This is a 2-week class that I created and taught at MIT during IAP (January term) 2023 and 2024.
The course is intended for students using empirical or quantitative methods in Economics, including students interested in econometrics, applied microeconomics and quantitative macroeconomics. It covers the basics of programming and numerical methods, with an emphasis on (a) best practices for replicability, (b) enhanced performance, and (c) improved approximation. The course aims to expand quantitative-focused students' toolboxes, familiarizing them with a variety of methods for different numerical problems (e.g. optimization, simulation, zero-solving, solving for equilibria, dynamic programming) and deepening their understanding of how the different algorithms work, their advantages and their disadvantages.
Prior experience in programming is helpful, but not a prerequisite. The programming language used throughout the course will be Julia.
There are no graded assignments, but students are encouraged to run the code in their own and experiment with it. The only way to learn this is by practicing!
- L1. Introduction to Programming in Julia
- L2. Replicability and Version Control using Git
- L3. Dynamic Programming and Function Approximation
- L4. Numerical Integration and Differentiation
- L5. Numerical Integration and Differentiation II
- L6. Numerical Optimization
- L7. Zero-Solvers and Fixed Point Algorithms
- L8. Review with an Economic Application (Dynamic Duopoly Models)