Resources
Everything I’ve built for people learning econometrics, free to use. If something is helpful in your own class, please take it.
ECON 320 Econometrics Lab12 weeks, 4 problem sets, final project
A semester of hands-on Python notebooks, from descriptive statistics and data visualization through multiple regression, specification problems such as omitted variable bias, and hypothesis testing with heteroskedasticity-robust standard errors, with the emphasis on questioning results and evaluating assumptions rather than just producing them. Each week pairs the completed lecture notebook with a separate practice exercise. Every notebook can be read in the browser or downloaded as a .ipynb file to run yourself. Syllabus.
Week 0 Getting started Slides · Exercise .ipynb
Week 1 Descriptive statistics and Python basics Lecture .ipynb · Exercise .ipynb
Week 2 Understanding and presenting data Lecture .ipynb · Exercise .ipynb
Week 3 OLS for simple linear regression Lecture .ipynb · Exercise .ipynb
Week 4 OLS for multiple linear regression Lecture .ipynb · Exercise .ipynb
Week 5 Qualitative data and dummy variables Lecture .ipynb · Exercise .ipynb
Week 6 Multicollinearity Lecture .ipynb · Exercise .ipynb
Week 7 Omitted variable bias Lecture .ipynb · Exercise .ipynb
Week 8 Asymptotic analysis Exercise .ipynb
Week 9 Inference: t- and F-tests Lecture .ipynb · Exercise .ipynb
Week 10 Heteroskedasticity Lecture .ipynb · Exercise .ipynb
Week 11 Endogeneity and instrumental variables Lecture .ipynb · Exercise .ipynb
Problem set 1 .ipynb · Problem set 2 .ipynb · Problem set 3 .ipynb · Problem set 4 .ipynb
Data projects for beginners4 projects with real data
Assignments I wrote for Economic Analysis for Social Networks at CUHK, when I was a master’s student. Each one is a small, self-contained project with real data.