From 77dbbf5f5cb0961659f91f5826a21bd71e888ad0 Mon Sep 17 00:00:00 2001 From: sidd3888 Date: Mon, 6 Nov 2023 01:39:37 +0530 Subject: [PATCH 1/8] Changes to model description and plot --- .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 207 +++++------------- 1 file changed, 50 insertions(+), 157 deletions(-) diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb index 6657619dc..df21b9e31 100644 --- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -15,7 +15,8 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "code_folding": [] + "code_folding": [], + "is_executing": true }, "outputs": [], "source": [ @@ -42,23 +43,23 @@ "\n", "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n", "\n", - "\\begin{equation}\n", + "\\begin{align*}\n", "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n", - "\\end{equation}\n", + "\\end{align*}\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", + "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\aleph_t$. 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 current income) and must choose how much of those resources to consume $C_t$ and hold the rest 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 $\\beta$. With probability $1-\\aleph_t$, the agent dies between period $t$ and $t+1$, 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", + "\\begin{align*}\n", + "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n", + "&\\text{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{align*}\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", + "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 $\\{\\aleph_t\\}$. To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *infinite horizon* consumer with constant income growth and survival probability.\n", "\n", "## Representing Agents in HARK\n", "\n", @@ -162,9 +163,9 @@ "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", + "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 (parameterization/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:" + "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 (index 0) of the solution list can be retrieved by:" ] }, { @@ -175,7 +176,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -191,7 +192,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "One of the results proven in the associated [the lecture notes](https://www.econ2.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", + "One of the results proven in the associated [lecture notes](https://www.econ2.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." ] @@ -203,7 +204,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -383,16 +384,16 @@ "\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](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory).\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](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\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", + "\\begin{align*}\n", + "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\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_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n", + "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n", + "\\end{align*}\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", @@ -497,75 +498,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPFRaw = 0.985648 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFNrm = 0.994897 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFAggLivPrb = 0.965935 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Thorn = APF = 0.995505 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PermGroFacAdj = 1.000611 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "uInvEpShkuInv = 0.988401 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "VAF = 0.929888 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WRPF = 0.289257 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DiscFacGPFNrmMax = 0.972357 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "GPFRaw = 0.985648 \n", + "GPFNrm = 0.994897 \n", + "GPFAggLivPrb = 0.965935 \n", + "Thorn = APF = 0.995505 \n", + "PermGroFacAdj = 1.000611 \n", + "uInvEpShkuInv = 0.988401 \n", + "VAF = 0.929888 \n", + "WRPF = 0.289257 \n", + "DiscFacGPFNrmMax = 0.972357 \n", "DiscFacGPFAggLivPrbMax = 1.015641 \n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -601,69 +548,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPFRaw = 0.985648 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFNrm = 1.023116 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFAggLivPrb = 0.965935 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Thorn = APF = 0.995505 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PermGroFacAdj = 0.973012 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "uInvEpShkuInv = 0.954556 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "VAF = 0.898046 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WRPF = 0.289257 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DiscFacGPFNrmMax = 0.906690 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "GPFRaw = 0.985648 \n", + "GPFNrm = 1.023116 \n", + "GPFAggLivPrb = 0.965935 \n", + "Thorn = APF = 0.995505 \n", + "PermGroFacAdj = 0.973012 \n", + "uInvEpShkuInv = 0.954556 \n", + "VAF = 0.898046 \n", + "WRPF = 0.289257 \n", + "DiscFacGPFNrmMax = 0.906690 \n", "DiscFacGPFAggLivPrbMax = 1.015641 \n" ] } @@ -716,7 +609,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.18" }, "latex_envs": { "LaTeX_envs_menu_present": true, From cb66d463dd5a82f95d6dbcf63469f88da44ea520 Mon Sep 17 00:00:00 2001 From: Siddarth Venkatesh <72447516+sidd3888@users.noreply.github.com> Date: Mon, 6 Nov 2023 18:39:42 +0530 Subject: [PATCH 2/8] Remove time subscript on PermGroFac The code and the earlier model assumes $\Gamma_t$ to be a constant, so changed the notation in the later model as well --- examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb index df21b9e31..57c311e47 100644 --- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -387,10 +387,10 @@ "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](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\n", "\n", "\\begin{align*}\n", - "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma_{t+1}\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ] \\\\\n", + "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\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", + "m_{t+1} &= R/(\\Gamma \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n", "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n", "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n", "\\end{align*}\n", From 1a2a1af4652f09559c8879c9d15000b365e9b5c8 Mon Sep 17 00:00:00 2001 From: Alan Lujan Date: Mon, 6 Nov 2023 08:34:49 -0500 Subject: [PATCH 3/8] notation review --- .