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Course Level: Graduate

Mathematical statistics and probability theory. Foundations of estimation and inference (including hypothesis testing). Basics of asymptotic theory. Multivariate regression models: ordinary least squares and generalized least squares (including serial correlation, heteroskedasticity, multicollinearity, and stochastic regressors).Basic maximum likelihood. Practice with statistical and econometric software. Usually offered every spring. Prerequisite: admission to PhD program or permission of department.