https://github.com/quantecon/imf_2024

Computational economics workshop at the IMF, March 2024
https://github.com/quantecon/imf_2024

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Computational economics workshop at the IMF, March 2024

• Host: GitHub
• URL: https://github.com/quantecon/imf_2024
• Owner: QuantEcon
• License: cc0-1.0
• Created: 2024-02-07T19:24:01.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-27T12:47:20.000Z (about 2 years ago)
• Last Synced: 2025-04-19T21:20:05.523Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 17.8 MB
• Stars: 7
• Watchers: 6
• Forks: 9
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Modern Computational Economics and Policy Applications

![](qe-logo-large.png)

A workshop for the IMF's Institute for Capacity Development

## Abstract

Open source scientific computing environments built around the Python

programming language have expanded rapidly in recent years. They now form the

dominant paradigm in artificial intelligence and many fields within the natural

sciences.  Economists can greatly enhance their modeling and data processing

capabilities by exploiting Python's scientific ecosystem.  This course will

cover the foundations of Python programming and Python scientific libraries, as

well as showing how they can be used in economic applications for rapid

development and high performance computing.

## Times and Dates

* Dates: March 25-27, 2024

* Times: 9:30 -- 12:30 and 14:00 -- 17:00 

* Location: room HQ2-3B-748 (in-person participants) 

## Instructors

[Chase Coleman](https://github.com/cc7768) is a computational economist based at New York University where

he is a visiting assistant professor. He was an early contributor at QuantEcon

and, along with other members of QuantEcon, has given lectures and workshops

on Python, Julia, and other open source computational tools at institutions and

universities all around the world.

[John Stachurski](https://johnstachurski.net/) is a mathematical and

computational economist based at the Australian National University who works on

algorithms at the intersection of dynamic programming, Markov dynamics,

economics, and finance.  His work is published in journals such as the Journal

of Finance, the Journal of Economic Theory, Automatica, Econometrica, and

Operations Research.  In 2016 he co-founded QuantEcon with Thomas J. Sargent. 

In addition, 2011 Nobel Laureate [Thomas J. Sargent](http://www.tomsargent.com/)

will join remotely and run a one hour session on the 27th.

## Syllabus

* Monday morning: Introduction 

  - Scientific computing: directions and trends (`intro_slides/sci_comp_intro.pdf`)

  - Python and the AI revolution (`ai_revolution/ai_revolution.pdf`)

  - A brief tour of Python's massive scientific computing ecosystem (`scientific_python/main.pdf`)

  - Working with Jupyter (free coding)

  - A taste of HPC with Python (`fun_with_jax.ipynb`)

* Monday afternoon: Python basics

  - Core Python  (`quick_python_intro.ipynb`)

  - NumPy / SciPy / Matplotlib / Numba (`quick_scientific_python_intro.ipynb`)

  - Exercises: Simulation (`simulation_exercises.ipynb`)

  - Exercises: Lorenz curves and Gini coefficients (`lorenz_gini.ipynb`)

* Tuesday morning: Markov models in Python

  - Markov chains: Basic concepts (`finite_markov.ipynb`)

  - Intermezzo: A quick introduction to JAX (`jax_intro.ipynb`)

  - Wealth distribution dynamics (`wealth_dynamics.ipynb`)

  - Exercises: Markov chain exercises (`markov_homework.ipynb`)

* Tuesday afternoon: Dynamic programming

  - Job search (`job_search.ipynb`)

  - A simple optimal savings problem (`opt_savings_1.ipynb`)

  - Alternative algorithms: VFI, HPI and OPI (`opt_savings_2.ipynb`)

  - The endogenous grid method (`egm.ipynb`)

* Wednesday morning: Heterogeneous agents

  - Heterogenous firms (`hopenhayn.ipynb`)

  - The Aiyagari model (`aiyagari.ipynb`)

* Wednesday afternoon: Further applications

  - Sovereign default (`arellano.ipynb`)

  - The Bianchi overborrowing model (`overborrowing.ipynb`, `bianchi.pdf`)

## Software

The main interface to Python will be either `jupyter-notebook` or `jupyter-lab`.

Access to the `ipython` REPL will also be useful.

Some work will be done remotely using Google Colab --- a Google account is

required.

Required Python libraries (much of which is found in the Anaconda Python distribution):

* `numpy`

* `scipy`

* `matplotlib`

* `pandas`

* `scikit-learn`

* `statsmodels`

* `numba`

* `f2py`

* `quantecon`

## Useful References

* https://www.anaconda.com/download

* https://github.com/google/jax/issues/13684 (for JS)