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 1321 ++++++++--------- 1 file changed, 644 insertions(+), 677 deletions(-) diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb index 57c311e47..49463f273 100644 --- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -1,677 +1,644 @@ -{ - "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": 1, - "metadata": { - "code_folding": [], - "is_executing": true - }, - "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", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import HARK\n", - "from copy import deepcopy\n", - "\n", - "mystr = lambda number: \"{:.4f}\".format(number)\n", - "from HARK.utilities import plot_funcs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Your First HARK Model: Perfect Foresight\n", - "\n", - "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n", - "\n", - "\\begin{align*}\n", - "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n", - "\\end{align*}\n", - "\n", - "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\aleph_t$. 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 current income) and must choose how much of those resources to consume $C_t$ and hold the rest 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 $\\beta$. With probability $1-\\aleph_t$, the agent dies between period $t$ and $t+1$, ending his problem.\n", - "\n", - "The agent's problem can be written in Bellman form as:\n", - "\n", - "\\begin{align*}\n", - "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n", - "&\\text{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{align*}\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 $\\{\\aleph_t\\}$. To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *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": 2, - "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](https://www.econ2.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", - "| $\\aleph$ | 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": 3, - "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": 4, - "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": 5, - "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 (parameterization/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 (index 0) of the solution list can be retrieved by:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PFexample.solution[0].cFunc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the results proven in the associated [lecture notes](https://www.econ2.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": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mPlotTop = 10\n", - "plot_funcs(PFexample.solution[0].cFunc, 0.0, 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": 8, - "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(\n", - " \"This agent's human wealth is \"\n", - " + str(humanWealth)\n", - " + \" times his current income level.\"\n", - ")\n", - "print(\n", - " \"This agent's consumption function is defined (consumption is positive) down to m_t = \"\n", - " + str(mMinimum)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Yikes! Let's take a look at the bottom of the consumption function. In the cell below, the bounds of the `plot_funcs` function are set to display down to the lowest defined value of the consumption function." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_funcs(PFexample.solution[0].cFunc, mMinimum, 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": 10, - "metadata": {}, - "outputs": [], - "source": [ - "NewExample = deepcopy(PFexample)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can assign new parameters to an `AgentType` with the `assign_parameter` method. For example, we could make the new agent less patient:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "nbsphinx-thumbnail": {} - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "NewExample.assign_parameters(DiscFac=0.90)\n", - "NewExample.solve()\n", - "mPlotBottom = mMinimum\n", - "plot_funcs(\n", - " [PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], mPlotBottom, mPlotTop\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(Note that you can pass a **list** of functions to `plot_funcs` 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": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "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", - "plot_funcs([PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], 0.0, 10.0)" - ] - }, - { - "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 *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](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\n", - "\n", - "\\begin{align*}\n", - "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\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 \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n", - "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n", - "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n", - "\\end{align*}\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": 13, - "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\": [\n", - " 0.1\n", - " ], # ... 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\": 50.0, # 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\": -10.0,\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": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", - "\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": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFRaw = 0.985648 \n", - "GPFNrm = 0.994897 \n", - "GPFAggLivPrb = 0.965935 \n", - "Thorn = APF = 0.995505 \n", - "PermGroFacAdj = 1.000611 \n", - "uInvEpShkuInv = 0.988401 \n", - "VAF = 0.929888 \n", - "WRPF = 0.289257 \n", - "DiscFacGPFNrmMax = 0.972357 \n", - "DiscFacGPFAggLivPrbMax = 1.015641 \n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "IndShockExample.solve()\n", - "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)" - ] - }, - { - "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 $\\texttt{update_income_process}$ so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFRaw = 0.985648 \n", - "GPFNrm = 1.023116 \n", - "GPFAggLivPrb = 0.965935 \n", - "Thorn = APF = 0.995505 \n", - "PermGroFacAdj = 0.973012 \n", - "uInvEpShkuInv = 0.954556 \n", - "VAF = 0.898046 \n", - "WRPF = 0.289257 \n", - "DiscFacGPFNrmMax = 0.906690 \n", - "DiscFacGPFAggLivPrbMax = 1.015641 \n" - ] - } - ], - "source": [ - "OtherExample = deepcopy(\n", - " IndShockExample\n", - ") # Make a copy so we can compare consumption functions\n", - "OtherExample.assign_parameters(\n", - " PermShkStd=[0.2]\n", - ") # Double permanent income risk (note that it's a one element list)\n", - "OtherExample.update_income_process() # 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": 