diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
new file mode 100644
index 000000000..696c25ff3
--- /dev/null
+++ b/.github/FUNDING.yml
@@ -0,0 +1,2 @@
+github: [numfocus]
+custom: ['https://numfocus.org/donate-to-econ-ark']
diff --git a/.github/workflows/hark.yml b/.github/workflows/hark.yml
new file mode 100644
index 000000000..b0dffba51
--- /dev/null
+++ b/.github/workflows/hark.yml
@@ -0,0 +1,41 @@
+name: HARK build on MacOS, Ubuntu and Windows
+
+on:
+ push:
+ branches:
+ - master
+ pull_request:
+ branches:
+ - master
+
+jobs:
+ build:
+
+ runs-on: ${{ matrix.os }}
+ strategy:
+ max-parallel: 4
+ matrix:
+ os: [ubuntu-latest, macos-latest, windows-latest]
+ python-version: [3.6, 3.7, 3.8]
+
+ steps:
+ - uses: actions/checkout@v1
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v1
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install -r requirements.txt
+# - name: Lint with flake8
+# run: |
+# pip install flake8
+# # stop the build if there are Python syntax errors or undefined names
+# flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+# # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+# flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+ - name: Test with pytest
+ run: |
+ pip install pytest
+ pytest
diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK-Add.bib b/.gitmodules
similarity index 100%
rename from Documentation/HARKmanual/ContributingToHARK/ContributingToHARK-Add.bib
rename to .gitmodules
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 80787b838..f172a6411 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -10,11 +10,14 @@ sphinx:
configuration: Documentation/conf.py
# Optionally build your docs in additional formats such as PDF and ePub
-formats:
- - pdf
+formats: []
# Optionally set the version of Python and requirements required to build your docs
python:
version: 3.7
install:
- requirements: requirements.txt
+
+submodules:
+ include: all
+ recursive: true
diff --git a/.travis.yml b/.travis.yml
index 8f0e9509a..d00c9c07b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,9 +2,12 @@ language: python
python:
- 2.7
- 3.6
+ - 3.7
+ - 3.8
cache: pip
install:
- pip install -r requirements.txt
+ - pip install .
script:
- pytest
# - flake8 HARK
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 60436e37f..472f51940 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,15 +1,75 @@
HARK
-Version 0.10.1.dev3
Release Notes
# Introduction
-This document contains the release notes for the 0.10.1 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents.
+This document contains the release notes of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents.
For more information on HARK, see [our Github organization](https://github.com/econ-ark).
## Changes
+
+### 0.10.4
+
+Release Date: 05-03-2020
+
+#### Major Changes
+ - Last release to support Python 2.7, future releases of econ-ark will support Python 3.6+ [#478](https://github.com/econ-ark/HARK/pull/478)
+ - Move non-reusable model code to examples directory, BayerLuetticke, FashionVictim now in examples instead of in HARK code [#442](https://github.com/econ-ark/HARK/pull/442)
+ - Load default parameters for ConsumptionSaving models [#466](https://github.com/econ-ark/HARK/pull/466)
+ - Improved implementaion of parallelNelderMead [#300](https://github.com/econ-ark/HARK/pull/300)
+
+#### Minor Changes
+ - Notebook utility functions for determining platform, GUI, latex (installation) are available in HARK.utilities [#512](https://github.com/econ-ark/HARK/pull/512)
+ - Few DemARKs moved to examples [#472](https://github.com/econ-ark/HARK/pull/472)
+ - MaxKinks available in ConsumerParameters again [#486](https://github.com/econ-ark/HARK/pull/486)
+
+
+### 0.10.3
+
+Release Date: 12-12-2019
+
+#### Major Changes
+
+- Added constrained perfect foresight model solution. ([#299](https://github.com/econ-ark/HARK/pull/299)
+
+#### Minor Changes
+
+- Fixed slicing error in minimizeNelderMead. ([#460](https://github.com/econ-ark/HARK/pull/460))
+- Fixed matplotlib GUI error. ([#444](https://github.com/econ-ark/HARK/pull/444))
+- Pinned sphinx dependency. ([#436](https://github.com/econ-ark/HARK/pull/436))
+- Fixed bug in ConsPortfolioModel in which the same risky rate of return would be drawn over and over. ([#433](https://github.com/econ-ark/HARK/pull/433))
+- Fixed sphinx dependency errors. ([#411](https://github.com/econ-ark/HARK/pull/411))
+- Refactored simultation.py. ([#408](https://github.com/econ-ark/HARK/pull/408))
+- AgentType.simulate() now throws informative errors if
+attributes required for simulation do not exist, or initializeSim() has
+never been called. ([#320](https://github.com/econ-ark/HARK/pull/320))
+
+### 0.10.2
+
+Release Date: 10-03-2019
+
+#### Minor Changes
+- Add some bugfixes and unit tests to HARK.core. ([#401](https://github.com/econ-ark/HARK/pull/401))
+- Fix error in discrete portfolio choice's AdjustPrb. ([#391](https://github.com/econ-ark/HARK/pull/391))
+
+### 0.10.1.dev5
+
+Release Date: 09-25-2019
+
+#### Minor Changes
+- Added portfolio choice between risky and safe assets (ConsPortfolioModel). ([#241](https://github.com/econ-ark/HARK/pull/241))
+
+### 0.10.1.dev4
+
+Release Date: 09-19-2019
+
+#### Minor Changes
+- Fixes cubic interpolation in KinkedRSolver. ([#386](https://github.com/econ-ark/HARK/pull/386))
+- Documentes the procedure for constructing value function inverses and fixes bug in which survival rate was not included in absolute patience factor. ([#383](https://github.com/econ-ark/HARK/pull/383))
+- Fixes problems that sometimes prevented multiprocessing from working. ([#377](https://github.com/econ-ark/HARK/pull/377))
+
### 0.10.1.dev3
Release Date: 07-23-2019
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 58ae30f38..952b83d11 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -21,6 +21,7 @@ See [details on our policy on Code of Conduct](./.github/CODE_OF_CONDUCT.md).
* [Submitting a Bug Report](./doc/guides/contributing/issues.md#submitting-a-bug-report)
* [Triaging a Bug Report](./doc/guides/contributing/issues.md#triaging-a-bug-report)
* [Resolving a Bug Report](./doc/guides/contributing/issues.md#resolving-a-bug-report)
+* [Linking your fork to your local clone](./doc/guides/contributing/issues.md#linking-your-fork-to-your-local-clone)
## [Pull Requests](./doc/guides/contributing/pull-requests.md)
diff --git a/Documentation/CONTRIBUTING.md b/Documentation/CONTRIBUTING.md
index 1b489aee9..eec0e3083 100644
--- a/Documentation/CONTRIBUTING.md
+++ b/Documentation/CONTRIBUTING.md
@@ -1,12 +1,12 @@
# Contributing to Econ-ARK
-### Welcome!
+## Welcome!
Thank you for considering contributing to Econ-ARK! We're a young project with a small but committed community that's hoping to grow while maintaining our friendly and responsive culture. Whether you're an economist or a technologist, a writer or a coder, an undergrad or a full professor, a professional or a hobbyist, there's a place for you in the Econ-ARK community.
We're still creating our contribution infrastructure, so this document is a work in progress. If you have any questions please feel free to @ or otherwise reach out project manager [Shauna](https://github.com/shaunagm), or lead developers [Chris](https://github.com/llorracc) and [Matt](https://github.com/mnwhite). If you prefer to connect through email, you can send it to __econ-ark at jhuecon dot org__.
-### How to Contribute
+## How to Contribute
We're open to all kinds of contributions, from bug reports to help with our docs to suggestions on how to improve our code. The best way to figure out if the contribution you'd like to make is something we'd merge or otherwise accept, is to open up an issue in our issue tracker. Please create an issue rather than immediately submitting pull request, unless the change you'd like to make is so minor you won't mind if the pull request is rejected. For bigger contributions, we want to proactively talk things through so we don't end up wasting your time.
@@ -21,7 +21,7 @@ If you'd like to help with those or any other kind of contribution, reach out to
We don't currently have guidelines for opening issues or pull requests, so include as much information as seems relevant to you, and we'll ask you if we need to know more.
-### Responding to Issues & Pull Requests
+## Responding to Issues & Pull Requests
We're trying to get better at managing our open issues and pull requests. We've created a new set of goals for all issues and pull requests in our Econ-ARK repos:
@@ -31,7 +31,7 @@ We're trying to get better at managing our open issues and pull requests. We've
If you've been waiting on us for more than two weeks for any reason, please feel free to give us a nudge. Correspondingly, we ask that you respond to any questions or requests from us within two weeks as well, even if it's just to say, "Sorry, I can't get to this for a while yet". If we don't hear back from you, we may close your issue or pull request. If you want to re-open it, just ask - we're glad to do so.
-### Getting Started
+## Getting Started
The [quick start guide](https://github.com/econ-ark/HARK#ii-quick-start-guide) in our README provides instructions for how to get started running HARK. This also serves as a setup guide for new contributors. If you run into any problems, please let us know by opening an issue in the issue tracker.
diff --git a/Documentation/HARKdoc.pdf b/Documentation/HARKdoc.pdf
deleted file mode 100644
index 3581b3a35..000000000
Binary files a/Documentation/HARKdoc.pdf and /dev/null differ
diff --git a/Documentation/HARKmanual.md b/Documentation/HARKmanual.md
index ee01bb1b2..31e196e86 100644
--- a/Documentation/HARKmanual.md
+++ b/Documentation/HARKmanual.md
@@ -1,7 +1,7 @@
## A User’s Guide for HARK:
Heterogeneous Agents Resources and toolKit
-May 21, 2018
+February 13, 2020
Christopher D Carroll1
Johns Hopkins University
@@ -16,7 +16,7 @@ University of Delaware
-
+
1 Carroll: (This is an excerpt of the full manual, available at http://econ-ark.org/HARK/Documentation/HARKmanual.pdf)
0.1 This section provides an overview of how users can contribute new code to HARK, what sort of
-contributions are most desired, and style conventions for the toolKit. Before attempting to
-extend HARK, we strongly recommend that you familiarize yourself with its features and models
-by looking at the source code.
-
-
0.1.1 The easiest and most straightforward way to contribute to HARK is by writing new general
-purpose tools for inclusion in one of the top-level modules. This might be a commonly
-known data analysis technique (e.g. a kernel density estimator) for HARK.utilities , an
-optimization method (local or global) for HARK.estimation , an interpolation method for
-HARKinterpolation , etc. If you’ve found a technique useful in your own research and
-believe it could be of use to others on entirely different projects, then it probably
-belongs in HARK. Likewise, if you read through a HARK module expecting to find a
-certain tool or function (because of similar functions that are present, or merely because
-it’s very useful ) but don’t, this is a good sign that HARK has a hole that you can
-fill.
Secondly, the development team wants to expand HARK to include more canonical models.
-Most of the models available in the toolKit at the time of its public beta release concern agents
-with (geometrically discounted) CRRA utility over a unitary consumption good, who receive
-permanent and transitory shocks to income and have access to a single risk free asset. This is an
-important class of models, but that description has an awful lot of qualifiers. In short, there are
-many more common features of dynamic household models that are not yet in HARK, but would
-be very valuable for the toolKit. New model contributions should “play nicely” with existing
-models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the
-greatest extent possible. For ideas of potential model extensions for HARK, see section
-
-
-?? .
Finally, contributions to HARK are not restricted to entirely new functionality. While we have
-done our best to provide flexible frameworks and useful tools, we have almost certainly
-overlooked an unexpected case that would not be (properly) handled by the toolKit as is.
-Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake
-or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as
-an Issue on GitHub so that someone else might. The “mistake” you find might not
-even be in the code itself, but a deficiency in the documentation: a misnamed (or
-incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing)
-comment, even a grammatical or spelling error. No matter how niggling, we want your
-fix.
-
For those who want to contribute to the HARK project without programming a new model or
-digging through the code for errors and omissions, consider reading through the Issues page on
-GitHub to see what other users have flagged for development. You might find several pieces of
-“low hanging fruit” for you to quickly fix.
-
0.1.2 Git is a distributed version control system that has gained increasing popularity in the decade
-since its creation; it is designed to enable collaboration and “non-linear workflow” among a
-(potentially disperse) programming team. As HARK is to be developed by volunteer researchers
-all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds
-as a command line tool, others have since extended git with further functionality and
-a user interface more friendly to those more familiar with GUIs. Foremost among
-these tools is GitHub.com, where the HARK project is hosted. This section provides a
-brief overview of how to use GitHub to interact with (and potentially contribute to)
-HARK.
-
To prepare to contribute to HARK, follow these simple steps:
-
- Make a GitHub account: Go to github.com ; the homepage should prompt
- you to choose a username and password. Signing up is quick, easy, and
- free.Install GitHub Desktop : Go to desktop.github.com and download the Desktop
-
-
- application for your operating system, then install it. After opening Desktop for the
- first time, enter your GitHub login to connect it to your account.
- Fork the HARK repository: Go to the HARK repository page , click on the Fork
- button in the upper right, and choose to fork HARK to your own GitHub account.
- This creates a new repository identical to the one hosted by Econ-Ark at the moment
- you fork it, but hosted on your account instead.
- Add the repository to Desktop: Open GitHub Desktop, click on the yourusername/HARK ,
- click the Clone button at the bottom of the dialogue window, and choose a directory
- on your local machine.
- You now control a “fork” of the main HARK repository, hosted by GitHub, and have a clone of
-this fork on your local machine. You are free to edit your fork of HARK in any way you’d like;
-other GitHub users can view your fork (and fork it themselves), but they cannot make changes to
-your fork unless you give them permission by adding them as a contributor. Changes made
-to the local copy of your fork do not get automatically sent to the remote copy on
-GitHub for public viewing. To make changes to your fork on GitHub, follow these
-steps:
-
- Make local changes: Edit your local copy of the repository in any way you’d like:
- add or delete lines from existing files; add, delete, or rename entire files or directories.
- Commit local changes: Open the repository in GitHub Desktop and click on the
- Changes tab. In the lower left, provide a short description of the changes made in
- the Summary field, and (optionally) longer comments about these changes in the
- Description field. Click the check-mark Commit button.
- Push the commit: Click on the Sync button near the upper right to “push” your
- local commits to the remote server; this will also pull down remote commits from
- other contributors to your fork that were made since the last time you synced.
- The left hand panel of the Changes tab has a summary of the files that have been changed,
-added, or removed since your last commit; the right hand panel shows the specific
-changes made to each changed file. After step 2, a record of your changes has been
-stored locally by the git system, but is not yet reflected in the public remote copy on
-
-
-GitHub.
Suppose you have mucked about in your fork for a while and have made changes to HARK
-that you think should be included in the main repository hosted by Econ-Ark. To make your
-dream a reality, follow these steps:
-
- Isolate the changes: If you mucked around for quite a while, making various changes
- here and there, and then later made some contribution that you want to include in
- the main repository, you should isolate the desired changes in a new branch of your
- fork . Make a new branch by clicking the button toward the upper left of GitHub
- Desktop, revert that branch to the commit just before you forked from the main
- repository,only the changes you want to push to the main
- repository .
- Issue a pull request: Go to HARK’s GitHub page and click the “New pull request”
- button. Click the text that says “compare across forks”, and select econ-ark/HARK
- as the base fork, master as the base, your fork as the head fork, and the branch to
- be merged as the “compare”. In the boxes below, give a name to your pull request
- and provide sufficient comments about your contribution so that a development team
- member can understand what you’ve done. Click the “Create pull request” button.
- Be patient: Someone from the development team will look at your contribution and
- either merge it into the main repository or return it with comments. See section 0.1.3
- for procedures followed by the HARK team when evaluating outside contributions.
-
-
-
- 0.1.3 Before submitting to HARK, the development team would like you to make sure that
-your intended contribution is listed as an Issue on the GitHub page. If you are going
-to fix or address an Issue that is already posted, reply to that post with a note
-indicating that you are about to work on it and an estimate of when you will make a
-pull request for it. If you want to do something that no one has mentioned yet as
-an Issue, make a new Issue on GitHub (briefly) describing what you intend to
-do.
After creating a pull request to submit your contribution, it will be evaluated by one or more
-members of the HARK team. Criteria that the HARK team will consider when choosing whether
-to accept a submission include:
-
- Does the contribution break previous code? If you change or expand an existing
- function, you should make sure that this does not cause some models or applications
- to work incorrectly. In the near future, HARK will have a core set of tests to run to
- check for full compatibility.
- Is the code well documented? Code submissions should have properly formatted
- docstrings for each new function, class, method, and module (see section 0.1.5 ) and
- enough comments for a reader to follow the algorithm.
- Is the contribution relevant? The development team intends for HARK to
- be diverse, but not everything goes in HARK. If your submission concerns a
- representative agent model or a method for estimating a reduced form model, then
- it probably doesn’t belong in HARK.
- Is the code demonstrated with an example? If your solve a new model in
- HARK, you should include a set of example parameters for other users to easily
- test. If you’ve contributed a new function, consider providing a snippet of code
-
-
- demonstrating its use.Is the code correct? Your code should actually do what it claims to do.
- Does the code use HARK conventions? To the best of your ability, contributions
- to HARK should follow the style guidelines in section 0.1.4 . We don’t demand
- perfection, as these are guidelines rather than rules.
- Is the code well integrated in HARK? In the beta release of HARK, we have
- tried to present a “taxonomy” of consumption-saving models that build up from each
- other. When possible, new models in HARK should try to continue this pattern. A
- submission that introduces a model in a new module that could have easily been made
- as an extension of an existing module might be rejected. However, we acknowledge
- that there is value in providing a solver for a new model, and that a future contributor
- might make her mark by integrating it into the existing hierarchy.
- Is this already in HARK? If your contribution is already in HARK, then it will
- almost certainly be rejected. Of course, if you submit a version that dominates the
- existing implementation (or even one that works better in a well described, non-trivial
- set of circumstances), it belongs in HARK.
- The HARK team intends to release more specific guidelines in the near future, so that contributors
-are better able to judge their submissions before issuing a pull request. We are in the process of
-recruiting “czars” to oversee particular aspects of HARK, providing their knowledge and
-expertise.
0.1.4 Object naming conventions in HARK are fairly different than existing standards, and differ
-somewhat between tool modules vs model or application modules. The following conventions
-apply throughout HARK:
-
-
-
- Functions and methods are always in “camel case”: no underscores, first letter is lower
- case, first letter of each subsequent word is capitalized. E.g. approxLognormal .
-
- Function and method names should accurately and concisely describe what the
- function does; the first word in the name must be a verb .
- Variable and class names should not have a verb as their first word.
-
- Class names should use no underscores and capitalize the first letter of each word;
- moreover, a class name must include a noun . E.g. ConsPerfForesightSolver .
- When naming variables in model modules, the HARK team strongly discourages using single
-letter names, like c for consumption. Instead, we encourage contributors to use longer, more
-descriptive variable names using additional words and common abbreviations to specify the
-variable more precisely. In /Documentation/NARK.pdf , we list standard single letter variable
-“bases” and an array of prefixes and suffixes to adjust them. Economic variables in model
-modules should (usually) not use underscores, instead using camel case to the greatest extent
-possible.
Conventions for naming variables in HARK’s tool modules are significantly closer to more
-commonly used standards. Variable names should be in all lower case, with underscores between
-words, e.g. data_to_match . The functions and classes in these modules are more general
-and almost surely do not have any inherent “economic content”; they are numerical
-or algorithmic objects, not variables that might appear in an equation in an article
-for a (non-computational) economics journal. Variable names in application modules
-(e.g. the files that execute the cstwMPC estimations) are a mix of the conventions for tool
-and model files, as appropriate for each variable. That is, variables that are directly
-related to “economic variables” in model modules should follow those conventions, while
-objects created solely for data manipulation or reporting should use the style of tool
-modules.
-
0.1.5 The HARK team wants the toolKit to be as accessible to users as possible; our greatest
-fearonline documentation for all
-of our tool and model modules, so that users can learn about what’s available in HARK without
-digging through the source code. When making contributions to HARK, the development team
-asks users to format their inline documentation to work with Sphinx by following a few simple
-rules.
-
- The top of every module should begin with a “docstring” providing a clear description
- of the contents of the module. The first sentence should concisely summarize
- the file, as it may appear in an index or summary of all modules without the
- remaining sentences. A docstring at the top of a module should be formatted
- as:"""
-
Specifies an economic model and provides methods for solving it. More
- specific description of the key features of the model and variations
- of it in this module. Maybe some comments about the solution method
- or limitations of the model. Your bank account routing number.
-
"""
-
- The line directly below the declaration of a function, method or class should begin a
- docstring describing that object. As with modules, the first sentence should concisely
- summarize the function or class, as it might be included in an index or summary. For
- functions and methods, the docstring should be formatted as:
- def functionName(input1,input2):
-
"""
-
-
-
Concise description of the function. More details about what
-
the function does, options or modes available, and maybe
- mathematical
-
methods used. Credit to a source if you poached their
- algorithm.
-
Parameters
-
––––––––––
-
input1: type
-
Description of what input1 represents.
-
input2: type
-
Description of what input2 represents.
-
Returns
-
–––––––
-
output_name: type
-
Description of the output(s) of the function. Might have
-
multiple entries. If no output, this is just "None".
-
"""
-
- Provide ample comments within a function or method so that a relatively intelligent
- reader can follow along with your algorithm. Short comments can follow at the end
- of a line, longer comments (or descriptions of the step or task about to be performed)
- should precede a block of code on the line(s) above it.
- Finally, if you write a new model module, the HARK team asks that you also provide a short
-mathematical writeup of the model as a PDF. This document does not need to go into great
-detail about the solution method for the model or the functions and classes included in
-the module, merely specify the economic model and provide a summary of how it
-is solved. See /Documentation/ConsumptionSavingModels.pdf for an example of
-this.
-
-
-
-
-
-
diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.idv b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.idv
deleted file mode 100644
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deleted file mode 100644
index bff48cadb..000000000
--- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.lg
+++ /dev/null
@@ -1,233 +0,0 @@
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-(This is an excerpt of the full manual, available at
-
-### 0.1 Contributing to HARK
-
-This section provides an overview of how users can contribute new code to
-HARK, what sort of contributions are most desired, and style conventions for
-the toolKit. Before attempting to extend HARK, we strongly recommend that you
-familiarize yourself with its features and models by looking at the source
-code.
-
-#### 0.1.1 What Does HARK Want?
-
-The easiest and most straightforward way to contribute to HARK is by writing
-new general purpose tools for inclusion in one of the top-level modules. This
-might be a commonly known data analysis technique (e.g. a kernel density
-estimator) for HARK.utilities, an optimization method (local or global) for
-HARK.estimation, an interpolation method for HARKinterpolation, etc. If you’ve
-found a technique useful in your own research and believe it could be of use
-to others on entirely different projects, then it probably belongs in HARK.
-Likewise, if you read through a HARK module expecting to find a certain tool
-or function (because of similar functions that are present, or merely because
-it’s very useful) but don’t, this is a good sign that HARK has a hole that you
-can fill.[1](ContributingToHARK2.html#fn1x0)
-
-Secondly, the development team wants to expand HARK to include more canonical
-models. Most of the models available in the toolKit at the time of its public
-beta release concern agents with (geometrically discounted) CRRA utility over
-a unitary consumption good, who receive permanent and transitory shocks to
-income and have access to a single risk free asset. This is an important class
-of models, but that description has an awful lot of qualifiers. In short,
-there are many more common features of dynamic household models that are not
-yet in HARK, but would be very valuable for the toolKit. New model
-contributions should “play nicely” with existing models and tools, following
-HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent
-possible. For ideas of potential model extensions for HARK, see section
-??.[2](ContributingToHARK3.html#fn2x0)
-
-Finally, contributions to HARK are not restricted to entirely new
-functionality. While we have done our best to provide flexible frameworks and
-useful tools, we have almost certainly overlooked an unexpected case that
-would not be (properly) handled by the toolKit as is. Moreover, there might
-even be flaws in our solution or simulation methods. If you find a mistake or
-error, we strongly encourage you to contribute a fix for the problem, or at
-least to flag it as an Issue on GitHub so that someone else might. The
-“mistake” you find might not even be in the code itself, but a deficiency in
-the documentation: a misnamed (or incorrectly typed) parameter or return
-variable in the docstring, an incorrect (or missing) comment, even a
-grammatical or spelling error. No matter how niggling, we want your fix.
-
-For those who want to contribute to the HARK project without programming a new
-model or digging through the code for errors and omissions, consider reading
-through the Issues page on GitHub to see what other users have flagged for
-development. You might find several pieces of “low hanging fruit” for you to
-quickly fix.
-
-#### 0.1.2 Git and GitHub
-
-Git is a distributed version control system that has gained increasing
-popularity in the decade since its creation; it is designed to enable
-collaboration and “non-linear workflow” among a (potentially disperse)
-programming team. As HARK is to be developed by volunteer researchers all
-around the world, git is an excellent fit for the toolKit. Originally designed
-by Linus Torvalds as a command line tool, others have since extended git with
-further functionality and a user interface more friendly to those more
-familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK
-project is hosted. This section provides a brief overview of how to use GitHub
-to interact with (and potentially contribute to) HARK.
-
-To prepare to contribute to HARK, follow these simple steps:
-
- 1. Make a GitHub account: Go to [github.com](https://www.github.com); the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.[3](ContributingToHARK4.html#fn3x0)
- 2. Install GitHub Desktop: Go to [desktop.github.com](https://desktop.github.com/) and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account.
- 3. Fork the HARK repository: Go to the [HARK repository page](https://github.com/econ-ark/HARK), click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead.
- 4. Add the repository to Desktop: Open GitHub Desktop, click on the  icon in the upper left, and select clone. Find the repository named yourusername/HARK, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine.
-
-You now control a “fork” of the main HARK repository, hosted by GitHub, and
-have a clone of this fork on your local machine. You are free to edit your
-fork of HARK in any way you’d like; other GitHub users can view your fork (and
-fork it themselves), but they cannot make changes to your fork unless you give
-them permission by adding them as a contributor. Changes made to the local
-copy of your fork do not get automatically sent to the remote copy on GitHub
-for public viewing. To make changes to your fork on GitHub, follow these
-steps:
-
- 1. Make local changes: Edit your local copy of the repository in any way you’d like: add or delete lines from existing files; add, delete, or rename entire files or directories.
- 2. Commit local changes: Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button.
- 3. Push the commit: Click on the Sync button near the upper right to “push” your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced.
-
-The left hand panel of the Changes tab has a summary of the files that have
-been changed, added, or removed since your last commit; the right hand panel
-shows the specific changes made to each changed file. After step 2, a record
-of your changes has been stored locally by the git system, but is not yet
-reflected in the public remote copy on
-GitHub.[4](ContributingToHARK5.html#fn4x0) The “timeline” summary of the
-repository at the top of the Desktop window shows the history of commits for
-the repository: each dot represents a commit that was pushed to the remote
-server, and each open ring represents a local commit that has not been pushed
-to the remote server; the large broken ring on the far right represents
-changes that have not been locally
-committed.[5](ContributingToHARK6.html#fn5x0) The History tab lists all
-previous commits, including the timestamp, contributor, and a record of
-changes made.
-
-Suppose you have mucked about in your fork for a while and have made changes
-to HARK that you think should be included in the main repository hosted by
-Econ-Ark. To make your dream a reality, follow these steps:
-
- 1. Isolate the changes: If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should isolate the desired changes in a new branch of your fork. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,[6](ContributingToHARK7.html#fn6x0) then edit this branch to include only the changes you want to push to the main repository.
- 2. Issue a pull request: Go to [HARK’s GitHub page](https://github.com/econ-ark/HARK) and click the “New pull request” button. Click the text that says “compare across forks”, and select econ-ark/HARK as the base fork, master as the base, your fork as the head fork, and the branch to be merged as the “compare”. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you’ve done. Click the “Create pull request” button.
- 3. Be patient: Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section 0.1.3 for procedures followed by the HARK team when evaluating outside contributions.
-
-#### 0.1.3 Submission Approval Process
-
-Before submitting to HARK, the development team would like you to make sure
-that your intended contribution is listed as an Issue on the GitHub page. If
-you are going to fix or address an Issue that is already posted, reply to that
-post with a note indicating that you are about to work on it and an estimate
-of when you will make a pull request for it. If you want to do something that
-no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly)
-describing what you intend to do.[7](ContributingToHARK8.html#fn7x0) This will
-both help prevent two people from working on the same thing (and one of them
-potentially wasting time on it), and lets the development team know what’s
-coming. Moreover, if you propose a contribution that is (a) already in HARK,
-(b) not in the spirit of HARK, or (c) a fix for something that’s not actually
-broken, this gives the team a chance to let you know not to spend effort on
-it.
-
-After creating a pull request to submit your contribution, it will be
-evaluated by one or more members of the HARK team. Criteria that the HARK team
-will consider when choosing whether to accept a submission include:
-
- 1. Does the contribution break previous code? If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility.
- 2. Is the code well documented? Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section 0.1.5) and enough comments for a reader to follow the algorithm.
- 3. Is the contribution relevant? The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn’t belong in HARK.
- 4. Is the code demonstrated with an example? If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you’ve contributed a new function, consider providing a snippet of code demonstrating its use.[8](ContributingToHARK9.html#fn8x0)
- 5. Is the code correct? Your code should actually do what it claims to do.
- 6. Does the code use HARK conventions? To the best of your ability, contributions to HARK should follow the style guidelines in section 0.1.4. We don’t demand perfection, as these are guidelines rather than rules.
- 7. Is the code well integrated in HARK? In the beta release of HARK, we have tried to present a “taxonomy” of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy.
- 8. Is this already in HARK? If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK.
-
-The HARK team intends to release more specific guidelines in the near future,
-so that contributors are better able to judge their submissions before issuing
-a pull request. We are in the process of recruiting “czars” to oversee
-particular aspects of HARK, providing their knowledge and
-expertise.[9](ContributingToHARK10.html#fn9x0) While czars might directly
-evaluate submissions as a HARK team member, their more important role is to
-help establish these guidelines for their domain, more specifically nailing
-down what we mean by “correct” or “well integrated”.
-
-#### 0.1.4 Naming Conventions
-
-Object naming conventions in HARK are fairly different than existing
-standards, and differ somewhat between tool modules vs model or application
-modules. The following conventions apply throughout HARK:
-
- * Functions and methods are always in “camel case”: no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. approxLognormal.
- * Function and method names should accurately and concisely describe what the function does; the first word in the name must be a verb.[10](ContributingToHARK11.html#fn10x0)
- * Variable and class names should not have a verb as their first word.
- * Class names should use no underscores and capitalize the first letter of each word; moreover, a class name must include a noun. E.g. ConsPerfForesightSolver.
-
-When naming variables in model modules, the HARK team strongly discourages
-using single letter names, like c for consumption. Instead, we encourage
-contributors to use longer, more descriptive variable names using additional
-words and common abbreviations to specify the variable more precisely. In
-/Documentation/NARK.pdf, we list standard single letter variable “bases” and
-an array of prefixes and suffixes to adjust them. Economic variables in model
-modules should (usually) not use underscores, instead using camel case to the
-greatest extent possible.[11](ContributingToHARK12.html#fn11x0) The
-development team prefers this standard so that users can translate between
-Python code and LaTeX script with minimal work.
-
-Conventions for naming variables in HARK’s tool modules are significantly
-closer to more commonly used standards. Variable names should be in all lower
-case, with underscores between words, e.g. data_to_match. The functions and
-classes in these modules are more general and almost surely do not have any
-inherent “economic content”; they are numerical or algorithmic objects, not
-variables that might appear in an equation in an article for a (non-
-computational) economics journal. Variable names in application modules (e.g.
-the files that execute the cstwMPC estimations) are a mix of the conventions
-for tool and model files, as appropriate for each variable. That is, variables
-that are directly related to “economic variables” in model modules should
-follow those conventions, while objects created solely for data manipulation
-or reporting should use the style of tool modules.
-
-#### 0.1.5 Documentation Conventions
-
-The HARK team wants the toolKit to be as accessible to users as possible; our
-greatest fear[12](ContributingToHARK13.html#fn12x0) is that a new user will
-open up our code, get hopelessly confused trying to read it, and then never
-look at HARK again. To prevent this tragic outcome, we have tried hard to
-provide comprehensive, accurate documentation and comments within the code
-describing our methods.[13](ContributingToHARK14.html#fn13x0) Moreover, HARK
-uses the Sphinx utility to generate a website with [online
-documentation](https://econ-ark.github.io/HARK/) for all of our tool and model
-modules, so that users can learn about what’s available in HARK without
-digging through the source code. When making contributions to HARK, the
-development team asks users to format their inline documentation to work with
-Sphinx by following a few simple rules.
-
- * The top of every module should begin with a “docstring” providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:[14](ContributingToHARK15.html#fn14x0)
-
-"""
-
-Specifies an economic model and provides methods for solving it. More specific
-description of the key features of the model and variations of it in this
-module. Maybe some comments about the solution method or limitations of the
-model. Your bank account routing number.
-
-"""
-
- * The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as:
-
-def functionName(input1,input2):
-
-"""
-
-Concise description of the function. More details about what
-
-the function does, options or modes available, and maybe mathematical
-
-methods used. Credit to a source if you poached their algorithm.
-
-Parameters
-
-––––––––––
-
-input1: type
-
-Description of what input1 represents.
-
-input2: type
-
-Description of what input2 represents.
-
-Returns
-
-–––––––
-
-output_name: type
-
-Description of the output(s) of the function. Might have
-
-multiple entries. If no output, this is just "None".
-
-"""
-
- * Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it.
-
-Finally, if you write a new model module, the HARK team asks that you also
-provide a short mathematical writeup of the model as a PDF. This document does
-not need to go into great detail about the solution method for the model or
-the functions and classes included in the module, merely specify the economic
-model and provide a summary of how it is solved. See
-/Documentation/ConsumptionSavingModels.pdf for an example of this.
-
diff --git a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.tex b/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.tex
deleted file mode 100644
index e81c4766e..000000000
--- a/Documentation/HARKmanual/ContributingToHARK/ContributingToHARK.tex
+++ /dev/null
@@ -1,666 +0,0 @@
-% !TeX spellcheck = en_GB
-
-\documentclass[12pt,titlepage,letterpaper]{econtex}
-\usepackage{econtexSetup}\usepackage{econtexShortcuts}
-\usepackage{graphicx}
-\usepackage{hyperref}
-\providecommand{\E}{\mathbb{E}}
-
-\newboolean{ContributingToHARKOnly}
-\setboolean{ContributingToHARKOnly}{true}
-
-\begin{document}
-\ifthenelse{\boolean{ContributingToHARKOnly}}{(This is an excerpt of the full manual, available at \url{http://econ-ark.org/HARK/Documentation/HARKmanual.pdf)}}{ % If ContributingToHARKOnly is true, do not include any text until that section
-\title{\LARGE A User's Guide for HARK: \\ Heterogeneous Agents Resources and toolKit}
-
-\date{\normalsize{\today}}
-
-\author{
-{Christopher D Carroll\authNum} \\ {\small Johns Hopkins University}
-\and
-{Alexander M Kaufman\authNum} \\ {\small Consumer Financial Protection Bureau}
-\and
-{David C Low\authNum} \\ {\small Consumer Financial Protection Bureau}
-\and
-{Nathan M Palmer\authNum} \\ {\small U.S.\ Treasury Office of Financial Research}
-\and
-{Matthew N White\authNum} \\ {\small University of Delaware}
-}
-
-
-\begin{authorsinfo}
-\name{
-Carroll: \url{http://econ.jhu.edu/people/ccarroll/}, email: \href{mailto:ccarroll@jhu.edu}{\texttt{ccarroll@jhu.edu}}
-}
-\name{
-Kaufman: \href{mailto:alexander.kaufman@cfpb.gov}{\texttt{alexander.kaufman@cfpb.gov}}
-}
-\name{
-Low: \href{mailto:david.c.low@cfpb.gov}{\texttt{david.c.low@cfpb.gov}}
-}
-\name{
-Palmer: \href{mailto:nathan.m.palmer@ofr.treasury.gov}{\texttt{nathan.m.palmer@ofr.treasury.gov}}
-}
-\name{
-White: \href{mailto:mnwecon@udel.edu}{\texttt{mnwecon@udel.edu}}
-}
-\end{authorsinfo}
-
-\thanks{The materials in this resource reflect work by the persons named herein and others, much of it accomplished at the CFPB. However, none of the materials here should be interpreted as reflecting official opinions of the CFPB or any employees of the CFPB. After appropriate technical review, these resources are expected to appear on the CFPB's official GitHub page. Views expressed in this presentation are those of the authors and not necessarily of the Office of Financial Research or other government organizations.}
-
-\maketitle
-
-\begin{center}
-\includegraphics[scale=0.8]{UserGuidePic.pdf}
-\end{center}
-
-\thispagestyle{empty}
-\newpage
-
-\tableofcontents
-
-\newpage
-
-\section{Introduction}\label{sec:Introduction}
-
-If you are willing to risk some mild psychological trauma, conjure to mind your first experience of hand-coding a structural economic model. Your clunky effort probably built on legacy code provided by an adviser or colleague -- which itself came from who-knows-what apocryphal sources. Efforts to combine elements from one model with those from another were likely frustrated by the ``Tower of Babel'' problem: Code from one source could not ``speak'' to code from another without your own intermediation as a translator, possibly between two unfamiliar languages and aided only by oracular comments that, at best, made sense only in the context of other (now missing) code.
-
-After months of effort, you may have had the character-improving experience of proudly explaining to your adviser that not only had you grafted two ideas together, you also found a trick that speeded the solution by an order of magnitude, only to be told that your breathtaking insight had been understood for many years, as reflected in an appendix to a 2008 paper; or, worse, your discovery was something that ``everybody knows'' but did not exist at all in published form!
-
-Learning by doing has value, but only within limits. We do not require young drivers to design an internal combustion engine before driving a car, nor must graduate students write their own matrix inversion algorithms before running an OLS regression.
-
-In recent years, considerable progress has been made in addressing these kinds of problems in many areas of economic modeling. Macroeconomists using representative agent models can send Dynare model files to each other; reduced form econometricians can choose from a host of econometric packages. But modelers whose questions require explicit structural modeling involving nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply be aggregated away) are mostly still stuck in the bad old days.
-
-The ultimate goal of the HARK project is to fix these problems. Specifically, our aim is to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents.\footnote{By ``heterogeneous,'' we mean that agents might differ before anything in the model has ``happened'' (\textit{ex-ante} heterogeneity); and agents might experience different stochastic events during the model (\textit{ex-post} heterogeneity).} Further, we seek to establish (with input from the community) standards for the description and specification of objects like discrete approximations to continuous distributions and interpolated function approximations, so that numeric methods can be quickly swapped without ugly ``patching.''
-
-We hope that HARK will make it much easier and faster for researchers to develop solution and estimation methods for new models. The open source nature of HARK will make it easier for other researchers to audit and verify new models and methods, and to collaborate on correcting deficiencies when found. As HARK expands to include more canonical models and more tools and utilities, we can all spend less time managing numerical minutiae and more time fretting about identification arguments and data accuracy.
-
-\subsection{Getting Started}\label{sec:GettingStarted}
-
-If you want to get started using HARK right away, this section provides a very easy quickstart guide for getting HARK up and running on your computer in just a few minutes. More information can be found in the \texttt{README.md} file in the HARK repository (step 4), but here is a quick version for those who want to jump right in:
-
-\begin{enumerate}
-\item \textbf{Download Anaconda:} Go to \href{https://www.anaconda.com/download/}{https://www.anaconda.com/download/} and download Anaconda for your operating system; be sure to get the version for Python 2.7.
-
-\item \textbf{Install Anaconda:} Follow the easy instructions on that page to install Anaconda.
-
-\item \textbf{Add extra packages:} (optional) If you want to use HARK's multithreading feature, you need to add two packages that aren't part of the default Anaconda distribution. Simply open a command prompt and do \texttt{conda install joblib} and \texttt{conda install dill}, accepting defaults to install.
-
-\item \textbf{Download HARK:} Go to \href{https://github.com/econ-ark/HARK}{https://github.com/econ-ark/HARK}, the home of the HARK repository. Click on the lime green button labeled ``Clone or download'' toward the upper right of the main panel, and select ``Download ZIP''.\footnote{This is the fastest way to get HARK running on a computer. Later, if you want to make your own contributions to HARK by adding code (or fixing ours!), you'll need to clone the repository on your computer. See section \ref{sec:GitHub} for more.}
-
-\item \textbf{Unzip HARK:} Unpack the \texttt{HARK.zip} file using any archive utility, like \href{http://www.peazip.org/}{Peazip}. Put the files anywhere you'd like, maintaining the internal directory structure.
-
-\item \textbf{Run Spyder:} Open a command prompt and do \texttt{spyder}. Spyder is an interactive development environment (IDE) for iPython, a slightly prettier, more interactive flavor of Python.
-
-\item \textbf{Open a HARK module:} Go to the directory where you put HARK and open any file with the \texttt{.py} extension; we recommend \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}.
-
-\item \textbf{Run the module:} Click on the green arrow ``play'' button toward the right side of the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue box pops up). Congratulations! HARK is now up and running on your computer.
-\end{enumerate}
-
-If you followed our recommendation to try \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}, you should see graphical representations of the solution to a few consumption-saving models with idiosyncratic shocks to permanent and transitory income (see \texttt{/Documentation/ConsumptionSavingModels.pdf} for details of these models). If you chose a different module, you might get some examples of some other model, results of a structural estimation, a polite notification that this particular module doesn't do much on its own, or no output at all. See the \texttt{README.md} for a full list of modules from which you can expect non-trivial output.
-
-\subsection{Structure of HARK}\label{sec:StructureOfHARK}
-
-HARK is written in Python, an object-oriented programming (OOP) language that has experienced increasing popularity in the scientific community in the past several years. A significant reason for the adoption of Python is the \texttt{numpy} and \texttt{scipy} packages, which offer a wide array of mathematical and statistical functions and tools; HARK makes liberal use of these libraries. Python's object-oriented nature allows models in HARK to be easily extended: more complex models can inherit functions and methods from more fundamental ``parent'' models, eliminating the need to reproduce or repurpose code.
-
-As implied in the previous section, we strongly encourage HARK users to use the Anaconda distribution of Python, which includes all commonly used mathematical and scientific packages, an interactive development environment for iPython (Spyder), and a package manager that allows users to quickly install or update packages not included in the default distribution (conda).
-
-For users unfamiliar with OOP, we strongly encourage you to review the background material on OOP provided by the good people at \href{http://quant-econ.net/}{QuantEcon} (for more on them, see below) at this link: \href{http://quant-econ.net/py/python_oop.html}{Object Oriented Programming}. Unlike non-OOP languages, OOP bundles together data and functions into \textit{objects}. These can be accessed via: \texttt{object\_name.data} and \texttt{object\_name.method\_name()}, respectively. For organizational purposes, definitions of multiple objects are stored in \textit{modules}, which are simply files with a \texttt{.py} extension. Modules can be accessed in Python via:\\
-
-\texttt{import module\_name as import\_name}
-\\
-
-\noindent
-This imports the module and gives it a local name of \texttt{import\_name}. We can access a function within this module by simply typing: \texttt{import\_name.function\_name()}. The following example will illustrate the usage of these commands. \texttt{CRRAutility} is the function object for calculating CRRA utility supplied by \texttt{HARK.utilities} module. \texttt{CRRAutility} is called \textit{attributes} of the module \texttt{HARK.utilities}. In order to calculate CRRA utility with a consumption of 1 and a coefficient of risk aversion of 2 we run:\\
-
-\texttt{import HARK.utilities as Hutil}
-
-\texttt{Hutil.CRRAutility(1,2)}\\
-
-Python modules in HARK can generally be categorized into three types: tools, models, and applications. \textbf{Tool modules} contain functions and classes with general purpose tools that have no inherent ``economic content'', but that can be used in many economic models as building blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include functions for data analysis (e.g.\ calculating Lorenz shares from data, or constructing a non-parametric kernel regression), functions to create and manipulate discrete approximations to continuous distributions, or classes for constructing interpolated approximations to non-parametric functions. Tool modules generally reside in HARK's root directory and have names like \texttt{HARK.simulation} and \texttt{HARK.interpolation}; they do not necessarily do anything when run.
-
-\textbf{Model modules} specify particular economic models, including classes to represent agents in the model (and the ``market structure'' in which they interact) and functions for solving the ``one period problem'' of those models. For example, \texttt{ConsIndShockModel.py} concerns consumption-saving models in which agents have CRRA utility over consumption and face idiosyncratic shocks to permanent and transitory income. The module includes classes for representing ``types'' of consumers, along with functions for solving (several flavors of) the one period consumption-saving problem. When run, model modules might demonstrate example specifications of their models, filling in the model parameters with arbitrary values. When \texttt{ConsIndShockModel.py} is run, it specifies an infinite horizon consumer with a particular discount factor, permanent income growth rate, coefficient of relative risk aversion (etc), who faces lognormal shocks to permanent and transitory income each period with a particular standard deviation; it then solves this consumer's problem and graphically displays the results.\footnote{Running \texttt{ConsIndShockModel.py} also demonstrates other variations of the consumption-saving problem, but their description is omitted here for brevity.} Model modules generally have \texttt{Model} in their name.
-
-\textbf{Application modules} use tool and model modules to solve, simulate, and/or estimate economic models \textit{for a particular purpose}. While tool modules have no particular economic content and model modules describe entire classes of economic models, applications are uses of a model for some research purpose. For example, \texttt{/Demos/SolvingMicroDSOPs/StructEstimation.py} uses a consumption-saving model from \texttt{ConsIndShockModel.py}, calibrating it with age-dependent sequences of permanent income growth, survival probabilities, and the standard deviation of income shocks (etc); it then estimates the coefficient of relative risk aversion and shifter for an age-varying sequence of discount factors that best fits simulated wealth profiles to empirical data from the Survey of Consumer Finance. A particular application might have multiple modules associated with it, all of which generally reside in one directory.
-
-\subsection{Other Resources}\label{sec:OtherResources}
-
-In the incredibly unlikely scenario in which this document does not fill all of the gaps in your knowledge and answer any questions you might have while reading it, here is a collection of potentially helpful other resources.
-
-\begin{itemize}
-
-\item \href{https://econ-ark.github.io/HARK/}{Online documentation} for HARK tool and model modules, produced using Sphinx.
-
-\item A \href{https://docs.python.org/2.7/tutorial/}{tutorial on Python 2.7}, straight from the source.
-
-\item \href{https://en.wikipedia.org/wiki/Object-oriented_programming}{Wikipedia article on object-oriented programming}, not the subject of an edit war.
-
-\item \href{http://quant-econ.net/}{QuantEcon}, a collection of lectures on quantitative economic modeling from a couple of economists, which definitely in no way influenced HARK whatsoever.
-
-\item A \href{https://www.atlassian.com/git/tutorials/}{tutorial on git}, the repository management and tracking system used by the HARK project, created by Linus Torvalds.
-
-\item A \href{https://guides.github.com/activities/hello-world/}{tutorial on GitHub}, a website that provides a useful framework for git that makes it significantly more user-friendly, despised by Linus Torvalds.
-
-\item \href{https://en.wikipedia.org/wiki/Linus_Torvalds}{Wikipedia article on Linus Torvalds}, just in case.
-
-\item The \href{http://www.numpy.org/}{homepage for NumPy}, a numerical package extensively used by HARK.
-
-\item The \href{http://www.sphinx-doc.org/}{homepage for Sphinx}, a tool for producing HTML documentation.
-
-\item A \href{http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/}{collection of lecture notes} by Chris Carroll, to catch you up on the basics of consumption-saving models.
-
-\item A \href{http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/}{particular set of lecture notes} by Chris Carroll, explaining many of the numeric techniques used in HARK and offering an example of a simple structural model that, in a stunning coincidence, is implemented in HARK as an example.
-
-\item A \href{http://www.econ2.jhu.edu/people/ccarroll/BufferStockTheory.pdf}{deep dive} into the theoretical foundations of buffer stock saving. Warning: dense.
-
-\end{itemize}
-
-
-\section{General Purpose Tools}\label{sec:GeneralPurposeTools}
-
-HARK's root directory contains six tool modules,\footnote{The ``taxonomy'' of these modules is in flux; the functions described here could be combined into fewer modules or further divided by purpose.} each containing a variety of functions and classes that can be used in many economic models-- or even for mathematical purposes that have nothing to do with economics. Some of the tool modules are very sparely populated at this time, while others are quite large. We expect that all of these modules will grow considerably in the near future, as new tools are ``low hanging fruit'' for contribution to the project.\footnote{That is, as the foundational, building-block elements of HARK, new tools are not difficult to program and do not require extensive integration with many moving parts.}
-
-\subsection{HARKcore}\label{sec:HARKcore}
-
-A key goal of the project is to create modularity and interoperability between models, making them easy to combine, adapt, and extend. To this end, the \texttt{HARK.core} module specifies a framework for economic models in HARK, creating a common structure for them on two levels that can be called ``microeconomic'' and ``macroeconomic''.
-
-Microeconomic models in HARK use the \texttt{AgentType} class to represent agents with an intertemporal optimization problem. Each of these models specifies a subclass of \texttt{AgentType}; an instance of the subclass represents agents who are \textit{ex-ante} homogeneous-- they have common values for all parameters that describe the problem. For example, \texttt{ConsIndShockModel} specifies the \texttt{IndShockConsumerType} class, which has methods specific to consumption-saving models with idiosyncratic shocks to income; an instance of the class might represent all consumers who have a CRRA of 3, discount factor of 0.98, etc. The \texttt{AgentType} class has a \texttt{solve} method that acts as a ``universal microeconomic solver'' for any properly formatted model, making it easier to set up a new model and to combine elements from different models; the solver is intended to encompass any model that can be framed as a sequence of one period problems. For a complete description, see section \ref{sec:Microeconomics}.
-
-Macroeconomic models in HARK use the \texttt{Market} class to represent a market (or other aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual agents in the model into aggregate outcomes that are ``passed back'' to the agents. For example, the market in a consumption-saving model might combine the individual asset holdings of all agents in the market to generate aggregate capital in the economy, yielding the interest rate on assets (as the marginal product of capital); the individual agents then learn the aggregate capital level and interest rate, conditioning their next action on this information. Objects that microeconomic agents treat as exogenous when solving (or simulating) their model are thus endogenous at the macroeconomic level. Like \texttt{AgentType}, the \texttt{Market} class also has a \texttt{solve} method, which seeks out a dynamic general equilibrium: a ``rule'' governing the dynamic evolution of macroeconomic objects such that if agents believe this rule and act accordingly, then their collective actions generate a sequence of macroeconomic outcomes that justify the belief in that rule. For a more complete description, see section \ref{sec:Macroeconomics}.
-
-Beyond the model frameworks, \texttt{HARK.core} also defines a ``supersuperclass'' called \texttt{HARKobject}. When solving a dynamic microeconomic model with an infinite horizon (or searching for a dynamic general equilibrium), it is often required to consider whether two solutions are sufficiently close to each other to warrant stopping the process (i.e.\ approximate convergence). It is thus necessary to calculate the ``distance'' between two solutions, so HARK specifies that classes should have a \texttt{distance} method that takes a single input and returns a non-negative value representing the (generally dimensionless) distance between the object in question and the input to the method. As a convenient default, \texttt{HARKobject} provides a ``universal distance metric'' that should be useful in many contexts.\footnote{Roughly speaking, the universal distance metric is a recursive supnorm, returning the largest distance between two instances, among attributes named in \texttt{distance\_criteria}. Those attributes might be complex objects themselves rather than real numbers, generating a recursive call to the universal distance metric.} When defining a new subclass of \texttt{HARKobject}, the user simply defines the attribute \texttt{distance\_criteria} as a list of strings naming the attributes of the class that should be compared when calculating the distance between two instances of that class. For example, the class \texttt{ConsumerSolution} has \texttt{distance\_criteria = ['cFunc']}, indicating that only the consumption function attribute of the solution matters when comparing the distance between two instances of \texttt{ConsumerSolution}. See \href{https://econ-ark.github.io/HARK/generated/HARK.core.html}{here} for online documentation.
-
-\subsection{HARK.utilities}\label{sec:HARKutilities}
-
-The \texttt{HARK.utilities} module carries a double meaning in its name, as it contains both utility functions (and their derivatives, inverses, and combinations thereof) in the economic modeling sense as well as utilities in the sense of general tools. Utility functions included at this time are constant relative risk aversion and constant absolute risk aversion. Other functions in \texttt{HARK.utilities} include some data manipulation tools (e.g.\ for calculating an average of data conditional on being within a percentile range of different data), functions for constructing discrete state space grids, convenience functions for retrieving information about functions, and basic plotting tools using \texttt{matplotlib.pyplot}.
-
-The module also includes functions for constructing discrete approximations to continuous distributions (e.g.\ \texttt{approxLognormal()} to approximate a log-normal distribution) as well as manipulating these representations (e.g.\ appending one outcome to an existing distribution, or combining independent univariate distributions into one multivariate distribution). As a convention in HARK, continuous distributions are approximated as finite discrete distributions when solving models; an $N$-dimensional random variable is formatted as a length $N+1$ list of 1D arrays, with the first element representing event probabilities and all other elements are realizations of the $N$ component RVs. This both simplifies solution methods (reducing numeric integrals to simple dot products) and allows users to easily test whether their chosen degree of discretization yields a sufficient approximation to the full distribution. See \href{https://econ-ark.github.io/HARK/generated/HARK.utilities.html}{here} for online documentation.
-
-\subsection{HARK.interpolation}\label{sec:HARKinterpolation}
-
-The \texttt{HARK.interpolation} module defines classes for representing interpolated function approximations. Interpolation methods in HARK all inherit from a superclass such as \texttt{HARKinterpolator1D} or \texttt{HARKinterpolator2D}, wrapper classes that ensures interoperability across interpolation methods. For example, \texttt{HARKinterpolator1D} specifies the methods \texttt{\_\_call\_\_} and \texttt{derivative} to accept an arbitrary array as an input and return an identically shaped array with the interpolated function evaluated at the values in the array or its first derivative, respectively. However, these methods do little on their own, merely reshaping arrays and referring to the \texttt{\_evaluate} and \texttt{\_der} methods, which are \textit{not actually defined in} \texttt{HARKinterpolator1D}. Each subclass of \texttt{HARKinterpolator1D} specifies their own implementation of \texttt{\_evaluate} and \texttt{\_der} particular to that interpolation method, accepting and returning only 1D arrays. In this way, subclasses of \texttt{HARKinterpolator1D} are easily interchangeable with each other, as all methods that the user interacts with are identical, varying only by ``internal'' methods.
-
-When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to know the ``distance'' between functions generated by successive iterations of a solution procedure. To this end, each interpolator class in HARK must define a \texttt{distance} method that takes as an input another instance of the same class and returns a non-negative real number representing the ``distance'' between the two. As each of the \texttt{HARKinterpolatorXD} classes inherits from \texttt{HARKobject}, all interpolator classes have the default ``universal'' distance method; the user must simply list the names of the relevant attributes in the attribute \texttt{distance\_criteria} of the class.
-
-Interpolation methods currently implemented in HARK include (multi)linear interpolation up to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D ``lower envelope'' interpolator. See \href{https://econ-ark.github.io/HARK/generated/HARKinterpolation.html}{here} for online documentation.
-
-\subsection{HARK.simulation}\label{sec:HARKsimulation}
-
-The \texttt{HARK.simulation} module provides tools for generating simulated data or shocks for post-solution use of models. Currently implemented distributions include normal, lognormal, Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use, these tools are used in the consumption-saving models of \texttt{ConsIndShockModel.py} to simulate permanent and transitory income shocks as well as unemployment events. See \href{https://econ-ark.github.io/HARK/generated/HARKsimulation.html}{here} for online documentation.
-
-\subsection{HARK.estimation}\label{sec:HARKestimation}
-
-Methods for optimizing an objective function for the purposes of estimating a model can be found in \texttt{HARK.estimation}. As of this writing, the implementation includes only minimization by the Nelder-Mead simplex method, minimization by a derivative-free Powell method variant, and two small tools for resampling data (i.e.\ for a bootstrap); the minimizers are merely convenience wrappers (with result reporting) for optimizers included in \texttt{scipy.optimize}. Future functionality will include more robust global search methods, including genetic algorithms, simulated annealing, and differential evolution. See \href{https://econ-ark.github.io/HARK/generated/HARKestimation.html}{here} for full documentation.
-
-\subsection{HARK.parallel}\label{sec:HARKparallel}
-
-By default, processes in Python are single-threaded, using only a single CPU core. The \texttt{HARK.parallel} module provides basic tools for using multiple CPU cores simultaneously, with minimal effort.\footnote{\texttt{HARK.parallel} uses two packages that aren't included in the default distribution of Anaconda: \texttt{joblib} and \texttt{dill}; see step 3 of the instructions in section \ref{sec:GettingStarted} for how to install them.} In particular, it provides the function \texttt{multiThreadCommands}, which takes two arguments: a list of \texttt{AgentType}s and a list of commands as strings; each command should be a method of the \texttt{AgentType}s. The function simply distributes the \texttt{AgentType}s across threads on different cores and executes each command in order, returning no output (the \texttt{AgentType}s themselves are changed by running the commands). Equivalent results would be achieved by simply looping over each type and running each method in the list. Indeed, \texttt{HARK.parallel} also has a function called \texttt{multiThreadCommandsFake} that does just that, with identical syntax to \texttt{multiThreadCommands}; multithreading in HARK can thus be easily turned on and off.\footnote{In the future, \texttt{HARK.parallel} might be absorbed into \texttt{HARK.core} and \texttt{HARK.estimation}, particularly if \texttt{joblib} and \texttt{dill} become part of the standard Anaconda distribution.} The module also has functions for a parallel implementation of the Nelder-Mead simplex algorithm, as described in Wiswall and Lee (2011). See \href{https://econ-ark.github.io/HARK/generated/HARK.parallel.html}{here} for full documentation.
-
-\section{Microeconomics: the AgentType Class}\label{sec:Microeconomics}
-
-The core of our microeconomic dynamic optimization framework is a flexible object-oriented representation of economic agents. The \texttt{HARK.core} module defines a superclass called \texttt{AgentType}; each model defines a subclass of \texttt{AgentType}, specifying additional model-specific features and methods while inheriting the methods of the superclass. Most importantly, the method \texttt{solve} acts as a ``universal solver'' applicable to any (properly formatted) discrete time model. This section describes the format of an instance of \texttt{AgentType} as it defines a dynamic microeconomic problem;\footnote{Each instance of \texttt{AgentType} represents an \textit{ex-ante} heterogeneous ``type'' of agent; \textit{ex-post} heterogeneity is achieved by simulating many agents of the same type, each of whom receives a unique sequence of shocks.}
-
-\subsection{Attributes of an AgentType}\label{sec:AttributesOfAnAgentType}
-
-A discrete time model in our framework is characterized by a sequence of ``periods'' that the agent will experience. A well-formed instance of \texttt{AgentType} includes the following attributes:
-\begin{itemize}
-\item \texttt{solveOnePeriod}: A function, or a list of functions, representing the solution method for a single period of the agent's problem. The inputs passed to a \texttt{solveOnePeriod} function include all data that characterize the agent's problem in that period, including the solution to the subsequent period's problem (designated as \texttt{solution\_next}). The output of these functions is a single \texttt{Solution} object, which can be passed to the solver for the previous period.
-
-\item \texttt{time\_inv}: A list of strings containing all of the variable names that are passed to at least one function in \texttt{solveOnePeriod} but do \textit{not} vary across periods. Each of these variables resides in a correspondingly named attribute of the \texttt{AgentType} instance.
-
-\item \texttt{time\_vary}: A list of strings naming the attributes of this instance that vary across periods. Each of these attributes is a list of period-specific values, which should be of the same length. If the solution method varies across periods, then \texttt{'solveOnePeriod'} is an element of \texttt{time\_vary}.\footnote{\texttt{time\_vary} may include attributes that are never used by a function in \texttt{solveOnePeriod}. Most saliently, the attribute \texttt{solution} is time-varying but is not used to solve individual periods.}
-
-\item \texttt{solution\_terminal}: An object representing the solution to the ``terminal'' period of the model. This might represent a known trivial solution that does not require numeric methods, the solution to some previously solved ``next phase'' of the model, a scrap value function, or an initial guess of the solution to an infinite horizon model.
-
-\item \texttt{pseudo\_terminal}: A Boolean flag indicating that \texttt{solution\_terminal} is not a proper terminal period solution (rather an initial guess, ``next phase'' solution, or scrap value) and should not be reported as part of the model's solution.
-
-\item \texttt{cycles}: A non-negative integer indicating the number of times the agent will experience the sequence of periods in the problem. For example, \texttt{cycles = 1} means that the sequence of periods is analogous to a lifecycle model, experienced once from beginning to end; \texttt{cycles = 2} means that the agent experiences the sequence twice, with the first period in the sequence following the last. An infinite horizon problem in which the sequence of periods repeats indefinitely is indicated with \texttt{cycles = 0}.
-
-\item \texttt{tolerance}: A positive real number indicating convergence tolerance, representing the maximum acceptable ``distance'' between successive cycle solutions in an infinite horizon model; it is irrelevant when \texttt{cycles > 0}. As the distance metric on the space of solutions is model-specific, the value of \texttt{tolerance} is generally dimensionless.
-
-\item \texttt{time\_flow}: A Boolean flag indicating the direction that time is ``flowing.'' When \texttt{True}, the variables listed in \texttt{time\_vary} are listed in ordinary chronological order, with index 0 being the first period; when \texttt{False}, these lists are in reverse chronological order, with index 0 holding the last period.
-\end{itemize}
-
-An instance of \texttt{AgentType} also has the attributes named in \texttt{time\_vary} and \texttt{time\_inv}, and may have other attributes that are not included in either (e.g.\ values not used in the model solution, but instead to construct objects used in the solution).
-
-\subsection{A Universal Solver}\label{sec:UniversalSolver}
-
-When an instance of \texttt{AgentType} invokes its \texttt{solve} method, the solution to the agent's problem is stored in the attribute \texttt{solution}. The solution is computed by recursively solving the sequence of periods defined by the variables listed in \texttt{time\_vary} and \texttt{time\_inv} using the functions in \texttt{solveOnePeriod}. The time-varying inputs are updated each period, including the successive period's solution as \texttt{solution\_next}; the same values of time invariant inputs in \texttt{time\_inv} are passed to the solver in every period. The first call to \texttt{solveOnePeriod} uses \texttt{solution\_terminal} as \texttt{solution\_next}. In a finite horizon problem, the sequence of periods is solved \texttt{cycles} times over; in an infinite horizon problem, the sequence of periods is solved until the solutions of successive cycles have a ``distance'' of less than \texttt{tolerance}.
-
-The output from a function in \texttt{solveOnePeriod} is an instance of a model-specific solution class. The attributes of a solution to one period of a problem might include behavioral functions, (marginal) value functions, and other variables characterizing the result. Each solution class must have a method called \texttt{distance()}, which returns the ``distance'' between itself and another instance of the same solution class, so as to define convergence as a stopping criterion; for many models, this will be the ``distance'' between a policy or value function in the solutions. If the solution class is defined as a subclass of \texttt{HARKobject}, it automatically inherits the default \texttt{distance} method, so that the user must only list the relevant object attributes in \texttt{distance\_criteria}.
-
-The \texttt{AgentType} also has methods named \texttt{preSolve} and \texttt{postSolve}, both of which take no arguments and do absolutely nothing. A subclass of \texttt{AgentType} can overwrite these blank methods with its own model specific methods. \texttt{preSolve} is automatically called near the beginning of the \texttt{solve} method, before solving the sequence of periods. It is used for specifying tasks that should be done before solving the sequence of periods, such as pre-constructing some objects repeatedly used by the solution method or finding an analytical terminal period solution. For example, the \texttt{IndShockConsumerType} class in \texttt{ConsIndShockModel} has a \texttt{preSolve} method that calls its \texttt{updateSolutionTerminal} method to ensure that \texttt{solution\_terminal} is consistent with the model parameters. The \texttt{postSolve} method is called shortly after the sequence of periods is fully solved; it can be used for ``post-processing'' of the solution or performing a step that is only useful after solution convergence. For example, the \texttt{TractableConsumerType} in \texttt{TractableBufferStockModel} has a \texttt{postSolve} method that constructs an interpolated consumption function from the list of stable arm points found during solution.
-
-Our universal solver is written in a very general way that should be applicable to any discrete time optimization problem-- because Python is so flexible in defining objects, the time-varying inputs for each period can take any form. Indeed, the solver does no ``real work'' itself, but merely provides a structure for describing models in the HARK framework, allowing interoperability among current and future modules.
-
-\subsection{The Flow of Time and Other Methods}\label{sec:OtherMethods}
-
-Because dynamic optimization problems are solved recursively in our framework, it is natural to list time-varying values in reverse chronological order-- the \texttt{solve()} method loops over the values in each time-varying list in the same direction that a human would read them. When simulating agents after the solution has been obtained, however, it is much more convenient for time-varying parameters to be listed in ordinary chronological order-- the direction in which they will be experienced by simulated agents. To allow the user to set the order in which ``time is flowing'' for an instance of \texttt{AgentType}, the HARK framework includes functionality to easily change ordering of time-varying values.
-
-The attribute \texttt{time\_flow} is \texttt{True} if variables are listed in ordinary chronological order and \texttt{False} otherwise. \texttt{AgentType} has the following methods for manipulating time:
-\begin{itemize}
-\item \texttt{timeReport()}: Prints to screen a description of the direction that time is flowing, for interactive convenience and as a reminder of the functionality.
-
-\item \texttt{timeFlip()}: Flips the direction of time. Each attribute listed in \texttt{time\_vary} is reversed in place, and the value of \texttt{time\_flow} is toggled.
-
-\item \texttt{timeFwd()}: Sets the direction of time to ordinary chronological order.
-
-\item \texttt{timeRev()}: Sets the direction of time to reverse chronological order.
-\end{itemize}
-
-These methods are invoked to more conveniently access time-varying objects. When a new time-varying attribute is added, its name should be appended to \texttt{time\_vary}, particularly if its values are used in the solution of the model (or is part of the solution itself). For example, the \texttt{solve()} method automatically adds the string \texttt{'solution'} to \texttt{time\_vary} if it is not already present. Note that attributes listed in \texttt{time\_vary} \textit{must} be lists if \texttt{solve()} or \texttt{timeFlip()} are used. Some values that could be considered ``time varying'' but are never used to solve the model are more conveniently represented as a \texttt{numpy.array} object (e.g.\ \ the history of a state or control variable from a simulation); because the \texttt{numpy.array} class does not have a \texttt{reverse()} method, these attributes should not be listed in \texttt{time\_vary}.
-
-The base \texttt{AgentType} is sparsely defined, as most ``real'' methods will be application-specific. Two final methods bear mentioning. First, the \texttt{\_\_call\_\_} method points to \texttt{assignParameters()}, a convenience method for adding or adjusting attributes (inherited from \texttt{HARKobject}). This method takes any number of keyword arguments, so that code can be parsimoniously written as, for example, \texttt{AgentInstance(attribute1 = value1, attribute2 = value2)}. Using Python's dictionary capabilities, many attributes can be conveniently set with minimal code. Second, the \texttt{resetRNG} method simply resets the \texttt{AgentType}'s random number generator (as the attribute \texttt{RNG}) using the value in the attribute \texttt{seed}.\footnote{Every instance of \texttt{AgentType} is created with a random number generator as an instance of the class \texttt{numpy.random.RandomState}, with a default \texttt{seed} of zero.} This method is useful for (\textit{inter alia}) ensuring that the same underlying sequence of shocks is used for every simulation run when a model is solved or estimated.
-
-
-\subsection{Sample Model: Perfect Foresight Consumption-Saving}\label{sec:PerfectForesight}
-
-To provide a concrete example of how the \texttt{AgentType} class works, consider the very simple case of a perfect foresight consumption-saving model. The agent has time-separable, additive CRRA preferences over consumption, discounting future utility at a constant rate; he receives a particular stream of labor income each period, and knows the interest rate on assets that he holds from one period to the next. His decision about how much to consume in a particular period can be expressed in Bellman form as:
-
-\begin{eqnarray*}
-V_t(M_t) &=& \max_{C_t} \utilFunc(C_t) + \beta \PLives_t \E [V_{t+1}(M_{t+1}) ], \\
-A_t &=& M_t - C_t, \\
-M_{t+1} &=& \Rfree A_t + Y_{t+1}, \\
-Y_{t+1} &=& \Gamma_{t+1} Y_t, \\
-\utilFunc(C) &=& \frac{C^{1-\CRRA}}{1-\CRRA}.
-\end{eqnarray*}
-An agent's problem is thus characterized by values of $\CRRA$, $\Rfree$, and $\beta$ plus sequences of survival probabilities $\PLives_t$ and income growth factors $\Gamma_t$ for $t=0,\cdots,T$. This problem has an analytical solution for both the value function and the consumption function.
-
-The \texttt{ConsIndShockModel} module defines the class \texttt{PerfForesightConsumerType} as a subclass of \texttt{AgentType} and provides solver functions for several variations of a consumption-saving model, including the perfect foresight problem.\footnote{As the name implies, the module is mostly concerned with models with idiosyncratic shocks to income.} A HARK user could specify and solve a ten period perfect foresight model with the following commands:
-
-\vspace{0.25cm}
-
-\noindent \texttt{MyConsumer = PerfForesightConsumerType(time\_flow=True, cycles=1,}
-
-\texttt{ CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98,}
-
-\texttt{ LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90],}
-
-\texttt{ PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02],}
-
-\texttt{ Nagents=1000)}
-
-\noindent \texttt{MyConsumer.solve()}
-
-\vspace{0.25cm}
-
-The first line makes a new instance of \texttt{ConsumerType}, specifying that time is currently ``flowing'' forward\footnote{This is relevant for the intepretation of the time-varying inputs \texttt{LivPrb} and \texttt{PermGroFac}.} and that the sequence of periods happens exactly once; the next three lines (all part of the same command) set the time invariant (\texttt{CRRA}, \texttt{Rfree}, \texttt{DiscFac}) and time varying parameters (\texttt{LivPrb}, \texttt{PermGroFac}). Note that \texttt{dir(MyConsumer)} shows all the attributes of \texttt{MyConsumer}. After running the \texttt{solve} method, \texttt{MyConsumer} will have an attribute called \texttt{solution}, which will be a list with eleven \texttt{ConsumerSolution} objects, representing the period-by-period solution to the model.\footnote{The last element of \texttt{solution} represents the terminal period solution, where the agent consumes all available resources because there is no future.} Each \texttt{ConsumerSolution} has several attributes:
-\begin{itemize}
-\item \texttt{cFunc}: Optimal consumption function for this period as a function of (normalized) market resources $m_t = M_t/Y_t$. Can be called like any other function: \texttt{MyConsumer.solution[0].cFunc(5)} returns consumption at $m_t = 5$.
-
-\item \texttt{vFunc}: Value function (over market resources $m_t$); can be evaluated like \texttt{cFunc}.
-
-\item \texttt{mNrmMin}: Minimum value of normalized market resources $m_t$ such that \texttt{cFunc} and \texttt{vFunc} are defined.
-
-\item \texttt{hNrm}: Normalized human wealth-- the PDV of future income (ignoring mortality) divided by current period income $Y_t$.
-
-\item \texttt{MPC}: The constant marginal propensity to consume (linear consumption function).
-
-\end{itemize}
-
-The following command generates the plot for the perfect foresight consumption function:
-
-\vspace{0.25cm}
-
-\noindent \texttt{mMin = MyConsumer.solution[0].mNrmMin}
-
-\noindent \texttt{plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10)}
-
-\vspace{0.25cm}
-
-To solve a version of this problem in which the sequence of ten periods happens three times (yielding a \texttt{solution} attribute with thirty-one elements), the user can do:
-
-\vspace{0.25cm}
-
-\noindent \texttt{MyConsumer(cycles = 3)}
-
-\noindent \texttt{MyConsumer.solve()}
-
-\vspace{0.25cm}
-
-To solve an infinite horizon problem in which the agent experiences the same one period problem indefinitely, the user can do:
-
-\vspace{0.25cm}
-
-\noindent \texttt{OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02,}
-
-\texttt{DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000)}
-
-\noindent \texttt{OtherConsumer.solve()}
-
-\vspace{0.25cm}
-
-This instance is specified as being infinite horizon by setting \texttt{cycles=0}. Note that the time-varying inputs are still specified as (one element) lists, even though they take on the same value in every period; this is because an infinite horizon model might consist of a multi-period sequence repeated indefinitely, rather than just one repeated period. The \texttt{solution} attribute of \texttt{OtherConsumer} will be a list containing one instance of \texttt{ConsumerSolution}, representing the solution to every period of the model.
-
-\section{Macroeconomics: the Market Class}\label{sec:Macroeconomics}
-
-The modeling framework of \texttt{AgentType} is deemed ``microeconomic'' because it pertains only to the dynamic optimization problem of agents, treating all inputs of the problem as exogenously fixed. In what we label as ``macroeconomic'' models, some of the inputs for the microeconomic models are endogenously determined by the collective states and controls of agents in the model. In a dynamic general equilibrium, there must be consistency between agents' beliefs about these macroeconomic objects, their individual behavior, and the realizations of the macroeconomic objects that result from individual choices.
-
-The \texttt{Market} class in \texttt{HARK.core} provides a framework for such macroeconomic models, with a \texttt{solve} method that searches for a dynamic general equilibrium. An instance of \texttt{Market} includes a list of \texttt{AgentType}s that compose the economy, a method for transforming microeconomic outcomes (states, controls, and/or shocks) into macroeconomic outcomes, and a method for interpreting a history or sequence of macroeconomic outcomes into a new ``dynamic rule'' for agents to believe. Agents treat the dynamic rule as an input to their microeconomic problem, conditioning their optimal policy functions on it. A dynamic general equilibrium is a fixed point dynamic rule: when agents act optimally while believing the equilibrium rule, their individual actions generate a macroeconomic history consistent with the equilibrium rule.
-
-\subsection{Down on the Farm}\label{sec:DownOnTheFarm}
-
-The \texttt{Market} class uses a farming metaphor to conceptualize the process for generating a history of macroeconomic outcomes in a model. Suppose all \texttt{AgentTypes} in the economy believe in some dynamic rule (i.e.\ the rule is stored as attributes of each \texttt{AgentType}, which directly or indirectly enters their dynamic optimization problem), and that they have each found the solution to their microeconomic model using their \texttt{solve} method. Further, the macroeconomic and microeconomic states have been reset to some initial orientation.
-
-To generate a history of macroeconomic outcomes, the \texttt{Market} repeatedly loops over the following steps a set number of times:
-\begin{enumerate}
-\item \texttt{sow}: Distribute the macroeconomic state variables to all \texttt{AgentType}s in the market.
-
-\item \texttt{cultivate}: Each \texttt{AgentType} executes their \texttt{marketAction} method, likely corresponding to simulating one period of the microeconomic model.
-
-\item \texttt{reap}: Microeconomic outcomes are gathered from each \texttt{AgentType} in the market.
-
-\item \texttt{mill}: Data gathered by \texttt{reap} is processed into new macroeconomic states according to some ``aggregate market process''.
-
-\item \texttt{store}: Relevant macroeconomic states are added to a running history of outcomes.
-\end{enumerate}
-This procedure is conducted by the \texttt{makeHistory} method of \texttt{Market} as a subroutine of its \texttt{solve} method. After making histories of the relevant macroeconomic variables, the market then executes its \texttt{calcDynamics} function with the macroeconomic history as inputs, generating a new dynamic rule to distribute to the \texttt{AgentType}s in the market. The process then begins again, with the agents solving their updated microeconomic models given the new dynamic rule; the \texttt{solve} loop continues until the ``distance'' between successive dynamic rules is sufficiently small.
-
-\subsection{Attributes of a Market}\label{sec:AttributesOfAMarket}
-
-To specify a complete instance of \texttt{Market}, the user should give it the following attributes:\footnote{For some purposes, it might be useful to specify a subclass of \texttt{Market}, defining \texttt{millRule} and/or \texttt{calcDynamics} as methods rather than functions.}
-\begin{itemize}
-\item \texttt{agents}: A list of \texttt{AgentType}s, representing the agents in the market. Each element in \texttt{agents} represents an \textit{ex-ante} heterogeneous type; each type could have many \textit{ex-post} heterogeneous agents.
-
-\item \texttt{sow\_vars}: A list of strings naming variables that are output from the aggregate market process, representing the macroeconomic outcomes. These variables will be distributed to the \texttt{agents} in the \texttt{sow} step.
-
-\item \texttt{reap\_vars}: A list of strings naming variables to be collected from the \texttt{agents} in the \texttt{reap} step, to be used as inputs for the aggregate market process.
-
-\item \texttt{const\_vars}: A list of strings naming variables used by the aggregate market process that \textit{do not} come from \texttt{agents}; they are constant or come from the \texttt{Market} itself.
-
-\item \texttt{track\_vars}: A list of strings naming variables generated by the aggregate market process that should be tracked as a history, to be used when calculating a new dynamic rule. Usually a subset of \texttt{sow\_vars}.
-
-\item \texttt{dyn\_vars}: A list of strings naming the variables that constitute a dynamic rule. These will be stored as attributes of the \texttt{agents} whenever a new rule is calculated.
-
-\item \texttt{millRule}: A function for the ``aggregate market process'', transforming microeconomic outcomes into macroeconomic outcomes. Its inputs are named in \texttt{reap\_vars} and \texttt{const\_vars}, and it returns a single object with attributes named in \texttt{sow\_vars} and/or \texttt{track\_vars}. Can be defined as a method of a subclass of \texttt{Market}.
-
-\item \texttt{calcDynamics}: A function that generates a new dynamic rule from a history of macroeconomic outcomes. Its inputs are named in \texttt{track\_vars}, and it returns a single object with attributes named in \texttt{dyn\_vars}.
-
-\item \texttt{act\_T}: The number of times that the \texttt{makeHistory} method should execute the ``farming loop'' when generating a new macroeconomic history.
-
-\item \texttt{tolerance}: The minimum acceptable ``distance'' between successive dynamic rules produced by \texttt{calcDynamics} to constitute a sufficiently converged solution.
-\end{itemize}
-
-Further, each \texttt{AgentType} in \texttt{agents} must have two methods not necessary for microeconomic models; neither takes any input (except \texttt{self}):
-\begin{itemize}
-\item \texttt{marketAction}: The microeconomic process to be run in the \texttt{cultivate} step. Likely uses the new macroeconomic outcomes named in \texttt{sow\_vars}; should store new values of relevant microeconomic outcomes in the attributes (of \texttt{self}) named in \texttt{reap\_vars}.
-
-\item \texttt{reset}: Reset, initialize, or prepare for a new ``farming loop'' to generate a macroeconomic history. Might reset its internal random number generator, set initial state varibles, clear personal histories, etc.
-\end{itemize}
-
-When solving macroeconomic models in HARK, the user should also define classes to represent the output from the aggregate market process in \texttt{millRule} and for the model-specific dynamic rule. The latter should have a \texttt{distance} method to test for solution convergence; if the class inherits from \texttt{HARKobject}, the user need only list relevant attributes in \texttt{distance\_criteria}.
-
-\subsection{Sample Model: FashionVictim}\label{sec:FashionVictim}
-
-To illustrate the \texttt{Market} class, consider a simple example in the emerging economic subfield of aesthemetrics, the \texttt{FashionVictimModel}.\footnote{This model is inspired by the paper ``The hipster effect: When anticonformists all look the same'' by Jonathan Touboul.}\footnote{For a more traditional macroeconomic model, the \texttt{ConsAggShocksModel} module includes a consumption-saving model with both idiosyncratic and aggregate shocks, in which individual asset holdings are aggregated into total productive capital, endogenizing the (dynamic) interest and wage rate.} This module defines a subclass of \texttt{AgentType} called \texttt{FashionVictimType}. Each period, fashion victims make a binary choice of style $s$: to dress as a jock (0) or punk (1). They receive utility directly from the outfit they wear and as a function of the proportion of the population who \textit{just wore} the same style; they also pay switching costs ($c_{pj}$,$c_{jp}$) if they change styles rather than keep the same as the previous period. Moreover, they receive an idiosyncratic type 1 extreme value (T1EV) preference shock to each style in each period. Defining the population punk proportion as $p$ and the conformity utility function as $f:[0,1]\rightarrow \mathbb{R}$, the current period utility function is thus:
-\begin{equation*}
-u(s_t;s_{t-1},p_t) = s_t f(p_t) + (1-s_t) f(1-p_t) + s_t U_p + (1-s_t) U_j - c_{pj} s_{t-1}(1-s_t) - c_{jp}(1-s_{t-1})s_t.
-\end{equation*}
-
-Fashion victims are forward looking and discount future utility at a constant rate of $\beta$ per period. To simplify the analysis, we assume they believe that the population punk proportion in the next period is a linear function of the punk proportion in the current period, subject to a uniformly distributed shock. No restrictions are put on the function $f$; fashion victims might be conformists who like to dress the same as others ($f'(p) > 0$) or hipsters who like to style themselves in the minority ($f'(p) < 0$).\footnote{In practice, $f$ is parameterized as the beta distribution for convenience.} A fashion victim's problem can be written in Bellman form as:
-\begin{equation*}
-V(s_{t-1},p_t) = \E \left[ \max_{s_t \in \{0,1\}} u(s_t;s_{t-1},p_t) + \eta_{s_t} + \beta \E \left[ V(s_t,p_{t+1}) \right] \right],
-\end{equation*}
-\begin{equation*}
-p_{t+1} = a p_t + b + \pi_{t+1}, \qquad \pi_{t+1} \sim U[-w,w], \qquad \eta_0,\eta_1 \sim T1EV.
-\end{equation*}
-
-An instance of \texttt{FashionVictimType} is thus characterized by values of $U_p$, $U_j$, $c_{pj}$, $c_{jp}$ and a function $f$, as well as beliefs about $p_{t+1}$ as a function of $p_t$ (summarized by slope $a$, intercept $b$, and uniform shock width $w$). Given this information, a \texttt{FashionVictimType}'s infinite horizon microeconomic model can be solved by backward induction in a few lines; the ``one period solver'' is given by \texttt{solveFashion}. However, while individual agents treat the dynamics of $p_t$ as exogenous, they are in fact endogenously determined by the actions of all the fashion victims in the market.\footnote{The market might consist of agents all of the same type, or might have several types with \textit{ex-ante} heterogeneity.} A dynamic general equilibrium of the ``macroeconomic fashion model'' is thus characterized by a triple of $(a,b,w)$ such that when fashion victims believe in this ``punk evolution rule'' and act optimally, their collective fashion choices exhibit this same rule when the model is simulated.
-
-The search for a dynamic general equilibrium is implemented in HARK's \texttt{Market} class with the following definitions:
-
-\vspace{0.25cm}
-
-\noindent \texttt{sow\_vars = ['pNow']} (macroeconomic outcome is $p_t$)
-
-\noindent \texttt{reap\_vars = ['sNow']} (microeconomic outcomes are $s_t$ for many agents)
-
-\noindent \texttt{track\_vars = ['pNow']} (must track history of $p_t$)
-
-\noindent \texttt{dyn\_vars = ['pNextSlope','pNextIntercept','pNextWidth']} (dynamic rule $(a,b,w)$)
-
-\noindent \texttt{millRule = calcPunkProp} (aggregate process: average the style choices of all agents)
-
-\noindent \texttt{calcDynamics = calcFashionEvoFunc} (calculate new $(a,b,w)$ with autoregression of $p_t$)
-
-\noindent \texttt{act\_T = 1000} (simulate 1000 periods of the fashion market)
-
-\noindent \texttt{tolerance = 0.01} (terminate solution when $(a,b,w)$ changes by less than 0.01)
-
-\vspace{0.25cm}
-
-The \texttt{agents} attribute has a list of 22 \texttt{FashionVictimType}s, which vary in their values of $U_p$ and $U_j$, and their $f$ functions. The \texttt{marketAction} method of \texttt{FashionVictimType} simulates one period of the microeconomic model: each agent receives style preference shocks $\eta_0$ and $\eta_1$, sees the current proportion of punks $p_t$ (sown to them as \texttt{pNow}), and chooses which style to wear, storing it in the binary array \texttt{sNow}, an attribute of \texttt{self}.
-
-The \texttt{millRule} for this market is extremely simple: it flattens the list of binary arrays of individual style choices (gathered in the \texttt{reap} step) and averages them into a new value of $p_t$, to be tracked as a history and \texttt{sow}n back to the \texttt{agents} to begin the cycle again. Once a history of 1000 values of $p_t$ has been generated with the \texttt{makeHistory} method, we can calculate a new dynamic fashion rule with \texttt{calcFashionEvoFunc} by regressing $p_t$ on $p_{t-1}$, approximating $w$ as twice the standard deviation of prediction errors.\footnote{This is an arbitrary way to calculate $w$, but accuracy is not important in a silly example.} The new fashion rule is an instance of the simple \text{FashionEvoFunc} class, whose only methods are inherited from \texttt{HARKobject}.
-
-When the \texttt{solve} method is run, the solver successively solves each agent's microeconomic problem, runs the \texttt{makeHistory} method to generate a 1000 period history of $p_t$, and calculates a new punk evolution rule based on this history; the solver terminates when consecutive rules differ by less than 0.01 in any dimension.
-
-}
-% \hypertarget{ContributingToHARK}{\section{Contributing to HARK}\label{sec:ContributingToHARK}}
-\section{Contributing to HARK}\label{sec:ContributingToHARK}
-
-%After reading this far into the user guide, it is safe to assume that you are eager to contribute to HARK's development.
-This section provides an overview of how users can contribute new code to HARK, what sort of contributions are most desired, and style conventions for the toolKit. Before attempting to extend HARK, we strongly recommend that you familiarize yourself with its features and models by looking at the source code.
-
-\subsection{What Does HARK Want?}\label{sec:WhatDoesHARKWant}
-
-The easiest and most straightforward way to contribute to HARK is by writing new general purpose tools for inclusion in one of the top-level modules. This might be a commonly known data analysis technique (e.g.\ a kernel density estimator) for \texttt{HARK.utilities}, an optimization method (local or global) for \texttt{HARK.estimation}, an interpolation method for \texttt{HARKinterpolation}, etc. If you've found a technique useful in your own research and believe it could be of use to others on entirely different projects, then it probably belongs in HARK. Likewise, if you read through a HARK module expecting to find a certain tool or function (because of similar functions that are present, or merely because \textit{it's very useful}) but don't, this is a good sign that HARK has a hole that you can fill.\footnote{Case in point: As of this moment, \texttt{HARK.utilities} has no function for making a discrete approximation to the normal distribution, even though there are several well known methods for doing so and it is an obviously useful function.}
-
-Secondly, the development team wants to expand HARK to include more canonical models. Most of the models available in the toolKit at the time of its public beta release concern agents with (geometrically discounted) CRRA utility over a unitary consumption good, who receive permanent and transitory shocks to income and have access to a single risk free asset. This is an important class of models, but that description has an awful lot of qualifiers. In short, there are many more common features of dynamic household models that are not yet in HARK, but would be very valuable for the toolKit. New model contributions should ``play nicely'' with existing models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent possible. For ideas of potential model extensions for HARK, see section \ref{sec:FutureModels}.\footnote{Although that list is limited to extensions and variations on consumption-saving models, HARK has no such restrictions. If there is an entirely different sort of dynamic model that fits in the HARK framework, it can be contributed.}
-
-Finally, contributions to HARK are not restricted to entirely new functionality. While we have done our best to provide flexible frameworks and useful tools, we have almost certainly overlooked an unexpected case that would not be (properly) handled by the toolKit as is. Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as an Issue on GitHub so that someone else might. The ``mistake'' you find might not even be in the code itself, but a deficiency in the documentation: a misnamed (or incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing) comment, even a grammatical or spelling error. No matter how niggling, we want your fix.
-
-For those who want to contribute to the HARK project without programming a new model or digging through the code for errors and omissions, consider reading through the Issues page on GitHub to see what other users have flagged for development. You might find several pieces of ``low hanging fruit'' for you to quickly fix.
-
-\subsection{Git and GitHub}\label{sec:GitHub}
-
-Git is a distributed version control system that has gained increasing popularity in the decade since its creation; it is designed to enable collaboration and ``non-linear workflow'' among a (potentially disperse) programming team. As HARK is to be developed by volunteer researchers all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds as a command line tool, others have since extended git with further functionality and a user interface more friendly to those more familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK project is hosted. This section provides a brief overview of how to use GitHub to interact with (and potentially contribute to) HARK.
-
-To prepare to contribute to HARK, follow these simple steps:
-\begin{enumerate}
-\item \textbf{Make a GitHub account:} Go to \href{https://www.github.com}{github.com}; the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.\footnote{There are non-free ways to sign up for GitHub, but you don't need to worry about them.}
-
-\item \textbf{Install GitHub Desktop}: Go to \href{https://desktop.github.com/}{desktop.github.com} and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account.
-
-\item \textbf{Fork the HARK repository:} Go to the \href{https://github.com/econ-ark/HARK}{HARK repository page}, click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead.
-
-\item \textbf{Add the repository to Desktop:} Open GitHub Desktop, click on the $+$ icon in the upper left, and select clone. Find the repository named \texttt{yourusername/HARK}, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine.
-\end{enumerate}
-
-You now control a ``fork'' of the main HARK repository, hosted by GitHub, and have a clone of this fork on your local machine. You are free to edit your fork of HARK in any way you'd like; other GitHub users can view your fork (and fork it themselves), but they cannot make changes to your fork unless you give them permission by adding them as a contributor. Changes made to the local copy of your fork do not get automatically sent to the remote copy on GitHub for public viewing. To make changes to your fork on GitHub, follow these steps:
-\begin{enumerate}
-\item \textbf{Make local changes:} Edit your local copy of the repository in any way you'd like: add or delete lines from existing files; add, delete, or rename entire files or directories.
-
-\item \textbf{Commit local changes:} Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button.
-
-\item \textbf{Push the commit:} Click on the Sync button near the upper right to ``push'' your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced.
-\end{enumerate}
-
-The left hand panel of the Changes tab has a summary of the files that have been changed, added, or removed since your last commit; the right hand panel shows the specific changes made to each changed file. After step 2, a record of your changes has been stored locally by the git system, but is not yet reflected in the public remote copy on GitHub.\footnote{You can make several local commits without syncing with the remote server. This is useful for working without an internet connection. Say, on an intercontinental flight.} The ``timeline'' summary of the repository at the top of the Desktop window shows the history of commits for the repository: each dot represents a commit that was pushed to the remote server, and each open ring represents a local commit that has not been pushed to the remote server; the large broken ring on the far right represents changes that have not been locally committed.\footnote{Clicking on a dot or ring will display the changes made in that commit (or uncommitted changes).} The History tab lists all previous commits, including the timestamp, contributor, and a record of changes made.
-
-Suppose you have mucked about in your fork for a while and have made changes to HARK that you think should be included in the main repository hosted by Econ-Ark. To make your dream a reality, follow these steps:
-\begin{enumerate}
-\item \textbf{Isolate the changes:} If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should \textit{isolate the desired changes in a new branch of your fork}. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,\footnote{Or since your last update from the main repository hosted by Econ-Ark.} then edit this branch to include \textit{only the changes you want to push to the main repository}.
-
-\item \textbf{Issue a pull request:} Go to \href{https://github.com/econ-ark/HARK}{HARK's GitHub page} and click the ``New pull request'' button. Click the text that says ``compare across forks'', and select \texttt{econ-ark/HARK} as the base fork, \texttt{master} as the base, your fork as the head fork, and the branch to be merged as the ``compare''. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you've done. Click the ``Create pull request'' button.
-
-\item \textbf{Be patient:} Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section \ref{sec:SubmissionApprovalProcess} for procedures followed by the HARK team when evaluating outside contributions.
-
-\end{enumerate}
-
-\subsection{Submission Approval Process}\label{sec:SubmissionApprovalProcess}
-
-Before submitting to HARK, the development team would like you to make sure that your intended contribution is listed as an Issue on the GitHub page. If you are going to fix or address an Issue that is already posted, reply to that post with a note indicating that you are about to work on it and an estimate of when you will make a pull request for it. If you want to do something that no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly) describing what you intend to do.\footnote{If your contribution is a tiny adjustment or to fix/add some documentation or comments, you shouldn't make an Issue.} This will both help prevent two people from working on the same thing (and one of them potentially wasting time on it), and lets the development team know what's coming. Moreover, if you propose a contribution that is (a) already in HARK, (b) not in the spirit of HARK, or (c) a fix for something that's not actually broken, this gives the team a chance to let you know not to spend effort on it.
-
-After creating a pull request to submit your contribution, it will be evaluated by one or more members of the HARK team. Criteria that the HARK team will consider when choosing whether to accept a submission include:
-\begin{enumerate}
-\item \textbf{Does the contribution break previous code?} If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility.
-
-\item \textbf{Is the code well documented?} Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section \ref{sec:DocumentationConventions}) and enough comments for a reader to follow the algorithm.
-
-\item \textbf{Is the contribution relevant?} The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn't belong in HARK.
-
-\item \textbf{Is the code demonstrated with an example?} If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you've contributed a new function, consider providing a snippet of code demonstrating its use.\footnote{In the future, HARK will make use of in-line testing.}
-
-\item \textbf{Is the code correct?} Your code should actually do what it claims to do.
-
-\item \textbf{Does the code use HARK conventions?} To the best of your ability, contributions to HARK should follow the style guidelines in section \ref{sec:NamingConventions}. We don't demand perfection, as these are guidelines rather than rules.
-
-\item \textbf{Is the code well integrated in HARK?} In the beta release of HARK, we have tried to present a ``taxonomy'' of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy.
-
-\item \textbf{Is this already in HARK?} If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK.
-\end{enumerate}
-
-The HARK team intends to release more specific guidelines in the near future, so that contributors are better able to judge their submissions before issuing a pull request. We are in the process of recruiting ``czars'' to oversee particular aspects of HARK, providing their knowledge and expertise.\footnote{If you have knowledge and/or expertise and would like to contribute as a czar, please contact a HARK team member so that we can judge your credentials even though we have way less expertise.} While czars might directly evaluate submissions as a HARK team member, their more important role is to help establish these guidelines for their domain, more specifically nailing down what we mean by ``correct'' or ``well integrated''.
-
-\subsection{Naming Conventions}\label{sec:NamingConventions}
-
-Object naming conventions in HARK are fairly different than existing standards, and differ somewhat between tool modules vs model or application modules. The following conventions apply throughout HARK:
-\begin{itemize}
-\item Functions and methods are always in ``camel case'': no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. \texttt{approxLognormal}.
-
-\item Function and method names should accurately and concisely describe what the function does; the first word in the name \textit{must be a verb}.\footnote{HARK standards have specified several ``keyword verbs'' that have specific meaning.}
-
-\item Variable and class names \textit{should not} have a verb as their first word.
-
-\item Class names should use no underscores and capitalize the first letter of each word; moreover, a class name \textit{must include a noun}. E.g.\ \texttt{ConsPerfForesightSolver}.
-\end{itemize}
-
-When naming variables in model modules, the HARK team strongly discourages using single letter names, like \texttt{c} for consumption. Instead, we encourage contributors to use longer, more descriptive variable names using additional words and common abbreviations to specify the variable more precisely. In \texttt{/Documentation/NARK.pdf}, we list standard single letter variable ``bases'' and an array of prefixes and suffixes to adjust them. Economic variables in model modules should (usually) not use underscores, instead using camel case to the greatest extent possible.\footnote{For ``non-economic'' variables that are only used temporarily, underscores are permissible.} The development team prefers this standard so that users can translate between Python code and LaTeX script with minimal work.
-
-Conventions for naming variables in HARK's tool modules are significantly closer to more commonly used standards. Variable names should be in all lower case, with underscores between words, e.g.\ \texttt{data\_to\_match}. The functions and classes in these modules are more general and almost surely do not have any inherent ``economic content''; they are numerical or algorithmic objects, not variables that might appear in an equation in an article for a (non-computational) economics journal. Variable names in application modules (e.g.\ the files that execute the \texttt{cstwMPC} estimations) are a mix of the conventions for tool and model files, as appropriate for each variable. That is, variables that are directly related to ``economic variables'' in model modules should follow those conventions, while objects created solely for data manipulation or reporting should use the style of tool modules.
-
-\subsection{Documentation Conventions}\label{sec:DocumentationConventions}
-
-The HARK team wants the toolKit to be as accessible to users as possible; our greatest fear\footnote{Other than spiders, of course.} is that a new user will open up our code, get hopelessly confused trying to read it, and then never look at HARK again. To prevent this tragic outcome, we have tried hard to provide comprehensive, accurate documentation and comments within the code describing our methods.\footnote{To the extent that documentation and comments are missing, we encourage users to rectify this in their contributions.} Moreover, HARK uses the Sphinx utility to generate a website with \href{https://econ-ark.github.io/HARK/}{online documentation} for all of our tool and model modules, so that users can learn about what's available in HARK without digging through the source code. When making contributions to HARK, the development team asks users to format their inline documentation to work with Sphinx by following a few simple rules.
-
-\begin{itemize}
-\item The top of every module should begin with a ``docstring'' providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:\footnote{The triple quote to begin and end a docstring can use either the apostrophe \texttt{'} or quotations marks \texttt{"}.}
-
-\texttt{"""}
-
-\texttt{Specifies an economic model and provides methods for solving it. More specific description of the key features of the model and variations of it in this module. Maybe some comments about the solution method or limitations of the model. Your bank account routing number.}
-
-\texttt{"""}
-
-\item The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as:
-
-\texttt{def functionName(input1,input2):}
-
-\hspace{0.9cm}\texttt{"""}
-
-\hspace{0.9cm}\texttt{Concise description of the function. More details about what}
-
-\hspace{0.9cm}\texttt{the function does, options or modes available, and maybe mathematical}
-
-\hspace{0.9cm}\texttt{methods used. Credit to a source if you poached their algorithm.}
-
-\vspace{0.5cm}
-
-\hspace{0.9cm}\texttt{Parameters}
-
-\hspace{0.9cm}\texttt{--------------------}
-
-\hspace{0.9cm}\texttt{input1: type}
-
-\hspace{1.8cm}\texttt{Description of what input1 represents.}
-
-\hspace{0.9cm}\texttt{input2: type}
-
-\hspace{1.8cm}\texttt{Description of what input2 represents.}
-
-\vspace{0.5cm}
-
-\hspace{0.9cm}\texttt{Returns}
-
-\hspace{0.9cm}\texttt{--------------}
-
-\hspace{0.9cm}\texttt{output\_name: type}
-
-\hspace{1.8cm}\texttt{Description of the output(s) of the function. Might have}
-
-\hspace{1.8cm}\texttt{multiple entries. If no output, this is just "None".}
-
-\hspace{0.9cm}\texttt{"""}
-
-\item Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it.
-\end{itemize}
-
-Finally, if you write a new model module, the HARK team asks that you also provide a short mathematical writeup of the model as a PDF. This document does not need to go into great detail about the solution method for the model or the functions and classes included in the module, merely specify the economic model and provide a summary of how it is solved. See \texttt{/Documentation/ConsumptionSavingModels.pdf} for an example of this.
-
-\ifthenelse{\boolean{ContributingToHARKOnly}}{}{
-\section{Future of HARK}\label{sec:FutureOfHARK}
-
-HARK is in a state of perpetual incompleteness, always expanding to include more tools and more models. As an open source project in the hands of many contributors around the world, the HARK team cannot fully predict future features of the toolKit. This section provides an overview of what we would like to see in the near future, as well as potential mechanisms to encourage researchers to contribute their code to the project.
-
-\subsection{Future Tools}\label{sec:FutureTools}
-
-The current frameworks in \texttt{HARK.core} concern agents who have ``isolated'' dynamic problems or interact with each other only through aggregate outcomes to which they contribute atomically. We hope to eventually provide a framework for models in which agents directly interact with each other; this may include a system for specifying the state of networks and how connections are formed and broken. \texttt{HARK.core} might also develop a framework more suited to industrial organization models and dynamic games.\footnote{It might already be possible to shoehorn some of these models into the \texttt{Market} framework.}
-
-The \texttt{HARK.estimation} module in particular is very sparsely populated, with its functions mostly serving as wrappers for ``off the shelf'' optimization methods from \texttt{scipy.optimize}. There are plenty of non-standard or more complex optimization techniques available, including global and/or stochastic search methods. We believe that additional optimizers are ``low hanging fruit'' as straightforward contributions to HARK and thus will be added in the near future. Moreover, there are plenty of distributions that have no approximator in \texttt{HARK.utilities} (or data generator in \texttt{HARK.simulation}), and these functions are among the easiest for new contributors to write. The data analysis tools in \texttt{HARK.utilities} only include functions that we happened to use for our current research projects, and we expect that this will be the mode of growth for future tools. There are some obvious holes that we believe will be filled quite quickly; for example, we provide a rudimentary non-parametric kernel regression function, but have not written a kernel density estimator to generate PDFs from data.
-
-The classes currently in \texttt{HARK.interpolation} are all variations on linear (or cubic) spline interpolation, and each is specifically written for a fixed number of dimensions. As these methods use full tensor grids, they are highly impractical for representing functions in higher dimensions, suffering greatly from the curse of dimensionality.\footnote{The HARK team only included interpolators for up to 4 dimensions, and some would argue this is already too many for tensor grid methods.} We are quite confident that \texttt{HARK.interpolation} will soon include techniques more applicable to high dimensional state spaces, including Smolnyak interpolation, other (adaptive) sparse grid methods, and regression-based interpolation. For lower-dimensional spaces, HARK will also soon provide Delaunay interpolation classes.
-
-The \texttt{HARK.parallel} module currently provides a simple interface for handling \textit{ex-ante} heterogeneity among agents. While useful for speeding up moderately-sized problems, the libraries used are only able to access the CPU of the local machine running the code. In the very near future, the HARK team will provide an interface (and example) for using Apache Spark, which bills itself as a ``fast and general engine for large-scale data processing''. Specifically, users will be able to easily scale up their computing resources and run HARK code on an Amazon EC2 session or other cluster.
-
-\subsection{Future Models}\label{sec:FutureModels}
-
-The economics models currently included in HARK mostly concern consumption-saving problems with constant relative risk aversion consumers who experience fully permanent and fully transitory shocks to income. This is obviously a very small subset of all consumption-saving models (which are themselves a small subset of all dynamic models economists might be interested in). Consider some examples of variants, features, and extensions that are not included in HARK, but could be in the future:
-\begin{itemize}
-\item Consumers with other utility functions, such as CARA or expo-power.
-
-\item Consumers with time-inconsistent preferences, e.g.\ hyperbolic discounting.
-
-\item Income shocks that are neither fully transitory nor fully permanent.
-
-\item Consumers with a bequest motive or terminal utility function.
-
-\item Utility from leisure and endogenous labor supply.
-
-\item Investment in a durable consumption good such as housing.
-
-\item Exogenous shocks to expenses.
-
-\item Multiple consumption goods, as with endogenous medical care.
-
-\item Portfolio allocation between a risky and riskless asset.
-
-\item Job search intensity as a control variable.
-
-\item Endogenous health evolution and health investment.
-\end{itemize}
-
-The HARK team is currently developing a module for mixing discrete and continuous choice models, in the style of Jorgensen, Rust, Iskhakov, and Schjerning (2015). Among many other models, this framework will enable HARK users to easily develop features like:
-\begin{itemize}
-\item Labor supply on the extensive margin: the timing of retirement.
-
-\item Selection of job type or sector (by riskiness, specific human capital, etc).
-
-\item Choice of medical insurance contract.
-
-\item Decision to rent or own housing.
-
-\item Endogenous default on debt (with ``macroeconomic'' pricing of debt).
-
-\item Household formation, dissolution, and reproduction.
-\end{itemize}
-
-Finally, we hope to provide a generic solver for any discrete choice model with T1EV shocks. The scope of HARK is limited only by what users choose to put in it. We hope that within a year of its debut, HARK will include a significant number of canonical models, providing more building blocks for future researchers to create novel combinations and new extensions.
-
-\subsection{Bounty Hunting}\label{sec:BountyHunting}
-
-While HARK users are free to write and contribute any tools and models that they believe could be helpful, building the toolKit in a ``bottom up'' style, the HARK team hopes to provide mechanisms for targeted ``top down'' development. The team is working to secure grant funding for HARK so as to offer ``bounties'' for particular models, tools, applications, or frameworks. Suppose the team decides that the model in Important Paper X should be included as part of HARK; we will issue a call for contributions to fill this hole, offering a modest prize of, say, \$20,000 to the programmer who submits the best implementation of the model, as judged by clearly stated criteria.\footnote{Actual bounties may be significantly smaller than this. But be honest: You're much more interested in contributing to HARK after reading that sentence.} The bounty would be announced as an Issue on HARK's GitHub page, as well as in a potential electronic digest of HARK news.
-
-Bounties might be placed on items as small as a single data analysis tool or as large as a complex estimation of a large general equilibrium model, with prizes scaling accordingly. We hasten to emphasize that we cannot guarantee funding for HARK, so the bounties (like everything else in this section) are purely speculative. As economists, the HARK team understands the incentive compatibility problems associated with asking researchers to contribute significant amounts of their time, effort, and unique skills in service of a tenuous concept like the Greater Good in exchange for even more tenuous Glory. We acknowledge that economic researchers are also optimizing agents with their own preferences over leisure and consumption, and agree that Greed is a valid mechanism to expand HARK.
-
-\subsection{All Aboard the Ark}\label{sec:AllAboardTheArk}
-
-As observant readers may have noticed, the HARK repository is hosted by a GitHub account named Econ-Ark, with no explanation as to what this might mean. Looking a few years into the future, the development team believes that HARK might be the first piece of a broader collection of computational economics resources. HARK is open source but somewhat akin to an exclusive club: contributions are required to adhere to standards and norms so as to foster modularity and interoperability among models and tools. Consistent with its namesake ship, the Econ-Ark has a significantly lower bar for admission, with a place on board for everything.
-
-On one level, the Ark might serve as a warehouse for any and all quantitative models and tools, for any economic purpose, in any subfield, and written in any language. Through nearly unanimous feedback, the HARK team understands that many economists are willing to share code that they have already written, but are reluctant to expend the considerable effort to translate and format it for HARK. In the future, we will set up a mechanism for code like this to be hosted on the Ark, with minimal requirements imposed on its authors (and correspondingly minimal warranty for potential users). Relevant projects on the Ark might become bounty targets for HARK, with users incentivized to port the original code into Python and to meet HARK standards; direct access to the original source code will significantly expedite the translation process, and provide a suitable benchmark for testing and verifying the contribution to HARK.
-
-On a different level, the Ark might also include cohesive toolkits like HARK, but geared toward purposes other than heterogeneous agents macroeconomics models. For example, the previously mentioned modules for networks among agents (and other agent-based modeling), industrial organization and dynamic games, and traditional discrete choice models might become their own toolkits, with some interoperability with HARK. In support of this, some of the tool modules currently in HARK might be ``promoted'' and become universally shared across all Econ-Ark toolkits.
-
-The dark secret of HARK is that it is named for heterogeneous agents macroeconomics, but designed and written by structural microeconomists. The development team believes these subfields to have considerable overlap in terms of methods and techniques, and that HARK might not be noticed by half of its potential audience due to its name. In a general sense, the Econ-Ark gives us cover to expand the scope of our efforts and/or rebrand the toolKit for an alternative audience.
-} % end \ifthenelse{\boolean{\ContributingToHARKOnly}}
-\end{document}
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-
-
-(This is an excerpt of the full manual, available at http://econ-ark.org/HARK/Documentation/HARKmanual.pdf)
0.1 This section provides an overview of how users can contribute new code to HARK, what sort of
-contributions are most desired, and style conventions for the toolKit. Before attempting to
-extend HARK, we strongly recommend that you familiarize yourself with its features and models
-by looking at the source code.
-
-
0.1.1 The easiest and most straightforward way to contribute to HARK is by writing new general
-purpose tools for inclusion in one of the top-level modules. This might be a commonly
-known data analysis technique (e.g. a kernel density estimator) for HARK.utilities , an
-optimization method (local or global) for HARK.estimation , an interpolation method for
-HARKinterpolation , etc. If you’ve found a technique useful in your own research and
-believe it could be of use to others on entirely different projects, then it probably
-belongs in HARK. Likewise, if you read through a HARK module expecting to find a
-certain tool or function (because of similar functions that are present, or merely because
-it’s very useful ) but don’t, this is a good sign that HARK has a hole that you can
-fill.
Secondly, the development team wants to expand HARK to include more canonical models.
