University of Pittsburgh, advanced Ph.D. level, Spring 2024
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 (26007)
University of Pittsburgh, master's level, Fall 2023
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.
Econometrics: A Mathematical Approach (ECO 312)
Princeton University, undergraduate level, Spring 2023
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.
Empirical Macro II (E724)
Indiana University, advanced Ph.D. level, Spring 2021, Spring 2022
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.
Econometric Theory and Practice (E471/M504)
Indiana University, advanced undergraduate and master's level,
Spring 2020, Fall 2020, Fall 2021
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.
Johns Hopkins University, advanced Ph.D. level, Fall 2018
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