that when fashion victims believe in this “punk +evolution rule” and act optimally, their collective fashion choices exhibit +this same rule when the model is simulated. + +The search for a dynamic general equilibrium is implemented in HARK’s Market +class with the following definitions: + +sow_vars = [’pNow’] (macroeconomic outcome is ![p + + t ](HARKmanual40x.png)) + +reap_vars = [’sNow’] (microeconomic outcomes are ![st ](HARKmanual41x.png) +for many agents) + +track_vars = [’pNow’] (must track history of ![pt ](HARKmanual42x.png)) + +dyn_vars = [’pNextSlope’,’pNextIntercept’,’pNextWidth’] (dynamic rule +![\(a,b,w \) ](HARKmanual43x.png)) + +millRule = calcPunkProp (aggregate process: average the style choices of all +agents) + +calcDynamics = calcFashionEvoFunc (calculate new ![\(a,b,w\) +](HARKmanual44x.png) with autoregression of ![pt ](HARKmanual45x.png)) + +act_T = 1000 (simulate 1000 periods of the fashion market) + +tolerance = 0.01 (terminate solution when ![\(a,b,w \) ](HARKmanual46x.png) +changes by less than 0.01) + +The agents attribute has a list of 22 FashionVictimTypes, which vary in their +values of ![Up ](HARKmanual47x.png) and ![Uj ](HARKmanual48x.png), and their +![f ](HARKmanual49x.png) functions. The marketAction method of +FashionVictimType simulates one period of the microeconomic model: each agent +receives style preference shocks ![η + + 0 ](HARKmanual50x.png) and ![η + + 1 ](HARKmanual51x.png), sees the current proportion of punks ![pt +](HARKmanual52x.png) (sown to them as pNow), and chooses which style to wear, +storing it in the binary array sNow, an attribute of self. + +The millRule for this market is extremely simple: it flattens the list of +binary arrays of individual style choices (gathered in the reap step) and +averages them into a new value of ![pt ](HARKmanual53x.png), to be tracked as +a history and sown back to the agents to begin the cycle again. Once a history +of 1000 values of ![p + + t ](HARKmanual54x.png) has been generated with the makeHistory method, we +can calculate a new dynamic fashion rule with calcFashionEvoFunc by regressing +![pt ](HARKmanual55x.png) on ![pt-1 ](HARKmanual56x.png), approximating ![w +](HARKmanual57x.png) as twice the standard deviation of prediction +errors.[21](HARKmanual21.html#fn21x0) The new fashion rule is an instance of +the simple FashionEvoFunc class, whose only methods are inherited from +HARKobject. + +When the solve method is run, the solver successively solves each agent’s +microeconomic problem, runs the makeHistory method to generate a 1000 period +history of ![pt ](HARKmanual59x.png), and calculates a new punk evolution +rule based on this history; the solver terminates when consecutive rules +differ by less than 0.01 in any dimension. + +### 0.5 Contributing to HARK + +This section provides an overview of how users can contribute new code to +HARK, what sort of contributions are most desired, and style conventions for +the toolKit. Before attempting to extend HARK, we strongly recommend that you +familiarize yourself with its features and models by looking at the source +code. + +#### 0.5.1 What Does HARK Want? + +The easiest and most straightforward way to contribute to HARK is by writing +new general purpose tools for inclusion in one of the top-level modules. This +might be a commonly known data analysis technique (e.g. a kernel density +estimator) for HARK.utilities, an optimization method (local or global) for +HARK.estimation, an interpolation method for HARKinterpolation, etc. If you’ve +found a technique useful in your own research and believe it could be of use +to others on entirely different projects, then it probably belongs in HARK. +Likewise, if you read through a HARK module expecting to find a certain tool +or function (because of similar functions that are present, or merely because +it’s very useful) but don’t, this is a good sign that HARK has a hole that you +can fill.[22](HARKmanual22.html#fn22x0) + +Secondly, the development team wants to expand HARK to include more canonical +models. Most of the models available in the toolKit at the time of its public +beta release concern agents with (geometrically discounted) CRRA utility over +a unitary consumption good, who receive permanent and transitory shocks to +income and have access to a single risk free asset. This is an important class +of models, but that description has an awful lot of qualifiers. In short, +there are many more common features of dynamic household models that are not +yet in HARK, but would be very valuable for the toolKit. New model +contributions should “play nicely” with existing models and tools, following +HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent +possible. For ideas of potential model extensions for HARK, see section +0.6.2.[23](HARKmanual23.html#fn23x0) + +Finally, contributions to HARK are not restricted to entirely new +functionality. While we have done our best to provide flexible frameworks and +useful tools, we have almost certainly overlooked an unexpected case that +would not be (properly) handled by the toolKit as is. Moreover, there might +even be flaws in our solution or simulation methods. If you find a mistake or +error, we strongly encourage you to contribute a fix for the problem, or at +least to flag it as an Issue on GitHub so that someone else might. The +“mistake” you find might not even be in the code itself, but a deficiency in +the documentation: a misnamed (or incorrectly typed) parameter or return +variable in the docstring, an incorrect (or missing) comment, even a +grammatical or spelling error. No matter how niggling, we want your fix. + +For those who want to contribute to the HARK project without programming a new +model or digging through the code for errors and omissions, consider reading +through the Issues page on GitHub to see what other users have flagged for +development. You might find several pieces of “low hanging fruit” for you to +quickly fix. + +#### 0.5.2 Git and GitHub + +Git is a distributed version control system that has gained increasing +popularity in the decade since its creation; it is designed to enable +collaboration and “non-linear workflow” among a (potentially disperse) +programming team. As HARK is to be developed by volunteer researchers all +around the world, git is an excellent fit for the toolKit. Originally designed +by Linus Torvalds as a command line tool, others have since extended git with +further functionality and a user interface more friendly to those more +familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK +project is hosted. This section provides a brief overview of how to use GitHub +to interact with (and potentially contribute to) HARK. + +To prepare to contribute to HARK, follow these simple steps: + + 1. Make a GitHub account: Go to [github.com](https://www.github.com); the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.[24](HARKmanual24.html#fn24x0) + 2. Install GitHub Desktop: Go to [desktop.github.com](https://desktop.github.com/) and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account. + 3. Fork the HARK repository: Go to the [HARK repository page](https://github.com/econ-ark/HARK), click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead. + 4. Add the repository to Desktop: Open GitHub Desktop, click on the ![+ ](HARKmanual60x.png) icon in the upper left, and select clone. Find the repository named yourusername/HARK, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine. + +You now control a “fork” of the main HARK repository, hosted by GitHub, and +have a clone of this fork on your local machine. You are free to edit your +fork of HARK in any way you’d like; other GitHub users can view your fork (and +fork it themselves), but they cannot make changes to your fork unless you give +them permission by adding them as a contributor. Changes made to the local +copy of your fork do not get automatically sent to the remote copy on GitHub +for public viewing. To make changes to your fork on GitHub, follow these +steps: + + 1. Make local changes: Edit your local copy of the repository in any way you’d like: add or delete lines from existing files; add, delete, or rename entire files or directories. + 2. Commit local changes: Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button. + 3. Push the commit: Click on the Sync button near the upper right to “push” your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced. + +The left hand panel of the Changes tab has a summary of the files that have +been changed, added, or removed since your last commit; the right hand panel +shows the specific changes made to each changed file. After step 2, a record +of your changes has been stored locally by the git system, but is not yet +reflected in the public remote copy on GitHub.[25](HARKmanual25.html#fn25x0) +The “timeline” summary of the repository at the top of the Desktop window +shows the history of commits for the repository: each dot represents a commit +that was pushed to the remote server, and each open ring represents a local +commit that has not been pushed to the remote server; the large broken ring on +the far right represents changes that have not been locally +committed.[26](HARKmanual26.html#fn26x0) The History tab lists all previous +commits, including the timestamp, contributor, and a record of changes made. + +Suppose you have mucked about in your fork for a while and have made changes +to HARK that you think should be included in the main repository hosted by +Econ-Ark. To make your dream a reality, follow these steps: + + 1. Isolate the changes: If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should isolate the desired changes in a new branch of your fork. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,[27](HARKmanual27.html#fn27x0) then edit this branch to include only the changes you want to push to the main repository. + 2. Issue a pull request: Go to [HARK’s GitHub page](https://github.com/econ-ark/HARK) and click the “New pull request” button. Click the text that says “compare across forks”, and select econ-ark/HARK as the base fork, master as the base, your fork as the head fork, and the branch to be merged as the “compare”. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you’ve done. Click the “Create pull request” button. + 3. Be patient: Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section 0.5.3 for procedures followed by the HARK team when evaluating outside contributions. + +#### 0.5.3 Submission Approval Process + +Before submitting to HARK, the development team would like you to make sure +that your intended contribution is listed as an Issue on the GitHub page. If +you are going to fix or address an Issue that is already posted, reply to that +post with a note indicating that you are about to work on it and an estimate +of when you will make a pull request for it. If you want to do something that +no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly) +describing what you intend to do.[28](HARKmanual28.html#fn28x0) This will both +help prevent two people from working on the same thing (and one of them +potentially wasting time on it), and lets the development team know what’s +coming. Moreover, if you propose a contribution that is (a) already in HARK, +(b) not in the spirit of HARK, or (c) a fix for something that’s not actually +broken, this gives the team a chance to let you know not to spend effort on +it. + +After creating a pull request to submit your contribution, it will be +evaluated by one or more members of the HARK team. Criteria that the HARK team +will consider when choosing whether to accept a submission include: + + 1. Does the contribution break previous code? If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility. + 2. Is the code well documented? Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section 0.5.5) and enough comments for a reader to follow the algorithm. + 3. Is the contribution relevant? The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn’t belong in HARK. + 4. Is the code demonstrated with an example? If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you’ve contributed a new function, consider providing a snippet of code demonstrating its use.[29](HARKmanual29.html#fn29x0) + 5. Is the code correct? Your code should actually do what it claims to do. + 6. Does the code use HARK conventions? To the best of your ability, contributions to HARK should follow the style guidelines in section 0.5.4. We don’t demand perfection, as these are guidelines rather than rules. + 7. Is the code well integrated in HARK? In the beta release of HARK, we have tried to present a “taxonomy” of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy. + 8. Is this already in HARK? If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK. + +The HARK team intends to release more specific guidelines in the near future, +so that contributors are better able to judge their submissions before issuing +a pull request. We are in the process of recruiting “czars” to oversee +particular aspects of HARK, providing their knowledge and +expertise.[30](HARKmanual30.html#fn30x0) While czars might directly evaluate +submissions as a HARK team member, their more important role is to help +establish these guidelines for their domain, more specifically nailing down +what we mean by “correct” or “well integrated”. + +#### 0.5.4 Naming Conventions + +Object naming conventions in HARK are fairly different than existing +standards, and differ somewhat between tool modules vs model or application +modules. The following conventions apply throughout HARK: + + * Functions and methods are always in “camel case”: no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. approxLognormal. + * Function and method names should accurately and concisely describe what the function does; the first word in the name must be a verb.[31](HARKmanual31.html#fn31x0) + * Variable and class names should not have a verb as their first word. + * Class names should use no underscores and capitalize the first letter of each word; moreover, a class name must include a noun. E.g. ConsPerfForesightSolver. + +When naming variables in model modules, the HARK team strongly discourages +using single letter names, like c for consumption. Instead, we encourage +contributors to use longer, more descriptive variable names using additional +words and common abbreviations to specify the variable more precisely. In +/Documentation/NARK.pdf, we list standard single letter variable “bases” and +an array of prefixes and suffixes to adjust them. Economic variables in model +modules should (usually) not use underscores, instead using camel case to the +greatest extent possible.[32](HARKmanual32.html#fn32x0) The development team +prefers this standard so that users can translate between Python code and +LaTeX script with minimal work. + +Conventions for naming variables in HARK’s tool modules are significantly +closer to more commonly used standards. Variable names should be in all lower +case, with underscores between words, e.g. data_to_match. The functions and +classes in these modules are more general and almost surely do not have any +inherent “economic content”; they are numerical or algorithmic objects, not +variables that might appear in an equation in an article for a (non- +computational) economics journal. Variable names in application modules (e.g. +the files that execute the cstwMPC estimations) are a mix of the conventions +for tool and model files, as appropriate for each variable. That is, variables +that are directly related to “economic variables” in model modules should +follow those conventions, while objects created solely for data manipulation +or reporting should use the style of tool modules. + +#### 0.5.5 Documentation Conventions + +The HARK team wants the toolKit to be as accessible to users as possible; our +greatest fear[33](HARKmanual33.html#fn33x0) is that a new user will open up +our code, get hopelessly confused trying to read it, and then never look at +HARK again. To prevent this tragic outcome, we have tried hard to provide +comprehensive, accurate documentation and comments within the code describing +our methods.[34](HARKmanual34.html#fn34x0) Moreover, HARK uses the Sphinx +utility to generate a website with [online documentation](https://econ- +ark.github.io/HARK/) for all of our tool and model modules, so that users can +learn about what’s available in HARK without digging through the source code. +When making contributions to HARK, the development team asks users to format +their inline documentation to work with Sphinx by following a few simple +rules. + + * The top of every module should begin with a “docstring” providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:[35](HARKmanual35.html#fn35x0) + +""" + +Specifies an economic model and provides methods for solving it. More specific +description of the key features of the model and variations of it in this +module. Maybe some comments about the solution method or limitations of the +model. Your bank account routing number. + +""" + + * The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as: + +def functionName(input1,input2): + +""" + +Concise description of the function. More details about what + +the function does, options or modes available, and maybe mathematical + +methods used. Credit to a source if you poached their algorithm. + +Parameters + +–––––––––– + +input1: type + +Description of what input1 represents. + +input2: type + +Description of what input2 represents. + +Returns + +––––––– + +output_name: type + +Description of the output(s) of the function. Might have + +multiple entries. If no output, this is just "None". + +""" + + * Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it. + +Finally, if you write a new model module, the HARK team asks that you also +provide a short mathematical writeup of the model as a PDF. This document does +not need to go into great detail about the solution method for the model or +the functions and classes included in the module, merely specify the economic +model and provide a summary of how it is solved. See +/Documentation/ConsumptionSavingModels.pdf for an example of this. + +### 0.6 Future of HARK + +HARK is in a state of perpetual incompleteness, always expanding to include +more tools and more models. As an open source project in the hands of many +contributors around the world, the HARK team cannot fully predict future +features of the toolKit. This section provides an overview of what we would +like to see in the near future, as well as potential mechanisms to encourage +researchers to contribute their code to the project. + +#### 0.6.1 Future Tools + +The current frameworks in HARK.core concern agents who have “isolated” dynamic +problems or interact with each other only through aggregate outcomes to which +they contribute atomically. We hope to eventually provide a framework for +models in which agents directly interact with each other; this may include a +system for specifying the state of networks and how connections are formed and +broken. HARK.core might also develop a framework more suited to industrial +organization models and dynamic games.[36](HARKmanual36.html#fn36x0) + +The HARK.estimation module in particular is very sparsely populated, with its +functions mostly serving as wrappers for “off the shelf” optimization methods +from scipy.optimize. There are plenty of non-standard or more complex +optimization techniques available, including global and/or stochastic search +methods. We believe that additional optimizers are “low hanging fruit” as +straightforward contributions to HARK and thus will be added in the near +future. Moreover, there are plenty of distributions that have no approximator +in HARK.utilities (or data generator in HARK.simulation), and these functions +are among the easiest for new contributors to write. The data analysis tools +in HARK.utilities only include functions that we happened to use for our +current research projects, and we expect that this will be the mode of growth +for future tools. There are some obvious holes that we believe will be filled +quite quickly; for example, we provide a rudimentary non-parametric kernel +regression function, but have not written a kernel density estimator to +generate PDFs from data. + +The classes currently in HARK.interpolation are all variations on linear (or +cubic) spline interpolation, and each is specifically written for a fixed +number of dimensions. As these methods use full tensor grids, they are highly +impractical for representing functions in higher dimensions, suffering greatly +from the curse of dimensionality.[37](HARKmanual37.html#fn37x0) We are quite +confident that HARK.interpolation will soon include techniques more applicable +to high dimensional state spaces, including Smolnyak interpolation, other +(adaptive) sparse grid methods, and regression-based interpolation. For lower- +dimensional spaces, HARK will also soon provide Delaunay interpolation +classes. + +The HARK.parallel module currently provides a simple interface for handling +ex-ante heterogeneity among agents. While useful for speeding up moderately- +sized problems, the libraries used are only able to access the CPU of the +local machine running the code. In the very near future, the HARK team will +provide an interface (and example) for using Apache Spark, which bills itself +as a “fast and general engine for large-scale data processing”. Specifically, +users will be able to easily scale up their computing resources and run HARK +code on an Amazon EC2 session or other cluster. + +#### 0.6.2 Future Models + +The economics models currently included in HARK mostly concern consumption- +saving problems with constant relative risk aversion consumers who experience +fully permanent and fully transitory shocks to income. This is obviously a +very small subset of all consumption-saving models (which are themselves a +small subset of all dynamic models economists might be interested in). +Consider some examples of variants, features, and extensions that are not +included in HARK, but could be in the future: + + * Consumers with other utility functions, such as CARA or expo-power. + * Consumers with time-inconsistent preferences, e.g. hyperbolic discounting. + * Income shocks that are neither fully transitory nor fully permanent. + * Consumers with a bequest motive or terminal utility function. + * Utility from leisure and endogenous labor supply. + * Investment in a durable consumption good such as housing. + * Exogenous shocks to expenses. + * Multiple consumption goods, as with endogenous medical care. + * Portfolio allocation between a risky and riskless asset. + * Job search intensity as a control variable. + * Endogenous health evolution and health investment. + +The HARK team is currently developing a module for mixing discrete and +continuous choice models, in the style of Jorgensen, Rust, Iskhakov, and +Schjerning (2015). Among many other models, this framework will enable HARK +users to easily develop features like: + + * Labor supply on the extensive margin: the timing of retirement. + * Selection of job type or sector (by riskiness, specific human capital, etc). + * Choice of medical insurance contract. + * Decision to rent or own housing. + * Endogenous default on debt (with “macroeconomic” pricing of debt). + * Household formation, dissolution, and reproduction. + +Finally, we hope to provide a generic solver for any discrete choice model +with T1EV shocks. The scope of HARK is limited only by what users choose to +put in it. We hope that within a year of its debut, HARK will include a +significant number of canonical models, providing more building blocks for +future researchers to create novel combinations and new extensions. + +#### 0.6.3 Bounty Hunting + +While HARK users are free to write and contribute any tools and models that +they believe could be helpful, building the toolKit in a “bottom up” style, +the HARK team hopes to provide mechanisms for targeted “top down” development. +The team is working to secure grant funding for HARK so as to offer “bounties” +for particular models, tools, applications, or frameworks. Suppose the team +decides that the model in Important Paper X should be included as part of +HARK; we will issue a call for contributions to fill this hole, offering a +modest prize of, say, $20,000 to the programmer who submits the best +implementation of the model, as judged by clearly stated +criteria.[38](HARKmanual38.html#fn38x0) The bounty would be announced as an +Issue on HARK’s GitHub page, as well as in a potential electronic digest of +HARK news. + +Bounties might be placed on items as small as a single data analysis tool or +as large as a complex estimation of a large general equilibrium model, with +prizes scaling accordingly. We hasten to emphasize that we cannot guarantee +funding for HARK, so the bounties (like everything else in this section) are +purely speculative. As economists, the HARK team understands the incentive +compatibility problems associated with asking researchers to contribute +significant amounts of their time, effort, and unique skills in service of a +tenuous concept like the Greater Good in exchange for even more tenuous Glory. +We acknowledge that economic researchers are also optimizing agents with their +own preferences over leisure and consumption, and agree that Greed is a valid +mechanism to expand HARK. + +#### 0.6.4 All Aboard the Ark + +As observant readers may have noticed, the HARK repository is hosted by a +GitHub account named Econ-Ark, with no explanation as to what this might mean. +Looking a few years into the future, the development team believes that HARK +might be the first piece of a broader collection of computational economics +resources. HARK is open source but somewhat akin to an exclusive club: +contributions are required to adhere to standards and norms so as to foster +modularity and interoperability among models and tools. Consistent with its +namesake ship, the Econ-Ark has a significantly lower bar for admission, with +a place on board for everything. + +On one level, the Ark might serve as a warehouse for any and all quantitative +models and tools, for any economic purpose, in any subfield, and written in +any language. Through nearly unanimous feedback, the HARK team understands +that many economists are willing to share code that they have already written, +but are reluctant to expend the considerable effort to translate and format it +for HARK. In the future, we will set up a mechanism for code like this to be +hosted on the Ark, with minimal requirements imposed on its authors (and +correspondingly minimal warranty for potential users). Relevant projects on +the Ark might become bounty targets for HARK, with users incentivized to port +the original code into Python and to meet HARK standards; direct access to the +original source code will significantly expedite the translation process, and +provide a suitable benchmark for testing and verifying the contribution to +HARK. + +On a different level, the Ark might also include cohesive toolkits like HARK, +but geared toward purposes other than heterogeneous agents macroeconomics +models. For example, the previously mentioned modules for networks among +agents (and other agent-based modeling), industrial organization and dynamic +games, and traditional discrete choice models might become their own toolkits, +with some interoperability with HARK. In support of this, some of the tool +modules currently in HARK might be “promoted” and become universally shared +across all Econ-Ark toolkits. + +The dark secret of HARK is that it is named for heterogeneous agents +macroeconomics, but designed and written by structural microeconomists. The +development team believes these subfields to have considerable overlap in +terms of methods and techniques, and that HARK might not be noticed by half of +its potential audience due to its name. 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0.1  Contributing to HARK

-

This section provides an overview of how users can contribute new code to HARK, what sort of -contributions are most desired, and style conventions for the toolKit. Before attempting to -extend HARK, we strongly recommend that you familiarize yourself with its features and models -by looking at the source code. -

-

0.1.1  What Does HARK Want?

-

The easiest and most straightforward way to contribute to HARK is by writing new general -purpose tools for inclusion in one of the top-level modules. This might be a commonly -known data analysis technique (e.g. a kernel density estimator) for HARK.utilities, an -optimization method (local or global) for HARK.estimation, an interpolation method for -HARKinterpolation, etc. If you’ve found a technique useful in your own research and -believe it could be of use to others on entirely different projects, then it probably -belongs in HARK. Likewise, if you read through a HARK module expecting to find a -certain tool or function (because of similar functions that are present, or merely because -it’s very useful) but don’t, this is a good sign that HARK has a hole that you can -fill.1 -

Secondly, the development team wants to expand HARK to include more canonical models. -Most of the models available in the toolKit at the time of its public beta release concern agents -with (geometrically discounted) CRRA utility over a unitary consumption good, who receive -permanent and transitory shocks to income and have access to a single risk free asset. This is an -important class of models, but that description has an awful lot of qualifiers. In short, there are -many more common features of dynamic household models that are not yet in HARK, but would -be very valuable for the toolKit. New model contributions should “play nicely” with existing -models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the -greatest extent possible. For ideas of potential model extensions for HARK, see section - - -??.2 -

Finally, contributions to HARK are not restricted to entirely new functionality. While we have -done our best to provide flexible frameworks and useful tools, we have almost certainly -overlooked an unexpected case that would not be (properly) handled by the toolKit as is. -Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake -or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as -an Issue on GitHub so that someone else might. The “mistake” you find might not -even be in the code itself, but a deficiency in the documentation: a misnamed (or -incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing) -comment, even a grammatical or spelling error. No matter how niggling, we want your -fix. -

For those who want to contribute to the HARK project without programming a new model or -digging through the code for errors and omissions, consider reading through the Issues page on -GitHub to see what other users have flagged for development. You might find several pieces of -“low hanging fruit” for you to quickly fix. -

0.1.2  Git and GitHub

-

Git is a distributed version control system that has gained increasing popularity in the decade -since its creation; it is designed to enable collaboration and “non-linear workflow” among a -(potentially disperse) programming team. As HARK is to be developed by volunteer researchers -all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds -as a command line tool, others have since extended git with further functionality and -a user interface more friendly to those more familiar with GUIs. Foremost among -these tools is GitHub.com, where the HARK project is hosted. This section provides a -brief overview of how to use GitHub to interact with (and potentially contribute to) -HARK. -

To prepare to contribute to HARK, follow these simple steps: -

    -
  1. Make a GitHub account: Go to github.com; the homepage should prompt - you to choose a username and password. Signing up is quick, easy, and - free.3 -
    2. -
  2. Install GitHub Desktop: Go to desktop.github.com and download the Desktop - - - application for your operating system, then install it. After opening Desktop for the - first time, enter your GitHub login to connect it to your account. -
    3. -
  3. Fork the HARK repository: Go to the HARK repository page, click on the Fork - button in the upper right, and choose to fork HARK to your own GitHub account. - This creates a new repository identical to the one hosted by Econ-Ark at the moment - you fork it, but hosted on your account instead. -
    4. -
  4. Add the repository to Desktop: Open GitHub Desktop, click on the + icon - in the upper left, and select clone. Find the repository named yourusername/HARK, - click the Clone button at the bottom of the dialogue window, and choose a directory - on your local machine.
-

You now control a “fork” of the main HARK repository, hosted by GitHub, and have a clone of -this fork on your local machine. You are free to edit your fork of HARK in any way you’d like; -other GitHub users can view your fork (and fork it themselves), but they cannot make changes to -your fork unless you give them permission by adding them as a contributor. Changes made -to the local copy of your fork do not get automatically sent to the remote copy on -GitHub for public viewing. To make changes to your fork on GitHub, follow these -steps: -

    -
  1. Make local changes: Edit your local copy of the repository in any way you’d like: - add or delete lines from existing files; add, delete, or rename entire files or directories. -
    2. -
  2. Commit local changes: Open the repository in GitHub Desktop and click on the - Changes tab. In the lower left, provide a short description of the changes made in - the Summary field, and (optionally) longer comments about these changes in the - Description field. Click the check-mark Commit button. -
    3. -
  3. Push the commit: Click on the Sync button near the upper right to “push” your - local commits to the remote server; this will also pull down remote commits from - other contributors to your fork that were made since the last time you synced.
-

(This is an excerpt of the full manual, available at http://econ-ark.org/HARK/Documentation/HARKmanual.pdf) -

The left hand panel of the Changes tab has a summary of the files that have been changed, -added, or removed since your last commit; the right hand panel shows the specific -changes made to each changed file. After step 2, a record of your changes has been -stored locally by the git system, but is not yet reflected in the public remote copy on - - -GitHub.4 -The “timeline” summary of the repository at the top of the Desktop window shows the history of -commits for the repository: each dot represents a commit that was pushed to the remote server, -and each open ring represents a local commit that has not been pushed to the remote server; -the large broken ring on the far right represents changes that have not been locally -committed.5 -The History tab lists all previous commits, including the timestamp, contributor, and a record of -changes made. -

Suppose you have mucked about in your fork for a while and have made changes to HARK -that you think should be included in the main repository hosted by Econ-Ark. To make your -dream a reality, follow these steps: -

    -
  1. Isolate the changes: If you mucked around for quite a while, making various changes - here and there, and then later made some contribution that you want to include in - the main repository, you should isolate the desired changes in a new branch of your - fork. Make a new branch by clicking the button toward the upper left of GitHub - Desktop, revert that branch to the commit just before you forked from the main - repository,6 - then edit this branch to include only the changes you want to push to the main - repository. -
    2. -
  2. Issue a pull request: Go to HARK’s GitHub page and click the “New pull request” - button. Click the text that says “compare across forks”, and select econ-ark/HARK - as the base fork, master as the base, your fork as the head fork, and the branch to - be merged as the “compare”. In the boxes below, give a name to your pull request - and provide sufficient comments about your contribution so that a development team - member can understand what you’ve done. Click the “Create pull request” button. -
    3. -
  3. Be patient: Someone from the development team will look at your contribution and - either merge it into the main repository or return it with comments. See section 0.1.3 - for procedures followed by the HARK team when evaluating outside contributions. -
- - -

0.1.3  Submission Approval Process

-

Before submitting to HARK, the development team would like you to make sure that -your intended contribution is listed as an Issue on the GitHub page. If you are going -to fix or address an Issue that is already posted, reply to that post with a note -indicating that you are about to work on it and an estimate of when you will make a -pull request for it. If you want to do something that no one has mentioned yet as -an Issue, make a new Issue on GitHub (briefly) describing what you intend to -do.7 -This will both help prevent two people from working on the same thing (and one of them -potentially wasting time on it), and lets the development team know what’s coming. Moreover, if -you propose a contribution that is (a) already in HARK, (b) not in the spirit of HARK, or (c) a -fix for something that’s not actually broken, this gives the team a chance to let you know not to -spend effort on it. -

After creating a pull request to submit your contribution, it will be evaluated by one or more -members of the HARK team. Criteria that the HARK team will consider when choosing whether -to accept a submission include: -

    -
  1. Does the contribution break previous code? If you change or expand an existing - function, you should make sure that this does not cause some models or applications - to work incorrectly. In the near future, HARK will have a core set of tests to run to - check for full compatibility. -
    2. -
  2. Is the code well documented? Code submissions should have properly formatted - docstrings for each new function, class, method, and module (see section 0.1.5) and - enough comments for a reader to follow the algorithm. -
    3. -
  3. Is the contribution relevant? The development team intends for HARK to - be diverse, but not everything goes in HARK. If your submission concerns a - representative agent model or a method for estimating a reduced form model, then - it probably doesn’t belong in HARK. -
    4. -
  4. Is the code demonstrated with an example? If your solve a new model in - HARK, you should include a set of example parameters for other users to easily - test. If you’ve contributed a new function, consider providing a snippet of code - - - demonstrating its use.8 -
    5. -
  5. Is the code correct? Your code should actually do what it claims to do. -
    6. -
  6. Does the code use HARK conventions? To the best of your ability, contributions - to HARK should follow the style guidelines in section 0.1.4. We don’t demand - perfection, as these are guidelines rather than rules. -
    7. -
  7. Is the code well integrated in HARK? In the beta release of HARK, we have - tried to present a “taxonomy” of consumption-saving models that build up from each - other. When possible, new models in HARK should try to continue this pattern. A - submission that introduces a model in a new module that could have easily been made - as an extension of an existing module might be rejected. However, we acknowledge - that there is value in providing a solver for a new model, and that a future contributor - might make her mark by integrating it into the existing hierarchy. -
    8. -
  8. Is this already in HARK? If your contribution is already in HARK, then it will - almost certainly be rejected. Of course, if you submit a version that dominates the - existing implementation (or even one that works better in a well described, non-trivial - set of circumstances), it belongs in HARK.
-

The HARK team intends to release more specific guidelines in the near future, so that contributors -are better able to judge their submissions before issuing a pull request. We are in the process of -recruiting “czars” to oversee particular aspects of HARK, providing their knowledge and -expertise.9 -While czars might directly evaluate submissions as a HARK team member, their more important -role is to help establish these guidelines for their domain, more specifically nailing down what we -mean by “correct” or “well integrated”. -

0.1.4  Naming Conventions

-

Object naming conventions in HARK are fairly different than existing standards, and differ -somewhat between tool modules vs model or application modules. The following conventions -apply throughout HARK: - - -

    -
  • Functions and methods are always in “camel case”: no underscores, first letter is lower - case, first letter of each subsequent word is capitalized. E.g. approxLognormal. -
    • -
  • Function and method names should accurately and concisely describe what the - function does; the first word in the name must be a verb.10 -
    • -
  • Variable and class names should not have a verb as their first word. -
    • -
  • Class names should use no underscores and capitalize the first letter of each word; - moreover, a class name must include a noun. E.g. ConsPerfForesightSolver.
-

When naming variables in model modules, the HARK team strongly discourages using single -letter names, like c for consumption. Instead, we encourage contributors to use longer, more -descriptive variable names using additional words and common abbreviations to specify the -variable more precisely. In /Documentation/NARK.pdf, we list standard single letter variable -“bases” and an array of prefixes and suffixes to adjust them. Economic variables in model -modules should (usually) not use underscores, instead using camel case to the greatest extent -possible.11 -The development team prefers this standard so that users can translate between Python code -and LaTeX script with minimal work. -

Conventions for naming variables in HARK’s tool modules are significantly closer to more -commonly used standards. Variable names should be in all lower case, with underscores between -words, e.g. data_to_match. The functions and classes in these modules are more general -and almost surely do not have any inherent “economic content”; they are numerical -or algorithmic objects, not variables that might appear in an equation in an article -for a (non-computational) economics journal. Variable names in application modules -(e.g. the files that execute the cstwMPC estimations) are a mix of the conventions for tool -and model files, as appropriate for each variable. That is, variables that are directly -related to “economic variables” in model modules should follow those conventions, while -objects created solely for data manipulation or reporting should use the style of tool -modules. -

0.1.5  Documentation Conventions

- - -

The HARK team wants the toolKit to be as accessible to users as possible; our greatest -fear12 is -that a new user will open up our code, get hopelessly confused trying to read it, and then never -look at HARK again. To prevent this tragic outcome, we have tried hard to provide -comprehensive, accurate documentation and comments within the code describing our -methods.13 -Moreover, HARK uses the Sphinx utility to generate a website with online documentation for all -of our tool and model modules, so that users can learn about what’s available in HARK without -digging through the source code. When making contributions to HARK, the development team -asks users to format their inline documentation to work with Sphinx by following a few simple -rules. -

    -
  • The top of every module should begin with a “docstring” providing a clear description - of the contents of the module. The first sentence should concisely summarize - the file, as it may appear in an index or summary of all modules without the - remaining sentences. A docstring at the top of a module should be formatted - as:14 -

    """ -

    Specifies an economic model and provides methods for solving it. More - specific description of the key features of the model and variations - of it in this module. Maybe some comments about the solution method - or limitations of the model. Your bank account routing number. -

    """ -

    • -
  • The line directly below the declaration of a function, method or class should begin a - docstring describing that object. As with modules, the first sentence should concisely - summarize the function or class, as it might be included in an index or summary. For - functions and methods, the docstring should be formatted as: -

    def functionName(input1,input2): -

       """ - - -

       Concise description of the function. More details about what -

       the function does, options or modes available, and maybe - mathematical -

       methods used. Credit to a source if you poached their - algorithm. -

       Parameters -

       –––––––––– -

       input1: type -

         Description of what input1 represents. -

       input2: type -

         Description of what input2 represents. -

       Returns -

       ––––––– -

       output_name: type -

         Description of the output(s) of the function. Might have -

         multiple entries. If no output, this is just "None". -

       """ -

    • -
  • Provide ample comments within a function or method so that a relatively intelligent - reader can follow along with your algorithm. Short comments can follow at the end - of a line, longer comments (or descriptions of the step or task about to be performed) - should precede a block of code on the line(s) above it.
-

