Midterm Topics

EC 421: Introduction to Econometrics

Author

Edward Rubin

Note: In general, you do not need to memorize proofs. Just understand the steps. I might ask how you get from one step to the next. I won’t ask you to write down a full proof.

Slide Set 1: Intro

  • The goal of econometrics
  • Regression notation
  • Basic concept of causality

Slide Set 2: Review I

  • Population vs. sample
    • Parameters vs. sample estimates
    • Estimators and uncertainty
  • Uncertainty
    • Standard errors
    • Hypothesis testing
      • t tests
      • F tests
      • Forming hypotheses
      • critical value
      • p-value
    • Confidence intervals
  • Linear regression and OLS
    • “Best-fit” line
    • Residuals
    • SSE
    • Estimators: bias and variance
    • Statistical inference
    • Variance (and standard error) of the OLS estimator
    • Regressions with R’s lm function

Slide Set 3: Review II

  • Simple and multiple linear regression
  • Model fit
    • R squared
    • Overfitting
    • Adjusted R squared
  • Omitted-variable bias
  • Interpreting coefficients
    • Simple linear regression
    • Multiple linear regression (ceterus paribus)
    • Continuous explanatory variables
    • Categorical explanatory variables
    • Interactions
    • Specifications
      • Linear-linear
      • Log-linear
      • Log-log
  • Inference vs. prediction

Slide Set 4: Heteroskedasticity

  • The meaning of each of our assumptions/requirements
  • Heteroskedasticity
    • What it is
    • What it looks like
    • Consequences for OLS
  • Tests for heteroskedasticity
    • Goldfeld-Quandt test
    • White test
    • Chi-squared distribution
    • Null and alternative hypotheses of each test
    • Interpretations/conclusions for each
    • Strengths and weaknesses of each test

Slide Set 5: Living with Heteroskedasticity

  • Misspecification
  • Weighted least squares
  • Heteroskedasticity-robust standard errors
  • Correlated disturbances and ‘clustering’

Slide Set 6: Asymptotics and Consistency

  • Asymptotics
    • Compared to ‘finite-sample’ attributes (probability limits vs. expected values)
    • Probability limits
  • Consistency
  • Signing the bias from omitted variables.
  • Measurement error and attenuation bias: What are they?
  • Examples of measurement error