ECON622

This is a graduate topics course in computational economics, with applications in datascience and machine learning.

Course materials

• See here for instructions on getting started with Github and Python.
  • Julia instructions are also provided for the second half of the course.
• Note the recommendations on GitHub Academic

Syllabus

See Syllabus for more details

Problem Sets

Jesse: see the schedule Paul: see problemsets.md

Lectures

Jesse For the first half of the course, see the schedule.

Paul

1. February 23: Environment and Introduction to Julia

   • Intro slides
   • Environment: read one or both of these on your own and install Julia, IJulia, and VSCode, preferrably before the first class
     • MoJuWo: Writing your code
     • Julia Environment
   • In class: Motivating econometric examples
   • Self-study: Introductory Examples or Chapter 1 of Scientific Programming in Julia

2. February 25: Integration

   • Slides
   • Self-study: Julia Essentials and Fundamental Types
   • Self-study: Chapter 2 of Scientific Programming in Julia

3. March 2: Nonlinear Equation Solving

   • Slides
   • Self-study: Design patterns: good practices and structured thinking

4. March 4: Project Best Practices

   • Slides
   • Self-study: Package development, unit tests, & CI
   • Self-study: Testing and Packages
   • Self-study: Git and Github

5. March 9: clean up example project, introduction to automatic differentiation

   • In class: automatic differentiation packages, slides
   • Self-study: Automatic Differentation in Scientific Programming in Julia
   • Self-study: Differentiation for Hackers
   • Self-study: Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and Julia
   • Optional:
     • Understanding automatic differentiation (in Julia)
     • Forward and Reverse Automatic Differentiation In A Nutshell
     • Intro to AD

6. March 11: Optimization

   • optimization, slides
   • Optimization algorithms
   • Optimization packages

7. March 16: Extremum Estimation

   • Extremum estimation and inference
   • Empirical likelihood
   • Bootstrap

8. March 18 Function Approximation

   • Slides

9. March 23 Code Performance

   • Coding for performance be sure to look at the 2023 branch for the recent additions
   • GPU usage
   • Self-study: SIMDscan: since it briefly came up in class, and I was curious about it, I made a little package for calculating things like cumulative sums and autoregressive simulations using SIMD
   • Self-study: Need for speed
   • Self-study: Performance Tips

10. March 25 Dynamic Programming

• Slides

11. March 30 Debiased Machine Learning

• My notes
• Self-study: CausalML, Chernozhukov, Newey, Quintas-Martinez, & Syrgkanis (2024)

24. April 1 Class Presentations
25. April 6 STAT HOLIDAY
26. April 8 Class Presentations

Look under "Releases" or switch to another branch for earlier versions of the course.