-Most of the models available in the toolKit at the time of its public beta release concern agents
-with (geometrically discounted) CRRA utility over a unitary consumption good, who receive
-permanent and transitory shocks to income and have access to a single risk free asset. This is an
-important class of models, but that description has an awful lot of qualifiers. In short, there are
-many more common features of dynamic household models that are not yet in HARK, but would
-be very valuable for the toolKit. New model contributions should “play nicely” with existing
-models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the
-greatest extent possible. For ideas of potential model extensions for HARK, see section
-
-
-?? .
Finally, contributions to HARK are not restricted to entirely new functionality. While we have
-done our best to provide flexible frameworks and useful tools, we have almost certainly
-overlooked an unexpected case that would not be (properly) handled by the toolKit as is.
-Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake
-or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as
-an Issue on GitHub so that someone else might. The “mistake” you find might not
-even be in the code itself, but a deficiency in the documentation: a misnamed (or
-incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing)
-comment, even a grammatical or spelling error. No matter how niggling, we want your
-fix.
-
For those who want to contribute to the HARK project without programming a new model or
-digging through the code for errors and omissions, consider reading through the Issues page on
-GitHub to see what other users have flagged for development. You might find several pieces of
-“low hanging fruit” for you to quickly fix.
-
0.1.2 Git is a distributed version control system that has gained increasing popularity in the decade
-since its creation; it is designed to enable collaboration and “non-linear workflow” among a
-(potentially disperse) programming team. As HARK is to be developed by volunteer researchers
-all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds
-as a command line tool, others have since extended git with further functionality and
-a user interface more friendly to those more familiar with GUIs. Foremost among
-these tools is GitHub.com, where the HARK project is hosted. This section provides a
-brief overview of how to use GitHub to interact with (and potentially contribute to)
-HARK.
-
To prepare to contribute to HARK, follow these simple steps:
-
- Make a GitHub account: Go to github.com ; the homepage should prompt
- you to choose a username and password. Signing up is quick, easy, and
- free.Install GitHub Desktop : Go to desktop.github.com and download the Desktop
-
-
- application for your operating system, then install it. After opening Desktop for the
- first time, enter your GitHub login to connect it to your account.
- Fork the HARK repository: Go to the HARK repository page , click on the Fork
- button in the upper right, and choose to fork HARK to your own GitHub account.
- This creates a new repository identical to the one hosted by Econ-Ark at the moment
- you fork it, but hosted on your account instead.
- Add the repository to Desktop: Open GitHub Desktop, click on the yourusername/HARK ,
- click the Clone button at the bottom of the dialogue window, and choose a directory
- on your local machine.
- You now control a “fork” of the main HARK repository, hosted by GitHub, and have a clone of
-this fork on your local machine. You are free to edit your fork of HARK in any way you’d like;
-other GitHub users can view your fork (and fork it themselves), but they cannot make changes to
-your fork unless you give them permission by adding them as a contributor. Changes made
-to the local copy of your fork do not get automatically sent to the remote copy on
-GitHub for public viewing. To make changes to your fork on GitHub, follow these
-steps:
-
- Make local changes: Edit your local copy of the repository in any way you’d like:
- add or delete lines from existing files; add, delete, or rename entire files or directories.
- Commit local changes: Open the repository in GitHub Desktop and click on the
- Changes tab. In the lower left, provide a short description of the changes made in
- the Summary field, and (optionally) longer comments about these changes in the
- Description field. Click the check-mark Commit button.
- Push the commit: Click on the Sync button near the upper right to “push” your
- local commits to the remote server; this will also pull down remote commits from
- other contributors to your fork that were made since the last time you synced.
- The left hand panel of the Changes tab has a summary of the files that have been changed,
-added, or removed since your last commit; the right hand panel shows the specific
-changes made to each changed file. After step 2, a record of your changes has been
-stored locally by the git system, but is not yet reflected in the public remote copy on
-
-
-GitHub.
Suppose you have mucked about in your fork for a while and have made changes to HARK
-that you think should be included in the main repository hosted by Econ-Ark. To make your
-dream a reality, follow these steps:
-
- Isolate the changes: If you mucked around for quite a while, making various changes
- here and there, and then later made some contribution that you want to include in
- the main repository, you should isolate the desired changes in a new branch of your
- fork . Make a new branch by clicking the button toward the upper left of GitHub
- Desktop, revert that branch to the commit just before you forked from the main
- repository,only the changes you want to push to the main
- repository .
- Issue a pull request: Go to HARK’s GitHub page and click the “New pull request”
- button. Click the text that says “compare across forks”, and select econ-ark/HARK
- as the base fork, master as the base, your fork as the head fork, and the branch to
- be merged as the “compare”. In the boxes below, give a name to your pull request
- and provide sufficient comments about your contribution so that a development team
- member can understand what you’ve done. Click the “Create pull request” button.
- Be patient: Someone from the development team will look at your contribution and
- either merge it into the main repository or return it with comments. See section 0.1.3
- for procedures followed by the HARK team when evaluating outside contributions.
-
-
-
- 0.1.3 Before submitting to HARK, the development team would like you to make sure that
-your intended contribution is listed as an Issue on the GitHub page. If you are going
-to fix or address an Issue that is already posted, reply to that post with a note
-indicating that you are about to work on it and an estimate of when you will make a
-pull request for it. If you want to do something that no one has mentioned yet as
-an Issue, make a new Issue on GitHub (briefly) describing what you intend to
-do.
After creating a pull request to submit your contribution, it will be evaluated by one or more
-members of the HARK team. Criteria that the HARK team will consider when choosing whether
-to accept a submission include:
-
- Does the contribution break previous code? If you change or expand an existing
- function, you should make sure that this does not cause some models or applications
- to work incorrectly. In the near future, HARK will have a core set of tests to run to
- check for full compatibility.
- Is the code well documented? Code submissions should have properly formatted
- docstrings for each new function, class, method, and module (see section 0.1.5 ) and
- enough comments for a reader to follow the algorithm.
- Is the contribution relevant? The development team intends for HARK to
- be diverse, but not everything goes in HARK. If your submission concerns a
- representative agent model or a method for estimating a reduced form model, then
- it probably doesn’t belong in HARK.
- Is the code demonstrated with an example? If your solve a new model in
- HARK, you should include a set of example parameters for other users to easily
- test. If you’ve contributed a new function, consider providing a snippet of code
-
-
- demonstrating its use.Is the code correct? Your code should actually do what it claims to do.
- Does the code use HARK conventions? To the best of your ability, contributions
- to HARK should follow the style guidelines in section 0.1.4 . We don’t demand
- perfection, as these are guidelines rather than rules.
- Is the code well integrated in HARK? In the beta release of HARK, we have
- tried to present a “taxonomy” of consumption-saving models that build up from each
- other. When possible, new models in HARK should try to continue this pattern. A
- submission that introduces a model in a new module that could have easily been made
- as an extension of an existing module might be rejected. However, we acknowledge
- that there is value in providing a solver for a new model, and that a future contributor
- might make her mark by integrating it into the existing hierarchy.
- Is this already in HARK? If your contribution is already in HARK, then it will
- almost certainly be rejected. Of course, if you submit a version that dominates the
- existing implementation (or even one that works better in a well described, non-trivial
- set of circumstances), it belongs in HARK.
- The HARK team intends to release more specific guidelines in the near future, so that contributors
-are better able to judge their submissions before issuing a pull request. We are in the process of
-recruiting “czars” to oversee particular aspects of HARK, providing their knowledge and
-expertise.
0.1.4 Object naming conventions in HARK are fairly different than existing standards, and differ
-somewhat between tool modules vs model or application modules. The following conventions
-apply throughout HARK:
-
-
-
- Functions and methods are always in “camel case”: no underscores, first letter is lower
- case, first letter of each subsequent word is capitalized. E.g. approxLognormal .
-
- Function and method names should accurately and concisely describe what the
- function does; the first word in the name must be a verb .
- Variable and class names should not have a verb as their first word.
-
- Class names should use no underscores and capitalize the first letter of each word;
- moreover, a class name must include a noun . E.g. ConsPerfForesightSolver .
- When naming variables in model modules, the HARK team strongly discourages using single
-letter names, like c for consumption. Instead, we encourage contributors to use longer, more
-descriptive variable names using additional words and common abbreviations to specify the
-variable more precisely. In /Documentation/NARK.pdf , we list standard single letter variable
-“bases” and an array of prefixes and suffixes to adjust them. Economic variables in model
-modules should (usually) not use underscores, instead using camel case to the greatest extent
-possible.
Conventions for naming variables in HARK’s tool modules are significantly closer to more
-commonly used standards. Variable names should be in all lower case, with underscores between
-words, e.g. data_to_match . The functions and classes in these modules are more general
-and almost surely do not have any inherent “economic content”; they are numerical
-or algorithmic objects, not variables that might appear in an equation in an article
-for a (non-computational) economics journal. Variable names in application modules
-(e.g. the files that execute the cstwMPC estimations) are a mix of the conventions for tool
-and model files, as appropriate for each variable. That is, variables that are directly
-related to “economic variables” in model modules should follow those conventions, while
-objects created solely for data manipulation or reporting should use the style of tool
-modules.
-
0.1.5 The HARK team wants the toolKit to be as accessible to users as possible; our greatest
-fearonline documentation for all
-of our tool and model modules, so that users can learn about what’s available in HARK without
-digging through the source code. When making contributions to HARK, the development team
-asks users to format their inline documentation to work with Sphinx by following a few simple
-rules.
-
- The top of every module should begin with a “docstring” providing a clear description
- of the contents of the module. The first sentence should concisely summarize
- the file, as it may appear in an index or summary of all modules without the
- remaining sentences. A docstring at the top of a module should be formatted
- as:"""
-
Specifies an economic model and provides methods for solving it. More
- specific description of the key features of the model and variations
- of it in this module. Maybe some comments about the solution method
- or limitations of the model. Your bank account routing number.
-
"""
-
- The line directly below the declaration of a function, method or class should begin a
- docstring describing that object. As with modules, the first sentence should concisely
- summarize the function or class, as it might be included in an index or summary. For
- functions and methods, the docstring should be formatted as:
- def functionName(input1,input2):
-
"""
-
-
-
Concise description of the function. More details about what
-
the function does, options or modes available, and maybe
- mathematical
-
methods used. Credit to a source if you poached their
- algorithm.
-
Parameters
-
––––––––––
-
input1: type
-
Description of what input1 represents.
-
input2: type
-
Description of what input2 represents.
-
Returns
-
–––––––
-
output_name: type
-
Description of the output(s) of the function. Might have
-
multiple entries. If no output, this is just "None".
-
"""
-
- Provide ample comments within a function or method so that a relatively intelligent
- reader can follow along with your algorithm. Short comments can follow at the end
- of a line, longer comments (or descriptions of the step or task about to be performed)
- should precede a block of code on the line(s) above it.
- Finally, if you write a new model module, the HARK team asks that you also provide a short
-mathematical writeup of the model as a PDF. This document does not need to go into great
-detail about the solution method for the model or the functions and classes included in
-the module, merely specify the economic model and provide a summary of how it
-is solved. See /Documentation/ConsumptionSavingModels.pdf for an example of
-this.
-
-
-
-
-
-
diff --git a/Documentation/HARKmanual/HARKmanual.4ct b/Documentation/HARKmanual/HARKmanual.4ct
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-
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-\Preamble{}
-\begin{document}\bibliographystyle{plainnat}\HCode{
-
A User’s Guide for HARK: Heterogeneous Agents Resources and toolKit
-
-
-
A User’s Guide for HARK:
-
May 21, 2018
-
-
Christopher D Carroll 1 Johns Hopkins University Alexander M Kaufman 2 Consumer Financial Protection Bureau David C Low 3 Consumer Financial Protection Bureau Nathan M Palmer 4 U.S. Treasury Office of Financial Research Matthew N White 5 University of Delaware
-
-
-
-
1 Carroll: http://econ.jhu.edu/people/ccarroll/ , email: ccarroll@jhu.edu 2 Kaufman: alexander.kaufman@cfpb.gov 3 Low: david.c.low@cfpb.gov 4 Palmer:
-nathan.m.palmer@ofr.treasury.gov 5 White: mnwecon@udel.edu
-
-
-
-
-
-
-
-
-
0.1 If you are willing to risk some mild psychological trauma, conjure to mind your first experience of
-hand-coding a structural economic model. Your clunky effort probably built on legacy code
-provided by an adviser or colleague – which itself came from who-knows-what apocryphal
-sources. Efforts to combine elements from one model with those from another were likely
-frustrated by the “Tower of Babel” problem: Code from one source could not “speak” to code
-from another without your own intermediation as a translator, possibly between two unfamiliar
-languages and aided only by oracular comments that, at best, made sense only in the context of
-other (now missing) code.
-
After months of effort, you may have had the character-improving experience of proudly
-explaining to your adviser that not only had you grafted two ideas together, you also found a
-trick that speeded the solution by an order of magnitude, only to be told that your breathtaking
-insight had been understood for many years, as reflected in an appendix to a 2008 paper; or,
-worse, your discovery was something that “everybody knows” but did not exist at all in published
-form!
-
Learning by doing has value, but only within limits. We do not require young drivers to design
-an internal combustion engine before driving a car, nor must graduate students write their own
-matrix inversion algorithms before running an OLS regression.
-
In recent years, considerable progress has been made in addressing these kinds of problems in
-many areas of economic modeling. Macroeconomists using representative agent models can send
-Dynare model files to each other; reduced form econometricians can choose from a host of
-econometric packages. But modelers whose questions require explicit structural modeling
-involving nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply be
-aggregated away) are mostly still stuck in the bad old days.
-
The ultimate goal of the HARK project is to fix these problems. Specifically, our aim is to
-produce an open source repository of highly modular, easily interoperable code for
-solving, simulating, and estimating dynamic economic models with heterogeneous
-agents.
We hope that HARK will make it much easier and faster for researchers to develop solution
-and estimation methods for new models. The open source nature of HARK will make
-it easier for other researchers to audit and verify new models and methods, and to
-collaborate on correcting deficiencies when found. As HARK expands to include more
-
-
-canonical models and more tools and utilities, we can all spend less time managing
-numerical minutiae and more time fretting about identification arguments and data
-accuracy.
-
0.1.1 If you want to get started using HARK right away, this section provides a very easy quickstart
-guide for getting HARK up and running on your computer in just a few minutes. More
-information can be found in the README.md file in the HARK repository (step 4), but here is a
-quick version for those who want to jump right in:
-
-
- Download Anaconda: Go to https://www.anaconda.com/download/ and
- download Anaconda for your operating system; be sure to get the version for Python
- 2.7.
- Install Anaconda: Follow the easy instructions on that page to install Anaconda.
- Add extra packages: (optional) If you want to use HARK’s multithreading feature,
- you need to add two packages that aren’t part of the default Anaconda distribution.
- Simply open a command prompt and do conda install joblib and conda install
- dill , accepting defaults to install.
- Download HARK: Go to https://github.com/econ-ark/HARK , the home
- of the HARK repository. Click on the lime green button labeled “Clone or
- download” toward the upper right of the main panel, and select “Download
- ZIP”.Unzip HARK: Unpack the HARK.zip file using any archive utility, like Peazip . Put
- the files anywhere you’d like, maintaining the internal directory structure.
- Run Spyder: Open a command prompt and do spyder . Spyder is an interactive
- development environment (IDE) for iPython, a slightly prettier, more interactive
- flavor of Python.
-
-
- Open a HARK module: Go to the directory where you put HARK and open any
- file with the .py extension; we recommend
- /Demos/ConsumptionSaving/ConsIndShockModel.py .
- Run the module: Click on the green arrow “play” button toward the right side of
- the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue
- box pops up). Congratulations! HARK is now up and running on your computer.
- If you followed our recommendation to try /Demos/ConsumptionSaving/ConsIndShockModel.py ,
-you should see graphical representations of the solution to a few consumption-saving
-models with idiosyncratic shocks to permanent and transitory income (see
-/Documentation/ConsumptionSavingModels.pdf for details of these models). If you chose a
-different module, you might get some examples of some other model, results of a structural
-estimation, a polite notification that this particular module doesn’t do much on its own, or no
-output at all. See the README.md for a full list of modules from which you can expect non-trivial
-output.
-
-
0.1.2 HARK is written in Python, an object-oriented programming (OOP) language that has
-experienced increasing popularity in the scientific community in the past several years. A
-significant reason for the adoption of Python is the numpy and scipy packages, which offer a
-wide array of mathematical and statistical functions and tools; HARK makes liberal
-use of these libraries. Python’s object-oriented nature allows models in HARK to
-be easily extended: more complex models can inherit functions and methods from
-more fundamental “parent” models, eliminating the need to reproduce or repurpose
-code.
-
As implied in the previous section, we strongly encourage HARK users to use the Anaconda
-distribution of Python, which includes all commonly used mathematical and scientific packages,
-an interactive development environment for iPython (Spyder), and a package manager that
-allows users to quickly install or update packages not included in the default distribution
-(conda).
-
For users unfamiliar with OOP, we strongly encourage you to review the background material
-on OOP provided by the good people at QuantEcon (for more on them, see below) at this link:
-Object Oriented Programming . Unlike non-OOP languages, OOP bundles together
-data and functions into objects . These can be accessed via: object_name.data and
-object_name.method_name() , respectively. For organizational purposes, definitions of multiple
-objects are stored in modules , which are simply files with a .py extension. Modules can be
-
-
-accessed in Python via:
import module_name as import_name
This imports the module and gives it a local name of import_name . We can access a function
-within this module by simply typing: import_name.function_name() . The following example
-will illustrate the usage of these commands. CRRAutility is the function object for calculating
-CRRA utility supplied by HARK.utilities module. CRRAutility is called attributes of the
-module HARK.utilities . In order to calculate CRRA utility with a consumption of 1 and a
-coefficient of risk aversion of 2 we run:
import HARK.utilities as Hutil
-
Hutil.CRRAutility(1,2)
Python modules in HARK can generally be categorized into three types: tools, models, and
-applications. Tool modules contain functions and classes with general purpose tools that have
-no inherent “economic content”, but that can be used in many economic models as building
-blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include
-functions for data analysis (e.g. calculating Lorenz shares from data, or constructing a
-non-parametric kernel regression), functions to create and manipulate discrete approximations to
-continuous distributions, or classes for constructing interpolated approximations to
-non-parametric functions. Tool modules generally reside in HARK’s root directory and have
-names like HARK.simulation and HARK.interpolation ; they do not necessarily do anything
-when run.
-
Model modules specify particular economic models, including classes to represent agents in
-the model (and the “market structure” in which they interact) and functions for solving the
-“one period problem” of those models. For example, ConsIndShockModel.py concerns
-consumption-saving models in which agents have CRRA utility over consumption and face
-idiosyncratic shocks to permanent and transitory income. The module includes classes for
-representing “types” of consumers, along with functions for solving (several flavors of) the one
-period consumption-saving problem. When run, model modules might demonstrate example
-specifications of their models, filling in the model parameters with arbitrary values. When
-ConsIndShockModel.py is run, it specifies an infinite horizon consumer with a particular
-discount factor, permanent income growth rate, coefficient of relative risk aversion (etc), who
-faces lognormal shocks to permanent and transitory income each period with a particular
-standard deviation; it then solves this consumer’s problem and graphically displays the
-results.Model in their name.
-
-
-
Application modules use tool and model modules to solve, simulate, and/or estimate economic
-models for a particular purpose . While tool modules have no particular economic content and
-model modules describe entire classes of economic models, applications are uses of a model for
-some research purpose. For example, /Demos/SolvingMicroDSOPs/StructEstimation.py uses a
-consumption-saving model from ConsIndShockModel.py , calibrating it with age-dependent
-sequences of permanent income growth, survival probabilities, and the standard deviation of
-income shocks (etc); it then estimates the coefficient of relative risk aversion and shifter for
-an age-varying sequence of discount factors that best fits simulated wealth profiles
-to empirical data from the Survey of Consumer Finance. A particular application
-might have multiple modules associated with it, all of which generally reside in one
-directory.
-
0.1.3 In the incredibly unlikely scenario in which this document does not fill all of the gaps in your
-knowledge and answer any questions you might have while reading it, here is a collection of
-potentially helpful other resources.
-
- Online documentation for HARK tool and model modules, produced using Sphinx.
- A tutorial on Python 2.7 , straight from the source.
-
- Wikipedia article on object-oriented programming , not the subject of an edit war.
- QuantEcon , a collection of lectures on quantitative economic modeling from a couple
- of economists, which definitely in no way influenced HARK whatsoever.
- A tutorial on git , the repository management and tracking system used by the HARK
- project, created by Linus Torvalds.
-
- A tutorial on GitHub , a website that provides a useful framework for git that makes
- it significantly more user-friendly, despised by Linus Torvalds.
-
- Wikipedia article on Linus Torvalds , just in case.
-
-
- The homepage for NumPy , a numerical package extensively used by HARK.
-
- The homepage for Sphinx , a tool for producing HTML documentation.
-
- A collection of lecture notes by Chris Carroll, to catch you up on the basics of
- consumption-saving models.
-
- A particular set of lecture notes by Chris Carroll, explaining many of the numeric
- techniques used in HARK and offering an example of a simple structural model that,
- in a stunning coincidence, is implemented in HARK as an example.
-
- A deep dive into the theoretical foundations of buffer stock saving. Warning: dense.
-
-
-
0.2 HARK’s root directory contains six tool
-modules,
0.2.1 A key goal of the project is to create modularity and interoperability between models, making
-them easy to combine, adapt, and extend. To this end, the HARK.core module specifies a
-framework for economic models in HARK, creating a common structure for them on two levels
-that can be called “microeconomic” and “macroeconomic”.
-
Microeconomic models in HARK use the AgentType class to represent agents with an
-intertemporal optimization problem. Each of these models specifies a subclass of AgentType ; an
-instance of the subclass represents agents who are ex-ante homogeneous– they have common
-values for all parameters that describe the problem. For example, ConsIndShockModel specifies
-the IndShockConsumerType class, which has methods specific to consumption-saving
-models with idiosyncratic shocks to income; an instance of the class might represent all
-consumers who have a CRRA of 3, discount factor of 0.98, etc. The AgentType class
-has a solve method that acts as a “universal microeconomic solver” for any properly
-formatted model, making it easier to set up a new model and to combine elements
-from different models; the solver is intended to encompass any model that can be
-framed as a sequence of one period problems. For a complete description, see section
-0.3 .
-
Macroeconomic models in HARK use the Market class to represent a market (or other
-aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual
-agents in the model into aggregate outcomes that are “passed back” to the agents. For example,
-the market in a consumption-saving model might combine the individual asset holdings of all
-agents in the market to generate aggregate capital in the economy, yielding the interest rate on
-assets (as the marginal product of capital); the individual agents then learn the aggregate capital
-level and interest rate, conditioning their next action on this information. Objects that
-microeconomic agents treat as exogenous when solving (or simulating) their model are thus
-endogenous at the macroeconomic level. Like AgentType , the Market class also has a
-solve method, which seeks out a dynamic general equilibrium: a “rule” governing the
-dynamic evolution of macroeconomic objects such that if agents believe this rule and
-act accordingly, then their collective actions generate a sequence of macroeconomic
-outcomes that justify the belief in that rule. For a more complete description, see section
-0.4 .
-
Beyond the model frameworks, HARK.core also defines a “supersuperclass” called HARKobject .
-When solving a dynamic microeconomic model with an infinite horizon (or searching for a
-dynamic general equilibrium), it is often required to consider whether two solutions
-are sufficiently close to each other to warrant stopping the process (i.e. approximate
-convergence). It is thus necessary to calculate the “distance” between two solutions,
-so HARK specifies that classes should have a distance method that takes a single
-input and returns a non-negative value representing the (generally dimensionless)
-distance between the object in question and the input to the method. As a convenient
-default, HARKobject provides a “universal distance metric” that should be useful in many
-
-
-contexts.HARKobject , the user simply defines the attribute
-distance_criteria as a list of strings naming the attributes of the class that should be
-compared when calculating the distance between two instances of that class. For example, the
-class ConsumerSolution has distance_criteria = [’cFunc’] , indicating that only the
-consumption function attribute of the solution matters when comparing the distance between
-two instances of ConsumerSolution . See here for online documentation.
-
0.2.2 The HARK.utilities module carries a double meaning in its name, as it contains both
-utility functions (and their derivatives, inverses, and combinations thereof) in the
-economic modeling sense as well as utilities in the sense of general tools. Utility functions
-included at this time are constant relative risk aversion and constant absolute risk
-aversion. Other functions in HARK.utilities include some data manipulation tools
-(e.g. for calculating an average of data conditional on being within a percentile range
-of different data), functions for constructing discrete state space grids, convenience
-functions for retrieving information about functions, and basic plotting tools using
-matplotlib.pyplot .
-
The module also includes functions for constructing discrete approximations to continuous
-distributions (e.g. approxLognormal() to approximate a log-normal distribution) as well as
-manipulating these representations (e.g. appending one outcome to an existing distribution, or
-combining independent univariate distributions into one multivariate distribution). As a
-convention in HARK, continuous distributions are approximated as finite discrete distributions
-when solving models; an here for online
-documentation.
-
-
0.2.3 The HARK.interpolation module defines classes for representing interpolated function
-
-
-approximations. Interpolation methods in HARK all inherit from a superclass such as
-HARKinterpolator1D or HARKinterpolator2D , wrapper classes that ensures interoperability
-across interpolation methods. For example, HARKinterpolator1D specifies the methods
-__call__ and derivative to accept an arbitrary array as an input and return an identically
-shaped array with the interpolated function evaluated at the values in the array or its first
-derivative, respectively. However, these methods do little on their own, merely reshaping arrays
-and referring to the _evaluate and _der methods, which are not actually defined in
-HARKinterpolator1D . Each subclass of HARKinterpolator1D specifies their own implementation
-of _evaluate and _der particular to that interpolation method, accepting and returning only 1D
-arrays. In this way, subclasses of HARKinterpolator1D are easily interchangeable with each
-other, as all methods that the user interacts with are identical, varying only by “internal”
-methods.
-
When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to
-know the “distance” between functions generated by successive iterations of a solution procedure.
-To this end, each interpolator class in HARK must define a distance method that takes as an
-input another instance of the same class and returns a non-negative real number representing the
-“distance” between the two. As each of the HARKinterpolatorXD classes inherits from
-HARKobject , all interpolator classes have the default “universal” distance method; the user must
-simply list the names of the relevant attributes in the attribute distance_criteria of the
-class.
-
Interpolation methods currently implemented in HARK include (multi)linear interpolation up
-to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D
-total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation
-over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D “lower
-envelope” interpolator. See here for online documentation.
-
-
0.2.4 The HARK.simulation module provides tools for generating simulated data or shocks for
-post-solution use of models. Currently implemented distributions include normal, lognormal,
-Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use,
-these tools are used in the consumption-saving models of ConsIndShockModel.py to simulate
-permanent and transitory income shocks as well as unemployment events. See here for online
-documentation.
-
-
-
-
0.2.5 Methods for optimizing an objective function for the purposes of estimating a model can be
-found in HARK.estimation . As of this writing, the implementation includes only minimization by
-the Nelder-Mead simplex method, minimization by a derivative-free Powell method
-variant, and two small tools for resampling data (i.e. for a bootstrap); the minimizers
-are merely convenience wrappers (with result reporting) for optimizers included in
-scipy.optimize . Future functionality will include more robust global search methods, including
-genetic algorithms, simulated annealing, and differential evolution. See here for full
-documentation.
-
-
0.2.6 By default, processes in Python are single-threaded, using only a single CPU core. The HARK.parallel
-module provides basic tools for using multiple CPU cores simultaneously, with minimal
-effort.multiThreadCommands , which takes two arguments: a list
-of AgentType s and a list of commands as strings; each command should be a method of the
-AgentType s. The function simply distributes the AgentType s across threads on different cores
-and executes each command in order, returning no output (the AgentType s themselves are
-changed by running the commands). Equivalent results would be achieved by simply looping over
-each type and running each method in the list. Indeed, HARK.parallel also has a
-function called multiThreadCommandsFake that does just that, with identical syntax to
-multiThreadCommands ; multithreading in HARK can thus be easily turned on and
-off.here for full documentation.
-
0.3 The core of our microeconomic dynamic optimization framework is a flexible object-oriented
-representation of economic agents. The HARK.core module defines a superclass called AgentType ;
-
-
-each model defines a subclass of AgentType , specifying additional model-specific features and
-methods while inheriting the methods of the superclass. Most importantly, the method solve
-acts as a “universal solver” applicable to any (properly formatted) discrete time model. This
-section describes the format of an instance of AgentType as it defines a dynamic microeconomic
-problem;
0.3.1 A discrete time model in our framework is characterized by a sequence of “periods” that the
-agent will experience. A well-formed instance of AgentType includes the following
-attributes:
-
- solveOnePeriod : A function, or a list of functions, representing the solution
- method for a single period of the agent’s problem. The inputs passed to a
- solveOnePeriod function include all data that characterize the agent’s problem in
- that period, including the solution to the subsequent period’s problem (designated
- as solution_next ). The output of these functions is a single Solution object, which
- can be passed to the solver for the previous period.
- time_inv : A list of strings containing all of the variable names that are passed to at
- least one function in solveOnePeriod but do not vary across periods. Each of these
- variables resides in a correspondingly named attribute of the AgentType instance.
- time_vary : A list of strings naming the attributes of this instance that vary
- across periods. Each of these attributes is a list of period-specific values, which
- should be of the same length. If the solution method varies across periods, then
- ’solveOnePeriod’ is an element of time_vary .solution_terminal : An object representing the solution to the “terminal” period
- of the model. This might represent a known trivial solution that does not require
- numeric methods, the solution to some previously solved “next phase” of the model,
- a scrap value function, or an initial guess of the solution to an infinite horizon model.
-
-
- pseudo_terminal : A Boolean flag indicating that solution_terminal is not a
- proper terminal period solution (rather an initial guess, “next phase” solution, or
- scrap value) and should not be reported as part of the model’s solution.
- cycles : A non-negative integer indicating the number of times the agent will
- experience the sequence of periods in the problem. For example, cycles = 1 means
- that the sequence of periods is analogous to a lifecycle model, experienced once from
- beginning to end; cycles = 2 means that the agent experiences the sequence twice,
- with the first period in the sequence following the last. An infinite horizon problem
- in which the sequence of periods repeats indefinitely is indicated with cycles = 0 .
- tolerance : A positive real number indicating convergence tolerance, representing
- the maximum acceptable “distance” between successive cycle solutions in an infinite
- horizon model; it is irrelevant when cycles > 0 . As the distance metric on the space
- of solutions is model-specific, the value of tolerance is generally dimensionless.
- time_flow : A Boolean flag indicating the direction that time is “flowing.” When
- True , the variables listed in time_vary are listed in ordinary chronological order,
- with index 0 being the first period; when False , these lists are in reverse chronological
- order, with index 0 holding the last period.
- An instance of AgentType also has the attributes named in time_vary and time_inv , and
-may have other attributes that are not included in either (e.g. values not used in the model
-solution, but instead to construct objects used in the solution).
-
-
0.3.2 When an instance of AgentType invokes its solve method, the solution to the agent’s problem is
-stored in the attribute solution . The solution is computed by recursively solving the sequence of
-periods defined by the variables listed in time_vary and time_inv using the functions in
-solveOnePeriod . The time-varying inputs are updated each period, including the
-successive period’s solution as solution_next ; the same values of time invariant inputs in
-time_inv are passed to the solver in every period. The first call to solveOnePeriod uses
-solution_terminal as solution_next . In a finite horizon problem, the sequence of
-periods is solved cycles times over; in an infinite horizon problem, the sequence of
-periods is solved until the solutions of successive cycles have a “distance” of less than
-tolerance .
-
-
-
The output from a function in solveOnePeriod is an instance of a model-specific solution
-class. The attributes of a solution to one period of a problem might include behavioral functions,
-(marginal) value functions, and other variables characterizing the result. Each solution class must
-have a method called distance() , which returns the “distance” between itself and another
-instance of the same solution class, so as to define convergence as a stopping criterion; for many
-models, this will be the “distance” between a policy or value function in the solutions. If the
-solution class is defined as a subclass of HARKobject , it automatically inherits the default
-distance method, so that the user must only list the relevant object attributes in
-distance_criteria .
-
The AgentType also has methods named preSolve and postSolve , both of which take no
-arguments and do absolutely nothing. A subclass of AgentType can overwrite these blank
-methods with its own model specific methods. preSolve is automatically called near the
-beginning of the solve method, before solving the sequence of periods. It is used for specifying
-tasks that should be done before solving the sequence of periods, such as pre-constructing some
-objects repeatedly used by the solution method or finding an analytical terminal period solution.
-For example, the IndShockConsumerType class in ConsIndShockModel has a preSolve method
-that calls its updateSolutionTerminal method to ensure that solution_terminal is
-consistent with the model parameters. The postSolve method is called shortly after the
-sequence of periods is fully solved; it can be used for “post-processing” of the solution or
-performing a step that is only useful after solution convergence. For example, the
-TractableConsumerType in TractableBufferStockModel has a postSolve method that
-constructs an interpolated consumption function from the list of stable arm points found during
-solution.
-
Our universal solver is written in a very general way that should be applicable to any discrete
-time optimization problem– because Python is so flexible in defining objects, the time-varying
-inputs for each period can take any form. Indeed, the solver does no “real work” itself, but merely
-provides a structure for describing models in the HARK framework, allowing interoperability
-among current and future modules.
-
-
0.3.3 Because dynamic optimization problems are solved recursively in our framework, it is natural to
-list time-varying values in reverse chronological order– the solve() method loops over the
-values in each time-varying list in the same direction that a human would read them.
-When simulating agents after the solution has been obtained, however, it is much more
-convenient for time-varying parameters to be listed in ordinary chronological order–
-the direction in which they will be experienced by simulated agents. To allow the
-user to set the order in which “time is flowing” for an instance of AgentType , the
-HARK framework includes functionality to easily change ordering of time-varying
-
-
-values.
-
The attribute time_flow is True if variables are listed in ordinary chronological
-order and False otherwise. AgentType has the following methods for manipulating
-time:
-
- timeReport() : Prints to screen a description of the direction that time is flowing,
- for interactive convenience and as a reminder of the functionality.
- timeFlip() : Flips the direction of time. Each attribute listed in time_vary is
- reversed in place, and the value of time_flow is toggled.
- timeFwd() : Sets the direction of time to ordinary chronological order.
- timeRev() : Sets the direction of time to reverse chronological order.
- These methods are invoked to more conveniently access time-varying objects. When a new
-time-varying attribute is added, its name should be appended to time_vary , particularly if its
-values are used in the solution of the model (or is part of the solution itself). For example, the
-solve() method automatically adds the string ’solution’ to time_vary if it is not already
-present. Note that attributes listed in time_vary must be lists if solve() or timeFlip()
-are used. Some values that could be considered “time varying” but are never used to
-solve the model are more conveniently represented as a numpy.array object (e.g. the
-history of a state or control variable from a simulation); because the numpy.array
-class does not have a reverse() method, these attributes should not be listed in
-time_vary .
-
The base AgentType is sparsely defined, as most “real” methods will be application-specific.
-Two final methods bear mentioning. First, the __call__ method points to assignParameters() ,
-a convenience method for adding or adjusting attributes (inherited from HARKobject ). This
-method takes any number of keyword arguments, so that code can be parsimoniously
-written as, for example, AgentInstance(attribute1 = value1, attribute2 =
-value2) . Using Python’s dictionary capabilities, many attributes can be conveniently
-set with minimal code. Second, the resetRNG method simply resets the AgentType ’s
-random number generator (as the attribute RNG ) using the value in the attribute
-seed .inter alia ) ensuring that the same underlying sequence of shocks is
-used for every simulation run when a model is solved or estimated.
-
-
-
0.3.4 To provide a concrete example of how the AgentType class works, consider the very simple case
-of a perfect foresight consumption-saving model. The agent has time-separable, additive CRRA
-preferences over consumption, discounting future utility at a constant rate; he receives a
-particular stream of labor income each period, and knows the interest rate on assets that he
-holds from one period to the next. His decision about how much to consume in a particular
-period can be expressed in Bellman form as:
-
-
The ConsIndShockModel module defines the class PerfForesightConsumerType
-as a subclass of AgentType and provides solver functions for several
-variations of a consumption-saving model, including the perfect foresight
-problem.
MyConsumer = PerfForesightConsumerType(time_flow=True, cycles=1,
-
CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98,
-
LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90],
-
PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02],
-
Nagents=1000)
-
MyConsumer.solve()
-
The first line makes a new instance of ConsumerType , specifying that time is currently “flowing”
-
-
-forwardCRRA , Rfree , DiscFac ) and time varying parameters (LivPrb ,
-PermGroFac ). Note that dir(MyConsumer) shows all the attributes of MyConsumer . After running
-the solve method, MyConsumer will have an attribute called solution , which will be a list with
-eleven ConsumerSolution objects, representing the period-by-period solution to the
-model.ConsumerSolution has several attributes:
-
- cFunc : Optimal consumption function for this period as a function of
- (normalized) market resources MyConsumer.solution[0].cFunc(5) returns consumption at vFunc : Value function (over market resources cFunc .
- mNrmMin : Minimum value of normalized market resources cFunc and
- vFunc are defined.
- hNrm : Normalized human wealth– the PDV of future income (ignoring mortality)
- divided by current period income MPC : The constant marginal propensity to consume (linear consumption function).
-
- The following command generates the plot for the perfect foresight consumption
-function:
-
mMin = MyConsumer.solution[0].mNrmMin
-
plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10)
-
To solve a version of this problem in which the sequence of ten periods happens
-three times (yielding a solution attribute with thirty-one elements), the user can
-do:
-
-
-
MyConsumer(cycles = 3)
-
MyConsumer.solve()
-
To solve an infinite horizon problem in which the agent experiences the same one period
-problem indefinitely, the user can do:
-
OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02,
-
DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000)
-
OtherConsumer.solve()
-
This instance is specified as being infinite horizon by setting cycles=0 . Note that the
-time-varying inputs are still specified as (one element) lists, even though they take on the same
-value in every period; this is because an infinite horizon model might consist of a multi-period
-sequence repeated indefinitely, rather than just one repeated period. The solution attribute of
-OtherConsumer will be a list containing one instance of ConsumerSolution , representing the
-solution to every period of the model.
-
0.4 The modeling framework of AgentType is deemed “microeconomic” because it pertains only to
-the dynamic optimization problem of agents, treating all inputs of the problem as
-exogenously fixed. In what we label as “macroeconomic” models, some of the inputs
-for the microeconomic models are endogenously determined by the collective states
-and controls of agents in the model. In a dynamic general equilibrium, there must be
-consistency between agents’ beliefs about these macroeconomic objects, their individual
-behavior, and the realizations of the macroeconomic objects that result from individual
-choices.
-
The Market class in HARK.core provides a framework for such macroeconomic models, with a
-solve method that searches for a dynamic general equilibrium. An instance of Market includes a
-list of AgentType s that compose the economy, a method for transforming microeconomic
-outcomes (states, controls, and/or shocks) into macroeconomic outcomes, and a method for
-interpreting a history or sequence of macroeconomic outcomes into a new “dynamic rule” for
-agents to believe. Agents treat the dynamic rule as an input to their microeconomic problem,
-conditioning their optimal policy functions on it. A dynamic general equilibrium is a fixed point
-dynamic rule: when agents act optimally while believing the equilibrium rule, their
-individual actions generate a macroeconomic history consistent with the equilibrium
-rule.
-
-
0.4.1 The Market class uses a farming metaphor to conceptualize the process for generating a
-history of macroeconomic outcomes in a model. Suppose all AgentTypes in the economy
-believe in some dynamic rule (i.e. the rule is stored as attributes of each AgentType ,
-which directly or indirectly enters their dynamic optimization problem), and that they
-have each found the solution to their microeconomic model using their solve method.
-Further, the macroeconomic and microeconomic states have been reset to some initial
-orientation.
-
To generate a history of macroeconomic outcomes, the Market repeatedly loops over the
-following steps a set number of times:
-
- sow : Distribute the macroeconomic state variables to all AgentType s in the market.
- cultivate : Each AgentType executes their marketAction method, likely
- corresponding to simulating one period of the microeconomic model.
- reap : Microeconomic outcomes are gathered from each AgentType in the market.
- mill : Data gathered by reap is processed into new macroeconomic states according
- to some “aggregate market process”.
- store : Relevant macroeconomic states are added to a running history of outcomes.
-This procedure is conducted by the makeHistory method of Market as a subroutine of its solve
-method. After making histories of the relevant macroeconomic variables, the market then
-executes its calcDynamics function with the macroeconomic history as inputs, generating a new
-dynamic rule to distribute to the AgentType s in the market. The process then begins again, with
-the agents solving their updated microeconomic models given the new dynamic rule; the solve
-loop continues until the “distance” between successive dynamic rules is sufficiently
-small.
-
-
0.4.2 To specify a complete instance of Market , the user should give it the following
-attributes:
- agents : A list of AgentType s, representing the agents in the market. Each element in
- agents represents an ex-ante heterogeneous type; each type could have many ex-post
- heterogeneous agents.
- sow_vars : A list of strings naming variables that are output from the aggregate
- market process, representing the macroeconomic outcomes. These variables will be
- distributed to the agents in the sow step.
- reap_vars : A list of strings naming variables to be collected from the agents in the
- reap step, to be used as inputs for the aggregate market process.
- const_vars : A list of strings naming variables used by the aggregate market process
- that do not come from agents ; they are constant or come from the Market itself.
- track_vars : A list of strings naming variables generated by the aggregate market
- process that should be tracked as a history, to be used when calculating a new
- dynamic rule. Usually a subset of sow_vars .
- dyn_vars : A list of strings naming the variables that constitute a dynamic rule. These
- will be stored as attributes of the agents whenever a new rule is calculated.
- millRule : A function for the “aggregate market process”, transforming
- microeconomic outcomes into macroeconomic outcomes. Its inputs are named in
- reap_vars and const_vars , and it returns a single object with attributes named in
- sow_vars and/or track_vars . Can be defined as a method of a subclass of Market .
- calcDynamics : A function that generates a new dynamic rule from a history of
- macroeconomic outcomes. Its inputs are named in track_vars , and it returns a single
- object with attributes named in dyn_vars .
- act_T : The number of times that the makeHistory method should execute the
- “farming loop” when generating a new macroeconomic history.
- tolerance : The minimum acceptable “distance” between successive dynamic rules
- produced by calcDynamics to constitute a sufficiently converged solution.
-
-
- Further, each AgentType in agents must have two methods not necessary for microeconomic
-models; neither takes any input (except self ):
-
- marketAction : The microeconomic process to be run in the cultivate step. Likely
- uses the new macroeconomic outcomes named in sow_vars ; should store new values
- of relevant microeconomic outcomes in the attributes (of self ) named in reap_vars .
- reset : Reset, initialize, or prepare for a new “farming loop” to generate a
- macroeconomic history. Might reset its internal random number generator, set initial
- state varibles, clear personal histories, etc.
- When solving macroeconomic models in HARK, the user should also define classes to represent
-the output from the aggregate market process in millRule and for the model-specific
-dynamic rule. The latter should have a distance method to test for solution convergence;
-if the class inherits from HARKobject , the user need only list relevant attributes in
-distance_criteria .
-
0.4.3 To illustrate the Market class, consider a simple example in the emerging economic subfield of aesthemetrics, the
-FashionVictimModel .AgentType called FashionVictimType . Each period, fashion
-victims make a binary choice of style just
-wore the same style; they also pay switching costs (
-
-
-
-
-
-
Fashion victims are forward looking and discount future utility at a constant rate of
-
-
-
-
-
-
-
-
-
An instance of FashionVictimType is thus characterized by values of FashionVictimType ’s infinite horizon microeconomic model can be solved by
-backward induction in a few lines; the “one period solver” is given by solveFashion .
-However, while individual agents treat the dynamics of
The search for a dynamic general equilibrium is implemented in HARK’s Market class with the
-following definitions:
-
sow_vars = [’pNow’] (macroeconomic outcome is
reap_vars = [’sNow’] (microeconomic outcomes are
track_vars = [’pNow’] (must track history of
dyn_vars = [’pNextSlope’,’pNextIntercept’,’pNextWidth’] (dynamic rule
millRule = calcPunkProp (aggregate process: average the style choices of all agents)
-
calcDynamics = calcFashionEvoFunc (calculate new
act_T = 1000 (simulate 1000 periods of the fashion market)
-
tolerance = 0.01 (terminate solution when
The agents attribute has a list of 22 FashionVictimType s, which vary in their values of marketAction method of FashionVictimType
-simulates one period of the microeconomic model: each agent receives style preference
-shocks pNow ),
-and chooses which style to wear, storing it in the binary array sNow , an attribute of
-self .
-
The millRule for this market is extremely simple: it flattens the list of binary arrays of
-individual style choices (gathered in the reap step) and averages them into a new value
-of sow n back to the agents to begin the cycle
-again. Once a history of 1000 values of makeHistory
-method, we can calculate a new dynamic fashion rule with calcFashionEvoFunc by
-
-
-regressing HARKobject .
-
When the solve method is run, the solver successively solves each agent’s microeconomic
-problem, runs the makeHistory method to generate a 1000 period history of
0.5 This section provides an overview of how users can contribute new code to HARK, what sort of
-contributions are most desired, and style conventions for the toolKit. Before attempting to
-extend HARK, we strongly recommend that you familiarize yourself with its features and models
-by looking at the source code.
-
-
0.5.1 The easiest and most straightforward way to contribute to HARK is by writing new general
-purpose tools for inclusion in one of the top-level modules. This might be a commonly
-known data analysis technique (e.g. a kernel density estimator) for HARK.utilities , an
-optimization method (local or global) for HARK.estimation , an interpolation method for
-HARKinterpolation , etc. If you’ve found a technique useful in your own research and
-believe it could be of use to others on entirely different projects, then it probably
-belongs in HARK. Likewise, if you read through a HARK module expecting to find a
-certain tool or function (because of similar functions that are present, or merely because
-it’s very useful ) but don’t, this is a good sign that HARK has a hole that you can
-fill.
Secondly, the development team wants to expand HARK to include more canonical models.
-Most of the models available in the toolKit at the time of its public beta release concern agents
-with (geometrically discounted) CRRA utility over a unitary consumption good, who receive
-
-
-permanent and transitory shocks to income and have access to a single risk free asset. This is an
-important class of models, but that description has an awful lot of qualifiers. In short, there are
-many more common features of dynamic household models that are not yet in HARK, but would
-be very valuable for the toolKit. New model contributions should “play nicely” with existing
-models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the
-greatest extent possible. For ideas of potential model extensions for HARK, see section
-0.6.2 .
Finally, contributions to HARK are not restricted to entirely new functionality. While we have
-done our best to provide flexible frameworks and useful tools, we have almost certainly
-overlooked an unexpected case that would not be (properly) handled by the toolKit as is.
-Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake
-or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as
-an Issue on GitHub so that someone else might. The “mistake” you find might not
-even be in the code itself, but a deficiency in the documentation: a misnamed (or
-incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing)
-comment, even a grammatical or spelling error. No matter how niggling, we want your
-fix.
-
For those who want to contribute to the HARK project without programming a new model or
-digging through the code for errors and omissions, consider reading through the Issues page on
-GitHub to see what other users have flagged for development. You might find several pieces of
-“low hanging fruit” for you to quickly fix.
-
0.5.2 Git is a distributed version control system that has gained increasing popularity in the decade
-since its creation; it is designed to enable collaboration and “non-linear workflow” among a
-(potentially disperse) programming team. As HARK is to be developed by volunteer researchers
-all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds
-as a command line tool, others have since extended git with further functionality and
-a user interface more friendly to those more familiar with GUIs. Foremost among
-these tools is GitHub.com, where the HARK project is hosted. This section provides a
-brief overview of how to use GitHub to interact with (and potentially contribute to)
-HARK.
-
To prepare to contribute to HARK, follow these simple steps:
-
- Make a GitHub account: Go to github.com ; the homepage should prompt
- you to choose a username and password. Signing up is quick, easy, and
-
-
- free.Install GitHub Desktop : Go to desktop.github.com and download the Desktop
- application for your operating system, then install it. After opening Desktop for the
- first time, enter your GitHub login to connect it to your account.
- Fork the HARK repository: Go to the HARK repository page , click on the Fork
- button in the upper right, and choose to fork HARK to your own GitHub account.
- This creates a new repository identical to the one hosted by Econ-Ark at the moment
- you fork it, but hosted on your account instead.
- Add the repository to Desktop: Open GitHub Desktop, click on the yourusername/HARK ,
- click the Clone button at the bottom of the dialogue window, and choose a directory
- on your local machine.
- You now control a “fork” of the main HARK repository, hosted by GitHub, and have a clone of
-this fork on your local machine. You are free to edit your fork of HARK in any way you’d like;
-other GitHub users can view your fork (and fork it themselves), but they cannot make changes to
-your fork unless you give them permission by adding them as a contributor. Changes made
-to the local copy of your fork do not get automatically sent to the remote copy on
-GitHub for public viewing. To make changes to your fork on GitHub, follow these
-steps:
-
- Make local changes: Edit your local copy of the repository in any way you’d like:
- add or delete lines from existing files; add, delete, or rename entire files or directories.
- Commit local changes: Open the repository in GitHub Desktop and click on the
- Changes tab. In the lower left, provide a short description of the changes made in
- the Summary field, and (optionally) longer comments about these changes in the
- Description field. Click the check-mark Commit button.
- Push the commit: Click on the Sync button near the upper right to “push” your
- local commits to the remote server; this will also pull down remote commits from
- other contributors to your fork that were made since the last time you synced.
-
-
- The left hand panel of the Changes tab has a summary of the files that have been changed,
-added, or removed since your last commit; the right hand panel shows the specific
-changes made to each changed file. After step 2, a record of your changes has been
-stored locally by the git system, but is not yet reflected in the public remote copy on
-GitHub.
Suppose you have mucked about in your fork for a while and have made changes to HARK
-that you think should be included in the main repository hosted by Econ-Ark. To make your
-dream a reality, follow these steps:
-
- Isolate the changes: If you mucked around for quite a while, making various changes
- here and there, and then later made some contribution that you want to include in
- the main repository, you should isolate the desired changes in a new branch of your
- fork . Make a new branch by clicking the button toward the upper left of GitHub
- Desktop, revert that branch to the commit just before you forked from the main
- repository,only the changes you want to push to the main
- repository .
- Issue a pull request: Go to HARK’s GitHub page and click the “New pull request”
- button. Click the text that says “compare across forks”, and select econ-ark/HARK
- as the base fork, master as the base, your fork as the head fork, and the branch to
- be merged as the “compare”. In the boxes below, give a name to your pull request
- and provide sufficient comments about your contribution so that a development team
- member can understand what you’ve done. Click the “Create pull request” button.
- Be patient: Someone from the development team will look at your contribution and
- either merge it into the main repository or return it with comments. See section 0.5.3
- for procedures followed by the HARK team when evaluating outside contributions.
-
- 0.5.3 Before submitting to HARK, the development team would like you to make sure that
-your intended contribution is listed as an Issue on the GitHub page. If you are going
-to fix or address an Issue that is already posted, reply to that post with a note
-indicating that you are about to work on it and an estimate of when you will make a
-pull request for it. If you want to do something that no one has mentioned yet as
-an Issue, make a new Issue on GitHub (briefly) describing what you intend to
-do.
After creating a pull request to submit your contribution, it will be evaluated by one or more
-members of the HARK team. Criteria that the HARK team will consider when choosing whether
-to accept a submission include:
-
- Does the contribution break previous code? If you change or expand an existing
- function, you should make sure that this does not cause some models or applications
- to work incorrectly. In the near future, HARK will have a core set of tests to run to
- check for full compatibility.
- Is the code well documented? Code submissions should have properly formatted
- docstrings for each new function, class, method, and module (see section 0.5.5 ) and
- enough comments for a reader to follow the algorithm.
- Is the contribution relevant? The development team intends for HARK to
- be diverse, but not everything goes in HARK. If your submission concerns a
- representative agent model or a method for estimating a reduced form model, then
- it probably doesn’t belong in HARK.
-
-
- Is the code demonstrated with an example? If your solve a new model in
- HARK, you should include a set of example parameters for other users to easily
- test. If you’ve contributed a new function, consider providing a snippet of code
- demonstrating its use.Is the code correct? Your code should actually do what it claims to do.
- Does the code use HARK conventions? To the best of your ability, contributions
- to HARK should follow the style guidelines in section 0.5.4 . We don’t demand
- perfection, as these are guidelines rather than rules.
- Is the code well integrated in HARK? In the beta release of HARK, we have
- tried to present a “taxonomy” of consumption-saving models that build up from each
- other. When possible, new models in HARK should try to continue this pattern. A
- submission that introduces a model in a new module that could have easily been made
- as an extension of an existing module might be rejected. However, we acknowledge
- that there is value in providing a solver for a new model, and that a future contributor
- might make her mark by integrating it into the existing hierarchy.
- Is this already in HARK? If your contribution is already in HARK, then it will
- almost certainly be rejected. Of course, if you submit a version that dominates the
- existing implementation (or even one that works better in a well described, non-trivial
- set of circumstances), it belongs in HARK.
- The HARK team intends to release more specific guidelines in the near future, so that contributors
-are better able to judge their submissions before issuing a pull request. We are in the process of
-recruiting “czars” to oversee particular aspects of HARK, providing their knowledge and
-expertise.
0.5.4 Object naming conventions in HARK are fairly different than existing standards, and differ
-somewhat between tool modules vs model or application modules. The following conventions
-apply throughout HARK:
-
- Functions and methods are always in “camel case”: no underscores, first letter is lower
- case, first letter of each subsequent word is capitalized. E.g. approxLognormal .
-
- Function and method names should accurately and concisely describe what the
- function does; the first word in the name must be a verb .
- Variable and class names should not have a verb as their first word.
-
- Class names should use no underscores and capitalize the first letter of each word;
- moreover, a class name must include a noun . E.g. ConsPerfForesightSolver .
- When naming variables in model modules, the HARK team strongly discourages using single
-letter names, like c for consumption. Instead, we encourage contributors to use longer, more
-descriptive variable names using additional words and common abbreviations to specify the
-variable more precisely. In /Documentation/NARK.pdf , we list standard single letter variable
-“bases” and an array of prefixes and suffixes to adjust them. Economic variables in model
-modules should (usually) not use underscores, instead using camel case to the greatest extent
-possible.
Conventions for naming variables in HARK’s tool modules are significantly closer to more
-commonly used standards. Variable names should be in all lower case, with underscores between
-words, e.g. data_to_match . The functions and classes in these modules are more general
-and almost surely do not have any inherent “economic content”; they are numerical
-or algorithmic objects, not variables that might appear in an equation in an article
-for a (non-computational) economics journal. Variable names in application modules
-(e.g. the files that execute the cstwMPC estimations) are a mix of the conventions for tool
-and model files, as appropriate for each variable. That is, variables that are directly
-related to “economic variables” in model modules should follow those conventions, while
-objects created solely for data manipulation or reporting should use the style of tool
-modules.
-
-
-
0.5.5 The HARK team wants the toolKit to be as accessible to users as possible; our greatest
-fearonline documentation for all
-of our tool and model modules, so that users can learn about what’s available in HARK without
-digging through the source code. When making contributions to HARK, the development team
-asks users to format their inline documentation to work with Sphinx by following a few simple
-rules.
-
- The top of every module should begin with a “docstring” providing a clear description
- of the contents of the module. The first sentence should concisely summarize
- the file, as it may appear in an index or summary of all modules without the
- remaining sentences. A docstring at the top of a module should be formatted
- as:"""
-
Specifies an economic model and provides methods for solving it. More
- specific description of the key features of the model and variations
- of it in this module. Maybe some comments about the solution method
- or limitations of the model. Your bank account routing number.
-
"""
-
- The line directly below the declaration of a function, method or class should begin a
- docstring describing that object. As with modules, the first sentence should concisely
- summarize the function or class, as it might be included in an index or summary. For
- functions and methods, the docstring should be formatted as:
- def functionName(input1,input2):
-
-
-
"""
-
Concise description of the function. More details about what
-
the function does, options or modes available, and maybe
- mathematical
-
methods used. Credit to a source if you poached their
- algorithm.
-
Parameters
-
––––––––––
-
input1: type
-
Description of what input1 represents.
-
input2: type
-
Description of what input2 represents.
-
Returns
-
–––––––
-
output_name: type
-
Description of the output(s) of the function. Might have
-
multiple entries. If no output, this is just "None".
-
"""
-
- Provide ample comments within a function or method so that a relatively intelligent
- reader can follow along with your algorithm. Short comments can follow at the end
- of a line, longer comments (or descriptions of the step or task about to be performed)
- should precede a block of code on the line(s) above it.
- Finally, if you write a new model module, the HARK team asks that you also provide a short
-mathematical writeup of the model as a PDF. This document does not need to go into great
-detail about the solution method for the model or the functions and classes included in
-the module, merely specify the economic model and provide a summary of how it
-is solved. See /Documentation/ConsumptionSavingModels.pdf for an example of
-this.
-
-
-
0.6 HARK is in a state of perpetual incompleteness, always expanding to include more
-tools and more models. As an open source project in the hands of many contributors
-around the world, the HARK team cannot fully predict future features of the toolKit.
-This section provides an overview of what we would like to see in the near future, as
-well as potential mechanisms to encourage researchers to contribute their code to the
-project.
-
-
0.6.1 The current frameworks in HARK.core concern agents who have “isolated” dynamic
-problems or interact with each other only through aggregate outcomes to which they
-contribute atomically. We hope to eventually provide a framework for models in which
-agents directly interact with each other; this may include a system for specifying the
-state of networks and how connections are formed and broken. HARK.core might also
-develop a framework more suited to industrial organization models and dynamic
-games.
The HARK.estimation module in particular is very sparsely populated, with its
-functions mostly serving as wrappers for “off the shelf” optimization methods from
-scipy.optimize . There are plenty of non-standard or more complex optimization
-techniques available, including global and/or stochastic search methods. We believe
-that additional optimizers are “low hanging fruit” as straightforward contributions
-to HARK and thus will be added in the near future. Moreover, there are plenty of
-distributions that have no approximator in HARK.utilities (or data generator in
-HARK.simulation ), and these functions are among the easiest for new contributors
-to write. The data analysis tools in HARK.utilities only include functions that we
-happened to use for our current research projects, and we expect that this will be the
-mode of growth for future tools. There are some obvious holes that we believe will be
-filled quite quickly; for example, we provide a rudimentary non-parametric kernel
-regression function, but have not written a kernel density estimator to generate PDFs from
-data.
-
The classes currently in HARK.interpolation are all variations on linear (or
-cubic) spline interpolation, and each is specifically written for a fixed number
-of dimensions. As these methods use full tensor grids, they are highly impractical
-for representing functions in higher dimensions, suffering greatly from the curse of
-
-
-dimensionality.HARK.interpolation will soon include techniques more applicable
-to high dimensional state spaces, including Smolnyak interpolation, other (adaptive) sparse grid
-methods, and regression-based interpolation. For lower-dimensional spaces, HARK will also soon
-provide Delaunay interpolation classes.
-
The HARK.parallel module currently provides a simple interface for handling ex-ante
-heterogeneity among agents. While useful for speeding up moderately-sized problems, the
-libraries used are only able to access the CPU of the local machine running the code. In the very
-near future, the HARK team will provide an interface (and example) for using Apache Spark,
-which bills itself as a “fast and general engine for large-scale data processing”. Specifically, users
-will be able to easily scale up their computing resources and run HARK code on an Amazon EC2
-session or other cluster.
-
0.6.2 The economics models currently included in HARK mostly concern consumption-saving problems
-with constant relative risk aversion consumers who experience fully permanent and fully
-transitory shocks to income. This is obviously a very small subset of all consumption-saving
-models (which are themselves a small subset of all dynamic models economists might be
-interested in). Consider some examples of variants, features, and extensions that are not included
-in HARK, but could be in the future:
-
- Consumers with other utility functions, such as CARA or expo-power.
-
- Consumers with time-inconsistent preferences, e.g. hyperbolic discounting.
-
- Income shocks that are neither fully transitory nor fully permanent.
-
- Consumers with a bequest motive or terminal utility function.
-
- Utility from leisure and endogenous labor supply.
-
- Investment in a durable consumption good such as housing.
-
-
-
- Exogenous shocks to expenses.
-
- Multiple consumption goods, as with endogenous medical care.
-
- Portfolio allocation between a risky and riskless asset.
-
- Job search intensity as a control variable.
-
- Endogenous health evolution and health investment.
- The HARK team is currently developing a module for mixing discrete and continuous
-choice models, in the style of Jorgensen, Rust, Iskhakov, and Schjerning (2015). Among
-many other models, this framework will enable HARK users to easily develop features
-like:
-
- Labor supply on the extensive margin: the timing of retirement.
-
- Selection of job type or sector (by riskiness, specific human capital, etc).
-
- Choice of medical insurance contract.
-
- Decision to rent or own housing.
-
- Endogenous default on debt (with “macroeconomic” pricing of debt).
-
- Household formation, dissolution, and reproduction.
- Finally, we hope to provide a generic solver for any discrete choice model with T1EV shocks.
-The scope of HARK is limited only by what users choose to put in it. We hope that
-within a year of its debut, HARK will include a significant number of canonical models,
-providing more building blocks for future researchers to create novel combinations and new
-extensions.
-
-
-
-
0.6.3 While HARK users are free to write and contribute any tools and models that they believe could
-be helpful, building the toolKit in a “bottom up” style, the HARK team hopes to provide
-mechanisms for targeted “top down” development. The team is working to secure grant funding for
-HARK so as to offer “bounties” for particular models, tools, applications, or frameworks. Suppose
-the team decides that the model in Important Paper X should be included as part of HARK; we
-will issue a call for contributions to fill this hole, offering a modest prize of, say, $20,000 to the
-programmer who submits the best implementation of the model, as judged by clearly stated
-criteria.
Bounties might be placed on items as small as a single data analysis tool or as large as a
-complex estimation of a large general equilibrium model, with prizes scaling accordingly. We
-hasten to emphasize that we cannot guarantee funding for HARK, so the bounties (like
-everything else in this section) are purely speculative. As economists, the HARK team
-understands the incentive compatibility problems associated with asking researchers to
-contribute significant amounts of their time, effort, and unique skills in service of a
-tenuous concept like the Greater Good in exchange for even more tenuous Glory. We
-acknowledge that economic researchers are also optimizing agents with their own preferences
-over leisure and consumption, and agree that Greed is a valid mechanism to expand
-HARK.
-
0.6.4 As observant readers may have noticed, the HARK repository is hosted by a GitHub account
-named Econ-Ark, with no explanation as to what this might mean. Looking a few years into the
-future, the development team believes that HARK might be the first piece of a broader collection
-of computational economics resources. HARK is open source but somewhat akin to an
-exclusive club: contributions are required to adhere to standards and norms so as to foster
-modularity and interoperability among models and tools. Consistent with its namesake ship,
-the Econ-Ark has a significantly lower bar for admission, with a place on board for
-everything.
-
On one level, the Ark might serve as a warehouse for any and all quantitative models and
-tools, for any economic purpose, in any subfield, and written in any language. Through
-nearly unanimous feedback, the HARK team understands that many economists are
-willing to share code that they have already written, but are reluctant to expend the
-
-
-considerable effort to translate and format it for HARK. In the future, we will set up a
-mechanism for code like this to be hosted on the Ark, with minimal requirements
-imposed on its authors (and correspondingly minimal warranty for potential users).
-Relevant projects on the Ark might become bounty targets for HARK, with users
-incentivized to port the original code into Python and to meet HARK standards;
-direct access to the original source code will significantly expedite the translation
-process, and provide a suitable benchmark for testing and verifying the contribution to
-HARK.
-
On a different level, the Ark might also include cohesive toolkits like HARK, but geared
-toward purposes other than heterogeneous agents macroeconomics models. For example, the
-previously mentioned modules for networks among agents (and other agent-based modeling),
-industrial organization and dynamic games, and traditional discrete choice models might become
-their own toolkits, with some interoperability with HARK. In support of this, some of the tool
-modules currently in HARK might be “promoted” and become universally shared across all
-Econ-Ark toolkits.
-
The dark secret of HARK is that it is named for heterogeneous agents macroeconomics, but
-designed and written by structural microeconomists. The development team believes these
-subfields to have considerable overlap in terms of methods and techniques, and that HARK
-might not be noticed by half of its potential audience due to its name. In a general sense, the
-Econ-Ark gives us cover to expand the scope of our efforts and/or rebrand the toolKit for an
-alternative audience.
-
-
-
-
-
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diff --git a/Documentation/HARKmanual/HARKmanual.md b/Documentation/HARKmanual/HARKmanual.md
deleted file mode 100644
index ee01bb1b2..000000000
--- a/Documentation/HARKmanual/HARKmanual.md
+++ /dev/null
@@ -1,1263 +0,0 @@
-## A User’s Guide for HARK:
-Heterogeneous Agents Resources and toolKit
-
-May 21, 2018
-
-Christopher D Carroll1
-Johns Hopkins University
-Alexander M Kaufman2
-Consumer Financial Protection Bureau
-David C Low3
-Consumer Financial Protection Bureau
-Nathan M Palmer4
-U.S. Treasury Office of Financial Research
-Matthew N White5
-University of Delaware
-
-
-
-
-
-1 Carroll: , the home of the HARK repository. Click on the lime green button labeled “Clone or download” toward the upper right of the main panel, and select “Download ZIP”.[3](HARKmanual3.html#fn3x0)
- 5. Unzip HARK: Unpack the HARK.zip file using any archive utility, like [Peazip](http://www.peazip.org/). Put the files anywhere you’d like, maintaining the internal directory structure.
- 6. Run Spyder: Open a command prompt and do spyder. Spyder is an interactive development environment (IDE) for iPython, a slightly prettier, more interactive flavor of Python.
- 7. Open a HARK module: Go to the directory where you put HARK and open any file with the .py extension; we recommend /Demos/ConsumptionSaving/ConsIndShockModel.py.
- 8. Run the module: Click on the green arrow “play” button toward the right side of the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue box pops up). Congratulations! HARK is now up and running on your computer.
-
-If you followed our recommendation to try
-/Demos/ConsumptionSaving/ConsIndShockModel.py, you should see graphical
-representations of the solution to a few consumption-saving models with
-idiosyncratic shocks to permanent and transitory income (see
-/Documentation/ConsumptionSavingModels.pdf for details of these models). If
-you chose a different module, you might get some examples of some other model,
-results of a structural estimation, a polite notification that this particular
-module doesn’t do much on its own, or no output at all. See the README.md for
-a full list of modules from which you can expect non-trivial output.
-
-#### 0.1.2 Structure of HARK
-
-HARK is written in Python, an object-oriented programming (OOP) language that
-has experienced increasing popularity in the scientific community in the past
-several years. A significant reason for the adoption of Python is the numpy
-and scipy packages, which offer a wide array of mathematical and statistical
-functions and tools; HARK makes liberal use of these libraries. Python’s
-object-oriented nature allows models in HARK to be easily extended: more
-complex models can inherit functions and methods from more fundamental
-“parent” models, eliminating the need to reproduce or repurpose code.
-
-As implied in the previous section, we strongly encourage HARK users to use
-the Anaconda distribution of Python, which includes all commonly used
-mathematical and scientific packages, an interactive development environment
-for iPython (Spyder), and a package manager that allows users to quickly
-install or update packages not included in the default distribution (conda).
-
-For users unfamiliar with OOP, we strongly encourage you to review the
-background material on OOP provided by the good people at
-[QuantEcon](http://quant-econ.net/) (for more on them, see below) at this
-link: [Object Oriented Programming](http://quant-econ.net/py/python_oop.html).
-Unlike non-OOP languages, OOP bundles together data and functions into
-objects. These can be accessed via: object_name.data and
-object_name.method_name(), respectively. For organizational purposes,
-definitions of multiple objects are stored in modules, which are simply files
-with a .py extension. Modules can be accessed in Python via:
-
-import module_name as import_name
-
-This imports the module and gives it a local name of import_name. We can
-access a function within this module by simply typing:
-import_name.function_name(). The following example will illustrate the usage
-of these commands. CRRAutility is the function object for calculating CRRA
-utility supplied by HARK.utilities module. CRRAutility is called attributes of
-the module HARK.utilities. In order to calculate CRRA utility with a
-consumption of 1 and a coefficient of risk aversion of 2 we run:
-
-import HARK.utilities as Hutil
-
-Hutil.CRRAutility(1,2)
-
-Python modules in HARK can generally be categorized into three types: tools,
-models, and applications. Tool modules contain functions and classes with
-general purpose tools that have no inherent “economic content”, but that can
-be used in many economic models as building blocks or utilities; they could
-plausibly be useful in non-economic settings. Tools might include functions
-for data analysis (e.g. calculating Lorenz shares from data, or constructing a
-non-parametric kernel regression), functions to create and manipulate discrete
-approximations to continuous distributions, or classes for constructing
-interpolated approximations to non-parametric functions. Tool modules
-generally reside in HARK’s root directory and have names like HARK.simulation
-and HARK.interpolation; they do not necessarily do anything when run.
-
-Model modules specify particular economic models, including classes to
-represent agents in the model (and the “market structure” in which they
-interact) and functions for solving the “one period problem” of those models.
-For example, ConsIndShockModel.py concerns consumption-saving models in which
-agents have CRRA utility over consumption and face idiosyncratic shocks to
-permanent and transitory income. The module includes classes for representing
-“types” of consumers, along with functions for solving (several flavors of)
-the one period consumption-saving problem. When run, model modules might
-demonstrate example specifications of their models, filling in the model
-parameters with arbitrary values. When ConsIndShockModel.py is run, it
-specifies an infinite horizon consumer with a particular discount factor,
-permanent income growth rate, coefficient of relative risk aversion (etc), who
-faces lognormal shocks to permanent and transitory income each period with a
-particular standard deviation; it then solves this consumer’s problem and
-graphically displays the results.[4](HARKmanual4.html#fn4x0) Model modules
-generally have Model in their name.
-
-Application modules use tool and model modules to solve, simulate, and/or
-estimate economic models for a particular purpose. While tool modules have no
-particular economic content and model modules describe entire classes of
-economic models, applications are uses of a model for some research purpose.
-For example, /Demos/SolvingMicroDSOPs/StructEstimation.py uses a consumption-
-saving model from ConsIndShockModel.py, calibrating it with age-dependent
-sequences of permanent income growth, survival probabilities, and the standard
-deviation of income shocks (etc); it then estimates the coefficient of
-relative risk aversion and shifter for an age-varying sequence of discount
-factors that best fits simulated wealth profiles to empirical data from the
-Survey of Consumer Finance. A particular application might have multiple
-modules associated with it, all of which generally reside in one directory.
-
-#### 0.1.3 Other Resources
-
-In the incredibly unlikely scenario in which this document does not fill all
-of the gaps in your knowledge and answer any questions you might have while
-reading it, here is a collection of potentially helpful other resources.
-
- * [Online documentation](https://econ-ark.github.io/HARK/) for HARK tool and model modules, produced using Sphinx.
- * A [tutorial on Python 2.7](https://docs.python.org/2.7/tutorial/), straight from the source.
- * [Wikipedia article on object-oriented programming](https://en.wikipedia.org/wiki/Object-oriented_programming), not the subject of an edit war.
- * [QuantEcon](http://quant-econ.net/), a collection of lectures on quantitative economic modeling from a couple of economists, which definitely in no way influenced HARK whatsoever.
- * A [tutorial on git](https://www.atlassian.com/git/tutorials/), the repository management and tracking system used by the HARK project, created by Linus Torvalds.
- * A [tutorial on GitHub](https://guides.github.com/activities/hello-world/), a website that provides a useful framework for git that makes it significantly more user-friendly, despised by Linus Torvalds.
- * [Wikipedia article on Linus Torvalds](https://en.wikipedia.org/wiki/Linus_Torvalds), just in case.
- * The [homepage for NumPy](http://www.numpy.org/), a numerical package extensively used by HARK.
- * The [homepage for Sphinx](http://www.sphinx-doc.org/), a tool for producing HTML documentation.
- * A [collection of lecture notes](http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/) by Chris Carroll, to catch you up on the basics of consumption-saving models.
- * A [particular set of lecture notes](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/) by Chris Carroll, explaining many of the numeric techniques used in HARK and offering an example of a simple structural model that, in a stunning coincidence, is implemented in HARK as an example.
- * A [deep dive](http://www.econ2.jhu.edu/people/ccarroll/BufferStockTheory.pdf) into the theoretical foundations of buffer stock saving. Warning: dense.
-
-### 0.2 General Purpose Tools
-
-HARK’s root directory contains six tool modules,[5](HARKmanual5.html#fn5x0)
-each containing a variety of functions and classes that can be used in many
-economic models– or even for mathematical purposes that have nothing to do
-with economics. Some of the tool modules are very sparely populated at this
-time, while others are quite large. We expect that all of these modules will
-grow considerably in the near future, as new tools are “low hanging fruit” for
-contribution to the project.[6](HARKmanual6.html#fn6x0)
-
-#### 0.2.1 HARKcore
-
-A key goal of the project is to create modularity and interoperability between
-models, making them easy to combine, adapt, and extend. To this end, the
-HARK.core module specifies a framework for economic models in HARK, creating a
-common structure for them on two levels that can be called “microeconomic” and
-“macroeconomic”.
-
-Microeconomic models in HARK use the AgentType class to represent agents with
-an intertemporal optimization problem. Each of these models specifies a
-subclass of AgentType; an instance of the subclass represents agents who are
-ex-ante homogeneous– they have common values for all parameters that describe
-the problem. For example, ConsIndShockModel specifies the IndShockConsumerType
-class, which has methods specific to consumption-saving models with
-idiosyncratic shocks to income; an instance of the class might represent all
-consumers who have a CRRA of 3, discount factor of 0.98, etc. The AgentType
-class has a solve method that acts as a “universal microeconomic solver” for
-any properly formatted model, making it easier to set up a new model and to
-combine elements from different models; the solver is intended to encompass
-any model that can be framed as a sequence of one period problems. For a
-complete description, see section 0.3.
-
-Macroeconomic models in HARK use the Market class to represent a market (or
-other aggregator) that combines the actions, states, and/or shocks (generally,
-outcomes) of individual agents in the model into aggregate outcomes that are
-“passed back” to the agents. For example, the market in a consumption-saving
-model might combine the individual asset holdings of all agents in the market
-to generate aggregate capital in the economy, yielding the interest rate on
-assets (as the marginal product of capital); the individual agents then learn
-the aggregate capital level and interest rate, conditioning their next action
-on this information. Objects that microeconomic agents treat as exogenous when
-solving (or simulating) their model are thus endogenous at the macroeconomic
-level. Like AgentType, the Market class also has a solve method, which seeks
-out a dynamic general equilibrium: a “rule” governing the dynamic evolution of
-macroeconomic objects such that if agents believe this rule and act
-accordingly, then their collective actions generate a sequence of
-macroeconomic outcomes that justify the belief in that rule. For a more
-complete description, see section 0.4.
-
-Beyond the model frameworks, HARK.core also defines a “supersuperclass” called
-HARKobject. When solving a dynamic microeconomic model with an infinite
-horizon (or searching for a dynamic general equilibrium), it is often required
-to consider whether two solutions are sufficiently close to each other to
-warrant stopping the process (i.e. approximate convergence). It is thus
-necessary to calculate the “distance” between two solutions, so HARK specifies
-that classes should have a distance method that takes a single input and
-returns a non-negative value representing the (generally dimensionless)
-distance between the object in question and the input to the method. As a
-convenient default, HARKobject provides a “universal distance metric” that
-should be useful in many contexts.[7](HARKmanual7.html#fn7x0) When defining a
-new subclass of HARKobject, the user simply defines the attribute
-distance_criteria as a list of strings naming the attributes of the class that
-should be compared when calculating the distance between two instances of that
-class. For example, the class ConsumerSolution has distance_criteria =
-[’cFunc’], indicating that only the consumption function attribute of the
-solution matters when comparing the distance between two instances of
-ConsumerSolution. See [here](https://econ-
-ark.github.io/HARK/generated/HARK.core.html) for online documentation.
-
-#### 0.2.2 HARK.utilities
-
-The HARK.utilities module carries a double meaning in its name, as it contains
-both utility functions (and their derivatives, inverses, and combinations
-thereof) in the economic modeling sense as well as utilities in the sense of
-general tools. Utility functions included at this time are constant relative
-risk aversion and constant absolute risk aversion. Other functions in
-HARK.utilities include some data manipulation tools (e.g. for calculating an
-average of data conditional on being within a percentile range of different
-data), functions for constructing discrete state space grids, convenience
-functions for retrieving information about functions, and basic plotting tools
-using matplotlib.pyplot.
-
-The module also includes functions for constructing discrete approximations to
-continuous distributions (e.g. approxLognormal() to approximate a log-normal
-distribution) as well as manipulating these representations (e.g. appending
-one outcome to an existing distribution, or combining independent univariate
-distributions into one multivariate distribution). As a convention in HARK,
-continuous distributions are approximated as finite discrete distributions
-when solving models; an -dimensional random variable
-is formatted as a length  list of 1D arrays, with
-the first element representing event probabilities and all other elements are
-realizations of the  component RVs. This both
-simplifies solution methods (reducing numeric integrals to simple dot
-products) and allows users to easily test whether their chosen degree of
-discretization yields a sufficient approximation to the full distribution. See
-[here](https://econ-ark.github.io/HARK/generated/HARK.utilities.html) for
-online documentation.
-
-#### 0.2.3 HARK.interpolation
-
-The HARK.interpolation module defines classes for representing interpolated
-function approximations. Interpolation methods in HARK all inherit from a
-superclass such as HARKinterpolator1D or HARKinterpolator2D, wrapper classes
-that ensures interoperability across interpolation methods. For example,
-HARKinterpolator1D specifies the methods __call__ and derivative to accept an
-arbitrary array as an input and return an identically shaped array with the
-interpolated function evaluated at the values in the array or its first
-derivative, respectively. However, these methods do little on their own,
-merely reshaping arrays and referring to the _evaluate and _der methods, which
-are not actually defined in HARKinterpolator1D. Each subclass of
-HARKinterpolator1D specifies their own implementation of _evaluate and _der
-particular to that interpolation method, accepting and returning only 1D
-arrays. In this way, subclasses of HARKinterpolator1D are easily
-interchangeable with each other, as all methods that the user interacts with
-are identical, varying only by “internal” methods.
-
-When evaluating a stopping criterion for an infinite horizon problem, it is
-often necessary to know the “distance” between functions generated by
-successive iterations of a solution procedure. To this end, each interpolator
-class in HARK must define a distance method that takes as an input another
-instance of the same class and returns a non-negative real number representing
-the “distance” between the two. As each of the HARKinterpolatorXD classes
-inherits from HARKobject, all interpolator classes have the default
-“universal” distance method; the user must simply list the names of the
-relevant attributes in the attribute distance_criteria of the class.
-
-Interpolation methods currently implemented in HARK include (multi)linear
-interpolation up to 4D, 1D cubic spline interpolation, (multi)linear
-interpolation over 1D interpolations (up to 4D total), (multi)linear
-interpolation over 2D interpolations (up to 4D total), linear interpolation
-over 3D interpolations, 2D curvilinear interpolation over irregular grids, and
-a 1D “lower envelope” interpolator. See [here](https://econ-
-ark.github.io/HARK/generated/HARKinterpolation.html) for online documentation.
-
-#### 0.2.4 HARK.simulation
-
-The HARK.simulation module provides tools for generating simulated data or
-shocks for post-solution use of models. Currently implemented distributions
-include normal, lognormal, Weibull (including exponential), uniform,
-Bernoulli, and discrete. As an example of their use, these tools are used in
-the consumption-saving models of ConsIndShockModel.py to simulate permanent
-and transitory income shocks as well as unemployment events. See
-[here](https://econ-ark.github.io/HARK/generated/HARKsimulation.html) for
-online documentation.
-
-#### 0.2.5 HARK.estimation
-
-Methods for optimizing an objective function for the purposes of estimating a
-model can be found in HARK.estimation. As of this writing, the implementation
-includes only minimization by the Nelder-Mead simplex method, minimization by
-a derivative-free Powell method variant, and two small tools for resampling
-data (i.e. for a bootstrap); the minimizers are merely convenience wrappers
-(with result reporting) for optimizers included in scipy.optimize. Future
-functionality will include more robust global search methods, including
-genetic algorithms, simulated annealing, and differential evolution. See
-[here](https://econ-ark.github.io/HARK/generated/HARKestimation.html) for full
-documentation.
-
-#### 0.2.6 HARK.parallel
-
-By default, processes in Python are single-threaded, using only a single CPU
-core. The HARK.parallel module provides basic tools for using multiple CPU
-cores simultaneously, with minimal effort.[8](HARKmanual8.html#fn8x0) In
-particular, it provides the function multiThreadCommands, which takes two
-arguments: a list of AgentTypes and a list of commands as strings; each
-command should be a method of the AgentTypes. The function simply distributes
-the AgentTypes across threads on different cores and executes each command in
-order, returning no output (the AgentTypes themselves are changed by running
-the commands). Equivalent results would be achieved by simply looping over
-each type and running each method in the list. Indeed, HARK.parallel also has
-a function called multiThreadCommandsFake that does just that, with identical
-syntax to multiThreadCommands; multithreading in HARK can thus be easily
-turned on and off.[9](HARKmanual9.html#fn9x0) The module also has functions
-for a parallel implementation of the Nelder-Mead simplex algorithm, as
-described in Wiswall and Lee (2011). See [here](https://econ-
-ark.github.io/HARK/generated/HARK.parallel.html) for full documentation.
-
-### 0.3 Microeconomics: the AgentType Class
-
-The core of our microeconomic dynamic optimization framework is a flexible
-object-oriented representation of economic agents. The HARK.core module
-defines a superclass called AgentType; each model defines a subclass of
-AgentType, specifying additional model-specific features and methods while
-inheriting the methods of the superclass. Most importantly, the method solve
-acts as a “universal solver” applicable to any (properly formatted) discrete
-time model. This section describes the format of an instance of AgentType as
-it defines a dynamic microeconomic problem;[10](HARKmanual10.html#fn10x0)
-
-#### 0.3.1 Attributes of an AgentType
-
-A discrete time model in our framework is characterized by a sequence of
-“periods” that the agent will experience. A well-formed instance of AgentType
-includes the following attributes:
-
- * solveOnePeriod: A function, or a list of functions, representing the solution method for a single period of the agent’s problem. The inputs passed to a solveOnePeriod function include all data that characterize the agent’s problem in that period, including the solution to the subsequent period’s problem (designated as solution_next). The output of these functions is a single Solution object, which can be passed to the solver for the previous period.
- * time_inv: A list of strings containing all of the variable names that are passed to at least one function in solveOnePeriod but do not vary across periods. Each of these variables resides in a correspondingly named attribute of the AgentType instance.
- * time_vary: A list of strings naming the attributes of this instance that vary across periods. Each of these attributes is a list of period-specific values, which should be of the same length. If the solution method varies across periods, then ’solveOnePeriod’ is an element of time_vary.[11](HARKmanual11.html#fn11x0)
- * solution_terminal: An object representing the solution to the “terminal” period of the model. This might represent a known trivial solution that does not require numeric methods, the solution to some previously solved “next phase” of the model, a scrap value function, or an initial guess of the solution to an infinite horizon model.
- * pseudo_terminal: A Boolean flag indicating that solution_terminal is not a proper terminal period solution (rather an initial guess, “next phase” solution, or scrap value) and should not be reported as part of the model’s solution.
- * cycles: A non-negative integer indicating the number of times the agent will experience the sequence of periods in the problem. For example, cycles = 1 means that the sequence of periods is analogous to a lifecycle model, experienced once from beginning to end; cycles = 2 means that the agent experiences the sequence twice, with the first period in the sequence following the last. An infinite horizon problem in which the sequence of periods repeats indefinitely is indicated with cycles = 0.
- * tolerance: A positive real number indicating convergence tolerance, representing the maximum acceptable “distance” between successive cycle solutions in an infinite horizon model; it is irrelevant when cycles > 0. As the distance metric on the space of solutions is model-specific, the value of tolerance is generally dimensionless.
- * time_flow: A Boolean flag indicating the direction that time is “flowing.” When True, the variables listed in time_vary are listed in ordinary chronological order, with index 0 being the first period; when False, these lists are in reverse chronological order, with index 0 holding the last period.
-
-An instance of AgentType also has the attributes named in time_vary and
-time_inv, and may have other attributes that are not included in either (e.g.
-values not used in the model solution, but instead to construct objects used
-in the solution).
-
-#### 0.3.2 A Universal Solver
-
-When an instance of AgentType invokes its solve method, the solution to the
-agent’s problem is stored in the attribute solution. The solution is computed
-by recursively solving the sequence of periods defined by the variables listed
-in time_vary and time_inv using the functions in solveOnePeriod. The time-
-varying inputs are updated each period, including the successive period’s
-solution as solution_next; the same values of time invariant inputs in
-time_inv are passed to the solver in every period. The first call to
-solveOnePeriod uses solution_terminal as solution_next. In a finite horizon
-problem, the sequence of periods is solved cycles times over; in an infinite
-horizon problem, the sequence of periods is solved until the solutions of
-successive cycles have a “distance” of less than tolerance.
-
-The output from a function in solveOnePeriod is an instance of a model-
-specific solution class. The attributes of a solution to one period of a
-problem might include behavioral functions, (marginal) value functions, and
-other variables characterizing the result. Each solution class must have a
-method called distance(), which returns the “distance” between itself and
-another instance of the same solution class, so as to define convergence as a
-stopping criterion; for many models, this will be the “distance” between a
-policy or value function in the solutions. If the solution class is defined as
-a subclass of HARKobject, it automatically inherits the default distance
-method, so that the user must only list the relevant object attributes in
-distance_criteria.
-
-The AgentType also has methods named preSolve and postSolve, both of which
-take no arguments and do absolutely nothing. A subclass of AgentType can
-overwrite these blank methods with its own model specific methods. preSolve is
-automatically called near the beginning of the solve method, before solving
-the sequence of periods. It is used for specifying tasks that should be done
-before solving the sequence of periods, such as pre-constructing some objects
-repeatedly used by the solution method or finding an analytical terminal
-period solution. For example, the IndShockConsumerType class in
-ConsIndShockModel has a preSolve method that calls its updateSolutionTerminal
-method to ensure that solution_terminal is consistent with the model
-parameters. The postSolve method is called shortly after the sequence of
-periods is fully solved; it can be used for “post-processing” of the solution
-or performing a step that is only useful after solution convergence. For
-example, the TractableConsumerType in TractableBufferStockModel has a
-postSolve method that constructs an interpolated consumption function from the
-list of stable arm points found during solution.
-
-Our universal solver is written in a very general way that should be
-applicable to any discrete time optimization problem– because Python is so
-flexible in defining objects, the time-varying inputs for each period can take
-any form. Indeed, the solver does no “real work” itself, but merely provides a
-structure for describing models in the HARK framework, allowing
-interoperability among current and future modules.
-
-#### 0.3.3 The Flow of Time and Other Methods
-
-Because dynamic optimization problems are solved recursively in our framework,
-it is natural to list time-varying values in reverse chronological order– the
-solve() method loops over the values in each time-varying list in the same
-direction that a human would read them. When simulating agents after the
-solution has been obtained, however, it is much more convenient for time-
-varying parameters to be listed in ordinary chronological order– the direction
-in which they will be experienced by simulated agents. To allow the user to
-set the order in which “time is flowing” for an instance of AgentType, the
-HARK framework includes functionality to easily change ordering of time-
-varying values.
-
-The attribute time_flow is True if variables are listed in ordinary
-chronological order and False otherwise. AgentType has the following methods
-for manipulating time:
-
- * timeReport(): Prints to screen a description of the direction that time is flowing, for interactive convenience and as a reminder of the functionality.
- * timeFlip(): Flips the direction of time. Each attribute listed in time_vary is reversed in place, and the value of time_flow is toggled.
- * timeFwd(): Sets the direction of time to ordinary chronological order.
- * timeRev(): Sets the direction of time to reverse chronological order.
-
-These methods are invoked to more conveniently access time-varying objects.
-When a new time-varying attribute is added, its name should be appended to
-time_vary, particularly if its values are used in the solution of the model
-(or is part of the solution itself). For example, the solve() method
-automatically adds the string ’solution’ to time_vary if it is not already
-present. Note that attributes listed in time_vary must be lists if solve() or
-timeFlip() are used. Some values that could be considered “time varying” but
-are never used to solve the model are more conveniently represented as a
-numpy.array object (e.g. the history of a state or control variable from a
-simulation); because the numpy.array class does not have a reverse() method,
-these attributes should not be listed in time_vary.
-
-The base AgentType is sparsely defined, as most “real” methods will be
-application-specific. Two final methods bear mentioning. First, the __call__
-method points to assignParameters(), a convenience method for adding or
-adjusting attributes (inherited from HARKobject). This method takes any number
-of keyword arguments, so that code can be parsimoniously written as, for
-example, AgentInstance(attribute1 = value1, attribute2 = value2). Using
-Python’s dictionary capabilities, many attributes can be conveniently set with
-minimal code. Second, the resetRNG method simply resets the AgentType’s random
-number generator (as the attribute RNG) using the value in the attribute
-seed.[12](HARKmanual12.html#fn12x0) This method is useful for (inter alia)
-ensuring that the same underlying sequence of shocks is used for every
-simulation run when a model is solved or estimated.
-
-#### 0.3.4 Sample Model: Perfect Foresight Consumption-Saving
-
-To provide a concrete example of how the AgentType class works, consider the
-very simple case of a perfect foresight consumption-saving model. The agent
-has time-separable, additive CRRA preferences over consumption, discounting
-future utility at a constant rate; he receives a particular stream of labor
-income each period, and knows the interest rate on assets that he holds from
-one period to the next. His decision about how much to consume in a particular
-period can be expressed in Bellman form as:
-
-![Vt\(Mt \) = max u\(Ct\) + β//Dt E\[Vt+1\(Mt+1 \)\],
-
- Ct
- At = Mt - Ct,
-
- Mt+1 = RAt + Yt+1,
-
- Yt+1 = Γ t+1Yt,
-
- C1- ρ
- u\(C \) = -----.
-
- 1 - ρ ](HARKmanual4x.png)
-
-An agent’s problem is thus characterized by values of , , and 
-plus sequences of survival probabilities  and
-income growth factors  for . This problem has an analytical solution for both the
-value function and the consumption function.
-
-The ConsIndShockModel module defines the class PerfForesightConsumerType as a
-subclass of AgentType and provides solver functions for several variations of
-a consumption-saving model, including the perfect foresight
-problem.[13](HARKmanual13.html#fn13x0) A HARK user could specify and solve a
-ten period perfect foresight model with the following commands:
-
-MyConsumer = PerfForesightConsumerType(time_flow=True, cycles=1,
-
-CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98,
-
-LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90],
-
-PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02],
-
-Nagents=1000)
-
-MyConsumer.solve()
-
-The first line makes a new instance of ConsumerType, specifying that time is
-currently “flowing” forward[14](HARKmanual14.html#fn14x0) and that the
-sequence of periods happens exactly once; the next three lines (all part of
-the same command) set the time invariant (CRRA, Rfree, DiscFac) and time
-varying parameters (LivPrb, PermGroFac). Note that dir(MyConsumer) shows all
-the attributes of MyConsumer. After running the solve method, MyConsumer will
-have an attribute called solution, which will be a list with eleven
-ConsumerSolution objects, representing the period-by-period solution to the
-model.[15](HARKmanual15.html#fn15x0) Each ConsumerSolution has several
-attributes:
-
- * cFunc: Optimal consumption function for this period as a function of (normalized) market resources . Can be called like any other function:
-MyConsumer.solution[0].cFunc(5) returns consumption at .
-
- * vFunc: Value function (over market resources ); can be evaluated like cFunc.
- * mNrmMin: Minimum value of normalized market resources  such that cFunc and vFunc are defined.
- * hNrm: Normalized human wealth– the PDV of future income (ignoring mortality) divided by current period income .
- * MPC: The constant marginal propensity to consume (linear consumption function).
-
-The following command generates the plot for the perfect foresight consumption
-function:
-
-mMin = MyConsumer.solution[0].mNrmMin
-
-plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10)
-
-To solve a version of this problem in which the sequence of ten periods
-happens three times (yielding a solution attribute with thirty-one elements),
-the user can do:
-
-MyConsumer(cycles = 3)
-
-MyConsumer.solve()
-
-To solve an infinite horizon problem in which the agent experiences the same
-one period problem indefinitely, the user can do:
-
-OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02,
-
-DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000)
-
-OtherConsumer.solve()
-
-This instance is specified as being infinite horizon by setting cycles=0. Note
-that the time-varying inputs are still specified as (one element) lists, even
-though they take on the same value in every period; this is because an
-infinite horizon model might consist of a multi-period sequence repeated
-indefinitely, rather than just one repeated period. The solution attribute of
-OtherConsumer will be a list containing one instance of ConsumerSolution,
-representing the solution to every period of the model.
-
-### 0.4 Macroeconomics: the Market Class
-
-The modeling framework of AgentType is deemed “microeconomic” because it
-pertains only to the dynamic optimization problem of agents, treating all
-inputs of the problem as exogenously fixed. In what we label as
-“macroeconomic” models, some of the inputs for the microeconomic models are
-endogenously determined by the collective states and controls of agents in the
-model. In a dynamic general equilibrium, there must be consistency between
-agents’ beliefs about these macroeconomic objects, their individual behavior,
-and the realizations of the macroeconomic objects that result from individual
-choices.
-
-The Market class in HARK.core provides a framework for such macroeconomic
-models, with a solve method that searches for a dynamic general equilibrium.
-An instance of Market includes a list of AgentTypes that compose the economy,
-a method for transforming microeconomic outcomes (states, controls, and/or
-shocks) into macroeconomic outcomes, and a method for interpreting a history
-or sequence of macroeconomic outcomes into a new “dynamic rule” for agents to
-believe. Agents treat the dynamic rule as an input to their microeconomic
-problem, conditioning their optimal policy functions on it. A dynamic general
-equilibrium is a fixed point dynamic rule: when agents act optimally while
-believing the equilibrium rule, their individual actions generate a
-macroeconomic history consistent with the equilibrium rule.
-
-#### 0.4.1 Down on the Farm
-
-The Market class uses a farming metaphor to conceptualize the process for
-generating a history of macroeconomic outcomes in a model. Suppose all
-AgentTypes in the economy believe in some dynamic rule (i.e. the rule is
-stored as attributes of each AgentType, which directly or indirectly enters
-their dynamic optimization problem), and that they have each found the
-solution to their microeconomic model using their solve method. Further, the
-macroeconomic and microeconomic states have been reset to some initial
-orientation.
-
-To generate a history of macroeconomic outcomes, the Market repeatedly loops
-over the following steps a set number of times:
-
- 1. sow: Distribute the macroeconomic state variables to all AgentTypes in the market.
- 2. cultivate: Each AgentType executes their marketAction method, likely corresponding to simulating one period of the microeconomic model.
- 3. reap: Microeconomic outcomes are gathered from each AgentType in the market.
- 4. mill: Data gathered by reap is processed into new macroeconomic states according to some “aggregate market process”.
- 5. store: Relevant macroeconomic states are added to a running history of outcomes.
-
-This procedure is conducted by the makeHistory method of Market as a
-subroutine of its solve method. After making histories of the relevant
-macroeconomic variables, the market then executes its calcDynamics function
-with the macroeconomic history as inputs, generating a new dynamic rule to
-distribute to the AgentTypes in the market. The process then begins again,
-with the agents solving their updated microeconomic models given the new
-dynamic rule; the solve loop continues until the “distance” between successive
-dynamic rules is sufficiently small.
-
-#### 0.4.2 Attributes of a Market
-
-To specify a complete instance of Market, the user should give it the
-following attributes:[16](HARKmanual16.html#fn16x0)
-
- * agents: A list of AgentTypes, representing the agents in the market. Each element in agents represents an ex-ante heterogeneous type; each type could have many ex-post heterogeneous agents.
- * sow_vars: A list of strings naming variables that are output from the aggregate market process, representing the macroeconomic outcomes. These variables will be distributed to the agents in the sow step.
- * reap_vars: A list of strings naming variables to be collected from the agents in the reap step, to be used as inputs for the aggregate market process.
- * const_vars: A list of strings naming variables used by the aggregate market process that do not come from agents; they are constant or come from the Market itself.
- * track_vars: A list of strings naming variables generated by the aggregate market process that should be tracked as a history, to be used when calculating a new dynamic rule. Usually a subset of sow_vars.
- * dyn_vars: A list of strings naming the variables that constitute a dynamic rule. These will be stored as attributes of the agents whenever a new rule is calculated.
- * millRule: A function for the “aggregate market process”, transforming microeconomic outcomes into macroeconomic outcomes. Its inputs are named in reap_vars and const_vars, and it returns a single object with attributes named in sow_vars and/or track_vars. Can be defined as a method of a subclass of Market.
- * calcDynamics: A function that generates a new dynamic rule from a history of macroeconomic outcomes. Its inputs are named in track_vars, and it returns a single object with attributes named in dyn_vars.
- * act_T: The number of times that the makeHistory method should execute the “farming loop” when generating a new macroeconomic history.
- * tolerance: The minimum acceptable “distance” between successive dynamic rules produced by calcDynamics to constitute a sufficiently converged solution.
-
-Further, each AgentType in agents must have two methods not necessary for
-microeconomic models; neither takes any input (except self):
-
- * marketAction: The microeconomic process to be run in the cultivate step. Likely uses the new macroeconomic outcomes named in sow_vars; should store new values of relevant microeconomic outcomes in the attributes (of self) named in reap_vars.
- * reset: Reset, initialize, or prepare for a new “farming loop” to generate a macroeconomic history. Might reset its internal random number generator, set initial state varibles, clear personal histories, etc.
-
-When solving macroeconomic models in HARK, the user should also define classes
-to represent the output from the aggregate market process in millRule and for
-the model-specific dynamic rule. The latter should have a distance method to
-test for solution convergence; if the class inherits from HARKobject, the user
-need only list relevant attributes in distance_criteria.
-
-#### 0.4.3 Sample Model: FashionVictim
-
-To illustrate the Market class, consider a simple example in the emerging
-economic subfield of aesthemetrics, the
-FashionVictimModel.[17](HARKmanual17.html#fn17x0)
-[18](HARKmanual18.html#fn18x0) This module defines a subclass of AgentType
-called FashionVictimType. Each period, fashion victims make a binary choice of
-style : to dress as a jock (0) or punk (1). They
-receive utility directly from the outfit they wear and as a function of the
-proportion of the population who just wore the same style; they also pay
-switching costs (,) if
-they change styles rather than keep the same as the previous period. Moreover,
-they receive an idiosyncratic type 1 extreme value (T1EV) preference shock to
-each style in each period. Defining the population punk proportion as  and the conformity utility function as ![f : \[0,1\] → ℝ
-](HARKmanual20x.png), the current period utility function is thus:
-
-
-
-Fashion victims are forward looking and discount future utility at a constant
-rate of  per period. To simplify the analysis, we
-assume they believe that the population punk proportion in the next period is
-a linear function of the punk proportion in the current period, subject to a
-uniformly distributed shock. No restrictions are put on the function ; fashion victims might be conformists who like to dress
-the same as others () or hipsters who like
-to style themselves in the minority ().[19](HARKmanual19.html#fn19x0) A fashion victim’s
-problem can be written in Bellman form as:
-
-![ \[ \]
-
-V\(st-1,pt\) = E smt∈a{x0,1}u \(st;st-1,pt\) + ηst + βE \[V \(st,pt+1\)\] ,
-
-](HARKmanual27x.png)
-
-![pt+1 = apt + b + πt+1, πt+1 ~ U\[- w,w \], η0,η1 ~ T 1EV.
-
-](HARKmanual28x.png)
-
-An instance of FashionVictimType is thus characterized by values of , , ,
- and a function , as well
-as beliefs about  as a function of  (summarized by slope , intercept
-, and uniform shock width ).
-Given this information, a FashionVictimType’s infinite horizon microeconomic
-model can be solved by backward induction in a few lines; the “one period
-solver” is given by solveFashion. However, while individual agents treat the
-dynamics of  as exogenous, they are in fact
-endogenously determined by the actions of all the fashion victims in the
-market.[20](HARKmanual20.html#fn20x0) A dynamic general equilibrium of the
-“macroeconomic fashion model” is thus characterized by a triple of  such that when fashion victims believe in this “punk
-evolution rule” and act optimally, their collective fashion choices exhibit
-this same rule when the model is simulated.
-
-The search for a dynamic general equilibrium is implemented in HARK’s Market
-class with the following definitions:
-
-sow_vars = [’pNow’] (macroeconomic outcome is )
-
-reap_vars = [’sNow’] (microeconomic outcomes are 
-for many agents)
-
-track_vars = [’pNow’] (must track history of )
-
-dyn_vars = [’pNextSlope’,’pNextIntercept’,’pNextWidth’] (dynamic rule
-)
-
-millRule = calcPunkProp (aggregate process: average the style choices of all
-agents)
-
-calcDynamics = calcFashionEvoFunc (calculate new  with autoregression of )
-
-act_T = 1000 (simulate 1000 periods of the fashion market)
-
-tolerance = 0.01 (terminate solution when 
-changes by less than 0.01)
-
-The agents attribute has a list of 22 FashionVictimTypes, which vary in their
-values of  and , and their  functions. The
-marketAction method of FashionVictimType simulates one period of the
-microeconomic model: each agent receives style preference shocks  and , sees the current
-proportion of punks  (sown to them as pNow), and
-chooses which style to wear, storing it in the binary array sNow, an attribute
-of self.
-
-The millRule for this market is extremely simple: it flattens the list of
-binary arrays of individual style choices (gathered in the reap step) and
-averages them into a new value of , to be tracked as
-a history and sown back to the agents to begin the cycle again. Once a history
-of 1000 values of  has been generated with the
-makeHistory method, we can calculate a new dynamic fashion rule with
-calcFashionEvoFunc by regressing  on , approximating  as twice
-the standard deviation of prediction errors.[21](HARKmanual21.html#fn21x0) The
-new fashion rule is an instance of the simple FashionEvoFunc class, whose only
-methods are inherited from HARKobject.
-
-When the solve method is run, the solver successively solves each agent’s
-microeconomic problem, runs the makeHistory method to generate a 1000 period
-history of , and calculates a new punk evolution
-rule based on this history; the solver terminates when consecutive rules
-differ by less than 0.01 in any dimension.
-
-### 0.5 Contributing to HARK
-
-This section provides an overview of how users can contribute new code to
-HARK, what sort of contributions are most desired, and style conventions for
-the toolKit. Before attempting to extend HARK, we strongly recommend that you
-familiarize yourself with its features and models by looking at the source
-code.
-
-#### 0.5.1 What Does HARK Want?
-
-The easiest and most straightforward way to contribute to HARK is by writing
-new general purpose tools for inclusion in one of the top-level modules. This
-might be a commonly known data analysis technique (e.g. a kernel density
-estimator) for HARK.utilities, an optimization method (local or global) for
-HARK.estimation, an interpolation method for HARKinterpolation, etc. If you’ve
-found a technique useful in your own research and believe it could be of use
-to others on entirely different projects, then it probably belongs in HARK.
-Likewise, if you read through a HARK module expecting to find a certain tool
-or function (because of similar functions that are present, or merely because
-it’s very useful) but don’t, this is a good sign that HARK has a hole that you
-can fill.[22](HARKmanual22.html#fn22x0)
-
-Secondly, the development team wants to expand HARK to include more canonical
-models. Most of the models available in the toolKit at the time of its public
-beta release concern agents with (geometrically discounted) CRRA utility over
-a unitary consumption good, who receive permanent and transitory shocks to
-income and have access to a single risk free asset. This is an important class
-of models, but that description has an awful lot of qualifiers. In short,
-there are many more common features of dynamic household models that are not
-yet in HARK, but would be very valuable for the toolKit. New model
-contributions should “play nicely” with existing models and tools, following
-HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent
-possible. For ideas of potential model extensions for HARK, see section
-0.6.2.[23](HARKmanual23.html#fn23x0)
-
-Finally, contributions to HARK are not restricted to entirely new
-functionality. While we have done our best to provide flexible frameworks and
-useful tools, we have almost certainly overlooked an unexpected case that
-would not be (properly) handled by the toolKit as is. Moreover, there might
-even be flaws in our solution or simulation methods. If you find a mistake or
-error, we strongly encourage you to contribute a fix for the problem, or at
-least to flag it as an Issue on GitHub so that someone else might. The
-“mistake” you find might not even be in the code itself, but a deficiency in
-the documentation: a misnamed (or incorrectly typed) parameter or return
-variable in the docstring, an incorrect (or missing) comment, even a
-grammatical or spelling error. No matter how niggling, we want your fix.
-
-For those who want to contribute to the HARK project without programming a new
-model or digging through the code for errors and omissions, consider reading
-through the Issues page on GitHub to see what other users have flagged for
-development. You might find several pieces of “low hanging fruit” for you to
-quickly fix.
-
-#### 0.5.2 Git and GitHub
-
-Git is a distributed version control system that has gained increasing
-popularity in the decade since its creation; it is designed to enable
-collaboration and “non-linear workflow” among a (potentially disperse)
-programming team. As HARK is to be developed by volunteer researchers all
-around the world, git is an excellent fit for the toolKit. Originally designed
-by Linus Torvalds as a command line tool, others have since extended git with
-further functionality and a user interface more friendly to those more
-familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK
-project is hosted. This section provides a brief overview of how to use GitHub
-to interact with (and potentially contribute to) HARK.
-
-To prepare to contribute to HARK, follow these simple steps:
-
- 1. Make a GitHub account: Go to [github.com](https://www.github.com); the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.[24](HARKmanual24.html#fn24x0)
- 2. Install GitHub Desktop: Go to [desktop.github.com](https://desktop.github.com/) and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account.
- 3. Fork the HARK repository: Go to the [HARK repository page](https://github.com/econ-ark/HARK), click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead.
- 4. Add the repository to Desktop: Open GitHub Desktop, click on the  icon in the upper left, and select clone. Find the repository named yourusername/HARK, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine.
-
-You now control a “fork” of the main HARK repository, hosted by GitHub, and
-have a clone of this fork on your local machine. You are free to edit your
-fork of HARK in any way you’d like; other GitHub users can view your fork (and
-fork it themselves), but they cannot make changes to your fork unless you give
-them permission by adding them as a contributor. Changes made to the local
-copy of your fork do not get automatically sent to the remote copy on GitHub
-for public viewing. To make changes to your fork on GitHub, follow these
-steps:
-
- 1. Make local changes: Edit your local copy of the repository in any way you’d like: add or delete lines from existing files; add, delete, or rename entire files or directories.
- 2. Commit local changes: Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button.
- 3. Push the commit: Click on the Sync button near the upper right to “push” your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced.
-
-The left hand panel of the Changes tab has a summary of the files that have
-been changed, added, or removed since your last commit; the right hand panel
-shows the specific changes made to each changed file. After step 2, a record
-of your changes has been stored locally by the git system, but is not yet
-reflected in the public remote copy on GitHub.[25](HARKmanual25.html#fn25x0)
-The “timeline” summary of the repository at the top of the Desktop window
-shows the history of commits for the repository: each dot represents a commit
-that was pushed to the remote server, and each open ring represents a local
-commit that has not been pushed to the remote server; the large broken ring on
-the far right represents changes that have not been locally
-committed.[26](HARKmanual26.html#fn26x0) The History tab lists all previous
-commits, including the timestamp, contributor, and a record of changes made.
-
-Suppose you have mucked about in your fork for a while and have made changes
-to HARK that you think should be included in the main repository hosted by
-Econ-Ark. To make your dream a reality, follow these steps:
-
- 1. Isolate the changes: If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should isolate the desired changes in a new branch of your fork. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,[27](HARKmanual27.html#fn27x0) then edit this branch to include only the changes you want to push to the main repository.
- 2. Issue a pull request: Go to [HARK’s GitHub page](https://github.com/econ-ark/HARK) and click the “New pull request” button. Click the text that says “compare across forks”, and select econ-ark/HARK as the base fork, master as the base, your fork as the head fork, and the branch to be merged as the “compare”. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you’ve done. Click the “Create pull request” button.
- 3. Be patient: Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section 0.5.3 for procedures followed by the HARK team when evaluating outside contributions.
