A graduate-level introduction to econometric theory. The main focus is the linear regression model. The course covers estimation and inference using ordinary least squares (OLS), generalized least squares (GLS), generalized method of moments (GMM) and instrumental variables (IV). It also covers maximum likelihood (ML) estimation of binary choice models (logit and probit), and some topics in time series.

Slides

1. Regression, identification, and the OLS estimator    [pdf] [slides]
2. Properties of the OLS estimator    [pdf] [slides]
3. Geometry of OLS and partitioned regression    [pdf] [slides]
4. Confidence intervals    [pdf] [slides]
5. Hypothesis testing    [pdf] [slides]
6. Effects at quantiles and quantile regression    [pdf] [slides]
7. Omitted and irrelevant regressors    [pdf] [slides]
8. Asymptotic theory    [pdf] [slides]

Lecture notes

1. Basics of probability
2. Linear regression and OLS
3. Geometry of OLS, partitioned regression, variance estimation, goodness of fit
4. Confidence intervals
5. Hypothesis testing
6. Properties of the adjusted $R^2$, model misspecification, structural change, forecasts
7. Large sample theory
8. Large sample properties of OLS, asymptotic confidence intervals and hypothesis testing
9. Heteroskedasticity and GLS
10. Endogeneity and IVs
11. GMM
12. Efficient GMM, 2SLS, overidentifying restrictions
13. Simultaneous equations
14. MLE
15. Probit and logit
16. Time series