Finally, if you write a new model module, the HARK team asks that you also provide a short -mathematical writeup of the model as a PDF. This document does not need to go into great -detail about the solution method for the model or the functions and classes included in -the module, merely specify the economic model and provide a summary of how it -is solved. 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margin-bottom:1pt; } -Css: .figure img.graphics {margin-left:10%;} ---- needs --- ContributingToHARK.idv[2] ==> ContributingToHARK0x.png --- ---- characters --- -Font("cmr","12","12","100") -Font("ecrm","1200","12","100") -Font("ecti","1200","12","100") -Font("ecbx","1200","12","100") -Font("ectt","1200","12","100") -Font("ecrm","0600","6","100") -Font("ecrm","1000","10","100") -Font("ectt","1000","10","100") diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.tex b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.tex deleted file mode 100644 index e81c4766e..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.tex +++ /dev/null @@ -1,666 +0,0 @@ -% !TeX spellcheck = en_GB - -\documentclass[12pt,titlepage,letterpaper]{econtex} -\usepackage{econtexSetup}\usepackage{econtexShortcuts} -\usepackage{graphicx} -\usepackage{hyperref} -\providecommand{\E}{\mathbb{E}} - -\newboolean{ContributingToHARKOnly} -\setboolean{ContributingToHARKOnly}{true} - -\begin{document} -\ifthenelse{\boolean{ContributingToHARKOnly}}{(This is an excerpt of the full manual, available at \url{http://econ-ark.org/HARK/Documentation/HARKmanual.pdf)}}{ % If ContributingToHARKOnly is true, do not include any text until that section -\title{\LARGE A User's Guide for HARK: \\ Heterogeneous Agents Resources and toolKit} - -\date{\normalsize{\today}} - -\author{ -{Christopher D Carroll\authNum} \\ {\small Johns Hopkins University} -\and -{Alexander M Kaufman\authNum} \\ {\small Consumer Financial Protection Bureau} -\and -{David C Low\authNum} \\ {\small Consumer Financial Protection Bureau} -\and -{Nathan M Palmer\authNum} \\ {\small U.S.\ Treasury Office of Financial Research} -\and -{Matthew N White\authNum} \\ {\small University of Delaware} -} - - -\begin{authorsinfo} -\name{ -Carroll: \url{http://econ.jhu.edu/people/ccarroll/}, email: \href{mailto:ccarroll@jhu.edu}{\texttt{ccarroll@jhu.edu}} -} -\name{ -Kaufman: \href{mailto:alexander.kaufman@cfpb.gov}{\texttt{alexander.kaufman@cfpb.gov}} -} -\name{ -Low: \href{mailto:david.c.low@cfpb.gov}{\texttt{david.c.low@cfpb.gov}} -} -\name{ -Palmer: \href{mailto:nathan.m.palmer@ofr.treasury.gov}{\texttt{nathan.m.palmer@ofr.treasury.gov}} -} -\name{ -White: \href{mailto:mnwecon@udel.edu}{\texttt{mnwecon@udel.edu}} -} -\end{authorsinfo} - -\thanks{The materials in this resource reflect work by the persons named herein and others, much of it accomplished at the CFPB. However, none of the materials here should be interpreted as reflecting official opinions of the CFPB or any employees of the CFPB. After appropriate technical review, these resources are expected to appear on the CFPB's official GitHub page. Views expressed in this presentation are those of the authors and not necessarily of the Office of Financial Research or other government organizations.} - -\maketitle - -\begin{center} -\includegraphics[scale=0.8]{UserGuidePic.pdf} -\end{center} - -\thispagestyle{empty} -\newpage - -\tableofcontents - -\newpage - -\section{Introduction}\label{sec:Introduction} - -If you are willing to risk some mild psychological trauma, conjure to mind your first experience of hand-coding a structural economic model. Your clunky effort probably built on legacy code provided by an adviser or colleague -- which itself came from who-knows-what apocryphal sources. Efforts to combine elements from one model with those from another were likely frustrated by the ``Tower of Babel'' problem: Code from one source could not ``speak'' to code from another without your own intermediation as a translator, possibly between two unfamiliar languages and aided only by oracular comments that, at best, made sense only in the context of other (now missing) code. - -After months of effort, you may have had the character-improving experience of proudly explaining to your adviser that not only had you grafted two ideas together, you also found a trick that speeded the solution by an order of magnitude, only to be told that your breathtaking insight had been understood for many years, as reflected in an appendix to a 2008 paper; or, worse, your discovery was something that ``everybody knows'' but did not exist at all in published form! - -Learning by doing has value, but only within limits. We do not require young drivers to design an internal combustion engine before driving a car, nor must graduate students write their own matrix inversion algorithms before running an OLS regression. - -In recent years, considerable progress has been made in addressing these kinds of problems in many areas of economic modeling. Macroeconomists using representative agent models can send Dynare model files to each other; reduced form econometricians can choose from a host of econometric packages. But modelers whose questions require explicit structural modeling involving nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply be aggregated away) are mostly still stuck in the bad old days. - -The ultimate goal of the HARK project is to fix these problems. Specifically, our aim is to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents.\footnote{By ``heterogeneous,'' we mean that agents might differ before anything in the model has ``happened'' (\textit{ex-ante} heterogeneity); and agents might experience different stochastic events during the model (\textit{ex-post} heterogeneity).} Further, we seek to establish (with input from the community) standards for the description and specification of objects like discrete approximations to continuous distributions and interpolated function approximations, so that numeric methods can be quickly swapped without ugly ``patching.'' - -We hope that HARK will make it much easier and faster for researchers to develop solution and estimation methods for new models. The open source nature of HARK will make it easier for other researchers to audit and verify new models and methods, and to collaborate on correcting deficiencies when found. As HARK expands to include more canonical models and more tools and utilities, we can all spend less time managing numerical minutiae and more time fretting about identification arguments and data accuracy. - -\subsection{Getting Started}\label{sec:GettingStarted} - -If you want to get started using HARK right away, this section provides a very easy quickstart guide for getting HARK up and running on your computer in just a few minutes. More information can be found in the \texttt{README.md} file in the HARK repository (step 4), but here is a quick version for those who want to jump right in: - -\begin{enumerate} -\item \textbf{Download Anaconda:} Go to \href{https://www.anaconda.com/download/}{https://www.anaconda.com/download/} and download Anaconda for your operating system; be sure to get the version for Python 2.7. - -\item \textbf{Install Anaconda:} Follow the easy instructions on that page to install Anaconda. - -\item \textbf{Add extra packages:} (optional) If you want to use HARK's multithreading feature, you need to add two packages that aren't part of the default Anaconda distribution. Simply open a command prompt and do \texttt{conda install joblib} and \texttt{conda install dill}, accepting defaults to install. - -\item \textbf{Download HARK:} Go to \href{https://github.com/econ-ark/HARK}{https://github.com/econ-ark/HARK}, the home of the HARK repository. Click on the lime green button labeled ``Clone or download'' toward the upper right of the main panel, and select ``Download ZIP''.\footnote{This is the fastest way to get HARK running on a computer. Later, if you want to make your own contributions to HARK by adding code (or fixing ours!), you'll need to clone the repository on your computer. See section \ref{sec:GitHub} for more.} - -\item \textbf{Unzip HARK:} Unpack the \texttt{HARK.zip} file using any archive utility, like \href{http://www.peazip.org/}{Peazip}. Put the files anywhere you'd like, maintaining the internal directory structure. - -\item \textbf{Run Spyder:} Open a command prompt and do \texttt{spyder}. Spyder is an interactive development environment (IDE) for iPython, a slightly prettier, more interactive flavor of Python. - -\item \textbf{Open a HARK module:} Go to the directory where you put HARK and open any file with the \texttt{.py} extension; we recommend \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}. - -\item \textbf{Run the module:} Click on the green arrow ``play'' button toward the right side of the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue box pops up). Congratulations! HARK is now up and running on your computer. -\end{enumerate} - -If you followed our recommendation to try \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}, you should see graphical representations of the solution to a few consumption-saving models with idiosyncratic shocks to permanent and transitory income (see \texttt{/Documentation/ConsumptionSavingModels.pdf} for details of these models). If you chose a different module, you might get some examples of some other model, results of a structural estimation, a polite notification that this particular module doesn't do much on its own, or no output at all. See the \texttt{README.md} for a full list of modules from which you can expect non-trivial output. - -\subsection{Structure of HARK}\label{sec:StructureOfHARK} - -HARK is written in Python, an object-oriented programming (OOP) language that has experienced increasing popularity in the scientific community in the past several years. A significant reason for the adoption of Python is the \texttt{numpy} and \texttt{scipy} packages, which offer a wide array of mathematical and statistical functions and tools; HARK makes liberal use of these libraries. Python's object-oriented nature allows models in HARK to be easily extended: more complex models can inherit functions and methods from more fundamental ``parent'' models, eliminating the need to reproduce or repurpose code. - -As implied in the previous section, we strongly encourage HARK users to use the Anaconda distribution of Python, which includes all commonly used mathematical and scientific packages, an interactive development environment for iPython (Spyder), and a package manager that allows users to quickly install or update packages not included in the default distribution (conda). - -For users unfamiliar with OOP, we strongly encourage you to review the background material on OOP provided by the good people at \href{http://quant-econ.net/}{QuantEcon} (for more on them, see below) at this link: \href{http://quant-econ.net/py/python_oop.html}{Object Oriented Programming}. Unlike non-OOP languages, OOP bundles together data and functions into \textit{objects}. These can be accessed via: \texttt{object\_name.data} and \texttt{object\_name.method\_name()}, respectively. For organizational purposes, definitions of multiple objects are stored in \textit{modules}, which are simply files with a \texttt{.py} extension. Modules can be accessed in Python via:\\ - -\texttt{import module\_name as import\_name} -\\ - -\noindent -This imports the module and gives it a local name of \texttt{import\_name}. We can access a function within this module by simply typing: \texttt{import\_name.function\_name()}. The following example will illustrate the usage of these commands. \texttt{CRRAutility} is the function object for calculating CRRA utility supplied by \texttt{HARK.utilities} module. \texttt{CRRAutility} is called \textit{attributes} of the module \texttt{HARK.utilities}. In order to calculate CRRA utility with a consumption of 1 and a coefficient of risk aversion of 2 we run:\\ - -\texttt{import HARK.utilities as Hutil} - -\texttt{Hutil.CRRAutility(1,2)}\\ - -Python modules in HARK can generally be categorized into three types: tools, models, and applications. \textbf{Tool modules} contain functions and classes with general purpose tools that have no inherent ``economic content'', but that can be used in many economic models as building blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include functions for data analysis (e.g.\ calculating Lorenz shares from data, or constructing a non-parametric kernel regression), functions to create and manipulate discrete approximations to continuous distributions, or classes for constructing interpolated approximations to non-parametric functions. Tool modules generally reside in HARK's root directory and have names like \texttt{HARK.simulation} and \texttt{HARK.interpolation}; they do not necessarily do anything when run. - -\textbf{Model modules} specify particular economic models, including classes to represent agents in the model (and the ``market structure'' in which they interact) and functions for solving the ``one period problem'' of those models. For example, \texttt{ConsIndShockModel.py} concerns consumption-saving models in which agents have CRRA utility over consumption and face idiosyncratic shocks to permanent and transitory income. The module includes classes for representing ``types'' of consumers, along with functions for solving (several flavors of) the one period consumption-saving problem. When run, model modules might demonstrate example specifications of their models, filling in the model parameters with arbitrary values. When \texttt{ConsIndShockModel.py} is run, it specifies an infinite horizon consumer with a particular discount factor, permanent income growth rate, coefficient of relative risk aversion (etc), who faces lognormal shocks to permanent and transitory income each period with a particular standard deviation; it then solves this consumer's problem and graphically displays the results.\footnote{Running \texttt{ConsIndShockModel.py} also demonstrates other variations of the consumption-saving problem, but their description is omitted here for brevity.} Model modules generally have \texttt{Model} in their name. - -\textbf{Application modules} use tool and model modules to solve, simulate, and/or estimate economic models \textit{for a particular purpose}. While tool modules have no particular economic content and model modules describe entire classes of economic models, applications are uses of a model for some research purpose. For example, \texttt{/Demos/SolvingMicroDSOPs/StructEstimation.py} uses a consumption-saving model from \texttt{ConsIndShockModel.py}, calibrating it with age-dependent sequences of permanent income growth, survival probabilities, and the standard deviation of income shocks (etc); it then estimates the coefficient of relative risk aversion and shifter for an age-varying sequence of discount factors that best fits simulated wealth profiles to empirical data from the Survey of Consumer Finance. A particular application might have multiple modules associated with it, all of which generally reside in one directory. - -\subsection{Other Resources}\label{sec:OtherResources} - -In the incredibly unlikely scenario in which this document does not fill all of the gaps in your knowledge and answer any questions you might have while reading it, here is a collection of potentially helpful other resources. - -\begin{itemize} - -\item \href{https://econ-ark.github.io/HARK/}{Online documentation} for HARK tool and model modules, produced using Sphinx. - -\item A \href{https://docs.python.org/2.7/tutorial/}{tutorial on Python 2.7}, straight from the source. - -\item \href{https://en.wikipedia.org/wiki/Object-oriented_programming}{Wikipedia article on object-oriented programming}, not the subject of an edit war. - -\item \href{http://quant-econ.net/}{QuantEcon}, a collection of lectures on quantitative economic modeling from a couple of economists, which definitely in no way influenced HARK whatsoever. - -\item A \href{https://www.atlassian.com/git/tutorials/}{tutorial on git}, the repository management and tracking system used by the HARK project, created by Linus Torvalds. - -\item A \href{https://guides.github.com/activities/hello-world/}{tutorial on GitHub}, a website that provides a useful framework for git that makes it significantly more user-friendly, despised by Linus Torvalds. - -\item \href{https://en.wikipedia.org/wiki/Linus_Torvalds}{Wikipedia article on Linus Torvalds}, just in case. - -\item The \href{http://www.numpy.org/}{homepage for NumPy}, a numerical package extensively used by HARK. - -\item The \href{http://www.sphinx-doc.org/}{homepage for Sphinx}, a tool for producing HTML documentation. - -\item A \href{http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/}{collection of lecture notes} by Chris Carroll, to catch you up on the basics of consumption-saving models. - -\item A \href{http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/}{particular set of lecture notes} by Chris Carroll, explaining many of the numeric techniques used in HARK and offering an example of a simple structural model that, in a stunning coincidence, is implemented in HARK as an example. - -\item A \href{http://www.econ2.jhu.edu/people/ccarroll/BufferStockTheory.pdf}{deep dive} into the theoretical foundations of buffer stock saving. Warning: dense. - -\end{itemize} - - -\section{General Purpose Tools}\label{sec:GeneralPurposeTools} - -HARK's root directory contains six tool modules,\footnote{The ``taxonomy'' of these modules is in flux; the functions described here could be combined into fewer modules or further divided by purpose.} each containing a variety of functions and classes that can be used in many economic models-- or even for mathematical purposes that have nothing to do with economics. Some of the tool modules are very sparely populated at this time, while others are quite large. We expect that all of these modules will grow considerably in the near future, as new tools are ``low hanging fruit'' for contribution to the project.\footnote{That is, as the foundational, building-block elements of HARK, new tools are not difficult to program and do not require extensive integration with many moving parts.} - -\subsection{HARKcore}\label{sec:HARKcore} - -A key goal of the project is to create modularity and interoperability between models, making them easy to combine, adapt, and extend. To this end, the \texttt{HARK.core} module specifies a framework for economic models in HARK, creating a common structure for them on two levels that can be called ``microeconomic'' and ``macroeconomic''. - -Microeconomic models in HARK use the \texttt{AgentType} class to represent agents with an intertemporal optimization problem. Each of these models specifies a subclass of \texttt{AgentType}; an instance of the subclass represents agents who are \textit{ex-ante} homogeneous-- they have common values for all parameters that describe the problem. For example, \texttt{ConsIndShockModel} specifies the \texttt{IndShockConsumerType} class, which has methods specific to consumption-saving models with idiosyncratic shocks to income; an instance of the class might represent all consumers who have a CRRA of 3, discount factor of 0.98, etc. The \texttt{AgentType} class has a \texttt{solve} method that acts as a ``universal microeconomic solver'' for any properly formatted model, making it easier to set up a new model and to combine elements from different models; the solver is intended to encompass any model that can be framed as a sequence of one period problems. For a complete description, see section \ref{sec:Microeconomics}. - -Macroeconomic models in HARK use the \texttt{Market} class to represent a market (or other aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual agents in the model into aggregate outcomes that are ``passed back'' to the agents. For example, the market in a consumption-saving model might combine the individual asset holdings of all agents in the market to generate aggregate capital in the economy, yielding the interest rate on assets (as the marginal product of capital); the individual agents then learn the aggregate capital level and interest rate, conditioning their next action on this information. Objects that microeconomic agents treat as exogenous when solving (or simulating) their model are thus endogenous at the macroeconomic level. Like \texttt{AgentType}, the \texttt{Market} class also has a \texttt{solve} method, which seeks out a dynamic general equilibrium: a ``rule'' governing the dynamic evolution of macroeconomic objects such that if agents believe this rule and act accordingly, then their collective actions generate a sequence of macroeconomic outcomes that justify the belief in that rule. For a more complete description, see section \ref{sec:Macroeconomics}. - -Beyond the model frameworks, \texttt{HARK.core} also defines a ``supersuperclass'' called \texttt{HARKobject}. When solving a dynamic microeconomic model with an infinite horizon (or searching for a dynamic general equilibrium), it is often required to consider whether two solutions are sufficiently close to each other to warrant stopping the process (i.e.\ approximate convergence). It is thus necessary to calculate the ``distance'' between two solutions, so HARK specifies that classes should have a \texttt{distance} method that takes a single input and returns a non-negative value representing the (generally dimensionless) distance between the object in question and the input to the method. As a convenient default, \texttt{HARKobject} provides a ``universal distance metric'' that should be useful in many contexts.\footnote{Roughly speaking, the universal distance metric is a recursive supnorm, returning the largest distance between two instances, among attributes named in \texttt{distance\_criteria}. Those attributes might be complex objects themselves rather than real numbers, generating a recursive call to the universal distance metric.} When defining a new subclass of \texttt{HARKobject}, the user simply defines the attribute \texttt{distance\_criteria} as a list of strings naming the attributes of the class that should be compared when calculating the distance between two instances of that class. For example, the class \texttt{ConsumerSolution} has \texttt{distance\_criteria = ['cFunc']}, indicating that only the consumption function attribute of the solution matters when comparing the distance between two instances of \texttt{ConsumerSolution}. See \href{https://econ-ark.github.io/HARK/generated/HARK.core.html}{here} for online documentation. - -\subsection{HARK.utilities}\label{sec:HARKutilities} - -The \texttt{HARK.utilities} module carries a double meaning in its name, as it contains both utility functions (and their derivatives, inverses, and combinations thereof) in the economic modeling sense as well as utilities in the sense of general tools. Utility functions included at this time are constant relative risk aversion and constant absolute risk aversion. Other functions in \texttt{HARK.utilities} include some data manipulation tools (e.g.\ for calculating an average of data conditional on being within a percentile range of different data), functions for constructing discrete state space grids, convenience functions for retrieving information about functions, and basic plotting tools using \texttt{matplotlib.pyplot}. - -The module also includes functions for constructing discrete approximations to continuous distributions (e.g.\ \texttt{approxLognormal()} to approximate a log-normal distribution) as well as manipulating these representations (e.g.\ appending one outcome to an existing distribution, or combining independent univariate distributions into one multivariate distribution). As a convention in HARK, continuous distributions are approximated as finite discrete distributions when solving models; an $N$-dimensional random variable is formatted as a length $N+1$ list of 1D arrays, with the first element representing event probabilities and all other elements are realizations of the $N$ component RVs. This both simplifies solution methods (reducing numeric integrals to simple dot products) and allows users to easily test whether their chosen degree of discretization yields a sufficient approximation to the full distribution. See \href{https://econ-ark.github.io/HARK/generated/HARK.utilities.html}{here} for online documentation. - -\subsection{HARK.interpolation}\label{sec:HARKinterpolation} - -The \texttt{HARK.interpolation} module defines classes for representing interpolated function approximations. Interpolation methods in HARK all inherit from a superclass such as \texttt{HARKinterpolator1D} or \texttt{HARKinterpolator2D}, wrapper classes that ensures interoperability across interpolation methods. For example, \texttt{HARKinterpolator1D} specifies the methods \texttt{\_\_call\_\_} and \texttt{derivative} to accept an arbitrary array as an input and return an identically shaped array with the interpolated function evaluated at the values in the array or its first derivative, respectively. However, these methods do little on their own, merely reshaping arrays and referring to the \texttt{\_evaluate} and \texttt{\_der} methods, which are \textit{not actually defined in} \texttt{HARKinterpolator1D}. Each subclass of \texttt{HARKinterpolator1D} specifies their own implementation of \texttt{\_evaluate} and \texttt{\_der} particular to that interpolation method, accepting and returning only 1D arrays. In this way, subclasses of \texttt{HARKinterpolator1D} are easily interchangeable with each other, as all methods that the user interacts with are identical, varying only by ``internal'' methods. - -When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to know the ``distance'' between functions generated by successive iterations of a solution procedure. To this end, each interpolator class in HARK must define a \texttt{distance} method that takes as an input another instance of the same class and returns a non-negative real number representing the ``distance'' between the two. As each of the \texttt{HARKinterpolatorXD} classes inherits from \texttt{HARKobject}, all interpolator classes have the default ``universal'' distance method; the user must simply list the names of the relevant attributes in the attribute \texttt{distance\_criteria} of the class. - -Interpolation methods currently implemented in HARK include (multi)linear interpolation up to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D ``lower envelope'' interpolator. See \href{https://econ-ark.github.io/HARK/generated/HARKinterpolation.html}{here} for online documentation. - -\subsection{HARK.simulation}\label{sec:HARKsimulation} - -The \texttt{HARK.simulation} module provides tools for generating simulated data or shocks for post-solution use of models. Currently implemented distributions include normal, lognormal, Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use, these tools are used in the consumption-saving models of \texttt{ConsIndShockModel.py} to simulate permanent and transitory income shocks as well as unemployment events. See \href{https://econ-ark.github.io/HARK/generated/HARKsimulation.html}{here} for online documentation. - -\subsection{HARK.estimation}\label{sec:HARKestimation} - -Methods for optimizing an objective function for the purposes of estimating a model can be found in \texttt{HARK.estimation}. As of this writing, the implementation includes only minimization by the Nelder-Mead simplex method, minimization by a derivative-free Powell method variant, and two small tools for resampling data (i.e.\ for a bootstrap); the minimizers are merely convenience wrappers (with result reporting) for optimizers included in \texttt{scipy.optimize}. Future functionality will include more robust global search methods, including genetic algorithms, simulated annealing, and differential evolution. See \href{https://econ-ark.github.io/HARK/generated/HARKestimation.html}{here} for full documentation. - -\subsection{HARK.parallel}\label{sec:HARKparallel} - -By default, processes in Python are single-threaded, using only a single CPU core. The \texttt{HARK.parallel} module provides basic tools for using multiple CPU cores simultaneously, with minimal effort.\footnote{\texttt{HARK.parallel} uses two packages that aren't included in the default distribution of Anaconda: \texttt{joblib} and \texttt{dill}; see step 3 of the instructions in section \ref{sec:GettingStarted} for how to install them.} In particular, it provides the function \texttt{multiThreadCommands}, which takes two arguments: a list of \texttt{AgentType}s and a list of commands as strings; each command should be a method of the \texttt{AgentType}s. The function simply distributes the \texttt{AgentType}s across threads on different cores and executes each command in order, returning no output (the \texttt{AgentType}s themselves are changed by running the commands). Equivalent results would be achieved by simply looping over each type and running each method in the list. Indeed, \texttt{HARK.parallel} also has a function called \texttt{multiThreadCommandsFake} that does just that, with identical syntax to \texttt{multiThreadCommands}; multithreading in HARK can thus be easily turned on and off.\footnote{In the future, \texttt{HARK.parallel} might be absorbed into \texttt{HARK.core} and \texttt{HARK.estimation}, particularly if \texttt{joblib} and \texttt{dill} become part of the standard Anaconda distribution.} The module also has functions for a parallel implementation of the Nelder-Mead simplex algorithm, as described in Wiswall and Lee (2011). See \href{https://econ-ark.github.io/HARK/generated/HARK.parallel.html}{here} for full documentation. - -\section{Microeconomics: the AgentType Class}\label{sec:Microeconomics} - -The core of our microeconomic dynamic optimization framework is a flexible object-oriented representation of economic agents. The \texttt{HARK.core} module defines a superclass called \texttt{AgentType}; each model defines a subclass of \texttt{AgentType}, specifying additional model-specific features and methods while inheriting the methods of the superclass. Most importantly, the method \texttt{solve} acts as a ``universal solver'' applicable to any (properly formatted) discrete time model. This section describes the format of an instance of \texttt{AgentType} as it defines a dynamic microeconomic problem;\footnote{Each instance of \texttt{AgentType} represents an \textit{ex-ante} heterogeneous ``type'' of agent; \textit{ex-post} heterogeneity is achieved by simulating many agents of the same type, each of whom receives a unique sequence of shocks.} - -\subsection{Attributes of an AgentType}\label{sec:AttributesOfAnAgentType} - -A discrete time model in our framework is characterized by a sequence of ``periods'' that the agent will experience. A well-formed instance of \texttt{AgentType} includes the following attributes: -\begin{itemize} -\item \texttt{solveOnePeriod}: A function, or a list of functions, representing the solution method for a single period of the agent's problem. The inputs passed to a \texttt{solveOnePeriod} function include all data that characterize the agent's problem in that period, including the solution to the subsequent period's problem (designated as \texttt{solution\_next}). The output of these functions is a single \texttt{Solution} object, which can be passed to the solver for the previous period. - -\item \texttt{time\_inv}: A list of strings containing all of the variable names that are passed to at least one function in \texttt{solveOnePeriod} but do \textit{not} vary across periods. Each of these variables resides in a correspondingly named attribute of the \texttt{AgentType} instance. - -\item \texttt{time\_vary}: A list of strings naming the attributes of this instance that vary across periods. Each of these attributes is a list of period-specific values, which should be of the same length. If the solution method varies across periods, then \texttt{'solveOnePeriod'} is an element of \texttt{time\_vary}.\footnote{\texttt{time\_vary} may include attributes that are never used by a function in \texttt{solveOnePeriod}. Most saliently, the attribute \texttt{solution} is time-varying but is not used to solve individual periods.} - -\item \texttt{solution\_terminal}: An object representing the solution to the ``terminal'' period of the model. This might represent a known trivial solution that does not require numeric methods, the solution to some previously solved ``next phase'' of the model, a scrap value function, or an initial guess of the solution to an infinite horizon model. - -\item \texttt{pseudo\_terminal}: A Boolean flag indicating that \texttt{solution\_terminal} is not a proper terminal period solution (rather an initial guess, ``next phase'' solution, or scrap value) and should not be reported as part of the model's solution. - -\item \texttt{cycles}: A non-negative integer indicating the number of times the agent will experience the sequence of periods in the problem. For example, \texttt{cycles = 1} means that the sequence of periods is analogous to a lifecycle model, experienced once from beginning to end; \texttt{cycles = 2} means that the agent experiences the sequence twice, with the first period in the sequence following the last. An infinite horizon problem in which the sequence of periods repeats indefinitely is indicated with \texttt{cycles = 0}. - -\item \texttt{tolerance}: A positive real number indicating convergence tolerance, representing the maximum acceptable ``distance'' between successive cycle solutions in an infinite horizon model; it is irrelevant when \texttt{cycles > 0}. As the distance metric on the space of solutions is model-specific, the value of \texttt{tolerance} is generally dimensionless. - -\item \texttt{time\_flow}: A Boolean flag indicating the direction that time is ``flowing.'' When \texttt{True}, the variables listed in \texttt{time\_vary} are listed in ordinary chronological order, with index 0 being the first period; when \texttt{False}, these lists are in reverse chronological order, with index 0 holding the last period. -\end{itemize} - -An instance of \texttt{AgentType} also has the attributes named in \texttt{time\_vary} and \texttt{time\_inv}, and may have other attributes that are not included in either (e.g.\ values not used in the model solution, but instead to construct objects used in the solution). - -\subsection{A Universal Solver}\label{sec:UniversalSolver} - -When an instance of \texttt{AgentType} invokes its \texttt{solve} method, the solution to the agent's problem is stored in the attribute \texttt{solution}. The solution is computed by recursively solving the sequence of periods defined by the variables listed in \texttt{time\_vary} and \texttt{time\_inv} using the functions in \texttt{solveOnePeriod}. The time-varying inputs are updated each period, including the successive period's solution as \texttt{solution\_next}; the same values of time invariant inputs in \texttt{time\_inv} are passed to the solver in every period. The first call to \texttt{solveOnePeriod} uses \texttt{solution\_terminal} as \texttt{solution\_next}. In a finite horizon problem, the sequence of periods is solved \texttt{cycles} times over; in an infinite horizon problem, the sequence of periods is solved until the solutions of successive cycles have a ``distance'' of less than \texttt{tolerance}. - -The output from a function in \texttt{solveOnePeriod} is an instance of a model-specific solution class. The attributes of a solution to one period of a problem might include behavioral functions, (marginal) value functions, and other variables characterizing the result. Each solution class must have a method called \texttt{distance()}, which returns the ``distance'' between itself and another instance of the same solution class, so as to define convergence as a stopping criterion; for many models, this will be the ``distance'' between a policy or value function in the solutions. If the solution class is defined as a subclass of \texttt{HARKobject}, it automatically inherits the default \texttt{distance} method, so that the user must only list the relevant object attributes in \texttt{distance\_criteria}. - -The \texttt{AgentType} also has methods named \texttt{preSolve} and \texttt{postSolve}, both of which take no arguments and do absolutely nothing. A subclass of \texttt{AgentType} can overwrite these blank methods with its own model specific methods. \texttt{preSolve} is automatically called near the beginning of the \texttt{solve} method, before solving the sequence of periods. It is used for specifying tasks that should be done before solving the sequence of periods, such as pre-constructing some objects repeatedly used by the solution method or finding an analytical terminal period solution. For example, the \texttt{IndShockConsumerType} class in \texttt{ConsIndShockModel} has a \texttt{preSolve} method that calls its \texttt{updateSolutionTerminal} method to ensure that \texttt{solution\_terminal} is consistent with the model parameters. The \texttt{postSolve} method is called shortly after the sequence of periods is fully solved; it can be used for ``post-processing'' of the solution or performing a step that is only useful after solution convergence. For example, the \texttt{TractableConsumerType} in \texttt{TractableBufferStockModel} has a \texttt{postSolve} method that constructs an interpolated consumption function from the list of stable arm points found during solution. - -Our universal solver is written in a very general way that should be applicable to any discrete time optimization problem-- because Python is so flexible in defining objects, the time-varying inputs for each period can take any form. Indeed, the solver does no ``real work'' itself, but merely provides a structure for describing models in the HARK framework, allowing interoperability among current and future modules. - -\subsection{The Flow of Time and Other Methods}\label{sec:OtherMethods} - -Because dynamic optimization problems are solved recursively in our framework, it is natural to list time-varying values in reverse chronological order-- the \texttt{solve()} method loops over the values in each time-varying list in the same direction that a human would read them. When simulating agents after the solution has been obtained, however, it is much more convenient for time-varying parameters to be listed in ordinary chronological order-- the direction in which they will be experienced by simulated agents. To allow the user to set the order in which ``time is flowing'' for an instance of \texttt{AgentType}, the HARK framework includes functionality to easily change ordering of time-varying values. - -The attribute \texttt{time\_flow} is \texttt{True} if variables are listed in ordinary chronological order and \texttt{False} otherwise. \texttt{AgentType} has the following methods for manipulating time: -\begin{itemize} -\item \texttt{timeReport()}: Prints to screen a description of the direction that time is flowing, for interactive convenience and as a reminder of the functionality. - -\item \texttt{timeFlip()}: Flips the direction of time. Each attribute listed in \texttt{time\_vary} is reversed in place, and the value of \texttt{time\_flow} is toggled. - -\item \texttt{timeFwd()}: Sets the direction of time to ordinary chronological order. - -\item \texttt{timeRev()}: Sets the direction of time to reverse chronological order. -\end{itemize} - -These methods are invoked to more conveniently access time-varying objects. When a new time-varying attribute is added, its name should be appended to \texttt{time\_vary}, particularly if its values are used in the solution of the model (or is part of the solution itself). For example, the \texttt{solve()} method automatically adds the string \texttt{'solution'} to \texttt{time\_vary} if it is not already present. Note that attributes listed in \texttt{time\_vary} \textit{must} be lists if \texttt{solve()} or \texttt{timeFlip()} are used. Some values that could be considered ``time varying'' but are never used to solve the model are more conveniently represented as a \texttt{numpy.array} object (e.g.\ \ the history of a state or control variable from a simulation); because the \texttt{numpy.array} class does not have a \texttt{reverse()} method, these attributes should not be listed in \texttt{time\_vary}. - -The base \texttt{AgentType} is sparsely defined, as most ``real'' methods will be application-specific. Two final methods bear mentioning. First, the \texttt{\_\_call\_\_} method points to \texttt{assignParameters()}, a convenience method for adding or adjusting attributes (inherited from \texttt{HARKobject}). This method takes any number of keyword arguments, so that code can be parsimoniously written as, for example, \texttt{AgentInstance(attribute1 = value1, attribute2 = value2)}. Using Python's dictionary capabilities, many attributes can be conveniently set with minimal code. Second, the \texttt{resetRNG} method simply resets the \texttt{AgentType}'s random number generator (as the attribute \texttt{RNG}) using the value in the attribute \texttt{seed}.\footnote{Every instance of \texttt{AgentType} is created with a random number generator as an instance of the class \texttt{numpy.random.RandomState}, with a default \texttt{seed} of zero.} This method is useful for (\textit{inter alia}) ensuring that the same underlying sequence of shocks is used for every simulation run when a model is solved or estimated. - - -\subsection{Sample Model: Perfect Foresight Consumption-Saving}\label{sec:PerfectForesight} - -To provide a concrete example of how the \texttt{AgentType} class works, consider the very simple case of a perfect foresight consumption-saving model. The agent has time-separable, additive CRRA preferences over consumption, discounting future utility at a constant rate; he receives a particular stream of labor income each period, and knows the interest rate on assets that he holds from one period to the next. His decision about how much to consume in a particular period can be expressed in Bellman form as: - -\begin{eqnarray*} -V_t(M_t) &=& \max_{C_t} \utilFunc(C_t) + \beta \PLives_t \E [V_{t+1}(M_{t+1}) ], \\ -A_t &=& M_t - C_t, \\ -M_{t+1} &=& \Rfree A_t + Y_{t+1}, \\ -Y_{t+1} &=& \Gamma_{t+1} Y_t, \\ -\utilFunc(C) &=& \frac{C^{1-\CRRA}}{1-\CRRA}. -\end{eqnarray*} -An agent's problem is thus characterized by values of $\CRRA$, $\Rfree$, and $\beta$ plus sequences of survival probabilities $\PLives_t$ and income growth factors $\Gamma_t$ for $t=0,\cdots,T$. This problem has an analytical solution for both the value function and the consumption function. - -The \texttt{ConsIndShockModel} module defines the class \texttt{PerfForesightConsumerType} as a subclass of \texttt{AgentType} and provides solver functions for several variations of a consumption-saving model, including the perfect foresight problem.\footnote{As the name implies, the module is mostly concerned with models with idiosyncratic shocks to income.} A HARK user could specify and solve a ten period perfect foresight model with the following commands: - -\vspace{0.25cm} - -\noindent \texttt{MyConsumer = PerfForesightConsumerType(time\_flow=True, cycles=1,} - -\texttt{ CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98,} - -\texttt{ LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90],} - -\texttt{ PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02],} - -\texttt{ Nagents=1000)} - -\noindent \texttt{MyConsumer.solve()} - -\vspace{0.25cm} - -The first line makes a new instance of \texttt{ConsumerType}, specifying that time is currently ``flowing'' forward\footnote{This is relevant for the intepretation of the time-varying inputs \texttt{LivPrb} and \texttt{PermGroFac}.} and that the sequence of periods happens exactly once; the next three lines (all part of the same command) set the time invariant (\texttt{CRRA}, \texttt{Rfree}, \texttt{DiscFac}) and time varying parameters (\texttt{LivPrb}, \texttt{PermGroFac}). Note that \texttt{dir(MyConsumer)} shows all the attributes of \texttt{MyConsumer}. After running the \texttt{solve} method, \texttt{MyConsumer} will have an attribute called \texttt{solution}, which will be a list with eleven \texttt{ConsumerSolution} objects, representing the period-by-period solution to the model.\footnote{The last element of \texttt{solution} represents the terminal period solution, where the agent consumes all available resources because there is no future.} Each \texttt{ConsumerSolution} has several attributes: -\begin{itemize} -\item \texttt{cFunc}: Optimal consumption function for this period as a function of (normalized) market resources $m_t = M_t/Y_t$. Can be called like any other function: \texttt{MyConsumer.solution[0].cFunc(5)} returns consumption at $m_t = 5$. - -\item \texttt{vFunc}: Value function (over market resources $m_t$); can be evaluated like \texttt{cFunc}. - -\item \texttt{mNrmMin}: Minimum value of normalized market resources $m_t$ such that \texttt{cFunc} and \texttt{vFunc} are defined. - -\item \texttt{hNrm}: Normalized human wealth-- the PDV of future income (ignoring mortality) divided by current period income $Y_t$. - -\item \texttt{MPC}: The constant marginal propensity to consume (linear consumption function). - -\end{itemize} - -The following command generates the plot for the perfect foresight consumption function: - -\vspace{0.25cm} - -\noindent \texttt{mMin = MyConsumer.solution[0].mNrmMin} - -\noindent \texttt{plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10)} - -\vspace{0.25cm} - -To solve a version of this problem in which the sequence of ten periods happens three times (yielding a \texttt{solution} attribute with thirty-one elements), the user can do: - -\vspace{0.25cm} - -\noindent \texttt{MyConsumer(cycles = 3)} - -\noindent \texttt{MyConsumer.solve()} - -\vspace{0.25cm} - -To solve an infinite horizon problem in which the agent experiences the same one period problem indefinitely, the user can do: - -\vspace{0.25cm} - -\noindent \texttt{OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02,} - -\texttt{DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000)} - -\noindent \texttt{OtherConsumer.solve()} - -\vspace{0.25cm} - -This instance is specified as being infinite horizon by setting \texttt{cycles=0}. Note that the time-varying inputs are still specified as (one element) lists, even though they take on the same value in every period; this is because an infinite horizon model might consist of a multi-period sequence repeated indefinitely, rather than just one repeated period. The \texttt{solution} attribute of \texttt{OtherConsumer} will be a list containing one instance of \texttt{ConsumerSolution}, representing the solution to every period of the model. - -\section{Macroeconomics: the Market Class}\label{sec:Macroeconomics} - -The modeling framework of \texttt{AgentType} is deemed ``microeconomic'' because it pertains only to the dynamic optimization problem of agents, treating all inputs of the problem as exogenously fixed. In what we label as ``macroeconomic'' models, some of the inputs for the microeconomic models are endogenously determined by the collective states and controls of agents in the model. In a dynamic general equilibrium, there must be consistency between agents' beliefs about these macroeconomic objects, their individual behavior, and the realizations of the macroeconomic objects that result from individual choices. - -The \texttt{Market} class in \texttt{HARK.core} provides a framework for such macroeconomic models, with a \texttt{solve} method that searches for a dynamic general equilibrium. An instance of \texttt{Market} includes a list of \texttt{AgentType}s that compose the economy, a method for transforming microeconomic outcomes (states, controls, and/or shocks) into macroeconomic outcomes, and a method for interpreting a history or sequence of macroeconomic outcomes into a new ``dynamic rule'' for agents to believe. Agents treat the dynamic rule as an input to their microeconomic problem, conditioning their optimal policy functions on it. A dynamic general equilibrium is a fixed point dynamic rule: when agents act optimally while believing the equilibrium rule, their individual actions generate a macroeconomic history consistent with the equilibrium rule. - -\subsection{Down on the Farm}\label{sec:DownOnTheFarm} - -The \texttt{Market} class uses a farming metaphor to conceptualize the process for generating a history of macroeconomic outcomes in a model. Suppose all \texttt{AgentTypes} in the economy believe in some dynamic rule (i.e.\ the rule is stored as attributes of each \texttt{AgentType}, which directly or indirectly enters their dynamic optimization problem), and that they have each found the solution to their microeconomic model using their \texttt{solve} method. Further, the macroeconomic and microeconomic states have been reset to some initial orientation. - -To generate a history of macroeconomic outcomes, the \texttt{Market} repeatedly loops over the following steps a set number of times: -\begin{enumerate} -\item \texttt{sow}: Distribute the macroeconomic state variables to all \texttt{AgentType}s in the market. - -\item \texttt{cultivate}: Each \texttt{AgentType} executes their \texttt{marketAction} method, likely corresponding to simulating one period of the microeconomic model. - -\item \texttt{reap}: Microeconomic outcomes are gathered from each \texttt{AgentType} in the market. - -\item \texttt{mill}: Data gathered by \texttt{reap} is processed into new macroeconomic states according to some ``aggregate market process''. - -\item \texttt{store}: Relevant macroeconomic states are added to a running history of outcomes. -\end{enumerate} -This procedure is conducted by the \texttt{makeHistory} method of \texttt{Market} as a subroutine of its \texttt{solve} method. After making histories of the relevant macroeconomic variables, the market then executes its \texttt{calcDynamics} function with the macroeconomic history as inputs, generating a new dynamic rule to distribute to the \texttt{AgentType}s in the market. The process then begins again, with the agents solving their updated microeconomic models given the new dynamic rule; the \texttt{solve} loop continues until the ``distance'' between successive dynamic rules is sufficiently small. - -\subsection{Attributes of a Market}\label{sec:AttributesOfAMarket} - -To specify a complete instance of \texttt{Market}, the user should give it the following attributes:\footnote{For some purposes, it might be useful to specify a subclass of \texttt{Market}, defining \texttt{millRule} and/or \texttt{calcDynamics} as methods rather than functions.} -\begin{itemize} -\item \texttt{agents}: A list of \texttt{AgentType}s, representing the agents in the market. Each element in \texttt{agents} represents an \textit{ex-ante} heterogeneous type; each type could have many \textit{ex-post} heterogeneous agents. - -\item \texttt{sow\_vars}: A list of strings naming variables that are output from the aggregate market process, representing the macroeconomic outcomes. These variables will be distributed to the \texttt{agents} in the \texttt{sow} step. - -\item \texttt{reap\_vars}: A list of strings naming variables to be collected from the \texttt{agents} in the \texttt{reap} step, to be used as inputs for the aggregate market process. - -\item \texttt{const\_vars}: A list of strings naming variables used by the aggregate market process that \textit{do not} come from \texttt{agents}; they are constant or come from the \texttt{Market} itself. - -\item \texttt{track\_vars}: A list of strings naming variables generated by the aggregate market process that should be tracked as a history, to be used when calculating a new dynamic rule. Usually a subset of \texttt{sow\_vars}. - -\item \texttt{dyn\_vars}: A list of strings naming the variables that constitute a dynamic rule. These will be stored as attributes of the \texttt{agents} whenever a new rule is calculated. - -\item \texttt{millRule}: A function for the ``aggregate market process'', transforming microeconomic outcomes into macroeconomic outcomes. Its inputs are named in \texttt{reap\_vars} and \texttt{const\_vars}, and it returns a single object with attributes named in \texttt{sow\_vars} and/or \texttt{track\_vars}. Can be defined as a method of a subclass of \texttt{Market}. - -\item \texttt{calcDynamics}: A function that generates a new dynamic rule from a history of macroeconomic outcomes. Its inputs are named in \texttt{track\_vars}, and it returns a single object with attributes named in \texttt{dyn\_vars}. - -\item \texttt{act\_T}: The number of times that the \texttt{makeHistory} method should execute the ``farming loop'' when generating a new macroeconomic history. - -\item \texttt{tolerance}: The minimum acceptable ``distance'' between successive dynamic rules produced by \texttt{calcDynamics} to constitute a sufficiently converged solution. -\end{itemize} - -Further, each \texttt{AgentType} in \texttt{agents} must have two methods not necessary for microeconomic models; neither takes any input (except \texttt{self}): -\begin{itemize} -\item \texttt{marketAction}: The microeconomic process to be run in the \texttt{cultivate} step. Likely uses the new macroeconomic outcomes named in \texttt{sow\_vars}; should store new values of relevant microeconomic outcomes in the attributes (of \texttt{self}) named in \texttt{reap\_vars}. - -\item \texttt{reset}: Reset, initialize, or prepare for a new ``farming loop'' to generate a macroeconomic history. Might reset its internal random number generator, set initial state varibles, clear personal histories, etc. -\end{itemize} - -When solving macroeconomic models in HARK, the user should also define classes to represent the output from the aggregate market process in \texttt{millRule} and for the model-specific dynamic rule. The latter should have a \texttt{distance} method to test for solution convergence; if the class inherits from \texttt{HARKobject}, the user need only list relevant attributes in \texttt{distance\_criteria}. - -\subsection{Sample Model: FashionVictim}\label{sec:FashionVictim} - -To illustrate the \texttt{Market} class, consider a simple example in the emerging economic subfield of aesthemetrics, the \texttt{FashionVictimModel}.\footnote{This model is inspired by the paper ``The hipster effect: When anticonformists all look the same'' by Jonathan Touboul.}\footnote{For a more traditional macroeconomic model, the \texttt{ConsAggShocksModel} module includes a consumption-saving model with both idiosyncratic and aggregate shocks, in which individual asset holdings are aggregated into total productive capital, endogenizing the (dynamic) interest and wage rate.} This module defines a subclass of \texttt{AgentType} called \texttt{FashionVictimType}. Each period, fashion victims make a binary choice of style $s$: to dress as a jock (0) or punk (1). They receive utility directly from the outfit they wear and as a function of the proportion of the population who \textit{just wore} the same style; they also pay switching costs ($c_{pj}$,$c_{jp}$) if they change styles rather than keep the same as the previous period. Moreover, they receive an idiosyncratic type 1 extreme value (T1EV) preference shock to each style in each period. Defining the population punk proportion as $p$ and the conformity utility function as $f:[0,1]\rightarrow \mathbb{R}$, the current period utility function is thus: -\begin{equation*} -u(s_t;s_{t-1},p_t) = s_t f(p_t) + (1-s_t) f(1-p_t) + s_t U_p + (1-s_t) U_j - c_{pj} s_{t-1}(1-s_t) - c_{jp}(1-s_{t-1})s_t. -\end{equation*} - -Fashion victims are forward looking and discount future utility at a constant rate of $\beta$ per period. To simplify the analysis, we assume they believe that the population punk proportion in the next period is a linear function of the punk proportion in the current period, subject to a uniformly distributed shock. No restrictions are put on the function $f$; fashion victims might be conformists who like to dress the same as others ($f'(p) > 0$) or hipsters who like to style themselves in the minority ($f'(p) < 0$).\footnote{In practice, $f$ is parameterized as the beta distribution for convenience.} A fashion victim's problem can be written in Bellman form as: -\begin{equation*} -V(s_{t-1},p_t) = \E \left[ \max_{s_t \in \{0,1\}} u(s_t;s_{t-1},p_t) + \eta_{s_t} + \beta \E \left[ V(s_t,p_{t+1}) \right] \right], -\end{equation*} -\begin{equation*} -p_{t+1} = a p_t + b + \pi_{t+1}, \qquad \pi_{t+1} \sim U[-w,w], \qquad \eta_0,\eta_1 \sim T1EV. -\end{equation*} - -An instance of \texttt{FashionVictimType} is thus characterized by values of $U_p$, $U_j$, $c_{pj}$, $c_{jp}$ and a function $f$, as well as beliefs about $p_{t+1}$ as a function of $p_t$ (summarized by slope $a$, intercept $b$, and uniform shock width $w$). Given this information, a \texttt{FashionVictimType}'s infinite horizon microeconomic model can be solved by backward induction in a few lines; the ``one period solver'' is given by \texttt{solveFashion}. However, while individual agents treat the dynamics of $p_t$ as exogenous, they are in fact endogenously determined by the actions of all the fashion victims in the market.\footnote{The market might consist of agents all of the same type, or might have several types with \textit{ex-ante} heterogeneity.} A dynamic general equilibrium of the ``macroeconomic fashion model'' is thus characterized by a triple of $(a,b,w)$ such that when fashion victims believe in this ``punk evolution rule'' and act optimally, their collective fashion choices exhibit this same rule when the model is simulated. - -The search for a dynamic general equilibrium is implemented in HARK's \texttt{Market} class with the following definitions: - -\vspace{0.25cm} - -\noindent \texttt{sow\_vars = ['pNow']} (macroeconomic outcome is $p_t$) - -\noindent \texttt{reap\_vars = ['sNow']} (microeconomic outcomes are $s_t$ for many agents) - -\noindent \texttt{track\_vars = ['pNow']} (must track history of $p_t$) - -\noindent \texttt{dyn\_vars = ['pNextSlope','pNextIntercept','pNextWidth']} (dynamic rule $(a,b,w)$) - -\noindent \texttt{millRule = calcPunkProp} (aggregate process: average the style choices of all agents) - -\noindent \texttt{calcDynamics = calcFashionEvoFunc} (calculate new $(a,b,w)$ with autoregression of $p_t$) - -\noindent \texttt{act\_T = 1000} (simulate 1000 periods of the fashion market) - -\noindent \texttt{tolerance = 0.01} (terminate solution when $(a,b,w)$ changes by less than 0.01) - -\vspace{0.25cm} - -The \texttt{agents} attribute has a list of 22 \texttt{FashionVictimType}s, which vary in their values of $U_p$ and $U_j$, and their $f$ functions. The \texttt{marketAction} method of \texttt{FashionVictimType} simulates one period of the microeconomic model: each agent receives style preference shocks $\eta_0$ and $\eta_1$, sees the current proportion of punks $p_t$ (sown to them as \texttt{pNow}), and chooses which style to wear, storing it in the binary array \texttt{sNow}, an attribute of \texttt{self}. - -The \texttt{millRule} for this market is extremely simple: it flattens the list of binary arrays of individual style choices (gathered in the \texttt{reap} step) and averages them into a new value of $p_t$, to be tracked as a history and \texttt{sow}n back to the \texttt{agents} to begin the cycle again. Once a history of 1000 values of $p_t$ has been generated with the \texttt{makeHistory} method, we can calculate a new dynamic fashion rule with \texttt{calcFashionEvoFunc} by regressing $p_t$ on $p_{t-1}$, approximating $w$ as twice the standard deviation of prediction errors.\footnote{This is an arbitrary way to calculate $w$, but accuracy is not important in a silly example.} The new fashion rule is an instance of the simple \text{FashionEvoFunc} class, whose only methods are inherited from \texttt{HARKobject}. - -When the \texttt{solve} method is run, the solver successively solves each agent's microeconomic problem, runs the \texttt{makeHistory} method to generate a 1000 period history of $p_t$, and calculates a new punk evolution rule based on this history; the solver terminates when consecutive rules differ by less than 0.01 in any dimension. - -} -% \hypertarget{ContributingToHARK}{\section{Contributing to HARK}\label{sec:ContributingToHARK}} -\section{Contributing to HARK}\label{sec:ContributingToHARK} - -%After reading this far into the user guide, it is safe to assume that you are eager to contribute to HARK's development. -This section provides an overview of how users can contribute new code to HARK, what sort of contributions are most desired, and style conventions for the toolKit. Before attempting to extend HARK, we strongly recommend that you familiarize yourself with its features and models by looking at the source code. - -\subsection{What Does HARK Want?}\label{sec:WhatDoesHARKWant} - -The easiest and most straightforward way to contribute to HARK is by writing new general purpose tools for inclusion in one of the top-level modules. This might be a commonly known data analysis technique (e.g.\ a kernel density estimator) for \texttt{HARK.utilities}, an optimization method (local or global) for \texttt{HARK.estimation}, an interpolation method for \texttt{HARKinterpolation}, etc. If you've found a technique useful in your own research and believe it could be of use to others on entirely different projects, then it probably belongs in HARK. Likewise, if you read through a HARK module expecting to find a certain tool or function (because of similar functions that are present, or merely because \textit{it's very useful}) but don't, this is a good sign that HARK has a hole that you can fill.\footnote{Case in point: As of this moment, \texttt{HARK.utilities} has no function for making a discrete approximation to the normal distribution, even though there are several well known methods for doing so and it is an obviously useful function.} - -Secondly, the development team wants to expand HARK to include more canonical models. Most of the models available in the toolKit at the time of its public beta release concern agents with (geometrically discounted) CRRA utility over a unitary consumption good, who receive permanent and transitory shocks to income and have access to a single risk free asset. This is an important class of models, but that description has an awful lot of qualifiers. In short, there are many more common features of dynamic household models that are not yet in HARK, but would be very valuable for the toolKit. New model contributions should ``play nicely'' with existing models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent possible. For ideas of potential model extensions for HARK, see section \ref{sec:FutureModels}.\footnote{Although that list is limited to extensions and variations on consumption-saving models, HARK has no such restrictions. If there is an entirely different sort of dynamic model that fits in the HARK framework, it can be contributed.} - -Finally, contributions to HARK are not restricted to entirely new functionality. While we have done our best to provide flexible frameworks and useful tools, we have almost certainly overlooked an unexpected case that would not be (properly) handled by the toolKit as is. Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as an Issue on GitHub so that someone else might. The ``mistake'' you find might not even be in the code itself, but a deficiency in the documentation: a misnamed (or incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing) comment, even a grammatical or spelling error. No matter how niggling, we want your fix. - -For those who want to contribute to the HARK project without programming a new model or digging through the code for errors and omissions, consider reading through the Issues page on GitHub to see what other users have flagged for development. You might find several pieces of ``low hanging fruit'' for you to quickly fix. - -\subsection{Git and GitHub}\label{sec:GitHub} - -Git is a distributed version control system that has gained increasing popularity in the decade since its creation; it is designed to enable collaboration and ``non-linear workflow'' among a (potentially disperse) programming team. As HARK is to be developed by volunteer researchers all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds as a command line tool, others have since extended git with further functionality and a user interface more friendly to those more familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK project is hosted. This section provides a brief overview of how to use GitHub to interact with (and potentially contribute to) HARK. - -To prepare to contribute to HARK, follow these simple steps: -\begin{enumerate} -\item \textbf{Make a GitHub account:} Go to \href{https://www.github.com}{github.com}; the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.\footnote{There are non-free ways to sign up for GitHub, but you don't need to worry about them.} - -\item \textbf{Install GitHub Desktop}: Go to \href{https://desktop.github.com/}{desktop.github.com} and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account. - -\item \textbf{Fork the HARK repository:} Go to the \href{https://github.com/econ-ark/HARK}{HARK repository page}, click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead. - -\item \textbf{Add the repository to Desktop:} Open GitHub Desktop, click on the $+$ icon in the upper left, and select clone. Find the repository named \texttt{yourusername/HARK}, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine. -\end{enumerate} - -You now control a ``fork'' of the main HARK repository, hosted by GitHub, and have a clone of this fork on your local machine. You are free to edit your fork of HARK in any way you'd like; other GitHub users can view your fork (and fork it themselves), but they cannot make changes to your fork unless you give them permission by adding them as a contributor. Changes made to the local copy of your fork do not get automatically sent to the remote copy on GitHub for public viewing. To make changes to your fork on GitHub, follow these steps: -\begin{enumerate} -\item \textbf{Make local changes:} Edit your local copy of the repository in any way you'd like: add or delete lines from existing files; add, delete, or rename entire files or directories. - -\item \textbf{Commit local changes:} Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button. - -\item \textbf{Push the commit:} Click on the Sync button near the upper right to ``push'' your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced. -\end{enumerate} - -The left hand panel of the Changes tab has a summary of the files that have been changed, added, or removed since your last commit; the right hand panel shows the specific changes made to each changed file. After step 2, a record of your changes has been stored locally by the git system, but is not yet reflected in the public remote copy on GitHub.\footnote{You can make several local commits without syncing with the remote server. This is useful for working without an internet connection. Say, on an intercontinental flight.} The ``timeline'' summary of the repository at the top of the Desktop window shows the history of commits for the repository: each dot represents a commit that was pushed to the remote server, and each open ring represents a local commit that has not been pushed to the remote server; the large broken ring on the far right represents changes that have not been locally committed.\footnote{Clicking on a dot or ring will display the changes made in that commit (or uncommitted changes).} The History tab lists all previous commits, including the timestamp, contributor, and a record of changes made. - -Suppose you have mucked about in your fork for a while and have made changes to HARK that you think should be included in the main repository hosted by Econ-Ark. To make your dream a reality, follow these steps: -\begin{enumerate} -\item \textbf{Isolate the changes:} If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should \textit{isolate the desired changes in a new branch of your fork}. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,\footnote{Or since your last update from the main repository hosted by Econ-Ark.} then edit this branch to include \textit{only the changes you want to push to the main repository}. - -\item \textbf{Issue a pull request:} Go to \href{https://github.com/econ-ark/HARK}{HARK's GitHub page} and click the ``New pull request'' button. Click the text that says ``compare across forks'', and select \texttt{econ-ark/HARK} as the base fork, \texttt{master} as the base, your fork as the head fork, and the branch to be merged as the ``compare''. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you've done. Click the ``Create pull request'' button. - -\item \textbf{Be patient:} Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section \ref{sec:SubmissionApprovalProcess} for procedures followed by the HARK team when evaluating outside contributions. - -\end{enumerate} - -\subsection{Submission Approval Process}\label{sec:SubmissionApprovalProcess} - -Before submitting to HARK, the development team would like you to make sure that your intended contribution is listed as an Issue on the GitHub page. If you are going to fix or address an Issue that is already posted, reply to that post with a note indicating that you are about to work on it and an estimate of when you will make a pull request for it. If you want to do something that no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly) describing what you intend to do.\footnote{If your contribution is a tiny adjustment or to fix/add some documentation or comments, you shouldn't make an Issue.} This will both help prevent two people from working on the same thing (and one of them potentially wasting time on it), and lets the development team know what's coming. Moreover, if you propose a contribution that is (a) already in HARK, (b) not in the spirit of HARK, or (c) a fix for something that's not actually broken, this gives the team a chance to let you know not to spend effort on it. - -After creating a pull request to submit your contribution, it will be evaluated by one or more members of the HARK team. Criteria that the HARK team will consider when choosing whether to accept a submission include: -\begin{enumerate} -\item \textbf{Does the contribution break previous code?} If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility. - -\item \textbf{Is the code well documented?} Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section \ref{sec:DocumentationConventions}) and enough comments for a reader to follow the algorithm. - -\item \textbf{Is the contribution relevant?} The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn't belong in HARK. - -\item \textbf{Is the code demonstrated with an example?} If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you've contributed a new function, consider providing a snippet of code demonstrating its use.\footnote{In the future, HARK will make use of in-line testing.} - -\item \textbf{Is the code correct?} Your code should actually do what it claims to do. - -\item \textbf{Does the code use HARK conventions?} To the best of your ability, contributions to HARK should follow the style guidelines in section \ref{sec:NamingConventions}. We don't demand perfection, as these are guidelines rather than rules. - -\item \textbf{Is the code well integrated in HARK?} In the beta release of HARK, we have tried to present a ``taxonomy'' of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy. - -\item \textbf{Is this already in HARK?} If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK. -\end{enumerate} - -The HARK team intends to release more specific guidelines in the near future, so that contributors are better able to judge their submissions before issuing a pull request. We are in the process of recruiting ``czars'' to oversee particular aspects of HARK, providing their knowledge and expertise.\footnote{If you have knowledge and/or expertise and would like to contribute as a czar, please contact a HARK team member so that we can judge your credentials even though we have way less expertise.} While czars might directly evaluate submissions as a HARK team member, their more important role is to help establish these guidelines for their domain, more specifically nailing down what we mean by ``correct'' or ``well integrated''. - -\subsection{Naming Conventions}\label{sec:NamingConventions} - -Object naming conventions in HARK are fairly different than existing standards, and differ somewhat between tool modules vs model or application modules. The following conventions apply throughout HARK: -\begin{itemize} -\item Functions and methods are always in ``camel case'': no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. \texttt{approxLognormal}. - -\item Function and method names should accurately and concisely describe what the function does; the first word in the name \textit{must be a verb}.\footnote{HARK standards have specified several ``keyword verbs'' that have specific meaning.} - -\item Variable and class names \textit{should not} have a verb as their first word. - -\item Class names should use no underscores and capitalize the first letter of each word; moreover, a class name \textit{must include a noun}. E.g.\ \texttt{ConsPerfForesightSolver}. -\end{itemize} - -When naming variables in model modules, the HARK team strongly discourages using single letter names, like \texttt{c} for consumption. Instead, we encourage contributors to use longer, more descriptive variable names using additional words and common abbreviations to specify the variable more precisely. In \texttt{/Documentation/NARK.pdf}, we list standard single letter variable ``bases'' and an array of prefixes and suffixes to adjust them. Economic variables in model modules should (usually) not use underscores, instead using camel case to the greatest extent possible.\footnote{For ``non-economic'' variables that are only used temporarily, underscores are permissible.} The development team prefers this standard so that users can translate between Python code and LaTeX script with minimal work. - -Conventions for naming variables in HARK's tool modules are significantly closer to more commonly used standards. Variable names should be in all lower case, with underscores between words, e.g.\ \texttt{data\_to\_match}. The functions and classes in these modules are more general and almost surely do not have any inherent ``economic content''; they are numerical or algorithmic objects, not variables that might appear in an equation in an article for a (non-computational) economics journal. Variable names in application modules (e.g.\ the files that execute the \texttt{cstwMPC} estimations) are a mix of the conventions for tool and model files, as appropriate for each variable. That is, variables that are directly related to ``economic variables'' in model modules should follow those conventions, while objects created solely for data manipulation or reporting should use the style of tool modules. - -\subsection{Documentation Conventions}\label{sec:DocumentationConventions} - -The HARK team wants the toolKit to be as accessible to users as possible; our greatest fear\footnote{Other than spiders, of course.} is that a new user will open up our code, get hopelessly confused trying to read it, and then never look at HARK again. To prevent this tragic outcome, we have tried hard to provide comprehensive, accurate documentation and comments within the code describing our methods.\footnote{To the extent that documentation and comments are missing, we encourage users to rectify this in their contributions.} Moreover, HARK uses the Sphinx utility to generate a website with \href{https://econ-ark.github.io/HARK/}{online documentation} for all of our tool and model modules, so that users can learn about what's available in HARK without digging through the source code. When making contributions to HARK, the development team asks users to format their inline documentation to work with Sphinx by following a few simple rules. - -\begin{itemize} -\item The top of every module should begin with a ``docstring'' providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:\footnote{The triple quote to begin and end a docstring can use either the apostrophe \texttt{'} or quotations marks \texttt{"}.} - -\texttt{"""} - -\texttt{Specifies an economic model and provides methods for solving it. More specific description of the key features of the model and variations of it in this module. Maybe some comments about the solution method or limitations of the model. Your bank account routing number.} - -\texttt{"""} - -\item The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as: - -\texttt{def functionName(input1,input2):} - -\hspace{0.9cm}\texttt{"""} - -\hspace{0.9cm}\texttt{Concise description of the function. More details about what} - -\hspace{0.9cm}\texttt{the function does, options or modes available, and maybe mathematical} - -\hspace{0.9cm}\texttt{methods used. Credit to a source if you poached their algorithm.} - -\vspace{0.5cm} - -\hspace{0.9cm}\texttt{Parameters} - -\hspace{0.9cm}\texttt{--------------------} - -\hspace{0.9cm}\texttt{input1: type} - -\hspace{1.8cm}\texttt{Description of what input1 represents.} - -\hspace{0.9cm}\texttt{input2: type} - -\hspace{1.8cm}\texttt{Description of what input2 represents.} - -\vspace{0.5cm} - -\hspace{0.9cm}\texttt{Returns} - -\hspace{0.9cm}\texttt{--------------} - -\hspace{0.9cm}\texttt{output\_name: type} - -\hspace{1.8cm}\texttt{Description of the output(s) of the function. Might have} - -\hspace{1.8cm}\texttt{multiple entries. If no output, this is just "None".} - -\hspace{0.9cm}\texttt{"""} - -\item Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it. -\end{itemize} - -Finally, if you write a new model module, the HARK team asks that you also provide a short mathematical writeup of the model as a PDF. This document does not need to go into great detail about the solution method for the model or the functions and classes included in the module, merely specify the economic model and provide a summary of how it is solved. See \texttt{/Documentation/ConsumptionSavingModels.pdf} for an example of this. - -\ifthenelse{\boolean{ContributingToHARKOnly}}{}{ -\section{Future of HARK}\label{sec:FutureOfHARK} - -HARK is in a state of perpetual incompleteness, always expanding to include more tools and more models. As an open source project in the hands of many contributors around the world, the HARK team cannot fully predict future features of the toolKit. This section provides an overview of what we would like to see in the near future, as well as potential mechanisms to encourage researchers to contribute their code to the project. - -\subsection{Future Tools}\label{sec:FutureTools} - -The current frameworks in \texttt{HARK.core} concern agents who have ``isolated'' dynamic problems or interact with each other only through aggregate outcomes to which they contribute atomically. We hope to eventually provide a framework for models in which agents directly interact with each other; this may include a system for specifying the state of networks and how connections are formed and broken. \texttt{HARK.core} might also develop a framework more suited to industrial organization models and dynamic games.\footnote{It might already be possible to shoehorn some of these models into the \texttt{Market} framework.} - -The \texttt{HARK.estimation} module in particular is very sparsely populated, with its functions mostly serving as wrappers for ``off the shelf'' optimization methods from \texttt{scipy.optimize}. There are plenty of non-standard or more complex optimization techniques available, including global and/or stochastic search methods. We believe that additional optimizers are ``low hanging fruit'' as straightforward contributions to HARK and thus will be added in the near future. Moreover, there are plenty of distributions that have no approximator in \texttt{HARK.utilities} (or data generator in \texttt{HARK.simulation}), and these functions are among the easiest for new contributors to write. The data analysis tools in \texttt{HARK.utilities} only include functions that we happened to use for our current research projects, and we expect that this will be the mode of growth for future tools. There are some obvious holes that we believe will be filled quite quickly; for example, we provide a rudimentary non-parametric kernel regression function, but have not written a kernel density estimator to generate PDFs from data. - -The classes currently in \texttt{HARK.interpolation} are all variations on linear (or cubic) spline interpolation, and each is specifically written for a fixed number of dimensions. As these methods use full tensor grids, they are highly impractical for representing functions in higher dimensions, suffering greatly from the curse of dimensionality.\footnote{The HARK team only included interpolators for up to 4 dimensions, and some would argue this is already too many for tensor grid methods.} We are quite confident that \texttt{HARK.interpolation} will soon include techniques more applicable to high dimensional state spaces, including Smolnyak interpolation, other (adaptive) sparse grid methods, and regression-based interpolation. For lower-dimensional spaces, HARK will also soon provide Delaunay interpolation classes. - -The \texttt{HARK.parallel} module currently provides a simple interface for handling \textit{ex-ante} heterogeneity among agents. While useful for speeding up moderately-sized problems, the libraries used are only able to access the CPU of the local machine running the code. In the very near future, the HARK team will provide an interface (and example) for using Apache Spark, which bills itself as a ``fast and general engine for large-scale data processing''. Specifically, users will be able to easily scale up their computing resources and run HARK code on an Amazon EC2 session or other cluster. - -\subsection{Future Models}\label{sec:FutureModels} - -The economics models currently included in HARK mostly concern consumption-saving problems with constant relative risk aversion consumers who experience fully permanent and fully transitory shocks to income. This is obviously a very small subset of all consumption-saving models (which are themselves a small subset of all dynamic models economists might be interested in). Consider some examples of variants, features, and extensions that are not included in HARK, but could be in the future: -\begin{itemize} -\item Consumers with other utility functions, such as CARA or expo-power. - -\item Consumers with time-inconsistent preferences, e.g.\ hyperbolic discounting. - -\item Income shocks that are neither fully transitory nor fully permanent. - -\item Consumers with a bequest motive or terminal utility function. - -\item Utility from leisure and endogenous labor supply. - -\item Investment in a durable consumption good such as housing. - -\item Exogenous shocks to expenses. - -\item Multiple consumption goods, as with endogenous medical care. - -\item Portfolio allocation between a risky and riskless asset. - -\item Job search intensity as a control variable. - -\item Endogenous health evolution and health investment. -\end{itemize} - -The HARK team is currently developing a module for mixing discrete and continuous choice models, in the style of Jorgensen, Rust, Iskhakov, and Schjerning (2015). Among many other models, this framework will enable HARK users to easily develop features like: -\begin{itemize} -\item Labor supply on the extensive margin: the timing of retirement. - -\item Selection of job type or sector (by riskiness, specific human capital, etc). - -\item Choice of medical insurance contract. - -\item Decision to rent or own housing. - -\item Endogenous default on debt (with ``macroeconomic'' pricing of debt). - -\item Household formation, dissolution, and reproduction. -\end{itemize} - -Finally, we hope to provide a generic solver for any discrete choice model with T1EV shocks. The scope of HARK is limited only by what users choose to put in it. We hope that within a year of its debut, HARK will include a significant number of canonical models, providing more building blocks for future researchers to create novel combinations and new extensions. - -\subsection{Bounty Hunting}\label{sec:BountyHunting} - -While HARK users are free to write and contribute any tools and models that they believe could be helpful, building the toolKit in a ``bottom up'' style, the HARK team hopes to provide mechanisms for targeted ``top down'' development. The team is working to secure grant funding for HARK so as to offer ``bounties'' for particular models, tools, applications, or frameworks. Suppose the team decides that the model in Important Paper X should be included as part of HARK; we will issue a call for contributions to fill this hole, offering a modest prize of, say, \$20,000 to the programmer who submits the best implementation of the model, as judged by clearly stated criteria.\footnote{Actual bounties may be significantly smaller than this. But be honest: You're much more interested in contributing to HARK after reading that sentence.} The bounty would be announced as an Issue on HARK's GitHub page, as well as in a potential electronic digest of HARK news. - -Bounties might be placed on items as small as a single data analysis tool or as large as a complex estimation of a large general equilibrium model, with prizes scaling accordingly. We hasten to emphasize that we cannot guarantee funding for HARK, so the bounties (like everything else in this section) are purely speculative. As economists, the HARK team understands the incentive compatibility problems associated with asking researchers to contribute significant amounts of their time, effort, and unique skills in service of a tenuous concept like the Greater Good in exchange for even more tenuous Glory. We acknowledge that economic researchers are also optimizing agents with their own preferences over leisure and consumption, and agree that Greed is a valid mechanism to expand HARK. - -\subsection{All Aboard the Ark}\label{sec:AllAboardTheArk} - -As observant readers may have noticed, the HARK repository is hosted by a GitHub account named Econ-Ark, with no explanation as to what this might mean. Looking a few years into the future, the development team believes that HARK might be the first piece of a broader collection of computational economics resources. HARK is open source but somewhat akin to an exclusive club: contributions are required to adhere to standards and norms so as to foster modularity and interoperability among models and tools. Consistent with its namesake ship, the Econ-Ark has a significantly lower bar for admission, with a place on board for everything. - -On one level, the Ark might serve as a warehouse for any and all quantitative models and tools, for any economic purpose, in any subfield, and written in any language. Through nearly unanimous feedback, the HARK team understands that many economists are willing to share code that they have already written, but are reluctant to expend the considerable effort to translate and format it for HARK. In the future, we will set up a mechanism for code like this to be hosted on the Ark, with minimal requirements imposed on its authors (and correspondingly minimal warranty for potential users). Relevant projects on the Ark might become bounty targets for HARK, with users incentivized to port the original code into Python and to meet HARK standards; direct access to the original source code will significantly expedite the translation process, and provide a suitable benchmark for testing and verifying the contribution to HARK. - -On a different level, the Ark might also include cohesive toolkits like HARK, but geared toward purposes other than heterogeneous agents macroeconomics models. For example, the previously mentioned modules for networks among agents (and other agent-based modeling), industrial organization and dynamic games, and traditional discrete choice models might become their own toolkits, with some interoperability with HARK. In support of this, some of the tool modules currently in HARK might be ``promoted'' and become universally shared across all Econ-Ark toolkits. - -The dark secret of HARK is that it is named for heterogeneous agents macroeconomics, but designed and written by structural microeconomists. The development team believes these subfields to have considerable overlap in terms of methods and techniques, and that HARK might not be noticed by half of its potential audience due to its name. In a general sense, the Econ-Ark gives us cover to expand the scope of our efforts and/or rebrand the toolKit for an alternative audience. -} % end \ifthenelse{\boolean{\ContributingToHARKOnly}} -\end{document} diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK0x.png b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK0x.png deleted file mode 100644 index 7418068e6e881693724ff086561704cbc8cd2156..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 160 zcmeAS@N?(olHy`uVBq!ia0vp^JRmj;Gmy+@pHv8>L<4+6T>t<74`iYN+qO3#1#Bfj ze!&bgSHDkF%2|AD4N!!$z$3Dlfr0M`2s2LA=92~r%6qyvhH%IxCp0iKa&W63U9u!) k$r7fZmS9dNy@p0c2D{6Q>xDOQ8h|YEboFyt=akR{012EVnE(I) diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK10.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK10.html deleted file mode 100644 index 2705a51c7..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK10.html +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - -

-

9If you have knowledge and/or expertise and would like to contribute as a czar, please contact a HARK - team member so that we can judge your credentials even though we have way less expertise.

- - diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK11.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK11.html deleted file mode 100644 index 30fc47b29..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK11.html +++ /dev/null @@ -1,17 +0,0 @@ - - - - - - - - - - -
-

10HARK standards have specified several “keyword verbs” that have specific meaning.

- diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK12.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK12.html deleted file mode 100644 index aef2b6fbe..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK12.html +++ /dev/null @@ -1,18 +0,0 @@ - - - - - - - - - - -
-

11For “non-economic” variables that are only used temporarily, underscores are permissible.

- - diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK13.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK13.html deleted file mode 100644 index c673a5ca3..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK13.html +++ /dev/null @@ -1,18 +0,0 @@ - - - - - - - - - - -
-

12Other than spiders, of course.

- - diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK14.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK14.html deleted file mode 100644 index 6813ef347..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK14.html +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - -
-

13To the extent that documentation and comments are missing, we encourage users to rectify this in their - contributions.

- - diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK15.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK15.html deleted file mode 100644 index 3a2d1ae95..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK15.html +++ /dev/null @@ -1,22 +0,0 @@ - - - - - - - - - - -
-

14The triple quote to begin and end a docstring can use either the apostrophe ’ or quotations marks - ".

- diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK2.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK2.html deleted file mode 100644 index d04c39fdf..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK2.html +++ /dev/null @@ -1,24 +0,0 @@ - - - - - - - - - - -
-

1Case in point: As of this moment, HARK.utilities has no function for making a discrete approximation to - the normal distribution, even though there are several well known methods for doing so and it is an obviously - useful function.

- - diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK3.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK3.html deleted file mode 100644 index 7d0289b70..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK3.html +++ /dev/null @@ -1,22 +0,0 @@ - - - - - - - - - - -
-

2Although that list is limited to extensions and variations on consumption-saving models, HARK has no - such restrictions. If there is an entirely different sort of dynamic model that fits in the HARK framework, it can - be contributed.

- - diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK4.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK4.html deleted file mode 100644 index ebdc9f885..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK4.html +++ /dev/null @@ -1,17 +0,0 @@ - - - - - - - - - - -
-

3There are non-free ways to sign up for GitHub, but you don’t need to worry about them.

- diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK5.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK5.html deleted file mode 100644 index db323e6d0..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK5.html +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - -
-

4You can make several local commits without syncing with the remote server. This is useful for working - without an internet connection. Say, on an intercontinental flight.

- - diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK6.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK6.html deleted file mode 100644 index fc13af666..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK6.html +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - -
-

5Clicking on a dot or ring will display the changes made in that commit (or uncommitted - changes).

- - diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK7.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK7.html deleted file mode 100644 index cc1259d54..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK7.html +++ /dev/null @@ -1,17 +0,0 @@ - - - - - - - - - - -
-

6Or since your last update from the main repository hosted by Econ-Ark.

- diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK8.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK8.html deleted file mode 100644 index 27661e64f..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK8.html +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - -
-

7If your contribution is a tiny adjustment or to fix/add some documentation or comments, you shouldn’t - make an Issue.

- - diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK9.html b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK9.html deleted file mode 100644 index a034e4a6e..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK9.html +++ /dev/null @@ -1,17 +0,0 @@ - - - - - - - - - - -
-

8In the future, HARK will make use of in-line testing.

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license: LaTeX Project Public License -%%% -%%% Further information is available at -%%% http://www.uni-kiel.de/agrarpol/ahenningsen/latex-bibtex.html -%%% -%%% This document class is based on the "scrreprt" class -%%% from the KOMA script bundle. For documentation, Google KOMA-Script Documentation -%%% -%%% The corresponding BibTeX style file "econtex.bst" should be used -%%% for the bibliography: \bibliographystyle{econtex} -%%% -%%% In addition to the options for scrreprt, this package includes an additional option -%%% titlepage = include a title page (with command "\maketitle", or "\maketitleWithForcedDate{}") -%%% -%%% If an abstract is defined by \begin{abstract}\end{abstract} before \maketitle is invoked, -%%% it will appear on the title page -%%% -%%% If the titlepage option is invoked: -%%% \keywords can be used to show keywords below the abstract -%%% \jelclass can be used to show JEL classifications below the abstract -%%% \thanks{text of the thank-you footnote} produces a footnote at the bottom of the page containing the text - -\ProvidesClass{econtex}[2016/05/27 LaTeX class for economics papers, handouts, and exam questions written by Christopher Carroll] -\NeedsTeXFormat{LaTeX2e} -% \RequirePackage{rotating} - -\RequirePackage{ifthen} -\RequirePackage{chngpage} - -% If unspecified whether pdf output (instead of dvi) is being used, determine that it SHOULD be -\newif\ifdvi\dvitrue -\@ifundefined{pdfoutput}{}{\ifnum\pdfoutput>0 \dvifalse\fi} - -% permit double spacing -\RequirePackage{setspace} -%\doublespacing - -% option to include a title page -\newcounter{IncludeTitlePage} -\setcounter{IncludeTitlePage}{0} -\DeclareOption{titlepage}{\setcounter{IncludeTitlePage}{1}} - -% pass any options on to the scrreprt class and load this class with some options -\DeclareOption*{\PassOptionsToClass{\CurrentOption}{scrreprt}} -\ProcessOptions\relax -\LoadClass[fontsize=12pt,english,numbers=noenddot,captions=tableheading,captions=nooneline]{scrreprt} -% pointlessnumbers = do not add final dot after ordinary section numbers -% tablecaptionabove = use \captionabove for captions above rather than below tables -% noonelinecaption = do not treat one-line captions differently - -% use new TeX encoding scheme (T1), which contains accented (European) letters, -% to make European hyphenations work -\RequirePackage[T1]{fontenc} - -% use babel package and set language to english -\RequirePackage[english]{babel} - -% spacing around formulas -\AtBeginDocument{ -\setlength{\abovedisplayshortskip}{6pt} -\setlength{\belowdisplayshortskip}{6pt} -\setlength{\abovedisplayskip}{6pt} -\setlength{\belowdisplayskip}{6pt} -\allowdisplaybreaks % Permits LaTeX to break long formulas across pages (?) -} - -%% Added the above info to the definition of normalsize -\def\normalsize{\@setfontsize \normalsize \@xiipt {14.5} -\abovedisplayskip 6\p@ \@plus 3\p@ \@minus 3\p@ -\belowdisplayskip \abovedisplayskip -\abovedisplayshortskip 6pt \belowdisplayshortskip 6pt -\let \@listi \@listI} -\normalsize - -% Don't indent the second and later lines of footnotes -\deffootnote[1em]{0.0em}{1em}{\textsuperscript{\thefootnotemark}} - -% spacing around captions and floats -\setlength{\abovecaptionskip}{6pt} -\setlength{\belowcaptionskip}{3pt} -\setlength{\floatsep}{0pt} -\setlength{\textfloatsep}{20pt} -\setlength{\intextsep}{15pt} - -\RequirePackage{calc} - -\newenvironment{Description} -{\begin{list}{}{\let\makelabel\Descriptionlabel - \setlength\labelwidth{75pt} - \setlength\labelsep{0pt} - \setlength\leftmargin{75pt} - \setlength\itemindent{0pt} -}} -{\end{list}} -\newcommand*\Descriptionlabel[1]{\textbf{#1}\hfil} - -\RequirePackage{cancel,verbatim, -amsmath,amssymb,amsfonts,amsthm -,threeparttable,dcolumn,multicol,multirow,booktabs % table environment extensions -,latexsym -,afterpage -,endnotes -,moreverb -,hhline % Allow double hlines -,tipa % Define more characters, in particular the \textthorn character -,color -} - -\RequirePackage{econtex} -\RequirePackage[mathscr]{eucal} -\RequirePackage[normalem]{ulem} -\providecommand\bmmax{0} % Prevent boldmath package from using up too much memory -\RequirePackage{bm} % Allow bold math -\RequirePackage{bbm} % Blackbord math - -% font for URLs -\RequirePackage{url} -\def\UrlFont{\ttfamily} - -\usepackage[econtex]{optional} % Enable the control of compilation using options passed on startup -\opt{EndFloats}{\RequirePackage[noheads,nolists,tablesfirst,nomarkers]{endfloat}} - -% page format, margins if PrintGeom option is invoked -\opt{PrintGeom}{ -\RequirePackage{geometry} -\geometry{letterpaper, tmargin=1.4in, bmargin=1.4in, lmargin=1.25in, - rmargin=1.25in, headheight=0in, headsep=0in, footskip=0.5in } -} - -\opt{JournalFormatting}{ % BEJM requires 1.5 inch margins -\RequirePackage{geometry} -\geometry{letterpaper, tmargin=1.5in, bmargin=1.5in, lmargin=1.5in, - rmargin=1.5in, headheight=0in, headsep=0in, footskip=0.5in } -} - -\providecommand{\textSizeDefault}{\normalsize} -\opt{bigWide}{\renewcommand{\textSizeDefault}{\large}} - -\providecommand{\abstractSizeDefault}{\small} -\opt{bigWide}{\renewcommand{\abstractSizeDefault}{\normalsize}} - -%% formatting section headers -\setkomafont{section}{\normalfont \Large \bfseries} -\setkomafont{subsection}{\normalfont \large} -\setkomafont{subsubsection}{\normalfont \itshape} - -% citations and bibliography -\RequirePackage[authoryear]{natbib} -\AtBeginDocument{\renewcommand{\refname}{\clearpage References} -\renewcommand\@makefnmark - {\mbox{\textsuperscript{\tiny\@thefnmark}}} % Restore footnote defn after titlepage redefines it -\ifdvi - \renewcommand{\ushort}{\underline} % ushort seems to not work properly for tex4ht (which compiles using dvi) -\fi -} - -% captions of figures and tables -\setcapwidth[c]{\textwidth} -% \setcapindent*{0pt} -\setkomafont{captionlabel}{\centering \bfseries} -\setkomafont{caption}{\centering} -\renewcommand{\captionformat}{~ } - -\newcommand{\forcedate}[1]{\newcommand{\@forcedate}{#1}} - -% titlepage with title, author(s), and date - -\renewcommand{\maketitle}{ -\ifthenelse{ \isundefined\@forcedate }{ - \ClassWarningNoLine{econtex}{{\protect\forcedate} is undefined in the .tex file; please define it and rerun}}{} - \ifthenelse{\equal{\theIncludeTitlePage}{1}}{ - \let\footnotesize\scriptsize - \thispagestyle{empty} - \vspace*{0mm} - \enlargethispage{0.4in} - }{} - \begin{center} - \begin{LARGE} - \begin{spacing}{1.00} - \textbf{\@title} - \end{spacing} - \end{LARGE}\medskip\medskip\medskip - {\normalsize \@date \par} - {\tiny{~}\par} - \ifthenelse{\equal{\theIncludeTitlePage}{1}}{ - \vspace*{3mm} - {\large - \lineskip .75em% - \begin{tabular}[t]{c}% - \@author - \end{tabular}\par}% - }{} - \end{center} -} - - -\newcommand{\maketitleWithForcedDate}[1]{ -\ClassWarningNoLine{econtex}{\protect\maketitleWithForcedDate{} is deprecated. To set a date different from the current one, just use the \protect\date{(string desired to signify paper date)}} - - \ifthenelse{\equal{\theIncludeTitlePage}{1}}{ - \let\footnotesize\scriptsize - \thispagestyle{empty} - \vspace*{0mm} - \enlargethispage{0.4in} - }{} - \begin{center} - \begin{LARGE} - \begin{spacing}{1.00} - \textbf{\@title} - \end{spacing} - \end{LARGE}\medskip\medskip\medskip - {\normalsize #1 \par} - {\tiny{~}\par} - \ifthenelse{\equal{\theIncludeTitlePage}{1}}{ - \vspace*{3mm} - {\large - \lineskip .75em% - \begin{tabular}[t]{c}% - \@author - \end{tabular}\par}% - }{} - \end{center} -} - - -% abstract, keywords, JEL classification -\newcommand{\keywords}[1]{\newcommand{\@keywords}{#1}} -\newcommand{\jelclass}[1]{\newcommand{\@jelclass}{#1}} -\newcommand{\aspublished}[1]{\newcommand{\@aspublished}{#1}} - -\renewenvironment{abstract}{% - \begin{spacing}{0.9} - \noindent {\tiny \phantom{.}} \\ % Trick to get proper spacing in html - \noindent \hrule height 0.4pt depth 0.0pt width \textwidth \relax - \vspace*{4mm} - \abstractSizeDefault - \noindent \textbf{Abstract}\\ - \indent -}{% - \noindent {\tiny \phantom{.}} \\ % Trick to get proper spacing in html - \noindent \hrule height 0.4pt depth 0.0pt width \textwidth \relax - \vspace*{0mm} - \ifthenelse{ \isundefined\@keywords }{ - \ClassWarningNoLine{econtex}{No keywords specified. - Please use the command \protect\keywords} - }{ - } - \end{spacing} - -\begin{quote} -\begin{Description} -\item[\textbf{Keywords~}] \@keywords -\ifthenelse{ \isundefined\@jelclass }{ -\ClassWarningNoLine{econtex}{No JEL classification specified. - Please use the command \protect\jelclass} -}{ -\item[\textbf{JEL codes~}] \@jelclass -} - -\end{Description} -\ifthenelse{ \isundefined\@aspublished }{ -}{ -{ - -\vspace{0.1in}\@aspublished} -}\end{quote} -} - -\setcounter{footnote}{0}% - -\medskip - -% avoid clubs and widows -\clubpenalty=1000 % penalty for breaking a page with only one sentence; set to 10000 to prohibit -\widowpenalty=10000 -% \displaywidowpenalty=10000 - -\hbadness 3000 % LaTeX default is 1000, which is too finicky - set to 10000 to get rid of complaints - -% increase stretchability of the spaces (avoid overfull hboxes) -\setlength{\emergencystretch}{3em} - - -\pagestyle{plain} -\thispagestyle{empty} - -\newcommand{\titlepagefinish}{\newpage -\renewcommand\@makefnmark - {\mbox{\textsuperscript{\tiny\@thefnmark}}} -} - - -\RequirePackage[multiple]{footmisc} -\RequirePackage{manyfoot,perpage} -\DefineFNsymbols{blank}[text]{{} {} {} {} {}} -\setfnsymbol{blank} -\DeclareNewFootnote{B}[fnsymbol] -\MakePerPage{footnoteB} - - -\renewcommand{\thanks}{\footnoteB} -\newcommand{\thanksFooter}{\footnoteB} - -\hfuzz2pt % Don't bother to report over-full boxes if over-edge is < 2pt - -%%%%%%%%%% Added by Amy Hendrickson, TeXnology Inc. -%% Amy change, Feb 28, 2011 ==>> - -%%% Generalization of the author and authorinfo commands, based on -%%% the original code. - -%% Sample use: -%% \author{Christopher D. Carroll\authNum \\ -%% \and -%% Jiri Slacalek\authNum \\ -%% \and -%% Martin Sommer\authNum \\ -%% \and -%% Another Name\authNum -%% \and -%% Still Another Name\authNum -%% } - -\newcounter{authornum} % Use either \num or (preferred) \authNum to denote authors -\def\num{\global\advance\c@authornum by 1\textsuperscript{\tiny \the\c@authornum}} -\def\authNum{\global\advance\c@authornum by 1\textsuperscript{\tiny \the\c@authornum}} - -%% \begin{authorsinfo}...\end{authorsinfo} - -%% Now used: -%% \begin{authorsinfo} -%% \name{First author: info} -%% \\ -%% \name{Second author: info} -%% \\ -%% \name{Third author: info} -%% (and so on for as many authors -%% as you have) -%% \end{authorsinfo} -%% (note: the \\ doesn't do anything, but makes it easier to -%% see the separate author names.) - -\newcounter{bottomauthor} -\def\authorsinfo{\global\c@bottomauthor=0\setbox0=\vbox\bgroup\footnotesize -\let\\ \relax -} - -\def\endauthorsinfo{\egroup -\footnoteB{\unvbox0} -} - -\def\name#1{\global\advance\c@bottomauthor by 1\textsuperscript{\tiny \the\c@bottomauthor}{#1}{~~~~}} - diff --git a/Documentation/HARKmanual/ContributingToHARK/econtex.sty b/Documentation/HARKmanual/ContributingToHARK/econtex.sty deleted file mode 100644 index fcefb5736..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/econtex.sty +++ /dev/null @@ -1,34 +0,0 @@ -%---------------Custom Definitions-------------- - -\def\liminf{\mathop{\rm lim\, inf}\limits} -\DeclareMathOperator{\Ex}{\mathbb{E}} % Expectations operator -\def\var{\operatorname{var}} % variance -\def\cov{\operatorname{cov}} % covariance -\def\std{\operatorname{std}} % standard deviation -\def\argmax{\operatornamewithlimits{arg\,max}} % argmax - -%---------------Custom Modifications---------------------- - -% \htline defines a thick horizontal line in table -\def\htline{\noalign{\hrule height 2\arrayrulewidth}} - -% \hdline defines a dotted horizontal line in table -\def\hdline{\noalign{.\dotfill{}.}} - -%To keep footnotes on a single page except in extreme cases -\interfootnotelinepenalty=5000 %from 0 to 10000 - -%To prevent hyphenation -\hyphenpenalty=5000 %from 0 to 10000, default is 200 - -%To prevent breaking math formula -\relpenalty=4000 %from 0 to 10000, default is 500 - -%To prevent breaking math formula after binary operator -\binoppenalty=10000 %from 0 to 10000, default is 700 - -%-------------------------------------------------------- - - - - diff --git a/Documentation/HARKmanual/ContributingToHARK/econtexSetup.sty b/Documentation/HARKmanual/ContributingToHARK/econtexSetup.sty deleted file mode 100644 index 29aa7a880..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/econtexSetup.sty +++ /dev/null @@ -1,236 +0,0 @@ -\RequirePackage{wasysym -,psibycus % Greek language package, including koppa -,ushort -%,lscape % Pages in landscape mode go inside \begin{landscape}\end{landscape} % CONFLICTS WITH EPSTOPDF -} - -\newenvironment{CDCPrivate} % Environment for extra material not wanted in the public document -{\marginpar{\tiny beginCDC}}{\marginpar{\tiny endCDC}} % CDCPrivate - -\provideboolean{Web} -\setboolean{Web}{false} % reset to true if running via dvi; search for \ifdvi below - -\provideboolean{bigPrint} -\setboolean{bigPrint}{true} -\setboolean{bigPrint}{false} - -\provideboolean{wideMargins} -\setboolean{wideMargins}{true} -\setboolean{wideMargins}{false} - -\provideboolean{BigAndWide} -\setboolean{BigAndWide}{true} -\setboolean{BigAndWide}{false} - -% Options to set if being run from the shell (so autocompiled) -\opt{FromShell}{ -\setboolean{bigPrint}{false} -\setboolean{wideMargins}{false} -\setboolean{BigAndWide}{false} -} - -\opt{bigWide}{ -\setboolean{bigPrint}{true} -\setboolean{wideMargins}{true} -\setboolean{BigAndWide}{true} -\providecommand{\textSizeDefault}{\large} -\providecommand{\abstractSizeDefault}{\large} -} - -\opt{Aquamacs}{ -%\setboolean{bigPrint}{true} -%\setboolean{wideMargins}{true} -%\setboolean{BigAndWide}{true} -%\providecommand{\textSizeDefault}{\large} -%\providecommand{\abstractSizeDefault}{\large} -} - -\provideboolean{KoppaOn} -\setboolean{KoppaOn}{true} -%\setboolean{KoppaOn}{false} - -\ifthenelse{\boolean{KoppaOn}}{ -\providecommand{\koppa}{\text{\greek{k+}}} -\providecommand{\Koppa}{\text{\greek{K+}}} -\providecommand{\sampi}{\text{\greek{s+}}} -\providecommand{\cigma}{\text{\greek{c+}}} -\providecommand{\Cigma}{\text{\greek{C+}}} -}{ -\def\koppa{\mathbf{q}}\providecommand{\koppa}{$\mathbf{q}$}\providecommand{\Koppa}{$\mathbf{Q}$} -} - -% Keep the elements in the argument #1 together on the same page -\newcommand{\together}[1]{\noindent\parbox{\linewidth}{#1}} - -% html version of document must be constructed by running pdflatex in -% dvi output mode; code below creates an ifdvi construct that detects -% whether running in dvi mode and sets the options necessary to generate -% html version of document using tex4ht - -%\makeatletter -%\newif\ifdvi\dvitrue -%\@ifundefined{pdfoutput}{}{\ifnum\pdfoutput>0 \dvifalse\fi} -%\makeatother - -% dvi compilation only occurs if making the html version -\ifdvi - \setboolean{Web}{true} - \RequirePackage{tex4ht} - \RequirePackage{graphicx} - \RequirePackage[tex4ht]{hyperref} - \provideboolean{bigPrint}\setboolean{bigPrint}{true} % HTM output looks better in a larger font size - \providecommand{\wideMargins}{\null{}} % Wide margins doesn't mean anything for html documents - \newcommand{\CDCFig}[1]{\centerline{\includegraphics{#1}}} - \newcommand{\CDCFigSideways}[1]{\centerline{\includegraphics{#1}}} - \DeclareGraphicsExtensions{.png} - \providecommand{\textSizeDefault}{\large} - \providecommand{\titlepagefinish}{\newpage\textSizeDefault} - \providecommand{\abstractSizeDefault}{\large} - \providecommand{\ushort}{\underline} % ushort seems to not work properly for tex4ht - \let\footnoterule\relax - \makeatletter - \renewenvironment{abstract}{% - \begin{spacing}{0.9} - \noindent {\tiny \phantom{.}} \\ % Trick to get proper spacing in html - \noindent \hrule height 0.4pt depth 0.0pt width \textwidth \relax - \vspace*{5mm} - \noindent \textbf{Abstract}\\ - \indent \abstractSizeDefault - }{% - \noindent {\tiny \phantom{.}} \\ % Trick to get proper spacing in html -% \noindent \hrule height 0.4pt depth 0.0pt width \textwidth \relax - \vspace*{3mm} - \ifthenelse{ \isundefined\@keywords }{ - \ClassWarningNoLine{bejournal}{No keywords specified. - Please use the command \protect\keywords} - }{ - } - \end{spacing} - \begin{quote} - \begin{Description} - \item[\textbf{~~~~~~~~~~~~Keywords~}] \@keywords - \ifthenelse{ \isundefined\@jelclass }{ - \ClassWarningNoLine{bejournal}{No JEL classification specified. - Please use the command \protect\jelclass} - }{ - \item[\textbf{~~~~~~~~~~~~JEL codes~}] \@jelclass - \end{Description} - } - \end{quote} - \makeatother - } -\else - \RequirePackage[pdftex]{graphicx} - \RequirePackage[plainpages=false]{hyperref} - \hypersetup{ - pdffitwindow = false, % 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-\providecommand{\hours}{\ensuremath{\mathfrak{h}}} -\providecommand{\Hours}{\ensuremath{\mathfrak{H}}} -\providecommand{\HRat}{\ensuremath{H}} -\providecommand{\hRat}{\ensuremath{h}} -\providecommand{\h}{\ensuremath{h}} -\providecommand{\H}{\ensuremath{H}} -\providecommand{\iFunc}{\ensuremath{\mathrm{i}}} -\providecommand{\IFunc}{\ensuremath{\mathrm{I}}} -\providecommand{\ILev}{\ensuremath{I}} -\providecommand{\iLev}{\ensuremath{i}} -\providecommand{\impg}{{\imath}_{\pGro}} -\providecommand{\ImpG}{{\Im}_{\PGro}} -\providecommand{\impr}{{\imath}_{\rfree}} -\providecommand{\ImpR}{{\Im}_{\Rfree}} -\providecommand{\Inc}{{Y}} -\providecommand{\inc}{{y}} -\providecommand{\InvEpShkInv}{\acute{\psi}} -\providecommand{\InvEpShkInv}{\underline{\psi}} -\providecommand{\Inv}{{I}} -\providecommand{\inv}{{i}} -\providecommand{\IRat}{\ensuremath{I}} -\providecommand{\iRat}{\ensuremath{i}} -\providecommand{\itc}{\ensuremath{\zeta}} -\providecommand{\jFunc}{\ensuremath{\mathrm{j}}} 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-\providecommand{\leiShare}{\ensuremath{\zeta}} % -\providecommand{\LGro}{\ensuremath{\Lambda}} -\providecommand{\lGro}{\ensuremath{\lambda}} -\providecommand{\LLevBF}{\ensuremath{\mathbf{L}}} -\providecommand{\lLevBF}{\ensuremath{\pmb{\ell}}} -\providecommand{\lLev}{\ensuremath{\ell}} -\providecommand{\LLev}{\ensuremath{L}} -\providecommand{\Lo}{\check} -\providecommand{\LRat}{\ensuremath{L}} -\providecommand{\MaxMaxMPC}{\ensuremath{\bar{\bar{\kappa}}}} -\providecommand{\MaxMinMPC}{\ensuremath{\hat{\underline{\kappa}}}} -\providecommand{\MaxMPC}{\ensuremath{\bar{\kappa}}} -\providecommand{\MaxMPS}{\ensuremath{\PatR}} -\providecommand{\Mean}{\ensuremath{\mathbb{M}}} % Mean -\providecommand{\mEss}{\check{m}^{e}} -\providecommand{\ME}{\MRat^{e}} -\providecommand{\mE}{\mRat^{e}} -\providecommand{\mFunc}{\ensuremath{\mathrm{m}}} -\providecommand{\MinMinMPC}{\ensuremath{\underline{\kappa}}} -\providecommand{\MinMPC}{\ensuremath{\uline{\kappa}}} -\providecommand{\MinMPS}{\ensuremath{\pZero^{1/\CRRA} \PatR}} -\providecommand{\MLevBF}{\ensuremath{\mathbf{M}}} -\providecommand{\mLevBF}{\ensuremath{\mathbf{m}}} -\providecommand{\mLevBF}{\ensuremath{\mathbf{m}}} -\providecommand{\mLevE}{\mLev^{e}} -\providecommand{\MLev}{\ensuremath{M}} -\providecommand{\mLev}{\ensuremath{m}} -\providecommand{\MPCE}{\MPC^{e}} -\providecommand{\MPCFunc}{\ensuremath{\pmb{\kappa}}} -\providecommand{\MPCPPF}{\ensuremath{\Pi}} -\providecommand{\MPCP}{\ensuremath{\pi}} -\providecommand{\MPCU}{\MPC^{u}} -\providecommand{\MPC}{\ensuremath{\kappa}} -\providecommand{\MPSFunc}{\ensuremath{\pmb{lambda}}} -\providecommand{\MPS}{\ensuremath{\lambda}} -\providecommand{\MRatE}{\MRat^{e}} -\providecommand{\mRatE}{\mRat^{e}} -\providecommand{\MRat}{\ensuremath{M}} -\providecommand{\mRat}{\ensuremath{m}} -\providecommand{\MSS}{\ensuremath{\breve{M}}} -\providecommand{\mSS}{\ensuremath{\breve{m}}} -\providecommand{\mTarg}{\check{m}} -\providecommand{\MU}{{M}^{u}} 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-\providecommand{\pShk}{\psi} % -\providecommand{\permShkInd}{\psi} % New -\providecommand{\pshk}{\psi} % -\providecommand{\PtyGro}{\ensuremath{\Phi}} -\providecommand{\ptyGro}{\ensuremath{\phi}} -\providecommand{\PtyLab}{\ensuremath{\mathrm{Z}}} % Labor productivity -\providecommand{\ptyLab}{\ensuremath{z}} % usually, log of \PtyLab -\providecommand{\PtyLev}{\ensuremath{A}} % Multiplies overall production function -\providecommand{\ptyLev}{\ensuremath{a}} % Usually, log of \PtyLev -\providecommand{\pZero}{\ensuremath{\wp}} -\providecommand{\QLev}{\ensuremath{Q}} -\providecommand{\q}{\ensuremath{\koppa}} -\providecommand{\RCpnd}{\ensuremath{\mathbf{R}}} -\providecommand{\RevFunc}{\ensuremath{\pmb{\Pi}}} -\providecommand{\revFunc}{\ensuremath{\pmb{\pi}}} -\providecommand{\Rev}{\ensuremath{\Pi}} -\providecommand{\rev}{\ensuremath{\pi}} -\providecommand{\rfree}{\ensuremath{\mathsf{r}}} % The net return on the safe asset at an annual rate -\providecommand{\Rfree}{\ensuremath{\mathsf{R}}} % The return factor on the safe asset - unfortunately mathfrak fonts don't come through for tth -\providecommand{\RFunc}{\ensuremath{\mathrm{R}}} -\providecommand{\RGross}{\ensuremath{\breve{\mathsf{R}}}} -\providecommand{\rGross}{\ensuremath{\breve{\mathsf{r}}}} -\providecommand{\RiskyAlt}{\ensuremath{\acute{\mathbf{R}}}} % The return on the risky asset -\providecommand{\riskyAlt}{\ensuremath{\acute{\mathbf{r}}}} % The net return on the risky asset annual rate -\providecommand{\riskyshare}{\ensuremath{\varsigma}} -\providecommand{\Risky}{\ensuremath{\mathbf{R}}} % The return on the risky asset -\providecommand{\risky}{\ensuremath{\mathbf{r}}} % The net return on the risky asset annual rate -\providecommand{\RLev}{\ensuremath{R}} % Rate of return (but better to use \Rfree or \Risky or something else more specific -\providecommand{\RnormWGro}{\ensuremath{\mathcal{R}_{\WGro}}} % Normalized version of riskless return factor -\providecommand{\rnormwGro}{\ensuremath{\mathit{r}_{\wGro}}} % Normalized version of riskless rate of return -\providecommand{\Rnorm}{\ensuremath{\mathcal{R}}} % Normalized version of riskless return factor -\providecommand{\rnorm}{\ensuremath{\mathit{r}}} % Normalized version of riskless rate of return -\providecommand{\Rport}{\ensuremath{\pmb{\mathfrak{R}}}} % Portfolio -weighted return -\providecommand{\rport}{\ensuremath{\pmb{\mathfrak{r}}}} % Portfolio -weighted return -\providecommand{\rProd}{\ensuremath{\mathit{r}}} -\providecommand{\Rprod}{\ensuremath{\mathscr{R}}} -\providecommand{\rprod}{\ensuremath{\mathscr{r}}} -\providecommand{\RProd}{\ensuremath{\mathsf{R}}} -\providecommand{\RSave}{\ensuremath{\ushort{\Rfree}}} -\providecommand{\rsave}{\ensuremath{\ushort{\rfree}}} -\providecommand{\RBoro}{\ensuremath{\bar{\Rfree}}} -\providecommand{\rboro}{\ensuremath{\bar{\rfree}}} -\providecommand{\R}{\Rfree} -\providecommand{\saveRate}{\grave{s}} % saving (income minus consumption) over income -\providecommand{\Save}{S} % Saving (income minus consumption) -\providecommand{\save}{s} % saving (income minus consumption) -\providecommand{\SDF}{\MLev} % Stochastic Discount Factor -\providecommand{\sdr}{\mRat} % Stochastic Discount rate -\providecommand{\Seniority}{\ensuremath{\mathsf{X}}} -\providecommand{\seniority}{\ensuremath{\mathsf{x}}} -\providecommand{\SeverancePayment}{\ensuremath{\mathcal{S}}} -\providecommand{\SeveranceRatio}{\ensuremath{\varsigma}} -\providecommand{\Severance}{\ensuremath{\varsigma}} -\providecommand{\SE}{\SRat^{e}} -\providecommand{\sE}{\sRat^{e}} -\providecommand{\sFunc}{\ensuremath{\mathrm{s}}} -\providecommand{\ShkLogZeroLogStd}{\sigma_{\cancel{\ShkMeanOneLog}}} % Std of that shock -\providecommand{\ShkLogZeroLogVar}{\sigma_{\cancel{\ShkMeanOneLog}}^{2}} % Variance of that shock -\providecommand{\ShkLogZeroLog}{\cancel{\ShkMeanOneLog}} % Log of that shock -\providecommand{\ShkLogZero}{\cancel{\ShkMeanOne}} % A shock whose expectation in logs is zero; cancellation of the nonzero mean for the mean one shock -\providecommand{\ShkMeanOneLogStd}{\sigma_{\ShkMeanOneLog}} % Std of that shock -\providecommand{\ShkMeanOneLogVar}{\sigma^{2}_{\ShkMeanOneLog}} % Log of that shock -\providecommand{\ShkMeanOneLog}{\theta} % Log of that shock -\providecommand{\ShkMeanOne}{\Theta} % A shock whose expectation in levels is always equal to one regardless of variance -\providecommand{\SLevBF}{\mathbf{S}} -\providecommand{\sLevBF}{\mathbf{s}} -\providecommand{\SLevE}{\SLev^{e}} -\providecommand{\sLevE}{\sLev^{e}} -\providecommand{\SLevU}{\SLev^{u}} -\providecommand{\sLevU}{\sLev^{u}} -\providecommand{\SLev}{\ensuremath{S}} -\providecommand{\sLev}{\ensuremath{s}} -\providecommand{\srate}{\ensuremath{\varsigma}} -\providecommand{\SRatE}{\SRat^{e}} -\providecommand{\sRatE}{\sRat^{e}} -\providecommand{\SRatU}{\SRat^{u}} -\providecommand{\sRatU}{\sRat^{u}} -\providecommand{\SRat}{\ensuremath{S}} -\providecommand{\sRat}{\ensuremath{s}} -\providecommand{\STargE}{\Target{\SRat}^{\null}} -\providecommand{\sTargE}{\Target{\sRat}^{\null}} -\providecommand{\STargTarg}{\Target{\Target{\SRat}}} -\providecommand{\sTargTarg}{\Target{\Target{\sRat}}} -\providecommand{\STarg}{\Target{\SRat}} -\providecommand{\sTarg}{\Target{\sRat}} -\providecommand{\Steady}{\bar} -\providecommand{\Stocks}{{S}} -\providecommand{\stocks}{{s}} -\providecommand{\straight}{\Pi} -\providecommand{\Surplus}{\ensuremath{Z}} -\providecommand{\surplus}{\ensuremath{z}} -\providecommand{\SU}{\SRat^{u}} -\providecommand{\sU}{\sRat^{u}} -\providecommand{\Target}{\check} -\providecommand{\TaxCombInv}{\ensuremath{\mathcal{T}^{-1}}} -\providecommand{\TaxComb}{\ensuremath{\mathcal{T}}} -\providecommand{\TaxCorp}{\ensuremath{\tau}} -\providecommand{\taxDep}{\ensuremath{\partial}} -\providecommand{\TaxFree}{\ensuremath{\cancel{\Tax}}} -\providecommand{\TaxLev}{T} -\providecommand{\TaxNetTrans}{\ensuremath{Z}} -\providecommand{\taxNetTrans}{\ensuremath{z}} -\providecommand{\TaxPaid}{\ensuremath{T}} -\providecommand{\TaxRate}{\ensuremath{t}} -\providecommand{\TaxUI}{\ensuremath{\tau}} -\providecommand{\Tax}{\ensuremath{\tau}} -\providecommand{\tax}{t} -\providecommand{\tBak}{\ensuremath{\pmb{n}}} -\providecommand{\TEatBak}{\ensuremath{\mathtt{q}}} -\providecommand{\TEat}{\ensuremath{\TEnd}} -\providecommand{\TEndBak}{\ensuremath{\mathsf{p}}} -\providecommand{\TEnd}{\ensuremath{T}} -\providecommand{\TermTime}{\ensuremath{T}} -\providecommand{\tFwd}{\ensuremath{n}} -\providecommand{\tHorOfm}{\ensuremath{\pmb{n}}} -\providecommand{\tHor}{\ensuremath{\mathsf{n}}} -\providecommand{\timeRate}{\ensuremath{\vartheta}} -\providecommand{\tinyAmount}{\ensuremath{\epsilon}} -\providecommand{\TLev}{\ensuremath{T}} -\providecommand{\TMap}{\mathscr{T}} -\providecommand{\tNow}{\ensuremath{t}} -\providecommand{\tShkAll}{\xi} % -\providecommand{\tShkEmpMin}{\underline{\theta}} % -\providecommand{\TShkEmp}{\Theta} % -\providecommand{\tShkEmp}{\theta} % -\providecommand{\tShkEmp}{\theta} % -\providecommand{\tranShkInd}{\theta} % New -\providecommand{\TranShkAgg}{\Theta} % New -\providecommand{\TShk}{\Xi} % -\providecommand{\tShk}{\xi} % -\providecommand{\tshk}{\xi} % -\providecommand{\tSS}{\ensuremath{t}} -\providecommand{\tThen}{\ensuremath{\tau}} -\providecommand{\uFunc}{{\ensuremath{\mathrm{u}}}} -\providecommand{\uInvEpShkuInv}{\underline{\underline{\psi}}} -\providecommand{\ULev}{\ensuremath{U}} -\providecommand{\unins}{\ensuremath{\zeta}} -\providecommand{\uPDVFunc}{\ensuremath{\mathbb{U}}} -\providecommand{\uPmt}{\ensuremath{\mu}} -\providecommand{\uPPP}{{\ensuremath{\mathrm{u}^{\prime\prime\prime}}}} -\providecommand{\uPP}{{\ensuremath{\mathrm{u}^{\prime\prime}}}} -\providecommand{\uP}{{\ensuremath{\mathrm{u}^{\prime}}}} -\providecommand{\urate}{\ensuremath{\mho}} -\providecommand{\utilFunc}{\ensuremath{\mathrm{u}}} -\providecommand{\util}{\ensuremath{u}} -\providecommand{\ValAlt}{\ensuremath{\mathcal{V}}} % middle-of-period Value function -\providecommand{\valfn}{\ensuremath{\mathrm{v}}} % middle-of-period value function -\providecommand{\Value}{\ensuremath{\mathrm{V}}} % middle-of-period Value function -\providecommand{\VEndFunc}{\ensuremath{\mathfrak{V}}} -\providecommand{\vEndFunc}{\ensuremath{\mathfrak{v}}} -\providecommand{\VEnd}{\ensuremath{\mathfrak{V}}} % end-of-period Value function -\providecommand{\vEnd}{\ensuremath{\mathfrak{v}}} % end-of-period value function -\providecommand{\vEss}{\check{v}^{e}} -\providecommand{\VE}{{V}^{e}} -\providecommand{\vE}{{v}^{e}} -\providecommand{\vFirm}{\ensuremath{\mathrm{e}}} -\providecommand{\VFunc}{\ensuremath{\mathrm{V}}} -\providecommand{\vFunc}{\ensuremath{\mathrm{v}}} -\providecommand{\VInv}{\ensuremath{\Lambda}} -\providecommand{\vInv}{\ensuremath{\scriptstyle \Lambda \displaystyle}} -\providecommand{\vk}{\ensuremath{\lambda}} -\providecommand{\vLevBF}{\ensuremath{\mathbf{v}}} -\providecommand{\VLevFunc}{\ensuremath{\pmb{\mathrm{V}}}} -\providecommand{\vLevFunc}{\ensuremath{\pmb{\mathrm{v}}}} -\providecommand{\VLev}{\ensuremath{V}} -\providecommand{\vLev}{\ensuremath{v}} -\providecommand{\vNorm}{\ensuremath{\mathrm{w}}} % end-of-period value function -\providecommand{\VNum}{\ensuremath{V}} -\providecommand{\vNum}{\ensuremath{v}} -\providecommand{\vOptAlt}{\ensuremath{\Alt{\tilde{\mathfrak{v}}}}} -\providecommand{\vOpt}{\ensuremath{\tilde{\mathfrak{v}}}} -\providecommand{\VRat}{\ensuremath{V}} -\providecommand{\vRat}{\ensuremath{v}} -\providecommand{\vTarg}{\Target{\vRat}} -\providecommand{\VU}{{V}^{u}} -\providecommand{\vU}{{v}^{u}} -\providecommand{\Wage}{\ensuremath{\mathsf{W}}} -\providecommand{\wage}{\ensuremath{\mathsf{w}}} -\providecommand{\WAllLev}{\ensuremath{\mathbf{O}}} -\providecommand{\wAllLev}{\ensuremath{\mathbf{o}}} -\providecommand{\WAllRat}{\ensuremath{O}} -\providecommand{\wAllRat}{\ensuremath{o}} -\providecommand{\WAll}{{O}} -\providecommand{\wAll}{{o}} -\providecommand{\WBeg}{\KLev} % Wealth as of the beginning of the period (before R is received, not including Y) -\providecommand{\wBeg}{\kLev} % wealth as of the beginning of the period (before R is received, not including Y) -\providecommand{\Wealth}{\ensuremath{O}} -\providecommand{\wealth}{\ensuremath{o}} -\providecommand{\WEndRat}{\ARat} % -\providecommand{\wEndRat}{\aRat} % -\providecommand{\WEnd}{\ALev} % Wealth as of the end of the period (after C has been chosen) -\providecommand{\wEnd}{\aLev} % wealth as of the end of the period (after C has been taken) -\providecommand{\Wend}{\ARat} % Wealth as of the end of the period (after C has been chosen) -\providecommand{\wend}{\aRat} % wealth as of the end of the period (after C has been taken) -\providecommand{\wFunc}{\mathrm{w}} -\providecommand{\WGroPF}{\ensuremath{\mathsf{G}}} -\providecommand{\WGro}{\ensuremath{\mathsf{G}}} -\providecommand{\wGro}{\ensuremath{\mathsf{g}}} -\providecommand{\whumMin}{\underline{\hRat}} % human wealth -- individual -\providecommand{\WHum}{\HLev} % Human wealth -- aggregate -\providecommand{\wHum}{\hLev} % human wealth -- individual -\providecommand{\Whum}{\HRat} % Human wealth -- aggregate -\providecommand{\whum}{\hRat} % human wealth -- individual -\providecommand{\WLev}{\ensuremath{W}} -\providecommand{\wLev}{\pmb{w}} -\providecommand{\WMid}{\BLev} % Wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\WMid}{\BLev} % Wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\wMid}{\bLev} % wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\Wmid}{\BRat} % Wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\wmid}{\bRat} % wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\WMkt}{\MLev} % Wealth as of the middle of the period (after R is received, including Y) -\providecommand{\wMkt}{\mLev} % wealth as of the middle of the period (after R is received, including Y) -\providecommand{\wmkt}{\mLev} % wealth as of the middle of the period (after R is received, including Y) -\providecommand{\wNet}{\ensuremath{x}} % -\providecommand{\WNet}{\ensuremath{X}} % Total wealth -\providecommand{\WPre}{{K}} -\providecommand{\wPre}{{k}} -\providecommand{\wRat}{\ensuremath{o}} % -\providecommand{\WRat}{\ensuremath{O}} % Ratio to permanent income -\providecommand{\wTot}{\ensuremath{\mathbf{o}}} % -\providecommand{\WTot}{\ensuremath{\mathbf{O}}} % Total wealth -\providecommand{\wtot}{\ensuremath{o}} % -\providecommand{\Wtot}{\ensuremath{O}} % Total wealth -\providecommand{\xFer}{\chi} -\providecommand{\XFer}{X} -\providecommand{\xFunc}{\mathrm{x}} -\providecommand{\XLev}{\ensuremath{X}} -\providecommand{\xLev}{\ensuremath{x}} -\providecommand{\xpend}{\ensuremath{\xi}} -\providecommand{\XperGro}{\ensuremath{\mathsf{X}}} -\providecommand{\xperGro}{\ensuremath{\mathsf{x}}} -\providecommand{\XRat}{\ensuremath{X}} -\providecommand{\xRat}{\ensuremath{x}} -\providecommand{\yFunc}{\mathrm{y}} -\providecommand{\yLevBF}{\ensuremath{\mathbf{y}}} -\providecommand{\YLev}{\ensuremath{Y}} -\providecommand{\yLev}{\ensuremath{y}} -\providecommand{\YRat}{\ensuremath{Y}} -\providecommand{\yRat}{\ensuremath{y}} -\providecommand{\yTargE}{\ensuremath{\check{y}^{e}}} -\providecommand{\yTarg}{\ensuremath{\check{y}}} -\providecommand{\zAgg}{\ensuremath{\pmb{Z}}} -\providecommand{\zFunc}{\mathrm{z}} -\providecommand{\ZLevBF}{\ensuremath{\mathbf{Z}}} -\providecommand{\zLevBF}{\ensuremath{\mathbf{z}}} -\providecommand{\zLevBF}{\ensuremath{\pmb{z}}} -\providecommand{\ZLev}{\ensuremath{Z}} -\providecommand{\zLev}{\ensuremath{z}} -\providecommand{\ZRat}{\ensuremath{Z}} -\providecommand{\zRat}{\ensuremath{z}} - -\newcommand{\discRte}{\omega} -\newcommand{\DieFac}{\pDead} -\newcommand{\LivFac}{\Alive} -\newcommand{\PopFac}{\PopGro} -\newcommand{\popRte}{\popGro} - - -\providecommand{\NFALev}{\NLev} -\providecommand{\NFARat}{\NRat} -\providecommand{\NI}{\ensuremath{Z}} -\providecommand{\GDPLev}{\pmb{P}} -\providecommand{\GDPRat}{P} -\providecommand{\GDPGro}{\gimel} -\providecommand{\gdpLev}{\pmb{p}} -\providecommand{\gdpRat}{p} -\providecommand{\weight}{\omega} - - -\providecommand{\bi}{} -\renewcommand{\bi}{\begin{itemize}} -\providecommand{\ei}{} -\renewcommand{\ei}{\end{itemize}} -\providecommand{\reqd}{} -\renewcommand{\reqd}{\item[$^{*}$]} -\providecommand{\recm}{} -\renewcommand{\recm}{\item[\phantom{$^{*}$}]} - -\providecommand{\Severance}{\kappa} -\newcommand{\pd}[2]{\frac{\partial#1}{\partial#2}} - diff --git a/Documentation/HARKmanual/ContributingToHARK/index.html b/Documentation/HARKmanual/ContributingToHARK/index.html deleted file mode 100644 index 93e48d479..000000000 --- a/Documentation/HARKmanual/ContributingToHARK/index.html +++ /dev/null @@ -1,446 +0,0 @@ - - - - - - - - - - - - - -

