Syllabus

Vector and Matrix Differentiation (updated: September 22, 2009)

Lecture 1: Basics of Probability (Updated: September 21, 2014)

Lecture 2: Linear regression model and OLS (updated: September 29, 2014)

Lecture 3: Geometry of LS, partitioned regression, goodness of fit (updated: October 7, 2016)

Lecture 4: Confidence intervals (updated: October 25, 2016)

Lecture 5: Hypothesis testing (updated: October 25, 2016)

Lecture 6: Properties of the adjusted coefficient of determination, misspecified models, structural change, dummy variables, forecasts (updated: November 2, 2010)

Lecture 7: Large sample theory (updated: November 7, 2013)

Lecture 8: Large sample properties of OLS, asymptotic confidence intervals and hypothesis testing (updated: December 21, 2010)

Lecture 9: Heteroskedasticity and GLS (updated: December 8, 2008)

Lecture 10: Endogeneity and IV estimation (updated: December 17, 2010) (updated: December 8, 2008)

Lecture 11: GMM, part I (updated: November 21, 2010)

Lecture 12: GMM, part II (updated: November 21, 2010)

Lecture 13: Simultaneous equations (updated: November 15, 2006)

Lecture 14: Maximum likelihood estimation (updated: December 8, 2008)

Lecture 15: Binary response models (updated: December 8, 2008)

Lecture 16: Time series topics. (updated: February 8, 2006)

Practice Questions:

Stata basics

Normal distribution

t-distribution