Topic 1: Linear Regression

Material covered in class

Lecture Notes 1: Introductory Regression Notes

• Properties of Estimators

• Consistency and Asymptotic Normality of OLS

• Inference

• Monte Carlo Simulations

Lecture Notes 2: Additional Regression Notes

• Bias and Variance of OLS

• Gauss-Markov Theorem, GLS, and FGLS

• Frisch-Waugh-Lovell Theorem

• Wald Statistic

• Functions of Parameters