-
-#### 0.5.3 Submission Approval Process
-
-Before submitting to HARK, the development team would like you to make sure
-that your intended contribution is listed as an Issue on the GitHub page. If
-you are going to fix or address an Issue that is already posted, reply to that
-post with a note indicating that you are about to work on it and an estimate
-of when you will make a pull request for it. If you want to do something that
-no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly)
-describing what you intend to do.[28](HARKmanual28.html#fn28x0) This will both
-help prevent two people from working on the same thing (and one of them
-potentially wasting time on it), and lets the development team know what’s
-coming. Moreover, if you propose a contribution that is (a) already in HARK,
-(b) not in the spirit of HARK, or (c) a fix for something that’s not actually
-broken, this gives the team a chance to let you know not to spend effort on
-it.
-
-After creating a pull request to submit your contribution, it will be
-evaluated by one or more members of the HARK team. Criteria that the HARK team
-will consider when choosing whether to accept a submission include:
-
- 1. Does the contribution break previous code? If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility.
- 2. Is the code well documented? Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section 0.5.5) and enough comments for a reader to follow the algorithm.
- 3. Is the contribution relevant? The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn’t belong in HARK.
- 4. Is the code demonstrated with an example? If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you’ve contributed a new function, consider providing a snippet of code demonstrating its use.[29](HARKmanual29.html#fn29x0)
- 5. Is the code correct? Your code should actually do what it claims to do.
- 6. Does the code use HARK conventions? To the best of your ability, contributions to HARK should follow the style guidelines in section 0.5.4. We don’t demand perfection, as these are guidelines rather than rules.
- 7. Is the code well integrated in HARK? In the beta release of HARK, we have tried to present a “taxonomy” of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy.
- 8. Is this already in HARK? If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK.
-
-The HARK team intends to release more specific guidelines in the near future,
-so that contributors are better able to judge their submissions before issuing
-a pull request. We are in the process of recruiting “czars” to oversee
-particular aspects of HARK, providing their knowledge and
-expertise.[30](HARKmanual30.html#fn30x0) While czars might directly evaluate
-submissions as a HARK team member, their more important role is to help
-establish these guidelines for their domain, more specifically nailing down
-what we mean by “correct” or “well integrated”.
-
-#### 0.5.4 Naming Conventions
-
-Object naming conventions in HARK are fairly different than existing
-standards, and differ somewhat between tool modules vs model or application
-modules. The following conventions apply throughout HARK:
-
- * Functions and methods are always in “camel case”: no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. approxLognormal.
- * Function and method names should accurately and concisely describe what the function does; the first word in the name must be a verb.[31](HARKmanual31.html#fn31x0)
- * Variable and class names should not have a verb as their first word.
- * Class names should use no underscores and capitalize the first letter of each word; moreover, a class name must include a noun. E.g. ConsPerfForesightSolver.
-
-When naming variables in model modules, the HARK team strongly discourages
-using single letter names, like c for consumption. Instead, we encourage
-contributors to use longer, more descriptive variable names using additional
-words and common abbreviations to specify the variable more precisely. In
-/Documentation/NARK.pdf, we list standard single letter variable “bases” and
-an array of prefixes and suffixes to adjust them. Economic variables in model
-modules should (usually) not use underscores, instead using camel case to the
-greatest extent possible.[32](HARKmanual32.html#fn32x0) The development team
-prefers this standard so that users can translate between Python code and
-LaTeX script with minimal work.
-
-Conventions for naming variables in HARK’s tool modules are significantly
-closer to more commonly used standards. Variable names should be in all lower
-case, with underscores between words, e.g. data_to_match. The functions and
-classes in these modules are more general and almost surely do not have any
-inherent “economic content”; they are numerical or algorithmic objects, not
-variables that might appear in an equation in an article for a (non-
-computational) economics journal. Variable names in application modules (e.g.
-the files that execute the cstwMPC estimations) are a mix of the conventions
-for tool and model files, as appropriate for each variable. That is, variables
-that are directly related to “economic variables” in model modules should
-follow those conventions, while objects created solely for data manipulation
-or reporting should use the style of tool modules.
-
-#### 0.5.5 Documentation Conventions
-
-The HARK team wants the toolKit to be as accessible to users as possible; our
-greatest fear[33](HARKmanual33.html#fn33x0) is that a new user will open up
-our code, get hopelessly confused trying to read it, and then never look at
-HARK again. To prevent this tragic outcome, we have tried hard to provide
-comprehensive, accurate documentation and comments within the code describing
-our methods.[34](HARKmanual34.html#fn34x0) Moreover, HARK uses the Sphinx
-utility to generate a website with [online documentation](https://econ-
-ark.github.io/HARK/) for all of our tool and model modules, so that users can
-learn about what’s available in HARK without digging through the source code.
-When making contributions to HARK, the development team asks users to format
-their inline documentation to work with Sphinx by following a few simple
-rules.
-
- * The top of every module should begin with a “docstring” providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:[35](HARKmanual35.html#fn35x0)
-
-"""
-
-Specifies an economic model and provides methods for solving it. More specific
-description of the key features of the model and variations of it in this
-module. Maybe some comments about the solution method or limitations of the
-model. Your bank account routing number.
-
-"""
-
- * The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as:
-
-def functionName(input1,input2):
-
-"""
-
-Concise description of the function. More details about what
-
-the function does, options or modes available, and maybe mathematical
-
-methods used. Credit to a source if you poached their algorithm.
-
-Parameters
-
-––––––––––
-
-input1: type
-
-Description of what input1 represents.
-
-input2: type
-
-Description of what input2 represents.
-
-Returns
-
-–––––––
-
-output_name: type
-
-Description of the output(s) of the function. Might have
-
-multiple entries. If no output, this is just "None".
-
-"""
-
- * Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it.
-
-Finally, if you write a new model module, the HARK team asks that you also
-provide a short mathematical writeup of the model as a PDF. This document does
-not need to go into great detail about the solution method for the model or
-the functions and classes included in the module, merely specify the economic
-model and provide a summary of how it is solved. See
-/Documentation/ConsumptionSavingModels.pdf for an example of this.
-
-### 0.6 Future of HARK
-
-HARK is in a state of perpetual incompleteness, always expanding to include
-more tools and more models. As an open source project in the hands of many
-contributors around the world, the HARK team cannot fully predict future
-features of the toolKit. This section provides an overview of what we would
-like to see in the near future, as well as potential mechanisms to encourage
-researchers to contribute their code to the project.
-
-#### 0.6.1 Future Tools
-
-The current frameworks in HARK.core concern agents who have “isolated” dynamic
-problems or interact with each other only through aggregate outcomes to which
-they contribute atomically. We hope to eventually provide a framework for
-models in which agents directly interact with each other; this may include a
-system for specifying the state of networks and how connections are formed and
-broken. HARK.core might also develop a framework more suited to industrial
-organization models and dynamic games.[36](HARKmanual36.html#fn36x0)
-
-The HARK.estimation module in particular is very sparsely populated, with its
-functions mostly serving as wrappers for “off the shelf” optimization methods
-from scipy.optimize. There are plenty of non-standard or more complex
-optimization techniques available, including global and/or stochastic search
-methods. We believe that additional optimizers are “low hanging fruit” as
-straightforward contributions to HARK and thus will be added in the near
-future. Moreover, there are plenty of distributions that have no approximator
-in HARK.utilities (or data generator in HARK.simulation), and these functions
-are among the easiest for new contributors to write. The data analysis tools
-in HARK.utilities only include functions that we happened to use for our
-current research projects, and we expect that this will be the mode of growth
-for future tools. There are some obvious holes that we believe will be filled
-quite quickly; for example, we provide a rudimentary non-parametric kernel
-regression function, but have not written a kernel density estimator to
-generate PDFs from data.
-
-The classes currently in HARK.interpolation are all variations on linear (or
-cubic) spline interpolation, and each is specifically written for a fixed
-number of dimensions. As these methods use full tensor grids, they are highly
-impractical for representing functions in higher dimensions, suffering greatly
-from the curse of dimensionality.[37](HARKmanual37.html#fn37x0) We are quite
-confident that HARK.interpolation will soon include techniques more applicable
-to high dimensional state spaces, including Smolnyak interpolation, other
-(adaptive) sparse grid methods, and regression-based interpolation. For lower-
-dimensional spaces, HARK will also soon provide Delaunay interpolation
-classes.
-
-The HARK.parallel module currently provides a simple interface for handling
-ex-ante heterogeneity among agents. While useful for speeding up moderately-
-sized problems, the libraries used are only able to access the CPU of the
-local machine running the code. In the very near future, the HARK team will
-provide an interface (and example) for using Apache Spark, which bills itself
-as a “fast and general engine for large-scale data processing”. Specifically,
-users will be able to easily scale up their computing resources and run HARK
-code on an Amazon EC2 session or other cluster.
-
-#### 0.6.2 Future Models
-
-The economics models currently included in HARK mostly concern consumption-
-saving problems with constant relative risk aversion consumers who experience
-fully permanent and fully transitory shocks to income. This is obviously a
-very small subset of all consumption-saving models (which are themselves a
-small subset of all dynamic models economists might be interested in).
-Consider some examples of variants, features, and extensions that are not
-included in HARK, but could be in the future:
-
- * Consumers with other utility functions, such as CARA or expo-power.
- * Consumers with time-inconsistent preferences, e.g. hyperbolic discounting.
- * Income shocks that are neither fully transitory nor fully permanent.
- * Consumers with a bequest motive or terminal utility function.
- * Utility from leisure and endogenous labor supply.
- * Investment in a durable consumption good such as housing.
- * Exogenous shocks to expenses.
- * Multiple consumption goods, as with endogenous medical care.
- * Portfolio allocation between a risky and riskless asset.
- * Job search intensity as a control variable.
- * Endogenous health evolution and health investment.
-
-The HARK team is currently developing a module for mixing discrete and
-continuous choice models, in the style of Jorgensen, Rust, Iskhakov, and
-Schjerning (2015). Among many other models, this framework will enable HARK
-users to easily develop features like:
-
- * Labor supply on the extensive margin: the timing of retirement.
- * Selection of job type or sector (by riskiness, specific human capital, etc).
- * Choice of medical insurance contract.
- * Decision to rent or own housing.
- * Endogenous default on debt (with “macroeconomic” pricing of debt).
- * Household formation, dissolution, and reproduction.
-
-Finally, we hope to provide a generic solver for any discrete choice model
-with T1EV shocks. The scope of HARK is limited only by what users choose to
-put in it. We hope that within a year of its debut, HARK will include a
-significant number of canonical models, providing more building blocks for
-future researchers to create novel combinations and new extensions.
-
-#### 0.6.3 Bounty Hunting
-
-While HARK users are free to write and contribute any tools and models that
-they believe could be helpful, building the toolKit in a “bottom up” style,
-the HARK team hopes to provide mechanisms for targeted “top down” development.
-The team is working to secure grant funding for HARK so as to offer “bounties”
-for particular models, tools, applications, or frameworks. Suppose the team
-decides that the model in Important Paper X should be included as part of
-HARK; we will issue a call for contributions to fill this hole, offering a
-modest prize of, say, $20,000 to the programmer who submits the best
-implementation of the model, as judged by clearly stated
-criteria.[38](HARKmanual38.html#fn38x0) The bounty would be announced as an
-Issue on HARK’s GitHub page, as well as in a potential electronic digest of
-HARK news.
-
-Bounties might be placed on items as small as a single data analysis tool or
-as large as a complex estimation of a large general equilibrium model, with
-prizes scaling accordingly. We hasten to emphasize that we cannot guarantee
-funding for HARK, so the bounties (like everything else in this section) are
-purely speculative. As economists, the HARK team understands the incentive
-compatibility problems associated with asking researchers to contribute
-significant amounts of their time, effort, and unique skills in service of a
-tenuous concept like the Greater Good in exchange for even more tenuous Glory.
-We acknowledge that economic researchers are also optimizing agents with their
-own preferences over leisure and consumption, and agree that Greed is a valid
-mechanism to expand HARK.
-
-#### 0.6.4 All Aboard the Ark
-
-As observant readers may have noticed, the HARK repository is hosted by a
-GitHub account named Econ-Ark, with no explanation as to what this might mean.
-Looking a few years into the future, the development team believes that HARK
-might be the first piece of a broader collection of computational economics
-resources. HARK is open source but somewhat akin to an exclusive club:
-contributions are required to adhere to standards and norms so as to foster
-modularity and interoperability among models and tools. Consistent with its
-namesake ship, the Econ-Ark has a significantly lower bar for admission, with
-a place on board for everything.
-
-On one level, the Ark might serve as a warehouse for any and all quantitative
-models and tools, for any economic purpose, in any subfield, and written in
-any language. Through nearly unanimous feedback, the HARK team understands
-that many economists are willing to share code that they have already written,
-but are reluctant to expend the considerable effort to translate and format it
-for HARK. In the future, we will set up a mechanism for code like this to be
-hosted on the Ark, with minimal requirements imposed on its authors (and
-correspondingly minimal warranty for potential users). Relevant projects on
-the Ark might become bounty targets for HARK, with users incentivized to port
-the original code into Python and to meet HARK standards; direct access to the
-original source code will significantly expedite the translation process, and
-provide a suitable benchmark for testing and verifying the contribution to
-HARK.
-
-On a different level, the Ark might also include cohesive toolkits like HARK,
-but geared toward purposes other than heterogeneous agents macroeconomics
-models. For example, the previously mentioned modules for networks among
-agents (and other agent-based modeling), industrial organization and dynamic
-games, and traditional discrete choice models might become their own toolkits,
-with some interoperability with HARK. In support of this, some of the tool
-modules currently in HARK might be “promoted” and become universally shared
-across all Econ-Ark toolkits.
-
-The dark secret of HARK is that it is named for heterogeneous agents
-macroeconomics, but designed and written by structural microeconomists. The
-development team believes these subfields to have considerable overlap in
-terms of methods and techniques, and that HARK might not be noticed by half of
-its potential audience due to its name. In a general sense, the Econ-Ark gives
-us cover to expand the scope of our efforts and/or rebrand the toolKit for an
-alternative audience.
-
diff --git a/Documentation/HARKmanual/HARKmanual.tex b/Documentation/HARKmanual/HARKmanual.tex
deleted file mode 100644
index c7c02df20..000000000
--- a/Documentation/HARKmanual/HARKmanual.tex
+++ /dev/null
@@ -1,666 +0,0 @@
-% !TeX spellcheck = en_GB
-
-\documentclass[12pt,titlepage,letterpaper]{econtex}
-\usepackage{econtexSetup}\usepackage{econtexShortcuts}
-\usepackage{graphicx}
-\usepackage{hyperref}
-\providecommand{\E}{\mathbb{E}}
-
-\newboolean{ContributingToHARKOnly}
-\setboolean{ContributingToHARKOnly}{false}
-
-\begin{document}
-\ifthenelse{\boolean{ContributingToHARKOnly}}{(This is an excerpt of the full manual, available at \url{http://econ-ark.org/HARK/Documentation/HARKmanual.pdf)}}{ % If ContributingToHARKOnly is true, do not include any text until that section
-\title{\LARGE A User's Guide for HARK: \\ Heterogeneous Agents Resources and toolKit}
-
-\date{\normalsize{\today}}
-
-\author{
-{Christopher D Carroll\authNum} \\ {\small Johns Hopkins University}
-\and
-{Alexander M Kaufman\authNum} \\ {\small Consumer Financial Protection Bureau}
-\and
-{David C Low\authNum} \\ {\small Consumer Financial Protection Bureau}
-\and
-{Nathan M Palmer\authNum} \\ {\small U.S.\ Treasury Office of Financial Research}
-\and
-{Matthew N White\authNum} \\ {\small University of Delaware}
-}
-
-
-\begin{authorsinfo}
-\name{
-Carroll: \url{http://econ.jhu.edu/people/ccarroll/}, email: \href{mailto:ccarroll@jhu.edu}{\texttt{ccarroll@jhu.edu}}
-}
-\name{
-Kaufman: \href{mailto:alexander.kaufman@cfpb.gov}{\texttt{alexander.kaufman@cfpb.gov}}
-}
-\name{
-Low: \href{mailto:david.c.low@cfpb.gov}{\texttt{david.c.low@cfpb.gov}}
-}
-\name{
-Palmer: \href{mailto:nathan.m.palmer@ofr.treasury.gov}{\texttt{nathan.m.palmer@ofr.treasury.gov}}
-}
-\name{
-White: \href{mailto:mnwecon@udel.edu}{\texttt{mnwecon@udel.edu}}
-}
-\end{authorsinfo}
-
-\thanks{The materials in this resource reflect work by the persons named herein and others, much of it accomplished at the CFPB. However, none of the materials here should be interpreted as reflecting official opinions of the CFPB or any employees of the CFPB. After appropriate technical review, these resources are expected to appear on the CFPB's official GitHub page. Views expressed in this presentation are those of the authors and not necessarily of the Office of Financial Research or other government organizations.}
-
-\maketitle
-
-\begin{center}
-\includegraphics[scale=0.8]{UserGuidePic.pdf}
-\end{center}
-
-\thispagestyle{empty}
-\newpage
-
-\tableofcontents
-
-\newpage
-
-\section{Introduction}\label{sec:Introduction}
-
-If you are willing to risk some mild psychological trauma, conjure to mind your first experience of hand-coding a structural economic model. Your clunky effort probably built on legacy code provided by an adviser or colleague -- which itself came from who-knows-what apocryphal sources. Efforts to combine elements from one model with those from another were likely frustrated by the ``Tower of Babel'' problem: Code from one source could not ``speak'' to code from another without your own intermediation as a translator, possibly between two unfamiliar languages and aided only by oracular comments that, at best, made sense only in the context of other (now missing) code.
-
-After months of effort, you may have had the character-improving experience of proudly explaining to your adviser that not only had you grafted two ideas together, you also found a trick that speeded the solution by an order of magnitude, only to be told that your breathtaking insight had been understood for many years, as reflected in an appendix to a 2008 paper; or, worse, your discovery was something that ``everybody knows'' but did not exist at all in published form!
-
-Learning by doing has value, but only within limits. We do not require young drivers to design an internal combustion engine before driving a car, nor must graduate students write their own matrix inversion algorithms before running an OLS regression.
-
-In recent years, considerable progress has been made in addressing these kinds of problems in many areas of economic modeling. Macroeconomists using representative agent models can send Dynare model files to each other; reduced form econometricians can choose from a host of econometric packages. But modelers whose questions require explicit structural modeling involving nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply be aggregated away) are mostly still stuck in the bad old days.
-
-The ultimate goal of the HARK project is to fix these problems. Specifically, our aim is to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents.\footnote{By ``heterogeneous,'' we mean that agents might differ before anything in the model has ``happened'' (\textit{ex-ante} heterogeneity); and agents might experience different stochastic events during the model (\textit{ex-post} heterogeneity).} Further, we seek to establish (with input from the community) standards for the description and specification of objects like discrete approximations to continuous distributions and interpolated function approximations, so that numeric methods can be quickly swapped without ugly ``patching.''
-
-We hope that HARK will make it much easier and faster for researchers to develop solution and estimation methods for new models. The open source nature of HARK will make it easier for other researchers to audit and verify new models and methods, and to collaborate on correcting deficiencies when found. As HARK expands to include more canonical models and more tools and utilities, we can all spend less time managing numerical minutiae and more time fretting about identification arguments and data accuracy.
-
-\subsection{Getting Started}\label{sec:GettingStarted}
-
-If you want to get started using HARK right away, this section provides a very easy quickstart guide for getting HARK up and running on your computer in just a few minutes. More information can be found in the \texttt{README.md} file in the HARK repository (step 4), but here is a quick version for those who want to jump right in:
-
-\begin{enumerate}
-\item \textbf{Download Anaconda:} Go to \href{https://www.anaconda.com/download/}{https://www.anaconda.com/download/} and download Anaconda for your operating system; be sure to get the version for Python 2.7.
-
-\item \textbf{Install Anaconda:} Follow the easy instructions on that page to install Anaconda.
-
-\item \textbf{Add extra packages:} (optional) If you want to use HARK's multithreading feature, you need to add two packages that aren't part of the default Anaconda distribution. Simply open a command prompt and do \texttt{conda install joblib} and \texttt{conda install dill}, accepting defaults to install.
-
-\item \textbf{Download HARK:} Go to \href{https://github.com/econ-ark/HARK}{https://github.com/econ-ark/HARK}, the home of the HARK repository. Click on the lime green button labeled ``Clone or download'' toward the upper right of the main panel, and select ``Download ZIP''.\footnote{This is the fastest way to get HARK running on a computer. Later, if you want to make your own contributions to HARK by adding code (or fixing ours!), you'll need to clone the repository on your computer. See section \ref{sec:GitHub} for more.}
-
-\item \textbf{Unzip HARK:} Unpack the \texttt{HARK.zip} file using any archive utility, like \href{http://www.peazip.org/}{Peazip}. Put the files anywhere you'd like, maintaining the internal directory structure.
-
-\item \textbf{Run Spyder:} Open a command prompt and do \texttt{spyder}. Spyder is an interactive development environment (IDE) for iPython, a slightly prettier, more interactive flavor of Python.
-
-\item \textbf{Open a HARK module:} Go to the directory where you put HARK and open any file with the \texttt{.py} extension; we recommend \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}.
-
-\item \textbf{Run the module:} Click on the green arrow ``play'' button toward the right side of the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue box pops up). Congratulations! HARK is now up and running on your computer.
-\end{enumerate}
-
-If you followed our recommendation to try \texttt{/Demos/ConsumptionSaving/ConsIndShockModel.py}, you should see graphical representations of the solution to a few consumption-saving models with idiosyncratic shocks to permanent and transitory income (see \texttt{/Documentation/ConsumptionSavingModels.pdf} for details of these models). If you chose a different module, you might get some examples of some other model, results of a structural estimation, a polite notification that this particular module doesn't do much on its own, or no output at all. See the \texttt{README.md} for a full list of modules from which you can expect non-trivial output.
-
-\subsection{Structure of HARK}\label{sec:StructureOfHARK}
-
-HARK is written in Python, an object-oriented programming (OOP) language that has experienced increasing popularity in the scientific community in the past several years. A significant reason for the adoption of Python is the \texttt{numpy} and \texttt{scipy} packages, which offer a wide array of mathematical and statistical functions and tools; HARK makes liberal use of these libraries. Python's object-oriented nature allows models in HARK to be easily extended: more complex models can inherit functions and methods from more fundamental ``parent'' models, eliminating the need to reproduce or repurpose code.
-
-As implied in the previous section, we strongly encourage HARK users to use the Anaconda distribution of Python, which includes all commonly used mathematical and scientific packages, an interactive development environment for iPython (Spyder), and a package manager that allows users to quickly install or update packages not included in the default distribution (conda).
-
-For users unfamiliar with OOP, we strongly encourage you to review the background material on OOP provided by the good people at \href{http://quant-econ.net/}{QuantEcon} (for more on them, see below) at this link: \href{http://quant-econ.net/py/python_oop.html}{Object Oriented Programming}. Unlike non-OOP languages, OOP bundles together data and functions into \textit{objects}. These can be accessed via: \texttt{object\_name.data} and \texttt{object\_name.method\_name()}, respectively. For organizational purposes, definitions of multiple objects are stored in \textit{modules}, which are simply files with a \texttt{.py} extension. Modules can be accessed in Python via:\\
-
-\texttt{import module\_name as import\_name}
-\\
-
-\noindent
-This imports the module and gives it a local name of \texttt{import\_name}. We can access a function within this module by simply typing: \texttt{import\_name.function\_name()}. The following example will illustrate the usage of these commands. \texttt{CRRAutility} is the function object for calculating CRRA utility supplied by \texttt{HARK.utilities} module. \texttt{CRRAutility} is called \textit{attributes} of the module \texttt{HARK.utilities}. In order to calculate CRRA utility with a consumption of 1 and a coefficient of risk aversion of 2 we run:\\
-
-\texttt{import HARK.utilities as Hutil}
-
-\texttt{Hutil.CRRAutility(1,2)}\\
-
-Python modules in HARK can generally be categorized into three types: tools, models, and applications. \textbf{Tool modules} contain functions and classes with general purpose tools that have no inherent ``economic content'', but that can be used in many economic models as building blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include functions for data analysis (e.g.\ calculating Lorenz shares from data, or constructing a non-parametric kernel regression), functions to create and manipulate discrete approximations to continuous distributions, or classes for constructing interpolated approximations to non-parametric functions. Tool modules generally reside in HARK's root directory and have names like \texttt{HARK.simulation} and \texttt{HARK.interpolation}; they do not necessarily do anything when run.
-
-\textbf{Model modules} specify particular economic models, including classes to represent agents in the model (and the ``market structure'' in which they interact) and functions for solving the ``one period problem'' of those models. For example, \texttt{ConsIndShockModel.py} concerns consumption-saving models in which agents have CRRA utility over consumption and face idiosyncratic shocks to permanent and transitory income. The module includes classes for representing ``types'' of consumers, along with functions for solving (several flavors of) the one period consumption-saving problem. When run, model modules might demonstrate example specifications of their models, filling in the model parameters with arbitrary values. When \texttt{ConsIndShockModel.py} is run, it specifies an infinite horizon consumer with a particular discount factor, permanent income growth rate, coefficient of relative risk aversion (etc), who faces lognormal shocks to permanent and transitory income each period with a particular standard deviation; it then solves this consumer's problem and graphically displays the results.\footnote{Running \texttt{ConsIndShockModel.py} also demonstrates other variations of the consumption-saving problem, but their description is omitted here for brevity.} Model modules generally have \texttt{Model} in their name.
-
-\textbf{Application modules} use tool and model modules to solve, simulate, and/or estimate economic models \textit{for a particular purpose}. While tool modules have no particular economic content and model modules describe entire classes of economic models, applications are uses of a model for some research purpose. For example, \texttt{/Demos/SolvingMicroDSOPs/StructEstimation.py} uses a consumption-saving model from \texttt{ConsIndShockModel.py}, calibrating it with age-dependent sequences of permanent income growth, survival probabilities, and the standard deviation of income shocks (etc); it then estimates the coefficient of relative risk aversion and shifter for an age-varying sequence of discount factors that best fits simulated wealth profiles to empirical data from the Survey of Consumer Finance. A particular application might have multiple modules associated with it, all of which generally reside in one directory.
-
-\subsection{Other Resources}\label{sec:OtherResources}
-
-In the incredibly unlikely scenario in which this document does not fill all of the gaps in your knowledge and answer any questions you might have while reading it, here is a collection of potentially helpful other resources.
-
-\begin{itemize}
-
-\item \href{https://econ-ark.github.io/HARK/}{Online documentation} for HARK tool and model modules, produced using Sphinx.
-
-\item A \href{https://docs.python.org/2.7/tutorial/}{tutorial on Python 2.7}, straight from the source.
-
-\item \href{https://en.wikipedia.org/wiki/Object-oriented_programming}{Wikipedia article on object-oriented programming}, not the subject of an edit war.
-
-\item \href{http://quant-econ.net/}{QuantEcon}, a collection of lectures on quantitative economic modeling from a couple of economists, which definitely in no way influenced HARK whatsoever.
-
-\item A \href{https://www.atlassian.com/git/tutorials/}{tutorial on git}, the repository management and tracking system used by the HARK project, created by Linus Torvalds.
-
-\item A \href{https://guides.github.com/activities/hello-world/}{tutorial on GitHub}, a website that provides a useful framework for git that makes it significantly more user-friendly, despised by Linus Torvalds.
-
-\item \href{https://en.wikipedia.org/wiki/Linus_Torvalds}{Wikipedia article on Linus Torvalds}, just in case.
-
-\item The \href{http://www.numpy.org/}{homepage for NumPy}, a numerical package extensively used by HARK.
-
-\item The \href{http://www.sphinx-doc.org/}{homepage for Sphinx}, a tool for producing HTML documentation.
-
-\item A \href{http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/}{collection of lecture notes} by Chris Carroll, to catch you up on the basics of consumption-saving models.
-
-\item A \href{http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/}{particular set of lecture notes} by Chris Carroll, explaining many of the numeric techniques used in HARK and offering an example of a simple structural model that, in a stunning coincidence, is implemented in HARK as an example.
-
-\item A \href{http://www.econ2.jhu.edu/people/ccarroll/BufferStockTheory.pdf}{deep dive} into the theoretical foundations of buffer stock saving. Warning: dense.
-
-\end{itemize}
-
-
-\section{General Purpose Tools}\label{sec:GeneralPurposeTools}
-
-HARK's root directory contains six tool modules,\footnote{The ``taxonomy'' of these modules is in flux; the functions described here could be combined into fewer modules or further divided by purpose.} each containing a variety of functions and classes that can be used in many economic models-- or even for mathematical purposes that have nothing to do with economics. Some of the tool modules are very sparely populated at this time, while others are quite large. We expect that all of these modules will grow considerably in the near future, as new tools are ``low hanging fruit'' for contribution to the project.\footnote{That is, as the foundational, building-block elements of HARK, new tools are not difficult to program and do not require extensive integration with many moving parts.}
-
-\subsection{HARKcore}\label{sec:HARKcore}
-
-A key goal of the project is to create modularity and interoperability between models, making them easy to combine, adapt, and extend. To this end, the \texttt{HARK.core} module specifies a framework for economic models in HARK, creating a common structure for them on two levels that can be called ``microeconomic'' and ``macroeconomic''.
-
-Microeconomic models in HARK use the \texttt{AgentType} class to represent agents with an intertemporal optimization problem. Each of these models specifies a subclass of \texttt{AgentType}; an instance of the subclass represents agents who are \textit{ex-ante} homogeneous-- they have common values for all parameters that describe the problem. For example, \texttt{ConsIndShockModel} specifies the \texttt{IndShockConsumerType} class, which has methods specific to consumption-saving models with idiosyncratic shocks to income; an instance of the class might represent all consumers who have a CRRA of 3, discount factor of 0.98, etc. The \texttt{AgentType} class has a \texttt{solve} method that acts as a ``universal microeconomic solver'' for any properly formatted model, making it easier to set up a new model and to combine elements from different models; the solver is intended to encompass any model that can be framed as a sequence of one period problems. For a complete description, see section \ref{sec:Microeconomics}.
-
-Macroeconomic models in HARK use the \texttt{Market} class to represent a market (or other aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual agents in the model into aggregate outcomes that are ``passed back'' to the agents. For example, the market in a consumption-saving model might combine the individual asset holdings of all agents in the market to generate aggregate capital in the economy, yielding the interest rate on assets (as the marginal product of capital); the individual agents then learn the aggregate capital level and interest rate, conditioning their next action on this information. Objects that microeconomic agents treat as exogenous when solving (or simulating) their model are thus endogenous at the macroeconomic level. Like \texttt{AgentType}, the \texttt{Market} class also has a \texttt{solve} method, which seeks out a dynamic general equilibrium: a ``rule'' governing the dynamic evolution of macroeconomic objects such that if agents believe this rule and act accordingly, then their collective actions generate a sequence of macroeconomic outcomes that justify the belief in that rule. For a more complete description, see section \ref{sec:Macroeconomics}.
-
-Beyond the model frameworks, \texttt{HARK.core} also defines a ``supersuperclass'' called \texttt{HARKobject}. When solving a dynamic microeconomic model with an infinite horizon (or searching for a dynamic general equilibrium), it is often required to consider whether two solutions are sufficiently close to each other to warrant stopping the process (i.e.\ approximate convergence). It is thus necessary to calculate the ``distance'' between two solutions, so HARK specifies that classes should have a \texttt{distance} method that takes a single input and returns a non-negative value representing the (generally dimensionless) distance between the object in question and the input to the method. As a convenient default, \texttt{HARKobject} provides a ``universal distance metric'' that should be useful in many contexts.\footnote{Roughly speaking, the universal distance metric is a recursive supnorm, returning the largest distance between two instances, among attributes named in \texttt{distance\_criteria}. Those attributes might be complex objects themselves rather than real numbers, generating a recursive call to the universal distance metric.} When defining a new subclass of \texttt{HARKobject}, the user simply defines the attribute \texttt{distance\_criteria} as a list of strings naming the attributes of the class that should be compared when calculating the distance between two instances of that class. For example, the class \texttt{ConsumerSolution} has \texttt{distance\_criteria = ['cFunc']}, indicating that only the consumption function attribute of the solution matters when comparing the distance between two instances of \texttt{ConsumerSolution}. See \href{https://econ-ark.github.io/HARK/generated/HARK.core.html}{here} for online documentation.
-
-\subsection{HARK.utilities}\label{sec:HARKutilities}
-
-The \texttt{HARK.utilities} module carries a double meaning in its name, as it contains both utility functions (and their derivatives, inverses, and combinations thereof) in the economic modeling sense as well as utilities in the sense of general tools. Utility functions included at this time are constant relative risk aversion and constant absolute risk aversion. Other functions in \texttt{HARK.utilities} include some data manipulation tools (e.g.\ for calculating an average of data conditional on being within a percentile range of different data), functions for constructing discrete state space grids, convenience functions for retrieving information about functions, and basic plotting tools using \texttt{matplotlib.pyplot}.
-
-The module also includes functions for constructing discrete approximations to continuous distributions (e.g.\ \texttt{approxLognormal()} to approximate a log-normal distribution) as well as manipulating these representations (e.g.\ appending one outcome to an existing distribution, or combining independent univariate distributions into one multivariate distribution). As a convention in HARK, continuous distributions are approximated as finite discrete distributions when solving models; an $N$-dimensional random variable is formatted as a length $N+1$ list of 1D arrays, with the first element representing event probabilities and all other elements are realizations of the $N$ component RVs. This both simplifies solution methods (reducing numeric integrals to simple dot products) and allows users to easily test whether their chosen degree of discretization yields a sufficient approximation to the full distribution. See \href{https://econ-ark.github.io/HARK/generated/HARK.utilities.html}{here} for online documentation.
-
-\subsection{HARK.interpolation}\label{sec:HARKinterpolation}
-
-The \texttt{HARK.interpolation} module defines classes for representing interpolated function approximations. Interpolation methods in HARK all inherit from a superclass such as \texttt{HARKinterpolator1D} or \texttt{HARKinterpolator2D}, wrapper classes that ensures interoperability across interpolation methods. For example, \texttt{HARKinterpolator1D} specifies the methods \texttt{\_\_call\_\_} and \texttt{derivative} to accept an arbitrary array as an input and return an identically shaped array with the interpolated function evaluated at the values in the array or its first derivative, respectively. However, these methods do little on their own, merely reshaping arrays and referring to the \texttt{\_evaluate} and \texttt{\_der} methods, which are \textit{not actually defined in} \texttt{HARKinterpolator1D}. Each subclass of \texttt{HARKinterpolator1D} specifies their own implementation of \texttt{\_evaluate} and \texttt{\_der} particular to that interpolation method, accepting and returning only 1D arrays. In this way, subclasses of \texttt{HARKinterpolator1D} are easily interchangeable with each other, as all methods that the user interacts with are identical, varying only by ``internal'' methods.
-
-When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to know the ``distance'' between functions generated by successive iterations of a solution procedure. To this end, each interpolator class in HARK must define a \texttt{distance} method that takes as an input another instance of the same class and returns a non-negative real number representing the ``distance'' between the two. As each of the \texttt{HARKinterpolatorXD} classes inherits from \texttt{HARKobject}, all interpolator classes have the default ``universal'' distance method; the user must simply list the names of the relevant attributes in the attribute \texttt{distance\_criteria} of the class.
-
-Interpolation methods currently implemented in HARK include (multi)linear interpolation up to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D ``lower envelope'' interpolator. See \href{https://econ-ark.github.io/HARK/generated/HARKinterpolation.html}{here} for online documentation.
-
-\subsection{HARK.simulation}\label{sec:HARKsimulation}
-
-The \texttt{HARK.simulation} module provides tools for generating simulated data or shocks for post-solution use of models. Currently implemented distributions include normal, lognormal, Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use, these tools are used in the consumption-saving models of \texttt{ConsIndShockModel.py} to simulate permanent and transitory income shocks as well as unemployment events. See \href{https://econ-ark.github.io/HARK/generated/HARKsimulation.html}{here} for online documentation.
-
-\subsection{HARK.estimation}\label{sec:HARKestimation}
-
-Methods for optimizing an objective function for the purposes of estimating a model can be found in \texttt{HARK.estimation}. As of this writing, the implementation includes only minimization by the Nelder-Mead simplex method, minimization by a derivative-free Powell method variant, and two small tools for resampling data (i.e.\ for a bootstrap); the minimizers are merely convenience wrappers (with result reporting) for optimizers included in \texttt{scipy.optimize}. Future functionality will include more robust global search methods, including genetic algorithms, simulated annealing, and differential evolution. See \href{https://econ-ark.github.io/HARK/generated/HARKestimation.html}{here} for full documentation.
-
-\subsection{HARK.parallel}\label{sec:HARKparallel}
-
-By default, processes in Python are single-threaded, using only a single CPU core. The \texttt{HARK.parallel} module provides basic tools for using multiple CPU cores simultaneously, with minimal effort.\footnote{\texttt{HARK.parallel} uses two packages that aren't included in the default distribution of Anaconda: \texttt{joblib} and \texttt{dill}; see step 3 of the instructions in section \ref{sec:GettingStarted} for how to install them.} In particular, it provides the function \texttt{multiThreadCommands}, which takes two arguments: a list of \texttt{AgentType}s and a list of commands as strings; each command should be a method of the \texttt{AgentType}s. The function simply distributes the \texttt{AgentType}s across threads on different cores and executes each command in order, returning no output (the \texttt{AgentType}s themselves are changed by running the commands). Equivalent results would be achieved by simply looping over each type and running each method in the list. Indeed, \texttt{HARK.parallel} also has a function called \texttt{multiThreadCommandsFake} that does just that, with identical syntax to \texttt{multiThreadCommands}; multithreading in HARK can thus be easily turned on and off.\footnote{In the future, \texttt{HARK.parallel} might be absorbed into \texttt{HARK.core} and \texttt{HARK.estimation}, particularly if \texttt{joblib} and \texttt{dill} become part of the standard Anaconda distribution.} The module also has functions for a parallel implementation of the Nelder-Mead simplex algorithm, as described in Wiswall and Lee (2011). See \href{https://econ-ark.github.io/HARK/generated/HARK.parallel.html}{here} for full documentation.
-
-\section{Microeconomics: the AgentType Class}\label{sec:Microeconomics}
-
-The core of our microeconomic dynamic optimization framework is a flexible object-oriented representation of economic agents. The \texttt{HARK.core} module defines a superclass called \texttt{AgentType}; each model defines a subclass of \texttt{AgentType}, specifying additional model-specific features and methods while inheriting the methods of the superclass. Most importantly, the method \texttt{solve} acts as a ``universal solver'' applicable to any (properly formatted) discrete time model. This section describes the format of an instance of \texttt{AgentType} as it defines a dynamic microeconomic problem;\footnote{Each instance of \texttt{AgentType} represents an \textit{ex-ante} heterogeneous ``type'' of agent; \textit{ex-post} heterogeneity is achieved by simulating many agents of the same type, each of whom receives a unique sequence of shocks.}
-
-\subsection{Attributes of an AgentType}\label{sec:AttributesOfAnAgentType}
-
-A discrete time model in our framework is characterized by a sequence of ``periods'' that the agent will experience. A well-formed instance of \texttt{AgentType} includes the following attributes:
-\begin{itemize}
-\item \texttt{solveOnePeriod}: A function, or a list of functions, representing the solution method for a single period of the agent's problem. The inputs passed to a \texttt{solveOnePeriod} function include all data that characterize the agent's problem in that period, including the solution to the subsequent period's problem (designated as \texttt{solution\_next}). The output of these functions is a single \texttt{Solution} object, which can be passed to the solver for the previous period.
-
-\item \texttt{time\_inv}: A list of strings containing all of the variable names that are passed to at least one function in \texttt{solveOnePeriod} but do \textit{not} vary across periods. Each of these variables resides in a correspondingly named attribute of the \texttt{AgentType} instance.
-
-\item \texttt{time\_vary}: A list of strings naming the attributes of this instance that vary across periods. Each of these attributes is a list of period-specific values, which should be of the same length. If the solution method varies across periods, then \texttt{'solveOnePeriod'} is an element of \texttt{time\_vary}.\footnote{\texttt{time\_vary} may include attributes that are never used by a function in \texttt{solveOnePeriod}. Most saliently, the attribute \texttt{solution} is time-varying but is not used to solve individual periods.}
-
-\item \texttt{solution\_terminal}: An object representing the solution to the ``terminal'' period of the model. This might represent a known trivial solution that does not require numeric methods, the solution to some previously solved ``next phase'' of the model, a scrap value function, or an initial guess of the solution to an infinite horizon model.
-
-\item \texttt{pseudo\_terminal}: A Boolean flag indicating that \texttt{solution\_terminal} is not a proper terminal period solution (rather an initial guess, ``next phase'' solution, or scrap value) and should not be reported as part of the model's solution.
-
-\item \texttt{cycles}: A non-negative integer indicating the number of times the agent will experience the sequence of periods in the problem. For example, \texttt{cycles = 1} means that the sequence of periods is analogous to a lifecycle model, experienced once from beginning to end; \texttt{cycles = 2} means that the agent experiences the sequence twice, with the first period in the sequence following the last. An infinite horizon problem in which the sequence of periods repeats indefinitely is indicated with \texttt{cycles = 0}.
-
-\item \texttt{tolerance}: A positive real number indicating convergence tolerance, representing the maximum acceptable ``distance'' between successive cycle solutions in an infinite horizon model; it is irrelevant when \texttt{cycles > 0}. As the distance metric on the space of solutions is model-specific, the value of \texttt{tolerance} is generally dimensionless.
-
-\item \texttt{time\_flow}: A Boolean flag indicating the direction that time is ``flowing.'' When \texttt{True}, the variables listed in \texttt{time\_vary} are listed in ordinary chronological order, with index 0 being the first period; when \texttt{False}, these lists are in reverse chronological order, with index 0 holding the last period.
-\end{itemize}
-
-An instance of \texttt{AgentType} also has the attributes named in \texttt{time\_vary} and \texttt{time\_inv}, and may have other attributes that are not included in either (e.g.\ values not used in the model solution, but instead to construct objects used in the solution).
-
-\subsection{A Universal Solver}\label{sec:UniversalSolver}
-
-When an instance of \texttt{AgentType} invokes its \texttt{solve} method, the solution to the agent's problem is stored in the attribute \texttt{solution}. The solution is computed by recursively solving the sequence of periods defined by the variables listed in \texttt{time\_vary} and \texttt{time\_inv} using the functions in \texttt{solveOnePeriod}. The time-varying inputs are updated each period, including the successive period's solution as \texttt{solution\_next}; the same values of time invariant inputs in \texttt{time\_inv} are passed to the solver in every period. The first call to \texttt{solveOnePeriod} uses \texttt{solution\_terminal} as \texttt{solution\_next}. In a finite horizon problem, the sequence of periods is solved \texttt{cycles} times over; in an infinite horizon problem, the sequence of periods is solved until the solutions of successive cycles have a ``distance'' of less than \texttt{tolerance}.
-
-The output from a function in \texttt{solveOnePeriod} is an instance of a model-specific solution class. The attributes of a solution to one period of a problem might include behavioral functions, (marginal) value functions, and other variables characterizing the result. Each solution class must have a method called \texttt{distance()}, which returns the ``distance'' between itself and another instance of the same solution class, so as to define convergence as a stopping criterion; for many models, this will be the ``distance'' between a policy or value function in the solutions. If the solution class is defined as a subclass of \texttt{HARKobject}, it automatically inherits the default \texttt{distance} method, so that the user must only list the relevant object attributes in \texttt{distance\_criteria}.
-
-The \texttt{AgentType} also has methods named \texttt{preSolve} and \texttt{postSolve}, both of which take no arguments and do absolutely nothing. A subclass of \texttt{AgentType} can overwrite these blank methods with its own model specific methods. \texttt{preSolve} is automatically called near the beginning of the \texttt{solve} method, before solving the sequence of periods. It is used for specifying tasks that should be done before solving the sequence of periods, such as pre-constructing some objects repeatedly used by the solution method or finding an analytical terminal period solution. For example, the \texttt{IndShockConsumerType} class in \texttt{ConsIndShockModel} has a \texttt{preSolve} method that calls its \texttt{updateSolutionTerminal} method to ensure that \texttt{solution\_terminal} is consistent with the model parameters. The \texttt{postSolve} method is called shortly after the sequence of periods is fully solved; it can be used for ``post-processing'' of the solution or performing a step that is only useful after solution convergence. For example, the \texttt{TractableConsumerType} in \texttt{TractableBufferStockModel} has a \texttt{postSolve} method that constructs an interpolated consumption function from the list of stable arm points found during solution.
-
-Our universal solver is written in a very general way that should be applicable to any discrete time optimization problem-- because Python is so flexible in defining objects, the time-varying inputs for each period can take any form. Indeed, the solver does no ``real work'' itself, but merely provides a structure for describing models in the HARK framework, allowing interoperability among current and future modules.
-
-\subsection{The Flow of Time and Other Methods}\label{sec:OtherMethods}
-
-Because dynamic optimization problems are solved recursively in our framework, it is natural to list time-varying values in reverse chronological order-- the \texttt{solve()} method loops over the values in each time-varying list in the same direction that a human would read them. When simulating agents after the solution has been obtained, however, it is much more convenient for time-varying parameters to be listed in ordinary chronological order-- the direction in which they will be experienced by simulated agents. To allow the user to set the order in which ``time is flowing'' for an instance of \texttt{AgentType}, the HARK framework includes functionality to easily change ordering of time-varying values.
-
-The attribute \texttt{time\_flow} is \texttt{True} if variables are listed in ordinary chronological order and \texttt{False} otherwise. \texttt{AgentType} has the following methods for manipulating time:
-\begin{itemize}
-\item \texttt{timeReport()}: Prints to screen a description of the direction that time is flowing, for interactive convenience and as a reminder of the functionality.
-
-\item \texttt{timeFlip()}: Flips the direction of time. Each attribute listed in \texttt{time\_vary} is reversed in place, and the value of \texttt{time\_flow} is toggled.
-
-\item \texttt{timeFwd()}: Sets the direction of time to ordinary chronological order.
-
-\item \texttt{timeRev()}: Sets the direction of time to reverse chronological order.
-\end{itemize}
-
-These methods are invoked to more conveniently access time-varying objects. When a new time-varying attribute is added, its name should be appended to \texttt{time\_vary}, particularly if its values are used in the solution of the model (or is part of the solution itself). For example, the \texttt{solve()} method automatically adds the string \texttt{'solution'} to \texttt{time\_vary} if it is not already present. Note that attributes listed in \texttt{time\_vary} \textit{must} be lists if \texttt{solve()} or \texttt{timeFlip()} are used. Some values that could be considered ``time varying'' but are never used to solve the model are more conveniently represented as a \texttt{numpy.array} object (e.g.\ \ the history of a state or control variable from a simulation); because the \texttt{numpy.array} class does not have a \texttt{reverse()} method, these attributes should not be listed in \texttt{time\_vary}.
-
-The base \texttt{AgentType} is sparsely defined, as most ``real'' methods will be application-specific. Two final methods bear mentioning. First, the \texttt{\_\_call\_\_} method points to \texttt{assignParameters()}, a convenience method for adding or adjusting attributes (inherited from \texttt{HARKobject}). This method takes any number of keyword arguments, so that code can be parsimoniously written as, for example, \texttt{AgentInstance(attribute1 = value1, attribute2 = value2)}. Using Python's dictionary capabilities, many attributes can be conveniently set with minimal code. Second, the \texttt{resetRNG} method simply resets the \texttt{AgentType}'s random number generator (as the attribute \texttt{RNG}) using the value in the attribute \texttt{seed}.\footnote{Every instance of \texttt{AgentType} is created with a random number generator as an instance of the class \texttt{numpy.random.RandomState}, with a default \texttt{seed} of zero.} This method is useful for (\textit{inter alia}) ensuring that the same underlying sequence of shocks is used for every simulation run when a model is solved or estimated.
-
-
-\subsection{Sample Model: Perfect Foresight Consumption-Saving}\label{sec:PerfectForesight}
-
-To provide a concrete example of how the \texttt{AgentType} class works, consider the very simple case of a perfect foresight consumption-saving model. The agent has time-separable, additive CRRA preferences over consumption, discounting future utility at a constant rate; he receives a particular stream of labor income each period, and knows the interest rate on assets that he holds from one period to the next. His decision about how much to consume in a particular period can be expressed in Bellman form as:
-
-\begin{eqnarray*}
-V_t(M_t) &=& \max_{C_t} \utilFunc(C_t) + \beta \PLives_t \E [V_{t+1}(M_{t+1}) ], \\
-A_t &=& M_t - C_t, \\
-M_{t+1} &=& \Rfree A_t + Y_{t+1}, \\
-Y_{t+1} &=& \Gamma_{t+1} Y_t, \\
-\utilFunc(C) &=& \frac{C^{1-\CRRA}}{1-\CRRA}.
-\end{eqnarray*}
-An agent's problem is thus characterized by values of $\CRRA$, $\Rfree$, and $\beta$ plus sequences of survival probabilities $\PLives_t$ and income growth factors $\Gamma_t$ for $t=0,\cdots,T$. This problem has an analytical solution for both the value function and the consumption function.
-
-The \texttt{ConsIndShockModel} module defines the class \texttt{PerfForesightConsumerType} as a subclass of \texttt{AgentType} and provides solver functions for several variations of a consumption-saving model, including the perfect foresight problem.\footnote{As the name implies, the module is mostly concerned with models with idiosyncratic shocks to income.} A HARK user could specify and solve a ten period perfect foresight model with the following commands:
-
-\vspace{0.25cm}
-
-\noindent \texttt{MyConsumer = PerfForesightConsumerType(time\_flow=True, cycles=1,}
-
-\texttt{ CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98,}
-
-\texttt{ LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90],}
-
-\texttt{ PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02],}
-
-\texttt{ Nagents=1000)}
-
-\noindent \texttt{MyConsumer.solve()}
-
-\vspace{0.25cm}
-
-The first line makes a new instance of \texttt{ConsumerType}, specifying that time is currently ``flowing'' forward\footnote{This is relevant for the intepretation of the time-varying inputs \texttt{LivPrb} and \texttt{PermGroFac}.} and that the sequence of periods happens exactly once; the next three lines (all part of the same command) set the time invariant (\texttt{CRRA}, \texttt{Rfree}, \texttt{DiscFac}) and time varying parameters (\texttt{LivPrb}, \texttt{PermGroFac}). Note that \texttt{dir(MyConsumer)} shows all the attributes of \texttt{MyConsumer}. After running the \texttt{solve} method, \texttt{MyConsumer} will have an attribute called \texttt{solution}, which will be a list with eleven \texttt{ConsumerSolution} objects, representing the period-by-period solution to the model.\footnote{The last element of \texttt{solution} represents the terminal period solution, where the agent consumes all available resources because there is no future.} Each \texttt{ConsumerSolution} has several attributes:
-\begin{itemize}
-\item \texttt{cFunc}: Optimal consumption function for this period as a function of (normalized) market resources $m_t = M_t/Y_t$. Can be called like any other function: \texttt{MyConsumer.solution[0].cFunc(5)} returns consumption at $m_t = 5$.
-
-\item \texttt{vFunc}: Value function (over market resources $m_t$); can be evaluated like \texttt{cFunc}.
-
-\item \texttt{mNrmMin}: Minimum value of normalized market resources $m_t$ such that \texttt{cFunc} and \texttt{vFunc} are defined.
-
-\item \texttt{hNrm}: Normalized human wealth-- the PDV of future income (ignoring mortality) divided by current period income $Y_t$.
-
-\item \texttt{MPC}: The constant marginal propensity to consume (linear consumption function).
-
-\end{itemize}
-
-The following command generates the plot for the perfect foresight consumption function:
-
-\vspace{0.25cm}
-
-\noindent \texttt{mMin = MyConsumer.solution[0].mNrmMin}
-
-\noindent \texttt{plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10)}
-
-\vspace{0.25cm}
-
-To solve a version of this problem in which the sequence of ten periods happens three times (yielding a \texttt{solution} attribute with thirty-one elements), the user can do:
-
-\vspace{0.25cm}
-
-\noindent \texttt{MyConsumer(cycles = 3)}
-
-\noindent \texttt{MyConsumer.solve()}
-
-\vspace{0.25cm}
-
-To solve an infinite horizon problem in which the agent experiences the same one period problem indefinitely, the user can do:
-
-\vspace{0.25cm}
-
-\noindent \texttt{OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02,}
-
-\texttt{DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000)}
-
-\noindent \texttt{OtherConsumer.solve()}
-
-\vspace{0.25cm}
-
-This instance is specified as being infinite horizon by setting \texttt{cycles=0}. Note that the time-varying inputs are still specified as (one element) lists, even though they take on the same value in every period; this is because an infinite horizon model might consist of a multi-period sequence repeated indefinitely, rather than just one repeated period. The \texttt{solution} attribute of \texttt{OtherConsumer} will be a list containing one instance of \texttt{ConsumerSolution}, representing the solution to every period of the model.
-
-\section{Macroeconomics: the Market Class}\label{sec:Macroeconomics}
-
-The modeling framework of \texttt{AgentType} is deemed ``microeconomic'' because it pertains only to the dynamic optimization problem of agents, treating all inputs of the problem as exogenously fixed. In what we label as ``macroeconomic'' models, some of the inputs for the microeconomic models are endogenously determined by the collective states and controls of agents in the model. In a dynamic general equilibrium, there must be consistency between agents' beliefs about these macroeconomic objects, their individual behavior, and the realizations of the macroeconomic objects that result from individual choices.
-
-The \texttt{Market} class in \texttt{HARK.core} provides a framework for such macroeconomic models, with a \texttt{solve} method that searches for a dynamic general equilibrium. An instance of \texttt{Market} includes a list of \texttt{AgentType}s that compose the economy, a method for transforming microeconomic outcomes (states, controls, and/or shocks) into macroeconomic outcomes, and a method for interpreting a history or sequence of macroeconomic outcomes into a new ``dynamic rule'' for agents to believe. Agents treat the dynamic rule as an input to their microeconomic problem, conditioning their optimal policy functions on it. A dynamic general equilibrium is a fixed point dynamic rule: when agents act optimally while believing the equilibrium rule, their individual actions generate a macroeconomic history consistent with the equilibrium rule.
-
-\subsection{Down on the Farm}\label{sec:DownOnTheFarm}
-
-The \texttt{Market} class uses a farming metaphor to conceptualize the process for generating a history of macroeconomic outcomes in a model. Suppose all \texttt{AgentTypes} in the economy believe in some dynamic rule (i.e.\ the rule is stored as attributes of each \texttt{AgentType}, which directly or indirectly enters their dynamic optimization problem), and that they have each found the solution to their microeconomic model using their \texttt{solve} method. Further, the macroeconomic and microeconomic states have been reset to some initial orientation.
-
-To generate a history of macroeconomic outcomes, the \texttt{Market} repeatedly loops over the following steps a set number of times:
-\begin{enumerate}
-\item \texttt{sow}: Distribute the macroeconomic state variables to all \texttt{AgentType}s in the market.
-
-\item \texttt{cultivate}: Each \texttt{AgentType} executes their \texttt{marketAction} method, likely corresponding to simulating one period of the microeconomic model.
-
-\item \texttt{reap}: Microeconomic outcomes are gathered from each \texttt{AgentType} in the market.
-
-\item \texttt{mill}: Data gathered by \texttt{reap} is processed into new macroeconomic states according to some ``aggregate market process''.
-
-\item \texttt{store}: Relevant macroeconomic states are added to a running history of outcomes.
-\end{enumerate}
-This procedure is conducted by the \texttt{makeHistory} method of \texttt{Market} as a subroutine of its \texttt{solve} method. After making histories of the relevant macroeconomic variables, the market then executes its \texttt{calcDynamics} function with the macroeconomic history as inputs, generating a new dynamic rule to distribute to the \texttt{AgentType}s in the market. The process then begins again, with the agents solving their updated microeconomic models given the new dynamic rule; the \texttt{solve} loop continues until the ``distance'' between successive dynamic rules is sufficiently small.
-
-\subsection{Attributes of a Market}\label{sec:AttributesOfAMarket}
-
-To specify a complete instance of \texttt{Market}, the user should give it the following attributes:\footnote{For some purposes, it might be useful to specify a subclass of \texttt{Market}, defining \texttt{millRule} and/or \texttt{calcDynamics} as methods rather than functions.}
-\begin{itemize}
-\item \texttt{agents}: A list of \texttt{AgentType}s, representing the agents in the market. Each element in \texttt{agents} represents an \textit{ex-ante} heterogeneous type; each type could have many \textit{ex-post} heterogeneous agents.
-
-\item \texttt{sow\_vars}: A list of strings naming variables that are output from the aggregate market process, representing the macroeconomic outcomes. These variables will be distributed to the \texttt{agents} in the \texttt{sow} step.
-
-\item \texttt{reap\_vars}: A list of strings naming variables to be collected from the \texttt{agents} in the \texttt{reap} step, to be used as inputs for the aggregate market process.
-
-\item \texttt{const\_vars}: A list of strings naming variables used by the aggregate market process that \textit{do not} come from \texttt{agents}; they are constant or come from the \texttt{Market} itself.
-
-\item \texttt{track\_vars}: A list of strings naming variables generated by the aggregate market process that should be tracked as a history, to be used when calculating a new dynamic rule. Usually a subset of \texttt{sow\_vars}.
-
-\item \texttt{dyn\_vars}: A list of strings naming the variables that constitute a dynamic rule. These will be stored as attributes of the \texttt{agents} whenever a new rule is calculated.
-
-\item \texttt{millRule}: A function for the ``aggregate market process'', transforming microeconomic outcomes into macroeconomic outcomes. Its inputs are named in \texttt{reap\_vars} and \texttt{const\_vars}, and it returns a single object with attributes named in \texttt{sow\_vars} and/or \texttt{track\_vars}. Can be defined as a method of a subclass of \texttt{Market}.
-
-\item \texttt{calcDynamics}: A function that generates a new dynamic rule from a history of macroeconomic outcomes. Its inputs are named in \texttt{track\_vars}, and it returns a single object with attributes named in \texttt{dyn\_vars}.
-
-\item \texttt{act\_T}: The number of times that the \texttt{makeHistory} method should execute the ``farming loop'' when generating a new macroeconomic history.
-
-\item \texttt{tolerance}: The minimum acceptable ``distance'' between successive dynamic rules produced by \texttt{calcDynamics} to constitute a sufficiently converged solution.
-\end{itemize}
-
-Further, each \texttt{AgentType} in \texttt{agents} must have two methods not necessary for microeconomic models; neither takes any input (except \texttt{self}):
-\begin{itemize}
-\item \texttt{marketAction}: The microeconomic process to be run in the \texttt{cultivate} step. Likely uses the new macroeconomic outcomes named in \texttt{sow\_vars}; should store new values of relevant microeconomic outcomes in the attributes (of \texttt{self}) named in \texttt{reap\_vars}.
-
-\item \texttt{reset}: Reset, initialize, or prepare for a new ``farming loop'' to generate a macroeconomic history. Might reset its internal random number generator, set initial state varibles, clear personal histories, etc.
-\end{itemize}
-
-When solving macroeconomic models in HARK, the user should also define classes to represent the output from the aggregate market process in \texttt{millRule} and for the model-specific dynamic rule. The latter should have a \texttt{distance} method to test for solution convergence; if the class inherits from \texttt{HARKobject}, the user need only list relevant attributes in \texttt{distance\_criteria}.
-
-\subsection{Sample Model: FashionVictim}\label{sec:FashionVictim}
-
-To illustrate the \texttt{Market} class, consider a simple example in the emerging economic subfield of aesthemetrics, the \texttt{FashionVictimModel}.\footnote{This model is inspired by the paper ``The hipster effect: When anticonformists all look the same'' by Jonathan Touboul.}\footnote{For a more traditional macroeconomic model, the \texttt{ConsAggShocksModel} module includes a consumption-saving model with both idiosyncratic and aggregate shocks, in which individual asset holdings are aggregated into total productive capital, endogenizing the (dynamic) interest and wage rate.} This module defines a subclass of \texttt{AgentType} called \texttt{FashionVictimType}. Each period, fashion victims make a binary choice of style $s$: to dress as a jock (0) or punk (1). They receive utility directly from the outfit they wear and as a function of the proportion of the population who \textit{just wore} the same style; they also pay switching costs ($c_{pj}$,$c_{jp}$) if they change styles rather than keep the same as the previous period. Moreover, they receive an idiosyncratic type 1 extreme value (T1EV) preference shock to each style in each period. Defining the population punk proportion as $p$ and the conformity utility function as $f:[0,1]\rightarrow \mathbb{R}$, the current period utility function is thus:
-\begin{equation*}
-u(s_t;s_{t-1},p_t) = s_t f(p_t) + (1-s_t) f(1-p_t) + s_t U_p + (1-s_t) U_j - c_{pj} s_{t-1}(1-s_t) - c_{jp}(1-s_{t-1})s_t.
-\end{equation*}
-
-Fashion victims are forward looking and discount future utility at a constant rate of $\beta$ per period. To simplify the analysis, we assume they believe that the population punk proportion in the next period is a linear function of the punk proportion in the current period, subject to a uniformly distributed shock. No restrictions are put on the function $f$; fashion victims might be conformists who like to dress the same as others ($f'(p) > 0$) or hipsters who like to style themselves in the minority ($f'(p) < 0$).\footnote{In practice, $f$ is parameterized as the beta distribution for convenience.} A fashion victim's problem can be written in Bellman form as:
-\begin{equation*}
-V(s_{t-1},p_t) = \E \left[ \max_{s_t \in \{0,1\}} u(s_t;s_{t-1},p_t) + \eta_{s_t} + \beta \E \left[ V(s_t,p_{t+1}) \right] \right],
-\end{equation*}
-\begin{equation*}
-p_{t+1} = a p_t + b + \pi_{t+1}, \qquad \pi_{t+1} \sim U[-w,w], \qquad \eta_0,\eta_1 \sim T1EV.
-\end{equation*}
-
-An instance of \texttt{FashionVictimType} is thus characterized by values of $U_p$, $U_j$, $c_{pj}$, $c_{jp}$ and a function $f$, as well as beliefs about $p_{t+1}$ as a function of $p_t$ (summarized by slope $a$, intercept $b$, and uniform shock width $w$). Given this information, a \texttt{FashionVictimType}'s infinite horizon microeconomic model can be solved by backward induction in a few lines; the ``one period solver'' is given by \texttt{solveFashion}. However, while individual agents treat the dynamics of $p_t$ as exogenous, they are in fact endogenously determined by the actions of all the fashion victims in the market.\footnote{The market might consist of agents all of the same type, or might have several types with \textit{ex-ante} heterogeneity.} A dynamic general equilibrium of the ``macroeconomic fashion model'' is thus characterized by a triple of $(a,b,w)$ such that when fashion victims believe in this ``punk evolution rule'' and act optimally, their collective fashion choices exhibit this same rule when the model is simulated.
-
-The search for a dynamic general equilibrium is implemented in HARK's \texttt{Market} class with the following definitions:
-
-\vspace{0.25cm}
-
-\noindent \texttt{sow\_vars = ['pNow']} (macroeconomic outcome is $p_t$)
-
-\noindent \texttt{reap\_vars = ['sNow']} (microeconomic outcomes are $s_t$ for many agents)
-
-\noindent \texttt{track\_vars = ['pNow']} (must track history of $p_t$)
-
-\noindent \texttt{dyn\_vars = ['pNextSlope','pNextIntercept','pNextWidth']} (dynamic rule $(a,b,w)$)
-
-\noindent \texttt{millRule = calcPunkProp} (aggregate process: average the style choices of all agents)
-
-\noindent \texttt{calcDynamics = calcFashionEvoFunc} (calculate new $(a,b,w)$ with autoregression of $p_t$)
-
-\noindent \texttt{act\_T = 1000} (simulate 1000 periods of the fashion market)
-
-\noindent \texttt{tolerance = 0.01} (terminate solution when $(a,b,w)$ changes by less than 0.01)
-
-\vspace{0.25cm}
-
-The \texttt{agents} attribute has a list of 22 \texttt{FashionVictimType}s, which vary in their values of $U_p$ and $U_j$, and their $f$ functions. The \texttt{marketAction} method of \texttt{FashionVictimType} simulates one period of the microeconomic model: each agent receives style preference shocks $\eta_0$ and $\eta_1$, sees the current proportion of punks $p_t$ (sown to them as \texttt{pNow}), and chooses which style to wear, storing it in the binary array \texttt{sNow}, an attribute of \texttt{self}.
-
-The \texttt{millRule} for this market is extremely simple: it flattens the list of binary arrays of individual style choices (gathered in the \texttt{reap} step) and averages them into a new value of $p_t$, to be tracked as a history and \texttt{sow}n back to the \texttt{agents} to begin the cycle again. Once a history of 1000 values of $p_t$ has been generated with the \texttt{makeHistory} method, we can calculate a new dynamic fashion rule with \texttt{calcFashionEvoFunc} by regressing $p_t$ on $p_{t-1}$, approximating $w$ as twice the standard deviation of prediction errors.\footnote{This is an arbitrary way to calculate $w$, but accuracy is not important in a silly example.} The new fashion rule is an instance of the simple \text{FashionEvoFunc} class, whose only methods are inherited from \texttt{HARKobject}.
-
-When the \texttt{solve} method is run, the solver successively solves each agent's microeconomic problem, runs the \texttt{makeHistory} method to generate a 1000 period history of $p_t$, and calculates a new punk evolution rule based on this history; the solver terminates when consecutive rules differ by less than 0.01 in any dimension.
-
-}
-% \hypertarget{ContributingToHARK}{\section{Contributing to HARK}\label{sec:ContributingToHARK}}
-\section{Contributing to HARK}\label{sec:ContributingToHARK}
-
-%After reading this far into the user guide, it is safe to assume that you are eager to contribute to HARK's development.
-This section provides an overview of how users can contribute new code to HARK, what sort of contributions are most desired, and style conventions for the toolKit. Before attempting to extend HARK, we strongly recommend that you familiarize yourself with its features and models by looking at the source code.
-
-\subsection{What Does HARK Want?}\label{sec:WhatDoesHARKWant}
-
-The easiest and most straightforward way to contribute to HARK is by writing new general purpose tools for inclusion in one of the top-level modules. This might be a commonly known data analysis technique (e.g.\ a kernel density estimator) for \texttt{HARK.utilities}, an optimization method (local or global) for \texttt{HARK.estimation}, an interpolation method for \texttt{HARKinterpolation}, etc. If you've found a technique useful in your own research and believe it could be of use to others on entirely different projects, then it probably belongs in HARK. Likewise, if you read through a HARK module expecting to find a certain tool or function (because of similar functions that are present, or merely because \textit{it's very useful}) but don't, this is a good sign that HARK has a hole that you can fill.\footnote{Case in point: As of this moment, \texttt{HARK.utilities} has no function for making a discrete approximation to the normal distribution, even though there are several well known methods for doing so and it is an obviously useful function.}
-
-Secondly, the development team wants to expand HARK to include more canonical models. Most of the models available in the toolKit at the time of its public beta release concern agents with (geometrically discounted) CRRA utility over a unitary consumption good, who receive permanent and transitory shocks to income and have access to a single risk free asset. This is an important class of models, but that description has an awful lot of qualifiers. In short, there are many more common features of dynamic household models that are not yet in HARK, but would be very valuable for the toolKit. New model contributions should ``play nicely'' with existing models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the greatest extent possible. For ideas of potential model extensions for HARK, see section \ref{sec:FutureModels}.\footnote{Although that list is limited to extensions and variations on consumption-saving models, HARK has no such restrictions. If there is an entirely different sort of dynamic model that fits in the HARK framework, it can be contributed.}
-
-Finally, contributions to HARK are not restricted to entirely new functionality. While we have done our best to provide flexible frameworks and useful tools, we have almost certainly overlooked an unexpected case that would not be (properly) handled by the toolKit as is. Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as an Issue on GitHub so that someone else might. The ``mistake'' you find might not even be in the code itself, but a deficiency in the documentation: a misnamed (or incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing) comment, even a grammatical or spelling error. No matter how niggling, we want your fix.
-
-For those who want to contribute to the HARK project without programming a new model or digging through the code for errors and omissions, consider reading through the Issues page on GitHub to see what other users have flagged for development. You might find several pieces of ``low hanging fruit'' for you to quickly fix.
-
-\subsection{Git and GitHub}\label{sec:GitHub}
-
-Git is a distributed version control system that has gained increasing popularity in the decade since its creation; it is designed to enable collaboration and ``non-linear workflow'' among a (potentially disperse) programming team. As HARK is to be developed by volunteer researchers all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds as a command line tool, others have since extended git with further functionality and a user interface more friendly to those more familiar with GUIs. Foremost among these tools is GitHub.com, where the HARK project is hosted. This section provides a brief overview of how to use GitHub to interact with (and potentially contribute to) HARK.
-
-To prepare to contribute to HARK, follow these simple steps:
-\begin{enumerate}
-\item \textbf{Make a GitHub account:} Go to \href{https://www.github.com}{github.com}; the homepage should prompt you to choose a username and password. Signing up is quick, easy, and free.\footnote{There are non-free ways to sign up for GitHub, but you don't need to worry about them.}
-
-\item \textbf{Install GitHub Desktop}: Go to \href{https://desktop.github.com/}{desktop.github.com} and download the Desktop application for your operating system, then install it. After opening Desktop for the first time, enter your GitHub login to connect it to your account.
-
-\item \textbf{Fork the HARK repository:} Go to the \href{https://github.com/econ-ark/HARK}{HARK repository page}, click on the Fork button in the upper right, and choose to fork HARK to your own GitHub account. This creates a new repository identical to the one hosted by Econ-Ark at the moment you fork it, but hosted on your account instead.
-
-\item \textbf{Add the repository to Desktop:} Open GitHub Desktop, click on the $+$ icon in the upper left, and select clone. Find the repository named \texttt{yourusername/HARK}, click the Clone button at the bottom of the dialogue window, and choose a directory on your local machine.
-\end{enumerate}
-
-You now control a ``fork'' of the main HARK repository, hosted by GitHub, and have a clone of this fork on your local machine. You are free to edit your fork of HARK in any way you'd like; other GitHub users can view your fork (and fork it themselves), but they cannot make changes to your fork unless you give them permission by adding them as a contributor. Changes made to the local copy of your fork do not get automatically sent to the remote copy on GitHub for public viewing. To make changes to your fork on GitHub, follow these steps:
-\begin{enumerate}
-\item \textbf{Make local changes:} Edit your local copy of the repository in any way you'd like: add or delete lines from existing files; add, delete, or rename entire files or directories.
-
-\item \textbf{Commit local changes:} Open the repository in GitHub Desktop and click on the Changes tab. In the lower left, provide a short description of the changes made in the Summary field, and (optionally) longer comments about these changes in the Description field. Click the check-mark Commit button.
-
-\item \textbf{Push the commit:} Click on the Sync button near the upper right to ``push'' your local commits to the remote server; this will also pull down remote commits from other contributors to your fork that were made since the last time you synced.
-\end{enumerate}
-
-The left hand panel of the Changes tab has a summary of the files that have been changed, added, or removed since your last commit; the right hand panel shows the specific changes made to each changed file. After step 2, a record of your changes has been stored locally by the git system, but is not yet reflected in the public remote copy on GitHub.\footnote{You can make several local commits without syncing with the remote server. This is useful for working without an internet connection. Say, on an intercontinental flight.} The ``timeline'' summary of the repository at the top of the Desktop window shows the history of commits for the repository: each dot represents a commit that was pushed to the remote server, and each open ring represents a local commit that has not been pushed to the remote server; the large broken ring on the far right represents changes that have not been locally committed.\footnote{Clicking on a dot or ring will display the changes made in that commit (or uncommitted changes).} The History tab lists all previous commits, including the timestamp, contributor, and a record of changes made.
-
-Suppose you have mucked about in your fork for a while and have made changes to HARK that you think should be included in the main repository hosted by Econ-Ark. To make your dream a reality, follow these steps:
-\begin{enumerate}
-\item \textbf{Isolate the changes:} If you mucked around for quite a while, making various changes here and there, and then later made some contribution that you want to include in the main repository, you should \textit{isolate the desired changes in a new branch of your fork}. Make a new branch by clicking the button toward the upper left of GitHub Desktop, revert that branch to the commit just before you forked from the main repository,\footnote{Or since your last update from the main repository hosted by Econ-Ark.} then edit this branch to include \textit{only the changes you want to push to the main repository}.
-
-\item \textbf{Issue a pull request:} Go to \href{https://github.com/econ-ark/HARK}{HARK's GitHub page} and click the ``New pull request'' button. Click the text that says ``compare across forks'', and select \texttt{econ-ark/HARK} as the base fork, \texttt{master} as the base, your fork as the head fork, and the branch to be merged as the ``compare''. In the boxes below, give a name to your pull request and provide sufficient comments about your contribution so that a development team member can understand what you've done. Click the ``Create pull request'' button.
-
-\item \textbf{Be patient:} Someone from the development team will look at your contribution and either merge it into the main repository or return it with comments. See section \ref{sec:SubmissionApprovalProcess} for procedures followed by the HARK team when evaluating outside contributions.
-
-\end{enumerate}
-
-\subsection{Submission Approval Process}\label{sec:SubmissionApprovalProcess}
-
-Before submitting to HARK, the development team would like you to make sure that your intended contribution is listed as an Issue on the GitHub page. If you are going to fix or address an Issue that is already posted, reply to that post with a note indicating that you are about to work on it and an estimate of when you will make a pull request for it. If you want to do something that no one has mentioned yet as an Issue, make a new Issue on GitHub (briefly) describing what you intend to do.\footnote{If your contribution is a tiny adjustment or to fix/add some documentation or comments, you shouldn't make an Issue.} This will both help prevent two people from working on the same thing (and one of them potentially wasting time on it), and lets the development team know what's coming. Moreover, if you propose a contribution that is (a) already in HARK, (b) not in the spirit of HARK, or (c) a fix for something that's not actually broken, this gives the team a chance to let you know not to spend effort on it.
-
-After creating a pull request to submit your contribution, it will be evaluated by one or more members of the HARK team. Criteria that the HARK team will consider when choosing whether to accept a submission include:
-\begin{enumerate}
-\item \textbf{Does the contribution break previous code?} If you change or expand an existing function, you should make sure that this does not cause some models or applications to work incorrectly. In the near future, HARK will have a core set of tests to run to check for full compatibility.
-
-\item \textbf{Is the code well documented?} Code submissions should have properly formatted docstrings for each new function, class, method, and module (see section \ref{sec:DocumentationConventions}) and enough comments for a reader to follow the algorithm.
-
-\item \textbf{Is the contribution relevant?} The development team intends for HARK to be diverse, but not everything goes in HARK. If your submission concerns a representative agent model or a method for estimating a reduced form model, then it probably doesn't belong in HARK.
-
-\item \textbf{Is the code demonstrated with an example?} If your solve a new model in HARK, you should include a set of example parameters for other users to easily test. If you've contributed a new function, consider providing a snippet of code demonstrating its use.\footnote{In the future, HARK will make use of in-line testing.}
-
-\item \textbf{Is the code correct?} Your code should actually do what it claims to do.
-
-\item \textbf{Does the code use HARK conventions?} To the best of your ability, contributions to HARK should follow the style guidelines in section \ref{sec:NamingConventions}. We don't demand perfection, as these are guidelines rather than rules.
-
-\item \textbf{Is the code well integrated in HARK?} In the beta release of HARK, we have tried to present a ``taxonomy'' of consumption-saving models that build up from each other. When possible, new models in HARK should try to continue this pattern. A submission that introduces a model in a new module that could have easily been made as an extension of an existing module might be rejected. However, we acknowledge that there is value in providing a solver for a new model, and that a future contributor might make her mark by integrating it into the existing hierarchy.
-
-\item \textbf{Is this already in HARK?} If your contribution is already in HARK, then it will almost certainly be rejected. Of course, if you submit a version that dominates the existing implementation (or even one that works better in a well described, non-trivial set of circumstances), it belongs in HARK.
-\end{enumerate}
-
-The HARK team intends to release more specific guidelines in the near future, so that contributors are better able to judge their submissions before issuing a pull request. We are in the process of recruiting ``czars'' to oversee particular aspects of HARK, providing their knowledge and expertise.\footnote{If you have knowledge and/or expertise and would like to contribute as a czar, please contact a HARK team member so that we can judge your credentials even though we have way less expertise.} While czars might directly evaluate submissions as a HARK team member, their more important role is to help establish these guidelines for their domain, more specifically nailing down what we mean by ``correct'' or ``well integrated''.
-
-\subsection{Naming Conventions}\label{sec:NamingConventions}
-
-Object naming conventions in HARK are fairly different than existing standards, and differ somewhat between tool modules vs model or application modules. The following conventions apply throughout HARK:
-\begin{itemize}
-\item Functions and methods are always in ``camel case'': no underscores, first letter is lower case, first letter of each subsequent word is capitalized. E.g. \texttt{approxLognormal}.
-
-\item Function and method names should accurately and concisely describe what the function does; the first word in the name \textit{must be a verb}.\footnote{HARK standards have specified several ``keyword verbs'' that have specific meaning.}
-
-\item Variable and class names \textit{should not} have a verb as their first word.
-
-\item Class names should use no underscores and capitalize the first letter of each word; moreover, a class name \textit{must include a noun}. E.g.\ \texttt{ConsPerfForesightSolver}.
-\end{itemize}
-
-When naming variables in model modules, the HARK team strongly discourages using single letter names, like \texttt{c} for consumption. Instead, we encourage contributors to use longer, more descriptive variable names using additional words and common abbreviations to specify the variable more precisely. In \texttt{/Documentation/NARK.pdf}, we list standard single letter variable ``bases'' and an array of prefixes and suffixes to adjust them. Economic variables in model modules should (usually) not use underscores, instead using camel case to the greatest extent possible.\footnote{For ``non-economic'' variables that are only used temporarily, underscores are permissible.} The development team prefers this standard so that users can translate between Python code and LaTeX script with minimal work.
-
-Conventions for naming variables in HARK's tool modules are significantly closer to more commonly used standards. Variable names should be in all lower case, with underscores between words, e.g.\ \texttt{data\_to\_match}. The functions and classes in these modules are more general and almost surely do not have any inherent ``economic content''; they are numerical or algorithmic objects, not variables that might appear in an equation in an article for a (non-computational) economics journal. Variable names in application modules (e.g.\ the files that execute the \texttt{cstwMPC} estimations) are a mix of the conventions for tool and model files, as appropriate for each variable. That is, variables that are directly related to ``economic variables'' in model modules should follow those conventions, while objects created solely for data manipulation or reporting should use the style of tool modules.
-
-\subsection{Documentation Conventions}\label{sec:DocumentationConventions}
-
-The HARK team wants the toolKit to be as accessible to users as possible; our greatest fear\footnote{Other than spiders, of course.} is that a new user will open up our code, get hopelessly confused trying to read it, and then never look at HARK again. To prevent this tragic outcome, we have tried hard to provide comprehensive, accurate documentation and comments within the code describing our methods.\footnote{To the extent that documentation and comments are missing, we encourage users to rectify this in their contributions.} Moreover, HARK uses the Sphinx utility to generate a website with \href{https://econ-ark.github.io/HARK/}{online documentation} for all of our tool and model modules, so that users can learn about what's available in HARK without digging through the source code. When making contributions to HARK, the development team asks users to format their inline documentation to work with Sphinx by following a few simple rules.
-
-\begin{itemize}
-\item The top of every module should begin with a ``docstring'' providing a clear description of the contents of the module. The first sentence should concisely summarize the file, as it may appear in an index or summary of all modules without the remaining sentences. A docstring at the top of a module should be formatted as:\footnote{The triple quote to begin and end a docstring can use either the apostrophe \texttt{'} or quotations marks \texttt{"}.}
-
-\texttt{"""}
-
-\texttt{Specifies an economic model and provides methods for solving it. More specific description of the key features of the model and variations of it in this module. Maybe some comments about the solution method or limitations of the model. Your bank account routing number.}
-
-\texttt{"""}
-
-\item The line directly below the declaration of a function, method or class should begin a docstring describing that object. As with modules, the first sentence should concisely summarize the function or class, as it might be included in an index or summary. For functions and methods, the docstring should be formatted as:
-
-\texttt{def functionName(input1,input2):}
-
-\hspace{0.9cm}\texttt{"""}
-
-\hspace{0.9cm}\texttt{Concise description of the function. More details about what}
-
-\hspace{0.9cm}\texttt{the function does, options or modes available, and maybe mathematical}
-
-\hspace{0.9cm}\texttt{methods used. Credit to a source if you poached their algorithm.}
-
-\vspace{0.5cm}
-
-\hspace{0.9cm}\texttt{Parameters}
-
-\hspace{0.9cm}\texttt{--------------------}
-
-\hspace{0.9cm}\texttt{input1: type}
-
-\hspace{1.8cm}\texttt{Description of what input1 represents.}
-
-\hspace{0.9cm}\texttt{input2: type}
-
-\hspace{1.8cm}\texttt{Description of what input2 represents.}
-
-\vspace{0.5cm}
-
-\hspace{0.9cm}\texttt{Returns}
-
-\hspace{0.9cm}\texttt{--------------}
-
-\hspace{0.9cm}\texttt{output\_name: type}
-
-\hspace{1.8cm}\texttt{Description of the output(s) of the function. Might have}
-
-\hspace{1.8cm}\texttt{multiple entries. If no output, this is just "None".}
-
-\hspace{0.9cm}\texttt{"""}
-
-\item Provide ample comments within a function or method so that a relatively intelligent reader can follow along with your algorithm. Short comments can follow at the end of a line, longer comments (or descriptions of the step or task about to be performed) should precede a block of code on the line(s) above it.
-\end{itemize}
-
-Finally, if you write a new model module, the HARK team asks that you also provide a short mathematical writeup of the model as a PDF. This document does not need to go into great detail about the solution method for the model or the functions and classes included in the module, merely specify the economic model and provide a summary of how it is solved. See \texttt{/Documentation/ConsumptionSavingModels.pdf} for an example of this.
-
-\ifthenelse{\boolean{ContributingToHARKOnly}}{}{
-\section{Future of HARK}\label{sec:FutureOfHARK}
-
-HARK is in a state of perpetual incompleteness, always expanding to include more tools and more models. As an open source project in the hands of many contributors around the world, the HARK team cannot fully predict future features of the toolKit. This section provides an overview of what we would like to see in the near future, as well as potential mechanisms to encourage researchers to contribute their code to the project.
-
-\subsection{Future Tools}\label{sec:FutureTools}
-
-The current frameworks in \texttt{HARK.core} concern agents who have ``isolated'' dynamic problems or interact with each other only through aggregate outcomes to which they contribute atomically. We hope to eventually provide a framework for models in which agents directly interact with each other; this may include a system for specifying the state of networks and how connections are formed and broken. \texttt{HARK.core} might also develop a framework more suited to industrial organization models and dynamic games.\footnote{It might already be possible to shoehorn some of these models into the \texttt{Market} framework.}
-
-The \texttt{HARK.estimation} module in particular is very sparsely populated, with its functions mostly serving as wrappers for ``off the shelf'' optimization methods from \texttt{scipy.optimize}. There are plenty of non-standard or more complex optimization techniques available, including global and/or stochastic search methods. We believe that additional optimizers are ``low hanging fruit'' as straightforward contributions to HARK and thus will be added in the near future. Moreover, there are plenty of distributions that have no approximator in \texttt{HARK.utilities} (or data generator in \texttt{HARK.simulation}), and these functions are among the easiest for new contributors to write. The data analysis tools in \texttt{HARK.utilities} only include functions that we happened to use for our current research projects, and we expect that this will be the mode of growth for future tools. There are some obvious holes that we believe will be filled quite quickly; for example, we provide a rudimentary non-parametric kernel regression function, but have not written a kernel density estimator to generate PDFs from data.
-
-The classes currently in \texttt{HARK.interpolation} are all variations on linear (or cubic) spline interpolation, and each is specifically written for a fixed number of dimensions. As these methods use full tensor grids, they are highly impractical for representing functions in higher dimensions, suffering greatly from the curse of dimensionality.\footnote{The HARK team only included interpolators for up to 4 dimensions, and some would argue this is already too many for tensor grid methods.} We are quite confident that \texttt{HARK.interpolation} will soon include techniques more applicable to high dimensional state spaces, including Smolnyak interpolation, other (adaptive) sparse grid methods, and regression-based interpolation. For lower-dimensional spaces, HARK will also soon provide Delaunay interpolation classes.
-
-The \texttt{HARK.parallel} module currently provides a simple interface for handling \textit{ex-ante} heterogeneity among agents. While useful for speeding up moderately-sized problems, the libraries used are only able to access the CPU of the local machine running the code. In the very near future, the HARK team will provide an interface (and example) for using Apache Spark, which bills itself as a ``fast and general engine for large-scale data processing''. Specifically, users will be able to easily scale up their computing resources and run HARK code on an Amazon EC2 session or other cluster.
-
-\subsection{Future Models}\label{sec:FutureModels}
-
-The economics models currently included in HARK mostly concern consumption-saving problems with constant relative risk aversion consumers who experience fully permanent and fully transitory shocks to income. This is obviously a very small subset of all consumption-saving models (which are themselves a small subset of all dynamic models economists might be interested in). Consider some examples of variants, features, and extensions that are not included in HARK, but could be in the future:
-\begin{itemize}
-\item Consumers with other utility functions, such as CARA or expo-power.
-
-\item Consumers with time-inconsistent preferences, e.g.\ hyperbolic discounting.
-
-\item Income shocks that are neither fully transitory nor fully permanent.
-
-\item Consumers with a bequest motive or terminal utility function.
-
-\item Utility from leisure and endogenous labor supply.
-
-\item Investment in a durable consumption good such as housing.
-
-\item Exogenous shocks to expenses.
-
-\item Multiple consumption goods, as with endogenous medical care.
-
-\item Portfolio allocation between a risky and riskless asset.
-
-\item Job search intensity as a control variable.
-
-\item Endogenous health evolution and health investment.
-\end{itemize}
-
-The HARK team is currently developing a module for mixing discrete and continuous choice models, in the style of Jorgensen, Rust, Iskhakov, and Schjerning (2015). Among many other models, this framework will enable HARK users to easily develop features like:
-\begin{itemize}
-\item Labor supply on the extensive margin: the timing of retirement.
-
-\item Selection of job type or sector (by riskiness, specific human capital, etc).
-
-\item Choice of medical insurance contract.
-
-\item Decision to rent or own housing.
-
-\item Endogenous default on debt (with ``macroeconomic'' pricing of debt).
-
-\item Household formation, dissolution, and reproduction.
-\end{itemize}
-
-Finally, we hope to provide a generic solver for any discrete choice model with T1EV shocks. The scope of HARK is limited only by what users choose to put in it. We hope that within a year of its debut, HARK will include a significant number of canonical models, providing more building blocks for future researchers to create novel combinations and new extensions.
-
-\subsection{Bounty Hunting}\label{sec:BountyHunting}
-
-While HARK users are free to write and contribute any tools and models that they believe could be helpful, building the toolKit in a ``bottom up'' style, the HARK team hopes to provide mechanisms for targeted ``top down'' development. The team is working to secure grant funding for HARK so as to offer ``bounties'' for particular models, tools, applications, or frameworks. Suppose the team decides that the model in Important Paper X should be included as part of HARK; we will issue a call for contributions to fill this hole, offering a modest prize of, say, \$20,000 to the programmer who submits the best implementation of the model, as judged by clearly stated criteria.\footnote{Actual bounties may be significantly smaller than this. But be honest: You're much more interested in contributing to HARK after reading that sentence.} The bounty would be announced as an Issue on HARK's GitHub page, as well as in a potential electronic digest of HARK news.
-
-Bounties might be placed on items as small as a single data analysis tool or as large as a complex estimation of a large general equilibrium model, with prizes scaling accordingly. We hasten to emphasize that we cannot guarantee funding for HARK, so the bounties (like everything else in this section) are purely speculative. As economists, the HARK team understands the incentive compatibility problems associated with asking researchers to contribute significant amounts of their time, effort, and unique skills in service of a tenuous concept like the Greater Good in exchange for even more tenuous Glory. We acknowledge that economic researchers are also optimizing agents with their own preferences over leisure and consumption, and agree that Greed is a valid mechanism to expand HARK.
-
-\subsection{All Aboard the Ark}\label{sec:AllAboardTheArk}
-
-As observant readers may have noticed, the HARK repository is hosted by a GitHub account named Econ-Ark, with no explanation as to what this might mean. Looking a few years into the future, the development team believes that HARK might be the first piece of a broader collection of computational economics resources. HARK is open source but somewhat akin to an exclusive club: contributions are required to adhere to standards and norms so as to foster modularity and interoperability among models and tools. Consistent with its namesake ship, the Econ-Ark has a significantly lower bar for admission, with a place on board for everything.
-
-On one level, the Ark might serve as a warehouse for any and all quantitative models and tools, for any economic purpose, in any subfield, and written in any language. Through nearly unanimous feedback, the HARK team understands that many economists are willing to share code that they have already written, but are reluctant to expend the considerable effort to translate and format it for HARK. In the future, we will set up a mechanism for code like this to be hosted on the Ark, with minimal requirements imposed on its authors (and correspondingly minimal warranty for potential users). Relevant projects on the Ark might become bounty targets for HARK, with users incentivized to port the original code into Python and to meet HARK standards; direct access to the original source code will significantly expedite the translation process, and provide a suitable benchmark for testing and verifying the contribution to HARK.
-
-On a different level, the Ark might also include cohesive toolkits like HARK, but geared toward purposes other than heterogeneous agents macroeconomics models. For example, the previously mentioned modules for networks among agents (and other agent-based modeling), industrial organization and dynamic games, and traditional discrete choice models might become their own toolkits, with some interoperability with HARK. In support of this, some of the tool modules currently in HARK might be ``promoted'' and become universally shared across all Econ-Ark toolkits.
-
-The dark secret of HARK is that it is named for heterogeneous agents macroeconomics, but designed and written by structural microeconomists. The development team believes these subfields to have considerable overlap in terms of methods and techniques, and that HARK might not be noticed by half of its potential audience due to its name. In a general sense, the Econ-Ark gives us cover to expand the scope of our efforts and/or rebrand the toolKit for an alternative audience.
-} % end \ifthenelse{\boolean{\ContributingToHARKOnly}}
-\end{document}
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-
-
-A User’s Guide for HARK: Heterogeneous Agents Resources and toolKit
-
-
-
A User’s Guide for HARK:
-
May 21, 2018
-
-
Christopher D Carroll 1 Johns Hopkins University Alexander M Kaufman 2 Consumer Financial Protection Bureau David C Low 3 Consumer Financial Protection Bureau Nathan M Palmer 4 U.S. Treasury Office of Financial Research Matthew N White 5 University of Delaware
-
-
-
-
1 Carroll: http://econ.jhu.edu/people/ccarroll/ , email: ccarroll@jhu.edu 2 Kaufman: alexander.kaufman@cfpb.gov 3 Low: david.c.low@cfpb.gov 4 Palmer:
-nathan.m.palmer@ofr.treasury.gov 5 White: mnwecon@udel.edu
-
-
-
-
-
-
-
-
-
0.1 If you are willing to risk some mild psychological trauma, conjure to mind your first experience of
-hand-coding a structural economic model. Your clunky effort probably built on legacy code
-provided by an adviser or colleague – which itself came from who-knows-what apocryphal
-sources. Efforts to combine elements from one model with those from another were likely
-frustrated by the “Tower of Babel” problem: Code from one source could not “speak” to code
-from another without your own intermediation as a translator, possibly between two unfamiliar
-languages and aided only by oracular comments that, at best, made sense only in the context of
-other (now missing) code.
-
After months of effort, you may have had the character-improving experience of proudly
-explaining to your adviser that not only had you grafted two ideas together, you also found a
-trick that speeded the solution by an order of magnitude, only to be told that your breathtaking
-insight had been understood for many years, as reflected in an appendix to a 2008 paper; or,
-worse, your discovery was something that “everybody knows” but did not exist at all in published
-form!
-
Learning by doing has value, but only within limits. We do not require young drivers to design
-an internal combustion engine before driving a car, nor must graduate students write their own
-matrix inversion algorithms before running an OLS regression.
-
In recent years, considerable progress has been made in addressing these kinds of problems in
-many areas of economic modeling. Macroeconomists using representative agent models can send
-Dynare model files to each other; reduced form econometricians can choose from a host of
-econometric packages. But modelers whose questions require explicit structural modeling
-involving nontrivial kinds of heterogeneity (that is, heterogeneity that cannot simply be
-aggregated away) are mostly still stuck in the bad old days.
-
The ultimate goal of the HARK project is to fix these problems. Specifically, our aim is to
-produce an open source repository of highly modular, easily interoperable code for
-solving, simulating, and estimating dynamic economic models with heterogeneous
-agents.
We hope that HARK will make it much easier and faster for researchers to develop solution
-and estimation methods for new models. The open source nature of HARK will make
-it easier for other researchers to audit and verify new models and methods, and to
-collaborate on correcting deficiencies when found. As HARK expands to include more
-
-
-canonical models and more tools and utilities, we can all spend less time managing
-numerical minutiae and more time fretting about identification arguments and data
-accuracy.
-
0.1.1 If you want to get started using HARK right away, this section provides a very easy quickstart
-guide for getting HARK up and running on your computer in just a few minutes. More
-information can be found in the README.md file in the HARK repository (step 4), but here is a
-quick version for those who want to jump right in:
-
-
- Download Anaconda: Go to https://www.anaconda.com/download/ and
- download Anaconda for your operating system; be sure to get the version for Python
- 2.7.
- Install Anaconda: Follow the easy instructions on that page to install Anaconda.
- Add extra packages: (optional) If you want to use HARK’s multithreading feature,
- you need to add two packages that aren’t part of the default Anaconda distribution.
- Simply open a command prompt and do conda install joblib and conda install
- dill , accepting defaults to install.
- Download HARK: Go to https://github.com/econ-ark/HARK , the home
- of the HARK repository. Click on the lime green button labeled “Clone or
- download” toward the upper right of the main panel, and select “Download
- ZIP”.Unzip HARK: Unpack the HARK.zip file using any archive utility, like Peazip . Put
- the files anywhere you’d like, maintaining the internal directory structure.
- Run Spyder: Open a command prompt and do spyder . Spyder is an interactive
- development environment (IDE) for iPython, a slightly prettier, more interactive
- flavor of Python.
-
-
- Open a HARK module: Go to the directory where you put HARK and open any
- file with the .py extension; we recommend
- /Demos/ConsumptionSaving/ConsIndShockModel.py .
- Run the module: Click on the green arrow “play” button toward the right side of
- the toolbar of icons at the top of the Spyder window (accept defaults if a dialogue
- box pops up). Congratulations! HARK is now up and running on your computer.
- If you followed our recommendation to try /Demos/ConsumptionSaving/ConsIndShockModel.py ,
-you should see graphical representations of the solution to a few consumption-saving
-models with idiosyncratic shocks to permanent and transitory income (see
-/Documentation/ConsumptionSavingModels.pdf for details of these models). If you chose a
-different module, you might get some examples of some other model, results of a structural
-estimation, a polite notification that this particular module doesn’t do much on its own, or no
-output at all. See the README.md for a full list of modules from which you can expect non-trivial
-output.
-
-
0.1.2 HARK is written in Python, an object-oriented programming (OOP) language that has
-experienced increasing popularity in the scientific community in the past several years. A
-significant reason for the adoption of Python is the numpy and scipy packages, which offer a
-wide array of mathematical and statistical functions and tools; HARK makes liberal
-use of these libraries. Python’s object-oriented nature allows models in HARK to
-be easily extended: more complex models can inherit functions and methods from
-more fundamental “parent” models, eliminating the need to reproduce or repurpose
-code.
-
As implied in the previous section, we strongly encourage HARK users to use the Anaconda
-distribution of Python, which includes all commonly used mathematical and scientific packages,
-an interactive development environment for iPython (Spyder), and a package manager that
-allows users to quickly install or update packages not included in the default distribution
-(conda).
-
For users unfamiliar with OOP, we strongly encourage you to review the background material
-on OOP provided by the good people at QuantEcon (for more on them, see below) at this link:
-Object Oriented Programming . Unlike non-OOP languages, OOP bundles together
-data and functions into objects . These can be accessed via: object_name.data and
-object_name.method_name() , respectively. For organizational purposes, definitions of multiple
-objects are stored in modules , which are simply files with a .py extension. Modules can be
-
-
-accessed in Python via:
import module_name as import_name
This imports the module and gives it a local name of import_name . We can access a function
-within this module by simply typing: import_name.function_name() . The following example
-will illustrate the usage of these commands. CRRAutility is the function object for calculating
-CRRA utility supplied by HARK.utilities module. CRRAutility is called attributes of the
-module HARK.utilities . In order to calculate CRRA utility with a consumption of 1 and a
-coefficient of risk aversion of 2 we run:
import HARK.utilities as Hutil
-
Hutil.CRRAutility(1,2)
Python modules in HARK can generally be categorized into three types: tools, models, and
-applications. Tool modules contain functions and classes with general purpose tools that have
-no inherent “economic content”, but that can be used in many economic models as building
-blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include
-functions for data analysis (e.g. calculating Lorenz shares from data, or constructing a
-non-parametric kernel regression), functions to create and manipulate discrete approximations to
-continuous distributions, or classes for constructing interpolated approximations to
-non-parametric functions. Tool modules generally reside in HARK’s root directory and have
-names like HARK.simulation and HARK.interpolation ; they do not necessarily do anything
-when run.
-
Model modules specify particular economic models, including classes to represent agents in
-the model (and the “market structure” in which they interact) and functions for solving the
-“one period problem” of those models. For example, ConsIndShockModel.py concerns
-consumption-saving models in which agents have CRRA utility over consumption and face
-idiosyncratic shocks to permanent and transitory income. The module includes classes for
-representing “types” of consumers, along with functions for solving (several flavors of) the one
-period consumption-saving problem. When run, model modules might demonstrate example
-specifications of their models, filling in the model parameters with arbitrary values. When
-ConsIndShockModel.py is run, it specifies an infinite horizon consumer with a particular
-discount factor, permanent income growth rate, coefficient of relative risk aversion (etc), who
-faces lognormal shocks to permanent and transitory income each period with a particular
-standard deviation; it then solves this consumer’s problem and graphically displays the
-results.Model in their name.
-
-
-
Application modules use tool and model modules to solve, simulate, and/or estimate economic
-models for a particular purpose . While tool modules have no particular economic content and
-model modules describe entire classes of economic models, applications are uses of a model for
-some research purpose. For example, /Demos/SolvingMicroDSOPs/StructEstimation.py uses a
-consumption-saving model from ConsIndShockModel.py , calibrating it with age-dependent
-sequences of permanent income growth, survival probabilities, and the standard deviation of
-income shocks (etc); it then estimates the coefficient of relative risk aversion and shifter for
-an age-varying sequence of discount factors that best fits simulated wealth profiles
-to empirical data from the Survey of Consumer Finance. A particular application
-might have multiple modules associated with it, all of which generally reside in one
-directory.
-
0.1.3 In the incredibly unlikely scenario in which this document does not fill all of the gaps in your
-knowledge and answer any questions you might have while reading it, here is a collection of
-potentially helpful other resources.
-
- Online documentation for HARK tool and model modules, produced using Sphinx.
- A tutorial on Python 2.7 , straight from the source.
-
- Wikipedia article on object-oriented programming , not the subject of an edit war.
- QuantEcon , a collection of lectures on quantitative economic modeling from a couple
- of economists, which definitely in no way influenced HARK whatsoever.
- A tutorial on git , the repository management and tracking system used by the HARK
- project, created by Linus Torvalds.
-
- A tutorial on GitHub , a website that provides a useful framework for git that makes
- it significantly more user-friendly, despised by Linus Torvalds.
-
- Wikipedia article on Linus Torvalds , just in case.
-
-
- The homepage for NumPy , a numerical package extensively used by HARK.
-
- The homepage for Sphinx , a tool for producing HTML documentation.
-
- A collection of lecture notes by Chris Carroll, to catch you up on the basics of
- consumption-saving models.
-
- A particular set of lecture notes by Chris Carroll, explaining many of the numeric
- techniques used in HARK and offering an example of a simple structural model that,
- in a stunning coincidence, is implemented in HARK as an example.
-
- A deep dive into the theoretical foundations of buffer stock saving. Warning: dense.
-
-
-
0.2 HARK’s root directory contains six tool
-modules,
0.2.1 A key goal of the project is to create modularity and interoperability between models, making
-them easy to combine, adapt, and extend. To this end, the HARK.core module specifies a
-framework for economic models in HARK, creating a common structure for them on two levels
-that can be called “microeconomic” and “macroeconomic”.
-
Microeconomic models in HARK use the AgentType class to represent agents with an
-intertemporal optimization problem. Each of these models specifies a subclass of AgentType ; an
-instance of the subclass represents agents who are ex-ante homogeneous– they have common
-values for all parameters that describe the problem. For example, ConsIndShockModel specifies
-the IndShockConsumerType class, which has methods specific to consumption-saving
-models with idiosyncratic shocks to income; an instance of the class might represent all
-consumers who have a CRRA of 3, discount factor of 0.98, etc. The AgentType class
-has a solve method that acts as a “universal microeconomic solver” for any properly
-formatted model, making it easier to set up a new model and to combine elements
-from different models; the solver is intended to encompass any model that can be
-framed as a sequence of one period problems. For a complete description, see section
-0.3 .
-
Macroeconomic models in HARK use the Market class to represent a market (or other
-aggregator) that combines the actions, states, and/or shocks (generally, outcomes) of individual
-agents in the model into aggregate outcomes that are “passed back” to the agents. For example,
-the market in a consumption-saving model might combine the individual asset holdings of all
-agents in the market to generate aggregate capital in the economy, yielding the interest rate on
-assets (as the marginal product of capital); the individual agents then learn the aggregate capital
-level and interest rate, conditioning their next action on this information. Objects that
-microeconomic agents treat as exogenous when solving (or simulating) their model are thus
-endogenous at the macroeconomic level. Like AgentType , the Market class also has a
-solve method, which seeks out a dynamic general equilibrium: a “rule” governing the
-dynamic evolution of macroeconomic objects such that if agents believe this rule and
-act accordingly, then their collective actions generate a sequence of macroeconomic
-outcomes that justify the belief in that rule. For a more complete description, see section
-0.4 .
-
Beyond the model frameworks, HARK.core also defines a “supersuperclass” called HARKobject .
-When solving a dynamic microeconomic model with an infinite horizon (or searching for a
-dynamic general equilibrium), it is often required to consider whether two solutions
-are sufficiently close to each other to warrant stopping the process (i.e. approximate
-convergence). It is thus necessary to calculate the “distance” between two solutions,
-so HARK specifies that classes should have a distance method that takes a single
-input and returns a non-negative value representing the (generally dimensionless)
-distance between the object in question and the input to the method. As a convenient
-default, HARKobject provides a “universal distance metric” that should be useful in many
-
-
-contexts.HARKobject , the user simply defines the attribute
-distance_criteria as a list of strings naming the attributes of the class that should be
-compared when calculating the distance between two instances of that class. For example, the
-class ConsumerSolution has distance_criteria = [’cFunc’] , indicating that only the
-consumption function attribute of the solution matters when comparing the distance between
-two instances of ConsumerSolution . See here for online documentation.
-
0.2.2 The HARK.utilities module carries a double meaning in its name, as it contains both
-utility functions (and their derivatives, inverses, and combinations thereof) in the
-economic modeling sense as well as utilities in the sense of general tools. Utility functions
-included at this time are constant relative risk aversion and constant absolute risk
-aversion. Other functions in HARK.utilities include some data manipulation tools
-(e.g. for calculating an average of data conditional on being within a percentile range
-of different data), functions for constructing discrete state space grids, convenience
-functions for retrieving information about functions, and basic plotting tools using
-matplotlib.pyplot .
-
The module also includes functions for constructing discrete approximations to continuous
-distributions (e.g. approxLognormal() to approximate a log-normal distribution) as well as
-manipulating these representations (e.g. appending one outcome to an existing distribution, or
-combining independent univariate distributions into one multivariate distribution). As a
-convention in HARK, continuous distributions are approximated as finite discrete distributions
-when solving models; an here for online
-documentation.
-
-
0.2.3 The HARK.interpolation module defines classes for representing interpolated function
-
-
-approximations. Interpolation methods in HARK all inherit from a superclass such as
-HARKinterpolator1D or HARKinterpolator2D , wrapper classes that ensures interoperability
-across interpolation methods. For example, HARKinterpolator1D specifies the methods
-__call__ and derivative to accept an arbitrary array as an input and return an identically
-shaped array with the interpolated function evaluated at the values in the array or its first
-derivative, respectively. However, these methods do little on their own, merely reshaping arrays
-and referring to the _evaluate and _der methods, which are not actually defined in
-HARKinterpolator1D . Each subclass of HARKinterpolator1D specifies their own implementation
-of _evaluate and _der particular to that interpolation method, accepting and returning only 1D
-arrays. In this way, subclasses of HARKinterpolator1D are easily interchangeable with each
-other, as all methods that the user interacts with are identical, varying only by “internal”
-methods.
-
When evaluating a stopping criterion for an infinite horizon problem, it is often necessary to
-know the “distance” between functions generated by successive iterations of a solution procedure.
-To this end, each interpolator class in HARK must define a distance method that takes as an
-input another instance of the same class and returns a non-negative real number representing the
-“distance” between the two. As each of the HARKinterpolatorXD classes inherits from
-HARKobject , all interpolator classes have the default “universal” distance method; the user must
-simply list the names of the relevant attributes in the attribute distance_criteria of the
-class.
-
Interpolation methods currently implemented in HARK include (multi)linear interpolation up
-to 4D, 1D cubic spline interpolation, (multi)linear interpolation over 1D interpolations (up to 4D
-total), (multi)linear interpolation over 2D interpolations (up to 4D total), linear interpolation
-over 3D interpolations, 2D curvilinear interpolation over irregular grids, and a 1D “lower
-envelope” interpolator. See here for online documentation.
-
-
0.2.4 The HARK.simulation module provides tools for generating simulated data or shocks for
-post-solution use of models. Currently implemented distributions include normal, lognormal,
-Weibull (including exponential), uniform, Bernoulli, and discrete. As an example of their use,
-these tools are used in the consumption-saving models of ConsIndShockModel.py to simulate
-permanent and transitory income shocks as well as unemployment events. See here for online
-documentation.
-
-
-
-
0.2.5 Methods for optimizing an objective function for the purposes of estimating a model can be
-found in HARK.estimation . As of this writing, the implementation includes only minimization by
-the Nelder-Mead simplex method, minimization by a derivative-free Powell method
-variant, and two small tools for resampling data (i.e. for a bootstrap); the minimizers
-are merely convenience wrappers (with result reporting) for optimizers included in
-scipy.optimize . Future functionality will include more robust global search methods, including
-genetic algorithms, simulated annealing, and differential evolution. See here for full
-documentation.
-
-
0.2.6 By default, processes in Python are single-threaded, using only a single CPU core. The HARK.parallel
-module provides basic tools for using multiple CPU cores simultaneously, with minimal
-effort.multiThreadCommands , which takes two arguments: a list
-of AgentType s and a list of commands as strings; each command should be a method of the
-AgentType s. The function simply distributes the AgentType s across threads on different cores
-and executes each command in order, returning no output (the AgentType s themselves are
-changed by running the commands). Equivalent results would be achieved by simply looping over
-each type and running each method in the list. Indeed, HARK.parallel also has a
-function called multiThreadCommandsFake that does just that, with identical syntax to
-multiThreadCommands ; multithreading in HARK can thus be easily turned on and
-off.here for full documentation.
-
0.3 The core of our microeconomic dynamic optimization framework is a flexible object-oriented
-representation of economic agents. The HARK.core module defines a superclass called AgentType ;
-
-
-each model defines a subclass of AgentType , specifying additional model-specific features and
-methods while inheriting the methods of the superclass. Most importantly, the method solve
-acts as a “universal solver” applicable to any (properly formatted) discrete time model. This
-section describes the format of an instance of AgentType as it defines a dynamic microeconomic
-problem;
0.3.1 A discrete time model in our framework is characterized by a sequence of “periods” that the
-agent will experience. A well-formed instance of AgentType includes the following
-attributes:
-
- solveOnePeriod : A function, or a list of functions, representing the solution
- method for a single period of the agent’s problem. The inputs passed to a
- solveOnePeriod function include all data that characterize the agent’s problem in
- that period, including the solution to the subsequent period’s problem (designated
- as solution_next ). The output of these functions is a single Solution object, which
- can be passed to the solver for the previous period.
- time_inv : A list of strings containing all of the variable names that are passed to at
- least one function in solveOnePeriod but do not vary across periods. Each of these
- variables resides in a correspondingly named attribute of the AgentType instance.
