/EC320

This is the homepage for your ECON 320 Summer Course.

Primary LanguageHTML

Welcome to EC 320: Introduction to Econometrics (Summer 2022) at the University of Oregon.

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.

Week 1

  1. Introduction
    .html | .pdf

  2. Statistics Review I
    .html | .pdf

  3. Statistics Review II
    .html | .pdf

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

Week 2

  1. Regression Logic
    .html | .pdf

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

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

  4. Classical Assumptions
    .html|.pdf

Week 3

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

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

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

Week 4

  1. Categorical Variables
    .html|.pdf

  2. Interactive Relationships
    .html|.pdf

  3. Nonlinear Relationships
    .html|.pdf

  4. Final Review
    .html|

Labs

  1. Lab 1 .html | .RMD

  2. Lab 2 .html | .RMD | data

  3. Lab 3 .html | .RMD

  4. Lab 4

Worksheets

  1. Week 1 .pdf

Contributors

I am indebted to Ed Rubin (@edrubin), Kyle Raze (@kyleraze),Phil Economides (@peconomi), and Jeni Putz for their contributions to course materials and the preparation they have put into previous work in this course. I also source some material from Nick Huntington-Klein (@NickCH-K), who maintains a trove of resources for learning causal inference.