Introduction to Econometrics

Welcome to EC 320: Introduction to Econometrics (Winter 2021) at the University of Oregon.

• Instructor: Boyoon Chang
• GE: Kyutaro Matsuzawa
• Syllabus

This course introduces the statistical techniques that help economists learn about the world using data. Using calculus and introductory statistics, students will cultivate a working understanding of the theory underpinning regression analysis—how it works, why it works, and when it can lead us astray. As the course progresses, students will apply the insights of theory to work with and learn from actual data using R, a statistical programming language. My goal is for students to leave the 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. What is Econometrics?
   .html | .pdf

2. Statistics Review I
   .html | .pdf

3. Statistics Review II
   .html | .pdf

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

5. Regression Logic
   .html | .pdf

6. Midterm Review
   .html | .pdf

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7. Simple Linear Regression: Estimation I
   .html | .pdf

8. Simple Linear Regression: Estimation II
   .html | .pdf

9. Classical Assumptions
   .html | .pdf

10. Simple Linear Regression: Inference
    .html | .pdf

11. Multiple Linear Regression: Estimation
    .html | .pdf

12. Multiple Linear Regression: Inference
    .html | .pdf

13. Midterm Review
    .html | .pdf

14. Categorical Variables
    .html | .pdf

15. Interactive Relationships
    .html | .pdf

16. Nonlinear Relationships
    .html | .pdf

17. Final Review
    .html

Labs

1. Introduction to R
   .html | Exercise 1

2. Introduction to function
   .html | Exercise 2

3. Data structure, preparation, and variable types
   .html | Exercise 3

4. Data manipulation, visualization, and regression
   .html | Exercise 4

5. Simple linear regression revisited
   .html | Exercise 5

6. Multiple regression and categorical variable
   .html | Exercise 6

7. Log transformation and interaction term
   .html | Exercise 7

8. Final Review
   .pdf

Due Dates

1. Exercise 1 : Due 01/07(Fri)
   .R | submit it here
2. Exercise 2: Due 01/12(Wed)
   .R | submit it here
3. Problem Set 1: Due 01/14(Fri)
   .pdf | submit it here
4. Exercise 3: Due 01/19(Wed)
   .R | submit it here
5. Exercise 4: Due 02/02(Wed)
   .R | submit it here
6. Problem Set 2: Due 02/04(Fri) 02/07(Mon)
   .pdf | submit it here
7. Exercise 5: Due 02/09(Wed)
   .R | submit it here
8. Exercise 6: Due 02/16(Wed)
   .R | submit it here
9. Problem Set 3: Due 02/18(Fri) 02/21(Mon)
   .pdf | submit it here
10. Exercise 7: Due 03/02(Wed)
    .R | submit it here
11. Problem Set 4: Due 03/04(Fri) 03/07(Mon)
    .pdf | submit it here

Other course content

For supplemental lecture documents, problem sets, and other materials, please see Canvas.

Contributors

I am indebted to Ed Rubin (@edrubin) and Kyle Raze(@kyleraze) for their generous contribution and offerings of course materials. I also source some material from Nick Huntington-Klein (@NickCH-K), who maintains a trove of resources for learning causal inference.