EC 421, Spring 2019

Welcome to Economics 421: Introduction to Econometrics (Spring 2019) at the University of Oregon (w/ Ed Rubin).

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

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). I create the slides with xaringan in R. Thanks go to Grant McDermott for helping/pushing me to get going with xaringan.

1. The 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 | PDF (no pauses) | .Rmd
3. Review of key metrics topics
   OLS properties and inference
   PDF | PDF (no pauses) | .Rmd
4. Heteroskedasticity
   Step 1 in relaxing our assumptions: non-constant variance in our disturbances. How can we test this assumption? What are the implications of violations?
   PDF | PDF (no pauses) | .Rmd
5. Heteroskedasticity II
   What do we do when we detect heteroskedasticity? Model specification, weighted least squares (WLS), and heteroskedasticity-robust standard errors (plus a simulation).
   PDF | PDF (no pauses) | .Rmd
6. Consistency
   Moving from small-sample properties to asymptopia (i.e., as N gets big).
   PDF | PDF (no pauses) | .Rmd
7. Time series
   What happens when you have repeated observations on an individual?
   PDF | PDF (no pauses) | .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

Problem sets

1. Problem set 1: Review of OLS
   PDF | Data | Solutions
2. Problem set 2: Unbiasedness, consistency, and heteroskedasticity
   PDF | Data | Solutions
3. Problem set 3: Time series and autocorrelation
   PDF | Data | Solutions
4. Problem set 4: Nonstationarity, causality, and instrumental variables
   PDF | Data | Solutions

Midterm

Midterm | Midterm solutions

Midterm review materials: Review topics | Review problems | Previous midterm | Previous midterm's solutions

Note: We will not provide solutions for the review problems.

Final

• Topics: topics that were fair game for the exam
• Review questions: no solutions; just review questions
• Previous final: the actual exam