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EC 421, Spring 2020

Welcome to Economics 421: Introduction to Econometrics (Spring 2020) 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
  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 | Data | Solutions
  2. Problem set 2: Heteroskedasticity, consistency, and time series | Data | Solutions
  3. Problem set 3: Time series and autocorrelation | Data | Solutions
  4. Problem set 4: Nonstationarity, causality, and instrumental variables | Data | Solutions

Midterm

Midterm exam

Midterm project: Prompt | Data

Final

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Introduction to Econometrics at the University of Oregon (EC421) during Spring quarter, 2020. Taught by Ed Rubin

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