This is an advanced graduate course on models and methods that are useful to conduct substantive empirical research in macroeconomics, finance, etc. The first half of the course covers the estimation and evaluation of dynamic stochastic general equilibrium models (DSGE). The second half of the course covers a diverse set of topics: estimation of heterogeneous agent models, big data and network connectedness, and new Keynesian models.

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 regression models, discrete choice models, and panel data models, as well as more advanced topics, such as experiments and quasi-experiments, big data and statistical learning, etc. 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.