17, - "metadata": {}, - "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 (ipykernel)", - "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": "3.9.18" - }, - "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 - }, - "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": 4 -} +{ + "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": 1, + "metadata": { + "code_folding": [], + "is_executing": true + }, + "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", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import HARK\n", + "from copy import deepcopy\n", + "\n", + "mystr = lambda number: \"{:.4f}\".format(number)\n", + "from HARK.utilities import plot_funcs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Your First HARK Model: Perfect Foresight\n", + "\n", + "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n", + "\n", + "\\begin{align*}\n", + "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n", + "\\end{align*}\n", + "\n", + "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb \\le 1$. 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 current income) and must choose how much of those resources to consume $C_t$ and hold the rest 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 $\\beta$. With probability $\\mathsf{D}_t = 1-\\LivPrb_t$, the agent dies between period $t$ and $t+1$, ending his problem.\n", + "\n", + "The agent's problem can be written in Bellman form as:\n", + "\n", + "\\begin{align*}\n", + "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n", + "&\\text{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{align*}\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 $\\{\\aleph_t\\}$. To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *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": 2, + "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](https://www.econ2.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 `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", + "| $\\aleph$ | 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": 3, + "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 `PF_dictionary`. Such a bundle is created passing `PF_dictionary` to the class $\\texttt{PerfForesightConsumerType}$:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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": 5, + "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 (parameterization/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 (index 0) of the solution list can be retrieved by:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PFexample.solution[0].cFunc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the results proven in the associated [lecture notes](https://www.econ2.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": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mPlotTop = 10\n", + "plot_funcs(PFexample.solution[0].cFunc, 0.0, 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": 8, + "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(\n", + " \"This agent's human wealth is \"\n", + " + str(humanWealth)\n", + " + \" times his current income level.\"\n", + ")\n", + "print(\n", + " \"This agent's consumption function is defined (consumption is positive) down to m_t = \"\n", + " + str(mMinimum)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yikes! Let's take a look at the bottom of the consumption function. In the cell below, the bounds of the `plot_funcs` function are set to display down to the lowest defined value of the consumption function." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_funcs(PFexample.solution[0].cFunc, mMinimum, 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": 10, + "metadata": {}, + "outputs": [], + "source": [ + "NewExample = deepcopy(PFexample)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can assign new parameters to an `AgentType` with the `assign_parameter` method. For example, we could make the new agent less patient:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "nbsphinx-thumbnail": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "NewExample.assign_parameters(DiscFac=0.90)\n", + "NewExample.solve()\n", + "mPlotBottom = mMinimum\n", + "plot_funcs(\n", + " [PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], mPlotBottom, mPlotTop\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Note that you can pass a **list** of functions to `plot_funcs` 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": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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", + "plot_funcs([PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], 0.0, 10.0)" + ] + }, + { + "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 *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](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\n", + "\n", + "\\begin{align*}\n", + "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\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 \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n", + "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n", + "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n", + "\\end{align*}\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": 13, + "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\": [\n", + " 0.1\n", + " ], # ... 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\": 50.0, # 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\": -10.0,\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": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", + "\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": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "IndShockExample.solve()\n", + "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)" + ] + }, + { + "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 `update_income_process` so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "OtherExample = deepcopy(\n", + " IndShockExample\n", + ") # Make a copy so we can compare consumption functions\n", + "OtherExample.assign_parameters(\n", + " PermShkStd=[0.2]\n", + ") # Double permanent income risk (note that it's a one element list)\n", + "OtherExample.update_income_process() # 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": 17, + "metadata": {}, + "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" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.12" + }, + "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 + }, + "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": 4 +} From 674050780e0f4ab7e75672f88d5c1456db16c932 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Fri, 8 Dec 2023 15:23:15 -0500 Subject: [PATCH 4/8] Very light editing of Gentle Intro Added one sentence and a couple words, adjusted commas. --- .