Welcome to Economics 421: Introduction to Econometrics (Winter 2026) at the University of Oregon (taught by Edward Rubin and Eric Wilken).
Materials to prepare for the in-class final exam (and its key):
- in-class review;
- past exams;
- list of topics to know;
- practice problems (no solutions)
The problem sets should also help you review.
Materials to prepare for the in-class midterm exam:
- notes from in-class review;
- past exams;
- list of topics to know;
- practice problems (no solutions)
The problem sets should also help you review.
For information on the course specifics, please see the syllabus.
Edward Rubin: Thursdays: 2:00p–3:30p (PLC 530)
Eric Wilken: Tuesdays: 3:00p–4:00p (See Canvas: Zoom or PLC 407)
Below are the tentatively planning topics for the problem sets.
Problem Set 0: Review
Due: Tuesday, 20 January 2026 by 11:59 PM
Files: assignment | data | solutions
Problem Set 1: Heteroskedasticity, Clustering, and OLS Assumptions
Due: Monday, 02 February 2026 by 11:59 PM
Files: assignment | data | solutions
Problem Set 2: Time series data, analyses, and nonstationarity
Due: Tuesday, 03 March 2026 by 11:59 PM
Files: assignment | data | solutions
Problem Set 3: Problem Set 3: Causality, Instrumental Variables, and Review
Due: Wednesday, 11 March 2026 by 11:59 PM
Files: assignment | data | solutions
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.
-
Introduction to "Introduction to Econometrics"
PDF | .Rmd
R Introduction and (R)eview
PDF | .Rmd -
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 -
Review of key topics from EC320
(the first course in our intro-to-metrics sequence)
PDF | .Rmd -
Autocorrelated disturbances
Implications, testing, and estimation. Also: introductionggplot2and user-defined functions.
PDF | .Rmd -
Nonstationarity
Introduciton, implications for OLS, testing, and estimation. Also: in-class exercise for model selection.
PDF | .Rmd -
Causality
Introduction to causality and the Neymam-Rubin causal model. Also: Recap of in-class model-selection exercise.
PDF | .Rmd -
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 -
Panel data, fixed effects, and DiD
Introduction to panel data, fixed effects, within variation, and difference-in-differences estimation.
PDF | .Rmd
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 | |
| 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 |
| Winter 2025 | exam key | exam key |
| Spring 2025 | exam | exam |
Note: If there is no key posted, then I do not have it and will not distribute it.
Here are links to previous years' course materials as well:
- Spring 2025
- Winter 2025
- Winter 2022
- Winter 2022
- Winter 2021
- Spring 2020
- Winter 2020
- Spring 2019
- Winter 2019
Please also see the syllabus for specific information on the homework and grade policies.
- Data Services at the UO library: includes help desk, consultations, and workshops
- RStudio Education: free online walkthroughs and cheatsheets
- swirl and learnr: interactive R tutorials
- R for Data Science: free online book walking you through R basics