Welcome to Economics 421: Introduction to Econometrics (Winter 2020) at the University of Oregon (taught by Edward Rubin).
For information on the course specifics, please see the syllabus.
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.
- The introduction to "Introduction to Econometrics"
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 - Heteroskedasticity: Tests and implications
PDF | .Rmd - Living with heteroskedasticity: Inference, WLS, and specification
PDF | .Rmd - Consistency and OLS in asymptopia
PDF | .Rmd - Introduction to time series
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
- Problem set 1: Review of OLS | Data | Solutions
- Problem set 2: Heteroskedasticity, consistency, and time series | Data | Solutions
- Problem set 3: Time series and autocorrelation | Data | Solutions
- Problem set 4: Nonstationarity, causality, and instrumental variables | Data | Solutions
- Topics: topics that were fair game for the exam
- Review questions: no solutions; just review questions
- Midterms from my previous classes
- Midterm: the actual exam
- Solutions: short solutions to the midterm
- Review questions: no solutions; just review questions
- Previous final
- Previous final, solutions
- Final: the actual exam
- Solutions: solution key to the final