Probability and Statistics II

Despite the "Probability and Statistics" label, this is really the graduate econometrics course where you learn to extract causal relationships from messy economic data and defend the assumptions that let you do so. You'll work through OLS and its breakdowns (heteroskedasticity, endogeneity, autocorrelation) into IV, GLS, GMM, and maximum likelihood, with weekly problem sets and lab work translating estimators into actual regression output on real datasets. It sits at the core of the MA sequence, building on Probability and Statistics I and feeding directly into time-series, panel, and microeconometrics electives, since almost any empirical paper you read or write later assumes you can reason fluently about identification and standard errors.