- time_vary : A list of strings naming the attributes of this instance that vary
- across periods. Each of these attributes is a list of period-specific values, which
- should be of the same length. If the solution method varies across periods, then
- ’solveOnePeriod’ is an element of time_vary .solution_terminal : An object representing the solution to the “terminal” period
- of the model. This might represent a known trivial solution that does not require
- numeric methods, the solution to some previously solved “next phase” of the model,
- a scrap value function, or an initial guess of the solution to an infinite horizon model.
-
-
- pseudo_terminal : A Boolean flag indicating that solution_terminal is not a
- proper terminal period solution (rather an initial guess, “next phase” solution, or
- scrap value) and should not be reported as part of the model’s solution.
- cycles : A non-negative integer indicating the number of times the agent will
- experience the sequence of periods in the problem. For example, cycles = 1 means
- that the sequence of periods is analogous to a lifecycle model, experienced once from
- beginning to end; cycles = 2 means that the agent experiences the sequence twice,
- with the first period in the sequence following the last. An infinite horizon problem
- in which the sequence of periods repeats indefinitely is indicated with cycles = 0 .
- tolerance : A positive real number indicating convergence tolerance, representing
- the maximum acceptable “distance” between successive cycle solutions in an infinite
- horizon model; it is irrelevant when cycles > 0 . As the distance metric on the space
- of solutions is model-specific, the value of tolerance is generally dimensionless.
- time_flow : A Boolean flag indicating the direction that time is “flowing.” When
- True , the variables listed in time_vary are listed in ordinary chronological order,
- with index 0 being the first period; when False , these lists are in reverse chronological
- order, with index 0 holding the last period.
- An instance of AgentType also has the attributes named in time_vary and time_inv , and
-may have other attributes that are not included in either (e.g. values not used in the model
-solution, but instead to construct objects used in the solution).
-
-
0.3.2 When an instance of AgentType invokes its solve method, the solution to the agent’s problem is
-stored in the attribute solution . The solution is computed by recursively solving the sequence of
-periods defined by the variables listed in time_vary and time_inv using the functions in
-solveOnePeriod . The time-varying inputs are updated each period, including the
-successive period’s solution as solution_next ; the same values of time invariant inputs in
-time_inv are passed to the solver in every period. The first call to solveOnePeriod uses
-solution_terminal as solution_next . In a finite horizon problem, the sequence of
-periods is solved cycles times over; in an infinite horizon problem, the sequence of
-periods is solved until the solutions of successive cycles have a “distance” of less than
-tolerance .
-
-
-
The output from a function in solveOnePeriod is an instance of a model-specific solution
-class. The attributes of a solution to one period of a problem might include behavioral functions,
-(marginal) value functions, and other variables characterizing the result. Each solution class must
-have a method called distance() , which returns the “distance” between itself and another
-instance of the same solution class, so as to define convergence as a stopping criterion; for many
-models, this will be the “distance” between a policy or value function in the solutions. If the
-solution class is defined as a subclass of HARKobject , it automatically inherits the default
-distance method, so that the user must only list the relevant object attributes in
-distance_criteria .
-
The AgentType also has methods named preSolve and postSolve , both of which take no
-arguments and do absolutely nothing. A subclass of AgentType can overwrite these blank
-methods with its own model specific methods. preSolve is automatically called near the
-beginning of the solve method, before solving the sequence of periods. It is used for specifying
-tasks that should be done before solving the sequence of periods, such as pre-constructing some
-objects repeatedly used by the solution method or finding an analytical terminal period solution.
-For example, the IndShockConsumerType class in ConsIndShockModel has a preSolve method
-that calls its updateSolutionTerminal method to ensure that solution_terminal is
-consistent with the model parameters. The postSolve method is called shortly after the
-sequence of periods is fully solved; it can be used for “post-processing” of the solution or
-performing a step that is only useful after solution convergence. For example, the
-TractableConsumerType in TractableBufferStockModel has a postSolve method that
-constructs an interpolated consumption function from the list of stable arm points found during
-solution.
-
Our universal solver is written in a very general way that should be applicable to any discrete
-time optimization problem– because Python is so flexible in defining objects, the time-varying
-inputs for each period can take any form. Indeed, the solver does no “real work” itself, but merely
-provides a structure for describing models in the HARK framework, allowing interoperability
-among current and future modules.
-
-
0.3.3 Because dynamic optimization problems are solved recursively in our framework, it is natural to
-list time-varying values in reverse chronological order– the solve() method loops over the
-values in each time-varying list in the same direction that a human would read them.
-When simulating agents after the solution has been obtained, however, it is much more
-convenient for time-varying parameters to be listed in ordinary chronological order–
-the direction in which they will be experienced by simulated agents. To allow the
-user to set the order in which “time is flowing” for an instance of AgentType , the
-HARK framework includes functionality to easily change ordering of time-varying
-
-
-values.
-
The attribute time_flow is True if variables are listed in ordinary chronological
-order and False otherwise. AgentType has the following methods for manipulating
-time:
-
- timeReport() : Prints to screen a description of the direction that time is flowing,
- for interactive convenience and as a reminder of the functionality.
- timeFlip() : Flips the direction of time. Each attribute listed in time_vary is
- reversed in place, and the value of time_flow is toggled.
- timeFwd() : Sets the direction of time to ordinary chronological order.
- timeRev() : Sets the direction of time to reverse chronological order.
- These methods are invoked to more conveniently access time-varying objects. When a new
-time-varying attribute is added, its name should be appended to time_vary , particularly if its
-values are used in the solution of the model (or is part of the solution itself). For example, the
-solve() method automatically adds the string ’solution’ to time_vary if it is not already
-present. Note that attributes listed in time_vary must be lists if solve() or timeFlip()
-are used. Some values that could be considered “time varying” but are never used to
-solve the model are more conveniently represented as a numpy.array object (e.g. the
-history of a state or control variable from a simulation); because the numpy.array
-class does not have a reverse() method, these attributes should not be listed in
-time_vary .
-
The base AgentType is sparsely defined, as most “real” methods will be application-specific.
-Two final methods bear mentioning. First, the __call__ method points to assignParameters() ,
-a convenience method for adding or adjusting attributes (inherited from HARKobject ). This
-method takes any number of keyword arguments, so that code can be parsimoniously
-written as, for example, AgentInstance(attribute1 = value1, attribute2 =
-value2) . Using Python’s dictionary capabilities, many attributes can be conveniently
-set with minimal code. Second, the resetRNG method simply resets the AgentType ’s
-random number generator (as the attribute RNG ) using the value in the attribute
-seed .inter alia ) ensuring that the same underlying sequence of shocks is
-used for every simulation run when a model is solved or estimated.
-
-
-
0.3.4 To provide a concrete example of how the AgentType class works, consider the very simple case
-of a perfect foresight consumption-saving model. The agent has time-separable, additive CRRA
-preferences over consumption, discounting future utility at a constant rate; he receives a
-particular stream of labor income each period, and knows the interest rate on assets that he
-holds from one period to the next. His decision about how much to consume in a particular
-period can be expressed in Bellman form as:
-
-
The ConsIndShockModel module defines the class PerfForesightConsumerType
-as a subclass of AgentType and provides solver functions for several
-variations of a consumption-saving model, including the perfect foresight
-problem.
MyConsumer = PerfForesightConsumerType(time_flow=True, cycles=1,
-
CRRA = 2.7, Rfree = 1.03, DiscFac = 0.98,
-
LivPrb = [0.99,0.98,0.97,0.96,0.95,0.94,0.93,0.92,0.91,0.90],
-
PermGroFac = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02],
-
Nagents=1000)
-
MyConsumer.solve()
-
The first line makes a new instance of ConsumerType , specifying that time is currently “flowing”
-
-
-forwardCRRA , Rfree , DiscFac ) and time varying parameters (LivPrb ,
-PermGroFac ). Note that dir(MyConsumer) shows all the attributes of MyConsumer . After running
-the solve method, MyConsumer will have an attribute called solution , which will be a list with
-eleven ConsumerSolution objects, representing the period-by-period solution to the
-model.ConsumerSolution has several attributes:
-
- cFunc : Optimal consumption function for this period as a function of
- (normalized) market resources MyConsumer.solution[0].cFunc(5) returns consumption at vFunc : Value function (over market resources cFunc .
- mNrmMin : Minimum value of normalized market resources cFunc and
- vFunc are defined.
- hNrm : Normalized human wealth– the PDV of future income (ignoring mortality)
- divided by current period income MPC : The constant marginal propensity to consume (linear consumption function).
-
- The following command generates the plot for the perfect foresight consumption
-function:
-
mMin = MyConsumer.solution[0].mNrmMin
-
plotFuncs(MyConsumer.solution[0].cFunc, mMin, mMin+10)
-
To solve a version of this problem in which the sequence of ten periods happens
-three times (yielding a solution attribute with thirty-one elements), the user can
-do:
-
-
-
MyConsumer(cycles = 3)
-
MyConsumer.solve()
-
To solve an infinite horizon problem in which the agent experiences the same one period
-problem indefinitely, the user can do:
-
OtherConsumer = PerfForesightConsumerType(cycles=0, CRRA=3.5, Rfree=1.02,
-
DiscFac=0.95, LivPrb = [0.99], PermGroFac = [1.01], Nagents=1000)
-
OtherConsumer.solve()
-
This instance is specified as being infinite horizon by setting cycles=0 . Note that the
-time-varying inputs are still specified as (one element) lists, even though they take on the same
-value in every period; this is because an infinite horizon model might consist of a multi-period
-sequence repeated indefinitely, rather than just one repeated period. The solution attribute of
-OtherConsumer will be a list containing one instance of ConsumerSolution , representing the
-solution to every period of the model.
-
0.4 The modeling framework of AgentType is deemed “microeconomic” because it pertains only to
-the dynamic optimization problem of agents, treating all inputs of the problem as
-exogenously fixed. In what we label as “macroeconomic” models, some of the inputs
-for the microeconomic models are endogenously determined by the collective states
-and controls of agents in the model. In a dynamic general equilibrium, there must be
-consistency between agents’ beliefs about these macroeconomic objects, their individual
-behavior, and the realizations of the macroeconomic objects that result from individual
-choices.
-
The Market class in HARK.core provides a framework for such macroeconomic models, with a
-solve method that searches for a dynamic general equilibrium. An instance of Market includes a
-list of AgentType s that compose the economy, a method for transforming microeconomic
-outcomes (states, controls, and/or shocks) into macroeconomic outcomes, and a method for
-interpreting a history or sequence of macroeconomic outcomes into a new “dynamic rule” for
-agents to believe. Agents treat the dynamic rule as an input to their microeconomic problem,
-conditioning their optimal policy functions on it. A dynamic general equilibrium is a fixed point
-dynamic rule: when agents act optimally while believing the equilibrium rule, their
-individual actions generate a macroeconomic history consistent with the equilibrium
-rule.
-
-
0.4.1 The Market class uses a farming metaphor to conceptualize the process for generating a
-history of macroeconomic outcomes in a model. Suppose all AgentTypes in the economy
-believe in some dynamic rule (i.e. the rule is stored as attributes of each AgentType ,
-which directly or indirectly enters their dynamic optimization problem), and that they
-have each found the solution to their microeconomic model using their solve method.
-Further, the macroeconomic and microeconomic states have been reset to some initial
-orientation.
-
To generate a history of macroeconomic outcomes, the Market repeatedly loops over the
-following steps a set number of times:
-
- sow : Distribute the macroeconomic state variables to all AgentType s in the market.
- cultivate : Each AgentType executes their marketAction method, likely
- corresponding to simulating one period of the microeconomic model.
- reap : Microeconomic outcomes are gathered from each AgentType in the market.
- mill : Data gathered by reap is processed into new macroeconomic states according
- to some “aggregate market process”.
- store : Relevant macroeconomic states are added to a running history of outcomes.
-This procedure is conducted by the makeHistory method of Market as a subroutine of its solve
-method. After making histories of the relevant macroeconomic variables, the market then
-executes its calcDynamics function with the macroeconomic history as inputs, generating a new
-dynamic rule to distribute to the AgentType s in the market. The process then begins again, with
-the agents solving their updated microeconomic models given the new dynamic rule; the solve
-loop continues until the “distance” between successive dynamic rules is sufficiently
-small.
-
-
0.4.2 To specify a complete instance of Market , the user should give it the following
-attributes:
- agents : A list of AgentType s, representing the agents in the market. Each element in
- agents represents an ex-ante heterogeneous type; each type could have many ex-post
- heterogeneous agents.
- sow_vars : A list of strings naming variables that are output from the aggregate
- market process, representing the macroeconomic outcomes. These variables will be
- distributed to the agents in the sow step.
- reap_vars : A list of strings naming variables to be collected from the agents in the
- reap step, to be used as inputs for the aggregate market process.
- const_vars : A list of strings naming variables used by the aggregate market process
- that do not come from agents ; they are constant or come from the Market itself.
- track_vars : A list of strings naming variables generated by the aggregate market
- process that should be tracked as a history, to be used when calculating a new
- dynamic rule. Usually a subset of sow_vars .
- dyn_vars : A list of strings naming the variables that constitute a dynamic rule. These
- will be stored as attributes of the agents whenever a new rule is calculated.
- millRule : A function for the “aggregate market process”, transforming
- microeconomic outcomes into macroeconomic outcomes. Its inputs are named in
- reap_vars and const_vars , and it returns a single object with attributes named in
- sow_vars and/or track_vars . Can be defined as a method of a subclass of Market .
- calcDynamics : A function that generates a new dynamic rule from a history of
- macroeconomic outcomes. Its inputs are named in track_vars , and it returns a single
- object with attributes named in dyn_vars .
- act_T : The number of times that the makeHistory method should execute the
- “farming loop” when generating a new macroeconomic history.
- tolerance : The minimum acceptable “distance” between successive dynamic rules
- produced by calcDynamics to constitute a sufficiently converged solution.
-
-
- Further, each AgentType in agents must have two methods not necessary for microeconomic
-models; neither takes any input (except self ):
-
- marketAction : The microeconomic process to be run in the cultivate step. Likely
- uses the new macroeconomic outcomes named in sow_vars ; should store new values
- of relevant microeconomic outcomes in the attributes (of self ) named in reap_vars .
- reset : Reset, initialize, or prepare for a new “farming loop” to generate a
- macroeconomic history. Might reset its internal random number generator, set initial
- state varibles, clear personal histories, etc.
- When solving macroeconomic models in HARK, the user should also define classes to represent
-the output from the aggregate market process in millRule and for the model-specific
-dynamic rule. The latter should have a distance method to test for solution convergence;
-if the class inherits from HARKobject , the user need only list relevant attributes in
-distance_criteria .
-
0.4.3 To illustrate the Market class, consider a simple example in the emerging economic subfield of aesthemetrics, the
-FashionVictimModel .AgentType called FashionVictimType . Each period, fashion
-victims make a binary choice of style just
-wore the same style; they also pay switching costs (
-
-
-
-
-
-
Fashion victims are forward looking and discount future utility at a constant rate of
-
-
-
-
-
-
-
-
-
An instance of FashionVictimType is thus characterized by values of FashionVictimType ’s infinite horizon microeconomic model can be solved by
-backward induction in a few lines; the “one period solver” is given by solveFashion .
-However, while individual agents treat the dynamics of
The search for a dynamic general equilibrium is implemented in HARK’s Market class with the
-following definitions:
-
sow_vars = [’pNow’] (macroeconomic outcome is
reap_vars = [’sNow’] (microeconomic outcomes are
track_vars = [’pNow’] (must track history of
dyn_vars = [’pNextSlope’,’pNextIntercept’,’pNextWidth’] (dynamic rule
millRule = calcPunkProp (aggregate process: average the style choices of all agents)
-
calcDynamics = calcFashionEvoFunc (calculate new
act_T = 1000 (simulate 1000 periods of the fashion market)
-
tolerance = 0.01 (terminate solution when
The agents attribute has a list of 22 FashionVictimType s, which vary in their values of marketAction method of FashionVictimType
-simulates one period of the microeconomic model: each agent receives style preference
-shocks pNow ),
-and chooses which style to wear, storing it in the binary array sNow , an attribute of
-self .
-
The millRule for this market is extremely simple: it flattens the list of binary arrays of
-individual style choices (gathered in the reap step) and averages them into a new value
-of sow n back to the agents to begin the cycle
-again. Once a history of 1000 values of makeHistory
-method, we can calculate a new dynamic fashion rule with calcFashionEvoFunc by
-
-
-regressing HARKobject .
-
When the solve method is run, the solver successively solves each agent’s microeconomic
-problem, runs the makeHistory method to generate a 1000 period history of
0.5 This section provides an overview of how users can contribute new code to HARK, what sort of
-contributions are most desired, and style conventions for the toolKit. Before attempting to
-extend HARK, we strongly recommend that you familiarize yourself with its features and models
-by looking at the source code.
-
-
0.5.1 The easiest and most straightforward way to contribute to HARK is by writing new general
-purpose tools for inclusion in one of the top-level modules. This might be a commonly
-known data analysis technique (e.g. a kernel density estimator) for HARK.utilities , an
-optimization method (local or global) for HARK.estimation , an interpolation method for
-HARKinterpolation , etc. If you’ve found a technique useful in your own research and
-believe it could be of use to others on entirely different projects, then it probably
-belongs in HARK. Likewise, if you read through a HARK module expecting to find a
-certain tool or function (because of similar functions that are present, or merely because
-it’s very useful ) but don’t, this is a good sign that HARK has a hole that you can
-fill.
Secondly, the development team wants to expand HARK to include more canonical models.
-Most of the models available in the toolKit at the time of its public beta release concern agents
-with (geometrically discounted) CRRA utility over a unitary consumption good, who receive
-
-
-permanent and transitory shocks to income and have access to a single risk free asset. This is an
-important class of models, but that description has an awful lot of qualifiers. In short, there are
-many more common features of dynamic household models that are not yet in HARK, but would
-be very valuable for the toolKit. New model contributions should “play nicely” with existing
-models and tools, following HARK style guidelines (see sections 5.4 and 5.5) to the
-greatest extent possible. For ideas of potential model extensions for HARK, see section
-0.6.2 .
Finally, contributions to HARK are not restricted to entirely new functionality. While we have
-done our best to provide flexible frameworks and useful tools, we have almost certainly
-overlooked an unexpected case that would not be (properly) handled by the toolKit as is.
-Moreover, there might even be flaws in our solution or simulation methods. If you find a mistake
-or error, we strongly encourage you to contribute a fix for the problem, or at least to flag it as
-an Issue on GitHub so that someone else might. The “mistake” you find might not
-even be in the code itself, but a deficiency in the documentation: a misnamed (or
-incorrectly typed) parameter or return variable in the docstring, an incorrect (or missing)
-comment, even a grammatical or spelling error. No matter how niggling, we want your
-fix.
-
For those who want to contribute to the HARK project without programming a new model or
-digging through the code for errors and omissions, consider reading through the Issues page on
-GitHub to see what other users have flagged for development. You might find several pieces of
-“low hanging fruit” for you to quickly fix.
-
0.5.2 Git is a distributed version control system that has gained increasing popularity in the decade
-since its creation; it is designed to enable collaboration and “non-linear workflow” among a
-(potentially disperse) programming team. As HARK is to be developed by volunteer researchers
-all around the world, git is an excellent fit for the toolKit. Originally designed by Linus Torvalds
-as a command line tool, others have since extended git with further functionality and
-a user interface more friendly to those more familiar with GUIs. Foremost among
-these tools is GitHub.com, where the HARK project is hosted. This section provides a
-brief overview of how to use GitHub to interact with (and potentially contribute to)
-HARK.
-
To prepare to contribute to HARK, follow these simple steps:
-
- Make a GitHub account: Go to github.com ; the homepage should prompt
- you to choose a username and password. Signing up is quick, easy, and
-
-
- free.Install GitHub Desktop : Go to desktop.github.com and download the Desktop
- application for your operating system, then install it. After opening Desktop for the
- first time, enter your GitHub login to connect it to your account.
- Fork the HARK repository: Go to the HARK repository page , click on the Fork
- button in the upper right, and choose to fork HARK to your own GitHub account.
- This creates a new repository identical to the one hosted by Econ-Ark at the moment
- you fork it, but hosted on your account instead.
- Add the repository to Desktop: Open GitHub Desktop, click on the yourusername/HARK ,
- click the Clone button at the bottom of the dialogue window, and choose a directory
- on your local machine.
- You now control a “fork” of the main HARK repository, hosted by GitHub, and have a clone of
-this fork on your local machine. You are free to edit your fork of HARK in any way you’d like;
-other GitHub users can view your fork (and fork it themselves), but they cannot make changes to
-your fork unless you give them permission by adding them as a contributor. Changes made
-to the local copy of your fork do not get automatically sent to the remote copy on
-GitHub for public viewing. To make changes to your fork on GitHub, follow these
-steps:
-
- Make local changes: Edit your local copy of the repository in any way you’d like:
- add or delete lines from existing files; add, delete, or rename entire files or directories.
- Commit local changes: Open the repository in GitHub Desktop and click on the
- Changes tab. In the lower left, provide a short description of the changes made in
- the Summary field, and (optionally) longer comments about these changes in the
- Description field. Click the check-mark Commit button.
- Push the commit: Click on the Sync button near the upper right to “push” your
- local commits to the remote server; this will also pull down remote commits from
- other contributors to your fork that were made since the last time you synced.
-
-
- The left hand panel of the Changes tab has a summary of the files that have been changed,
-added, or removed since your last commit; the right hand panel shows the specific
-changes made to each changed file. After step 2, a record of your changes has been
-stored locally by the git system, but is not yet reflected in the public remote copy on
-GitHub.
Suppose you have mucked about in your fork for a while and have made changes to HARK
-that you think should be included in the main repository hosted by Econ-Ark. To make your
-dream a reality, follow these steps:
-
- Isolate the changes: If you mucked around for quite a while, making various changes
- here and there, and then later made some contribution that you want to include in
- the main repository, you should isolate the desired changes in a new branch of your
- fork . Make a new branch by clicking the button toward the upper left of GitHub
- Desktop, revert that branch to the commit just before you forked from the main
- repository,only the changes you want to push to the main
- repository .
- Issue a pull request: Go to HARK’s GitHub page and click the “New pull request”
- button. Click the text that says “compare across forks”, and select econ-ark/HARK
- as the base fork, master as the base, your fork as the head fork, and the branch to
- be merged as the “compare”. In the boxes below, give a name to your pull request
- and provide sufficient comments about your contribution so that a development team
- member can understand what you’ve done. Click the “Create pull request” button.
- Be patient: Someone from the development team will look at your contribution and
- either merge it into the main repository or return it with comments. See section 0.5.3
- for procedures followed by the HARK team when evaluating outside contributions.
-
- 0.5.3 Before submitting to HARK, the development team would like you to make sure that
-your intended contribution is listed as an Issue on the GitHub page. If you are going
-to fix or address an Issue that is already posted, reply to that post with a note
-indicating that you are about to work on it and an estimate of when you will make a
-pull request for it. If you want to do something that no one has mentioned yet as
-an Issue, make a new Issue on GitHub (briefly) describing what you intend to
-do.
After creating a pull request to submit your contribution, it will be evaluated by one or more
-members of the HARK team. Criteria that the HARK team will consider when choosing whether
-to accept a submission include:
-
- Does the contribution break previous code? If you change or expand an existing
- function, you should make sure that this does not cause some models or applications
- to work incorrectly. In the near future, HARK will have a core set of tests to run to
- check for full compatibility.
- Is the code well documented? Code submissions should have properly formatted
- docstrings for each new function, class, method, and module (see section 0.5.5 ) and
- enough comments for a reader to follow the algorithm.
- Is the contribution relevant? The development team intends for HARK to
- be diverse, but not everything goes in HARK. If your submission concerns a
- representative agent model or a method for estimating a reduced form model, then
- it probably doesn’t belong in HARK.
-
-
- Is the code demonstrated with an example? If your solve a new model in
- HARK, you should include a set of example parameters for other users to easily
- test. If you’ve contributed a new function, consider providing a snippet of code
- demonstrating its use.Is the code correct? Your code should actually do what it claims to do.
- Does the code use HARK conventions? To the best of your ability, contributions
- to HARK should follow the style guidelines in section 0.5.4 . We don’t demand
- perfection, as these are guidelines rather than rules.
- Is the code well integrated in HARK? In the beta release of HARK, we have
- tried to present a “taxonomy” of consumption-saving models that build up from each
- other. When possible, new models in HARK should try to continue this pattern. A
- submission that introduces a model in a new module that could have easily been made
- as an extension of an existing module might be rejected. However, we acknowledge
- that there is value in providing a solver for a new model, and that a future contributor
- might make her mark by integrating it into the existing hierarchy.
- Is this already in HARK? If your contribution is already in HARK, then it will
- almost certainly be rejected. Of course, if you submit a version that dominates the
- existing implementation (or even one that works better in a well described, non-trivial
- set of circumstances), it belongs in HARK.
- The HARK team intends to release more specific guidelines in the near future, so that contributors
-are better able to judge their submissions before issuing a pull request. We are in the process of
-recruiting “czars” to oversee particular aspects of HARK, providing their knowledge and
-expertise.
0.5.4 Object naming conventions in HARK are fairly different than existing standards, and differ
-somewhat between tool modules vs model or application modules. The following conventions
-apply throughout HARK:
-
- Functions and methods are always in “camel case”: no underscores, first letter is lower
- case, first letter of each subsequent word is capitalized. E.g. approxLognormal .
-
- Function and method names should accurately and concisely describe what the
- function does; the first word in the name must be a verb .
- Variable and class names should not have a verb as their first word.
-
- Class names should use no underscores and capitalize the first letter of each word;
- moreover, a class name must include a noun . E.g. ConsPerfForesightSolver .
- When naming variables in model modules, the HARK team strongly discourages using single
-letter names, like c for consumption. Instead, we encourage contributors to use longer, more
-descriptive variable names using additional words and common abbreviations to specify the
-variable more precisely. In /Documentation/NARK.pdf , we list standard single letter variable
-“bases” and an array of prefixes and suffixes to adjust them. Economic variables in model
-modules should (usually) not use underscores, instead using camel case to the greatest extent
-possible.
Conventions for naming variables in HARK’s tool modules are significantly closer to more
-commonly used standards. Variable names should be in all lower case, with underscores between
-words, e.g. data_to_match . The functions and classes in these modules are more general
-and almost surely do not have any inherent “economic content”; they are numerical
-or algorithmic objects, not variables that might appear in an equation in an article
-for a (non-computational) economics journal. Variable names in application modules
-(e.g. the files that execute the cstwMPC estimations) are a mix of the conventions for tool
-and model files, as appropriate for each variable. That is, variables that are directly
-related to “economic variables” in model modules should follow those conventions, while
-objects created solely for data manipulation or reporting should use the style of tool
-modules.
-
-
-
0.5.5 The HARK team wants the toolKit to be as accessible to users as possible; our greatest
-fearonline documentation for all
-of our tool and model modules, so that users can learn about what’s available in HARK without
-digging through the source code. When making contributions to HARK, the development team
-asks users to format their inline documentation to work with Sphinx by following a few simple
-rules.
-
- The top of every module should begin with a “docstring” providing a clear description
- of the contents of the module. The first sentence should concisely summarize
- the file, as it may appear in an index or summary of all modules without the
- remaining sentences. A docstring at the top of a module should be formatted
- as:"""
-
Specifies an economic model and provides methods for solving it. More
- specific description of the key features of the model and variations
- of it in this module. Maybe some comments about the solution method
- or limitations of the model. Your bank account routing number.
-
"""
-
- The line directly below the declaration of a function, method or class should begin a
- docstring describing that object. As with modules, the first sentence should concisely
- summarize the function or class, as it might be included in an index or summary. For
- functions and methods, the docstring should be formatted as:
- def functionName(input1,input2):
-
-
-
"""
-
Concise description of the function. More details about what
-
the function does, options or modes available, and maybe
- mathematical
-
methods used. Credit to a source if you poached their
- algorithm.
-
Parameters
-
––––––––––
-
input1: type
-
Description of what input1 represents.
-
input2: type
-
Description of what input2 represents.
-
Returns
-
–––––––
-
output_name: type
-
Description of the output(s) of the function. Might have
-
multiple entries. If no output, this is just "None".
-
"""
-
- Provide ample comments within a function or method so that a relatively intelligent
- reader can follow along with your algorithm. Short comments can follow at the end
- of a line, longer comments (or descriptions of the step or task about to be performed)
- should precede a block of code on the line(s) above it.
- Finally, if you write a new model module, the HARK team asks that you also provide a short
-mathematical writeup of the model as a PDF. This document does not need to go into great
-detail about the solution method for the model or the functions and classes included in
-the module, merely specify the economic model and provide a summary of how it
-is solved. See /Documentation/ConsumptionSavingModels.pdf for an example of
-this.
-
-
-
0.6 HARK is in a state of perpetual incompleteness, always expanding to include more
-tools and more models. As an open source project in the hands of many contributors
-around the world, the HARK team cannot fully predict future features of the toolKit.
-This section provides an overview of what we would like to see in the near future, as
-well as potential mechanisms to encourage researchers to contribute their code to the
-project.
-
-
0.6.1 The current frameworks in HARK.core concern agents who have “isolated” dynamic
-problems or interact with each other only through aggregate outcomes to which they
-contribute atomically. We hope to eventually provide a framework for models in which
-agents directly interact with each other; this may include a system for specifying the
-state of networks and how connections are formed and broken. HARK.core might also
-develop a framework more suited to industrial organization models and dynamic
-games.
The HARK.estimation module in particular is very sparsely populated, with its
-functions mostly serving as wrappers for “off the shelf” optimization methods from
-scipy.optimize . There are plenty of non-standard or more complex optimization
-techniques available, including global and/or stochastic search methods. We believe
-that additional optimizers are “low hanging fruit” as straightforward contributions
-to HARK and thus will be added in the near future. Moreover, there are plenty of
-distributions that have no approximator in HARK.utilities (or data generator in
-HARK.simulation ), and these functions are among the easiest for new contributors
-to write. The data analysis tools in HARK.utilities only include functions that we
-happened to use for our current research projects, and we expect that this will be the
-mode of growth for future tools. There are some obvious holes that we believe will be
-filled quite quickly; for example, we provide a rudimentary non-parametric kernel
-regression function, but have not written a kernel density estimator to generate PDFs from
-data.
-
The classes currently in HARK.interpolation are all variations on linear (or
-cubic) spline interpolation, and each is specifically written for a fixed number
-of dimensions. As these methods use full tensor grids, they are highly impractical
-for representing functions in higher dimensions, suffering greatly from the curse of
-
-
-dimensionality.HARK.interpolation will soon include techniques more applicable
-to high dimensional state spaces, including Smolnyak interpolation, other (adaptive) sparse grid
-methods, and regression-based interpolation. For lower-dimensional spaces, HARK will also soon
-provide Delaunay interpolation classes.
-
The HARK.parallel module currently provides a simple interface for handling ex-ante
-heterogeneity among agents. While useful for speeding up moderately-sized problems, the
-libraries used are only able to access the CPU of the local machine running the code. In the very
-near future, the HARK team will provide an interface (and example) for using Apache Spark,
-which bills itself as a “fast and general engine for large-scale data processing”. Specifically, users
-will be able to easily scale up their computing resources and run HARK code on an Amazon EC2
-session or other cluster.
-
0.6.2 The economics models currently included in HARK mostly concern consumption-saving problems
-with constant relative risk aversion consumers who experience fully permanent and fully
-transitory shocks to income. This is obviously a very small subset of all consumption-saving
-models (which are themselves a small subset of all dynamic models economists might be
-interested in). Consider some examples of variants, features, and extensions that are not included
-in HARK, but could be in the future:
-
- Consumers with other utility functions, such as CARA or expo-power.
-
- Consumers with time-inconsistent preferences, e.g. hyperbolic discounting.
-
- Income shocks that are neither fully transitory nor fully permanent.
-
- Consumers with a bequest motive or terminal utility function.
-
- Utility from leisure and endogenous labor supply.
-
- Investment in a durable consumption good such as housing.
-
-
-
- Exogenous shocks to expenses.
-
- Multiple consumption goods, as with endogenous medical care.
-
- Portfolio allocation between a risky and riskless asset.
-
- Job search intensity as a control variable.
-
- Endogenous health evolution and health investment.
- The HARK team is currently developing a module for mixing discrete and continuous
-choice models, in the style of Jorgensen, Rust, Iskhakov, and Schjerning (2015). Among
-many other models, this framework will enable HARK users to easily develop features
-like:
-
- Labor supply on the extensive margin: the timing of retirement.
-
- Selection of job type or sector (by riskiness, specific human capital, etc).
-
- Choice of medical insurance contract.
-
- Decision to rent or own housing.
-
- Endogenous default on debt (with “macroeconomic” pricing of debt).
-
- Household formation, dissolution, and reproduction.
- Finally, we hope to provide a generic solver for any discrete choice model with T1EV shocks.
-The scope of HARK is limited only by what users choose to put in it. We hope that
-within a year of its debut, HARK will include a significant number of canonical models,
-providing more building blocks for future researchers to create novel combinations and new
-extensions.
-
-
-
-
0.6.3 While HARK users are free to write and contribute any tools and models that they believe could
-be helpful, building the toolKit in a “bottom up” style, the HARK team hopes to provide
-mechanisms for targeted “top down” development. The team is working to secure grant funding for
-HARK so as to offer “bounties” for particular models, tools, applications, or frameworks. Suppose
-the team decides that the model in Important Paper X should be included as part of HARK; we
-will issue a call for contributions to fill this hole, offering a modest prize of, say, $20,000 to the
-programmer who submits the best implementation of the model, as judged by clearly stated
-criteria.
Bounties might be placed on items as small as a single data analysis tool or as large as a
-complex estimation of a large general equilibrium model, with prizes scaling accordingly. We
-hasten to emphasize that we cannot guarantee funding for HARK, so the bounties (like
-everything else in this section) are purely speculative. As economists, the HARK team
-understands the incentive compatibility problems associated with asking researchers to
-contribute significant amounts of their time, effort, and unique skills in service of a
-tenuous concept like the Greater Good in exchange for even more tenuous Glory. We
-acknowledge that economic researchers are also optimizing agents with their own preferences
-over leisure and consumption, and agree that Greed is a valid mechanism to expand
-HARK.
-
0.6.4 As observant readers may have noticed, the HARK repository is hosted by a GitHub account
-named Econ-Ark, with no explanation as to what this might mean. Looking a few years into the
-future, the development team believes that HARK might be the first piece of a broader collection
-of computational economics resources. HARK is open source but somewhat akin to an
-exclusive club: contributions are required to adhere to standards and norms so as to foster
-modularity and interoperability among models and tools. Consistent with its namesake ship,
-the Econ-Ark has a significantly lower bar for admission, with a place on board for
-everything.
-
On one level, the Ark might serve as a warehouse for any and all quantitative models and
-tools, for any economic purpose, in any subfield, and written in any language. Through
-nearly unanimous feedback, the HARK team understands that many economists are
-willing to share code that they have already written, but are reluctant to expend the
-
-
-considerable effort to translate and format it for HARK. In the future, we will set up a
-mechanism for code like this to be hosted on the Ark, with minimal requirements
-imposed on its authors (and correspondingly minimal warranty for potential users).
-Relevant projects on the Ark might become bounty targets for HARK, with users
-incentivized to port the original code into Python and to meet HARK standards;
-direct access to the original source code will significantly expedite the translation
-process, and provide a suitable benchmark for testing and verifying the contribution to
-HARK.
-
On a different level, the Ark might also include cohesive toolkits like HARK, but geared
-toward purposes other than heterogeneous agents macroeconomics models. For example, the
-previously mentioned modules for networks among agents (and other agent-based modeling),
-industrial organization and dynamic games, and traditional discrete choice models might become
-their own toolkits, with some interoperability with HARK. In support of this, some of the tool
-modules currently in HARK might be “promoted” and become universally shared across all
-Econ-Ark toolkits.
-
The dark secret of HARK is that it is named for heterogeneous agents macroeconomics, but
-designed and written by structural microeconomists. The development team believes these
-subfields to have considerable overlap in terms of methods and techniques, and that HARK
-might not be noticed by half of its potential audience due to its name. In a general sense, the
-Econ-Ark gives us cover to expand the scope of our efforts and/or rebrand the toolKit for an
-alternative audience.
-
-
-
-
-
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+\documentclass[12pt]{econtex}
+\usepackage{econtexSetup}
+\usepackage{econtexShortcuts}
+
+\usepackage{cancel}
+
+\begin{document}
+
+
+\title{Suggested Naming Conventions in ARK}
+
+\forcedate{July 15, 2018}
+
+\vspace{2in}
+
+\keywords{ARK, Variable, Function, Object, Operator, Naming, Guidelines, Conventions}
+\jelclass{None}
+
+
+\maketitle
+
+
+\begin{abstract}
+ Sharing of code is easier when different contributors use similar names for similar objects. While we will not enforce the
+ recommendations below, contributors can make their code more attractive to others
+ by using names consistent with our guidelines.
+\end{abstract}
+
+\thanks{The \texttt{NARK} repo that generates this document
+also contains \LaTeX~configuration files that permit you to use the same name for an object in
+your writing (your \texttt{paper.tex} file) and in your code (helping to minimize confusion in translating between paper and code).}
+
+
+\pagebreak
+
+\section{Principles}
+Our aim has been to balance:
+\begin{itemize}
+\item Brevity (nobody wants to type long names over and over)
+\item Uniqueness (in case you want to search-and-replace)
+\item Mnemonic quality (it should be easy to remember what represents what)
+\item Ubiquity (objects defined herein will appear in many projects)
+\item Combinatoriality (easy to mix and match our recommendations)
+\end{itemize}
+
+
+\pagebreak
+
+\section{Variables}
+
+\subsection{Single-Letter}
+
+We generally discourage the use of single-letter variable names. The reasons for this advice are presented \textit{ad nauseum} in introductory computer programming texts (which, we know, few economists consult); here we will note only that the extent to which your code will be influential depends upon the extent to which someone else can easily read it, which is harder if you have used variable names which could mean almost anything.
+
+It is only slightly less objectionable to name a variable after a familiar letter in another commonly used alphabet (say, \texttt{delta}). Your future self (and other users) will not know which of the many possible meanings of $\delta$ you had in mind.
+
+But, because brevity is a virtue, a single letter \textit{in combination with a modifier or two} (`\texttt{hMin}' as the value of minimum human wealth, say) is fine -- so long as the reader has some reason to expect that the lower-case letter \texttt{h} signifies human wealth (as they will, if they consult Table~\ref{table:RomanLetters}).
+
+That is the spirit in which we offer preferred interpretations for the Roman letters below. The upper case version is an aggregated version of the variable (at the level of the whole economy, say, or of the whole market being studied), while the lower case indicates the level of an individual consumer or firm or other subaggregate entity.
+
+A few exceptions to these rules are explicitly noted below the table.
+
+When an alternative is needed with a meaning similar to, but distinct from, the definitions below, please use a multi-letter name to represent it. For example, please do not use $W$ for wealth (if some measure of wealth that differs from $\ALev$, $\BLev$, $\HLev$, or $\NLev$ is needed); instead use, say, \texttt{Wlth} or \texttt{Wealth}. (Some examples follow in a subsequent section).
+
+Finally, a few of the definitions below are actually prohibitions; these are based on many years of experience which have shown that use of the prohibited variable name generates more confusion than clarity.
+
+\begin{table}[h]
+\begin{center}
+\begin{tabular}{|lcl|} \hline
+Letter & & Meaning
+\\ \hline
+ \ALev & & \textit{A}ssets \textit{A}fter \textit{A}ll \textit{A}ctions have been \textit{A}ccomplished (end of period)
+\\ \BLev & & \textit{B}eginning \textit{B}ank \textit{B}alances \textit{B}efore any \textit{B}ehavior (\textit{b}eginning-of-period)
+\\ \CLev & & \textit{C}onsumption \textit{C}hoice \textit{C}onnects $B$ to $A$
+\\ \DLev & & \textit{D}ebt
+\\ \ELev & & PROHIBITED: Too many possible meanings (expectations, effort, expenses)
+\\ \FLev & & Production \textit{F}unction
+\\ \GLev & & \textit{G}rowth
+\\ \HLev & & \textit{H}uman wealth
+\\ \ILev & & \textit{I}nvestment
+\\ \JLev & & Ad\textit{J}ustment costs (e.g., in a $Q$ model)
+\\ \KLev & & Capital or beginning of period nonhuman assets
+\\ \LLev & & PROHIBITED: Is it Labor or Leisure or Land or ...?
+\\ \MLev & & \textit{M}arket resources (the sum of capital, capital income, and labor income)
+\\ \NLev & & \textit{N}et wealth including human wealth ($=B + H$)
+\\ \OLev & & PROHIBITED: Too similar to the number 0; too many possible meanings
+\\ \PLev & & PROHIBITED: Is it prices, permanent income, present value, profits, ...?
+\\ \QLev & & Hayashi/Abel $Q$ (or similar asset price)
+\\ \RLev & & \textit{R}eturn (see the variants articulated below)
+\\ \SLev & & PROHIBITED: ``saving'' (flow)? ``savings'' (stock)? or the ``saving rate'' (ratio)?
+\\ \TLev & & This is a tough one. See the discussion below.
+\\ \ULev & & \textit{U}tility
+\\ \VLev & & \textit{V}alue
+\\ \WLev & & \textit{W}age
+\\ \XLev & & e\textit{X}penditures (as distinct from consumption; e.g., for durables)
+\\ \YLev & & Noncapital income (usually, the sum of transfer and labor income)
+\\ \ZLev & & Lei\textit{Z}ure in consumption/leisure tradeoff
+\\ \hline
+\end{tabular}
+\end{center}
+\caption{Preferred Usages of Roman Letters}
+\label{table:RomanLetters}
+\end{table}
+
+\subsection{Exceptions to the Rules}
+
+The letter $\TLev$ is an exception to the rule that lower- and upper-case versions of variables are individual and aggregate quantities. We reserve the capital letter to designate the end of the horizon (death, or the end of the economy, occurs at the end of period $\TLev$). The lower case version $t$ is so ubiquitiously used as the current time period that we do not want to resist the overwhelming force of tradition to prohibit its use in that capacity.
+
+Finally, the following are exempted from the prohibition on single-letter variable names because they are used so frequently that the prohibition would be more trouble than it is worth: $a$, $b$, $c$, $m$.
+
+
+\pagebreak
+
+\subsection{Strings}
+
+There are more objects that are likely to be used extensively in ARK projects than there are Roman letters. We present preferred usages for some of those commonly-needed variables here.
+
+\begin{table}[h]
+\begin{center}
+\begin{tabular}{|rcl|}
+\hline
+Name & - & Description
+ \\ \hline
+ \texttt{CND} & - & Consumption of Nondurable Good
+ \\ \texttt{Cst} & - & Cost of something
+ \\ \texttt{Dgd} & - & Stock of durable good
+ \\ \texttt{Dvd} & - & Dividends
+ \\ \texttt{Hse} & - & Quantity of housing (\textit{not} value, which is quantity $\times$ price)
+ \\ \texttt{Nlq} & - & Quantity that is \textbf{N}ot \textbf{l}i\textbf{q}uid (e.g., $\mathtt{aNlq}$ is illiquid financial)
+ \\ \texttt{Lbr} & - & Quantity of labor
+ \\ \texttt{Pop} & - & Size of population
+ \\ \texttt{Tax} & - & Tax -- should be modified by \texttt{Rte} or \texttt{Amt} articulated below
+ \\ \texttt{Perm} & - & Permanent income
+ \\ \texttt{Tran} & - & Transitory income
+\\ \hline
+\end{tabular}
+\caption{String Variables}
+\label{table:Strings}
+\end{center}
+\end{table}
+
+
+
+\section{Factors and Rates}
+ When measuring change over time, lower-case variables reflect rates
+ while the corresponding upper-case variable connects adjacent discrete periods.\footnote{This
+ convention rarely conflicts with the usage we endorse elsewhere of
+ indicating individual-level variables by the lower and aggregate
+ variables by the upper case.}$^{,}$\footnote{If there is a need for the continuous-time
+representation, we endorse use of the discrete-time rate defined below. Any
+author who needs a continuous-time rate, a discrete-time rate, and a discrete-time factor
+is invited to invent their own notation.} So, for example, if the time interval is a year and the annual interest rate is $\rfree=0.03$ or three percent, then
+the annual interest factor is $\Rfree=1.03$.\footnote{In the rare cases where it is necessary to distinguish between a continuous-time rate and a discrete-time rate -- for example, when there is an analytical result available in continuous time -- the variable in question can be modified by \texttt{Cnt} or \texttt{Dsc}.}
+
+
+\begin{table}[h]
+ \centering
+ \begin{tabular}{|ccc|}
+ \hline
+Code & Output & Description
+\\ \hline
+ \verb|\Rfree| & $\Rfree$ & Riskfree interest factor
+\\ \verb|\rfree| & $\rfree$ & Riskfree interest rate
+\\ \verb|\Risky| & $\Risky$ & The return factor on a risky asset
+\\ \verb|\risky| & $\risky$ & The return rate on a risky asset
+\\ \verb|\Rport| & $\Rport$ & The return factor on the entire portfolio
+\\ \verb|\rport| & $\rport$ & The return rate on the entire portfolio
+\\ \verb|\RSave| & $\RSave$ & Return factor earned on positive end-of-period assets
+\\ \verb|\rsave| & $\rsave$ & Return rate earned on positive end-of-period assets
+\\ \verb|\RBoro| & $\RBoro$ & Return factor paid on debts
+\\ \verb|\rboro| & $\rboro$ & Return rate paid on debts
+\\ \hline
+ \end{tabular}
+ \caption{Factors and Rates}
+ \label{table:Factors}
+\end{table}
+
+We depart from the upper-lower case scheme when the natural letter to use has an even more urgent use elsewhere in our scheme.
+A particularly common example occurs in the case of models like \cite{blanchardFinite} in which
+individual agents are subject to a Poisson probability of death. Because death was common in the
+middle ages, we use the archaic Gothic font for the death rate; and the probability of survival is the cancellation of the probability of death:
+\begin{table}[h]
+ \centering
+ \begin{tabular}{|>{\ttfamily}cccl|}
+ \hline
+ Code & \LaTeX & Description & \\
+ \hline
+ \verb|\DieFac| & $\DieFac$ & Probabilty of death &
+\\ \verb|\LivFac| & $\LivFac$ & Probability to not die $=(1-\DieFac)$ &
+\\ \hline
+ \end{tabular}
+ \caption{Special Cases: Factors and Rates}
+ \label{table:SpecialFactors}
+\end{table}
+
+\section{Parameters}
+ Some parameters are worth defining because they are likely to be
+ used in a high proportion of models; others are subject to enough
+ constraints when used (such as the need for similar-looking upper-
+ and lower-case Greek representations), as to be worth standardizing.
+
+Programmers should
+use the corresponding variable name without the backslash as the name of the corresponding object
+in their code. For example, the Coefficient of Relative Risk Aversion is $\verb|\CRRA|$ in a \LaTeX
+document and \texttt{CRRA} in a software module.
+
+\begin{table}[h]
+ \centering
+ \begin{tabular}{|>{\ttfamily}rcll|}
+ \hline
+ Name & \LaTeX & Description & Illustration
+ \\ \hline
+ \verb|\CARA| & $\CARA$ & Coefficient of Absolute Risk Aversion &$\uFunc(\bullet)=-\CARA^{-1} e^{-\CARA \bullet}$
+ \\ \verb|\CRRA| & $\CRRA$ & Coefficient of Relative Risk Aversion & $\uFunc(\bullet)=(1-\CRRA)^{-1}\bullet^{1-\CRRA}$
+ \\ \verb|\DiscFac| & $\DiscFac$ & Time Discount Factor & $\uFunc^{\prime}(\cLev_{t}) = \Rfree \DiscFac \uFunc^{\prime}(\cLev_{t+1})$
+ \\ \verb|\discRte| & $\discRte$ & Time Discount rate & $\discRte=\DiscFac^{-1}-1$
+ \\ \verb|\DeprFac| & $\DeprFac$ & Depreciation Factor (Hebrew \texttt{daleth}) & $\Kap_{t+1} = \DeprFac \Kap_{t}+\ILev_{t}$
+ \\ \verb|\deprRte| & $\deprRte$ & Depreciation Rate & $\DeprFac = 1-\deprRte$
+ \\ \verb|\TranShkAgg| & $\TranShkAgg$ & Transitory shock (aggregate) & $\Ex_{t}[\TranShkAgg_{t+n}]=1$ if $\TranShkAgg$ iid
+ \\ \verb|\tranShkInd| & $\tranShkInd$ & Transitory shock (individual) & $\Ex_{t}[\tranShkInd_{t+n}]=1$ if $\tranShkInd$ iid
+ \\ \verb|\PermShkAgg| & $\PermShkAgg$ & Permanent shock (aggregate) & $\Ex_{t}[\PermShkAgg_{t+n}]=1$ if $\PermShkAgg$ iid
+ \\ \verb|\permShkInd| & $\permShkInd$ & Permanent shock (individual) & $\Ex_{t}[\permShkInd_{t+n}]=1$ if $\permShkInd$ iid
+ \\ \verb|\PopGro| & $\PopGro$ & Population Growth Factor & $\mathtt{Pop}_{t+1} = \PopGro \mathtt{Pop}_{t}$
+ \\ \verb|\popGro| & $\popGro$ & Population Growth rate & $\PopGro = 1 + \popGro$
+ \\ \verb|\PtyGro| & $\PtyGro$ & Productivity Growth Factor & $\GLev = \PtyGro \PopGro$
+ \\ \verb|\ptyGro| & $\ptyGro$ & Productivity Growth rate & $\PtyGro = (1+\ptyGro)$
+ \\ \verb|\leiShare| & $\leiShare$ & Leisure share, Cobb-Douglas utility & $\uFunc(c,z)=(1-\CRRA)^{-1}(c^{1-\leiShare}z^{\leiShare})^{1-\CRRA}$
+ \\ \verb|\MPC| & $\MPC$ & Marginal Propensity to Consume & $\cFunc^{\prime}(\mRat)=\partial c/\partial m$
+ \\ \verb|\Pat| & $\Pat$ & Absolute Patience Factor (\texttt{Thorn}) & $\Pat = (\Rfree \DiscFac)^{1/\CRRA} $
+ \\ \verb|\PatPGro| & $\PatPGro$ & Growth Patience Factor (\texttt{Thorn}) & $\Pat = (\Rfree \DiscFac)^{1/\CRRA}/\PtyGro $
+ \\ \verb|\PatR| & $\PatR$ & Return Patience Factor (\texttt{Thorn}) & $\Pat = (\Rfree \DiscFac)^{1/\CRRA}/\Rfree $
+ \\ \verb|\pat| & $\pat$ & Absolute Patience rate (\texttt{thorn}) & $\pat = (\Rfree \DiscFac)^{1/\CRRA}-1 \approx \CRRA^{-1}(\rfree-\discRte) $
+ \\ \verb|\patpGro| & $\patpGro$ & Growth Patience rate (\texttt{thorn}) & $\patpGro = \pat -\ptyGro $
+ \\ \verb|\patr| & $\patr$ & Return Patience rate (\texttt{thorn}) & $\patr =\pat -\rfree $
+ \\ \verb|\riskyshare| & $\riskyshare$ & Portfolio share in risky assets & $\Rport_{t+1}=(1-\riskyshare)\Rfree+\riskyshare\Risky_{t+1}$
+ \\ \hline
+ \end{tabular}
+ \caption{Parameters}
+ \label{table:Parameters}
+\end{table}
+
+Mnemonics:
+\begin{itemize}
+\item Hebrew \texttt{daleth} is the fourth letter of the Hebrew alphabet (as d and $\delta$ are of the Roman and Greek) and is an etymological and linguistic cousin of those letters
+\item $\discRte$ is the lower case Greek letter \texttt{omega}, because people say ``OMG, I've got to think about the future.''
+\item You are invited to scrutinize $\Xi$ yourself to imagine reasons it could represent something to do with population growth.
+\item The glorious letter $\Pat$ (pronounced `\href{http://en.wikipedia.org/wiki/Thorn_(letter)}{thorn}') enriched Old English, Gothic, and some other defunct alphabets; sadly, it remains in use today only in Iceland. It is useful because having to type the many symbols in the object $(\Rfree \DiscFac)^{1/\CRRA}$ over and over again is a \textit{thorn} in the side of economists working with dynamic models! (It is the `absolute patience factor' because if it is less than one the consumer wants to bring resources from the future to the present and is therefore absolutely impatient; for a fuller discussion of this terminology, see \cite{carrollTractable}.)
+\end{itemize}
+
+
+
+\section{Operators}
+A few operators are so universally used that it will be useful to define them.
+
+\begin{table}[h]
+ \centering
+ \begin{tabular}{|>{\ttfamily}rcll|}
+ \hline
+ Name & \LaTeX & Description & Illustration
+ \\ \hline
+ \verb|\Ex| & $\Ex$ & The expectation as of date $t$ & $\Ex_{t}[\uFunc^{\prime}(\cRat_{t+1})]$
+\\ \verb|\PDV| & $\PDV$ & Present Discounted Value & $\PDV_{t}^{T}(y)$ is human wealth
+ \\ \hline
+ \end{tabular}
+ \caption{Operators}
+ \label{table:Operators}
+\end{table}
+
+
+
+
+\section{Modifiers}
+\begin{table}[h]
+ \centering
+ \begin{tabular}{|>{\ttfamily}ccc|}
+ \hline
+ \textit{[object]}\texttt{Agg} & - & Value of something at the aggregate level (as opposed to \texttt{Ind})
+\\ \textit{[object]}\texttt{Ind} & - & Value of something at the level of an individual (as opposed to \texttt{Agg})
+\\ \textit{[object]}\texttt{Lvl} & - & Level
+\\ \textit{[object]}\texttt{Rto} & - & Ratio
+\\ \textit{[object]}\texttt{Bot} & - & Lower value in some range
+\\ \textit{[object]}\texttt{Top} & - & Upper value in some range
+\\ \textit{[object]}\texttt{Min} & - & Minimum possible value
+\\ \textit{[object]}\texttt{Max} & - & Maximum possible value
+\\ \textit{[object]}\texttt{Cnt} & - & Continuous-time value
+\\ \textit{[object]}\texttt{Dsc} & - & Discrete-time value
+\\ \textit{[object]}\texttt{Shk} & - & Shock
+\\ \textit{[object]}\texttt{Trg} & - & The `target' value of a variable
+\\ \textit{[object]}\texttt{Rte} & - & A `rate' variable like the discount rate $\discRte$
+\\ \textit{[object]}\texttt{Fac} & - & A factor variable like the discount factor $\DiscFac$
+\\ \textit{[object]}\texttt{Amt} & - & An amount, like \texttt{TaxAmt} which might be lump-sum
+\\ \hline
+ \end{tabular}
+ \caption{General Purpose Modifiers}
+ \label{table:General}
+\end{table}
+
+
+Shocks will generally be represented by finite vectors of outcomes and their probabilities. For example, permanent income is called \texttt{Perm} and shocks are designated \texttt{PermShk}
+\begin{table}[ht]
+ \centering
+ \begin{tabular}{|>{\ttfamily}ccc|}
+ \hline
+ \textit{[object]}\texttt{Prbs} & - & Probabilities of outcomes (e.g. \texttt{PermShkPrbs} for permanent shocks)
+\\ \textit{[object]}\texttt{Vals} & - & Values (e.g., for mean one shock \texttt{PermShkVals} . \texttt{PermShkPrbs} = 1)
+\\ \textit{[object]}\texttt{Dstn} & - & Representation of a probability distribution (includes both Prbs and Vals)
+\\ \hline
+ \end{tabular}
+ \caption{Probabilities}
+ \label{table:Probabilities}
+\end{table}
+
+
+
+Timing can be confusing because there can be multiple ordered steps
+within a `period.' We will use \texttt{Prev}, \texttt{Curr}, \texttt{Next} to refer
+to steps relative to the local moment within a period, and $t$ variables to refer to succeeding periods:
+\begin{table}[h]
+ \centering
+ \begin{tabular}{|>{\ttfamily}lcl|}
+ \hline
+ \textit{[object]}tm2 & - & object in period $t$ minus 2
+\\ \textit{[object]}tm1 & - & object in period $t$ minus 1
+\\ \textit{[object]}Now & - & object in period $t$
+\\ \textit{[object]}t\phantom{p1} & - & object in period $t$ (alternative definition)
+\\ \textit{[object]}tp1 & - & object in $t$ plus 1
+\\ \textit{[object]}tpn & - & object in $t$ plus $n$
+\\ \textit{[object]}Prev & - & object in previous subperiod
+\\ \textit{[object]}Curr & - & object in current subperiod
+\\ \textit{[object]}Next & - & object in next subperiod
+\\ \hline
+ \end{tabular}
+ \caption{Timing}
+ \label{table:Timing}
+\end{table}
+
+\input econtexBibMake
+
+\end{document}
+
+
diff --git a/Documentation/NARK/econtex.bst b/Documentation/NARK/econtex.bst
new file mode 100644
index 000000000..d826b51ae
--- /dev/null
+++ b/Documentation/NARK/econtex.bst
@@ -0,0 +1,1294 @@
+%%% BEJournals Bibliography Style
+%%% Modified from econometrica.bst by KCB (see documentation below)
+%%% Modified by Christopher Carroll to print full names of authors when available rather than initials
+%%% For use with harvard.sty and LaTeX2e (NFSS) or LaTeX 2.09
+%%% 24-aug-94
+%%%
+%%% Based on makebst, but with modifications by
+%%% kcb@hss.caltech.edu (KC Border)
+%%% Please report any problems to me.
+%%%
+%%% The main changes from the ouput of makebst are:
+%%% 1. Addition of a function to replace repeated authors with a 3 em dash.
+%%% (Search for compare.names, this function is portable.)
+%%% 2. Modification of output.nonnull to allow commas inside quotation marks
+%%% and no comma after year.
+%%% 3.Eliminated feature (bug?) of the harvard style of citation.
+%%% It creates a short label for subsequent citations. This alleged
+%%% feature shortens both Aliprantis, Border, and Luxemburg (1991) and
+%%% Aliprantis, Brown, and Burkinshaw (1991) to Aliprantis et~al. (1991).
+%%% This creates the need to label these as
+%%% Aliprantis, Border, and Luxemburg (1991a) and
+%%% Aliprantis, Brown, and Burkinshaw (1991b), despite the fact
+%%% that these entries have different authors. I used my own format
+%%% for creating the labels, and used the same label for
+%%% both the short and long forms. I know that the
+%%% _Chicago_Manual_of_Style recommends using Aliprantis et~al,
+%% but Econometrica regularly writes out three authors,
+%% and it is rare to cite an article with more than four authors in
+%%% Econometrica. (I couldn't find an example of even four being cited,
+%%% but I didn't spend a lot of time looking.)
+%%% 4. Addition of macros to handle editions. (My own idiosycracy.)
+%%% 5. A new entry type called issue for special issues of a journal.
+%%% (Another idiosyncracy.)
+%%% 6. Many details to conform to Econometrica's quirks.
+%%% (Search for KCB )
+%%% 7. I wrote this for use with LaTeX2e and the NFSS2. However, I did
+%%% add some macros so it will work with LaTeX 2.09.
+%%% (Search for \ifx\undefined\emph )
+%%% The remainder of this file was generated by makebst, except for
+%%% modifications marked with KCB.
+%%
+%% This is file `em.bst', generated
+%% on <1994/8/17> with the docstrip utility (2.2h).
+%%
+%% The original source files were:
+%%
+%% genbst.mbs (with options: `ay,har,nm-rev1,nmft-sc,dt-beg,yr-par,tit-qq,atit-u,thtit-a,volp-com,edby,blk-com,pp,ed,abr,and-com,nfss')
+%% ----------------------------------------
+%% *** Econometrica style ***
+%%
+%% (Here are the specifications of the source file)
+%% \def\filename{genbst.mbs}
+%% \def\filedate{1994 July 1}
+%% \def\fileversion{1.3}
+%% For use with BibTeX version 0.99a or later, LaTeX version 2.09
+%%-------------------------------------------------------------------
+%% NOTICE:
+%% This file may be used for non-profit purposes.
+%% It may not be distributed in exchange for money,
+%% other than distribution costs.
+%%
+%% The author provides it `as is' and does not guarantee it in any way.
+%%
+%% Copyright (C) 1994 Patrick W. Daly
+%% Max-Planck-Institut f\"ur Aeronomie
+%% Postfach 20
+%% D-37189 Katlenburg-Lindau
+%% Germany
+%%
+%% E-mail:
+%% SPAN-- nsp::linmpi::daly (note nsp also known as ecd1)
+%% Internet-- daly@linmpi.dnet.gwdg.de
+%%-----------------------------------------------------------
+%% \CharacterTable
+%% {Upper-case \A\B\C\D\E\F\G\H\I\J\K\L\M\N\O\P\Q\R\S\T\U\V\W\X\Y\Z
+%% Lower-case \a\b\c\d\e\f\g\h\i\j\k\l\m\n\o\p\q\r\s\t\u\v\w\x\y\z
+%% Digits \0\1\2\3\4\5\6\7\8\9
+%% Exclamation \! Double quote \" Hash (number) \#
+%% Dollar \$ Percent \% Ampersand \&
+%% Acute accent \' Left paren \( Right paren \)
+%% Asterisk \* Plus \+ Comma \,
+%% Minus \- Point \. Solidus \/
+%% Colon \: Semicolon \; Less than \<
+%% Equals \= Greater than \> Question mark \?
+%% Commercial at \@ Left bracket \[ Backslash \\
+%% Right bracket \] Circumflex \^ Underscore \_
+%% Grave accent \` Left brace \{ Vertical bar \|
+%% Right brace \} Tilde \~}
+%%---------------------------------------------------------------------
+ % This is an author-year citation style bibliography. As such, it is
+ % non-standard LaTeX, and requires a style option to function properly.
+ % The appropriate style file is: harvard.sty
+ % The form of the bibitem entries is
+ % \harvarditem[Jones et al.]{Jones, Baker, and Smith}{1990}{key}...
+ % There are style changes that may be specified in the text with
+ % the command \citationstyle.
+ % \citeasnoun{key} ==>> Jones et al. (1990)
+ % \cite{key} ==>> (Jones et al. 1990)
+ % \cite[chap. 2]{key} ==>> (Jones et al. 1990, chap. 2)
+ % On the first citation of any work, the full author list is used.
+%%---------------------------------------------------------------------
+
+ENTRY
+ { address
+ author
+ booktitle
+ chapter
+ edition
+ editor
+ howpublished
+ institution
+ journal
+ key
+ month
+ note
+ number
+ organization
+ pages
+ publisher
+ school
+ series
+ title
+ type
+ volume
+ year
+ }
+ {}
+ { label extra.label sort.label } %%%KCB: removed long.label
+
+INTEGERS { output.state before.all mid.sentence after.sentence after.block }
+
+%%%KCB: new output state, no.comma
+INTEGERS { no.comma}
+
+FUNCTION {init.state.consts}
+{ #0 'before.all :=
+ #1 'mid.sentence :=
+ #2 'after.sentence :=
+ #3 'after.block :=
+ #4 'no.comma := %%%KCB
+}
+
+STRINGS { s t }
+
+
+%%%KCB: revise to use new output state
+FUNCTION {output.nonnull}
+{ 's :=
+ output.state no.comma = %%%KCB: if in this state, write a space
+ { " " * write$ before.all 'output.state := } %%%KCB
+ { output.state mid.sentence = %%%KCB: add brace at beginning
+ { ", " * write$ }
+ { output.state after.block =
+ { add.period$ write$
+ newline$
+ "\newblock " write$
+ }
+ { output.state before.all =
+ 'write$
+ { add.period$ " " * write$ }
+ if$
+ }
+ if$
+ }
+ if$
+ mid.sentence 'output.state :=
+ } %%%KCB: matching brace
+ if$ %%%KCB: test for no.comma state
+ s
+}
+
+FUNCTION {output}
+{ duplicate$ empty$
+ 'pop$
+ 'output.nonnull
+ if$
+}
+
+FUNCTION {output.check}
+{ 't :=
+ duplicate$ empty$
+ { pop$ "empty " t * " in " * cite$ * warning$ }
+ 'output.nonnull
+ if$
+}
+
+FUNCTION {output.bibitem}
+{ newline$
+ "\harvarditem[" write$
+ label write$
+ "]{" label * "}{" * write$ %%%KCB: replaced long.label with label
+ year duplicate$ empty$
+ { pop$ "????" }
+ 'skip$
+ if$
+ extra.label * "}{" * write$
+ cite$ write$
+ "}" write$
+ newline$
+ ""
+ before.all 'output.state :=
+}
+
+FUNCTION {fin.entry}
+{ add.period$
+ write$
+ newline$
+}
+
+FUNCTION {new.block}
+{ output.state before.all =
+ 'skip$
+ { after.block 'output.state := }
+ if$
+}
+
+FUNCTION {new.sentence}
+{ output.state after.block =
+ 'skip$
+ { output.state before.all =
+ 'skip$
+ { after.sentence 'output.state := }
+ if$
+ }
+ if$
+}
+
+FUNCTION {not}
+{ { #0 }
+ { #1 }
+ if$
+}
+
+FUNCTION {and}
+{ 'skip$
+ { pop$ #0 }
+ if$
+}
+
+FUNCTION {or}
+{ { pop$ #1 }
+ 'skip$
+ if$
+}
+
+FUNCTION {new.block.checkb}
+{ empty$
+ swap$ empty$
+ and
+ 'skip$
+ 'new.block
+ if$
+}
+
+FUNCTION {field.or.null}
+{ duplicate$ empty$
+ { pop$ "" }
+ 'skip$
+ if$
+}
+
+FUNCTION {emphasize}
+{ duplicate$ empty$
+ { pop$ "" }
+ { "\emph{" swap$ * "}" * }
+ if$
+}
+
+INTEGERS { nameptr namesleft numnames }
+
+%%%KCB: Here is a portable set of routines to check for repeated authors
+%%% and replace them with a 3 em dash
+%%% According to _Chicago_Manual_of_Style_, a dash is used to
+%%% replace an author's name only if the entire list is the same, so:
+%%% Compare names. See if prev.name is identical to CurrList.
+%%% If not, then just output CurrList.
+%%% If so, replace with \BySame
+%%% I use a conservative matching scheme,
+%%% and check to see if full names are identical,
+%%% even though we only use initials.
+%%% This may mean that you have to correct your .bib database
+%%% to ensure consistency.
+%%%
+%%% sample usage:
+%%% search for {format.author}
+
+INTEGERS {currNum prevNum}
+
+STRINGS {PrevList CurrList prev.name PrevTemp CurrTemp}
+
+%%%KCB: Initialize prev.name ot some nonsense string:
+
+FUNCTION {init.prev.name}
+{
+"My puppy Harley" 'prev.name :=
+}
+
+%%%KCB: Must EXECUTE {init.prev.name} after READ
+
+%%%KCB: compare.names
+FUNCTION {compare.names}
+{ prev.name 'PrevList :=
+ duplicate$ 'prev.name := % make current list the new previous list
+ 'CurrList :=
+ PrevList num.names$ 'prevNum :=
+ CurrList num.names$ 'currNum :=
+%
+ prevNum currNum = %% If prevNum = currNum
+ { "" 'PrevTemp := %% Then take this branch
+ "" 'CurrTemp :=
+ #1 'nameptr := %%% start with first name in each list
+ { nameptr prevNum > not }%% while nameptr <= prevNum
+%% Format both lists the same way.
+%% Use full names: bib file might need to be corrected
+ {
+ PrevList nameptr "{ff }{vv~}{ll}{ jj}" format.name$ PrevTemp * 'PrevTemp :=
+ CurrList nameptr "{ff }{vv~}{ll}{ jj}" format.name$ CurrTemp * 'CurrTemp :=
+ nameptr #1 + 'nameptr := }
+ while$
+%% Now compare :
+ PrevTemp CurrTemp = %% If PrevTemp = CurrTemp
+ { "\BySame{}" }
+ { CurrList }
+ if$
+ }
+ { CurrList } %% If not prevNum = currNum
+ if$ %% If prevNum = currNum
+}
+
+%%%
+%%%
+%%%KCB: End of new routine.
+
+
+FUNCTION {format.names}
+{ 's :=
+ #1 'nameptr :=
+ s num.names$ 'numnames :=
+ numnames 'namesleft :=
+ { namesleft #0 > }
+ { nameptr #1 >
+ { s nameptr "{ff~}{vv~}{ll}{, jj}" format.name$ 't := }
+ { s nameptr "{vv~}{ll}{, jj}{, ff}" format.name$ 't := }
+ if$
+ nameptr #1 >
+ {
+ namesleft #1 >
+ { ", " * t * }
+ {
+ ", " *
+ t "others" =
+ { " et~al." * }
+ { " {and} " * t * } %%%CDC: removed \small
+ if$
+ }
+ if$
+ }
+ 't
+ if$
+ nameptr #1 + 'nameptr :=
+ namesleft #1 - 'namesleft :=
+ }
+ while$
+}
+
+
+FUNCTION {format.names.ed}
+{ 's :=
+ #1 'nameptr :=
+ s num.names$ 'numnames :=
+ numnames 'namesleft :=
+ { namesleft #0 > }
+ { s nameptr
+ "{ff~}{vv~}{ll}{, jj}"
+ format.name$ 't :=
+ nameptr #1 >
+ {
+ namesleft #1 >
+ { ", " * t * }
+ {
+ ", " *
+ t "others" =
+ { " et~al." * }
+ { " {and} " * t * } %%%CDC: removed \small
+ if$
+ }
+ if$
+ }
+ 't
+ if$
+ nameptr #1 + 'nameptr :=
+ namesleft #1 - 'namesleft :=
+ }
+ while$
+}
+
+FUNCTION {format.key}
+{ empty$
+ { key field.or.null }
+ { "" }
+ if$
+}
+
+FUNCTION {format.authors}
+{ author empty$
+ { "" }
+ { "\textsc{" author compare.names format.names * "} " * }
+ if$
+}
+
+FUNCTION {format.editors}
+{ editor empty$
+ { "" }
+ { "\textsc{" editor compare.names format.names * " }" *
+ editor num.names$ #1 >
+ { " (eds.)" * }
+ { " (ed.)" * }
+ if$
+ }
+ if$
+}
+
+FUNCTION {format.in.editors}
+{ editor empty$
+ { "" }
+ { editor format.names.ed
+ }
+ if$
+}
+
+FUNCTION {format.title}
+{ title empty$
+ { "" }
+ { title
+ "``" swap$ * ",'' " *
+ }
+no.comma 'output.state := %%%KCB:
+ if$
+}
+
+FUNCTION {n.dashify}
+{ 't :=
+ ""
+ { t empty$ not }
+ { t #1 #1 substring$ "-" =
+ { t #1 #2 substring$ "--" = not
+ { "--" *
+ t #2 global.max$ substring$ 't :=
+ }
+ { { t #1 #1 substring$ "-" = }
+ { "-" *
+ t #2 global.max$ substring$ 't :=
+ }
+ while$
+ }
+ if$
+ }
+ { t #1 #1 substring$ *
+ t #2 global.max$ substring$ 't :=
+ }
+ if$
+ }
+ while$
+}
+
+FUNCTION {word.in}
+{ "in " }
+
+FUNCTION {format.date}
+{ year duplicate$ empty$
+ { "empty year in " cite$ * "; set to ????" * warning$
+ pop$ "????" }
+ 'skip$
+ if$
+ " (" swap$ * extra.label * "):" * %%%KCB: added :
+ before.all 'output.state := %%%KCB:
+}
+
+FUNCTION {format.btitle}
+{ title emphasize
+no.comma 'output.state := %%%KCB:
+}
+
+FUNCTION {tie.or.space.connect}
+{ duplicate$ text.length$ #3 <
+ { "~" }
+ { " " }
+ if$
+ swap$ * *
+}
+
+FUNCTION {either.or.check}
+{ empty$
+ 'pop$
+ { "can't use both " swap$ * " fields in " * cite$ * warning$ }
+ if$
+}
+
+FUNCTION {format.bvolume}
+{ volume empty$
+ { "" }
+ { "vol." volume tie.or.space.connect
+ series empty$
+ 'skip$
+ { " of " * series emphasize * }
+ if$
+ "volume and number" number either.or.check
+ }
+ if$
+}
+
+FUNCTION {format.number.series}
+{ volume empty$
+ { number empty$
+ { series field.or.null }
+ { output.state mid.sentence =
+ { "no." }
+ { "No." }
+ if$
+ number tie.or.space.connect
+ series empty$
+ { "there's a number but no series in " cite$ * warning$ }
+ { " in " * series * }
+ if$
+ }
+ if$
+ }
+ { "" }
+ if$
+}
+
+FUNCTION {format.edition}
+{ edition empty$
+ { "" }
+ { output.state mid.sentence =
+ { edition "l" change.case$ " edn." * }
+ { edition "t" change.case$ " edn." * }
+ if$
+ }
+ if$
+}
+
+INTEGERS { multiresult }
+
+FUNCTION {multi.page.check}
+{ 't :=
+ #0 'multiresult :=
+ { multiresult not
+ t empty$ not
+ and
+ }
+ { t #1 #1 substring$
+ duplicate$ "-" =
+ swap$ duplicate$ "," =
+ swap$ "+" =
+ or or
+ { #1 'multiresult := }
+ { t #2 global.max$ substring$ 't := }
+ if$
+ }
+ while$
+ multiresult
+}
+
+FUNCTION {format.pages}
+{ pages empty$
+ { "" }
+ { pages multi.page.check
+ { "pp." pages n.dashify tie.or.space.connect }
+ { "p." pages tie.or.space.connect }
+ if$
+ }
+ if$
+}
+
+FUNCTION {format.vol.num.pages}
+{ volume field.or.null
+ number empty$
+ 'skip$
+ { "(" number * ")" * *
+ volume empty$
+ { "there's a number but no volume in " cite$ * warning$ }
+ 'skip$
+ if$
+ }
+ if$
+ pages empty$
+ 'skip$
+ { duplicate$ empty$
+ { pop$ format.pages }
+ { ", " * pages n.dashify * }
+ if$
+ }
+ if$
+}
+
+FUNCTION {format.chapter.pages}
+{ chapter empty$
+ 'format.pages
+ { type empty$
+ { "chap." }
+ { type "l" change.case$ }
+ if$
+ chapter tie.or.space.connect
+ pages empty$
+ 'skip$
+ { ", " * format.pages * }
+ if$
+ }
+ if$
+}
+
+FUNCTION {format.in.ed.booktitle}
+{ booktitle empty$
+ { "" }
+ { editor empty$
+ { word.in booktitle emphasize * }
+ { word.in booktitle emphasize * ", ed. by " * %%%KCB: abbrv ed.
+ format.in.editors * }
+ if$
+ }
+ if$
+}
+
+FUNCTION {format.thesis.type}
+{ type empty$
+ 'skip$
+ { pop$
+ type "t" change.case$
+ }
+ if$
+}
+
+FUNCTION {format.tr.number}
+{ type empty$
+ { "Discussion Paper" } %%%KCB: changed default
+ 'type
+ if$
+ number empty$
+ { "t" change.case$ }
+ { number tie.or.space.connect }
+ if$
+}
+
+FUNCTION {format.article.crossref}
+{
+ word.in
+ "\cite{" * crossref * "}" *
+}
+
+FUNCTION {format.book.crossref}
+{ volume empty$
+ { "empty volume in " cite$ * "'s crossref of " * crossref * warning$
+ word.in
+ }
+ { "vol." volume tie.or.space.connect
+ " of " *
+ }
+ if$
+ "\cite{" * crossref * "}" *
+}
+
+FUNCTION {format.incoll.inproc.crossref}
+{
+ word.in
+ "\cite{" * crossref * "}" *
+}
+
+FUNCTION {article}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ format.date "year" output.check
+ format.title "title" output.check
+ crossref missing$
+ { journal emphasize "journal" output.check
+ format.vol.num.pages output
+ }
+ { format.article.crossref output.nonnull
+ format.pages output
+ }
+ if$
+ note output
+ fin.entry
+}
+
+FUNCTION {book}
+{ output.bibitem
+ author empty$
+ { format.editors "author and editor" output.check
+ editor format.key output
+ }
+ { format.authors output.nonnull
+ crossref missing$
+ { "author and editor" editor either.or.check }
+ 'skip$
+ if$
+ }
+ if$
+ format.date "year" output.check
+ format.btitle "title" output.check
+ mid.sentence 'output.state := %%%KCB:
+ crossref missing$
+ { format.bvolume output
+ format.number.series output
+ new.sentence
+ publisher "publisher" output.check
+ address output
+ }
+ {
+ format.book.crossref output.nonnull
+ }
+ if$
+ format.edition output
+ note output
+ fin.entry
+}
+
+FUNCTION {booklet}
+{ output.bibitem
+ format.authors output
+ author format.key output
+ format.date "year" output.check
+ format.title "title" output.check
+ howpublished output
+ address output
+ note output
+ fin.entry
+}
+
+FUNCTION {inbook}
+{ output.bibitem
+ author empty$
+ { format.editors "author and editor" output.check
+ editor format.key output
+ }
+ { format.authors output.nonnull
+ crossref missing$
+ { "author and editor" editor either.or.check }
+ 'skip$
+ if$
+ }
+ if$
+ format.date "year" output.check
+ format.btitle "title" output.check
+ crossref missing$
+ { format.bvolume output
+ format.chapter.pages "chapter and pages" output.check
+ format.number.series output
+ new.sentence
+ publisher "publisher" output.check
+ address output
+ }
+ { format.chapter.pages "chapter and pages" output.check
+ format.book.crossref output.nonnull
+ }
+ if$
+ format.edition output
+ note output
+ fin.entry
+}
+
+FUNCTION {incollection}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ format.date "year" output.check
+ format.title "title" output.check
+ crossref missing$
+ { format.in.ed.booktitle "booktitle" output.check
+ format.bvolume output
+ format.number.series output
+ format.chapter.pages output
+ new.sentence
+ publisher "publisher" output.check
+ address output
+ format.edition output
+ }
+ { format.incoll.inproc.crossref output.nonnull
+ format.chapter.pages output
+ }
+ if$
+ note output
+ fin.entry
+}
+
+FUNCTION {inproceedings}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ format.date "year" output.check
+ format.title "title" output.check
+ crossref missing$
+ { format.in.ed.booktitle "booktitle" output.check
+ format.bvolume output
+ format.number.series output
+ format.pages output
+ address output
+ new.sentence
+ organization output
+ publisher output
+ }
+ { format.incoll.inproc.crossref output.nonnull
+ format.pages output
+ }
+ if$
+ note output
+ fin.entry
+}
+
+FUNCTION {conference} { inproceedings }
+
+%%%KCB: special issue of a journal with a special editor
+
+FUNCTION {issue}
+{ output.bibitem
+ format.editors output
+ editor format.key output
+ format.date "year" output.check
+ format.btitle "title" output.check
+ crossref missing$
+ { journal emphasize "journal" output.check
+ format.vol.num.pages output
+ }
+ { format.article.crossref output.nonnull
+ format.pages output
+ }
+ if$
+ note output
+ fin.entry
+}
+
+FUNCTION {manual}
+{ output.bibitem
+ format.authors output
+ author format.key output
+ format.date "year" output.check
+ format.btitle "title" output.check
+ organization output
+ address output
+ format.edition output
+ note output
+ fin.entry
+}
+
+FUNCTION {mastersthesis}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ format.date "year" output.check
+ format.title "title" output.check
+ "Master's thesis" format.thesis.type output.nonnull
+ school "school" output.check
+ address output
+ note output
+ fin.entry
+}
+
+FUNCTION {misc}
+{ output.bibitem
+ format.authors output
+ author format.key output
+ format.date "year" output.check
+ format.title output
+ howpublished output
+ note output
+ fin.entry
+}
+
+FUNCTION {phdthesis}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ format.date "year" output.check
+ format.title "title" output.check
+ "Ph.D. thesis" format.thesis.type output.nonnull
+ school "school" output.check
+ address output
+ note output
+ fin.entry
+}
+
+FUNCTION {proceedings}
+{ output.bibitem
+ format.editors output
+ editor format.key output
+ format.date "year" output.check
+ format.btitle "title" output.check
+ format.bvolume output
+ format.number.series output
+ address output
+ new.sentence
+ organization output
+ publisher output
+ note output
+ fin.entry
+}
+
+FUNCTION {techreport}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ format.date "year" output.check
+ format.title "title" output.check
+ format.tr.number output.nonnull
+ institution "institution" output.check
+ address output
+ note output
+ fin.entry
+}
+
+FUNCTION {unpublished}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ format.date "year" output.check
+ format.title "title" output.check
+ note "note" output.check
+ fin.entry
+}
+
+FUNCTION {default.type} { misc }
+
+MACRO {jan} {"January"}
+
+MACRO {feb} {"February"}
+
+MACRO {mar} {"March"}
+
+MACRO {apr} {"April"}
+
+MACRO {may} {"May"}
+
+MACRO {jun} {"June"}
+
+MACRO {jul} {"July"}
+
+MACRO {aug} {"August"}
+
+MACRO {sep} {"September"}
+
+MACRO {oct} {"October"}
+
+MACRO {nov} {"November"}
+
+MACRO {dec} {"December"}
+
+%%%KCB: removed computer science journal macros
+%%%KCB: added following macros for editions
+MACRO {first} {"1st "}
+MACRO {second} {"2d "}
+MACRO {third} {"3d "}
+MACRO {fourth} {"4th "}
+MACRO {fifth} {"5th "}
+MACRO {sixth} {"6th"}
+MACRO {seventh} {"7th "}
+MACRO {eighth} {"8th "}
+MACRO {ninth} {"9th "}
+MACRO {tenth} {"10th "}
+
+READ
+
+FUNCTION {sortify}
+{ purify$
+ "l" change.case$
+}
+
+INTEGERS { len }
+
+FUNCTION {chop.word}
+{ 's :=
+ 'len :=
+ s #1 len substring$ =
+ { s len #1 + global.max$ substring$ }
+ 's
+ if$
+}
+
+
+%%%KCB: Change format of labels:
+FUNCTION {format.lab.names}
+{ 's :=
+ #1 'nameptr :=
+ s num.names$ 'numnames :=
+ numnames 'namesleft :=
+ { namesleft #0 > }
+ { s nameptr "{vv~}{ll}" format.name$ 't :=
+ nameptr #1 >
+ { namesleft #1 >
+ { ", " * t * }
+ { numnames #2 >
+ { "," * }
+ 'skip$
+ if$
+ t "others" =
+ { " et~al." * }
+ { " and " * t * }
+ if$
+ }
+ if$
+ }
+ 't
+ if$
+ nameptr #1 + 'nameptr :=
+ namesleft #1 - 'namesleft :=
+ }
+ while$
+}
+
+%%%KCB: removed FUNCTION {format.long.lab.names}
+
+FUNCTION {author.key.label}
+{ author empty$
+ { key empty$
+ { cite$ #1 #3 substring$ }
+ 'key
+ if$
+ }
+ { author format.lab.names }
+ if$
+}
+
+FUNCTION {author.editor.key.label}
+{ author empty$
+ { editor empty$
+ { key empty$
+ { cite$ #1 #3 substring$ }
+ 'key
+ if$
+ }
+ { editor format.lab.names }
+ if$
+ }
+ { author format.lab.names }
+ if$
+}
+
+FUNCTION {editor.key.label}
+{ editor empty$
+ { key empty$
+ { cite$ #1 #3 substring$ }
+ 'key
+ if$
+ }
+ { editor format.lab.names }
+ if$
+}
+
+%%%KCB: use entire year field as label
+
+FUNCTION {calc.label}
+{ type$ "book" =
+ type$ "inbook" =
+ or
+ type$ "issue" = %%%KCB: need for new entry type
+ or %%%KCB
+ 'author.editor.key.label
+ { type$ "proceedings" =
+ 'editor.key.label
+ 'author.key.label
+ if$
+ }
+ if$
+ ", "
+ *
+ year field.or.null
+ *
+ 'label :=
+}
+
+FUNCTION {calc.short.label}
+{ type$ "book" =
+ type$ "inbook" =
+ or
+ 'author.editor.key.label
+ { type$ "proceedings" =
+ 'editor.key.label
+ 'author.key.label
+ if$
+ }
+ if$
+ 'label :=
+}
+
+%%%KCB: removed FUNCTION {calc.long.label}
+
+FUNCTION {sort.format.names}
+{ 's :=
+ #1 'nameptr :=
+ ""
+ s num.names$ 'numnames :=
+ numnames 'namesleft :=
+ { namesleft #0 > }
+ { nameptr #1 >
+ { " " * }
+ 'skip$
+ if$
+ s nameptr
+ "{vv{ } }{ll{ }}{ f{ }}{ jj{ }}"
+ format.name$ 't :=
+ nameptr numnames = t "others" = and
+ { "et al" * }
+ { t sortify * }
+ if$
+ nameptr #1 + 'nameptr :=
+ namesleft #1 - 'namesleft :=
+ }
+ while$
+}
+
+FUNCTION {sort.format.title}
+{ 't :=
+ "A " #2
+ "An " #3
+ "The " #4 t chop.word
+ chop.word
+ chop.word
+ sortify
+ #1 global.max$ substring$
+}
+
+FUNCTION {author.sort}
+{ author empty$
+ { key empty$
+ { "to sort, need author or key in " cite$ * warning$
+ ""
+ }
+ { key sortify }
+ if$
+ }
+ { author sort.format.names }
+ if$
+}
+
+FUNCTION {author.editor.sort}
+{ author empty$
+ { editor empty$
+ { key empty$
+ { "to sort, need author, editor, or key in " cite$ * warning$
+ ""
+ }
+ { key sortify }
+ if$
+ }
+ { editor sort.format.names }
+ if$
+ }
+ { author sort.format.names }
+ if$
+}
+
+FUNCTION {editor.sort}
+{ editor empty$
+ { key empty$
+ { "to sort, need editor or key in " cite$ * warning$
+ ""
+ }
+ { key sortify }
+ if$
+ }
+ { editor sort.format.names }
+ if$
+}
+
+FUNCTION {presort}
+{ calc.label
+ label sortify
+ " "
+ *
+ type$ "book" =
+ type$ "inbook" =
+ or
+ type$ "issue" = %%%KCB: need for new entry type
+ or %%%KCB
+ 'author.editor.sort
+ { type$ "proceedings" =
+ 'editor.sort
+ 'author.sort
+ if$
+ }
+ if$
+ #1 entry.max$ substring$
+ 'sort.label :=
+ sort.label
+ *
+ " "
+ *
+ title field.or.null
+ sort.format.title
+ *
+ #1 entry.max$ substring$
+ 'sort.key$ :=
+}
+
+ITERATE {presort}
+
+SORT
+
+STRINGS { last.label next.extra }
+
+INTEGERS { last.extra.num }
+
+FUNCTION {initialize.extra.label.stuff}
+{ #0 int.to.chr$ 'last.label :=
+ "" 'next.extra :=
+ #0 'last.extra.num :=
+}
+
+FUNCTION {forward.pass}
+{ last.label label =
+ { last.extra.num #1 + 'last.extra.num :=
+ last.extra.num int.to.chr$ 'extra.label :=
+ }
+ { "a" chr.to.int$ 'last.extra.num :=
+ "" 'extra.label :=
+ label 'last.label :=
+ }
+ if$
+}
+
+FUNCTION {reverse.pass}
+{ next.extra "b" =
+ { "a" 'extra.label := }
+ 'skip$
+ if$
+ extra.label 'next.extra :=
+}
+
+EXECUTE {initialize.extra.label.stuff}
+
+EXECUTE {init.prev.name} %%%KCB: needed for replacing repeated authors
+
+ITERATE {forward.pass}
+
+REVERSE {reverse.pass}
+
+FUNCTION {bib.sort.order}
+{ sort.label
+ " "
+ *
+ year field.or.null sortify
+ *
+ " "
+ *
+ title field.or.null
+ sort.format.title
+ *
+ #1 entry.max$ substring$
+ 'sort.key$ :=
+ calc.short.label
+%%%KCB: removed calc.long.label
+}
+
+ITERATE {bib.sort.order}
+
+SORT
+
+FUNCTION {begin.bib}
+{ preamble$ empty$
+ 'skip$
+ { preamble$ write$ newline$ }
+ if$
+%%%KCB: add defn of \BySame, can be overridden in document
+"\ifx\undefined\BySame" write$ newline$
+ "\newcommand{\BySame}{\leavevmode\rule[.5ex]{3em}{.5pt}\ }"
+ write$ newline$
+ "\fi" write$ newline$
+%%% KCB: For those who still cling to LaTeX 2.09
+"\ifx\undefined\textsc" write$ newline$
+ "\newcommand{\textsc}[1]{{\sc #1}}"
+ write$ newline$
+ "\newcommand{\emph}[1]{{\em #1\/}}"
+ write$ newline$
+"\let\tmpsmall\small" write$ newline$
+"\renewcommand{\small}{\tmpsmall\sc}" write$ newline$
+ "\fi" write$ newline$
+%%%
+ "\begin{thebibliography}{}" write$ newline$
+}
+
+EXECUTE {begin.bib}
+
+EXECUTE {init.state.consts}
+
+ITERATE {call.type$}
+
+FUNCTION {end.bib}
+{ newline$
+ "\end{thebibliography}" write$ newline$
+"Done." top$ %%%KCB: Signal end for NeXTeX's TexView
+}
+
+EXECUTE {end.bib}
+%% End of customized bst file
diff --git a/Documentation/HARKmanual/econtex.cls b/Documentation/NARK/econtex.cls
similarity index 97%
rename from Documentation/HARKmanual/econtex.cls
rename to Documentation/NARK/econtex.cls
index 4d9605a07..fdf0f7726 100644
--- a/Documentation/HARKmanual/econtex.cls
+++ b/Documentation/NARK/econtex.cls
@@ -10,13 +10,13 @@
%%% Further information is available at
%%% http://www.uni-kiel.de/agrarpol/ahenningsen/latex-bibtex.html
%%%
-%%% This document class is based on the "scrreprt" class
+%%% This document class is based on the "scrartcl" class
%%% from the KOMA script bundle. For documentation, Google KOMA-Script Documentation
%%%
%%% The corresponding BibTeX style file "econtex.bst" should be used
%%% for the bibliography: \bibliographystyle{econtex}
%%%
-%%% In addition to the options for scrreprt, this package includes an additional option
+%%% In addition to the options for scrartcl, this package includes an additional option
%%% titlepage = include a title page (with command "\maketitle", or "\maketitleWithForcedDate{}")
%%%
%%% If an abstract is defined by \begin{abstract}\end{abstract} before \maketitle is invoked,
@@ -47,10 +47,10 @@
\setcounter{IncludeTitlePage}{0}
\DeclareOption{titlepage}{\setcounter{IncludeTitlePage}{1}}
-% pass any options on to the scrreprt class and load this class with some options
-\DeclareOption*{\PassOptionsToClass{\CurrentOption}{scrreprt}}
+% pass any options on to the scrartcl class and load this class with some options
+\DeclareOption*{\PassOptionsToClass{\CurrentOption}{scrartcl}}
\ProcessOptions\relax
-\LoadClass[fontsize=12pt,english,numbers=noenddot,captions=tableheading,captions=nooneline]{scrreprt}
+\LoadClass[fontsize=12pt,english,numbers=noenddot,captions=tableheading,captions=nooneline]{scrartcl}
% pointlessnumbers = do not add final dot after ordinary section numbers
% tablecaptionabove = use \captionabove for captions above rather than below tables
% noonelinecaption = do not treat one-line captions differently
diff --git a/Documentation/HARKmanual/ContributingToHARK/econtex.sty b/Documentation/NARK/econtex.sty
similarity index 100%
rename from Documentation/HARKmanual/ContributingToHARK/econtex.sty
rename to Documentation/NARK/econtex.sty
diff --git a/Documentation/NARK/econtexBibMake.tex b/Documentation/NARK/econtexBibMake.tex
new file mode 100755
index 000000000..dafc34bf8
--- /dev/null
+++ b/Documentation/NARK/econtexBibMake.tex
@@ -0,0 +1,7 @@
+
+% \write18{if [ ! -f economics.bib ]; then touch economics.bib ; fi}
+\write18{if [ ! -f \jobname.bib ]; then touch \jobname.bib ; fi}
+\write18{if [ ! -f \jobname-Add.bib ]; then touch \jobname-Add.bib ; fi}
+
+\bibliographystyle{econtex}
+\bibliography{economics,\jobname,\jobname-Add}
diff --git a/Documentation/HARKmanual/ContributingToHARK/econtexSetup.sty b/Documentation/NARK/econtexSetup.sty
similarity index 100%
rename from Documentation/HARKmanual/ContributingToHARK/econtexSetup.sty
rename to Documentation/NARK/econtexSetup.sty
diff --git a/Documentation/HARKmanual/ContributingToHARK/econtexShortcuts.sty b/Documentation/NARK/econtexShortcuts.sty
old mode 100644
new mode 100755
similarity index 100%
rename from Documentation/HARKmanual/ContributingToHARK/econtexShortcuts.sty
rename to Documentation/NARK/econtexShortcuts.sty
diff --git a/Documentation/UserGuidePic.jpg b/Documentation/UserGuidePic.jpg
deleted file mode 100644
index 19e13846d..000000000
Binary files a/Documentation/UserGuidePic.jpg and /dev/null differ
diff --git a/Documentation/UserGuidePic.pdf b/Documentation/UserGuidePic.pdf
deleted file mode 100644
index 70f495951..000000000
Binary files a/Documentation/UserGuidePic.pdf and /dev/null differ
diff --git a/Documentation/HARKmanual/HARKmanual.pdf b/Documentation/_static/HARKmanual.pdf
similarity index 100%
rename from Documentation/HARKmanual/HARKmanual.pdf
rename to Documentation/_static/HARKmanual.pdf
diff --git a/Documentation/api-consumptionsaving.rst b/Documentation/api-consumptionsaving.rst
new file mode 100644
index 000000000..275e9a906
--- /dev/null
+++ b/Documentation/api-consumptionsaving.rst
@@ -0,0 +1,14 @@
+HARK.ConsumptionSaving
+===================
+
+.. toctree::
+ :maxdepth: 2
+
+ api-cs-ConsAggShockModel
+ api-cs-ConsGenIncProcessModel
+ api-cs-ConsIndShockModel
+ api-cs-ConsMarkovModel
+ api-cs-ConsPortfolioModel
+ api-cs-ConsPrefShochModel
+ api-cs-ConsRepAgentModel
+ api-cs-TractableBufferStockModel
diff --git a/Documentation/api-core.rst b/Documentation/api-core.rst
new file mode 100644
index 000000000..36a718efc
--- /dev/null
+++ b/Documentation/api-core.rst
@@ -0,0 +1,5 @@
+HARK.core
+=========
+
+.. automodule:: HARK.core
+ :members:
diff --git a/Documentation/api-cs-ConsAggShockModel.rst b/Documentation/api-cs-ConsAggShockModel.rst
new file mode 100644
index 000000000..48fc6c99e
--- /dev/null
+++ b/Documentation/api-cs-ConsAggShockModel.rst
@@ -0,0 +1,6 @@
+HARK.ConsumptionSaving.ConsAggShockModel
+========================================
+
+.. automodule:: HARK.ConsumptionSaving.ConsAggShockModel
+ :members:
+
diff --git a/Documentation/api-cs-ConsGenIncProcessModel.rst b/Documentation/api-cs-ConsGenIncProcessModel.rst
new file mode 100644
index 000000000..9bb4d1334
--- /dev/null
+++ b/Documentation/api-cs-ConsGenIncProcessModel.rst
@@ -0,0 +1,5 @@
+HARK.ConsumptionSaving.ConsGenIncProcessModel
+================================================
+
+.. automodule:: HARK.ConsumptionSaving.ConsGenIncProcessModel
+ :members:
diff --git a/Documentation/api-cs-ConsIndShockModel.rst b/Documentation/api-cs-ConsIndShockModel.rst
new file mode 100644
index 000000000..6b9ec13ad
--- /dev/null
+++ b/Documentation/api-cs-ConsIndShockModel.rst
@@ -0,0 +1,7 @@
+HARK.ConsumptionSaving.ConsIndShockModel
+========================================
+
+.. automodule:: HARK.ConsumptionSaving.ConsIndShockModel
+ :members:
+
+
diff --git a/Documentation/api-cs-ConsMarkovModel.rst b/Documentation/api-cs-ConsMarkovModel.rst
new file mode 100644
index 000000000..eb904daa6
--- /dev/null
+++ b/Documentation/api-cs-ConsMarkovModel.rst
@@ -0,0 +1,5 @@
+HARK.ConsumptionSaving.ConsMarkovModel
+======================================
+
+.. automodule:: HARK.ConsumptionSaving.ConsMarkovModel
+ :members:
diff --git a/Documentation/api-cs-ConsMedModel.rst b/Documentation/api-cs-ConsMedModel.rst
new file mode 100644
index 000000000..e2aff86cc
--- /dev/null
+++ b/Documentation/api-cs-ConsMedModel.rst
@@ -0,0 +1,5 @@
+HARK.ConsumptionSaving.ConsMedModel
+=====================================
+
+.. automodule:: HARK.ConsumptionSaving.ConsMedModel
+ :members:
diff --git a/Documentation/api-cs-ConsPortfolioModel.rst b/Documentation/api-cs-ConsPortfolioModel.rst
new file mode 100644
index 000000000..b4fd0482e
--- /dev/null
+++ b/Documentation/api-cs-ConsPortfolioModel.rst
@@ -0,0 +1,5 @@
+HARK.ConsumptionSaving.ConsPortfolioModel
+=========================================
+
+.. automodule:: HARK.ConsumptionSaving.ConsPortfolioModel
+ :members:
diff --git a/Documentation/api-cs-ConsPrefShockModel.rst b/Documentation/api-cs-ConsPrefShockModel.rst
new file mode 100644
index 000000000..c0a6acd13
--- /dev/null
+++ b/Documentation/api-cs-ConsPrefShockModel.rst
@@ -0,0 +1,6 @@
+HARK.ConsumptionSaving.ConsPrefShockModel
+=========================================
+
+.. automodule:: HARK.ConsumptionSaving.ConsPrefShockModel
+ :members:
+
diff --git a/Documentation/api-cs-ConsRepAgentModel.rst b/Documentation/api-cs-ConsRepAgentModel.rst
new file mode 100644
index 000000000..fba7e08b9
--- /dev/null
+++ b/Documentation/api-cs-ConsRepAgentModel.rst
@@ -0,0 +1,6 @@
+HARK.ConsumptionSaving.ConsRepAgentModel
+========================================
+
+.. automodule:: HARK.ConsumptionSaving.ConsRepAgentModel
+ :members:
+
diff --git a/Documentation/api-cs-TractableBufferStockModel.rst b/Documentation/api-cs-TractableBufferStockModel.rst
new file mode 100644
index 000000000..984651786
--- /dev/null
+++ b/Documentation/api-cs-TractableBufferStockModel.rst
@@ -0,0 +1,5 @@
+HARK.ConsumptionSaving.TractableBufferStockModel
+================================================
+
+.. automodule:: HARK.ConsumptionSaving.TractableBufferStockModel
+ :members:
diff --git a/Documentation/api-dcegm.rst b/Documentation/api-dcegm.rst
new file mode 100644
index 000000000..81260ff36
--- /dev/null
+++ b/Documentation/api-dcegm.rst
@@ -0,0 +1,5 @@
+HARK.dcegm
+===========
+
+.. automodule:: HARK.dcegm
+ :members:
diff --git a/Documentation/api-estimation.rst b/Documentation/api-estimation.rst
new file mode 100644
index 000000000..637677c6d
--- /dev/null
+++ b/Documentation/api-estimation.rst
@@ -0,0 +1,5 @@
+HARK.estimation
+=================
+
+.. automodule:: HARK.estimation
+ :members:
diff --git a/Documentation/api-interpolation.rst b/Documentation/api-interpolation.rst
new file mode 100644
index 000000000..484cd0c91
--- /dev/null
+++ b/Documentation/api-interpolation.rst
@@ -0,0 +1,5 @@
+HARK.interpolation
+=====================
+
+.. automodule:: HARK.interpolation
+ :members:
diff --git a/Documentation/api-parallel.rst b/Documentation/api-parallel.rst
new file mode 100644
index 000000000..f7656730c
--- /dev/null
+++ b/Documentation/api-parallel.rst
@@ -0,0 +1,5 @@
+HARK.parallel
+==============
+
+.. automodule:: HARK.parallel
+ :members:
diff --git a/Documentation/api-simulation.rst b/Documentation/api-simulation.rst
new file mode 100644
index 000000000..676919926
--- /dev/null
+++ b/Documentation/api-simulation.rst
@@ -0,0 +1,5 @@
+HARK.simulation
+================
+
+.. automodule:: HARK.simulation
+ :members:
diff --git a/Documentation/api-utilities.rst b/Documentation/api-utilities.rst
new file mode 100644
index 000000000..da26dfe59
--- /dev/null
+++ b/Documentation/api-utilities.rst
@@ -0,0 +1,6 @@
+HARK.utilities
+================
+
+.. automodule:: HARK.utilities
+ :members:
+
diff --git a/Documentation/api-validators.rst b/Documentation/api-validators.rst
new file mode 100644
index 000000000..10905e44b
--- /dev/null
+++ b/Documentation/api-validators.rst
@@ -0,0 +1,5 @@
+HARK.validators
+===============
+
+.. automodule:: HARK.validators
+ :members:
diff --git a/Documentation/api.rst b/Documentation/api.rst
new file mode 100644
index 000000000..afaedfff7
--- /dev/null
+++ b/Documentation/api.rst
@@ -0,0 +1,59 @@
+API Documentation
+==================
+
+Tools
+-----------
+
+.. automodule:: HARK.core
+ :members:
+
+.. automodule:: HARK.dcegm
+ :members:
+
+.. automodule:: HARK.estimation
+ :members:
+
+.. automodule:: HARK.interpolation
+ :members:
+
+.. automodule:: HARK.parallel
+ :members:
+
+.. automodule:: HARK.simulation
+ :members:
+
+.. automodule:: HARK.utilities
+ :members:
+
+.. automodule:: HARK.validators
+ :members:
+
+Consumption Saving
+--------------------
+
+.. automodule:: HARK.ConsumptionSaving.ConsAggShockModel
+ :members:
+
+.. automodule:: HARK.ConsumptionSaving.ConsGenIncProcessModel
+ :members:
+
+.. automodule:: HARK.ConsumptionSaving.ConsIndShockModel
+ :members:
+
+.. automodule:: HARK.ConsumptionSaving.ConsMarkovModel
+ :members:
+
+.. automodule:: HARK.ConsumptionSaving.ConsMedModel
+ :members:
+
+.. automodule:: HARK.ConsumptionSaving.ConsPortfolioModel
+ :members:
+
+.. automodule:: HARK.ConsumptionSaving.ConsPrefShockModel
+ :members:
+
+.. automodule:: HARK.ConsumptionSaving.ConsRepAgentModel
+ :members:
+
+.. automodule:: HARK.ConsumptionSaving.TractableBufferStockModel
+ :members:
diff --git a/Documentation/conf.py b/Documentation/conf.py
index 426933b47..26af2177e 100644
--- a/Documentation/conf.py
+++ b/Documentation/conf.py
@@ -40,7 +40,7 @@
'sphinx.ext.mathjax',
'sphinx.ext.autosummary',
'sphinx.ext.imgconverter',
- 'numpydoc',
+ 'sphinx.ext.napoleon',
'nbsphinx',
'recommonmark',
]
@@ -70,17 +70,17 @@
# General information about the project.
project = u'HARK'
-copyright = u'2016, TEMP'
-author = u'TEMP'
+copyright = u'2020, Econ-ARK team'
+author = u'Econ-ARK team'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
-version = u'0.97'
+version = u'latest'
# The full version, including alpha/beta/rc tags.
-release = u'0.97'
+release = u'latest'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
diff --git a/Documentation/example_notebooks/ConsPortfolioModelDoc.ipynb b/Documentation/example_notebooks/ConsPortfolioModelDoc.ipynb
new file mode 120000
index 000000000..148cec20a
--- /dev/null
+++ b/Documentation/example_notebooks/ConsPortfolioModelDoc.ipynb
@@ -0,0 +1 @@
+../../examples/ConsPortfolioModelDoc/ConsPortfolioModelDoc.ipynb
\ No newline at end of file
diff --git a/Documentation/example_notebooks/GenIncProcessModel.ipynb b/Documentation/example_notebooks/GenIncProcessModel.ipynb
new file mode 120000
index 000000000..95382b26a
--- /dev/null
+++ b/Documentation/example_notebooks/GenIncProcessModel.ipynb
@@ -0,0 +1 @@
+../../examples/GenIncProcessModel/GenIncProcessModel.ipynb
\ No newline at end of file
diff --git a/Documentation/example_notebooks/Gentle-Intro-To-HARK.ipynb b/Documentation/example_notebooks/Gentle-Intro-To-HARK.ipynb
new file mode 120000
index 000000000..85e84e64c
--- /dev/null
+++ b/Documentation/example_notebooks/Gentle-Intro-To-HARK.ipynb
@@ -0,0 +1 @@
+../../examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb
\ No newline at end of file
diff --git a/Documentation/example_notebooks/IndShockConsumerType.ipynb b/Documentation/example_notebooks/IndShockConsumerType.ipynb
new file mode 120000
index 000000000..2bc279db3
--- /dev/null
+++ b/Documentation/example_notebooks/IndShockConsumerType.ipynb
@@ -0,0 +1 @@
+../../examples/ConsIndShockModel/IndShockConsumerType.ipynb
\ No newline at end of file
diff --git a/Documentation/example_notebooks/KinkedRconsumerType.ipynb b/Documentation/example_notebooks/KinkedRconsumerType.ipynb
new file mode 120000
index 000000000..fd9844aa1
--- /dev/null
+++ b/Documentation/example_notebooks/KinkedRconsumerType.ipynb
@@ -0,0 +1 @@
+../../examples/ConsIndShockModel/KinkedRconsumerType.ipynb
\ No newline at end of file
diff --git a/Documentation/example_notebooks/LifecycleModelExample.ipynb b/Documentation/example_notebooks/LifecycleModelExample.ipynb
new file mode 120000
index 000000000..564f6c0d2
--- /dev/null
+++ b/Documentation/example_notebooks/LifecycleModelExample.ipynb
@@ -0,0 +1 @@
+../../examples/LifecycleModel/LifecycleModelExample.ipynb
\ No newline at end of file
diff --git a/Documentation/example_notebooks/PerfForesightConsumerType.ipynb b/Documentation/example_notebooks/PerfForesightConsumerType.ipynb
new file mode 120000
index 000000000..23345c01c
--- /dev/null
+++ b/Documentation/example_notebooks/PerfForesightConsumerType.ipynb
@@ -0,0 +1 @@
+../../examples/ConsIndShockModel/PerfForesightConsumerType.ipynb
\ No newline at end of file
diff --git a/Documentation/example_notebooks/README.md b/Documentation/example_notebooks/README.md
new file mode 100644
index 000000000..bef0ac27f
--- /dev/null
+++ b/Documentation/example_notebooks/README.md
@@ -0,0 +1,5 @@
+This directory contains symlinks to the notebooks in the top level `examples/` directory.
+
+These symlinks are referenced in the sphinx documentation, e.g. in `index.rst`.
+
+`nbsphinx`, the sphinx notebook extension, sees the `.ipynb` extension, and resolves the link, properly converting the notebook into Sphinx themed HTML.
\ No newline at end of file
diff --git a/Documentation/index.rst b/Documentation/index.rst
index 39af57ebc..b85960b8f 100644
--- a/Documentation/index.rst
+++ b/Documentation/index.rst
@@ -1,46 +1,62 @@
Welcome to HARK's documentation!
================================
+Heterogenous Agents Resources & toolKit (HARK) is a toolkit for the structural modeling of economic choices of optimizing and non-optimizing heterogeneous agents.
+
+HARK is software written in Python.
+This is the documentation for that software.
+It is aimed at computational researchers and other
+software developers who are building new models with HARK,
+or are contributing to the core software.
+
+If you are a student trying to learn economics concepts,
+you might want to look at the `DemARK
+
-
-.. autosummary::
- :toctree: generated
-
- HARK.core
- HARK.dcegm
- HARK.estimation
- HARK.interpolation
- HARK.parallel
- HARK.simulation
- HARK.utilities
- HARK.validators
- HARK.ConsumptionSaving.ConsAggShockModel
- HARK.FashionVictim.FashionVictimModel
+ api-core
+ api-dcegm
+ api-estimation
+ api-interpolation
+ api-parallel
+ api-simulation
+ api-utilities
+ api-validators
+ api-consumptionsaving
+
Indices and tables
==================
diff --git a/Documentation/introduction.md b/Documentation/introduction.md
new file mode 100644
index 000000000..d626a88da
--- /dev/null
+++ b/Documentation/introduction.md
@@ -0,0 +1,12 @@
+# Introduction
+
+This document will tell you how to get HARK up and
+running on your machine, how to get started using it, and give you an
+overview of the main elements of the toolkit.
