Introduction to Econometrics

This repository contains materials for EC 320: Introduction to Econometrics, taught in the Spring of 2022 by Emmett Saulnier.

This course introduces the statistical techniques that help economists learn about the world using data. We will focus much of our attention on regression analysis, the workhorse of applied econometrics. Using calculus and introductory statistics, we will cultivate a working understanding of the theory underpinning regression analysis—how it works, why it works, and when it can lead us astray. We will apply the insights of theory to work with and learn from actual data using R, a statistical programming language. To the extent that you invest the requisite time and effort, you can leave this course with marketable skills in data analysis and—most importantly—a more sophisticated understanding of the notion that correlation does not necessarily imply causation.

Lectures

The HTML versions of the lecture slides allow you to view animations and interactive features, provided that you have an internet connection. The PDF slides don't require an internet connection, but they cannot display the animations or interactive features.

1. Introduction .html | .pdf

2. Statistics Review I .html | .pdf

3. Statistics Review II .html | .pdf

4. The Fundamental Problem of Econometrics .html | .pdf

5. Logic of Regression .html | .pdf

6. Simple Linear Regression 1 .html | .pdf

7. Simple Linear Regression 2 .html | .pdf

8. Classical Assumptions .html | .pdf

9. Inference .html | .pdf

10. Multiple Regression Estimation .html | .pdf

11. Multiple Regression Inference .html | .pdf

12. Nonlinear Relationships .html | .pdf

13. Categorical Variables .html | .pdf

14. Interactive Relationships .html | .pdf

15. Model Specification .html | .pdf

16. Difference-in-Differences .html | .pdf

Labs

Each bullet point represents a given week.

1. Introduction to R .html

2. Working with Data .html

3. Grouping, Summarizing, and Plotting Data .html

4. Simple Linear Regression .html

5. Midterm Review

6. Hypothesis Testing .html

7. Multivariate Linear Regression .html

8. Transformations, Dummies, and Interactions .html

9. Plots .html

Contributors

Material for this course has contributions from Ed Rubin (@edrubin), Kyle Raze (@kyleraze), and Philip Economides(@peconomi), who have taught the class prior to me and graciously made their work public. I also source some material from Nick Huntington-Klein (@NickCH-K), who maintains a trove of resources for learning causal inference.