This is an advanced graduate course on models and methods that are useful to conduct substantive empirical research in macroeconomics, finance, etc. It focuses on the estimation and evaluation of dynamic stochastic general equilibrium models (DSGE). If time permits, we will also discuss a diverse set of potential topics, such as estimation of heterogeneous agent models and big data and network connectedness.

This course introduces students to basic econometric concepts and their applications. Compared to E371, we will put more emphasis on the mathematical and statistical foundations of econometric methods. The course covers linear and nonlinear regression models, discrete choice models, and panel data models. We will also discuss more advanced topics, such as experiments and quasi-experiments as well as big data and statistical learning, if time permits. All concepts are motivated by real-world applications. Students will learn how to apply econometric methods to data using the statistical software R.

This is an advanced graduate course on major econometric techniques and models that are used in empirical microeconomics. We first introduce econometric theories of nonlinear extremal estimation, nonparametric estimation, and semiparametric estimation. Then, we discuss applications of these theories to limited dependent variable models, selection models, panel data models, and endogenous treatment models with unobserved heterogeneity.