+
+Other useful resources are:
+
+ * Documentation: [ReadTheDocs](https://hark.readthedocs.io/)
+ * User guide: [HARKmanual](http://econ-ark.github.io/HARK/Documentation/HARKmanual/)
+ * Demonstrations of HARK functionality: [DemARK](https://github.com/econ-ark/DemARK/)
+ * Replications and Explorations Made using the ARK : [REMARK](https://github.com/econ-ark/REMARK/)
diff --git a/Documentation/license.md b/Documentation/license.md
new file mode 100644
index 000000000..ee2dae560
--- /dev/null
+++ b/Documentation/license.md
@@ -0,0 +1,5 @@
+# License
+
+All of HARK is licensed under the Apache License, Version 2.0 (ALv2). Please see
+the LICENSE file for the text of the license. More information can be found at:
+http://www.apache.org/dev/apply-license.html
diff --git a/Documentation/readme.md b/Documentation/list-of-files.md
similarity index 52%
rename from Documentation/readme.md
rename to Documentation/list-of-files.md
index 20e40d06b..5967dd7b2 100644
--- a/Documentation/readme.md
+++ b/Documentation/list-of-files.md
@@ -1,189 +1,9 @@
-# Heterogeneous Agents Resources and toolKit (HARK)
-pre-release 0.10.1.dev3
-
-Click the Badge for Citation Info.
-[](https://zenodo.org/badge/latestdoi/50448254)
-
-
-Table of Contents:
-
-* [1. Introduction](#i-introduction)
-* [2. Quick start guide](#ii-quick-start-guide)
- * [Installing](#Installing-HARK)
- * [Installing Anaconda](#Using-HARK-with-Anaconda)
- * [Learning HARK](#Learning-HARK)
-* [3. List of files in repository](#iii-list-of-files-in-repository)
-* [4. Warnings and disclaimers](#iv-warnings-and-disclaimers)
-* [5. License Information](#v-license)
-
-
-## I. INTRODUCTION
-
-This document will tell you how to get HARK up and
-running on your machine, how to get started using it, and give you an
-overview of the main elements of the toolkit.
-
-Other useful resources are:
-
- * Documentation: [ReadTheDocs](https://hark.readthedocs.io/)
- * User guide: [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf)
- * In the [HARK repository](https://github.com/econ-ark/HARK)
- * Demonstrations of HARK functionality: [DemARK](https://github.com/econ-ark/DemARK/)
- * Replications and Explorations Made using the ARK : [REMARK](https://github.com/econ-ark/REMARK/)
-
-## II. QUICK START GUIDE
-
-### Installing HARK
-
-HARK is an open source project that is compatible with both python 2 and 3. But we recommend
-using python 3; eventually support for python 2 will end.
-
-#### Installing HARK with pip
-
-The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/).
-
-To install HARK with pip, at a command line type `pip install econ-ark`.
-
-(If you want to install a release that is not the default stable release, for instance if you want to install a development release, you'll need to use a "pinned" release number: `pip install econ-ark==0.10.1.dev1`, substituting "0.10.1.dev1" for your desired release number.)
-
-If you are installing via pip, we recommend using a virtual environment such as [virtualenv](https://virtualenv.pypa.io/en/latest/). Creation of a virtual environment isolates the installation of `econ-ark` from the installations of any other python tools and packages.
-
-To install `virtualenv`, then to create an environment named `econ-ark`, and finally to activate that environment:
-
-```
-cd [directory where you want to store the econ-ark virtual environment]
-pip install virtualenv
-virtualenv econ-ark
-source econ-ark/bin/activate
-```
-
-----
-#### Using HARK with Anaconda
-
-If you intend ever to use the toolkit for anything other than running the precooked material we have provided, you should probably install [Anaconda](https://anaconda.com/why-anaconda), which will install python along with many packages that are frequently used in scientific computing.
-
-1. Download Anaconda for your operating system and follow the installation instructions [at Anaconda.com](https://www.anaconda.com/distribution/#download-section).
-
-1. Anaconda includes its own virtual environment system called `conda` which stores environments in a preset location (so you don't have to choose). So in order to create and activate an econ-ark virtual environment:
-```
-conda create -n econ-ark anaconda
-conda activate econ-ark
-conda install -c conda-forge econ-ark
-```
-1. Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`.
-
-1. To verify that spyder has access to HARK try typing `pip install econ-ark` into the iPython shell within Spyder. If you have successfully installed HARK as above, you should see a lot of messages saying 'Requirement satisfied'.
-
- * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file.
-
- * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)).
-
- * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone
-
- * If you don't want to clone HARK, but just to download it, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory.
-
- Once you've got a copy of HARK in a directory, return to Spyder and navigate to that directory where you put HARK. This can be done within Spyder by doing `import os` and then using `os.chdir()` to change directories. `chdir` works just like cd at a command prompt on most operating systems, except that it takes a string as input: `os.chdir('Music')` moves to the Music subdirectory of the current working directory.
-
-6) Most of the modules in HARK are just collections of tools. There are a few demonstration
-applications that use the tools that you automatically get when you install HARK -- they are listed below in [Application Modules](#application-modules). A much larger set of uses of HARK can be found at two repositories:
- * [DemARK](https://github.com/econ-ark/DemARK): Demonstrations of the use of HARK
- * [REMARK](https://github.com/econ-ark/REMARK): Replications of existing papers made using HARK
-
-You will want to obtain your own local copy of these repos using:
-```
-git clone https://github.com/econ-ark/DemARK.git
-```
-and similarly for the REMARK repo. Once you have downloaded them, you will find that each repo contains a `notebooks` directory that contains a number of [jupyter notebooks](https://jupyter.org/). If you have the jupyter notebook tool installed (it is installed as part of Anaconda), you should be able to launch the
-jupyter notebook app from the command line with the command:
-
-```
-jupyter notebook
-```
-and from there you can open the notebooks and execute them.
-
-#### Learning HARK
-
-We have a set of 30-second [Elevator Spiels](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#capsule-summaries-of-what-the-econ-ark-project-is) describing the project, tailored to people with several different kinds of background.
-
-The most broadly applicable advice is to go to [Econ-ARK](https://econ-ark.org) and click on "Notebooks", and choose [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) which will launch as a [jupyter notebook](https://jupyter.org/).
-
-##### [For people with a technical/scientific/computing background but little economics background](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-people-with-a-technicalscientificcomputing-background-but-no-economics-background)
-
-* [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb)
-
-##### [For economists who have done some structural modeling](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-done-some-structural-modeling)
-
-* A full replication of the [Iskhakov, Jørgensen, Rust, and Schjerning](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE643) paper for solving the discrete-continuous retirement saving problem
- * An informal discussion of the issues involved is [here](https://github.com/econ-ark/DemARK/blob/master/notebooks/DCEGM-Upper-Envelope.ipynb) (part of the [DemARK](https://github.com/econ-ark/DemARK) repo)
-
-* [Structural-Estimates-From-Empirical-MPCs](https://github.com/econ-ark/DemARK/blob/master/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb) is an example of the use of the toolkit in a discussion of a well known paper. (Yes, it is easy enough to use that you can estimate a structural model on somebody else's data in the limited time available for writing a discussion)
-
-##### [For economists who have not yet done any structural modeling but might be persuadable to start](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-not-yet-done-any-structural-modeling-but-might-be-persuadable-to-start)
-
-* Start with [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) to get your feet wet
-
-* A simple indirect inference/simulated method of moments structural estimation along the lines of Gourinchas and Parker's 2002 Econometrica paper or Cagetti's 2003 paper is performed by the [SolvingMicroDSOPs](https://github.com/econ-ark/REMARK/tree/master/REMARKs/SolvingMicroDSOPs) [REMARK](https://github.com/econ-ark/REMARK); this code implements the solution methods described in the corresponding section of [these lecture notes](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/)
-
-##### [For Other Developers of Software for Computational Economics](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-other-developers-of-software-for-computational-economics)
-
-
-* Our workhorse module is [ConsIndShockModel.py](https://github.com/econ-ark/HARK/blob/master/HARK/ConsumptionSaving/ConsIndShockModel.py)
- * which is explored and explained (a bit) in [this jupyter notebook](https://github.com/econ-ark/DemARK/blob/master/notebooks/ConsIndShockModel.ipynb)
-
-### Making changes to HARK
-
-If you want to make changes or contributions (yay!) to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files (and it's a bad idea to mess with the original code anyway because you'll likely forget what changes you made). If you are adept at GitHub, you can [fork](https://help.github.com/en/articles/fork-a-repo) the repo. If you are less experienced, you should download a personal copy of HARK again using `git clone` (see above) or the GitHub Desktop app.
-
-1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). If you get a permission denied error, you may need to setup SSH for GitHub, or you can clone using HTTPS: 'git clone https://github.com/econ-ark/HARK.git'.
-
-2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))).
-
-For Mac or Linux:
-
-Install virtualenv if you need to and then type:
-
-```
-virtualenv econ-ark
-source econ-ark/bin/activate
-```
-For Windows:
-```
-virtualenv econ-ark
-econ-ark\\Scripts\\activate.bat
-```
-
-3. Once the virtualenv is activated, you may see `(econ-ark)` in your command prompt (depending on how your machine is configured)
-
-3. Make sure to change to HARK directory, and install HARK's requirements into the virtual environment with `pip install -r requirements.txt`.
-
-4. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`.
-
-### Trouble with installation?
-
-We've done our best to give correct, thorough instructions on how to install HARK but we know this information may be inaccurate or incomplete. Please let us know if you run into trouble so we can update this guide! Here's a list of platforms and versions this guide has been verified for:
-
-| Installation Type | Platform | Python Version | Date Tested | Tested By |
-| ------------- |:-------------:| -----:| -----:|-----:|
-| basic pip install | Linux (16.04) | 3 | 2019-04-24 | @shaunagm |
-| anaconda | Linux (16.04) | 3 | 2019-04-24 | @shaunagm |
-| basic pip install | MacOS 10.13.2 "High Sierra" | 2.7| 2019-04-26 | @llorracc |
-
-### Next steps
-
-To learn more about how to use HARK, check out our [user manual](Documentation/HARKmanual.pdf).
-
-For help making changes to HARK, check out our [contributing guide](CONTRIBUTING.md).
-
-
-## III. LIST OF FILES IN REPOSITORY
+# List of Files in Repository
This section contains descriptions of the main files in the repo.
Documentation files:
-* [README.md](README.md): The file you are currently reading.
* [Documentation/HARKdoc.pdf](Documentation/HARKdoc.pdf): A mini-user guide produced for a December 2015 workshop on HARK, unofficially representing the alpha version. (Substantially out of date).
-* [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf): A user guide for HARK, written for the beta release at CEF 2016 in Bordeaux. Should contain 90% fewer lies relative to HARKdoc.pdf.
- * [Documentation/HARKmanual.tex](Documentation/HARKmanual.tex): LaTeX source for the user guide. Open source code probably requires an open source manual as well.
* [Documentation/ConsumptionSavingModels.pdf](Documentation/ConsumptionSavingModels.pdf): Mathematical descriptions of the various consumption-saving models in HARK and how they map into the code.
* [Documentation/ConsumptionSavingModels.tex](Documentation/ConsumptionSavingModels.tex): LaTeX source for the "models" writeup.
* [Documentation/NARK.pdf](Documentation/NARK.pdf): Variable naming conventions for HARK, plus concordance with LaTeX variable definitions. Still in development.
@@ -390,14 +210,3 @@ Other files that you don't need to worry about:
* Image for the front cover of the user guide, showing the consumption
function for the KinkyPref model.
-
-## IV. WARNINGS AND DISCLAIMERS
-
-This is a beta version of HARK. The code has not been extensively tested as it should be. We hope it is useful, but there are absolutely no guarantees (expressed or implied) that it works or will do what you want. Use at your own risk. And please, let us know if you find bugs by posting an issue to [the GitHub page](https://github.com/econ-ark/HARK)!
-
-
-## V. License
-
-All of HARK is licensed under the Apache License, Version 2.0 (ALv2). Please see
-the LICENSE file for the text of the license. More information can be found at:
-http://www.apache.org/dev/apply-license.html
diff --git a/Documentation/notebooks/DCT-Copula-Illustration.ipynb b/Documentation/notebooks/DCT-Copula-Illustration.ipynb
deleted file mode 120000
index 413f61774..000000000
--- a/Documentation/notebooks/DCT-Copula-Illustration.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../DCT-Copula-Illustration.ipynb
\ No newline at end of file
diff --git a/Documentation/notebooks/Journey_1_PhD.ipynb b/Documentation/notebooks/Journey_1_PhD.ipynb
index 63aa8582a..dc4851a1e 100644
--- a/Documentation/notebooks/Journey_1_PhD.ipynb
+++ b/Documentation/notebooks/Journey_1_PhD.ipynb
@@ -33,9 +33,11 @@
"$$\n",
"U(C)=\\frac{C^{1-\\rho}}{1-\\rho}\n",
"$$\n",
+ "\n",
"Now assume that every consumer faces some uncertainty on hers income (e.g. it follows AR (1) process), which is idiosyncratic - the realizations of each shock is (potentially) different for each agent. In this setting the bellman equation looks like:\n",
+ "\n",
"\\begin{eqnarray*}\n",
- "V(M_t, Y_t) &=& \\max_{C_t} U(C_t) + E\\[\\beta V(M_{t+1}, Y_{t+1})\\], \\\\\n",
+ "V(M_t, Y_t) &=& \\max_{C_t} U(C_t) + E[\\beta V(M_{t+1}, Y_{t+1})], \\\\\n",
"& s.t. & \\\\\n",
"A_t &=& M_t - C_t, \\\\\n",
"M_{t+1} &=& R (M_{t}-C_{t}) + Y_t, \\\\\n",
@@ -109,7 +111,8 @@
"metadata": {},
"source": [
"## 4 Agent-type class \n",
- "Agent-type class enables you to build the macroeconomic models, such as presented in the introduction. It is also the essential part of the macroeconomic model in HARK. Therefore, to use HARK, you always need to use agent-type classes! \n",
+ "Agent-type class enables you to build the macroeconomic models, such as presented in the introduction. It is also the essential part of the macroeconomic model in HARK. Therefore, to use HARK, you always need to use agent-type classes!\n",
+ "\n",
"### 4.1 Introductory example\n",
"As an example, let's solve the stochastic model from the introduction. Assume the income process of the agent i in the period t: $Y_{i,t}$, is given by: \n",
"\n",
@@ -125,25 +128,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "ename": "TypeError",
- "evalue": "'<' not supported between instances of 'NoneType' and 'NoneType'",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mHARK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutilities\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotFuncs\u001b[0m \u001b[1;31m#useful function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mExample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIndShockConsumerType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_idiosyncratic_shocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[1;32m~\\Desktop\\HARK\\HARK\\ConsumptionSaving\\ConsIndShockModel.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, cycles, time_flow, verbose, quiet, **kwds)\u001b[0m\n\u001b[0;32m 1793\u001b[0m \u001b[1;31m# Add consumer-type specific objects, copying to create independent versions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1794\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolveOnePeriod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msolveConsIndShock\u001b[0m \u001b[1;31m# idiosyncratic shocks solver\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1795\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Make assets grid, income process, terminal solution\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1797\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Desktop\\HARK\\HARK\\ConsumptionSaving\\ConsIndShockModel.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1848\u001b[0m '''\n\u001b[0;32m 1849\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdateIncomeProcess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1850\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdateAssetsGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1851\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdateSolutionTerminal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1852\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Desktop\\HARK\\HARK\\ConsumptionSaving\\ConsIndShockModel.py\u001b[0m in \u001b[0;36mupdateAssetsGrid\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1831\u001b[0m \u001b[0mnone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1832\u001b[0m '''\n\u001b[1;32m-> 1833\u001b[1;33m \u001b[0maXtraGrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconstructAssetsGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1834\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maXtraGrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maXtraGrid\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1835\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddToTimeInv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'aXtraGrid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Desktop\\HARK\\HARK\\ConsumptionSaving\\ConsIndShockModel.py\u001b[0m in \u001b[0;36mconstructAssetsGrid\u001b[1;34m(parameters)\u001b[0m\n\u001b[0;32m 2441\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2442\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maXtraGrid\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2443\u001b[1;33m \u001b[0mj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maXtraGrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearchsorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2444\u001b[0m \u001b[0maXtraGrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maXtraGrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'NoneType' and 'NoneType'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import sys #set path of the notebook \n",
"import os\n",
@@ -153,7 +140,7 @@
"import Documentation.notebooks.Jounery_1_param as Params #imported paramters \n",
"from HARK.utilities import plotFuncs #useful function\n",
"\n",
- "Example = IndShockConsumerType(**Params.init_idiosyncratic_shocks) \n"
+ "Example = IndShockConsumerType() \n"
]
},
{
@@ -427,7 +414,38 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.5"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
}
},
"nbformat": 4,
diff --git a/Documentation/quick-start.md b/Documentation/quick-start.md
new file mode 100644
index 000000000..222810ce8
--- /dev/null
+++ b/Documentation/quick-start.md
@@ -0,0 +1,143 @@
+# Quick Start
+
+## Installing HARK
+
+HARK is an open source project that is compatible with both python 2 and 3. But we recommend
+using python 3; eventually support for python 2 will end.
+
+### Installing HARK with pip
+
+The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/).
+
+To install HARK with pip, at a command line type `pip install econ-ark`.
+
+(If you want to install a release that is not the default stable release, for instance if you want to install a development release, you'll need to use a "pinned" release number: `pip install econ-ark==0.10.1.dev1`, substituting "0.10.1.dev1" for your desired release number.)
+
+If you are installing via pip, we recommend using a virtual environment such as [virtualenv](https://virtualenv.pypa.io/en/latest/). Creation of a virtual environment isolates the installation of `econ-ark` from the installations of any other python tools and packages.
+
+To install `virtualenv`, then to create an environment named `econ-ark`, and finally to activate that environment:
+
+```
+cd [directory where you want to store the econ-ark virtual environment]
+pip install virtualenv
+virtualenv econ-ark
+source econ-ark/bin/activate
+```
+
+----
+### Using HARK with Anaconda
+
+If you intend ever to use the toolkit for anything other than running the precooked material we have provided, you should probably install [Anaconda](https://anaconda.com/why-anaconda), which will install python along with many packages that are frequently used in scientific computing.
+
+1. Download Anaconda for your operating system and follow the installation instructions [at Anaconda.com](https://www.anaconda.com/distribution/#download-section).
+
+1. Anaconda includes its own virtual environment system called `conda` which stores environments in a preset location (so you don't have to choose). So in order to create and activate an econ-ark virtual environment:
+```
+conda create -n econ-ark anaconda
+conda activate econ-ark
+conda install -c conda-forge econ-ark
+```
+1. Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`.
+
+1. To verify that spyder has access to HARK try typing `pip install econ-ark` into the iPython shell within Spyder. If you have successfully installed HARK as above, you should see a lot of messages saying 'Requirement satisfied'.
+
+ * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file.
+
+ * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)).
+
+ * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone
+
+ * If you don't want to clone HARK, but just to download it, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory.
+
+ Once you've got a copy of HARK in a directory, return to Spyder and navigate to that directory where you put HARK. This can be done within Spyder by doing `import os` and then using `os.chdir()` to change directories. `chdir` works just like cd at a command prompt on most operating systems, except that it takes a string as input: `os.chdir('Music')` moves to the Music subdirectory of the current working directory.
+
+6) Most of the modules in HARK are just collections of tools. There are a few demonstration
+applications that use the tools that you automatically get when you install HARK -- they are listed below in [Application Modules](#application-modules). A much larger set of uses of HARK can be found at two repositories:
+ * [DemARK](https://github.com/econ-ark/DemARK): Demonstrations of the use of HARK
+ * [REMARK](https://github.com/econ-ark/REMARK): Replications of existing papers made using HARK
+
+You will want to obtain your own local copy of these repos using:
+```
+git clone https://github.com/econ-ark/DemARK.git
+```
+and similarly for the REMARK repo. Once you have downloaded them, you will find that each repo contains a `notebooks` directory that contains a number of [jupyter notebooks](https://jupyter.org/). If you have the jupyter notebook tool installed (it is installed as part of Anaconda), you should be able to launch the
+jupyter notebook app from the command line with the command:
+
+```
+jupyter notebook
+```
+and from there you can open the notebooks and execute them.
+
+### Learning HARK
+
+We have a set of 30-second [Elevator Spiels](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#capsule-summaries-of-what-the-econ-ark-project-is) describing the project, tailored to people with several different kinds of background.
+
+The most broadly applicable advice is to go to [Econ-ARK](https://econ-ark.org) and click on "Notebooks", and choose [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) which will launch as a [jupyter notebook](https://jupyter.org/).
+
+#### [For people with a technical/scientific/computing background but little economics background](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-people-with-a-technicalscientificcomputing-background-but-no-economics-background)
+
+* [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb)
+
+#### [For economists who have done some structural modeling](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-done-some-structural-modeling)
+
+* A full replication of the [Iskhakov, Jørgensen, Rust, and Schjerning](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE643) paper for solving the discrete-continuous retirement saving problem
+ * An informal discussion of the issues involved is [here](https://github.com/econ-ark/DemARK/blob/master/notebooks/DCEGM-Upper-Envelope.ipynb) (part of the [DemARK](https://github.com/econ-ark/DemARK) repo)
+
+* [Structural-Estimates-From-Empirical-MPCs](https://github.com/econ-ark/DemARK/blob/master/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb) is an example of the use of the toolkit in a discussion of a well known paper. (Yes, it is easy enough to use that you can estimate a structural model on somebody else's data in the limited time available for writing a discussion)
+
+#### [For economists who have not yet done any structural modeling but might be persuadable to start](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-not-yet-done-any-structural-modeling-but-might-be-persuadable-to-start)
+
+* Start with [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) to get your feet wet
+
+* A simple indirect inference/simulated method of moments structural estimation along the lines of Gourinchas and Parker's 2002 Econometrica paper or Cagetti's 2003 paper is performed by the [SolvingMicroDSOPs](https://github.com/econ-ark/REMARK/tree/master/REMARKs/SolvingMicroDSOPs) [REMARK](https://github.com/econ-ark/REMARK); this code implements the solution methods described in the corresponding section of [these lecture notes](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/)
+
+#### [For Other Developers of Software for Computational Economics](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-other-developers-of-software-for-computational-economics)
+
+
+* Our workhorse module is [ConsIndShockModel.py](https://github.com/econ-ark/HARK/blob/master/HARK/ConsumptionSaving/ConsIndShockModel.py)
+ * which is explored and explained (a bit) in [this jupyter notebook](https://github.com/econ-ark/DemARK/blob/master/notebooks/ConsIndShockModel.ipynb)
+
+## Making changes to HARK
+
+If you want to make changes or contributions to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files (and it's a bad idea to mess with the original code anyway because you'll likely forget what changes you made). If you are adept at GitHub, you can [fork](https://help.github.com/en/articles/fork-a-repo) the repo. If you are less experienced, you should download a personal copy of HARK again using `git clone` (see above) or the GitHub Desktop app.
+
+1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). If you get a permission denied error, you may need to setup SSH for GitHub, or you can clone using HTTPS: 'git clone https://github.com/econ-ark/HARK.git'.
+
+2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))).
+
+For Mac or Linux:
+
+Install virtualenv if you need to and then type:
+
+```
+virtualenv econ-ark
+source econ-ark/bin/activate
+```
+For Windows:
+```
+virtualenv econ-ark
+econ-ark\\Scripts\\activate.bat
+```
+
+3. Once the virtualenv is activated, you may see `(econ-ark)` in your command prompt (depending on how your machine is configured)
+
+3. Make sure to change to HARK directory, and install HARK's requirements into the virtual environment with `pip install -r requirements.txt`.
+
+4. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`.
+
+## Trouble with installation?
+
+We've done our best to give correct, thorough instructions on how to install HARK but we know this information may be inaccurate or incomplete. Please let us know if you run into trouble so we can update this guide! Here's a list of platforms and versions this guide has been verified for:
+
+| Installation Type | Platform | Python Version | Date Tested | Tested By |
+| ------------- |:-------------:| -----:| -----:|-----:|
+| basic pip install | Linux (16.04) | 3 | 2019-04-24 | @shaunagm |
+| anaconda | Linux (16.04) | 3 | 2019-04-24 | @shaunagm |
+| basic pip install | MacOS 10.13.2 "High Sierra" | 2.7| 2019-04-26 | @llorracc |
+
+## Next steps
+
+To learn more about how to use HARK, check out our [user manual](_static/HARKmanual.pdf).
+
+For help making changes to HARK, check out our [contributing guide](CONTRIBUTING.md).
+
diff --git a/Documentation/warnings-and-disclaimers.md b/Documentation/warnings-and-disclaimers.md
new file mode 100644
index 000000000..a223f075c
--- /dev/null
+++ b/Documentation/warnings-and-disclaimers.md
@@ -0,0 +1,4 @@
+# IV. WARNINGS AND DISCLAIMERS
+
+This is a beta version of HARK. The code has not been extensively tested as it should be. We hope it is useful, but there are absolutely no guarantees (expressed or implied) that it works or will do what you want. Use at your own risk. And please, let us know if you find bugs by posting an issue to [the GitHub page](https://github.com/econ-ark/HARK)!
+
diff --git a/Examples/Gentle-Intro-To-HARK.ipynb b/Examples/Gentle-Intro-To-HARK.ipynb
deleted file mode 100644
index 2a56d9fc3..000000000
--- a/Examples/Gentle-Intro-To-HARK.ipynb
+++ /dev/null
@@ -1,540 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# A Gentle Introduction to HARK\n",
- "\n",
- "This notebook provides a simple, hands-on tutorial for first time HARK users -- and potentially first time Python users. It does not go \"into the weeds\" - we have hidden some code cells that do boring things that you don't need to digest on your first experience with HARK. Our aim is to convey a feel for how the toolkit works.\n",
- "\n",
- "For readers for whom this is your very first experience with Python, we have put important Python concepts in $\\textbf{boldface}$. For those for whom this is the first time they have used a Jupyter notebook, we have put Jupyter instructions in $\\textit{italics}.$ Only cursory definitions (if any) are provided here. If you want to learn more, there are many online Python and Jupyter tutorials."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "# This cell has a bit of initial setup. You can click the triangle to the left to expand it.\n",
- "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n",
- "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n",
- "# The most common problem beginners have is to execute a cell before all its predecessors\n",
- "# If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n",
- "%matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "# The first step is to be able to bring things in from different directories\n",
- "import os\n",
- "import sys\n",
- "module_path = os.path.abspath(os.path.join('..'))\n",
- "if module_path not in sys.path:\n",
- " sys.path.append(module_path)\n",
- "\n",
- "from copy import deepcopy\n",
- "from time import clock\n",
- "\n",
- "import numpy as np\n",
- "\n",
- "import HARK\n",
- "from Examples.util import log_progress\n",
- "from HARK.utilities import plotFuncs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Your First HARK Model: Perfect Foresight\n",
- "\n",
- "$$\\newcommand{\\CRRA}{\\rho}\\newcommand{\\DiscFac}{\\beta}$$\n",
- "We start with almost the simplest possible consumption model: A consumer with CRRA utility \n",
- "\n",
- "\\begin{equation}\n",
- "U(C) = \\frac{C^{1-\\CRRA}}{1-\\rho}\n",
- "\\end{equation}\n",
- "\n",
- "has perfect foresight about everything except the (stochastic) date of death, which occurs with constant probability implying a \"survival probability\" $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb < 1$. Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and currrent income) and must choose how much of those resources to consume $C_t$ and how much to retain in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$. Between periods, the agent dies with probability $\\mathsf{D}_t$, ending his problem.\n",
- "\n",
- "The agent's problem can be written in Bellman form as:\n",
- "\n",
- "\\begin{eqnarray*}\n",
- "V_t(M_t,P_t) &=& \\max_{C_t}~U(C_t) + \\beta \\aleph V_{t+1}(M_{t+1},P_{t+1}), \\\\\n",
- "& s.t. & \\\\\n",
- "%A_t &=& M_t - C_t, \\\\\n",
- "M_{t+1} &=& R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n",
- "P_{t+1} &=& \\Gamma_{t+1} P_t, \\\\\n",
- "\\end{eqnarray*}\n",
- "\n",
- "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\mathsf{\\aleph}_t\\}$. To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an $\\textit{infinite horizon}$ consumer with constant income growth and survival probability.\n",
- "\n",
- "## Representing Agents in HARK\n",
- "\n",
- "HARK represents agents solving this type of problem as $\\textbf{instances}$ of the $\\textbf{class}$ $\\texttt{PerfForesightConsumerType}$, a $\\textbf{subclass}$ of $\\texttt{AgentType}$. To make agents of this class, we must import the class itself into our workspace. (Run the cell below in order to do this)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The $\\texttt{PerfForesightConsumerType}$ class contains within itself the python code that constructs the solution for the perfect foresight model we are studying here, as specifically articulated in [these lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/). \n",
- "\n",
- "To create an instance of $\\texttt{PerfForesightConsumerType}$, we simply call the class as if it were a function, passing as arguments the specific parameter values we want it to have. In the hidden cell below, we define a $\\textbf{dictionary}$ named $\\texttt{PF_dictionary}$ with these parameter values:\n",
- "\n",
- "| Param | Description | Code | Value |\n",
- "| :---: | --- | --- | :---: |\n",
- "| $\\rho$ | Relative risk aversion | $\\texttt{CRRA}$ | 2.5 |\n",
- "| $\\beta$ | Discount factor | $\\texttt{DiscFac}$ | 0.96 |\n",
- "| $R$ | Risk free interest factor | $\\texttt{Rfree}$ | 1.03 |\n",
- "| $\\newcommand{\\LivFac}{\\aleph}\\LivFac$ | Survival probability | $\\texttt{LivPrb}$ | 0.98 |\n",
- "| $\\Gamma$ | Income growth factor | $\\texttt{PermGroFac}$ | 1.01 |\n",
- "\n",
- "\n",
- "For now, don't worry about the specifics of dictionaries. All you need to know is that a dictionary lets us pass many arguments wrapped up in one simple data structure."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "# This cell defines a parameter dictionary. You can expand it if you want to see what that looks like.\n",
- "PF_dictionary = {\n",
- " 'CRRA' : 2.5,\n",
- " 'DiscFac' : 0.96,\n",
- " 'Rfree' : 1.03,\n",
- " 'LivPrb' : [0.98],\n",
- " 'PermGroFac' : [1.01],\n",
- " 'T_cycle' : 1,\n",
- " 'cycles' : 0,\n",
- " 'AgentCount' : 10000\n",
- "}\n",
- "\n",
- "# To those curious enough to open this hidden cell, you might notice that we defined\n",
- "# a few extra parameters in that dictionary: T_cycle, cycles, and AgentCount. Don't\n",
- "# worry about these for now."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's make an $\\textbf{object}$ named $\\texttt{PFexample}$ which is an $\\textbf{instance}$ of the $\\texttt{PerfForesightConsumerType}$ class. The object $\\texttt{PFexample}$ will bundle together the abstract mathematical description of the solution embodied in $\\texttt{PerfForesightConsumerType}$, and the specific set of parameter values defined in $\\texttt{PF_dictionary}$. Such a bundle is created passing $\\texttt{PF_dictionary}$ to the class $\\texttt{PerfForesightConsumerType}$:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "PFexample = PerfForesightConsumerType(**PF_dictionary) \n",
- "# the asterisks ** basically say \"here come some arguments\" to PerfForesightConsumerType"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In $\\texttt{PFexample}$, we now have _defined_ the problem of a particular infinite horizon perfect foresight consumer who knows how to solve this problem. \n",
- "\n",
- "## Solving an Agent's Problem\n",
- "\n",
- "To tell the agent actually to solve the problem, we call the agent's $\\texttt{solve}$ $\\textbf{method}$. (A $\\textbf{method}$ is essentially a function that an object runs that affects the object's own internal characteristics -- in this case, the method adds the consumption function to the contents of $\\texttt{PFexample}$.)\n",
- "\n",
- "The cell below calls the $\\texttt{solve}$ method for $\\texttt{PFexample}$"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "PFexample.solve()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Running the $\\texttt{solve}$ method creates the $\\textbf{attribute}$ of $\\texttt{PFexample}$ named $\\texttt{solution}$. In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (paramterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem. \n",
- "\n",
- "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem. The consumption function stored as the first element (element 0) of the solution list can be retrieved by:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "PFexample.solution[0].cFunc"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "One of the results proven in the associated [the lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$. \n",
- "\n",
- "This is why $\\texttt{cFunc}$ can be represented by a linear interpolation. It can be plotted between an $m$ ratio of 0 and 10 using the command below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mPlotTop=10\n",
- "plotFuncs(PFexample.solution[0].cFunc,0.,mPlotTop)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The figure illustrates one of the surprising features of the perfect foresight model: A person with zero money should be spending at a rate more than double their income (that is, $\\texttt{cFunc}(0.) \\approx 2.08$ - the intersection on the vertical axis). How can this be?\n",
- "\n",
- "The answer is that we have not incorporated any constraint that would prevent the agent from borrowing against the entire PDV of future earnings-- human wealth. How much is that? What's the minimum value of $m_t$ where the consumption function is defined? We can check by retrieving the $\\texttt{hNrm}$ **attribute** of the solution, which calculates the value of human wealth normalized by permanent income:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "humanWealth = PFexample.solution[0].hNrm\n",
- "mMinimum = PFexample.solution[0].mNrmMin\n",
- "print(\"This agent's human wealth is \" + str(humanWealth) + ' times his current income level.')\n",
- "print(\"This agent's consumption function is defined (consumption is positive) down to m_t = \" + str(mMinimum))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Yikes! Let's take a look at the bottom of the consumption function. In the cell below, set the bounds of the $\\texttt{plotFuncs}$ function to display down to the lowest defined value of the consumption function."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# YOUR FIRST HANDS-ON EXERCISE!\n",
- "# Fill in the value for \"mPlotBottom\" to plot the consumption function from the point where it is zero.\n",
- "# plotFuncs(PFexample.solution[0].cFunc,mPlotBottom,mPlotTop)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Changing Agent Parameters\n",
- "\n",
- "Suppose you wanted to change one (or more) of the parameters of the agent's problem and see what that does. We want to compare consumption functions before and after we change parameters, so let's make a new instance of $\\texttt{PerfForesightConsumerType}$ by copying $\\texttt{PFexample}$."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "NewExample = deepcopy(PFexample)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In Python, you can set an $\\textbf{attribute}$ of an object just like any other variable. For example, we could make the new agent less patient:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "NewExample.DiscFac = 0.90\n",
- "NewExample.solve()\n",
- "mPlotBottom = mMinimum\n",
- "plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],mPlotBottom,mPlotTop)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "(Note that you can pass a **list** of functions to $\\texttt{plotFuncs}$ as the first argument rather than just a single function. Lists are written inside of [square brackets].)\n",
- "\n",
- "Let's try to deal with the \"problem\" of massive human wealth by making another consumer who has essentially no future income. We can virtually eliminate human wealth by making the permanent income growth factor $\\textit{very}$ small.\n",
- "\n",
- "In $\\texttt{PFexample}$, the agent's income grew by 1 percent per period -- his $\\texttt{PermGroFac}$ took the value 1.01. What if our new agent had a growth factor of 0.01 -- his income $\\textit{shrinks}$ by 99 percent each period? In the cell below, set $\\texttt{NewExample}$'s discount factor back to its original value, then set its $\\texttt{PermGroFac}$ attribute so that the growth factor is 0.01 each period.\n",
- "\n",
- "Important: Recall that the model at the top of this document said that an agent's problem is characterized by a sequence of income growth factors, but we tabled that concept. Because $\\texttt{PerfForesightConsumerType}$ treats $\\texttt{PermGroFac}$ as a $\\textit{time-varying}$ attribute, it must be specified as a $\\textbf{list}$ (with a single element in this case)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Revert NewExample's discount factor and make his future income minuscule\n",
- "# print(\"your lines here\")\n",
- "\n",
- "# Compare the old and new consumption functions\n",
- "plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],0.,10.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now $\\texttt{NewExample}$'s consumption function has the same slope (MPC) as $\\texttt{PFexample}$, but it emanates from (almost) zero-- he has basically no future income to borrow against!\n",
- "\n",
- "If you'd like, use the cell above to alter $\\texttt{NewExample}$'s other attributes (relative risk aversion, etc) and see how the consumption function changes. However, keep in mind that \\textit{no solution exists} for some combinations of parameters. HARK should let you know if this is the case if you try to solve such a model.\n",
- "\n",
- "\n",
- "## Your Second HARK Model: Adding Income Shocks\n",
- "\n",
- "Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines. Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints.\n",
- "\n",
- "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$. Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$. As with the perfect foresight problem, this model can be framed in terms of $\\textit{normalized}$ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory).\n",
- "\n",
- "\\begin{eqnarray*}\n",
- "v_t(m_t) &=& \\max_{c_t} ~ U(c_t) ~ + \\phantom{\\LivFac} \\beta \\mathbb{E} [(\\Gamma_{t+1}\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ], \\\\\n",
- "a_t &=& m_t - c_t, \\\\\n",
- "a_t &\\geq& \\underline{a}, \\\\\n",
- "m_{t+1} &=& R/(\\Gamma_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n",
- "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &=& 1, \\\\\n",
- "u(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n",
- "\\end{eqnarray*}\n",
- "\n",
- "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$. To create an $\\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shock joint. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks? Can't the joint distribution of permanent and transitory shocks be just about anything?\n",
- "\n",
- "$\\textit{Yes}$, and HARK can handle whatever correlation structure a user might care to specify. However, the default behavior of $\\texttt{IndShockConsumerType}$ is that the distribution of permanent income shocks is mean one lognormal, and the distribution of transitory shocks is mean one lognormal augmented with a point mass representing unemployment. The distributions are independent of each other by default, and are approximated with $N$ point equiprobable distributions.\n",
- "\n",
- "Let's make an infinite horizon instance of $\\texttt{IndShockConsumerType}$ with the same parameters as our original perfect foresight agent, plus the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary:\n",
- "\n",
- "\n",
- "| Param | Description | Code | Value |\n",
- "| :---: | --- | --- | :---: |\n",
- "| $\\underline{a}$ | Artificial borrowing constraint | $\\texttt{BoroCnstArt}$ | 0.0 |\n",
- "| $\\sigma_\\psi$ | Underlying stdev of permanent income shocks | $\\texttt{PermShkStd}$ | 0.1 |\n",
- "| $\\sigma_\\theta$ | Underlying stdev of transitory income shocks | $\\texttt{TranShkStd}$ | 0.1 |\n",
- "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | 7 |\n",
- "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | 7 |\n",
- "| $\\mho$ | Unemployment probability | $\\texttt{UnempPrb}$ | 0.05 |\n",
- "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | 0.3 |"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "code_folding": [
- 2
- ]
- },
- "outputs": [],
- "source": [
- "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n",
- "\n",
- "IndShockDictionary = {\n",
- " 'CRRA': 2.5, # The dictionary includes our original parameters...\n",
- " 'Rfree': 1.03,\n",
- " 'DiscFac': 0.96,\n",
- " 'LivPrb': [0.98],\n",
- " 'PermGroFac': [1.01],\n",
- " 'PermShkStd': [0.1], # ... and the new parameters for constructing the income process. \n",
- " 'PermShkCount': 7,\n",
- " 'TranShkStd': [0.1],\n",
- " 'TranShkCount': 7,\n",
- " 'UnempPrb': 0.05,\n",
- " 'IncUnemp': 0.3,\n",
- " 'BoroCnstArt': 0.0,\n",
- " 'aXtraMin': 0.001, # aXtra parameters specify how to construct the grid of assets.\n",
- " 'aXtraMax': 50., # Don't worry about these for now\n",
- " 'aXtraNestFac': 3,\n",
- " 'aXtraCount': 48,\n",
- " 'aXtraExtra': [None],\n",
- " 'vFuncBool': False, # These booleans indicate whether the value function should be calculated\n",
- " 'CubicBool': False, # and whether to use cubic spline interpolation. You can ignore them.\n",
- " 'aNrmInitMean' : -10.,\n",
- " 'aNrmInitStd' : 0.0, # These parameters specify the (log) distribution of normalized assets\n",
- " 'pLvlInitMean' : 0.0, # and permanent income for agents at \"birth\". They are only relevant in\n",
- " 'pLvlInitStd' : 0.0, # simulation and you don't need to worry about them.\n",
- " 'PermGroFacAgg' : 1.0,\n",
- " 'T_retire': 0, # What's this about retirement? ConsIndShock is set up to be able to\n",
- " 'UnempPrbRet': 0.0, # handle lifecycle models as well as infinite horizon problems. Swapping\n",
- " 'IncUnempRet': 0.0, # out the structure of the income process is easy, but ignore for now.\n",
- " 'T_age' : None,\n",
- " 'T_cycle' : 1,\n",
- " 'cycles' : 0,\n",
- " 'AgentCount': 10000,\n",
- " 'tax_rate':0.0,\n",
- "}\n",
- " \n",
- "# Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to\n",
- "# think about them for now."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As before, we need to import the relevant subclass of $\\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
- "IndShockExample = IndShockConsumerType(**IndShockDictionary)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can solve our new agent's problem just like before, using the $\\texttt{solve}$ method."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "IndShockExample.solve()\n",
- "plotFuncs(IndShockExample.solution[0].cFunc,0.,10.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Changing Constructed Attributes\n",
- "\n",
- "In the parameter dictionary above, we chose values for HARK to use when constructing its numeric representation of $F_t$, the joint distribution of permanent and transitory income shocks. When $\\texttt{IndShockExample}$ was created, those parameters ($\\texttt{TranShkStd}$, etc) were used by the $\\textbf{constructor}$ or $\\textbf{initialization}$ method of $\\texttt{IndShockConsumerType}$ to construct an attribute called $\\texttt{IncomeDstn}$.\n",
- "\n",
- "Suppose you were interested in changing (say) the amount of permanent income risk. From the section above, you might think that you could simply change the attribute $\\texttt{TranShkStd}$, solve the model again, and it would work.\n",
- "\n",
- "That's $\\textit{almost}$ true-- there's one extra step. $\\texttt{TranShkStd}$ is a primitive input, but it's not the thing you $\\textit{actually}$ want to change. Changing $\\texttt{TranShkStd}$ doesn't actually update the income distribution... unless you tell it to (just like changing an agent's preferences does not change the consumption function that was stored for the old set of parameters -- until you invoke the $\\texttt{solve}$ method again). In the cell below, we invoke the method $\\texttt{updateIncomeProcess}$ so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "OtherExample = deepcopy(IndShockExample) # Make a copy so we can compare consumption functions\n",
- "OtherExample.PermShkStd = [0.2] # Double permanent income risk (note that it's a one element list)\n",
- "OtherExample.updateIncomeProcess() # Call the method to reconstruct the representation of F_t\n",
- "OtherExample.solve()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the cell below, use your blossoming HARK skills to plot the consumption function for $\\texttt{IndShockExample}$ and $\\texttt{OtherExample}$ on the same figure."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Use the line(s) below to plot the consumptions functions against each other\n"
- ]
- }
- ],
- "metadata": {
- "jupytext": {
- "formats": "ipynb,py",
- "metadata_filter": {
- "cells": "collapsed"
- }
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- },
- "varInspector": {
- "cols": {
- "lenName": 16,
- "lenType": 16,
- "lenVar": 40
- },
- "kernels_config": {
- "python": {
- "delete_cmd_postfix": "",
- "delete_cmd_prefix": "del ",
- "library": "var_list.py",
- "varRefreshCmd": "print(var_dic_list())"
- },
- "r": {
- "delete_cmd_postfix": ") ",
- "delete_cmd_prefix": "rm(",
- "library": "var_list.r",
- "varRefreshCmd": "cat(var_dic_list()) "
- }
- },
- "types_to_exclude": [
- "module",
- "function",
- "builtin_function_or_method",
- "instance",
- "_Feature"
- ],
- "window_display": false
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/Examples/tests/test_gentle_intro_to_hark.py b/Examples/tests/test_gentle_intro_to_hark.py
deleted file mode 100644
index 3a2d6c574..000000000
--- a/Examples/tests/test_gentle_intro_to_hark.py
+++ /dev/null
@@ -1,43 +0,0 @@
-'''
-Tests that the gentle intro to HARK notebook runs correctly
-'''
-from __future__ import print_function, division
-from __future__ import absolute_import
-
-from builtins import str
-from builtins import zip
-from builtins import range
-from builtins import object
-
-import os
-import sys
-
-import nbformat
-import unittest
-from nbconvert.preprocessors import ExecutePreprocessor
-
-class TestGentleIntroToHark(unittest.TestCase):
-
- def test_notebook_runs(self):
- # we only test that the notebook works in python3
- if sys.version_info[0] < 3:
- return
-
- test_path = os.path.dirname(os.path.realpath(__file__))
- nb_path = os.path.join(test_path, '..', 'Gentle-Intro-To-HARK.ipynb')
- with open(nb_path) as nb_f:
- nb = nbformat.read(nb_f, as_version=nbformat.NO_CONVERT)
-
- ep = ExecutePreprocessor(timeout=60, kernel_name='python3')
- ep.allow_errors = True
- # this actually runs the notebook
- ep.preprocess(nb, {})
-
- errors = []
- for cell in nb.cells:
- if 'outputs' in cell:
- for output in cell['outputs']:
- if output.output_type == 'error':
- errors.append(output)
-
- self.assertFalse(errors)
diff --git a/Examples/util.py b/Examples/util.py
deleted file mode 100644
index 876172940..000000000
--- a/Examples/util.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from ipywidgets import IntProgress, HTML, VBox
-from IPython.display import display
-
-def log_progress(sequence, every=None, size=None, name='Items'):
- is_iterator = False
- if size is None:
- try:
- size = len(sequence)
- except TypeError:
- is_iterator = True
- if size is not None:
- if every is None:
- if size <= 200:
- every = 1
- else:
- every = int(size / 200) # every 0.5%
- else:
- assert every is not None, 'sequence is iterator, set every'
-
- if is_iterator:
- progress = IntProgress(min=0, max=1, value=1)
- progress.bar_style = 'info'
- else:
- progress = IntProgress(min=0, max=size, value=0)
- label = HTML()
- box = VBox(children=[label, progress])
- display(box)
-
- index = 0
- try:
- for index, record in enumerate(sequence, 1):
- if index == 1 or index % every == 0:
- if is_iterator:
- label.value = '{name}: {index} / ?'.format(
- name=name,
- index=index
- )
- else:
- progress.value = index
- label.value = u'{name}: {index} / {size}'.format(
- name=name,
- index=index,
- size=size
- )
- yield record
- except:
- progress.bar_style = 'danger'
- raise
- else:
- progress.bar_style = 'success'
- progress.value = index
- label.value = "{name}: {index}".format(
- name=name,
- index=str(index or '?')
- )
diff --git a/GOVERNANCE.md b/GOVERNANCE.md
deleted file mode 100644
index ee018ce74..000000000
--- a/GOVERNANCE.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# HARK Project Governance
-
-This document is not yet drafted. Please contact the [TSC](./README.md#tsc-technical-steering-committee) if you have any questions.
\ No newline at end of file
diff --git a/HARK/BayerLuetticke/Assets/One/EX1SS_nm50.p b/HARK/BayerLuetticke/Assets/One/EX1SS_nm50.p
deleted file mode 100755
index 5b860f7db..000000000
--- a/HARK/BayerLuetticke/Assets/One/EX1SS_nm50.p
+++ /dev/null
@@ -1,576 +0,0 @@
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\ No newline at end of file
diff --git a/HARK/BayerLuetticke/Assets/One/__init__.py b/HARK/BayerLuetticke/Assets/One/__init__.py
deleted file mode 100644
index d61028b5c..000000000
--- a/HARK/BayerLuetticke/Assets/One/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .FluctuationsOneAssetIOUs import *
-from .FluctuationsOneAssetIOUsBond import *
-from .SteadyStateOneAssetIOUs import *
-from .SteadyStateOneAssetIOUsBond import *
diff --git a/HARK/BayerLuetticke/Assets/__init__.py b/HARK/BayerLuetticke/Assets/__init__.py
deleted file mode 100644
index c2e3a1007..000000000
--- a/HARK/BayerLuetticke/Assets/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .One import *
-from .Two import *
\ No newline at end of file
diff --git a/HARK/BayerLuetticke/__init__.py b/HARK/BayerLuetticke/__init__.py
deleted file mode 100644
index f7b730eab..000000000
--- a/HARK/BayerLuetticke/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUs import *
-from HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUs import *
-from HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond import *
-from HARK.BayerLuetticke.Assets.One.SteadyStateOneAssetIOUsBond import *
-from HARK.BayerLuetticke.Assets.Two.SteadyStateTwoAsset import *
-from HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset import *
diff --git a/HARK/BayerLuetticke/notebooks/DCT-Copula-Illustration.ipynb b/HARK/BayerLuetticke/notebooks/DCT-Copula-Illustration.ipynb
deleted file mode 100644
index 7f4d74649..000000000
--- a/HARK/BayerLuetticke/notebooks/DCT-Copula-Illustration.ipynb
+++ /dev/null
@@ -1,1741 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Dimensionality Reduction in [Bayer and Luetticke (2018)](https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13071)\n",
- "\n",
- "[](https://mybinder.org/v2/gh/econ-ark/HARK/BayerLuetticke?filepath=HARK%2FBayerLuetticke%2FDCT-Copula-Illustration.ipynb)\n",
- "\n",
- "This companion to the [main notebook](TwoAsset.ipynb) explains in more detail how the authors reduce the dimensionality of their problem\n",
- "\n",
- "- Based on original slides by Christian Bayer and Ralph Luetticke \n",
- "- Original Jupyter notebook by Seungcheol Lee \n",
- "- Further edits by Chris Carroll, Tao Wang \n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Preliminaries\n",
- "\n",
- "In Steady-state Equilibrium (StE) in the model, in any given period, a consumer in state $s$ (which comprises liquid assets $m$, illiquid assets $k$, and human capital $\\newcommand{hLev}{h}\\hLev$) has two key choices:\n",
- "1. To adjust ('a') or not adjust ('n') their holdings of illiquid assets $k$\n",
- "1. Contingent on that choice, decide the level of consumption, yielding consumption functions:\n",
- " * $c_n(s)$ - nonadjusters\n",
- " * $c_a(s)$ - adjusters\n",
- "\n",
- "The usual envelope theorem applies here, so marginal value wrt the liquid asset equals marginal utility with respect to consumption:\n",
- "$[\\frac{d v}{d m} = \\frac{d u}{d c}]$.\n",
- "In practice, the authors solve their problem using the marginal value of money $\\texttt{Vm} = dv/dm$, but because the marginal utility function is invertible it is trivial to recover $\\texttt{c}$ from $(u^{\\prime})^{-1}(\\texttt{Vm} )$. The consumption function is therefore computed from the $\\texttt{Vm}$ function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "code_folding": []
- },
- "outputs": [],
- "source": [
- "# Setup stuff\n",
- "\n",
- "# This is a jupytext paired notebook that autogenerates a corresponding .py file\n",
- "# which can be executed from a terminal command line via \"ipython [name].py\"\n",
- "# But a terminal does not permit inline figures, so we need to test jupyter vs terminal\n",
- "# Google \"how can I check if code is executed in the ipython notebook\"\n",
- "def in_ipynb():\n",
- " try:\n",
- " if str(type(get_ipython())) == \"\":\n",
- " return True\n",
- " else:\n",
- " return False\n",
- " except NameError:\n",
- " return False\n",
- "\n",
- "# Determine whether to make the figures inline (for spyder or jupyter)\n",
- "# vs whatever is the automatic setting that will apply if run from the terminal\n",
- "if in_ipynb():\n",
- " # %matplotlib inline generates a syntax error when run from the shell\n",
- " # so do this instead\n",
- " get_ipython().run_line_magic('matplotlib', 'inline') \n",
- "else:\n",
- " get_ipython().run_line_magic('matplotlib', 'auto') \n",
- " \n",
- "# The tools for navigating the filesystem\n",
- "import sys\n",
- "import os\n",
- "\n",
- "# Find pathname to this file:\n",
- "my_file_path = os.path.dirname(os.path.abspath(\"DCT-Copula-Illustration.ipynb\"))\n",
- "\n",
- "# Relative directory for pickled code\n",
- "code_dir = os.path.join(my_file_path, \"../Assets/Two\") \n",
- "\n",
- "sys.path.insert(0, code_dir)\n",
- "sys.path.insert(0, my_file_path)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "code_folding": []
- },
- "outputs": [],
- "source": [
- "# Load precalculated Stationary Equilibrium (StE) object EX3SS\n",
- "\n",
- "import pickle\n",
- "os.chdir(code_dir) # Go to the directory with pickled code\n",
- "\n",
- "## EX3SS_20.p is the information in the stationary equilibrium \n",
- "## (20: the number of illiquid and liquid weath gridpoints)\n",
- "### The comments above are original, but it seems that there are 30 not 20 points now\n",
- "\n",
- "EX3SS=pickle.load(open(\"EX3SS_20.p\", \"rb\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Dimensions\n",
- "\n",
- "The imported StE solution to the problem represents the functions at a set of gridpoints of\n",
- " * liquid assets ($n_m$ points), illiquid assets ($n_k$), and human capital ($n_h$)\n",
- " * In the code these are $\\{\\texttt{nm,nk,nh}\\}$\n",
- "\n",
- "So even if the grids are fairly sparse for each state variable, the total number of combinations of the idiosyncratic state gridpoints is large: $n = n_m \\times n_k \\times n_h$. So, e.g., $\\bar{c}$ is a set of size $n$ containing the level of consumption at each possible _combination_ of gridpoints.\n",
- "\n",
- "In the \"real\" micro problem, it would almost never happen that a continuous variable like $m$ would end up being exactly equal to one of the prespecified gridpoints. But the functions need to be evaluated at such non-grid points. This is addressed by linear interpolation. That is, if, say, the grid had $m_{8} = 40$ and $m_{9} = 50$ then and a consumer ended up with $m = 45$ then the approximation is that $\\tilde{c}(45) = 0.5 \\bar{c}_{8} + 0.5 \\bar{c}_{9}$.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "code_folding": [
- 0
- ],
- "lines_to_next_cell": 2,
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "c_n is of dimension: (30, 30, 4)\n",
- "c_a is of dimension: (30, 30, 4)\n",
- "Vk is of dimension:(30, 30, 4)\n",
- "Vm is of dimension:(30, 30, 4)\n",
- "For convenience, these are all constructed from the same exogenous grids:\n",
- "30 gridpoints for liquid assets;\n",
- "30 gridpoints for illiquid assets;\n",
- "4 gridpoints for individual productivity.\n",
- "\n",
- "Therefore, the joint distribution is of size: \n",
- "30 * 30 * 4 = 3600\n"
- ]
- }
- ],
- "source": [
- "# Show dimensions of the consumer's problem (state space)\n",
- "\n",
- "print('c_n is of dimension: ' + str(EX3SS['mutil_c_n'].shape))\n",
- "print('c_a is of dimension: ' + str(EX3SS['mutil_c_a'].shape))\n",
- "\n",
- "print('Vk is of dimension:' + str(EX3SS['Vk'].shape))\n",
- "print('Vm is of dimension:' + str(EX3SS['Vm'].shape))\n",
- "\n",
- "print('For convenience, these are all constructed from the same exogenous grids:')\n",
- "print(str(len(EX3SS['grid']['m']))+' gridpoints for liquid assets;')\n",
- "print(str(len(EX3SS['grid']['k']))+' gridpoints for illiquid assets;')\n",
- "print(str(len(EX3SS['grid']['h']))+' gridpoints for individual productivity.')\n",
- "print('')\n",
- "print('Therefore, the joint distribution is of size: ')\n",
- "print(str(EX3SS['mpar']['nm'])+\n",
- " ' * '+str(EX3SS['mpar']['nk'])+\n",
- " ' * '+str(EX3SS['mpar']['nh'])+\n",
- " ' = '+ str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh']))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Dimension Reduction\n",
- "\n",
- "The authors use different dimensionality reduction methods for the consumer's problem and the distribution across idiosyncratic states"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Representing the consumer's problem with Basis Functions\n",
- "\n",
- "The idea is to find an efficient \"compressed\" representation of our functions (e.g., the consumption function), which BL do using tools originally developed for image compression. The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent how the colors _change_ from one pixel to nearby ones. Similarly, consumption at a given point $s_{i}$ is likely to be close to consumption point at another point $s_{j}$ that is \"close\" in the state space (similar wealth, income, etc), so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points.\n",
- "\n",
- "Like linear interpolation, the [DCT transformation](https://en.wikipedia.org/wiki/Discrete_cosine_transform) is a method of representing a continuous function using a finite set of numbers. It uses a set of independent [basis functions](https://en.wikipedia.org/wiki/Basis_function) to do this.\n",
- "\n",
- "But it turns out that some of those basis functions are much more important than others in representing the steady-state functions. Dimension reduction is accomplished by basically ignoring all basis functions that make \"small enough\" contributions to the representation of the function. \n",
- "\n",
- "##### When might this go wrong?\n",
- "\n",
- "Suppose the consumption function changes in a recession in ways that change behavior radically at some states. Like, suppose unemployment almost never happens in steady state, but it can happen in temporary recessions. Suppose further that, even for employed people, in a recession, _worries_ about unemployment cause many of them to prudently withdraw some of their illiquid assets -- behavior opposite of what people in the same state would be doing during expansions. In that case, the basis functions that represented the steady state function would have had no incentive to be able to represent well the part of the space that is never seen in steady state, so any functions that might help do so might well have been dropped in the dimension reduction stage.\n",
- "\n",
- "On the whole, it seems unlikely that this kind of thing is a major problem, because the vast majority of the variation that people experience is idiosyncratic. There is always unemployment, for example; it just moves up and down a bit with aggregate shocks, but since the experience of unemployment is in fact well represented in the steady state the method should have no trouble capturing it.\n",
- "\n",
- "Where the method might have more trouble is in representing economies in which there are multiple equilibria in which behavior is quite different."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### For the distribution of agents across states: Copula\n",
- "\n",
- "The other tool the authors use is the [\"copula\"](https://en.wikipedia.org/wiki/Copula_(probability_theory)), which allows us to represent the distribution of people across idiosyncratic states efficiently\n",
- "\n",
- "The copula is computed from the joint distribution of states in StE and will be used to transform the [marginal distributions](https://en.wikipedia.org/wiki/Marginal_distribution) back to joint distributions. (For an illustration of how the assumptions used when modeling asset price distributions using copulas can fail see [Salmon](https://www.wired.com/2009/02/wp-quant/))\n",
- "\n",
- " * A copula is a representation of the joint distribution expressed using a mapping between the uniform joint CDF and the marginal distributions of the variables\n",
- " \n",
- " * The crucial assumption is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same\n",
- " * An example of when this might not hold is the following. Suppose that in expansions, the people at the top of the distribution of illiquid assets (the top 1 percent, say) are also at the top 1 percent of liquid assets. But in recessions the bottom 99 percent get angry at the top 1 percent of illiquid asset holders and confiscate part of their liquid assets (the illiquid assets can't be confiscated quickly because they are illiquid). Now the people in the top 99 percent of illiquid assets might be in the _bottom_ 1 percent of liquid assets.\n",
- " \n",
- "- In this case we just need to represent how the mapping from ranks into levels of assets\n",
- "\n",
- "- This reduces the number of points for which we need to track transitions from $3600 = 30 \\times 30 \\times 4$ to $64 = 30+30+4$. Or the total number of points we need to contemplate goes from $3600^2 \\approx 13 $million to $64^2=4096$. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "code_folding": [
- 0
- ],
- "lines_to_next_cell": 2
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The copula consists of two parts: gridpoints and values at those gridpoints:\n",
- " gridpoints have dimensionality of (3600, 3)\n",
- " where the first element is total number of gridpoints\n",
- " and the second element is number of idiosyncratic state variables\n",
- " whose values also are of dimension of 3600\n",
- " each entry of which is the probability that all three of the\n",
- " state variables are below the corresponding point.\n"
- ]
- }
- ],
- "source": [
- "# Get some specs about the copula, which is precomputed in the EX3SS object\n",
- "\n",
- "print('The copula consists of two parts: gridpoints and values at those gridpoints:'+ \\\n",
- " '\\n gridpoints have dimensionality of '+str(EX3SS['Copula']['grid'].shape) + \\\n",
- " '\\n where the first element is total number of gridpoints' + \\\n",
- " '\\n and the second element is number of idiosyncratic state variables' + \\\n",
- " '\\n whose values also are of dimension of '+str(EX3SS['Copula']['value'].shape[0]) + \\\n",
- " '\\n each entry of which is the probability that all three of the'\n",
- " '\\n state variables are below the corresponding point.')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "## Import BL codes\n",
- "\n",
- "import sys \n",
- "\n",
- "# Relative directory for BL codes \n",
- "sys.path.insert(0,'../../../..') # comment by TW: this is not the same as in TwoAsset.ipynb. \n",
- "from HARK.BayerLuetticke.Assets.Two.FluctuationsTwoAsset import FluctuationsTwoAsset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "## Import other necessary libraries\n",
- "\n",
- "import numpy as np\n",
- "#from numpy.linalg import matrix_rank\n",
- "import scipy as sc\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import time\n",
- "import scipy.fftpack as sf # scipy discrete fourier transforms\n",
- "from mpl_toolkits.mplot3d import Axes3D\n",
- "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
- "from matplotlib import cm\n",
- "from matplotlib import lines\n",
- "import seaborn as sns\n",
- "import copy as cp\n",
- "from scipy import linalg #linear algebra "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "## Choose an aggregate shock to perturb(one of three shocks: MP, TFP, Uncertainty)\n",
- "\n",
- "# EX3SS['par']['aggrshock'] = 'MP'\n",
- "# EX3SS['par']['rhoS'] = 0.0 # Persistence of variance\n",
- "# EX3SS['par']['sigmaS'] = 0.001 # STD of variance shocks\n",
- "\n",
- "#EX3SS['par']['aggrshock'] = 'TFP'\n",
- "#EX3SS['par']['rhoS'] = 0.95\n",
- "#EX3SS['par']['sigmaS'] = 0.0075\n",
- " \n",
- "EX3SS['par']['aggrshock'] = 'Uncertainty'\n",
- "EX3SS['par']['rhoS'] = 0.84 # Persistence of variance\n",
- "EX3SS['par']['sigmaS'] = 0.54 # STD of variance shocks\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "## Choose an accuracy of approximation with DCT\n",
- "\n",
- "### Determines number of basis functions chosen -- enough to match this accuracy\n",
- "### EX3SS is precomputed steady-state pulled in above\n",
- "EX3SS['par']['accuracy'] = 0.99999"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "## Implement state reduction and DCT\n",
- "### Do state reduction on steady state\n",
- "EX3SR=FluctuationsTwoAsset(**EX3SS) # Takes StE result as input and get ready to invoke state reduction operation\n",
- "SR=EX3SR.StateReduc() # StateReduc is operated "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "code_folding": [
- 0
- ],
- "lines_to_next_cell": 2
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "What are the results from the state reduction?\n",
- "\n",
- "\n",
- "To achieve an accuracy of 0.99999\n",
- "\n",
- "The dimension of the policy functions is reduced to 154 from 3600\n",
- "The dimension of the marginal value functions is reduced to 94 from (30, 30, 4)\n",
- "The total number of control variables is 259=154+94+ # of other macro controls\n",
- "\n",
- "\n",
- "The copula represents the joint distribution with a vector of size (64, 60)\n",
- "The dimension of states including exogenous state, is 66\n",
- "It simply stacks all grids of different \n",
- " state variables regardless of their joint distributions. \n",
- " This is due to the assumption that the rank order remains the same.\n",
- "The total number of state variables is 62=60+ the number of macro states (like the interest rate)\n"
- ]
- }
- ],
- "source": [
- "# Measuring the effectiveness of the state reduction\n",
- "\n",
- "print('What are the results from the state reduction?')\n",
- "#print('Newly added attributes after the operation include \\n'+str(set(SR.keys())-set(EX3SS.keys())))\n",
- "\n",
- "print('\\n')\n",
- "\n",
- "print('To achieve an accuracy of '+str(EX3SS['par']['accuracy'])+'\\n') \n",
- "\n",
- "print('The dimension of the policy functions is reduced to '+str(SR['indexMUdct'].shape[0]) \\\n",
- " +' from '+str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh'])\n",
- " )\n",
- "print('The dimension of the marginal value functions is reduced to '+str(SR['indexVKdct'].shape[0]) \\\n",
- " + ' from ' + str(EX3SS['Vk'].shape))\n",
- "print('The total number of control variables is '+str(SR['Contr'].shape[0])+'='+str(SR['indexMUdct'].shape[0]) + \\\n",
- " '+'+str(SR['indexVKdct'].shape[0])+'+ # of other macro controls')\n",
- "print('\\n')\n",
- "print('The copula represents the joint distribution with a vector of size '+str(SR['Gamma_state'].shape) )\n",
- "print('The dimension of states including exogenous state, is ' +str(SR['Xss'].shape[0]))\n",
- "\n",
- "print('It simply stacks all grids of different\\\n",
- " \\n state variables regardless of their joint distributions.\\\n",
- " \\n This is due to the assumption that the rank order remains the same.')\n",
- "print('The total number of state variables is '+str(SR['State'].shape[0]) + '='+\\\n",
- " str(SR['Gamma_state'].shape[1])+'+ the number of macro states (like the interest rate)')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Graphical Illustration\n",
- "\n",
- "#### Policy/value functions\n",
- "\n",
- "Taking the consumption function as an example, we plot consumption by adjusters and non-adjusters over a range of $k$ and $m$ that encompasses 100 as well 90 percent of the mass of the distribution function,respectively. \n",
- "\n",
- "We plot the functions for the each of the 4 values of the wage $h$.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "code_folding": [
- 0
- ],
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "## Graphical illustration\n",
- "\n",
- "xi = EX3SS['par']['xi']\n",
- "invmutil = lambda x : (1./x)**(1./xi) \n",
- "\n",
- "### convert marginal utilities back to consumption function\n",
- "mut_StE = EX3SS['mutil_c']\n",
- "mut_n_StE = EX3SS['mutil_c_n'] # marginal utility of non-adjusters\n",
- "mut_a_StE = EX3SS['mutil_c_a'] # marginal utility of adjusters \n",
- "\n",
- "c_StE = invmutil(mut_StE)\n",
- "cn_StE = invmutil(mut_n_StE)\n",
- "ca_StE = invmutil(mut_a_StE)\n",
- "\n",
- "\n",
- "### grid values \n",
- "dim_StE = mut_StE.shape\n",
- "mgrid = EX3SS['grid']['m']\n",
- "kgrid = EX3SS['grid']['k']\n",
- "hgrid = EX3SS['grid']['h']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "## Define some functions to be used next\n",
- "\n",
- "def dct3d(x):\n",
- " x0=sf.dct(x.copy(),axis=0,norm='ortho')\n",
- " x1=sf.dct(x0.copy(),axis=1,norm='ortho')\n",
- " x2=sf.dct(x1.copy(),axis=2,norm='ortho')\n",
- " return x2\n",
- "\n",
- "def idct3d(x):\n",
- " x2 = sf.idct(x.copy(),axis=2,norm='ortho')\n",
- " x1 = sf.idct(x2.copy(),axis=1,norm='ortho')\n",
- " x0 = sf.idct(x1.copy(),axis=0,norm='ortho') \n",
- " return x0\n",
- "\n",
- "def DCTApprox(fullgrids,dct_index):\n",
- " dim=fullgrids.shape\n",
- " dctcoefs = dct3d(fullgrids)\n",
- " dctcoefs_rdc = np.zeros(dim)\n",
- " dctcoefs_rdc[dct_index]=dctcoefs[dct_index]\n",
- " approxgrids = idct3d(dctcoefs_rdc)\n",
- " return approxgrids"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Depending on the accuracy level, the DCT operation choses the necessary number of basis functions used to approximate consumption function at the full grids. This is illustrated in the p31-p34 in this [slides](https://www.dropbox.com/s/46fdxh0aphazm71/presentation_method.pdf?dl=0). We show this for both 1-dimensional (m or k) or 2-dimenstional grids (m and k) in the following. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "code_folding": [
- 0
- ],
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "154 basis functions used.\n",
- "37 basis functions used.\n",
- "14 basis functions used.\n",
- "5 basis functions used.\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "## 2D graph of consumption function: c(m) fixing k and h\n",
- "\n",
- "\n",
- "## list of accuracy levels \n",
- "Accuracy_BL = 0.99999 # From BL\n",
- "Accuracy_Less0 = 0.999\n",
- "Accuracy_Less1 = 0.99\n",
- "Accuracy_Less2 = 0.95\n",
- "\n",
- "acc_lst = np.array([Accuracy_BL,Accuracy_Less0,Accuracy_Less1,Accuracy_Less2])\n",
- "\n",
- "## c(m) fixing k and h\n",
- "fig = plt.figure(figsize=(8,8))\n",
- "fig.suptitle('c at full grids and c approximated by DCT in different accuracy levels' \n",
- " '\\n non-adjusters, fixing k and h',\n",
- " fontsize=(13))\n",
- "fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.3)\n",
- "\n",
- "for idx in range(len(acc_lst)):\n",
- " EX3SS_cp =cp.deepcopy(EX3SS) \n",
- " EX3SS_cp['par']['accuracy'] = acc_lst[idx]\n",
- " EX3SR_cp=FluctuationsTwoAsset(**EX3SS_cp) # Takes StE result as input and get ready to invoke state reduction operation\n",
- " SR_cp=EX3SR_cp.StateReduc()\n",
- " mut_rdc_idx_flt_cp = SR_cp['indexMUdct']\n",
- " mut_rdc_idx_cp = np.unravel_index(mut_rdc_idx_flt_cp,dim_StE,order='F')\n",
- " nb_bf_cp = len(mut_rdc_idx_cp[0])\n",
- " print(str(nb_bf_cp) +\" basis functions used.\")\n",
- " c_n_approx_cp = DCTApprox(cn_StE,mut_rdc_idx_cp)\n",
- " c_a_approx_cp = DCTApprox(ca_StE,mut_rdc_idx_cp)\n",
- " cn_diff_cp = c_n_approx_cp-cn_StE\n",
- " \n",
- " # choose the fix grid of h and k\n",
- " hgrid_fix=2 # fix level of h as an example \n",
- " kgrid_fix=10 # fix level of k as an example\n",
- " \n",
- " # get the corresponding c function approximated by dct\n",
- " cVec = c_a_approx_cp[:,kgrid_fix,hgrid_fix]\n",
- " \n",
- " ## plots \n",
- " ax = fig.add_subplot(2,2,idx+1)\n",
- " ax.plot(mgrid,cVec,label='c approximated by DCT')\n",
- " ax.plot(mgrid,ca_StE[:,kgrid_fix,hgrid_fix],'--',label='c at full grids')\n",
- " ax.plot(mgrid,cVec,'r*')\n",
- " ax.set_xlabel('m',fontsize=13)\n",
- " ax.set_ylabel(r'$c(m)$',fontsize=13)\n",
- " ax.set_title(r'accuracy=${}$'.format(acc_lst[idx]))\n",
- " ax.legend(loc=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "154 basis functions used.\n",
- "37 basis functions used.\n",
- "14 basis functions used.\n",
- "5 basis functions used.\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "## 2D graph of consumption function: c(k) fixing m and h\n",
- "\n",
- "fig = plt.figure(figsize=(8,8))\n",
- "fig.suptitle('c at full grids and c approximated by DCT in different accuracy levels' \n",
- " '\\n non-adjusters, fixing m and h',\n",
- " fontsize=(13))\n",
- "fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.3)\n",
- "\n",
- "for idx in range(len(acc_lst)):\n",
- " EX3SS_cp =cp.deepcopy(EX3SS)\n",
- " EX3SS_cp['par']['accuracy'] = acc_lst[idx]\n",
- " EX3SR_cp=FluctuationsTwoAsset(**EX3SS_cp) # Takes StE result as input and get ready to invoke state reduction operation\n",
- " SR_cp=EX3SR_cp.StateReduc()\n",
- " mut_rdc_idx_flt_cp= SR_cp['indexMUdct']\n",
- " mut_rdc_idx_cp = np.unravel_index(mut_rdc_idx_flt_cp,dim_StE,order='F')\n",
- " nb_bf_cp = len(mut_rdc_idx_cp[0])\n",
- " print(str(nb_bf_cp) +\" basis functions used.\")\n",
- " c_n_approx_cp = DCTApprox(cn_StE,mut_rdc_idx_cp)\n",
- " c_a_approx_cp = DCTApprox(ca_StE,mut_rdc_idx_cp)\n",
- " cn_diff_cp = c_n_approx_cp-cn_StE\n",
- " \n",
- " # choose the fix grid of h and m \n",
- " hgrid_fix=2 # fix level of h as an example \n",
- " mgrid_fix=10 # fix level of k as an example\n",
- " \n",
- " # get the corresponding c function approximated by dct\n",
- " cVec = c_n_approx_cp[mgrid_fix,:,hgrid_fix]\n",
- "\n",
- " ## plots \n",
- " ax = fig.add_subplot(2,2,idx+1)\n",
- " ax.plot(kgrid,cVec,label='c approximated by DCT')\n",
- " ax.plot(kgrid,cn_StE[mgrid_fix,:,hgrid_fix],'--',label='c at full grids')\n",
- " ax.plot(kgrid,cVec,'r*')\n",
- " ax.set_xlabel('k',fontsize=13)\n",
- " ax.set_ylabel(r'$c(k)$',fontsize=13)\n",
- " ax.set_title(r'accuracy=${}$'.format(acc_lst[idx]))\n",
- " ax.legend(loc=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Input: plot the graph for bottom x (0-1) of the distribution.\n",
- "0.99\n",
- "Input:choose the accuracy level for DCT, i.e. 0.99999 in the basline of Bayer and Luetticke\n",
- "0.99999\n"
- ]
- }
- ],
- "source": [
- "## Set the population density for plotting graphs \n",
- "\n",
- "print('Input: plot the graph for bottom x (0-1) of the distribution.')\n",
- "mass_pct = float(input())\n",
- "\n",
- "print('Input:choose the accuracy level for DCT, i.e. 0.99999 in the basline of Bayer and Luetticke')\n",
- "Accuracy_BS = float(input()) ## baseline accuracy level "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "# Restore the solution corresponding to the original BL accuracy\n",
- "\n",
- "EX3SS['par']['accuracy'] = Accuracy_BS\n",
- "EX3SR=FluctuationsTwoAsset(**EX3SS) # Takes StE result as input and get ready to invoke state reduction operation\n",
- "SR=EX3SR.StateReduc() # StateReduc is operated \n",
- "\n",
- "## meshgrids for plots\n",
- "\n",
- "mmgrid,kkgrid = np.meshgrid(mgrid,kgrid)\n",
- "\n",
- "## indexMUdct is one dimension, needs to be unraveled to 3 dimensions\n",
- "mut_rdc_idx_flt = SR['indexMUdct']\n",
- "mut_rdc_idx = np.unravel_index(mut_rdc_idx_flt,dim_StE,order='F')\n",
- "\n",
- "## Note: the following chunk of codes can be used to recover the indices of grids selected by DCT. not used here.\n",
- "#nb_dct = len(mut_StE.flatten()) \n",
- "#mut_rdc_bool = np.zeros(nb_dct) # boolean array of 30 x 30 x 4 \n",
- "#for i in range(nb_dct):\n",
- "# mut_rdc_bool[i]=i in list(SR['indexMUdct'])\n",
- "#mut_rdc_bool_3d = (mut_rdc_bool==1).reshape(dim_StE)\n",
- "#mut_rdc_mask_3d = (mut_rdc_bool).reshape(dim_StE)\n",
- "\n",
- "## For BL accuracy level, get dct compressed c functions at all grids \n",
- "\n",
- "c_n_approx = DCTApprox(cn_StE,mut_rdc_idx)\n",
- "c_a_approx = DCTApprox(ca_StE,mut_rdc_idx)\n",
- "\n",
- "\n",
- "# Get the joint distribution calculated elsewhere\n",
- "\n",
- "joint_distr = EX3SS['joint_distr']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "## Functions used to plot consumption functions at the trimmed grids\n",
- "\n",
- "def WhereToTrim2d(joint_distr,mass_pct):\n",
- " \"\"\"\n",
- " parameters\n",
- " -----------\n",
- " marginal1: marginal pdf in the 1st dimension\n",
- " marginal2: marginal pdf in the 2nd dimension\n",
- " mass_pct: bottom percentile to keep \n",
- " \n",
- " returns\n",
- " ----------\n",
- " trim1_idx: idx for trimming in the 1s dimension\n",
- " trim2_idx: idx for trimming in the 1s dimension\n",
- " \"\"\"\n",
- " \n",
- " marginal1 = joint_distr.sum(axis=0)\n",
- " marginal2 = joint_distr.sum(axis=1)\n",
- " ## this can handle cases where the joint_distr itself is a marginal distr from 3d, \n",
- " ## i.e. marginal.cumsum().max() =\\= 1 \n",
- " trim1_idx = (np.abs(marginal1.cumsum()-mass_pct*marginal1.cumsum().max())).argmin() \n",
- " trim2_idx = (np.abs(marginal2.cumsum()-mass_pct*marginal2.cumsum().max())).argmin()\n",
- " return trim1_idx,trim2_idx\n",
- "\n",
- "def TrimMesh2d(grids1,grids2,trim1_idx,trim2_idx,drop=True):\n",
- " if drop ==True:\n",
- " grids_trim1 = grids1.copy()\n",
- " grids_trim2 = grids2.copy()\n",
- " grids_trim1=grids_trim1[:trim1_idx]\n",
- " grids_trim2=grids_trim2[:trim2_idx]\n",
- " grids1_trimmesh, grids2_trimmesh = np.meshgrid(grids_trim1,grids_trim2)\n",
- " else:\n",
- " grids_trim1 = grids1.copy()\n",
- " grids_trim2 = grids2.copy()\n",
- " grids_trim1[trim1_idx:]=np.nan\n",
- " grids_trim2[trim2_idx:]=np.nan\n",
- " grids1_trimmesh, grids2_trimmesh = np.meshgrid(grids_trim1,grids_trim2)\n",
- " \n",
- " return grids1_trimmesh,grids2_trimmesh"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [],
- "source": [
- "## Other configurations for plotting \n",
- "\n",
- "distr_min = 0\n",
- "distr_max = np.nanmax(joint_distr)\n",
- "fontsize_lg = 13 \n",
- "\n",
- "## lower bound for grid \n",
- "mmin = np.nanmin(mgrid)\n",
- "kmin = np.nanmin(kgrid)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "code_folding": [
- 0
- ],
- "lines_to_next_cell": 2
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# For non-adjusters: 3D surface plots of consumption function at full grids and approximated by DCT\n",
- "## at all grids and grids after dct first for non-adjusters and then for adjusters\n",
- "\n",
- "\n",
- "fig = plt.figure(figsize=(14,14))\n",
- "fig.suptitle('Consumption of non-adjusters at grid points of m and k \\n where ' +str(int(mass_pct*100))+ ' % of the agents are distributed \\n (for each h)',\n",
- " fontsize=(fontsize_lg))\n",
- "for hgrid_id in range(EX3SS['mpar']['nh']):\n",
- " \n",
- " ## get the grids and distr for fixed h\n",
- " hgrid_fix = hgrid_id \n",
- " distr_fix = joint_distr[:,:,hgrid_fix]\n",
- " c_n_approx_fix = c_n_approx[:,:,hgrid_fix]\n",
- " c_n_StE_fix = cn_StE[:,:,hgrid_fix]\n",
- " \n",
- " ## additions to the above cell\n",
- " ## for each h grid, take the 90% mass of m and k as the maximum of the m and k axis \n",
- " mk_marginal = joint_distr[:,:,hgrid_fix]\n",
- " mmax_idx, kmax_idx = WhereToTrim2d(mk_marginal,mass_pct)\n",
- " mmax, kmax = mgrid[mmax_idx],kgrid[kmax_idx]\n",
- " mmgrid_trim,kkgrid_trim = TrimMesh2d(mgrid,kgrid,mmax_idx,kmax_idx)\n",
- " \n",
- " c_n_approx_trim = c_n_approx_fix.copy()\n",
- " c_n_approx_trim = c_n_approx_trim[:kmax_idx:,:mmax_idx] # the dimension is transposed for meshgrid.\n",
- " distr_fix_trim = distr_fix.copy()\n",
- "\n",
- " cn_StE_trim = c_n_StE_fix.copy()\n",
- " cn_StE_trim = cn_StE_trim[:kmax_idx,:mmax_idx] \n",
- " distr_fix_trim = distr_fix_trim[:kmax_idx,:mmax_idx]\n",
- " \n",
- " ## find the maximum z \n",
- " zmax = np.nanmax(c_n_approx_trim)\n",
- " \n",
- " \n",
- " ## plots \n",
- " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n",
- " scatter = ax.scatter(mmgrid_trim,kkgrid_trim,cn_StE_trim,\n",
- " marker='v',\n",
- " color='red')\n",
- " surface = ax.plot_surface(mmgrid_trim,kkgrid_trim,c_n_approx_trim,\n",
- " cmap='Blues')\n",
- " fake2Dline = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='b',\n",
- " marker='o') # fake line for making the legend for surface\n",
- " \n",
- " ax.contourf(mmgrid_trim,kkgrid_trim,distr_fix_trim, \n",
- " zdir='z',\n",
- " offset=np.min(distr_fix_trim),\n",
- " cmap=cm.YlOrRd,\n",
- " vmin=distr_min, \n",
- " vmax=distr_max)\n",
- " fake2Dline2 = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='orange',\n",
- " marker='o') # fakeline for making the legend for surface\n",
- " \n",
- " ax.set_xlabel('m',fontsize=fontsize_lg)\n",
- " ax.set_ylabel('k',fontsize=fontsize_lg)\n",
- " ax.set_zlabel(r'$c_n(m,k)$',fontsize=fontsize_lg)\n",
- " #ax.set_xlim([mmin,mmax])\n",
- " ax.set_ylim([kmax,kmin])\n",
- " ax.set_zlim([0,zmax])\n",
- " ax.set_title(r'$h({})$'.format(hgrid_fix))\n",
- " plt.gca().invert_xaxis()\n",
- " #plt.gca().invert_yaxis()\n",
- " ax.view_init(20, 70)\n",
- " ax.legend([scatter,fake2Dline,fake2Dline2], \n",
- " ['Full-grid c','Approximated c','Joint distribution'],\n",
- " loc=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# For adjusters: 3D surface plots of consumption function at full grids and approximated by DCT \n",
- "\n",
- " \n",
- "fig = plt.figure(figsize=(14,14))\n",
- "fig.suptitle('Consumption of adjusters at grid points of m and k \\n where ' +str(int(mass_pct*100))+ '% of agents are distributed \\n (for each h)',\n",
- " fontsize=(fontsize_lg))\n",
- "for hgrid_id in range(EX3SS['mpar']['nh']):\n",
- " \n",
- " ## get the grids and distr for fixed h\n",
- " hgrid_fix=hgrid_id\n",
- " c_a_StE_fix = ca_StE[:,:,hgrid_fix]\n",
- " c_a_approx_fix = c_a_approx[:,:,hgrid_fix]\n",
- " distr_fix = joint_distr[:,:,hgrid_fix]\n",
- " \n",
- " ## additions to the above cell\n",
- " ## for each h grid, take the 90% mass of m and k as the maximum of the m and k axis \n",
- " mk_marginal = joint_distr[:,:,hgrid_fix]\n",
- " mmax_idx, kmax_idx = WhereToTrim2d(mk_marginal,mass_pct)\n",
- " mmax, kmax = mgrid[mmax_idx],kgrid[kmax_idx]\n",
- " mmgrid_trim,kkgrid_trim = TrimMesh2d(mgrid,kgrid,mmax_idx,kmax_idx)\n",
- " c_a_approx_trim =c_a_approx_fix.copy()\n",
- " c_a_approx_trim = c_a_approx_trim[:kmax_idx,:mmax_idx]\n",
- " distr_fix_trim = distr_fix.copy()\n",
- " ca_StE_trim =c_a_StE_fix.copy()\n",
- " ca_StE_trim = ca_StE_trim[:kmax_idx,:mmax_idx] \n",
- " distr_fix_trim = distr_fix_trim[:kmax_idx,:mmax_idx]\n",
- "\n",
- " \n",
- " # get the maximum z\n",
- " zmax = np.nanmax(c_a_approx_trim)\n",
- " \n",
- " ## plots \n",
- " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n",
- " ax.scatter(mmgrid_trim,kkgrid_trim,ca_StE_trim,marker='v',color='red',\n",
- " label='full-grid c:adjuster')\n",
- " ax.plot_surface(mmgrid_trim,kkgrid_trim,c_a_approx_trim,cmap='Blues',\n",
- " label='approximated c: adjuster')\n",
- " fake2Dline = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='b',\n",
- " marker='o') # fake line for making the legend for surface\n",
- " ax.contourf(mmgrid_trim,kkgrid_trim,distr_fix_trim, \n",
- " zdir='z',\n",
- " offset=np.min(distr_fix_trim),\n",
- " cmap=cm.YlOrRd,\n",
- " vmin=distr_min,\n",
- " vmax=distr_max)\n",
- " fake2Dline2 = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='orange',\n",
- " marker='o') # fakeline for making the legend for surface\n",
- " ax.set_xlabel('m',fontsize=fontsize_lg)\n",
- " ax.set_ylabel('k',fontsize=fontsize_lg)\n",
- " ax.set_zlabel(r'$c_a(m,k)$',fontsize=fontsize_lg)\n",
- " #ax.set_xlim([mmin,mmax])\n",
- " ax.set_ylim([kmax,kmin])\n",
- " plt.gca().invert_xaxis()\n",
- " #plt.gca().invert_yaxis()\n",
- " ax.set_zlim([0,zmax])\n",
- " ax.set_title(r'$h({})$'.format(hgrid_fix))\n",
- " ax.view_init(20, 70)\n",
- " ax.legend([scatter,fake2Dline,fake2Dline2], \n",
- " ['Full-grid c','Approx c','Joint distribution'],\n",
- " loc=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "## 3D scatter plots of the difference of full-grid c and approximated c for non-adjusters\n",
- "\n",
- "fig = plt.figure(figsize=(14,14))\n",
- "fig.suptitle('Approximation errors of non-adjusters at grid points of m and k \\n where ' +str(int(mass_pct*100))+ '% of agents are distributed \\n (for each h)',\n",
- " fontsize=(fontsize_lg))\n",
- "for hgrid_id in range(EX3SS['mpar']['nh']):\n",
- " \n",
- " ## get the grids and distr for fixed h\n",
- " hgrid_fix = hgrid_id \n",
- " cn_diff = c_n_approx-cn_StE\n",
- " cn_diff_fix = cn_diff[:,:,hgrid_fix]\n",
- " distr_fix = joint_distr[:,:,hgrid_fix]\n",
- "\n",
- "\n",
- " ## additions to the above cell\n",
- " ## for each h grid, take the 90% mass of m and k as the maximum of the m and k axis \n",
- " mk_marginal = joint_distr[:,:,hgrid_fix]\n",
- " mmax_idx, kmax_idx = WhereToTrim2d(mk_marginal,mass_pct)\n",
- " mmax, kmax = mgrid[mmax_idx],kgrid[kmax_idx]\n",
- " mmgrid_trim,kkgrid_trim = TrimMesh2d(mgrid,kgrid,mmax_idx,kmax_idx)\n",
- " c_n_diff_trim = cn_diff_fix.copy()\n",
- " c_n_diff_trim = c_n_diff_trim[:kmax_idx,:mmax_idx] # first k and then m because c is is nk x nm \n",
- " distr_fix_trim = distr_fix.copy()\n",
- " distr_fix_trim = distr_fix_trim[:kmax_idx,:mmax_idx]\n",
- "\n",
- "\n",
- " ## plots \n",
- " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n",
- " \n",
- " ax.plot_surface(mmgrid_trim,kkgrid_trim,c_n_diff_trim, \n",
- " rstride=1, \n",
- " cstride=1,\n",
- " cmap=cm.coolwarm, \n",
- " edgecolor='none')\n",
- " fake2Dline_pos = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='r',\n",
- " marker='o') # fakeline for making the legend for surface\n",
- " fake2Dline_neg = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='b',\n",
- " marker='o') # fakeline for making the legend for surface\n",
- " ax.contourf(mmgrid_trim,kkgrid_trim,distr_fix_trim,\n",
- " zdir='z',\n",
- " offset=np.min(c_n_diff_trim),\n",
- " cmap=cm.YlOrRd,\n",
- " vmin=distr_min,\n",
- " vmax=distr_max)\n",
- " fake2Dline2 = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='orange',\n",
- " marker='o') # fakeline for making the legend for contour\n",
- " ax.set_xlabel('m',fontsize=fontsize_lg)\n",
- " ax.set_ylabel('k',fontsize=fontsize_lg)\n",
- " ax.set_zlabel(r'$c_a(m,k)$',fontsize=fontsize_lg)\n",
- " #ax.set_xlim([mmin,mmax])\n",
- " ax.set_ylim([kmax,kmin])\n",
- " plt.gca().invert_xaxis()\n",
- " #plt.gca().invert_yaxis()\n",
- " ax.set_title(r'$h({})$'.format(hgrid_fix))\n",
- " ax.view_init(20, 40)\n",
- " ax.legend([fake2Dline_pos,fake2Dline_neg,fake2Dline2], \n",
- " ['Positive approx errors','Negative approx errors','Joint distribution'],\n",
- " loc=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "154 basis functions used.\n",
- "37 basis functions used.\n",
- "14 basis functions used.\n",
- "5 basis functions used.\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Difference of full-grid c and DCT compressed c for each level of accuracy\n",
- "\n",
- "\n",
- "fig = plt.figure(figsize=(14,14))\n",