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 1222 ++++++++--------- 1 file changed, 578 insertions(+), 644 deletions(-) diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb index 49463f273..bd4e204c5 100644 --- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -1,644 +1,578 @@ -{ - "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": 1, - "metadata": { - "code_folding": [], - "is_executing": true - }, - "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", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import HARK\n", - "from copy import deepcopy\n", - "\n", - "mystr = lambda number: \"{:.4f}\".format(number)\n", - "from HARK.utilities import plot_funcs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Your First HARK Model: Perfect Foresight\n", - "\n", - "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n", - "\n", - "\\begin{align*}\n", - "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n", - "\\end{align*}\n", - "\n", - "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb \\le 1$. 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 current income) and must choose how much of those resources to consume $C_t$ and hold the rest 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 $\\beta$. With probability $\\mathsf{D}_t = 1-\\LivPrb_t$, the agent dies between period $t$ and $t+1$, ending his problem.\n", - "\n", - "The agent's problem can be written in Bellman form as:\n", - "\n", - "\\begin{align*}\n", - "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n", - "&\\text{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{align*}\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 $\\{\\aleph_t\\}$. To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *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": 2, - "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](https://www.econ2.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 `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", - "| $\\aleph$ | 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": 3, - "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 `PF_dictionary`. Such a bundle is created passing `PF_dictionary` to the class $\\texttt{PerfForesightConsumerType}$:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "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": 5, - "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 (parameterization/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 (index 0) of the solution list can be retrieved by:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PFexample.solution[0].cFunc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the results proven in the associated [lecture notes](https://www.econ2.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": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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iA0YdLIZhsGHDBrKyskhLS7vr5ZqamigsLOQ3v/kNwcHD+3JFRUVs3759tNNERMSPdPcPsePIWcprLgKQHBNGaX4mmQkPmDtMfMqog+XZZ5+ltraW6urqu17G4/Hw3e9+l+3bt/PQQw8N+7Y3b97Mhg0b7nzc1dVFQkLCaKeKiIiPevvTdjYdqOVSRx8WC6xZlETBkjnYQ3SqIl82qsewrFu3jsOHD3Pq1CmSkpLuermOjg4iIyMJCvp/f/G8Xi+GYRAUFMSxY8f47//9v3/j19NjWEREJpfeQTfFRxvY+24LALOjplKS5+TRpHs/xED8i2mPYTEMg3Xr1nHo0CFOnjx5z1gBiIiIoK6u7kuf++d//mdOnDjBgQMHvvH6IiIy+Zw+f4OC/Wdoud4LwOqFiRTmziXMppcGk7sb0d+OtWvX8uqrr1JRUUF4eDhXrlwBwOFwMGXKFOD2j3MuXbrE3r17sVqtX3l8y4wZM7Db7fd83IuIiEw+/UMeSo818nL1OQwD4hx2dq1ykpUSbfY08QMjCpaysjIAsrOzv/T5PXv28NRTTwFw+fJlPv/883EZJyIik4PrQgcby100X7v9ul158+PZuvRhIuwhJi8TfzGm12GZKHoMi4iIfxp0e9n9ZhNlVc14vAYx4TaKV6aTMy/W7GkyAXzmdVhERETu5mxrFxvKXTRc6QZgmTOO7ctSiQwLNXmZ+CMFi4iIjCu3x0vZyWZ2n2hiyGMQFRbKzhVp5KbPMnua+DEFi4iIjJumtm427j9D7cXbr4S+JDWWnU+mEz3NZvIy8XcKFhERGTOP1+CV6s8oOfYJg24vEfZgXliexvLMOCwWvbOyjJ2CRURExuR8ew8F+89Q03ITgOw5MRSvzGCmw27yMplMFCwiIjIqXq/BvvdbKHq9gb4hD9NswWx9Yh75CxJ0qiLjTsEiIiIjdvFmL5sO1PJO83UAHkuezq5VGcRHTjV5mUxWChYRERk2wzAor7nAjiMfc2vAjT3EyubceaxemIjVqlMVuX8ULCIiMixtXf0UHqzlrcZrAMxPjKQkz0lSdJjJyyQQKFhEROSeDMOgwtXKtsp6OvuGCA22UrD4IdZkPUiQTlVkgihYRETkrtpvDbDlUB1v1LcBkBHvoDTPSUpsuMnLJNAoWERE5GsdrbvMlsMfcaNnkGCrhfU5KTydnUxIkNXsaRKAFCwiIvIlHb2DbKusp8LVCsDcmeGU5jtJjXOYvEwCmYJFRETuONHQRuHBOq52D2C1wDPZyTyXk4ItOMjsaRLgFCwiIkJ3/xA7jpylvOYiAMkxYZTmZ5KZ8IC5w0T+k4JFRCTAvf1pO5sO1HKpow+LBdYsSqJgyRzsITpVEd+hYBERCVC9g26Kjzaw990WAGZHTaUkz8mjSVEmLxP5KgWLiEgAOn3+BgX7z9ByvReA1QsTKcydS5hNdwvim/Q3U0QkgPQPeSg91sjL1ecwDIhz2Nm1yklWSrTZ00TuScEiIhIgXBc62FjuovlaDwB58+PZuvRhIuwhJi8T+WYKFhGRSW7Q7WX3m02UVTXj8RrEhNsoXplOzrxYs6eJDJuCRURkEjvb2sWGchcNV7oBWOaMY/uyVCLDQk1eJjIyChYRkUnI7fFSdrKZ3SeaGPIYRIWFsnNFGrnps8yeJjIqChYRkUmmqa2bjfvPUHuxE4AlqbHsfDKd6Gk2k5eJjJ6CRURkkvB4DV6p/oySY58w6PYSYQ/mheVpLM+Mw2KxmD1PZEwULCIik8D59h4K9p+hpuUmANlzYihemcFMh93kZSLjQ8EiIuLHvF6Dfe+3UPR6A31DHqbZgtn6xDzyFyToVEUmFQWLiIifunizl00Hanmn+ToAjyVPZ9eqDOIjp5q8TGT8KVhERPyMYRiU11xgx5GPuTXgxh5iZXPuPFYvTMRq1amKTE4KFhERP9LW1U/hwVrearwGwPzESErynCRFh5m8TOT+UrCIiPgBwzCocLWyrbKezr4hQoOtFCx+iDVZDxKkUxUJAAoWEREf135rgC2H6nijvg2AjHgHpXlOUmLDTV4mMnEULCIiPuxo3WW2HP6IGz2DBFstrM9J4ensZEKCrGZPE5lQChYRER/U0TvItsp6KlytAMydGU5pvpPUOIfJy0TMoWAREfExJxraKDxYx9XuAawWeCY7medyUrAFB5k9TcQ0ChYRER/R3T/EjiNnKa+5CEByTBil+ZlkJjxg7jARH6BgERHxAW9/2s6mA7Vc6ujDYoE1i5IoWDIHe4hOVURAwSIiYqreQTfFRxvY+24LALOjplKS5+TRpCiTl4n4FgWLiIhJTp+/QcH+M7Rc7wVg9cJECnPnEmbTP80i/5X+rxARmWD9Qx5KjzXycvU5DAPiHHZ2rXKSlRJt9jQRn6VgERGZQK4LHWwsd9F8rQeAvPnxbF36MBH2EJOXifg2BYuIyAQYdHvZ/WYTZVXNeLwGMeE2ilemkzMv1uxpIn5BwSIicp+dbe1iQ7mLhivdACxzxrF9WSqRYaEmLxPxHwoWEZH7xO3xUnaymd0nmhjyGESFhbJzRRq56bPMnibidxQsIiL3QVNbNxv3n6H2YicAS1Jj2flkOtHTbCYvE/FPChYRkXHk8Rq8Uv0ZJcc+YdDtJcIezAvL01ieGYfFYjF7nojfUrCIiIyT8+09FOw/Q03LTQCy58RQvDKDmQ67yctE/J+CRURkjLxeg33vt1D0egN9Qx6m2YLZ+sQ88hck6FRFZJwoWERExuDizV42HajlnebrADyWPJ1dqzKIj5xq8jKRyUXBIiIyCoZhUF5zgR1HPubWgBt7iJXNufNYvTARq1WnKiLjTcEiIjJCbV39FB6s5a3GawDMT4ykJM9JUnSYyctEJi8Fi4jIMBmGQYWrlW2V9XT2DREabKVg8UOsyXqQIJ2qiNxXChYRkWFovzXAlkN1vFHfBkBGvIPSPCcpseEmLxMJDAoWEZFvcLTuMlsOf8SNnkGCrRbW56TwdHYyIUFWs6eJBAwFi4jIXXT0DrKtsp4KVysAc2eGU5rvJDXOYfIykcCjYBER+RonGtooPFjH1e4BrBZ4JjuZ53JSsAUHmT1NJCApWEREfkt3/xA7jpylvOYiAMkxYZTmZ5KZ8IC5w0QCnIJFROQ/vf1pO5sO1HKpow+LBdYsSqJgyRzsITpVETGbgkVEAl7voJviow3sfbcFgNlRUynJc/JoUpTJy0TkCwoWEQlop8/foGD/GVqu9wKwemEihblzCbPpn0cRX6L/I0UkIPUPeSg91sjL1ecwDIhz2Nm1yklWSrTZ00TkayhYRCTguC50sLHcRfO1HgDy5sezdenDRNhDTF4mInejYBGRgDHo9rL7zSbKqprxeA1iwm0Ur0wnZ16s2dNE5BsoWEQkIJxt7WJDuYuGK90ALHPGsX1ZKpFhoSYvE5HhULCIyKTm9ngpO9nM7hNNDHkMosJC2bkijdz0WWZPE5ERULCIyKTV1NbNxv1nqL3YCcCS1Fh2PplO9DSbyctEZKQULCIy6Xi8Bq9Uf0bJsU8YdHuJsAfzwvI0lmfGYbFYzJ4nIqOgYBGRSeV8ew8F+89Q03ITgOw5MRSvzGCmw27yMhEZCwWLiEwKXq/BvvdbKHq9gb4hD9NswWx9Yh75CxJ0qiIyCShYRMTvXbzZy6YDtbzTfB2Ax5Kns2tVBvGRU01eJiLjRcEiIn7LMAzKay6w48jH3BpwYw+xsjl3HqsXJmK16lRFZDKxjuTCRUVFPPLII4SHhzNjxgxWrFhBY2PjPa9TXV3NokWLmD59OlOmTGHu3Ln8/d///ZhGi4i0dfXzw1+c5kcH67g14GZ+YiRH1z/ODx77HcWKyCQ0ohOWqqoq1q5dyyOPPILb7WbLli0sXryYs2fPEhYW9rXXCQsL49lnnyUjI4OwsDCqq6v5i7/4C8LCwvjzP//zcflDiEjgMAyDClcr2yrr6ewbIjTYSsHih1iT9SBBChWRSctiGIYx2itfu3aNGTNmUFVVxeOPPz7s661cuZKwsDD+5V/+ZViX7+rqwuFw0NnZSURExGjnioifa781wJZDdbxR3wZARryD0jwnKbHhJi8Tka8znvffY3oMS2fn7RdjioqKGvZ1PvzwQ9555x1+8pOf3PUyAwMDDAwM3Pm4q6tr9CNFZFI4WneZLYc/4kbPIMFWC+tzUng6O5mQoBH9ZFtE/NSog8UwDDZs2EBWVhZpaWnfePn4+HiuXbuG2+3mxz/+MX/2Z39218sWFRWxffv20U4TkUmko3eQbZX1VLhaAZg7M5zSfCepcQ6Tl4nIRBr1j4TWrl3La6+9RnV1NfHx8d94+XPnznHr1i3ee+89CgsLefHFF/nTP/3Tr73s152wJCQk6EdCIgHmREMbhQfruNo9gNUCz2Qn81xOCrbgILOnicgwmP4joXXr1lFZWcmpU6eGFSsASUlJAKSnp9PW1saPf/zjuwaLzWbDZtN7fYgEqu7+IXYcOUt5zUUAkmPCKM3PJDPhAXOHiYhpRhQshmGwbt06Dh06xMmTJ+9EyEgZhvGlExQRkS+8/Wk7mw7UcqmjD4sF1ixKomDJHOwhOlURCWQjCpa1a9fy6quvUlFRQXh4OFeuXAHA4XAwZcoUADZv3sylS5fYu3cvAP/0T//E7NmzmTt3LnD7dVlKSkpYt27deP45RMTP9Q66KT7awN53WwCYHTWVkjwnjyYN/0H9IjJ5jShYysrKAMjOzv7S5/fs2cNTTz0FwOXLl/n888/v/J7X62Xz5s2cO3eO4OBgkpOTKS4u5i/+4i/GtlxEJo3T529QsP8MLdd7AVi9MJHC3LmE2fRi3CJy25heh2Wi6HVYRCan/iEPpccaebn6HIYBcQ47u1Y5yUqJNnuaiIwD0x90KyIyVq4LHWwsd9F8rQeAvPnxbF36MBH2EJOXiYgvUrCIyIQadHvZ/WYTZVXNeLwGMeE2ilemkzMv1uxpIuLDFCwiMmHOtnaxodxFw5VuAJY549i+LJXIsFCTl4mIr1OwiMh95/Z4KTvZzO4TTQx5DKLCQtm5Io3c9FlmTxMRP6FgEZH7qqmtm437z1B78fZ7jy1JjWXnk+lET9OLQ4rI8ClYROS+8HgNXqn+jJJjnzDo9hJhD+aF5Wksz4zDYrGYPU9E/IyCRUTG3fn2Hgr2n6Gm5SYA2XNiKF6ZwUyH3eRlIuKvFCwiMm68XoN977dQ9HoDfUMeptmC2frEPPIXJOhURUTGRMEiIuPi4s1eNh2o5Z3m6wA8ljydXasyiI+cavIyEZkMFCwiMiaGYVBec4EdRz7m1oAbe4iVzbnzWL0wEatVpyoiMj4ULCIyam1d/RQerOWtxmsAzE+MpCTPSVJ0mMnLRGSyUbCIyIgZhkGFq5VtlfV09g0RGmylYPFDrMl6kCCdqojIfaBgEZERab81wJZDdbxR3wZARryD0jwnKbHhJi8TkclMwSIiw3a07jJbDn/EjZ5Bgq0W1uek8HR2MiFBVrOnicgkp2ARkW/U0TvItsp6KlytAMydGU5pvpPUOIfJy0QkUChYROSeTjS0UXiwjqvdA1gt8Ex2Ms/lpGALDjJ7mogEEAWLiHyt7v4hdhw5S3nNRQCSY8Iozc8kM+EBc4eJSEBSsIjIV7z9aTubDtRyqaMPiwXWLEqiYMkc7CE6VRERcyhYROSO3kE3xUcb2PtuCwCzo6ZSkufk0aQok5eJSKBTsIgIAKfP36Bg/xlarvcCsHphIoW5cwmz6Z8JETGf/iUSCXD9Qx5KjzXycvU5DAPiHHZ2rXKSlRJt9jQRkTsULCIBzHWhg43lLpqv9QCQNz+erUsfJsIeYvIyEZEvU7CIBKBBt5fdbzZRVtWMx2sQE26jeGU6OfNizZ4mIvK1FCwiAeZsaxcbyl00XOkGYJkzju3LUokMCzV5mYjI3SlYRAKE2+Ol7GQzu080MeQxiAoLZeeKNHLTZ5k9TUTkGylYRAJAU1s3G/efofZiJwBLUmPZ+WQ60dNsJi8TERkeBYvIJObxGrxS/Rklxz5h0O0lwh7MC8vTWJ4Zh8ViMXueiMiwKVhEJqnz7T0U7D9DTctNALLnxFC8MoOZDrvJy0RERk7BIjLJeL0G+95voej1BvqGPEyzBbP1iXnkL0jQqYqI+C0Fi8gkcvFmL5sO1PJO83UAHkuezq5VGcRHTjV5mYjI2ChYRCYBwzAor7nAjiMfc2vAjT3EyubceaxemIjVqlMVEfF/ChYRP9fW1U/hwVrearwGwPzESErynCRFh5m8TERk/ChYRPyUYRhUuFrZVllPZ98QocFWChY/xJqsBwnSqYqITDIKFhE/1H5rgC2H6nijvg2AjHgHpXlOUmLDTV4mInJ/KFhE/MzRustsOfwRN3oGCbZaWJ+TwtPZyYQEWc2eJiJy3yhYRPxER+8g2yrrqXC1AjB3Zjil+U5S4xwmLxMRuf8ULCJ+4ERDG4UH67jaPYDVAs9kJ/NcTgq24CCzp4mITAgFi4gP6+4fYseRs5TXXAQgOSaM0vxMMhMeMHeYiMgEU7CI+Ki3P21n04FaLnX0YbHAmkVJFCyZgz1EpyoiEngULCI+pnfQTfHRBva+2wLA7KiplOQ5eTQpyuRlIiLmUbCI+JDT529QsP8MLdd7AVi9MJHC3LmE2fS/qogENv0rKOID+oc8lB5r5OXqcxgGxDns7FrlJCsl2uxpIiI+QcEiYjLXhQ42lrtovtYDQN78eLYufZgIe4jJy0REfIeCRcQkg24vu99soqyqGY/XICbcRvHKdHLmxZo9TUTE5yhYRExwtrWLDeUuGq50A7DMGcf2ZalEhoWavExExDcpWEQmkNvjpexkM7tPNDHkMYgKC2XnijRy02eZPU1ExKcpWEQmSFNbNxv3n6H2YicAS1Jj2flkOtHTbCYvExHxfQoWkfvM4zV4pfozSo59wqDbS4Q9mBeWp7E8Mw6LxWL2PBERv6BgEbmPzrf3ULD/DDUtNwHInhND8coMZjrsJi8TEfEvChaR+8DrNdj3fgtFrzfQN+Rhmi2YrU/MI39Bgk5VRERGQcEiMs4u3uxl04Fa3mm+DsBjydPZtSqD+MipJi8TEfFfChaRcWIYBuU1F9hx5GNuDbixh1jZnDuP1QsTsVp1qiIiMhYKFpFx0NbVT+HBWt5qvAbA/MRISvKcJEWHmbxMRGRyULCIjIFhGFS4WtlWWU9n3xChwVYKFj/EmqwHCdKpiojIuFGwiIxS+60Bthyq4436NgAy4h2U5jlJiQ03eZmIyOSjYBEZhaN1l9ly+CNu9AwSbLWwPieFp7OTCQmymj1NRGRSUrCIjEBH7yDbKuupcLUCMHdmOKX5TlLjHCYvExGZ3BQsIsN0oqGNwoN1XO0ewGqBZ7KTeS4nBVtwkNnTREQmPQWLyDfo7h9ix5GzlNdcBCA5JozS/EwyEx4wd5iISABRsIjcw9uftrPpQC2XOvqwWGDNoiQKlszBHqJTFRGRiaRgEfkavYNuio82sPfdFgBmR02lJM/Jo0lRJi8TEQlMChaR/+L0+RsU7D9Dy/VeAFYvTKQwdy5hNv3vIiJiFv0LLPKf+oc8lB5r5OXqcxgGxDns7FrlJCsl2uxpIiIBT8EiArgudLCx3EXztR4A8ubHs3Xpw0TYQ0xeJiIioGCRADfo9rL7zSbKqprxeA1iwm0Ur0wnZ16s2dNEROS3KFgkYJ1t7WJDuYuGK90ALHPGsX1ZKpFhoSYvExGR/0rBIgHH7fFSdrKZ3SeaGPIYRIWFsnNFGrnps8yeJiIid6FgkYDS1NbNxv1nqL3YCcCS1Fh2PplO9DSbyctEROReFCwSEDxeg1eqP6Pk2CcMur1E2IN5YXkayzPjsFgsZs8TEZFvoGCRSe98ew8F+89Q03ITgOw5MRSvzGCmw27yMhERGS7rSC5cVFTEI488Qnh4ODNmzGDFihU0Njbe8zq//vWv+fa3v01MTAwRERH83u/9Hm+88caYRosMh9drsPfd8+T+w2+oabnJNFswf/s/0tnz1COKFRERPzOiYKmqqmLt2rW89957HD9+HLfbzeLFi+np6bnrdU6dOsW3v/1tXn/9dT744AP+23/7byxdupQPP/xwzONF7ubizV6+98r7/E1FPX1DHh5Lns6/Pf/7/PEjs/UjIBERP2QxDMMY7ZWvXbvGjBkzqKqq4vHHHx/29VJTU/njP/5j/uZv/mZYl+/q6sLhcNDZ2UlERMRo50oAMAyD8poL7DjyMbcG3NhDrGzOncfqhYlYrQoVEZGJNJ7332N6DEtn5+1nWkRFDf8N4bxeL93d3fe8zsDAAAMDA3c+7urqGv1ICRhtXf0UHqzlrcZrAMxPjKQkz0lSdJjJy0REZKxGHSyGYbBhwwaysrJIS0sb9vVKS0vp6ekhPz//rpcpKipi+/bto50mAcYwDCpcrWyrrKezb4jQYCsFix9iTdaDBOlURURkUhj1j4TWrl3La6+9RnV1NfHx8cO6zi9/+Uv+7M/+jIqKCv7wD//wrpf7uhOWhIQE/UhIvqL91gBbDtXxRn0bABnxDkrznKTEhpu8TERETP+R0Lp166isrOTUqVPDjpVf/epXrFmzhv37998zVgBsNhs2m17IS+7taN1lthz+iBs9gwRbLazPSeHp7GRCgkb0WHIREfEDIwoWwzBYt24dhw4d4uTJkyQlJQ3rer/85S/54Q9/yC9/+Uu+853vjGqoyBc6egfZVllPhasVgLkzwynNd5Ia5zB5mYiI3C8jCpa1a9fy6quvUlFRQXh4OFeuXAHA4XAwZcoUADZv3sylS5fYu3cvcDtWvv/97/MP//APLFy48M51pkyZgsOhOxgZmRMNbRQerONq9wBWCzyTncxzOSnYgoPMniYiIvfRiB7DcrfXr9izZw9PPfUUAE899RTnz5/n5MmTAGRnZ1NVVfWV6/zgBz/gF7/4xbC+rp7WLN39Q+w4cpbymosAJMeEUZqfSWbCA+YOExGRuxrP++8xvQ7LRFGwBLa3P21n04FaLnX0YbHAmkVJFCyZgz1EpyoiIr7M9AfdikyE3kE3xUcb2PtuCwCzo6ZSkufk0aThv+6PiIhMDgoW8Umnz9+gYP8ZWq73ArB6YSKFuXMJs+mvrIhIINK//uJT+oc8lB5r5OXqcxgGxDns7FrlJCsl2uxpIiJiIgWL+AzXhQ42lrtovnb7zTTz5sezdenDRNhDTF4mIiJmU7CI6QbdXna/2URZVTMer0FMuI3ilenkzIs1e5qIiPgIBYuY6mxrFxvKXTRc6QZgmTOO7ctSiQwLNXmZiIj4EgWLmMLt8VJ2spndJ5oY8hhEhYWyc0UauemzzJ4mIiI+SMEiE66prZuN+89Qe7ETgCWpsex8Mp3oaXr/KBER+XoKFpkwHq/BK9WfUXLsEwbdXiLswbywPI3lmXF3fRVlERERULDIBDnf3kPB/jPUtNwEIHtODMUrM5jpsJu8TERE/IGCRe4rr9dg3/stFL3eQN+Qh2m2YLY+MY/8BQk6VRERkWFTsMh9c/FmL5sO1PJO83UAHkuezq5VGcRHTjV5mYiI+BsFi4w7wzAor7nAjiMfc2vAjT3EyubceaxemIjVqlMVEREZOQWLjKu2rn4KD9byVuM1AOYnRlKS5yQpOszkZSIi4s8ULDIuDMOgwtXKtsp6OvuGCA22UrD4IdZkPUiQTlVERGSMFCwyZu23BthyqI436tsAyIh3UJrnJCU23ORlIiIyWShYZEyO1l1my+GPuNEzSLDVwvqcFJ7OTiYkyGr2NBERmUQULDIqHb2DbKusp8LVCsDcmeGU5jtJjXOYvExERCYjBYuM2ImGNgoP1nG1ewCrBZ7JTua5nBRswUFmTxMRkUlKwSLD1t0/xI4jZymvuQhAckwYpfmZZCY8YO4wERGZ9BQsMixvf9rOpgO1XOrow2KBNYuSKFgyB3uITlVEROT+U7DIPfUOuik+2sDed1sAmB01lZI8J48mRZm8TEREAomCRe7q9PkbFOw/Q8v1XgBWL0ykMHcuYTb9tRERkYmlex75iv4hD6XHGnm5+hyGAXEOO7tWOclKiTZ7moiIBCgFi3yJ60IHG8tdNF/rASBvfjxblz5MhD3E5GUiIhLIFCwCwKDby+43myirasbjNYgJt1G8Mp2cebFmTxMREVGwCJxt7WJDuYuGK90ALHPGsX1ZKpFhoSYvExERuU3BEsDcHi9lJ5vZfaKJIY9BVFgoO1ekkZs+y+xpIiIiX6JgCVBNbd1s3H+G2oudACxJjWXnk+lET7OZvExEROSrFCwBxuM1eKX6M0qOfcKg20uEPZgXlqexPDMOi8Vi9jwREZGvpWAJIOfbeyjYf4aalpsAZM+JoXhlBjMddpOXiYiI3JuCJQB4vQb73m+h6PUG+oY8TLMFs/WJeeQvSNCpioiI+AUFyyR38WYvmw7U8k7zdQAeS57OrlUZxEdONXmZiIjI8ClYJinDMCivucCOIx9za8CNPcTK5tx5rF6YiNWqUxUREfEvCpZJqK2rn8KDtbzVeA2A+YmRlOQ5SYoOM3mZiIjI6ChYJhHDMKhwtbKtsp7OviFCg60ULH6INVkPEqRTFRER8WMKlkmi/dYAWw7V8UZ9GwAZ8Q5K85ykxIabvExERGTsFCyTwNG6y2w5/BE3egYJtlpYn5PC09nJhARZzZ4mIiIyLhQsfqyjd5BtlfVUuFoBmDsznNJ8J6lxDpOXiYiIjC8Fi5860dBG4cE6rnYPYLXAM9nJPJeTgi04yOxpIiIi407B4me6+4fYceQs5TUXAUiOCaM0P5PMhAfMHSYiInIfKVj8yNuftrPpQC2XOvqwWGDNoiQKlszBHqJTFRERmdwULH6gd9BN8dEG9r7bAsDsqKmU5Dl5NCnK5GUiIiITQ8Hi406fv0HB/jO0XO8FYPXCRApz5xJm07dOREQCh+71fFT/kIfSY428XH0Ow4A4h51dq5xkpUSbPU1ERGTCKVh8kOtCBxvLXTRf6wEgb348W5c+TIQ9xORlIiIi5lCw+JBBt5fdbzZRVtWMx2sQE26jeGU6OfNizZ4mIiJiKgWLjzjb2sWGchcNV7oBWOaMY/uyVCLDQk1eJiIiYj4Fi8ncHi9lJ5vZfaKJIY9BVFgoO1ekkZs+y+xpIiIiPkPBYqKmtm427j9D7cVOAJakxrLzyXSip9lMXiYiIuJbFCwm8HgNXqn+jJJjnzDo9hJhD+aF5Wksz4zDYrGYPU9ERMTnKFgm2Pn2Hgr2n6Gm5SYA2XNiKF6ZwUyH3eRlIiIivkvBMkG8XoN977dQ9HoDfUMeptmC2frEPPIXJOhURURE5BsoWCbAxZu9bDpQyzvN1wF4LHk6u1ZlEB851eRlIiIi/kHBch8ZhkF5zQV2HPmYWwNu7CFWNufOY/XCRKxWnaqIiIgMl4LlPmnr6qfwYC1vNV4DYH5iJCV5TpKiw0xeJiIi4n8ULOPMMAwqXK1sq6yns2+I0GArBYsfYk3WgwTpVEVERGRUFCzjqP3WAFsO1fFGfRsAGfEOSvOcpMSGm7xMRETEvylYxsnRustsOfwRN3oGCbZaWJ+TwtPZyYQEWc2eJiIi4vcULGPU0TvItsp6KlytAMydGU5pvpPUOIfJy0RERCYPBcsYnGhoo/BgHVe7B7Ba4JnsZJ7LScEWHGT2NBERkUlFwTIK3f1D7DhylvKaiwAkx4RRmp9JZsID5g4TERGZpBQsI/T2p+1sOlDLpY4+LBZYsyiJgiVzsIfoVEVEROR+UbAMU++gm+KjDex9twWA2VFTKclz8mhSlMnLREREJj8FyzCcPn+Dgv1naLneC8DqhYkU5s4lzKb/fCIiIhNB97j30D/kofRYIy9Xn8MwIM5hZ9cqJ1kp0WZPExERCSgKlrtwXehgY7mL5ms9AOTNj2fr0oeJsIeYvExERCTwKFj+i0G3l91vNlFW1YzHaxATbqN4ZTo582LNniYiIhKwFCy/5WxrFxvKXTRc6QZgmTOO7ctSiQwLNXmZiIhIYFOwAG6Pl7KTzew+0cSQxyAqLJSdK9LITZ9l9jQREREBRvRGN0VFRTzyyCOEh4czY8YMVqxYQWNj4z2vc/nyZb773e8yZ84crFYrzz///Fj2jrumtm5Wlr1D6fFPGPIYLEmN5dhfPq5YERER8SEjCpaqqirWrl3Le++9x/Hjx3G73SxevJienp67XmdgYICYmBi2bNmC0+kc8+Dx4vEavHSqme/8YzW1FzuJsAfzsz/O5Offm0/0NJvZ80REROS3WAzDMEZ75WvXrjFjxgyqqqp4/PHHv/Hy2dnZZGZm8rOf/WxEX6erqwuHw0FnZycRERGjXPv/nG/voWD/GWpabt7eNSeG4pUZzHTYx3zbIiIictt43n+P6TEsnZ2dAERF+cervXq9Bvveb6Ho9Qb6hjxMswWz9Yl55C9IwGKxmD1PRERE7mLUwWIYBhs2bCArK4u0tLTx3MTAwAADAwN3Pu7q6hrzbV682cumA7W803wdgMeSp7NrVQbxkVPHfNsiIiJyf406WJ599llqa2uprq4ezz3A7Qf3bt++fVxuyzAMymsusOPIx9wacGMPsbI5dx6rFyZitepURURExB+MKljWrVtHZWUlp06dIj4+frw3sXnzZjZs2HDn466uLhISEkZ8O21d/RQerOWtxmsAzE+MpCTPSVJ02LhtFRERkftvRMFiGAbr1q3j0KFDnDx5kqSkpPsyymazYbON/pk6hmFQ4WplW2U9nX1DhAZbKVj8EGuyHiRIpyoiIiJ+Z0TBsnbtWl599VUqKioIDw/nypUrADgcDqZMmQLcPh25dOkSe/fuvXM9l8sFwK1bt7h27Roul4vQ0FAefvjhcfpj/D/ttwbYcqiON+rbAMiId1Ca5yQlNnzcv5aIiIhMjBE9rfluz6TZs2cPTz31FABPPfUU58+f5+TJk/e8XmJiIufPnx/W1x3u06KO1l1my+GPuNEzSLDVwvqcFJ7OTiYkaEQvNyMiIiLjwLSnNQ+nbX7xi1+M6npj0dE7yLbKeipcrQDMnRlOab6T1DjHff26IiIiMjH8/r2ETjS0UXiwjqvdA1gt8Ex2Ms/lpGALDjJ7moiIiIwTvw2W7v4hdhw5S3nNRQCSY8Iozc8kM+EBc4eJiIjIuPPLYHn703Y2HajlUkcfFgusWZREwZI52EN0qiIiIjIZ+VWw9A66Kan4iL3vtgAwO2oqJXlOHk3yj7cGEBERkdHxq2D5H2XvcKnn9jOOVi9MpDB3LmE2v/ojiIiIyCj41b39hRt9xM+IYtcqJ1kp0WbPERERkQniV8GyIjOOn/zxo0TYQ8yeIiIiIhPIr15R7SdPpitWREREApBfBYuIiIgEJgWLiIiI+DwFi4iIiPg8BYuIiIj4PAWLiIiI+DwFi4iIiPg8BYuIiIj4PAWLiIiI+DwFi4iIiPg8BYuIiIj4PAWLiIiI+DwFi4iIiPg8BYuIiIj4PAWLiIiI+LxgswcMh2EYAHR1dZm8RERERIbri/vtL+7Hx8IvguX69esAJCQkmLxERERERur69es4HI4x3YZfBEtUVBQAn3/++Zj/wDI2XV1dJCQkcOHCBSIiIsyeE9D0vfAd+l74Fn0/fEdnZyezZ8++cz8+Fn4RLFbr7YfaOBwO/eXzEREREfpe+Ah9L3yHvhe+Rd8P3/HF/fiYbmMcdoiIiIjcVwoWERER8Xl+ESw2m41t27Zhs9nMnhLw9L3wHfpe+A59L3yLvh++Yzy/FxZjPJ5rJCIiInIf+cUJi4iIiAQ2BYuIiIj4PAWLiIiI+DwFi4iIiPg8nw+Wf/7nfyYpKQm73c78+fP5zW9+Y/akgFRUVMQjjzxCeHg4M2bMYMWKFTQ2Npo9K+AVFRVhsVh4/vnnzZ4SsC5dusT3vvc9pk+fztSpU8nMzOSDDz4we1bAcbvd/K//9b9ISkpiypQpPPjgg7zwwgt4vV6zpwWEU6dOsXTpUuLi4rBYLBw+fPhLv28YBj/+8Y+Ji4tjypQpZGdnU19fP6Kv4dPB8qtf/Yrnn3+eLVu28OGHH/L7v//75Obm8vnnn5s9LeBUVVWxdu1a3nvvPY4fP47b7Wbx4sX09PSYPS1gnT59mpdeeomMjAyzpwSsmzdvsmjRIkJCQjh69Chnz56ltLSUBx54wOxpAedv//Zv+fnPf86LL77Ixx9/zK5du/jf//t/84//+I9mTwsIPT09OJ1OXnzxxa/9/V27dvF3f/d3vPjii5w+fZqZM2fy7W9/m+7u7uF/EcOHPfroo8bTTz/9pc/NnTvXKCwsNGmRfOHq1asGYFRVVZk9JSB1d3cbKSkpxvHjx40/+IM/MNavX2/2pID0ox/9yMjKyjJ7hhiG8Z3vfMf44Q9/+KXPrVy50vje975n0qLABRiHDh2687HX6zVmzpxpFBcX3/lcf3+/4XA4jJ///OfDvl2fPWEZHBzkgw8+YPHixV/6/OLFi3nnnXdMWiVf6OzsBBiXN7SSkVu7di3f+c53+MM//EOzpwS0yspKFixYQF5eHjNmzOBb3/oW/+f//B+zZwWkrKws3nzzTT755BMAzpw5Q3V1NX/0R39k8jI5d+4cV65c+dL9uc1m4w/+4A9GdH/us29+2N7ejsfjITY29kufj42N5cqVKyatErj9s8gNGzaQlZVFWlqa2XMCzr/+67/yH//xH5w+fdrsKQHvs88+o6ysjA0bNvDXf/3X/Pu//zvPPfccNpuN73//+2bPCyg/+tGP6OzsZO7cuQQFBeHxeNi5cyd/+qd/ava0gPfFffbX3Z+3tLQM+3Z8Nli+YLFYvvSxYRhf+ZxMrGeffZba2lqqq6vNnhJwLly4wPr16zl27Bh2u93sOQHP6/WyYMECfvrTnwLwrW99i/r6esrKyhQsE+xXv/oV+/bt49VXXyU1NRWXy8Xzzz9PXFwcP/jBD8yeJ4z9/txngyU6OpqgoKCvnKZcvXr1K5UmE2fdunVUVlZy6tQp4uPjzZ4TcD744AOuXr3K/Pnz73zO4/Fw6tQpXnzxRQYGBggKCjJxYWCZNWsWDz/88Jc+N2/ePA4ePGjSosD1V3/1VxQWFvInf/InAKSnp9PS0kJRUZGCxWQzZ84Ebp+0zJo1687nR3p/7rOPYQkNDWX+/PkcP378S58/fvw4jz32mEmrApdhGDz77LP8+te/5sSJEyQlJZk9KSDl5ORQV1eHy+W682vBggX8z//5P3G5XIqVCbZo0aKvPL3/k08+ITEx0aRFgau3txer9ct3aUFBQXpasw9ISkpi5syZX7o/HxwcpKqqakT35z57wgKwYcMGVq9ezYIFC/i93/s9XnrpJT7//HOefvpps6cFnLVr1/Lqq69SUVFBeHj4nZMvh8PBlClTTF4XOMLDw7/yuKGwsDCmT5+uxxOZ4C//8i957LHH+OlPf0p+fj7//u//zksvvcRLL71k9rSAs3TpUnbu3Mns2bNJTU3lww8/5O/+7u/44Q9/aPa0gHDr1i0+/fTTOx+fO3cOl8tFVFQUs2fP5vnnn+enP/0pKSkppKSk8NOf/pSpU6fy3e9+d/hfZLyexnS//NM//ZORmJhohIaGGr/7u7+rp9GaBPjaX3v27DF7WsDT05rN9X//7/810tLSDJvNZsydO9d46aWXzJ4UkLq6uoz169cbs2fPNux2u/Hggw8aW7ZsMQYGBsyeFhDeeuutr72P+MEPfmAYxu2nNm/bts2YOXOmYbPZjMcff9yoq6sb0dewGIZhjFdhiYiIiNwPPvsYFhEREZEvKFhERETE5ylYRERExOcpWERERMTnKVhERETE5ylYRERExOcpWERERMTnKVhERETE5ylYRERExOcpWERERMTnKVhERETE5ylYRERExOf9/3pK1A6IkNumAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mPlotTop = 10\n", - "plot_funcs(PFexample.solution[0].cFunc, 0.0, 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": 8, - "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(\n", - " \"This agent's human wealth is \"\n", - " + str(humanWealth)\n", - " + \" times his current income level.\"\n", - ")\n", - "print(\n", - " \"This agent's consumption function is defined (consumption is positive) down to m_t = \"\n", - " + str(mMinimum)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Yikes! Let's take a look at the bottom of the consumption function. In the cell below, the bounds of the `plot_funcs` function are set to display down to the lowest defined value of the consumption function." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_funcs(PFexample.solution[0].cFunc, mMinimum, 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": 10, - "metadata": {}, - "outputs": [], - "source": [ - "NewExample = deepcopy(PFexample)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can assign new parameters to an `AgentType` with the `assign_parameter` method. For example, we could make the new agent less patient:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "nbsphinx-thumbnail": {} - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "NewExample.assign_parameters(DiscFac=0.90)\n", - "NewExample.solve()\n", - "mPlotBottom = mMinimum\n", - "plot_funcs(\n", - " [PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], mPlotBottom, mPlotTop\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(Note that you can pass a **list** of functions to `plot_funcs` 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": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "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", - "plot_funcs([PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], 0.0, 10.0)" - ] - }, - { - "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 *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](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly the normalized utility and continuation value are $u$ and $v_t$.