IMF Workshop 2025

Modern Computational Economics and Policy Applications

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 significantly enhance their modeling and data processing capabilities by building or updating their knowledge of Python's scientific ecosystem. This course will cover the Python scientific libraries, emphasizing new developments driven by artificial intelligence. We will discuss their use for policy applications, with a slight bias towards modeling and macroeconomics.

Relative to the course we ran at the IMF in 2024, there will be less emphasis on Python foundations and more emphasis on AI and its implications for economic policy analysis.

Times and Dates

• Dates: December 2-4, 2025
• Times: 9:30 -- 12:30 and 14:00 -- 17:00
• Location: IMF HQ2-03B-748

Instructors

Chase Coleman is a lecturer in computational economics at Rice University. He was an early contributor at QuantEcon and has given lectures and workshops on Python, Julia, and other open source computational tools at institutions and universities all around the world.

John Stachurski is a mathematical and computational economist current based at Kyoto 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, Econometrica, and Operations Research. In 2016 he co-founded QuantEcon with Thomas J. Sargent.

Syllabus

• Tuesday morning: Course overview, AI pair programming
  • Introduction
  • Coding with AI
  • Quick Colab intro
  • Python for Scientific Computing
  • JAX
  • NumPy vs Numba vs JAX
• Tuesday afternoon: Household problems (DP and EGM) via JAX
  • Optimal Savings I: Cake Eating
  • Optimal Savings II: Numerical Cake Eating
  • Optimal Savings III: Stochastic Returns
  • Optimal Savings IV: Time Iteration
  • Optimal Savings V: The Endogenous Grid Method
  • Optimal Savings VI: EGM with JAX
  • The Income Fluctuation Problem I: Discretization and VFI
  • The Income Fluctuation Problem II: Optimistic Policy Iteration
  • The Income Fluctuation Problem III: The Endogenous Grid Method
  • The Income Fluctuation Problem IV: Transient Income Shocks
  • The Income Fluctuation Problem V: Stochastic Returns on Assets
• Wednesday morning: Data wrangling: Pandas and polars
  • Modern data landscape and why Python
  • Data wrangling with pandas
  • Practice time! Parts 1+2
  • Introduction to polars
  • Practice time! Parts 3+4
• Wednesday afternoon: Data science and Bayesian analysis
  • Bayesian stats refresher
  • Toy GP notebook
  • Gaussian processes
  • GP Kernels
  • Aiyagari with Jax/GPs
• Thursday morning: Introduction to deep learning
  • Introduction to Deep Learning
  • Simple Neural Network Regression with Keras and JAX
  • Neural Network Regression with JAX
  • Policy Gradient-Based Optimal Savings
• Thursday afternoon: Reinforcement learning
  • Introduction to Reinforcement Learning
  • Temporal difference learning
  • Temporal difference exercises
  • Continuous state

Computing Environment

For live coding at the workshop, we recommend Colab.

Prerequisites

We will assume attendees are familiar with the basics of (a) Python and (b) Colab and/or Jupyter notebooks.

Those who lack such foundations but wish to attend should read the following lectures in the QuantEcon Python Programming lecture series.

• https://python-programming.quantecon.org/python_by_example.html
• https://python-programming.quantecon.org/functions.html

You can run these lectures by clicking the "play" icon top right, selecting Colab, and clicking on "Launch Notebook".