- "fig.suptitle('Approximation errors in different levels of accuracy \\n where ' +str(int(mass_pct*100))+ '% of agents are distributed \\n (non-adjusters)',\n",
- " fontsize=(fontsize_lg))\n",
- "\n",
- "for idx in range(len(acc_lst)):\n",
- " EX3SS_cp =cp.deepcopy(EX3SS)\n",
- " EX3SS_cp['par']['accuracy'] = acc_lst[idx]\n",
- " EX3SR_cp=FluctuationsTwoAsset(**EX3SS_cp) # Takes StE result as input and get ready to invoke state reduction operation\n",
- " SR_cp=EX3SR_cp.StateReduc()\n",
- " mut_rdc_idx_flt_cp = SR_cp['indexMUdct']\n",
- " mut_rdc_idx_cp = np.unravel_index(mut_rdc_idx_flt_cp,dim_StE,order='F')\n",
- " nb_bf_cp = len(mut_rdc_idx_cp[0])\n",
- " print(str(nb_bf_cp) +\" basis functions used.\")\n",
- " c_n_approx_cp = DCTApprox(cn_StE,mut_rdc_idx_cp)\n",
- " cn_diff_cp = c_n_approx_cp-cn_StE\n",
- " \n",
- " hgrid_fix=1 # fix level of h as an example \n",
- " c_n_diff_cp_fix = cn_diff_cp[:,:,hgrid_fix]\n",
- " distr_fix = joint_distr[:,:,hgrid_fix]\n",
- " \n",
- " ## for each h grid, take the 90% mass of m and k as the maximum of the m and k axis \n",
- " mk_marginal = joint_distr[:,:,hgrid_fix]\n",
- " mmax_idx, kmax_idx = WhereToTrim2d(mk_marginal,mass_pct)\n",
- " mmax, kmax = mgrid[mmax_idx],kgrid[kmax_idx]\n",
- " mmgrid_trim,kkgrid_trim = TrimMesh2d(mgrid,kgrid,mmax_idx,kmax_idx)\n",
- " c_n_diff_cp_trim = c_n_diff_cp_fix.copy()\n",
- " c_n_diff_cp_trim = c_n_diff_cp_trim[:kmax_idx:,:mmax_idx]\n",
- " distr_fix_trim = distr_fix.copy()\n",
- " distr_fix_trim = distr_fix_trim[:kmax_idx,:mmax_idx]\n",
- " \n",
- " ## plots \n",
- " ax = fig.add_subplot(2,2,idx+1, projection='3d')\n",
- " ax.plot_surface(mmgrid_trim,kkgrid_trim,c_n_diff_cp_trim, \n",
- " rstride=1, \n",
- " cstride=1,\n",
- " cmap=cm.coolwarm, \n",
- " edgecolor='none')\n",
- " fake2Dline_pos = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='r',\n",
- " marker='o') # fakeline for making the legend for surface\n",
- " fake2Dline_neg = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='b',\n",
- " marker='o') # fakeline for making the legend for surface\n",
- " dst_contour = ax.contourf(mmgrid_trim,kkgrid_trim,distr_fix_trim, \n",
- " zdir='z',\n",
- " offset=np.min(-2),\n",
- " cmap=cm.YlOrRd,\n",
- " vmin=distr_min, \n",
- " vmax=distr_max)\n",
- " fake2Dline2 = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='orange',\n",
- " marker='o') # fakeline for making the legend for contour\n",
- " ax.set_xlabel('m',fontsize=13)\n",
- " ax.set_ylabel('k',fontsize=13)\n",
- " ax.set_zlabel('Difference of c functions',fontsize=13)\n",
- " #ax.set_xlim([mmin,mmax])\n",
- " ax.set_ylim([kmax,kmin])\n",
- " plt.gca().invert_xaxis()\n",
- " #plt.gca().invert_yaxis()\n",
- " ax.set_zlim([-2,2]) # these are magic numbers. need to fix\n",
- " ax.set_title(r'accuracy=${}$'.format(acc_lst[idx]))\n",
- " ax.view_init(10, 60)\n",
- " ax.legend([fake2Dline_pos,fake2Dline_neg,fake2Dline2], \n",
- " ['+ approx errors','- approx errors','Joint distribution'],\n",
- " loc=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "code_folding": [
- 0
- ],
- "lines_to_next_cell": 2
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "154 basis functions used.\n",
- "37 basis functions used.\n",
- "14 basis functions used.\n",
- "5 basis functions used.\n"
- ]
- },
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Difference of full-grid c and DCT compressed c for difference levels of accuracy\n",
- "\n",
- "fig = plt.figure(figsize=(14,14))\n",
- "fig.suptitle('Differences of approximation errors between adjusters/non-adjusters \\n where ' +str(int(mass_pct*100))+ '% of agents are distributed \\n in different accuracy levels',\n",
- " fontsize=(fontsize_lg))\n",
- "\n",
- "for idx in range(len(acc_lst)):\n",
- " EX3SS_cp =cp.deepcopy(EX3SS)\n",
- " EX3SS_cp['par']['accuracy'] = acc_lst[idx]\n",
- " EX3SR_cp=FluctuationsTwoAsset(**EX3SS_cp) # Takes StE result as input and get ready to invoke state reduction operation\n",
- " SR_cp=EX3SR_cp.StateReduc()\n",
- " mut_rdc_idx_flt_cp = SR_cp['indexMUdct']\n",
- " mut_rdc_idx_cp = np.unravel_index(mut_rdc_idx_flt_cp,dim_StE,order='F')\n",
- " nb_bf_cp = len(mut_rdc_idx_cp[0])\n",
- " print(str(nb_bf_cp) +\" basis functions used.\")\n",
- " c_n_approx_cp = DCTApprox(cn_StE,mut_rdc_idx_cp)\n",
- " c_a_approx_cp = DCTApprox(ca_StE,mut_rdc_idx_cp)\n",
- " cn_diff_cp = c_n_approx_cp-cn_StE\n",
- " ca_diff_cp = c_a_approx_cp-ca_StE\n",
- " c_diff_cp_apx_error = ca_diff_cp - cn_diff_cp\n",
- " \n",
- " hgrid_fix=1 # fix level of h as an example \n",
- " c_diff_cp_apx_error_fix = c_diff_cp_apx_error[:,:,hgrid_fix]\n",
- " distr_fix = joint_distr[:,:,hgrid_fix]\n",
- "\n",
- "\n",
- " ## additions to the above cell\n",
- " ## for each h grid, take the 90% mass of m and k as the maximum of the m and k axis \n",
- " mk_marginal = joint_distr[:,:,hgrid_fix]\n",
- " mmax_idx, kmax_idx = WhereToTrim2d(mk_marginal,mass_pct)\n",
- " mmax, kmax = mgrid[mmax_idx],kgrid[kmax_idx]\n",
- " mmgrid_trim,kkgrid_trim = TrimMesh2d(mgrid,kgrid,mmax_idx,kmax_idx)\n",
- " c_diff_cp_apx_error_trim = c_diff_cp_apx_error_fix.copy()\n",
- " c_diff_cp_apx_error_trim = c_diff_cp_apx_error_trim[:kmax_idx,:mmax_idx]\n",
- " distr_fix_trim = distr_fix.copy()\n",
- " distr_fix_trim = distr_fix_trim[:kmax_idx,:mmax_idx]\n",
- " \n",
- " ## get the scale \n",
- " zmin = np.nanmin(c_diff_cp_apx_error)\n",
- " zmax = np.nanmax(c_diff_cp_apx_error)\n",
- " \n",
- " ## plots \n",
- " ax = fig.add_subplot(2,2,idx+1, projection='3d')\n",
- " ax.plot_surface(mmgrid_trim,kkgrid_trim,c_diff_cp_apx_error_trim, \n",
- " rstride=1, \n",
- " cstride=1,\n",
- " cmap=cm.coolwarm, \n",
- " edgecolor='none',\n",
- " label='Difference of full-grid and approximated consumption functions')\n",
- " fake2Dline_pos = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='r',\n",
- " marker='o') # fakeline for making the legend for surface\n",
- " fake2Dline_neg = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='b',\n",
- " marker='o') # fakeline for making the legend for surface\n",
- " ax.contourf(mmgrid_trim,kkgrid_trim,distr_fix_trim,\n",
- " zdir='z',\n",
- " offset=np.min(-0.2),\n",
- " cmap=cm.YlOrRd,\n",
- " vmin=distr_min, \n",
- " vmax=distr_max)\n",
- " fake2Dline2 = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='orange',\n",
- " marker='o') # fakeline for making the legend for contour\n",
- " ax.set_xlabel('m',fontsize=fontsize_lg)\n",
- " ax.set_ylabel('k',fontsize=fontsize_lg)\n",
- " ax.set_zlabel('Difference of approximation errors',fontsize=fontsize_lg)\n",
- " #ax.set_xlim([mmin,mmax])\n",
- " ax.set_ylim([kmax,kmin])\n",
- " plt.gca().invert_xaxis()\n",
- " #plt.gca().invert_yaxis()\n",
- " ax.set_zlim([-0.2,0.2]) # these are magic numbers. need to fix\n",
- " ax.set_title(r'accuracy=${}$'.format(acc_lst[idx]))\n",
- " ax.view_init(10, 60)\n",
- " ax.legend([fake2Dline_pos,fake2Dline_neg,fake2Dline2],\n",
- " ['+ diff','- diff','Joint distribution'],\n",
- " loc=0)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Observation\n",
- "\n",
- "- For a given grid value of productivity, the remaining grid points after DCT to represent the whole consumption function are concentrated in low values of $k$ and $m$. This is because the slopes of the surfaces of marginal utility are changing the most in these regions. For larger values of $k$ and $m$ the functions become smooth and only slightly concave, so they can be represented by many fewer points\n",
- "- For different grid values of productivity (2 sub plots), the numbers of grid points in the DCT operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of gridpoints of 154 after DCT operation, as we noted above for marginal utility function. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Distribution of states \n",
- "\n",
- "- We first plot the distribution of $k$ fixing $m$ and $h$. Next, we plot the joint distribution of $m$ and $k$ only fixing $h$ in 3-dimenstional space. \n",
- "- The joint-distribution can be represented by marginal distributions of $m$, $k$ and $h$ and a copula that describes the correlation between the three states. The former is straightfoward. We plot the copula only. The copula is essentially a multivariate cummulative distribution function where each marginal is uniform. (Translation from the uniform to the appropriate nonuniform distribution is handled at a separate stage).\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "### Marginalize along h grids\n",
- "\n",
- "joint_distr = EX3SS['joint_distr']\n",
- "joint_distr_km = EX3SS['joint_distr'].sum(axis=2)\n",
- "\n",
- "### Plot distributions in 2 dimensional graph \n",
- "\n",
- "fig = plt.figure(figsize=(10,10))\n",
- "plt.suptitle('Marginal distribution of k at different m \\n(for each h)')\n",
- "\n",
- "for hgrid_id in range(EX3SS['mpar']['nh']):\n",
- " ax = plt.subplot(2,2,hgrid_id+1)\n",
- " ax.set_title(r'$h({})$'.format(hgrid_id))\n",
- " ax.set_xlabel('k',size=fontsize_lg)\n",
- " for id in range(EX3SS['mpar']['nm']): \n",
- " ax.plot(kgrid,joint_distr[id,:,hgrid_id])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "## Plot joint distribution of k and m in 3d graph\n",
- "#for only 90 percent of the distributions \n",
- "\n",
- "fig = plt.figure(figsize=(14,14))\n",
- "fig.suptitle('Joint distribution of m and k \\n where ' +str(int(mass_pct*100))+ '% agents are distributed \\n(for each h)',\n",
- " fontsize=(fontsize_lg))\n",
- "\n",
- "for hgrid_id in range(EX3SS['mpar']['nh']):\n",
- " \n",
- " ## get the distr for fixed h\n",
- " hgrid_fix = hgrid_id \n",
- " joint_km = joint_distr[:,:,hgrid_fix]\n",
- " \n",
- " ## additions to the above cell\n",
- " ## for each h grid, take the 90% mass of m and k as the maximum of the m and k axis \n",
- " mk_marginal = joint_distr[:,:,hgrid_fix]\n",
- " mmax_idx, kmax_idx = WhereToTrim2d(mk_marginal,mass_pct)\n",
- " mmax, kmax = mgrid[mmax_idx],kgrid[kmax_idx]\n",
- " mmgrid_trim,kkgrid_trim = TrimMesh2d(mgrid,kgrid,mmax_idx,kmax_idx)\n",
- " joint_km_trim = joint_km.copy()\n",
- " joint_km_trim = joint_km_trim[:kmax_idx,:mmax_idx]\n",
- " \n",
- " # get the maximum z\n",
- " zmax = np.nanmax(joint_distr)\n",
- " \n",
- " ## plots \n",
- " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n",
- " ax.plot_surface(mmgrid_trim,kkgrid_trim,joint_km_trim, \n",
- " rstride=1, \n",
- " cstride=1,\n",
- " cmap=cm.YlOrRd, \n",
- " edgecolor='none',\n",
- " vmin=distr_min, \n",
- " vmax=distr_max)\n",
- " fake2Dline = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='orange',\n",
- " marker='o') # fakeline for making the legend for contour\n",
- " ax.set_xlabel('m',fontsize=fontsize_lg)\n",
- " ax.set_ylabel('k',fontsize=fontsize_lg)\n",
- " ax.set_zlabel('Probability',fontsize=fontsize_lg)\n",
- " ax.set_title(r'$h({})$'.format(hgrid_id))\n",
- " #ax.set_xlim([mmin,mmax])\n",
- " ax.set_ylim([kmax,kmin])\n",
- " ax.set_zlim([0,zmax])\n",
- " plt.gca().invert_xaxis()\n",
- " #plt.gca().invert_yaxis()\n",
- " ax.view_init(20, 60)\n",
- " ax.legend([fake2Dline], \n",
- " ['joint distribution'],\n",
- " loc=0)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Notice the CDFs in StE copula have 4 modes, corresponding to the number of $h$ gridpoints. Each of the four parts of the cdf is a joint-distribution of $m$ and $k$. It can be presented in 3-dimensional graph as below. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "code_folding": [
- 0
- ]
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "## Plot the copula \n",
- "# same plot as above for only 90 percent of the distributions \n",
- "\n",
- "\n",
- "cdf=EX3SS['Copula']['value'].reshape(4,30,30) # important: 4,30,30 not 30,30,4? \n",
- "\n",
- "fig = plt.figure(figsize=(14,14))\n",
- "fig.suptitle('Copula of m and k \\n where ' +str(int(mass_pct*100))+ '% agents are distributed \\n(for each h)',\n",
- " fontsize=(fontsize_lg))\n",
- "for hgrid_id in range(EX3SS['mpar']['nh']):\n",
- " \n",
- " hgrid_fix = hgrid_id \n",
- " cdf_fix = cdf[hgrid_fix,:,:]\n",
- " \n",
- " ## additions to the above cell\n",
- " ## for each h grid, take the 90% mass of m and k as the maximum of the m and k axis \n",
- " mk_marginal = joint_distr[:,:,hgrid_fix]\n",
- " mmax_idx, kmax_idx = WhereToTrim2d(mk_marginal,mass_pct)\n",
- " mmax, kmax = mgrid[mmax_idx],kgrid[kmax_idx]\n",
- " mmgrid_trim,kkgrid_trim = TrimMesh2d(mgrid,kgrid,mmax_idx,kmax_idx)\n",
- " cdf_fix_trim = cdf_fix.copy()\n",
- " cdf_fix_trim = cdf_fix_trim[:kmax_idx,:mmax_idx]\n",
- " \n",
- " ## plots \n",
- " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n",
- " ax.plot_surface(mmgrid_trim,kkgrid_trim,cdf_fix_trim, \n",
- " rstride=1, \n",
- " cstride=1,\n",
- " cmap =cm.Greens, \n",
- " edgecolor='None')\n",
- " fake2Dline = lines.Line2D([0],[0], \n",
- " linestyle=\"none\", \n",
- " c='green',\n",
- " marker='o')\n",
- " ax.set_xlabel('m',fontsize=fontsize_lg)\n",
- " ax.set_ylabel('k',fontsize=fontsize_lg)\n",
- " ax.set_title(r'$h({})$'.format(hgrid_id))\n",
- " \n",
- " ## for each h grid, take the 95% mass of m and k as the maximum of the m and k axis \n",
- " \n",
- " marginal_mk = joint_distr[:,:,hgrid_id]\n",
- " marginal_m = marginal_mk.sum(axis=0)\n",
- " marginal_k = marginal_mk.sum(axis=1)\n",
- " mmax = mgrid[(np.abs(marginal_m.cumsum()-mass_pct*marginal_m.cumsum().max())).argmin()]\n",
- " kmax = kgrid[(np.abs(marginal_k.cumsum()-mass_pct*marginal_k.cumsum().max())).argmin()]\n",
- " #ax.set_xlim([mmin,mmax])\n",
- " ax.set_ylim([kmax,kmin])\n",
- " plt.gca().invert_xaxis()\n",
- " #plt.gca().invert_yaxis()\n",
- " ax.view_init(30, 60)\n",
- " ax.legend([fake2Dline], \n",
- " ['Marginal cdf of the copula'],\n",
- " loc=0)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## More to do:\n",
- "\n",
- "1. Figure out median value of h and normalize c, m, and k by it"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Given the assumption that the copula remains the same after aggregate risk is introduced, we can use the same copula and the marginal distributions to recover the full joint-distribution of the states. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Summary: what do we achieve after the transformation?\n",
- "\n",
- "- Using the DCT, the dimension of the policy and value functions are reduced from 3600 to 154 and 94, respectively.\n",
- "- By marginalizing the joint distribution with the fixed copula assumption, the marginal distribution is of dimension 64 compared to its joint distribution of a dimension of 3600.\n",
- "\n",
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diff --git a/HARK/BayerLuetticke/notebooks/OneAsset-HANK.ipynb b/HARK/BayerLuetticke/notebooks/OneAsset-HANK.ipynb
deleted file mode 100644
index b24247bfb..000000000
--- a/HARK/BayerLuetticke/notebooks/OneAsset-HANK.ipynb
+++ /dev/null
@@ -1,642 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# A One Asset HANK Model \n",
- "\n",
- "This notebook solves a New Keynesian model in which there is only a single liquid asset. This is the second model described in \":\n",
- " return True\n",
- " else:\n",
- " return False\n",
- " except NameError:\n",
- " return False\n",
- "\n",
- "# Determine whether to make the figures inline (for spyder or jupyter)\n",
- "# vs whatever is the automatic setting that will apply if run from the terminal\n",
- "if in_ipynb():\n",
- " # %matplotlib inline generates a syntax error when run from the shell\n",
- " # so do this instead\n",
- " get_ipython().run_line_magic('matplotlib', 'inline') \n",
- "else:\n",
- " get_ipython().run_line_magic('matplotlib', 'auto') \n",
- " \n",
- "# The tools for navigating the filesystem\n",
- "import sys\n",
- "import os\n",
- "\n",
- "# Find pathname to this file:\n",
- "my_file_path = os.path.dirname(os.path.abspath(\"OneAsset-HANK.ipynb\"))\n",
- "\n",
- "# Relative directory for pickled code\n",
- "code_dir = os.path.join(my_file_path, \"../Assets/One/\") \n",
- "\n",
- "sys.path.insert(0, code_dir)\n",
- "sys.path.insert(0, my_file_path)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "code_folding": [
- 0
- ],
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "# Ignore system warnings while running the notebook\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')\n",
- "\n",
- "# Load Stationary equilibrium (StE) object EX2SS\n",
- "\n",
- "import pickle\n",
- "os.chdir(code_dir) # Go to the directory with pickled code\n",
- "\n",
- "## EX2SS.p is the information in the stationary equilibrium (20: the number of illiquid and liquid weath grids )\n",
- "EX2SS=pickle.load(open(\"EX2SS.p\", \"rb\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "from HARK.BayerLuetticke.Assets.One.FluctuationsOneAssetIOUsBond import FluctuationsOneAssetIOUs, SGU_solver, plot_IRF"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "code_folding": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Elapsed time is 0.05101699999999987 seconds.\n",
- "Use Schmitt Grohe Uribe Algorithm\n",
- " A *E[xprime uprime] =B*[x u]\n",
- " A = (dF/dxprimek dF/duprime), B =-(dF/dx dF/du)\n",
- "Computing Jacobian F1=DF/DXprime F3 =DF/DX\n",
- "Total number of parallel blocks: 3 .\n",
- "Block number: 0 done.\n",
- "Block number: 1 done.\n",
- "Block number: 2 done.\n",
- "Elapsed time is 25.685692000000003 seconds.\n",
- "Computing Jacobian F2 - DF/DYprime\n",
- "Total number of parallel blocks: 3 .\n",
- "Block number: 0 done.\n",
- "Block number: 1 done.\n",
- "Block number: 2 done.\n",
- "Elapsed time is 0.9454619999999991 seconds.\n"
- ]
- },
- {
- "data": {
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Uncertainty Shock\n",
- " \n",
- "EX2SS['par']['aggrshock'] = 'Uncertainty'\n",
- "EX2SS['par']['rhoS'] = 0.84 # Persistence of variance\n",
- "EX2SS['par']['sigmaS'] = 0.54 # STD of variance shocks\n",
- "\n",
- "EX2SR=FluctuationsOneAssetIOUs(**EX2SS)\n",
- "SR=EX2SR.StateReduc()\n",
- "\n",
- "SGUresult=SGU_solver(SR['Xss'],SR['Yss'],SR['Gamma_state'],SR['Gamma_control'],SR['InvGamma'],SR['Copula'],\n",
- " SR['par'],SR['mpar'],SR['grid'],SR['targets'],SR['P_H'],SR['aggrshock'],SR['oc'])\n",
- "\n",
- "plot_IRF(SR['mpar'],SR['par'],SGUresult['gx'],SGUresult['hx'],SR['joint_distr'],\n",
- " SR['Gamma_state'],SR['grid'],SR['targets'],SR['os'],SR['oc'],SR['Output'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Elapsed time is 0.04381399999999758 seconds.\n",
- "Use Schmitt Grohe Uribe Algorithm\n",
- " A *E[xprime uprime] =B*[x u]\n",
- " A = (dF/dxprimek dF/duprime), B =-(dF/dx dF/du)\n",
- "Computing Jacobian F1=DF/DXprime F3 =DF/DX\n",
- "Total number of parallel blocks: 3 .\n",
- "Block number: 0 done.\n",
- "Block number: 1 done.\n",
- "Block number: 2 done.\n",
- "Elapsed time is 26.346356 seconds.\n",
- "Computing Jacobian F2 - DF/DYprime\n",
- "Total number of parallel blocks: 3 .\n",
- "Block number: 0 done.\n",
- "Block number: 1 done.\n",
- "Block number: 2 done.\n",
- "Elapsed time is 0.9628140000000016 seconds.\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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Rt8/BzJq4+5rw1fOAeUHmEYlVO3blcc1LaXy7PJP7z+3EwB5HBB1JYkjUlQPwTzNLIfS20grg2mDjiMSeLTtzGTxmOnMytvDIRV0479jkoCNJjIm6cnD3y4POIBLLtu/K44oXprNg9RaevKwrvTsVt3tPZN+irhxE5OBl5+Yz5MUZzFu1hWcGHsfpHRoFHUliVLQeyioiBygnr4AbXpnFtB828chFXVQMckj2Ww5m9o/S3CYiwckvcG6dMJv/LFrPiHOPoV9Ks6AjSYwrzZbD6cXcdlZZBxGRg+Pu3PX2XD5MX8NdZx/NZd1bBB1J4kCJ+xzM7HrgBqC1maUXGqoF/C/SwURk/9yd+z5YyPi0ldz8u6N0HoOUmX3tkH4V+Bh4ELij0O3b3H1TRFOJSKk8Nvl7XvjfDwz+TUtuPb1t0HEkjpRYDu6+BdgCXGpmCUCj8P1rmllNd/+pnDKKSDGe/3o5j3/+PRelJnP37ztoSgwpU/s9lNXMbgLuBdYBBeGbHegcuVgisi+vTf+J+z9cyO+PacKD53emkibRkzJWmvMcbgHauXtmpMOIyP69N2c1d70zl57tGvDoxSmaXVUiojRHK60k9PaSiARs8oJ1/N/42XRreTjPDDyOKpV1qpJERmm2HJYDU8zsQ2DX7hvd/ZGIpRKRIr5ZupEbXp1Fx6a1ef7KVJISE4KOJHGsNOXwU/ijSvhDRMrZrJ9+ZshLabSqV4Oxg7tRKykx6EgS5/ZbDu7+t/IIIiLFW7hmK4NemE6DWlV5+epuHFZDr9Ek8vZ1Etxj7n6Lmb1P6OikPbh734gmExGWb9jO5aOnUaNqZcZd3Z2GtZOCjiQVxL62HF4Ofx5ZHkFEZE+rNu9k4PPTcIdxQ7rT/PDqQUeSCmRfJ8HNDH/+0syqALtPv1zs7rnlEU6kolq/LZsBz01l2648Xh/agyMb1Aw6klQwpTkJrifwIqFV2QxobmZXuvtXkY0mUjFtzsrhitHTWb9tFy9f3Z2OTesEHUkqoNIcrfQwcIa7LwYws7bAa8BxkQwmUhFt35XHlWNmsHzDDsYMPp7jjjgs6EhSQZXmDJrE3cUA4O5LAB1HJ1LGCq/i9uSArvzmqPpBR5IKrDRbDmlmNppfd1APAGZGLpJIxZOb/+sqbo9dnKJV3CRwpSmH64EbgZsJ7XP4CngqkqFEKpKCAuf/JswJreJ2Xiet4iZRoTQnwe0ysyeAzwnNyrrY3XMinkykghjx0ULen7OaO846mgHdjwg6jghQujWkfw8sAx4HngCWmtkhLRNqZv3NbL6ZFZhZ6l5jd5rZUjNbbGZnHsrXEYl2z3+9nNH/DS3Wc+0prYOOI/KL0h6t9Ft3XwpgZkcCHxJaJe5gzQPOB54tfKOZdQAuAToCTYHJZtbW3fMP4WuJRKX35qzm/g8XcvYxjbVYj0Sd0hyttH53MYQtB9Yfyhd194WFj4AqpB/wurvvcvcfgKVAt0P5WiLR6NtlmQybMIduLQ/nkYtStFiPRJ3SbDnMN7OPgAmE5ljqD8wws/MB3P3tMszTDJha6HpG+DaRuLFo7VaGvpzGEfWq89wVmnpbolNpyiGJ0BKhp4avbwAOB84hVBbFloOZTQYaFzM03N0nlvC1inv5VGTSv/DzDwWGArRo0aKk7CJRZfXmnQx6YQbVqyQw9qpu1KmuU4YkOpXmaKXBB/PE7t7rIB6WATQvdD0ZWF3C848CRgGkpqYWWyAi0WTLzlwGjZnOjl15TLjuBJrVrRZ0JJESRdsag+8Bl5hZVTNrBbQBpgecSeSQ7crLZ+hLafywcQfPXn4c7ZvUDjqSyD4FUg5mdp6ZZQAnAB+a2acA7j6f0L6NBcAnwI06Ukli3e6T3Kb9sImR/btwoqbFkBhQmn0OZc7d3wHeKWFsBDCifBOJRM6IjxbyYfoa7jr7aJ39LDGjNCfB/dHMalvIaDObZWZnlEc4kVi3+yS3QSe25JqTdZKbxI7SvK10lbtvBc4AGgCDgYcimkokDrxf+CS3PjrJTWJLacph90/02cAYd59D8YecikjYt8syua3QSW4JOslNYkxpymGmmU0iVA6fmlktQhPwiUgxdJKbxIPS7JC+GkgBlrt7lpnVI/TWkojsRSe5SbwosRzM7Gh3X0SoGABa6z1TkZLpJDeJJ/vacvg/QtNTPFzMmAO/i0gikRhU+CS3Fwd300luEvNKLAd3Hxr+/NvyiyMSewqf5Pb4JSk6yU3iQmnOc+gf3gmNmf3FzN42s2MjH00kNugkN4lHpTla6W5332ZmJwFnAi8Cz0Q2lkhs0EluEq9KUw675zb6PfB0eLrtKpGLJBIbdJKbxLPSlMMqM3sWuAj4yMyqlvJxInFLJ7lJvCvNH/mLgE+B3u6+mdBCP7dHNJVIFFu8dptOcpO4t99ycPes8FKgW8ysBZAILIp4MpEotHZLNoPGTKdaok5yk/hWmqOV+prZ98APwJfhzx9HOphItNmaHTrJbVt2HmMGH6+T3CSuleZtpfuAHsASd28F9AL+F9FUIlEmJ6+A68fNZOn67Tw9sCsdm9YJOpJIRJWmHHLdPROoZGaV3P0Lfp1SQyTuuTt/fiud/y3N5B8XdObkNg2CjiQScaWZeG+zmdUEvgJeMbP1QF5kY4lEj5GTFvPOd6sYdkZbLjguOeg4IuWiNFsO/YAs4FZC6zovA86JZCiRaDFu6o88+cUyLu3WnBt/e1TQcUTKzX63HNx9R/higZl9CGS6u0c2lkjwJi9Yxz0T5/Hbdg24r18nneQmFUqJWw5m1sPMpuyeS8nM5gHzgHVm1rv8IoqUv9krN/OH176jU7M6PHFZVyon6LxPqVj2teXwBHAXUAf4D3CWu081s6OB1wi9xSQSd37M3MHVY2dQv1YVRl95PDWqlmbXnEh82dfLocruPsnd3wDWuvtUgPACQCJxKXP7Lq58YToF7rw4uBsNalUNOpJIIPZVDoXXid6519gh7XMITwM+38wKzCy10O0tzWynmc0Of2j2Vyk3O3PyGfJSGmu2ZPP8lam0blAz6EgigdnX9nIXM9sKGFAtfJnw9aRD/LrzgPOBZ4sZW+buOo9CylV+gXPz698xe+Vmnh5wHMcdcXjQkUQCta+V4CI2m5i7LwR09IdEBXfn3vfm89mCdfytb0d6d2ocdCSRwEXjIRitzOw7M/vSzE4u6U5mNtTM0swsbcOGDeWZT+LMs18t5+WpPzL0lNZceWLLoOOIRIWIHYZhZpOB4l6CDQ8vGFScNUALd880s+OAd82so7tv3fuO7j4KGAWQmpqq8y7koEycvYqHPl5En85NuKP30UHHEYkaESsHd+91EI/ZBewKX55pZsuAtkBaGccT4ZtlGxn2xhy6tzqchy/qQiUt2CPyi6h6W8nMGphZQvhya6ANsDzYVBKPFq/dxrUvzaRlvRqMujyVqpW1YI9IYYGUg5mdZ2YZwAnAh2b2aXjoFCDdzOYAbwLXufumIDJK/PplwZ4qWrBHpCSBnPrp7u8A7xRz+1vAW+WfSCqKwgv2jL+2hxbsESlBVL2tJBJJWrBHpPQ0aYxUCIUX7Hm4fxct2COyH9pykAph94I9t52uBXtESkPlIHHv5W9X/LJgz02/04I9IqWhcpC49s53Gdw9cT692jfUgj0iB0DlIHHrswXrGPZGOie0rqcFe0QOkH5bJC59s2wjN746i05Na/PclakkJeokN5EDoXKQuDN75WaueTGNIw6vztjB3aipldxEDpjKQeLK4rXbGDRmOofXrMK4Id05rEaVoCOJxCSVg8SNnzKzuHz0NKokVOKVq3vQqPahrkklUnFpe1viwrqt2QwYPZWc/ALGDz2BFvWqBx1JJKZpy0Fi3s87chj4/DQ2bc9h7OButGtcK+hIIjFPWw4S07bvymPQmOn8uCmLsYOPJ6V53aAjicQFbTlIzMrOzWfIizOYt3orT13WlROPrB90JJG4oXKQmJSbX8BNr85i2g+beLh/F3p1aBR0JJG4onKQmFNQ4Nz+xhwmL1zP3/t25NxjmwUdSSTuqBwkprg7f31vPu/OXs3tZ7bj8hNaBh1JJC6pHCSmjJy0mJen/si1p7Tmhp5HBh1HJG6pHCRmPPvlsl+m3r7jrKM1w6pIBKkcJCa8Nv0nHvx4EX06N+H+c49RMYhEmMpBot77c1Zz1ztz6dmuAY9clEJCJRWDSKSpHCSqfbFoPbeOn83xRxzO0wOOo0pl/ciKlAf9pknUmrY8k+vGzeToJrV4flAq1apoTQaR8hJIOZjZv8xskZmlm9k7Zla30NidZrbUzBab2ZlB5JPgzc3YwtUvppF8WDVeHNyN2kmJQUcSqVCC2nL4DOjk7p2BJcCdAGbWAbgE6Aj0Bp4yM71crGCWrt/OlWOmU6daIuOGdKdezapBRxKpcAIpB3ef5O554atTgeTw5X7A6+6+y91/AJYC3YLIKMGYv3oLl4yaSiUzxg3pTpM61YKOJFIhRcM+h6uAj8OXmwErC41lhG8rwsyGmlmamaVt2LAhwhGlPExdnsklz04lMcF4fWh3WtWvEXQkkQorYlN2m9lkoHExQ8PdfWL4PsOBPOCV3Q8r5v5e3PO7+yhgFEBqamqx95HYMWn+Wm567TuaH1aNl6/uTtO62mIQCVLEysHde+1r3MyuBPoAp7n77j/uGUDzQndLBlZHJqFEiwkzVnLH2+kck1yXMYOO53Ct+ywSuKCOVuoN/Bno6+5ZhYbeAy4xs6pm1gpoA0wPIqOUj2e+XMaf3krnN0fV59Uh3VUMIlEiqJXgngCqAp+Fp0GY6u7Xuft8M5sALCD0dtON7p4fUEaJoIIC56FPFjHqq+X06dyERy5K0QluIlEkkHJw96P2MTYCGFGOcaSc5eYXcMdbc3lrVgZXnHAE957TkUqaEkMkqmgNaSlXO3PyuenVWXy+aD239mrLzacdpUn0RKKQykHKzZasXIa8NIO0H3/mvnM7cXmPI4KOJCIlUDlIuVi3NZsrX5jOsg3b+felx9Knc9OgI4nIPqgcJOJ+2LiDy0dPY9OOHMYM6sZJbeoHHUlE9kPlIBE1b9UWBo2ZTn6B89o1PejSvO7+HyQigVM5SMR8uyyTa15Ko3ZSZV4a2p2jGtYMOpKIlJLKQSLik3lrufm172hRrzovX91NE+iJxBiVg5S516f/xF3vzKVzeDqMw3TWs0jMUTlImXF3npqyjH99uphT2jbgmYFdqV5FP2IisUi/uVImCgqcER8tZPR/f6Bvl6aM7N9F02GIxDCVgxyy3PwC/vRmOu98t4pBJ7bknj4dNB2GSIxTOcghydy+i9vemMOUxRu47fS23PQ7TYchEg9UDnLQPpq7hrvfncfW7FxGnNeJAd01HYZIvFA5yAHL3L6LeybO58O5azimWR1e7d+Ddo1rBR1LRMqQykEOyIfpa7h74jy2Zedy+5ntGHpKaxITtONZJN6oHKRUNm7fxT0T5/HR3LV0Tq7Dvy7U1oJIPFM5yH59kL6aeybOZ3t2Href2Y5rT2lNZW0tiMQ1lYOUqOjWQhdtLYhUECoHKcLd+XDuGm0tiFRgKgfZw4Ztoa2Fj+etpUtyHf7VvwttG2lrQaSiUTkIENpa+CB9DfdMnMeOXfn8uffRXHNyK20tiFRQKgdhw7Zd3P3uPD6ZH9paGNm/C220tSBSoakcKjB35/30NfxVWwsispdAysHM/gWcA+QAy4DB7r7ZzFoCC4HF4btOdffrgsgY7/bYWmhel5EXdtbWgoj8Iqgth8+AO909z8z+AdwJ/Dk8tszdUwLKFffcnffmrOav780nKyefO846miEnaWtBRPYUSDm4+6RCV6cCFwaRo6LIyStg2g+ZfLZgHZ8tWMeaLdmkNK/LyP6dOaqhthZEpKho2OdwFTC+0PVWZvYdsBX4i7t/XdyDzGwoMBSgRYsWEQ8Za7Zl5/Llkg1Mmr+OLxavZ1t2HtUSEzilbX3+1Lsdfbs0I0FrLohICSJWDmY2GWhczNBwd58Yvs9wIA94JTy2Bmjh7plmdhzwrpl1dPetez+Ju48CRgGkpqZ6JL6HWLNuazafLVjHpAXr+HbZRnLznXo1qnBWp8ac0aExJ7WpT1JiQtAxRekx+isAAAf9SURBVCQGRKwc3L3XvsbN7EqgD3Cau3v4MbuAXeHLM81sGdAWSItUzljm7ixdv51J4UKYs3IzAC3rVWfwb1pxeodGdG1xmLYQROSABXW0Um9CO6BPdfesQrc3ADa5e76ZtQbaAMuDyHiwvlqygSqVK3FY9SrUrZ5InWqJZfpqPb/A+e6nn0OFMH8tKzJD/3xdmtfl9jPbcUaHRhzVsKZWYxORQxLUPocngKrAZ+E/YrsPWT0F+LuZ5QH5wHXuvimgjAfl5te/Y3NW7h63VUtM+KUodpdG6KMKdavtffnX8aqVQ6WSnZvPf7/fyKQFa/l84Xoyd+SQmGCccGR9rj65Nae3b0TjOklBfLsiEqeCOlrpqBJufwt4q5zjlKlXh/Tg56wcNmflsnln+HP4+s9ZuWzZmcPS9dt/uZybX/Lukt2lsjkrl525+dSqWpmeRzfkjA6NOLVdA2onJZbjdyYiFUk0HK0UVzo0rV3q+7o7WTn5v5ZJMYWyeWcuNaokcFr7RvRoXY8qlXU+gohEnsohQGZGjaqVqVG1MsmHBZ1GRORXehkqIiJFqBxERKQIlYOIiBShchARkSJUDiIiUoTKQUREilA5iIhIESoHEREpwsITosY0M9sA/HgIT1Ef2FhGcSIh2vNB9GeM9nwQ/RmjPR8o44E6wt0bFDcQF+VwqMwszd1Tg85RkmjPB9GfMdrzQfRnjPZ8oIxlSW8riYhIESoHEREpQuUQMiroAPsR7fkg+jNGez6I/ozRng+Uscxon4OIiBShLQcRESlC5SAiIkVU6HIws95mttjMlprZHUHn2ZuZNTezL8xsoZnNN7M/Bp2pOGaWYGbfmdkHQWcpjpnVNbM3zWxR+N/yhKAzFWZmt4b/f+eZ2WtmFviC4Gb2gpmtN7N5hW473Mw+M7Pvw58DXaKqhIz/Cv8/p5vZO2ZWN5ryFRobZmZuZvWDyFYaFbYczCwBeBI4C+gAXGpmHYJNVUQecJu7twd6ADdGYUaAPwILgw6xD48Dn7j70UAXoiirmTUDbgZS3b0TkABcEmwqAMYCvfe67Q7gc3dvA3wevh6ksRTN+BnQyd07A0uAO8s7VCFjKZoPM2sOnA78VN6BDkSFLQegG7DU3Ze7ew7wOtAv4Ex7cPc17j4rfHkboT9qzYJNtSczSwZ+DzwfdJbimFlt4BRgNIC757j75mBTFVEZqGZmlYHqwOqA8+DuXwGb9rq5H/Bi+PKLwLnlGmovxWV090nunhe+OhVILvdgv2Yp7t8Q4FHgT0BUHw1UkcuhGbCy0PUMouwPb2Fm1hI4FpgWbJIiHiP0g14QdJAStAY2AGPCb309b2Y1gg61m7uvAkYSehW5Btji7pOCTVWiRu6+BkIvXICGAefZn6uAj4MOUZiZ9QVWufucoLPsT0UuByvmtqhscjOrCbwF3OLuW4POs5uZ9QHWu/vMoLPsQ2WgK/C0ux8L7CD4t0N+EX7fvh/QCmgK1DCzgcGmin1mNpzQ27KvBJ1lNzOrDgwH7gk6S2lU5HLIAJoXup5MFGzO783MEgkVwyvu/nbQefbyG6Cvma0g9Lbc78xsXLCRisgAMtx99xbXm4TKIlr0An5w9w3ungu8DZwYcKaSrDOzJgDhz+sDzlMsM7sS6AMM8Og6ketIQi8C5oR/Z5KBWWbWONBUJajI5TADaGNmrcysCqGdgO8FnGkPZmaE3itf6O6PBJ1nb+5+p7snu3tLQv9+/3H3qHrV6+5rgZVm1i5802nAggAj7e0noIeZVQ//f59GFO0w38t7wJXhy1cCEwPMUiwz6w38Gejr7llB5ynM3ee6e0N3bxn+nckAuoZ/RqNOhS2H8E6rm4BPCf0yTnD3+cGmKuI3wOWEXpHPDn+cHXSoGPQH4BUzSwdSgAcCzvOL8BbNm8AsYC6h38nAp1cws9eAb4F2ZpZhZlcDDwGnm9n3hI62eSgKMz4B1AI+C/++PBNl+WKGps8QEZEiKuyWg4iIlEzlICIiRagcRESkCJWDiIgUoXIQEZEiVA4iJTCzZDObGJ6FdLmZPWFmVcvouQeZWdOyeC6RSFA5iBQjfELa28C74VlI2wDVgH+WwXMnAIMITZdxII+rfKhfW6S0VA4ixfsdkO3uYwDcPR+4FbjCzG4ysyd239HMPjCznuHLT5tZWnh9hr8Vus8KM7vHzP4LXAqkEjoxb7aZVTOz48zsSzObaWafFpqmYoqZPWBmXxKaGl2kXOiViEjxOgJ7TCjo7lvDc+Ls6/dmuLtvCm8dfG5mnd09PTyW7e4nAZjZEGCYu6eF58/6N9DP3TeY2cXACEKzigLUdfdTy+5bE9k/lYNI8YziZ+ktbjbfwi4ys6GEfreaEFpIanc5jC/hMe2AToSmfIDQgj9rCo2X9DiRiFE5iBRvPnBB4RvCCwc1AjKBtoWGksLjrYBhwPHu/rOZjd09FrajhK9lwHx3L2n50pIeJxIx2ucgUrzPgepmdgX8shP5YUITu/0ApJhZpfCSj93Cj6lN6A/5FjNrRGgJ2pJsIzRBHMBioIGF17Y2s0Qz61jW35DIgVA5iBQjvA7AecCF4VlIM4ECdx8B/I9QQcwltIrb7qVc5wDfEdrqeCF8v5KMBZ4xs9mE3ka6EPiHmc0BZhO9azpIBaFZWUVKwcxOBF4Dzo/yle9EyoTKQUREitDbSiIiUoTKQUREilA5iIhIESoHEREpQuUgIiJFqBxERKSI/w/RvK6KksQtZgAAAABJRU5ErkJggg==\n",
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\n",
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Monetary Policy Shock\n",
- "\n",
- "EX2SS['par']['aggrshock'] = 'MP'\n",
- "EX2SS['par']['rhoS'] = 0.0 # Persistence of variance\n",
- "EX2SS['par']['sigmaS'] = 0.001 # STD of variance shocks\n",
- "\n",
- "EX2SR=FluctuationsOneAssetIOUs(**EX2SS)\n",
- "SR=EX2SR.StateReduc()\n",
- "\n",
- "SGUresult=SGU_solver(SR['Xss'],SR['Yss'],SR['Gamma_state'],SR['Gamma_control'],SR['InvGamma'],SR['Copula'],\n",
- " SR['par'],SR['mpar'],SR['grid'],SR['targets'],SR['P_H'],SR['aggrshock'],SR['oc'])\n",
- "\n",
- "plot_IRF(SR['mpar'],SR['par'],SGUresult['gx'],SGUresult['hx'],SR['joint_distr'],\n",
- " SR['Gamma_state'],SR['grid'],SR['targets'],SR['os'],SR['oc'],SR['Output'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Elapsed time is 0.04532199999999875 seconds.\n",
- "Use Schmitt Grohe Uribe Algorithm\n",
- " A *E[xprime uprime] =B*[x u]\n",
- " A = (dF/dxprimek dF/duprime), B =-(dF/dx dF/du)\n",
- "Computing Jacobian F1=DF/DXprime F3 =DF/DX\n",
- "Total number of parallel blocks: 3 .\n",
- "Block number: 0 done.\n",
- "Block number: 1 done.\n",
- "Block number: 2 done.\n",
- "Elapsed time is 24.163849 seconds.\n",
- "Computing Jacobian F2 - DF/DYprime\n",
- "Total number of parallel blocks: 3 .\n",
- "Block number: 0 done.\n",
- "Block number: 1 done.\n",
- "Block number: 2 done.\n",
- "Elapsed time is 0.8783719999999988 seconds.\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
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bNCamUSDzV2fZHUUpr7JsUy7jlm7kz92acUOPFnbHcTtaQLyAj48wKDGGHzblcrDghN1xlPIKWYeOcf+sNNpHNODFa+rODLvnQguIlxiSGENRsdG5sZSqBUXFJdw9YyXHi4p5++ZuBNfzszuSW9IC4iXiYxrSKqw+c1fvsTuKUh7v1a9+Z+XOg/xrWBfahIfYHcdtaQHxEiLC4MQYftmaR87hQrvjKOWxvlqbxeQftzG6VyyDusTYHcetaQHxIkMSozEGvlyrg+lKVce2fUd55JM1JLVozJNXdbI7jtvTAuJF2kY0oFN0Q+3GUqoajp0o5s4PUvHzFd4a0Y16fvrvsTL6HfIyQxJjWLXzoE7xrtQ5enZOOhuy8xl3fRLNGgfZHccjaAHxMoO6RAMwb422QpSqqo9X7GJ2aib3Xt6W3h0i7I7jMbSAeJkWTYPpdl5j5ulFhUpVScaeQzwzJ52L2oZyfx1eHKo6tIB4ocGJMazPOszmnHy7oyjl1g5bi0M1DvZn/A1d8fXRiwXPhS0FRESaishiEdlkfWxSwXFfi8hBEZl/2vZWIrLcev5HIlLPNck9w9VdovERmJum3VhKVcQYwyOzV5N54Bhv3dSNsJAAuyN5HLtaII8DS40x7YCl1uPyvAbcUs72fwH/tp5/ALjNKSk9VESDQC5sE8q8NVkYY+yOo5RbmvzjNhZmZPPElR1Jjm1qdxyPZFcBGQpMte5PBa4p7yBjzFLglH4YcUxI0wf4pLLn12WDu8Swbd9R0ncftjuKUm5nxfb9vPLV7wyIj+S2i1vZHcdj2VVAIo0xWQDWx3M57SEUOGiMOWk9zgSaVXSwiNwhIikikpKbm1vtwJ5mYEIU/r6iZ2MpdZp9R45zz8yVNG8SxGu6OFSNOK2AiMgSEUkv5za0pi9dzrYK+2mMMRONMcnGmOTw8PAafmrP0Ti4Hpe2C2fe6j2UlGg3llLwv8WhDhYU8faIbjQM1MWhasJpU0waY/pVtE9EskUk2hiTJSLRQM45vPQ+oLGI+FmtkOaAvs0ux5CkGJb+nkPKjgP0bKV9vEpN+GYzP23O41/XdiY+RheHqqkqtUBEZGlVtp2DucAo6/4oYE5Vn2gco8LfAsOq8/y6pF+nSAL9fZinU5soxfKteYxfupE/dW3G8GRdHKo2nLWAiEigiDQFwkSkiXX6bVMRiQVqMk3lq0B/EdkE9LceIyLJIjKpzOdfBswG+opIpogMsHY9BjwoIptxjIlMrkEWr1U/wI++nSJZsDaLk8UldsdRyjb7j57g/llptAytzwu6OFStqawL66/AAziKRSr/G384DLxV3U9qjMkD+pazPQUYU+bxJRU8fyvQs7qfvy4ZkhjDl2uy+HlLHpe2rztjQEqVKr3eY//RE3w2qhchAbo4VG05awvEGDPeGNMKeNgY09oY08q6JRpjJrgoo6qBy9qH0yDAT2foVXXW+z9tZ+nvOTxxVUcSmum4R22qUik2xrwpIr2A2LLPMcZMc1IuVUsC/X0ZkBDFwvS9vPSnBAL8fO2OpJTLrM08xCtfradfp0hG94q1O47Xqeog+nRgLHAx0MO6JTsxl6pFgxNjyD9+ku821J3rYJTKLyzing9XEhYSwGvDuui4hxNUtTMwGYgzOi+GR7qoTSih9esxd/UeBsRH2R1HKaczxvD0F+ns2l/ArDsupEl9nS7PGap6IWE6oP95PJSfrw9XdY5m6fpsjh4/WfkTlPJws1MzmZO2h7/3a6/XQDlRVQtIGLBORBaKyNzSmzODqdo1ODGGwqISlqzPtjuKUk61OSeff8zJ4MLWodx1eVu743i1qnZhPefMEMr5kls2IbpRIPNW72FoUoVThynl0QqLirln5iqC6/ky7oYkXd/DyarUAjHGfA9sB/yt+yuAlU7MpWqZj48wqEs032/M5WDBCbvjKOUUL365jt/35jN2eCKRDQPtjuP1qnoW1u04pk9/19rUDPjCWaGUcwxJbEZRseHr9L12R1Gq1n21NosPft3JHZe25nJd19wlqjoGcjdwEY4r0DHGbOLcpmBXbiChWUNiQ4N1infldXbtL+DRT9eQ2KIxD1/Rwe44dUZVC8hxY8wf/R4i4sdZplBX7klEGJIYwy9b8sjJL7Q7jlK1oqi4hPtmrQIDb97QlXp+di1zVPdU9Tv9vYg8CQSJSH8cExzOc14s5SyDE2MoMbBgTZbdUZSqFW8s3siqnQd55drOnBcabHecOqWqBeRxIBdYi2OCxQXA084KpZynXWQDOkY10LmxlFf4YWMu73y3hRt7nsegLjWZIFxVR1ULSBDwnjHmOmPMMOA9a5vyQEOSYli58yC79hfYHUWpasvJL+TBj9NoHxnCs4Pi7I5TJ1W1gCzl1IIRBCyp/TjKFQZb79TmazeW8lAlJYYHP1rNkeMnmXBTN4Lq6SShdqhqAQk0xhwpfWDd185GD9WiaTBdz2us3VjKY73z/RZ+3LyP5wbH0z6ygd1x6qyqFpCjItKt9IGIdAeOOSeScoXBXWJYn3WYzTn5dkdR6pyk7tjPG4s3MqhLNNf30KVp7VTVAnI/MFtEllnLzH4E3OO8WMrZBnWJxkdg7mrtxlKe42DBCe77MI1mjYN4+c+ddYp2m1U6F5aI+AD1gI5ABxzL2v5ujClycjblRBENA7mgdSjzV+/h7/3a6R+icnvGGB77dA3Zhwv59M5eNAz0tztSnVdpC8QYUwK8bowpMsakG2PWavHwDoMTY9i67ygZew7bHUWpSn3w6w4WZmTz2MCOJLZobHccRdW7sBaJyLWib1O9ypUJUfj5CPN0MF25uYw9h3jhy/Vc3iGc2y5uZXccZalqAXkQx9XnJ0TksIjki4i+bfVwjYPrcWn7cOat3kNJic5Mo9zT0eMnuffDVTQJ9mfsdYn46BTtbqOq07k3MMb4GGP8jTENrccNnR1OOd+QxBj2HCokdecBu6MoVa5n52Swbd9Rxl3fldCQALvjqDKqOp27iMjNIvKM9biFiPR0bjTlCv3iIgnw89FuLOWWPluZyacrM7m3TzsubBNqdxx1mqp2Yb0NXAjcZD0+ArzllETKpUIC/OjXKZIFa7M4WVxidxyl/rA55whPf5FOz1ZNua+PLk3rjqpaQM43xtwNFAIYYw7gOLVXeYHBiTHsO3KCX7bm2R1FKQCOnSjm7hkrCfL35c0bu+Lnq1O0u6Oq/lSKRMQXaw0QEQkH9O2ql+jdIZwGAX7MTdNuLOUenp+XwcacfP59fZIuTevGqlpA/gN8DkSIyEvAj8DLTkulXCrQ35cr4qP4OmMvx08W2x1H1XGfr8pk1opd3NW7DZe2D7c7jjqLqp6FNQN4FHgFyAKuMcbMdmYw5VqDE6PJLzzJ9xty7Y6i6rDNOUd46vN0esY25e/92tsdR1XirFOZiEgg8DegLY7FpN41xpx0RTDlWhe1DaNp/XrMXb2HK+Kj7I6j6qDScY9Af1/+o+MeHqGyn9BUIBlH8bgSGOv0RMoW/r4+XJkQxdL1ORSc0PcIyvWen5fBhmzHuEdUIx338ASVFZA4Y8zNxph3gWHApbXxSUWkqYgsFpFN1scmFRz3tYgcFJH5p22fIiLbRCTNuiXVRq66bkhiDMeKilm8LtvuKKqO+WLVbmat2MXdl7fhMh338BiVFZA/Jk2s5a6rx4Glxph2OFY7fLyC414Dbqlg3yPGmCTrllaL2eqsHrFNiWoYyDyd4l250JbcIzz5+Vod9/BAlRWQRGvuq8Mikg90qaW5sIbi6B7D+nhNeQcZY5YCuuKRi/j4CIO6RPP9xhwOFeiEy8r5Cov+N+4x/sYkHffwMGf9aRljfK25r0rnv/KrpbmwIo0xWdbnyAIiqvEaL4nIGhH5t4hUOEGOiNwhIikikpKbq2cYVWZIUgxFxYavM7QVopzv+XkZ/L43nzeGJxLdKMjuOOocOa3ci8gSEUkv5za0Fl7+CRwLXPUAmgKPVXSgMWaiMSbZGJMcHq59q5Xp3KwRLUODtRtLOd2ctN18+Jvjeo/eHarzHlLZrdIVCavLGNOvon0iki0i0caYLBGJBnLO8bVL/7sdF5H3gYdrEFWVISIMSYzhrW83k5NfSEQDPRtG1b4tuUd48rO19IhtwoP9ddzDU9nV4TgXGGXdHwXMOZcnW0UHa4Gra4D0Wk1Xxw1OjKHEwFdr99odRXmh0nGPen4+er2Hh7PrJ/cq0F9ENgH9rceISLKITCo9SESW4VjIqq+IZIrIAGvXDBFZi+P6lDDgRZem93LtIxsQF92Q6b/uoFgXmlK17Pl56xzjHtcn6biHh3NaF9bZGGPygL7lbE8BxpR5fEkFz+/jvHQK4K7L23DPzFUsWJvF4MQYu+MoL+EY99jJnb3bcLmOe3g8bTuqcl2VEE27iBD+s3STLnerakXpuEdyyyY8pOMeXkELiCqXj49wX992bMo5woJ0PSNL1UzZcY83b9JxD2+hP0VVoas6O1oh45doK0TVzD/n67iHN9ICoirkq60QVQvmpO1m5vKd/O0yHffwNlpA1FlpK0TVxNay4x5X6LiHt9ECos7K10e4V1shqhoKi4q5e+aqP6738NdxD6+jP1FVqas7R9NWz8hS5+if89exPuswbwxPIqaxjnt4Iy0gqlKlYyEbs4/wVbpena4qN3f1nv+Ne3TUcQ9vpQVEVUlpK2T80o3aClFntW3fUZ74dA3dddzD62kBUVWirRBVFYVFxdw1YyX+fj68qeMeXk9/uqrKru4cTZvw+joWoir0/LzScY9EHfeoA7SAqCorbYVsyM7n6wxthahTTf91xx/zXPXpGGl3HOUCWkDUORnUJYY24fX1uhB1il+25PH83Az6dIzg4Ss62B1HuYgWEHVOtBWiTrczr4A7Z6QSG1af8Tck4esjdkdSLqIFRJ0zbYWoUkeOn2TMtBUYA5NGJtMg0N/uSMqFtICoc1a2FbJQWyF1VkmJ4YFZaWzJPcrbI7oRG1bf7kjKxbSAqGoZ1CWG1uH1Ga9nZNVZry/ewJL12Tw7KI6L2obZHUfZQAuIqhZfH+H+vu34fa+2QuqiOWm7eevbLdzYswUjL2xpdxxlEy0gqtq0FVI3rd51kEc/WUPP2KY8PyQBER00r6u0gKhq01ZI3ZNzuJA7pqcQFhLAOzd3o56f/gupy/Snr2pEWyF1R2FRMbdPTyW/8CSTRiUTGhJgdyRlMy0gqkZ8fYT7+jhaIYvWaSvEWxljePKztazedZA3hifRKbqh3ZGUG9ACompscGIMrcPqM06vC/FaE3/YymerdvNg//YMTIiyO45yE1pAVI2VXheirRDv9M3v2bz69e9c3Tmae/u0tTuOciNaQFSt0FaId9qck899H6YRF92Q167romdcqVNoAVG1wrF2elurFZJtdxxVCw4WnOC2qSkE+vswcWQywfX87I6k3IwWEFVrBndxtEL0jCzPd7K4hLtnriTrYCHv3tKdZrq2hyqHFhBVa/x8fbi3b1vWZx3WVoiHe/HL9fy0OY8X/5RA95ZN7Y6j3JQWEFWrtBXi+T78bSdTft7ObRe3YnhyC7vjKDemBUTVKj9fH+7po60QT7V8ax7PfJHOpe3DeeLKjnbHUW7OlgIiIk1FZLGIbLI+NinnmCQR+UVEMkRkjYhcX2ZfKxFZbj3/IxGp59qvQJ3NkMQYWoU51k43RlshnmLX/gLunLGS85oG8+aNXfHz1feX6uzs+g15HFhqjGkHLLUen64AGGmMiQcGAuNEpLG171/Av63nHwBuc0FmVUV+vj7c26ct67QV4jGOHj/J7dNSKCou4b+jkmkUpAtDqcrZVUCGAlOt+1OBa04/wBiz0Rizybq/B8gBwsVxInof4JOzPV/Zq7QVMn6JtkLcXUmJ4cGP09iYnc+Em7rRJjzE7kjKQ9hVQCKNMVkA1seIsx0sIj2BesAWIBQ4aIw5ae3OBJo5MauqBm2FeI5xSzayMCObJ6/qxGXtw+2OozyI0wqIiCwRkfRybkPP8XWigenAX4wxJUB5l8JW+BZXRO4QkRQRScnNzT23L0LVyJDEGGJDg7UV4sbmr9nDf77ZzLDuzbnt4lZ2x1EexmkFxBjTzxiTUM5tDpBtFYbSAtoUJuYAABIWSURBVJFT3muISEPgS+BpY8yv1uZ9QGMRKb0stjmw5yw5Jhpjko0xyeHh+u7KlRytkHasyzrMYm2FuJ2ft+zj4dmr6XZeY176ky4Mpc6dXV1Yc4FR1v1RwJzTD7DOrPocmGaMmV263Tjeyn4LDDvb85V7GJrkaIWM01aIW/lx0z5unbKC85oGM3FkMgF+vnZHUh7IrgLyKtBfRDYB/a3HiEiyiEyyjhkOXAqMFpE065Zk7XsMeFBENuMYE5ns2viqqrQV4n6+35jLbVNXEBtanw9vv4AwXRhKVZPUpXeFycnJJiUlxe4Ydc7J4hL6vfE9gf6+zLnnIn23a6NvN+Tw1+mptA0P4YMx59O0vl5CpSonIqnGmOTTt+uVQsrp/Hx9ePKqTvy+N5/HP12rXVk2Wbo+m79OS6V9ZAgzb9fioWpOC4hyiSvio3j4ivZ8vmo3b36z2e44dc6ijL387YNUOkY3YMZtF9A4WIuHqjmd4F+5zN2Xt2XrvqO8sXgjsWH1GZIYY3ekOuHr9L3cM3Ml8c0aMe3WnnqVuao12gJRLiMivPLnzvRs1ZSHZ68mdccBuyN5vQVrs7h75kq6NG/E9Nu0eKjapQVEuVSAny/v3tydmEaB3DEthV37C+yO5LXmrd7DvR+uomuLxky77XwaBmrxULVLC4hyuSb16zF5dA9Olhj+MmUFh44V2R3J68xJ2839s1bRvWUTpt7ak5AA7a1WtU8LiLJFm/AQ3rm5G9v3HeWemSspKi6xO5LX+GxlJn//KI2erZoy5S89qK/FQzmJFhBlm15twnj5z51Ztmkf/5iboaf31oLZKbt4aPZqLmwTyvujexJcT4uHch797VK2Gp7cgm37jvLOd1toHVafMZe0tjuSx/poxU4e/2wtF7cN478jkwn01ws2lXNpAVG2e+SKDmzfd5SXFqynZWh9+sdF2h3J48xcvpMnP1/LZe3DefeW7lo8lEtoF5aynY+P8MbwJDo3a8T9s1aRvvuQ3ZE8yvRftvPk52u5vIMWD+VaWkCUWwiq58ukkck0DvJnzNQU9h4qtDuSR5jy0zaemZNBv04R/J8WD+ViWkCU24hoGMikUT3ILyzitqkrKDhxsvIn1WGTf9zGc/PWcUVcJG+P6K6TVCqX0wKi3EpcTEPevKkr67MOc/+sNIpL9Mys8vz3h628MH8dVyZE8daIbtTz0z9l5Xr6W6fcTp+OkTwzKI7F67L519e/2x3H7bzz3RZeWrCeq7tE858bu+Lvq3/Gyh56FpZyS6N7xbJt31Em/rCVVmH1ubHneXZHcgsTvtnE2EUbGZIYwxvDE/HT4qFspAVEuSUR4dlBcezIK+CZL9Jp0SSYi9uF2R3LNvuPnuDFL9fx2crd/KlrM14b1kWLh7Kd/gYqt+Xn68OEm7rSJjyEO2eksjkn3+5ILmeM4YtVu+n3xvfMTdvDfX3aMvY6bXko96C/hcqtNQj0Z/LoZAL8fPjLlBXkHTludySX2bW/gFHvr+CBj9I4r2kw8++7mAev6ICvj9gdTSlAu7AoKioiMzOTwsK6dd1BYGAgzZs3x9/f/af4bt4kmP+OTOaGib/y1+mpfDDmfK++3uFkcQlTft7O64s24iPw3OA4brkwVguHcjt1voBkZmbSoEEDYmNjEakbf6DGGPLy8sjMzKRVq1Z2x6mSruc14fXhidwzcxWPfbqGcdcneeXPK2PPIR7/dC1rdx+ib8cIXrgmgZjGQXbHUqpcdb6AFBYW1qniAY4B6tDQUHJzc+2Ock4GdYlhR14Bry3cQKuw+jzQr73dkWrNsRPFjFu6kUnLttEkuB4TburK1Z2j69TvpfI8db6AAHXyj9RTv+a7erdha+5Rxi3ZRKuw+gxNamZ3pBr7afM+nvhsLTv3F3B9cguevKoTjYLdv2tRKS0gyqOICC//OYFdBwp4ZPYa1u05zOiLYolu5HndPAeOnuDFL9fz6cpMYkODmXn7+fRqU3dPVVaeRwuI8jil66o/Myed/y7byuQft3F1l2huv6Q1Cc0a2R2vUsYY5q7ewz/nrePQsSLu6t2G+/q28+oTA5R30tN43UBISAgA27dvJygoiKSkJOLi4hg5ciRFRY71wr/77jsaNWpEUlISSUlJ9OvX76yvOW3aNBISEoiPjycuLo6xY8c6/etwpSb16zHhpm58/8jljLwwliXrshn05o/cMPEXlqzLpsRN59DKPFDAX6as4P5ZaTRvEsS8ey/m0YEdtXgoj6QtkDKen5fBuj2Ha/U142Ia8o/B8VU+vk2bNqSlpVFcXEz//v35+OOPGTFiBACXXHIJ8+fPr/Q1vvrqK8aNG8eiRYuIiYmhsLCQ6dOnV/trcGctmgbz7OA4Hujfjlm/7eT9n7YzZloKrcPqc+vFrbi2W3OC6tn/z7m4xFin5m4A4NlBcYzqpafmKs+mBcRN+fr60rNnT3bv3n3Oz33llVcYO3YsMTExgOOaj9tvv722I7qVhoH+3HFpG/5yUSsWrM1i0rJtPP1FOq8v2sDNF7TklgtbEtEg0JZs6/Yc5onP1rA68xCXdwjnhWsSaN4k2JYsStUmLSBlnEtLwdkKCwtZvnw548eP/2PbsmXLSEpKAuC6667jqaeeKve56enpdO/e3SU53Y2/rw9Dk5oxJDGG37btZ9KP25jw7Wbe/X4rQ5NiGHNJazpENXBJliPHT/LWt5uZ+MNWGgf5M/6GJIYkxnjsGXBKnU4LiJvZsmULSUlJbNq0iWHDhtGlS5c/9lW1C0s5ztY6v3Uo57cOZWvuEd7/aTuzU3cxOzWTS9qFcfslrbmkXVit/DM/VFDE5tx8NuccYVP2ETbnOj7uPngMgGHdm/PUVZ1oUr9ejT+XUu5EC4ibKR0DycrKonfv3sydO5chQ4ac02vEx8eTmppKnz59nJTSs7QOD+GFaxJ4sH97Zv62kyk/b2fke7/RIbIBt13SiqFJMZWu5meMIffIcTZbBaJsscjN/9/8XAF+PrQJD6F7yyZc36MFvdqEkhzb1NlfolK2sKWAiEhT4CMgFtgODDfGHDjtmCTgHaAhUAy8ZIz5yNo3BbgMOGQdPtoYk+aK7K4SHR3Nq6++yiuvvHLOBeSJJ57g0UcfZf78+URFRXH8+HHeffdd7rvvPiel9QxN6tfj7svbMuaSVsxN28PkH7fx6Cdr+H9fb2DUhS25+YKWNAryZ/fBY2zOPcKWU1oU+Rwu/N8Suw0C/GgTEULv9uG0jQihbUQI7SIa0KxJkA6MqzrDrhbI48BSY8yrIvK49fix044pAEYaYzaJSAyQKiILjTEHrf2PGGM+cWFml7vmmmt47rnnWLZs2Tk976qrriI7O5t+/fphjEFEuPXWW52U0vME+PlyXXILhnVvzo+b9zFp2TZeX7yRCd9uxkeEY0XFfxwbWr8ebSNCGJwYQ7uIENpGNKBtRAiRDQN0LEPVeWKM68+XF5ENQG9jTJaIRAPfGWM6VPKc1cAwq6BMAeafawFJTk42KSkpp2xbv349nTp1OrcvwEvU5a/9dBuz85m5fCc+Io7WRGQIbcNDdNxCKUBEUo0xyadvt6sFEmmMyQKwikjE2Q4WkZ5APWBLmc0vicizwFLgcWNMuQtFiMgdwB0A552ny6Kq8rWPbMBzQ9znLDylPIHTCoiILAGiytlV/rmnFb9ONDAdGGWMKbE2PwHsxVFUJuLo/vpnec83xky0jiE5Odk9L0+uppdeeonZs2efsu1sp/cqpVRtcloBMcZUONeGiGSLSHSZLqycCo5rCHwJPG2M+bXMa2dZd4+LyPvAwzXM6pH92U899VS1i4UdXZdKKe9i11xYc4FR1v1RwJzTDxCResDnwDRjzOzT9kVbHwW4BkivbpDAwEDy8vLq1D/U0gWlAgPtuTJbKeUd7BoDeRX4WERuA3YC1wGISDLwN2PMGGA4cCkQKiKjreeVnq47Q0TCAQHSgL9VN0jz5s3JzMz0uMWVaqp0SVullKouW87Cskt5Z2EppZQ6u4rOwtLp3JVSSlWLFhCllFLVogVEKaVUtdSpMRARyQV2VPPpYcC+WozjDO6e0d3zgftndPd8oBlrg7vla2mMCT99Y50qIDUhIinlDSK5E3fP6O75wP0zuns+0Iy1wd3zldIuLKWUUtWiBUQppVS1aAGpuol2B6gCd8/o7vnA/TO6ez7QjLXB3fMBOgailFKqmrQFopRSqlq0gCillKoWLSBVICIDRWSDiGy2luB1GyLSQkS+FZH1IpIhIvfbnakiIuIrIqtEZL7dWU4nIo1F5BMR+d36Xl5od6bTicjfrZ9xuoh8KCK2T6csIu+JSI6IpJfZ1lREFovIJutjEzfL95r1c14jIp+LSGO78lWUscy+h0XEiEiYHdkqowWkEiLiC7wFXAnEATeKSJy9qU5xEnjIGNMJuAC4283ylXU/sN7uEBUYD3xtjOkIJOJmOUWkGXAfkGyMSQB8gRvsTQXAFGDgadseB5YaY9phrRjq6lBlTOHMfIuBBGNMF2AjjgXq7DSFMzMiIi2A/jhmLHdLWkAq1xPYbIzZaow5AcwChtqc6Q/GmCxjzErrfj6Of3zN7E11JhFpDlwNTLI7y+mshcsuBSYDGGNOGGMO2puqXH5AkIj4AcHAHpvzYIz5Adh/2uahwFTr/lQca/bYorx8xphFxpiT1sNfAVvXNajgewjwb+BRwG3PdNICUrlmwK4yjzNxw3/QACISC3QFltubpFzjcPwxlFR2oA1aA7nA+1YX2yQRqW93qLKMMbuBsTjejWYBh4wxi+xNVaHI0lVDrY8RNuc5m1uBr+wOcToRGQLsNsastjvL2WgBqVx5a9263TsCEQkBPgUeMMYctjtPWSIyCMgxxqTanaUCfkA34B1jTFfgKPZ2u5zBGkcYCrQCYoD6InKzvak8m4g8haMLeIbdWcoSkWDgKeBZu7NURgtI5TKBFmUeN8cNug7KEhF/HMVjhjHmM7vzlOMiYIiIbMfRBdhHRD6wN9IpMoFMY0xpy+0THAXFnfQDthljco0xRcBnQC+bM1Uku8yy09FAjs15ziAio4BBwAjjfhfDtcHxRmG19TfTHFgpIlG2piqHFpDKrQDaiUgra532G3Cs6e4WrHXhJwPrjTFv2J2nPMaYJ4wxzY0xsTi+f98YY9zm3bMxZi+wS0Q6WJv6AutsjFSencAFIhJs/cz74mYD/WXMBUZZ90cBc2zMcgYRGQg8BgwxxhTYned0xpi1xpgIY0ys9TeTCXSzfk/dihaQSliDbfcAC3H8wX5sjMmwN9UpLgJuwfGuPs26XWV3KA90LzBDRNYAScDLNuc5hdU6+gRYCazF8bdr+3QXIvIh8AvQQUQyReQ24FWgv4hswnEW0atulm8C0ABYbP29/J9d+c6S0SPoVCZKKaWqRVsgSimlqkULiFJKqWrRAqKUUqpatIAopZSqFi0gSimlqkULiFI1ICLNRWSONfPsVhGZICIBtfTao0UkpjZeSyln0AKiVDVZF/R9BnxhzTzbDggC/l8tvLYvMBrHtCXn8jy/mn5upapKC4hS1dcHKDTGvA9gjCkG/g6MFJF7RGRC6YEiMl9Eelv33xGRFGttj+fLHLNdRJ4VkR+BG4FkHBc3polIkIh0F5HvRSRVRBaWmS7kOxF5WUS+xzFlvlIuoe9WlKq+eOCUCSKNMYet+YvO9rf1lDFmv9XKWCoiXYwxa6x9hcaYiwFEZAzwsDEmxZrv7E1gqDEmV0SuB17CMZssQGNjzGW196UpVTktIEpVn1D+zMzlzeBc1nARuQPH3180joXKSgvIRxU8pwOQgGP6DXAsKJVVZn9Fz1PKabSAKFV9GcC1ZTdYi1NFAnlA+zK7Aq39rYCHgR7GmAMiMqV0n+VoBZ9LgAxjTEVL7Vb0PKWcRsdAlKq+pUCwiIyEPwa+X8cxWd82IElEfKylSXtaz2mI45/9IRGJxLFUckXycUz6B7ABCBdrrXYR8ReR+Nr+gpQ6F1pAlKomax2JPwHDrJln84ASY8xLwE84ishaHCsJli47vBpYhaP18p51XEWmAP8nImk4uqyGAf8SkdVAGu67HoiqI3Q2XqVqiYj0Aj4E/uzGqy8qVWu0gCillKoW7cJSSilVLVpAlFJKVYsWEKWUUtWiBUQppVS1aAFRSilVLVpAlFJKVcv/B45pF+TRS8YsAAAAAElFTkSuQmCC\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Productivity Shock\n",
- "\n",
- "EX2SS['par']['aggrshock'] = 'TFP'\n",
- "EX2SS['par']['rhoS'] = 0.95\n",
- "EX2SS['par']['sigmaS'] = 0.0075\n",
- "\n",
- "EX2SR=FluctuationsOneAssetIOUs(**EX2SS)\n",
- "SR=EX2SR.StateReduc()\n",
- "\n",
- "SGUresult = SGU_solver(SR['Xss'],SR['Yss'],SR['Gamma_state'],SR['Gamma_control'],SR['InvGamma'],SR['Copula'],\n",
- " SR['par'],SR['mpar'],SR['grid'],SR['targets'],SR['P_H'],SR['aggrshock'],SR['oc'])\n",
- "\n",
- "plot_IRF(SR['mpar'],SR['par'],SGUresult['gx'],SGUresult['hx'],SR['joint_distr'],\n",
- " SR['Gamma_state'],SR['grid'],SR['targets'],SR['os'],SR['oc'],SR['Output'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "cite2c": {
- "citations": {
- "6202365/ECL3ZAR7": {
- "author": [
- {
- "family": "Bayer",
- "given": "Christian"
- },
- {
- "family": "Luetticke",
- "given": "Ralph"
- }
- ],
- "id": "6202365/ECL3ZAR7",
- "issued": {
- "year": 2018
- },
- "note": "bibtex:blSolving",
- "publisher": "CEPR Discussion Papers",
- "title": "Solving heterogeneous agent models in discrete time with many idiosyncratic states by perturbation methods",
- "type": "report"
- }
- }
- },
- "jupytext": {
- "formats": "ipynb,py:percent"
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- },
- "latex_envs": {
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- "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
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- "toc": {
- "base_numbering": 1,
- "nav_menu": {},
- "number_sections": true,
- "sideBar": true,
- "skip_h1_title": false,
- "title_cell": "Table of Contents",
- "title_sidebar": "Contents",
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- "toc_position": {},
- "toc_section_display": true,
- "toc_window_display": false
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- "nbformat": 4,
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diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py
index fd1c99702..dc1c0a43f 100644
--- a/HARK/ConsumptionSaving/ConsAggShockModel.py
+++ b/HARK/ConsumptionSaving/ConsAggShockModel.py
@@ -19,7 +19,12 @@
from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution, IndShockConsumerType
from HARK import HARKobject, Market, AgentType
from copy import deepcopy
-import matplotlib.pyplot as plt
+# import matplotlib.pyplot as plt
+import HARK.ConsumptionSaving.ConsumerParameters as Params
+
+__all__ = ['MargValueFunc2D', 'AggShockConsumerType', 'AggShockMarkovConsumerType',
+'CobbDouglasEconomy', 'SmallOpenEconomy', 'CobbDouglasMarkovEconomy',
+'SmallOpenMarkovEconomy', 'CobbDouglasAggVars', 'AggregateSavingRule', 'AggShocksDynamicRule']
utility = CRRAutility
utilityP = CRRAutilityP
@@ -79,6 +84,9 @@ def __init__(self, time_flow=True, **kwds):
Make a new instance of AggShockConsumerType, an extension of
IndShockConsumerType. Sets appropriate solver and input lists.
'''
+ params = Params.init_agg_shocks.copy()
+ params.update(kwds)
+ kwds = params
AgentType.__init__(self, solution_terminal=deepcopy(IndShockConsumerType.solution_terminal_),
time_flow=time_flow, pseudo_terminal=False, **kwds)
@@ -375,6 +383,9 @@ class AggShockMarkovConsumerType(AggShockConsumerType):
state is subject to Markov-style discrete state evolution.
'''
def __init__(self, **kwds):
+ params = Params.init_agg_mrkv_shocks.copy()
+ params.update(kwds)
+ kwds = params
AggShockConsumerType.__init__(self, **kwds)
self.addToTimeInv('MrkvArray')
self.solveOnePeriod = solveConsAggMarkov
@@ -886,7 +897,11 @@ class CobbDouglasEconomy(Market):
Note: The current implementation assumes a constant labor supply, but
this will be generalized in the future.
'''
- def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds):
+ def __init__(self,
+ agents=[],
+ tolerance=0.0001,
+ act_T=1200,
+ **kwds):
'''
Make a new instance of CobbDouglasEconomy by filling in attributes
specific to this kind of market.
@@ -906,6 +921,9 @@ def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds):
-------
None
'''
+ params = Params.init_cobb_douglas.copy()
+ params.update(kwds)
+ kwds = params
Market.__init__(self, agents=agents,
sow_vars=['MaggNow', 'AaggNow', 'RfreeNow',
'wRteNow', 'PermShkAggNow', 'TranShkAggNow', 'KtoLnow'],
@@ -1330,7 +1348,11 @@ class CobbDouglasMarkovEconomy(CobbDouglasEconomy):
productivity growth factor can vary over time.
'''
- def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds):
+ def __init__(self,
+ agents=[],
+ tolerance=0.0001,
+ act_T=1200,
+ **kwds):
'''
Make a new instance of CobbDouglasMarkovEconomy by filling in attributes
specific to this kind of market.
@@ -1350,6 +1372,9 @@ def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds):
-------
None
'''
+ params = Params.init_mrkv_cobb_douglas.copy()
+ params.update(kwds)
+ kwds = params
CobbDouglasEconomy.__init__(self, agents=agents, tolerance=tolerance, act_T=act_T, **kwds)
self.sow_vars.append('MrkvNow')
@@ -1768,199 +1793,3 @@ def __init__(self, AFunc):
'''
self.AFunc = AFunc
self.distance_criteria = ['AFunc']
-
-
-###############################################################################
-
-def main():
- import HARK.ConsumptionSaving.ConsumerParameters as Params
- from time import clock
- from HARK.utilities import plotFuncs
-
- def mystr(number): return "{:.4f}".format(number)
-
- solve_agg_shocks_micro = False # Solve an AggShockConsumerType's microeconomic problem
- solve_agg_shocks_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy
-
- solve_markov_micro = False # Solve an AggShockMarkovConsumerType's microeconomic problem
- solve_markov_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy
- solve_krusell_smith = True # Solve a simple Krusell-Smith-style two state, two shock model
- solve_poly_state = False # Solve a CobbDouglasEconomy with many states, potentially utilizing the "state jumper"
-
- # EXAMPLE IMPLEMENTATIONS OF AggShockConsumerType ###
-
- if solve_agg_shocks_micro or solve_agg_shocks_market:
- # Make an aggregate shocks consumer type
- AggShockExample = AggShockConsumerType(**Params.init_agg_shocks)
- AggShockExample.cycles = 0
-
- # Make a Cobb-Douglas economy for the agents
- EconomyExample = CobbDouglasEconomy(agents=[AggShockExample], **Params.init_cobb_douglas)
- EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks
-
- # Have the consumers inherit relevant objects from the economy
- AggShockExample.getEconomyData(EconomyExample)
-
- if solve_agg_shocks_micro:
- # Solve the microeconomic model for the aggregate shocks example type (and display results)
- t_start = clock()
- AggShockExample.solve()
- t_end = clock()
- print('Solving an aggregate shocks consumer took ' + mystr(t_end-t_start) + ' seconds.')
- print('Consumption function at each aggregate market resources-to-labor ratio gridpoint:')
- m_grid = np.linspace(0, 10, 200)
- AggShockExample.unpackcFunc()
- for M in AggShockExample.Mgrid.tolist():
- mMin = AggShockExample.solution[0].mNrmMin(M)
- c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin, M*np.ones_like(m_grid))
- plt.plot(m_grid+mMin, c_at_this_M)
- plt.ylim(0., None)
- plt.show()
-
- if solve_agg_shocks_market:
- # Solve the "macroeconomic" model by searching for a "fixed point dynamic rule"
- t_start = clock()
- print('Now solving for the equilibrium of a Cobb-Douglas economy. This might take a few minutes...')
- EconomyExample.solve()
- t_end = clock()
- print('Solving the "macroeconomic" aggregate shocks model took ' + str(t_end - t_start) + ' seconds.')
-
- print('Aggregate savings as a function of aggregate market resources:')
- plotFuncs(EconomyExample.AFunc, 0, 2*EconomyExample.kSS)
- print('Consumption function at each aggregate market resources gridpoint (in general equilibrium):')
- AggShockExample.unpackcFunc()
- m_grid = np.linspace(0, 10, 200)
- AggShockExample.unpackcFunc()
- for M in AggShockExample.Mgrid.tolist():
- mMin = AggShockExample.solution[0].mNrmMin(M)
- c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin, M*np.ones_like(m_grid))
- plt.plot(m_grid+mMin, c_at_this_M)
- plt.ylim(0., None)
- plt.show()
-
- # EXAMPLE IMPLEMENTATIONS OF AggShockMarkovConsumerType #
-
- if solve_markov_micro or solve_markov_market or solve_krusell_smith:
- # Make a Markov aggregate shocks consumer type
- AggShockMrkvExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks)
- AggShockMrkvExample.IncomeDstn[0] = 2*[AggShockMrkvExample.IncomeDstn[0]]
- AggShockMrkvExample.cycles = 0
-
- # Make a Cobb-Douglas economy for the agents
- MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample], **Params.init_mrkv_cobb_douglas)
- MrkvEconomyExample.DampingFac = 0.2 # Turn down damping
- MrkvEconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks
- AggShockMrkvExample.getEconomyData(
- MrkvEconomyExample) # Have the consumers inherit relevant objects from the economy
-
- if solve_markov_micro:
- # Solve the microeconomic model for the Markov aggregate shocks example type (and display results)
- t_start = clock()
- AggShockMrkvExample.solve()
- t_end = clock()
- print('Solving an aggregate shocks Markov consumer took ' + mystr(t_end-t_start) + ' seconds.')
-
- print('Consumption function at each aggregate market \
- resources-to-labor ratio gridpoint (for each macro state):')
- m_grid = np.linspace(0, 10, 200)
- AggShockMrkvExample.unpackcFunc()
- for i in range(2):
- for M in AggShockMrkvExample.Mgrid.tolist():
- mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M)
- c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin, M*np.ones_like(m_grid))
- plt.plot(m_grid+mMin, c_at_this_M)
- plt.ylim(0., None)
- plt.show()
-
- if solve_markov_market:
- # Solve the "macroeconomic" model by searching for a "fixed point dynamic rule"
- t_start = clock()
- print('Now solving a two-state Markov economy. This should take a few minutes...')
- MrkvEconomyExample.solve()
- t_end = clock()
- print('Solving the "macroeconomic" aggregate shocks model took ' + str(t_end - t_start) + ' seconds.')
-
- print('Consumption function at each aggregate market \
- resources-to-labor ratio gridpoint (for each macro state):')
- m_grid = np.linspace(0, 10, 200)
- AggShockMrkvExample.unpackcFunc()
- for i in range(2):
- for M in AggShockMrkvExample.Mgrid.tolist():
- mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M)
- c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin, M*np.ones_like(m_grid))
- plt.plot(m_grid+mMin, c_at_this_M)
- plt.ylim(0., None)
- plt.show()
-
- if solve_krusell_smith:
- # Make a Krusell-Smith agent type
- # NOTE: These agents aren't exactly like KS, as they don't have serially correlated unemployment
- KSexampleType = deepcopy(AggShockMrkvExample)
- KSexampleType.IncomeDstn[0] = [[np.array([0.96, 0.04]), np.array([1.0, 1.0]), np.array([1.0/0.96, 0.0])],
- [np.array([0.90, 0.10]), np.array([1.0, 1.0]), np.array([1.0/0.90, 0.0])]]
-
- # Make a KS economy
- KSeconomy = deepcopy(MrkvEconomyExample)
- KSeconomy.agents = [KSexampleType]
- KSeconomy.AggShkDstn = [[np.array([1.0]), np.array([1.0]), np.array([1.05])],
- [np.array([1.0]), np.array([1.0]), np.array([0.95])]]
- KSeconomy.PermGroFacAgg = [1.0, 1.0]
- KSexampleType.getEconomyData(KSeconomy)
- KSeconomy.makeAggShkHist()
-
- # Solve the K-S model
- t_start = clock()
- print('Now solving a Krusell-Smith-style economy. This should take about a minute...')
- KSeconomy.solve()
- t_end = clock()
- print('Solving the Krusell-Smith model took ' + str(t_end - t_start) + ' seconds.')
-
- if solve_poly_state:
- StateCount = 15 # Number of Markov states
- GrowthAvg = 1.01 # Average permanent income growth factor
- GrowthWidth = 0.02 # PermGroFacAgg deviates from PermGroFacAgg in this range
- Persistence = 0.90 # Probability of staying in the same Markov state
- PermGroFacAgg = np.linspace(GrowthAvg-GrowthWidth, GrowthAvg+GrowthWidth, num=StateCount)
-
- # Make the Markov array with chosen states and persistence
- PolyMrkvArray = np.zeros((StateCount, StateCount))
- for i in range(StateCount):
- for j in range(StateCount):
- if i == j:
- PolyMrkvArray[i, j] = Persistence
- elif (i == (j-1)) or (i == (j+1)):
- PolyMrkvArray[i, j] = 0.5*(1.0 - Persistence)
- PolyMrkvArray[0, 0] += 0.5*(1.0 - Persistence)
- PolyMrkvArray[StateCount-1, StateCount-1] += 0.5*(1.0 - Persistence)
-
- # Make a consumer type to inhabit the economy
- PolyStateExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks)
- PolyStateExample.MrkvArray = PolyMrkvArray
- PolyStateExample.PermGroFacAgg = PermGroFacAgg
- PolyStateExample.IncomeDstn[0] = StateCount*[PolyStateExample.IncomeDstn[0]]
- PolyStateExample.cycles = 0
-
- # Make a Cobb-Douglas economy for the agents
- PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample], **Params.init_mrkv_cobb_douglas)
- PolyStateEconomy.MrkvArray = PolyMrkvArray
- PolyStateEconomy.PermGroFacAgg = PermGroFacAgg
- PolyStateEconomy.PermShkAggStd = StateCount*[0.006]
- PolyStateEconomy.TranShkAggStd = StateCount*[0.003]
- PolyStateEconomy.slope_prev = StateCount*[1.0]
- PolyStateEconomy.intercept_prev = StateCount*[0.0]
- PolyStateEconomy.update()
- PolyStateEconomy.makeAggShkDstn()
- PolyStateEconomy.makeAggShkHist() # Simulate a history of aggregate shocks
- PolyStateExample.getEconomyData(
- PolyStateEconomy) # Have the consumers inherit relevant objects from the economy
-
- # Solve the many state model
- t_start = clock()
- print('Now solving an economy with ' + str(StateCount) + ' Markov states. This might take a while...')
- PolyStateEconomy.solve()
- t_end = clock()
- print('Solving a model with ' + str(StateCount) + ' states took ' + str(t_end - t_start) + ' seconds.')
-
-
-if __name__ == '__main__':
- main()
diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py
index 748e0b1fd..83f51501e 100644
--- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py
+++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py
@@ -18,6 +18,11 @@
getPercentiles
from HARK.simulation import drawLognormal, drawDiscrete, drawUniform
from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType
+import HARK.ConsumptionSaving.ConsumerParameters as Params
+
+__all__ = ['ValueFunc2D', 'MargValueFunc2D', 'MargMargValueFunc2D', 'pLvlFuncAR1',
+'ConsGenIncProcessSolver', 'GenIncProcessConsumerType',
+'IndShockExplicitPermIncConsumerType', 'PersistentShockConsumerType']
utility = CRRAutility
utilityP = CRRAutilityP
@@ -965,7 +970,7 @@ class GenIncProcessConsumerType(IndShockConsumerType):
solution_terminal_ = ConsumerSolution(cFunc=cFunc_terminal_, mNrmMin=0.0, hNrm=0.0, MPCmin=1.0, MPCmax=1.0)
poststate_vars_ = ['aLvlNow', 'pLvlNow']
- def __init__(self, cycles=1, time_flow=True, **kwds):
+ def __init__(self, cycles=0, time_flow=True, **kwds):
'''
Instantiate a new ConsumerType with given data.
See ConsumerParameters.init_explicit_perm_inc for a dictionary of the
@@ -982,6 +987,10 @@ def __init__(self, cycles=1, time_flow=True, **kwds):
-------
None
'''
+ params = Params.init_explicit_perm_inc.copy()
+ params.update(kwds)
+ kwds = params
+
# Initialize a basic ConsumerType
IndShockConsumerType.__init__(self, cycles=cycles, time_flow=time_flow, **kwds)
self.solveOnePeriod = solveConsGenIncProcess # idiosyncratic shocks solver with explicit persistent income
@@ -1305,151 +1314,3 @@ def updatepLvlNextFunc(self):
self.addToTimeVary('pLvlNextFunc')
if not orig_time:
self.timeRev()
-
-
-###############################################################################
-
-def main():
- import HARK.ConsumptionSaving.ConsumerParameters as Params
- from HARK.utilities import plotFuncs
- from time import clock
- import matplotlib.pyplot as plt
- def mystr(number): return "{:.4f}".format(number)
-
- do_simulation = False
-
- # Display information about the pLvlGrid used in these examples
- print('The infinite horizon examples presented here use a grid of persistent income levels (pLvlGrid)')
- print('based on percentiles of the long run distribution of pLvl for the given parameters. These percentiles')
- print('are specified in the attribute pLvlPctiles. Here, the lowest percentile is ' +
- str(Params.init_explicit_perm_inc['pLvlPctiles'][0]*100) + ' and the highest')
- print('percentile is ' + str(Params.init_explicit_perm_inc['pLvlPctiles'][-1]*100) + '.\n')
-
- # Make and solve an example "explicit permanent income" consumer with idiosyncratic shocks
- ExplicitExample = IndShockExplicitPermIncConsumerType(**Params.init_explicit_perm_inc)
- t_start = clock()
- ExplicitExample.solve()
- t_end = clock()
- print('Solving an explicit permanent income consumer took ' + mystr(t_end-t_start) + ' seconds.')
-
- # Plot the consumption function at various permanent income levels
- print('Consumption function by pLvl for explicit permanent income consumer:')
- pLvlGrid = ExplicitExample.pLvlGrid[0]
- mLvlGrid = np.linspace(0, 20, 300)
- for p in pLvlGrid:
- M_temp = mLvlGrid + ExplicitExample.solution[0].mLvlMin(p)
- C = ExplicitExample.solution[0].cFunc(M_temp, p*np.ones_like(M_temp))
- plt.plot(M_temp, C)
- plt.xlim(0., 20.)
- plt.ylim(0., None)
- plt.xlabel('Market resource level mLvl')
- plt.ylabel('Consumption level cLvl')
- plt.show()
-
- # Now solve the *exact same* problem, but with the permanent income normalization
- NormalizedExample = IndShockConsumerType(**Params.init_explicit_perm_inc)
- t_start = clock()
- NormalizedExample.solve()
- t_end = clock()
- print('Solving the equivalent problem with permanent income normalized out took ' +
- mystr(t_end-t_start) + ' seconds.')
-
- # Show that the normalized consumption function for the "explicit permanent income" consumer
- # is almost identical for every permanent income level (and the same as the normalized problem's
- # cFunc), but is less accurate due to extrapolation outside the bounds of pLvlGrid.
- print('Normalized consumption function by pLvl for explicit permanent income consumer:')
- pLvlGrid = ExplicitExample.pLvlGrid[0]
- mNrmGrid = np.linspace(0, 20, 300)
- for p in pLvlGrid:
- M_temp = mNrmGrid*p + ExplicitExample.solution[0].mLvlMin(p)
- C = ExplicitExample.solution[0].cFunc(M_temp, p*np.ones_like(M_temp))
- plt.plot(M_temp/p, C/p)
- plt.xlim(0., 20.)
- plt.ylim(0., None)
- plt.xlabel('Normalized market resources mNrm')
- plt.ylabel('Normalized consumption cNrm')
- plt.show()
- print('Consumption function for normalized problem (without explicit permanent income):')
- mNrmMin = NormalizedExample.solution[0].mNrmMin
- plotFuncs(NormalizedExample.solution[0].cFunc, mNrmMin, mNrmMin+20)
-
- print('The "explicit permanent income" solution deviates from the solution to the normalized problem because')
- print('of errors from extrapolating beyond the bounds of the pLvlGrid. The error is largest for pLvl values')
- print('near the upper and lower bounds, and propagates toward the center of the distribution.\n')
-
- # Plot the value function at various permanent income levels
- if ExplicitExample.vFuncBool:
- pGrid = np.linspace(0.1, 3.0, 24)
- M = np.linspace(0.001, 5, 300)
- for p in pGrid:
- M_temp = M+ExplicitExample.solution[0].mLvlMin(p)
- C = ExplicitExample.solution[0].vFunc(M_temp, p*np.ones_like(M_temp))
- plt.plot(M_temp, C)
- plt.ylim([-200, 0])
- plt.xlabel('Market resource level mLvl')
- plt.ylabel('Value v')
- plt.show()
-
- # Simulate some data
- if do_simulation:
- ExplicitExample.T_sim = 500
- ExplicitExample.track_vars = ['mLvlNow', 'cLvlNow', 'pLvlNow']
- ExplicitExample.makeShockHistory() # This is optional
- ExplicitExample.initializeSim()
- ExplicitExample.simulate()
- plt.plot(np.mean(ExplicitExample.mLvlNow_hist, axis=1))
- plt.xlabel('Simulated time period')
- plt.ylabel('Average market resources mLvl')
- plt.show()
-
-###############################################################################
-
- # Make and solve an example "persistent idisyncratic shocks" consumer
- PersistentExample = PersistentShockConsumerType(**Params.init_persistent_shocks)
- t_start = clock()
- PersistentExample.solve()
- t_end = clock()
- print('Solving a persistent income shocks consumer took ' + mystr(t_end-t_start) + ' seconds.')
-
- # Plot the consumption function at various levels of persistent income pLvl
- print('Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of '
- + str(PersistentExample.PrstIncCorr) + ':')
- pLvlGrid = PersistentExample.pLvlGrid[0]
- mLvlGrid = np.linspace(0, 20, 300)
- for p in pLvlGrid:
- M_temp = mLvlGrid + PersistentExample.solution[0].mLvlMin(p)
- C = PersistentExample.solution[0].cFunc(M_temp, p*np.ones_like(M_temp))
- plt.plot(M_temp, C)
- plt.xlim(0., 20.)
- plt.ylim(0., None)
- plt.xlabel('Market resource level mLvl')
- plt.ylabel('Consumption level cLvl')
- plt.show()
-
- # Plot the value function at various persistent income levels
- if PersistentExample.vFuncBool:
- pGrid = PersistentExample.pLvlGrid[0]
- M = np.linspace(0.001, 5, 300)
- for p in pGrid:
- M_temp = M+PersistentExample.solution[0].mLvlMin(p)
- C = PersistentExample.solution[0].vFunc(M_temp, p*np.ones_like(M_temp))
- plt.plot(M_temp, C)
- plt.ylim([-200, 0])
- plt.xlabel('Market resource level mLvl')
- plt.ylabel('Value v')
- plt.show()
-
- # Simulate some data
- if do_simulation:
- PersistentExample.T_sim = 500
- PersistentExample.track_vars = ['mLvlNow', 'cLvlNow', 'pLvlNow']
- PersistentExample.initializeSim()
- PersistentExample.simulate()
- plt.plot(np.mean(PersistentExample.mLvlNow_hist, axis=1))
- plt.xlabel('Simulated time period')
- plt.ylabel('Average market resources mLvl')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index a9d45f26f..7dee73e0d 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -24,11 +24,18 @@
from HARK import AgentType, Solution, NullFunc, HARKobject
from HARK.utilities import warnings # Because of "patch" to warnings modules
from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp
-from HARK.simulation import drawDiscrete, drawBernoulli, drawLognormal, drawUniform
+from HARK.simulation import drawDiscrete, drawLognormal, drawUniform
from HARK.utilities import approxMeanOneLognormal, addDiscreteOutcomeConstantMean,\
combineIndepDstns, makeGridExpMult, CRRAutility, CRRAutilityP, \
CRRAutilityPP, CRRAutilityP_inv, CRRAutility_invP, CRRAutility_inv, \
CRRAutilityP_invP
+import HARK.ConsumptionSaving.ConsumerParameters as Params
+
+
+__all__ = ['ConsumerSolution', 'ValueFunc', 'MargValueFunc', 'MargMargValueFunc',
+'ConsPerfForesightSolver', 'ConsIndShockSetup', 'ConsIndShockSolverBasic',
+'ConsIndShockSolver', 'ConsKinkedRsolver', 'PerfForesightConsumerType',
+'IndShockConsumerType', 'KinkedRconsumerType']
utility = CRRAutility
utilityP = CRRAutilityP
@@ -91,18 +98,11 @@ def __init__(self, cFunc=None, vFunc=None,
None
'''
# Change any missing function inputs to NullFunc
- if cFunc is None:
- cFunc = NullFunc()
- if vFunc is None:
- vFunc = NullFunc()
- if vPfunc is None:
- vPfunc = NullFunc()
- if vPPfunc is None:
- vPPfunc = NullFunc()
- self.cFunc = cFunc
- self.vFunc = vFunc
- self.vPfunc = vPfunc
- self.vPPfunc = vPPfunc
+ self.cFunc = cFunc if cFunc is not None else NullFunc()
+ self.vFunc = vFunc if vFunc is not None else NullFunc()
+ self.vPfunc = vPfunc if vPfunc is not None else NullFunc()
+ # vPFunc = NullFunc() if vPfunc is None else vPfunc
+ self.vPPfunc = vPPfunc if vPPfunc is not None else NullFunc()
self.mNrmMin = mNrmMin
self.hNrm = hNrm
self.MPCmin = MPCmin
@@ -316,7 +316,7 @@ class ConsPerfForesightSolver(object):
A class for solving a one period perfect foresight consumption-saving problem.
An instance of this class is created by the function solvePerfForesight in each period.
'''
- def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
+ def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks):
'''
Constructor for a new ConsPerfForesightSolver.
@@ -335,20 +335,26 @@ def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
Risk free interest factor on end-of-period assets.
PermGroFac : float
Expected permanent income growth factor at the end of this period.
+ BoroCnstArt : float or None
+ Artificial borrowing constraint, as a multiple of permanent income.
+ Can be None, indicating no artificial constraint.
+ MaxKinks : int
+ Maximum number of kink points to allow in the consumption function;
+ additional points will be thrown out. Only relevant in infinite
+ horizon model with artificial borrowing constraint.
Returns:
----------
None
'''
# We ask that HARK users define single-letter variables they use in a dictionary
- # attribute called notation.
- # Do that first.
+ # attribute called notation. Do that first.
self.notation = {'a': 'assets after all actions',
'm': 'market resources at decision time',
'c': 'consumption'}
- self.assignParameters(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac)
+ self.assignParameters(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks)
- def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
+ def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks):
'''
Saves necessary parameters as attributes of self for use by other methods.
@@ -367,10 +373,16 @@ def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
Risk free interest factor on end-of-period assets.
PermGroFac : float
Expected permanent income growth factor at the end of this period.
+ BoroCnstArt : float or None
+ Artificial borrowing constraint, as a multiple of permanent income.
+ Can be None, indicating no artificial constraint.
+ MaxKinks : int
+ Maximum number of kink points to allow in the consumption function;
+ additional points will be thrown out.
Returns
-------
- none
+ None
'''
self.solution_next = solution_next
self.DiscFac = DiscFac
@@ -378,6 +390,8 @@ def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
self.CRRA = CRRA
self.Rfree = Rfree
self.PermGroFac = PermGroFac
+ self.MaxKinks = MaxKinks
+ self.BoroCnstArt = BoroCnstArt
def defUtilityFuncs(self):
'''
@@ -386,11 +400,11 @@ def defUtilityFuncs(self):
Parameters
----------
- none
+ None
Returns
-------
- none
+ None
'''
self.u = lambda c : utility(c,gam=self.CRRA) # utility function
self.uP = lambda c : utilityP(c,gam=self.CRRA) # marginal utility function
@@ -398,20 +412,32 @@ def defUtilityFuncs(self):
def defValueFuncs(self):
'''
- Defines the value and marginal value function for this period.
+ Defines the value and marginal value functions for this period.
+ Uses the fact that for a perfect foresight CRRA utility problem,
+ if the MPC in period t is :math:`\kappa_{t}`, and relative risk
+ aversion :math:`\rho`, then the inverse value vFuncNvrs has a
+ constant slope of :math:`\kappa_{t}^{-\rho/(1-\rho)}` and
+ vFuncNvrs has value of zero at the lower bound of market resources
+ mNrmMin. See PerfForesightConsumerType.ipynb documentation notebook
+ for a brief explanation and the links below for a fuller treatment.
+
+ https://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/#vFuncAnalytical
+ https://econ.jhu.edu/people/ccarroll/SolvingMicroDSOPs/#vFuncPF
Parameters
----------
- none
+ None
Returns
-------
- none
+ None
'''
- MPCnvrs = self.MPC**(-self.CRRA/(1.0-self.CRRA))
- vFuncNvrs = LinearInterp(np.array([self.mNrmMin, self.mNrmMin+1.0]),np.array([0.0, MPCnvrs]))
- self.vFunc = ValueFunc(vFuncNvrs,self.CRRA)
- self.vPfunc = MargValueFunc(self.cFunc,self.CRRA)
+
+ # See the PerfForesightConsumerType.ipynb documentation notebook for the derivations
+ vFuncNvrsSlope = self.MPCmin**(-self.CRRA/(1.0-self.CRRA))
+ vFuncNvrs = LinearInterp(np.array([self.mNrmMinNow, self.mNrmMinNow+1.0]),np.array([0.0, vFuncNvrsSlope]))
+ self.vFunc = ValueFunc(vFuncNvrs,self.CRRA)
+ self.vPfunc = MargValueFunc(self.cFunc,self.CRRA)
def makePFcFunc(self):
'''
@@ -419,24 +445,92 @@ def makePFcFunc(self):
Parameters
----------
- none
+ None
Returns
-------
- none
+ None
'''
- # Calculate human wealth this period (and lower bound of m)
+ # Use a local value of BoroCnstArt to prevent comparing None and float below.
+ if self.BoroCnstArt is None:
+ BoroCnstArt = -np.inf
+ else:
+ BoroCnstArt = self.BoroCnstArt
+
+ # Calculate human wealth this period
self.hNrmNow = (self.PermGroFac/self.Rfree)*(self.solution_next.hNrm + 1.0)
- self.mNrmMin = -self.hNrmNow
- # Calculate the (constant) marginal propensity to consume
+
+ # Calculate the lower bound of the marginal propensity to consume
PatFac = ((self.Rfree*self.DiscFacEff)**(1.0/self.CRRA))/self.Rfree
- self.MPC = 1.0/(1.0 + PatFac/self.solution_next.MPCmin)
- # Construct the consumption function
- self.cFunc = LinearInterp([self.mNrmMin, self.mNrmMin+1.0],[0.0, self.MPC])
- # Add two attributes to enable calculation of steady state market resources
+ self.MPCmin = 1.0/(1.0 + PatFac/self.solution_next.MPCmin)
+
+ # Extract the discrete kink points in next period's consumption function;
+ # don't take the last one, as it only defines the extrapolation and is not a kink.
+ mNrmNext = self.solution_next.cFunc.x_list[:-1]
+ cNrmNext = self.solution_next.cFunc.y_list[:-1]
+
+ # Calculate the end-of-period asset values that would reach those kink points
+ # next period, then invert the first order condition to get consumption. Then
+ # find the endogenous gridpoint (kink point) today that corresponds to each kink
+ aNrmNow = (self.PermGroFac/self.Rfree)*(mNrmNext-1.0)
+ cNrmNow = (self.DiscFacEff*self.Rfree)**(-1./self.CRRA)*(self.PermGroFac*cNrmNext)
+ mNrmNow = aNrmNow + cNrmNow
+
+ # Add an additional point to the list of gridpoints for the extrapolation,
+ # using the new value of the lower bound of the MPC.
+ mNrmNow = np.append(mNrmNow, mNrmNow[-1] + 1.0)
+ cNrmNow = np.append(cNrmNow, cNrmNow[-1] + self.MPCmin)
+
+ # If the artificial borrowing constraint binds, combine the constrained and
+ # unconstrained consumption functions.
+ if BoroCnstArt > mNrmNow[0]:
+ # Find the highest index where constraint binds
+ cNrmCnst = mNrmNow - BoroCnstArt
+ CnstBinds = cNrmCnst < cNrmNow
+ idx = np.where(CnstBinds)[0][-1]
+
+ if idx < (mNrmNow.size-1):
+ # If it is not the *very last* index, find the the critical level
+ # of mNrm where the artificial borrowing contraint begins to bind.
+ d0 = cNrmNow[idx] - cNrmCnst[idx]
+ d1 = cNrmCnst[idx+1] - cNrmNow[idx+1]
+ m0 = mNrmNow[idx]
+ m1 = mNrmNow[idx+1]
+ alpha = d0/(d0 + d1)
+ mCrit = m0 + alpha*(m1 - m0)
+
+ # Adjust the grids of mNrm and cNrm to account for the borrowing constraint.
+ cCrit = mCrit - BoroCnstArt
+ mNrmNow = np.concatenate(([BoroCnstArt, mCrit], mNrmNow[(idx+1):]))
+ cNrmNow = np.concatenate(([0., cCrit], cNrmNow[(idx+1):]))
+
+ else:
+ # If it *is* the very last index, then there are only three points
+ # that characterize the consumption function: the artificial borrowing
+ # constraint, the constraint kink, and the extrapolation point.
+ mXtra = cNrmNow[-1] - cNrmCnst[-1]/(1.0 - self.MPCmin)
+ mCrit = mNrmNow[-1] + mXtra
+ cCrit = mCrit - BoroCnstArt
+ mNrmNow = np.array([BoroCnstArt, mCrit, mCrit + 1.0])
+ cNrmNow = np.array([0., cCrit, cCrit + self.MPCmin])
+
+ # If the mNrm and cNrm grids have become too large, throw out the last
+ # kink point, being sure to adjust the extrapolation.
+ if mNrmNow.size > self.MaxKinks:
+ mNrmNow = np.concatenate((mNrmNow[:-2], [mNrmNow[-3] + 1.0]))
+ cNrmNow = np.concatenate((cNrmNow[:-2], [cNrmNow[-3] + self.MPCmin]))
+
+ # Construct the consumption function as a linear interpolation.
+ self.cFunc = LinearInterp(mNrmNow, cNrmNow)
+
+ # Calculate the upper bound of the MPC as the slope of the bottom segment.
+ self.MPCmax = (cNrmNow[1] - cNrmNow[0]) / (mNrmNow[1] - mNrmNow[0])
+
+ # Add two attributes to enable calculation of steady state market resources.
self.ExIncNext = 1.0 # Perfect foresight income of 1
- self.mNrmMinNow = self.mNrmMin # Relabeling for compatibility with addSSmNrm
-
+ self.mNrmMinNow = mNrmNow[0] # Relabeling for compatibility with addSSmNrm
+
+
def addSSmNrm(self,solution):
'''
Finds steady state (normalized) market resources and adds it to the
@@ -474,7 +568,7 @@ def solve(self):
Parameters
----------
- none
+ None
Returns
-------
@@ -486,13 +580,13 @@ def solve(self):
self.makePFcFunc()
self.defValueFuncs()
solution = ConsumerSolution(cFunc=self.cFunc, vFunc=self.vFunc, vPfunc=self.vPfunc,
- mNrmMin=self.mNrmMin, hNrm=self.hNrmNow,
- MPCmin=self.MPC, MPCmax=self.MPC)
- #solution = self.addSSmNrm(solution)
+ mNrmMin=self.mNrmMinNow, hNrm=self.hNrmNow,
+ MPCmin=self.MPCmin, MPCmax=self.MPCmax)
+ solution = self.addSSmNrm(solution)
return solution
-def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
+def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks):
'''
Solves a single period consumption-saving problem for a consumer with perfect foresight.
@@ -511,15 +605,23 @@ def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
Risk free interest factor on end-of-period assets.
PermGroFac : float
Expected permanent income growth factor at the end of this period.
+ BoroCnstArt : float or None
+ Artificial borrowing constraint, as a multiple of permanent income.
+ Can be None, indicating no artificial constraint.
+ MaxKinks : int
+ Maximum number of kink points to allow in the consumption function;
+ additional points will be thrown out. Only relevant in infinite horizon
+ models with artificial borrowing constraint.
Returns
-------
- solution : ConsumerSolution
- The solution to this period's problem.
+ solution_now : ConsumerSolution
+ The solution to this period's problem.
'''
- solver = ConsPerfForesightSolver(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac)
- solution = solver.solve()
- return solution
+
+ solver = ConsPerfForesightSolver(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac,BoroCnstArt,MaxKinks)
+ solution_now = solver.solve()
+ return solution_now
###############################################################################
@@ -624,10 +726,9 @@ def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,
none
'''
ConsPerfForesightSolver.assignParameters(self,solution_next,DiscFac,LivPrb,
- CRRA,Rfree,PermGroFac)
- self.BoroCnstArt = BoroCnstArt
- self.IncomeDstn = IncomeDstn
+ CRRA,Rfree,PermGroFac,BoroCnstArt,None)
self.aXtraGrid = aXtraGrid
+ self.IncomeDstn = IncomeDstn
self.vFuncBool = vFuncBool
self.CubicBool = CubicBool
@@ -706,6 +807,9 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac):
self.MPCmaxNow = 1.0/(1.0 + (self.WorstIncPrb**(1.0/self.CRRA))*
self.PatFac/solution_next.MPCmax)
+ self.cFuncLimitIntercept = self.MPCminNow*self.hNrmNow
+ self.cFuncLimitSlope = self.MPCminNow
+
def defBoroCnst(self,BoroCnstArt):
'''
@@ -810,7 +914,7 @@ def prepareToCalcEndOfPrdvP(self):
ShkPrbs_temp = (np.tile(self.ShkPrbsNext,(aNrmCount,1))).transpose()
# Get cash on hand next period
- mNrmNext = self.Rfree/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + TranShkVals_temp
+ mNrmNext = self.Rfree/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + TranShkVals_temp # CDC 20191205: This should be divided by LivPrb[0] for Blanchard insurance
# Store and report the results
self.PermShkVals_temp = PermShkVals_temp
@@ -971,7 +1075,7 @@ def makeLinearcFunc(self,mNrm,cNrm):
cFuncUnc : LinearInterp
The unconstrained consumption function for this period.
'''
- cFuncUnc = LinearInterp(mNrm,cNrm,self.MPCminNow*self.hNrmNow,self.MPCminNow)
+ cFuncUnc = LinearInterp(mNrm, cNrm, self.cFuncLimitIntercept, self.cFuncLimitSlope)
return cFuncUnc
def solve(self):
@@ -1305,7 +1409,6 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,
-------
None
'''
- assert CubicBool==False,'KinkedR will only work with linear interpolation (for now)'
assert Rboro>=Rsave, 'Interest factor on debt less than interest factor on savings!'
# Initialize the solver. Most of the steps are exactly the same as in
@@ -1316,7 +1419,32 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,
# Assign the interest rates as class attributes, to use them later.
self.Rboro = Rboro
self.Rsave = Rsave
+
+ def makeCubiccFunc(self,mNrm,cNrm):
+ '''
+ Makes a cubic spline interpolation that contains the kink of the unconstrained
+ consumption function for this period.
+
+ Parameters
+ ----------
+ mNrm : np.array
+ Corresponding market resource points for interpolation.
+ cNrm : np.array
+ Consumption points for interpolation.
+
+ Returns
+ -------
+ cFuncUnc : CubicInterp
+ The unconstrained consumption function for this period.
+ '''
+ # Call the makeCubiccFunc from ConsIndShockSolver.
+ cFuncNowUncKink = super().makeCubiccFunc(mNrm, cNrm)
+
+ # Change the coeffients at the kinked points.
+ cFuncNowUncKink.coeffs[self.i_kink + 1] = [cNrm[self.i_kink], mNrm[self.i_kink + 1] - mNrm[self.i_kink], 0, 0]
+ return cFuncNowUncKink
+
def prepareToCalcEndOfPrdvP(self):
'''
Prepare to calculate end-of-period marginal value by creating an array
@@ -1356,7 +1484,8 @@ def prepareToCalcEndOfPrdvP(self):
# Make a 1D array of the interest factor at each asset gridpoint
Rfree_vec = self.Rsave*np.ones(aXtraCount)
if KinkBool:
- Rfree_vec[0:(np.sum(aNrmNow<=0)-1)] = self.Rboro
+ self.i_kink = np.sum(aNrmNow<=0)-1 # Save the index of the kink point as an attribute
+ Rfree_vec[0:self.i_kink] = self.Rboro
self.Rfree = Rfree_vec
Rfree_temp = np.tile(Rfree_vec,(ShkCount,1))
@@ -1453,8 +1582,8 @@ class PerfForesightConsumerType(AgentType):
'''
A perfect foresight consumer type who has no uncertainty other than mortality.
His problem is defined by a coefficient of relative risk aversion, intertemporal
- discount factor, interest factor, and time sequences of the permanent income
- growth rate and survival probability.
+ discount factor, interest factor, an artificial borrowing constraint (maybe)
+ and time sequences of the permanent income growth rate and survival probability.
'''
# Define some universal values for all consumer types
cFunc_terminal_ = LinearInterp([0.0, 1.0],[0.0,1.0]) # c=m in terminal period
@@ -1463,11 +1592,16 @@ class PerfForesightConsumerType(AgentType):
vFunc = vFunc_terminal_, mNrmMin=0.0, hNrm=0.0,
MPCmin=1.0, MPCmax=1.0)
time_vary_ = ['LivPrb','PermGroFac']
- time_inv_ = ['CRRA','Rfree','DiscFac']
+ time_inv_ = ['CRRA','Rfree','DiscFac','MaxKinks','BoroCnstArt']
poststate_vars_ = ['aNrmNow','pLvlNow']
shock_vars_ = []
- def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds):
+ def __init__(self,
+ cycles=1,
+ time_flow=True,
+ verbose=False,
+ quiet=False,
+ **kwds):
'''
Instantiate a new consumer type with given data.
See ConsumerParameters.init_perfect_foresight for a dictionary of
@@ -1484,6 +1618,10 @@ def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds):
-------
None
'''
+ params = Params.init_perfect_foresight.copy()
+ params.update(kwds)
+ kwds = params
+
# Initialize a basic AgentType
AgentType.__init__(self,solution_terminal=deepcopy(self.solution_terminal_),
cycles=cycles,time_flow=time_flow,pseudo_terminal=False,**kwds)
@@ -1498,6 +1636,22 @@ def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds):
self.solveOnePeriod = solvePerfForesight # solver for perfect foresight model
+ def preSolve(self):
+ self.updateSolutionTerminal() # Solve the terminal period problem
+
+ # Fill in BoroCnstArt and MaxKinks if they're not specified or are irrelevant.
+ if not hasattr(self,'BoroCnstArt'): # If no borrowing constraint specified...
+ self.BoroCnstArt = None # ...assume the user wanted none
+ if not hasattr(self,'MaxKinks'):
+ if self.cycles > 0: # If it's not an infinite horizon model...
+ self.MaxKinks = np.inf # ...there's no need to set MaxKinks
+ elif self.BoroCnstArt is None: # If there's no borrowing constraint...
+ self.MaxKinks = np.inf # ...there's no need to set MaxKinks
+ else:
+ raise(AttributeError('PerfForesightConsumerType requires the attribute MaxKinks to be specified when BoroCnstArt is not None and cycles == 0.'))
+
+
+
def checkRestrictions(self):
"""
A method to check that various restrictions are met for the model class.
@@ -1695,15 +1849,113 @@ def getPostStates(self):
self.aLvlNow = self.aNrmNow*self.pLvlNow # Useful in some cases to precalculate asset level
return None
+ def checkAIC(self, thorn,verbose,public_call):
+ '''
+ Evaluate and report on the Absolute Impatience Condition
+ '''
+ AIF = thorn
+
+ self.AIF = AIF
+ if AIF<1:
+ self.AIC = True
+ if public_call or verbose:
+ print('The value of the absolute impatience factor (AIF) for the supplied parameter values satisfies the Absolute Impatience Condition.', end = " ")
+ self.violated = False
+ if verbose:
+ print(' Because the AIF < 1, the absolute amount of consumption is expected to fall over time.')
+ print()
+ else:
+ self.AIC = False
+ print('The given type violates the Absolute Impatience Condition with the supplied parameter values; the AIF is %1.5f ' % (AIF), end=" ")
+ self.violated = True
+ if verbose:
+ print(' Because the AIF > 1, the absolute amount of consumption is expected to grow over time')
+ print()
+
+ def checkGICPF(self,thorn,verbose,public_call):
+ '''
+ Evaluate and report on the Growth Impatience Condition
+ '''
+ GIFPF = thorn/self.PermGroFac[0]
+ self.GIFPF = GIFPF
+
+ if GIFPF<1:
+ self.GICPF = True
+ if public_call or verbose:
+ print('The value of the Growth Impatience Factor for the supplied parameter values satisfies the Perfect Foresight Growth Impatience Condition.', end = " ")
+ if verbose:
+ print(' Therefore, for a perfect foresight consumer, the ratio of individual wealth to permanent income will fall indefinitely.')
+ print()
+ else:
+ self.GICPF = False
+ self.violated = True
+ print('The given parameter values violate the Perfect Foresight Growth Impatience Condition for this consumer type; the GIFPF is: %2.4f' % (GIFPF), end = " ")
+ if verbose:
+ print(' Therefore, for a perfect foresight consumer the ratio of individual wealth to permanent income is expected to grow toward infinity.')
+ print()
+
+ def checkRIC(self, thorn,verbose,public_call):
+ '''
+ Evaluate and report on the Return Impatience Condition
+ '''
+
+ RIF = thorn/self.Rfree
+ self.RIF = RIF
+ if RIF<1:
+ self.RIC = True
+ if public_call or verbose:
+ print('The return impatience factor value for the supplied parameter values satisfies the Return Impatience Condition.', end = " ")
+ if verbose:
+ print('Therefore, the limiting consumption function is not c(m)=0 for all m')
+ print()
+ else:
+ self.RIC = False
+ self.violated = True
+ print('The given type violates the Return Impatience Condition with the supplied parameter values; the factor is %1.5f ' % (RIF), end = " ")
+ if verbose:
+ print('Therefore, the limiting consumption function is c(m)=0 for all m')
+ print()
+
+ def checkFHWC(self,verbose,public_call):
+ '''
+ Evaluate and report on the Finite Human Wealth Condition
+ '''
+
+ FHWF = self.PermGroFac[0]/self.Rfree
+ self.FHWF = FHWF
+ if FHWF<1:
+ self.hNrm = 1.0/(1.0-self.PermGroFac[0]/self.Rfree)
+ self.FHWC = True
+ if public_call or verbose:
+ print('The Finite Human wealth factor value for the supplied parameter values satisfies the Finite Human Wealth Condition.', end = " ")
+ if verbose:
+ print('Therefore, the limiting consumption function is not c(m)=Infinity')
+ print('and human wealth normalized by permanent income is %2.5f' % (self.hNrm))
+ self.cNrmPDV = 1.0/(1.0-self.Thorn/self.Rfree)
+ print('and the PDV of future consumption growth is %2.5f' % (self.cNrmPDV) )
+ print()
+ else:
+ self.FHWC = False
+ print('The given type violates the Finite Human Wealth Condition; the Finite Human wealth factor value %2.5f ' % (FHWF), end = " ")
+ self.violated = True
+ if verbose:
+ print('Therefore, the limiting consumption function is c(m)=Infinity for all m')
+ print()
+ if verbose and self.violated and verbose_reference:
+ print('[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/')
+ return self.violated
+
+
+
def checkConditions(self,verbose=False,verbose_reference=False,public_call=False):
'''
- This method checks whether the instance's type satisfies the growth impatience condition
- (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return
- impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of
- autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate
- solutions under infinite horizon with a 1 period cycle. Depending on the model at hand, a
- different combination of these conditions must be satisfied. To check which conditions are
- relevant to the model at hand, a reference to the relevant theoretical literature is made.
+ This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC),
+ the Return Impatience Condition (RIC), the Finite Human Wealth Condition (FHWC) and the perfect foresight
+ model's version of the Finite Value of the Growth Impatience Condition (GIC_PF) and
+ Autarky Condition (FVAC_PF). Depending on the configuration of parameter values, some
+ combination of these conditions must be satisfied in order for the problem to have
+ a nondegenerate solution. To check which conditions are required, in the verbose mode
+ a reference to the relevant theoretical literature is made.
Parameters
----------
@@ -1721,42 +1973,15 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False
if self.cycles!=0 or self.T_cycle > 1:
return
- violated = False
-
- #Evaluate and report on the return impatience condition
-
- RIF = (self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree
- if RIF<1:
- if public_call:
- print('The return impatience factor value for the supplied parameter values satisfies the return impatience condition.')
- else:
- violated = True
- print('The given type violates the Return Impatience Condition with the supplied parameter values; the factor is %1.5f ' % (RIF))
-
- #Evaluate and report on the absolute impatience condition
- AIF = self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA)
- if AIF<1:
- if public_call:
- print('The absolute impatience factor value for the supplied parameter values satisfies the absolute impatience condition.')
- else:
- print('The given type violates the absolute impatience condition with the supplied parameter values; the AIF is %1.5f ' % (AIF))
- if verbose:
- violated = True
- print(' Therefore, the absolute amount of consumption is expected to grow over time')
-
- #Evaluate and report on the finite human wealth condition
- FHWF = self.PermGroFac[0]/self.Rfree
- if FHWF<1:
- if public_call:
- print('The finite human wealth factor value for the supplied parameter values satisfies the finite human wealth condition.')
- else:
- print('The given type violates the finite human wealth condition; the finite human wealth factor value %2.5f ' % (FHWF))
- violated = True
- if verbose and violated and verbose_reference:
- print('[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/')
+ self.violated = False
- return violated
+ Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA)
+ self.Thorn = Thorn
+ self.checkAIC(Thorn,verbose,public_call)
+ self.checkGICPF(Thorn,verbose,public_call)
+ self.checkRIC(Thorn,verbose,public_call)
+ self.checkFHWC(verbose,public_call)
class IndShockConsumerType(PerfForesightConsumerType):
'''
@@ -1767,9 +1992,15 @@ class IndShockConsumerType(PerfForesightConsumerType):
period assets, and an artificial borrowing constraint.
'''
time_inv_ = PerfForesightConsumerType.time_inv_ + ['BoroCnstArt','vFuncBool','CubicBool']
+ time_inv_.remove('MaxKinks') # This is in the PerfForesight model but not ConsIndShock
shock_vars_ = ['PermShkNow','TranShkNow']
- def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds):
+ def __init__(self,
+ cycles=1,
+ time_flow=True,
+ verbose=False,
+ quiet=False,
+ **kwds):
'''
Instantiate a new ConsumerType with given data.
See ConsumerParameters.init_idiosyncratic_shocks for a dictionary of
@@ -1786,9 +2017,18 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds):
-------
None
'''
+
+ params = Params.init_idiosyncratic_shocks.copy()
+ params.update(kwds)
+ kwds = params
+
# Initialize a basic AgentType
- PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,
- verbose=verbose,quiet=quiet, **kwds)
+ PerfForesightConsumerType.__init__(self,
+ cycles=cycles,
+ time_flow=time_flow,
+ verbose=verbose,
+ quiet=quiet,
+ **kwds)
# Add consumer-type specific objects, copying to create independent versions
self.solveOnePeriod = solveConsIndShock # idiosyncratic shocks solver
@@ -1906,7 +2146,8 @@ def calcBoundingValues(self):
horizon model with only one period repeated indefinitely. Store results
as attributes of self. Human wealth is the present discounted value of
expected future income after receiving income this period, ignoring mort-
- ality. The maximum MPC is the limit of the MPC as m --> mNrmMin. The
+ ality (because your income matters to you only if you are still alive).
+ The maximum MPC is the limit of the MPC as m --> mNrmMin. The
minimum MPC is the limit of the MPC as m --> infty.
Parameters
@@ -2009,7 +2250,7 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True):
# Calculate expected marginal value and implied optimal consumption
ExvPnextGrid = self.DiscFac*self.Rfree*self.LivPrb[0]*self.PermGroFac[0]**(-self.CRRA)* \
np.sum(PermShkVals_tiled**(-self.CRRA)*vPnextArray*ShkPrbs_tiled,axis=0)
- cOptGrid = ExvPnextGrid**(-1.0/self.CRRA)
+ cOptGrid = ExvPnextGrid**(-1.0/self.CRRA) # This is the 'Endogenous Gridpoints' step
# Calculate Euler error and store an interpolated function
EulerErrorNrmGrid = (cNowGrid - cOptGrid)/cOptGrid
@@ -2025,14 +2266,103 @@ def preSolve(self):
if not self.quiet:
self.checkConditions(verbose=self.verbose,public_call=False)
+ def checkGICInd(self,Thorn,verbose,public_call):
+ '''
+ Check Individual Growth Impatience Factor.
+ '''
+
+ if self.GIFInd<=1:
+ self.GICInd = True
+ if public_call or verbose:
+ print('The value of the Individual Growth Impatience Factor for the supplied parameter values satisfies the Individual Growth Impatience Condition.', end = " ")
+ if verbose:
+ print('Therefore, a target level of the individual market resources ratio m exists (see '+self.url+'/#onetarget for more).')
+ print()
+ else:
+ self.GICInd = False
+ self.violated = True
+ print('The given parameter values violate the Individual Growth Impatience Condition; the GIFInd is: %2.4f' % (self.GIFInd), end = " ")
+ if verbose:
+ print('')
+ print('Therefore, a target ratio of individual market resources to individual permanent income does not exist. (see '+self.url+'/#onetarget for more).')
+ print()
+
+ def checkCIGAgg(self, Thorn,verbose,public_call):
+
+ if self.GIFAgg<=1:
+ self.GICAgg = True
+ if public_call or verbose:
+ print('The value of the Aggregate Growth Impatience Factor for the supplied parameter values satisfies the Aggregate Growth Impatience Condition.', end = " ")
+ if verbose:
+ print('Therefore, it is possible that a target level of the ratio of aggregate market resources to aggregate permanent income exists.') # Need to provide reference
+ print()
+ else:
+ self.GICAgg = False
+ self.violated = True
+ print('The given parameter values violate the Aggregate Growth Impatience Condition; the GIFAgg is: %2.4f' % (self.GIFAgg), end = " ")
+ if verbose:
+ print('')
+ print('Therefore, a target ratio of aggregate resources to aggregate permanent income does not exist.') # Need to provide reference
+ print()
+
+ def checkWRIC(self, verbose,public_call):
+ '''
+ Evaluate and report on the Weak Return Impatience Condition
+ [url]/#WRIF modified to incorporate LivPrb
+ '''
+ WRIF=(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA)/self.Rfree
+ self.WRIF = WRIF
+ if WRIF<=1:
+ self.WRIC = True
+ if public_call or verbose:
+ print('The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition (see '+self.url+'/#WRIC for more).')
