This is an advanced graduate course on econometric methodology. It is designed for students interested in econometric theory as well as those interested in applying econometric methods in empirical research. We will cover the following topics: limited dependent variable models, panel data models, treatment effects, shrinkage estimation, and machine learning and double robustness. If time permits, we will also discuss a diverse set of potential topics, such as the estimation of heterogeneous agent models as well as big data and network connectedness.

Quantitative Methods presents a framework for data-driven decision making under conditions of uncertainty and partial information, and it covers data analysis methods and techniques used in economic applications. The class will use R throughout; among the topics covered are graphical and descriptive data analysis, conditional probability, random variables, distribution functions, sampling, estimation, confidence intervals, hypothesis testing, and an introduction to regression methods.

This course is an introduction to econometrics. Econometrics is a sub-discipline of statistics that provides methods for inferring economic structure from data. This course has two goals. The first goal is to give you means to evaluate an econometric analysis critically and logically. Second, you should be able to analyze a data set methodically and comprehensively using the tools of econometrics.

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.