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Heterogeneous Agents Resources and toolKit (HARK)

HARK is a toolkit for the structural modeling of economic choices of optimizing and non-optimizing heterogeneous agents. For more information on using HARK, see the Econ-ARK Website.

The Econ-ARK project is fiscally sponsored by NumFOCUS. Consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.

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This project is bound by a Code of Conduct.

Questions/Comments/Help

We have a Gitter Econ-ARK community.

Table of Contents

• Install
• Usage
• Citation
• Support
• Release Types
• Documentation
• Introduction
  • For Students: A Gentle Introduction to Hark
  • For Economists: Structural Modeling with Hark
  • For Computational Economics Developers
• Contributing to HARK
• Disclaimer

Install

Install from Anaconda Cloud by running:

conda install -c conda-forge econ-ark

Install from PyPI by running:

pip install econ-ark

Once HARK is installed, you can copy its example notebooks into a local working directory of your choice from within a Python environment:

from HARK import install_examples

install_examples()

Follow the simple prompts to make an examples subdirectory inside the directory you specify.

To use the interactive notebooks, you will need to install Jupyter: pip install jupyter. Moreover, the math and text formatting in some notebooks might require the latex-envs extension. To set this up, follow these steps:

1. Install the package: pip install jupyter_latex_envs
2. Install associated JS files: jupyter contrib nbextension install --user
3. Enable the extension: jupyter nbextension enable latex_envs --user --py

You can launch Jupyter from the command line with jupyter notebook, then navigate to the directory where you have installed the example notebooks. We recommend starting with /examples/Gentle-Intro/Gentle-Intro-to-HARK.ipynb, which links to the other introductory notebooks near the bottom.

Usage

We start with almost the simplest possible consumption model: A consumer with constant relative risk aversion (CRRA) utility who has perfect foresight about everything except the (stochastic) date of death.

<div align="center"> <img height="52px" src="https://github.com/econ-ark/HARK/blob/main/docs/images/usage-crra-utility-function.png"> </div>

The agent's one period problem can be recursively