Overview

This course provides hands-on experience with econometric methods, from foundational data analysis to advanced causal inference techniques. Using the Python programming language, students learn how to manage data, run regressions, test hypotheses, and interpret results in real-world economic contexts. Topics include descriptive statistics, data visualization, simple and multiple linear regression, qualitative data handling, model specification issues, inference methods, and robust standard errors. In an era of expanding generative AI tools, we place special emphasis on developing the ability to question results, evaluate assumptions, and draw sound conclusions from data.

Learning objectives

1. Organize, clean, and present economic data clearly and effectively.
2. Apply OLS to estimate linear regression models (simple and multiple) to analyze relationships between variables.
3. Incorporate qualitative data into regression models using dummy variables and interactions.
4. Diagnose and address common specification issues such as multicollinearity and omitted variable bias.
5. Perform hypothesis testing (t-tests, F-tests) and compute heteroskedasticity-robust standard errors.
6. Think critically before drawing causal conclusions.

Happy learning!

Key materials

• Syllabus (PDF)

Weekly topics and materials

Assignments

• Problem set 1 – HTML | Notebook
• Problem set 2 – HTML | Notebook
• Problem set 3 – HTML | Notebook
• Problem set 4 – HTML | Notebook

Project

• Final project – Guidelines | Datasets information