+ print()
+ else:
+ self.WRIC = False
+ self.violated = True
+ print('The given type violates the Weak Return Impatience Condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF), end = " ")
+ if verbose:
+ print('')
+ print('Therefore, a nondegenerate solution is not available (see '+self.url+'/#WRIC for more.')
+ print()
+
+ def checkFVAC(self,verbose,public_call):
+ '''
+ Evaluate and report on the Finite Value of Autarky Condition
+ Hyperlink to paper: [url]/#Autarky-Value
+ '''
+ EpShkuInv = np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA))
+ if self.CRRA != 1.0:
+ uInvEpShkuInv = EpShkuInv**(1/(1-self.CRRA)) # The term that gives a utility-consequence-adjusted utility growth
+ else:
+ uInvEpShkuInv = 1.0
+
+ self.uInvEpShkuInv = uInvEpShkuInv
+ FVAF=self.LivPrb[0]*self.DiscFac*self.uInvEpShkuInv
+ self.FVAF = FVAF
+ if FVAF<=1:
+ self.FVAC = True
+ if public_call or verbose:
+ print('The Finite Value of Autarky Factor (FVAV) for the supplied parameter values satisfies the Finite Value of Autarky Condition.')
+ if self.WRIC:
+ print('Since both WRIC and FVAC are satisfied, the problem has a nondegenerate solution')
+ else:
+ self.FVAC = False
+ print('The given type violates the Finite Value of Autarky Condition with the supplied parameter values. The FVAF is %2.4f' %(FVAF), end = " ")
+ self.violated = True
+ if public_call or verbose:
+ print('Therefore, a nondegenerate solution is not available (see '+self.url+'/#Conditions-Under-Which-the-Problem-Defines-a-Contraction-Mapping')
+ print()
+
+
+
+
def checkConditions(self,verbose=False,public_call=True):
'''
- This method checks whether the instance's type satisfies the growth impatience condition
- (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return
- impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of
- autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate
- solutions under infinite horizon with a 1 period cycle. Depending on the model at hand, a
- different combination of these conditions must be satisfied. (For an exposition of the
+ This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), Weak Return
+ Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of
+ Autarky Condition (FVAC). When combinations of these conditions are satisfied, the
+ solution to the problem exhibits different characteristics. (For an exposition of the
conditions, see http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/)
Parameters
@@ -2046,50 +2376,116 @@ def checkConditions(self,verbose=False,public_call=True):
-------
None
'''
- violated = PerfForesightConsumerType.checkConditions(self, verbose=verbose, verbose_reference=False)
+ self.violated = False # PerfForesightConsumerType.checkConditions(self, verbose=False, verbose_reference=False)
if self.cycles!=0 or self.T_cycle > 1:
return
- EPermShkInv=np.dot(self.PermShkDstn[0][0],1/self.PermShkDstn[0][1])
- PermGroFacAdj=self.PermGroFac[0]*EPermShkInv
- Thorn=self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA)
- GIF=Thorn/PermGroFacAdj
- #Evaluate and report on the growth impatience condition
- if GIF<1:
- if public_call:
- print('The growth impatience factor value for the supplied parameter values satisfies the growth impatience condition.')
- else:
- violated = True
- print('The given parameter values violate the growth impatience condition for this consumer type; the GIF is: %2.4f' % (GIF))
- if verbose:
- print(' Therefore, a target level of wealth does not exist.')
+ # For theory, see hyperlink targets to expressions in
+ # url=http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory
+ # For example, the hyperlink to the relevant section of the paper
+ self.url='http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory'
+ # would be referenced below as:
+ # [url]/#Uncertainty-Modified-Conditions
+
+ self.InvPermShkDstn=deepcopy(self.PermShkDstn)
+ self.InvPermShkDstn[0][1]=1/self.PermShkDstn[0][1]
+ EPermShkInv=np.dot(self.InvPermShkDstn[0][0],1/self.PermShkDstn[0][1]) # $\Ex_{t}[\psi^{-1}_{t+1}]$ (in first eqn in sec)
+
+ InvEPermShkInv=(1/EPermShkInv) # $\underline{\psi}$ in the paper (\bar{\isp} in private version)
+ PermGroFacAdj=self.PermGroFac[0]*InvEPermShkInv # [url]/#PGroAdj
+ # [url]/#Pat, adjusted to include mortality
+# Thorn = ((self.Rfree/self.LivPrb[0])*(self.DiscFac*self.LivPrb[0]))**(1/self.CRRA)
+ Thorn = ((self.Rfree*self.DiscFac))**(1/self.CRRA)
+ GIFPF = Thorn/(self.PermGroFac[0] ) # [url]/#GIF
+ GIFInd = Thorn/(self.PermGroFac[0]*InvEPermShkInv) # [url]/#GIFI
+ GIFAgg = Thorn*self.LivPrb[0]/self.PermGroFac[0] # Lower bound of aggregate wealth growth if all inheritances squandered
+
+# self.Rnorm = self.Rfree*EPermShkInv/(self.PermGroFac[0]*self.LivPrb[0])
+ self.GIFPF = GIFPF
+ self.GIFInd = GIFInd
+ self.GIFAgg = GIFAgg
+ self.Thorn = Thorn
+ self.PermGroFacAdj = PermGroFacAdj
+ self.EPermShkInv = EPermShkInv
+ self.InvEPermShkInv = InvEPermShkInv
+ self.DiscFacGIFPFMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge
+ self.DiscFacGIFIndMax = ((self.PermGroFac[0]*InvEPermShkInv)**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge
+ self.DiscFacGIFAggMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree*self.LivPrb[0]) # DiscFac at growth impatience knife edge
+
+ self.checkGICPF(Thorn,verbose,public_call)
+ self.checkGICInd(Thorn,verbose,public_call)
+ self.checkCIGAgg(Thorn,verbose,public_call)
+ self.checkWRIC(verbose,public_call)
+ self.checkFVAC(verbose, public_call)
+
+ if verbose and self.violated:
+ print('\n[!] For more information on the conditions, see Tables 3 and 4 in "Theoretical Foundations of Buffer Stock Saving" at '+self.url+'/#Factors-Defined-And-Compared')
+ print('')
+
+ if verbose:
+ print('GIFPF = %2.6f ' % (GIFPF))
+ print('GIFInd = %2.6f ' % (GIFInd))
+ print('GIFAgg = %2.6f ' % (GIFAgg))
+ print('Thorn = AIF = %2.6f ' % (Thorn))
+ print('PermGroFacAdj = %2.6f ' % (PermGroFacAdj))
+ print('uInvEpShkuInv = %2.6f ' % (self.uInvEpShkuInv))
+ print('FVAF = %2.6f ' % (self.FVAF))
+ print('WRIF = %2.6f ' % (self.WRIF))
+ print('DiscFacGIFIndMax = %2.6f ' % (self.DiscFacGIFIndMax))
+ print('DiscFacGIFAggMax = %2.6f ' % (self.DiscFacGIFAggMax))
+
+ def Ex_Mtp1_over_Ex_Ptp1(self,mRat,verbose=False):
+ cRat = self.solution[-1].cFunc(mRat)
+ aRat = mRat-cRat
+ Ex_Ptp1 = PermGroFac[0]
+ Ex_bLev_tp1 = aRat*self.Rfree
+ Ex_Mtp1 = Ex_bLev_tp1
+ return Ex_Mtp1/Ex_Ptp1
+
+ def Ex_mtp1(self,mRat,verbose=False):
+ cRat = self.solution[-1].cFunc(mRat)
+ aRat = mRat-cRat
+ Ex_bRat_tp1 = aRat*self.Rfree*self.EPermShkInv/self.PermGroFac[0]
+ Ex_Mtp1 = (Ex_bRat_tp1 + 1)*Ex_Ptp1 # mean TranShk and PermShk are 1
+ return Ex_Mtp1/Ex_Ptp1
+
+ def calcTargets(self,verbose=False):
+ '''
+ If the problem is one that satisfies the conditions required for target ratios of different
+ variables to permanent income to exist, and has been solved to within the self-defined
+ tolerance, this method calculates the target values of market resources, consumption,
+ and assets.
- #Evaluate and report on the weak return impatience condition
- WRIF=(self.LivPrb[0]*(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree
- if WRIF<1:
- if public_call:
- print('The weak return impatience factor value for the supplied parameter values satisfies the weak return impatience condition.')
- else:
- violated = True
- print('The given type violates the weak return impatience condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF))
- if verbose:
- print(' Therefore, a nondegenerate solution is not available.')
-
- #Evaluate and report on the finite value of autarky condition
- EPermShkValFunc=np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA))
- FVAF=self.LivPrb[0]*self.DiscFac*EPermShkValFunc*(self.PermGroFac[0]**(1-self.CRRA))
- if FVAF<1:
- if public_call:
- print('The finite value of autarky factor value for the supplied parameter values satisfies the finite value of autarky condition.')
- else:
- print('The given type violates the finite value of autarky condition with the supplied parameter values. The FVAF is %2.4f' %(FVAF))
- violated = True
- if verbose:
- print(' Therefore, a nondegenerate solution is not available.')
+ Parameters
+ ----------
+ verbose : boolean
+ Specifies different levels of verbosity of feedback. When False, it prints no results.
+ When True, it reports all target values, and passes the verbosity indicator to the
+ checkConditions method which responds accordingly.
+
+ Returns
+ -------
+ None
+ '''
+ infinite_horizon = cycles_left == 0
+ if not infinite_horizon:
+ print('The calcTargets method works only for infinite horizon models.')
+ return
+
+
+ # To be written.
+ # Defining:
+ ## Rnorm = Rfree/(PermGroFac[0]*PermShk)
+ ## EPermShkInv = E[PermShk**(-1)]
+ ## InvEPermShkInv = 1/EPermShkInv
+ ## ExRnorm = E[Rfree/(PermGroFac[0]*PermShk)] = Rfree EPermShkInv / PermGroFac[0]
+ ## InvExRnorm = 1/ExRnorm
+ ## The "sustainable consumption" locus is given by
+ # cSust = InvExRnorm + m*(1-InvExRnorm)
- if verbose and violated:
- print('\n[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/')
+ # The target level of m, mTarg, will be the value such that
+ # cSust[m] = cFunc[m]
class KinkedRconsumerType(IndShockConsumerType):
@@ -2120,6 +2516,10 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
-------
None
'''
+ params = Params.init_kinked_R.copy()
+ params.update(kwds)
+ kwds = params
+
# Initialize a basic AgentType
PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds)
@@ -2223,15 +2623,11 @@ def getRfree(self):
def checkConditions(self,verbose=False):
'''
- This method checks whether the instance's type satisfies the growth impatience condition
- (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return
- impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of
- autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate
- infinite horizon solutions with a 1 period cycle. Depending on the model at hand, a
- different combination of these conditions must be satisfied. To check which conditions are
- relevant to the model at hand, a reference to the relevant theoretical literature is made.
-
- SHOULD BE INHERITED FROM ConsIndShockModel
+ This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC),
+ the Return Impatience Condition (RIC), the Growth Impatience Condition (GIC), the Weak Return
+ Impatience Condition (WRIC), the Finite Human Wealth Condition (FHWC) and the Finite Value of
+ Autarky Condition (FVAC). To check which conditions are relevant to the model at hand, a
+ reference to the relevant theoretical literature is made.
Parameters
----------
@@ -2444,154 +2840,3 @@ def constructAssetsGrid(parameters):
aXtraGrid = np.insert(aXtraGrid, j, a)
return aXtraGrid
-
-####################################################################################################
-
-def main():
- import HARK.ConsumptionSaving.ConsumerParameters as Params
- from HARK.utilities import plotFuncsDer, plotFuncs
- from time import clock
- mystr = lambda number : "{:.4f}".format(number)
-
- do_simulation = True
-
- # Make and solve an example perfect foresight consumer
- PFexample = PerfForesightConsumerType(**Params.init_perfect_foresight)
- PFexample.cycles = 0 # Make this type have an infinite horizon
-
- start_time = clock()
- PFexample.solve()
- end_time = clock()
- print('Solving a perfect foresight consumer took ' + mystr(end_time-start_time) + ' seconds.')
- PFexample.unpackcFunc()
- PFexample.timeFwd()
-
- # Plot the perfect foresight consumption function
- print('Linear consumption function:')
- mMin = PFexample.solution[0].mNrmMin
- plotFuncs(PFexample.cFunc[0],mMin,mMin+10)
-
- if do_simulation:
- PFexample.T_sim = 120 # Set number of simulation periods
- PFexample.track_vars = ['mNrmNow']
- PFexample.initializeSim()
- PFexample.simulate()
-
-###############################################################################
-
- # Make and solve an example consumer with idiosyncratic income shocks
- IndShockExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks)
- IndShockExample.cycles = 0 # Make this type have an infinite horizon
-
- start_time = clock()
- IndShockExample.solve()
- end_time = clock()
- print('Solving a consumer with idiosyncratic shocks took ' + mystr(end_time-start_time) + ' seconds.')
- IndShockExample.unpackcFunc()
- IndShockExample.timeFwd()
-
- # Plot the consumption function and MPC for the infinite horizon consumer
- print('Concave consumption function:')
- plotFuncs(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5)
- print('Marginal consumption function:')
- plotFuncsDer(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5)
-
- # Compare the consumption functions for the perfect foresight and idiosyncratic
- # shock types. Risky income cFunc asymptotically approaches perfect foresight cFunc.
- print('Consumption functions for perfect foresight vs idiosyncratic shocks:')
- plotFuncs([PFexample.cFunc[0],IndShockExample.cFunc[0]],IndShockExample.solution[0].mNrmMin,100)
-
- # Compare the value functions for the two types
- if IndShockExample.vFuncBool:
- print('Value functions for perfect foresight vs idiosyncratic shocks:')
- plotFuncs([PFexample.solution[0].vFunc,IndShockExample.solution[0].vFunc],
- IndShockExample.solution[0].mNrmMin+0.5,10)
-
- # Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, and pLvlNow_hist
- if do_simulation:
- IndShockExample.T_sim = 120
- IndShockExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow']
- IndShockExample.makeShockHistory() # This is optional, simulation will draw shocks on the fly if it isn't run.
- IndShockExample.initializeSim()
- IndShockExample.simulate()
-
- ###########################################################################
-
- # Make and solve an idiosyncratic shocks consumer with a finite lifecycle
- LifecycleExample = IndShockConsumerType(**Params.init_lifecycle)
- LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once
-
- start_time = clock()
- LifecycleExample.solve()
- end_time = clock()
- print('Solving a lifecycle consumer took ' + mystr(end_time-start_time) + ' seconds.')
- LifecycleExample.unpackcFunc()
- LifecycleExample.timeFwd()
-
- # Plot the consumption functions during working life
- print('Consumption functions while working:')
- mMin = min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)])
- plotFuncs(LifecycleExample.cFunc[:LifecycleExample.T_retire],mMin,5)
-
- # Plot the consumption functions during retirement
- print('Consumption functions while retired:')
- plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire:],0,5)
- LifecycleExample.timeRev()
-
- # Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, pLvlNow_hist, and t_age_hist
- if do_simulation:
- LifecycleExample.T_sim = 120
- LifecycleExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_age']
- LifecycleExample.initializeSim()
- LifecycleExample.simulate()
-
-###############################################################################
-
- # Make and solve a "cyclical" consumer type who lives the same four quarters repeatedly.
- # The consumer has income that greatly fluctuates throughout the year.
- CyclicalExample = IndShockConsumerType(**Params.init_cyclical)
- CyclicalExample.cycles = 0
-
- start_time = clock()
- CyclicalExample.solve()
- end_time = clock()
- print('Solving a cyclical consumer took ' + mystr(end_time-start_time) + ' seconds.')
- CyclicalExample.unpackcFunc()
- CyclicalExample.timeFwd()
-
- # Plot the consumption functions for the cyclical consumer type
- print('Quarterly consumption functions:')
- mMin = min([X.mNrmMin for X in CyclicalExample.solution])
- plotFuncs(CyclicalExample.cFunc,mMin,5)
-
- # Simulate some data; results stored in cHist, mHist, bHist, aHist, MPChist, and pHist
- if do_simulation:
- CyclicalExample.T_sim = 480
- CyclicalExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow','t_cycle']
- CyclicalExample.initializeSim()
- CyclicalExample.simulate()
-
-###############################################################################
-
- # Make and solve an agent with a kinky interest rate
- KinkyExample = KinkedRconsumerType(**Params.init_kinked_R)
- KinkyExample.cycles = 0 # Make the Example infinite horizon
-
- start_time = clock()
- KinkyExample.solve()
- end_time = clock()
- print('Solving a kinky consumer took ' + mystr(end_time-start_time) + ' seconds.')
- KinkyExample.unpackcFunc()
- print('Kinky consumption function:')
- KinkyExample.timeFwd()
- plotFuncs(KinkyExample.cFunc[0],KinkyExample.solution[0].mNrmMin,5)
-
- if do_simulation:
- KinkyExample.T_sim = 120
- KinkyExample.track_vars = ['mNrmNow','cNrmNow','pLvlNow']
- KinkyExample.initializeSim()
- KinkyExample.simulate()
-
-
-if __name__ == '__main__':
- main()
diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py
new file mode 100644
index 000000000..cf34826e5
--- /dev/null
+++ b/HARK/ConsumptionSaving/ConsLaborModel.py
@@ -0,0 +1,617 @@
+"""
+Subclasses of AgentType representing consumers who make decisions about how much
+labor to supply, as well as a consumption-saving decision.
+
+It currently only has
+one model: labor supply on the intensive margin (unit interval) with CRRA utility
+from a composite good (of consumption and leisure), with transitory and permanent
+productivity shocks. Agents choose their quantities of labor and consumption after
+observing both of these shocks, so the transitory shock is a state variable.
+"""
+import sys
+
+from copy import copy
+import numpy as np
+from HARK.core import Solution
+from HARK.utilities import CRRAutilityP, CRRAutilityP_inv
+from HARK.interpolation import LinearInterp, LinearInterpOnInterp1D, VariableLowerBoundFunc2D, BilinearInterp, ConstantFunction
+from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, MargValueFunc
+from HARK.ConsumptionSaving.ConsGenIncProcessModel import ValueFunc2D, MargValueFunc2D
+import HARK.ConsumptionSaving.ConsumerParameters as Params
+import matplotlib.pyplot as plt
+
+class ConsumerLaborSolution(Solution):
+ '''
+ A class for representing one period of the solution to a Consumer Labor problem.
+ '''
+ distance_criteria = ['cFunc','LbrFunc']
+
+ def __init__(self, cFunc=None, LbrFunc=None, vFunc=None, vPfunc=None, bNrmMin=None):
+ '''
+ The constructor for a new ConsumerSolution object.
+
+ Parameters
+ ----------
+ cFunc : function
+ The consumption function for this period, defined over normalized
+ bank balances and the transitory productivity shock: cNrm = cFunc(bNrm,TranShk).
+ LbrFunc : function
+ The labor supply function for this period, defined over normalized
+ bank balances 0.751784276198: Lbr = LbrFunc(bNrm,TranShk).
+ vFunc : function
+ The beginning-of-period value function for this period, defined over
+ normalized bank balances 0.751784276198: v = vFunc(bNrm,TranShk).
+ vPfunc : function
+ The beginning-of-period marginal value (of bank balances) function for
+ this period, defined over normalized bank balances 0.751784276198: vP = vPfunc(bNrm,TranShk).
+ bNrmMin: float
+ The minimum allowable bank balances for this period, as a function of
+ the transitory shock. cFunc, LbrFunc, etc are undefined for bNrm < bNrmMin(TranShk).
+
+ Returns
+ -------
+ None
+ '''
+ if cFunc is not None:
+ self.cFunc = cFunc
+ if LbrFunc is not None:
+ self.LbrFunc = LbrFunc
+ if vFunc is not None:
+ self.vFunc = vFunc
+ if vPfunc is not None:
+ self.vPfunc = vPfunc
+ if bNrmMin is not None:
+ self.bNrmMin = bNrmMin
+
+
+def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,CRRA,
+ Rfree,PermGroFac,BoroCnstArt,aXtraGrid,TranShkGrid,vFuncBool,
+ CubicBool,WageRte,LbrCost):
+ '''
+ Solves one period of the consumption-saving model with endogenous labor supply
+ on the intensive margin by using the endogenous grid method to invert the first
+ order conditions for optimal composite consumption and between consumption and
+ leisure, obviating any search for optimal controls.
+
+ Parameters
+ ----------
+ solution_next : ConsumerLaborSolution
+ The solution to the next period's problem; must have the attributes
+ vPfunc and bNrmMinFunc representing marginal value of bank balances and
+ minimum (normalized) bank balances as a function of the transitory shock.
+ PermShkDstn: [np.array]
+ Discrete distribution of permanent productivity shocks.
+ TranShkDstn: [np.array]
+ Discrete distribution of transitory productivity shocks.
+ LivPrb : float
+ Survival probability; likelihood of being alive at the beginning of
+ the succeeding period.
+ DiscFac : float
+ Intertemporal discount factor.
+ CRRA : float
+ Coefficient of relative risk aversion over the composite good.
+ Rfree : float
+ Risk free interest rate on assets retained at the end of the period.
+ PermGroFac : float
+ Expected permanent income growth factor for next period.
+ BoroCnstArt: float or None
+ Borrowing constraint for the minimum allowable assets to end the
+ period with. Currently not handled, must be None.
+ aXtraGrid: np.array
+ Array of "extra" end-of-period asset values-- assets above the
+ absolute minimum acceptable level.
+ TranShkGrid: np.array
+ Grid of transitory shock values to use as a state grid for interpolation.
+ vFuncBool: boolean
+ An indicator for whether the value function should be computed and
+ included in the reported solution. Not yet handled, must be False.
+ CubicBool: boolean
+ An indicator for whether the solver should use cubic or linear interpolation.
+ Cubic interpolation is not yet handled, must be False.
+ WageRte: float
+ Wage rate per unit of labor supplied.
+ LbrCost: float
+ Cost parameter for supplying labor: u_t = U(x_t), x_t = c_t*z_t^LbrCost,
+ where z_t is leisure = 1 - Lbr_t.
+
+ Returns
+ -------
+ solution_now : ConsumerLaborSolution
+ The solution to this period's problem, including a consumption function
+ cFunc, a labor supply function LbrFunc, and a marginal value function vPfunc;
+ each are defined over normalized bank balances and transitory prod shock.
+ Also includes bNrmMinNow, the minimum permissible bank balances as a function
+ of the transitory productivity shock.
+ '''
+ # Make sure the inputs for this period are valid: CRRA > LbrCost/(1+LbrCost)
+ # and CubicBool = False. CRRA condition is met automatically when CRRA >= 1.
+ frac = 1./(1.+LbrCost)
+ if CRRA <= frac*LbrCost:
+ print('Error: make sure CRRA coefficient is strictly greater than alpha/(1+alpha).')
+ sys.exit()
+ if BoroCnstArt is not None:
+ print('Error: Model cannot handle artificial borrowing constraint yet. ')
+ sys.exit()
+ if vFuncBool or CubicBool is True:
+ print('Error: Model cannot handle cubic interpolation yet.')
+ sys.exit()
+
+ # Unpack next period's solution and the productivity shock distribution, and define the inverse (marginal) utilty function
+ vPfunc_next = solution_next.vPfunc
+ TranShkPrbs = TranShkDstn[0]
+ TranShkVals = TranShkDstn[1]
+ PermShkPrbs = PermShkDstn[0]
+ PermShkVals = PermShkDstn[1]
+ TranShkCount = TranShkPrbs.size
+ PermShkCount = PermShkPrbs.size
+ uPinv = lambda X : CRRAutilityP_inv(X,gam=CRRA)
+
+ # Make tiled versions of the grid of a_t values and the components of the shock distribution
+ aXtraCount = aXtraGrid.size
+ bNrmGrid = aXtraGrid # Next period's bank balances before labor income
+ bNrmGrid_rep = np.tile(np.reshape(bNrmGrid,(aXtraCount,1)),(1,TranShkCount)) # Replicated axtraGrid of b_t values (bNowGrid) for each transitory (productivity) shock
+ TranShkVals_rep = np.tile(np.reshape(TranShkVals,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock values for each a_t state
+ TranShkPrbs_rep = np.tile(np.reshape(TranShkPrbs,(1,TranShkCount)),(aXtraCount,1)) # Replicated transitory shock probabilities for each a_t state
+
+ # Construct a function that gives marginal value of next period's bank balances *just before* the transitory shock arrives
+ vPNext = vPfunc_next(bNrmGrid_rep, TranShkVals_rep) # Next period's marginal value at every transitory shock and every bank balances gridpoint
+ vPbarNext = np.sum(vPNext*TranShkPrbs_rep, axis = 1) # Integrate out the transitory shocks (in TranShkVals direction) to get expected vP just before the transitory shock
+ vPbarNvrsNext = uPinv(vPbarNext) # Transformed marginal value through the inverse marginal utility function to "decurve" it
+ vPbarNvrsFuncNext = LinearInterp(np.insert(bNrmGrid,0,0.0),np.insert(vPbarNvrsNext,0,0.0)) # Linear interpolation over b_{t+1}, adding a point at minimal value of b = 0.
+ vPbarFuncNext = MargValueFunc(vPbarNvrsFuncNext,CRRA) # "Recurve" the intermediate marginal value function through the marginal utility function
+
+ # Get next period's bank balances at each permanent shock from each end-of-period asset values
+ aNrmGrid_rep = np.tile(np.reshape(aXtraGrid,(aXtraCount,1)),(1,PermShkCount)) # Replicated grid of a_t values for each permanent (productivity) shock
+ PermShkVals_rep = np.tile(np.reshape(PermShkVals,(1,PermShkCount)),(aXtraCount,1)) # Replicated permanent shock values for each a_t value
+ PermShkPrbs_rep = np.tile(np.reshape(PermShkPrbs,(1,PermShkCount)),(aXtraCount,1)) # Replicated permanent shock probabilities for each a_t value
+ bNrmNext = (Rfree/(PermGroFac*PermShkVals_rep))*aNrmGrid_rep
+
+ # Calculate marginal value of end-of-period assets at each a_t gridpoint
+ vPbarNext = (PermGroFac*PermShkVals_rep)**(-CRRA)*vPbarFuncNext(bNrmNext) # Get marginal value of bank balances next period at each shock
+ EndOfPrdvP = DiscFac*Rfree*LivPrb*np.sum(vPbarNext*PermShkPrbs_rep, axis=1, keepdims=True) # Take expectation across permanent income shocks
+
+ # Compute scaling factor for each transitory shock
+ TranShkScaleFac_temp = frac*(WageRte*TranShkGrid)**(LbrCost*frac)*(LbrCost**(-LbrCost*frac)+LbrCost**(frac))
+ TranShkScaleFac = np.reshape(TranShkScaleFac_temp,(1,TranShkGrid.size)) # Flip it to be a row vector
+
+ # Use the first order condition to compute an array of "composite good" x_t values corresponding to (a_t,theta_t) values
+ xNow = (np.dot(EndOfPrdvP,TranShkScaleFac))**(-1./(CRRA-LbrCost*frac))
+
+ # Transform the composite good x_t values into consumption c_t and leisure z_t values
+ TranShkGrid_rep = np.tile(np.reshape(TranShkGrid,(1,TranShkGrid.size)),(aXtraCount,1))
+ xNowPow = xNow**frac # Will use this object multiple times in math below
+ cNrmNow = (((WageRte*TranShkGrid_rep)/LbrCost)**(LbrCost*frac))*xNowPow # Find optimal consumption from optimal composite good
+ LsrNow = (LbrCost/(WageRte*TranShkGrid_rep))**frac*xNowPow # Find optimal leisure from optimal composite good
+
+ # The zero-th transitory shock is TranShk=0, and the solution is to not work: Lsr = 1, Lbr = 0.
+ cNrmNow[:,0] = uPinv(EndOfPrdvP.flatten())
+ LsrNow[:,0] = 1.0
+
+ # Agent cannot choose to work a negative amount of time. When this occurs, set
+ # leisure to one and recompute consumption using simplified first order condition.
+ violates_labor_constraint = LsrNow > 1. # Find where labor would be negative if unconstrained
+ EndOfPrdvP_temp = np.tile(np.reshape(EndOfPrdvP,(aXtraCount,1)),(1,TranShkCount))
+ cNrmNow[violates_labor_constraint] = uPinv(EndOfPrdvP_temp[violates_labor_constraint])
+ LsrNow[violates_labor_constraint] = 1. # Set up z =1, upper limit
+
+ # Calculate the endogenous bNrm states by inverting the within-period transition
+ aNrmNow_rep = np.tile(np.reshape(aXtraGrid,(aXtraCount,1)),(1,TranShkGrid.size))
+ bNrmNow = aNrmNow_rep - WageRte*TranShkGrid_rep + cNrmNow + WageRte*TranShkGrid_rep*LsrNow
+
+ # Add an extra gridpoint at the absolute minimal valid value for b_t for each TranShk;
+ # this corresponds to working 100% of the time and consuming nothing.
+ bNowArray = np.concatenate((np.reshape(-WageRte*TranShkGrid,(1,TranShkGrid.size)), bNrmNow),axis=0)
+ cNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)),cNrmNow),axis=0) # Consume nothing
+ LsrNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)),LsrNow),axis=0) # And no leisure!
+ LsrNowArray[0,0] = 1.0 # Don't work at all if TranShk=0, even if bNrm=0
+ LbrNowArray = 1.0 - LsrNowArray # Labor is the complement of leisure
+
+ # Get (pseudo-inverse) marginal value of bank balances using end of period
+ # marginal value of assets (envelope condition), adding a column of zeros
+ # zeros on the left edge, representing the limit at the minimum value of b_t.
+ vPnvrsNowArray = np.concatenate((np.zeros((1,TranShkGrid.size)), uPinv(EndOfPrdvP_temp))) # Concatenate
+
+ # Construct consumption and marginal value functions for this period
+ bNrmMinNow = LinearInterp(TranShkGrid,bNowArray[0,:])
+
+ # Loop over each transitory shock and make a linear interpolation to get lists
+ # of optimal consumption, labor and (pseudo-inverse) marginal value by TranShk
+ cFuncNow_list = []
+ LbrFuncNow_list = []
+ vPnvrsFuncNow_list = []
+ for j in range(TranShkGrid.size):
+ bNrmNow_temp = bNowArray[:,j] - bNowArray[0,j] # Adjust bNrmNow for this transitory shock, so bNrmNow_temp[0] = 0
+ cFuncNow_list.append(LinearInterp(bNrmNow_temp,cNowArray[:,j])) # Make consumption function for this transitory shock
+ LbrFuncNow_list.append(LinearInterp(bNrmNow_temp,LbrNowArray[:,j])) # Make labor function for this transitory shock
+ vPnvrsFuncNow_list.append(LinearInterp(bNrmNow_temp,vPnvrsNowArray[:,j])) # Make pseudo-inverse marginal value function for this transitory shock
+
+ # Make linear interpolation by combining the lists of consumption, labor and marginal value functions
+ cFuncNowBase = LinearInterpOnInterp1D(cFuncNow_list,TranShkGrid)
+ LbrFuncNowBase = LinearInterpOnInterp1D(LbrFuncNow_list,TranShkGrid)
+ vPnvrsFuncNowBase = LinearInterpOnInterp1D(vPnvrsFuncNow_list,TranShkGrid)
+
+ # Construct consumption, labor, pseudo-inverse marginal value functions with
+ # bNrmMinNow as the lower bound. This removes the adjustment in the loop above.
+ cFuncNow = VariableLowerBoundFunc2D(cFuncNowBase,bNrmMinNow)
+ LbrFuncNow = VariableLowerBoundFunc2D(LbrFuncNowBase,bNrmMinNow)
+ vPnvrsFuncNow = VariableLowerBoundFunc2D(vPnvrsFuncNowBase,bNrmMinNow)
+
+ # Construct the marginal value function by "recurving" its pseudo-inverse
+ vPfuncNow = MargValueFunc2D(vPnvrsFuncNow,CRRA)
+
+ # Make a solution object for this period and return it
+ solution = ConsumerLaborSolution(cFunc=cFuncNow,LbrFunc=LbrFuncNow,vPfunc=vPfuncNow,bNrmMin=bNrmMinNow)
+ return solution
+
+
+class LaborIntMargConsumerType(IndShockConsumerType):
+
+ '''
+ A class representing agents who make a decision each period about how much
+ to consume vs save and how much labor to supply (as a fraction of their time).
+ They get CRRA utility from a composite good x_t = c_t*z_t^alpha, and discount
+ future utility flows at a constant factor.
+ '''
+ time_vary_ = copy(IndShockConsumerType.time_vary_)
+ time_vary_ += ['WageRte']
+ time_inv_ = copy(IndShockConsumerType.time_inv_)
+
+ def __init__(self,cycles=1,time_flow=True,**kwds):
+ '''
+ Instantiate a new consumer type with given data.
+ See ConsumerParameters.init_labor_intensive for a dictionary of
+ the keywords that should be passed to the constructor.
+
+ Parameters
+ ----------
+ cycles : int
+ Number of times the sequence of periods should be solved.
+ time_flow : boolean
+ Whether time is currently "flowing" forward for this instance.
+
+ Returns
+ -------
+ None
+ '''
+ params = Params.init_labor_intensive.copy()
+ params.update(kwds)
+ kwds = params
+
+ IndShockConsumerType.__init__(self,cycles = cycles,time_flow=time_flow,**kwds)
+ self.pseudo_terminal = False
+ self.solveOnePeriod = solveConsLaborIntMarg
+ self.update()
+
+ def update(self):
+ '''
+ Update the income process, the assets grid, and the terminal solution.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ '''
+ self.updateIncomeProcess()
+ self.updateAssetsGrid()
+ self.updateTranShkGrid()
+ self.updateLbrCost()
+
+ def updateLbrCost(self):
+ '''
+ Construct the age-varying cost of working LbrCost using the attribute LbrCostCoeffs.
+ This attribute should be a 1D array of arbitrary length, representing polynomial
+ coefficients (over t_cycle) for (log) LbrCost.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ '''
+ Coeffs = self.LbrCostCoeffs
+ N = len(Coeffs)
+ age_vec = np.arange(self.T_cycle)
+ LbrCostBase = np.zeros(self.T_cycle)
+ for n in range(N):
+ LbrCostBase += Coeffs[n]*age_vec**n
+ LbrCost = np.exp(LbrCostBase)
+ time_orig = self.time_flow
+ self.timeFwd()
+ self.LbrCost = LbrCost.tolist()
+ self.addToTimeVary('LbrCost')
+ if not time_orig:
+ self.timeRev()
+
+
+ def calcBoundingValues(self):
+ '''
+ Calculate human wealth plus minimum and maximum MPC in an infinite
+ horizon model with only one period repeated indefinitely. Store results
+ as attributes of self. Human wealth is the present discounted value of
+ expected future income after receiving income this period, ignoring mort-
+ ality. The maximum MPC is the limit of the MPC as m --> mNrmMin. The
+ minimum MPC is the limit of the MPC as m --> infty.
+
+ NOT YET IMPLEMENTED FOR THIS CLASS
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ '''
+ raise NotImplementedError()
+
+ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True):
+ '''
+ Creates a "normalized Euler error" function for this instance, mapping
+ from market resources to "consumption error per dollar of consumption."
+ Stores result in attribute eulerErrorFunc as an interpolated function.
+ Has option to use approximate income distribution stored in self.IncomeDstn
+ or to use a (temporary) very dense approximation.
+
+ NOT YET IMPLEMENTED FOR THIS CLASS
+
+ Parameters
+ ----------
+ mMax : float
+ Maximum normalized market resources for the Euler error function.
+ approx_inc_dstn : Boolean
+ Indicator for whether to use the approximate discrete income distri-
+ bution stored in self.IncomeDstn[0], or to use a very accurate
+ discrete approximation instead. When True, uses approximation in
+ IncomeDstn; when False, makes and uses a very dense approximation.
+
+ Returns
+ -------
+ None
+ '''
+ raise NotImplementedError()
+
+
+ def getStates(self):
+ '''
+ Calculates updated values of normalized bank balances and permanent income
+ level for each agent. Uses pLvlNow, aNrmNow, PermShkNow. Calls the getStates
+ method for the parent class, then erases mNrmNow, which cannot be calculated
+ until after getControls in this model.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ '''
+ IndShockConsumerType.getStates(self)
+ self.mNrmNow[:] = np.nan # Delete market resource calculation
+
+
+ def getControls(self):
+ '''
+ Calculates consumption and labor supply for each consumer of this type
+ using the consumption and labor functions in each period of the cycle.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ '''
+ cNrmNow = np.zeros(self.AgentCount) + np.nan
+ MPCnow = np.zeros(self.AgentCount) + np.nan
+ LbrNow = np.zeros(self.AgentCount) + np.nan
+ for t in range(self.T_cycle):
+ these = t == self.t_cycle
+ cNrmNow[these] = self.solution[t].cFunc(self.bNrmNow[these], self.TranShkNow[these]) # Assign consumption values
+ MPCnow[these] = self.solution[t].cFunc.derivativeX(self.bNrmNow[these], self.TranShkNow[these]) # Assign marginal propensity to consume values (derivative)
+ LbrNow[these] = self.solution[t].LbrFunc(self.bNrmNow[these], self.TranShkNow[these]) # Assign labor supply
+ self.cNrmNow = cNrmNow
+ self.MPCnow = MPCnow
+ self.LbrNow = LbrNow
+
+
+ def getPostStates(self):
+ '''
+ Calculates end-of-period assets for each consumer of this type.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ '''
+ mNrmNow = np.zeros(self.AgentCount) + np.nan
+ aNrmNow = np.zeros(self.AgentCount) + np.nan
+ for t in range(self.T_cycle):
+ these = t == self.t_cycle
+ mNrmNow[these] = self.bNrmNow[these] + self.LbrNow[these]*self.TranShkNow[these] # mNrm = bNrm + yNrm
+ aNrmNow[these] = mNrmNow[these] - self.cNrmNow[these] # aNrm = mNrm - cNrm
+ self.mNrmNow = mNrmNow
+ self.aNrmNow = aNrmNow
+
+
+ def updateTranShkGrid(self):
+ '''
+ Create a time-varying list of arrays for TranShkGrid using TranShkDstn,
+ which is created by the method updateIncomeProcess(). Simply takes the
+ transitory shock values and uses them as a state grid; can be improved.
+ Creates the attribute TranShkGrid.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ '''
+ time_orig=self.time_flow
+ self.timeFwd()
+
+ TranShkGrid = [] # Create an empty list for TranShkGrid that will be updated
+ for t in range(self.T_cycle):
+ TranShkGrid.append(self.TranShkDstn[t][1]) # Update/ Extend the list of TranShkGrid with the TranShkVals for each TranShkPrbs
+ self.TranShkGrid = TranShkGrid # Save that list in self (time-varying)
+ self.addToTimeVary('TranShkGrid') # Run the method addToTimeVary from AgentType to add TranShkGrid as one parameter of time_vary list
+
+ if not time_orig:
+ self.timeRev()
+
+
+ def updateSolutionTerminal(self):
+ '''
+ Updates the terminal period solution and solves for optimal consumption
+ and labor when there is no future.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ '''
+ if self.time_flow: # To make sure we pick the last element of the list, depending on the direction time is flowing
+ t=-1
+ else:
+ t=0
+ TranShkGrid = self.TranShkGrid[t]
+ LbrCost = self.LbrCost[t]
+ WageRte = self.WageRte[t]
+
+ bNrmGrid = np.insert(self.aXtraGrid,0,0.0) # Add a point at b_t = 0 to make sure that bNrmGrid goes down to 0
+ bNrmCount = bNrmGrid.size # 201
+ TranShkCount = TranShkGrid.size # = (7,)
+ bNrmGridTerm = np.tile(np.reshape(bNrmGrid,(bNrmCount,1)),(1,TranShkCount)) # Replicated bNrmGrid for each transitory shock theta_t
+ TranShkGridTerm = np.tile(TranShkGrid,(bNrmCount,1)) # Tile the grid of transitory shocks for the terminal solution. (201,7)
+
+ # Array of labor (leisure) values for terminal solution
+ LsrTerm = np.minimum((LbrCost/(1.+LbrCost))*(bNrmGridTerm/(WageRte*TranShkGridTerm)+1.),1.0)
+ LsrTerm[0,0] = 1.0
+ LbrTerm = 1.0 - LsrTerm
+
+ # Calculate market resources in terminal period, which is consumption
+ mNrmTerm = bNrmGridTerm + LbrTerm*WageRte*TranShkGridTerm
+ cNrmTerm = mNrmTerm # Consume everything we have
+
+ # Make a bilinear interpolation to represent the labor and consumption functions
+ LbrFunc_terminal = BilinearInterp(LbrTerm,bNrmGrid,TranShkGrid)
+ cFunc_terminal = BilinearInterp(cNrmTerm,bNrmGrid,TranShkGrid)
+
+ # Compute the effective consumption value using consumption value and labor value at the terminal solution
+ xEffTerm = LsrTerm**LbrCost*cNrmTerm
+ vNvrsFunc_terminal = BilinearInterp(xEffTerm,bNrmGrid,TranShkGrid)
+ vFunc_terminal = ValueFunc2D(vNvrsFunc_terminal, self.CRRA)
+
+ # Using the envelope condition at the terminal solution to estimate the marginal value function
+ vPterm = LsrTerm**LbrCost*CRRAutilityP(xEffTerm,gam=self.CRRA)
+ vPnvrsTerm = CRRAutilityP_inv(vPterm,gam=self.CRRA) # Evaluate the inverse of the CRRA marginal utility function at a given marginal value, vP
+
+ vPnvrsFunc_terminal = BilinearInterp(vPnvrsTerm,bNrmGrid,TranShkGrid)
+ vPfunc_terminal = MargValueFunc2D(vPnvrsFunc_terminal,self.CRRA) # Get the Marginal Value function
+
+ bNrmMin_terminal = ConstantFunction(0.) # Trivial function that return the same real output for any input
+
+ self.solution_terminal = ConsumerLaborSolution(cFunc=cFunc_terminal,LbrFunc=LbrFunc_terminal,\
+ vFunc=vFunc_terminal,vPfunc=vPfunc_terminal,bNrmMin=bNrmMin_terminal)
+
+
+ def plotcFunc(self,t,bMin=None,bMax=None,ShkSet=None):
+ '''
+ Plot the consumption function by bank balances at a given set of transitory shocks.
+
+ Parameters
+ ----------
+ t : int
+ Time index of the solution for which to plot the consumption function.
+ bMin : float or None
+ Minimum value of bNrm at which to begin the plot. If None, defaults
+ to the minimum allowable value of bNrm for each transitory shock.
+ bMax : float or None
+ Maximum value of bNrm at which to end the plot. If None, defaults
+ to bMin + 20.
+ ShkSet : [float] or None
+ Array or list of transitory shocks at which to plot the consumption
+ function. If None, defaults to the TranShkGrid for this time period.
+
+ Returns
+ -------
+ None
+ '''
+ if ShkSet is None:
+ ShkSet = self.TranShkGrid[t]
+
+ for j in range(len(ShkSet)):
+ TranShk = ShkSet[j]
+ if bMin is None:
+ bMin_temp = self.solution[t].bNrmMin(TranShk)
+ else:
+ bMin_temp = bMin
+ if bMax is None:
+ bMax_temp = bMin_temp + 20.
+ else:
+ bMax_temp = bMax
+
+ B = np.linspace(bMin_temp, bMax_temp, 300)
+ C = self.solution[t].cFunc(B, TranShk*np.ones_like(B))
+ plt.plot(B,C)
+ plt.xlabel('Beginning of period bank balances')
+ plt.ylabel('Normalized consumption level')
+ plt.show()
+
+
+ def plotLbrFunc(self,t,bMin=None,bMax=None,ShkSet=None):
+ '''
+ Plot the labor supply function by bank balances at a given set of transitory shocks.
+
+ Parameters
+ ----------
+ t : int
+ Time index of the solution for which to plot the labor supply function.
+ bMin : float or None
+ Minimum value of bNrm at which to begin the plot. If None, defaults
+ to the minimum allowable value of bNrm for each transitory shock.
+ bMax : float or None
+ Maximum value of bNrm at which to end the plot. If None, defaults
+ to bMin + 20.
+ ShkSet : [float] or None
+ Array or list of transitory shocks at which to plot the labor supply
+ function. If None, defaults to the TranShkGrid for this time period.
+
+ Returns
+ -------
+ None
+ '''
+ if ShkSet is None:
+ ShkSet = self.TranShkGrid[t]
+
+ for j in range(len(ShkSet)):
+ TranShk = ShkSet[j]
+ if bMin is None:
+ bMin_temp = self.solution[t].bNrmMin(TranShk)
+ else:
+ bMin_temp = bMin
+ if bMax is None:
+ bMax_temp = bMin_temp + 20.
+ else:
+ bMax_temp = bMax
+
+ B = np.linspace(bMin_temp, bMax_temp, 300)
+ L = self.solution[t].LbrFunc(B, TranShk*np.ones_like(B))
+ plt.plot(B,L)
+ plt.xlabel('Beginning of period bank balances')
+ plt.ylabel('Labor supply')
+ plt.show()
diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py
index d1f90280a..2641dd9eb 100644
--- a/HARK/ConsumptionSaving/ConsMarkovModel.py
+++ b/HARK/ConsumptionSaving/ConsMarkovModel.py
@@ -17,6 +17,8 @@
from HARK.utilities import CRRAutility, CRRAutilityP, CRRAutilityPP, CRRAutilityP_inv, \
CRRAutility_invP, CRRAutility_inv, CRRAutilityP_invP
+__all__ = ['ConsMarkovSolver', 'MarkovConsumerType']
+
utility = CRRAutility
utilityP = CRRAutilityP
utilityPP = CRRAutilityPP
@@ -594,7 +596,7 @@ def makevFunc(self,solution):
return vFuncNow
-def solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac,
+def _solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac,
MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool):
'''
Solves a single period consumption-saving problem with risky income and
@@ -682,7 +684,7 @@ class MarkovConsumerType(IndShockConsumerType):
def __init__(self,cycles=1,time_flow=True,**kwds):
IndShockConsumerType.__init__(self,cycles=1,time_flow=True,**kwds)
- self.solveOnePeriod = solveConsMarkov
+ self.solveOnePeriod = _solveConsMarkov
self.poststate_vars += ['MrkvNow']
if not hasattr(self, 'global_markov'):
self.global_markov = False
@@ -703,15 +705,19 @@ def checkMarkovInputs(self):
StateCount = self.MrkvArray[0].shape[0]
# Check that arrays are the right shape
- assert self.Rfree.shape == (StateCount,),'Rfree not the right shape!'
+ if not isinstance(self.Rfree, np.ndarray) or self.Rfree.shape != (StateCount, ):
+ raise ValueError('Rfree not the right shape, it should an array of Rfree of all the states.')
# Check that arrays in lists are the right shape
for MrkvArray_t in self.MrkvArray:
- assert MrkvArray_t.shape == (StateCount,StateCount),'MrkvArray not the right shape!'
+ if not isinstance(MrkvArray_t, np.ndarray) or MrkvArray_t.shape != (StateCount, StateCount):
+ raise ValueError('MrkvArray not the right shape, it should be of the size states*statres.')
for LivPrb_t in self.LivPrb:
- assert LivPrb_t.shape == (StateCount,),'Array in LivPrb is not the right shape!'
- for PermGroFac_t in self.LivPrb:
- assert PermGroFac_t.shape == (StateCount,),'Array in PermGroFac is not the right shape!'
+ if not isinstance(LivPrb_t, np.ndarray) or LivPrb_t.shape != (StateCount, ):
+ raise ValueError('Array in LivPrb is not the right shape, it should be an array of length equal to number of states')
+ for PermGroFac_t in self.PermGroFac:
+ if not isinstance(PermGroFac_t, np.ndarray) or PermGroFac_t.shape != (StateCount, ):
+ raise ValueError('Array in PermGroFac is not the right shape, it should be an array of length equal to number of states')
# Now check the income distribution.
# Note IncomeDstn is (potentially) time-varying, so it is in time_vary.
@@ -719,8 +725,10 @@ def checkMarkovInputs(self):
# at a particular point in time. Each income distribution at a point in time should itself
# be a list, with each element corresponding to the income distribution
# conditional on a particular Markov state.
+ # TODO: should this be a numpy array too?
for IncomeDstn_t in self.IncomeDstn:
- assert len(IncomeDstn_t) == StateCount,'List in IncomeDstn is not the right length!'
+ if not isinstance(IncomeDstn_t, list) or len(IncomeDstn_t) != StateCount:
+ raise ValueError('List in IncomeDstn is not the right length, it should be length equal to number of states')
def preSolve(self):
"""
@@ -966,165 +974,3 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True):
None
'''
raise NotImplementedError()
-
-
-
-###############################################################################
-
-def main():
- import HARK.ConsumptionSaving.ConsumerParameters as Params
- from HARK.utilities import plotFuncs
- from time import clock
- from copy import copy
- mystr = lambda number : "{:.4f}".format(number)
-
- do_simulation = True
-
- # Define the Markov transition matrix for serially correlated unemployment
- unemp_length = 5 # Averange length of unemployment spell
- urate_good = 0.05 # Unemployment rate when economy is in good state
- urate_bad = 0.12 # Unemployment rate when economy is in bad state
- bust_prob = 0.01 # Probability of economy switching from good to bad
- recession_length = 20 # Averange length of bad state
- p_reemploy =1.0/unemp_length
- p_unemploy_good = p_reemploy*urate_good/(1-urate_good)
- p_unemploy_bad = p_reemploy*urate_bad/(1-urate_bad)
- boom_prob = 1.0/recession_length
- MrkvArray = np.array([[(1-p_unemploy_good)*(1-bust_prob),p_unemploy_good*(1-bust_prob),
- (1-p_unemploy_good)*bust_prob,p_unemploy_good*bust_prob],
- [p_reemploy*(1-bust_prob),(1-p_reemploy)*(1-bust_prob),
- p_reemploy*bust_prob,(1-p_reemploy)*bust_prob],
- [(1-p_unemploy_bad)*boom_prob,p_unemploy_bad*boom_prob,
- (1-p_unemploy_bad)*(1-boom_prob),p_unemploy_bad*(1-boom_prob)],
- [p_reemploy*boom_prob,(1-p_reemploy)*boom_prob,
- p_reemploy*(1-boom_prob),(1-p_reemploy)*(1-boom_prob)]])
-
- # Make a consumer with serially correlated unemployment, subject to boom and bust cycles
- init_serial_unemployment = copy(Params.init_idiosyncratic_shocks)
- init_serial_unemployment['MrkvArray'] = [MrkvArray]
- init_serial_unemployment['UnempPrb'] = 0 # to make income distribution when employed
- init_serial_unemployment['global_markov'] = False
- SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment)
- SerialUnemploymentExample.cycles = 0
- SerialUnemploymentExample.vFuncBool = False # for easy toggling here
-
- # Replace the default (lognormal) income distribution with a custom one
- employed_income_dist = [np.ones(1),np.ones(1),np.ones(1)] # Definitely get income
- unemployed_income_dist = [np.ones(1),np.ones(1),np.zeros(1)] # Definitely don't
- SerialUnemploymentExample.IncomeDstn = [[employed_income_dist,unemployed_income_dist,employed_income_dist,
- unemployed_income_dist]]
-
- # Interest factor, permanent growth rates, and survival probabilities are constant arrays
- SerialUnemploymentExample.Rfree = np.array(4*[SerialUnemploymentExample.Rfree])
- SerialUnemploymentExample.PermGroFac = [np.array(4*SerialUnemploymentExample.PermGroFac)]
- SerialUnemploymentExample.LivPrb = [SerialUnemploymentExample.LivPrb*np.ones(4)]
-
- # Solve the serial unemployment consumer's problem and display solution
- SerialUnemploymentExample.timeFwd()
- start_time = clock()
- SerialUnemploymentExample.solve()
- end_time = clock()
- print('Solving a Markov consumer with serially correlated unemployment took ' + mystr(end_time-start_time) + ' seconds.')
- print('Consumption functions for each discrete state:')
- plotFuncs(SerialUnemploymentExample.solution[0].cFunc,0,50)
- if SerialUnemploymentExample.vFuncBool:
- print('Value functions for each discrete state:')
- plotFuncs(SerialUnemploymentExample.solution[0].vFunc,5,50)
-
- # Simulate some data; results stored in cHist, mNrmNow_hist, cNrmNow_hist, and MrkvNow_hist
- if do_simulation:
- SerialUnemploymentExample.T_sim = 120
- SerialUnemploymentExample.MrkvPrbsInit = [0.25,0.25,0.25,0.25]
- SerialUnemploymentExample.track_vars = ['mNrmNow','cNrmNow']
- SerialUnemploymentExample.makeShockHistory() # This is optional
- SerialUnemploymentExample.initializeSim()
- SerialUnemploymentExample.simulate()
-
-###############################################################################
-
- # Make a consumer who occasionally gets "unemployment immunity" for a fixed period
- UnempPrb = 0.05 # Probability of becoming unemployed each period
- ImmunityPrb = 0.01 # Probability of becoming "immune" to unemployment
- ImmunityT = 6 # Number of periods of immunity
-
- StateCount = ImmunityT+1 # Total number of Markov states
- IncomeDstnReg = [np.array([1-UnempPrb,UnempPrb]), np.array([1.0,1.0]), np.array([1.0/(1.0-UnempPrb),0.0])] # Ordinary income distribution
- IncomeDstnImm = [np.array([1.0]), np.array([1.0]), np.array([1.0])] # Income distribution when unemployed
- IncomeDstn = [IncomeDstnReg] + ImmunityT*[IncomeDstnImm] # Income distribution for each Markov state, in a list
-
- # Make the Markov transition array. MrkvArray[i,j] is the probability of transitioning
- # to state j in period t+1 from state i in period t.
- MrkvArray = np.zeros((StateCount,StateCount))
- MrkvArray[0,0] = 1.0 - ImmunityPrb # Probability of not becoming immune in ordinary state: stay in ordinary state
- MrkvArray[0,ImmunityT] = ImmunityPrb # Probability of becoming immune in ordinary state: begin immunity periods
- for j in range(ImmunityT):
- MrkvArray[j+1,j] = 1.0 # When immune, have 100% chance of transition to state with one fewer immunity periods remaining
-
- init_unemployment_immunity = copy(Params.init_idiosyncratic_shocks)
- init_unemployment_immunity['MrkvArray'] = [MrkvArray]
- ImmunityExample = MarkovConsumerType(**init_unemployment_immunity)
- ImmunityExample.assignParameters(Rfree = np.array(np.array(StateCount*[1.03])), # Interest factor same in all states
- PermGroFac = [np.array(StateCount*[1.01])], # Permanent growth factor same in all states
- LivPrb = [np.array(StateCount*[0.98])], # Same survival probability in all states
- BoroCnstArt = None, # No artificial borrowing constraint
- cycles = 0) # Infinite horizon
- ImmunityExample.IncomeDstn = [IncomeDstn]
-
- # Solve the unemployment immunity problem and display the consumption functions
- start_time = clock()
- ImmunityExample.solve()
- end_time = clock()
- print('Solving an "unemployment immunity" consumer took ' + mystr(end_time-start_time) + ' seconds.')
- print('Consumption functions for each discrete state:')
- mNrmMin = np.min([ImmunityExample.solution[0].mNrmMin[j] for j in range(StateCount)])
- plotFuncs(ImmunityExample.solution[0].cFunc,mNrmMin,10)
-
-###############################################################################
-
- # Make a consumer with serially correlated permanent income growth
- UnempPrb = 0.05 # Unemployment probability
- StateCount = 5 # Number of permanent income growth rates
- Persistence = 0.5 # Probability of getting the same permanent income growth rate next period
-
- IncomeDstnReg = [np.array([1-UnempPrb,UnempPrb]), np.array([1.0,1.0]), np.array([1.0,0.0])]
- IncomeDstn = StateCount*[IncomeDstnReg] # Same simple income distribution in each state
-
- # Make the state transition array for this type: Persistence probability of remaining in the same state, equiprobable otherwise
- MrkvArray = Persistence*np.eye(StateCount) + (1.0/StateCount)*(1.0-Persistence)*np.ones((StateCount,StateCount))
-
- init_serial_growth = copy(Params.init_idiosyncratic_shocks)
- init_serial_growth['MrkvArray'] = [MrkvArray]
- SerialGroExample = MarkovConsumerType(**init_serial_growth)
- SerialGroExample.assignParameters(Rfree = np.array(np.array(StateCount*[1.03])), # Same interest factor in each Markov state
- PermGroFac = [np.array([0.97,0.99,1.01,1.03,1.05])], # Different permanent growth factor in each Markov state
- LivPrb = [np.array(StateCount*[0.98])], # Same survival probability in all states
- cycles = 0)
- SerialGroExample.IncomeDstn = [IncomeDstn]
-
-
- # Solve the serially correlated permanent growth shock problem and display the consumption functions
- start_time = clock()
- SerialGroExample.solve()
- end_time = clock()
- print('Solving a serially correlated growth consumer took ' + mystr(end_time-start_time) + ' seconds.')
- print('Consumption functions for each discrete state:')
- plotFuncs(SerialGroExample.solution[0].cFunc,0,10)
-
-###############################################################################
-
- # Make a consumer with serially correlated interest factors
- SerialRExample = deepcopy(SerialGroExample) # Same as the last problem...
- SerialRExample.assignParameters(PermGroFac = [np.array(StateCount*[1.01])], # ...but now the permanent growth factor is constant...
- Rfree = np.array([1.01,1.02,1.03,1.04,1.05])) # ...and the interest factor is what varies across states
-
- # Solve the serially correlated interest rate problem and display the consumption functions
- start_time = clock()
- SerialRExample.solve()
- end_time = clock()
- print('Solving a serially correlated interest consumer took ' + mystr(end_time-start_time) + ' seconds.')
- print('Consumption functions for each discrete state:')
- plotFuncs(SerialRExample.solution[0].cFunc,0,10)
-
-
-if __name__ == '__main__':
- main()
diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py
index 75b2502ad..32aaea065 100644
--- a/HARK/ConsumptionSaving/ConsMedModel.py
+++ b/HARK/ConsumptionSaving/ConsMedModel.py
@@ -18,8 +18,12 @@
from HARK.ConsumptionSaving.ConsGenIncProcessModel import ConsGenIncProcessSolver,\
PersistentShockConsumerType, ValueFunc2D, MargValueFunc2D,\
MargMargValueFunc2D, VariableLowerBoundFunc2D
+import HARK.ConsumptionSaving.ConsumerParameters as Params
from copy import deepcopy
+__all__ = ['MedShockPolicyFunc', 'cThruXfunc', 'MedThruXfunc',
+'MedShockConsumerType', 'ConsMedShockSolver']
+
utility_inv = CRRAutility_inv
utilityP_inv = CRRAutilityP_inv
utility = CRRAutility
@@ -509,7 +513,7 @@ class MedShockConsumerType(PersistentShockConsumerType):
'''
shock_vars_ = PersistentShockConsumerType.shock_vars_ + ['MedShkNow']
- def __init__(self,cycles=1,time_flow=True,**kwds):
+ def __init__(self,cycles=0,time_flow=True,**kwds):
'''
Instantiate a new ConsumerType with given data, and construct objects
to be used during solution (income distribution, assets grid, etc).
@@ -527,6 +531,10 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
-------
None
'''
+ params = Params.init_medical_shocks.copy()
+ params.update(kwds)
+ kwds = params
+
PersistentShockConsumerType.__init__(self,cycles=cycles,**kwds)
self.solveOnePeriod = solveConsMedShock # Choose correct solver
self.addToTimeInv('CRRAmed')
@@ -1356,89 +1364,3 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR
solver.prepareToSolve() # Do some preparatory work
solution_now = solver.solve() # Solve the period
return solution_now
-
-
-###############################################################################
-
-def main():
- import HARK.ConsumptionSaving.ConsumerParameters as Params
- from HARK.utilities import CRRAutility_inv
- from time import clock
- import matplotlib.pyplot as plt
- mystr = lambda number : "{:.4f}".format(number)
-
- do_simulation = True
-
- # Make and solve an example medical shocks consumer type
- MedicalExample = MedShockConsumerType(**Params.init_medical_shocks)
- t_start = clock()
- MedicalExample.solve()
- t_end = clock()
- print('Solving a medical shocks consumer took ' + mystr(t_end-t_start) + ' seconds.')
-
- # Plot the consumption function
- M = np.linspace(0,30,300)
- pLvl = 1.0
- P = pLvl*np.ones_like(M)
- for j in range(MedicalExample.MedShkDstn[0][0].size):
- MedShk = MedicalExample.MedShkDstn[0][1][j]*np.ones_like(M)
- M_temp = M + MedicalExample.solution[0].mLvlMin(pLvl)
- C = MedicalExample.solution[0].cFunc(M_temp,P,MedShk)
- plt.plot(M_temp,C)
- print('Consumption function by medical need shock (constant permanent income)')
- plt.show()
-
- # Plot the medical care function
- for j in range(MedicalExample.MedShkDstn[0][0].size):
- MedShk = MedicalExample.MedShkDstn[0][1][j]*np.ones_like(M)
- Med = MedicalExample.solution[0].MedFunc(M_temp,P,MedShk)
- plt.plot(M_temp,Med)
- print('Medical care function by medical need shock (constant permanent income)')
- plt.ylim([0,20])
- plt.show()
-
- # Plot the savings function
- for j in range(MedicalExample.MedShkDstn[0][0].size):
- MedShk = MedicalExample.MedShkDstn[0][1][j]*np.ones_like(M)
- Sav = M_temp - MedicalExample.solution[0].cFunc(M_temp,P,MedShk) - MedicalExample.MedPrice[0]*\
- MedicalExample.solution[0].MedFunc(M_temp,P,MedShk)
- plt.plot(M_temp,Sav)
- print('End of period savings by medical need shock (constant permanent income)')
- plt.show()
-
- # Plot the marginal value function
- M = np.linspace(0.0,30,300)
- for p in range(MedicalExample.pLvlGrid[0].size):
- pLvl = MedicalExample.pLvlGrid[0][p]
- M_temp = pLvl*M + MedicalExample.solution[0].mLvlMin(pLvl)
- P = pLvl*np.ones_like(M)
- vP = MedicalExample.solution[0].vPfunc(M_temp,P)**(-1.0/MedicalExample.CRRA)
- plt.plot(M_temp,vP)
- print('Marginal value function (pseudo inverse)')
- plt.show()
-
- if MedicalExample.vFuncBool:
- # Plot the value function
- M = np.linspace(0.0,1,300)
- for p in range(MedicalExample.pLvlGrid[0].size):
- pLvl = MedicalExample.pLvlGrid[0][p]
- M_temp = pLvl*M + MedicalExample.solution[0].mLvlMin(pLvl)
- P = pLvl*np.ones_like(M)
- v = CRRAutility_inv(MedicalExample.solution[0].vFunc(M_temp,P),gam=MedicalExample.CRRA)
- plt.plot(M_temp,v)
- print('Value function (pseudo inverse)')
- plt.show()
-
- if do_simulation:
- t_start = clock()
- MedicalExample.T_sim = 100
- MedicalExample.track_vars = ['mLvlNow','cLvlNow','MedNow']
- MedicalExample.makeShockHistory()
- MedicalExample.initializeSim()
- MedicalExample.simulate()
- t_end = clock()
- print('Simulating ' + str(MedicalExample.AgentCount) + ' agents for ' + str(MedicalExample.T_sim) + ' periods took ' + mystr(t_end-t_start) + ' seconds.')
-
-if __name__ == '__main__':
- main()
-
diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py
new file mode 100644
index 000000000..87cecd7ce
--- /dev/null
+++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py
@@ -0,0 +1,1456 @@
+# FIXME RiskyShareLimitFunc currently doesn't work for time varying CRRA,
+# Rfree and Risky-parameters. This should be possible by creating a list of
+# functions instead.
+
+import math # we're using math for log and exp, might want to just use numpy?
+import scipy.optimize as sciopt # we're using scipy optimize to optimize and fsolve
+import scipy.integrate # used to calculate the expectation over returns
+import scipy.stats as stats # for densities related to the returns distributions
+from copy import (
+ deepcopy,
+) # it's convenient to copy things some times instead of re-creating them
+
+# Solution is inherited from in the PortfolioSolution class, NullFunc is used
+# throughout HARK when no input is given and AgentType is used for .preSolve
+from HARK import Solution, NullFunc, AgentType
+
+from HARK.ConsumptionSaving.ConsIndShockModel import (
+ PerfForesightConsumerType, # for .__init__
+ IndShockConsumerType, # PortfolioConsumerType inherits from it
+ ConsIndShockSolver, # ConsIndShockPortfolioSolver inherits from it
+ ValueFunc, # to do the re-curving of value functions for interpolation
+ MargValueFunc, # same as above, but for marginal value functions
+ utility_inv, # inverse CRRA
+)
+
+from HARK.utilities import (
+ approxLognormal, # for approximating the lognormal returns factor
+ combineIndepDstns, # for combining the existing
+)
+
+from HARK.simulation import drawLognormal # random draws for simulating agents
+
+from HARK.interpolation import (
+ LinearInterp, # piece-wise linear interpolation
+ LowerEnvelope, # lower envelope for consumption function around borrowing constraint
+)
+
+
+import numpy as np # for array operations
+
+__all__ = [
+ "PortfolioSolution",
+ "ContinuousDomain",
+ "DiscreteDomain",
+ "PortfolioConsumerType",
+ "ConsIndShockPortfolioSolver",
+ "LogNormalPortfolioConsumerType",
+]
+# REMARK: The Campbell and Viceira (2002) approximation can be calculated from
+# the code below. TODO clean up
+# def CambellVicApprox()
+#
+# # We assume fixed distribution of risky shocks throughout, so we can
+# # calculate the limiting solution once and for all.
+# phi = math.log(self.RiskyAvg/self.Rfree)
+# RiskyAvgSqrd = self.RiskyAvg**2
+# RiskyVar = self.RiskyStd**2
+# #
+# # mu = math.log(self.RiskyAvg/(math.sqrt(1+RiskyVar/RiskyAvgSqrd)))
+# # sigma = math.sqrt(math.log(1+RiskyVar/RiskyAvgSqrd))
+# #
+# # RiskyShareLimit = phi/(self.CRRA*sigma**2)
+
+
+def _PerfForesightLogNormalPortfolioShare(Rfree, RiskyAvg, RiskyStd, CRRA):
+ """
+ Calculate the optimal portfolio share in the perfect foresight model. This
+ does not depend on resources today or the time period.
+
+ Parameters
+ ----------
+ Rfree : Number
+ The risk free interest factor
+ RiskyAvg : Number
+ The average risk factor
+ RiskyStd : Number
+ The standard deviation of the risk factor
+ CRRA : Number
+ The CRRA utility parameter
+
+ Returns
+ -------
+ optShare : Number
+ The optimal portfolio share in the perfect foresight portofolio model.
+ """
+ PortfolioObjective = lambda share: _PerfForesightLogNormalPortfolioObjective(
+ share, Rfree, RiskyAvg, RiskyStd, CRRA
+ )
+ optShare = sciopt.minimize_scalar(
+ PortfolioObjective, bounds=(0.0, 1.0), method="bounded"
+ ).x
+ return optShare
+
+
+def _PerfForesightDiscretePortfolioShare(Rfree, RiskyDstn, CRRA):
+ """
+ Calculate the optimal portfolio share in the perfect foresight model. This
+ Does not depend on resources today or the time period. This version assumes
+ that the return factor distribution is characterized by a discrete distribution
+ or can be approximated using the input values.
+
+ Parameters
+ ----------
+ Rfree : Number
+ The risk free interest factor
+ RiskyDstn : numpy.array
+ A numpy array with first element being a probability vector and the
+ second element being the values of the returns factor associated with
+ the probabilities
+ CRRA : Number
+ The CRRA utility parameter
+
+ Returns
+ -------
+ optShare : Number
+ The optimal portfolio share in the perfect foresight portofolio model.
+ """
+ PortfolioObjective = lambda share: _PerfForesightDiscretePortfolioObjective(
+ share, Rfree, RiskyDstn, CRRA
+ )
+ optShare = sciopt.minimize_scalar(
+ PortfolioObjective, bounds=(0.0, 1.0), method="bounded"
+ ).x
+ return optShare
+
+
+# Switch here based on knowledge about risky.
+# It can either be "discrete" in which case it is only the number of draws that
+# are used, or it can be continuous in which case bounds and a pdf has to be supplied.
+def _PerfForesightLogNormalPortfolioIntegrand(share, Rfree, RiskyAvg, RiskyStd, CRRA):
+ """
+ Returns a function to evaluate the integrand for calculating the expectation
+ in the perfect foresight porfolio problem with lognormal return factors.
+
+ Parameters
+ ----------
+ Rfree : Number
+ The risk free interest factor
+ RiskyAvg : Number
+ The average risk factor
+ RiskyStd : Number
+ The standard deviation of the risk factor
+ CRRA : Number
+ The CRRA utility parameter
+
+ Returns
+ -------
+ integrand : function (lambda)
+ Can be used to evaluate the integrand and the sent to a quadrature procedure.
+ """
+ muNorm = np.log(RiskyAvg / np.sqrt(1 + RiskyStd ** 2 / RiskyAvg ** 2))
+ sigmaNorm = np.sqrt(np.log(1 + RiskyStd ** 2 / RiskyAvg ** 2))
+ sharedobjective = lambda r: (Rfree + share * (r - Rfree)) ** (1 - CRRA)
+ pdf = lambda r: stats.lognorm.pdf(r, s=sigmaNorm, scale=np.exp(muNorm))
+
+ integrand = lambda r: sharedobjective(r) * pdf(r)
+ return integrand
+
+
+def _PerfForesightLogNormalPortfolioObjective(share, Rfree, RiskyAvg, RiskyStd, CRRA):
+ """
+ Returns the integral used in the perfect foresight portoflio choice problem
+ with lognormal return factors evaluated at share.
+
+ Parameters
+ ----------
+ Rfree : Number
+ The risk free interest factor
+ RiskyAvg : Number
+ The average risk factor
+ RiskyStd : Number
+ The standard deviation of the risk factor
+ CRRA : Number
+ The CRRA utility parameter
+
+ Returns
+ -------
+ integrand : function (lambda)
+ Can be used to evaluate the integrand and the sent to a quadrature procedure.
+ """
+ integrand = _PerfForesightLogNormalPortfolioIntegrand(
+ share, Rfree, RiskyAvg, RiskyStd, CRRA
+ )
+ a = 0.0 # Cannot be negative
+ b = 5.0 # This is just an upper bound. pdf should be 0 here.
+ return -((1 - CRRA) ** -1) * scipy.integrate.quad(integrand, a, b)[0]
+
+
+def _PerfForesightDiscretePortfolioObjective(share, Rfree, RiskyDstn, CRRA):
+ """
+ Returns the integral used in the perfect foresight portoflio choice problem
+ with discretely distributed return factors evaluated at share.
+
+ Parameters
+ ----------
+ Rfree : Number
+ The risk free interest factor
+ RiskyDstn : numpy.array
+ A numpy array with first element being a probability vector and the
+ second element being the values of the returns factor associated with
+ the probabilities
+ CRRA : Number
+ The CRRA utility parameter
+
+ Returns
+ -------
+ integrand : function (lambda)
+ Can be used to evaluate the integrand and the sent to a quadrature procedure.
+ """
+ vals = (Rfree + share * (RiskyDstn[1] - Rfree)) ** (1 - CRRA)
+ weights = RiskyDstn[0]
+
+ return -((1 - CRRA) ** -1) * np.dot(vals, weights)
+
+
+def _calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc_adj, CRRA):
+ """
+ Set up the value function at the point of consumption, but before portofolio
+ choice. Depends on the probability of getting the porfolio choice this period,
+ the possible shares today (if AdjustPrb is not 1) and the value function
+ given portfolio choice (so at next sub-period).
+
+ Parameters
+ ----------
+ AdjustPrb : number
+ The probability of adjusting the portfolio this period
+ AdjustCount : integer
+ The number of adjustment states the agent can be in from the set of
+ possible states = (adjuster, nonadjuster)
+ ShareNowCount : integer
+ The number of current portfolio share. Only relevant if nonadjustment is
+ possible.
+ vFunc_adj : list of lists
+ The values of being in first index state (0 is adjuster, 1 is non-adjuster)
+ and possibly second index state (indicating the current portfolio share).
+ The latter is relevant for non-adjusters only.
+ CRRA : Number
+ The CRRA utility parameter
+
+ Returns
+ -------
+ integrand : function (lambda)
+ Can be used to evaluate the integrand and the sent to a quadrature procedure.
+ """
+
+ # AdjustCount could in principle just be inferred from AdjustPrb instead of
+ # cartying it arround FIXME / TODO
+ wFunc = []
+ if AdjustCount == 1:
+ # Just take the adjuster
+ for ShareIndex in range(ShareNowCount[0]):
+ wFunc.append(vFunc_adj[0][ShareIndex])
+ else:
+ # Calculate stuff
+ for ShareIndex in range(ShareNowCount[1]):
+ # TODO FIXME better grid
+ evalgrid = np.linspace(0, 100, 200)
+ evVals = AdjustPrb * vFunc_adj[0][ShareIndex](evalgrid) + (
+ 1 - AdjustPrb
+ ) * vFunc_adj[1][ShareIndex](evalgrid)
+ with np.errstate(
+ divide="ignore", over="ignore", under="ignore", invalid="ignore"
+ ):
+ evValsNvrs = utility_inv(evVals, gam=CRRA)
+ wFunc.append(ValueFunc(LinearInterp(evVals, evValsNvrs), CRRA))
+
+ return wFunc
+
+
+def RiskyDstnFactory(RiskyAvg=1.0, RiskyStd=0.0):
+ """
+ A class for generating functions that generate nodes and weights for a log-
+ normal distribution as parameterized by the input `RiskyAvg` and `RiskyStd`
+ values. The returned function takes a number of points to request and returns
+ a list of lists where the first list contains the weights (probabilities) and
+ the second list contains the values.
+ """
+ RiskyAvgSqrd = RiskyAvg ** 2
+ RiskyVar = RiskyStd ** 2
+
+ mu = math.log(RiskyAvg / (math.sqrt(1 + RiskyVar / RiskyAvgSqrd)))
+ sigma = math.sqrt(math.log(1 + RiskyVar / RiskyAvgSqrd))
+
+ return lambda RiskyCount: approxLognormal(RiskyCount, mu=mu, sigma=sigma)
+
+
+def LogNormalRiskyDstnDraw(RiskyAvg=1.0, RiskyStd=0.0):
+ """
+ A class for generating functions that draw random values from a log-normal
+ distribution as parameterized by the input `RiskyAvg` and `RiskyStd`
+ values. The returned function takes no argument and returns a value.
+ """
+ RiskyAvgSqrd = RiskyAvg ** 2
+ RiskyVar = RiskyStd ** 2
+
+ mu = math.log(RiskyAvg / (math.sqrt(1 + RiskyVar / RiskyAvgSqrd)))
+ sigma = math.sqrt(math.log(1 + RiskyVar / RiskyAvgSqrd))
+
+ return lambda rngSeed: drawLognormal(1, mu=mu, sigma=sigma, seed=rngSeed)
+
+
+class PortfolioSolution(Solution):
+ distance_criteria = ["cFunc"]
+
+ def __init__(
+ self,
+ cFunc=None,
+ vFunc=None,
+ wFunc=None,
+ vPfunc=None,
+ RiskyShareFunc=None,
+ vPPfunc=None,
+ mNrmMin=None,
+ hNrm=None,
+ MPCmin=None,
+ MPCmax=None,
+ ):
+ """We implement three different ways to allow portfolio choice.
+ The agent can choose:
+
+ * any portfolio share ('continuous choice')
+ * only a specified set of portfolio shares ('discrete shares'):
+ * With probability 1 (agent always gets to choose)
+ * With probability 0 < p < 1 (stochastic chance to choose)
+
+ We allow two choices for the description of the
+ distribution of the stochastic variable:
+ 1. A generic discrete probability distribution
+ * Nodes and their probabilities are specified
+ 2. A true lognormal distribution
+ * The mean return and the standard deviation are specified
+
+ In the discrete shares case, the user also must input a function that draws
+ from the distribution in drawRiskyFunc
+
+ Other assumptions:
+ * distributions are time constant
+ * probability of being allowed to reoptimize is time constant
+ * If p < 1, you must specify the PortfolioSet discretely
+ """
+ # Change any missing function inputs to NullFunc
+ if cFunc is None:
+ cFunc = NullFunc()
+ if vFunc is None:
+ vFunc = NullFunc()
+ if RiskyShareFunc is None:
+ RiskyShareFunc = NullFunc()
+ if vPfunc is None:
+ vPfunc = NullFunc()
+ if vPPfunc is None:
+ vPPfunc = NullFunc()
+ self.cFunc = cFunc
+ self.vFunc = vFunc
+ self.vPfunc = vPfunc
+ self.RiskyShareFunc = RiskyShareFunc
+ self.wFunc = wFunc
+ # self.vPPfunc = vPPfunc
+ # self.mNrmMin = mNrmMin
+ # self.hNrm = hNrm
+ # self.MPCmin = MPCmin
+ # self.MPCmax = MPCmax
+
+
+# These domains are convenient for switching to relavent code paths internally.
+# It might be simpler to just pass in vectors instead of DiscreteDomain.
+class ContinuousDomain(object):
+ def __init__(self, lower, upper, points=[np.nan]):
+ if lower > upper:
+ raise Exception("lower bounds is larger than upper bound")
+ else:
+ self.lower = lower
+ self.upper = upper
+ self.points = points
+
+ def getPoints(self):
+ return self.points
+
+ def len(self):
+ return len(self.points)
+
+
+class DiscreteDomain(object):
+ def __init__(self, points):
+ self.points = points
+ self.lower = np.amin(points)
+ self.upper = np.amax(points)
+
+ def len(self):
+ return len(self.points)
+
+ def getPoints(self):
+ return self.points
+
+
+class PortfolioConsumerType(IndShockConsumerType):
+
+ # We add CantAdjust to the standard set of poststate_vars_ here. We call it
+ # CantAdjust over CanAdjust, because this allows us to index into the
+ # "CanAdjust = 1- CantAdjust" at all times (it's the 0th offset).
+ poststate_vars_ = ["aNrmNow", "pLvlNow", "RiskyShareNow", "CantAdjust"]
+ time_inv_ = deepcopy(IndShockConsumerType.time_inv_)
+ time_inv_ = time_inv_ + ["approxRiskyDstn", "RiskyCount", "RiskyShareCount"]
+ time_inv_ = time_inv_ + ["RiskyShareLimitFunc", "PortfolioDomain"]
+ time_inv_ = time_inv_ + ["AdjustPrb", "PortfolioGrid", "AdjustCount"]
+
+ def __init__(self, cycles=1, time_flow=True, verbose=False, quiet=False, **kwds):
+
+ # Initialize a basic AgentType
+ PerfForesightConsumerType.__init__(
+ self,
+ cycles=cycles,
+ time_flow=time_flow,
+ verbose=verbose,
+ quiet=quiet,
+ **kwds
+ )
+
+ # Check that an adjustment probability is set. If not, default to always.
+ if not hasattr(self, "AdjustPrb"):
+ self.AdjustPrb = 1.0
+ self.AdjustCount = 1
+ elif self.AdjustPrb == 1.0:
+ # Always adjust, so there's just one possibility
+ self.AdjustCount = 1
+ else:
+ # If AdjustPrb was set and was below 1.0, there's a chance that
+ # the consumer cannot adjust in a given period.
+ self.AdjustCount = 2
+
+ if not hasattr(self, "PortfolioDomain"):
+ if self.AdjustPrb < 1.0:
+ raise Exception(
+ "Please supply a PortfolioDomain when setting AdjustPrb < 1.0."
+ )
+ else:
+ self.PortfolioDomain = ContinuousDomain(0, 1)
+
+ if isinstance(self.PortfolioDomain, DiscreteDomain):
+ self.DiscreteCase = True
+ if self.vFuncBool == False:
+ if self.verbose:
+ print(
+ "Setting vFuncBool to True to accomodate dicrete portfolio optimization."
+ )
+
+ self.vFuncBool = True
+ else:
+ self.DiscreteCase = False
+ if self.AdjustPrb < 1.0:
+ raise Exception(
+ "Occational inability to re-optimize portfolio (AdjustPrb < 1.0) is currently not possible with continuous choice of the portfolio share."
+ )
+
+ # Now we can set up the PortfolioGrid! This is the portfolio values
+ # you can enter the period with. It's exact for discrete , for continuous
+ # domain it's the interpolation points.
+ self.PortfolioGrid = self.PortfolioDomain.getPoints()
+
+ if self.BoroCnstArt is not 0.0:
+ if self.verbose:
+ print(
+ "Setting BoroCnstArt to 0.0 as this is required by PortfolioConsumerType."
+ )
+ self.BoroCnstArt = 0.0
+
+ self.ShareNowCount = [1]
+ if self.DiscreteCase and self.AdjustCount > 1:
+ self.ShareNow = self.PortfolioDomain.getPoints()
+ self.ShareNowCount.append(len(self.PortfolioDomain.getPoints()))
+
+ # Chose specialized solver for Portfolio choice model
+ self.solveOnePeriod = solveConsPortfolio
+
+ self.update()
+
+ self.RiskyShareLimitFunc = lambda RiskyDstn: _PerfForesightDiscretePortfolioShare(
+ self.Rfree, RiskyDstn, self.CRRA
+ )
+
+ def preSolve(self):
+ AgentType.preSolve(self)
+ self.updateSolutionTerminal()
+
+ def updateSolutionTerminal(self):
+ """
+ Updates the terminal period solution for a portfolio shock consumer.
+ Only fills in the consumption function and marginal value function.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ """
+
+ # repeat according to number of portfolio adjustment situations
+ # TODO FIXME this is technically incorrect, too many in [0]
+ cFunc_terminal = LinearInterp([0.0, 1.0], [0.0, 1.0]) # c=m in terminal period
+ vFunc_terminal = LinearInterp([0.0, 1.0], [0.0, 0.0]) # This is overwritten
+ RiskyShareFunc_terminal = LinearInterp(
+ [0.0, 1.0], [0.0, 0.0]
+ ) # c=m in terminal period
+
+ if isinstance(self.PortfolioDomain, DiscreteDomain):
+ PortfolioGridCount = len(self.PortfolioDomain.points)
+ else:
+ # This should be "PortfolioGridCount" that was set earlier,
+ PortfolioGridCount = 1
+
+ vFunc_terminal = PortfolioGridCount * [ValueFunc(cFunc_terminal, self.CRRA)]
+ vFunc_terminal = self.AdjustCount * [vFunc_terminal]
+
+ vPfunc_terminal = PortfolioGridCount * [
+ MargValueFunc(cFunc_terminal, self.CRRA)
+ ]
+ vPfunc_terminal = self.AdjustCount * [vPfunc_terminal]
+
+ cFunc_terminal = PortfolioGridCount * [cFunc_terminal]
+ cFunc_terminal = self.AdjustCount * [cFunc_terminal]
+
+ RiskyShareFunc_terminal = PortfolioGridCount * [RiskyShareFunc_terminal]
+ RiskyShareFunc_terminal = self.AdjustCount * [RiskyShareFunc_terminal]
+
+ wFunc_terminal = _calcwFunc(
+ self.AdjustPrb,
+ self.AdjustCount,
+ self.ShareNowCount,
+ vFunc_terminal,
+ self.CRRA,
+ )
+
+ self.solution_terminal = PortfolioSolution(
+ cFunc=cFunc_terminal,
+ RiskyShareFunc=RiskyShareFunc_terminal,
+ vFunc=vFunc_terminal,
+ wFunc=wFunc_terminal,
+ vPfunc=vPfunc_terminal,
+ mNrmMin=0.0,
+ hNrm=None,
+ MPCmin=None,
+ MPCmax=None,
+ )
+
+ def getPostStates(self):
+ """
+ Calculates end-of-period assets for each consumer of this type.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ """
+ # Calculate post decision ressources
+ self.aNrmNow = self.mNrmNow - self.cNrmNow
+ self.aLvlNow = (
+ self.aNrmNow * self.pLvlNow
+ ) # Useful in some cases to precalculate asset level
+
+ # We calculate the risky share given post decision assets aNrmNow. We
+ # do this for all agents that have self.CantAdjust == 0 and save the
+ # non-adjusters for the next section.
+ RiskyShareNow = np.zeros(self.AgentCount) + np.nan
+
+ for t in range(self.T_cycle):
+ # We need to take into account whether they have drawn a portfolio
+ # adjust shock or not.
+ these_adjust = self.CantAdjust == 0
+ these_t = t == self.t_cycle
+
+ # First take adjusters
+ these = np.logical_and(these_adjust, these_t)
+ RiskyShareNow[these] = self.solution[t].RiskyShareFunc[0][0](
+ self.aNrmNow[these]
+ ) # should be redefined on mNrm in solve and calculated in getControls
+
+ these_cant_adjust = self.CantAdjust == 1
+ these = np.logical_and(these_cant_adjust, these_t)
+ RiskyShareNow[these] = self.RiskySharePrev[
+ these
+ ] # should be redefined on mNrm in solve and calculated in getControls
+
+ # Store the result in self
+ self.RiskyShareNow = RiskyShareNow
+ return None
+
+ # Simulation methods
+ def getStates(self):
+ """
+ Calculates updated values of normalized market resources and permanent income level for each
+ agent. Uses pLvlNow, aNrmNow, PermShkNow, TranShkNow.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ """
+ RiskySharePrev = self.RiskyShareNow
+ self.RiskySharePrev = RiskySharePrev # Save this for the non-adjusters!
+ pLvlPrev = self.pLvlNow
+ aNrmPrev = self.aNrmNow
+
+ RfreeNow = self.getRfree()
+ # In the current interpretation, everyone gets the same random return.
+ # This is because they all invest in the same stock/market index.
+ # As a result, we simply draw *one* realization from RiskyDstn.
+ RiskyNow = self.getRisky()
+
+ # Calculate the portfolio return from last period to current period.
+ RportNow = RfreeNow + RiskySharePrev * (RiskyNow - RfreeNow)
+
+ # Calculate new states: normalized market resources and permanent income level
+ self.pLvlNow = pLvlPrev * self.PermShkNow # Updated permanent income level
+ self.PlvlAggNow = (
+ self.PlvlAggNow * self.PermShkAggNow
+ ) # Updated aggregate permanent productivity level
+ ReffNow = (
+ RportNow / self.PermShkNow
+ ) # "Effective" interest factor on normalized assets
+ self.bNrmNow = ReffNow * aNrmPrev # Bank balances before labor income
+ self.mNrmNow = self.bNrmNow + self.TranShkNow # Market resources after income
+
+ # Figure out who can adjust their portfolio this period.
+ self.CantAdjust = stats.bernoulli.rvs(1 - self.AdjustPrb, size=self.AgentCount)
+
+ # New agents are always allowed to optimize their portfolio, because they
+ # have no past portfolio to "keep".
+ self.CantAdjust[self.new_agents] = 0.0
+ return None
+
+ def simBirth(self, which_agents):
+ """
+ Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as
+ well as time variables t_age and t_cycle. Normalized assets and permanent income levels
+ are drawn from lognormal distributions given by aNrmInitMean and aNrmInitStd (etc).
+
+ Parameters
+ ----------
+ which_agents : np.array(Bool)
+ Boolean array of size self.AgentCount indicating which agents should be "born".
+
+ Returns
+ -------
+ None
+ """
+ # Get and store states for newly born agents
+ N = np.sum(which_agents) # Number of new consumers to make
+ self.aNrmNow[which_agents] = drawLognormal(
+ N,
+ mu=self.aNrmInitMean,
+ sigma=self.aNrmInitStd,
+ seed=self.RNG.randint(0, 2 ** 31 - 1),
+ )
+ pLvlInitMeanNow = self.pLvlInitMean + np.log(
+ self.PlvlAggNow
+ ) # Account for newer cohorts having higher permanent income
+ self.pLvlNow[which_agents] = drawLognormal(
+ N,
+ mu=pLvlInitMeanNow,
+ sigma=self.pLvlInitStd,
+ seed=self.RNG.randint(0, 2 ** 31 - 1),
+ )
+
+ self.t_age[which_agents] = 0 # How many periods since each agent was born
+ self.t_cycle[
+ which_agents
+ ] = 0 # Which period of the cycle each agent is currently in
+ self.new_agents = (
+ which_agents
+ ) # store for portfolio choice forced to be allowed in first period
+ return None
+
+ def getControls(self):
+ """
+ Calculates consumption for each consumer of this type using the consumption functions.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ None
+ """
+ cNrmNow = np.zeros(self.AgentCount) + np.nan
+ MPCnow = np.zeros(self.AgentCount) + np.nan
+
+ these_cant_adjust = self.CantAdjust == 1
+ these_can_adjust = self.CantAdjust == 0
+
+ for t in range(self.T_cycle):
+ these_t = t == self.t_cycle
+ these = np.logical_and(these_t, these_can_adjust)
+ cNrmNow[these], MPCnow[these] = (
+ self.solution[t].cFunc[0][0].eval_with_derivative(self.mNrmNow[these])
+ )
+
+ if any(these_cant_adjust):
+ for portfolio_index, portfolio_value in enumerate(self.ShareNow):
+ these_portfolio = np.equal(portfolio_value, self.RiskySharePrev)
+ these = np.logical_and(these_t, these_portfolio)
+
+ cNrmNow[these], MPCnow[these] = (
+ self.solution[t]
+ .cFunc[1][portfolio_index]
+ .eval_with_derivative(self.mNrmNow[these])
+ )
+
+ self.cNrmNow = cNrmNow
+ self.MPCnow = MPCnow
+ return None
+
+ def getRisky(self):
+ return self.drawRiskyFunc(self.RNG.randint(0, 2 ** 31 - 1))
+
+
+class ConsIndShockPortfolioSolver(ConsIndShockSolver):
+ """
+ A class for solving a one period consumption-saving problem with portfolio choice.
+ An instance of this class is created by the function solveConsPortfolio in each period.
+ """
+
+ def __init__(
+ self,
+ solution_next,
+ IncomeDstn,
+ LivPrb,
+ DiscFac,
+ CRRA,
+ Rfree,
+ PermGroFac,
+ BoroCnstArt,
+ aXtraGrid,
+ vFuncBool,
+ CubicBool,
+ approxRiskyDstn,
+ RiskyCount,
+ RiskyShareCount,
+ RiskyShareLimitFunc,
+ AdjustPrb,
+ PortfolioGrid,
+ AdjustCount,
+ PortfolioDomain,
+ ):
+
+ ConsIndShockSolver.__init__(
+ self,
+ solution_next,
+ IncomeDstn,
+ LivPrb,
+ DiscFac,
+ CRRA,
+ Rfree,
+ PermGroFac,
+ BoroCnstArt,
+ aXtraGrid,
+ vFuncBool,
+ CubicBool,
+ )
+
+ self.PortfolioDomain = PortfolioDomain
+ if isinstance(self.PortfolioDomain, DiscreteDomain):
+ self.DiscreteCase = True
+ else:
+ self.DiscreteCase = False
+
+ self.AdjustPrb = AdjustPrb
+ self.PortfolioGrid = PortfolioGrid
+ self.AdjustCount = AdjustCount
+ self.ShareNowCount = [1]
+ if self.DiscreteCase:
+ self.ShareNow = self.PortfolioDomain.getPoints()
+ self.ShareNowCount.append(len(self.PortfolioDomain.getPoints()))
+
+ # Store the Risky asset shock distribution
+ self.RiskyDstn = approxRiskyDstn(RiskyCount)
+ self.RiskyShareLimit = RiskyShareLimitFunc(self.RiskyDstn)
+
+ # Store the number of grid points used approximate the FOC in the port-
+ # folio sub-problem.
+ self.RiskyShareCount = RiskyShareCount
+
+ self.vFuncsNext = solution_next.vFunc
+ self.vPfuncsNext = solution_next.vPfunc
+
+ self.updateShockDstn()
+ self.makeRshareGrid()
+
+ def makeEndOfPrdvFunc(self, AdjustIndex, ShareIndex):
+ """
+ Construct the end-of-period value function for this period, storing it
+ as an attribute of self for use by other methods.
+
+ Parameters
+ ----------
+ none
+
+ Returns
+ -------
+ none
+ """
+ if not self.DiscreteCase:
+ raise Exception(
+ "vFuncBool == True is not supported for continuous portfolio choice."
+ )
+
+ # We will need to index vFuncNext wrt the state next period given choices
+ # today.
+ VLvlNext = (
+ self.PermShkVals_temp ** (1.0 - self.CRRA)
+ * self.PermGroFac ** (1.0 - self.CRRA)
+ ) * self.vFuncsNext[AdjustIndex][ShareIndex](
+ self.mNrmNext[AdjustIndex][ShareIndex]
+ )
+ EndOfPrdv = self.DiscFacEff * np.sum(VLvlNext * self.ShkPrbs_temp, axis=0)
+ EndOfPrdvNvrs = self.uinv(
+ EndOfPrdv
+ ) # value transformed through inverse utility
+ # Manually input (0,0) pair
+ EndOfPrdvNvrs = np.insert(EndOfPrdvNvrs, 0, 0.0)
+ aNrm_temp = np.insert(self.aNrmNow, 0, 0.0)
+
+ EndOfPrdvNvrsFunc = LinearInterp(aNrm_temp, EndOfPrdvNvrs)
+ self.EndOfPrdvFunc = ValueFunc(EndOfPrdvNvrsFunc, self.CRRA)
+
+ def makevFunc(self, solution, AdjustIndex, ShareIndex):
+ """
+ Creates the value function for this period, defined over market resources m.
+ self must have the attribute EndOfPrdvFunc in order to execute.
+
+ Parameters
+ ----------
+ solution : ConsumerSolution
+ The solution to this single period problem, which must include the
+ consumption function.
+
+ Returns
+ -------
+ vFuncNow : ValueFunc
+ A representation of the value function for this period, defined over
+ normalized market resources m: v = vFuncNow(m).
+ """
+ # Compute expected value and marginal value on a grid of market resources
+ mNrm_temp = self.mNrmMinNow + self.aXtraGrid
+ cNrmNow = solution.cFunc[AdjustIndex][ShareIndex](mNrm_temp)
+ aNrmNow = mNrm_temp - cNrmNow
+ vNrmNow = self.u(cNrmNow) + self.EndOfPrdvFunc(aNrmNow)
+
+ # Construct the beginning-of-period value function
+ vNvrs = self.uinv(vNrmNow) # value transformed through inverse utility
+ # Manually insert (0,0) pair.
+ mNrm_temp = np.insert(
+ mNrm_temp, 0, 0.0
+ ) # np.insert(mNrm_temp,0,self.mNrmMinNow)
+ vNvrs = np.insert(vNvrs, 0, 0.0)
+ vNvrsFuncNow = LinearInterp(mNrm_temp, vNvrs)
+ vFuncNow = ValueFunc(vNvrsFuncNow, self.CRRA)
+ return vFuncNow
+
+ def addvFunc(self, solution):
+ """
+ Creates the value function for this period and adds it to the solution.
+
+ Parameters
+ ----------
+ solution : ConsumerSolution
+ The solution to this single period problem, likely including the
+ consumption function, marginal value function, etc.
+
+ Returns
+ -------
+ solution : ConsumerSolution
+ The single period solution passed as an input, but now with the
+ value function (defined over market resources m) as an attribute.
+ """
+ if not self.DiscreteCase:
+ raise Exception(
+ "You're not supposed to be here. Continuous choice portfolio domain does not support vFuncBool == True or AdjustPrb < 1.0."
+ )
+
+ vFunc = self.AdjustCount * [[]]
+
+ for AdjustIndex in range(self.AdjustCount): # nonadjuster possible!
+ # this is where we add to vFunc based on non-adjustment.
+ # Basically repeat the above with the share updated to be the "prev"
+ # share an. We need to keep mNrmNext at two major indeces: adjust and
+ # non-adjust. Adjust will just have one element, but non-adjust will need
+ # one for each of the possible current ("prev") values.
+ for ShareIndex in range(
+ self.ShareNowCount[AdjustIndex]
+ ): # for all share level indeces in the adjuster (1) case
+ self.makeEndOfPrdvFunc(AdjustIndex, ShareIndex)
+ vFunc[AdjustIndex].append(
+ self.makevFunc(solution, AdjustIndex, ShareIndex)
+ )
+
+ solution.vFunc = vFunc
+ return solution
+
+ def updateShockDstn(self):
+ self.ShockDstn = combineIndepDstns(self.IncomeDstn, self.RiskyDstn)
+
+ def makeRshareGrid(self):
+ # We set this up such that attempts to use RshareGrid will fail hard
+ # if we're in the discrete case
+ if not self.DiscreteCase:
+ self.RshareGrid = np.linspace(0, 1, self.RiskyShareCount)
+ return self.RshareGrid
+ return []
+
+ def prepareToCalcRiskyShare(self):
+ """
+ Prepare variables used to find optimal portfolio shares. Branches to either
+ the discrete or continuous portfolio choice set.
+ """
+ if self.DiscreteCase:
+ self.prepareToCalcRiskyShareDiscrete()
+ else:
+ self.prepareToCalcRiskyShareContinuous()
+
+ def prepareToCalcRiskyShareContinuous(self):
+ # Hard restriction on aNrm. We'd need to define more elaborate model
+ # specifics if a could become negative (or a positive return shock
+ # would make you worse off!)
+ aNrmPort = self.aXtraGrid[self.aXtraGrid >= 0]
+
+ self.aNrmPort = aNrmPort
+ RshareGrid = self.makeRshareGrid()
+ self.RshareNow = np.array([])
+ vHatP = np.zeros((len(aNrmPort), len(RshareGrid)))
+
+ # Evaluate the non-constant part of the first order conditions wrt the
+ # portfolio share. This requires the implied resources tomorrow given
+ # todays shocks to be evaluated.
+ i_a = 0
+ for a in aNrmPort:
+ # for all possible a's today
+ i_s = 0
+ for s in RshareGrid:
+ Rtilde = self.RiskyShkValsNext - self.Rfree
+ Reff = self.Rfree + Rtilde * s
+ mNext = (
+ a * Reff / (self.PermGroFac * self.PermShkValsNext)
+ + self.TranShkValsNext
+ )
+ vHatP_a_s = (
+ Rtilde
+ * self.PermShkValsNext ** (-self.CRRA)
+ * self.vPfuncNext(mNext)
+ )
+ vHatP[i_a, i_s] = np.dot(vHatP_a_s, self.ShkPrbsNext)
+ i_s += 1
+ i_a += 1
+
+ self.vHatP = vHatP
+
+ def prepareToCalcRiskyShareDiscrete(self):
+ # Hard restriction on aNrm. We'd need to define more elaborate model
+ # specifics if a could become negative (or a positive return shock
+ # would make you worse off!)
+ aNrmPort = self.aXtraGrid[self.aXtraGrid >= 0]
+
+ self.aNrmPort = aNrmPort
+ RshareGrid = self.ShareNow
+ self.RshareNow = np.array([])
+ vHat = np.zeros((len(aNrmPort), len(RshareGrid)))
+
+ # Evaluate the non-constant part of the first order conditions wrt the
+ # portfolio share. This requires the implied resources tomorrow given
+ # todays shocks to be evaluated.
+ i_a = 0
+ for a in aNrmPort:
+ # for all possible a's today
+ i_s = 0
+ for s in RshareGrid:
+ Rtilde = self.RiskyShkValsNext - self.Rfree
+ Reff = self.Rfree + Rtilde * s
+ mNrmNext = (
+ a * Reff / (self.PermGroFac * self.PermShkValsNext)
+ + self.TranShkValsNext
+ )
+
+ VLvlNext = (
+ self.PermShkValsNext ** (1.0 - self.CRRA)
+ * self.PermGroFac ** (1.0 - self.CRRA)
+ ) * self.vFuncNext(mNrmNext)
+ vHat_a_s = self.DiscFacEff * np.sum(VLvlNext * self.ShkPrbsNext, axis=0)
+
+ vHat[i_a, i_s] = vHat_a_s
+ i_s += 1
+ i_a += 1
+
+ self.vHat = vHat
+
+ def calcRiskyShare(self):
+ if self.DiscreteCase:
+ RiskyShareFunc = self.calcRiskyShareDiscrete()
+ else:
+ RiskyShareFunc = self.calcRiskyShareContinuous()
+
+ return RiskyShareFunc
+
+ def calcRiskyShareContinuous(self):
+ # This should be fixed by an insert 0
+ aGrid = np.array([0.0])
+ Rshare = np.array([1.0])
+
+ i_a = 0
+ for a in self.aNrmPort:
+ aGrid = np.append(aGrid, a)
+ if self.vHatP[i_a, -1] >= 0.0:
+ Rshare = np.append(Rshare, 1.0)
+ elif self.vHatP[i_a, 0] < 0.0:
+ Rshare = np.append(Rshare, 0.0)
+ else:
+ residual = LinearInterp(self.RshareGrid, self.vHatP[i_a, :])
+ zero = sciopt.fsolve(residual, Rshare[-1])
+ Rshare = np.append(Rshare, zero)
+ i_a += 1
+ RiskyShareFunc = LinearInterp(
+ aGrid, Rshare, intercept_limit=self.RiskyShareLimit, slope_limit=0
+ ) # HAVE to specify the slope limit
+ return RiskyShareFunc
+
+ def calcRiskyShareDiscrete(self):
+ # Based on the end-of-period value function, we calculate the best
+ # choice today for a range of a values (those given in aNrmPort).
+
+ # Should just use insert below ( at 0)
+ aGrid = np.array([0.0])
+ Rshare = np.array([1.0]) # is it true for AdjustPrb < 1?
+
+ i_a = 0
+ # For all positive aNrms
+ for a in self.aNrmPort:
+ # all values at portfolio shares should be calculated
+ # argmax gives optimal portfolio
+ share_argmax = np.argmax(self.vHat[i_a, :])
+ Rshare = np.append(Rshare, self.ShareNow[share_argmax])
+ i_a += 1
+
+ # TODO FIXME find limiting share for perf foresight
+ RiskyShareFunc = scipy.interpolate.interp1d(
+ np.insert(self.aNrmPort, 0, 0.0),
+ Rshare,
+ kind="zero",
+ bounds_error=False,
+ fill_value=Rshare[-1],
+ )
+ return RiskyShareFunc
+
+ def prepareToCalcEndOfPrdvP(self):
+ """
+ Prepare to calculate end-of-period marginal value by creating an array
+ of market resources that the agent could have next period, considering
+ the grid of end-of-period assets and the distribution of shocks he might
+ experience next period. This method adds extra steps because it first
+ solves the portfolio problem given the end-of-period assets to be able
+ to get next period resources.
+
+ Parameters
+ ----------
+ none
+
+ Returns
+ -------
+ aNrmNow : np.array
+ A 1D array of end-of-period assets; also stored as attribute of self.
+ """
+
+ # We define aNrmNow all the way from BoroCnstNat up to max(self.aXtraGrid)
+ # even if BoroCnstNat < BoroCnstArt, so we can construct the consumption
+ # function as the lower envelope of the (by the artificial borrowing con-
+ # straint) uconstrained consumption function, and the artificially con-
+ # strained consumption function.
+ aNrmNow = np.asarray(self.aXtraGrid)
+ ShkCount = self.TranShkValsNext.size
+ aNrm_temp = np.tile(aNrmNow, (ShkCount, 1))
+
+ # Tile arrays of the income shocks and put them into useful shapes
+ aNrmCount = aNrmNow.shape[0]
+ PermShkVals_temp = (np.tile(self.PermShkValsNext, (aNrmCount, 1))).transpose()
+ TranShkVals_temp = (np.tile(self.TranShkValsNext, (aNrmCount, 1))).transpose()
+ RiskyShkVals_temp = (np.tile(self.RiskyShkValsNext, (aNrmCount, 1))).transpose()
+ ShkPrbs_temp = (np.tile(self.ShkPrbsNext, (aNrmCount, 1))).transpose()
+
+ if self.AdjustCount == 1:
+ mNrmNext = [[]]
+ else:
+ mNrmNext = [[], []]
+
+ for AdjustIndex in range(self.AdjustCount):
+ for ShareIndex in range(self.ShareNowCount[AdjustIndex]):
+ # Calculate share at current aNrm. If non-adjusting, it's just
+ # self.RiskySharePrev, else we use the recently calculated RiskyShareFunc
+ # First generate mNrmNext for adjusters
+ if AdjustIndex == 0: # adjust
+ sAt_aNrm = self.RiskyShareFunc(aNrmNow)
+ # Then generate for non-adjusters
+ else: # non-adjuster
+ sAt_aNrm = self.ShareNow[ShareIndex]
+
+ # Get cash on hand next period.
+ # Compose possible return factors
+ self.Rtilde = RiskyShkVals_temp - self.Rfree
+ # Combine into effective returns factors, taking into account the share
+ self.Reff = self.Rfree + self.Rtilde * sAt_aNrm
+ # Apply the permanent growth factor and possible permanent shocks
+ mNrmPreTran = (
+ self.Reff / (self.PermGroFac * PermShkVals_temp) * aNrm_temp
+ )
+ # Add transitory income
+ mNrmNext[AdjustIndex].append(mNrmPreTran + TranShkVals_temp)
+
+ # Store and report the results
+ self.PermShkVals_temp = PermShkVals_temp
+ self.ShkPrbs_temp = ShkPrbs_temp
+ self.mNrmNext = mNrmNext
+ self.aNrmNow = aNrmNow
+ return aNrmNow
+
+ def calcEndOfPrdvP(self):
+ """
+ Calculate end-of-period marginal value of assets at each point in aNrmNow.
+ Does so by taking a weighted sum of next period marginal values across
+ income shocks (in a preconstructed grid self.mNrmNext).
+
+ Parameters
+ ----------
+ none
+
+ Returns
+ -------
+ EndOfPrdvP : np.array
+ A 1D array of end-of-period marginal value of assets
+ """
+
+ EndOfPrdvP = self.AdjustCount * [[]]
+ for AdjustIndex in range(self.AdjustCount):
+ for ShareIndex in range(self.ShareNowCount[AdjustIndex]):
+ mNrmNext = self.mNrmNext[AdjustIndex][ShareIndex]
+ EndOfPrdvP[AdjustIndex].append(
+ np.sum(
+ self.DiscFacEff
+ * self.Reff
+ * self.PermGroFac ** (-self.CRRA)
+ * self.PermShkVals_temp ** (-self.CRRA)
+ * self.vPfuncNext(mNrmNext)
+ * self.ShkPrbs_temp,
+ axis=0,
+ )
+ )
+
+ return EndOfPrdvP
+
+ def setAndUpdateValues(self, solution_next, IncomeDstn, LivPrb, DiscFac):
+ """
+ Unpacks some of the inputs (and calculates simple objects based on them),
+ storing the results in self for use by other methods. These include:
+ income shocks and probabilities, next period's marginal value function
+ (etc), the probability of getting the worst income shock next period,
+ the patience factor, human wealth, and the bounding MPCs.
+
+ Parameters
+ ----------
+ solution_next : ConsumerSolution
+ The solution to next period's one period problem.
+ IncomeDstn : [np.array]
+ A list containing three arrays of floats, representing a discrete
+ approximation to the income process between the period being solved
+ and the one immediately following (in solution_next). Order: event
+ probabilities, permanent shocks, transitory shocks.
+ LivPrb : float
+ Survival probability; likelihood of being alive at the beginning of
+ the succeeding period.
+ DiscFac : float
+ Intertemporal discount factor for future utility.