\n", - "\n", - "\\begin{align*}\n", - "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\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 \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n", - "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n", - "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n", - "\\end{align*}\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": 13, - "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\": [\n", - " 0.1\n", - " ], # ... 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\": 50.0, # 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\": -10.0,\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": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", - "\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": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "IndShockExample.solve()\n", - "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)" - ] - }, - { - "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 `update_income_process` so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "OtherExample = deepcopy(\n", - " IndShockExample\n", - ") # Make a copy so we can compare consumption functions\n", - "OtherExample.assign_parameters(\n", - " PermShkStd=[0.2]\n", - ") # Double permanent income risk (note that it's a one element list)\n", - "OtherExample.update_income_process() # 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": 17, - "metadata": {}, - "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" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - }, - "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 - }, - "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": 4 -} +{ + "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": [ + 0 + ], + "is_executing": true + }, + "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", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import HARK\n", + "from copy import deepcopy\n", + "\n", + "mystr = lambda number: \"{:.4f}\".format(number)\n", + "from HARK.utilities import plot_funcs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Your First HARK Model: Perfect Foresight\n", + "\n", + "We start with almost the simplest possible consumption model: A consumer with CRRA utility\n", + "\n", + "\\begin{align*}\n", + "U(C) = \\frac{C^{1-\\rho}}{1-\\rho}\n", + "\\end{align*}\n", + "\n", + "has perfect foresight about everything except the (stochastic) date of death, which may occur in each period, implying a \"survival probability\" each period of $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb_t \\le 1$, and a complementary death probability of $\\mathsf{D}_t = 1 - \\LivPrb_t$; death ends the consumer's decision problem. 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 current income) and must choose how much of those resources to consume $C_t$ and hold the rest 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 $\\beta$.\n", + "\n", + "The agent's problem can be written in Bellman form as:\n", + "\n", + "\\begin{align*}\n", + "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\beta \\aleph_t V_{t+1}(M_{t+1},P_{t+1})\\\\\n", + "&\\text{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{align*}\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 $\\{\\aleph_t\\}$. To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an *infinite horizon* consumer with constant income growth and survival probability: $\\Gamma_t = \\Gamma$ and $\\LivPrb_t = \\LivPrb$ for all $t$.\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](https://www.econ2.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 `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", + "| $\\aleph$ | 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 **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 `PF_dictionary`. Such a bundle is created passing `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 in a dictionary\" 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": null, + "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 (parameterization/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 (index 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 [lecture notes](https://www.econ2.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", + "plot_funcs(PFexample.solution[0].cFunc, 0.0, 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-- their \"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(\n", + " \"This agent's human wealth is \"\n", + " + str(humanWealth)\n", + " + \" times his current income level.\"\n", + ")\n", + "print(\n", + " \"This agent's consumption function is defined (consumption is positive) down to m_t = \"\n", + " + str(mMinimum)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yikes! Let's take a look at the bottom of the consumption function. In the cell below, the bounds of the `plot_funcs` function are set to display down to the lowest defined value of the consumption function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_funcs(PFexample.solution[0].cFunc, mMinimum, 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": [ + "You can assign new parameters to an `AgentType` with the `assign_parameter` method. For example, we could make the new agent less patient:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "nbsphinx-thumbnail": {} + }, + "outputs": [], + "source": [ + "NewExample.assign_parameters(DiscFac=0.90)\n", + "NewExample.solve()\n", + "mPlotBottom = mMinimum\n", + "plot_funcs(\n", + " [PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], mPlotBottom, mPlotTop\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Note that you can pass a **list** of functions to `plot_funcs` 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", + "# WRITE YOUR CODE HERE!\n", + "\n", + "# Compare the old and new consumption functions\n", + "plot_funcs([PFexample.solution[0].cFunc, NewExample.solution[0].cFunc], 0.0, 10.0)" + ] + }, + { + "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 *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](https://www.econ2.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). Accordingly, the normalized utility and continuation value are $u$ and $v_t$.