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

• Instructor: Philip Economides
• GE: Micaela Wood
• 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.

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?
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2. Statistics Review I
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3. Statistics Review II
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4. The Fundamental Problem of Econometrics
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5. Regression Logic
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6. Simple Linear Regression: Estimation I
   .html | .pdf | Handout

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

8. Classical Assumptions
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9. Midterm Review
   

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

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

13. Nonlinear Relationships
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14. Categorical Variables
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15. Interactive Relationships
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16. Model Specifications
    .html | .pdf

17. Heteroskedasticity
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18. Final Review
    

Posted every week and made available on Canvas.

Problem Set 1: Review Content
Available: 01/03/2022 | Due: 01/10/2022

Quiz 1 - Basics
Available: 01/13/2022 | Due: 01/17/2022

Problem Set 2: Fundamentals and Regressions
Available: 01/17/2022 | Due: 01/24/2022

Problem Set 3: Simple Linear Regressions
Available: 01/24/2022 | Due: 01/31/2022

Midterm Exam
Date: 02/07/2022

Problem Set 4: Inference and Multiple Linear Regressions
Available: 02/09/2022 | Due: 02/18/2022

Quiz 2 - Regressions
Available: 02/21/2022 | Due: 02/23/2022

Data Project
Available: 01/03/2022 | Due: 03/01/2022

Problem Set 5: Deeper Topics
Available: 02/23/2022 | Due: 03/07/2022

Final Exam
Date: 03/16/2022

Each bullet point represents a given week

1. Introduction to R and R Markdown

2. Data wrangling using tidyverse
   .html | Data

3. Visualization using ggplot2

4. Regression Analysis & Hypothesis Testing

5. No lab

6. Hypothesis Testing and Confidence Intervals

7. Omitted Variable Bias Simulation

8. Maps with ggplot2!

9. Interaction Terms and Non-Linear Relationships

10. Heteroskedasticity and Autocorrelation

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

I am indebted to Ed Rubin (@edrubin) and Kyle Raze (@kyleraze) for their contributions to course materials and the preparation has 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.