+
+ Returns
+ -------
+ None
+ """
+
+ # TODO: this does not yet
+ # calc sUnderbar -> mertonsammuelson
+ # calc MPC kappaUnderbar
+ # calc human wealth
+
+ self.DiscFacEff = DiscFac * LivPrb # "effective" discount factor
+ self.ShkPrbsNext = self.ShockDstn[
+ 0
+ ] # but ConsumtionSolver doesn't store the risky shocks
+ self.PermShkValsNext = self.ShockDstn[
+ 1
+ ] # but ConsumtionSolver doesn't store the risky shocks
+ self.TranShkValsNext = self.ShockDstn[
+ 2
+ ] # but ConsumtionSolver doesn't store the risky shocks
+ self.RiskyShkValsNext = self.ShockDstn[
+ 3
+ ] # but ConsumtionSolver doesn't store the risky shocks
+ self.PermShkMinNext = np.min(self.PermShkValsNext)
+ self.TranShkMinNext = np.min(self.TranShkValsNext)
+ self.vPfuncNext = solution_next.vPfunc
+ self.WorstIncPrb = np.sum(
+ self.ShkPrbsNext[
+ (self.PermShkValsNext * self.TranShkValsNext)
+ == (self.PermShkMinNext * self.TranShkMinNext)
+ ]
+ )
+
+ if self.CubicBool:
+ self.vPPfuncNext = solution_next.vPPfunc
+
+ if self.vFuncBool:
+ self.vFuncNext = solution_next.wFunc
+
+ # Update the bounding MPCs and PDV of human wealth:
+ # self.PatFac = ((self.Rfree*self.DiscFacEff)**(1.0/self.CRRA))/self.Rfree
+ # self.MPCminNow = 1.0/(1.0 + self.PatFac/solution_next.MPCmin)
+ # self.ExIncNext = np.dot(self.ShkPrbsNext,self.TranShkValsNext*self.PermShkValsNext)
+ # self.hNrmNow = self.PermGroFac/self.Rfree*(self.ExIncNext + solution_next.hNrm)
+ # self.MPCmaxNow = 1.0/(1.0 + (self.WorstIncPrb**(1.0/self.CRRA))*
+ # self.PatFac/solution_next.MPCmax)
+
+ def defBoroCnst(self, BoroCnstArt):
+ """
+ Defines the constrained portion of the consumption function as cFuncNowCnst,
+ an attribute of self. Uses the artificial and natural borrowing constraints.
+
+ Parameters
+ ----------
+ BoroCnstArt : float or None
+ Borrowing constraint for the minimum allowable assets to end the
+ period with. If it is less than the natural borrowing constraint,
+ then it is irrelevant; BoroCnstArt=None indicates no artificial bor-
+ rowing constraint.
+
+ Returns
+ -------
+ none
+ """
+ # Calculate the minimum allowable value of money resources in this period
+ self.BoroCnstNat = 0.0 # (self.solution_next.mNrmMin - self.TranShkMinNext)*\
+ # (self.PermGroFac*self.PermShkMinNext)/self.Rfree
+
+ if BoroCnstArt is None:
+ self.mNrmMinNow = self.BoroCnstNat
+ else:
+ self.mNrmMinNow = np.max([self.BoroCnstNat, BoroCnstArt])
+
+ # Can we just put in *some value* for MPCmaxNow here?
+ # if self.BoroCnstNat < self.mNrmMinNow:
+ # self.MPCmaxEff = 1.0 # If actually constrained, MPC near limit is 1
+ # else:
+ # self.MPCmaxEff = self.MPCmaxNow
+
+ # Define the borrowing constraint (limiting consumption function)
+ self.cFuncNowCnst = LinearInterp(np.array([0.0, 1.0]), np.array([0.0, 1.0]))
+
+ def solve(self):
+ """
+ Solves a one period consumption saving problem with risky income and
+ a portfolio choice over a riskless and a risky asset.
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ solution : ConsumerSolution
+ The solution to the one period problem.
+ """
+
+ # TODO FIXME
+ # This code is a mix of looping over states ( for the Risky Share funcs)
+ # and implicit looping such as in prepareToCalcEndOfPrdvP. I think it would
+ # be best to simply have a major loop here, and keep the methods atomic.
+
+ if self.AdjustCount == 1:
+ cFuncs = [[]]
+ vPfuncs = [[]]
+ RiskyShareFuncs = [[]]
+ else:
+ cFuncs = [[], []]
+ vPfuncs = [[], []]
+ RiskyShareFuncs = [[], []]
+
+ for AdjustIndex in range(self.AdjustCount):
+ for PortfolioGridIdx in range(self.ShareNowCount[AdjustIndex]):
+ if self.DiscreteCase:
+ self.vFuncNext = self.vFuncsNext[AdjustIndex][PortfolioGridIdx]
+
+ self.vPfuncNext = self.vPfuncsNext[AdjustIndex][PortfolioGridIdx]
+
+ self.PortfolioGridIdx = PortfolioGridIdx
+
+ # If it can adjust, solve the first sub-problem: the portfolio choice
+ if AdjustIndex == 0:
+ self.prepareToCalcRiskyShare()
+ self.RiskyShareFunc = self.calcRiskyShare()
+ else:
+ val = self.PortfolioGrid[PortfolioGridIdx]
+ self.RiskyShareFunc = scipy.interpolate.interp1d(
+ np.array([0.0, 1.0]),
+ np.repeat(val, 2),
+ kind="zero",
+ bounds_error=False,
+ fill_value=val,
+ )
+
+ RiskyShareFuncs[AdjustIndex].append(self.RiskyShareFunc)
+
+ # Then solve the consumption choice given optimal portfolio choice
+ aNrm = self.prepareToCalcEndOfPrdvP()
+
+ # Seems like this could already have been done above? TODO:Calculate
+ # and save both and compare.
+ EndOfPrdvP = self.calcEndOfPrdvP()
+
+ # Todo!
+ self.cFuncLimitIntercept = None
+ self.cFuncLimitSlope = None
+
+ # Generate all the solutions
+ for AdjustIndex in range(
+ self.AdjustCount
+ ): # stupid name, should be related to adjusting
+ for PortfolioGridIdx in range(self.ShareNowCount[AdjustIndex]):
+ cs_solution = self.makeBasicSolution(
+ EndOfPrdvP[AdjustIndex][PortfolioGridIdx],
+ aNrm,
+ self.makeLinearcFunc,
+ )
+ cFuncs[AdjustIndex].append(cs_solution.cFunc)
+ vPfuncs[AdjustIndex].append(cs_solution.vPfunc)
+ # This is a good place to make it defined at m!!!
+
+ # solution = self.addMPCandHumanWealth(solution)
+ solution = PortfolioSolution(
+ cFunc=cFuncs, vPfunc=vPfuncs, RiskyShareFunc=RiskyShareFuncs
+ )
+
+ if self.vFuncBool:
+ solution = self.addvFunc(solution)
+ solution.wFunc = _calcwFunc(
+ self.AdjustPrb,
+ self.AdjustCount,
+ self.ShareNowCount,
+ solution.vFunc,
+ self.CRRA,
+ )
+
+ return solution
+
+
+# The solveOnePeriod function!
+
+
+def solveConsPortfolio(
+ solution_next,
+ IncomeDstn,
+ LivPrb,
+ DiscFac,
+ CRRA,
+ Rfree,
+ PermGroFac,
+ BoroCnstArt,
+ aXtraGrid,
+ vFuncBool,
+ CubicBool,
+ approxRiskyDstn,
+ RiskyCount,
+ RiskyShareCount,
+ RiskyShareLimitFunc,
+ AdjustPrb,
+ PortfolioGrid,
+ AdjustCount,
+ PortfolioDomain,
+):
+
+ # construct solver instance
+ solver = ConsIndShockPortfolioSolver(
+ solution_next,
+ IncomeDstn,
+ LivPrb,
+ DiscFac,
+ CRRA,
+ Rfree,
+ PermGroFac,
+ BoroCnstArt,
+ aXtraGrid,
+ vFuncBool,
+ CubicBool,
+ approxRiskyDstn,
+ RiskyCount,
+ RiskyShareCount,
+ RiskyShareLimitFunc,
+ AdjustPrb,
+ PortfolioGrid,
+ AdjustCount,
+ PortfolioDomain,
+ )
+
+ # Do some preparatory work
+ solver.prepareToSolve()
+
+ # Solve and return solution
+ portsolution = solver.solve()
+
+ return portsolution
+
+
+class LogNormalPortfolioConsumerType(PortfolioConsumerType):
+ """
+ A consumer type with a portfolio choice. This agent type has log-normal return
+ factors. Their problem is defined by a coefficient of relative risk aversion,
+ intertemporal discount factor, interest factor, and time sequences of the
+ permanent income growth rate, survival probability, and return factor averages
+ and standard deviations.
+ """
+
+ # time_inv_ = PortfolioConsumerType.time_inv_ + ['approxRiskyDstn', 'RiskyCount', 'RiskyShareCount', 'RiskyShareLimitFunc', 'AdjustPrb', 'PortfolioGrid']
+
+ def __init__(self, cycles=1, time_flow=True, verbose=False, quiet=False, **kwds):
+
+ PortfolioConsumerType.__init__(
+ self,
+ cycles=cycles,
+ time_flow=time_flow,
+ verbose=verbose,
+ quiet=quiet,
+ **kwds
+ )
+
+ self.approxRiskyDstn = RiskyDstnFactory(
+ RiskyAvg=self.RiskyAvg, RiskyStd=self.RiskyStd
+ )
+ # Needed to simulate. Is a function that given 0 inputs it returns a draw
+ # from the risky asset distribution. Only one is needed, because everyone
+ # draws the same shock.
+ self.drawRiskyFunc = LogNormalRiskyDstnDraw(
+ RiskyAvg=self.RiskyAvg, RiskyStd=self.RiskyStd
+ )
+
+ self.RiskyShareLimitFunc = lambda _: _PerfForesightLogNormalPortfolioShare(
+ self.Rfree, self.RiskyAvg, self.RiskyStd, self.CRRA
+ )
diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py
index 636c8c3dd..340c3aff2 100644
--- a/HARK/ConsumptionSaving/ConsPrefShockModel.py
+++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py
@@ -15,6 +15,9 @@
from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, ConsIndShockSolver, \
ValueFunc, MargValueFunc, KinkedRconsumerType, ConsKinkedRsolver
from HARK.interpolation import LinearInterpOnInterp1D, LinearInterp, CubicInterp, LowerEnvelope
+import HARK.ConsumptionSaving.ConsumerParameters as Params
+
+__all__ = ['PrefShockConsumerType', 'KinkyPrefConsumerType', 'ConsPrefShockSolver', 'ConsKinkyPrefSolver']
class PrefShockConsumerType(IndShockConsumerType):
'''
@@ -23,7 +26,10 @@ class PrefShockConsumerType(IndShockConsumerType):
'''
shock_vars_ = IndShockConsumerType.shock_vars_ + ['PrefShkNow']
- def __init__(self,cycles=1,time_flow=True,**kwds):
+ def __init__(self,
+ cycles=1,
+ time_flow=True,
+ **kwds):
'''
Instantiate a new ConsumerType with given data, and construct objects
to be used during solution (income distribution, assets grid, etc).
@@ -41,7 +47,14 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
-------
None
'''
- IndShockConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds)
+ params = Params.init_preference_shocks.copy()
+ params.update(kwds)
+ kwds = params
+
+ IndShockConsumerType.__init__(self,
+ cycles=cycles,
+ time_flow=time_flow,
+ **kwds)
self.solveOnePeriod = solveConsPrefShock # Choose correct solver
def preSolve(self):
@@ -206,6 +219,9 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
-------
None
'''
+ params = Params.init_kinky_pref.copy()
+ params.update(kwds)
+ kwds = params
IndShockConsumerType.__init__(self,**kwds)
self.solveOnePeriod = solveConsKinkyPref # Choose correct solver
self.addToTimeInv('Rboro','Rsave')
@@ -596,99 +612,3 @@ def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn,
solver.prepareToSolve()
solution = solver.solve()
return solution
-
-###############################################################################
-
-def main():
- import HARK.ConsumptionSaving.ConsumerParameters as Params
- import matplotlib.pyplot as plt
- from HARK.utilities import plotFuncs
- from time import clock
- mystr = lambda number : "{:.4f}".format(number)
-
- do_simulation = True
-
- # Make and solve a preference shock consumer
- PrefShockExample = PrefShockConsumerType(**Params.init_preference_shocks)
- PrefShockExample.cycles = 0 # Infinite horizon
-
- t_start = clock()
- PrefShockExample.solve()
- t_end = clock()
- print('Solving a preference shock consumer took ' + str(t_end-t_start) + ' seconds.')
-
- # Plot the consumption function at each discrete shock
- m = np.linspace(PrefShockExample.solution[0].mNrmMin,5,200)
- print('Consumption functions at each discrete shock:')
- for j in range(PrefShockExample.PrefShkDstn[0][1].size):
- PrefShk = PrefShockExample.PrefShkDstn[0][1][j]
- c = PrefShockExample.solution[0].cFunc(m,PrefShk*np.ones_like(m))
- plt.plot(m,c)
- plt.xlim([0.,None])
- plt.ylim([0.,None])
- plt.show()
-
- print('Consumption function (and MPC) when shock=1:')
- c = PrefShockExample.solution[0].cFunc(m,np.ones_like(m))
- k = PrefShockExample.solution[0].cFunc.derivativeX(m,np.ones_like(m))
- plt.plot(m,c)
- plt.plot(m,k)
- plt.xlim([0.,None])
- plt.ylim([0.,None])
- plt.show()
-
- if PrefShockExample.vFuncBool:
- print('Value function (unconditional on shock):')
- plotFuncs(PrefShockExample.solution[0].vFunc,PrefShockExample.solution[0].mNrmMin+0.5,5)
-
- # Test the simulator for the pref shock class
- if do_simulation:
- PrefShockExample.T_sim = 120
- PrefShockExample.track_vars = ['cNrmNow']
- PrefShockExample.makeShockHistory() # This is optional
- PrefShockExample.initializeSim()
- PrefShockExample.simulate()
-
- ###########################################################################
-
- # Make and solve a "kinky preferece" consumer, whose model combines KinkedR and PrefShock
- KinkyPrefExample = KinkyPrefConsumerType(**Params.init_kinky_pref)
- KinkyPrefExample.cycles = 0 # Infinite horizon
-
- t_start = clock()
- KinkyPrefExample.solve()
- t_end = clock()
- print('Solving a kinky preference consumer took ' + str(t_end-t_start) + ' seconds.')
-
- # Plot the consumption function at each discrete shock
- m = np.linspace(KinkyPrefExample.solution[0].mNrmMin,5,200)
- print('Consumption functions at each discrete shock:')
- for j in range(KinkyPrefExample.PrefShkDstn[0][1].size):
- PrefShk = KinkyPrefExample.PrefShkDstn[0][1][j]
- c = KinkyPrefExample.solution[0].cFunc(m,PrefShk*np.ones_like(m))
- plt.plot(m,c)
- plt.ylim([0.,None])
- plt.show()
-
- print('Consumption function (and MPC) when shock=1:')
- c = KinkyPrefExample.solution[0].cFunc(m,np.ones_like(m))
- k = KinkyPrefExample.solution[0].cFunc.derivativeX(m,np.ones_like(m))
- plt.plot(m,c)
- plt.plot(m,k)
- plt.ylim([0.,None])
- plt.show()
-
- if KinkyPrefExample.vFuncBool:
- print('Value function (unconditional on shock):')
- plotFuncs(KinkyPrefExample.solution[0].vFunc,KinkyPrefExample.solution[0].mNrmMin+0.5,5)
-
- # Test the simulator for the kinky preference class
- if do_simulation:
- KinkyPrefExample.T_sim = 120
- KinkyPrefExample.track_vars = ['cNrmNow','PrefShkNow']
- KinkyPrefExample.initializeSim()
- KinkyPrefExample.simulate()
-
-
-if __name__ == '__main__':
- main()
diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py
index 1f58c2c3b..b0b755ccb 100644
--- a/HARK/ConsumptionSaving/ConsRepAgentModel.py
+++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py
@@ -13,6 +13,8 @@
from HARK.simulation import drawUniform, drawDiscrete
from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, MargValueFunc
+__all__ = ['RepAgentConsumerType', 'RepAgentMarkovConsumerType']
+
def solveConsRepAgent(solution_next,DiscFac,CRRA,IncomeDstn,CapShare,DeprFac,PermGroFac,aXtraGrid):
'''
Solve one period of the simple representative agent consumption-saving model.
@@ -330,61 +332,3 @@ def getControls(self):
t = self.t_cycle[0]
i = self.MrkvNow[0]
self.cNrmNow = self.solution[t].cFunc[i](self.mNrmNow)
-
-
-###############################################################################
-def main():
- from copy import deepcopy
- from time import clock
- from HARK.utilities import plotFuncs
- import HARK.ConsumptionSaving.ConsumerParameters as Params
-
- # Make a quick example dictionary
- RA_params = deepcopy(Params.init_idiosyncratic_shocks)
- RA_params['DeprFac'] = 0.05
- RA_params['CapShare'] = 0.36
- RA_params['UnempPrb'] = 0.0
- RA_params['LivPrb'] = [1.0]
-
- # Make and solve a rep agent model
- RAexample = RepAgentConsumerType(**RA_params)
- t_start = clock()
- RAexample.solve()
- t_end = clock()
- print('Solving a representative agent problem took ' + str(t_end-t_start) + ' seconds.')
- plotFuncs(RAexample.solution[0].cFunc,0,20)
-
- # Simulate the representative agent model
- RAexample.T_sim = 2000
- RAexample.track_vars = ['cNrmNow','mNrmNow','Rfree','wRte']
- RAexample.initializeSim()
- t_start = clock()
- RAexample.simulate()
- t_end = clock()
- print('Simulating a representative agent for ' + str(RAexample.T_sim) + ' periods took ' + str(t_end-t_start) + ' seconds.')
-
- # Make and solve a Markov representative agent
- RA_markov_params = deepcopy(RA_params)
- RA_markov_params['PermGroFac'] = [[0.97,1.03]]
- RA_markov_params['MrkvArray'] = np.array([[0.99,0.01],[0.01,0.99]])
- RA_markov_params['MrkvNow'] = 0
- RAmarkovExample = RepAgentMarkovConsumerType(**RA_markov_params)
- RAmarkovExample.IncomeDstn[0] = 2*[RAmarkovExample.IncomeDstn[0]]
- t_start = clock()
- RAmarkovExample.solve()
- t_end = clock()
- print('Solving a two state representative agent problem took ' + str(t_end-t_start) + ' seconds.')
- plotFuncs(RAmarkovExample.solution[0].cFunc,0,10)
-
- # Simulate the two state representative agent model
- RAmarkovExample.T_sim = 2000
- RAmarkovExample.track_vars = ['cNrmNow','mNrmNow','Rfree','wRte','MrkvNow']
- RAmarkovExample.initializeSim()
- t_start = clock()
- RAmarkovExample.simulate()
- t_end = clock()
- print('Simulating a two state representative agent for ' + str(RAexample.T_sim) + ' periods took ' + str(t_end-t_start) + ' seconds.')
-
-if __name__ == '__main__':
- main()
-
diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py
index dbeca20e0..fed4c2bd5 100644
--- a/HARK/ConsumptionSaving/ConsumerParameters.py
+++ b/HARK/ConsumptionSaving/ConsumerParameters.py
@@ -16,6 +16,8 @@
DiscFac = 0.96 # Intertemporal discount factor
LivPrb = [0.98] # Survival probability
PermGroFac = [1.01] # Permanent income growth factor
+BoroCnstArt = None # Artificial borrowing constraint
+MaxKinks = 400 # Maximum number of grid points to allow in cFunc (should be large)
AgentCount = 10000 # Number of agents of this type (only matters for simulation)
aNrmInitMean = 0.0 # Mean of log initial assets (only matters for simulation)
aNrmInitStd = 1.0 # Standard deviation of log initial assets (only for simulation)
@@ -31,6 +33,8 @@
'DiscFac': DiscFac,
'LivPrb': LivPrb,
'PermGroFac': PermGroFac,
+ 'BoroCnstArt': BoroCnstArt,
+ 'MaxKinks': MaxKinks,
'AgentCount': AgentCount,
'aNrmInitMean' : aNrmInitMean,
'aNrmInitStd' : aNrmInitStd,
@@ -135,7 +139,7 @@
init_kinked_R['Rboro'] = Rboro
init_kinked_R['Rsave'] = Rsave
init_kinked_R['BoroCnstArt'] = None # kinked R is a bit silly if borrowing not allowed
-init_kinked_R['CubicBool'] = False # kinked R currently only compatible with linear cFunc
+init_kinked_R['CubicBool'] = True # kinked R is now compatible with linear cFunc and cubic cFunc
init_kinked_R['aXtraCount'] = 48 # ...so need lots of extra gridpoints to make up for it
@@ -207,7 +211,6 @@
'CRRA': CRRAPF,
'PermGroFacAgg': PermGroFacAgg,
'AggregateL':1.0,
- 'act_T':1200,
'intercept_prev': intercept_prev,
'slope_prev': slope_prev,
'verbose': verbose_cobb_douglas,
@@ -246,13 +249,11 @@
pLvlPctiles = np.concatenate(([0.001, 0.005, 0.01, 0.03], np.linspace(0.05, 0.95, num=19),[0.97, 0.99, 0.995, 0.999]))
PrstIncCorr = 0.98 # Serial correlation coefficient for permanent income
-cycles = 0
# Make a dictionary for the "explicit permanent income" idiosyncratic shocks model
init_explicit_perm_inc = copy(init_idiosyncratic_shocks)
init_explicit_perm_inc['pLvlPctiles'] = pLvlPctiles
init_explicit_perm_inc['PermGroFac'] = [1.0] # long run permanent income growth doesn't work yet
-init_explicit_perm_inc['cycles'] = cycles
init_explicit_perm_inc['aXtraMax'] = 30
init_explicit_perm_inc['aXtraExtra'] = [0.005,0.01]
@@ -280,3 +281,78 @@
init_medical_shocks['MedShkCountTail'] = MedShkCountTail
init_medical_shocks['MedPrice'] = MedPrice
init_medical_shocks['aXtraCount'] = 32
+
+# -----------------------------------------------------------------------------
+# -------- Define additional parameters for the baby labor model --------------
+# -----------------------------------------------------------------------------
+
+LbrDisutilCoeffs = [-1.5,0.1,0.05] # Constant, linear, and quadratic coefficients
+ # on transformed labor disutility factor by age
+
+# Make a dictionary for "baby labor" model
+init_baby_labor = copy(init_lifecycle)
+init_baby_labor['LbrDisutilCoeffs'] = LbrDisutilCoeffs
+init_baby_labor['TranShkCount'] = 1 # No transitory shocks in baby labor model
+init_baby_labor['TranShkStd'] = [0.0]*init_lifecycle['T_cycle']
+init_baby_labor['T_retire'] = 0 # turn off retirement
+init_baby_labor['PermShkStd'] = [0.1]*init_lifecycle['T_cycle']
+init_baby_labor['UnempPrb'] = 0.0 # turn off unemployment
+init_baby_labor['UnempPrbRet'] = 0.0
+init_baby_labor['PermShkCount'] = 25 # Crank up permanent shock count
+init_baby_labor['aXtraCount'] = 200 # Might be important to have many gridpoints
+
+
+# -----------------------------------------------------------------------------
+# ----Define additional parameters for the Endogenous Labor Supply model-------
+# -----------------------------------------------------------------------------
+
+LivPrb = [0.99] # Survival probability
+PermGroFac = [1.02] # Permanent income growth factor
+LbrCostCoeffs = [-1.0] # Labor cost coefficents list of one element
+WageRte = [1.0] # Wage Rate
+
+BoroCnstArt = None
+CubicBool = False # Use cubic spline interpolation when True, linear interpolation when False
+vFuncBool = False # Whether to calculate the value function during solution
+
+StateMin = 0.001
+StateMax = 20.0
+StateCount = 15
+ExponentialGrid = True
+
+# Make a dictionary for Endogenous Labor supply model - intensive margin
+init_labor_intensive = copy(init_lifecycle)
+init_labor_intensive['TranShkStd'] = [0.1]*init_lifecycle['T_cycle']
+init_labor_intensive['PermShkStd'] = [0.1]*init_lifecycle['T_cycle']
+init_labor_intensive['IncUnemp'] = 0.0
+init_labor_intensive['UnempPrb'] = 0.05
+init_labor_intensive['UnempPrbRet'] = 0.0
+init_labor_intensive['TranShkCount'] = 15 # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks
+init_labor_intensive['PermShkCount'] = 16 # Crank up permanent shock count
+init_labor_intensive['BoroCnstArt'] = BoroCnstArt
+init_labor_intensive['CubicBool'] = CubicBool
+init_labor_intensive['vFuncBool'] = vFuncBool
+init_labor_intensive ['T_retire'] = 0 # Turn off retirement
+init_labor_intensive ['aXtraCount'] = 200 # May be important to have a larger number of gridpoints (than 48 initially)
+init_labor_intensive ['aXtraMax'] = 80.
+init_labor_intensive ['LbrCostCoeffs'] = LbrCostCoeffs
+init_labor_intensive ['WageRte'] = WageRte
+init_labor_intensive ['LivPrb'] = LivPrb
+init_labor_intensive ['PermGroFac'] = PermGroFac
+init_labor_intensive ['StateMin'] = StateMin
+init_labor_intensive ['StateMax'] = StateMax
+init_labor_intensive ['StateCount'] = StateCount
+init_labor_intensive ['ExponentialGrid'] = ExponentialGrid
+init_labor_intensive ['T_cycle'] = 1
+
+# Make a dictionary for Endogenous Labor supply model with finite lifecycle
+init_labor_lifecycle = copy(init_labor_intensive)
+init_labor_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02]
+init_labor_lifecycle['PermShkStd'] = [0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1]
+init_labor_lifecycle['TranShkStd'] = [0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1]
+init_labor_lifecycle['LivPrb'] = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] # Living probability decreases as time moves forward.
+init_labor_lifecycle['WageRte'] = [1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0] # Wage rate in a lifecycle
+init_labor_lifecycle['LbrCostCoeffs'] = [-2.0, 0.4] # Assume labor cost coeffs is a polynomial of degree 1
+init_labor_lifecycle['T_cycle'] = 10
+#init_labor_lifecycle['T_retire'] = 7 # IndexError at line 774 in interpolation.py.
+init_labor_lifecycle['T_age'] = 11 # Make sure that old people die at terminal age and don't turn into newborns!
\ No newline at end of file
diff --git a/HARK/ConsumptionSaving/RepAgentModel.py b/HARK/ConsumptionSaving/RepAgentModel.py
deleted file mode 100644
index 6dbae740a..000000000
--- a/HARK/ConsumptionSaving/RepAgentModel.py
+++ /dev/null
@@ -1,11 +0,0 @@
-'''
-This file appears to be an old version of what is now ConsRepAgentModel.py.
-Its previous contents have been entirely removed and replaced with a universal
-import from ConsRepAgentModel. Whenever a user imports from this file, they
-will get a warning that they should import from ConsRepAgentModel instead.
-'''
-
-import warnings
-from HARK.ConsumptionSaving.ConsRepAgentModel import *
-
-warnings.warn('Please import from ConsRepAgentModel rather than RepAgentModel. This module will be removed in a future version of HARK.')
\ No newline at end of file
diff --git a/HARK/ConsumptionSaving/TractableBufferStockModel.py b/HARK/ConsumptionSaving/TractableBufferStockModel.py
index 057b4c3d5..f47425fe2 100644
--- a/HARK/ConsumptionSaving/TractableBufferStockModel.py
+++ b/HARK/ConsumptionSaving/TractableBufferStockModel.py
@@ -34,6 +34,8 @@
from copy import copy
from scipy.optimize import newton, brentq
+__all__ = ['TractableConsumerSolution', 'TractableConsumerType']
+
# If you want to run the "tractable" version of cstwMPC, uncomment the line below
# and have TractableConsumerType inherit from cstwMPCagent rather than AgentType
#from HARK.cstwMPC.cstwMPC import cstwMPCagent
@@ -465,104 +467,3 @@ def getPostStates(self):
'''
self.aLvlNow = self.mLvlNow - self.cLvlNow
return None
-
-
-###############################################################################
-
-def main():
- # Import the HARK library. The assumption is that this code is in a folder
- # contained in the HARK folder. Also import the ConsumptionSavingModel
- import numpy as np # numeric Python
- from HARK.utilities import plotFuncs # basic plotting tools
- from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType # An alternative, much longer way to solve the TBS model
- from time import clock # timing utility
-
- do_simulation = True
-
- # Define the model primitives
- base_primitives = {'UnempPrb' : .00625, # Probability of becoming unemployed
- 'DiscFac' : 0.975, # Intertemporal discount factor
- 'Rfree' : 1.01, # Risk-free interest factor on assets
- 'PermGroFac' : 1.0025, # Permanent income growth factor (uncompensated)
- 'CRRA' : 1.0} # Coefficient of relative risk aversion
-
- # Define a dictionary to be used in case of simulation
- simulation_values = {'aLvlInitMean' : 0.0, # Mean of log initial assets for new agents
- 'aLvlInitStd' : 1.0, # Stdev of log initial assets for new agents
- 'AgentCount' : 10000, # Number of agents to simulate
- 'T_sim' : 120, # Number of periods to simulate
- 'T_cycle' : 1} # Number of periods in the cycle
-
- # Make and solve a tractable consumer type
- ExampleType = TractableConsumerType(**base_primitives)
- t_start = clock()
- ExampleType.solve()
- t_end = clock()
- print('Solving a tractable consumption-savings model took ' + str(t_end-t_start) + ' seconds.')
-
- # Plot the consumption function and whatnot
- m_upper = 1.5*ExampleType.mTarg
- conFunc_PF = lambda m: ExampleType.h*ExampleType.PFMPC + ExampleType.PFMPC*m
- #plotFuncs([ExampleType.solution[0].cFunc,ExampleType.mSSfunc,ExampleType.cSSfunc],0,m_upper)
- plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper)
-
- if do_simulation:
- ExampleType(**simulation_values) # Set attributes needed for simulation
- ExampleType.track_vars = ['mLvlNow']
- ExampleType.makeShockHistory()
- ExampleType.initializeSim()
- ExampleType.simulate()
-
-
- # Now solve the same model using backward induction rather than the analytic method of TBS.
- # The TBS model is equivalent to a Markov model with two states, one of them absorbing (permanent unemployment).
- MrkvArray = np.array([[1.0-base_primitives['UnempPrb'],base_primitives['UnempPrb']],[0.0,1.0]]) # Define the two state, absorbing unemployment Markov array
- init_consumer_objects = {"CRRA":base_primitives['CRRA'],
- "Rfree":np.array(2*[base_primitives['Rfree']]), # Interest factor (same in both states)
- "PermGroFac":[np.array(2*[base_primitives['PermGroFac']/(1.0-base_primitives['UnempPrb'])])], # Unemployment-compensated permanent growth factor
- "BoroCnstArt":None, # Artificial borrowing constraint
- "PermShkStd":[0.0], # Permanent shock standard deviation
- "PermShkCount":1, # Number of shocks in discrete permanent shock distribution
- "TranShkStd":[0.0], # Transitory shock standard deviation
- "TranShkCount":1, # Number of shocks in discrete permanent shock distribution
- "T_cycle":1, # Number of periods in cycle
- "UnempPrb":0.0, # Unemployment probability (not used, as the unemployment here is *permanent*, not transitory)
- "UnempPrbRet":0.0, # Unemployment probability when retired (irrelevant here)
- "T_retire":0, # Age at retirement (turned off)
- "IncUnemp":0.0, # Income when unemployed (irrelevant)
- "IncUnempRet":0.0, # Income when unemployed and retired (irrelevant)
- "aXtraMin":0.001, # Minimum value of assets above minimum in grid
- "aXtraMax":ExampleType.mUpperBnd, # Maximum value of assets above minimum in grid
- "aXtraCount":48, # Number of points in assets grid
- "aXtraExtra":[None], # Additional points to include in assets grid
- "aXtraNestFac":3, # Degree of exponential nesting when constructing assets grid
- "LivPrb":[np.array([1.0,1.0])], # Survival probability
- "DiscFac":base_primitives['DiscFac'], # Intertemporal discount factor
- 'AgentCount':1, # Number of agents in a simulation (irrelevant)
- 'tax_rate':0.0, # Tax rate on labor income (irrelevant)
- 'vFuncBool':False, # Whether to calculate the value function
- 'CubicBool':True, # Whether to use cubic splines (False --> linear splines)
- 'MrkvArray':[MrkvArray] # State transition probabilities
- }
- MarkovType = MarkovConsumerType(**init_consumer_objects) # Make a basic consumer type
- employed_income_dist = [np.ones(1),np.ones(1),np.ones(1)] # Income distribution when employed
- unemployed_income_dist = [np.ones(1),np.ones(1),np.zeros(1)] # Income distribution when permanently unemployed
- MarkovType.IncomeDstn = [[employed_income_dist,unemployed_income_dist]] # set the income distribution in each state
- MarkovType.cycles = 0
-
- # Solve the "Markov TBS" model
- t_start = clock()
- MarkovType.solve()
- t_end = clock()
- MarkovType.unpackcFunc()
-
- print('Solving the same model "the long way" took ' + str(t_end-t_start) + ' seconds.')
- #plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper)
- plotFuncs(MarkovType.cFunc[0],0,m_upper)
- diffFunc = lambda m : ExampleType.solution[0].cFunc(m) - MarkovType.cFunc[0][0](m)
- print('Difference between the (employed) consumption functions:')
- plotFuncs(diffFunc,0,m_upper)
-
-if __name__ == '__main__':
- main()
-
diff --git a/HARK/ConsumptionSaving/__init__.py b/HARK/ConsumptionSaving/__init__.py
index e69de29bb..44b489c35 100644
--- a/HARK/ConsumptionSaving/__init__.py
+++ b/HARK/ConsumptionSaving/__init__.py
@@ -0,0 +1,10 @@
+# from HARK.ConsumptionSaving.ConsumerParameters import *
+# from HARK.ConsumptionSaving.ConsAggShockModel import *
+# from HARK.ConsumptionSaving.ConsGenIncProcessModel import *
+# from HARK.ConsumptionSaving.ConsIndShockModel import *
+# from HARK.ConsumptionSaving.ConsMarkovModel import *
+# from HARK.ConsumptionSaving.ConsMedModel import *
+# from HARK.ConsumptionSaving.ConsPortfolioModel import *
+# from HARK.ConsumptionSaving.ConsPrefShockModel import *
+# from HARK.ConsumptionSaving.ConsRepAgentModel import *
+# from HARK.ConsumptionSaving.TractableBufferStockModel import *
diff --git a/HARK/FashionVictim/__init__.py b/HARK/ConsumptionSaving/tests/__init__.py
similarity index 100%
rename from HARK/FashionVictim/__init__.py
rename to HARK/ConsumptionSaving/tests/__init__.py
diff --git a/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py b/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py
new file mode 100644
index 000000000..6de68dfd3
--- /dev/null
+++ b/HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py
@@ -0,0 +1,70 @@
+import numpy as np
+from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
+import HARK.ConsumptionSaving.ConsumerParameters as Params
+from copy import copy
+import unittest
+
+class test_ConsMarkovSolver(unittest.TestCase):
+ def setUp(self):
+
+ # Define the Markov transition matrix for serially correlated unemployment
+ unemp_length = 5 # Averange length of unemployment spell
+ urate_good = 0.05 # Unemployment rate when economy is in good state
+ urate_bad = 0.12 # Unemployment rate when economy is in bad state
+ bust_prob = 0.01 # Probability of economy switching from good to bad
+ recession_length = 20 # Averange length of bad state
+ p_reemploy = 1.0 / unemp_length
+ p_unemploy_good = p_reemploy * urate_good / (1 - urate_good)
+ p_unemploy_bad = p_reemploy * urate_bad / (1 - urate_bad)
+ boom_prob = 1.0 / recession_length
+ self.MrkvArray = np.array(
+ [
+ [
+ (1 - p_unemploy_good) * (1 - bust_prob),
+ p_unemploy_good * (1 - bust_prob),
+ (1 - p_unemploy_good) * bust_prob,
+ p_unemploy_good * bust_prob,
+ ],
+ [
+ p_reemploy * (1 - bust_prob),
+ (1 - p_reemploy) * (1 - bust_prob),
+ p_reemploy * bust_prob,
+ (1 - p_reemploy) * bust_prob,
+ ],
+ [
+ (1 - p_unemploy_bad) * boom_prob,
+ p_unemploy_bad * boom_prob,
+ (1 - p_unemploy_bad) * (1 - boom_prob),
+ p_unemploy_bad * (1 - boom_prob),
+ ],
+ [
+ p_reemploy * boom_prob,
+ (1 - p_reemploy) * boom_prob,
+ p_reemploy * (1 - boom_prob),
+ (1 - p_reemploy) * (1 - boom_prob),
+ ],
+ ]
+ )
+
+ init_serial_unemployment = copy(Params.init_idiosyncratic_shocks)
+ init_serial_unemployment["MrkvArray"] = [self.MrkvArray]
+ self.model = MarkovConsumerType(**init_serial_unemployment)
+
+ def test_checkMarkovInputs(self):
+ # check Rfree
+ self.assertRaises(ValueError, self.model.checkMarkovInputs)
+ # fix Rfree
+ self.model.Rfree = np.array(4 * [self.model.Rfree])
+ # check MrkvArray, first mess up the setup
+ self.model.MrkvArray = np.random.rand(3, 3)
+ self.assertRaises(ValueError, self.model.checkMarkovInputs)
+ # then fix it back
+ self.model.MrkvArray = self.MrkvArray
+ # check LivPrb
+ self.assertRaises(ValueError, self.model.checkMarkovInputs)
+ # fix LivPrb
+ self.model.LivPrb = [np.array(4 * self.model.LivPrb)]
+ # check PermGroFac
+ self.assertRaises(ValueError, self.model.checkMarkovInputs)
+ # fix PermGroFac
+ self.model.PermGroFac = [np.array(4 * self.model.PermGroFac)]
diff --git a/HARK/ConsumptionSaving/tests/test_ConsPortfolioModel.py b/HARK/ConsumptionSaving/tests/test_ConsPortfolioModel.py
new file mode 100644
index 000000000..456f9ed2d
--- /dev/null
+++ b/HARK/ConsumptionSaving/tests/test_ConsPortfolioModel.py
@@ -0,0 +1,47 @@
+import copy
+import HARK.ConsumptionSaving.ConsPortfolioModel as cpm
+import HARK.ConsumptionSaving.ConsumerParameters as param
+import unittest
+
+class testPortfolioConsumerType(unittest.TestCase):
+
+ def setUp(self):
+
+ # Parameters from Mehra and Prescott (1985):
+ Avg = 1.08 # equity premium
+ Std = 0.20 # standard deviation of rate-of-return shocks
+
+ RiskyDstnFunc = cpm.RiskyDstnFactory(RiskyAvg=Avg, RiskyStd=Std) # Generates nodes for integration
+ RiskyDrawFunc = cpm.LogNormalRiskyDstnDraw(RiskyAvg=Avg, RiskyStd=Std) # Function to generate draws from a lognormal distribution
+
+ init_portfolio = copy.copy(param.init_idiosyncratic_shocks) # Default parameter values for inf horiz model - including labor income with transitory and permanent shocks
+ init_portfolio['approxRiskyDstn'] = RiskyDstnFunc
+ init_portfolio['drawRiskyFunc'] = RiskyDrawFunc
+ init_portfolio['RiskyCount'] = 2 # Number of points in the approximation; 2 points is minimum
+ init_portfolio['RiskyShareCount'] = 25 # How many discrete points to allow for portfolio share
+ init_portfolio['Rfree'] = 1.0 # Riskfree return factor (interest rate is zero)
+ init_portfolio['CRRA'] = 6.0 # Relative risk aversion
+
+ # Uninteresting technical parameters:
+ init_portfolio['aXtraMax'] = 100
+ init_portfolio['aXtraCount'] = 50
+ init_portfolio['BoroCnstArt'] = 0.0 # important for theoretical reasons
+ init_portfolio['DiscFac'] = 0.92 # Make them impatient even wrt a riskfree return of 1.08
+
+ # Create portfolio choice consumer type
+ self.pcct = cpm.PortfolioConsumerType(**init_portfolio)
+
+ # %% {"code_folding": []}
+ # Solve the model under the given parameters
+
+ self.pcct.solve()
+
+ def test_RiskyShareFunc(self):
+
+ self.assertAlmostEqual(
+ self.pcct.solution[0].RiskyShareFunc[0][0](2).tolist(),
+ 0.8796982720076255)
+
+ self.assertAlmostEqual(
+ self.pcct.solution[0].RiskyShareFunc[0][0](8).tolist(),
+ 0.69738175)
diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
new file mode 100644
index 000000000..1e58fb27e
--- /dev/null
+++ b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
@@ -0,0 +1,118 @@
+from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType
+import HARK.ConsumptionSaving.ConsumerParameters as Params
+import numpy as np
+import unittest
+
+class testIndShockConsumerType(unittest.TestCase):
+
+ def test_getShocks(self):
+ agent = IndShockConsumerType(
+ AgentCount = 2,
+ T_sim = 10
+ )
+
+ agent.solve()
+
+ agent.initializeSim()
+ agent.simBirth(np.array([True,False]))
+ agent.simOnePeriod()
+ agent.simBirth(np.array([False,True]))
+
+ agent.getShocks()
+
+ self.assertEqual(agent.PermShkNow[0],
+ 1.0050166461586711)
+ self.assertEqual(agent.PermShkNow[1],
+ 1.0050166461586711)
+ self.assertEqual(agent.TranShkNow[0],
+ 1.1176912196531754)
+
+class testBufferStock(unittest.TestCase):
+ """ Tests of the results of the BufferStock REMARK.
+ """
+
+ def setUp(self):
+ # Make a dictionary containing all parameters needed to solve the model
+ self.base_params = Params.init_idiosyncratic_shocks
+
+ # Set the parameters for the baseline results in the paper
+ # using the variable values defined in the cell above
+ self.base_params['PermGroFac'] = [1.03]
+ self.base_params['Rfree'] = 1.04
+ self.base_params['DiscFac'] = 0.96
+ self.base_params['CRRA'] = 2.00
+ self.base_params['UnempPrb'] = 0.005
+ self.base_params['IncUnemp'] = 0.0
+ self.base_params['PermShkStd'] = [0.1]
+ self.base_params['TranShkStd'] = [0.1]
+ self.base_params['LivPrb'] = [1.0]
+ self.base_params['CubicBool'] = True
+ self.base_params['T_cycle'] = 1
+ self.base_params['BoroCnstArt'] = None
+
+ def test_baseEx(self):
+ baseEx = IndShockConsumerType(**self.base_params)
+ baseEx.cycles = 100 # Make this type have a finite horizon (Set T = 100)
+
+ baseEx.solve()
+ baseEx.unpackcFunc()
+
+ m = np.linspace(0,9.5,1000)
+
+ c_m = baseEx.cFunc[0](m)
+ c_t1 = baseEx.cFunc[-2](m)
+ c_t5 = baseEx.cFunc[-6](m)
+ c_t10 = baseEx.cFunc[-11](m)
+
+ self.assertAlmostEqual(c_m[500], 1.4008090582203356)
+ self.assertAlmostEqual(c_t1[500], 2.9227437159255216)
+ self.assertAlmostEqual(c_t5[500], 1.7350607327187664)
+ self.assertAlmostEqual(c_t10[500], 1.4991390649979213)
+ self.assertAlmostEqual(c_t10[600], 1.6101476268581576)
+ self.assertAlmostEqual(c_t10[700], 1.7196531041366991)
+
+ def test_GICFails(self):
+ GIC_fail_dictionary = dict(self.base_params)
+ GIC_fail_dictionary['Rfree'] = 1.08
+ GIC_fail_dictionary['PermGroFac'] = [1.00]
+
+ GICFailExample = IndShockConsumerType(
+ cycles=0, # cycles=0 makes this an infinite horizon consumer
+ **GIC_fail_dictionary)
+
+ GICFailExample.solve()
+ GICFailExample.unpackcFunc()
+ m = np.linspace(0,5,1000)
+ c_m = GICFailExample.cFunc[0](m)
+
+ self.assertAlmostEqual(c_m[500], 0.7772637042393458)
+ self.assertAlmostEqual(c_m[700], 0.8392649061916746)
+
+ self.assertFalse(GICFailExample.GICPF)
+
+ def test_infinite_horizon(self):
+ baseEx_inf = IndShockConsumerType(cycles=0,
+ **self.base_params)
+
+ baseEx_inf.solve()
+ baseEx_inf.unpackcFunc()
+
+ m1 = np.linspace(1,baseEx_inf.solution[0].mNrmSS,50) # m1 defines the plot range on the left of target m value (e.g. m <= target m)
+ c_m1 = baseEx_inf.cFunc[0](m1)
+
+ self.assertAlmostEqual(c_m1[0], 0.8527887545025995)
+ self.assertAlmostEqual(c_m1[-1], 1.0036279936408656)
+
+ x1 = np.linspace(0,25,1000)
+ cfunc_m = baseEx_inf.cFunc[0](x1)
+
+ self.assertAlmostEqual(cfunc_m[500], 1.8902146173138235)
+ self.assertAlmostEqual(cfunc_m[700], 2.1591451850267176)
+
+ m = np.linspace(0.001,8,1000)
+
+ # Use the HARK method derivative to get the derivative of cFunc, and the values are just the MPC
+ MPC = baseEx_inf.cFunc[0].derivative(m)
+
+ self.assertAlmostEqual(MPC[500], 0.08415000641504392)
+ self.assertAlmostEqual(MPC[700], 0.07173144137912524)
diff --git a/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py
new file mode 100644
index 000000000..0d4ae1d33
--- /dev/null
+++ b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py
@@ -0,0 +1,77 @@
+from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType
+import numpy as np
+import unittest
+
+class testPerfForesightConsumerType(unittest.TestCase):
+
+ def setUp(self):
+ self.agent = PerfForesightConsumerType()
+ self.agent_infinite = PerfForesightConsumerType(cycles=0)
+
+ def test_default_solution(self):
+ self.agent.solve()
+ c = self.agent.solution[0].cFunc
+
+ self.assertEqual(c.x_list[0], -0.9805825242718447)
+ self.assertEqual(c.x_list[1], 0.01941747572815533)
+ self.assertEqual(c.y_list[0], 0)
+ self.assertEqual(c.y_list[1], 0.511321002804608)
+ self.assertEqual(c.decay_extrap, False)
+
+ def test_checkConditions(self):
+ self.agent_infinite.checkConditions()
+
+ self.assertTrue(self.agent_infinite.AIC)
+ self.assertTrue(self.agent_infinite.GICPF)
+ self.assertTrue(self.agent_infinite.RIC)
+ self.assertTrue(self.agent_infinite.FHWC)
+
+ def test_simulation(self):
+
+ self.agent_infinite.solve()
+
+ # Create parameter values necessary for simulation
+ SimulationParams = {
+ "AgentCount" : 10000, # Number of agents of this type
+ "T_sim" : 120, # Number of periods to simulate
+ "aNrmInitMean" : -6.0, # Mean of log initial assets
+ "aNrmInitStd" : 1.0, # Standard deviation of log initial assets
+ "pLvlInitMean" : 0.0, # Mean of log initial permanent income
+ "pLvlInitStd" : 0.0, # Standard deviation of log initial permanent income
+ "PermGroFacAgg" : 1.0, # Aggregate permanent income growth factor
+ "T_age" : None, # Age after which simulated agents are automatically killed
+ }
+
+ self.agent_infinite(**SimulationParams) # This implicitly uses the assignParameters method of AgentType
+
+ # Create PFexample object
+ self.agent_infinite.track_vars = ['mNrmNow']
+ self.agent_infinite.initializeSim()
+ self.agent_infinite.simulate()
+
+ self.assertAlmostEqual(
+ np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[40],
+ -23.008063500363942
+ )
+
+ self.assertAlmostEqual(
+ np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[100],
+ -27.164608851546927
+ )
+
+ ## Try now with the manipulation at time step 80
+
+ self.agent_infinite.initializeSim()
+ self.agent_infinite.simulate(80)
+ self.agent_infinite.aNrmNow += -5. # Adjust all simulated consumers' assets downward by 5
+ self.agent_infinite.simulate(40)
+
+ self.assertAlmostEqual(
+ np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[40],
+ -23.008063500363942
+ )
+
+ self.assertAlmostEqual(
+ np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[100],
+ -29.140261331951606
+ )
diff --git a/HARK/tests/test_modelInits.py b/HARK/ConsumptionSaving/tests/test_modelInits.py
similarity index 100%
rename from HARK/tests/test_modelInits.py
rename to HARK/ConsumptionSaving/tests/test_modelInits.py
diff --git a/HARK/tests/test_modelcomparisons.py b/HARK/ConsumptionSaving/tests/test_modelcomparisons.py
similarity index 85%
rename from HARK/tests/test_modelcomparisons.py
rename to HARK/ConsumptionSaving/tests/test_modelcomparisons.py
index d8b3f6ee7..5177b4cd3 100644
--- a/HARK/tests/test_modelcomparisons.py
+++ b/HARK/ConsumptionSaving/tests/test_modelcomparisons.py
@@ -11,7 +11,7 @@
import numpy as np
# Bring in the HARK models we want to test
-from HARK.ConsumptionSaving.ConsIndShockModel import solvePerfForesight, IndShockConsumerType
+from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType, IndShockConsumerType
from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType
@@ -30,16 +30,22 @@ def setUp(self):
"""
# Set up and solve infinite type
import HARK.ConsumptionSaving.ConsumerParameters as Params
-
- InfiniteType = IndShockConsumerType(**Params.init_idiosyncratic_shocks)
- InfiniteType.assignParameters(LivPrb=[1.],
- DiscFac=0.955,
- PermGroFac=[1.],
- PermShkStd=[0.],
- TempShkStd=[0.],
- T_total=1, T_retire=0, BoroCnstArt=None, UnempPrb=0.,
- cycles=0
- ) # This is what makes the type infinite horizon
+
+ # Define a test dictionary that should have the same solution in the
+ # perfect foresight and idiosyncratic shocks models.
+ test_dictionary = deepcopy(Params.init_idiosyncratic_shocks)
+ test_dictionary['LivPrb'] = [1.]
+ test_dictionary['DiscFac'] = 0.955
+ test_dictionary['PermGroFac'] = [1.]
+ test_dictionary['PermShkStd'] = [0.]
+ test_dictionary['TranShkStd'] = [0.]
+ test_dictionary['UnempPrb'] = 0.
+ test_dictionary['T_cycle'] = 1
+ test_dictionary['T_retire'] = 0
+ test_dictionary['BoroCnstArt'] = None
+
+ InfiniteType = IndShockConsumerType(**test_dictionary)
+ InfiniteType.cycles = 0
InfiniteType.updateIncomeProcess()
InfiniteType.solve()
@@ -47,8 +53,8 @@ def setUp(self):
InfiniteType.unpackcFunc()
# Make and solve a perfect foresight consumer type with the same parameters
- PerfectForesightType = deepcopy(InfiniteType)
- PerfectForesightType.solveOnePeriod = solvePerfForesight
+ PerfectForesightType = PerfForesightConsumerType(**test_dictionary)
+ PerfectForesightType.cycles = 0
PerfectForesightType.solve()
PerfectForesightType.unpackcFunc()
diff --git a/HARK/GOVERNANCE.md b/HARK/GOVERNANCE.md
deleted file mode 100644
index 03baf25bb..000000000
--- a/HARK/GOVERNANCE.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Econ-ARK Project Governance
-
-Econ-ARK project governance is still being drafted. Please refer to the [**Technical Steering Commitee**]() for questions.
\ No newline at end of file
diff --git a/HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py b/HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py
index 430e6f286..76a6e849f 100644
--- a/HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py
+++ b/HARK/SolvingMicroDSOPs/Calibration/EstimationParameters.py
@@ -4,22 +4,6 @@
'''
from __future__ import print_function
-# ---------------------------------------------------------------------------------
-# Debugging flags
-# ---------------------------------------------------------------------------------
-
-show_PermGroFacAgg_error = False
-# Error Notes:
-# This sets a "quick fix" to the error, AttributeError: 'TempConsumerType' object has no attribute 'PermGroFacAgg'
-# If you set this flag to "True" you will see the error. A more thorough fix is to
-# fix the place where this error was introduced (Set to "True" and it will appear;
-# this was almost certainly introduced when the code was extended to be used in the
-# GE setting). An even more thorough solution, which moves beyond the scope of
-# fixing this error, is adding unit tests to ID when changes to some code will
-# break things elsewhere.
-# Note: alternatively, decide that the "init_consumer_objects['PermGroFacAgg'] = 1.0"
-# line below fixes it properly ('feature not a bug') and remove all this text.
-
# ---------------------------------------------------------------------------------
# - Define all of the model parameters for SolvingMicroDSOPs and ConsumerExamples -
# ---------------------------------------------------------------------------------
@@ -159,12 +143,6 @@
'CubicBool':CubicBool
}
-if show_PermGroFacAgg_error:
- pass # do nothing
-else:
- print("***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***")
- init_consumer_objects['PermGroFacAgg'] = 1.0
-
if __name__ == '__main__':
print("Sorry, EstimationParameters doesn't actually do anything on its own.")
diff --git a/HARK/SolvingMicroDSOPs/Calibration/SCFdata.csv b/HARK/SolvingMicroDSOPs/Calibration/SCFdata.csv
deleted file mode 100644
index c26c88fc2..000000000
--- a/HARK/SolvingMicroDSOPs/Calibration/SCFdata.csv
+++ /dev/null
@@ -1,10476 +0,0 @@
-wealth_income_ratio,age_group,weight
--7.278331,1,.7919394
--3.352119,1,1.077116
--2.806893,1,.6973649
--2.053807,1,1.283598
--1.629471,1,.8323343
- -1.42016,1 ,.818994
--1.274307,1,.8566858
- -1.11702,1,.7354656
--1.091087,1,.9976413
--.9850314,1,.7813509
--.9656265,1,.8889729
--.9253121,1,1.057292
- -.899695,1,.9466035
--.8299303,1 ,.890588
- -.812595,1,1.869985
--.7882341,1,.8057681
--.7869177,1,.8014933
--.6918034,1,1.634311
--.6831914,1 ,.881384
--.6659861,1,.9229636
--.6609519,1,1.142049
--.6391214,1,1.008117
- -.599839,1,1.076386
--.5505417,1,.8633595
--.5389788,1,.6785128
--.5188019,1,1.081038
--.5126372,1,.7898264
--.5109465,1,1.280624
--.4946782,1,.9332498
--.4766116,1,1.566128
- -.465877,1,1.863954
--.4540315,1,.9191576
--.4486161,1,.6700751
--.4470559,1,.7423913
--.4295197,1,1.073492
--.4249749,1,1.151995
--.4122496,1,1.007923
--.4054797,1,.6364754
--.3538077,1,1.721592
--.3473591,1,1.037425
--.3467194,1,.7459993
--.3283586,1,.6364754
--.3246621,1,1.160917
--.3025178,1,1.706382
--.2955485,1,1.027948
--.2953791,1,1.639488
--.2915711,1,.7920528
--.2819163,1,1.289271
--.2807718,1,.9229636
--.2757939,1,.9146178
- -.268415,1,1.064077
--.2563871,1 ,1.18782
- -.248923,1,1.042683
--.2484222,1,.7309287
--.2345723,1,.6592572
--.2079935,1,.8332959
- -.207988,1,1.043408
--.1961349,1 ,.961073
--.1798025,1,1.017332
--.1780192,1,1.099453
--.1769906,1 ,1.36187
--.1571241,1 ,1.10623
--.1527696,1,.9191576
--.1497698,1,1.376706
--.1445973,1,.9229636
--.1374587,1,.8474965
--.1153859,1 ,.861033
--.1146951,1,1.604324
--.1132503,1,1.407474
--.1108532,1,.7911471
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--.0998007,1,.9284371
--.0804299,1,1.113341
--.0799255,1,.9347388
- -.077218,1,1.524214
--.0705655,1,1.261582
--.0624957,1,.7340624
--.0593102,1 ,1.007
--.0585578,1,1.052421
--.0584118,1,.8179243
- -.056384,1,1.944564
--.0503634,1,.7485954
--.0454822,1,1.056103
--.0443961,1,.6700697
--.0369744,1,.7300747
--.0358081,1,.8620316
--.0258416,1,.7970695
--.0221961,1 ,.842134
--.0208272,1,.7354656
- -.017334,1,1.685536
--.0163349,1,1.154953
--.0161709,1,.8776652
--.0143902,1,.8478131
- -.01375,1,1.006895
--.0067059,1,1.458626
--.0059591,1,.9462281
--.0044075,1 ,1.10151
--.0042296,1,.8581439
- 0,1,1.113853
- 0,1,.9396044
- 0,1 ,1.26719
- 0,1,.8226658
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diff --git a/HARK/SolvingMicroDSOPs/Calibration/SetupSCFdata.py b/HARK/SolvingMicroDSOPs/Calibration/SetupSCFdata.py
deleted file mode 100644
index 445f7ad03..000000000
--- a/HARK/SolvingMicroDSOPs/Calibration/SetupSCFdata.py
+++ /dev/null
@@ -1,89 +0,0 @@
-'''
-Sets up the SCF data for use in the SolvingMicroDSOPs estimation.
-'''
-from __future__ import division # Use new division function
-from __future__ import print_function
-from __future__ import absolute_import
-from builtins import zip
-from builtins import str
-from builtins import range
-
-import os, sys
-
-# Find pathname to this file:
-my_file_path = os.path.dirname(os.path.abspath(__file__))
-
-# Pathnames to the other files:
-calibration_dir = os.path.join(my_file_path, "../Calibration/") # Relative directory for primitive parameter files
-tables_dir = os.path.join(my_file_path, "../Tables/") # Relative directory for primitive parameter files
-figures_dir = os.path.join(my_file_path, "../Figures/") # Relative directory for primitive parameter files
-code_dir = os.path.join(my_file_path, "../Code/") # Relative directory for primitive parameter files
-
-
-# Import modules from local repository. If local repository is part of HARK,
-# this will import from HARK. Otherwise manual pathname specification is in
-# order.
-try:
- # Import from core HARK code first:
- from HARK.SolvingMicroDSOPs.Calibration.EstimationParameters import initial_age, empirical_cohort_age_groups
-
-except:
- # Need to rely on the manual insertion of pathnames to all files in do_all.py
- # NOTE sys.path.insert(0, os.path.abspath(tables_dir)), etc. may need to be
- # copied from do_all.py to here
-
- # Import files first:
- from EstimationParameters import initial_age, empirical_cohort_age_groups
-
-
-# The following libraries are part of the standard python distribution
-import numpy as np # Numerical Python
-import csv # Comma-separated variable reader
-
-# Set the path to the empirical data:
-scf_data_path = data_location = os.path.dirname(os.path.abspath(__file__)) # os.path.abspath('./') #'./'
-
-# Open the file handle and create a reader object and a csv header
-infile = open(scf_data_path + '/SCFdata.csv', 'r')
-csv_reader = csv.reader(infile)
-data_csv_header = next(csv_reader)
-
-# Pull the column index from the data_csv_header
-data_column_index = data_csv_header.index('wealth_income_ratio') # scf_w_col
-age_group_column_index = data_csv_header.index('age_group') # scf_ages_col
-data_weight_column_index = data_csv_header.index('weight') # scf_weights_col
-
-# Initialize empty lists for the data
-w_to_y_data = [] # Ratio of wealth to permanent income
-empirical_weights = [] # Weighting for this observation
-empirical_groups = [] # Which age group this observation belongs to (1-7)
-
-# Read in the data from the datafile by looping over each record (row) in the file.
-for record in csv_reader:
- w_to_y_data.append(np.float64(record[data_column_index]))
- empirical_groups.append(np.float64(record[age_group_column_index]))
- empirical_weights.append(np.float64(record[data_weight_column_index]))
-
-# Generate a single array of SCF data, useful for resampling for bootstrap
-scf_data_array = np.array([w_to_y_data,empirical_groups,empirical_weights]).T
-
-# Convert SCF data to numpy's array format for easier math
-w_to_y_data = np.array(w_to_y_data)
-empirical_groups = np.array(empirical_groups)
-empirical_weights = np.array(empirical_weights)
-
-# Close the data file
-infile.close()
-
-# Generate a mapping between the real ages in the groups and the indices of simulated data
-simulation_map_cohorts_to_age_indices = []
-for ages in empirical_cohort_age_groups:
- simulation_map_cohorts_to_age_indices.append(np.array(ages) - initial_age)
-
-
-if __name__ == '__main__':
- print("Sorry, SetupSCFdata doesn't actually do anything on its own.")
- print("This module is imported by StructEstimation, providing data for")
- print("the example estimation. Please see that module if you want more")
- print("interesting output.")
-
diff --git a/HARK/SolvingMicroDSOPs/Code/StructEstimation.py b/HARK/SolvingMicroDSOPs/Code/StructEstimation.py
deleted file mode 100644
index 478d99531..000000000
--- a/HARK/SolvingMicroDSOPs/Code/StructEstimation.py
+++ /dev/null
@@ -1,392 +0,0 @@
-'''
-Demonstrates an example estimation of microeconomic dynamic stochastic optimization
-problem, as described in Section 9 of Chris Carroll's SolvingMicroDSOPs.pdf notes.
-The estimation attempts to match the age-conditional wealth profile of simulated
-consumers to the median wealth holdings of seven age groups in the 2004 SCF by
-varying only two parameters: the coefficient of relative risk aversion and a scaling
-factor for an age-varying sequence of discount factors. The estimation uses a
-consumption-saving model with idiosyncratic shocks to permanent and transitory
-income as defined in ConsIndShockModel.
-'''
-from __future__ import print_function
-from __future__ import absolute_import
-
-from builtins import str
-from builtins import range
-
-import os
-import sys
-import csv
-import numpy as np # Numeric Python
-import pylab # Python reproductions of some Matlab functions
-from time import time, clock # Timing utility
-
-# Import modules from core HARK libraries:
-import HARK.ConsumptionSaving.ConsIndShockModel as Model # The consumption-saving micro model
-from HARK.simulation import drawDiscrete # Method for sampling from a discrete distribution
-from HARK.estimation import minimizeNelderMead, bootstrapSampleFromData # Estimation methods
-
-# Find pathname to this file:
-my_file_path = os.path.dirname(os.path.abspath(__file__))
-
-# Pathnames to the other files:
-calibration_dir = os.path.join(my_file_path, "../Calibration/") # Relative directory for primitive parameter files
-tables_dir = os.path.join(my_file_path, "../Tables/") # Relative directory for primitive parameter files
-figures_dir = os.path.join(my_file_path, "../Figures/") # Relative directory for primitive parameter files
-code_dir = os.path.join(my_file_path, "../Code/") # Relative directory for primitive parameter files
-
-
-# Import modules from local repository. If local repository is part of HARK,
-# this will import from HARK. Otherwise manual pathname specification is in
-# order.
-try:
- # Import from core HARK code first:
- from HARK.SolvingMicroDSOPs.Calibration import EstimationParameters as Params # Parameters for the consumer type and the estimation
- from HARK.SolvingMicroDSOPs.Calibration import SetupSCFdata as Data # SCF 2004 data on household wealth
-
-except:
- # Need to rely on the manual insertion of pathnames to all files in do_all.py
- # NOTE sys.path.insert(0, os.path.abspath(tables_dir)), etc. may need to be
- # copied from do_all.py to here
- import EstimationParameters as Params # Parameters for the consumer type and the estimation
- import SetupSCFdata as Data # SCF 2004 data on household wealth
-
-
-
-# Set booleans to determine which tasks should be done
-local_estimate_model = True # Whether to estimate the model
-local_compute_standard_errors = False # Whether to get standard errors via bootstrap
-local_make_contour_plot = True # Whether to make a contour map of the objective function
-
-#=====================================================
-# Define objects and functions used for the estimation
-#=====================================================
-
-class TempConsumerType(Model.IndShockConsumerType):
- '''
- A very lightly edited version of IndShockConsumerType. Uses an alternate method of making new
- consumers and specifies DiscFac as being age-dependent. Called "temp" because only used here.
- '''
- def __init__(self,cycles=1,time_flow=True,**kwds):
- '''
- Make a new consumer type.
-
- Parameters
- ----------
- cycles : int
- Number of times the sequence of periods should be solved.
- time_flow : boolean
- Whether time is currently "flowing" forward for this instance.
-
- Returns
- -------
- None
- '''
- # Initialize a basic AgentType
- Model.IndShockConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds)
- self.addToTimeVary('DiscFac') # This estimation uses age-varying discount factors as
- self.delFromTimeInv('DiscFac')# estimated by Cagetti (2003), so switch from time_inv to time_vary
-
- def simBirth(self,which_agents):
- '''
- Alternate method for simulating initial states for simulated agents, drawing from a finite
- distribution. Used to overwrite IndShockConsumerType.simBirth, which uses lognormal distributions.
-
- Parameters
- ----------
- which_agents : np.array(Bool)
- Boolean array of size self.AgentCount indicating which agents should be "born".
-
- Returns
- -------
- None
- '''
- # Get and store states for newly born agents
- self.aNrmNow[which_agents] = self.aNrmInit[which_agents] # Take directly from pre-specified distribution
- self.pLvlNow[which_agents] = 1.0 # No variation in permanent income needed
- self.t_age[which_agents] = 0 # How many periods since each agent was born
- self.t_cycle[which_agents] = 0 # Which period of the cycle each agents is currently in
- return None
-
-
-# Make a lifecycle consumer to be used for estimation, including simulated shocks (plus an initial distribution of wealth)
-EstimationAgent = TempConsumerType(**Params.init_consumer_objects) # Make a TempConsumerType for estimation
-EstimationAgent(T_sim = EstimationAgent.T_cycle+1) # Set the number of periods to simulate
-EstimationAgent.track_vars = ['bNrmNow'] # Choose to track bank balances as wealth
-EstimationAgent.aNrmInit = drawDiscrete(N=Params.num_agents,
- P=Params.initial_wealth_income_ratio_probs,
- X=Params.initial_wealth_income_ratio_vals,
- seed=Params.seed) # Draw initial assets for each consumer
-EstimationAgent.makeShockHistory()
-
-# Define the objective function for the simulated method of moments estimation
-def smmObjectiveFxn(DiscFacAdj, CRRA,
- agent = EstimationAgent,
- DiscFacAdj_bound = Params.DiscFacAdj_bound,
- CRRA_bound = Params.CRRA_bound,
- empirical_data = Data.w_to_y_data,
- empirical_weights = Data.empirical_weights,
- empirical_groups = Data.empirical_groups,
- map_simulated_to_empirical_cohorts = Data.simulation_map_cohorts_to_age_indices):
- '''
- The objective function for the SMM estimation. Given values of discount factor
- adjuster DiscFacAdj, coeffecient of relative risk aversion CRRA, a base consumer
- agent type, empirical data, and calibrated parameters, this function calculates
- the weighted distance between data and the simulated wealth-to-permanent
- income ratio.
-
- Steps:
- a) solve for consumption functions for (DiscFacAdj, CRRA)
- b) simulate wealth holdings for many consumers over time
- c) sum distances between empirical data and simulated medians within
- seven age groupings
-
- Parameters
- ----------
- DiscFacAdj : float
- An adjustment factor to a given age-varying sequence of discount factors.
- I.e. DiscFac[t] = DiscFacAdj*DiscFac_timevary[t].
- CRRA : float
- Coefficient of relative risk aversion.
- agent : ConsumerType
- The consumer type to be used in the estimation, with all necessary para-
- meters defined except the discount factor and CRRA.
- DiscFacAdj_bound : (float,float)
- Lower and upper bounds on DiscFacAdj; if outside these bounds, the function
- simply returns a "penalty value".
- DiscFacAdj_bound : (float,float)
- Lower and upper bounds on CRRA; if outside these bounds, the function
- simply returns a "penalty value".
- empirical_data : np.array
- Array of wealth-to-permanent-income ratios in the data.
- empirical_weights : np.array
- Weights for each observation in empirical_data.
- empirical_groups : np.array
- Array of integers listing the age group for each observation in empirical_data.
- map_simulated_to_empirical_cohorts : [np.array]
- List of arrays of "simulation ages" for each age grouping. E.g. if the
- 0th element is [1,2,3,4,5], then these time indices from the simulation
- correspond to the 0th empirical age group.
-
- Returns
- -------
- distance_sum : float
- Sum of distances between empirical data observations and the corresponding
- median wealth-to-permanent-income ratio in the simulation.
- '''
- original_time_flow = agent.time_flow
- agent.timeFwd() # Make sure time is flowing forward for the agent
-
- # A quick check to make sure that the parameter values are within bounds.
- # Far flung falues of DiscFacAdj or CRRA might cause an error during solution or
- # simulation, so the objective function doesn't even bother with them.
- if DiscFacAdj < DiscFacAdj_bound[0] or DiscFacAdj > DiscFacAdj_bound[1] or CRRA < CRRA_bound[0] or CRRA > CRRA_bound[1]:
- return 1e30
-
- # Update the agent with a new path of DiscFac based on this DiscFacAdj (and a new CRRA)
- agent(DiscFac = [b*DiscFacAdj for b in Params.DiscFac_timevary], CRRA = CRRA)
-
- # Solve the model for these parameters, then simulate wealth data
- agent.solve() # Solve the microeconomic model
- agent.unpackcFunc() # "Unpack" the consumption function for convenient access
- max_sim_age = max([max(ages) for ages in map_simulated_to_empirical_cohorts])+1
- agent.initializeSim() # Initialize the simulation by clearing histories, resetting initial values
- agent.simulate(max_sim_age) # Simulate histories of consumption and wealth
- sim_w_history = agent.bNrmNow_hist # Take "wealth" to mean bank balances before receiving labor income
-
- # Find the distance between empirical data and simulated medians for each age group
- group_count = len(map_simulated_to_empirical_cohorts)
- distance_sum = 0
- for g in range(group_count):
- cohort_indices = map_simulated_to_empirical_cohorts[g] # The simulated time indices corresponding to this age group
- sim_median = np.median(sim_w_history[cohort_indices,]) # The median of simulated wealth-to-income for this age group
- group_indices = empirical_groups == (g+1) # groups are numbered from 1
- distance_sum += np.dot(np.abs(empirical_data[group_indices] - sim_median),empirical_weights[group_indices]) # Weighted distance from each empirical observation to the simulated median for this age group
-
- # Restore time to its original direction and report the result
- if not original_time_flow:
- agent.timeRev()
- return distance_sum
-
-# Make a single-input lambda function for use in the optimizer
-smmObjectiveFxnReduced = lambda parameters_to_estimate : smmObjectiveFxn(DiscFacAdj=parameters_to_estimate[0],CRRA=parameters_to_estimate[1])
-'''
-A "reduced form" of the SMM objective function, compatible with the optimizer.
-Identical to smmObjectiveFunction, but takes only a single input as a length-2
-list representing [DiscFacAdj,CRRA].
-'''
-
-# Define the bootstrap procedure
-def calculateStandardErrorsByBootstrap(initial_estimate,N,seed=0,verbose=False):
- '''
- Calculates standard errors by repeatedly re-estimating the model with datasets
- resampled from the actual data.
-
- Parameters
- ----------
- initial_estimate : [float,float]
- The estimated [DiscFacAdj,CRRA], for use as an initial guess for each
- re-estimation in the bootstrap procedure.
- N : int
- Number of times to resample data and re-estimate the model.
- seed : int
- Seed for the random number generator.
- verbose : boolean
- Indicator for whether extra output should be printed for the user.
-
- Returns
- -------
- standard_errors : [float,float]
- Standard errors calculated by bootstrap: [DiscFacAdj_std_error, CRRA_std_error].
- '''
- t_0 = time()
-
- # Generate a list of seeds for generating bootstrap samples
- RNG = np.random.RandomState(seed)
- seed_list = RNG.randint(2**31-1,size=N)
-
- # Estimate the model N times, recording each set of estimated parameters
- estimate_list = []
- for n in range(N):
- t_start = time()
-
- # Bootstrap a new dataset by resampling from the original data
- bootstrap_data = (bootstrapSampleFromData(Data.scf_data_array,seed=seed_list[n])).T
- w_to_y_data_bootstrap = bootstrap_data[0,]
- empirical_groups_bootstrap = bootstrap_data[1,]
- empirical_weights_bootstrap = bootstrap_data[2,]
-
- # Make a temporary function for use in this estimation run
- smmObjectiveFxnBootstrap = lambda parameters_to_estimate : smmObjectiveFxn(DiscFacAdj=parameters_to_estimate[0],
- CRRA=parameters_to_estimate[1],
- empirical_data = w_to_y_data_bootstrap,
- empirical_weights = empirical_weights_bootstrap,
- empirical_groups = empirical_groups_bootstrap)
-
- # Estimate the model with the bootstrap data and add to list of estimates
- this_estimate = minimizeNelderMead(smmObjectiveFxnBootstrap,initial_estimate)
- estimate_list.append(this_estimate)
- t_now = time()
-
- # Report progress of the bootstrap
- if verbose:
- print('Finished bootstrap estimation #' + str(n+1) + ' of ' + str(N) + ' in ' + str(t_now-t_start) + ' seconds (' + str(t_now-t_0) + ' cumulative)')
-
- # Calculate the standard errors for each parameter
- estimate_array = (np.array(estimate_list)).T
- DiscFacAdj_std_error = np.std(estimate_array[0])
- CRRA_std_error = np.std(estimate_array[1])
-
- return [DiscFacAdj_std_error, CRRA_std_error]
-
-
-#=================================================================
-# Done defining objects and functions. Now run them (if desired).
-#=================================================================
-
-def main(estimate_model=local_estimate_model, compute_standard_errors=local_compute_standard_errors, make_contour_plot=local_make_contour_plot):
- """
- Run the main estimation procedure for SolvingMicroDSOP.
-
- Parameters
- ----------
- estimate_model : bool
- Whether to estimate the model using Nelder-Mead. When True, this is a low-time, low-memory operation.
-
- compute_standard_errors : bool
- Whether to compute standard errors on the estiamtion of the model.
-
- make_contour_plot : bool
- Whether to make the contour plot associate with the estiamte.
-
- Returns
- -------
- None
- """
-
-
- # Estimate the model using Nelder-Mead
- if estimate_model:
- initial_guess = [Params.DiscFacAdj_start,Params.CRRA_start]
- print('--------------------------------------------------------------------------------')
- print('Now estimating the model using Nelder-Mead from an initial guess of ' + str(initial_guess) + '...')
- print('--------------------------------------------------------------------------------')
- t_start_estimate = clock()
- model_estimate = minimizeNelderMead(smmObjectiveFxnReduced,initial_guess,verbose=True)
- t_end_estimate = clock()
- time_to_estimate = t_end_estimate-t_start_estimate
- print('Time to execute all:', round(time_to_estimate/60.,2), 'min,', time_to_estimate, 'sec')
- print('Estimated values: DiscFacAdj=' + str(model_estimate[0]) + ', CRRA=' + str(model_estimate[1]))
-
- # Create the simple estimate table
- estimate_results_file = os.path.join(tables_dir, 'estimate_results.csv')
- with open(estimate_results_file, 'wt') as f:
- writer = csv.writer(f)
- writer.writerow(['DiscFacAdj', 'CRRA'])
- writer.writerow([model_estimate[0], model_estimate[1]])
-
-
- if compute_standard_errors and not estimate_model:
- print("To run the bootstrap you must first estimate the model by setting estimate_model = True.")
-
- # Compute standard errors by bootstrap
- if compute_standard_errors and estimate_model:
-
-
- # Estimate the model:
- print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- print("Computing standard errors using",Params.bootstrap_size,"bootstrap replications.")
- print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- try:
- t_bootstrap_guess = time_to_estimate * Params.bootstrap_size
- print("This will take approximately", round(t_bootstrap_guess/60.,2), "min, ", t_bootstrap_guess, "sec")
- except:
- pass
- t_start_bootstrap = clock()
- std_errors = calculateStandardErrorsByBootstrap(model_estimate,N=Params.bootstrap_size,seed=Params.seed,verbose=True)
- t_end_bootstrap = clock()
- time_to_bootstrap = t_end_bootstrap-t_start_bootstrap
- print('Time to execute all:', round(time_to_bootstrap/60.,2), 'min,', time_to_bootstrap, 'sec')
- print('Standard errors: DiscFacAdj--> ' + str(std_errors[0]) + ', CRRA--> ' + str(std_errors[1]))
-
- # Create the simple bootstrap table
- bootstrap_results_file = os.path.join(tables_dir, 'bootstrap_results.csv')
- with open(bootstrap_results_file, 'wt') as f:
- writer = csv.writer(f)
- writer.writerow(['DiscFacAdj', 'DiscFacAdj_standard_error', 'CRRA', 'CRRA_standard_error'])
- writer.writerow([model_estimate[0], std_errors[0], model_estimate[1], std_errors[1]])
-
- # Make a contour plot of the objective function
- if make_contour_plot:
- print('````````````````````````````````````````````````````````````````````````````````')
- print("Creating the contour plot.")
- print('````````````````````````````````````````````````````````````````````````````````')
- t_start_contour = clock()
- grid_density = 20 # Number of parameter values in each dimension
- level_count = 100 # Number of contour levels to plot
- DiscFacAdj_list = np.linspace(0.85,1.05,grid_density)
- CRRA_list = np.linspace(2,8,grid_density)
- CRRA_mesh, DiscFacAdj_mesh = pylab.meshgrid(CRRA_list,DiscFacAdj_list)
- smm_obj_levels = np.empty([grid_density,grid_density])
- for j in range(grid_density):
- DiscFacAdj = DiscFacAdj_list[j]
- for k in range(grid_density):
- CRRA = CRRA_list[k]
- smm_obj_levels[j,k] = smmObjectiveFxn(DiscFacAdj,CRRA)
- smm_contour = pylab.contourf(CRRA_mesh,DiscFacAdj_mesh,smm_obj_levels,level_count)
- t_end_contour = clock()
- time_to_contour = t_end_contour-t_start_contour
- print('Time to execute all:', round(time_to_contour/60.,2), 'min,', time_to_contour, 'sec')
- pylab.colorbar(smm_contour)
- pylab.plot(model_estimate[1],model_estimate[0],'*r',ms=15)
- pylab.xlabel(r'coefficient of relative risk aversion $\rho$',fontsize=14)
- pylab.ylabel(r'discount factor adjustment $\beth$',fontsize=14)
- pylab.savefig(os.path.join(figures_dir, 'SMMcontour.pdf'))
- pylab.savefig(os.path.join(figures_dir, 'SMMcontour.png'))
- pylab.show()
-
-
-
-if __name__ == '__main__':
- main()
-
diff --git a/HARK/SolvingMicroDSOPs/Figures/SMMcontour.pdf b/HARK/SolvingMicroDSOPs/Figures/SMMcontour.pdf
deleted file mode 100644
index 958c22f07..000000000
Binary files a/HARK/SolvingMicroDSOPs/Figures/SMMcontour.pdf and /dev/null differ
diff --git a/HARK/SolvingMicroDSOPs/Figures/SMMcontour.png b/HARK/SolvingMicroDSOPs/Figures/SMMcontour.png
deleted file mode 100644
index 527ffca66..000000000
Binary files a/HARK/SolvingMicroDSOPs/Figures/SMMcontour.png and /dev/null differ
diff --git a/HARK/SolvingMicroDSOPs/Tables/estimate_results.csv b/HARK/SolvingMicroDSOPs/Tables/estimate_results.csv
deleted file mode 100644
index 17084a8fc..000000000
--- a/HARK/SolvingMicroDSOPs/Tables/estimate_results.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-DiscFacAdj,CRRA
-1.004880962644363,4.8605098948425285
diff --git a/HARK/SolvingMicroDSOPs/do_all.py b/HARK/SolvingMicroDSOPs/do_all.py
deleted file mode 100644
index 1fb92f330..000000000
--- a/HARK/SolvingMicroDSOPs/do_all.py
+++ /dev/null
@@ -1,148 +0,0 @@
-'''
-Run all of the plots and tables in SolvingMicroDSOPs.
-
-To execute, do the following on the Python command line:
-
- from HARK.[YOUR-MODULE-NAME-HERE].do_all import run_replication
- run_replication()
-
-You will be presented with an interactive prompt that asks what level of
-replication you would like to have.
-
-More Details
-------------
-
-This example script allows the user to create all of the Figures and Tables
-modules for SolvingMicroDSOPs.StructuralEstimation.
-
-This is example is kept as simple and minimal as possible to illustrate the
-format of a "replication archive."
-
-The file structure is as follows:
-
-./SolvingMicroDSOPs/
- Calibration/ # Directory that contain the necessary code and data to parameterize the model
- Code/ # The main estimation code, in this case StructuralEstimation.py
- Figures/ # Any Figures created by the main code
- Tables/ # Any tables created by the main code
-
-Because computational modeling can be very memory- and time-intensive, this file
-also allows the user to choose whether to run files based on there resouce
-requirements. Files are categorized as one of the following three:
-
-- low_resource: low RAM needed and runs quickly, say less than 1-5 minutes
-- medium_resource: moderate RAM needed and runs moderately quickly, say 5-10+ mintues
-- high_resource: high RAM needed (and potentially parallel computing required), and high time to run, perhaps even hours, days, or longer.
-
-The designation is purposefully vague and left up the to researcher to specify
-more clearly below. Using time taken on an example machine is entirely reasonable
-here.
-
-Finally, this code may serve as example code for efforts that fall outside
-the HARK package structure for one reason or another. Therefore this script will
-attempt to import the necessary MicroDSOP sub-modules as though they are part of
-the HARK package; if that fails, this script reverts to manaully updating the
-Python PATH with the locations of the MicroDSOP directory structure so it can
-still run.
-'''
-
-from __future__ import division, print_function
-from builtins import str, range
-
-import os, sys
-
-# Find pathname to this file:
-my_file_path = os.path.dirname(os.path.abspath(__file__))
-
-# Pathnames to the other files:
-calibration_dir = os.path.join(my_file_path, "Calibration") # Relative directory for primitive parameter files
-tables_dir = os.path.join(my_file_path, "Tables") # Relative directory for primitive parameter files
-figures_dir = os.path.join(my_file_path, "Figures") # Relative directory for primitive parameter files
-code_dir = os.path.join(my_file_path, "Code") # Relative directory for primitive parameter files
-
-# Import modules from local repository. If local repository is part of HARK,
-# this will import from HARK. Otherwise manual pathname specification is in
-# order.
-try:
- # Import from core HARK code first:
- from HARK.SolvingMicroDSOPs.Code import StructEstimation as struct
-except:
- print("**************** Manually specifying pathnames for modules *******************")
- # It appears that the current module is not part of HARK, therefore we will
- # manually add the pathnames to the various files directly to the beginning
- # of the Python path. This will be needed for all files that will run in
- # lower directories.
- sys.path.insert(0, calibration_dir)
- sys.path.insert(0, tables_dir)
- sys.path.insert(0, figures_dir)
- sys.path.insert(0, code_dir)
- sys.path.insert(0, my_file_path)
-
- # Manual import needed, should draw from first instance at start of Python
- # PATH added above:
- import StructEstimation as struct
-
-
-# Define settings for "main()" function in StructuralEstiamtion.py based on
-# resource requirements:
-
-low_resource = {'estimate_model':True, 'make_contour_plot':False, 'compute_standard_errors':False}
-# Author note:
-# This takes approximately 90 seconds on a laptop with the following specs:
-# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz
-
-medium_resource = {'estimate_model':True, 'make_contour_plot':True, 'compute_standard_errors':False}
-# Author note:
-# This takes approximately 7 minutes on a laptop with the following specs:
-# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz
-
-high_resource = {'estimate_model':True, 'make_contour_plot':False, 'compute_standard_errors':True}
-# Author note:
-# This takes approximately 30 minutes on a laptop with the following specs:
-# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz
-
-all_replications = {'estimate_model':True, 'make_contour_plot':True, 'compute_standard_errors':True}
-# Author note:
-# This takes approximately 40 minutes on a laptop with the following specs:
-# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz
-
-
-# Ask the user which replication to run, and run it:
-def run_replication():
- which_replication = input("""Which replication would you like to run? (See documentation in do_all.py for details.) Please enter the option number to run that option; default is in brackets:
-
- [1] low-resource: ~90 sec; output ./Tables/estimate_results.csv
-
- 2 medium-resource: ~7 min; output ./Figures/SMMcontour.pdf
- ./Figures/SMMcontour.png
- 3 high-resource: ~30 min; output ./Tables/bootstrap_results.csv
-
- 4 all: ~40 min; output: all above.
-
- q quit: exit without executing.\n\n""")
-
-
- if which_replication == 'q':
- return
-
- elif which_replication == '1' or which_replication == '':
- print("Running low-resource replication...")
- struct.main(**low_resource)
-
- elif which_replication == '2':
- print("Running medium-resource replication...")
- struct.main(**medium_resource)
-
- elif which_replication == '3':
- print("Running high-resource replication...")
- struct.main(**high_resource)
-
- elif which_replication == '4':
- print("Running all replications...")
- struct.main(**all_replications)
-
- else:
- return
-
-if __name__ == '__main__':
- run_replication()
diff --git a/HARK/__init__.py b/HARK/__init__.py
index bf0a93b4c..bb66298db 100644
--- a/HARK/__init__.py
+++ b/HARK/__init__.py
@@ -1,6 +1,14 @@
from __future__ import absolute_import
-from .core import *
+import sys
+
+if sys.version_info[:2] < (3, 6):
+ print(
+ "WARNING: Econ-Ark v0.10.4 will be the last supported version for Python %d.%d, "
+ "please upgrade your system to the latest release, read more at https://www.python.org/doc/sunset-python-2/"
+ % sys.version_info[:2]
+ )
-__version__ = '0.10.1.dev3'
-from .BayerLuetticke import *
+from .core import *
+
+__version__ = "0.10.4"
diff --git a/HARK/cAndCwithStickyE/Calibration/CRRA.txt b/HARK/cAndCwithStickyE/Calibration/CRRA.txt
deleted file mode 100644
index d8263ee98..000000000
--- a/HARK/cAndCwithStickyE/Calibration/CRRA.txt
+++ /dev/null
@@ -1 +0,0 @@
-2
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/CapShare.txt b/HARK/cAndCwithStickyE/Calibration/CapShare.txt
deleted file mode 100644
index dc1eb07da..000000000
--- a/HARK/cAndCwithStickyE/Calibration/CapShare.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.36
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/DeprFacAnn.txt b/HARK/cAndCwithStickyE/Calibration/DeprFacAnn.txt
deleted file mode 100644
index 9fd9e4f87..000000000
--- a/HARK/cAndCwithStickyE/Calibration/DeprFacAnn.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.94
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/DiePrb.txt b/HARK/cAndCwithStickyE/Calibration/DiePrb.txt
deleted file mode 100644
index ff3247710..000000000
--- a/HARK/cAndCwithStickyE/Calibration/DiePrb.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.005
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/KYratioSS.txt b/HARK/cAndCwithStickyE/Calibration/KYratioSS.txt
deleted file mode 100644
index 3cacc0b93..000000000
--- a/HARK/cAndCwithStickyE/Calibration/KYratioSS.txt
+++ /dev/null
@@ -1 +0,0 @@
-12
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/PermShkAggVar.txt b/HARK/cAndCwithStickyE/Calibration/PermShkAggVar.txt
deleted file mode 100644
index 51af6eb09..000000000
--- a/HARK/cAndCwithStickyE/Calibration/PermShkAggVar.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.00004
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/PermShkVarAnn.txt b/HARK/cAndCwithStickyE/Calibration/PermShkVarAnn.txt
deleted file mode 100644
index 11ba96a30..000000000
--- a/HARK/cAndCwithStickyE/Calibration/PermShkVarAnn.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.012
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/TranShkAggVar.txt b/HARK/cAndCwithStickyE/Calibration/TranShkAggVar.txt
deleted file mode 100644
index 4e6943788..000000000
--- a/HARK/cAndCwithStickyE/Calibration/TranShkAggVar.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.00001
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/TranShkVarAnn.txt b/HARK/cAndCwithStickyE/Calibration/TranShkVarAnn.txt
deleted file mode 100644
index 1c7da8c00..000000000
--- a/HARK/cAndCwithStickyE/Calibration/TranShkVarAnn.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.03
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/UnempPrb.txt b/HARK/cAndCwithStickyE/Calibration/UnempPrb.txt
deleted file mode 100644
index 30e2fb4d6..000000000
--- a/HARK/cAndCwithStickyE/Calibration/UnempPrb.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.05
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/UpdatePrb.txt b/HARK/cAndCwithStickyE/Calibration/UpdatePrb.txt
deleted file mode 100644
index 2a64fae10..000000000
--- a/HARK/cAndCwithStickyE/Calibration/UpdatePrb.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.25
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Calibration/betaSOE.txt b/HARK/cAndCwithStickyE/Calibration/betaSOE.txt
deleted file mode 100644
index d6fcd6a7c..000000000
--- a/HARK/cAndCwithStickyE/Calibration/betaSOE.txt
+++ /dev/null
@@ -1 +0,0 @@
-0.97
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Figures/ValueVsPermShkAggVar.pdf b/HARK/cAndCwithStickyE/Figures/ValueVsPermShkAggVar.pdf
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diff --git a/HARK/cAndCwithStickyE/Figures/uCostvsPiInv.pdf b/HARK/cAndCwithStickyE/Figures/uCostvsPiInv.pdf
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diff --git a/HARK/cAndCwithStickyE/README.txt b/HARK/cAndCwithStickyE/README.txt
deleted file mode 100644
index 17d62012c..000000000
--- a/HARK/cAndCwithStickyE/README.txt
+++ /dev/null
@@ -1,85 +0,0 @@
-This document decribes the file structure and how to run the code in cAndCwithStickyE.
-The vast majority of the project is written in Python 2.7, but some empirical work uses Stata.
-
-To run the code, you should install the Anaconda distribution of Python 2.7 (from
-www.continuum.io), run Spyder, and execute the MAIN file; see notes below. All
-packages used by the StickyE project are included in the base distribution of Anaconda
-or included in this archive. The only exception occurs if you try to run the "beta
-dist" specification of the HA-DSGE model, which is commented out in the parameters
-file. To successfully run this exercise, you must install the packages joblib and
-dill by typing "conda install joblib" and "conda install dill" at a command prompt.
-
-NOTE ON SPYDER: Anaconda comes with an IDE called Spyder. On most operating systems,
-Spyder can be run by typing "spyder" at a command prompt. On Windows 10, this does
-not seem to work, and Spyder needs to be opened from the Start menu (usually in the
-Anaconda2 submenu).
-
-NOTE ON STATA: The MAIN file includes a boolean called use_stata. If set to True, the
-regressions will be run in Stata; if False, they will be run using the statsmodels.api
-package in Python. The only difference is that the Stata code is able to produce the
-KP statistic, but Python is not. To successfully use this option, you must set the
-stata_exe variable in the StickyEparams.py to point to a valid Stata executable.
-You must set a valid path in stata_exe if the make_emp_table or make_histogram booleans
-are set to True.
-
-NOTE ON MEMORY: The heterogeneous agents models are set to simulate 20,000 households
-for about 21,000 periods, saving the entire history of several household-level variables
-in in double precision. As such, running the SOE or HA-DSGE models is very memory
-intensive. We recommend that you run the code on a computer with at least 32GB of RAM
-to ensure that memory problems are not encountered. To run the SOE and HA-DSGE models
-back-to-back on the same call to the MAIN file, we recommend that you have at least
-64 GB of memory.
-
-NOTE ON TIME: On a reasonably modern CPU, the RA model can solve and simulate in about
-25-30 seconds (and has basically no memory requirement). The SOE model takes about
-15-20 minutes to solve and simulate (but see note on memory). The HA-DSGE mode takes
-roughly 16 hours to solve the sticky and frictionless versions, and will display progress
-toward finding the equilibrium aggregate saving rule after each iteration. The very
-long run time for HA-DSGE is largely due to the extremely large number of periods.
-Exercises that examine the relationship between parameters and value-at-birth (e.g. the
-"cost of stickiness" exercise) take about 4 hours each to run. Time series regressions
-take a few seconds to run in Python or two minutes in Stata (if use_stata is True).
-
-
-StickyE project files include:
-
-1) StickyE_MAIN.py
-This is the file to run to produce all the results and tables in the paper.
-There are a number of switches at the beginning of the file for the user to choose
-which results and files to produce. Note: Setting do_DSGE_markov=True will result in VERY long run time.
-
-2) StickyEparams.py
-This file loads the calibrated parameters, defines non-economic parameters (grid sizes,
-etc), constructs parameters calculated from the primitive calibrated parameters, and
-defines dictionaries for constructing objects in the MAIN file. It also sets:
-i) The number of periods and number of agents to simulate. This has direct consequences for run time.
-ii) The Stata path - this must be set to a valid Stata executable on the user's
-machine if you want to reproduce the Stata parts of the code.
-
-3) StickyEmodel.py
-This file contains the model code, building heavily off the rest of the HARK toolbox.
-
-4) StickyEtools.py
-This file does most of the post-processing after simulation. It runs regression, calculates
-equilibrium outcomes, etc. It is a collection of functions that are called by StickyE_MAIN.py
-
-5) StickyETimeSeries.do
-This is a Stata do file. The regressions are run in Stata in order to produce the KP statistic.
-
-Relative locations of other folders are defined near the top of StickyEparams.py.
-Referenced folders include:
-
-1) ./Results (results_dir)
-This folder contains saved data from the model simulations.
-
-2) ./Tables (tables_dir)
-This folder contains the Latex code output by functions in StickyEtools.py.
-Some of these tables appear in the paper.
-
-3) ./Figures (figures_dir)
-This folder contains figures produced by functions in StickyEtools.py.
-Some of these figures appear in the paper.
-
-4) ./Calibration (calibration_dir)
-This folder contains one line parameter values that are read by StickyEparams.py
-and by the LaTeX file that produces the paper.
diff --git a/HARK/cAndCwithStickyE/Results/DSGEmarkovFrictionlessBirthValue.csv b/HARK/cAndCwithStickyE/Results/DSGEmarkovFrictionlessBirthValue.csv
deleted file mode 100644
index 6afcc4cbb..000000000
--- a/HARK/cAndCwithStickyE/Results/DSGEmarkovFrictionlessBirthValue.csv
+++ /dev/null
@@ -1 +0,0 @@
--24.311498913568347,-24.068031555558235,-23.950381149098533,-23.582359316383222,-23.270495399257591,-22.866481004855782,-22.274778904479014,-21.869074849354746,-21.482237363934196,-21.200148944861038,-20.982374811308251
diff --git a/HARK/cAndCwithStickyE/Results/DSGEmarkovFrictionlessData.txt b/HARK/cAndCwithStickyE/Results/DSGEmarkovFrictionlessData.txt
deleted file mode 100644
index 08d1587fb..000000000
--- a/HARK/cAndCwithStickyE/Results/DSGEmarkovFrictionlessData.txt
+++ /dev/null
@@ -1,20010 +0,0 @@
-time_period DeltaLogC_me DeltaLogC DeltaLogY A BigTheta Delta8LogC Delta8LogY Delta8LogC_me Measurement_Error MrkvState R
-0.0 0.0015189105452846074 0.0027785416188439993 -0.0020592476157209544 41.42615307955284 0.99557930605502465 0.0040080354253672346 0.029980322405552307 0.0011488669372288762 0.0014619124040379835 7.0 1.0179217090649488
-1.0 -0.0064817738066116903 -0.0018613177614139054 0.0047031251502711192 41.786965772619631 0.99831453862132957 0.0097266720003424401 0.02392464416633297 0.0092957658900112428 -0.0031585436411597986 6.0 1.0177058101913274
-2.0 -0.0074113603832190733 -0.010552768816099595 0.0037207564381689946 41.88629386972233 0.99999510607391928 -0.0064871462247987066 0.022162170167449251 -0.0062743016804922835 -1.7135208279270469e-05 6.0 1.0176556208877661
-3.0 0.005609203171325905 0.0043221595779029398 0.00096908161811710904 41.840241418292528 0.99831453862132957 -0.00087083319423980621 0.022608569238057437 -0.0011671028217186685 0.0012699083851436901 6.0 1.0176788663460108
-4.0 -0.0041239884688787087 -0.0013823438089913265 0.0030593938974901869 42.310963201809784 0.99831453862132957 0.0080531209412596783 0.025516114222416755 0.0088169990472545812 -0.0014717362747436974 5.0 1.0174117557863434
-5.0 -0.0059950143776155707 -0.0080094125988614262 -0.00057036570621682176 42.12938778881378 1.0016785742323322 -0.0082824156399249338 0.0081935195965820769 -0.0086665641614716571 0.00054266194650215459 6.0 1.0175336077722219
-6.0 0.016783230511911718 0.01710403715179562 0.0070752150579137616 42.227284725734769 0.99557930605502465 -0.0053591346441785315 0.021876824316102498 -0.0018824820027285305 0.00022185530661825587 6.0 1.0174847971410252
-7.0 -0.0022893419681261629 -0.0020762576394592891 -0.01247957750602649 42.663450258631826 0.99557930605502465 0.00032263772371701749 0.0044183813339969055 -2.2370749218070252e-06 8.7709779513819328e-06 5.0 1.0172382636442268
-8.0 -0.015910832267591962 -0.01554520721906183 -0.0019784515639126015 42.971127452049963 1.0016785742323322 -0.018001111114188811 0.0044991773858052584 -0.011882997651809418 -0.00035685407057875067 4.0 1.017057610345188
-9.0 -0.0061305924646104237 -0.0073724807400585568 0.023162176276848554 42.908906841353222 1.0044324750173943 -0.023512274092833463 0.022958228512382696 -0.021385334749458196 0.00088503420486937237 5.0 1.017118833438424
-10.0 0.018874274320085455 0.017300498295314631 -0.0063437819731921499 43.096734350058888 0.99999510607391928 0.0043409930185807633 0.01289369010102155 0.00054997665839770504 0.0024588102296402041 5.0 1.0170281975148148
-11.0 -0.0068949824004921334 -0.002463736904421393 0.0010343138024522178 43.66676536755925 0.99999510607391928 -0.0024449034637435696 0.012958922285356658 -0.0047216764206672035 -0.0019724352664305506 4.0 1.016726243201536
-12.0 -0.01100910962711199 -0.015083167733243585 -0.01086779286164561 44.081038370950786 0.99831453862132957 -0.016145727387995829 -0.00096826447377913868 -0.020257455457038165 0.0021016228397010557 4.0 1.0165329871601678
-13.0 -0.00023589672160273523 0.0013984437917204662 0.0070183188112078396 44.358726507088996 0.99557930605502465 -0.0067378709974139361 0.0066204200436455231 -0.007903009778793102 0.00046728232637786214 4.0 1.0164051063719068
-14.0 -0.0021802427161391191 -0.0026227483480160962 -0.011570959020213486 45.000068289746146 0.99557930605502465 -0.026464656497225653 -0.012025754034481723 -0.028251257365772164 0.00090978795825484235 3.0 1.0160845204919693
-15.0 -0.018567994962489676 -0.01533517930258832 -0.0057753246008688643 45.34523073047778 0.99999510607391928 -0.039723578160354683 -0.0053215011293240985 -0.041492414792853843 -0.0023230277016465244 3.0 1.015931174306637
-16.0 0.0048305060427205615 0.0022312808210599999 0.024701745235447915 45.746628971780773 0.99831453862132957 -0.021947090120232854 0.021358695670036419 -0.025516008163487286 0.00027619752001405102 3.0 1.0157552341204774
-17.0 0.0027517488395938372 -6.2027415363152816e-06 -0.0053410361711268353 46.284059381510865 1.0044324750173943 -0.014580812121710612 -0.0071445167779389702 -0.018516852831246405 0.0030341491011441875 2.0 1.0154939694255822
-18.0 -0.016729042761064739 -0.011487950337144975 -0.011440218286034996 46.746023540326966 1.0016785742323322 -0.043369260754170219 -0.012240953090781817 -0.037169045447885851 -0.0022069433227755618 2.0 1.0152985414741607
-19.0 -0.00042430711297941626 0.001410900401604992 0.0063953369263906081 47.061479246484446 0.99557930605502465 -0.039494623448143834 -0.0068799299668434255 -0.038426601378165198 -0.0040421508373599891 3.0 1.0151962129063625
-20.0 0.012253300848415971 0.011774315818319336 -0.0017085102729537376 47.571791900865584 0.99557930605502465 -0.012637139896580912 0.0022793526218484474 -0.0076993480710703388 -0.0035631658072633426 2.0 1.0149569857266474
-21.0 -0.0069929605490474389 -0.011099929944867221 -0.013944733283000065 47.83207623486679 0.99557930605502465 -0.025135513633168599 -0.018683699472359455 -0.022523030964899604 0.0005438035885564444 2.0 1.0148513343384313
-22.0 0.0052270908708199337 0.00089641408348614671 0.0010897389567107573 48.09110080576599 1.0016785742323322 -0.021616351201666356 -0.0060230014954352146 -0.020698612730639165 0.0048744803758902222 2.0 1.0147471261905021
-23.0 0.0030333369940673816 0.0056111775406520181 0.024004899274188723 48.138739687178223 1.0044324750173943 -0.00066999435842601818 0.02375722237962237 0.0032009656448297452 0.0022966398293055813 3.0 1.0147569499319007
-24.0 0.016439602686901394 0.015317678223917025 -0.010320855739099858 48.306346704775933 0.99831453862132957 0.012416403044431007 -0.011265378594925404 0.010467927697548479 0.0034185642922899681 4.0 1.0147189060451591
-25.0 0.017125820863541891 0.02034174049036816 -0.0014950988615546048 48.069261288262226 0.99999510607391928 0.032764346276335482 -0.007419441285353174 0.037269834917539796 0.00020264466546369801 5.0 1.0148427079049023
-26.0 0.018389075983359482 0.01573444632981752 0.010129847602864603 48.164083352507603 1.0044324750173943 0.059986742943297977 0.014150624603546425 0.060141588611880681 0.0028572743190056447 5.0 1.0148046522142258
-27.0 -0.0063914946588527477 -0.0029798341319887678 -0.0052838603514431363 48.678829387693071 1.0044324750173943 0.055596008409704217 0.0024714273257126799 0.053773495225875406 -0.00055438620785832043 4.0 1.0145714505911148
-28.0 -0.0093397632757664106 -0.011147437831444951 0.0039402323395786813 48.460726268767736 1.0016785742323322 0.03267425475993993 0.0081201699382450997 0.031692642816741445 0.0012532883478202032 5.0 1.0146863874425109
-29.0 0.011077296906458395 0.012876937555858753 0.010859081065914508 49.006135528179179 0.99831453862132957 0.056651122260665904 0.032923984287159674 0.063950486426603143 -0.00054635230158014145 4.0 1.0144433983681347
-30.0 -0.015030281770683551 -0.014453936928752548 -0.0063381195640281364 49.220104394747523 0.99831453862132957 0.041300771248427209 0.025496125766420775 0.042050124265795047 -0.0011226971435111506 4.0 1.0143604427391568
-31.0 -0.001752760427146477 -0.0031466914676883395 0.0030420832608776746 49.882605894165572 1.0016785742323322 0.032542902240086852 0.0045333097531097301 0.030826553690445985 0.00027123389703071702 3.0 1.0140790330121454
-32.0 -0.010952820737852059 -0.0097084309916768141 -0.0041488174508604401 49.769706135117104 1.0044324750173943 0.0075167930244930126 0.01070534804134915 0.0096627152586934717 -0.00097315584914453557 4.0 1.0141500473243359
-33.0 0.023164120412300926 0.019516661115507927 0.0082765502566124211 49.551036834258369 0.99999510607391928 0.00669171364963278 0.020476997159516175 0.0043501086306425574 0.0026743034476484641 5.0 1.0142617125321141
-34.0 0.016267068204058738 0.018542796631236674 -0.0063124101885859013 49.451814414544515 1.0016785742323322 0.0095000639510519347 0.0040347393680656707 0.0013699789746907665 0.00039857502047052596 5.0 1.0142997237466982
-35.0 -0.0007888775066849929 -0.0012082657802398311 2.9159925443666967e-05 49.688677126434342 0.99831453862132957 0.011271632302800871 0.009347759644952474 0.011666999560769775 0.00081796329402536363 5.0 1.0142091899809837
-36.0 -0.001726668638420692 -0.00021864046166225615 -0.0018854909239870328 49.915518333856291 0.99831453862132957 0.022200429672583566 0.0035220363813867599 0.022800932710376504 -0.00069006488273307717 5.0 1.0141231476614587
-37.0 0.0019846258950594886 -0.001273461330645792 -0.0020875403850273179 50.139662516259882 1.0016785742323322 0.008050030786079021 -0.0094245850695550658 0.0026422291556839617 0.0025680223429722015 5.0 1.014038755913766
-38.0 -0.0084037612350893742 -0.0043397137988966106 -0.0029121649793175403 50.643085603926259 1.0016785742323322 0.018164253915934958 -0.0059986304848444697 0.015929454495643453 -0.0014960250932205465 4.0 1.0138234297274475
-39.0 -0.01165603163393894 -0.01450146126555904 0.013033280710602653 50.857097882245448 1.0016785742323322 0.0068094841180642574 0.0039925669648805083 0.0093879162091506374 0.0013494045383995537 4.0 1.013744800971131
-40.0 -0.0082729702568491437 -0.006206441367414911 -0.019437386536024275 51.483776591853648 0.99999510607391928 0.01031147374232616 -0.011296002120283328 0.011159597622009909 -0.00071712435103469196 3.0 1.0134899455890545
-41.0 -0.017245394505605111 -0.016042471811276771 -0.010551208626603324 51.924036365018026 0.99557930605502465 -0.025247659184458537 -0.030123761003499071 -0.022174039146061253 -0.0019200470453630217 3.0 1.0133332065513065
-42.0 0.0022469445924155618 0.0013270222155048195 0.00061886186761708154 52.009866146442207 1.0016785742323322 -0.042463433600190392 -0.023192488947296089 -0.037995516925588585 -0.001000124668452287 3.0 1.0133029035011414
-43.0 0.0014488400695048531 0.0020931671648596051 0.015752062019286084 52.13682033264076 0.99557930605502465 -0.039162000655090956 -0.0074695868534536709 -0.038847511044792338 -0.0016444517638070232 3.0 1.0132582311375746
-44.0 -0.0040184252373441642 -0.00522815806284882 -0.019742389979521657 52.852646075805133 0.99557930605502465 -0.04417151825627752 -0.025326485908988297 -0.047486745529070756 -0.00043471893830237526 2.0 1.0129824347698049
-45.0 -0.013559430201964373 -0.013301002339963258 0.0055992939837376902 53.239198941464572 0.99831453862132957 -0.056199059265594986 -0.017639651540223289 -0.056069105222980392 -0.00069314680030350723 2.0 1.0128506067791299
-46.0 -0.0077920111245322055 -0.0091231818545169652 -0.021331229074794442 53.422017195899002 0.99831453862132957 -0.060982527321215341 -0.036058715635700185 -0.055478774044072976 0.0006380239296812526 2.0 1.0127888047369504
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-20003.0 0.017204096831730631 0.016239522259787087 0.012536124576111085 33.680163915918278 0.99999510607391928 0.069050910082417261 0.065783824530837087 0.06726384601565627 0.00070841947319956586 10.0 1.0227444642720238
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-20007.0 0.0099251409086891229 0.0113697665943846 0.012264242242066459 32.968990928642611 1.0044324750173943 0.063057655432298354 0.07210728451537296 0.062817855127942579 -0.0014593010859774379 10.0 1.023268349578569
-20008.0 0.0074873470401541908 0.0052687610553459052 0.003326763498881391 33.028306801222833 1.0044324750173943 0.060312268473780684 0.065892633273310075 0.059053914671953756 0.00075928489883128516 10.0 1.0232239481659333
diff --git a/HARK/cAndCwithStickyE/Results/DSGEmarkovFrictionlessResults.csv b/HARK/cAndCwithStickyE/Results/DSGEmarkovFrictionlessResults.csv
deleted file mode 100644
index 8501615e6..000000000
--- a/HARK/cAndCwithStickyE/Results/DSGEmarkovFrictionlessResults.csv
+++ /dev/null
@@ -1 +0,0 @@
-56.847792401,3.44465152065,0.276427085691,0.00965865960185,0.00743251267234,0.00387845391934,1.01458943395,0.597611960896,0.796007731615,0.863239758053,0.123056245946,0.0542195437699,0.0534679195361
\ No newline at end of file
diff --git a/HARK/cAndCwithStickyE/Results/DSGEmarkovStickyBirthValue.csv b/HARK/cAndCwithStickyE/Results/DSGEmarkovStickyBirthValue.csv
deleted file mode 100644
index 59d5d93c6..000000000
--- a/HARK/cAndCwithStickyE/Results/DSGEmarkovStickyBirthValue.csv
+++ /dev/null
@@ -1 +0,0 @@
--24.321233175889635,-24.078152103307058,-23.961036115445367,-23.593752276046803,-23.282284372129595,-22.878201207882963,-22.286075315090393,-21.879602769758616,-21.491854325722905,-21.208419487452186,-20.990138513482528