(This is an excerpt of the full manual, available at http://econ-ark.org/HARK/Documentation/HARKmanual.pdf) -

0.1  Contributing to HARK

-

This section provides an overview of how users can contribute new code to HARK, what sort of -contributions are most desired, and style conventions for the toolKit. Before attempting to -extend HARK, we strongly recommend that you familiarize yourself with its features and models -by looking at the source code. -

-

0.1.1  What Does HARK Want?

-

The easiest and most straightforward way to contribute to HARK is by writing new general -purpose tools for inclusion in one of the top-level modules. This might be a commonly -known data analysis technique (e.g. a kernel density estimator) for HARK.utilities, an -optimization method (local or global) for HARK.estimation, an interpolation method for -HARKinterpolation, etc. If you’ve found a technique useful in your own research and -believe it could be of use to others on entirely different projects, then it probably -belongs in HARK. Likewise, if you read through a HARK module expecting to find a -certain tool or function (because of similar functions that are present, or merely because -it’s very useful) but don’t, this is a good sign that HARK has a hole that you can -fill.1 -

Secondly, the development team wants to expand HARK to include more canonical models. -Most of the models available in the toolKit at the time of its public beta release concern agents -with (geometrically discounted) CRRA utility over a unitary consumption good, who receive -permanent and transitory shocks to income and have access to a single risk free asset. This is an -important class of models, but that description has an awful lot of qualifiers. In short, there are -many more common features of dynamic household models that are not yet in HARK, but would -be very valuable for the toolKit. New model contributions should “play nicely” with existing -models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the -greatest extent possible. For ideas of potential model extensions for HARK, see section - - -??.2 -

Finally, contributions to HARK are not restricted to entirely new functionality. While we have -done our best to provide flexible frameworks and useful tools, we have almost certainly -overlooked an unexpected case that would not be (properly) handled by the toolKit as is. -Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake -or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as -an Issue on GitHub so that someone else might. The “mistake” you find might not -even be in the code itself, but a deficiency in the documentation: a misnamed (or -incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing) -comment, even a grammatical or spelling error. No matter how niggling, we want your -fix. -

For those who want to contribute to the HARK project without programming a new model or -digging through the code for errors and omissions, consider reading through the Issues page on -GitHub to see what other users have flagged for development. You might find several pieces of -“low hanging fruit” for you to quickly fix. -

0.1.2  Git and GitHub

-

Git is a distributed version control system that has gained increasing popularity in the decade -since its creation; it is designed to enable collaboration and “non-linear workflow” among a -(potentially disperse) programming team. As HARK is to be developed by volunteer researchers -all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds -as a command line tool, others have since extended git with further functionality and -a user interface more friendly to those more familiar with GUIs. Foremost among -these tools is GitHub.com, where the HARK project is hosted. This section provides a -brief overview of how to use GitHub to interact with (and potentially contribute to) -HARK. -

To prepare to contribute to HARK, follow these simple steps: -

    -
  1. Make a GitHub account: Go to github.com; the homepage should prompt - you to choose a username and password. Signing up is quick, easy, and - free.3 -
    2. -
  2. Install GitHub Desktop: Go to desktop.github.com and download the Desktop - - - application for your operating system, then install it. After opening Desktop for the - first time, enter your GitHub login to connect it to your account. -
    3. -
  3. Fork the HARK repository: Go to the HARK repository page, click on the Fork - button in the upper right, and choose to fork HARK to your own GitHub account. - This creates a new repository identical to the one hosted by Econ-Ark at the moment - you fork it, but hosted on your account instead. -
    4. -
  4. Add the repository to Desktop: Open GitHub Desktop, click on the + icon - in the upper left, and select clone. Find the repository named yourusername/HARK, - click the Clone button at the bottom of the dialogue window, and choose a directory - on your local machine.
-

You now control a “fork” of the main HARK repository, hosted by GitHub, and have a clone of -this fork on your local machine. You are free to edit your fork of HARK in any way you’d like; -other GitHub users can view your fork (and fork it themselves), but they cannot make changes to -your fork unless you give them permission by adding them as a contributor. Changes made -to the local copy of your fork do not get automatically sent to the remote copy on -GitHub for public viewing. To make changes to your fork on GitHub, follow these -steps: -

    -
  1. Make local changes: Edit your local copy of the repository in any way you’d like: - add or delete lines from existing files; add, delete, or rename entire files or directories. -
    2. -
  2. Commit local changes: Open the repository in GitHub Desktop and click on the - Changes tab. In the lower left, provide a short description of the changes made in - the Summary field, and (optionally) longer comments about these changes in the - Description field. Click the check-mark Commit button. -
    3. -
  3. Push the commit: Click on the Sync button near the upper right to “push” your - local commits to the remote server; this will also pull down remote commits from - other contributors to your fork that were made since the last time you synced.
-

The left hand panel of the Changes tab has a summary of the files that have been changed, -added, or removed since your last commit; the right hand panel shows the specific -changes made to each changed file. After step 2, a record of your changes has been -stored locally by the git system, but is not yet reflected in the public remote copy on - - -GitHub.4 -The “timeline” summary of the repository at the top of the Desktop window shows the history of -commits for the repository: each dot represents a commit that was pushed to the remote server, -and each open ring represents a local commit that has not been pushed to the remote server; -the large broken ring on the far right represents changes that have not been locally -committed.5 -The History tab lists all previous commits, including the timestamp, contributor, and a record of -changes made. -

Suppose you have mucked about in your fork for a while and have made changes to HARK -that you think should be included in the main repository hosted by Econ-Ark. To make your -dream a reality, follow these steps: -

    -
  1. Isolate the changes: If you mucked around for quite a while, making various changes - here and there, and then later made some contribution that you want to include in - the main repository, you should isolate the desired changes in a new branch of your - fork. Make a new branch by clicking the button toward the upper left of GitHub - Desktop, revert that branch to the commit just before you forked from the main - repository,6 - then edit this branch to include only the changes you want to push to the main - repository. -
    2. -
  2. Issue a pull request: Go to HARK’s GitHub page and click the “New pull request” - button. Click the text that says “compare across forks”, and select econ-ark/HARK - as the base fork, master as the base, your fork as the head fork, and the branch to - be merged as the “compare”. In the boxes below, give a name to your pull request - and provide sufficient comments about your contribution so that a development team - member can understand what you’ve done. Click the “Create pull request” button. -
    3. -
  3. Be patient: Someone from the development team will look at your contribution and - either merge it into the main repository or return it with comments. See section 0.1.3 - for procedures followed by the HARK team when evaluating outside contributions. -
- - -

0.1.3  Submission Approval Process

-

Before submitting to HARK, the development team would like you to make sure that -your intended contribution is listed as an Issue on the GitHub page. If you are going -to fix or address an Issue that is already posted, reply to that post with a note -indicating that you are about to work on it and an estimate of when you will make a -pull request for it. If you want to do something that no one has mentioned yet as -an Issue, make a new Issue on GitHub (briefly) describing what you intend to -do.7 -This will both help prevent two people from working on the same thing (and one of them -potentially wasting time on it), and lets the development team know what’s coming. Moreover, if -you propose a contribution that is (a) already in HARK, (b) not in the spirit of HARK, or (c) a -fix for something that’s not actually broken, this gives the team a chance to let you know not to -spend effort on it. -

After creating a pull request to submit your contribution, it will be evaluated by one or more -members of the HARK team. Criteria that the HARK team will consider when choosing whether -to accept a submission include: -

    -
  1. Does the contribution break previous code? If you change or expand an existing - function, you should make sure that this does not cause some models or applications - to work incorrectly. In the near future, HARK will have a core set of tests to run to - check for full compatibility. -
    2. -
  2. Is the code well documented? Code submissions should have properly formatted - docstrings for each new function, class, method, and module (see section 0.1.5) and - enough comments for a reader to follow the algorithm. -
    3. -
  3. Is the contribution relevant? The development team intends for HARK to - be diverse, but not everything goes in HARK. If your submission concerns a - representative agent model or a method for estimating a reduced form model, then - it probably doesn’t belong in HARK. -
    4. -
  4. Is the code demonstrated with an example? If your solve a new model in - HARK, you should include a set of example parameters for other users to easily - test. If you’ve contributed a new function, consider providing a snippet of code - - - demonstrating its use.8 -
    5. -
  5. Is the code correct? Your code should actually do what it claims to do. -
    6. -
  6. Does the code use HARK conventions? To the best of your ability, contributions - to HARK should follow the style guidelines in section 0.1.4. We don’t demand - perfection, as these are guidelines rather than rules. -
    7. -
  7. Is the code well integrated in HARK? In the beta release of HARK, we have - tried to present a “taxonomy” of consumption-saving models that build up from each - other. When possible, new models in HARK should try to continue this pattern. A - submission that introduces a model in a new module that could have easily been made - as an extension of an existing module might be rejected. However, we acknowledge - that there is value in providing a solver for a new model, and that a future contributor - might make her mark by integrating it into the existing hierarchy. -
    8. -
  8. Is this already in HARK? If your contribution is already in HARK, then it will - almost certainly be rejected. Of course, if you submit a version that dominates the - existing implementation (or even one that works better in a well described, non-trivial - set of circumstances), it belongs in HARK.
-

The HARK team intends to release more specific guidelines in the near future, so that contributors -are better able to judge their submissions before issuing a pull request. We are in the process of -recruiting “czars” to oversee particular aspects of HARK, providing their knowledge and -expertise.9 -While czars might directly evaluate submissions as a HARK team member, their more important -role is to help establish these guidelines for their domain, more specifically nailing down what we -mean by “correct” or “well integrated”. -

0.1.4  Naming Conventions

-

Object naming conventions in HARK are fairly different than existing standards, and differ -somewhat between tool modules vs model or application modules. The following conventions -apply throughout HARK: - - -

    -
  • Functions and methods are always in “camel case”: no underscores, first letter is lower - case, first letter of each subsequent word is capitalized. E.g. approxLognormal. -
    • -
  • Function and method names should accurately and concisely describe what the - function does; the first word in the name must be a verb.10 -
    • -
  • Variable and class names should not have a verb as their first word. -
    • -
  • Class names should use no underscores and capitalize the first letter of each word; - moreover, a class name must include a noun. E.g. ConsPerfForesightSolver.
-

When naming variables in model modules, the HARK team strongly discourages using single -letter names, like c for consumption. Instead, we encourage contributors to use longer, more -descriptive variable names using additional words and common abbreviations to specify the -variable more precisely. In /Documentation/NARK.pdf, we list standard single letter variable -“bases” and an array of prefixes and suffixes to adjust them. Economic variables in model -modules should (usually) not use underscores, instead using camel case to the greatest extent -possible.11 -The development team prefers this standard so that users can translate between Python code -and LaTeX script with minimal work. -

Conventions for naming variables in HARK’s tool modules are significantly closer to more -commonly used standards. Variable names should be in all lower case, with underscores between -words, e.g. data_to_match. The functions and classes in these modules are more general -and almost surely do not have any inherent “economic content”; they are numerical -or algorithmic objects, not variables that might appear in an equation in an article -for a (non-computational) economics journal. Variable names in application modules -(e.g. the files that execute the cstwMPC estimations) are a mix of the conventions for tool -and model files, as appropriate for each variable. That is, variables that are directly -related to “economic variables” in model modules should follow those conventions, while -objects created solely for data manipulation or reporting should use the style of tool -modules. -

0.1.5  Documentation Conventions

- - -

The HARK team wants the toolKit to be as accessible to users as possible; our greatest -fear12 is -that a new user will open up our code, get hopelessly confused trying to read it, and then never -look at HARK again. To prevent this tragic outcome, we have tried hard to provide -comprehensive, accurate documentation and comments within the code describing our -methods.13 -Moreover, HARK uses the Sphinx utility to generate a website with online documentation for all -of our tool and model modules, so that users can learn about what’s available in HARK without -digging through the source code. When making contributions to HARK, the development team -asks users to format their inline documentation to work with Sphinx by following a few simple -rules. -

    -
  • The top of every module should begin with a “docstring” providing a clear description - of the contents of the module. The first sentence should concisely summarize - the file, as it may appear in an index or summary of all modules without the - remaining sentences. A docstring at the top of a module should be formatted - as:14 -

    """ -

    Specifies an economic model and provides methods for solving it. More - specific description of the key features of the model and variations - of it in this module. Maybe some comments about the solution method - or limitations of the model. Your bank account routing number. -

    """ -

    • -
  • The line directly below the declaration of a function, method or class should begin a - docstring describing that object. As with modules, the first sentence should concisely - summarize the function or class, as it might be included in an index or summary. For - functions and methods, the docstring should be formatted as: -

    def functionName(input1,input2): -

       """ - - -

       Concise description of the function. More details about what -

       the function does, options or modes available, and maybe - mathematical -

       methods used. Credit to a source if you poached their - algorithm. -

       Parameters -

       –––––––––– -

       input1: type -

         Description of what input1 represents. -

       input2: type -

         Description of what input2 represents. -

       Returns -

       ––––––– -

       output_name: type -

         Description of the output(s) of the function. Might have -

         multiple entries. If no output, this is just "None". -

       """ -

    • -
  • Provide ample comments within a function or method so that a relatively intelligent - reader can follow along with your algorithm. Short comments can follow at the end - of a line, longer comments (or descriptions of the step or task about to be performed) - should precede a block of code on the line(s) above it.
-

Finally, if you write a new model module, the HARK team asks that you also provide a short -mathematical writeup of the model as a PDF. This document does not need to go into great -detail about the solution method for the model or the functions and classes included in -the module, merely specify the economic model and provide a summary of how it -is solved. See /Documentation/ConsumptionSavingModels.pdf for an example of -this. - - - - - - diff --git a/Documentation/HARKmanual/HARKmanual-Add.bib b/Documentation/HARKmanual/HARKmanual-Add.bib deleted file mode 100644 index e69de29bb..000000000 diff --git a/Documentation/HARKmanual/HARKmanual.4ct b/Documentation/HARKmanual/HARKmanual.4ct deleted file mode 100644 index b28c8a1e0..000000000 --- a/Documentation/HARKmanual/HARKmanual.4ct +++ /dev/null @@ -1,34 +0,0 @@ -\expandafter\ifx\csname doTocEntry\endcsname\relax \expandafter\endinput\fi -\doTocEntry\toclikechapter{}{\csname a:TocLink\endcsname{1}{x1-1000}{QQ2-1-1}{Contents}}{3}\relax -\doTocEntry\tocsection{0.1}{\csname a:TocLink\endcsname{1}{x1-20000.1}{QQ2-1-2}{Introduction}}{5}\relax -\doTocEntry\tocsubsection{0.1.1}{\csname a:TocLink\endcsname{1}{x1-30000.1.1}{QQ2-1-3}{Getting Started}}{6}\relax -\doTocEntry\tocsubsection{0.1.2}{\csname a:TocLink\endcsname{1}{x1-40000.1.2}{QQ2-1-4}{Structure of HARK}}{7}\relax 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Heterogeneous Agents Resources and toolKit - - - - - - - -

-

-

A User’s Guide for HARK:
-Heterogeneous Agents Resources and toolKit

-
May 21, 2018
-  -
Christopher D Carroll1 -
Johns Hopkins University
Alexander M Kaufman2 -
Consumer Financial Protection Bureau
David C Low3 -
Consumer Financial Protection Bureau
Nathan M Palmer4 -
U.S. Treasury Office of Financial Research
Matthew N White5 -
University of Delaware

-
-
-

-

PIC

-

- -

1 Carroll: http://econ.jhu.edu/people/ccarroll/, email: ccarroll@jhu.edu      2 -Kaufman: alexander.kaufman@cfpb.gov      3 Low: david.c.low@cfpb.gov      4 Palmer: -nathan.m.palmer@ofr.treasury.gov      5 White: mnwecon@udel.edu      - - - - -

-

-

Contents

-
-  0.1  Introduction -
  0.1.1  Getting Started -
  0.1.2  Structure of HARK -
  0.1.3  Other Resources -
 0.2  General Purpose Tools -
  0.2.1  HARKcore -
  0.2.2  HARK.utilities -
  0.2.3  HARK.interpolation -
  0.2.4  HARK.simulation -
  0.2.5  HARK.estimation -
  0.2.6  HARK.parallel -
 0.3  Microeconomics: the AgentType Class -
  0.3.1  Attributes of an AgentType -
  0.3.2  A Universal Solver -
  0.3.3  The Flow of Time and Other Methods -
  0.3.4  Sample Model: Perfect Foresight Consumption-Saving -
 0.4  Macroeconomics: the Market Class -
  0.4.1  Down on the Farm -
  0.4.2  Attributes of a Market -
  0.4.3  Sample Model: FashionVictim -
 0.5  Contributing to HARK -
  0.5.1  What Does HARK Want? -
  0.5.2  Git and GitHub -
  0.5.3  Submission Approval Process -
  0.5.4  Naming Conventions -
  0.5.5  Documentation Conventions -
 0.6  Future of HARK -
  0.6.1  Future Tools -
  0.6.2  Future Models -
  0.6.3  Bounty Hunting - - -
  0.6.4  All Aboard the Ark -
-

- - -

0.1  Introduction

-

If you are willing to risk some mild psychological trauma, conjure to mind your first experience of -hand-coding a structural economic model. Your clunky effort probably built on legacy code -provided by an adviser or colleague – which itself came from who-knows-what apocryphal -sources. Efforts to combine elements from one model with those from another were likely -frustrated by the “Tower of Babel” problem: Code from one source could not “speak” to code -from another without your own intermediation as a translator, possibly between two unfamiliar -languages and aided only by oracular comments that, at best, made sense only in the context of -other (now missing) code. -

After months of effort, you may have had the character-improving experience of proudly -explaining to your adviser that not only had you grafted two ideas together, you also found a -trick that speeded the solution by an order of magnitude, only to be told that your breathtaking -insight had been understood for many years, as reflected in an appendix to a 2008 paper; or, -worse, your discovery was something that “everybody knows” but did not exist at all in published -form! -

Learning by doing has value, but only within limits. We do not require young drivers to design -an internal combustion engine before driving a car, nor must graduate students write their own -matrix inversion algorithms before running an OLS regression. -

In recent years, considerable progress has been made in addressing these kinds of problems in -many areas of economic modeling. Macroeconomists using representative agent models can send -Dynare model files to each other; reduced form econometricians can choose from a host of -econometric packages. But modelers whose questions require explicit structural modeling -involving nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply be -aggregated away) are mostly still stuck in the bad old days. -

The ultimate goal of the HARK project is to fix these problems. Specifically, our aim is to -produce an open source repository of highly modular, easily interoperable code for -solving, simulating, and estimating dynamic economic models with heterogeneous -agents.2 -Further, we seek to establish (with input from the community) standards for the description and -specification of objects like discrete approximations to continuous distributions and interpolated -function approximations, so that numeric methods can be quickly swapped without ugly -“patching.” -

We hope that HARK will make it much easier and faster for researchers to develop solution -and estimation methods for new models. The open source nature of HARK will make -it easier for other researchers to audit and verify new models and methods, and to -collaborate on correcting deficiencies when found. As HARK expands to include more - - -canonical models and more tools and utilities, we can all spend less time managing -numerical minutiae and more time fretting about identification arguments and data -accuracy. -

0.1.1  Getting Started

-

If you want to get started using HARK right away, this section provides a very easy quickstart -guide for getting HARK up and running on your computer in just a few minutes. More -information can be found in the README.md file in the HARK repository (step 4), but here is a -quick version for those who want to jump right in: -

-

    -
  1. Download Anaconda: Go to https://www.anaconda.com/download/ and - download Anaconda for your operating system; be sure to get the version for Python - 2.7. -
    2. -
  2. Install Anaconda: Follow the easy instructions on that page to install Anaconda. -
    3. -
  3. Add extra packages: (optional) If you want to use HARK’s multithreading feature, - you need to add two packages that aren’t part of the default Anaconda distribution. - Simply open a command prompt and do conda install joblib and conda install - dill, accepting defaults to install. -
    4. -
  4. Download HARK: Go to https://github.com/econ-ark/HARK, the home - of the HARK repository. Click on the lime green button labeled “Clone or - download” toward the upper right of the main panel, and select “Download - ZIP”.3 -
    5. -
  5. Unzip HARK: Unpack the HARK.zip file using any archive utility, like Peazip. Put - the files anywhere you’d like, maintaining the internal directory structure. -
    6. -
  6. Run Spyder: Open a command prompt and do spyder. Spyder is an interactive - development environment (IDE) for iPython, a slightly prettier, more interactive - flavor of Python. - - -
    7. -
  7. Open a HARK module: Go to the directory where you put HARK and open any - file with the .py extension; we recommend - /Demos/ConsumptionSaving/ConsIndShockModel.py. -
    8. -
  8. Run the module: Click on the green arrow “play” button toward the right side of - the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue - box pops up). Congratulations! HARK is now up and running on your computer.
-

If you followed our recommendation to try /Demos/ConsumptionSaving/ConsIndShockModel.py, -you should see graphical representations of the solution to a few consumption-saving -models with idiosyncratic shocks to permanent and transitory income (see -/Documentation/ConsumptionSavingModels.pdf for details of these models). If you chose a -different module, you might get some examples of some other model, results of a structural -estimation, a polite notification that this particular module doesn’t do much on its own, or no -output at all. See the README.md for a full list of modules from which you can expect non-trivial -output. -

-

0.1.2  Structure of HARK

-

HARK is written in Python, an object-oriented programming (OOP) language that has -experienced increasing popularity in the scientific community in the past several years. A -significant reason for the adoption of Python is the numpy and scipy packages, which offer a -wide array of mathematical and statistical functions and tools; HARK makes liberal -use of these libraries. Python’s object-oriented nature allows models in HARK to -be easily extended: more complex models can inherit functions and methods from -more fundamental “parent” models, eliminating the need to reproduce or repurpose -code. -

As implied in the previous section, we strongly encourage HARK users to use the Anaconda -distribution of Python, which includes all commonly used mathematical and scientific packages, -an interactive development environment for iPython (Spyder), and a package manager that -allows users to quickly install or update packages not included in the default distribution -(conda). -

For users unfamiliar with OOP, we strongly encourage you to review the background material -on OOP provided by the good people at QuantEcon (for more on them, see below) at this link: -Object Oriented Programming. Unlike non-OOP languages, OOP bundles together -data and functions into objects. These can be accessed via: object_name.data and -object_name.method_name(), respectively. For organizational purposes, definitions of multiple -objects are stored in modules, which are simply files with a .py extension. Modules can be - - -accessed in Python via:
-

import module_name as import_name
-

This imports the module and gives it a local name of import_name. We can access a function -within this module by simply typing: import_name.function_name(). The following example -will illustrate the usage of these commands. CRRAutility is the function object for calculating -CRRA utility supplied by HARK.utilities module. CRRAutility is called attributes of the -module HARK.utilities. In order to calculate CRRA utility with a consumption of 1 and a -coefficient of risk aversion of 2 we run:
-

import HARK.utilities as Hutil -

Hutil.CRRAutility(1,2)
-

Python modules in HARK can generally be categorized into three types: tools, models, and -applications. Tool modules contain functions and classes with general purpose tools that have -no inherent “economic content”, but that can be used in many economic models as building -blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include -functions for data analysis (e.g. calculating Lorenz shares from data, or constructing a -non-parametric kernel regression), functions to create and manipulate discrete approximations to -continuous distributions, or classes for constructing interpolated approximations to -non-parametric functions. Tool modules generally reside in HARK’s root directory and have -names like HARK.simulation and HARK.interpolation; they do not necessarily do anything -when run. -

Model modules specify particular economic models, including classes to represent agents in -the model (and the “market structure” in which they interact) and functions for solving the -“one period problem” of those models. For example, ConsIndShockModel.py concerns -consumption-saving models in which agents have CRRA utility over consumption and face -idiosyncratic shocks to permanent and transitory income. The module includes classes for -representing “types” of consumers, along with functions for solving (several flavors of) the one -period consumption-saving problem. When run, model modules might demonstrate example -specifications of their models, filling in the model parameters with arbitrary values. When -ConsIndShockModel.py is run, it specifies an infinite horizon consumer with a particular -discount factor, permanent income growth rate, coefficient of relative risk aversion (etc), who -faces lognormal shocks to permanent and transitory income each period with a particular -standard deviation; it then solves this consumer’s problem and graphically displays the -results.4 -Model modules generally have Model in their name. - - -

Application modules use tool and model modules to solve, simulate, and/or estimate economic -models for a particular purpose. While tool modules have no particular economic content and -model modules describe entire classes of economic models, applications are uses of a model for -some research purpose. For example, /Demos/SolvingMicroDSOPs/StructEstimation.py uses a -consumption-saving model from ConsIndShockModel.py, calibrating it with age-dependent -sequences of permanent income growth, survival probabilities, and the standard deviation of -income shocks (etc); it then estimates the coefficient of relative risk aversion and shifter for -an age-varying sequence of discount factors that best fits simulated wealth profiles -to empirical data from the Survey of Consumer Finance. A particular application -might have multiple modules associated with it, all of which generally reside in one -directory. -

0.1.3  Other Resources

-

In the incredibly unlikely scenario in which this document does not fill all of the gaps in your -knowledge and answer any questions you might have while reading it, here is a collection of -potentially helpful other resources. -

-

-

0.2  General Purpose Tools

-

HARK’s root directory contains six tool -modules,5 -each containing a variety of functions and classes that can be used in many -economic models– or even for mathematical purposes that have nothing to do with -economics. Some of the tool modules are very sparely populated at this time, while -others are quite large. We expect that all of these modules will grow considerably -in the near future, as new tools are “low hanging fruit” for contribution to the -project.6 - - -

0.2.1  HARKcore

-

A key goal of the project is to create modularity and interoperability between models, making -them easy to combine, adapt, and extend. To this end, the HARK.core module specifies a -framework for economic models in HARK, creating a common structure for them on two levels -that can be called “microeconomic” and “macroeconomic”. -

Microeconomic models in HARK use the AgentType class to represent agents with an -intertemporal optimization problem. Each of these models specifies a subclass of AgentType; an -instance of the subclass represents agents who are ex-ante homogeneous– they have common -values for all parameters that describe the problem. For example, ConsIndShockModel specifies -the IndShockConsumerType class, which has methods specific to consumption-saving -models with idiosyncratic shocks to income; an instance of the class might represent all -consumers who have a CRRA of 3, discount factor of 0.98, etc. The AgentType class -has a solve method that acts as a “universal microeconomic solver” for any properly -formatted model, making it easier to set up a new model and to combine elements -from different models; the solver is intended to encompass any model that can be -framed as a sequence of one period problems. For a complete description, see section -0.3. -

Macroeconomic models in HARK use the Market class to represent a market (or other -aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual -agents in the model into aggregate outcomes that are “passed back” to the agents. For example, -the market in a consumption-saving model might combine the individual asset holdings of all -agents in the market to generate aggregate capital in the economy, yielding the interest rate on -assets (as the marginal product of capital); the individual agents then learn the aggregate capital -level and interest rate, conditioning their next action on this information. Objects that -microeconomic agents treat as exogenous when solving (or simulating) their model are thus -endogenous at the macroeconomic level. Like AgentType, the Market class also has a -solve method, which seeks out a dynamic general equilibrium: a “rule” governing the -dynamic evolution of macroeconomic objects such that if agents believe this rule and -act accordingly, then their collective actions generate a sequence of macroeconomic -outcomes that justify the belief in that rule. For a more complete description, see section -0.4. -

Beyond the model frameworks, HARK.core also defines a “supersuperclass” called HARKobject. -When solving a dynamic microeconomic model with an infinite horizon (or searching for a -dynamic general equilibrium), it is often required to consider whether two solutions -are sufficiently close to each other to warrant stopping the process (i.e. approximate -convergence). It is thus necessary to calculate the “distance” between two solutions, -so HARK specifies that classes should have a distance method that takes a single -input and returns a non-negative value representing the (generally dimensionless) -distance between the object in question and the input to the method. As a convenient -default, HARKobject provides a “universal distance metric” that should be useful in many - - -contexts.7 -When defining a new subclass of HARKobject, the user simply defines the attribute -distance_criteria as a list of strings naming the attributes of the class that should be -compared when calculating the distance between two instances of that class. For example, the -class ConsumerSolution has distance_criteria = [’cFunc’], indicating that only the -consumption function attribute of the solution matters when comparing the distance between -two instances of ConsumerSolution. See here for online documentation. -

0.2.2  HARK.utilities

-

The HARK.utilities module carries a double meaning in its name, as it contains both -utility functions (and their derivatives, inverses, and combinations thereof) in the -economic modeling sense as well as utilities in the sense of general tools. Utility functions -included at this time are constant relative risk aversion and constant absolute risk -aversion. Other functions in HARK.utilities include some data manipulation tools -(e.g. for calculating an average of data conditional on being within a percentile range -of different data), functions for constructing discrete state space grids, convenience -functions for retrieving information about functions, and basic plotting tools using -matplotlib.pyplot. -

The module also includes functions for constructing discrete approximations to continuous -distributions (e.g. approxLognormal() to approximate a log-normal distribution) as well as -manipulating these representations (e.g. appending one outcome to an existing distribution, or -combining independent univariate distributions into one multivariate distribution). As a -convention in HARK, continuous distributions are approximated as finite discrete distributions -when solving models; an N -dimensional random variable is formatted as a length N + 1 list of -1D arrays, with the first element representing event probabilities and all other elements are -realizations of the N component RVs. This both simplifies solution methods (reducing numeric -integrals to simple dot products) and allows users to easily test whether their chosen degree of -discretization yields a sufficient approximation to the full distribution. See here for online -documentation. -

-

0.2.3  HARK.interpolation

-

The HARK.interpolation module defines classes for representing interpolated function - - -approximations. Interpolation methods in HARK all inherit from a superclass such as -HARKinterpolator1D or HARKinterpolator2D, wrapper classes that ensures interoperability -across interpolation methods. For example, HARKinterpolator1D specifies the methods -__call__ and derivative to accept an arbitrary array as an input and return an identically -shaped array with the interpolated function evaluated at the values in the array or its first -derivative, respectively. However, these methods do little on their own, merely reshaping arrays -and referring to the _evaluate and _der methods, which are not actually defined in -HARKinterpolator1D. Each subclass of HARKinterpolator1D specifies their own implementation -of _evaluate and _der particular to that interpolation method, accepting and returning only 1D -arrays. In this way, subclasses of HARKinterpolator1D are easily interchangeable with each -other, as all methods that the user interacts with are identical, varying only by “internal” -methods. -

When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to -know the “distance” between functions generated by successive iterations of a solution procedure. -To this end, each interpolator class in HARK must define a distance method that takes as an -input another instance of the same class and returns a non-negative real number representing the -“distance” between the two. As each of the HARKinterpolatorXD classes inherits from -HARKobject, all interpolator classes have the default “universal” distance method; the user must -simply list the names of the relevant attributes in the attribute distance_criteria of the -class. -

Interpolation methods currently implemented in HARK include (multi)linear interpolation up -to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D -total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation -over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D “lower -envelope” interpolator. See here for online documentation. -

-

0.2.4  HARK.simulation

-

The HARK.simulation module provides tools for generating simulated data or shocks for -post-solution use of models. Currently implemented distributions include normal, lognormal, -Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use, -these tools are used in the consumption-saving models of ConsIndShockModel.py to simulate -permanent and transitory income shocks as well as unemployment events. See here for online -documentation. -

- - -

0.2.5  HARK.estimation

-

Methods for optimizing an objective function for the purposes of estimating a model can be -found in HARK.estimation. As of this writing, the implementation includes only minimization by -the Nelder-Mead simplex method, minimization by a derivative-free Powell method -variant, and two small tools for resampling data (i.e. for a bootstrap); the minimizers -are merely convenience wrappers (with result reporting) for optimizers included in -scipy.optimize. Future functionality will include more robust global search methods, including -genetic algorithms, simulated annealing, and differential evolution. See here for full -documentation. -

-

0.2.6  HARK.parallel

-

By default, processes in Python are single-threaded, using only a single CPU core. The HARK.parallel -module provides basic tools for using multiple CPU cores simultaneously, with minimal -effort.8 -In particular, it provides the function multiThreadCommands, which takes two arguments: a list -of AgentTypes and a list of commands as strings; each command should be a method of the -AgentTypes. The function simply distributes the AgentTypes across threads on different cores -and executes each command in order, returning no output (the AgentTypes themselves are -changed by running the commands). Equivalent results would be achieved by simply looping over -each type and running each method in the list. Indeed, HARK.parallel also has a -function called multiThreadCommandsFake that does just that, with identical syntax to -multiThreadCommands; multithreading in HARK can thus be easily turned on and -off.9 -The module also has functions for a parallel implementation of the Nelder-Mead simplex -algorithm, as described in Wiswall and Lee (2011). See here for full documentation. -

0.3  Microeconomics: the AgentType Class

-

The core of our microeconomic dynamic optimization framework is a flexible object-oriented -representation of economic agents. The HARK.core module defines a superclass called AgentType; - - -each model defines a subclass of AgentType, specifying additional model-specific features and -methods while inheriting the methods of the superclass. Most importantly, the method solve -acts as a “universal solver” applicable to any (properly formatted) discrete time model. This -section describes the format of an instance of AgentType as it defines a dynamic microeconomic -problem;10 -

0.3.1  Attributes of an AgentType

-

A discrete time model in our framework is characterized by a sequence of “periods” that the -agent will experience. A well-formed instance of AgentType includes the following -attributes: -

    -
  • solveOnePeriod: A function, or a list of functions, representing the solution - method for a single period of the agent’s problem. The inputs passed to a - solveOnePeriod function include all data that characterize the agent’s problem in - that period, including the solution to the subsequent period’s problem (designated - as solution_next). The output of these functions is a single Solution object, which - can be passed to the solver for the previous period. -
    • -
  • time_inv: A list of strings containing all of the variable names that are passed to at - least one function in solveOnePeriod but do not vary across periods. Each of these - variables resides in a correspondingly named attribute of the AgentType instance. -
    • -
  • time_vary: A list of strings naming the attributes of this instance that vary - across periods. Each of these attributes is a list of period-specific values, which - should be of the same length. If the solution method varies across periods, then - ’solveOnePeriod’ is an element of time_vary.11 -
    • -
  • solution_terminal: An object representing the solution to the “terminal” period - of the model. This might represent a known trivial solution that does not require - numeric methods, the solution to some previously solved “next phase” of the model, - a scrap value function, or an initial guess of the solution to an infinite horizon model. - - -
    • -
  • pseudo_terminal: A Boolean flag indicating that solution_terminal is not a - proper terminal period solution (rather an initial guess, “next phase” solution, or - scrap value) and should not be reported as part of the model’s solution. -
    • -
  • cycles: A non-negative integer indicating the number of times the agent will - experience the sequence of periods in the problem. For example, cycles = 1 means - that the sequence of periods is analogous to a lifecycle model, experienced once from - beginning to end; cycles = 2 means that the agent experiences the sequence twice, - with the first period in the sequence following the last. An infinite horizon problem - in which the sequence of periods repeats indefinitely is indicated with cycles = 0. -
    • -
  • tolerance: A positive real number indicating convergence tolerance, representing - the maximum acceptable “distance” between successive cycle solutions in an infinite - horizon model; it is irrelevant when cycles > 0. As the distance metric on the space - of solutions is model-specific, the value of tolerance is generally dimensionless. -
    • -
  • time_flow: A Boolean flag indicating the direction that time is “flowing.” When - True, the variables listed in time_vary are listed in ordinary chronological order, - with index 0 being the first period; when False, these lists are in reverse chronological - order, with index 0 holding the last period.
-

An instance of AgentType also has the attributes named in time_vary and time_inv, and -may have other attributes that are not included in either (e.g. values not used in the model -solution, but instead to construct objects used in the solution). -

-

0.3.2  A Universal Solver

-

When an instance of AgentType invokes its solve method, the solution to the agent’s problem is -stored in the attribute solution. The solution is computed by recursively solving the sequence of -periods defined by the variables listed in time_vary and time_inv using the functions in -solveOnePeriod. The time-varying inputs are updated each period, including the -successive period’s solution as solution_next; the same values of time invariant inputs in -time_inv are passed to the solver in every period. The first call to solveOnePeriod uses -solution_terminal as solution_next. In a finite horizon problem, the sequence of -periods is solved cycles times over; in an infinite horizon problem, the sequence of -periods is solved until the solutions of successive cycles have a “distance” of less than -tolerance. - - -

The output from a function in solveOnePeriod is an instance of a model-specific solution -class. The attributes of a solution to one period of a problem might include behavioral functions, -(marginal) value functions, and other variables characterizing the result. Each solution class must -have a method called distance(), which returns the “distance” between itself and another -instance of the same solution class, so as to define convergence as a stopping criterion; for many -models, this will be the “distance” between a policy or value function in the solutions. If the -solution class is defined as a subclass of HARKobject, it automatically inherits the default -distance method, so that the user must only list the relevant object attributes in -distance_criteria. -

The AgentType also has methods named preSolve and postSolve, both of which take no -arguments and do absolutely nothing. A subclass of AgentType can overwrite these blank -methods with its own model specific methods. preSolve is automatically called near the -beginning of the solve method, before solving the sequence of periods. It is used for specifying -tasks that should be done before solving the sequence of periods, such as pre-constructing some -objects repeatedly used by the solution method or finding an analytical terminal period solution. -For example, the IndShockConsumerType class in ConsIndShockModel has a preSolve method -that calls its updateSolutionTerminal method to ensure that solution_terminal is -consistent with the model parameters. The postSolve method is called shortly after the -sequence of periods is fully solved; it can be used for “post-processing” of the solution or -performing a step that is only useful after solution convergence. For example, the -TractableConsumerType in TractableBufferStockModel has a postSolve method that -constructs an interpolated consumption function from the list of stable arm points found during -solution. -

Our universal solver is written in a very general way that should be applicable to any discrete -time optimization problem– because Python is so flexible in defining objects, the time-varying -inputs for each period can take any form. Indeed, the solver does no “real work” itself, but merely -provides a structure for describing models in the HARK framework, allowing interoperability -among current and future modules. -

-

0.3.3  The Flow of Time and Other Methods

-

Because dynamic optimization problems are solved recursively in our framework, it is natural to -list time-varying values in reverse chronological order– the solve() method loops over the -values in each time-varying list in the same direction that a human would read them. -When simulating agents after the solution has been obtained, however, it is much more -convenient for time-varying parameters to be listed in ordinary chronological order– -the direction in which they will be experienced by simulated agents. To allow the -user to set the order in which “time is flowing” for an instance of AgentType, the -HARK framework includes functionality to easily change ordering of time-varying - - -values. -

The attribute time_flow is True if variables are listed in ordinary chronological -order and False otherwise. AgentType has the following methods for manipulating -time: -

    -
  • timeReport(): Prints to screen a description of the direction that time is flowing, - for interactive convenience and as a reminder of the functionality. -
    • -
  • timeFlip(): Flips the direction of time. Each attribute listed in time_vary is - reversed in place, and the value of time_flow is toggled. -
    • -
  • timeFwd(): Sets the direction of time to ordinary chronological order. -
    • -
  • timeRev(): Sets the direction of time to reverse chronological order.
-

These methods are invoked to more conveniently access time-varying objects. When a new -time-varying attribute is added, its name should be appended to time_vary, particularly if its -values are used in the solution of the model (or is part of the solution itself). For example, the -solve() method automatically adds the string ’solution’ to time_vary if it is not already -present. Note that attributes listed in time_vary must be lists if solve() or timeFlip() -are used. Some values that could be considered “time varying” but are never used to -solve the model are more conveniently represented as a numpy.array object (e.g.  the -history of a state or control variable from a simulation); because the numpy.array -class does not have a reverse() method, these attributes should not be listed in -time_vary. -

The base AgentType is sparsely defined, as most “real” methods will be application-specific. -Two final methods bear mentioning. First, the __call__ method points to assignParameters(), -a convenience method for adding or adjusting attributes (inherited from HARKobject). This -method takes any number of keyword arguments, so that code can be parsimoniously -written as, for example, AgentInstance(attribute1 = value1, attribute2 = -value2). Using Python’s dictionary capabilities, many attributes can be conveniently -set with minimal code. Second, the resetRNG method simply resets the AgentType’s -random number generator (as the attribute RNG) using the value in the attribute -seed.12 -This method is useful for (inter alia) ensuring that the same underlying sequence of shocks is -used for every simulation run when a model is solved or estimated. - - -

0.3.4  Sample Model: Perfect Foresight Consumption-Saving

-

To provide a concrete example of how the AgentType class works, consider the very simple case -of a perfect foresight consumption-saving model. The agent has time-separable, additive CRRA -preferences over consumption, discounting future utility at a constant rate; he receives a -particular stream of labor income each period, and knows the interest rate on assets that he -holds from one period to the next. His decision about how much to consume in a particular -period can be expressed in Bellman form as: -

- Vt(Mt ) = max u(Ct) + β//Dt E[Vt+1(Mt+1 )], - Ct - At = Mt - Ct, - - Mt+1 = RAt + Yt+1, - Yt+1 = Γ t+1Yt, - C1- ρ - u(C ) = -----. - 1 - ρ
An -agent’s problem is thus characterized by values of ρ , R , and β plus sequences -of survival probabilities //Dt and income growth factors Γ t for t = 0,⋅⋅⋅,T . This -problem has an analytical solution for both the value function and the consumption -function. -

The ConsIndShockModel module defines the class PerfForesightConsumerType -as a subclass of AgentType and provides solver functions for several -variations of a consumption-saving model, including the perfect foresight -problem.13 -A HARK user could specify and solve a ten period perfect foresight model with the following -commands: -

MyConsumer = PerfForesightConsumerType(time_flow=True, cycles=1, -

CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98, -

LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90], -

PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02], -

Nagents=1000) -

MyConsumer.solve() -

The first line makes a new instance of ConsumerType, specifying that time is currently “flowing” - - -forward14 -and that the sequence of periods happens exactly once; the next three lines (all part of the same -command) set the time invariant (CRRA, Rfree, DiscFac) and time varying parameters (LivPrb, -PermGroFac). Note that dir(MyConsumer) shows all the attributes of MyConsumer. After running -the solve method, MyConsumer will have an attribute called solution, which will be a list with -eleven ConsumerSolution objects, representing the period-by-period solution to the -model.15 -Each ConsumerSolution has several attributes: -

    -
  • cFunc: Optimal consumption function for this period as a function of - (normalized) market resources m = M ∕Y - t t t . Can be called like any other function: - MyConsumer.solution[0].cFunc(5) returns consumption at mt = 5 . -
    • -
  • vFunc: Value function (over market resources mt ); can be evaluated like cFunc. -
    • -
  • mNrmMin: Minimum value of normalized market resources mt such that cFunc and - vFunc are defined. -
    • -
  • hNrm: Normalized human wealth– the PDV of future income (ignoring mortality) - divided by current period income Yt . -
    • -
  • MPC: The constant marginal propensity to consume (linear consumption function). -
-

The following command generates the plot for the perfect foresight consumption -function: -

mMin = MyConsumer.solution[0].mNrmMin -

plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10) -

To solve a version of this problem in which the sequence of ten periods happens -three times (yielding a solution attribute with thirty-one elements), the user can -do: - - -

MyConsumer(cycles = 3) -

MyConsumer.solve() -

To solve an infinite horizon problem in which the agent experiences the same one period -problem indefinitely, the user can do: -

OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02, -

DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000) -

OtherConsumer.solve() -

This instance is specified as being infinite horizon by setting cycles=0. Note that the -time-varying inputs are still specified as (one element) lists, even though they take on the same -value in every period; this is because an infinite horizon model might consist of a multi-period -sequence repeated indefinitely, rather than just one repeated period. The solution attribute of -OtherConsumer will be a list containing one instance of ConsumerSolution, representing the -solution to every period of the model. -

0.4  Macroeconomics: the Market Class

-

The modeling framework of AgentType is deemed “microeconomic” because it pertains only to -the dynamic optimization problem of agents, treating all inputs of the problem as -exogenously fixed. In what we label as “macroeconomic” models, some of the inputs -for the microeconomic models are endogenously determined by the collective states -and controls of agents in the model. In a dynamic general equilibrium, there must be -consistency between agents’ beliefs about these macroeconomic objects, their individual -behavior, and the realizations of the macroeconomic objects that result from individual -choices. -

The Market class in HARK.core provides a framework for such macroeconomic models, with a -solve method that searches for a dynamic general equilibrium. An instance of Market includes a -list of AgentTypes that compose the economy, a method for transforming microeconomic -outcomes (states, controls, and/or shocks) into macroeconomic outcomes, and a method for -interpreting a history or sequence of macroeconomic outcomes into a new “dynamic rule” for -agents to believe. Agents treat the dynamic rule as an input to their microeconomic problem, -conditioning their optimal policy functions on it. A dynamic general equilibrium is a fixed point -dynamic rule: when agents act optimally while believing the equilibrium rule, their -individual actions generate a macroeconomic history consistent with the equilibrium -rule. -

-

0.4.1  Down on the Farm

- - -

The Market class uses a farming metaphor to conceptualize the process for generating a -history of macroeconomic outcomes in a model. Suppose all AgentTypes in the economy -believe in some dynamic rule (i.e. the rule is stored as attributes of each AgentType, -which directly or indirectly enters their dynamic optimization problem), and that they -have each found the solution to their microeconomic model using their solve method. -Further, the macroeconomic and microeconomic states have been reset to some initial -orientation. -

To generate a history of macroeconomic outcomes, the Market repeatedly loops over the -following steps a set number of times: -

    -
  1. sow: Distribute the macroeconomic state variables to all AgentTypes in the market. -
    2. -
  2. cultivate: Each AgentType executes their marketAction method, likely - corresponding to simulating one period of the microeconomic model. -
    3. -
  3. reap: Microeconomic outcomes are gathered from each AgentType in the market. -
    4. -
  4. mill: Data gathered by reap is processed into new macroeconomic states according - to some “aggregate market process”. -
    5. -
  5. store: Relevant macroeconomic states are added to a running history of outcomes.
-

This procedure is conducted by the makeHistory method of Market as a subroutine of its solve -method. After making histories of the relevant macroeconomic variables, the market then -executes its calcDynamics function with the macroeconomic history as inputs, generating a new -dynamic rule to distribute to the AgentTypes in the market. The process then begins again, with -the agents solving their updated microeconomic models given the new dynamic rule; the solve -loop continues until the “distance” between successive dynamic rules is sufficiently -small. -

-

0.4.2  Attributes of a Market

-

To specify a complete instance of Market, the user should give it the following -attributes:16 - - -

    -
  • agents: A list of AgentTypes, representing the agents in the market. Each element in - agents represents an ex-ante heterogeneous type; each type could have many ex-post - heterogeneous agents. -
    • -
  • sow_vars: A list of strings naming variables that are output from the aggregate - market process, representing the macroeconomic outcomes. These variables will be - distributed to the agents in the sow step. -
    • -
  • reap_vars: A list of strings naming variables to be collected from the agents in the - reap step, to be used as inputs for the aggregate market process. -
    • -
  • const_vars: A list of strings naming variables used by the aggregate market process - that do not come from agents; they are constant or come from the Market itself. -
    • -
  • track_vars: A list of strings naming variables generated by the aggregate market - process that should be tracked as a history, to be used when calculating a new - dynamic rule. Usually a subset of sow_vars. -
    • -
  • dyn_vars: A list of strings naming the variables that constitute a dynamic rule. These - will be stored as attributes of the agents whenever a new rule is calculated. -
    • -
  • millRule: A function for the “aggregate market process”, transforming - microeconomic outcomes into macroeconomic outcomes. Its inputs are named in - reap_vars and const_vars, and it returns a single object with attributes named in - sow_vars and/or track_vars. Can be defined as a method of a subclass of Market. -
    • -
  • calcDynamics: A function that generates a new dynamic rule from a history of - macroeconomic outcomes. Its inputs are named in track_vars, and it returns a single - object with attributes named in dyn_vars. -
    • -
  • act_T: The number of times that the makeHistory method should execute the - “farming loop” when generating a new macroeconomic history. -
    • -
  • tolerance: The minimum acceptable “distance” between successive dynamic rules - produced by calcDynamics to constitute a sufficiently converged solution.
- - -

Further, each AgentType in agents must have two methods not necessary for microeconomic -models; neither takes any input (except self): -

    -
  • marketAction: The microeconomic process to be run in the cultivate step. Likely - uses the new macroeconomic outcomes named in sow_vars; should store new values - of relevant microeconomic outcomes in the attributes (of self) named in reap_vars. -
    • -
  • reset: Reset, initialize, or prepare for a new “farming loop” to generate a - macroeconomic history. Might reset its internal random number generator, set initial - state varibles, clear personal histories, etc.
-

When solving macroeconomic models in HARK, the user should also define classes to represent -the output from the aggregate market process in millRule and for the model-specific -dynamic rule. The latter should have a distance method to test for solution convergence; -if the class inherits from HARKobject, the user need only list relevant attributes in -distance_criteria. -

0.4.3  Sample Model: FashionVictim

-

To illustrate the Market class, consider a simple example in the emerging economic subfield of aesthemetrics, the -FashionVictimModel.17 18 -This module defines a subclass of AgentType called FashionVictimType. Each period, fashion -victims make a binary choice of style s : to dress as a jock (0) or punk (1). They receive utility -directly from the outfit they wear and as a function of the proportion of the population who just -wore the same style; they also pay switching costs (cpj ,cjp ) if they change styles rather than -keep the same as the previous period. Moreover, they receive an idiosyncratic type 1 extreme -value (T1EV) preference shock to each style in each period. Defining the population punk -proportion as p and the conformity utility function as f : [0,1] → ℝ , the current period utility -function is thus: -
-
- - -u(s ;s ,p) = s f(p )+ (1- s )f(1- p )+s U + (1- s )U - c s (1- s )- c (1 - s )s . - t t- 1 t t t t t t p t j pj t-1 t jp t-1 t -
-

