This is the homepage for the QuantEcon scientific and high performance computing workshop to be held at the Central Bank of Chile in May 2024.

• John Stachurski (Australian National University)

Bio: John Stachurski is a mathematical and computational economist 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.

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.

• An overview of modern scientific computing
• AI and its impact on economic modeling
• Quick introduction to Python

• Linear regression with Python
• Accelerating Python using Numba and Fortran
• Inventory dynamics
• Gini coefficients and Lorenz curves
• Wealth dynamics (simple model)
• Markov chains

• Introduction to JAX and GPU computing
• Automatic differentiation
• Autodiff application: Epstein-Zin preferences
• Wealth dynamics revisited
• Inventory dynamics revisited
• Job search

• Dynamic programming: theory and algorithms
• Optimal savings problems (JAX)
• Endogenous grid method (JAX)

• Aiyagari model
• Arellano sovereign default model
• Bianchi overborrowing model
• Hopenhayn industry model

• May 13th - 17th

All participants should bring laptop computers. If possible, participants should bring laptops with the ability to install open source software. For those without such permissions, a cloud computing option will be provided. The courses assume knowledge of the fundamentals of linear algebra, analysis, dynamic optimization and probability.

Suitable background can be found in the first few chapters of Dynamic Programming.