ECON526 - Fall 2025

This is a MA-level course in quantitative economics, data science, and causal inference in economics.

This course will have a combination of coding, theory, and development of mathematical background. All coding is done in Python.

Course materials

All materials will be on github, and canvas will be used to submit assignments/communication.

Course notes:

• Link to Jesse's Lecture Slides
• Paul's HTML Slides, source

There is no assigned physical textbook, but we will be using lecture notes from:

• Causal Inference for The Brave and True
• QuantEcon Python and Data Science Lectures

Computing Environment

See here for instructions. All course code will be done in python

• Get a GitHub ID and apply for the Student Developer Pack to get further free features
• We strongly recommend using VS Code as your primary code editor and uv for your python and package management.
• After setup you can clone a variety of repositories onto your local machine using a terminal, using either git directly (e.g. in terminal go git clone https://github.com/ubcecon/ECON526.git), or VS Code (recommended). See instructions and more other useful code repositories here

Syllabus

See Syllabus for more details

Problem Sets and Exams

The course has one midterm, weekly to bi-weekly problem sets, and a final data project due the last day of class.

1. September 8 Midnight: Problem Set 0 - covers Math Camp material, so you can get started right away.
2. September 14 Midnight: Problem Set 1 - short assignment checking your installation of Jupyter.
3. September 21 Midnight: Problem Set 2
4. September 28 Midnight: Problem Set 3
5. October 5 Midnight: Problem Set 4
6. NOT TO HAND IN Midterm Practice Problems
7. October 2 (LAB SESSION): Midterm Logistics Practice
8. October 8: IN CLASS MIDTERM
9. See Canvas for additional problem sets
10. December 15: Data Project Due

See the /problem_sets folder within this repository for the problem sets as jupyter notebooks.

• The pyproject.toml and uv.lock files provide the package setup. Simple run uv sync (more details here)
• Problem Set 0 can be done on paper and scanned, but other problem sets must be submitted as .ipynb and exported html files. See instructions here

Lectures

The course is structured into two parts:

Jesse

• September 3: Linear Algebra Foundations, PDF, and Extra and Self Study Materials

• September 8: Linear Algebra Foundations, PDF, and Extra and Self Study Materials

• September 10: Least Squares, Uniqueness, and Regularization, PDF, and Extra and Self Study Materials

• September 15: Applications of Linear Algebra and Eigenvalues, PDF, and Extra and Self Study Materials

• September 17: Latent Variables and Unsupervised Learning, PDF, and Extra and Self Study Materials

• September 22: Latent Variables and Unsupervised Learning, PDF, and Extra and Self Study Materials

• September 24: Linear and Nonlinear Dynamics, PDF, and Extra and Self Study Materials

• September 29: Probability, Conditioning, and Independence, PDF, and Extra and Self Study Materials

• October 1: Probability, Conditioning, and Independence, PDF, and Extra and Self Study Materials and start Stochastic Processes, Markov Chains, and Expectations, PDF, and Extra and Self Study Materials

• October 6: Midterm Practice Problems and Stochastic Processes, Markov Chains, and Expectations, PDF, and Extra and Self Study Materials

• October 8 (IN CLASS MIDTERM)

• October 13 (Statutory holiday)

• October 15: Large Language Models and Embeddings, PDF,

Paul

Go here for a list of topics, reading, and slides.

Here is the source for my slides.

See "Sources and Further Reading" (2nd last slide) on each set of slides for additional reading.

Important Dates

• November 11 (Midterm Break)
• November 13 (Midterm Break)

• December 15
  • PROJECT DUE