Introduction to Econometrics at the University of Oregon (EC421) during Winter quarter, 2019. Taught by Edward Rubin
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Data
LectureNotes update autocor notes Feb 19, 2019
ProblemSets ps2 updates; sl6/7 final; sl8 start Feb 5, 2019
Syllabus update syllabus (behind 1 lecture); typo in 06 slides; create 07 slides Jan 31, 2019
.gitignore
README.md update autocor notes Feb 19, 2019

README.md

EC 421, Winter 2019

Welcome to Economics 421: Introduction to Econometrics (Winter 2019) at the University of Oregon (taught by Edward 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 | .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. Living with heteroskedasticity: Inference, WLS, and specification | 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

Problem sets

  1. Problem set 1: Review of OLS | PDF | Data
  2. Problem set 2: Heteroskedasticity, consistency, and time series | PDF | Data