This Ph.D.-level course covers a variety of techniques that are used in econometric analysis. The class builds heavily on material developed in 41902, and it is strongly recommended that students have taken 41902 or equivalent before enrolling in this course. Some topics that may be covered are (i) heteroscedasticity and correlation robust inference methods including HAC, clustering, bootstrap methods, and randomization inference; (ii) causal inference methods including instrumental variables estimation, difference-in-differences estimation, and estimators of treatment effects under treatment effect heterogeneity; (iii) an introduction to nonparametric and high-dimensional statistical methods.

Business 41901 and 41902.

Journal articles and book chapters will be used in this course. A few references that may be useful are Hayashi Econometrics, Wooldridge Econometric Analysis of Cross Section and Panel Data, Angrist and Pischke Mostly Harmless Econometrics, Angrist and Pischke Mastering Metics, and Imbens and Rubin Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction.

Based on a project, midterm, final, and problem sets.