Econometric Theory and Methods
This course covers a number of econometric models and techniques that are commonly used in applied microeconomics. The core topics include a general framework for estimators (which includes maximum likelihood and generalized method of moments), discrete outcome models, sample selection (and related limited dependent variable or switching models), duration and count models, time series models, panel data models, variance estimation (including clustering and the bootstrap), and non-parametric techniques. The course is designed for PhD students who have completed 90-907 (PhD Econometrics I) or an equivalent course.
Demonstrate that a broad class of linear and non-linear statistical models fit within the M-estimator framework. Derive consistency and asymptotic normality of M-estimators. Utilize statistical techniques outside of the traditional M-estimator framework such as non-parametric estimation, bootstrap, etc. Understand how to use these methods in cross-sectional versus time-series versus panel data settings. Apply these estimators to data as part of course assignments.
90905 and 90906:
Statistical Theory for Social and Policy Research (90-905) and Introduction to Econometric Theory (90-906), or equivalent PhD-level econometrics with extensive coverage of linear regressions and sufficient treatment of asymptotic theory. It is assumed that students are familiar with basic linear algebra, multivariate calculus, probability theory, and statistical convergence concepts.