-

Fashion victims are forward looking and discount future utility at a constant rate of -β per period. To simplify the analysis, we assume they believe that the population -punk proportion in the next period is a linear function of the punk proportion in the -current period, subject to a uniformly distributed shock. No restrictions are put on -the function f ; fashion victims might be conformists who like to dress the same -as others (f′(p) > 0 ) or hipsters who like to style themselves in the minority -(f′(p) < 0 ).19 -A fashion victim’s problem can be written in Bellman form as: -
-
- [ ] - -V(st-1,pt) = E smt∈a{x0,1}u (st;st-1,pt) + ηst + βE [V (st,pt+1)] , -
-

-
-
-pt+1 = apt + b + πt+1, πt+1 ~ U[- w,w ], η0,η1 ~ T 1EV. - - -
-

-

An instance of FashionVictimType is thus characterized by values of Up , Uj , cpj , cjp -and a function f , as well as beliefs about pt+1 as a function of pt (summarized -by slope a , intercept b , and uniform shock width w ). Given this information, -a FashionVictimType’s infinite horizon microeconomic model can be solved by -backward induction in a few lines; the “one period solver” is given by solveFashion. -However, while individual agents treat the dynamics of pt as exogenous, they are -in fact endogenously determined by the actions of all the fashion victims in the -market.20 -A dynamic general equilibrium of the “macroeconomic fashion model” is thus characterized by a -triple of (a,b,w) such that when fashion victims believe in this “punk evolution rule” and act -optimally, their collective fashion choices exhibit this same rule when the model is -simulated. -

The search for a dynamic general equilibrium is implemented in HARK’s Market class with the -following definitions: -

sow_vars = [’pNow’] (macroeconomic outcome is pt ) -

reap_vars = [’sNow’] (microeconomic outcomes are st for many agents) -

track_vars = [’pNow’] (must track history of pt ) -

dyn_vars = [’pNextSlope’,’pNextIntercept’,’pNextWidth’] (dynamic rule (a,b,w ) ) -

millRule = calcPunkProp (aggregate process: average the style choices of all agents) -

calcDynamics = calcFashionEvoFunc (calculate new (a,b,w) with autoregression of -pt ) -

act_T = 1000 (simulate 1000 periods of the fashion market) -

tolerance = 0.01 (terminate solution when (a,b,w ) changes by less than 0.01) -

The agents attribute has a list of 22 FashionVictimTypes, which vary in their values of Up -and U - j , and their f functions. The marketAction method of FashionVictimType -simulates one period of the microeconomic model: each agent receives style preference -shocks η0 and η1 , sees the current proportion of punks pt (sown to them as pNow), -and chooses which style to wear, storing it in the binary array sNow, an attribute of -self. -

The millRule for this market is extremely simple: it flattens the list of binary arrays of -individual style choices (gathered in the reap step) and averages them into a new value -of pt , to be tracked as a history and sown back to the agents to begin the cycle -again. Once a history of 1000 values of pt has been generated with the makeHistory -method, we can calculate a new dynamic fashion rule with calcFashionEvoFunc by - - -regressing p - t on p - t- 1 , approximating w as twice the standard deviation of prediction -errors.21 -The new fashion rule is an instance of the simple FashionEvoFunc class, whose only methods are -inherited from HARKobject. -

When the solve method is run, the solver successively solves each agent’s microeconomic -problem, runs the makeHistory method to generate a 1000 period history of pt , and calculates -a new punk evolution rule based on this history; the solver terminates when consecutive rules -differ by less than 0.01 in any dimension. -

0.5  Contributing to HARK

-

This section provides an overview of how users can contribute new code to HARK, what sort of -contributions are most desired, and style conventions for the toolKit. Before attempting to -extend HARK, we strongly recommend that you familiarize yourself with its features and models -by looking at the source code. -

-

0.5.1  What Does HARK Want?

-

The easiest and most straightforward way to contribute to HARK is by writing new general -purpose tools for inclusion in one of the top-level modules. This might be a commonly -known data analysis technique (e.g. a kernel density estimator) for HARK.utilities, an -optimization method (local or global) for HARK.estimation, an interpolation method for -HARKinterpolation, etc. If you’ve found a technique useful in your own research and -believe it could be of use to others on entirely different projects, then it probably -belongs in HARK. Likewise, if you read through a HARK module expecting to find a -certain tool or function (because of similar functions that are present, or merely because -it’s very useful) but don’t, this is a good sign that HARK has a hole that you can -fill.22 -

Secondly, the development team wants to expand HARK to include more canonical models. -Most of the models available in the toolKit at the time of its public beta release concern agents -with (geometrically discounted) CRRA utility over a unitary consumption good, who receive - - -permanent and transitory shocks to income and have access to a single risk free asset. This is an -important class of models, but that description has an awful lot of qualifiers. In short, there are -many more common features of dynamic household models that are not yet in HARK, but would -be very valuable for the toolKit. New model contributions should “play nicely” with existing -models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the -greatest extent possible. For ideas of potential model extensions for HARK, see section -0.6.2.23 -

Finally, contributions to HARK are not restricted to entirely new functionality. While we have -done our best to provide flexible frameworks and useful tools, we have almost certainly -overlooked an unexpected case that would not be (properly) handled by the toolKit as is. -Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake -or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as -an Issue on GitHub so that someone else might. The “mistake” you find might not -even be in the code itself, but a deficiency in the documentation: a misnamed (or -incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing) -comment, even a grammatical or spelling error. No matter how niggling, we want your -fix. -

For those who want to contribute to the HARK project without programming a new model or -digging through the code for errors and omissions, consider reading through the Issues page on -GitHub to see what other users have flagged for development. You might find several pieces of -“low hanging fruit” for you to quickly fix. -

0.5.2  Git and GitHub

-

Git is a distributed version control system that has gained increasing popularity in the decade -since its creation; it is designed to enable collaboration and “non-linear workflow” among a -(potentially disperse) programming team. As HARK is to be developed by volunteer researchers -all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds -as a command line tool, others have since extended git with further functionality and -a user interface more friendly to those more familiar with GUIs. Foremost among -these tools is GitHub.com, where the HARK project is hosted. This section provides a -brief overview of how to use GitHub to interact with (and potentially contribute to) -HARK. -

To prepare to contribute to HARK, follow these simple steps: -

    -
  1. Make a GitHub account: Go to github.com; the homepage should prompt - you to choose a username and password. Signing up is quick, easy, and - - - free.24 -
    2. -
  2. Install GitHub Desktop: Go to desktop.github.com and download the Desktop - application for your operating system, then install it. After opening Desktop for the - first time, enter your GitHub login to connect it to your account. -
    3. -
  3. Fork the HARK repository: Go to the HARK repository page, click on the Fork - button in the upper right, and choose to fork HARK to your own GitHub account. - This creates a new repository identical to the one hosted by Econ-Ark at the moment - you fork it, but hosted on your account instead. -
    4. -
  4. Add the repository to Desktop: Open GitHub Desktop, click on the + icon - in the upper left, and select clone. Find the repository named yourusername/HARK, - click the Clone button at the bottom of the dialogue window, and choose a directory - on your local machine.
-

You now control a “fork” of the main HARK repository, hosted by GitHub, and have a clone of -this fork on your local machine. You are free to edit your fork of HARK in any way you’d like; -other GitHub users can view your fork (and fork it themselves), but they cannot make changes to -your fork unless you give them permission by adding them as a contributor. Changes made -to the local copy of your fork do not get automatically sent to the remote copy on -GitHub for public viewing. To make changes to your fork on GitHub, follow these -steps: -

    -
  1. Make local changes: Edit your local copy of the repository in any way you’d like: - add or delete lines from existing files; add, delete, or rename entire files or directories. -
    2. -
  2. Commit local changes: Open the repository in GitHub Desktop and click on the - Changes tab. In the lower left, provide a short description of the changes made in - the Summary field, and (optionally) longer comments about these changes in the - Description field. Click the check-mark Commit button. -
    3. -
  3. Push the commit: Click on the Sync button near the upper right to “push” your - local commits to the remote server; this will also pull down remote commits from - other contributors to your fork that were made since the last time you synced.
- - -

The left hand panel of the Changes tab has a summary of the files that have been changed, -added, or removed since your last commit; the right hand panel shows the specific -changes made to each changed file. After step 2, a record of your changes has been -stored locally by the git system, but is not yet reflected in the public remote copy on -GitHub.25 -The “timeline” summary of the repository at the top of the Desktop window shows the history of -commits for the repository: each dot represents a commit that was pushed to the remote server, -and each open ring represents a local commit that has not been pushed to the remote server; -the large broken ring on the far right represents changes that have not been locally -committed.26 -The History tab lists all previous commits, including the timestamp, contributor, and a record of -changes made. -

Suppose you have mucked about in your fork for a while and have made changes to HARK -that you think should be included in the main repository hosted by Econ-Ark. To make your -dream a reality, follow these steps: -

    -
  1. Isolate the changes: If you mucked around for quite a while, making various changes - here and there, and then later made some contribution that you want to include in - the main repository, you should isolate the desired changes in a new branch of your - fork. Make a new branch by clicking the button toward the upper left of GitHub - Desktop, revert that branch to the commit just before you forked from the main - repository,27 - then edit this branch to include only the changes you want to push to the main - repository. -
    2. -
  2. Issue a pull request: Go to HARK’s GitHub page and click the “New pull request” - button. Click the text that says “compare across forks”, and select econ-ark/HARK - as the base fork, master as the base, your fork as the head fork, and the branch to - be merged as the “compare”. In the boxes below, give a name to your pull request - and provide sufficient comments about your contribution so that a development team - member can understand what you’ve done. Click the “Create pull request” button. -
    3. - - -
  3. Be patient: Someone from the development team will look at your contribution and - either merge it into the main repository or return it with comments. See section 0.5.3 - for procedures followed by the HARK team when evaluating outside contributions. -
-

0.5.3  Submission Approval Process

-

Before submitting to HARK, the development team would like you to make sure that -your intended contribution is listed as an Issue on the GitHub page. If you are going -to fix or address an Issue that is already posted, reply to that post with a note -indicating that you are about to work on it and an estimate of when you will make a -pull request for it. If you want to do something that no one has mentioned yet as -an Issue, make a new Issue on GitHub (briefly) describing what you intend to -do.28 -This will both help prevent two people from working on the same thing (and one of them -potentially wasting time on it), and lets the development team know what’s coming. Moreover, if -you propose a contribution that is (a) already in HARK, (b) not in the spirit of HARK, or (c) a -fix for something that’s not actually broken, this gives the team a chance to let you know not to -spend effort on it. -

After creating a pull request to submit your contribution, it will be evaluated by one or more -members of the HARK team. Criteria that the HARK team will consider when choosing whether -to accept a submission include: -

    -
  1. Does the contribution break previous code? If you change or expand an existing - function, you should make sure that this does not cause some models or applications - to work incorrectly. In the near future, HARK will have a core set of tests to run to - check for full compatibility. -
    2. -
  2. Is the code well documented? Code submissions should have properly formatted - docstrings for each new function, class, method, and module (see section 0.5.5) and - enough comments for a reader to follow the algorithm. -
    3. -
  3. Is the contribution relevant? The development team intends for HARK to - be diverse, but not everything goes in HARK. If your submission concerns a - representative agent model or a method for estimating a reduced form model, then - it probably doesn’t belong in HARK. - - -
    4. -
  4. Is the code demonstrated with an example? If your solve a new model in - HARK, you should include a set of example parameters for other users to easily - test. If you’ve contributed a new function, consider providing a snippet of code - demonstrating its use.29 -
    5. -
  5. Is the code correct? Your code should actually do what it claims to do. -
    6. -
  6. Does the code use HARK conventions? To the best of your ability, contributions - to HARK should follow the style guidelines in section 0.5.4. We don’t demand - perfection, as these are guidelines rather than rules. -
    7. -
  7. Is the code well integrated in HARK? In the beta release of HARK, we have - tried to present a “taxonomy” of consumption-saving models that build up from each - other. When possible, new models in HARK should try to continue this pattern. A - submission that introduces a model in a new module that could have easily been made - as an extension of an existing module might be rejected. However, we acknowledge - that there is value in providing a solver for a new model, and that a future contributor - might make her mark by integrating it into the existing hierarchy. -
    8. -
  8. Is this already in HARK? If your contribution is already in HARK, then it will - almost certainly be rejected. Of course, if you submit a version that dominates the - existing implementation (or even one that works better in a well described, non-trivial - set of circumstances), it belongs in HARK.
-

The HARK team intends to release more specific guidelines in the near future, so that contributors -are better able to judge their submissions before issuing a pull request. We are in the process of -recruiting “czars” to oversee particular aspects of HARK, providing their knowledge and -expertise.30 -While czars might directly evaluate submissions as a HARK team member, their more important -role is to help establish these guidelines for their domain, more specifically nailing down what we -mean by “correct” or “well integrated”. - - -

0.5.4  Naming Conventions

-

Object naming conventions in HARK are fairly different than existing standards, and differ -somewhat between tool modules vs model or application modules. The following conventions -apply throughout HARK: -

    -
  • Functions and methods are always in “camel case”: no underscores, first letter is lower - case, first letter of each subsequent word is capitalized. E.g. approxLognormal. -
    • -
  • Function and method names should accurately and concisely describe what the - function does; the first word in the name must be a verb.31 -
    • -
  • Variable and class names should not have a verb as their first word. -
    • -
  • Class names should use no underscores and capitalize the first letter of each word; - moreover, a class name must include a noun. E.g. ConsPerfForesightSolver.
-

When naming variables in model modules, the HARK team strongly discourages using single -letter names, like c for consumption. Instead, we encourage contributors to use longer, more -descriptive variable names using additional words and common abbreviations to specify the -variable more precisely. In /Documentation/NARK.pdf, we list standard single letter variable -“bases” and an array of prefixes and suffixes to adjust them. Economic variables in model -modules should (usually) not use underscores, instead using camel case to the greatest extent -possible.32 -The development team prefers this standard so that users can translate between Python code -and LaTeX script with minimal work. -

Conventions for naming variables in HARK’s tool modules are significantly closer to more -commonly used standards. Variable names should be in all lower case, with underscores between -words, e.g. data_to_match. The functions and classes in these modules are more general -and almost surely do not have any inherent “economic content”; they are numerical -or algorithmic objects, not variables that might appear in an equation in an article -for a (non-computational) economics journal. Variable names in application modules -(e.g. the files that execute the cstwMPC estimations) are a mix of the conventions for tool -and model files, as appropriate for each variable. That is, variables that are directly -related to “economic variables” in model modules should follow those conventions, while -objects created solely for data manipulation or reporting should use the style of tool -modules. - - -

0.5.5  Documentation Conventions

-

The HARK team wants the toolKit to be as accessible to users as possible; our greatest -fear33 is -that a new user will open up our code, get hopelessly confused trying to read it, and then never -look at HARK again. To prevent this tragic outcome, we have tried hard to provide -comprehensive, accurate documentation and comments within the code describing our -methods.34 -Moreover, HARK uses the Sphinx utility to generate a website with online documentation for all -of our tool and model modules, so that users can learn about what’s available in HARK without -digging through the source code. When making contributions to HARK, the development team -asks users to format their inline documentation to work with Sphinx by following a few simple -rules. -

    -
  • The top of every module should begin with a “docstring” providing a clear description - of the contents of the module. The first sentence should concisely summarize - the file, as it may appear in an index or summary of all modules without the - remaining sentences. A docstring at the top of a module should be formatted - as:35 -

    """ -

    Specifies an economic model and provides methods for solving it. More - specific description of the key features of the model and variations - of it in this module. Maybe some comments about the solution method - or limitations of the model. Your bank account routing number. -

    """ -

    • -
  • The line directly below the declaration of a function, method or class should begin a - docstring describing that object. As with modules, the first sentence should concisely - summarize the function or class, as it might be included in an index or summary. For - functions and methods, the docstring should be formatted as: -

    def functionName(input1,input2): - - -

       """ -

       Concise description of the function. More details about what -

       the function does, options or modes available, and maybe - mathematical -

       methods used. Credit to a source if you poached their - algorithm. -

       Parameters -

       –––––––––– -

       input1: type -

         Description of what input1 represents. -

       input2: type -

         Description of what input2 represents. -

       Returns -

       ––––––– -

       output_name: type -

         Description of the output(s) of the function. Might have -

         multiple entries. If no output, this is just "None". -

       """ -

    • -
  • Provide ample comments within a function or method so that a relatively intelligent - reader can follow along with your algorithm. Short comments can follow at the end - of a line, longer comments (or descriptions of the step or task about to be performed) - should precede a block of code on the line(s) above it.
-

Finally, if you write a new model module, the HARK team asks that you also provide a short -mathematical writeup of the model as a PDF. This document does not need to go into great -detail about the solution method for the model or the functions and classes included in -the module, merely specify the economic model and provide a summary of how it -is solved. See /Documentation/ConsumptionSavingModels.pdf for an example of -this. - - -

0.6  Future of HARK

-

HARK is in a state of perpetual incompleteness, always expanding to include more -tools and more models. As an open source project in the hands of many contributors -around the world, the HARK team cannot fully predict future features of the toolKit. -This section provides an overview of what we would like to see in the near future, as -well as potential mechanisms to encourage researchers to contribute their code to the -project. -

-

0.6.1  Future Tools

-

The current frameworks in HARK.core concern agents who have “isolated” dynamic -problems or interact with each other only through aggregate outcomes to which they -contribute atomically. We hope to eventually provide a framework for models in which -agents directly interact with each other; this may include a system for specifying the -state of networks and how connections are formed and broken. HARK.core might also -develop a framework more suited to industrial organization models and dynamic -games.36 -

The HARK.estimation module in particular is very sparsely populated, with its -functions mostly serving as wrappers for “off the shelf” optimization methods from -scipy.optimize. There are plenty of non-standard or more complex optimization -techniques available, including global and/or stochastic search methods. We believe -that additional optimizers are “low hanging fruit” as straightforward contributions -to HARK and thus will be added in the near future. Moreover, there are plenty of -distributions that have no approximator in HARK.utilities (or data generator in -HARK.simulation), and these functions are among the easiest for new contributors -to write. The data analysis tools in HARK.utilities only include functions that we -happened to use for our current research projects, and we expect that this will be the -mode of growth for future tools. There are some obvious holes that we believe will be -filled quite quickly; for example, we provide a rudimentary non-parametric kernel -regression function, but have not written a kernel density estimator to generate PDFs from -data. -

The classes currently in HARK.interpolation are all variations on linear (or -cubic) spline interpolation, and each is specifically written for a fixed number -of dimensions. As these methods use full tensor grids, they are highly impractical -for representing functions in higher dimensions, suffering greatly from the curse of - - -dimensionality.37 -We are quite confident that HARK.interpolation will soon include techniques more applicable -to high dimensional state spaces, including Smolnyak interpolation, other (adaptive) sparse grid -methods, and regression-based interpolation. For lower-dimensional spaces, HARK will also soon -provide Delaunay interpolation classes. -

The HARK.parallel module currently provides a simple interface for handling ex-ante -heterogeneity among agents. While useful for speeding up moderately-sized problems, the -libraries used are only able to access the CPU of the local machine running the code. In the very -near future, the HARK team will provide an interface (and example) for using Apache Spark, -which bills itself as a “fast and general engine for large-scale data processing”. Specifically, users -will be able to easily scale up their computing resources and run HARK code on an Amazon EC2 -session or other cluster. -

0.6.2  Future Models

-

The economics models currently included in HARK mostly concern consumption-saving problems -with constant relative risk aversion consumers who experience fully permanent and fully -transitory shocks to income. This is obviously a very small subset of all consumption-saving -models (which are themselves a small subset of all dynamic models economists might be -interested in). Consider some examples of variants, features, and extensions that are not included -in HARK, but could be in the future: -

    -
  • Consumers with other utility functions, such as CARA or expo-power. -
    • -
  • Consumers with time-inconsistent preferences, e.g. hyperbolic discounting. -
    • -
  • Income shocks that are neither fully transitory nor fully permanent. -
    • -
  • Consumers with a bequest motive or terminal utility function. -
    • -
  • Utility from leisure and endogenous labor supply. -
    • -
  • Investment in a durable consumption good such as housing. - - -
    • -
  • Exogenous shocks to expenses. -
    • -
  • Multiple consumption goods, as with endogenous medical care. -
    • -
  • Portfolio allocation between a risky and riskless asset. -
    • -
  • Job search intensity as a control variable. -
    • -
  • Endogenous health evolution and health investment.
-

The HARK team is currently developing a module for mixing discrete and continuous -choice models, in the style of Jorgensen, Rust, Iskhakov, and Schjerning (2015). Among -many other models, this framework will enable HARK users to easily develop features -like: -

    -
  • Labor supply on the extensive margin: the timing of retirement. -
    • -
  • Selection of job type or sector (by riskiness, specific human capital, etc). -
    • -
  • Choice of medical insurance contract. -
    • -
  • Decision to rent or own housing. -
    • -
  • Endogenous default on debt (with “macroeconomic” pricing of debt). -
    • -
  • Household formation, dissolution, and reproduction.
-

Finally, we hope to provide a generic solver for any discrete choice model with T1EV shocks. -The scope of HARK is limited only by what users choose to put in it. We hope that -within a year of its debut, HARK will include a significant number of canonical models, -providing more building blocks for future researchers to create novel combinations and new -extensions. - - -

-

0.6.3  Bounty Hunting

-

While HARK users are free to write and contribute any tools and models that they believe could -be helpful, building the toolKit in a “bottom up” style, the HARK team hopes to provide -mechanisms for targeted “top down” development. The team is working to secure grant funding for -HARK so as to offer “bounties” for particular models, tools, applications, or frameworks. Suppose -the team decides that the model in Important Paper X should be included as part of HARK; we -will issue a call for contributions to fill this hole, offering a modest prize of, say, $20,000 to the -programmer who submits the best implementation of the model, as judged by clearly stated -criteria.38 -The bounty would be announced as an Issue on HARK’s GitHub page, as well as in a potential -electronic digest of HARK news. -

Bounties might be placed on items as small as a single data analysis tool or as large as a -complex estimation of a large general equilibrium model, with prizes scaling accordingly. We -hasten to emphasize that we cannot guarantee funding for HARK, so the bounties (like -everything else in this section) are purely speculative. As economists, the HARK team -understands the incentive compatibility problems associated with asking researchers to -contribute significant amounts of their time, effort, and unique skills in service of a -tenuous concept like the Greater Good in exchange for even more tenuous Glory. We -acknowledge that economic researchers are also optimizing agents with their own preferences -over leisure and consumption, and agree that Greed is a valid mechanism to expand -HARK. -

0.6.4  All Aboard the Ark

-

As observant readers may have noticed, the HARK repository is hosted by a GitHub account -named Econ-Ark, with no explanation as to what this might mean. Looking a few years into the -future, the development team believes that HARK might be the first piece of a broader collection -of computational economics resources. HARK is open source but somewhat akin to an -exclusive club: contributions are required to adhere to standards and norms so as to foster -modularity and interoperability among models and tools. Consistent with its namesake ship, -the Econ-Ark has a significantly lower bar for admission, with a place on board for -everything. -

On one level, the Ark might serve as a warehouse for any and all quantitative models and -tools, for any economic purpose, in any subfield, and written in any language. Through -nearly unanimous feedback, the HARK team understands that many economists are -willing to share code that they have already written, but are reluctant to expend the - - -considerable effort to translate and format it for HARK. In the future, we will set up a -mechanism for code like this to be hosted on the Ark, with minimal requirements -imposed on its authors (and correspondingly minimal warranty for potential users). -Relevant projects on the Ark might become bounty targets for HARK, with users -incentivized to port the original code into Python and to meet HARK standards; -direct access to the original source code will significantly expedite the translation -process, and provide a suitable benchmark for testing and verifying the contribution to -HARK. -

On a different level, the Ark might also include cohesive toolkits like HARK, but geared -toward purposes other than heterogeneous agents macroeconomics models. For example, the -previously mentioned modules for networks among agents (and other agent-based modeling), -industrial organization and dynamic games, and traditional discrete choice models might become -their own toolkits, with some interoperability with HARK. In support of this, some of the tool -modules currently in HARK might be “promoted” and become universally shared across all -Econ-Ark toolkits. -

The dark secret of HARK is that it is named for heterogeneous agents macroeconomics, but -designed and written by structural microeconomists. The development team believes these -subfields to have considerable overlap in terms of methods and techniques, and that HARK -might not be noticed by half of its potential audience due to its name. 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a/Documentation/HARKmanual/HARKmanual.tex b/Documentation/HARKmanual/HARKmanual.tex deleted file mode 100644 index 50876f019..000000000 --- a/Documentation/HARKmanual/HARKmanual.tex +++ /dev/null @@ -1,670 +0,0 @@ -% !TeX spellcheck = en_GB - -\documentclass[12pt,titlepage,letterpaper]{econtex} -\usepackage{econtexSetup}\usepackage{econtexShortcuts} -\usepackage{graphicx} -\usepackage{hyperref} -\providecommand{\E}{\mathbb{E}} - -\newboolean{ContributingToHARKOnly} -\setboolean{ContributingToHARKOnly}{false} - -\begin{document} -\ifthenelse{\boolean{ContributingToHARKOnly}}{(This is an excerpt of the full manual, available at \url{http://econ-ark.org/HARK/Documentation/HARKmanual.pdf)}}{ % If ContributingToHARKOnly is true, do not include any text until that section -\title{\LARGE A User's Guide for HARK: \\ Heterogeneous Agents Resources and toolKit} - -\date{\normalsize{\today}} - -\author{ -{Christopher D Carroll\authNum} \\ {\small Johns Hopkins University} -\and -{Alexander M Kaufman\authNum} \\ {\small Consumer Financial Protection Bureau} -\and -{David C Low\authNum} \\ {\small Consumer Financial Protection Bureau} -\and -{Nathan M Palmer\authNum} \\ {\small U.S.\ Treasury Office of Financial Research} -\and -{Matthew N White\authNum} \\ {\small University of Delaware} -} - - -\begin{authorsinfo} -\name{ -Carroll: \url{http://econ.jhu.edu/people/ccarroll/}, email: \href{mailto:ccarroll@jhu.edu}{\texttt{ccarroll@jhu.edu}} -} -\name{ -Kaufman: \href{mailto:alexander.kaufman@cfpb.gov}{\texttt{alexander.kaufman@cfpb.gov}} -} -\name{ -Low: \href{mailto:david.c.low@cfpb.gov}{\texttt{david.c.low@cfpb.gov}} -} -\name{ -Palmer: \href{mailto:nathan.m.palmer@ofr.treasury.gov}{\texttt{nathan.m.palmer@ofr.treasury.gov}} -} -\name{ -White: \href{mailto:mnwecon@udel.edu}{\texttt{mnwecon@udel.edu}} -} -\end{authorsinfo} - -\thanks{The materials in this resource reflect work by the persons named herein and others, much of it accomplished at the CFPB. However, none of the materials here should be interpreted as reflecting official opinions of the CFPB or any employees of the CFPB. After appropriate technical review, these resources are expected to appear on the CFPB's official GitHub page. Views expressed in this presentation are those of the authors and not necessarily of the Office of Financial Research or other government organizations.} - -\maketitle - -\begin{center} - \ifdvi - \includegraphics[width=4in]{UserGuidePic.png} - \else - \includegraphics[width=2in]{UserGuidePic.pdf} - \fi -\end{center} - -\thispagestyle{empty} -\newpage - -\tableofcontents - -\newpage - -\section{Introduction}\label{sec:Introduction} - -If you are willing to risk some mild psychological trauma, conjure to mind your first experience of hand-coding a structural economic model. Your clunky effort probably built on legacy code provided by an adviser or colleague -- which itself came from who-knows-what apocryphal sources. Efforts to combine elements from one model with those from another were likely frustrated by the ``Tower of Babel'' problem: Code from one source could not ``speak'' to code from another without your own intermediation as a translator, possibly between two unfamiliar languages and aided only by oracular comments that, at best, made sense only in the context of other (now missing) code. - -After months of effort, you may have had the character-improving experience of proudly explaining to your adviser that not only had you grafted two ideas together, you also found a trick that speeded the solution by an order of magnitude, only to be told that your breathtaking insight had been understood for many years, as reflected in an appendix to a 2008 paper; or, worse, your discovery was something that ``everybody knows'' but did not exist at all in published form! - -Learning by doing has value, but only within limits. We do not require young drivers to design an internal combustion engine before driving a car, nor must graduate students write their own matrix inversion algorithms before running an OLS regression. - -In recent years, considerable progress has been made in addressing these kinds of problems in many areas of economic modeling. Macroeconomists using representative agent models can send Dynare model files to each other; reduced form econometricians can choose from a host of econometric packages. But modelers whose questions require explicit structural modeling involving nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply be aggregated away) are mostly still stuck in the bad old days. - -The ultimate goal of the HARK project is to fix these problems. Specifically, our aim is to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents.\footnote{By ``heterogeneous,'' we mean that agents might differ before anything in the model has ``happened'' (\textit{ex-ante} heterogeneity); and agents might experience different stochastic events during the model (\textit{ex-post} heterogeneity).} Further, we seek to establish (with input from the community) standards for the description and specification of objects like discrete approximations to continuous distributions and interpolated function approximations, so that numeric methods can be quickly swapped without ugly ``patching.'' - -We hope that HARK will make it much easier and faster for researchers to develop solution and estimation methods for new models. The open source nature of HARK will make it easier for other researchers to audit and verify new models and methods, and to collaborate on correcting deficiencies when found. As HARK expands to include more canonical models and more tools and utilities, we can all spend less time managing numerical minutiae and more time fretting about identification arguments and data accuracy. - -\subsection{Getting Started}\label{sec:GettingStarted} - -If you want to get started using HARK right away, this section provides a very easy quickstart guide for getting HARK up and running on your computer in just a few minutes. More information can be found in the \texttt{README.md} file in the HARK repository (step 4), but here is a quick version for those who want to jump right in: - -\begin{enumerate} -\item \textbf{Download Anaconda:} Go to \href{https://www.anaconda.com/download/}{https://www.anaconda.com/download/} and download Anaconda for your operating system; be sure to get the version for Python 2.7. - -\item \textbf{Install Anaconda:} Follow the easy instructions on that page to install Anaconda. - -\item \textbf{Add extra packages:} (optional) If you want to use HARK's multithreading feature, you need to add two packages that aren't part of the default Anaconda distribution. Simply open a command prompt and do \texttt{conda install joblib} and \texttt{conda install dill}, accepting defaults to install. - -\item \textbf{Download HARK:} Go to \href{https://github.com/econ-ark/HARK}{https://github.com/econ-ark/HARK}, the home of the HARK repository. Click on the lime green button labeled ``Clone or download'' toward the upper right of the main panel, and select ``Download ZIP''.\footnote{This is the fastest way to get HARK running on a computer. Later, if you want to make your own contributions to HARK by adding code (or fixing ours!), you'll need to clone the repository on your computer. See section \ref{sec:GitHub} for more.} - -\item \textbf{Unzip HARK:} Unpack the \texttt{HARK.zip} file using any archive utility, like \href{http://www.peazip.org/}{Peazip}. Put the files anywhere you'd like, maintaining the internal directory structure. - -\item \textbf{Run Spyder:} Open a command prompt and do \texttt{spyder}. Spyder is an interactive development environment (IDE) for iPython, a slightly prettier, more interactive flavor of Python. - -\item \textbf{Open a HARK module:} Go to the directory where you put HARK and open any file with the \texttt{.py} extension; we recommend \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}. - -\item \textbf{Run the module:} Click on the green arrow ``play'' button toward the right side of the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue box pops up). Congratulations! HARK is now up and running on your computer. -\end{enumerate} - -If you followed our recommendation to try \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}, you should see graphical representations of the solution to a few consumption-saving models with idiosyncratic shocks to permanent and transitory income (see \texttt{/Documentation/ConsumptionSavingModels.pdf} for details of these models). If you chose a different module, you might get some examples of some other model, results of a structural estimation, a polite notification that this particular module doesn't do much on its own, or no output at all. See the \texttt{README.md} for a full list of modules from which you can expect non-trivial output. - -\subsection{Structure of HARK}\label{sec:StructureOfHARK} - -HARK is written in Python, an object-oriented programming (OOP) language that has experienced increasing popularity in the scientific community in the past several years. A significant reason for the adoption of Python is the \texttt{numpy} and \texttt{scipy} packages, which offer a wide array of mathematical and statistical functions and tools; HARK makes liberal use of these libraries. Python's object-oriented nature allows models in HARK to be easily extended: more complex models can inherit functions and methods from more fundamental ``parent'' models, eliminating the need to reproduce or repurpose code. - -As implied in the previous section, we strongly encourage HARK users to use the Anaconda distribution of Python, which includes all commonly used mathematical and scientific packages, an interactive development environment for iPython (Spyder), and a package manager that allows users to quickly install or update packages not included in the default distribution (conda). - -For users unfamiliar with OOP, we strongly encourage you to review the background material on OOP provided by the good people at \href{http://quant-econ.net/}{QuantEcon} (for more on them, see below) at this link: \href{http://quant-econ.net/py/python_oop.html}{Object Oriented Programming}. Unlike non-OOP languages, OOP bundles together data and functions into \textit{objects}. These can be accessed via: \texttt{object\_name.data} and \texttt{object\_name.method\_name()}, respectively. For organizational purposes, definitions of multiple objects are stored in \textit{modules}, which are simply files with a \texttt{.py} extension. Modules can be accessed in Python via:\\ - -\texttt{import module\_name as import\_name} -\\ - -\noindent -This imports the module and gives it a local name of \texttt{import\_name}. We can access a function within this module by simply typing: \texttt{import\_name.function\_name()}. The following example will illustrate the usage of these commands. \texttt{CRRAutility} is the function object for calculating CRRA utility supplied by \texttt{HARK.utilities} module. \texttt{CRRAutility} is called \textit{attributes} of the module \texttt{HARK.utilities}. In order to calculate CRRA utility with a consumption of 1 and a coefficient of risk aversion of 2 we run:\\ - -\texttt{import HARK.utilities as Hutil} - -\texttt{Hutil.CRRAutility(1,2)}\\ - -Python modules in HARK can generally be categorized into three types: tools, models, and applications. \textbf{Tool modules} contain functions and classes with general purpose tools that have no inherent ``economic content'', but that can be used in many economic models as building blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include functions for data analysis (e.g.\ calculating Lorenz shares from data, or constructing a non-parametric kernel regression), functions to create and manipulate discrete approximations to continuous distributions, or classes for constructing interpolated approximations to non-parametric functions. Tool modules generally reside in HARK's root directory and have names like \texttt{HARK.simulation} and \texttt{HARK.interpolation}; they do not necessarily do anything when run. - -\textbf{Model modules} specify particular economic models, including classes to represent agents in the model (and the ``market structure'' in which they interact) and functions for solving the ``one period problem'' of those models. For example, \texttt{ConsIndShockModel.py} concerns consumption-saving models in which agents have CRRA utility over consumption and face idiosyncratic shocks to permanent and transitory income. The module includes classes for representing ``types'' of consumers, along with functions for solving (several flavors of) the one period consumption-saving problem. When run, model modules might demonstrate example specifications of their models, filling in the model parameters with arbitrary values. When \texttt{ConsIndShockModel.py} is run, it specifies an infinite horizon consumer with a particular discount factor, permanent income growth rate, coefficient of relative risk aversion (etc), who faces lognormal shocks to permanent and transitory income each period with a particular standard deviation; it then solves this consumer's problem and graphically displays the results.\footnote{Running \texttt{ConsIndShockModel.py} also demonstrates other variations of the consumption-saving problem, but their description is omitted here for brevity.} Model modules generally have \texttt{Model} in their name. - -\textbf{Application modules} use tool and model modules to solve, simulate, and/or estimate economic models \textit{for a particular purpose}. While tool modules have no particular economic content and model modules describe entire classes of economic models, applications are uses of a model for some research purpose. For example, \texttt{/Demos/SolvingMicroDSOPs/StructEstimation.py} uses a consumption-saving model from \texttt{ConsIndShockModel.py}, calibrating it with age-dependent sequences of permanent income growth, survival probabilities, and the standard deviation of income shocks (etc); it then estimates the coefficient of relative risk aversion and shifter for an age-varying sequence of discount factors that best fits simulated wealth profiles to empirical data from the Survey of Consumer Finance. A particular application might have multiple modules associated with it, all of which generally reside in one directory. - -\subsection{Other Resources}\label{sec:OtherResources} - -In the incredibly unlikely scenario in which this document does not fill all of the gaps in your knowledge and answer any questions you might have while reading it, here is a collection of potentially helpful other resources. - -\begin{itemize} - -\item \href{https://econ-ark.github.io/HARK/}{Online documentation} for HARK tool and model modules, produced using Sphinx. - -\item A \href{https://docs.python.org/2.7/tutorial/}{tutorial on Python 2.7}, straight from the source. - -\item \href{https://en.wikipedia.org/wiki/Object-oriented_programming}{Wikipedia article on object-oriented programming}, not the subject of an edit war. - -\item \href{http://quant-econ.net/}{QuantEcon}, a collection of lectures on quantitative economic modeling from a couple of economists, which definitely in no way influenced HARK whatsoever. - -\item A \href{https://www.atlassian.com/git/tutorials/}{tutorial on git}, the repository management and tracking system used by the HARK project, created by Linus Torvalds. - -\item A \href{https://guides.github.com/activities/hello-world/}{tutorial on GitHub}, a website that provides a useful framework for git that makes it significantly more user-friendly, despised by Linus Torvalds. - -\item \href{https://en.wikipedia.org/wiki/Linus_Torvalds}{Wikipedia article on Linus Torvalds}, just in case. - -\item The \href{http://www.numpy.org/}{homepage for NumPy}, a numerical package extensively used by HARK. - -\item The \href{http://www.sphinx-doc.org/}{homepage for Sphinx}, a tool for producing HTML documentation. - -\item A \href{http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/}{collection of lecture notes} by Chris Carroll, to catch you up on the basics of consumption-saving models. - -\item A \href{http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/}{particular set of lecture notes} by Chris Carroll, explaining many of the numeric techniques used in HARK and offering an example of a simple structural model that, in a stunning coincidence, is implemented in HARK as an example. - -\item A \href{http://www.econ2.jhu.edu/people/ccarroll/BufferStockTheory.pdf}{deep dive} into the theoretical foundations of buffer stock saving. Warning: dense. - -\end{itemize} - - -\section{General Purpose Tools}\label{sec:GeneralPurposeTools} - -HARK's root directory contains six tool modules,\footnote{The ``taxonomy'' of these modules is in flux; the functions described here could be combined into fewer modules or further divided by purpose.} each containing a variety of functions and classes that can be used in many economic models-- or even for mathematical purposes that have nothing to do with economics. Some of the tool modules are very sparely populated at this time, while others are quite large. We expect that all of these modules will grow considerably in the near future, as new tools are ``low hanging fruit'' for contribution to the project.\footnote{That is, as the foundational, building-block elements of HARK, new tools are not difficult to program and do not require extensive integration with many moving parts.} - -\subsection{HARKcore}\label{sec:HARKcore} - -A key goal of the project is to create modularity and interoperability between models, making them easy to combine, adapt, and extend. To this end, the \texttt{HARK.core} module specifies a framework for economic models in HARK, creating a common structure for them on two levels that can be called ``microeconomic'' and ``macroeconomic''. - -Microeconomic models in HARK use the \texttt{AgentType} class to represent agents with an intertemporal optimization problem. Each of these models specifies a subclass of \texttt{AgentType}; an instance of the subclass represents agents who are \textit{ex-ante} homogeneous-- they have common values for all parameters that describe the problem. For example, \texttt{ConsIndShockModel} specifies the \texttt{IndShockConsumerType} class, which has methods specific to consumption-saving models with idiosyncratic shocks to income; an instance of the class might represent all consumers who have a CRRA of 3, discount factor of 0.98, etc. The \texttt{AgentType} class has a \texttt{solve} method that acts as a ``universal microeconomic solver'' for any properly formatted model, making it easier to set up a new model and to combine elements from different models; the solver is intended to encompass any model that can be framed as a sequence of one period problems. For a complete description, see section \ref{sec:Microeconomics}. - -Macroeconomic models in HARK use the \texttt{Market} class to represent a market (or other aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual agents in the model into aggregate outcomes that are ``passed back'' to the agents. For example, the market in a consumption-saving model might combine the individual asset holdings of all agents in the market to generate aggregate capital in the economy, yielding the interest rate on assets (as the marginal product of capital); the individual agents then learn the aggregate capital level and interest rate, conditioning their next action on this information. Objects that microeconomic agents treat as exogenous when solving (or simulating) their model are thus endogenous at the macroeconomic level. Like \texttt{AgentType}, the \texttt{Market} class also has a \texttt{solve} method, which seeks out a dynamic general equilibrium: a ``rule'' governing the dynamic evolution of macroeconomic objects such that if agents believe this rule and act accordingly, then their collective actions generate a sequence of macroeconomic outcomes that justify the belief in that rule. For a more complete description, see section \ref{sec:Macroeconomics}. - -Beyond the model frameworks, \texttt{HARK.core} also defines a ``supersuperclass'' called \texttt{HARKobject}. When solving a dynamic microeconomic model with an infinite horizon (or searching for a dynamic general equilibrium), it is often required to consider whether two solutions are sufficiently close to each other to warrant stopping the process (i.e.\ approximate convergence). It is thus necessary to calculate the ``distance'' between two solutions, so HARK specifies that classes should have a \texttt{distance} method that takes a single input and returns a non-negative value representing the (generally dimensionless) distance between the object in question and the input to the method. As a convenient default, \texttt{HARKobject} provides a ``universal distance metric'' that should be useful in many contexts.\footnote{Roughly speaking, the universal distance metric is a recursive supnorm, returning the largest distance between two instances, among attributes named in \texttt{distance\_criteria}. Those attributes might be complex objects themselves rather than real numbers, generating a recursive call to the universal distance metric.} When defining a new subclass of \texttt{HARKobject}, the user simply defines the attribute \texttt{distance\_criteria} as a list of strings naming the attributes of the class that should be compared when calculating the distance between two instances of that class. For example, the class \texttt{ConsumerSolution} has \texttt{distance\_criteria = ['cFunc']}, indicating that only the consumption function attribute of the solution matters when comparing the distance between two instances of \texttt{ConsumerSolution}. See \href{https://econ-ark.github.io/HARK/generated/HARK.core.html}{here} for online documentation. - -\subsection{HARK.utilities}\label{sec:HARKutilities} - -The \texttt{HARK.utilities} module carries a double meaning in its name, as it contains both utility functions (and their derivatives, inverses, and combinations thereof) in the economic modeling sense as well as utilities in the sense of general tools. Utility functions included at this time are constant relative risk aversion and constant absolute risk aversion. Other functions in \texttt{HARK.utilities} include some data manipulation tools (e.g.\ for calculating an average of data conditional on being within a percentile range of different data), functions for constructing discrete state space grids, convenience functions for retrieving information about functions, and basic plotting tools using \texttt{matplotlib.pyplot}. - -The module also includes functions for constructing discrete approximations to continuous distributions (e.g.\ \texttt{approxLognormal()} to approximate a log-normal distribution) as well as manipulating these representations (e.g.\ appending one outcome to an existing distribution, or combining independent univariate distributions into one multivariate distribution). As a convention in HARK, continuous distributions are approximated as finite discrete distributions when solving models; an $N$-dimensional random variable is formatted as a length $N+1$ list of 1D arrays, with the first element representing event probabilities and all other elements are realizations of the $N$ component RVs. This both simplifies solution methods (reducing numeric integrals to simple dot products) and allows users to easily test whether their chosen degree of discretization yields a sufficient approximation to the full distribution. See \href{https://econ-ark.github.io/HARK/generated/HARK.utilities.html}{here} for online documentation. - -\subsection{HARK.interpolation}\label{sec:HARKinterpolation} - -The \texttt{HARK.interpolation} module defines classes for representing interpolated function approximations. Interpolation methods in HARK all inherit from a superclass such as \texttt{HARKinterpolator1D} or \texttt{HARKinterpolator2D}, wrapper classes that ensures interoperability across interpolation methods. For example, \texttt{HARKinterpolator1D} specifies the methods \texttt{\_\_call\_\_} and \texttt{derivative} to accept an arbitrary array as an input and return an identically shaped array with the interpolated function evaluated at the values in the array or its first derivative, respectively. However, these methods do little on their own, merely reshaping arrays and referring to the \texttt{\_evaluate} and \texttt{\_der} methods, which are \textit{not actually defined in} \texttt{HARKinterpolator1D}. Each subclass of \texttt{HARKinterpolator1D} specifies their own implementation of \texttt{\_evaluate} and \texttt{\_der} particular to that interpolation method, accepting and returning only 1D arrays. In this way, subclasses of \texttt{HARKinterpolator1D} are easily interchangeable with each other, as all methods that the user interacts with are identical, varying only by ``internal'' methods. - -When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to know the ``distance'' between functions generated by successive iterations of a solution procedure. To this end, each interpolator class in HARK must define a \texttt{distance} method that takes as an input another instance of the same class and returns a non-negative real number representing the ``distance'' between the two. As each of the \texttt{HARKinterpolatorXD} classes inherits from \texttt{HARKobject}, all interpolator classes have the default ``universal'' distance method; the user must simply list the names of the relevant attributes in the attribute \texttt{distance\_criteria} of the class. - -Interpolation methods currently implemented in HARK include (multi)linear interpolation up to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D ``lower envelope'' interpolator. See \href{https://econ-ark.github.io/HARK/generated/HARKinterpolation.html}{here} for online documentation. - -\subsection{HARK.simulation}\label{sec:HARKsimulation} - -The \texttt{HARK.simulation} module provides tools for generating simulated data or shocks for post-solution use of models. Currently implemented distributions include normal, lognormal, Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use, these tools are used in the consumption-saving models of \texttt{ConsIndShockModel.py} to simulate permanent and transitory income shocks as well as unemployment events. See \href{https://econ-ark.github.io/HARK/generated/HARKsimulation.html}{here} for online documentation. - -\subsection{HARK.estimation}\label{sec:HARKestimation} - -Methods for optimizing an objective function for the purposes of estimating a model can be found in \texttt{HARK.estimation}. As of this writing, the implementation includes only minimization by the Nelder-Mead simplex method, minimization by a derivative-free Powell method variant, and two small tools for resampling data (i.e.\ for a bootstrap); the minimizers are merely convenience wrappers (with result reporting) for optimizers included in \texttt{scipy.optimize}. Future functionality will include more robust global search methods, including genetic algorithms, simulated annealing, and differential evolution. See \href{https://econ-ark.github.io/HARK/generated/HARKestimation.html}{here} for full documentation. - -\subsection{HARK.parallel}\label{sec:HARKparallel} - -By default, processes in Python are single-threaded, using only a single CPU core. The \texttt{HARK.parallel} module provides basic tools for using multiple CPU cores simultaneously, with minimal effort.\footnote{\texttt{HARK.parallel} uses two packages that aren't included in the default distribution of Anaconda: \texttt{joblib} and \texttt{dill}; see step 3 of the instructions in section \ref{sec:GettingStarted} for how to install them.} In particular, it provides the function \texttt{multiThreadCommands}, which takes two arguments: a list of \texttt{AgentType}s and a list of commands as strings; each command should be a method of the \texttt{AgentType}s. The function simply distributes the \texttt{AgentType}s across threads on different cores and executes each command in order, returning no output (the \texttt{AgentType}s themselves are changed by running the commands). Equivalent results would be achieved by simply looping over each type and running each method in the list. Indeed, \texttt{HARK.parallel} also has a function called \texttt{multiThreadCommandsFake} that does just that, with identical syntax to \texttt{multiThreadCommands}; multithreading in HARK can thus be easily turned on and off.\footnote{In the future, \texttt{HARK.parallel} might be absorbed into \texttt{HARK.core} and \texttt{HARK.estimation}, particularly if \texttt{joblib} and \texttt{dill} become part of the standard Anaconda distribution.} The module also has functions for a parallel implementation of the Nelder-Mead simplex algorithm, as described in Wiswall and Lee (2011). See \href{https://econ-ark.github.io/HARK/generated/HARK.parallel.html}{here} for full documentation. - -\section{Microeconomics: the AgentType Class}\label{sec:Microeconomics} - -The core of our microeconomic dynamic optimization framework is a flexible object-oriented representation of economic agents. The \texttt{HARK.core} module defines a superclass called \texttt{AgentType}; each model defines a subclass of \texttt{AgentType}, specifying additional model-specific features and methods while inheriting the methods of the superclass. Most importantly, the method \texttt{solve} acts as a ``universal solver'' applicable to any (properly formatted) discrete time model. This section describes the format of an instance of \texttt{AgentType} as it defines a dynamic microeconomic problem;\footnote{Each instance of \texttt{AgentType} represents an \textit{ex-ante} heterogeneous ``type'' of agent; \textit{ex-post} heterogeneity is achieved by simulating many agents of the same type, each of whom receives a unique sequence of shocks.} - -\subsection{Attributes of an AgentType}\label{sec:AttributesOfAnAgentType} - -A discrete time model in our framework is characterized by a sequence of ``periods'' that the agent will experience. A well-formed instance of \texttt{AgentType} includes the following attributes: -\begin{itemize} -\item \texttt{solveOnePeriod}: A function, or a list of functions, representing the solution method for a single period of the agent's problem. The inputs passed to a \texttt{solveOnePeriod} function include all data that characterize the agent's problem in that period, including the solution to the subsequent period's problem (designated as \texttt{solution\_next}). The output of these functions is a single \texttt{Solution} object, which can be passed to the solver for the previous period. - -\item \texttt{time\_inv}: A list of strings containing all of the variable names that are passed to at least one function in \texttt{solveOnePeriod} but do \textit{not} vary across periods. Each of these variables resides in a correspondingly named attribute of the \texttt{AgentType} instance. - -\item \texttt{time\_vary}: A list of strings naming the attributes of this instance that vary across periods. Each of these attributes is a list of period-specific values, which should be of the same length. If the solution method varies across periods, then \texttt{'solveOnePeriod'} is an element of \texttt{time\_vary}.\footnote{\texttt{time\_vary} may include attributes that are never used by a function in \texttt{solveOnePeriod}. Most saliently, the attribute \texttt{solution} is time-varying but is not used to solve individual periods.} - -\item \texttt{solution\_terminal}: An object representing the solution to the ``terminal'' period of the model. This might represent a known trivial solution that does not require numeric methods, the solution to some previously solved ``next phase'' of the model, a scrap value function, or an initial guess of the solution to an infinite horizon model. - -\item \texttt{pseudo\_terminal}: A Boolean flag indicating that \texttt{solution\_terminal} is not a proper terminal period solution (rather an initial guess, ``next phase'' solution, or scrap value) and should not be reported as part of the model's solution. - -\item \texttt{cycles}: A non-negative integer indicating the number of times the agent will experience the sequence of periods in the problem. For example, \texttt{cycles = 1} means that the sequence of periods is analogous to a lifecycle model, experienced once from beginning to end; \texttt{cycles = 2} means that the agent experiences the sequence twice, with the first period in the sequence following the last. An infinite horizon problem in which the sequence of periods repeats indefinitely is indicated with \texttt{cycles = 0}. - -\item \texttt{tolerance}: A positive real number indicating convergence tolerance, representing the maximum acceptable ``distance'' between successive cycle solutions in an infinite horizon model; it is irrelevant when \texttt{cycles > 0}. As the distance metric on the space of solutions is model-specific, the value of \texttt{tolerance} is generally dimensionless. - -\item \texttt{time\_flow}: A Boolean flag indicating the direction that time is ``flowing.'' When \texttt{True}, the variables listed in \texttt{time\_vary} are listed in ordinary chronological order, with index 0 being the first period; when \texttt{False}, these lists are in reverse chronological order, with index 0 holding the last period. -\end{itemize} - -An instance of \texttt{AgentType} also has the attributes named in \texttt{time\_vary} and \texttt{time\_inv}, and may have other attributes that are not included in either (e.g.\ values not used in the model solution, but instead to construct objects used in the solution). - -\subsection{A Universal Solver}\label{sec:UniversalSolver} - -When an instance of \texttt{AgentType} invokes its \texttt{solve} method, the solution to the agent's problem is stored in the attribute \texttt{solution}. The solution is computed by recursively solving the sequence of periods defined by the variables listed in \texttt{time\_vary} and \texttt{time\_inv} using the functions in \texttt{solveOnePeriod}. The time-varying inputs are updated each period, including the successive period's solution as \texttt{solution\_next}; the same values of time invariant inputs in \texttt{time\_inv} are passed to the solver in every period. The first call to \texttt{solveOnePeriod} uses \texttt{solution\_terminal} as \texttt{solution\_next}. In a finite horizon problem, the sequence of periods is solved \texttt{cycles} times over; in an infinite horizon problem, the sequence of periods is solved until the solutions of successive cycles have a ``distance'' of less than \texttt{tolerance}. - -The output from a function in \texttt{solveOnePeriod} is an instance of a model-specific solution class. The attributes of a solution to one period of a problem might include behavioral functions, (marginal) value functions, and other variables characterizing the result. Each solution class must have a method called \texttt{distance()}, which returns the ``distance'' between itself and another instance of the same solution class, so as to define convergence as a stopping criterion; for many models, this will be the ``distance'' between a policy or value function in the solutions. If the solution class is defined as a subclass of \texttt{HARKobject}, it automatically inherits the default \texttt{distance} method, so that the user must only list the relevant object attributes in \texttt{distance\_criteria}. - -The \texttt{AgentType} also has methods named \texttt{preSolve} and \texttt{postSolve}, both of which take no arguments and do absolutely nothing. A subclass of \texttt{AgentType} can overwrite these blank methods with its own model specific methods. \texttt{preSolve} is automatically called near the beginning of the \texttt{solve} method, before solving the sequence of periods. It is used for specifying tasks that should be done before solving the sequence of periods, such as pre-constructing some objects repeatedly used by the solution method or finding an analytical terminal period solution. For example, the \texttt{IndShockConsumerType} class in \texttt{ConsIndShockModel} has a \texttt{preSolve} method that calls its \texttt{updateSolutionTerminal} method to ensure that \texttt{solution\_terminal} is consistent with the model parameters. The \texttt{postSolve} method is called shortly after the sequence of periods is fully solved; it can be used for ``post-processing'' of the solution or performing a step that is only useful after solution convergence. For example, the \texttt{TractableConsumerType} in \texttt{TractableBufferStockModel} has a \texttt{postSolve} method that constructs an interpolated consumption function from the list of stable arm points found during solution. - -Our universal solver is written in a very general way that should be applicable to any discrete time optimization problem-- because Python is so flexible in defining objects, the time-varying inputs for each period can take any form. Indeed, the solver does no ``real work'' itself, but merely provides a structure for describing models in the HARK framework, allowing interoperability among current and future modules. - -\subsection{The Flow of Time and Other Methods}\label{sec:OtherMethods} - -Because dynamic optimization problems are solved recursively in our framework, it is natural to list time-varying values in reverse chronological order-- the \texttt{solve()} method loops over the values in each time-varying list in the same direction that a human would read them. When simulating agents after the solution has been obtained, however, it is much more convenient for time-varying parameters to be listed in ordinary chronological order-- the direction in which they will be experienced by simulated agents. To allow the user to set the order in which ``time is flowing'' for an instance of \texttt{AgentType}, the HARK framework includes functionality to easily change ordering of time-varying values. - -The attribute \texttt{time\_flow} is \texttt{True} if variables are listed in ordinary chronological order and \texttt{False} otherwise. \texttt{AgentType} has the following methods for manipulating time: -\begin{itemize} -\item \texttt{timeReport()}: Prints to screen a description of the direction that time is flowing, for interactive convenience and as a reminder of the functionality. - -\item \texttt{timeFlip()}: Flips the direction of time. Each attribute listed in \texttt{time\_vary} is reversed in place, and the value of \texttt{time\_flow} is toggled. - -\item \texttt{timeFwd()}: Sets the direction of time to ordinary chronological order. - -\item \texttt{timeRev()}: Sets the direction of time to reverse chronological order. -\end{itemize} - -These methods are invoked to more conveniently access time-varying objects. When a new time-varying attribute is added, its name should be appended to \texttt{time\_vary}, particularly if its values are used in the solution of the model (or is part of the solution itself). For example, the \texttt{solve()} method automatically adds the string \texttt{'solution'} to \texttt{time\_vary} if it is not already present. Note that attributes listed in \texttt{time\_vary} \textit{must} be lists if \texttt{solve()} or \texttt{timeFlip()} are used. Some values that could be considered ``time varying'' but are never used to solve the model are more conveniently represented as a \texttt{numpy.array} object (e.g.\ \ the history of a state or control variable from a simulation); because the \texttt{numpy.array} class does not have a \texttt{reverse()} method, these attributes should not be listed in \texttt{time\_vary}. - -The base \texttt{AgentType} is sparsely defined, as most ``real'' methods will be application-specific. Two final methods bear mentioning. First, the \texttt{\_\_call\_\_} method points to \texttt{assignParameters()}, a convenience method for adding or adjusting attributes (inherited from \texttt{HARKobject}). This method takes any number of keyword arguments, so that code can be parsimoniously written as, for example, \texttt{AgentInstance(attribute1 = value1, attribute2 = value2)}. Using Python's dictionary capabilities, many attributes can be conveniently set with minimal code. Second, the \texttt{resetRNG} method simply resets the \texttt{AgentType}'s random number generator (as the attribute \texttt{RNG}) using the value in the attribute \texttt{seed}.\footnote{Every instance of \texttt{AgentType} is created with a random number generator as an instance of the class \texttt{numpy.random.RandomState}, with a default \texttt{seed} of zero.} This method is useful for (\textit{inter alia}) ensuring that the same underlying sequence of shocks is used for every simulation run when a model is solved or estimated. - - -\subsection{Sample Model: Perfect Foresight Consumption-Saving}\label{sec:PerfectForesight} - -To provide a concrete example of how the \texttt{AgentType} class works, consider the very simple case of a perfect foresight consumption-saving model. The agent has time-separable, additive CRRA preferences over consumption, discounting future utility at a constant rate; he receives a particular stream of labor income each period, and knows the interest rate on assets that he holds from one period to the next. His decision about how much to consume in a particular period can be expressed in Bellman form as: - -\begin{eqnarray*} -V_t(M_t) &=& \max_{C_t} \utilFunc(C_t) + \beta \PLives_t \E [V_{t+1}(M_{t+1}) ], \\ -A_t &=& M_t - C_t, \\ -M_{t+1} &=& \Rfree A_t + Y_{t+1}, \\ -Y_{t+1} &=& \Gamma_{t+1} Y_t, \\ -\utilFunc(C) &=& \frac{C^{1-\CRRA}}{1-\CRRA}. -\end{eqnarray*} -An agent's problem is thus characterized by values of $\CRRA$, $\Rfree$, and $\beta$ plus sequences of survival probabilities $\PLives_t$ and income growth factors $\Gamma_t$ for $t=0,\cdots,T$. This problem has an analytical solution for both the value function and the consumption function. - -The \texttt{ConsIndShockModel} module defines the class \texttt{PerfForesightConsumerType} as a subclass of \texttt{AgentType} and provides solver functions for several variations of a consumption-saving model, including the perfect foresight problem.\footnote{As the name implies, the module is mostly concerned with models with idiosyncratic shocks to income.} A HARK user could specify and solve a ten period perfect foresight model with the following commands: - -\vspace{0.25cm} - -\noindent \texttt{MyConsumer = PerfForesightConsumerType(time\_flow=True, cycles=1,} - -\texttt{ CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98,} - -\texttt{ LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90],} - -\texttt{ PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02],} - -\texttt{ Nagents=1000)} - -\noindent \texttt{MyConsumer.solve()} - -\vspace{0.25cm} - -The first line makes a new instance of \texttt{ConsumerType}, specifying that time is currently ``flowing'' forward\footnote{This is relevant for the intepretation of the time-varying inputs \texttt{LivPrb} and \texttt{PermGroFac}.} and that the sequence of periods happens exactly once; the next three lines (all part of the same command) set the time invariant (\texttt{CRRA}, \texttt{Rfree}, \texttt{DiscFac}) and time varying parameters (\texttt{LivPrb}, \texttt{PermGroFac}). Note that \texttt{dir(MyConsumer)} shows all the attributes of \texttt{MyConsumer}. After running the \texttt{solve} method, \texttt{MyConsumer} will have an attribute called \texttt{solution}, which will be a list with eleven \texttt{ConsumerSolution} objects, representing the period-by-period solution to the model.\footnote{The last element of \texttt{solution} represents the terminal period solution, where the agent consumes all available resources because there is no future.} Each \texttt{ConsumerSolution} has several attributes: -\begin{itemize} -\item \texttt{cFunc}: Optimal consumption function for this period as a function of (normalized) market resources $m_t = M_t/Y_t$. Can be called like any other function: \texttt{MyConsumer.solution[0].cFunc(5)} returns consumption at $m_t = 5$. - -\item \texttt{vFunc}: Value function (over market resources $m_t$); can be evaluated like \texttt{cFunc}. - -\item \texttt{mNrmMin}: Minimum value of normalized market resources $m_t$ such that \texttt{cFunc} and \texttt{vFunc} are defined. - -\item \texttt{hNrm}: Normalized human wealth-- the PDV of future income (ignoring mortality) divided by current period income $Y_t$. - -\item \texttt{MPC}: The constant marginal propensity to consume (linear consumption function). - -\end{itemize} - -The following command generates the plot for the perfect foresight consumption function: - -\vspace{0.25cm} - -\noindent \texttt{mMin = MyConsumer.solution[0].mNrmMin} - -\noindent \texttt{plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10)} - -\vspace{0.25cm} - -To solve a version of this problem in which the sequence of ten periods happens three times (yielding a \texttt{solution} attribute with thirty-one elements), the user can do: - -\vspace{0.25cm} - -\noindent \texttt{MyConsumer(cycles = 3)} - -\noindent \texttt{MyConsumer.solve()} - -\vspace{0.25cm} - -To solve an infinite horizon problem in which the agent experiences the same one period problem indefinitely, the user can do: - -\vspace{0.25cm} - -\noindent \texttt{OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02,} - -\texttt{DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000)} - -\noindent \texttt{OtherConsumer.solve()} - -\vspace{0.25cm} - -This instance is specified as being infinite horizon by setting \texttt{cycles=0}. Note that the time-varying inputs are still specified as (one element) lists, even though they take on the same value in every period; this is because an infinite horizon model might consist of a multi-period sequence repeated indefinitely, rather than just one repeated period. The \texttt{solution} attribute of \texttt{OtherConsumer} will be a list containing one instance of \texttt{ConsumerSolution}, representing the solution to every period of the model. - -\section{Macroeconomics: the Market Class}\label{sec:Macroeconomics} - -The modeling framework of \texttt{AgentType} is deemed ``microeconomic'' because it pertains only to the dynamic optimization problem of agents, treating all inputs of the problem as exogenously fixed. In what we label as ``macroeconomic'' models, some of the inputs for the microeconomic models are endogenously determined by the collective states and controls of agents in the model. In a dynamic general equilibrium, there must be consistency between agents' beliefs about these macroeconomic objects, their individual behavior, and the realizations of the macroeconomic objects that result from individual choices. - -The \texttt{Market} class in \texttt{HARK.core} provides a framework for such macroeconomic models, with a \texttt{solve} method that searches for a dynamic general equilibrium. An instance of \texttt{Market} includes a list of \texttt{AgentType}s that compose the economy, a method for transforming microeconomic outcomes (states, controls, and/or shocks) into macroeconomic outcomes, and a method for interpreting a history or sequence of macroeconomic outcomes into a new ``dynamic rule'' for agents to believe. Agents treat the dynamic rule as an input to their microeconomic problem, conditioning their optimal policy functions on it. A dynamic general equilibrium is a fixed point dynamic rule: when agents act optimally while believing the equilibrium rule, their individual actions generate a macroeconomic history consistent with the equilibrium rule. - -\subsection{Down on the Farm}\label{sec:DownOnTheFarm} - -The \texttt{Market} class uses a farming metaphor to conceptualize the process for generating a history of macroeconomic outcomes in a model. Suppose all \texttt{AgentTypes} in the economy believe in some dynamic rule (i.e.\ the rule is stored as attributes of each \texttt{AgentType}, which directly or indirectly enters their dynamic optimization problem), and that they have each found the solution to their microeconomic model using their \texttt{solve} method. Further, the macroeconomic and microeconomic states have been reset to some initial orientation. - -To generate a history of macroeconomic outcomes, the \texttt{Market} repeatedly loops over the following steps a set number of times: -\begin{enumerate} -\item \texttt{sow}: Distribute the macroeconomic state variables to all \texttt{AgentType}s in the market. - -\item \texttt{cultivate}: Each \texttt{AgentType} executes their \texttt{marketAction} method, likely corresponding to simulating one period of the microeconomic model. - -\item \texttt{reap}: Microeconomic outcomes are gathered from each \texttt{AgentType} in the market. - -\item \texttt{mill}: Data gathered by \texttt{reap} is processed into new macroeconomic states according to some ``aggregate market process''. - -\item \texttt{store}: Relevant macroeconomic states are added to a running history of outcomes. -\end{enumerate} -This procedure is conducted by the \texttt{makeHistory} method of \texttt{Market} as a subroutine of its \texttt{solve} method. After making histories of the relevant macroeconomic variables, the market then executes its \texttt{calcDynamics} function with the macroeconomic history as inputs, generating a new dynamic rule to distribute to the \texttt{AgentType}s in the market. The process then begins again, with the agents solving their updated microeconomic models given the new dynamic rule; the \texttt{solve} loop continues until the ``distance'' between successive dynamic rules is sufficiently small. - -\subsection{Attributes of a Market}\label{sec:AttributesOfAMarket} - -To specify a complete instance of \texttt{Market}, the user should give it the following attributes:\footnote{For some purposes, it might be useful to specify a subclass of \texttt{Market}, defining \texttt{millRule} and/or \texttt{calcDynamics} as methods rather than functions.} -\begin{itemize} -\item \texttt{agents}: A list of \texttt{AgentType}s, representing the agents in the market. Each element in \texttt{agents} represents an \textit{ex-ante} heterogeneous type; each type could have many \textit{ex-post} heterogeneous agents. - -\item \texttt{sow\_vars}: A list of strings naming variables that are output from the aggregate market process, representing the macroeconomic outcomes. These variables will be distributed to the \texttt{agents} in the \texttt{sow} step. - -\item \texttt{reap\_vars}: A list of strings naming variables to be collected from the \texttt{agents} in the \texttt{reap} step, to be used as inputs for the aggregate market process. - -\item \texttt{const\_vars}: A list of strings naming variables used by the aggregate market process that \textit{do not} come from \texttt{agents}; they are constant or come from the \texttt{Market} itself. - -\item \texttt{track\_vars}: A list of strings naming variables generated by the aggregate market process that should be tracked as a history, to be used when calculating a new dynamic rule. Usually a subset of \texttt{sow\_vars}. - -\item \texttt{dyn\_vars}: A list of strings naming the variables that constitute a dynamic rule. These will be stored as attributes of the \texttt{agents} whenever a new rule is calculated. - -\item \texttt{millRule}: A function for the ``aggregate market process'', transforming microeconomic outcomes into macroeconomic outcomes. Its inputs are named in \texttt{reap\_vars} and \texttt{const\_vars}, and it returns a single object with attributes named in \texttt{sow\_vars} and/or \texttt{track\_vars}. Can be defined as a method of a subclass of \texttt{Market}. - -\item \texttt{calcDynamics}: A function that generates a new dynamic rule from a history of macroeconomic outcomes. Its inputs are named in \texttt{track\_vars}, and it returns a single object with attributes named in \texttt{dyn\_vars}. - -\item \texttt{act\_T}: The number of times that the \texttt{makeHistory} method should execute the ``farming loop'' when generating a new macroeconomic history. - -\item \texttt{tolerance}: The minimum acceptable ``distance'' between successive dynamic rules produced by \texttt{calcDynamics} to constitute a sufficiently converged solution. -\end{itemize} - -Further, each \texttt{AgentType} in \texttt{agents} must have two methods not necessary for microeconomic models; neither takes any input (except \texttt{self}): -\begin{itemize} -\item \texttt{marketAction}: The microeconomic process to be run in the \texttt{cultivate} step. Likely uses the new macroeconomic outcomes named in \texttt{sow\_vars}; should store new values of relevant microeconomic outcomes in the attributes (of \texttt{self}) named in \texttt{reap\_vars}. - -\item \texttt{reset}: Reset, initialize, or prepare for a new ``farming loop'' to generate a macroeconomic history. Might reset its internal random number generator, set initial state varibles, clear personal histories, etc. -\end{itemize} - -When solving macroeconomic models in HARK, the user should also define classes to represent the output from the aggregate market process in \texttt{millRule} and for the model-specific dynamic rule. The latter should have a \texttt{distance} method to test for solution convergence; if the class inherits from \texttt{HARKobject}, the user need only list relevant attributes in \texttt{distance\_criteria}. - -\subsection{Sample Model: FashionVictim}\label{sec:FashionVictim} - -To illustrate the \texttt{Market} class, consider a simple example in the emerging economic subfield of aesthemetrics, the \texttt{FashionVictimModel}.\footnote{This model is inspired by the paper ``The hipster effect: When anticonformists all look the same'' by Jonathan Touboul.}\footnote{For a more traditional macroeconomic model, the \texttt{ConsAggShocksModel} module includes a consumption-saving model with both idiosyncratic and aggregate shocks, in which individual asset holdings are aggregated into total productive capital, endogenizing the (dynamic) interest and wage rate.} This module defines a subclass of \texttt{AgentType} called \texttt{FashionVictimType}. Each period, fashion victims make a binary choice of style $s$: to dress as a jock (0) or punk (1). They receive utility directly from the outfit they wear and as a function of the proportion of the population who \textit{just wore} the same style; they also pay switching costs ($c_{pj}$,$c_{jp}$) if they change styles rather than keep the same as the previous period. Moreover, they receive an idiosyncratic type 1 extreme value (T1EV) preference shock to each style in each period. Defining the population punk proportion as $p$ and the conformity utility function as $f:[0,1]\rightarrow \mathbb{R}$, the current period utility function is thus: -\begin{equation*} -u(s_t;s_{t-1},p_t) = s_t f(p_t) + (1-s_t) f(1-p_t) + s_t U_p + (1-s_t) U_j - c_{pj} s_{t-1}(1-s_t) - c_{jp}(1-s_{t-1})s_t. -\end{equation*} - -Fashion victims are forward looking and discount future utility at a constant rate of $\beta$ per period. To simplify the analysis, we assume they believe that the population punk proportion in the next period is a linear function of the punk proportion in the current period, subject to a uniformly distributed shock. No restrictions are put on the function $f$; fashion victims might be conformists who like to dress the same as others ($f'(p) > 0$) or hipsters who like to style themselves in the minority ($f'(p) < 0$).\footnote{In practice, $f$ is parameterized as the beta distribution for convenience.} A fashion victim's problem can be written in Bellman form as: -\begin{equation*} -V(s_{t-1},p_t) = \E \left[ \max_{s_t \in \{0,1\}} u(s_t;s_{t-1},p_t) + \eta_{s_t} + \beta \E \left[ V(s_t,p_{t+1}) \right] \right], -\end{equation*} -\begin{equation*} -p_{t+1} = a p_t + b + \pi_{t+1}, \qquad \pi_{t+1} \sim U[-w,w], \qquad \eta_0,\eta_1 \sim T1EV. -\end{equation*} - -An instance of \texttt{FashionVictimType} is thus characterized by values of $U_p$, $U_j$, $c_{pj}$, $c_{jp}$ and a function $f$, as well as beliefs about $p_{t+1}$ as a function of $p_t$ (summarized by slope $a$, intercept $b$, and uniform shock width $w$). Given this information, a \texttt{FashionVictimType}'s infinite horizon microeconomic model can be solved by backward induction in a few lines; the ``one period solver'' is given by \texttt{solveFashion}. However, while individual agents treat the dynamics of $p_t$ as exogenous, they are in fact endogenously determined by the actions of all the fashion victims in the market.\footnote{The market might consist of agents all of the same type, or might have several types with \textit{ex-ante} heterogeneity.} A dynamic general equilibrium of the ``macroeconomic fashion model'' is thus characterized by a triple of $(a,b,w)$ such that when fashion victims believe in this ``punk evolution rule'' and act optimally, their collective fashion choices exhibit this same rule when the model is simulated. - -The search for a dynamic general equilibrium is implemented in HARK's \texttt{Market} class with the following definitions: - -\vspace{0.25cm} - -\noindent \texttt{sow\_vars = ['pNow']} (macroeconomic outcome is $p_t$) - -\noindent \texttt{reap\_vars = ['sNow']} (microeconomic outcomes are $s_t$ for many agents) - -\noindent \texttt{track\_vars = ['pNow']} (must track history of $p_t$) - -\noindent \texttt{dyn\_vars = ['pNextSlope','pNextIntercept','pNextWidth']} (dynamic rule $(a,b,w)$) - -\noindent \texttt{millRule = calcPunkProp} (aggregate process: average the style choices of all agents) - -\noindent \texttt{calcDynamics = calcFashionEvoFunc} (calculate new $(a,b,w)$ with autoregression of $p_t$) - -\noindent \texttt{act\_T = 1000} (simulate 1000 periods of the fashion market) - -\noindent \texttt{tolerance = 0.01} (terminate solution when $(a,b,w)$ changes by less than 0.01) - -\vspace{0.25cm} - -The \texttt{agents} attribute has a list of 22 \texttt{FashionVictimType}s, which vary in their values of $U_p$ and $U_j$, and their $f$ functions. The \texttt{marketAction} method of \texttt{FashionVictimType} simulates one period of the microeconomic model: each agent receives style preference shocks $\eta_0$ and $\eta_1$, sees the current proportion of punks $p_t$ (sown to them as \texttt{pNow}), and chooses which style to wear, storing it in the binary array \texttt{sNow}, an attribute of \texttt{self}. - -The \texttt{millRule} for this market is extremely simple: it flattens the list of binary arrays of individual style choices (gathered in the \texttt{reap} step) and averages them into a new value of $p_t$, to be tracked as a history and \texttt{sow}n back to the \texttt{agents} to begin the cycle again. Once a history of 1000 values of $p_t$ has been generated with the \texttt{makeHistory} method, we can calculate a new dynamic fashion rule with \texttt{calcFashionEvoFunc} by regressing $p_t$ on $p_{t-1}$, approximating $w$ as twice the standard deviation of prediction errors.\footnote{This is an arbitrary way to calculate $w$, but accuracy is not important in a silly example.} The new fashion rule is an instance of the simple \text{FashionEvoFunc} class, whose only methods are inherited from \texttt{HARKobject}. - -When the \texttt{solve} method is run, the solver successively solves each agent's microeconomic problem, runs the \texttt{makeHistory} method to generate a 1000 period history of $p_t$, and calculates a new punk evolution rule based on this history; the solver terminates when consecutive rules differ by less than 0.01 in any dimension. - -} -% \hypertarget{ContributingToHARK}{\section{Contributing to HARK}\label{sec:ContributingToHARK}} -\section{Contributing to HARK}\label{sec:ContributingToHARK} - -%After reading this far into the user guide, it is safe to assume that you are eager to contribute to HARK's development. -This section provides an overview of how users can contribute new code to HARK, what sort of contributions are most desired, and style conventions for the toolKit. Before attempting to extend HARK, we strongly recommend that you familiarize yourself with its features and models by looking at the source code. - -\subsection{What Does HARK Want?}\label{sec:WhatDoesHARKWant} - -The easiest and most straightforward way to contribute to HARK is by writing new general purpose tools for inclusion in one of the top-level modules. This might be a commonly known data analysis technique (e.g.\ a kernel density estimator) for \texttt{HARK.utilities}, an optimization method (local or global) for \texttt{HARK.estimation}, an interpolation method for \texttt{HARKinterpolation}, etc. If you've found a technique useful in your own research and believe it could be of use to others on entirely different projects, then it probably belongs in HARK. Likewise, if you read through a HARK module expecting to find a certain tool or function (because of similar functions that are present, or merely because \textit{it's very useful}) but don't, this is a good sign that HARK has a hole that you can fill.\footnote{Case in point: As of this moment, \texttt{HARK.utilities} has no function for making a discrete approximation to the normal distribution, even though there are several well known methods for doing so and it is an obviously useful function.} - -Secondly, the development team wants to expand HARK to include more canonical models. Most of the models available in the toolKit at the time of its public beta release concern agents with (geometrically discounted) CRRA utility over a unitary consumption good, who receive permanent and transitory shocks to income and have access to a single risk free asset. This is an important class of models, but that description has an awful lot of qualifiers. In short, there are many more common features of dynamic household models that are not yet in HARK, but would be very valuable for the toolKit. New model contributions should ``play nicely'' with existing models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent possible. For ideas of potential model extensions for HARK, see section \ref{sec:FutureModels}.\footnote{Although that list is limited to extensions and variations on consumption-saving models, HARK has no such restrictions. If there is an entirely different sort of dynamic model that fits in the HARK framework, it can be contributed.} - -Finally, contributions to HARK are not restricted to entirely new functionality. While we have done our best to provide flexible frameworks and useful tools, we have almost certainly overlooked an unexpected case that would not be (properly) handled by the toolKit as is. Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as an Issue on GitHub so that someone else might. The ``mistake'' you find might not even be in the code itself, but a deficiency in the documentation: a misnamed (or incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing) comment, even a grammatical or spelling error. No matter how niggling, we want your fix. - -For those who want to contribute to the HARK project without programming a new model or digging through the code for errors and omissions, consider reading through the Issues page on GitHub to see what other users have flagged for development. You might find several pieces of ``low hanging fruit'' for you to quickly fix. - -\subsection{Git and GitHub}\label{sec:GitHub} - -Git is a distributed version control system that has gained increasing popularity in the decade since its creation; it is designed to enable collaboration and ``non-linear workflow'' among a (potentially disperse) programming team. As HARK is to be developed by volunteer researchers all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds as a command line tool, others have since extended git with further functionality and a user interface more friendly to those more familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK project is hosted. This section provides a brief overview of how to use GitHub to interact with (and potentially contribute to) HARK. - -To prepare to contribute to HARK, follow these simple steps: -\begin{enumerate} -\item \textbf{Make a GitHub account:} Go to \href{https://www.github.com}{github.com}; the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.\footnote{There are non-free ways to sign up for GitHub, but you don't need to worry about them.} - -\item \textbf{Install GitHub Desktop}: Go to \href{https://desktop.github.com/}{desktop.github.com} and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account. - -\item \textbf{Fork the HARK repository:} Go to the \href{https://github.com/econ-ark/HARK}{HARK repository page}, click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead. - -\item \textbf{Add the repository to Desktop:} Open GitHub Desktop, click on the $+$ icon in the upper left, and select clone. Find the repository named \texttt{yourusername/HARK}, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine. -\end{enumerate} - -You now control a ``fork'' of the main HARK repository, hosted by GitHub, and have a clone of this fork on your local machine. You are free to edit your fork of HARK in any way you'd like; other GitHub users can view your fork (and fork it themselves), but they cannot make changes to your fork unless you give them permission by adding them as a contributor. Changes made to the local copy of your fork do not get automatically sent to the remote copy on GitHub for public viewing. To make changes to your fork on GitHub, follow these steps: -\begin{enumerate} -\item \textbf{Make local changes:} Edit your local copy of the repository in any way you'd like: add or delete lines from existing files; add, delete, or rename entire files or directories. - -\item \textbf{Commit local changes:} Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button. - -\item \textbf{Push the commit:} Click on the Sync button near the upper right to ``push'' your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced. -\end{enumerate} - -The left hand panel of the Changes tab has a summary of the files that have been changed, added, or removed since your last commit; the right hand panel shows the specific changes made to each changed file. After step 2, a record of your changes has been stored locally by the git system, but is not yet reflected in the public remote copy on GitHub.\footnote{You can make several local commits without syncing with the remote server. This is useful for working without an internet connection. Say, on an intercontinental flight.} The ``timeline'' summary of the repository at the top of the Desktop window shows the history of commits for the repository: each dot represents a commit that was pushed to the remote server, and each open ring represents a local commit that has not been pushed to the remote server; the large broken ring on the far right represents changes that have not been locally committed.\footnote{Clicking on a dot or ring will display the changes made in that commit (or uncommitted changes).} The History tab lists all previous commits, including the timestamp, contributor, and a record of changes made. - -Suppose you have mucked about in your fork for a while and have made changes to HARK that you think should be included in the main repository hosted by Econ-Ark. To make your dream a reality, follow these steps: -\begin{enumerate} -\item \textbf{Isolate the changes:} If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should \textit{isolate the desired changes in a new branch of your fork}. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,\footnote{Or since your last update from the main repository hosted by Econ-Ark.} then edit this branch to include \textit{only the changes you want to push to the main repository}. - -\item \textbf{Issue a pull request:} Go to \href{https://github.com/econ-ark/HARK}{HARK's GitHub page} and click the ``New pull request'' button. Click the text that says ``compare across forks'', and select \texttt{econ-ark/HARK} as the base fork, \texttt{master} as the base, your fork as the head fork, and the branch to be merged as the ``compare''. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you've done. Click the ``Create pull request'' button. - -\item \textbf{Be patient:} Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section \ref{sec:SubmissionApprovalProcess} for procedures followed by the HARK team when evaluating outside contributions. - -\end{enumerate} - -\subsection{Submission Approval Process}\label{sec:SubmissionApprovalProcess} - -Before submitting to HARK, the development team would like you to make sure that your intended contribution is listed as an Issue on the GitHub page. If you are going to fix or address an Issue that is already posted, reply to that post with a note indicating that you are about to work on it and an estimate of when you will make a pull request for it. If you want to do something that no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly) describing what you intend to do.\footnote{If your contribution is a tiny adjustment or to fix/add some documentation or comments, you shouldn't make an Issue.} This will both help prevent two people from working on the same thing (and one of them potentially wasting time on it), and lets the development team know what's coming. Moreover, if you propose a contribution that is (a) already in HARK, (b) not in the spirit of HARK, or (c) a fix for something that's not actually broken, this gives the team a chance to let you know not to spend effort on it. - -After creating a pull request to submit your contribution, it will be evaluated by one or more members of the HARK team. Criteria that the HARK team will consider when choosing whether to accept a submission include: -\begin{enumerate} -\item \textbf{Does the contribution break previous code?} If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility. - -\item \textbf{Is the code well documented?} Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section \ref{sec:DocumentationConventions}) and enough comments for a reader to follow the algorithm. - -\item \textbf{Is the contribution relevant?} The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn't belong in HARK. - -\item \textbf{Is the code demonstrated with an example?} If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you've contributed a new function, consider providing a snippet of code demonstrating its use.\footnote{In the future, HARK will make use of in-line testing.} - -\item \textbf{Is the code correct?} Your code should actually do what it claims to do. - -\item \textbf{Does the code use HARK conventions?} To the best of your ability, contributions to HARK should follow the style guidelines in section \ref{sec:NamingConventions}. We don't demand perfection, as these are guidelines rather than rules. - -\item \textbf{Is the code well integrated in HARK?} In the beta release of HARK, we have tried to present a ``taxonomy'' of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy. - -\item \textbf{Is this already in HARK?} If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK. -\end{enumerate} - -The HARK team intends to release more specific guidelines in the near future, so that contributors are better able to judge their submissions before issuing a pull request. We are in the process of recruiting ``czars'' to oversee particular aspects of HARK, providing their knowledge and expertise.\footnote{If you have knowledge and/or expertise and would like to contribute as a czar, please contact a HARK team member so that we can judge your credentials even though we have way less expertise.} While czars might directly evaluate submissions as a HARK team member, their more important role is to help establish these guidelines for their domain, more specifically nailing down what we mean by ``correct'' or ``well integrated''. - -\subsection{Naming Conventions}\label{sec:NamingConventions} - -Object naming conventions in HARK are fairly different than existing standards, and differ somewhat between tool modules vs model or application modules. The following conventions apply throughout HARK: -\begin{itemize} -\item Functions and methods are always in ``camel case'': no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. \texttt{approxLognormal}. - -\item Function and method names should accurately and concisely describe what the function does; the first word in the name \textit{must be a verb}.\footnote{HARK standards have specified several ``keyword verbs'' that have specific meaning.} - -\item Variable and class names \textit{should not} have a verb as their first word. - -\item Class names should use no underscores and capitalize the first letter of each word; moreover, a class name \textit{must include a noun}. E.g.\ \texttt{ConsPerfForesightSolver}. -\end{itemize} - -When naming variables in model modules, the HARK team strongly discourages using single letter names, like \texttt{c} for consumption. Instead, we encourage contributors to use longer, more descriptive variable names using additional words and common abbreviations to specify the variable more precisely. In \texttt{/Documentation/NARK.pdf}, we list standard single letter variable ``bases'' and an array of prefixes and suffixes to adjust them. Economic variables in model modules should (usually) not use underscores, instead using camel case to the greatest extent possible.\footnote{For ``non-economic'' variables that are only used temporarily, underscores are permissible.} The development team prefers this standard so that users can translate between Python code and LaTeX script with minimal work. - -Conventions for naming variables in HARK's tool modules are significantly closer to more commonly used standards. Variable names should be in all lower case, with underscores between words, e.g.\ \texttt{data\_to\_match}. The functions and classes in these modules are more general and almost surely do not have any inherent ``economic content''; they are numerical or algorithmic objects, not variables that might appear in an equation in an article for a (non-computational) economics journal. Variable names in application modules (e.g.\ the files that execute the \texttt{cstwMPC} estimations) are a mix of the conventions for tool and model files, as appropriate for each variable. That is, variables that are directly related to ``economic variables'' in model modules should follow those conventions, while objects created solely for data manipulation or reporting should use the style of tool modules. - -\subsection{Documentation Conventions}\label{sec:DocumentationConventions} - -The HARK team wants the toolKit to be as accessible to users as possible; our greatest fear\footnote{Other than spiders, of course.} is that a new user will open up our code, get hopelessly confused trying to read it, and then never look at HARK again. To prevent this tragic outcome, we have tried hard to provide comprehensive, accurate documentation and comments within the code describing our methods.\footnote{To the extent that documentation and comments are missing, we encourage users to rectify this in their contributions.} Moreover, HARK uses the Sphinx utility to generate a website with \href{https://econ-ark.github.io/HARK/}{online documentation} for all of our tool and model modules, so that users can learn about what's available in HARK without digging through the source code. When making contributions to HARK, the development team asks users to format their inline documentation to work with Sphinx by following a few simple rules. - -\begin{itemize} -\item The top of every module should begin with a ``docstring'' providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:\footnote{The triple quote to begin and end a docstring can use either the apostrophe \texttt{'} or quotations marks \texttt{"}.} - -\texttt{"""} - -\texttt{Specifies an economic model and provides methods for solving it. More specific description of the key features of the model and variations of it in this module. Maybe some comments about the solution method or limitations of the model. Your bank account routing number.} - -\texttt{"""} - -\item The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as: - -\texttt{def functionName(input1,input2):} - -\hspace{0.9cm}\texttt{"""} - -\hspace{0.9cm}\texttt{Concise description of the function. More details about what} - -\hspace{0.9cm}\texttt{the function does, options or modes available, and maybe mathematical} - -\hspace{0.9cm}\texttt{methods used. Credit to a source if you poached their algorithm.} - -\vspace{0.5cm} - -\hspace{0.9cm}\texttt{Parameters} - -\hspace{0.9cm}\texttt{--------------------} - -\hspace{0.9cm}\texttt{input1: type} - -\hspace{1.8cm}\texttt{Description of what input1 represents.} - -\hspace{0.9cm}\texttt{input2: type} - -\hspace{1.8cm}\texttt{Description of what input2 represents.} - -\vspace{0.5cm} - -\hspace{0.9cm}\texttt{Returns} - -\hspace{0.9cm}\texttt{--------------} - -\hspace{0.9cm}\texttt{output\_name: type} - -\hspace{1.8cm}\texttt{Description of the output(s) of the function. Might have} - -\hspace{1.8cm}\texttt{multiple entries. If no output, this is just "None".} - -\hspace{0.9cm}\texttt{"""} - -\item Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it. -\end{itemize} - -Finally, if you write a new model module, the HARK team asks that you also provide a short mathematical writeup of the model as a PDF. This document does not need to go into great detail about the solution method for the model or the functions and classes included in the module, merely specify the economic model and provide a summary of how it is solved. See \texttt{/Documentation/ConsumptionSavingModels.pdf} for an example of this. - -\ifthenelse{\boolean{ContributingToHARKOnly}}{}{ -\section{Future of HARK}\label{sec:FutureOfHARK} - -HARK is in a state of perpetual incompleteness, always expanding to include more tools and more models. As an open source project in the hands of many contributors around the world, the HARK team cannot fully predict future features of the toolKit. This section provides an overview of what we would like to see in the near future, as well as potential mechanisms to encourage researchers to contribute their code to the project. - -\subsection{Future Tools}\label{sec:FutureTools} - -The current frameworks in \texttt{HARK.core} concern agents who have ``isolated'' dynamic problems or interact with each other only through aggregate outcomes to which they contribute atomically. We hope to eventually provide a framework for models in which agents directly interact with each other; this may include a system for specifying the state of networks and how connections are formed and broken. \texttt{HARK.core} might also develop a framework more suited to industrial organization models and dynamic games.\footnote{It might already be possible to shoehorn some of these models into the \texttt{Market} framework.} - -The \texttt{HARK.estimation} module in particular is very sparsely populated, with its functions mostly serving as wrappers for ``off the shelf'' optimization methods from \texttt{scipy.optimize}. There are plenty of non-standard or more complex optimization techniques available, including global and/or stochastic search methods. We believe that additional optimizers are ``low hanging fruit'' as straightforward contributions to HARK and thus will be added in the near future. Moreover, there are plenty of distributions that have no approximator in \texttt{HARK.utilities} (or data generator in \texttt{HARK.simulation}), and these functions are among the easiest for new contributors to write. The data analysis tools in \texttt{HARK.utilities} only include functions that we happened to use for our current research projects, and we expect that this will be the mode of growth for future tools. There are some obvious holes that we believe will be filled quite quickly; for example, we provide a rudimentary non-parametric kernel regression function, but have not written a kernel density estimator to generate PDFs from data. - -The classes currently in \texttt{HARK.interpolation} are all variations on linear (or cubic) spline interpolation, and each is specifically written for a fixed number of dimensions. As these methods use full tensor grids, they are highly impractical for representing functions in higher dimensions, suffering greatly from the curse of dimensionality.\footnote{The HARK team only included interpolators for up to 4 dimensions, and some would argue this is already too many for tensor grid methods.} We are quite confident that \texttt{HARK.interpolation} will soon include techniques more applicable to high dimensional state spaces, including Smolnyak interpolation, other (adaptive) sparse grid methods, and regression-based interpolation. For lower-dimensional spaces, HARK will also soon provide Delaunay interpolation classes. - -The \texttt{HARK.parallel} module currently provides a simple interface for handling \textit{ex-ante} heterogeneity among agents. While useful for speeding up moderately-sized problems, the libraries used are only able to access the CPU of the local machine running the code. In the very near future, the HARK team will provide an interface (and example) for using Apache Spark, which bills itself as a ``fast and general engine for large-scale data processing''. Specifically, users will be able to easily scale up their computing resources and run HARK code on an Amazon EC2 session or other cluster. - -\subsection{Future Models}\label{sec:FutureModels} - -The economics models currently included in HARK mostly concern consumption-saving problems with constant relative risk aversion consumers who experience fully permanent and fully transitory shocks to income. This is obviously a very small subset of all consumption-saving models (which are themselves a small subset of all dynamic models economists might be interested in). Consider some examples of variants, features, and extensions that are not included in HARK, but could be in the future: -\begin{itemize} -\item Consumers with other utility functions, such as CARA or expo-power. - -\item Consumers with time-inconsistent preferences, e.g.\ hyperbolic discounting. - -\item Income shocks that are neither fully transitory nor fully permanent. - -\item Consumers with a bequest motive or terminal utility function. - -\item Utility from leisure and endogenous labor supply. - -\item Investment in a durable consumption good such as housing. - -\item Exogenous shocks to expenses. - -\item Multiple consumption goods, as with endogenous medical care. - -\item Portfolio allocation between a risky and riskless asset. - -\item Job search intensity as a control variable. - -\item Endogenous health evolution and health investment. -\end{itemize} - -The HARK team is currently developing a module for mixing discrete and continuous choice models, in the style of Jorgensen, Rust, Iskhakov, and Schjerning (2015). Among many other models, this framework will enable HARK users to easily develop features like: -\begin{itemize} -\item Labor supply on the extensive margin: the timing of retirement. - -\item Selection of job type or sector (by riskiness, specific human capital, etc). - -\item Choice of medical insurance contract. - -\item Decision to rent or own housing. - -\item Endogenous default on debt (with ``macroeconomic'' pricing of debt). - -\item Household formation, dissolution, and reproduction. -\end{itemize} - -Finally, we hope to provide a generic solver for any discrete choice model with T1EV shocks. The scope of HARK is limited only by what users choose to put in it. We hope that within a year of its debut, HARK will include a significant number of canonical models, providing more building blocks for future researchers to create novel combinations and new extensions. - -\subsection{Bounty Hunting}\label{sec:BountyHunting} - -While HARK users are free to write and contribute any tools and models that they believe could be helpful, building the toolKit in a ``bottom up'' style, the HARK team hopes to provide mechanisms for targeted ``top down'' development. The team is working to secure grant funding for HARK so as to offer ``bounties'' for particular models, tools, applications, or frameworks. Suppose the team decides that the model in Important Paper X should be included as part of HARK; we will issue a call for contributions to fill this hole, offering a modest prize of, say, \$20,000 to the programmer who submits the best implementation of the model, as judged by clearly stated criteria.\footnote{Actual bounties may be significantly smaller than this. But be honest: You're much more interested in contributing to HARK after reading that sentence.} The bounty would be announced as an Issue on HARK's GitHub page, as well as in a potential electronic digest of HARK news. - -Bounties might be placed on items as small as a single data analysis tool or as large as a complex estimation of a large general equilibrium model, with prizes scaling accordingly. We hasten to emphasize that we cannot guarantee funding for HARK, so the bounties (like everything else in this section) are purely speculative. As economists, the HARK team understands the incentive compatibility problems associated with asking researchers to contribute significant amounts of their time, effort, and unique skills in service of a tenuous concept like the Greater Good in exchange for even more tenuous Glory. We acknowledge that economic researchers are also optimizing agents with their own preferences over leisure and consumption, and agree that Greed is a valid mechanism to expand HARK. - -\subsection{All Aboard the Ark}\label{sec:AllAboardTheArk} - -As observant readers may have noticed, the HARK repository is hosted by a GitHub account named Econ-Ark, with no explanation as to what this might mean. Looking a few years into the future, the development team believes that HARK might be the first piece of a broader collection of computational economics resources. HARK is open source but somewhat akin to an exclusive club: contributions are required to adhere to standards and norms so as to foster modularity and interoperability among models and tools. Consistent with its namesake ship, the Econ-Ark has a significantly lower bar for admission, with a place on board for everything. - -On one level, the Ark might serve as a warehouse for any and all quantitative models and tools, for any economic purpose, in any subfield, and written in any language. Through nearly unanimous feedback, the HARK team understands that many economists are willing to share code that they have already written, but are reluctant to expend the considerable effort to translate and format it for HARK. In the future, we will set up a mechanism for code like this to be hosted on the Ark, with minimal requirements imposed on its authors (and correspondingly minimal warranty for potential users). Relevant projects on the Ark might become bounty targets for HARK, with users incentivized to port the original code into Python and to meet HARK standards; direct access to the original source code will significantly expedite the translation process, and provide a suitable benchmark for testing and verifying the contribution to HARK. - -On a different level, the Ark might also include cohesive toolkits like HARK, but geared toward purposes other than heterogeneous agents macroeconomics models. For example, the previously mentioned modules for networks among agents (and other agent-based modeling), industrial organization and dynamic games, and traditional discrete choice models might become their own toolkits, with some interoperability with HARK. In support of this, some of the tool modules currently in HARK might be ``promoted'' and become universally shared across all Econ-Ark toolkits. - -The dark secret of HARK is that it is named for heterogeneous agents macroeconomics, but designed and written by structural microeconomists. The development team believes these subfields to have considerable overlap in terms of methods and techniques, and that HARK might not be noticed by half of its potential audience due to its name. In a general sense, the Econ-Ark gives us cover to expand the scope of our efforts and/or rebrand the toolKit for an alternative audience. -} % end \ifthenelse{\boolean{\ContributingToHARKOnly}} -\end{document} diff --git a/Documentation/HARKmanual/HARKmanual0x.png b/Documentation/HARKmanual/HARKmanual0x.png deleted file mode 100644 index c90e43696abd70d0940a2718dfa3484e4c51f5ba..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 25965 zcmd3OWmuGL6y^*Jt;EnG-6g3=cY`zx4I|ys9YYUDN=t}kZxJhIiYCJrP8Y#j>Q2NNy4n4oxEPet3Tjk6J&6vf(l*_a@QHx((4suP2E9Q98Fg#~l~Hcv{td zGHgYQnM<3yM&lPOd3%J8FaCGJidLc8?HEPQ$jZvP5It%6U8NhgsY!x*K^)x|O)w~N zvp;1!g3K;CTk!aJsv2^AeFfgB|CN!OtBy8n%FoXar@;!B9;$oPyyT1_q^lRXbdbPK z-$dHN3M}@TjGf&g_IpGigO;9NX&J8i6djU5PAlHeSzlkjPUGWTO}?Xf_&1)8-+RC2 zGUjqhj1T#(U;kYRtE4^k75gedf}o3O==ts0yX{`A9xLel`Y+&({dtdZC;zR`m(0V(h*_)ZPI*+JN~+Q$&7PnAb2}%fJAudFip~9N=hsXT4j;QhGC1wEWV9 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-

