Skip to content

Latest commit

 

History

History
140 lines (106 loc) · 6.2 KB

syllabus.md

File metadata and controls

140 lines (106 loc) · 6.2 KB
Raw

ECON622 - Fall 2024

Computational Economics with Data Science Applications

Classes will held in-person.

Course Description

This is a graduate topics course in computational economics. We intend this to be useful for a large number of fields, but it is most useful for anyone likely to:

  1. Estimate a structural model
  2. Solve a dynamic model
  3. Collect and use data beyond what is possible in Stata (e.g medium/big data, textual data, etc.); or
  4. Implement econometric techniques that go beyond what is available in Stata
  5. Understand how new ML techniques can be applied to economics

A key purpose of this class is to teach specific techniques, algorithms, and tools to ensure that students write robust, correct, and tested code - and hopefully open the research opportunities for students to move to the cutting edge of quantitative economics. Beyond the necessary algorithms and new programming languages, another goal is to ensure that economists are using modern software engineering tools to allow collaboration - as most projects involve multiple coauthors and research assistants. Finally, all of the practice in this class will be done with the goal of showing how code used in research can be shared as open-source with the economics research community (and the scientific computing community as a whole).

Grading The only way to learn how to apply new programming languages and methods to economic problems is practice. To aid in this, a significant portion of the grade will be regular problem sets. The remainder of the grade will be a computational project.

  • Nearly weekly problem sets: 40%
  • Final Project: 40%
  • Presentation: 15%
  • Participation: 5%

While the problem sets will be frequent, many will be short to force practice (and will not be weighed identically) Assume you will get the full participation mark if you rarely miss class.

Students may work together on assignments, but each student should write their own answers. If you work closely together with someone and consquently have very similar code, you should state with whom you worked on your assignment.

The use of generative AI tools such as GitHub Copilot or ChatGPT is allowed as long as you disclose their use.

The final project topics are very open, and the main criteria is that you either (1) learn/use/apply a computational tool to a research topic of your interest or (2) contribute to an open-source computational economics project as a public good.

There will be short presentations in the last week of class. The topic of the presentation is flexible. It should be about computation and economics. It may be related to your final project. For example, you could present a summary of your plan for your project and any difficulties encountered so far. It could be about someone else's paper on a technique that might be useful for your project.

Topics

The course will be taught in 2 parts, one with each instructor.

We may not cover all these topics. A tentative schedule, based on last year's course is availabe on the course webpage.

Paul

This part of the course will introduce Julia and illustrate how it can be used for econometrics, especially structural estimation.

  1. Introduction to Julia

    • Learning the Julia programming language, with simple applications
    • Generic and Functional programming, multiple dispatch

  2. Software engineering tools: source-code control, unit testing, and continuous integration

    • Git and Github version tracking, diffs, collaboration, Pull Requests, etc.
    • Reproducible environments: package managers, and virtual environments
    • Unit and regression testing frameworks, benchmarking, and continuous-integration

  3. Extremum estimators & optimization

    • Review of extremum estimators
    • Introduction to optimization algorithms
    • Automatic Differentiation
    • Inference for extremum estimators

Jesse

This section will concentration on machine learning and deep learning techniques, and built computational tools such as working with gradients. Much of the code with be introduced using python toolkits such as JAX and PyTorch

  1. Iterative and matrix-free methods, pre-conditioning and regularization
  2. Introduction to Pytorch, JAX, and "ML Devops"
  3. Reverse-mode and forward-mode AD. Differentiable everything!
  4. Probabilistic Programming Languages (PPLs), Bayesian methods, and intro to generative models
  5. Gaussian Processes and Intro to Bayesian Optimization
  6. Neural Networks and Function Approximation
  7. Intro to Neural Networks, Function Approximation, and Representation Learning
  8. Deep Learning and Dynamic Models
  9. Double-descent, regularization, and generalization

UBC values and policies

UBC provides resources to support student learning and to maintain healthy lifestyles but recognizes that sometimes crises arise and so there are additional resources to access including those for survivors of sexual violence. UBC values respect for the person and ideas of all members of the academic community. Harassment and discrimination are not tolerated nor is suppression of academic freedom. UBC provides appropriate accommodation for students with disabilities and for religious and cultural observances. UBC values academic honesty and students are expected to acknowledge the ideas generated by others and to uphold the highest academic standards in all of their actions. Details of the policies and how to access support are available here https://senate.ubc.ca/policiesresources-support-student-success