Teaching at Vanderbilt University (2017 - )

ECON 3750 "Econometrics for Big Data" (Undergraduate Level)

Econometric methods for analyzing large datasets. Model selection, regularization, classification, resampling, tree-based methods, support vector machines, and double/debiased machine learning. Forecasting stock prices, prediction of housing prices, and determination of wages. Prerequisite: 3010 or 3012; either 3032, 3035, 3050; or MATH 2820L with MATH 2810 or 2820.

ECON 4050 "Topics in Econometrics: Big Data" (Undergraduate Level)

This course introduces state-of-the-art econometric methods to analyze big data. Topics include statistical learning, linear regression, classification, resampling methods, linear model selection and regularization, nonlinear model selection and regularization, tree-based methods, support vector machines, and double/debiased machine learning. Prerequisite: ECON 3050.

ECON 6500 "Statistical Analysis" (Masters Level)

ECON 6600 "Econometrics" (Masters Level)

Interpretation of statistical materials, the principles of statistical inference, the use of available statistics for problems of economic analysis, and the importance of statistics in economic policy and administration.

ECON 6600 "Econometrics" (Masters Level)

ECON 6600 "Econometrics" (Masters Level)

Introduction to econometrics. Conditional expectation, projection, least squares, normal equations, maximum likelihood, large sample asymptotics, restricted estimation, hypothesis testing, resampling methods, multivariate regression, instrumental variables, generalized method of moments, and time series.  Prerequisite: ECON 6500.

ECON 7550 "Econometric Methods for Big Data" (Masters Level)

ECON 7550"Econometric Methods for Big Data" (Masters Level)

Econometric methods for analyzing large datasets with modern statistical and machine learning techniques. Model selection, regularization, classification, resampling, double machine learning, trees, support vector machines, and neural networks. Research projects in development economics and related fields.  Prerequisites: ECON 6500 and ECON 6600

ECON 8300 "Statistical Analysis" (Ph.D Level)

ECON 8300 "Statistical Analysis" (Ph.D Level)

Probability and statistics for econometricians. Measure and integration theory, distribution families, finite sample statistics, asymptotic statistics, estimation, hypothesis testing, and Bayesian inference.

ECON 8310 "Econometrics I" (Ph.D Level)

Introduction to econometrics. Conditional expectation, projection, least squares, normal equations, maximum likelihood, large sample asymptotics, restricted estimation, hypothesis testing, resampling methods, multivariate regression, instrumental variables, generalized method of moments, and time series.  Prerequisite: ECON 8300.

Teaching at Johns Hopkins University (2012 - 2017)

180.356 "Big Data" (Undergraduate Level)

This course introduces state-of-the-art econometric methods to analyze big data. Topics include statistical learning, linear regression, classification, resampling methods, linear model selection and regularization, nonlinear model selection and regularization, tree-based methods, and support vector machines.

180.636 "Statistics" (Ph.D Level)

This course covers two broad topics, probability theory and statistical inference, as prerequisites for the subsequent econometric courses. For the first part, we introduce theories of measure and integration. For the second part, we discuss finite sample statistics, estimation, hypothesis testing, and asymptotic statistics. Examples are drawn from economics and econometrics. The course is limited to graduate students in economics.

180.637 "Microeconometrics I" (Ph.D Level) 

This is an advanced graduate course on major econometric techniques and models that are used in empirical microeconomics. The first half of the course introduces econometric theories of nonlinear extremal estimation, nonparametric estimation, and semiparametric estimation. The second half of the course illustrates applications of these theories to limited dependent variable models, selection models, and endogenous treatment models with unobserved heterogeneity.