10Each instance of AgentType represents an ex-ante heterogeneous “type” of agent; ex-post heterogeneity is - achieved by simulating many agents of the same type, each of whom receives a unique sequence of - shocks.

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11time_vary may include attributes that are never used by a function in solveOnePeriod. Most saliently, the - attribute solution is time-varying but is not used to solve individual periods.

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-

12Every instance of AgentType is created with a random number generator as an instance of the class - numpy.random.RandomState, with a default seed of zero.

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-

13As the name implies, the module is mostly concerned with models with idiosyncratic shocks to - income.

- - diff --git a/Documentation/HARKmanual/HARKmanual13x.png b/Documentation/HARKmanual/HARKmanual13x.png deleted file mode 100644 index 878c05278e0c91060f3e65236cac10fc0736feee..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 327 zcmV-N0l5B&P)h<^Y8 diff --git a/Documentation/HARKmanual/HARKmanual14.html b/Documentation/HARKmanual/HARKmanual14.html deleted file mode 100644 index c014774cc..000000000 --- a/Documentation/HARKmanual/HARKmanual14.html +++ /dev/null @@ -1,22 +0,0 @@ - - - - - - - - - - -
-

14This is relevant for the intepretation of the time-varying inputs LivPrb and PermGroFac.

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-

15The last element of solution represents the terminal period solution, where the agent consumes all - available resources because there is no future.

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-

16For some purposes, it might be useful to specify a subclass of Market, defining millRule and/or - calcDynamics as methods rather than functions.

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-

17This model is inspired by the paper “The hipster effect: When anticonformists all look the same” by - Jonathan Touboul.

- - diff --git a/Documentation/HARKmanual/HARKmanual17x.png b/Documentation/HARKmanual/HARKmanual17x.png deleted file mode 100644 index a4853bf0f4a56b121b88c2cc9ca4be4292291fb0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 337 zcmV-X0j~auP)qzQX`ujK`+G*D1u zIDiq@q1~hD;hP%}|Hfq!c+Yua{V_=>3j478Gq%`9`dQ;66^*ZgDE>hz_VZZjXAsBN zy&vSne@5tw`hrmO`&mQm)nel>vkm5TrtCzSMW+Y-5B?^`xqBTWa6eC1QRjK$`$yX} j(bB3K`(r|{yvVs5fK=|VAl*do00000NkvXXu0mjfk64PF diff --git a/Documentation/HARKmanual/HARKmanual18.html b/Documentation/HARKmanual/HARKmanual18.html deleted file mode 100644 index b71ea6859..000000000 --- a/Documentation/HARKmanual/HARKmanual18.html +++ /dev/null @@ -1,26 +0,0 @@ - - - - - - - - - - -
-

18For a more traditional macroeconomic model, the ConsAggShocksModel module includes a - consumption-saving model with both idiosyncratic and aggregate shocks, in which individual asset - holdings are aggregated into total productive capital, endogenizing the (dynamic) interest and wage - rate.

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19In practice, f is parameterized as the beta distribution for convenience.

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-

2By “heterogeneous,” we mean that agents might differ before anything in the model has “happened” - (ex-ante heterogeneity); and agents might experience different stochastic events during the model (ex-post - heterogeneity).

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-

20The market might consist of agents all of the same type, or might have several types with ex-ante - heterogeneity.

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21This is an arbitrary way to calculate w , but accuracy is not important in a silly example.

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-

22Case in point: As of this moment, HARK.utilities has no function for making a discrete approximation to - the normal distribution, even though there are several well known methods for doing so and it is an obviously - useful function.

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-

23Although that list is limited to extensions and variations on consumption-saving models, HARK has no - such restrictions. If there is an entirely different sort of dynamic model that fits in the HARK framework, it can - be contributed.

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-

24There are non-free ways to sign up for GitHub, but you don’t need to worry about them.

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-

25You can make several local commits without syncing with the remote server. This is useful for working - without an internet connection. Say, on an intercontinental flight.

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-

26Clicking on a dot or ring will display the changes made in that commit (or uncommitted - changes).

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-

27Or since your last update from the main repository hosted by Econ-Ark.

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-

28If your contribution is a tiny adjustment or to fix/add some documentation or comments, you shouldn’t - make an Issue.

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-

29In the future, HARK will make use of in-line testing.

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-

3This is the fastest way to get HARK running on a computer. Later, if you want to make your own - contributions to HARK by adding code (or fixing ours!), you’ll need to clone the repository on your computer. See - section 0.5.2 for more.

- diff --git a/Documentation/HARKmanual/HARKmanual30.html b/Documentation/HARKmanual/HARKmanual30.html deleted file mode 100644 index 429ae7a6d..000000000 --- a/Documentation/HARKmanual/HARKmanual30.html +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - -
-

30If you have knowledge and/or expertise and would like to contribute as a czar, please contact a HARK - team member so that we can judge your credentials even though we have way less expertise.

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-

31HARK standards have specified several “keyword verbs” that have specific meaning.

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-

32For “non-economic” variables that are only used temporarily, underscores are permissible.

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-

33Other than spiders, of course.

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34To the extent that documentation and comments are missing, we encourage users to rectify this in their - contributions.

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-

35The triple quote to begin and end a docstring can use either the apostrophe ’ or quotations marks - ".

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-

36It might already be possible to shoehorn some of these models into the Market framework.

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37The HARK team only included interpolators for up to 4 dimensions, and some would argue this is already - too many for tensor grid methods.

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-

38Actual bounties may be significantly smaller than this. But be honest: You’re much more interested in - contributing to HARK after reading that sentence.

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4Running ConsIndShockModel.py also demonstrates other variations of the consumption-saving problem, - but their description is omitted here for brevity.

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-

5The “taxonomy” of these modules is in flux; the functions described here could be combined into fewer - modules or further divided by purpose.

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6That is, as the foundational, building-block elements of HARK, new tools are not difficult to program and - do not require extensive integration with many moving parts.

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7Roughly speaking, the universal distance metric is a recursive supnorm, returning the largest distance - between two instances, among attributes named in distance_criteria. Those attributes might be complex - objects themselves rather than real numbers, generating a recursive call to the universal distance - metric.

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8HARK.parallel uses two packages that aren’t included in the default distribution of Anaconda: joblib - and dill; see step 3 of the instructions in section 0.1.1 for how to install them.

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9In the future, HARK.parallel might be absorbed into HARK.core and HARK.estimation, particularly if - joblib and dill become part of the standard Anaconda distribution.

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--- a/Documentation/HARKmanual/econtex.cls +++ /dev/null @@ -1,360 +0,0 @@ -%%% LaTeX class for economics -%%% -%%% author: Christopher Carroll -%%% license: LaTeX Project Public License -%%% -%%% Modified from style itaxpf by Arne Henningsen -%%% version: 1.0 (09 July 2007) -%%% license: LaTeX Project Public License -%%% -%%% Further information is available at -%%% http://www.uni-kiel.de/agrarpol/ahenningsen/latex-bibtex.html -%%% -%%% This document class is based on the "scrreprt" class -%%% from the KOMA script bundle. For documentation, Google KOMA-Script Documentation -%%% -%%% The corresponding BibTeX style file "econtex.bst" should be used -%%% for the bibliography: \bibliographystyle{econtex} -%%% -%%% In addition to the options for scrreprt, this package includes an additional option -%%% titlepage = include a title page (with command "\maketitle", or "\maketitleWithForcedDate{}") -%%% -%%% If an abstract is defined by \begin{abstract}\end{abstract} before \maketitle is invoked, -%%% it will appear on the title page -%%% -%%% If the titlepage option is invoked: -%%% \keywords can be used to show keywords below the abstract -%%% \jelclass can be used to show JEL classifications below the abstract -%%% \thanks{text of the thank-you footnote} produces a footnote at the bottom of the page containing the text - -\ProvidesClass{econtex}[2016/05/27 LaTeX class for economics papers, handouts, and exam questions written by Christopher Carroll] -\NeedsTeXFormat{LaTeX2e} -% \RequirePackage{rotating} - -\RequirePackage{ifthen} -\RequirePackage{chngpage} - -% If unspecified whether pdf output (instead of dvi) is being used, determine that it SHOULD be -\newif\ifdvi\dvitrue -\@ifundefined{pdfoutput}{}{\ifnum\pdfoutput>0 \dvifalse\fi} - -% permit double spacing -\RequirePackage{setspace} -%\doublespacing - -% option to include a title page -\newcounter{IncludeTitlePage} -\setcounter{IncludeTitlePage}{0} -\DeclareOption{titlepage}{\setcounter{IncludeTitlePage}{1}} - -% pass any options on to the scrreprt class and load this class with some options -\DeclareOption*{\PassOptionsToClass{\CurrentOption}{scrreprt}} -\ProcessOptions\relax -\LoadClass[fontsize=12pt,english,numbers=noenddot,captions=tableheading,captions=nooneline]{scrreprt} -% pointlessnumbers = do not add final dot after ordinary section numbers -% tablecaptionabove = use \captionabove for captions above rather than below tables -% noonelinecaption = do not treat one-line captions differently - -% use new TeX encoding scheme (T1), which contains accented (European) letters, -% to make European hyphenations work -\RequirePackage[T1]{fontenc} - -% use babel package and set language to english -\RequirePackage[english]{babel} - -% spacing around formulas -\AtBeginDocument{ -\setlength{\abovedisplayshortskip}{6pt} -\setlength{\belowdisplayshortskip}{6pt} -\setlength{\abovedisplayskip}{6pt} -\setlength{\belowdisplayskip}{6pt} -\allowdisplaybreaks % Permits LaTeX to break long formulas across pages (?) -} - -%% Added the above info to the definition of normalsize -\def\normalsize{\@setfontsize \normalsize \@xiipt {14.5} -\abovedisplayskip 6\p@ \@plus 3\p@ \@minus 3\p@ -\belowdisplayskip \abovedisplayskip -\abovedisplayshortskip 6pt \belowdisplayshortskip 6pt -\let \@listi \@listI} -\normalsize - -% Don't indent the second and later lines of footnotes -\deffootnote[1em]{0.0em}{1em}{\textsuperscript{\thefootnotemark}} - -% spacing around captions and floats -\setlength{\abovecaptionskip}{6pt} -\setlength{\belowcaptionskip}{3pt} -\setlength{\floatsep}{0pt} -\setlength{\textfloatsep}{20pt} -\setlength{\intextsep}{15pt} - -\RequirePackage{calc} - 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-\def\name#1{\global\advance\c@bottomauthor by 1\textsuperscript{\tiny \the\c@bottomauthor}{#1}{~~~~}} - diff --git a/Documentation/HARKmanual/econtex.sty b/Documentation/HARKmanual/econtex.sty deleted file mode 100644 index fcefb5736..000000000 --- a/Documentation/HARKmanual/econtex.sty +++ /dev/null @@ -1,34 +0,0 @@ -%---------------Custom Definitions-------------- - -\def\liminf{\mathop{\rm lim\, inf}\limits} -\DeclareMathOperator{\Ex}{\mathbb{E}} % Expectations operator -\def\var{\operatorname{var}} % variance -\def\cov{\operatorname{cov}} % covariance -\def\std{\operatorname{std}} % standard deviation -\def\argmax{\operatornamewithlimits{arg\,max}} % argmax - -%---------------Custom Modifications---------------------- - -% \htline defines a thick horizontal line in table -\def\htline{\noalign{\hrule height 2\arrayrulewidth}} - -% \hdline defines a dotted horizontal line in table -\def\hdline{\noalign{.\dotfill{}.}} - -%To keep footnotes on a single page except in extreme cases 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-\provideboolean{SpaceBetweenAnswers} -\setboolean{SpaceBetweenAnswers}{true} -\providecommand{\AnswerOrBlankSpace}{\ifthenelse{\boolean{SpaceBetweenAnswers}}} - -\opt{Answers}{\provideboolean{AnswersTF}\setboolean{AnswersTF}{true}} - -\newcommand{\ifAnswers}{\ifthenelse{\boolean{AnswersTF}}} -\newcommand{\answer}[2]{\ifthenelse{\boolean{AnswersTF}}{ - -{\it Answer:} -\begin{quote} -#1 -\end{quote} -} -{#2}} - - - - - diff --git a/Documentation/HARKmanual/econtexShortcuts.sty b/Documentation/HARKmanual/econtexShortcuts.sty deleted file mode 100644 index c31a9e7c1..000000000 --- a/Documentation/HARKmanual/econtexShortcuts.sty +++ /dev/null @@ -1,624 +0,0 @@ -\providecommand{\aAgg}{\ensuremath{\mathsf{A}}} -\providecommand{\aboveMin}{\blacktriangle} -\providecommand{\adjPar}{\ensuremath{\omega}} -\providecommand{\adj}{\ensuremath{\mathrm{j}}} -\providecommand{\aE}{\aRat^{e}} -\providecommand{\aFunc}{\ensuremath{\mathrm{a}}} -\providecommand{\Age}{\ensuremath{Z}} -\providecommand{\age}{\ensuremath{z}} -\providecommand{\ALevBF}{\ensuremath{\mathbf{A}}} -\providecommand{\aLevBF}{\ensuremath{\mathbf{a}}} -\providecommand{\ALev}{\ensuremath{A}} -\providecommand{\aLev}{\ensuremath{a}} -\providecommand{\Alive}{\ensuremath{\cancel{\mathfrak{D}}}} -\providecommand{\Alt}{\grave} -\providecommand{\aMin}{\ensuremath{\underline{\aRat}}} -\providecommand{\ARat}{\ensuremath{A}} -\providecommand{\aRat}{\ensuremath{a}} -\providecommand{\ASS}{\ensuremath{\ensuremath{A}}} -\providecommand{\aSS}{\ensuremath{\ensuremath{a}}} -\providecommand{\ATarg}{\ensuremath{\check{A}}} -\providecommand{\aTarg}{\ensuremath{\check{a}}} -\providecommand{\BE}{\BRat^{e}} -\providecommand{\bE}{\bRat^{e}} -\providecommand{\BLevBF}{\ensuremath{\mathbf{B}}} -\providecommand{\bLevBF}{\ensuremath{\mathbf{b}}} -\providecommand{\BLevE}{\ensuremath{\BLev^{e}}} -\providecommand{\bLevE}{\ensuremath{\bLev^{e}}} -\providecommand{\BLevU}{\ensuremath{\BLev^{u}}} -\providecommand{\bLevU}{\ensuremath{\bLev^{u}}} -\providecommand{\BLev}{\ensuremath{B}} -\providecommand{\bLev}{\ensuremath{b}} -\providecommand{\bMin}{\ensuremath{\underline{\bRat}}} -\providecommand{\bRatE}{\ensuremath{b}^{e}} -\providecommand{\BRatE}{\ensuremath{B}^{e}} -\providecommand{\bRatU}{\ensuremath{b}^{u}} -\providecommand{\BRat}{\ensuremath{B}} -\providecommand{\bRat}{\ensuremath{b}} -\providecommand{\bTargE}{\ensuremath{\check{b}^{e}}} -\providecommand{\BTargTarg}{\Target{\Target{\BRat}}} -\providecommand{\bTargTarg}{\Target{\Target{\bRat}}} -\providecommand{\BTarg}{\ensuremath{\check{B}}} -\providecommand{\bTarg}{\ensuremath{\check{b}}} -\providecommand{\BU}{{B}^{u}} -\providecommand{\bU}{{b}^{u}} -\providecommand{\cAgg}{\ensuremath{\pmb{C}}} -\providecommand{\CARA}{\ensuremath{\alpha}} -\providecommand{\ccRat}{\ensuremath{\mathsf{c}}} -\providecommand{\CDF}{\ensuremath{\mathcal{F}}} -\providecommand{\CEndFunc}{\ensuremath{\mathfrak{C}}} -\providecommand{\cEndFunc}{\ensuremath{\mathfrak{c}}} -\providecommand{\cEss}{{c}^{e}} -\providecommand{\CE}{\CRat^{e}} -\providecommand{\cE}{\cRat^{e}} -\providecommand{\cFuncAbove}{\ensuremath{\bar{\mathrm{c}}}} -\providecommand{\cFuncBelow}{\ensuremath{\uline{\mathrm{c}}}} -\providecommand{\cFuncMax}{\ensuremath{\bar{\bar{\mathrm{c}}}}} -\providecommand{\CFunc}{\ensuremath{\mathrm{C}}} -\providecommand{\cFunc}{\ensuremath{\mathrm{c}}} -\providecommand{\cFunc}{\ensuremath{\mathrm{c}}} -\providecommand{\CGroOverG}{\ensuremath{\Upsilon}} -\providecommand{\CGroOverR}{\ensuremath{\Phi}} -\providecommand{\CGroPF}{\ensuremath{\Lambda}} -\providecommand{\cGroPF}{\ensuremath{\lambda}} -\providecommand{\CGroPS}{\ensuremath{\chi}} % Precautionary Saving boost to consumption growth -\providecommand{\chiFunc}{\pmb{\chi}} -\providecommand{\Chi}{\ensuremath{\mathrm{X}}} % capital chi is sometimes useful, and not native to LaTeX -\providecommand{\CLevBF}{\ensuremath{\mathbf{C}}} -\providecommand{\cLevBF}{\ensuremath{\mathbf{c}}} -\providecommand{\CLevE}{\CLev^{e}} -\providecommand{\cLevE}{\cLev^{e}} -\providecommand{\cLevFunc}{\ensuremath{\pmb{\cFunc}}} -\providecommand{\CLevU}{\CLev^{u}} -\providecommand{\cLevU}{\cLev^{u}} -\providecommand{\cLev}{\ensuremath{c}} -\providecommand{\CLev}{\ensuremath{C}} -\providecommand{\Cons}{\ensuremath{C}} -\providecommand{\cons}{\ensuremath{c}} -\providecommand{\corr}{\varrho} -\providecommand{\cPDVFunc}{\ensuremath{\mathbb{C}}} -\providecommand{\CPDV}{\ensuremath{\text{PDV($C$)}}} -\providecommand{\cPPP}{\cons^{\prime\prime\prime}} -\providecommand{\cPP}{\cons^{\prime\prime}} -\providecommand{\cP}{\cons^{\prime}} -\providecommand{\cRatBF}{\ensuremath{\pmb{c}}} -\providecommand{\CRatE}{\CRat^{e}} -\providecommand{\cRatE}{\cRat^{e}} -\providecommand{\CRatU}{\CRat^{u}} -\providecommand{\cRatU}{\cRat^{u}} -\providecommand{\CRat}{\ensuremath{C}} -\providecommand{\cRat}{\ensuremath{c}} -\providecommand{\CRRA}{\ensuremath{\rho}} -\providecommand{\CTargE}{\CTarg^{\null}} -\providecommand{\cTargE}{\ensuremath{\Target{c}^{e}}} -\providecommand{\cTargTarg}{\Target{\Target{\cRat}}} -\providecommand{\CTarg}{\ensuremath{\Target{C}}} -\providecommand{\cTarg}{\ensuremath{\Target{c}}} -\providecommand{\curr}{1} % Permits a change of notation to T-1 and T, t and t+1, or whatever -\providecommand{\Curr}{t} % Permits a change of notation to T-1 and T, t and t+1, or whatever -\providecommand{\CU}{\CRat^{u}} -\providecommand{\cU}{\cRat^{u}} -\providecommand{\debtLim}{\mathsf{d}} -\providecommand{\Debt}{\ensuremath{D}} -\providecommand{\debt}{\ensuremath{d}} -\providecommand{\DeprFac}{\ensuremath{\daleth}} -\providecommand{\Depr}{\ensuremath{\daleth}} -\providecommand{\depr}{\ensuremath{\delta}} -\providecommand{\deprRte}{\ensuremath{\delta}} % New -\providecommand{\DiscAltuAdj}{\ensuremath{\underline{\underline{\beth}}}} -\providecommand{\DiscAlt}{\ensuremath{\beth}} -\providecommand{\Discount}{\ensuremath{\beta}} -\providecommand{\DiscRate}{\ensuremath{\vartheta}} -\providecommand{\DiscRte}{\ensuremath{\vartheta}} % New -\providecommand{\DiscFac}{\ensuremath{\beta}} % New -\providecommand{\DivGro}{\ensuremath{\mathsf{G}}} -\providecommand{\divGro}{\ensuremath{\mathsf{g}}} -\providecommand{\Div}{\ensuremath{D}} -\providecommand{\div}{\ensuremath{d}} -\providecommand{\DLev}{\ensuremath{D}} -\providecommand{\Dvdnd}{\ensuremath{\mathbf{D}}} -\providecommand{\dvdnd}{\ensuremath{d}} -\providecommand{\edvdnd}{\grave{\dvdnd}} -\providecommand{\EEndMap}{\ensuremath{\mathsf{E}}} -\providecommand{\effUnits}{\ensuremath{X}} -\providecommand{\eFunc}{\ensuremath{\mathrm{e}}} -\providecommand{\ELev}{\ensuremath{E}} -\providecommand{\ek}{\ensuremath{\lambda}} -\providecommand{\EmpGro}{\ensuremath{\Xi}} -\providecommand{\empGro}{\ensuremath{\xi}} -\providecommand{\empState}{\xi} % employment state indicator variable -\providecommand{\EpremLog}{\varphi} % Not using regular \eprem because want to distinguish between \varphi = log E_{t}[\Phi_{t+1}] and \phi_{t} = E[\log \Phi_{t}] -\providecommand{\EPrem}{\Phi} % equity premium -\providecommand{\eprem}{\phi} % log equity premium -\providecommand{\EpShkInv}{\Ex[\pShk^{-1}]} -\providecommand{\erate}{\ensuremath{\cancel{\mho}}} -\providecommand{\error}{\ensuremath{\epsilon}} -\providecommand{\Err}{\ensuremath{Z}} -\providecommand{\err}{\ensuremath{z}} -\providecommand{\err}{\ensuremath{z}} -\providecommand{\Estdr}{\ensuremath{\sigma_{\risky}}} % Standard deviation of log return on risky asset -\providecommand{\Evarr}{\ensuremath{\sigma_{\risky}^{2}}} % Variance of log return on risky asset -\providecommand{\EVarr}{\sigma_{\Risky}^{2}} % Variance of level return on risky asset (when returns norm dist) -\providecommand{\expend}{\ensuremath{\xi}} -\providecommand{\ExplDel}{@!\#\$\%\&!} -\providecommand{\Ex}{\ensuremath{\mathbb{E}}} % Expectations -\providecommand{\FDist}{\ensuremath{\mathcal{F}}} -\providecommand{\fDist}{\ensuremath{\mathcal{f}}} -\providecommand{\FFunc}{\ensuremath{\mathrm{F}}} -\providecommand{\fFunc}{\ensuremath{\mathrm{f}}} -\providecommand{\FLev}{\ensuremath{F}} -\providecommand{\fLev}{\ensuremath{f}} -\providecommand{\fPP}{\ensuremath{\mathrm{f}^{\prime\prime}}} -\providecommand{\FPP}{\ensuremath{\mathrm{F}^{\prime\prime}}} -\providecommand{\fP}{\ensuremath{\mathrm{f}^{\prime}}} -\providecommand{\FP}{\ensuremath{\mathrm{F}^{\prime}}} -\providecommand{\GLev}{\ensuremath{G}} -\providecommand{\GovNW}{\ensuremath{N}} -\providecommand{\govNW}{\ensuremath{n}} -\providecommand{\GovSpend}{\ensuremath{X}} -\providecommand{\govSpend}{\ensuremath{x}} -\providecommand{\Habit}{\ensuremath{H}} -\providecommand{\Habit}{\ensuremath{H}} -\providecommand{\habit}{\ensuremath{h}} -\providecommand{\Ham}{\ensuremath{\mathcal{H}}} % Hamiltonian -\providecommand{\HARKcore}{\texttt{HARKcore}} -\providecommand{\hEndMin}{\ensuremath{\underline{\mathfrak{h}}}} -\providecommand{\hEnd}{\ensuremath{\mathfrak{h}}} -\providecommand{\hFunc}{\ensuremath{\mathrm{h}}} -\providecommand{\Hi}{\hat} -\providecommand{\HLev}{\ensuremath{H}} -\providecommand{\HLevBF}{\ensuremath{\mathbf{H}}} -\providecommand{\hLevBF}{\ensuremath{\mathbf{h}}} -\providecommand{\HLev}{\ensuremath{\pmb{H}}} -\providecommand{\hLev}{\ensuremath{\pmb{h}}} -\providecommand{\hMin}{\ensuremath{\underline{\h}}} -\providecommand{\HMin}{\ensuremath{\underline{H}}} -\providecommand{\hours}{\ensuremath{\mathfrak{h}}} -\providecommand{\Hours}{\ensuremath{\mathfrak{H}}} -\providecommand{\HRat}{\ensuremath{H}} -\providecommand{\hRat}{\ensuremath{h}} -\providecommand{\h}{\ensuremath{h}} -\providecommand{\H}{\ensuremath{H}} -\providecommand{\iFunc}{\ensuremath{\mathrm{i}}} -\providecommand{\IFunc}{\ensuremath{\mathrm{I}}} -\providecommand{\ILev}{\ensuremath{I}} -\providecommand{\iLev}{\ensuremath{i}} -\providecommand{\impg}{{\imath}_{\pGro}} -\providecommand{\ImpG}{{\Im}_{\PGro}} -\providecommand{\impr}{{\imath}_{\rfree}} -\providecommand{\ImpR}{{\Im}_{\Rfree}} -\providecommand{\Inc}{{Y}} -\providecommand{\inc}{{y}} -\providecommand{\InvEpShkInv}{\acute{\psi}} -\providecommand{\InvEpShkInv}{\underline{\psi}} -\providecommand{\Inv}{{I}} -\providecommand{\inv}{{i}} -\providecommand{\IRat}{\ensuremath{I}} -\providecommand{\iRat}{\ensuremath{i}} -\providecommand{\itc}{\ensuremath{\zeta}} -\providecommand{\jFunc}{\ensuremath{\mathrm{j}}} -\providecommand{\JLev}{\ensuremath{J}} -\providecommand{\kapRent}{\ensuremath{\varkappa}} -\providecommand{\kapShare}{\ensuremath{\alpha}} -\providecommand{\Kap}{{K}} -\providecommand{\kap}{{k}} -\providecommand{\KFunc}{\ensuremath{\mathrm{K}}} -\providecommand{\kFunc}{\ensuremath{\mathrm{k}}} -\providecommand{\KLevBF}{\ensuremath{\mathbf{K}}} -\providecommand{\kLevBF}{\ensuremath{\mathbf{k}}} -\providecommand{\KLev}{\ensuremath{K}} -\providecommand{\kLev}{\ensuremath{k}} -\providecommand{\kPriceAfterITC}{\ensuremath{\mathscr{P}}} -\providecommand{\kPrice}{\ensuremath{\mathsf{P}}} -\providecommand{\KRat}{\ensuremath{K}} -\providecommand{\kRat}{\ensuremath{k}} -\providecommand{\kTargE}{\ensuremath{\Target{k}^{e}}} -\providecommand{\kTarg}{\ensuremath{\Target{k}}} -\providecommand{\labor}{\ensuremath{\ell}} % -\providecommand{\Labor}{\ensuremath{L}} -\providecommand{\labShare}{\ensuremath{\nu}} -\providecommand{\Leisure}{Z} % -\providecommand{\leisure}{z} % -\providecommand{\leiShare}{\ensuremath{\zeta}} % -\providecommand{\LGro}{\ensuremath{\Lambda}} -\providecommand{\lGro}{\ensuremath{\lambda}} -\providecommand{\LLevBF}{\ensuremath{\mathbf{L}}} -\providecommand{\lLevBF}{\ensuremath{\pmb{\ell}}} -\providecommand{\lLev}{\ensuremath{\ell}} -\providecommand{\LLev}{\ensuremath{L}} -\providecommand{\Lo}{\check} -\providecommand{\LRat}{\ensuremath{L}} -\providecommand{\MaxMaxMPC}{\ensuremath{\bar{\bar{\kappa}}}} -\providecommand{\MaxMinMPC}{\ensuremath{\hat{\underline{\kappa}}}} -\providecommand{\MaxMPC}{\ensuremath{\bar{\kappa}}} -\providecommand{\MaxMPS}{\ensuremath{\PatR}} -\providecommand{\Mean}{\ensuremath{\mathbb{M}}} % Mean -\providecommand{\mEss}{\check{m}^{e}} -\providecommand{\ME}{\MRat^{e}} -\providecommand{\mE}{\mRat^{e}} -\providecommand{\mFunc}{\ensuremath{\mathrm{m}}} -\providecommand{\MinMinMPC}{\ensuremath{\underline{\kappa}}} -\providecommand{\MinMPC}{\ensuremath{\uline{\kappa}}} -\providecommand{\MinMPS}{\ensuremath{\pZero^{1/\CRRA} \PatR}} -\providecommand{\MLevBF}{\ensuremath{\mathbf{M}}} -\providecommand{\mLevBF}{\ensuremath{\mathbf{m}}} -\providecommand{\mLevBF}{\ensuremath{\mathbf{m}}} -\providecommand{\mLevE}{\mLev^{e}} -\providecommand{\MLev}{\ensuremath{M}} -\providecommand{\mLev}{\ensuremath{m}} -\providecommand{\MPCE}{\MPC^{e}} -\providecommand{\MPCFunc}{\ensuremath{\pmb{\kappa}}} -\providecommand{\MPCPPF}{\ensuremath{\Pi}} -\providecommand{\MPCP}{\ensuremath{\pi}} -\providecommand{\MPCU}{\MPC^{u}} -\providecommand{\MPC}{\ensuremath{\kappa}} -\providecommand{\MPSFunc}{\ensuremath{\pmb{lambda}}} -\providecommand{\MPS}{\ensuremath{\lambda}} -\providecommand{\MRatE}{\MRat^{e}} -\providecommand{\mRatE}{\mRat^{e}} -\providecommand{\MRat}{\ensuremath{M}} -\providecommand{\mRat}{\ensuremath{m}} -\providecommand{\MSS}{\ensuremath{\breve{M}}} -\providecommand{\mSS}{\ensuremath{\breve{m}}} -\providecommand{\mTarg}{\check{m}} -\providecommand{\MU}{{M}^{u}} -\providecommand{\mU}{{m}^{u}} -\providecommand{\m}{\ensuremath{m}} -\providecommand{\M}{\ensuremath{M}} -\providecommand{\next}{2} % via search-and-replace -\providecommand{\Next}{t+1} % -\providecommand{\nFunc}{\ensuremath{\mathrm{n}}} -\providecommand{\NLev}{\ensuremath{N}} -\providecommand{\nLev}{\ensuremath{n}} -\providecommand{\NRat}{\ensuremath{N}} -\providecommand{\nRat}{\ensuremath{n}} -\providecommand{\Num}{\ensuremath{N}} -\providecommand{\n}{\ensuremath{n}} -\providecommand{\OLevBF}{\ensuremath{O}} -\providecommand{\oLevBF}{\ensuremath{\mathbf{o}}} -\providecommand{\OLev}{\ensuremath{O}} -\providecommand{\oLev}{\ensuremath{\pmb{o}}} -\providecommand{\ORat}{\ensuremath{O}} -\providecommand{\oRat}{\ensuremath{o}} -\providecommand{\Pareto}{\zeta} -\providecommand{\PatPGroAdj}{\text{\pmb{\Thorn}}_{\acute{\PGro}}} -\providecommand{\patpGroAdj}{\text{\thorn}_{\acute{\pGro}}} -\providecommand{\patpGrohat}{\hat{\text{\thorn}}_{\pGro}} -\providecommand{\PatPGro}{\text{\pmb{\Thorn}}_{\PGro}} -\providecommand{\patpGro}{\text{\thorn}_{\pGro}} -\providecommand{\PatR}{\text{\pmb{\Thorn}}_{\Rfree}} -\providecommand{\patr}{\text{\thorn}_{\rfree}} -\providecommand{\PatU}{\text{\pmb{\Thorn}}_{\urate}} -\providecommand{\patu}{\text{\thorn}_{\urate}} -\providecommand{\PatWGro}{\text{\pmb{\Thorn}}_{\WGro}} -\providecommand{\patwGro}{\text{\thorn}_{\wGro}} -\providecommand{\Pat}{\text{\pmb{\Thorn}}} -\providecommand{\pat}{\text{\thorn}} -\providecommand{\pDeadRate}{\ensuremath{\grave{\cancel{\mathsf{d}}}}} -\providecommand{\pDead}{\ensuremath{\mathfrak{D}}} -\providecommand{\pDieRate}{\ensuremath{\grave{\mathsf{d}}}} -\providecommand{\pDies}{\ensuremath{\mathrm{d}}} % Continuous time death rate -\providecommand{\PDies}{\ensuremath{\mathsf{D}}} % Discrete-time one-period death rate -\providecommand{\PDV}{\ensuremath{\mathbb{P}}} % PDV -\providecommand{\PGroAdj}{\ensuremath{\underline{\PGro}}} -\providecommand{\pGroAdj}{\ensuremath{\underline{\pGro}}} -\providecommand{\PGrouAdj}{\ensuremath{\underline{\underline{\PGro}}}} -\providecommand{\pGrouAdj}{\ensuremath{\underline{\underline{{\pGro}}}}} -\providecommand{\PGro}{\ensuremath{\Gamma}} -\providecommand{\pGro}{\ensuremath{\gamma}} -\providecommand{\phiFunc}{\ensuremath{\digamma}} -\providecommand{\PIHMPC}{\ensuremath{\varkappa}} -\providecommand{\PInc}{\ensuremath{P}} -\providecommand{\PLabor}{\ensuremath{P}} % Permanent labor income in levels -\providecommand{\Plabor}{P} % Permanent labor income in levels -\providecommand{\PLevBF}{\ensuremath{\mathbf{P}}} -\providecommand{\pLevBF}{\ensuremath{\mathbf{p}}} -\providecommand{\PLev}{\ensuremath{P}} -\providecommand{\pLev}{\ensuremath{\pmb{p}}} -\providecommand{\PLev}{\ensuremath{P}} -\providecommand{\PLives}{\ensuremath{\cancel{\PDies}}} -\providecommand{\pLives}{\ensuremath{\cancel{\pDies}}} -\providecommand{\pNotZero}{\ensuremath{\cancel{\wp}}} -\providecommand{\PopE}{\mathcal{E}} -\providecommand{\popE}{e} -\providecommand{\PopGro}{\ensuremath{\Xi}} -\providecommand{\popGro}{\ensuremath{\xi}} -\providecommand{\PopLev}{\ensuremath{\pmb{N}}} -\providecommand{\PopU}{\mathcal{U}} -\providecommand{\Pop}{\ensuremath{L}} -\providecommand{\PostITC}{\ensuremath{\cancel{\zeta}}} -\providecommand{\power}{\ensuremath{\eta}} -\providecommand{\pPDVFunc}{\ensuremath{\mathbb{P}}} -\providecommand{\PPDV}{\ensuremath{\text{PDV($P$)}}} -\providecommand{\pRat}{\ensuremath{p}} -\providecommand{\Price}{\ensuremath{\mathsf{P}}} -\providecommand{\ProdFunc}{\ensuremath{\mathrm{F}}} -\providecommand{\ProdFunc}{\ensuremath{\mathrm{F}}} -\providecommand{\prodFunc}{\ensuremath{\mathrm{f}}} -\providecommand{\prudEx}{\ensuremath{\omega}} -\providecommand{\prud}{\ensuremath{\eta}} -\providecommand{\pSav}{\ensuremath{\phi}} -\providecommand{\pShkMin}{\underline{\psi}} % -\providecommand{\PShk}{\Psi} % -\providecommand{\PermShkAgg}{\Psi} % New -\providecommand{\pShk}{\psi} % -\providecommand{\permShkInd}{\psi} % New -\providecommand{\pshk}{\psi} % -\providecommand{\PtyGro}{\ensuremath{\Phi}} -\providecommand{\ptyGro}{\ensuremath{\phi}} -\providecommand{\PtyLab}{\ensuremath{\mathrm{Z}}} % Labor productivity -\providecommand{\ptyLab}{\ensuremath{z}} % usually, log of \PtyLab -\providecommand{\PtyLev}{\ensuremath{A}} % Multiplies overall production function -\providecommand{\ptyLev}{\ensuremath{a}} % Usually, log of \PtyLev -\providecommand{\pZero}{\ensuremath{\wp}} -\providecommand{\QLev}{\ensuremath{Q}} -\providecommand{\q}{\ensuremath{\koppa}} -\providecommand{\RCpnd}{\ensuremath{\mathbf{R}}} -\providecommand{\RevFunc}{\ensuremath{\pmb{\Pi}}} -\providecommand{\revFunc}{\ensuremath{\pmb{\pi}}} -\providecommand{\Rev}{\ensuremath{\Pi}} -\providecommand{\rev}{\ensuremath{\pi}} -\providecommand{\rfree}{\ensuremath{\mathsf{r}}} % The net return on the safe asset at an annual rate -\providecommand{\Rfree}{\ensuremath{\mathsf{R}}} % The return factor on the safe asset - unfortunately mathfrak fonts don't come through for tth -\providecommand{\RFunc}{\ensuremath{\mathrm{R}}} -\providecommand{\RGross}{\ensuremath{\breve{\mathsf{R}}}} -\providecommand{\rGross}{\ensuremath{\breve{\mathsf{r}}}} -\providecommand{\RiskyAlt}{\ensuremath{\acute{\mathbf{R}}}} % The return on the risky asset -\providecommand{\riskyAlt}{\ensuremath{\acute{\mathbf{r}}}} % The net return on the risky asset annual rate -\providecommand{\riskyshare}{\ensuremath{\varsigma}} -\providecommand{\Risky}{\ensuremath{\mathbf{R}}} % The return on the risky asset -\providecommand{\risky}{\ensuremath{\mathbf{r}}} % The net return on the risky asset annual rate -\providecommand{\RLev}{\ensuremath{R}} % Rate of return (but better to use \Rfree or \Risky or something else more specific -\providecommand{\RnormWGro}{\ensuremath{\mathcal{R}_{\WGro}}} % Normalized version of riskless return factor -\providecommand{\rnormwGro}{\ensuremath{\mathit{r}_{\wGro}}} % Normalized version of riskless rate of return -\providecommand{\Rnorm}{\ensuremath{\mathcal{R}}} % Normalized version of riskless return factor -\providecommand{\rnorm}{\ensuremath{\mathit{r}}} % Normalized version of riskless rate of return -\providecommand{\Rport}{\ensuremath{\pmb{\mathfrak{R}}}} % Portfolio -weighted return -\providecommand{\rport}{\ensuremath{\pmb{\mathfrak{r}}}} % Portfolio -weighted return -\providecommand{\rProd}{\ensuremath{\mathit{r}}} -\providecommand{\Rprod}{\ensuremath{\mathscr{R}}} -\providecommand{\rprod}{\ensuremath{\mathscr{r}}} -\providecommand{\RProd}{\ensuremath{\mathsf{R}}} -\providecommand{\RSave}{\ensuremath{\ushort{\Rfree}}} -\providecommand{\rsave}{\ensuremath{\ushort{\rfree}}} -\providecommand{\RBoro}{\ensuremath{\bar{\Rfree}}} -\providecommand{\rboro}{\ensuremath{\bar{\rfree}}} -\providecommand{\R}{\Rfree} -\providecommand{\saveRate}{\grave{s}} % saving (income minus consumption) over income -\providecommand{\Save}{S} % Saving (income minus consumption) -\providecommand{\save}{s} % saving (income minus consumption) -\providecommand{\SDF}{\MLev} % Stochastic Discount Factor -\providecommand{\sdr}{\mRat} % Stochastic Discount rate -\providecommand{\Seniority}{\ensuremath{\mathsf{X}}} -\providecommand{\seniority}{\ensuremath{\mathsf{x}}} -\providecommand{\SeverancePayment}{\ensuremath{\mathcal{S}}} -\providecommand{\SeveranceRatio}{\ensuremath{\varsigma}} -\providecommand{\Severance}{\ensuremath{\varsigma}} -\providecommand{\SE}{\SRat^{e}} -\providecommand{\sE}{\sRat^{e}} -\providecommand{\sFunc}{\ensuremath{\mathrm{s}}} -\providecommand{\ShkLogZeroLogStd}{\sigma_{\cancel{\ShkMeanOneLog}}} % Std of that shock -\providecommand{\ShkLogZeroLogVar}{\sigma_{\cancel{\ShkMeanOneLog}}^{2}} % Variance of that shock -\providecommand{\ShkLogZeroLog}{\cancel{\ShkMeanOneLog}} % Log of that shock -\providecommand{\ShkLogZero}{\cancel{\ShkMeanOne}} % A shock whose expectation in logs is zero; cancellation of the nonzero mean for the mean one shock -\providecommand{\ShkMeanOneLogStd}{\sigma_{\ShkMeanOneLog}} % Std of that shock -\providecommand{\ShkMeanOneLogVar}{\sigma^{2}_{\ShkMeanOneLog}} % Log of that shock -\providecommand{\ShkMeanOneLog}{\theta} % Log of that shock -\providecommand{\ShkMeanOne}{\Theta} % A shock whose expectation in levels is always equal to one regardless of variance -\providecommand{\SLevBF}{\mathbf{S}} -\providecommand{\sLevBF}{\mathbf{s}} -\providecommand{\SLevE}{\SLev^{e}} -\providecommand{\sLevE}{\sLev^{e}} -\providecommand{\SLevU}{\SLev^{u}} -\providecommand{\sLevU}{\sLev^{u}} -\providecommand{\SLev}{\ensuremath{S}} -\providecommand{\sLev}{\ensuremath{s}} -\providecommand{\srate}{\ensuremath{\varsigma}} -\providecommand{\SRatE}{\SRat^{e}} -\providecommand{\sRatE}{\sRat^{e}} -\providecommand{\SRatU}{\SRat^{u}} -\providecommand{\sRatU}{\sRat^{u}} -\providecommand{\SRat}{\ensuremath{S}} -\providecommand{\sRat}{\ensuremath{s}} -\providecommand{\STargE}{\Target{\SRat}^{\null}} -\providecommand{\sTargE}{\Target{\sRat}^{\null}} -\providecommand{\STargTarg}{\Target{\Target{\SRat}}} -\providecommand{\sTargTarg}{\Target{\Target{\sRat}}} -\providecommand{\STarg}{\Target{\SRat}} -\providecommand{\sTarg}{\Target{\sRat}} -\providecommand{\Steady}{\bar} -\providecommand{\Stocks}{{S}} -\providecommand{\stocks}{{s}} -\providecommand{\straight}{\Pi} -\providecommand{\Surplus}{\ensuremath{Z}} -\providecommand{\surplus}{\ensuremath{z}} -\providecommand{\SU}{\SRat^{u}} -\providecommand{\sU}{\sRat^{u}} -\providecommand{\Target}{\check} -\providecommand{\TaxCombInv}{\ensuremath{\mathcal{T}^{-1}}} -\providecommand{\TaxComb}{\ensuremath{\mathcal{T}}} -\providecommand{\TaxCorp}{\ensuremath{\tau}} -\providecommand{\taxDep}{\ensuremath{\partial}} -\providecommand{\TaxFree}{\ensuremath{\cancel{\Tax}}} -\providecommand{\TaxLev}{T} -\providecommand{\TaxNetTrans}{\ensuremath{Z}} -\providecommand{\taxNetTrans}{\ensuremath{z}} -\providecommand{\TaxPaid}{\ensuremath{T}} -\providecommand{\TaxRate}{\ensuremath{t}} -\providecommand{\TaxUI}{\ensuremath{\tau}} -\providecommand{\Tax}{\ensuremath{\tau}} -\providecommand{\tax}{t} -\providecommand{\tBak}{\ensuremath{\pmb{n}}} -\providecommand{\TEatBak}{\ensuremath{\mathtt{q}}} -\providecommand{\TEat}{\ensuremath{\TEnd}} -\providecommand{\TEndBak}{\ensuremath{\mathsf{p}}} -\providecommand{\TEnd}{\ensuremath{T}} -\providecommand{\TermTime}{\ensuremath{T}} -\providecommand{\tFwd}{\ensuremath{n}} -\providecommand{\tHorOfm}{\ensuremath{\pmb{n}}} -\providecommand{\tHor}{\ensuremath{\mathsf{n}}} -\providecommand{\timeRate}{\ensuremath{\vartheta}} -\providecommand{\tinyAmount}{\ensuremath{\epsilon}} -\providecommand{\TLev}{\ensuremath{T}} -\providecommand{\TMap}{\mathscr{T}} -\providecommand{\tNow}{\ensuremath{t}} -\providecommand{\tShkAll}{\xi} % -\providecommand{\tShkEmpMin}{\underline{\theta}} % -\providecommand{\TShkEmp}{\Theta} % -\providecommand{\tShkEmp}{\theta} % -\providecommand{\tShkEmp}{\theta} % -\providecommand{\tranShkInd}{\theta} % New -\providecommand{\TranShkAgg}{\Theta} % New -\providecommand{\TShk}{\Xi} % -\providecommand{\tShk}{\xi} % -\providecommand{\tshk}{\xi} % -\providecommand{\tSS}{\ensuremath{t}} -\providecommand{\tThen}{\ensuremath{\tau}} -\providecommand{\uFunc}{{\ensuremath{\mathrm{u}}}} -\providecommand{\uInvEpShkuInv}{\underline{\underline{\psi}}} -\providecommand{\ULev}{\ensuremath{U}} -\providecommand{\unins}{\ensuremath{\zeta}} -\providecommand{\uPDVFunc}{\ensuremath{\mathbb{U}}} -\providecommand{\uPmt}{\ensuremath{\mu}} -\providecommand{\uPPP}{{\ensuremath{\mathrm{u}^{\prime\prime\prime}}}} -\providecommand{\uPP}{{\ensuremath{\mathrm{u}^{\prime\prime}}}} -\providecommand{\uP}{{\ensuremath{\mathrm{u}^{\prime}}}} -\providecommand{\urate}{\ensuremath{\mho}} -\providecommand{\utilFunc}{\ensuremath{\mathrm{u}}} -\providecommand{\util}{\ensuremath{u}} -\providecommand{\ValAlt}{\ensuremath{\mathcal{V}}} % middle-of-period Value function -\providecommand{\valfn}{\ensuremath{\mathrm{v}}} % middle-of-period value function -\providecommand{\Value}{\ensuremath{\mathrm{V}}} % middle-of-period Value function -\providecommand{\VEndFunc}{\ensuremath{\mathfrak{V}}} -\providecommand{\vEndFunc}{\ensuremath{\mathfrak{v}}} -\providecommand{\VEnd}{\ensuremath{\mathfrak{V}}} % end-of-period Value function -\providecommand{\vEnd}{\ensuremath{\mathfrak{v}}} % end-of-period value function -\providecommand{\vEss}{\check{v}^{e}} -\providecommand{\VE}{{V}^{e}} -\providecommand{\vE}{{v}^{e}} -\providecommand{\vFirm}{\ensuremath{\mathrm{e}}} -\providecommand{\VFunc}{\ensuremath{\mathrm{V}}} -\providecommand{\vFunc}{\ensuremath{\mathrm{v}}} -\providecommand{\VInv}{\ensuremath{\Lambda}} -\providecommand{\vInv}{\ensuremath{\scriptstyle \Lambda \displaystyle}} -\providecommand{\vk}{\ensuremath{\lambda}} -\providecommand{\vLevBF}{\ensuremath{\mathbf{v}}} -\providecommand{\VLevFunc}{\ensuremath{\pmb{\mathrm{V}}}} -\providecommand{\vLevFunc}{\ensuremath{\pmb{\mathrm{v}}}} -\providecommand{\VLev}{\ensuremath{V}} -\providecommand{\vLev}{\ensuremath{v}} -\providecommand{\vNorm}{\ensuremath{\mathrm{w}}} % end-of-period value function -\providecommand{\VNum}{\ensuremath{V}} -\providecommand{\vNum}{\ensuremath{v}} -\providecommand{\vOptAlt}{\ensuremath{\Alt{\tilde{\mathfrak{v}}}}} -\providecommand{\vOpt}{\ensuremath{\tilde{\mathfrak{v}}}} -\providecommand{\VRat}{\ensuremath{V}} -\providecommand{\vRat}{\ensuremath{v}} -\providecommand{\vTarg}{\Target{\vRat}} -\providecommand{\VU}{{V}^{u}} -\providecommand{\vU}{{v}^{u}} -\providecommand{\Wage}{\ensuremath{\mathsf{W}}} -\providecommand{\wage}{\ensuremath{\mathsf{w}}} -\providecommand{\WAllLev}{\ensuremath{\mathbf{O}}} -\providecommand{\wAllLev}{\ensuremath{\mathbf{o}}} -\providecommand{\WAllRat}{\ensuremath{O}} -\providecommand{\wAllRat}{\ensuremath{o}} -\providecommand{\WAll}{{O}} -\providecommand{\wAll}{{o}} -\providecommand{\WBeg}{\KLev} % Wealth as of the beginning of the period (before R is received, not including Y) -\providecommand{\wBeg}{\kLev} % wealth as of the beginning of the period (before R is received, not including Y) -\providecommand{\Wealth}{\ensuremath{O}} -\providecommand{\wealth}{\ensuremath{o}} -\providecommand{\WEndRat}{\ARat} % -\providecommand{\wEndRat}{\aRat} % -\providecommand{\WEnd}{\ALev} % Wealth as of the end of the period (after C has been chosen) -\providecommand{\wEnd}{\aLev} % wealth as of the end of the period (after C has been taken) -\providecommand{\Wend}{\ARat} % Wealth as of the end of the period (after C has been chosen) -\providecommand{\wend}{\aRat} % wealth as of the end of the period (after C has been taken) -\providecommand{\wFunc}{\mathrm{w}} -\providecommand{\WGroPF}{\ensuremath{\mathsf{G}}} -\providecommand{\WGro}{\ensuremath{\mathsf{G}}} -\providecommand{\wGro}{\ensuremath{\mathsf{g}}} -\providecommand{\whumMin}{\underline{\hRat}} % human wealth -- individual -\providecommand{\WHum}{\HLev} % Human wealth -- aggregate -\providecommand{\wHum}{\hLev} % human wealth -- individual -\providecommand{\Whum}{\HRat} % Human wealth -- aggregate -\providecommand{\whum}{\hRat} % human wealth -- individual -\providecommand{\WLev}{\ensuremath{W}} -\providecommand{\wLev}{\pmb{w}} -\providecommand{\WMid}{\BLev} % Wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\WMid}{\BLev} % Wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\wMid}{\bLev} % wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\Wmid}{\BRat} % Wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\wmid}{\bRat} % wealth as of the middle of the period (after R is received, not including Y) -\providecommand{\WMkt}{\MLev} % Wealth as of the middle of the period (after R is received, including Y) -\providecommand{\wMkt}{\mLev} % wealth as of the middle of the period (after R is received, including Y) -\providecommand{\wmkt}{\mLev} % wealth as of the middle of the period (after R is received, including Y) -\providecommand{\wNet}{\ensuremath{x}} % -\providecommand{\WNet}{\ensuremath{X}} % Total wealth -\providecommand{\WPre}{{K}} -\providecommand{\wPre}{{k}} -\providecommand{\wRat}{\ensuremath{o}} % -\providecommand{\WRat}{\ensuremath{O}} % Ratio to permanent income -\providecommand{\wTot}{\ensuremath{\mathbf{o}}} % -\providecommand{\WTot}{\ensuremath{\mathbf{O}}} % Total wealth -\providecommand{\wtot}{\ensuremath{o}} % -\providecommand{\Wtot}{\ensuremath{O}} % Total wealth -\providecommand{\xFer}{\chi} -\providecommand{\XFer}{X} -\providecommand{\xFunc}{\mathrm{x}} -\providecommand{\XLev}{\ensuremath{X}} -\providecommand{\xLev}{\ensuremath{x}} -\providecommand{\xpend}{\ensuremath{\xi}} -\providecommand{\XperGro}{\ensuremath{\mathsf{X}}} -\providecommand{\xperGro}{\ensuremath{\mathsf{x}}} -\providecommand{\XRat}{\ensuremath{X}} -\providecommand{\xRat}{\ensuremath{x}} -\providecommand{\yFunc}{\mathrm{y}} -\providecommand{\yLevBF}{\ensuremath{\mathbf{y}}} -\providecommand{\YLev}{\ensuremath{Y}} -\providecommand{\yLev}{\ensuremath{y}} -\providecommand{\YRat}{\ensuremath{Y}} -\providecommand{\yRat}{\ensuremath{y}} -\providecommand{\yTargE}{\ensuremath{\check{y}^{e}}} -\providecommand{\yTarg}{\ensuremath{\check{y}}} -\providecommand{\zAgg}{\ensuremath{\pmb{Z}}} -\providecommand{\zFunc}{\mathrm{z}} -\providecommand{\ZLevBF}{\ensuremath{\mathbf{Z}}} -\providecommand{\zLevBF}{\ensuremath{\mathbf{z}}} -\providecommand{\zLevBF}{\ensuremath{\pmb{z}}} -\providecommand{\ZLev}{\ensuremath{Z}} -\providecommand{\zLev}{\ensuremath{z}} -\providecommand{\ZRat}{\ensuremath{Z}} -\providecommand{\zRat}{\ensuremath{z}} - -\newcommand{\discRte}{\omega} -\newcommand{\DieFac}{\pDead} -\newcommand{\LivFac}{\Alive} -\newcommand{\PopFac}{\PopGro} -\newcommand{\popRte}{\popGro} - - -\providecommand{\NFALev}{\NLev} -\providecommand{\NFARat}{\NRat} -\providecommand{\NI}{\ensuremath{Z}} -\providecommand{\GDPLev}{\pmb{P}} -\providecommand{\GDPRat}{P} -\providecommand{\GDPGro}{\gimel} -\providecommand{\gdpLev}{\pmb{p}} -\providecommand{\gdpRat}{p} -\providecommand{\weight}{\omega} - - -\providecommand{\bi}{} -\renewcommand{\bi}{\begin{itemize}} -\providecommand{\ei}{} -\renewcommand{\ei}{\end{itemize}} -\providecommand{\reqd}{} -\renewcommand{\reqd}{\item[$^{*}$]} -\providecommand{\recm}{} -\renewcommand{\recm}{\item[\phantom{$^{*}$}]} - -\providecommand{\Severance}{\kappa} -\newcommand{\pd}[2]{\frac{\partial#1}{\partial#2}} - diff --git a/Documentation/HARKmanual/index.html b/Documentation/HARKmanual/index.html deleted file mode 100644 index 574c0fe75..000000000 --- a/Documentation/HARKmanual/index.html +++ /dev/null @@ -1,2083 +0,0 @@ - - - - - - A User’s Guide for HARK: Heterogeneous Agents Resources and toolKit - - - - - - - -

