Course Detail
Course Components:
The first course in a two-course sequence in econometrics. Designed to give doctoral students the tools and methods to conduct empirical research. Topics include: discrete and continuous random variables; probability theory; combinatorics; marginal and conditional probability; independence; moments; transformations; Bayes Theorem; convergence, law of large numbers, central limit theorem; maximum likelihood; method of moments; Bayesian inference; sufficient statistics; sampling distributions; confidence intervals; consistency and unbiasedness; Fisher; Rao-Blackwell; hypothesis testing; asymptotics of MLE; Simpson’s paradox; linear regression; omitted variables, misspecification, and instrumental variables; hypothesis testing; Hausman test; asymptotics of OLS and GLS; quantile regression; nonlinear estimators; inference with Monte Carlo simulations; bootstrapping; and clustering.