\n", + "\n", + "\\begin{align*}\n", + "v_t(m_t) &= \\max_{c_t} u(c_t) + \\aleph\\beta \\mathbb{E} [(\\Gamma\\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 \\psi_{t+1}) a_t + \\theta_{t+1} \\\\\n", + "\\mathbb{E}[\\psi_t]&=\\mathbb{E}[\\theta_t] = 1 \\\\\n", + "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}\n", + "\\end{align*}\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": [ + 0, + 2 + ] + }, + "outputs": [], + "source": [ + "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n", + "\n", + "IndShockDictionary = {\n", + " # The dictionary includes our original parameters...\n", + " \"CRRA\": 2.5,\n", + " \"Rfree\": 1.03,\n", + " \"DiscFac\": 0.96,\n", + " \"LivPrb\": [0.98],\n", + " \"PermGroFac\": [1.01],\n", + " # ... and the new parameters for constructing the income process.\n", + " \"PermShkStd\": [0.1], \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\": 50.0, # 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\": -10.0,\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", + "\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", + "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)" + ] + }, + { + "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 small 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 `update_income_process` so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "OtherExample = deepcopy(\n", + " IndShockExample\n", + ") # Make a copy so we can compare consumption functions\n", + "OtherExample.assign_parameters(\n", + " PermShkStd=[0.2]\n", + ") # Double permanent income risk (note that it's a one element list)\n", + "OtherExample.update_income_process() # Call the method to reconstruct the representation of F_t\n", + "OtherExample.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, agents in HARK have an $\\texttt{update}$ method that calls *all* of their various internal $\\texttt{update_X}$ methods (e.g. $\\texttt{update\\_income\\_process}$). If you change a parameter that describes a constructed attribute of your agent, invoking the $\\texttt{update}$ method will re-construct that attribute using the new parameter value.\n", + "\n", + "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" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "collapsed,code_folding", + "formats": "ipynb" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + }, + "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 + }, + "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": 4 +} From fa8bc418b6b3db30be66759f2b91cb6c88ddd1de Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Fri, 8 Dec 2023 15:34:18 -0500 Subject: [PATCH 5/8] Run Gentle Introduction to satisfy Sphinx --- .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb index bd4e204c5..1f6e38361 100644 --- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "code_folding": [ 0 @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "code_folding": [ 0 @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "nbsphinx-thumbnail": {} }, From 174a152d6348ccafdbeaba7b0980800fa0aa75e0 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Fri, 8 Dec 2023 15:58:30 -0500 Subject: [PATCH 6/8] Try again with additional output Output was mysteriously missing in some notebook cells; trying again for tests. --- .../Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 108 +++++++++++++++--- 1 file changed, 95 insertions(+), 13 deletions(-) diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb index 1f6e38361..0d97f377f 100644 --- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -176,7 +176,18 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "PFexample.solution[0].cFunc" ] @@ -194,7 +205,18 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "mPlotTop = 10\n", "plot_funcs(PFexample.solution[0].cFunc, 0.0, mPlotTop)" @@ -213,7 +235,16 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [], + "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", @@ -239,7 +270,18 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_funcs(PFexample.solution[0].cFunc, mMinimum, mPlotTop)" ] @@ -275,7 +317,18 @@ "metadata": { "nbsphinx-thumbnail": {} }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "NewExample.assign_parameters(DiscFac=0.90)\n", "NewExample.solve()\n", @@ -300,9 +353,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Revert NewExample's discount factor and make his future income minuscule\n", "# WRITE YOUR CODE HERE!\n", @@ -355,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "code_folding": [ 0, @@ -416,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -434,9 +498,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "IndShockExample.solve()\n", "plot_funcs(IndShockExample.solution[0].cFunc, 0.0, 10.0)" @@ -457,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -482,12 +557,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Use the line(s) below to plot the consumptions functions against each other" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 6ca702545fb25dee061039f5a7cf9fea0cf5f7a3 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Fri, 8 Dec 2023 16:08:47 -0500 Subject: [PATCH 7/8] Try to satisfy lint --- examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb index 0d97f377f..8d460eed8 100644 --- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -422,8 +422,7 @@ "execution_count": 13, "metadata": { "code_folding": [ - 0, - 2 + 0 ] }, "outputs": [], @@ -431,15 +430,13 @@ "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n", "\n", "IndShockDictionary = {\n", - " # The dictionary includes our original parameters...\n", - " \"CRRA\": 2.5,\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", - " # ... and the new parameters for constructing the income process.\n", " \"PermShkStd\": [0.1], \n", - " \"PermShkCount\": 7,\n", + " \"PermShkCount\": 7, # and the new parameters for constructing the income process.\n", " \"TranShkStd\": [0.1],\n", " \"TranShkCount\": 7,\n", " \"UnempPrb\": 0.05,\n", From 53d54ac34b24a266a8d66a5b45388d61d6c7ec2b Mon Sep 17 00:00:00 2001 From: Alan Lujan Date: Fri, 8 Dec 2023 19:38:04 -0500 Subject: [PATCH 8/8] fix ruff format --- examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb index 18b34a54a..94f33e9b3 100644 --- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -427,13 +427,13 @@ "# 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", + " \"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], \n", - " \"PermShkCount\": 7, # and the new parameters for constructing the income process.\n", + " \"PermShkStd\": [0.1],\n", + " \"PermShkCount\": 7, # and the new parameters for constructing the income process.\n", " \"TranShkStd\": [0.1],\n", " \"TranShkCount\": 7,\n", " \"UnempPrb\": 0.05,\n",