-

-

A User’s Guide for HARK:
-Heterogeneous Agents Resources and toolKit

-
May 21, 2018
-  -
Christopher D Carroll1 -
Johns Hopkins University
Alexander M Kaufman2 -
Consumer Financial Protection Bureau
David C Low3 -
Consumer Financial Protection Bureau
Nathan M Palmer4 -
U.S. Treasury Office of Financial Research
Matthew N White5 -
University of Delaware

-
-
-

-

PIC

-

- -

1 Carroll: http://econ.jhu.edu/people/ccarroll/, email: ccarroll@jhu.edu      2 -Kaufman: alexander.kaufman@cfpb.gov      3 Low: david.c.low@cfpb.gov      4 Palmer: -nathan.m.palmer@ofr.treasury.gov      5 White: mnwecon@udel.edu      - - - - -

-

-

Contents

-
-  0.1  Introduction -
  0.1.1  Getting Started -
  0.1.2  Structure of HARK -
  0.1.3  Other Resources -
 0.2  General Purpose Tools -
  0.2.1  HARKcore -
  0.2.2  HARK.utilities -
  0.2.3  HARK.interpolation -
  0.2.4  HARK.simulation -
  0.2.5  HARK.estimation -
  0.2.6  HARK.parallel -
 0.3  Microeconomics: the AgentType Class -
  0.3.1  Attributes of an AgentType -
  0.3.2  A Universal Solver -
  0.3.3  The Flow of Time and Other Methods -
  0.3.4  Sample Model: Perfect Foresight Consumption-Saving -
 0.4  Macroeconomics: the Market Class -
  0.4.1  Down on the Farm -
  0.4.2  Attributes of a Market -
  0.4.3  Sample Model: FashionVictim -
 0.5  Contributing to HARK -
  0.5.1  What Does HARK Want? -
  0.5.2  Git and GitHub -
  0.5.3  Submission Approval Process -
  0.5.4  Naming Conventions -
  0.5.5  Documentation Conventions -
 0.6  Future of HARK -
  0.6.1  Future Tools -
  0.6.2  Future Models -
  0.6.3  Bounty Hunting - - -
  0.6.4  All Aboard the Ark -
-

- - -

0.1  Introduction

-

If you are willing to risk some mild psychological trauma, conjure to mind your first experience of -hand-coding a structural economic model. Your clunky effort probably built on legacy code -provided by an adviser or colleague – which itself came from who-knows-what apocryphal -sources. Efforts to combine elements from one model with those from another were likely -frustrated by the “Tower of Babel” problem: Code from one source could not “speak” to code -from another without your own intermediation as a translator, possibly between two unfamiliar -languages and aided only by oracular comments that, at best, made sense only in the context of -other (now missing) code. -

After months of effort, you may have had the character-improving experience of proudly -explaining to your adviser that not only had you grafted two ideas together, you also found a -trick that speeded the solution by an order of magnitude, only to be told that your breathtaking -insight had been understood for many years, as reflected in an appendix to a 2008 paper; or, -worse, your discovery was something that “everybody knows” but did not exist at all in published -form! -

Learning by doing has value, but only within limits. We do not require young drivers to design -an internal combustion engine before driving a car, nor must graduate students write their own -matrix inversion algorithms before running an OLS regression. -

In recent years, considerable progress has been made in addressing these kinds of problems in -many areas of economic modeling. Macroeconomists using representative agent models can send -Dynare model files to each other; reduced form econometricians can choose from a host of -econometric packages. But modelers whose questions require explicit structural modeling -involving nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply be -aggregated away) are mostly still stuck in the bad old days. -

The ultimate goal of the HARK project is to fix these problems. Specifically, our aim is to -produce an open source repository of highly modular, easily interoperable code for -solving, simulating, and estimating dynamic economic models with heterogeneous -agents.2 -Further, we seek to establish (with input from the community) standards for the description and -specification of objects like discrete approximations to continuous distributions and interpolated -function approximations, so that numeric methods can be quickly swapped without ugly -“patching.” -

We hope that HARK will make it much easier and faster for researchers to develop solution -and estimation methods for new models. The open source nature of HARK will make -it easier for other researchers to audit and verify new models and methods, and to -collaborate on correcting deficiencies when found. As HARK expands to include more - - -canonical models and more tools and utilities, we can all spend less time managing -numerical minutiae and more time fretting about identification arguments and data -accuracy. -

0.1.1  Getting Started

-

If you want to get started using HARK right away, this section provides a very easy quickstart -guide for getting HARK up and running on your computer in just a few minutes. More -information can be found in the README.md file in the HARK repository (step 4), but here is a -quick version for those who want to jump right in: -

-

    -
  1. Download Anaconda: Go to https://www.anaconda.com/download/ and - download Anaconda for your operating system; be sure to get the version for Python - 2.7. -
    2. -
  2. Install Anaconda: Follow the easy instructions on that page to install Anaconda. -
    3. -
  3. Add extra packages: (optional) If you want to use HARK’s multithreading feature, - you need to add two packages that aren’t part of the default Anaconda distribution. - Simply open a command prompt and do conda install joblib and conda install - dill, accepting defaults to install. -
    4. -
  4. Download HARK: Go to https://github.com/econ-ark/HARK, the home - of the HARK repository. Click on the lime green button labeled “Clone or - download” toward the upper right of the main panel, and select “Download - ZIP”.3 -
    5. -
  5. Unzip HARK: Unpack the HARK.zip file using any archive utility, like Peazip. Put - the files anywhere you’d like, maintaining the internal directory structure. -
    6. -
  6. Run Spyder: Open a command prompt and do spyder. Spyder is an interactive - development environment (IDE) for iPython, a slightly prettier, more interactive - flavor of Python. - - -
    7. -
  7. Open a HARK module: Go to the directory where you put HARK and open any - file with the .py extension; we recommend - /Demos/ConsumptionSaving/ConsIndShockModel.py. -
    8. -
  8. Run the module: Click on the green arrow “play” button toward the right side of - the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue - box pops up). Congratulations! HARK is now up and running on your computer.
-

If you followed our recommendation to try /Demos/ConsumptionSaving/ConsIndShockModel.py, -you should see graphical representations of the solution to a few consumption-saving -models with idiosyncratic shocks to permanent and transitory income (see -/Documentation/ConsumptionSavingModels.pdf for details of these models). If you chose a -different module, you might get some examples of some other model, results of a structural -estimation, a polite notification that this particular module doesn’t do much on its own, or no -output at all. See the README.md for a full list of modules from which you can expect non-trivial -output. -

-

0.1.2  Structure of HARK

-

HARK is written in Python, an object-oriented programming (OOP) language that has -experienced increasing popularity in the scientific community in the past several years. A -significant reason for the adoption of Python is the numpy and scipy packages, which offer a -wide array of mathematical and statistical functions and tools; HARK makes liberal -use of these libraries. Python’s object-oriented nature allows models in HARK to -be easily extended: more complex models can inherit functions and methods from -more fundamental “parent” models, eliminating the need to reproduce or repurpose -code. -

As implied in the previous section, we strongly encourage HARK users to use the Anaconda -distribution of Python, which includes all commonly used mathematical and scientific packages, -an interactive development environment for iPython (Spyder), and a package manager that -allows users to quickly install or update packages not included in the default distribution -(conda). -

For users unfamiliar with OOP, we strongly encourage you to review the background material -on OOP provided by the good people at QuantEcon (for more on them, see below) at this link: -Object Oriented Programming. Unlike non-OOP languages, OOP bundles together -data and functions into objects. These can be accessed via: object_name.data and -object_name.method_name(), respectively. For organizational purposes, definitions of multiple -objects are stored in modules, which are simply files with a .py extension. Modules can be - - -accessed in Python via:
-

import module_name as import_name
-

This imports the module and gives it a local name of import_name. We can access a function -within this module by simply typing: import_name.function_name(). The following example -will illustrate the usage of these commands. CRRAutility is the function object for calculating -CRRA utility supplied by HARK.utilities module. CRRAutility is called attributes of the -module HARK.utilities. In order to calculate CRRA utility with a consumption of 1 and a -coefficient of risk aversion of 2 we run:
-

import HARK.utilities as Hutil -

Hutil.CRRAutility(1,2)
-

Python modules in HARK can generally be categorized into three types: tools, models, and -applications. Tool modules contain functions and classes with general purpose tools that have -no inherent “economic content”, but that can be used in many economic models as building -blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include -functions for data analysis (e.g. calculating Lorenz shares from data, or constructing a -non-parametric kernel regression), functions to create and manipulate discrete approximations to -continuous distributions, or classes for constructing interpolated approximations to -non-parametric functions. Tool modules generally reside in HARK’s root directory and have -names like HARK.simulation and HARK.interpolation; they do not necessarily do anything -when run. -

Model modules specify particular economic models, including classes to represent agents in -the model (and the “market structure” in which they interact) and functions for solving the -“one period problem” of those models. For example, ConsIndShockModel.py concerns -consumption-saving models in which agents have CRRA utility over consumption and face -idiosyncratic shocks to permanent and transitory income. The module includes classes for -representing “types” of consumers, along with functions for solving (several flavors of) the one -period consumption-saving problem. When run, model modules might demonstrate example -specifications of their models, filling in the model parameters with arbitrary values. When -ConsIndShockModel.py is run, it specifies an infinite horizon consumer with a particular -discount factor, permanent income growth rate, coefficient of relative risk aversion (etc), who -faces lognormal shocks to permanent and transitory income each period with a particular -standard deviation; it then solves this consumer’s problem and graphically displays the -results.4 -Model modules generally have Model in their name. - - -

Application modules use tool and model modules to solve, simulate, and/or estimate economic -models for a particular purpose. While tool modules have no particular economic content and -model modules describe entire classes of economic models, applications are uses of a model for -some research purpose. For example, /Demos/SolvingMicroDSOPs/StructEstimation.py uses a -consumption-saving model from ConsIndShockModel.py, calibrating it with age-dependent -sequences of permanent income growth, survival probabilities, and the standard deviation of -income shocks (etc); it then estimates the coefficient of relative risk aversion and shifter for -an age-varying sequence of discount factors that best fits simulated wealth profiles -to empirical data from the Survey of Consumer Finance. A particular application -might have multiple modules associated with it, all of which generally reside in one -directory. -

0.1.3  Other Resources

-

In the incredibly unlikely scenario in which this document does not fill all of the gaps in your -knowledge and answer any questions you might have while reading it, here is a collection of -potentially helpful other resources. -

-

-

0.2  General Purpose Tools

-

HARK’s root directory contains six tool -modules,5 -each containing a variety of functions and classes that can be used in many -economic models– or even for mathematical purposes that have nothing to do with -economics. Some of the tool modules are very sparely populated at this time, while -others are quite large. We expect that all of these modules will grow considerably -in the near future, as new tools are “low hanging fruit” for contribution to the -project.6 - - -

0.2.1  HARKcore

-

A key goal of the project is to create modularity and interoperability between models, making -them easy to combine, adapt, and extend. To this end, the HARK.core module specifies a -framework for economic models in HARK, creating a common structure for them on two levels -that can be called “microeconomic” and “macroeconomic”. -

Microeconomic models in HARK use the AgentType class to represent agents with an -intertemporal optimization problem. Each of these models specifies a subclass of AgentType; an -instance of the subclass represents agents who are ex-ante homogeneous– they have common -values for all parameters that describe the problem. For example, ConsIndShockModel specifies -the IndShockConsumerType class, which has methods specific to consumption-saving -models with idiosyncratic shocks to income; an instance of the class might represent all -consumers who have a CRRA of 3, discount factor of 0.98, etc. The AgentType class -has a solve method that acts as a “universal microeconomic solver” for any properly -formatted model, making it easier to set up a new model and to combine elements -from different models; the solver is intended to encompass any model that can be -framed as a sequence of one period problems. For a complete description, see section -0.3. -

Macroeconomic models in HARK use the Market class to represent a market (or other -aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual -agents in the model into aggregate outcomes that are “passed back” to the agents. For example, -the market in a consumption-saving model might combine the individual asset holdings of all -agents in the market to generate aggregate capital in the economy, yielding the interest rate on -assets (as the marginal product of capital); the individual agents then learn the aggregate capital -level and interest rate, conditioning their next action on this information. Objects that -microeconomic agents treat as exogenous when solving (or simulating) their model are thus -endogenous at the macroeconomic level. Like AgentType, the Market class also has a -solve method, which seeks out a dynamic general equilibrium: a “rule” governing the -dynamic evolution of macroeconomic objects such that if agents believe this rule and -act accordingly, then their collective actions generate a sequence of macroeconomic -outcomes that justify the belief in that rule. For a more complete description, see section -0.4. -

Beyond the model frameworks, HARK.core also defines a “supersuperclass” called HARKobject. -When solving a dynamic microeconomic model with an infinite horizon (or searching for a -dynamic general equilibrium), it is often required to consider whether two solutions -are sufficiently close to each other to warrant stopping the process (i.e. approximate -convergence). It is thus necessary to calculate the “distance” between two solutions, -so HARK specifies that classes should have a distance method that takes a single -input and returns a non-negative value representing the (generally dimensionless) -distance between the object in question and the input to the method. As a convenient -default, HARKobject provides a “universal distance metric” that should be useful in many - - -contexts.7 -When defining a new subclass of HARKobject, the user simply defines the attribute -distance_criteria as a list of strings naming the attributes of the class that should be -compared when calculating the distance between two instances of that class. For example, the -class ConsumerSolution has distance_criteria = [’cFunc’], indicating that only the -consumption function attribute of the solution matters when comparing the distance between -two instances of ConsumerSolution. See here for online documentation. -

0.2.2  HARK.utilities

-

The HARK.utilities module carries a double meaning in its name, as it contains both -utility functions (and their derivatives, inverses, and combinations thereof) in the -economic modeling sense as well as utilities in the sense of general tools. Utility functions -included at this time are constant relative risk aversion and constant absolute risk -aversion. Other functions in HARK.utilities include some data manipulation tools -(e.g. for calculating an average of data conditional on being within a percentile range -of different data), functions for constructing discrete state space grids, convenience -functions for retrieving information about functions, and basic plotting tools using -matplotlib.pyplot. -

The module also includes functions for constructing discrete approximations to continuous -distributions (e.g. approxLognormal() to approximate a log-normal distribution) as well as -manipulating these representations (e.g. appending one outcome to an existing distribution, or -combining independent univariate distributions into one multivariate distribution). As a -convention in HARK, continuous distributions are approximated as finite discrete distributions -when solving models; an N -dimensional random variable is formatted as a length N + 1 list of -1D arrays, with the first element representing event probabilities and all other elements are -realizations of the N component RVs. This both simplifies solution methods (reducing numeric -integrals to simple dot products) and allows users to easily test whether their chosen degree of -discretization yields a sufficient approximation to the full distribution. See here for online -documentation. -

-

0.2.3  HARK.interpolation

-

The HARK.interpolation module defines classes for representing interpolated function - - -approximations. Interpolation methods in HARK all inherit from a superclass such as -HARKinterpolator1D or HARKinterpolator2D, wrapper classes that ensures interoperability -across interpolation methods. For example, HARKinterpolator1D specifies the methods -__call__ and derivative to accept an arbitrary array as an input and return an identically -shaped array with the interpolated function evaluated at the values in the array or its first -derivative, respectively. However, these methods do little on their own, merely reshaping arrays -and referring to the _evaluate and _der methods, which are not actually defined in -HARKinterpolator1D. Each subclass of HARKinterpolator1D specifies their own implementation -of _evaluate and _der particular to that interpolation method, accepting and returning only 1D -arrays. In this way, subclasses of HARKinterpolator1D are easily interchangeable with each -other, as all methods that the user interacts with are identical, varying only by “internal” -methods. -

When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to -know the “distance” between functions generated by successive iterations of a solution procedure. -To this end, each interpolator class in HARK must define a distance method that takes as an -input another instance of the same class and returns a non-negative real number representing the -“distance” between the two. As each of the HARKinterpolatorXD classes inherits from -HARKobject, all interpolator classes have the default “universal” distance method; the user must -simply list the names of the relevant attributes in the attribute distance_criteria of the -class. -

Interpolation methods currently implemented in HARK include (multi)linear interpolation up -to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D -total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation -over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D “lower -envelope” interpolator. See here for online documentation. -

-

0.2.4  HARK.simulation

-

The HARK.simulation module provides tools for generating simulated data or shocks for -post-solution use of models. Currently implemented distributions include normal, lognormal, -Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use, -these tools are used in the consumption-saving models of ConsIndShockModel.py to simulate -permanent and transitory income shocks as well as unemployment events. See here for online -documentation. -

- - -

0.2.5  HARK.estimation

-

Methods for optimizing an objective function for the purposes of estimating a model can be -found in HARK.estimation. As of this writing, the implementation includes only minimization by -the Nelder-Mead simplex method, minimization by a derivative-free Powell method -variant, and two small tools for resampling data (i.e. for a bootstrap); the minimizers -are merely convenience wrappers (with result reporting) for optimizers included in -scipy.optimize. Future functionality will include more robust global search methods, including -genetic algorithms, simulated annealing, and differential evolution. See here for full -documentation. -

-

0.2.6  HARK.parallel

-

By default, processes in Python are single-threaded, using only a single CPU core. The HARK.parallel -module provides basic tools for using multiple CPU cores simultaneously, with minimal -effort.8 -In particular, it provides the function multiThreadCommands, which takes two arguments: a list -of AgentTypes and a list of commands as strings; each command should be a method of the -AgentTypes. The function simply distributes the AgentTypes across threads on different cores -and executes each command in order, returning no output (the AgentTypes themselves are -changed by running the commands). Equivalent results would be achieved by simply looping over -each type and running each method in the list. Indeed, HARK.parallel also has a -function called multiThreadCommandsFake that does just that, with identical syntax to -multiThreadCommands; multithreading in HARK can thus be easily turned on and -off.9 -The module also has functions for a parallel implementation of the Nelder-Mead simplex -algorithm, as described in Wiswall and Lee (2011). See here for full documentation. -

0.3  Microeconomics: the AgentType Class

-

The core of our microeconomic dynamic optimization framework is a flexible object-oriented -representation of economic agents. The HARK.core module defines a superclass called AgentType; - - -each model defines a subclass of AgentType, specifying additional model-specific features and -methods while inheriting the methods of the superclass. Most importantly, the method solve -acts as a “universal solver” applicable to any (properly formatted) discrete time model. This -section describes the format of an instance of AgentType as it defines a dynamic microeconomic -problem;10 -

0.3.1  Attributes of an AgentType

-

A discrete time model in our framework is characterized by a sequence of “periods” that the -agent will experience. A well-formed instance of AgentType includes the following -attributes: -

    -
  • solveOnePeriod: A function, or a list of functions, representing the solution - method for a single period of the agent’s problem. The inputs passed to a - solveOnePeriod function include all data that characterize the agent’s problem in - that period, including the solution to the subsequent period’s problem (designated - as solution_next). The output of these functions is a single Solution object, which - can be passed to the solver for the previous period. -
    • -
  • time_inv: A list of strings containing all of the variable names that are passed to at - least one function in solveOnePeriod but do not vary across periods. Each of these - variables resides in a correspondingly named attribute of the AgentType instance. -
    • -
  • time_vary: A list of strings naming the attributes of this instance that vary - across periods. Each of these attributes is a list of period-specific values, which - should be of the same length. If the solution method varies across periods, then - ’solveOnePeriod’ is an element of time_vary.11 -
    • -
  • solution_terminal: An object representing the solution to the “terminal” period - of the model. This might represent a known trivial solution that does not require - numeric methods, the solution to some previously solved “next phase” of the model, - a scrap value function, or an initial guess of the solution to an infinite horizon model. - - -
    • -
  • pseudo_terminal: A Boolean flag indicating that solution_terminal is not a - proper terminal period solution (rather an initial guess, “next phase” solution, or - scrap value) and should not be reported as part of the model’s solution. -
    • -
  • cycles: A non-negative integer indicating the number of times the agent will - experience the sequence of periods in the problem. For example, cycles = 1 means - that the sequence of periods is analogous to a lifecycle model, experienced once from - beginning to end; cycles = 2 means that the agent experiences the sequence twice, - with the first period in the sequence following the last. An infinite horizon problem - in which the sequence of periods repeats indefinitely is indicated with cycles = 0. -
    • -
  • tolerance: A positive real number indicating convergence tolerance, representing - the maximum acceptable “distance” between successive cycle solutions in an infinite - horizon model; it is irrelevant when cycles > 0. As the distance metric on the space - of solutions is model-specific, the value of tolerance is generally dimensionless. -
    • -
  • time_flow: A Boolean flag indicating the direction that time is “flowing.” When - True, the variables listed in time_vary are listed in ordinary chronological order, - with index 0 being the first period; when False, these lists are in reverse chronological - order, with index 0 holding the last period.
-

An instance of AgentType also has the attributes named in time_vary and time_inv, and -may have other attributes that are not included in either (e.g. values not used in the model -solution, but instead to construct objects used in the solution). -

-

0.3.2  A Universal Solver

-

When an instance of AgentType invokes its solve method, the solution to the agent’s problem is -stored in the attribute solution. The solution is computed by recursively solving the sequence of -periods defined by the variables listed in time_vary and time_inv using the functions in -solveOnePeriod. The time-varying inputs are updated each period, including the -successive period’s solution as solution_next; the same values of time invariant inputs in -time_inv are passed to the solver in every period. The first call to solveOnePeriod uses -solution_terminal as solution_next. In a finite horizon problem, the sequence of -periods is solved cycles times over; in an infinite horizon problem, the sequence of -periods is solved until the solutions of successive cycles have a “distance” of less than -tolerance. - - -

The output from a function in solveOnePeriod is an instance of a model-specific solution -class. The attributes of a solution to one period of a problem might include behavioral functions, -(marginal) value functions, and other variables characterizing the result. Each solution class must -have a method called distance(), which returns the “distance” between itself and another -instance of the same solution class, so as to define convergence as a stopping criterion; for many -models, this will be the “distance” between a policy or value function in the solutions. If the -solution class is defined as a subclass of HARKobject, it automatically inherits the default -distance method, so that the user must only list the relevant object attributes in -distance_criteria. -

The AgentType also has methods named preSolve and postSolve, both of which take no -arguments and do absolutely nothing. A subclass of AgentType can overwrite these blank -methods with its own model specific methods. preSolve is automatically called near the -beginning of the solve method, before solving the sequence of periods. It is used for specifying -tasks that should be done before solving the sequence of periods, such as pre-constructing some -objects repeatedly used by the solution method or finding an analytical terminal period solution. -For example, the IndShockConsumerType class in ConsIndShockModel has a preSolve method -that calls its updateSolutionTerminal method to ensure that solution_terminal is -consistent with the model parameters. The postSolve method is called shortly after the -sequence of periods is fully solved; it can be used for “post-processing” of the solution or -performing a step that is only useful after solution convergence. For example, the -TractableConsumerType in TractableBufferStockModel has a postSolve method that -constructs an interpolated consumption function from the list of stable arm points found during -solution. -

Our universal solver is written in a very general way that should be applicable to any discrete -time optimization problem– because Python is so flexible in defining objects, the time-varying -inputs for each period can take any form. Indeed, the solver does no “real work” itself, but merely -provides a structure for describing models in the HARK framework, allowing interoperability -among current and future modules. -

-

0.3.3  The Flow of Time and Other Methods

-

Because dynamic optimization problems are solved recursively in our framework, it is natural to -list time-varying values in reverse chronological order– the solve() method loops over the -values in each time-varying list in the same direction that a human would read them. -When simulating agents after the solution has been obtained, however, it is much more -convenient for time-varying parameters to be listed in ordinary chronological order– -the direction in which they will be experienced by simulated agents. To allow the -user to set the order in which “time is flowing” for an instance of AgentType, the -HARK framework includes functionality to easily change ordering of time-varying - - -values. -

The attribute time_flow is True if variables are listed in ordinary chronological -order and False otherwise. AgentType has the following methods for manipulating -time: -

    -
  • timeReport(): Prints to screen a description of the direction that time is flowing, - for interactive convenience and as a reminder of the functionality. -
    • -
  • timeFlip(): Flips the direction of time. Each attribute listed in time_vary is - reversed in place, and the value of time_flow is toggled. -
    • -
  • timeFwd(): Sets the direction of time to ordinary chronological order. -
    • -
  • timeRev(): Sets the direction of time to reverse chronological order.
-

These methods are invoked to more conveniently access time-varying objects. When a new -time-varying attribute is added, its name should be appended to time_vary, particularly if its -values are used in the solution of the model (or is part of the solution itself). For example, the -solve() method automatically adds the string ’solution’ to time_vary if it is not already -present. Note that attributes listed in time_vary must be lists if solve() or timeFlip() -are used. Some values that could be considered “time varying” but are never used to -solve the model are more conveniently represented as a numpy.array object (e.g.  the -history of a state or control variable from a simulation); because the numpy.array -class does not have a reverse() method, these attributes should not be listed in -time_vary. -

The base AgentType is sparsely defined, as most “real” methods will be application-specific. -Two final methods bear mentioning. First, the __call__ method points to assignParameters(), -a convenience method for adding or adjusting attributes (inherited from HARKobject). This -method takes any number of keyword arguments, so that code can be parsimoniously -written as, for example, AgentInstance(attribute1 = value1, attribute2 = -value2). Using Python’s dictionary capabilities, many attributes can be conveniently -set with minimal code. Second, the resetRNG method simply resets the AgentType’s -random number generator (as the attribute RNG) using the value in the attribute -seed.12 -This method is useful for (inter alia) ensuring that the same underlying sequence of shocks is -used for every simulation run when a model is solved or estimated. - - -

0.3.4  Sample Model: Perfect Foresight Consumption-Saving

-

To provide a concrete example of how the AgentType class works, consider the very simple case -of a perfect foresight consumption-saving model. The agent has time-separable, additive CRRA -preferences over consumption, discounting future utility at a constant rate; he receives a -particular stream of labor income each period, and knows the interest rate on assets that he -holds from one period to the next. His decision about how much to consume in a particular -period can be expressed in Bellman form as: -

- Vt(Mt ) = max u(Ct) + β//Dt E[Vt+1(Mt+1 )], - Ct - At = Mt - Ct, - - Mt+1 = RAt + Yt+1, - Yt+1 = Γ t+1Yt, - C1- ρ - u(C ) = -----. - 1 - ρ
An -agent’s problem is thus characterized by values of ρ , R , and β plus sequences -of survival probabilities //Dt and income growth factors Γ t for t = 0,⋅⋅⋅,T . This -problem has an analytical solution for both the value function and the consumption -function. -

The ConsIndShockModel module defines the class PerfForesightConsumerType -as a subclass of AgentType and provides solver functions for several -variations of a consumption-saving model, including the perfect foresight -problem.13 -A HARK user could specify and solve a ten period perfect foresight model with the following -commands: -

MyConsumer = PerfForesightConsumerType(time_flow=True, cycles=1, -

CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98, -

LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90], -

PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02], -

Nagents=1000) -

MyConsumer.solve() -

The first line makes a new instance of ConsumerType, specifying that time is currently “flowing” - - -forward14 -and that the sequence of periods happens exactly once; the next three lines (all part of the same -command) set the time invariant (CRRA, Rfree, DiscFac) and time varying parameters (LivPrb, -PermGroFac). Note that dir(MyConsumer) shows all the attributes of MyConsumer. After running -the solve method, MyConsumer will have an attribute called solution, which will be a list with -eleven ConsumerSolution objects, representing the period-by-period solution to the -model.15 -Each ConsumerSolution has several attributes: -

    -
  • cFunc: Optimal consumption function for this period as a function of - (normalized) market resources m = M ∕Y - t t t . Can be called like any other function: - MyConsumer.solution[0].cFunc(5) returns consumption at mt = 5 . -
    • -
  • vFunc: Value function (over market resources mt ); can be evaluated like cFunc. -
    • -
  • mNrmMin: Minimum value of normalized market resources mt such that cFunc and - vFunc are defined. -
    • -
  • hNrm: Normalized human wealth– the PDV of future income (ignoring mortality) - divided by current period income Yt . -
    • -
  • MPC: The constant marginal propensity to consume (linear consumption function). -
-

The following command generates the plot for the perfect foresight consumption -function: -

mMin = MyConsumer.solution[0].mNrmMin -

plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10) -

To solve a version of this problem in which the sequence of ten periods happens -three times (yielding a solution attribute with thirty-one elements), the user can -do: - - -

MyConsumer(cycles = 3) -

MyConsumer.solve() -

To solve an infinite horizon problem in which the agent experiences the same one period -problem indefinitely, the user can do: -

OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02, -

DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000) -

OtherConsumer.solve() -

This instance is specified as being infinite horizon by setting cycles=0. Note that the -time-varying inputs are still specified as (one element) lists, even though they take on the same -value in every period; this is because an infinite horizon model might consist of a multi-period -sequence repeated indefinitely, rather than just one repeated period. The solution attribute of -OtherConsumer will be a list containing one instance of ConsumerSolution, representing the -solution to every period of the model. -

0.4  Macroeconomics: the Market Class

-

The modeling framework of AgentType is deemed “microeconomic” because it pertains only to -the dynamic optimization problem of agents, treating all inputs of the problem as -exogenously fixed. In what we label as “macroeconomic” models, some of the inputs -for the microeconomic models are endogenously determined by the collective states -and controls of agents in the model. In a dynamic general equilibrium, there must be -consistency between agents’ beliefs about these macroeconomic objects, their individual -behavior, and the realizations of the macroeconomic objects that result from individual -choices. -

The Market class in HARK.core provides a framework for such macroeconomic models, with a -solve method that searches for a dynamic general equilibrium. An instance of Market includes a -list of AgentTypes that compose the economy, a method for transforming microeconomic -outcomes (states, controls, and/or shocks) into macroeconomic outcomes, and a method for -interpreting a history or sequence of macroeconomic outcomes into a new “dynamic rule” for -agents to believe. Agents treat the dynamic rule as an input to their microeconomic problem, -conditioning their optimal policy functions on it. A dynamic general equilibrium is a fixed point -dynamic rule: when agents act optimally while believing the equilibrium rule, their -individual actions generate a macroeconomic history consistent with the equilibrium -rule. -

-

0.4.1  Down on the Farm

- - -

The Market class uses a farming metaphor to conceptualize the process for generating a -history of macroeconomic outcomes in a model. Suppose all AgentTypes in the economy -believe in some dynamic rule (i.e. the rule is stored as attributes of each AgentType, -which directly or indirectly enters their dynamic optimization problem), and that they -have each found the solution to their microeconomic model using their solve method. -Further, the macroeconomic and microeconomic states have been reset to some initial -orientation. -

To generate a history of macroeconomic outcomes, the Market repeatedly loops over the -following steps a set number of times: -

    -
  1. sow: Distribute the macroeconomic state variables to all AgentTypes in the market. -
    2. -
  2. cultivate: Each AgentType executes their marketAction method, likely - corresponding to simulating one period of the microeconomic model. -
    3. -
  3. reap: Microeconomic outcomes are gathered from each AgentType in the market. -
    4. -
  4. mill: Data gathered by reap is processed into new macroeconomic states according - to some “aggregate market process”. -
    5. -
  5. store: Relevant macroeconomic states are added to a running history of outcomes.
-

This procedure is conducted by the makeHistory method of Market as a subroutine of its solve -method. After making histories of the relevant macroeconomic variables, the market then -executes its calcDynamics function with the macroeconomic history as inputs, generating a new -dynamic rule to distribute to the AgentTypes in the market. The process then begins again, with -the agents solving their updated microeconomic models given the new dynamic rule; the solve -loop continues until the “distance” between successive dynamic rules is sufficiently -small. -

-

0.4.2  Attributes of a Market

-

To specify a complete instance of Market, the user should give it the following -attributes:16 - - -

    -
  • agents: A list of AgentTypes, representing the agents in the market. Each element in - agents represents an ex-ante heterogeneous type; each type could have many ex-post - heterogeneous agents. -
    • -
  • sow_vars: A list of strings naming variables that are output from the aggregate - market process, representing the macroeconomic outcomes. These variables will be - distributed to the agents in the sow step. -
    • -
  • reap_vars: A list of strings naming variables to be collected from the agents in the - reap step, to be used as inputs for the aggregate market process. -
    • -
  • const_vars: A list of strings naming variables used by the aggregate market process - that do not come from agents; they are constant or come from the Market itself. -
    • -
  • track_vars: A list of strings naming variables generated by the aggregate market - process that should be tracked as a history, to be used when calculating a new - dynamic rule. Usually a subset of sow_vars. -
    • -
  • dyn_vars: A list of strings naming the variables that constitute a dynamic rule. These - will be stored as attributes of the agents whenever a new rule is calculated. -
    • -
  • millRule: A function for the “aggregate market process”, transforming - microeconomic outcomes into macroeconomic outcomes. Its inputs are named in - reap_vars and const_vars, and it returns a single object with attributes named in - sow_vars and/or track_vars. Can be defined as a method of a subclass of Market. -
    • -
  • calcDynamics: A function that generates a new dynamic rule from a history of - macroeconomic outcomes. Its inputs are named in track_vars, and it returns a single - object with attributes named in dyn_vars. -
    • -
  • act_T: The number of times that the makeHistory method should execute the - “farming loop” when generating a new macroeconomic history. -
    • -
  • tolerance: The minimum acceptable “distance” between successive dynamic rules - produced by calcDynamics to constitute a sufficiently converged solution.
- - -

Further, each AgentType in agents must have two methods not necessary for microeconomic -models; neither takes any input (except self): -

    -
  • marketAction: The microeconomic process to be run in the cultivate step. Likely - uses the new macroeconomic outcomes named in sow_vars; should store new values - of relevant microeconomic outcomes in the attributes (of self) named in reap_vars. -
    • -
  • reset: Reset, initialize, or prepare for a new “farming loop” to generate a - macroeconomic history. Might reset its internal random number generator, set initial - state varibles, clear personal histories, etc.
-

When solving macroeconomic models in HARK, the user should also define classes to represent -the output from the aggregate market process in millRule and for the model-specific -dynamic rule. The latter should have a distance method to test for solution convergence; -if the class inherits from HARKobject, the user need only list relevant attributes in -distance_criteria. -

0.4.3  Sample Model: FashionVictim

-

To illustrate the Market class, consider a simple example in the emerging economic subfield of aesthemetrics, the -FashionVictimModel.17 18 -This module defines a subclass of AgentType called FashionVictimType. Each period, fashion -victims make a binary choice of style s : to dress as a jock (0) or punk (1). They receive utility -directly from the outfit they wear and as a function of the proportion of the population who just -wore the same style; they also pay switching costs (cpj ,cjp ) if they change styles rather than -keep the same as the previous period. Moreover, they receive an idiosyncratic type 1 extreme -value (T1EV) preference shock to each style in each period. Defining the population punk -proportion as p and the conformity utility function as f : [0,1] → ℝ , the current period utility -function is thus: -
-
- - -u(s ;s ,p) = s f(p )+ (1- s )f(1- p )+s U + (1- s )U - c s (1- s )- c (1 - s )s . - t t- 1 t t t t t t p t j pj t-1 t jp t-1 t -
-

-

Fashion victims are forward looking and discount future utility at a constant rate of -β per period. To simplify the analysis, we assume they believe that the population -punk proportion in the next period is a linear function of the punk proportion in the -current period, subject to a uniformly distributed shock. No restrictions are put on -the function f ; fashion victims might be conformists who like to dress the same -as others (f′(p) > 0 ) or hipsters who like to style themselves in the minority -(f′(p) < 0 ).19 -A fashion victim’s problem can be written in Bellman form as: -
-
- [ ] - -V(st-1,pt) = E smt∈a{x0,1}u (st;st-1,pt) + ηst + βE [V (st,pt+1)] , -
-

-
-
-pt+1 = apt + b + πt+1, πt+1 ~ U[- w,w ], η0,η1 ~ T 1EV. - - -
-

-

An instance of FashionVictimType is thus characterized by values of Up , Uj , cpj , cjp -and a function f , as well as beliefs about pt+1 as a function of pt (summarized -by slope a , intercept b , and uniform shock width w ). Given this information, -a FashionVictimType’s infinite horizon microeconomic model can be solved by -backward induction in a few lines; the “one period solver” is given by solveFashion. -However, while individual agents treat the dynamics of pt as exogenous, they are -in fact endogenously determined by the actions of all the fashion victims in the -market.20 -A dynamic general equilibrium of the “macroeconomic fashion model” is thus characterized by a -triple of (a,b,w) such that when fashion victims believe in this “punk evolution rule” and act -optimally, their collective fashion choices exhibit this same rule when the model is -simulated. -

The search for a dynamic general equilibrium is implemented in HARK’s Market class with the -following definitions: -

sow_vars = [’pNow’] (macroeconomic outcome is pt ) -

reap_vars = [’sNow’] (microeconomic outcomes are st for many agents) -

track_vars = [’pNow’] (must track history of pt ) -

dyn_vars = [’pNextSlope’,’pNextIntercept’,’pNextWidth’] (dynamic rule (a,b,w ) ) -

millRule = calcPunkProp (aggregate process: average the style choices of all agents) -

calcDynamics = calcFashionEvoFunc (calculate new (a,b,w) with autoregression of -pt ) -

act_T = 1000 (simulate 1000 periods of the fashion market) -

tolerance = 0.01 (terminate solution when (a,b,w ) changes by less than 0.01) -

The agents attribute has a list of 22 FashionVictimTypes, which vary in their values of Up -and U - j , and their f functions. The marketAction method of FashionVictimType -simulates one period of the microeconomic model: each agent receives style preference -shocks η0 and η1 , sees the current proportion of punks pt (sown to them as pNow), -and chooses which style to wear, storing it in the binary array sNow, an attribute of -self. -

The millRule for this market is extremely simple: it flattens the list of binary arrays of -individual style choices (gathered in the reap step) and averages them into a new value -of pt , to be tracked as a history and sown back to the agents to begin the cycle -again. Once a history of 1000 values of pt has been generated with the makeHistory -method, we can calculate a new dynamic fashion rule with calcFashionEvoFunc by - - -regressing p - t on p - t- 1 , approximating w as twice the standard deviation of prediction -errors.21 -The new fashion rule is an instance of the simple FashionEvoFunc class, whose only methods are -inherited from HARKobject. -

When the solve method is run, the solver successively solves each agent’s microeconomic -problem, runs the makeHistory method to generate a 1000 period history of pt , and calculates -a new punk evolution rule based on this history; the solver terminates when consecutive rules -differ by less than 0.01 in any dimension. -

0.5  Contributing to HARK

-

This section provides an overview of how users can contribute new code to HARK, what sort of -contributions are most desired, and style conventions for the toolKit. Before attempting to -extend HARK, we strongly recommend that you familiarize yourself with its features and models -by looking at the source code. -

-

0.5.1  What Does HARK Want?

-

The easiest and most straightforward way to contribute to HARK is by writing new general -purpose tools for inclusion in one of the top-level modules. This might be a commonly -known data analysis technique (e.g. a kernel density estimator) for HARK.utilities, an -optimization method (local or global) for HARK.estimation, an interpolation method for -HARKinterpolation, etc. If you’ve found a technique useful in your own research and -believe it could be of use to others on entirely different projects, then it probably -belongs in HARK. Likewise, if you read through a HARK module expecting to find a -certain tool or function (because of similar functions that are present, or merely because -it’s very useful) but don’t, this is a good sign that HARK has a hole that you can -fill.22 -

Secondly, the development team wants to expand HARK to include more canonical models. -Most of the models available in the toolKit at the time of its public beta release concern agents -with (geometrically discounted) CRRA utility over a unitary consumption good, who receive - - -permanent and transitory shocks to income and have access to a single risk free asset. This is an -important class of models, but that description has an awful lot of qualifiers. In short, there are -many more common features of dynamic household models that are not yet in HARK, but would -be very valuable for the toolKit. New model contributions should “play nicely” with existing -models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the -greatest extent possible. For ideas of potential model extensions for HARK, see section -0.6.2.23 -

Finally, contributions to HARK are not restricted to entirely new functionality. While we have -done our best to provide flexible frameworks and useful tools, we have almost certainly -overlooked an unexpected case that would not be (properly) handled by the toolKit as is. -Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake -or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as -an Issue on GitHub so that someone else might. The “mistake” you find might not -even be in the code itself, but a deficiency in the documentation: a misnamed (or -incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing) -comment, even a grammatical or spelling error. No matter how niggling, we want your -fix. -

For those who want to contribute to the HARK project without programming a new model or -digging through the code for errors and omissions, consider reading through the Issues page on -GitHub to see what other users have flagged for development. You might find several pieces of -“low hanging fruit” for you to quickly fix. -

0.5.2  Git and GitHub

-

Git is a distributed version control system that has gained increasing popularity in the decade -since its creation; it is designed to enable collaboration and “non-linear workflow” among a -(potentially disperse) programming team. As HARK is to be developed by volunteer researchers -all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds -as a command line tool, others have since extended git with further functionality and -a user interface more friendly to those more familiar with GUIs. Foremost among -these tools is GitHub.com, where the HARK project is hosted. This section provides a -brief overview of how to use GitHub to interact with (and potentially contribute to) -HARK. -

To prepare to contribute to HARK, follow these simple steps: -

    -
  1. Make a GitHub account: Go to github.com; the homepage should prompt - you to choose a username and password. Signing up is quick, easy, and - - - free.24 -
    2. -
  2. Install GitHub Desktop: Go to desktop.github.com and download the Desktop - application for your operating system, then install it. After opening Desktop for the - first time, enter your GitHub login to connect it to your account. -
    3. -
  3. Fork the HARK repository: Go to the HARK repository page, click on the Fork - button in the upper right, and choose to fork HARK to your own GitHub account. - This creates a new repository identical to the one hosted by Econ-Ark at the moment - you fork it, but hosted on your account instead. -
    4. -
  4. Add the repository to Desktop: Open GitHub Desktop, click on the + icon - in the upper left, and select clone. Find the repository named yourusername/HARK, - click the Clone button at the bottom of the dialogue window, and choose a directory - on your local machine.
-

You now control a “fork” of the main HARK repository, hosted by GitHub, and have a clone of -this fork on your local machine. You are free to edit your fork of HARK in any way you’d like; -other GitHub users can view your fork (and fork it themselves), but they cannot make changes to -your fork unless you give them permission by adding them as a contributor. Changes made -to the local copy of your fork do not get automatically sent to the remote copy on -GitHub for public viewing. To make changes to your fork on GitHub, follow these -steps: -

    -
  1. Make local changes: Edit your local copy of the repository in any way you’d like: - add or delete lines from existing files; add, delete, or rename entire files or directories. -
    2. -
  2. Commit local changes: Open the repository in GitHub Desktop and click on the - Changes tab. In the lower left, provide a short description of the changes made in - the Summary field, and (optionally) longer comments about these changes in the - Description field. Click the check-mark Commit button. -
    3. -
  3. Push the commit: Click on the Sync button near the upper right to “push” your - local commits to the remote server; this will also pull down remote commits from - other contributors to your fork that were made since the last time you synced.
- - -

The left hand panel of the Changes tab has a summary of the files that have been changed, -added, or removed since your last commit; the right hand panel shows the specific -changes made to each changed file. After step 2, a record of your changes has been -stored locally by the git system, but is not yet reflected in the public remote copy on -GitHub.25 -The “timeline” summary of the repository at the top of the Desktop window shows the history of -commits for the repository: each dot represents a commit that was pushed to the remote server, -and each open ring represents a local commit that has not been pushed to the remote server; -the large broken ring on the far right represents changes that have not been locally -committed.26 -The History tab lists all previous commits, including the timestamp, contributor, and a record of -changes made. -

Suppose you have mucked about in your fork for a while and have made changes to HARK -that you think should be included in the main repository hosted by Econ-Ark. To make your -dream a reality, follow these steps: -

    -
  1. Isolate the changes: If you mucked around for quite a while, making various changes - here and there, and then later made some contribution that you want to include in - the main repository, you should isolate the desired changes in a new branch of your - fork. Make a new branch by clicking the button toward the upper left of GitHub - Desktop, revert that branch to the commit just before you forked from the main - repository,27 - then edit this branch to include only the changes you want to push to the main - repository. -
    2. -
  2. Issue a pull request: Go to HARK’s GitHub page and click the “New pull request” - button. Click the text that says “compare across forks”, and select econ-ark/HARK - as the base fork, master as the base, your fork as the head fork, and the branch to - be merged as the “compare”. In the boxes below, give a name to your pull request - and provide sufficient comments about your contribution so that a development team - member can understand what you’ve done. Click the “Create pull request” button. -
    3. - - -
  3. Be patient: Someone from the development team will look at your contribution and - either merge it into the main repository or return it with comments. See section 0.5.3 - for procedures followed by the HARK team when evaluating outside contributions. -
-

0.5.3  Submission Approval Process

-

Before submitting to HARK, the development team would like you to make sure that -your intended contribution is listed as an Issue on the GitHub page. If you are going -to fix or address an Issue that is already posted, reply to that post with a note -indicating that you are about to work on it and an estimate of when you will make a -pull request for it. If you want to do something that no one has mentioned yet as -an Issue, make a new Issue on GitHub (briefly) describing what you intend to -do.28 -This will both help prevent two people from working on the same thing (and one of them -potentially wasting time on it), and lets the development team know what’s coming. Moreover, if -you propose a contribution that is (a) already in HARK, (b) not in the spirit of HARK, or (c) a -fix for something that’s not actually broken, this gives the team a chance to let you know not to -spend effort on it. -

After creating a pull request to submit your contribution, it will be evaluated by one or more -members of the HARK team. Criteria that the HARK team will consider when choosing whether -to accept a submission include: -

    -
  1. Does the contribution break previous code? If you change or expand an existing - function, you should make sure that this does not cause some models or applications - to work incorrectly. In the near future, HARK will have a core set of tests to run to - check for full compatibility. -
    2. -
  2. Is the code well documented? Code submissions should have properly formatted - docstrings for each new function, class, method, and module (see section 0.5.5) and - enough comments for a reader to follow the algorithm. -
    3. -
  3. Is the contribution relevant? The development team intends for HARK to - be diverse, but not everything goes in HARK. If your submission concerns a - representative agent model or a method for estimating a reduced form model, then - it probably doesn’t belong in HARK. - - -
    4. -
  4. Is the code demonstrated with an example? If your solve a new model in - HARK, you should include a set of example parameters for other users to easily - test. If you’ve contributed a new function, consider providing a snippet of code - demonstrating its use.29 -
    5. -
  5. Is the code correct? Your code should actually do what it claims to do. -
    6. -
  6. Does the code use HARK conventions? To the best of your ability, contributions - to HARK should follow the style guidelines in section 0.5.4. We don’t demand - perfection, as these are guidelines rather than rules. -
    7. -
  7. Is the code well integrated in HARK? In the beta release of HARK, we have - tried to present a “taxonomy” of consumption-saving models that build up from each - other. When possible, new models in HARK should try to continue this pattern. A - submission that introduces a model in a new module that could have easily been made - as an extension of an existing module might be rejected. However, we acknowledge - that there is value in providing a solver for a new model, and that a future contributor - might make her mark by integrating it into the existing hierarchy. -
    8. -
  8. Is this already in HARK? If your contribution is already in HARK, then it will - almost certainly be rejected. Of course, if you submit a version that dominates the - existing implementation (or even one that works better in a well described, non-trivial - set of circumstances), it belongs in HARK.
-

The HARK team intends to release more specific guidelines in the near future, so that contributors -are better able to judge their submissions before issuing a pull request. We are in the process of -recruiting “czars” to oversee particular aspects of HARK, providing their knowledge and -expertise.30 -While czars might directly evaluate submissions as a HARK team member, their more important -role is to help establish these guidelines for their domain, more specifically nailing down what we -mean by “correct” or “well integrated”. - - -

0.5.4  Naming Conventions

-

Object naming conventions in HARK are fairly different than existing standards, and differ -somewhat between tool modules vs model or application modules. The following conventions -apply throughout HARK: -

    -
  • Functions and methods are always in “camel case”: no underscores, first letter is lower - case, first letter of each subsequent word is capitalized. E.g. approxLognormal. -
    • -
  • Function and method names should accurately and concisely describe what the - function does; the first word in the name must be a verb.31 -
    • -
  • Variable and class names should not have a verb as their first word. -
    • -
  • Class names should use no underscores and capitalize the first letter of each word; - moreover, a class name must include a noun. E.g. ConsPerfForesightSolver.
-

When naming variables in model modules, the HARK team strongly discourages using single -letter names, like c for consumption. Instead, we encourage contributors to use longer, more -descriptive variable names using additional words and common abbreviations to specify the -variable more precisely. In /Documentation/NARK.pdf, we list standard single letter variable -“bases” and an array of prefixes and suffixes to adjust them. Economic variables in model -modules should (usually) not use underscores, instead using camel case to the greatest extent -possible.32 -The development team prefers this standard so that users can translate between Python code -and LaTeX script with minimal work. -

Conventions for naming variables in HARK’s tool modules are significantly closer to more -commonly used standards. Variable names should be in all lower case, with underscores between -words, e.g. data_to_match. The functions and classes in these modules are more general -and almost surely do not have any inherent “economic content”; they are numerical -or algorithmic objects, not variables that might appear in an equation in an article -for a (non-computational) economics journal. Variable names in application modules -(e.g. the files that execute the cstwMPC estimations) are a mix of the conventions for tool -and model files, as appropriate for each variable. That is, variables that are directly -related to “economic variables” in model modules should follow those conventions, while -objects created solely for data manipulation or reporting should use the style of tool -modules. - - -

0.5.5  Documentation Conventions

-

The HARK team wants the toolKit to be as accessible to users as possible; our greatest -fear33 is -that a new user will open up our code, get hopelessly confused trying to read it, and then never -look at HARK again. To prevent this tragic outcome, we have tried hard to provide -comprehensive, accurate documentation and comments within the code describing our -methods.34 -Moreover, HARK uses the Sphinx utility to generate a website with online documentation for all -of our tool and model modules, so that users can learn about what’s available in HARK without -digging through the source code. When making contributions to HARK, the development team -asks users to format their inline documentation to work with Sphinx by following a few simple -rules. -

    -
  • The top of every module should begin with a “docstring” providing a clear description - of the contents of the module. The first sentence should concisely summarize - the file, as it may appear in an index or summary of all modules without the - remaining sentences. A docstring at the top of a module should be formatted - as:35 -

    """ -

    Specifies an economic model and provides methods for solving it. More - specific description of the key features of the model and variations - of it in this module. Maybe some comments about the solution method - or limitations of the model. Your bank account routing number. -

    """ -

    • -
  • The line directly below the declaration of a function, method or class should begin a - docstring describing that object. As with modules, the first sentence should concisely - summarize the function or class, as it might be included in an index or summary. For - functions and methods, the docstring should be formatted as: -

    def functionName(input1,input2): - - -

       """ -

       Concise description of the function. More details about what -

       the function does, options or modes available, and maybe - mathematical -

       methods used. Credit to a source if you poached their - algorithm. -

       Parameters -

       –––––––––– -

       input1: type -

         Description of what input1 represents. -

       input2: type -

         Description of what input2 represents. -

       Returns -

       ––––––– -

       output_name: type -

         Description of the output(s) of the function. Might have -

         multiple entries. If no output, this is just "None". -

       """ -

    • -
  • Provide ample comments within a function or method so that a relatively intelligent - reader can follow along with your algorithm. Short comments can follow at the end - of a line, longer comments (or descriptions of the step or task about to be performed) - should precede a block of code on the line(s) above it.
-

Finally, if you write a new model module, the HARK team asks that you also provide a short -mathematical writeup of the model as a PDF. This document does not need to go into great -detail about the solution method for the model or the functions and classes included in -the module, merely specify the economic model and provide a summary of how it -is solved. See /Documentation/ConsumptionSavingModels.pdf for an example of -this. - - -

