Econometric Theory and Methods
Description: This course covers a number of econometric models and techniques that are commonly used in applied microeconomics. The core topics for this course are: nonlinear models (including discrete choice models, sample selection, and duration models), time series and panel data models, as well as non-parametric and semi-parametric techniques (including kernel estimation, bootstrap, and sub-sampling). The course is designed for PhD students who have completed their first-year econometrics sequence.
Learning Outcomes: 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.