EC 421, Winter 2025

Welcome to Economics 421: Introduction to Econometrics (Winter 2025) at the University of Oregon (taught by Edward Rubin).

Syllabus

For information on the course specifics, please see the syllabus.

Assignments

Problem Set 0: Review
Due: Tuesday, 28 January 2025 by 11:59 PM. Submit via Canvas.
Files: assignment | data | solutions

Problem Set 1: Heteroskedasticity, Clustering, and OLS Assumptions
Due: Saturday, 08 February 2025 by 11:59 PM. Submit via Canvas.
Files: assignment | data

Note: You can use the box in the top right corner with the ⋯ to download the files. (You can also use the button with the download arrow.)

Midterm prep materials

In addition to the past exams linked below, here are some additional materials to help you prepare for the midterm:

• topics list lists the main topics you should know from each of the sets of slides;
• practice questions provides a bunch of review questions to help test your understanding.

Note: I will not post answers to the practice questions, but you can certainly ask about them in the review sessions (Tuesday in class; Wednesday in lab) or in office hours.

Lecture slides

The slides below (linked by their topic) are .html files that will only work properly if you are connected to the internet. If you're going off grid, grab the PDFs (you'll miss out on gifs and interactive plots, but the equations will render correctly).

Note: Links to topics that we have not yet covered lead to older slides. I will update links to the new slides as we work our way through the term/slides.

In case you're interested, I created the slides with xaringan in R. If you are thinking of making your own slides/documents, I would suggest quarto.

1. Introduction to "Introduction to Econometrics"
   PDF | .Rmd

2. Review of key math/stat/metrics topics
   Density functions, deriving the OLS estimators, properties of estimators, statistical inference (standard errors, confidence intervals, hypothesis testing), simulation
   PDF | .Rmd

3. Review of key topics from EC320
   (the first course in our intro-to-metrics sequence)
   PDF | .Rmd

4. Heteroskedasticity: Tests and implications
   PDF | .Rmd

5. Heteroskedasticity: Living with it
   PDF | .Rmd

6. Consistency and OLS in asymptopia
   PDF | .Rmd

7. Introduction to time series
   PDF | .Rmd

8. Autocorrelated disturbances
   Implications, testing, and estimation. Also: introduction ggplot2 and user-defined functions.
   PDF | .Rmd

9. Nonstationarity
   Introduciton, implications for OLS, testing, and estimation. Also: in-class exercise for model selection.
   PDF | .Rmd

10. Causality
    Introduction to causality and the Neymam-Rubin causal model. Also: Recap of in-class model-selection exercise.
    PDF | .Rmd

11. Instrumental Variables
    Review the Neymam-Rubin causal model; introduction to instrumental variables (IV) and two-stage least squares (2SLS). Applications to causal inference and measurement error. Venn diagrams.
    PDF | .Rmd

Exams

See the syllabus for specific information on the exams and grades.

Here are some exams from previous years:

Term	Midterm	Final
Winter 2019	exam key	exam key
Spring 2019	exam key	exam key
Winter 2020	exam key	exam key
Winter 2021	exam
Spring 2020	exam	exam key
Winter 2022		home exam home key
Spring 2022	exam key
Winter 2023	home key in-class exam in-class key	home exam home key in-class exam in-class key
Spring 2023	home exam in-class exam	home exam in-class exam

Note: If there is no key posted, then I do not have it and will not distribute it.

Previous years

Here are links to previous years' course materials as well:

• Winter 2022
• Winter 2021
• Spring 2020
• Winter 2020
• Spring 2019
• Winter 2019

Homework

Please also see the syllabus for specific information on the homework and grade policies.