0.6  Future of HARK

-

HARK is in a state of perpetual incompleteness, always expanding to include more -tools and more models. As an open source project in the hands of many contributors -around the world, the HARK team cannot fully predict future features of the toolKit. -This section provides an overview of what we would like to see in the near future, as -well as potential mechanisms to encourage researchers to contribute their code to the -project. -

-

0.6.1  Future Tools

-

The current frameworks in HARK.core concern agents who have “isolated” dynamic -problems or interact with each other only through aggregate outcomes to which they -contribute atomically. We hope to eventually provide a framework for models in which -agents directly interact with each other; this may include a system for specifying the -state of networks and how connections are formed and broken. HARK.core might also -develop a framework more suited to industrial organization models and dynamic -games.36 -

The HARK.estimation module in particular is very sparsely populated, with its -functions mostly serving as wrappers for “off the shelf” optimization methods from -scipy.optimize. There are plenty of non-standard or more complex optimization -techniques available, including global and/or stochastic search methods. We believe -that additional optimizers are “low hanging fruit” as straightforward contributions -to HARK and thus will be added in the near future. Moreover, there are plenty of -distributions that have no approximator in HARK.utilities (or data generator in -HARK.simulation), and these functions are among the easiest for new contributors -to write. The data analysis tools in HARK.utilities only include functions that we -happened to use for our current research projects, and we expect that this will be the -mode of growth for future tools. There are some obvious holes that we believe will be -filled quite quickly; for example, we provide a rudimentary non-parametric kernel -regression function, but have not written a kernel density estimator to generate PDFs from -data. -

The classes currently in HARK.interpolation are all variations on linear (or -cubic) spline interpolation, and each is specifically written for a fixed number -of dimensions. As these methods use full tensor grids, they are highly impractical -for representing functions in higher dimensions, suffering greatly from the curse of - - -dimensionality.37 -We are quite confident that HARK.interpolation will soon include techniques more applicable -to high dimensional state spaces, including Smolnyak interpolation, other (adaptive) sparse grid -methods, and regression-based interpolation. For lower-dimensional spaces, HARK will also soon -provide Delaunay interpolation classes. -

The HARK.parallel module currently provides a simple interface for handling ex-ante -heterogeneity among agents. While useful for speeding up moderately-sized problems, the -libraries used are only able to access the CPU of the local machine running the code. In the very -near future, the HARK team will provide an interface (and example) for using Apache Spark, -which bills itself as a “fast and general engine for large-scale data processing”. Specifically, users -will be able to easily scale up their computing resources and run HARK code on an Amazon EC2 -session or other cluster. -

0.6.2  Future Models

-

The economics models currently included in HARK mostly concern consumption-saving problems -with constant relative risk aversion consumers who experience fully permanent and fully -transitory shocks to income. This is obviously a very small subset of all consumption-saving -models (which are themselves a small subset of all dynamic models economists might be -interested in). Consider some examples of variants, features, and extensions that are not included -in HARK, but could be in the future: -

    -
  • Consumers with other utility functions, such as CARA or expo-power. -
    • -
  • Consumers with time-inconsistent preferences, e.g. hyperbolic discounting. -
    • -
  • Income shocks that are neither fully transitory nor fully permanent. -
    • -
  • Consumers with a bequest motive or terminal utility function. -
    • -
  • Utility from leisure and endogenous labor supply. -
    • -
  • Investment in a durable consumption good such as housing. - - -
    • -
  • Exogenous shocks to expenses. -
    • -
  • Multiple consumption goods, as with endogenous medical care. -
    • -
  • Portfolio allocation between a risky and riskless asset. -
    • -
  • Job search intensity as a control variable. -
    • -
  • Endogenous health evolution and health investment.
-

The HARK team is currently developing a module for mixing discrete and continuous -choice models, in the style of Jorgensen, Rust, Iskhakov, and Schjerning (2015). Among -many other models, this framework will enable HARK users to easily develop features -like: -

    -
  • Labor supply on the extensive margin: the timing of retirement. -
    • -
  • Selection of job type or sector (by riskiness, specific human capital, etc). -
    • -
  • Choice of medical insurance contract. -
    • -
  • Decision to rent or own housing. -
    • -
  • Endogenous default on debt (with “macroeconomic” pricing of debt). -
    • -
  • Household formation, dissolution, and reproduction.
-

Finally, we hope to provide a generic solver for any discrete choice model with T1EV shocks. -The scope of HARK is limited only by what users choose to put in it. We hope that -within a year of its debut, HARK will include a significant number of canonical models, -providing more building blocks for future researchers to create novel combinations and new -extensions. - - -

-

0.6.3  Bounty Hunting

-

While HARK users are free to write and contribute any tools and models that they believe could -be helpful, building the toolKit in a “bottom up” style, the HARK team hopes to provide -mechanisms for targeted “top down” development. The team is working to secure grant funding for -HARK so as to offer “bounties” for particular models, tools, applications, or frameworks. Suppose -the team decides that the model in Important Paper X should be included as part of HARK; we -will issue a call for contributions to fill this hole, offering a modest prize of, say, $20,000 to the -programmer who submits the best implementation of the model, as judged by clearly stated -criteria.38 -The bounty would be announced as an Issue on HARK’s GitHub page, as well as in a potential -electronic digest of HARK news. -

Bounties might be placed on items as small as a single data analysis tool or as large as a -complex estimation of a large general equilibrium model, with prizes scaling accordingly. We -hasten to emphasize that we cannot guarantee funding for HARK, so the bounties (like -everything else in this section) are purely speculative. As economists, the HARK team -understands the incentive compatibility problems associated with asking researchers to -contribute significant amounts of their time, effort, and unique skills in service of a -tenuous concept like the Greater Good in exchange for even more tenuous Glory. We -acknowledge that economic researchers are also optimizing agents with their own preferences -over leisure and consumption, and agree that Greed is a valid mechanism to expand -HARK. -

0.6.4  All Aboard the Ark

-

As observant readers may have noticed, the HARK repository is hosted by a GitHub account -named Econ-Ark, with no explanation as to what this might mean. Looking a few years into the -future, the development team believes that HARK might be the first piece of a broader collection -of computational economics resources. HARK is open source but somewhat akin to an -exclusive club: contributions are required to adhere to standards and norms so as to foster -modularity and interoperability among models and tools. Consistent with its namesake ship, -the Econ-Ark has a significantly lower bar for admission, with a place on board for -everything. -

On one level, the Ark might serve as a warehouse for any and all quantitative models and -tools, for any economic purpose, in any subfield, and written in any language. Through -nearly unanimous feedback, the HARK team understands that many economists are -willing to share code that they have already written, but are reluctant to expend the - - -considerable effort to translate and format it for HARK. In the future, we will set up a -mechanism for code like this to be hosted on the Ark, with minimal requirements -imposed on its authors (and correspondingly minimal warranty for potential users). -Relevant projects on the Ark might become bounty targets for HARK, with users -incentivized to port the original code into Python and to meet HARK standards; -direct access to the original source code will significantly expedite the translation -process, and provide a suitable benchmark for testing and verifying the contribution to -HARK. -

On a different level, the Ark might also include cohesive toolkits like HARK, but geared -toward purposes other than heterogeneous agents macroeconomics models. For example, the -previously mentioned modules for networks among agents (and other agent-based modeling), -industrial organization and dynamic games, and traditional discrete choice models might become -their own toolkits, with some interoperability with HARK. In support of this, some of the tool -modules currently in HARK might be “promoted” and become universally shared across all -Econ-Ark toolkits. -

The dark secret of HARK is that it is named for heterogeneous agents macroeconomics, but -designed and written by structural microeconomists. The development team believes these -subfields to have considerable overlap in terms of methods and techniques, and that HARK -might not be noticed by half of its potential audience due to its name. In a general sense, the -Econ-Ark gives us cover to expand the scope of our efforts and/or rebrand the toolKit for an -alternative audience. - - - - - From a95afc5e1aee0caf81a7391b6aa4ecede6372e4c Mon Sep 17 00:00:00 2001 From: Sebastian Benthall Date: Sat, 15 Feb 2020 06:58:43 -0500 Subject: [PATCH 208/242] RTD point to gh manual (#523) * update RTD link to manual to HARK GH-pages version * remove reference to manual in 'list of files' in the HARK repository --- Documentation/introduction.md | 3 +-- Documentation/list-of-files.md | 3 --- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/Documentation/introduction.md b/Documentation/introduction.md index ef113cbdf..d626a88da 100644 --- a/Documentation/introduction.md +++ b/Documentation/introduction.md @@ -7,7 +7,6 @@ overview of the main elements of the toolkit. Other useful resources are: * Documentation: [ReadTheDocs](https://hark.readthedocs.io/) - * User guide: [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf) - * In the [HARK repository](https://github.com/econ-ark/HARK) + * User guide: [HARKmanual](http://econ-ark.github.io/HARK/Documentation/HARKmanual/) * Demonstrations of HARK functionality: [DemARK](https://github.com/econ-ark/DemARK/) * Replications and Explorations Made using the ARK : [REMARK](https://github.com/econ-ark/REMARK/) diff --git a/Documentation/list-of-files.md b/Documentation/list-of-files.md index d43320865..5967dd7b2 100644 --- a/Documentation/list-of-files.md +++ b/Documentation/list-of-files.md @@ -3,10 +3,7 @@ This section contains descriptions of the main files in the repo. Documentation files: -* [README.md](README.md): The file you are currently reading. * [Documentation/HARKdoc.pdf](Documentation/HARKdoc.pdf): A mini-user guide produced for a December 2015 workshop on HARK, unofficially representing the alpha version. (Substantially out of date). -* [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf): A user guide for HARK, written for the beta release at CEF 2016 in Bordeaux. Should contain 90% fewer lies relative to HARKdoc.pdf. - * [Documentation/HARKmanual.tex](Documentation/HARKmanual.tex): LaTeX source for the user guide. Open source code probably requires an open source manual as well. * [Documentation/ConsumptionSavingModels.pdf](Documentation/ConsumptionSavingModels.pdf): Mathematical descriptions of the various consumption-saving models in HARK and how they map into the code. * [Documentation/ConsumptionSavingModels.tex](Documentation/ConsumptionSavingModels.tex): LaTeX source for the "models" writeup. * [Documentation/NARK.pdf](Documentation/NARK.pdf): Variable naming conventions for HARK, plus concordance with LaTeX variable definitions. Still in development. From 0a473993ecb94910bb4d05e2b0503dffc1ce7090 Mon Sep 17 00:00:00 2001 From: Saptarshi Kumar Date: Sat, 15 Feb 2020 18:23:00 +0530 Subject: [PATCH 209/242] Added updateLbrCost Function --- HARK/ConsumptionSaving/ConsLaborModel.py | 34 ++++++++++++++++++++++-- 1 file changed, 32 insertions(+), 2 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 1188e85bb..3c5f9c431 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -253,7 +253,7 @@ class LaborIntMargConsumerType(IndShockConsumerType): future utility flows at a constant factor. ''' time_vary_ = copy(IndShockConsumerType.time_vary_) - time_vary_ += ['LbrCost','WageRte'] + time_vary_ += ['WageRte'] time_inv_ = copy(IndShockConsumerType.time_inv_) def __init__(self,cycles=1,time_flow=True,**kwds): @@ -297,7 +297,37 @@ def update(self): self.updateIncomeProcess() self.updateAssetsGrid() self.updateTranShkGrid() - + self.updateLbrCost() + + def updateLbrCost(self): + ''' + Construct the age-varying cost of working LbrCost using the attribute LbrCostCoeffs. + This attribute should be a 1D array of arbitrary length, representing polynomial + coefficients (over t_cycle) for (log) LbrCost. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + Coeffs = self.LbrCostCoeffs + N = len(Coeffs) + age_vec = np.arange(self.T_cycle) + LbrCostBase = np.zeros(self.T_cycle) + for n in range(N): + LbrCostBase += Coeffs[n]*age_vec**n + LbrCost = np.exp(LbrCostBase) + time_orig = self.time_flow + self.timeFwd() + self.LbrCost = LbrCost + self.addToTimeVary('LbrCost') + if not time_orig: + self.timeRev() + + def calcBoundingValues(self): ''' Calculate human wealth plus minimum and maximum MPC in an infinite From e2bd354c57736218d5861bde291b59e66ff2e704 Mon Sep 17 00:00:00 2001 From: Saptarshi Kumar Date: Sat, 15 Feb 2020 23:47:12 +0530 Subject: [PATCH 210/242] Updated ConsumerParameters --- HARK/ConsumptionSaving/ConsumerParameters.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 181eebd17..1e1547307 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -308,7 +308,7 @@ LivPrb = [0.99] # Survival probability PermGroFac = [1.02] # Permanent income growth factor -LbrCost = [0.36] # Labor cost list of one element +LbrCostCoeffs = [0.36] # Labor cost coefficents list of one element WageRte = [1.0] # Wage Rate BoroCnstArt = None @@ -335,7 +335,7 @@ init_labor_intensive ['T_retire'] = 0 # Turn off retirement init_labor_intensive ['aXtraCount'] = 200 # May be important to have a larger number of gridpoints (than 48 initially) init_labor_intensive ['aXtraMax'] = 80. -init_labor_intensive ['LbrCost'] = LbrCost +init_labor_intensive ['LbrCostCoeffs'] = LbrCostCoeffs init_labor_intensive ['WageRte'] = WageRte init_labor_intensive ['LivPrb'] = LivPrb init_labor_intensive ['PermGroFac'] = PermGroFac @@ -352,7 +352,7 @@ init_labor_lifecycle['TranShkStd'] = [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0.1,0.2,0.3] init_labor_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] # Living probability decreases as time moves forward. init_labor_lifecycle['WageRte'] = [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9] # Wage rate in a lifecycle -init_labor_lifecycle['LbrCost'] = LbrCost * 10 # Assume labor cost is constant during the lifecycle +init_labor_lifecycle['LbrCostCoeffs'] = LbrCostCoeffs * 2 # Assume labor cost coeffs is two-dimensional init_labor_lifecycle['T_cycle'] = 10 #init_labor_lifecycle['T_retire'] = 7 # IndexError at line 774 in interpolation.py. init_labor_lifecycle['T_age'] = 11 # Make sure that old people die at terminal age and don't turn into newborns! \ No newline at end of file From 75e57ec18beee197c7ef63c88bb1026fd29525e8 Mon Sep 17 00:00:00 2001 From: sb Date: Sat, 15 Feb 2020 16:16:56 -0500 Subject: [PATCH 211/242] adding tests for IndShockConsumerType class, based on BufferStock REMARK. fixes #510 --- HARK/tests/test_IndShockConsumerType.py | 92 +++++++++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 HARK/tests/test_IndShockConsumerType.py diff --git a/HARK/tests/test_IndShockConsumerType.py b/HARK/tests/test_IndShockConsumerType.py new file mode 100644 index 000000000..774d445d2 --- /dev/null +++ b/HARK/tests/test_IndShockConsumerType.py @@ -0,0 +1,92 @@ +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType +import HARK.ConsumptionSaving.ConsumerParameters as Params +import numpy as np +import unittest + +class testBufferStock(unittest.TestCase): + """ Tests of the results of the BufferStock REMARK. + """ + + def setUp(self): + # Make a dictionary containing all parameters needed to solve the model + self.base_params = Params.init_idiosyncratic_shocks + + # Set the parameters for the baseline results in the paper + # using the variable values defined in the cell above + self.base_params['PermGroFac'] = [1.03] + self.base_params['Rfree'] = 1.04 + self.base_params['DiscFac'] = 0.96 + self.base_params['CRRA'] = 2.00 + self.base_params['UnempPrb'] = 0.005 + self.base_params['IncUnemp'] = 0.0 + self.base_params['PermShkStd'] = [0.1] + self.base_params['TranShkStd'] = [0.1] + self.base_params['LivPrb'] = [1.0] + self.base_params['CubicBool'] = True + self.base_params['T_cycle'] = 1 + self.base_params['BoroCnstArt'] = None + + def test_baseEx(self): + baseEx = IndShockConsumerType(**self.base_params) + baseEx.cycles = 100 # Make this type have a finite horizon (Set T = 100) + + baseEx.solve() + baseEx.unpackcFunc() + + m = np.linspace(0,9.5,1000) + + c_m = baseEx.cFunc[0](m) # c_m can be used to define the limiting infinite-horizon consumption rule here + c_t1 = baseEx.cFunc[-2](m) # c_t1 defines the second-to-last period consumption rule + c_t5 = baseEx.cFunc[-6](m) # c_t5 defines the T-5 period consumption rule + c_t10 = baseEx.cFunc[-11](m) + + self.assertEqual(c_m[500], 1.4008090582203356) + self.assertEqual(c_t1[500], 2.9227437159255216) + self.assertEqual(c_t5[500], 1.7350607327187664) + self.assertEqual(c_t10[500], 1.4991390649979213) + self.assertEqual(c_t10[600], 1.6101476268581576) + self.assertEqual(c_t10[700], 1.7196531041366991) + + def test_GICFails(self): + GIC_fail_dictionary = dict(self.base_params) + GIC_fail_dictionary['Rfree'] = 1.08 + GIC_fail_dictionary['PermGroFac'] = [1.00] + + GICFailExample = IndShockConsumerType( + cycles=0, # cycles=0 makes this an infinite horizon consumer + **GIC_fail_dictionary) + + GICFailExample.solve() # Above, we set up the problem but did not solve it + GICFailExample.unpackcFunc() # Make the consumption function easily accessible for plotting + m = np.linspace(0,5,1000) + c_m = GICFailExample.cFunc[0](m) + + self.assertEqual(c_m[500], 0.7772637042393458) + self.assertEqual(c_m[700], 0.8392649061916746) + + def test_infinite_horizon(self): + baseEx_inf = IndShockConsumerType(cycles=0, + **self.base_params) + + baseEx_inf.solve() + baseEx_inf.unpackcFunc() + + m1 = np.linspace(1,baseEx_inf.solution[0].mNrmSS,50) # m1 defines the plot range on the left of target m value (e.g. m <= target m) + c_m1 = baseEx_inf.cFunc[0](m1) + + self.assertEqual(c_m1[0], 0.8527887545025995) + self.assertEqual(c_m1[-1], 1.0036279936408656) + + x1 = np.linspace(0,25,1000) + cfunc_m = baseEx_inf.cFunc[0](x1) + + self.assertEqual(cfunc_m[500], 1.8902146173138235) + self.assertEqual(cfunc_m[700], 2.1591451850267176) + + m = np.linspace(0.001,8,1000) + + # Use the HARK method derivative to get the derivative of cFunc, and the values are just the MPC + MPC = baseEx_inf.cFunc[0].derivative(m) + + self.assertEqual(MPC[500], 0.08415000641504392) + self.assertEqual(MPC[700], 0.07173144137912524) From 68f41443377f0648dee5e7da7a04d586bf044914 Mon Sep 17 00:00:00 2001 From: sb Date: Sat, 15 Feb 2020 17:05:14 -0500 Subject: [PATCH 212/242] using assertAlmostEqual for tests to avoid failures due to operating system --- HARK/tests/test_IndShockConsumerType.py | 28 ++++++++++++------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/HARK/tests/test_IndShockConsumerType.py b/HARK/tests/test_IndShockConsumerType.py index 774d445d2..f7f8401d2 100644 --- a/HARK/tests/test_IndShockConsumerType.py +++ b/HARK/tests/test_IndShockConsumerType.py @@ -40,12 +40,12 @@ def test_baseEx(self): c_t5 = baseEx.cFunc[-6](m) # c_t5 defines the T-5 period consumption rule c_t10 = baseEx.cFunc[-11](m) - self.assertEqual(c_m[500], 1.4008090582203356) - self.assertEqual(c_t1[500], 2.9227437159255216) - self.assertEqual(c_t5[500], 1.7350607327187664) - self.assertEqual(c_t10[500], 1.4991390649979213) - self.assertEqual(c_t10[600], 1.6101476268581576) - self.assertEqual(c_t10[700], 1.7196531041366991) + self.assertAlmostEqual(c_m[500], 1.4008090582203356) + self.assertAlmostEqual(c_t1[500], 2.9227437159255216) + self.assertAlmostEqual(c_t5[500], 1.7350607327187664) + self.assertAlmostEqual(c_t10[500], 1.4991390649979213) + self.assertAlmostEqual(c_t10[600], 1.6101476268581576) + self.assertAlmostEqual(c_t10[700], 1.7196531041366991) def test_GICFails(self): GIC_fail_dictionary = dict(self.base_params) @@ -61,8 +61,8 @@ def test_GICFails(self): m = np.linspace(0,5,1000) c_m = GICFailExample.cFunc[0](m) - self.assertEqual(c_m[500], 0.7772637042393458) - self.assertEqual(c_m[700], 0.8392649061916746) + self.assertAlmostEqual(c_m[500], 0.7772637042393458) + self.assertAlmostEqual(c_m[700], 0.8392649061916746) def test_infinite_horizon(self): baseEx_inf = IndShockConsumerType(cycles=0, @@ -74,19 +74,19 @@ def test_infinite_horizon(self): m1 = np.linspace(1,baseEx_inf.solution[0].mNrmSS,50) # m1 defines the plot range on the left of target m value (e.g. m <= target m) c_m1 = baseEx_inf.cFunc[0](m1) - self.assertEqual(c_m1[0], 0.8527887545025995) - self.assertEqual(c_m1[-1], 1.0036279936408656) + self.assertAlmostEqual(c_m1[0], 0.8527887545025995) + self.assertAlmostEqual(c_m1[-1], 1.0036279936408656) x1 = np.linspace(0,25,1000) cfunc_m = baseEx_inf.cFunc[0](x1) - self.assertEqual(cfunc_m[500], 1.8902146173138235) - self.assertEqual(cfunc_m[700], 2.1591451850267176) + self.assertAlmostEqual(cfunc_m[500], 1.8902146173138235) + self.assertAlmostEqual(cfunc_m[700], 2.1591451850267176) m = np.linspace(0.001,8,1000) # Use the HARK method derivative to get the derivative of cFunc, and the values are just the MPC MPC = baseEx_inf.cFunc[0].derivative(m) - self.assertEqual(MPC[500], 0.08415000641504392) - self.assertEqual(MPC[700], 0.07173144137912524) + self.assertAlmostEqual(MPC[500], 0.08415000641504392) + self.assertAlmostEqual(MPC[700], 0.07173144137912524) From 1c8df30926a60a9aefe2629fea27d572baaa20aa Mon Sep 17 00:00:00 2001 From: sb Date: Sat, 15 Feb 2020 17:24:32 -0500 Subject: [PATCH 213/242] adding tests for failed GIC and AIC conditions on GICFailedExample. See #525 --- HARK/tests/test_IndShockConsumerType.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/HARK/tests/test_IndShockConsumerType.py b/HARK/tests/test_IndShockConsumerType.py index f7f8401d2..143deed90 100644 --- a/HARK/tests/test_IndShockConsumerType.py +++ b/HARK/tests/test_IndShockConsumerType.py @@ -64,6 +64,9 @@ def test_GICFails(self): self.assertAlmostEqual(c_m[500], 0.7772637042393458) self.assertAlmostEqual(c_m[700], 0.8392649061916746) + self.assertFalse(GICFailExample.GIC) + self.assertFalse(GICFailExample.AIC) + def test_infinite_horizon(self): baseEx_inf = IndShockConsumerType(cycles=0, **self.base_params) From 6c1acc7e067339d7ad8866d24f3d080d16e15c3a Mon Sep 17 00:00:00 2001 From: sb Date: Sat, 15 Feb 2020 17:41:19 -0500 Subject: [PATCH 214/242] removing comments from tests that was causing unicode error on Travis --- HARK/tests/test_IndShockConsumerType.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/HARK/tests/test_IndShockConsumerType.py b/HARK/tests/test_IndShockConsumerType.py index 143deed90..e7b1e69f4 100644 --- a/HARK/tests/test_IndShockConsumerType.py +++ b/HARK/tests/test_IndShockConsumerType.py @@ -35,9 +35,9 @@ def test_baseEx(self): m = np.linspace(0,9.5,1000) - c_m = baseEx.cFunc[0](m) # c_m can be used to define the limiting infinite-horizon consumption rule here - c_t1 = baseEx.cFunc[-2](m) # c_t1 defines the second-to-last period consumption rule - c_t5 = baseEx.cFunc[-6](m) # c_t5 defines the T-5 period consumption rule + c_m = baseEx.cFunc[0](m) + c_t1 = baseEx.cFunc[-2](m) + c_t5 = baseEx.cFunc[-6](m) c_t10 = baseEx.cFunc[-11](m) self.assertAlmostEqual(c_m[500], 1.4008090582203356) @@ -56,8 +56,8 @@ def test_GICFails(self): cycles=0, # cycles=0 makes this an infinite horizon consumer **GIC_fail_dictionary) - GICFailExample.solve() # Above, we set up the problem but did not solve it - GICFailExample.unpackcFunc() # Make the consumption function easily accessible for plotting + GICFailExample.solve() + GICFailExample.unpackcFunc() m = np.linspace(0,5,1000) c_m = GICFailExample.cFunc[0](m) From 770074a5a26e2a7280117f335390be8a8ec4a932 Mon Sep 17 00:00:00 2001 From: Saptarshi Kumar Date: Sun, 16 Feb 2020 11:20:07 +0530 Subject: [PATCH 215/242] Updated Comments --- HARK/ConsumptionSaving/ConsumerParameters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 1e1547307..1a9425530 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -352,7 +352,7 @@ init_labor_lifecycle['TranShkStd'] = [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0.1,0.2,0.3] init_labor_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] # Living probability decreases as time moves forward. init_labor_lifecycle['WageRte'] = [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9] # Wage rate in a lifecycle -init_labor_lifecycle['LbrCostCoeffs'] = LbrCostCoeffs * 2 # Assume labor cost coeffs is two-dimensional +init_labor_lifecycle['LbrCostCoeffs'] = LbrCostCoeffs * 2 # Assume labor cost coeffs is a polynomial of degree 1 init_labor_lifecycle['T_cycle'] = 10 #init_labor_lifecycle['T_retire'] = 7 # IndexError at line 774 in interpolation.py. init_labor_lifecycle['T_age'] = 11 # Make sure that old people die at terminal age and don't turn into newborns! \ No newline at end of file From efb7c9429d60b64072b3fb89d3a5e5dd00966a0c Mon Sep 17 00:00:00 2001 From: Saptarshi Kumar Date: Sun, 16 Feb 2020 20:48:53 +0530 Subject: [PATCH 216/242] Updated ConsumerParameters to meaningful values --- HARK/ConsumptionSaving/ConsumerParameters.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 1a9425530..a219b884a 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -308,7 +308,7 @@ LivPrb = [0.99] # Survival probability PermGroFac = [1.02] # Permanent income growth factor -LbrCostCoeffs = [0.36] # Labor cost coefficents list of one element +LbrCostCoeffs = [-1.0] # Labor cost coefficents list of one element WageRte = [1.0] # Wage Rate BoroCnstArt = None @@ -352,7 +352,7 @@ init_labor_lifecycle['TranShkStd'] = [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0.1,0.2,0.3] init_labor_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] # Living probability decreases as time moves forward. init_labor_lifecycle['WageRte'] = [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9] # Wage rate in a lifecycle -init_labor_lifecycle['LbrCostCoeffs'] = LbrCostCoeffs * 2 # Assume labor cost coeffs is a polynomial of degree 1 +init_labor_lifecycle['LbrCostCoeffs'] = [-2.0, 0.4] # Assume labor cost coeffs is a polynomial of degree 1 init_labor_lifecycle['T_cycle'] = 10 #init_labor_lifecycle['T_retire'] = 7 # IndexError at line 774 in interpolation.py. init_labor_lifecycle['T_age'] = 11 # Make sure that old people die at terminal age and don't turn into newborns! \ No newline at end of file From ed739223d3fa03c64c7a74cb43975bdb8a2ba529 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Sun, 16 Feb 2020 10:43:17 -0500 Subject: [PATCH 217/242] Small fix to updateLbrCost, adjust sim methods updateLbrCost method didn't work as intended, as LbrCost was an array, not a list; added call to tolist(). Fixed documentation error in a simulation method that someone else flagged. Also added two line getStates method, which isn't strictly necessary. Inherited method calculates mNrm "prematurely", which is then overwritten by a later sim method in this model. New getStates method simply "erases" that premature calculation. --- HARK/ConsumptionSaving/ConsLaborModel.py | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py index 3c5f9c431..cf34826e5 100644 --- a/HARK/ConsumptionSaving/ConsLaborModel.py +++ b/HARK/ConsumptionSaving/ConsLaborModel.py @@ -322,7 +322,7 @@ def updateLbrCost(self): LbrCost = np.exp(LbrCostBase) time_orig = self.time_flow self.timeFwd() - self.LbrCost = LbrCost + self.LbrCost = LbrCost.tolist() self.addToTimeVary('LbrCost') if not time_orig: self.timeRev() @@ -374,11 +374,31 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): None ''' raise NotImplementedError() + + + def getStates(self): + ''' + Calculates updated values of normalized bank balances and permanent income + level for each agent. Uses pLvlNow, aNrmNow, PermShkNow. Calls the getStates + method for the parent class, then erases mNrmNow, which cannot be calculated + until after getControls in this model. + + Parameters + ---------- + None + + Returns + ------- + None + ''' + IndShockConsumerType.getStates(self) + self.mNrmNow[:] = np.nan # Delete market resource calculation def getControls(self): ''' - Calculates consumption for each consumer of this type using the consumption functions. + Calculates consumption and labor supply for each consumer of this type + using the consumption and labor functions in each period of the cycle. Parameters ---------- From 24293a394e4874bdeced9ef4e8c8fa2a8634fa88 Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 17 Feb 2020 16:20:00 -0500 Subject: [PATCH 218/242] moving test into ConsumptionSaving/tests --- HARK/{ => ConsumptionSaving}/tests/test_IndShockConsumerType.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename HARK/{ => ConsumptionSaving}/tests/test_IndShockConsumerType.py (100%) diff --git a/HARK/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py similarity index 100% rename from HARK/tests/test_IndShockConsumerType.py rename to HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py From 91c2128ce30def837aa193707ee5dadc6d410428 Mon Sep 17 00:00:00 2001 From: sb Date: Tue, 18 Feb 2020 10:30:47 -0500 Subject: [PATCH 219/242] __init__.py for tests directory --- HARK/ConsumptionSaving/tests/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 HARK/ConsumptionSaving/tests/__init__.py diff --git a/HARK/ConsumptionSaving/tests/__init__.py b/HARK/ConsumptionSaving/tests/__init__.py new file mode 100644 index 000000000..e69de29bb From 4ac7f35e5bcdc77cbe51d6df22a2f6e3f39873cc Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Tue, 18 Feb 2020 14:23:20 -0500 Subject: [PATCH 220/242] Tiny labor supply example fixes Changed some division symbols to correctly use integer division, and adjusted lifecycle example dictionary. --- HARK/ConsumptionSaving/ConsumerParameters.py | 6 +++--- examples/ConsumptionSaving/example_ConsLaborModel.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index a219b884a..fed4c2bd5 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -348,10 +348,10 @@ # Make a dictionary for Endogenous Labor supply model with finite lifecycle init_labor_lifecycle = copy(init_labor_intensive) init_labor_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02] -init_labor_lifecycle['PermShkStd'] = [0.1,0.2,0.1,0.2,0.1,0.2,0.1,0.2,0.1,0.2] -init_labor_lifecycle['TranShkStd'] = [0.3,0.2,0.1,0.3,0.2,0.1,0.3,0.1,0.2,0.3] +init_labor_lifecycle['PermShkStd'] = [0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1] +init_labor_lifecycle['TranShkStd'] = [0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1] init_labor_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] # Living probability decreases as time moves forward. -init_labor_lifecycle['WageRte'] = [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9] # Wage rate in a lifecycle +init_labor_lifecycle['WageRte'] = [1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0] # Wage rate in a lifecycle init_labor_lifecycle['LbrCostCoeffs'] = [-2.0, 0.4] # Assume labor cost coeffs is a polynomial of degree 1 init_labor_lifecycle['T_cycle'] = 10 #init_labor_lifecycle['T_retire'] = 7 # IndexError at line 774 in interpolation.py. diff --git a/examples/ConsumptionSaving/example_ConsLaborModel.py b/examples/ConsumptionSaving/example_ConsLaborModel.py index ded047a38..58b343a29 100644 --- a/examples/ConsumptionSaving/example_ConsLaborModel.py +++ b/examples/ConsumptionSaving/example_ConsLaborModel.py @@ -146,7 +146,7 @@ b_max = -np.inf for t in range(LifecycleExample.T_cycle): TranShkSet = LifecycleExample.TranShkGrid[t] - Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less + Shk = TranShkSet[int(len(TranShkSet)//2)] # Use the median shock, more or less B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) C = LifecycleExample.solution[t].cFunc(B_temp,Shk*np.ones_like(B_temp)) plt.plot(B_temp, C) @@ -165,7 +165,7 @@ b_max = -np.inf for t in range(LifecycleExample.T_cycle): TranShkSet = LifecycleExample.TranShkGrid[t] - Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less + Shk = TranShkSet[int(len(TranShkSet)//2)] # Use the median shock, more or less B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) MPC = LifecycleExample.solution[t].cFunc.derivativeX(B_temp,Shk*np.ones_like(B_temp)) plt.plot(B_temp, MPC) @@ -184,7 +184,7 @@ b_max = -np.inf for t in range(LifecycleExample.T_cycle): TranShkSet = LifecycleExample.TranShkGrid[t] - Shk = TranShkSet[int(len(TranShkSet)/2)] # Use the median shock, more or less + Shk = TranShkSet[int(len(TranShkSet)//2)] # Use the median shock, more or less B_temp = B + LifecycleExample.solution[t].bNrmMin(Shk) L = LifecycleExample.solution[t].LbrFunc(B_temp,Shk*np.ones_like(B_temp)) plt.plot(B_temp, L) From bfaa7f18cdc02175e6c256a9ae95517089e7f4cb Mon Sep 17 00:00:00 2001 From: llorracc <@users.noreply.github.com> Date: Wed, 19 Feb 2020 00:22:07 +0100 Subject: [PATCH 221/242] Update checkConditions() --- HARK/ConsumptionSaving/ConsIndShockModel.py | 262 +++++++++++++++----- 1 file changed, 195 insertions(+), 67 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index db3784b43..7e3928bdc 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -915,7 +915,7 @@ def prepareToCalcEndOfPrdvP(self): ShkPrbs_temp = (np.tile(self.ShkPrbsNext,(aNrmCount,1))).transpose() # Get cash on hand next period - mNrmNext = self.Rfree/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + TranShkVals_temp + mNrmNext = self.Rfree/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + TranShkVals_temp # CDC 20191205: This should be divided by LivPrb[0] for Blanchard insurance # Store and report the results self.PermShkVals_temp = PermShkVals_temp @@ -1854,12 +1854,12 @@ def getPostStates(self): def checkConditions(self,verbose=False,verbose_reference=False,public_call=False): ''' This method checks whether the instance's type satisfies the Growth Impatience Condition - (GIC), Return Impatience Condition (RIC), Absolute Impatience Condition (AIC), Weak Return - Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of - Autarky Condition (FVAC). These are the conditions that are sufficient for nondegenerate - solutions under infinite horizon with a 1 period cycle. Depending on the model at hand, a - different combination of these conditions must be satisfied. To check which conditions are - relevant to the model at hand, a reference to the relevant theoretical literature is made. + (GIC), Return Impatience Condition (RIC), Absolute Impatience Condition (AIC), Return + Impatience Condition (RIC), Finite Human Wealth Condition (FHWC) and Finite Value of + Autarky Condition (FVAC). Depending on the configuration of parameter values, some + combination of these conditions must be satisfied in order for the problem to have + a nondegenerate solution. To check which conditions are required, in the verbose mode + a reference to the relevant theoretical literature is made. Parameters ---------- @@ -1888,18 +1888,18 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False self.AIF = AIF if AIF<1: self.AIC = True - if public_call: - print('The value of the absolute impatience factor for the supplied parameter values satisfies the Absolute Impatience Condition.', end = " ") - if verbose: - violated = False - print('Therefore, the absolute amount of consumption is expected to fall over time.') + if public_call or verbose: + print('The value of the absolute impatience factor (AIF) for the supplied parameter values satisfies the Absolute Impatience Condition.', end = " ") + if verbose: + violated = False + print(' Because the AIF < 1, the absolute amount of consumption is expected to fall over time.') print() else: self.AIC = False print('The given type violates the Absolute Impatience Condition with the supplied parameter values; the AIF is %1.5f ' % (AIF), end=" ") if verbose: violated = True - print('Therefore, the absolute amount of consumption is expected to grow over time') + print(' Because the AIF > 1, the absolute amount of consumption is expected to grow over time') print() #Evaluate and report on the Growth Impatience Condition @@ -1908,17 +1908,17 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False if GIF<1: self.GIC = True - if public_call: - print('The value of the growth impatience factor for the supplied parameter values satisfies the Growth Impatience Condition.', end = " ") - if verbose: - print(' Therefore, the ratio of individual wealth to permanent income will fall indefinitely.') + if public_call or verbose: + print('The value of the Growth Impatience Factor for the supplied parameter values satisfies the Growth Impatience Condition.', end = " ") + if verbose: + print(' Therefore, the ratio of individual wealth to permanent income will fall indefinitely.') print() else: self.GIC = False violated = True print('The given parameter values violate the Growth Impatience Condition for this consumer type; the GIF is: %2.4f' % (GIF), end = " ") if verbose: - print(' Therefore, the ratio of individual wealth to permanent income grow toward infinity.') + print(' Therefore, the ratio of individual wealth to permanent income is expected to grow toward infinity.') print() @@ -1928,17 +1928,17 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False self.RIF = RIF if RIF<1: self.RIC = True - if public_call: + if public_call or verbose: print('The return impatience factor value for the supplied parameter values satisfies the Return Impatience Condition.', end = " ") - if verbose: - print('Therefore, the limiting consumption function is not c(m)=0') + if verbose: + print('Therefore, the limiting consumption function is not c(m)=0 for all m') print() else: self.RIC = False violated = True print('The given type violates the Return Impatience Condition with the supplied parameter values; the factor is %1.5f ' % (RIF), end = " ") if verbose: - print('Therefore, the limiting consumption function is c(m)=0') + print('Therefore, the limiting consumption function is c(m)=0 for all m') print() #Evaluate and report on the Finite Human Wealth Condition @@ -1947,13 +1947,13 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False if FHWF<1: self.hNrm = 1.0/(1.0-self.PermGroFac[0]/self.Rfree) self.FHWC = True - if public_call: + if public_call or verbose: print('The Finite Human wealth factor value for the supplied parameter values satisfies the Finite Human Wealth Condition.', end = " ") - if verbose: - print('Therefore, the limiting consumption function is not c(m)=Infinity') - print('and human wealth normalized by permanent income is %2.5f' % (self.hNrm)) - self.cNrmPDV = 1.0/(1.0-self.Thorn/self.Rfree) - print('and the PDV of future consumption growth is %2.5f' % (self.cNrmPDV) ) + if verbose: + print('Therefore, the limiting consumption function is not c(m)=Infinity') + print('and human wealth normalized by permanent income is %2.5f' % (self.hNrm)) + self.cNrmPDV = 1.0/(1.0-self.Thorn/self.Rfree) + print('and the PDV of future consumption growth is %2.5f' % (self.cNrmPDV) ) print() else: self.FHWC = False @@ -1964,7 +1964,6 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False print() if verbose and violated and verbose_reference: print('[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') - return violated @@ -2131,7 +2130,8 @@ def calcBoundingValues(self): horizon model with only one period repeated indefinitely. Store results as attributes of self. Human wealth is the present discounted value of expected future income after receiving income this period, ignoring mort- - ality. The maximum MPC is the limit of the MPC as m --> mNrmMin. The + ality (because your income matters to you only if you are still alive). + The maximum MPC is the limit of the MPC as m --> mNrmMin. The minimum MPC is the limit of the MPC as m --> infty. Parameters @@ -2234,7 +2234,7 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): # Calculate expected marginal value and implied optimal consumption ExvPnextGrid = self.DiscFac*self.Rfree*self.LivPrb[0]*self.PermGroFac[0]**(-self.CRRA)* \ np.sum(PermShkVals_tiled**(-self.CRRA)*vPnextArray*ShkPrbs_tiled,axis=0) - cOptGrid = ExvPnextGrid**(-1.0/self.CRRA) + cOptGrid = ExvPnextGrid**(-1.0/self.CRRA) # This is the 'Endogenous Gridpoints' step # Calculate Euler error and store an interpolated function EulerErrorNrmGrid = (cNowGrid - cOptGrid)/cOptGrid @@ -2255,9 +2255,8 @@ def checkConditions(self,verbose=False,public_call=True): This method checks whether the instance's type satisfies the Growth Impatience Condition (GIC), Return Impatience Condition (RIC), Absolute Impatience Condition (AIC), Weak Return Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of - Autarky Condition (FVAC). These are the conditions that are sufficient for nondegenerate - infinite horizon solutions when there is a 1 period cycle. Depending on the model at hand, a - different combination of these conditions must be satisfied. (For an exposition of the + Autarky Condition (FVAC). When combinations of these conditions are satisfied, the + solution to the problem exhibits different characteristics. (For an exposition of the conditions, see http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) Parameters @@ -2271,71 +2270,203 @@ def checkConditions(self,verbose=False,public_call=True): ------- None ''' - violated = PerfForesightConsumerType.checkConditions(self, verbose=verbose, verbose_reference=False) + violated = False # PerfForesightConsumerType.checkConditions(self, verbose=False, verbose_reference=False) if self.cycles!=0 or self.T_cycle > 1: return - EPermShkInv=np.dot(self.PermShkDstn[0][0],1/self.PermShkDstn[0][1]) - PermGroFacAdj=self.PermGroFac[0]*EPermShkInv - Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) - GIF = Thorn/PermGroFacAdj - self.GIF = GIF - self.Thorn = Thorn - self.PermGroFacAdj = PermGroFacAdj - self.EPermShkInv = EPermShkInv + # For theory, see hyperlink targets to expressions in + # url=http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory + # For example, the hyperlink to the relevant section of the paper + url='http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory' + # would be referenced below as: + # [url]/#Uncertainty-Modified-Conditions + self.InvPermShkDstn=deepcopy(self.PermShkDstn) + self.InvPermShkDstn[0][1]=1/self.PermShkDstn[0][1] + EPermShkInv=np.dot(self.InvPermShkDstn[0][0],1/self.PermShkDstn[0][1]) # $\Ex_{t}[\psi^{-1}_{t+1}]$ (in first eqn in sec) + InvEPermShkInv=(1/EPermShkInv) # $\underline{\psi}$ in the paper (\bar{\isp} in private version) + PermGroFacAdj=self.PermGroFac[0]*InvEPermShkInv # [url]/#PGroAdj + # [url]/#Pat, adjusted to include mortality +# Thorn = ((self.Rfree/self.LivPrb[0])*(self.DiscFac*self.LivPrb[0]))**(1/self.CRRA) + Thorn = ((self.Rfree*self.DiscFac))**(1/self.CRRA) + GIF = Thorn/(self.PermGroFac[0] ) # [url]/#GIF + GIFInd = Thorn/(self.PermGroFac[0]*InvEPermShkInv) # [url]/#GIFI + GIFAgg = Thorn*self.LivPrb[0]/self.PermGroFac[0] # [url]#/GIFA + +# self.Rnorm = self.Rfree*EPermShkInv/(self.PermGroFac[0]*self.LivPrb[0]) + self.GIF = GIF + self.GIFInd = GIFInd + self.GIFAgg = GIFAgg + self.Thorn = Thorn + self.PermGroFacAdj = PermGroFacAdj + self.EPermShkInv = EPermShkInv + self.InvEPermShkInv = InvEPermShkInv + self.DiscFacGIFMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge + self.DiscFacGIFIMax = ((self.PermGroFac[0]*InvEPermShkInv)**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge + self.DiscFacGIFAMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree*self.LivPrb[0]) # DiscFac at growth impatience knife edge #Evaluate and report on the Growth Impatience Condition - if GIF<1: + # [url]/#GIC + if GIF<=1: self.GIC = True - if public_call: - print('The value of the growth impatience factor for the supplied parameter values satisfies the Growth Impatience Condition.', end = " ") - if verbose: - print('Therefore, a target level of wealth exists.') + if public_call or verbose: + print('The value of the Growth Impatience Factor for the supplied parameter values satisfies the Growth Impatience Condition.', end = " ") + if verbose: + print('Therefore, a target level of the ratio of expected market resources to expected permanent income exists (see '+url+'/#onetarget).') print() else: self.GIC = False violated = True - print('The given parameter values violate the Growth Impatience Condition for this consumer type; the GIF is: %2.4f' % (GIF), end = " ") + print('The given parameter values violate the Growth Impatience Condition; the GIF is: %2.4f' % (GIF), end = " ") + if verbose: + print('') + print('Therefore, a target level of wealth does not exist. (see '+url+'/#onetarget)') + print() + + if GIFInd<=1: + self.GICInd = True + if public_call or verbose: + print('The value of the Individual Growth Impatience Factor for the supplied parameter values satisfies the Individual Growth Impatience Condition.', end = " ") + if verbose: + print('Therefore, a target level of the individual market resources ratio m exists (see '+url+'/#onetarget).') + print() + else: + self.GICInd = False + violated = True + print('The given parameter values violate the Individual Growth Impatience Condition; the GIFInd is: %2.4f' % (GIFInd), end = " ") if verbose: - print('Therefore, a target level of wealth does not exist.') + print('') + print('Therefore, a target ratio of individual market resources to individual permanent income does not exist. (see '+url+'/#onetarget)') + print() + + if GIFAgg<=1: + self.GICAgg = True + if public_call or verbose: + print('The value of the Aggregate Growth Impatience Factor for the supplied parameter values satisfies the Aggregate Growth Impatience Condition.', end = " ") + if verbose: + print('Therefore, it is possible that a target level of the ratio of expected market resources to expected permanent income exists (see '+url+'/#onetarget).') + print() + else: + self.GICAgg = False + violated = True + print('The given parameter values violate the Aggregate Growth Impatience Condition; the GIFAgg is: %2.4f' % (GIFAgg), end = " ") + if verbose: + print('') + print('Therefore, a target ratio of aggregate resources to aggreg permanent income does not exist. (see '+url+'/#onetarget)') print() #Evaluate and report on the Weak Return Impatience Condition - WRIF=(self.LivPrb[0]*(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree + # [url]/#WRIF modified to incorporate LivPrb + WRIF=(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA)/self.Rfree self.WRIF = WRIF - if WRIF<1: + if WRIF<=1: self.WRIC = True - if public_call: - print('The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition.') + if public_call or verbose: + print('The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition (see '+url+'/#WRIC).') + print() else: self.WRIC = False violated = True print('The given type violates the Weak Return Impatience Condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF), end = " ") if verbose: - print('Therefore, a nondegenerate solution is not available.') + print('') + print('Therefore, a nondegenerate solution is not available (see '+url+'/#WRIC.') print() #Evaluate and report on the Finite Value of Autarky Condition - EPermShkValFunc=np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA)) - self.EPermShkValFunc = EPermShkValFunc - FVAF=self.LivPrb[0]*self.DiscFac*EPermShkValFunc*(self.PermGroFac[0]**(1-self.CRRA)) + # Hyperlink to paper: [url]/#Autarky-Value + EpShkuInv = np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA)) + if self.CRRA != 1.0: + uInvEpShkuInv = EpShkuInv**(1/(1-self.CRRA)) # The term that gives a utility-consequence-adjusted utility growth + else: + uInvEpShkuInv = 1.0 + + self.uInvEpShkuInv = uInvEpShkuInv + FVAF=self.LivPrb[0]*self.DiscFac*self.uInvEpShkuInv self.FVAF = FVAF - if FVAF<1: + if FVAF<=1: self.FVAC = True - if public_call: + if public_call or verbose: print('The Finite Value of autarky factor value for the supplied parameter values satisfies the Finite Value of Autarky Condition.') + if self.WRIC: + print('Since both WRIC and FVAC are satisfied, the problem has a nondegenerate solution') else: self.FVAC = False print('The given type violates the Finite Value of Autarky Condition with the supplied parameter values. The FVAF is %2.4f' %(FVAF), end = " ") violated = True - if verbose: - print('Therefore, a nondegenerate solution is not available.') + if public_call or verbose: + print('Therefore, a nondegenerate solution is not available (see '+url+'/#Conditions-Under-Which-the-Problem-Defines-a-Contraction-Mapping') print() if verbose and violated: - print('\n[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') + print('\n[!] For more information on the conditions, see Tables 3 and 4 in "Theoretical Foundations of Buffer Stock Saving" at '+url+'/#Factors-Defined-And-Compared') + print('') + + if verbose: + print('GIF = %2.6f ' % (GIF)) + print('GIFInd = %2.6f ' % (GIFInd)) + print('GIFAgg = %2.6f ' % (GIFAgg)) + print('Thorn = %2.6f ' % (Thorn)) + print('PermGroFacAdj = %2.6f ' % (PermGroFacAdj)) + print('uInvEpShkuInv = %2.6f ' % (uInvEpShkuInv)) + print('FVAF = %2.6f ' % (FVAF)) + print('WRIF = %2.6f ' % (WRIF)) + print('DiscFacGIFMax = %2.6f ' % (self.DiscFacGIFMax)) + print('DiscFacGIFAMax= %2.6f ' % (self.DiscFacGIFAMax)) + print('DiscFacGIFAMax= %2.6f ' % (self.DiscFacGIFIMax)) + + def Ex_Mtp1_over_Ex_Ptp1(self,mRat,verbose=False): + cRat = self.solution[-1].cFunc(mRat) + aRat = mRat-cRat + Ex_Ptp1 = PermGroFac[0] + Ex_bLev_tp1 = aRat*self.Rfree + Ex_Mtp1 = Ex_bLev_tp1 + return Ex_Mtp1/Ex_Ptp1 + + def Ex_mtp1(self,mRat,verbose=False): + cRat = self.solution[-1].cFunc(mRat) + aRat = mRat-cRat + Ex_bRat_tp1 = aRat*self.Rfree*self.EPermShkInv/self.PermGroFac[0] + Ex_Mtp1 = (Ex_bRat_tp1 + 1)*Ex_Ptp1 # mean TranShk and PermShk are 1 + return Ex_Mtp1/Ex_Ptp1 + + def calcTargets(self,verbose=False): + ''' + If the problem is one that satisfies the conditions required for target ratios of different + variables to permanent income to exist, and has been solved to within the self-defined + tolerance, this method calculates the target values of market resources, consumption, + and assets. + + Parameters + ---------- + verbose : boolean + Specifies different levels of verbosity of feedback. When False, it prints no results. + When True, it reports all target values, and passes the verbosity indicator to the + checkConditions method which responds accordingly. + + Returns + ------- + None + ''' + infinite_horizon = cycles_left == 0 + if not infinite_horizon: + print('The calcTargets method works only for infinite horizon models.') + return + + + # To be written. + # Defining: + ## Rnorm = Rfree/(PermGroFac[0]*PermShk) + ## EPermShkInv = E[PermShk**(-1)] + ## InvEPermShkInv = 1/EPermShkInv + ## ExRnorm = E[Rfree/(PermGroFac[0]*PermShk)] = Rfree EPermShkInv / PermGroFac[0] + ## InvExRnorm = 1/ExRnorm + ## The "sustainable consumption" locus is given by + # cSust = InvExRnorm + m*(1-InvExRnorm) + + # The target level of m, mTarg, will be the value such that + # cSust[m] = cFunc[m] class KinkedRconsumerType(IndShockConsumerType): @@ -2349,10 +2480,7 @@ class KinkedRconsumerType(IndShockConsumerType): time_inv_.remove('Rfree') time_inv_ += ['Rboro', 'Rsave'] - def __init__(self, - cycles=1, - time_flow=True, - **kwds): + def __init__(self,cycles=1,time_flow=True,**kwds): ''' Instantiate a new ConsumerType with given data. See ConsumerParameters.init_kinked_R for a dictionary of From fe1b6544f6d88a6139c95a35616c33ef129a8aa8 Mon Sep 17 00:00:00 2001 From: llorracc <@users.noreply.github.com> Date: Thu, 20 Feb 2020 00:09:59 +0100 Subject: [PATCH 222/242] Further small tweaks to factor calculations --- HARK/ConsumptionSaving/ConsIndShockModel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 7e3928bdc..881bd419d 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -2388,7 +2388,7 @@ def checkConditions(self,verbose=False,public_call=True): if FVAF<=1: self.FVAC = True if public_call or verbose: - print('The Finite Value of autarky factor value for the supplied parameter values satisfies the Finite Value of Autarky Condition.') + print('The Finite Value of Autarky Factor (FVAV) for the supplied parameter values satisfies the Finite Value of Autarky Condition.') if self.WRIC: print('Since both WRIC and FVAC are satisfied, the problem has a nondegenerate solution') else: From 4d6d4b4e6eada0ca1a5f4f836b5046e8541efb86 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 20 Feb 2020 10:06:46 +0530 Subject: [PATCH 223/242] add future imports back and py3.6 support, absolute_imports for v0.10.4 --- HARK/ConsumptionSaving/ConsAggShockModel.py | 4 ++++ .../ConsGenIncProcessModel.py | 4 ++++ HARK/ConsumptionSaving/ConsIndShockModel.py | 9 ++++++++- HARK/ConsumptionSaving/ConsMarkovModel.py | 3 +++ HARK/ConsumptionSaving/ConsMedModel.py | 4 ++++ HARK/ConsumptionSaving/ConsPrefShockModel.py | 4 ++++ HARK/ConsumptionSaving/ConsRepAgentModel.py | 4 ++++ HARK/ConsumptionSaving/ConsumerParameters.py | 1 + .../TractableBufferStockModel.py | 3 +++ HARK/ConsumptionSaving/__init__.py | 20 +++++++++---------- .../tests/test_ConsMarkovModel.py | 2 +- HARK/__init__.py | 2 +- 12 files changed, 47 insertions(+), 13 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index 838792bbf..dc1c0a43f 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -4,6 +4,10 @@ basic solver. Also includes a subclass of Market called CobbDouglas economy, used for solving "macroeconomic" models with aggregate shocks. ''' +from __future__ import division, print_function +from __future__ import absolute_import +from builtins import str +from builtins import range import numpy as np import scipy.stats as stats from HARK.interpolation import LinearInterp, LinearInterpOnInterp1D, ConstantFunction, IdentityFunction,\ diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index b21bb7218..83f51501e 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -4,6 +4,10 @@ ConsIndShockModel by explicitly tracking persistent income as a state variable, and allows (log) persistent income to follow an AR1 process rather than random walk. ''' +from __future__ import division, print_function +from __future__ import absolute_import +from builtins import str +from builtins import range from copy import deepcopy import numpy as np from HARK import AgentType, HARKobject diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 10a98d362..863f6479d 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -12,11 +12,16 @@ See NARK for information on variable naming conventions. See HARK documentation for mathematical descriptions of the models being solved. ''' +from __future__ import division +from __future__ import print_function +from __future__ import absolute_import +from builtins import str +from builtins import range +from builtins import object from copy import copy, deepcopy import numpy as np from scipy.optimize import newton from HARK import AgentType, Solution, NullFunc, HARKobject -import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import warnings # Because of "patch" to warnings modules from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp from HARK.simulation import drawDiscrete, drawLognormal, drawUniform @@ -24,6 +29,8 @@ combineIndepDstns, makeGridExpMult, CRRAutility, CRRAutilityP, \ CRRAutilityPP, CRRAutilityP_inv, CRRAutility_invP, CRRAutility_inv, \ CRRAutilityP_invP +import HARK.ConsumptionSaving.ConsumerParameters as Params + __all__ = ['ConsumerSolution', 'ValueFunc', 'MargValueFunc', 'MargMargValueFunc', 'ConsPerfForesightSolver', 'ConsIndShockSetup', 'ConsIndShockSolverBasic', diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py index 85331e5a1..2641dd9eb 100644 --- a/HARK/ConsumptionSaving/ConsMarkovModel.py +++ b/HARK/ConsumptionSaving/ConsMarkovModel.py @@ -4,6 +4,9 @@ include a Markov state; the interest factor, permanent growth factor, and income distribution can vary with the discrete state. ''' +from __future__ import division, print_function +from __future__ import absolute_import +from builtins import range from copy import deepcopy import numpy as np from HARK import AgentType diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py index 982f74b7d..32aaea065 100644 --- a/HARK/ConsumptionSaving/ConsMedModel.py +++ b/HARK/ConsumptionSaving/ConsMedModel.py @@ -1,6 +1,10 @@ ''' Consumption-saving models that also include medical spending. ''' +from __future__ import division, print_function +from __future__ import absolute_import +from builtins import str +from builtins import range import numpy as np from scipy.optimize import brentq from HARK import HARKobject diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index 0e3108908..340c3aff2 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -6,6 +6,10 @@ 2) A combination of (1) and ConsKinkedR, demonstrating how to construct a new model by inheriting from multiple classes. ''' +from __future__ import division, print_function +from __future__ import absolute_import +from builtins import str +from builtins import range import numpy as np from HARK.utilities import approxMeanOneLognormal from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, ConsIndShockSolver, \ diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py index f4c5183ea..b0b755ccb 100644 --- a/HARK/ConsumptionSaving/ConsRepAgentModel.py +++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py @@ -4,6 +4,10 @@ take a heterogeneous agents approach. In RA models, all attributes are either time invariant or exist on a short cycle; models must be infinite horizon. ''' +from __future__ import division, print_function +from __future__ import absolute_import +from builtins import str +from builtins import range import numpy as np from HARK.interpolation import LinearInterp from HARK.simulation import drawUniform, drawDiscrete diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index a4ae56863..53ca2688a 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -3,6 +3,7 @@ consumption-saving models. These models can be found in ConsIndShockModel, ConsAggShockModel, ConsPrefShockModel, and ConsMarkovModel. ''' +from __future__ import division, print_function from copy import copy import numpy as np diff --git a/HARK/ConsumptionSaving/TractableBufferStockModel.py b/HARK/ConsumptionSaving/TractableBufferStockModel.py index 1fcdc615b..f47425fe2 100644 --- a/HARK/ConsumptionSaving/TractableBufferStockModel.py +++ b/HARK/ConsumptionSaving/TractableBufferStockModel.py @@ -20,6 +20,9 @@ Despite the non-standard solution method, the iterative process can be embedded in the HARK framework, as shown below. ''' +from __future__ import division, print_function +from __future__ import absolute_import +from builtins import str import numpy as np # Import the HARK library. diff --git a/HARK/ConsumptionSaving/__init__.py b/HARK/ConsumptionSaving/__init__.py index 99245567a..44b489c35 100644 --- a/HARK/ConsumptionSaving/__init__.py +++ b/HARK/ConsumptionSaving/__init__.py @@ -1,10 +1,10 @@ -from HARK.ConsumptionSaving.ConsAggShockModel import * -from HARK.ConsumptionSaving.ConsGenIncProcessModel import * -from HARK.ConsumptionSaving.ConsIndShockModel import * -from HARK.ConsumptionSaving.ConsMarkovModel import * -from HARK.ConsumptionSaving.ConsMedModel import * -from HARK.ConsumptionSaving.ConsPortfolioModel import * -from HARK.ConsumptionSaving.ConsPrefShockModel import * -from HARK.ConsumptionSaving.ConsRepAgentModel import * -from HARK.ConsumptionSaving.TractableBufferStockModel import * -from HARK.ConsumptionSaving.ConsumerParameters import * +# from HARK.ConsumptionSaving.ConsumerParameters import * +# from HARK.ConsumptionSaving.ConsAggShockModel import * +# from HARK.ConsumptionSaving.ConsGenIncProcessModel import * +# from HARK.ConsumptionSaving.ConsIndShockModel import * +# from HARK.ConsumptionSaving.ConsMarkovModel import * +# from HARK.ConsumptionSaving.ConsMedModel import * +# from HARK.ConsumptionSaving.ConsPortfolioModel import * +# from HARK.ConsumptionSaving.ConsPrefShockModel import * +# from HARK.ConsumptionSaving.ConsRepAgentModel import * +# from HARK.ConsumptionSaving.TractableBufferStockModel import * diff --git a/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py b/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py index 7de568b28..6de68dfd3 100644 --- a/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py +++ b/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py @@ -1,5 +1,5 @@ import numpy as np -from HARK import MarkovConsumerType +from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType import HARK.ConsumptionSaving.ConsumerParameters as Params from copy import copy import unittest diff --git a/HARK/__init__.py b/HARK/__init__.py index c235d788e..f15296917 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,4 +1,4 @@ +from __future__ import absolute_import __version__ = '0.10.3' from HARK.core import * -from HARK.ConsumptionSaving import * From 6177dd0718904d8a5002b2dfac7eddb3cb0716bd Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 20 Feb 2020 10:11:10 +0530 Subject: [PATCH 224/242] move consumptionsaving tests to test dir --- .../tests/test_modelInits.py | 66 ++++++++ .../tests/test_modelcomparisons.py | 157 ++++++++++++++++++ 2 files changed, 223 insertions(+) create mode 100644 HARK/ConsumptionSaving/tests/test_modelInits.py create mode 100644 HARK/ConsumptionSaving/tests/test_modelcomparisons.py diff --git a/HARK/ConsumptionSaving/tests/test_modelInits.py b/HARK/ConsumptionSaving/tests/test_modelInits.py new file mode 100644 index 000000000..4d4d9b2e2 --- /dev/null +++ b/HARK/ConsumptionSaving/tests/test_modelInits.py @@ -0,0 +1,66 @@ +""" +This file tests whether HARK's models are initialized correctly. +""" + + +# Bring in modules we need +import unittest +import numpy as np +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType +from HARK.ConsumptionSaving.ConsIndShockModel import KinkedRconsumerType +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType +from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType +from HARK.utilities import plotFuncsDer, plotFuncs +from copy import copy + +class testInitialization(unittest.TestCase): + # We don't need a setUp method for the tests to run, but it's convenient + # if we want to test various things on the same model in different test_* + # methods. + def test_PerfForesightConsumerType(self): + try: + model = PerfForesightConsumerType(**Params.init_perfect_foresight) + except: + self.fail("PerfForesightConsumerType failed to initialize with Params.init_perfect_foresight.") + + def test_IndShockConsumerType(self): + try: + model = IndShockConsumerType(**Params.init_lifecycle) + except: + self.fail("IndShockConsumerType failed to initialize with Params.init_lifecycle.") + + def test_KinkedRconsumerType(self): + try: + model = KinkedRconsumerType(**Params.init_kinked_R) + except: + self.fail("KinkedRconsumerType failed to initialize with Params.init_kinked_R.") + + def test_MarkovConsumerType(self): + try: + unemp_length = 5 # Averange length of unemployment spell + urate_good = 0.05 # Unemployment rate when economy is in good state + urate_bad = 0.12 # Unemployment rate when economy is in bad state + bust_prob = 0.01 # Probability of economy switching from good to bad + recession_length = 20 # Averange length of bad state + p_reemploy =1.0/unemp_length + p_unemploy_good = p_reemploy*urate_good/(1-urate_good) + p_unemploy_bad = p_reemploy*urate_bad/(1-urate_bad) + boom_prob = 1.0/recession_length + MrkvArray = np.array([[(1-p_unemploy_good)*(1-bust_prob),p_unemploy_good*(1-bust_prob), + (1-p_unemploy_good)*bust_prob,p_unemploy_good*bust_prob], + [p_reemploy*(1-bust_prob),(1-p_reemploy)*(1-bust_prob), + p_reemploy*bust_prob,(1-p_reemploy)*bust_prob], + [(1-p_unemploy_bad)*boom_prob,p_unemploy_bad*boom_prob, + (1-p_unemploy_bad)*(1-boom_prob),p_unemploy_bad*(1-boom_prob)], + [p_reemploy*boom_prob,(1-p_reemploy)*boom_prob, + p_reemploy*(1-boom_prob),(1-p_reemploy)*(1-boom_prob)]]) + + # Make a consumer with serially correlated unemployment, subject to boom and bust cycles + init_serial_unemployment = copy(Params.init_idiosyncratic_shocks) + init_serial_unemployment['MrkvArray'] = [MrkvArray] + init_serial_unemployment['UnempPrb'] = 0 # to make income distribution when employed + init_serial_unemployment['global_markov'] = False + SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment) + except: + self.fail("MarkovConsumerType failed to initialize with boom/bust unemployment.") diff --git a/HARK/ConsumptionSaving/tests/test_modelcomparisons.py b/HARK/ConsumptionSaving/tests/test_modelcomparisons.py new file mode 100644 index 000000000..5177b4cd3 --- /dev/null +++ b/HARK/ConsumptionSaving/tests/test_modelcomparisons.py @@ -0,0 +1,157 @@ +""" +This file implements unit tests for several of the ConsumptionSaving models in HARK. +These tests compare the output of different models in specific cases in which those models +should yield the same output. The code will pass these tests if and only if the output is close +"enough". +""" + +# Bring in modules we need +import unittest +from copy import deepcopy +import numpy as np + +# Bring in the HARK models we want to test +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType +from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType +from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType + + +class Compare_PerfectForesight_and_Infinite(unittest.TestCase): + """ + Class to compare output of the perfect foresight and infinite horizon models. + When income uncertainty is removed from the infinite horizon model, it reduces in theory to + the perfect foresight model. This class implements tests to make sure it reduces in practice + to the perfect foresight model as well. + """ + + def setUp(self): + """ + Prepare to compare the models by initializing and solving them + """ + # Set up and solve infinite type + import HARK.ConsumptionSaving.ConsumerParameters as Params + + # Define a test dictionary that should have the same solution in the + # perfect foresight and idiosyncratic shocks models. + test_dictionary = deepcopy(Params.init_idiosyncratic_shocks) + test_dictionary['LivPrb'] = [1.] + test_dictionary['DiscFac'] = 0.955 + test_dictionary['PermGroFac'] = [1.] + test_dictionary['PermShkStd'] = [0.] + test_dictionary['TranShkStd'] = [0.] + test_dictionary['UnempPrb'] = 0. + test_dictionary['T_cycle'] = 1 + test_dictionary['T_retire'] = 0 + test_dictionary['BoroCnstArt'] = None + + InfiniteType = IndShockConsumerType(**test_dictionary) + InfiniteType.cycles = 0 + + InfiniteType.updateIncomeProcess() + InfiniteType.solve() + InfiniteType.timeFwd() + InfiniteType.unpackcFunc() + + # Make and solve a perfect foresight consumer type with the same parameters + PerfectForesightType = PerfForesightConsumerType(**test_dictionary) + PerfectForesightType.cycles = 0 + + PerfectForesightType.solve() + PerfectForesightType.unpackcFunc() + PerfectForesightType.timeFwd() + + self.InfiniteType = InfiniteType + self.PerfectForesightType = PerfectForesightType + + def test_consumption(self): + """" + Now compare the consumption functions and make sure they are "close" + """ + def diffFunc(m): return self.PerfectForesightType.solution[0].cFunc(m) - \ + self.InfiniteType.cFunc[0](m) + points = np.arange(0.5, 10., .01) + difference = diffFunc(points) + max_difference = np.max(np.abs(difference)) + + self.assertLess(max_difference, 0.01) + + +class Compare_TBS_and_Markov(unittest.TestCase): + """ + Class to compare output of the Tractable Buffer Stock and Markov models. + The only uncertainty in the TBS model is over when the agent will enter an absorbing state + with 0 income. With the right transition arrays and income processes, this is just a special + case of the Markov model. So with the right inputs, we should be able to solve the two + different models and get the same outputs. + """ + def setUp(self): + # Set up and solve TBS + base_primitives = {'UnempPrb': .015, + 'DiscFac': 0.9, + 'Rfree': 1.1, + 'PermGroFac': 1.05, + 'CRRA': .95} + TBSType = TractableConsumerType(**base_primitives) + TBSType.solve() + + # Set up and solve Markov + MrkvArray = [np.array([[1.0-base_primitives['UnempPrb'], base_primitives['UnempPrb']],[0.0, 1.0]])] + Markov_primitives = {"CRRA": base_primitives['CRRA'], + "Rfree": np.array(2*[base_primitives['Rfree']]), + "PermGroFac": [np.array(2*[base_primitives['PermGroFac'] / + (1.0-base_primitives['UnempPrb'])])], + "BoroCnstArt": None, + "PermShkStd": [0.0], + "PermShkCount": 1, + "TranShkStd": [0.0], + "TranShkCount": 1, + "T_total": 1, + "UnempPrb": 0.0, + "UnempPrbRet": 0.0, + "T_retire": 0, + "IncUnemp": 0.0, + "IncUnempRet": 0.0, + "aXtraMin": 0.001, + "aXtraMax": TBSType.mUpperBnd, + "aXtraCount": 48, + "aXtraExtra": [None], + "aXtraNestFac": 3, + "LivPrb":[np.array([1.0,1.0]),], + "DiscFac": base_primitives['DiscFac'], + 'Nagents': 1, + 'psi_seed': 0, + 'xi_seed': 0, + 'unemp_seed': 0, + 'tax_rate': 0.0, + 'vFuncBool': False, + 'CubicBool': True, + 'MrkvArray': MrkvArray, + 'T_cycle':1 + } + + MarkovType = MarkovConsumerType(**Markov_primitives) + MarkovType.cycles = 0 + employed_income_dist = [np.ones(1), np.ones(1), np.ones(1)] + unemployed_income_dist = [np.ones(1), np.ones(1), np.zeros(1)] + MarkovType.IncomeDstn = [[employed_income_dist, unemployed_income_dist]] + + MarkovType.solve() + MarkovType.unpackcFunc() + + self.TBSType = TBSType + self.MarkovType = MarkovType + + def test_consumption(self): + # Now compare the consumption functions and make sure they are "close" + + def diffFunc(m): return self.TBSType.solution[0].cFunc(m) - self.MarkovType.cFunc[0][0](m) + points = np.arange(0.1, 10., .01) + difference = diffFunc(points) + max_difference = np.max(np.abs(difference)) + + self.assertLess(max_difference, 0.01) + + +if __name__ == '__main__': + # Run all the tests + unittest.main() From abef8e37ce959e631344917c7a18705a5b39b752 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 20 Feb 2020 10:11:37 +0530 Subject: [PATCH 225/242] move consumptionsaving tests to test dir --- HARK/tests/test_modelInits.py | 66 ------------ HARK/tests/test_modelcomparisons.py | 157 ---------------------------- 2 files changed, 223 deletions(-) delete mode 100644 HARK/tests/test_modelInits.py delete mode 100644 HARK/tests/test_modelcomparisons.py diff --git a/HARK/tests/test_modelInits.py b/HARK/tests/test_modelInits.py deleted file mode 100644 index 4d4d9b2e2..000000000 --- a/HARK/tests/test_modelInits.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -This file tests whether HARK's models are initialized correctly. -""" - - -# Bring in modules we need -import unittest -import numpy as np -import HARK.ConsumptionSaving.ConsumerParameters as Params -from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType -from HARK.ConsumptionSaving.ConsIndShockModel import KinkedRconsumerType -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType -from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType -from HARK.utilities import plotFuncsDer, plotFuncs -from copy import copy - -class testInitialization(unittest.TestCase): - # We don't need a setUp method for the tests to run, but it's convenient - # if we want to test various things on the same model in different test_* - # methods. - def test_PerfForesightConsumerType(self): - try: - model = PerfForesightConsumerType(**Params.init_perfect_foresight) - except: - self.fail("PerfForesightConsumerType failed to initialize with Params.init_perfect_foresight.") - - def test_IndShockConsumerType(self): - try: - model = IndShockConsumerType(**Params.init_lifecycle) - except: - self.fail("IndShockConsumerType failed to initialize with Params.init_lifecycle.") - - def test_KinkedRconsumerType(self): - try: - model = KinkedRconsumerType(**Params.init_kinked_R) - except: - self.fail("KinkedRconsumerType failed to initialize with Params.init_kinked_R.") - - def test_MarkovConsumerType(self): - try: - unemp_length = 5 # Averange length of unemployment spell - urate_good = 0.05 # Unemployment rate when economy is in good state - urate_bad = 0.12 # Unemployment rate when economy is in bad state - bust_prob = 0.01 # Probability of economy switching from good to bad - recession_length = 20 # Averange length of bad state - p_reemploy =1.0/unemp_length - p_unemploy_good = p_reemploy*urate_good/(1-urate_good) - p_unemploy_bad = p_reemploy*urate_bad/(1-urate_bad) - boom_prob = 1.0/recession_length - MrkvArray = np.array([[(1-p_unemploy_good)*(1-bust_prob),p_unemploy_good*(1-bust_prob), - (1-p_unemploy_good)*bust_prob,p_unemploy_good*bust_prob], - [p_reemploy*(1-bust_prob),(1-p_reemploy)*(1-bust_prob), - p_reemploy*bust_prob,(1-p_reemploy)*bust_prob], - [(1-p_unemploy_bad)*boom_prob,p_unemploy_bad*boom_prob, - (1-p_unemploy_bad)*(1-boom_prob),p_unemploy_bad*(1-boom_prob)], - [p_reemploy*boom_prob,(1-p_reemploy)*boom_prob, - p_reemploy*(1-boom_prob),(1-p_reemploy)*(1-boom_prob)]]) - - # Make a consumer with serially correlated unemployment, subject to boom and bust cycles - init_serial_unemployment = copy(Params.init_idiosyncratic_shocks) - init_serial_unemployment['MrkvArray'] = [MrkvArray] - init_serial_unemployment['UnempPrb'] = 0 # to make income distribution when employed - init_serial_unemployment['global_markov'] = False - SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment) - except: - self.fail("MarkovConsumerType failed to initialize with boom/bust unemployment.") diff --git a/HARK/tests/test_modelcomparisons.py b/HARK/tests/test_modelcomparisons.py deleted file mode 100644 index 5177b4cd3..000000000 --- a/HARK/tests/test_modelcomparisons.py +++ /dev/null @@ -1,157 +0,0 @@ -""" -This file implements unit tests for several of the ConsumptionSaving models in HARK. -These tests compare the output of different models in specific cases in which those models -should yield the same output. The code will pass these tests if and only if the output is close -"enough". -""" - -# Bring in modules we need -import unittest -from copy import deepcopy -import numpy as np - -# Bring in the HARK models we want to test -from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType -from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType -from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType - - -class Compare_PerfectForesight_and_Infinite(unittest.TestCase): - """ - Class to compare output of the perfect foresight and infinite horizon models. - When income uncertainty is removed from the infinite horizon model, it reduces in theory to - the perfect foresight model. This class implements tests to make sure it reduces in practice - to the perfect foresight model as well. - """ - - def setUp(self): - """ - Prepare to compare the models by initializing and solving them - """ - # Set up and solve infinite type - import HARK.ConsumptionSaving.ConsumerParameters as Params - - # Define a test dictionary that should have the same solution in the - # perfect foresight and idiosyncratic shocks models. - test_dictionary = deepcopy(Params.init_idiosyncratic_shocks) - test_dictionary['LivPrb'] = [1.] - test_dictionary['DiscFac'] = 0.955 - test_dictionary['PermGroFac'] = [1.] - test_dictionary['PermShkStd'] = [0.] - test_dictionary['TranShkStd'] = [0.] - test_dictionary['UnempPrb'] = 0. - test_dictionary['T_cycle'] = 1 - test_dictionary['T_retire'] = 0 - test_dictionary['BoroCnstArt'] = None - - InfiniteType = IndShockConsumerType(**test_dictionary) - InfiniteType.cycles = 0 - - InfiniteType.updateIncomeProcess() - InfiniteType.solve() - InfiniteType.timeFwd() - InfiniteType.unpackcFunc() - - # Make and solve a perfect foresight consumer type with the same parameters - PerfectForesightType = PerfForesightConsumerType(**test_dictionary) - PerfectForesightType.cycles = 0 - - PerfectForesightType.solve() - PerfectForesightType.unpackcFunc() - PerfectForesightType.timeFwd() - - self.InfiniteType = InfiniteType - self.PerfectForesightType = PerfectForesightType - - def test_consumption(self): - """" - Now compare the consumption functions and make sure they are "close" - """ - def diffFunc(m): return self.PerfectForesightType.solution[0].cFunc(m) - \ - self.InfiniteType.cFunc[0](m) - points = np.arange(0.5, 10., .01) - difference = diffFunc(points) - max_difference = np.max(np.abs(difference)) - - self.assertLess(max_difference, 0.01) - - -class Compare_TBS_and_Markov(unittest.TestCase): - """ - Class to compare output of the Tractable Buffer Stock and Markov models. - The only uncertainty in the TBS model is over when the agent will enter an absorbing state - with 0 income. With the right transition arrays and income processes, this is just a special - case of the Markov model. So with the right inputs, we should be able to solve the two - different models and get the same outputs. - """ - def setUp(self): - # Set up and solve TBS - base_primitives = {'UnempPrb': .015, - 'DiscFac': 0.9, - 'Rfree': 1.1, - 'PermGroFac': 1.05, - 'CRRA': .95} - TBSType = TractableConsumerType(**base_primitives) - TBSType.solve() - - # Set up and solve Markov - MrkvArray = [np.array([[1.0-base_primitives['UnempPrb'], base_primitives['UnempPrb']],[0.0, 1.0]])] - Markov_primitives = {"CRRA": base_primitives['CRRA'], - "Rfree": np.array(2*[base_primitives['Rfree']]), - "PermGroFac": [np.array(2*[base_primitives['PermGroFac'] / - (1.0-base_primitives['UnempPrb'])])], - "BoroCnstArt": None, - "PermShkStd": [0.0], - "PermShkCount": 1, - "TranShkStd": [0.0], - "TranShkCount": 1, - "T_total": 1, - "UnempPrb": 0.0, - "UnempPrbRet": 0.0, - "T_retire": 0, - "IncUnemp": 0.0, - "IncUnempRet": 0.0, - "aXtraMin": 0.001, - "aXtraMax": TBSType.mUpperBnd, - "aXtraCount": 48, - "aXtraExtra": [None], - "aXtraNestFac": 3, - "LivPrb":[np.array([1.0,1.0]),], - "DiscFac": base_primitives['DiscFac'], - 'Nagents': 1, - 'psi_seed': 0, - 'xi_seed': 0, - 'unemp_seed': 0, - 'tax_rate': 0.0, - 'vFuncBool': False, - 'CubicBool': True, - 'MrkvArray': MrkvArray, - 'T_cycle':1 - } - - MarkovType = MarkovConsumerType(**Markov_primitives) - MarkovType.cycles = 0 - employed_income_dist = [np.ones(1), np.ones(1), np.ones(1)] - unemployed_income_dist = [np.ones(1), np.ones(1), np.zeros(1)] - MarkovType.IncomeDstn = [[employed_income_dist, unemployed_income_dist]] - - MarkovType.solve() - MarkovType.unpackcFunc() - - self.TBSType = TBSType - self.MarkovType = MarkovType - - def test_consumption(self): - # Now compare the consumption functions and make sure they are "close" - - def diffFunc(m): return self.TBSType.solution[0].cFunc(m) - self.MarkovType.cFunc[0][0](m) - points = np.arange(0.1, 10., .01) - difference = diffFunc(points) - max_difference = np.max(np.abs(difference)) - - self.assertLess(max_difference, 0.01) - - -if __name__ == '__main__': - # Run all the tests - unittest.main() From 9d7f61c9df65c101c5d7f0feb7fe4b319b80f7e7 Mon Sep 17 00:00:00 2001 From: llorracc <@users.noreply.github.com> Date: Thu, 20 Feb 2020 17:44:41 +0100 Subject: [PATCH 226/242] Final updates for checkConditions before turnover to Seb and Mridul --- HARK/ConsumptionSaving/ConsIndShockModel.py | 116 ++++++++++---------- 1 file changed, 55 insertions(+), 61 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 2f639471a..bc77c42fd 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1852,10 +1852,10 @@ def getPostStates(self): def checkConditions(self,verbose=False,verbose_reference=False,public_call=False): ''' - This method checks whether the instance's type satisfies the Growth Impatience Condition - (GIC), Return Impatience Condition (RIC), Absolute Impatience Condition (AIC), Return - Impatience Condition (RIC), Finite Human Wealth Condition (FHWC) and Finite Value of - Autarky Condition (FVAC). Depending on the configuration of parameter values, some + This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), + the Return Impatience Condition (RIC), the Finite Human Wealth Condition (FHWC) and the perfect foresight + model's version of the Finite Value of the Growth Impatience Condition (GIC_PF) and + Autarky Condition (FVAC_PF). Depending on the configuration of parameter values, some combination of these conditions must be satisfied in order for the problem to have a nondegenerate solution. To check which conditions are required, in the verbose mode a reference to the relevant theoretical literature is made. @@ -1902,22 +1902,22 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False print() #Evaluate and report on the Growth Impatience Condition - GIF = Thorn/self.PermGroFac[0] - self.GIF = GIF + GIFPF = Thorn/self.PermGroFac[0] + self.GIFPF = GIFPF - if GIF<1: - self.GIC = True + if GIFPF<1: + self.GICPF = True if public_call or verbose: - print('The value of the Growth Impatience Factor for the supplied parameter values satisfies the Growth Impatience Condition.', end = " ") + print('The value of the Growth Impatience Factor for the supplied parameter values satisfies the Perfect Foresight Growth Impatience Condition.', end = " ") if verbose: - print(' Therefore, the ratio of individual wealth to permanent income will fall indefinitely.') + print(' Therefore, for a perfect foresight consumer, the ratio of individual wealth to permanent income will fall indefinitely.') print() else: - self.GIC = False + self.GICPF = False violated = True - print('The given parameter values violate the Growth Impatience Condition for this consumer type; the GIF is: %2.4f' % (GIF), end = " ") + print('The given parameter values violate the Perfect Foresight Growth Impatience Condition for this consumer type; the GIFPF is: %2.4f' % (GIFPF), end = " ") if verbose: - print(' Therefore, the ratio of individual wealth to permanent income is expected to grow toward infinity.') + print(' Therefore, for a perfect foresight consumer the ratio of individual wealth to permanent income is expected to grow toward infinity.') print() @@ -2251,8 +2251,7 @@ def preSolve(self): def checkConditions(self,verbose=False,public_call=True): ''' - This method checks whether the instance's type satisfies the Growth Impatience Condition - (GIC), Return Impatience Condition (RIC), Absolute Impatience Condition (AIC), Weak Return + This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), Weak Return Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of Autarky Condition (FVAC). When combinations of these conditions are satisfied, the solution to the problem exhibits different characteristics. (For an exposition of the @@ -2289,38 +2288,38 @@ def checkConditions(self,verbose=False,public_call=True): # [url]/#Pat, adjusted to include mortality # Thorn = ((self.Rfree/self.LivPrb[0])*(self.DiscFac*self.LivPrb[0]))**(1/self.CRRA) Thorn = ((self.Rfree*self.DiscFac))**(1/self.CRRA) - GIF = Thorn/(self.PermGroFac[0] ) # [url]/#GIF + GIFPF = Thorn/(self.PermGroFac[0] ) # [url]/#GIF GIFInd = Thorn/(self.PermGroFac[0]*InvEPermShkInv) # [url]/#GIFI - GIFAgg = Thorn*self.LivPrb[0]/self.PermGroFac[0] # [url]#/GIFA + GIFAgg = Thorn*self.LivPrb[0]/self.PermGroFac[0] # Lower bound of aggregate wealth growth if all inheritances squandered -# self.Rnorm = self.Rfree*EPermShkInv/(self.PermGroFac[0]*self.LivPrb[0]) - self.GIF = GIF - self.GIFInd = GIFInd - self.GIFAgg = GIFAgg - self.Thorn = Thorn - self.PermGroFacAdj = PermGroFacAdj - self.EPermShkInv = EPermShkInv - self.InvEPermShkInv = InvEPermShkInv - self.DiscFacGIFMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge - self.DiscFacGIFIMax = ((self.PermGroFac[0]*InvEPermShkInv)**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge - self.DiscFacGIFAMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree*self.LivPrb[0]) # DiscFac at growth impatience knife edge +# self.Rnorm = self.Rfree*EPermShkInv/(self.PermGroFac[0]*self.LivPrb[0]) + self.GIFPF = GIFPF + self.GIFInd = GIFInd + self.GIFAgg = GIFAgg + self.Thorn = Thorn + self.PermGroFacAdj = PermGroFacAdj + self.EPermShkInv = EPermShkInv + self.InvEPermShkInv = InvEPermShkInv + self.DiscFacGIFPFMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge + self.DiscFacGIFIndMax = ((self.PermGroFac[0]*InvEPermShkInv)**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge + self.DiscFacGIFAggMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree*self.LivPrb[0]) # DiscFac at growth impatience knife edge #Evaluate and report on the Growth Impatience Condition # [url]/#GIC - if GIF<=1: - self.GIC = True + if GIFPF<1: + self.GICPF = True if public_call or verbose: - print('The value of the Growth Impatience Factor for the supplied parameter values satisfies the Growth Impatience Condition.', end = " ") + print('The value of the Perfect Foresight Growth Impatience Factor for the supplied parameter values satisfies the Perfect Foresight Growth Impatience Condition.', end = " ") if verbose: - print('Therefore, a target level of the ratio of expected market resources to expected permanent income exists (see '+url+'/#onetarget).') + print(' Therefore, in the absence of any risk, the ratio of individual wealth to permanent income would fall indefinitely.') print() else: - self.GIC = False + self.GICPF = False violated = True - print('The given parameter values violate the Growth Impatience Condition; the GIF is: %2.4f' % (GIF), end = " ") + print('The given parameter values violate the Perfect Foresight Growth Impatience Condition; the GIFPF is: %2.4f' % (GIFPF), end = " ") if verbose: print('') - print('Therefore, a target level of wealth does not exist. (see '+url+'/#onetarget)') + print('Therefore, the ratio of wealth to permanent income approaches infinity in the perfect foresight model. (see '+url+' for more).') print() if GIFInd<=1: @@ -2328,7 +2327,7 @@ def checkConditions(self,verbose=False,public_call=True): if public_call or verbose: print('The value of the Individual Growth Impatience Factor for the supplied parameter values satisfies the Individual Growth Impatience Condition.', end = " ") if verbose: - print('Therefore, a target level of the individual market resources ratio m exists (see '+url+'/#onetarget).') + print('Therefore, a target level of the individual market resources ratio m exists (see '+url+'/#onetarget for more).') print() else: self.GICInd = False @@ -2336,7 +2335,7 @@ def checkConditions(self,verbose=False,public_call=True): print('The given parameter values violate the Individual Growth Impatience Condition; the GIFInd is: %2.4f' % (GIFInd), end = " ") if verbose: print('') - print('Therefore, a target ratio of individual market resources to individual permanent income does not exist. (see '+url+'/#onetarget)') + print('Therefore, a target ratio of individual market resources to individual permanent income does not exist. (see '+url+'/#onetarget for more).') print() if GIFAgg<=1: @@ -2344,7 +2343,7 @@ def checkConditions(self,verbose=False,public_call=True): if public_call or verbose: print('The value of the Aggregate Growth Impatience Factor for the supplied parameter values satisfies the Aggregate Growth Impatience Condition.', end = " ") if verbose: - print('Therefore, it is possible that a target level of the ratio of expected market resources to expected permanent income exists (see '+url+'/#onetarget).') + print('Therefore, it is possible that a target level of the ratio of aggregate market resources to aggregate permanent income exists.') # Need to provide reference print() else: self.GICAgg = False @@ -2352,7 +2351,7 @@ def checkConditions(self,verbose=False,public_call=True): print('The given parameter values violate the Aggregate Growth Impatience Condition; the GIFAgg is: %2.4f' % (GIFAgg), end = " ") if verbose: print('') - print('Therefore, a target ratio of aggregate resources to aggreg permanent income does not exist. (see '+url+'/#onetarget)') + print('Therefore, a target ratio of aggregate resources to aggregate permanent income does not exist.') # Need to provide reference print() #Evaluate and report on the Weak Return Impatience Condition @@ -2362,7 +2361,7 @@ def checkConditions(self,verbose=False,public_call=True): if WRIF<=1: self.WRIC = True if public_call or verbose: - print('The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition (see '+url+'/#WRIC).') + print('The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition (see '+url+'/#WRIC for more).') print() else: self.WRIC = False @@ -2370,7 +2369,7 @@ def checkConditions(self,verbose=False,public_call=True): print('The given type violates the Weak Return Impatience Condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF), end = " ") if verbose: print('') - print('Therefore, a nondegenerate solution is not available (see '+url+'/#WRIC.') + print('Therefore, a nondegenerate solution is not available (see '+url+'/#WRIC for more.') print() #Evaluate and report on the Finite Value of Autarky Condition @@ -2403,17 +2402,16 @@ def checkConditions(self,verbose=False,public_call=True): print('') if verbose: - print('GIF = %2.6f ' % (GIF)) - print('GIFInd = %2.6f ' % (GIFInd)) - print('GIFAgg = %2.6f ' % (GIFAgg)) - print('Thorn = %2.6f ' % (Thorn)) - print('PermGroFacAdj = %2.6f ' % (PermGroFacAdj)) - print('uInvEpShkuInv = %2.6f ' % (uInvEpShkuInv)) - print('FVAF = %2.6f ' % (FVAF)) - print('WRIF = %2.6f ' % (WRIF)) - print('DiscFacGIFMax = %2.6f ' % (self.DiscFacGIFMax)) - print('DiscFacGIFAMax= %2.6f ' % (self.DiscFacGIFAMax)) - print('DiscFacGIFAMax= %2.6f ' % (self.DiscFacGIFIMax)) + print('GIFPF = %2.6f ' % (GIFPF)) + print('GIFInd = %2.6f ' % (GIFInd)) + print('GIFAgg = %2.6f ' % (GIFAgg)) + print('Thorn = AIF = %2.6f ' % (Thorn)) + print('PermGroFacAdj = %2.6f ' % (PermGroFacAdj)) + print('uInvEpShkuInv = %2.6f ' % (uInvEpShkuInv)) + print('FVAF = %2.6f ' % (FVAF)) + print('WRIF = %2.6f ' % (WRIF)) + print('DiscFacGIFIndMax = %2.6f ' % (self.DiscFacGIFIndMax)) + print('DiscFacGIFAggMax = %2.6f ' % (self.DiscFacGIFAggMax)) def Ex_Mtp1_over_Ex_Ptp1(self,mRat,verbose=False): cRat = self.solution[-1].cFunc(mRat) @@ -2603,15 +2601,11 @@ def getRfree(self): def checkConditions(self,verbose=False): ''' - This method checks whether the instance's type satisfies the Growth Impatience Condition - (GIC), Return Impatience Condition (RIC), Absolute Impatience Condition (AIC), Weak Return - Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of - Autarky Condition (FVAC). These are the conditions that are sufficient for nondegenerate - infinite horizon solutions with a 1 period cycle. Depending on the model at hand, a - different combination of these conditions must be satisfied. To check which conditions are - relevant to the model at hand, a reference to the relevant theoretical literature is made. - - SHOULD BE INHERITED FROM ConsIndShockModel + This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), + the Return Impatience Condition (RIC), the Growth Impatience Condition (GIC), the Weak Return + Impatience Condition (WRIC), the Finite Human Wealth Condition (FHWC) and the Finite Value of + Autarky Condition (FVAC). To check which conditions are relevant to the model at hand, a + reference to the relevant theoretical literature is made. Parameters ---------- From dac07bf32c74af9ad56df37f7e8571b11ad2812d Mon Sep 17 00:00:00 2001 From: sb Date: Fri, 21 Feb 2020 09:54:16 -0500 Subject: [PATCH 227/242] update IndShockConsumerType conditions check --- HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py index e7b1e69f4..f6f3f980d 100644 --- a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py +++ b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py @@ -64,8 +64,7 @@ def test_GICFails(self): self.assertAlmostEqual(c_m[500], 0.7772637042393458) self.assertAlmostEqual(c_m[700], 0.8392649061916746) - self.assertFalse(GICFailExample.GIC) - self.assertFalse(GICFailExample.AIC) + self.assertFalse(GICFailExample.GICPF) def test_infinite_horizon(self): baseEx_inf = IndShockConsumerType(cycles=0, From a71e9504e0906eb8654175e64a2bb53a2d7dfde3 Mon Sep 17 00:00:00 2001 From: sb Date: Fri, 21 Feb 2020 10:24:50 -0500 Subject: [PATCH 228/242] functionalize out the conditions (AIC, GICPF, RIC, FHWC) from checkConditions() --- HARK/ConsumptionSaving/ConsIndShockModel.py | 89 ++++++++++++--------- 1 file changed, 52 insertions(+), 37 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index bc77c42fd..efebbaeae 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1850,40 +1850,12 @@ def getPostStates(self): self.aLvlNow = self.aNrmNow*self.pLvlNow # Useful in some cases to precalculate asset level return None - def checkConditions(self,verbose=False,verbose_reference=False,public_call=False): + def checkAIC(self, thorn): ''' - This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), - the Return Impatience Condition (RIC), the Finite Human Wealth Condition (FHWC) and the perfect foresight - model's version of the Finite Value of the Growth Impatience Condition (GIC_PF) and - Autarky Condition (FVAC_PF). Depending on the configuration of parameter values, some - combination of these conditions must be satisfied in order for the problem to have - a nondegenerate solution. To check which conditions are required, in the verbose mode - a reference to the relevant theoretical literature is made. - - Parameters - ---------- - verbose : boolean - Specifies different levels of verbosity of feedback. When False, it only reports whether the - instance's type fails to satisfy a particular condition. When True, it reports all results, i.e. - the factor values for all conditions. - - Returns - ------- - None + Evaluate and report on the Absolute Impatience Condition ''' - # This method only checks for the conditions for infinite horizon models - # with a 1 period cycle. If these conditions are not met, we exit early. - if self.cycles!=0 or self.T_cycle > 1: - return + AIF = thorn - violated = False - - Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) - AIF = Thorn - - #Evaluate and report on the Absolute Impatience Condition - - self.Thorn = Thorn self.AIF = AIF if AIF<1: self.AIC = True @@ -1900,9 +1872,12 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False violated = True print(' Because the AIF > 1, the absolute amount of consumption is expected to grow over time') print() - - #Evaluate and report on the Growth Impatience Condition - GIFPF = Thorn/self.PermGroFac[0] + + def checkGICPF(self,thorn): + ''' + Evaluate and report on the Growth Impatience Condition + ''' + GIFPF = thorn/self.PermGroFac[0] self.GIFPF = GIFPF if GIFPF<1: @@ -1919,9 +1894,11 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False if verbose: print(' Therefore, for a perfect foresight consumer the ratio of individual wealth to permanent income is expected to grow toward infinity.') print() - - #Evaluate and report on the Return Impatience Condition + def checkRIC(self, thorn): + ''' + Evaluate and report on the Return Impatience Condition + ''' RIF = Thorn/self.Rfree self.RIF = RIF @@ -1940,7 +1917,11 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False print('Therefore, the limiting consumption function is c(m)=0 for all m') print() - #Evaluate and report on the Finite Human Wealth Condition + def checkFHWC(self): + ''' + Evaluate and report on the Finite Human Wealth Condition + ''' + FHWF = self.PermGroFac[0]/self.Rfree self.FHWF = FHWF if FHWF<1: @@ -1966,6 +1947,40 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False return violated + def checkConditions(self,verbose=False,verbose_reference=False,public_call=False): + ''' + This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), + the Return Impatience Condition (RIC), the Finite Human Wealth Condition (FHWC) and the perfect foresight + model's version of the Finite Value of the Growth Impatience Condition (GIC_PF) and + Autarky Condition (FVAC_PF). Depending on the configuration of parameter values, some + combination of these conditions must be satisfied in order for the problem to have + a nondegenerate solution. To check which conditions are required, in the verbose mode + a reference to the relevant theoretical literature is made. + + Parameters + ---------- + verbose : boolean + Specifies different levels of verbosity of feedback. When False, it only reports whether the + instance's type fails to satisfy a particular condition. When True, it reports all results, i.e. + the factor values for all conditions. + + Returns + ------- + None + ''' + # This method only checks for the conditions for infinite horizon models + # with a 1 period cycle. If these conditions are not met, we exit early. + if self.cycles!=0 or self.T_cycle > 1: + return + + Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) + self.Thorn = thorn + + self.checkAIC(Thorn) + self.checkGICPF(Thorn) + self.checkRIC(Thorn) + self.checkFHWC() + class IndShockConsumerType(PerfForesightConsumerType): ''' A consumer type with idiosyncratic shocks to permanent and transitory income. From c5f0ee5b56e532a320f2ee9f97311c100b98071d Mon Sep 17 00:00:00 2001 From: sb Date: Fri, 21 Feb 2020 11:00:50 -0500 Subject: [PATCH 229/242] functionalize out IndShockConsumerType conditions --- HARK/ConsumptionSaving/ConsIndShockModel.py | 177 ++++++++++---------- 1 file changed, 91 insertions(+), 86 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index efebbaeae..7cba62da6 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1850,7 +1850,7 @@ def getPostStates(self): self.aLvlNow = self.aNrmNow*self.pLvlNow # Useful in some cases to precalculate asset level return None - def checkAIC(self, thorn): + def checkAIC(self, thorn,verbose,public_call): ''' Evaluate and report on the Absolute Impatience Condition ''' @@ -1873,7 +1873,7 @@ def checkAIC(self, thorn): print(' Because the AIF > 1, the absolute amount of consumption is expected to grow over time') print() - def checkGICPF(self,thorn): + def checkGICPF(self,thorn,verbose,public_call): ''' Evaluate and report on the Growth Impatience Condition ''' @@ -1895,7 +1895,7 @@ def checkGICPF(self,thorn): print(' Therefore, for a perfect foresight consumer the ratio of individual wealth to permanent income is expected to grow toward infinity.') print() - def checkRIC(self, thorn): + def checkRIC(self, thorn,verbose,public_call): ''' Evaluate and report on the Return Impatience Condition ''' @@ -1917,7 +1917,7 @@ def checkRIC(self, thorn): print('Therefore, the limiting consumption function is c(m)=0 for all m') print() - def checkFHWC(self): + def checkFHWC(self,verbose,public_call): ''' Evaluate and report on the Finite Human Wealth Condition ''' @@ -1947,6 +1947,7 @@ def checkFHWC(self): return violated + def checkConditions(self,verbose=False,verbose_reference=False,public_call=False): ''' This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), @@ -1976,10 +1977,10 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) self.Thorn = thorn - self.checkAIC(Thorn) - self.checkGICPF(Thorn) - self.checkRIC(Thorn) - self.checkFHWC() + self.checkAIC(Thorn,verbose,public_call) + self.checkGICPF(Thorn,verbose,public_call) + self.checkRIC(Thorn,verbose,public_call) + self.checkFHWC(verbose,public_call) class IndShockConsumerType(PerfForesightConsumerType): ''' @@ -2264,80 +2265,12 @@ def preSolve(self): if not self.quiet: self.checkConditions(verbose=self.verbose,public_call=False) - def checkConditions(self,verbose=False,public_call=True): + def checkGICInd(self,Thorn,verbose,public_call): ''' - This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), Weak Return - Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of - Autarky Condition (FVAC). When combinations of these conditions are satisfied, the - solution to the problem exhibits different characteristics. (For an exposition of the - conditions, see http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) - - Parameters - ---------- - verbose : boolean - Specifies different levels of verbosity of feedback. When False, it only reports whether the - instance's type fails to satisfy a particular condition. When True, it reports all results, i.e. - the factor values for all conditions. - - Returns - ------- - None + Check Individual Growth Impatience Factor. ''' - violated = False # PerfForesightConsumerType.checkConditions(self, verbose=False, verbose_reference=False) - - if self.cycles!=0 or self.T_cycle > 1: - return - - # For theory, see hyperlink targets to expressions in - # url=http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory - # For example, the hyperlink to the relevant section of the paper - url='http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory' - # would be referenced below as: - # [url]/#Uncertainty-Modified-Conditions - - self.InvPermShkDstn=deepcopy(self.PermShkDstn) - self.InvPermShkDstn[0][1]=1/self.PermShkDstn[0][1] - EPermShkInv=np.dot(self.InvPermShkDstn[0][0],1/self.PermShkDstn[0][1]) # $\Ex_{t}[\psi^{-1}_{t+1}]$ (in first eqn in sec) - InvEPermShkInv=(1/EPermShkInv) # $\underline{\psi}$ in the paper (\bar{\isp} in private version) - PermGroFacAdj=self.PermGroFac[0]*InvEPermShkInv # [url]/#PGroAdj - # [url]/#Pat, adjusted to include mortality -# Thorn = ((self.Rfree/self.LivPrb[0])*(self.DiscFac*self.LivPrb[0]))**(1/self.CRRA) - Thorn = ((self.Rfree*self.DiscFac))**(1/self.CRRA) - GIFPF = Thorn/(self.PermGroFac[0] ) # [url]/#GIF - GIFInd = Thorn/(self.PermGroFac[0]*InvEPermShkInv) # [url]/#GIFI - GIFAgg = Thorn*self.LivPrb[0]/self.PermGroFac[0] # Lower bound of aggregate wealth growth if all inheritances squandered - -# self.Rnorm = self.Rfree*EPermShkInv/(self.PermGroFac[0]*self.LivPrb[0]) - self.GIFPF = GIFPF - self.GIFInd = GIFInd - self.GIFAgg = GIFAgg - self.Thorn = Thorn - self.PermGroFacAdj = PermGroFacAdj - self.EPermShkInv = EPermShkInv - self.InvEPermShkInv = InvEPermShkInv - self.DiscFacGIFPFMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge - self.DiscFacGIFIndMax = ((self.PermGroFac[0]*InvEPermShkInv)**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge - self.DiscFacGIFAggMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree*self.LivPrb[0]) # DiscFac at growth impatience knife edge - - #Evaluate and report on the Growth Impatience Condition - # [url]/#GIC - if GIFPF<1: - self.GICPF = True - if public_call or verbose: - print('The value of the Perfect Foresight Growth Impatience Factor for the supplied parameter values satisfies the Perfect Foresight Growth Impatience Condition.', end = " ") - if verbose: - print(' Therefore, in the absence of any risk, the ratio of individual wealth to permanent income would fall indefinitely.') - print() - else: - self.GICPF = False - violated = True - print('The given parameter values violate the Perfect Foresight Growth Impatience Condition; the GIFPF is: %2.4f' % (GIFPF), end = " ") - if verbose: - print('') - print('Therefore, the ratio of wealth to permanent income approaches infinity in the perfect foresight model. (see '+url+' for more).') - print() - if GIFInd<=1: + if self.GIFInd<=1: self.GICInd = True if public_call or verbose: print('The value of the Individual Growth Impatience Factor for the supplied parameter values satisfies the Individual Growth Impatience Condition.', end = " ") @@ -2347,13 +2280,15 @@ def checkConditions(self,verbose=False,public_call=True): else: self.GICInd = False violated = True - print('The given parameter values violate the Individual Growth Impatience Condition; the GIFInd is: %2.4f' % (GIFInd), end = " ") + print('The given parameter values violate the Individual Growth Impatience Condition; the GIFInd is: %2.4f' % (self.GIFInd), end = " ") if verbose: print('') print('Therefore, a target ratio of individual market resources to individual permanent income does not exist. (see '+url+'/#onetarget for more).') print() - if GIFAgg<=1: + def checkCIGAgg(self, Thorn,verbose,public_call): + + if self.GIFAgg<=1: self.GICAgg = True if public_call or verbose: print('The value of the Aggregate Growth Impatience Factor for the supplied parameter values satisfies the Aggregate Growth Impatience Condition.', end = " ") @@ -2363,14 +2298,17 @@ def checkConditions(self,verbose=False,public_call=True): else: self.GICAgg = False violated = True - print('The given parameter values violate the Aggregate Growth Impatience Condition; the GIFAgg is: %2.4f' % (GIFAgg), end = " ") + print('The given parameter values violate the Aggregate Growth Impatience Condition; the GIFAgg is: %2.4f' % (self.GIFAgg), end = " ") if verbose: print('') print('Therefore, a target ratio of aggregate resources to aggregate permanent income does not exist.') # Need to provide reference print() - #Evaluate and report on the Weak Return Impatience Condition - # [url]/#WRIF modified to incorporate LivPrb + def checkWRIC(self, verbose,public_call): + ''' + Evaluate and report on the Weak Return Impatience Condition + [url]/#WRIF modified to incorporate LivPrb + ''' WRIF=(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA)/self.Rfree self.WRIF = WRIF if WRIF<=1: @@ -2387,8 +2325,11 @@ def checkConditions(self,verbose=False,public_call=True): print('Therefore, a nondegenerate solution is not available (see '+url+'/#WRIC for more.') print() - #Evaluate and report on the Finite Value of Autarky Condition - # Hyperlink to paper: [url]/#Autarky-Value + def checkFVAC(self,verbose,public_call): + ''' + Evaluate and report on the Finite Value of Autarky Condition + Hyperlink to paper: [url]/#Autarky-Value + ''' EpShkuInv = np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA)) if self.CRRA != 1.0: uInvEpShkuInv = EpShkuInv**(1/(1-self.CRRA)) # The term that gives a utility-consequence-adjusted utility growth @@ -2412,6 +2353,70 @@ def checkConditions(self,verbose=False,public_call=True): print('Therefore, a nondegenerate solution is not available (see '+url+'/#Conditions-Under-Which-the-Problem-Defines-a-Contraction-Mapping') print() + + + + def checkConditions(self,verbose=False,public_call=True): + ''' + This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), Weak Return + Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of + Autarky Condition (FVAC). When combinations of these conditions are satisfied, the + solution to the problem exhibits different characteristics. (For an exposition of the + conditions, see http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) + + Parameters + ---------- + verbose : boolean + Specifies different levels of verbosity of feedback. When False, it only reports whether the + instance's type fails to satisfy a particular condition. When True, it reports all results, i.e. + the factor values for all conditions. + + Returns + ------- + None + ''' + violated = False # PerfForesightConsumerType.checkConditions(self, verbose=False, verbose_reference=False) + + if self.cycles!=0 or self.T_cycle > 1: + return + + # For theory, see hyperlink targets to expressions in + # url=http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory + # For example, the hyperlink to the relevant section of the paper + url='http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory' + # would be referenced below as: + # [url]/#Uncertainty-Modified-Conditions + + self.InvPermShkDstn=deepcopy(self.PermShkDstn) + self.InvPermShkDstn[0][1]=1/self.PermShkDstn[0][1] + EPermShkInv=np.dot(self.InvPermShkDstn[0][0],1/self.PermShkDstn[0][1]) # $\Ex_{t}[\psi^{-1}_{t+1}]$ (in first eqn in sec) + + InvEPermShkInv=(1/EPermShkInv) # $\underline{\psi}$ in the paper (\bar{\isp} in private version) + PermGroFacAdj=self.PermGroFac[0]*InvEPermShkInv # [url]/#PGroAdj + # [url]/#Pat, adjusted to include mortality +# Thorn = ((self.Rfree/self.LivPrb[0])*(self.DiscFac*self.LivPrb[0]))**(1/self.CRRA) + Thorn = ((self.Rfree*self.DiscFac))**(1/self.CRRA) + GIFPF = Thorn/(self.PermGroFac[0] ) # [url]/#GIF + GIFInd = Thorn/(self.PermGroFac[0]*InvEPermShkInv) # [url]/#GIFI + GIFAgg = Thorn*self.LivPrb[0]/self.PermGroFac[0] # Lower bound of aggregate wealth growth if all inheritances squandered + +# self.Rnorm = self.Rfree*EPermShkInv/(self.PermGroFac[0]*self.LivPrb[0]) + self.GIFPF = GIFPF + self.GIFInd = GIFInd + self.GIFAgg = GIFAgg + self.Thorn = Thorn + self.PermGroFacAdj = PermGroFacAdj + self.EPermShkInv = EPermShkInv + self.InvEPermShkInv = InvEPermShkInv + self.DiscFacGIFPFMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge + self.DiscFacGIFIndMax = ((self.PermGroFac[0]*InvEPermShkInv)**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge + self.DiscFacGIFAggMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree*self.LivPrb[0]) # DiscFac at growth impatience knife edge + + self.checkGICPF(Thorn,verbose,public_call) + self.checkGICInd(Thorn,verbose,public_call) + self.checkCIGAgg(Thorn,verbose,public_call) + self.checkWRIC(verbose,public_call) + if verbose and violated: print('\n[!] For more information on the conditions, see Tables 3 and 4 in "Theoretical Foundations of Buffer Stock Saving" at '+url+'/#Factors-Defined-And-Compared') print('') From d575be3fe33cdb7374e7ff6b8f1038ce25843ebd Mon Sep 17 00:00:00 2001 From: sb Date: Fri, 21 Feb 2020 11:30:31 -0500 Subject: [PATCH 230/242] violated now stored in the agent object to persist across tests --- HARK/ConsumptionSaving/ConsIndShockModel.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 7cba62da6..7813703f2 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1861,15 +1861,15 @@ def checkAIC(self, thorn,verbose,public_call): self.AIC = True if public_call or verbose: print('The value of the absolute impatience factor (AIF) for the supplied parameter values satisfies the Absolute Impatience Condition.', end = " ") + self.violated = False if verbose: - violated = False print(' Because the AIF < 1, the absolute amount of consumption is expected to fall over time.') print() else: self.AIC = False print('The given type violates the Absolute Impatience Condition with the supplied parameter values; the AIF is %1.5f ' % (AIF), end=" ") + self.violated = True if verbose: - violated = True print(' Because the AIF > 1, the absolute amount of consumption is expected to grow over time') print() @@ -1889,7 +1889,7 @@ def checkGICPF(self,thorn,verbose,public_call): print() else: self.GICPF = False - violated = True + self.violated = True print('The given parameter values violate the Perfect Foresight Growth Impatience Condition for this consumer type; the GIFPF is: %2.4f' % (GIFPF), end = " ") if verbose: print(' Therefore, for a perfect foresight consumer the ratio of individual wealth to permanent income is expected to grow toward infinity.') @@ -1911,7 +1911,7 @@ def checkRIC(self, thorn,verbose,public_call): print() else: self.RIC = False - violated = True + self.violated = True print('The given type violates the Return Impatience Condition with the supplied parameter values; the factor is %1.5f ' % (RIF), end = " ") if verbose: print('Therefore, the limiting consumption function is c(m)=0 for all m') @@ -1938,7 +1938,7 @@ def checkFHWC(self,verbose,public_call): else: self.FHWC = False print('The given type violates the Finite Human Wealth Condition; the Finite Human wealth factor value %2.5f ' % (FHWF), end = " ") - violated = True + self.violated = True if verbose: print('Therefore, the limiting consumption function is c(m)=Infinity for all m') print() @@ -2279,7 +2279,7 @@ def checkGICInd(self,Thorn,verbose,public_call): print() else: self.GICInd = False - violated = True + self.violated = True print('The given parameter values violate the Individual Growth Impatience Condition; the GIFInd is: %2.4f' % (self.GIFInd), end = " ") if verbose: print('') @@ -2297,7 +2297,7 @@ def checkCIGAgg(self, Thorn,verbose,public_call): print() else: self.GICAgg = False - violated = True + self.violated = True print('The given parameter values violate the Aggregate Growth Impatience Condition; the GIFAgg is: %2.4f' % (self.GIFAgg), end = " ") if verbose: print('') @@ -2318,7 +2318,7 @@ def checkWRIC(self, verbose,public_call): print() else: self.WRIC = False - violated = True + self.violated = True print('The given type violates the Weak Return Impatience Condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF), end = " ") if verbose: print('') @@ -2348,7 +2348,7 @@ def checkFVAC(self,verbose,public_call): else: self.FVAC = False print('The given type violates the Finite Value of Autarky Condition with the supplied parameter values. The FVAF is %2.4f' %(FVAF), end = " ") - violated = True + self.violated = True if public_call or verbose: print('Therefore, a nondegenerate solution is not available (see '+url+'/#Conditions-Under-Which-the-Problem-Defines-a-Contraction-Mapping') print() @@ -2417,7 +2417,7 @@ def checkConditions(self,verbose=False,public_call=True): self.checkCIGAgg(Thorn,verbose,public_call) self.checkWRIC(verbose,public_call) - if verbose and violated: + if verbose and self.violated: print('\n[!] For more information on the conditions, see Tables 3 and 4 in "Theoretical Foundations of Buffer Stock Saving" at '+url+'/#Factors-Defined-And-Compared') print('') From 0e8f1eb6f105feb0af04e4aacd733656460672cb Mon Sep 17 00:00:00 2001 From: sb Date: Fri, 21 Feb 2020 11:46:53 -0500 Subject: [PATCH 231/242] moving the getShocks() test into ConsumptionSaving/tests/ --- .../tests/test_IndShockConsumerType.py | 24 ++++++++++++++ HARK/tests/test_IndShockConsumerType.py | 31 ------------------- 2 files changed, 24 insertions(+), 31 deletions(-) delete mode 100644 HARK/tests/test_IndShockConsumerType.py diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py index f6f3f980d..1e58fb27e 100644 --- a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py +++ b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py @@ -3,6 +3,30 @@ import numpy as np import unittest +class testIndShockConsumerType(unittest.TestCase): + + def test_getShocks(self): + agent = IndShockConsumerType( + AgentCount = 2, + T_sim = 10 + ) + + agent.solve() + + agent.initializeSim() + agent.simBirth(np.array([True,False])) + agent.simOnePeriod() + agent.simBirth(np.array([False,True])) + + agent.getShocks() + + self.assertEqual(agent.PermShkNow[0], + 1.0050166461586711) + self.assertEqual(agent.PermShkNow[1], + 1.0050166461586711) + self.assertEqual(agent.TranShkNow[0], + 1.1176912196531754) + class testBufferStock(unittest.TestCase): """ Tests of the results of the BufferStock REMARK. """ diff --git a/HARK/tests/test_IndShockConsumerType.py b/HARK/tests/test_IndShockConsumerType.py deleted file mode 100644 index 41a3eda70..000000000 --- a/HARK/tests/test_IndShockConsumerType.py +++ /dev/null @@ -1,31 +0,0 @@ -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType - -import numpy as np -import unittest - -class testIndShockConsumerType(unittest.TestCase): - - def test_getShocks(self): - agent = IndShockConsumerType( - AgentCount = 2, - T_sim = 10 - ) - - agent.solve() - - agent.initializeSim() - agent.simBirth(np.array([True,False])) - agent.simOnePeriod() - agent.simBirth(np.array([False,True])) - - agent.getShocks() - - self.assertEqual(agent.PermShkNow[0], - 1.0050166461586711) - self.assertEqual(agent.PermShkNow[1], - 1.0050166461586711) - self.assertEqual(agent.TranShkNow[0], - 1.1176912196531754) - - - From 5f19fb706869c29bead0e5f677830a686d76e16c Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Sat, 22 Feb 2020 01:20:36 +0530 Subject: [PATCH 232/242] fix up notebook utilities (#537) (cherry picked from commit 90d566d0d2c8dc7c8c452d0444ef8f2a1ce56110) --- HARK/utilities.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/HARK/utilities.py b/HARK/utilities.py index 7f4cd7230..97463a491 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -1337,6 +1337,7 @@ def test_latex_installation(pf): os.system('apt-get update') os.system('apt-get install texlive texlive-latex-extra texlive-xetex dvipng') latexExists=True + return True else: raise ImportError('Please install a full distribution of LaTeX on your computer then rerun. \n \ A full distribution means textlive, texlive-latex-extras, texlive-xetex, dvipng, and ghostscript') @@ -1358,7 +1359,7 @@ def in_ipynb(): return False -def setup_latex_env_notebook(pf): +def setup_latex_env_notebook(pf, latexExists): """ This is needed for use of the latex_envs notebook extension which allows the use of environments in Markdown. @@ -1367,13 +1368,15 @@ def setup_latex_env_notebook(pf): pf: str (platform) output of determine_platform() """ - if test_latex_installation(pf): + import os + from matplotlib import rc + import matplotlib.pyplot as plt + plt.rc('font', family='serif') + plt.rc('text', usetex=latexExists) + if latexExists: latex_preamble = r'\usepackage{amsmath}\usepackage{amsfonts}\usepackage[T1]{fontenc}' - import os - from os import path - import matplotlib.pyplot as plt latexdefs_path = os.getcwd()+'/latexdefs.tex' - if path.isfile(latexdefs_path): + if os.path.isfile(latexdefs_path): latex_preamble = latex_preamble+r'\input{'+latexdefs_path+r'}' else: # the required latex_envs package needs this file to exist even if it is empty from pathlib import Path From 8e948537d6ac54a3f29db8cb6948fe47c514a392 Mon Sep 17 00:00:00 2001 From: Sebastian Benthall Date: Mon, 24 Feb 2020 07:10:24 -0500 Subject: [PATCH 233/242] url -> self.url in checkConditions print logic. fixed #539 (#540) * url -> self.url in checkConditions print logic. fixed #539 * further fixes to bugs introduced in checkConditions refactor --- HARK/ConsumptionSaving/ConsIndShockModel.py | 22 ++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 7813703f2..a4f78e83f 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1900,7 +1900,7 @@ def checkRIC(self, thorn,verbose,public_call): Evaluate and report on the Return Impatience Condition ''' - RIF = Thorn/self.Rfree + RIF = thorn/self.Rfree self.RIF = RIF if RIF<1: self.RIC = True @@ -1942,9 +1942,9 @@ def checkFHWC(self,verbose,public_call): if verbose: print('Therefore, the limiting consumption function is c(m)=Infinity for all m') print() - if verbose and violated and verbose_reference: + if verbose and self.violated and verbose_reference: print('[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') - return violated + return self.violated @@ -1975,7 +1975,7 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False return Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) - self.Thorn = thorn + self.Thorn = Thorn self.checkAIC(Thorn,verbose,public_call) self.checkGICPF(Thorn,verbose,public_call) @@ -2275,7 +2275,7 @@ def checkGICInd(self,Thorn,verbose,public_call): if public_call or verbose: print('The value of the Individual Growth Impatience Factor for the supplied parameter values satisfies the Individual Growth Impatience Condition.', end = " ") if verbose: - print('Therefore, a target level of the individual market resources ratio m exists (see '+url+'/#onetarget for more).') + print('Therefore, a target level of the individual market resources ratio m exists (see '+self.url+'/#onetarget for more).') print() else: self.GICInd = False @@ -2283,7 +2283,7 @@ def checkGICInd(self,Thorn,verbose,public_call): print('The given parameter values violate the Individual Growth Impatience Condition; the GIFInd is: %2.4f' % (self.GIFInd), end = " ") if verbose: print('') - print('Therefore, a target ratio of individual market resources to individual permanent income does not exist. (see '+url+'/#onetarget for more).') + print('Therefore, a target ratio of individual market resources to individual permanent income does not exist. (see '+self.url+'/#onetarget for more).') print() def checkCIGAgg(self, Thorn,verbose,public_call): @@ -2314,7 +2314,7 @@ def checkWRIC(self, verbose,public_call): if WRIF<=1: self.WRIC = True if public_call or verbose: - print('The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition (see '+url+'/#WRIC for more).') + print('The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition (see '+self.url+'/#WRIC for more).') print() else: self.WRIC = False @@ -2322,7 +2322,7 @@ def checkWRIC(self, verbose,public_call): print('The given type violates the Weak Return Impatience Condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF), end = " ") if verbose: print('') - print('Therefore, a nondegenerate solution is not available (see '+url+'/#WRIC for more.') + print('Therefore, a nondegenerate solution is not available (see '+self.url+'/#WRIC for more.') print() def checkFVAC(self,verbose,public_call): @@ -2350,7 +2350,7 @@ def checkFVAC(self,verbose,public_call): print('The given type violates the Finite Value of Autarky Condition with the supplied parameter values. The FVAF is %2.4f' %(FVAF), end = " ") self.violated = True if public_call or verbose: - print('Therefore, a nondegenerate solution is not available (see '+url+'/#Conditions-Under-Which-the-Problem-Defines-a-Contraction-Mapping') + print('Therefore, a nondegenerate solution is not available (see '+self.url+'/#Conditions-Under-Which-the-Problem-Defines-a-Contraction-Mapping') print() @@ -2383,7 +2383,7 @@ def checkConditions(self,verbose=False,public_call=True): # For theory, see hyperlink targets to expressions in # url=http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory # For example, the hyperlink to the relevant section of the paper - url='http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory' + self.url='http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory' # would be referenced below as: # [url]/#Uncertainty-Modified-Conditions @@ -2418,7 +2418,7 @@ def checkConditions(self,verbose=False,public_call=True): self.checkWRIC(verbose,public_call) if verbose and self.violated: - print('\n[!] For more information on the conditions, see Tables 3 and 4 in "Theoretical Foundations of Buffer Stock Saving" at '+url+'/#Factors-Defined-And-Compared') + print('\n[!] For more information on the conditions, see Tables 3 and 4 in "Theoretical Foundations of Buffer Stock Saving" at '+self.url+'/#Factors-Defined-And-Compared') print('') if verbose: From e1b52e5d42927d1d18f2ceb3ceff2deae6b4d5bf Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Mon, 24 Feb 2020 18:15:53 +0530 Subject: [PATCH 234/242] fix checkConditions refactor in ConsIndShockModel (#541) --- HARK/ConsumptionSaving/ConsIndShockModel.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index a4f78e83f..ac2377240 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -2375,7 +2375,7 @@ def checkConditions(self,verbose=False,public_call=True): ------- None ''' - violated = False # PerfForesightConsumerType.checkConditions(self, verbose=False, verbose_reference=False) + self.violated = False # PerfForesightConsumerType.checkConditions(self, verbose=False, verbose_reference=False) if self.cycles!=0 or self.T_cycle > 1: return @@ -2416,6 +2416,7 @@ def checkConditions(self,verbose=False,public_call=True): self.checkGICInd(Thorn,verbose,public_call) self.checkCIGAgg(Thorn,verbose,public_call) self.checkWRIC(verbose,public_call) + self.checkFVAC(verbose, public_call) if verbose and self.violated: print('\n[!] For more information on the conditions, see Tables 3 and 4 in "Theoretical Foundations of Buffer Stock Saving" at '+self.url+'/#Factors-Defined-And-Compared') @@ -2427,9 +2428,9 @@ def checkConditions(self,verbose=False,public_call=True): print('GIFAgg = %2.6f ' % (GIFAgg)) print('Thorn = AIF = %2.6f ' % (Thorn)) print('PermGroFacAdj = %2.6f ' % (PermGroFacAdj)) - print('uInvEpShkuInv = %2.6f ' % (uInvEpShkuInv)) - print('FVAF = %2.6f ' % (FVAF)) - print('WRIF = %2.6f ' % (WRIF)) + print('uInvEpShkuInv = %2.6f ' % (self.uInvEpShkuInv)) + print('FVAF = %2.6f ' % (self.FVAF)) + print('WRIF = %2.6f ' % (self.WRIF)) print('DiscFacGIFIndMax = %2.6f ' % (self.DiscFacGIFIndMax)) print('DiscFacGIFAggMax = %2.6f ' % (self.DiscFacGIFAggMax)) From 78210a5d4c667eb7847b6114113e680bcafe5708 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Wed, 26 Feb 2020 09:56:34 +0100 Subject: [PATCH 235/242] add jpg to make_figs (#544) --- HARK/utilities.py | 1 + 1 file changed, 1 insertion(+) diff --git a/HARK/utilities.py b/HARK/utilities.py index 97463a491..f7e64a06a 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -1409,6 +1409,7 @@ def make_figs(figure_name, saveFigs, drawFigs, target_dir="Figures"): os.makedirs(Figures_dir) # If dir does not exist, create it # Save the figures in several formats print("Saving figure {} in {}".format(figure_name, target_dir)) + plt.savefig(os.path.join(target_dir, '{}.jpg'.format(figure_name))) # For web/html plt.savefig(os.path.join(target_dir, '{}.png'.format(figure_name))) # For web/html plt.savefig(os.path.join(target_dir, '{}.pdf'.format(figure_name))) # For LaTeX plt.savefig(os.path.join(target_dir, '{}.svg'.format(figure_name))) # For html5 From cf4972fad57afb09ec46f2c8a4c801d17934c538 Mon Sep 17 00:00:00 2001 From: sb Date: Wed, 26 Feb 2020 13:22:45 -0500 Subject: [PATCH 236/242] moving test_PerfForesightConsumerType to ConsumptionSaving/tests/ --- .../tests/test_PerfForesightConsumerType.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename HARK/{ => ConsumptionSaving}/tests/test_PerfForesightConsumerType.py (100%) diff --git a/HARK/tests/test_PerfForesightConsumerType.py b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py similarity index 100% rename from HARK/tests/test_PerfForesightConsumerType.py rename to HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py From 16c84c1ad52b7c3e825549eb145a154d8209fee1 Mon Sep 17 00:00:00 2001 From: sb Date: Wed, 26 Feb 2020 14:36:07 -0500 Subject: [PATCH 237/242] adding test_ConsPortfolioModel, based on ConsPortfolioModel Documentation DemARK --- .../tests/test_ConsPortfolioModel.py | 47 +++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 HARK/ConsumptionSaving/tests/test_ConsPortfolioModel.py diff --git a/HARK/ConsumptionSaving/tests/test_ConsPortfolioModel.py b/HARK/ConsumptionSaving/tests/test_ConsPortfolioModel.py new file mode 100644 index 000000000..456f9ed2d --- /dev/null +++ b/HARK/ConsumptionSaving/tests/test_ConsPortfolioModel.py @@ -0,0 +1,47 @@ +import copy +import HARK.ConsumptionSaving.ConsPortfolioModel as cpm +import HARK.ConsumptionSaving.ConsumerParameters as param +import unittest + +class testPortfolioConsumerType(unittest.TestCase): + + def setUp(self): + + # Parameters from Mehra and Prescott (1985): + Avg = 1.08 # equity premium + Std = 0.20 # standard deviation of rate-of-return shocks + + RiskyDstnFunc = cpm.RiskyDstnFactory(RiskyAvg=Avg, RiskyStd=Std) # Generates nodes for integration + RiskyDrawFunc = cpm.LogNormalRiskyDstnDraw(RiskyAvg=Avg, RiskyStd=Std) # Function to generate draws from a lognormal distribution + + init_portfolio = copy.copy(param.init_idiosyncratic_shocks) # Default parameter values for inf horiz model - including labor income with transitory and permanent shocks + init_portfolio['approxRiskyDstn'] = RiskyDstnFunc + init_portfolio['drawRiskyFunc'] = RiskyDrawFunc + init_portfolio['RiskyCount'] = 2 # Number of points in the approximation; 2 points is minimum + init_portfolio['RiskyShareCount'] = 25 # How many discrete points to allow for portfolio share + init_portfolio['Rfree'] = 1.0 # Riskfree return factor (interest rate is zero) + init_portfolio['CRRA'] = 6.0 # Relative risk aversion + + # Uninteresting technical parameters: + init_portfolio['aXtraMax'] = 100 + init_portfolio['aXtraCount'] = 50 + init_portfolio['BoroCnstArt'] = 0.0 # important for theoretical reasons + init_portfolio['DiscFac'] = 0.92 # Make them impatient even wrt a riskfree return of 1.08 + + # Create portfolio choice consumer type + self.pcct = cpm.PortfolioConsumerType(**init_portfolio) + + # %% {"code_folding": []} + # Solve the model under the given parameters + + self.pcct.solve() + + def test_RiskyShareFunc(self): + + self.assertAlmostEqual( + self.pcct.solution[0].RiskyShareFunc[0][0](2).tolist(), + 0.8796982720076255) + + self.assertAlmostEqual( + self.pcct.solution[0].RiskyShareFunc[0][0](8).tolist(), + 0.69738175) From 615c418f5bb6b62b988c98e4febda6b229f4efa2 Mon Sep 17 00:00:00 2001 From: sb Date: Wed, 26 Feb 2020 15:00:45 -0500 Subject: [PATCH 238/242] adding checkConditions() test for PerfForesightConsumerType --- .../tests/test_PerfForesightConsumerType.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py index 049c0fee0..14830c468 100644 --- a/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py +++ b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py @@ -4,10 +4,13 @@ class testPerfForesightConsumerType(unittest.TestCase): + def setUp(self): + self.agent = PerfForesightConsumerType() + self.agent_infinite = PerfForesightConsumerType(cycles=0) + def test_default_solution(self): - agent = PerfForesightConsumerType() - agent.solve() - c = agent.solution[0].cFunc + self.agent.solve() + c = self.agent.solution[0].cFunc self.assertEqual(c.x_list[0], -0.9805825242718447) self.assertEqual(c.x_list[1], 0.01941747572815533) @@ -15,4 +18,10 @@ def test_default_solution(self): self.assertEqual(c.y_list[1], 0.511321002804608) self.assertEqual(c.decay_extrap, False) - + def test_checkConditions(self): + self.agent_infinite.checkConditions() + + self.assertTrue(self.agent_infinite.AIC) + self.assertTrue(self.agent_infinite.GIC) + self.assertTrue(self.agent_infinite.RIC) + self.assertTrue(self.agent_infinite.FHWC) From a30ff3d3b60f523a82f777c3ebd5a1ab7dae6a91 Mon Sep 17 00:00:00 2001 From: sb Date: Wed, 26 Feb 2020 15:03:20 -0500 Subject: [PATCH 239/242] fixing self.violated in checkConditions(), and the PerfForsightConsumerType test --- HARK/ConsumptionSaving/ConsIndShockModel.py | 2 ++ HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index ac2377240..0257f2733 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1974,6 +1974,8 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False if self.cycles!=0 or self.T_cycle > 1: return + self.violated = False + Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) self.Thorn = Thorn diff --git a/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py index 14830c468..7e20befc1 100644 --- a/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py +++ b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py @@ -22,6 +22,6 @@ def test_checkConditions(self): self.agent_infinite.checkConditions() self.assertTrue(self.agent_infinite.AIC) - self.assertTrue(self.agent_infinite.GIC) + self.assertTrue(self.agent_infinite.GICPF) self.assertTrue(self.agent_infinite.RIC) self.assertTrue(self.agent_infinite.FHWC) From f778419488170c4ee96c4b6edbc8a33ad1eaff53 Mon Sep 17 00:00:00 2001 From: sb Date: Wed, 26 Feb 2020 16:05:05 -0500 Subject: [PATCH 240/242] adding simulation test for PerfForesightConsumerType --- .../tests/test_PerfForesightConsumerType.py | 52 ++++++++++++++++++- 1 file changed, 51 insertions(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py index 7e20befc1..0d4ae1d33 100644 --- a/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py +++ b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py @@ -1,5 +1,5 @@ from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType - +import numpy as np import unittest class testPerfForesightConsumerType(unittest.TestCase): @@ -25,3 +25,53 @@ def test_checkConditions(self): self.assertTrue(self.agent_infinite.GICPF) self.assertTrue(self.agent_infinite.RIC) self.assertTrue(self.agent_infinite.FHWC) + + def test_simulation(self): + + self.agent_infinite.solve() + + # Create parameter values necessary for simulation + SimulationParams = { + "AgentCount" : 10000, # Number of agents of this type + "T_sim" : 120, # Number of periods to simulate + "aNrmInitMean" : -6.0, # Mean of log initial assets + "aNrmInitStd" : 1.0, # Standard deviation of log initial assets + "pLvlInitMean" : 0.0, # Mean of log initial permanent income + "pLvlInitStd" : 0.0, # Standard deviation of log initial permanent income + "PermGroFacAgg" : 1.0, # Aggregate permanent income growth factor + "T_age" : None, # Age after which simulated agents are automatically killed + } + + self.agent_infinite(**SimulationParams) # This implicitly uses the assignParameters method of AgentType + + # Create PFexample object + self.agent_infinite.track_vars = ['mNrmNow'] + self.agent_infinite.initializeSim() + self.agent_infinite.simulate() + + self.assertAlmostEqual( + np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[40], + -23.008063500363942 + ) + + self.assertAlmostEqual( + np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[100], + -27.164608851546927 + ) + + ## Try now with the manipulation at time step 80 + + self.agent_infinite.initializeSim() + self.agent_infinite.simulate(80) + self.agent_infinite.aNrmNow += -5. # Adjust all simulated consumers' assets downward by 5 + self.agent_infinite.simulate(40) + + self.assertAlmostEqual( + np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[40], + -23.008063500363942 + ) + + self.assertAlmostEqual( + np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[100], + -29.140261331951606 + ) From 614a5519ba04de247e545f88a1fecde183ac6848 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 5 Mar 2020 22:51:56 +0530 Subject: [PATCH 241/242] remove the quick fix from estimation parameters (#552) --- .../Calibration/EstimationParameters.py | 22 ------------------- 1 file changed, 22 deletions(-) diff --git a/HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py b/HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py index 430e6f286..76a6e849f 100644 --- a/HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py +++ b/HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py @@ -4,22 +4,6 @@ ''' from __future__ import print_function -# --------------------------------------------------------------------------------- -# Debugging flags -# --------------------------------------------------------------------------------- - -show_PermGroFacAgg_error = False -# Error Notes: -# This sets a "quick fix" to the error, AttributeError: 'TempConsumerType' object has no attribute 'PermGroFacAgg' -# If you set this flag to "True" you will see the error. A more thorough fix is to -# fix the place where this error was introduced (Set to "True" and it will appear; -# this was almost certainly introduced when the code was extended to be used in the -# GE setting). An even more thorough solution, which moves beyond the scope of -# fixing this error, is adding unit tests to ID when changes to some code will -# break things elsewhere. -# Note: alternatively, decide that the "init_consumer_objects['PermGroFacAgg'] = 1.0" -# line below fixes it properly ('feature not a bug') and remove all this text. - # --------------------------------------------------------------------------------- # - Define all of the model parameters for SolvingMicroDSOPs and ConsumerExamples - # --------------------------------------------------------------------------------- @@ -159,12 +143,6 @@ 'CubicBool':CubicBool } -if show_PermGroFacAgg_error: - pass # do nothing -else: - print("***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***") - init_consumer_objects['PermGroFacAgg'] = 1.0 - if __name__ == '__main__': print("Sorry, EstimationParameters doesn't actually do anything on its own.") From 08c7ec52291373c6660da24c7d874f70e99a1867 Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Thu, 5 Mar 2020 23:10:47 +0530 Subject: [PATCH 242/242] Release 0.10.4 (#553) * bump up version to 0.10.4 * update changelogs for 0.10.4 --- CHANGELOG.md | 20 ++++++++++++++++++-- HARK/__init__.py | 2 +- 2 files changed, 19 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9c0a55404..472f51940 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,15 +1,31 @@ HARK -Version 0.10.3 Release Notes # Introduction -This document contains the release notes for the 0.10.3 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. +This document contains the release notes of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. For more information on HARK, see [our Github organization](https://github.com/econ-ark). ## Changes + +### 0.10.4 + +Release Date: 05-03-2020 + +#### Major Changes + - Last release to support Python 2.7, future releases of econ-ark will support Python 3.6+ [#478](https://github.com/econ-ark/HARK/pull/478) + - Move non-reusable model code to examples directory, BayerLuetticke, FashionVictim now in examples instead of in HARK code [#442](https://github.com/econ-ark/HARK/pull/442) + - Load default parameters for ConsumptionSaving models [#466](https://github.com/econ-ark/HARK/pull/466) + - Improved implementaion of parallelNelderMead [#300](https://github.com/econ-ark/HARK/pull/300) + +#### Minor Changes + - Notebook utility functions for determining platform, GUI, latex (installation) are available in HARK.utilities [#512](https://github.com/econ-ark/HARK/pull/512) + - Few DemARKs moved to examples [#472](https://github.com/econ-ark/HARK/pull/472) + - MaxKinks available in ConsumerParameters again [#486](https://github.com/econ-ark/HARK/pull/486) + + ### 0.10.3 Release Date: 12-12-2019 diff --git a/HARK/__init__.py b/HARK/__init__.py index a3fda968f..bb66298db 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -11,4 +11,4 @@ from .core import * -__version__ = "0.10.3" +__version__ = "0.10.4"

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