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, and support vector machines. 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, and support vector machines. Prerequisite: ECON 3050.


ECON 6600 "Econometrics" (Masters Level)
Econometrics for policy analysis. Linear regression, M-estimation, probit and logit models, generalized method of moments, instrumental variable regression, and introduction to nonparametric econometrics. Prerequisite: ECON 6500.


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, and generalized method of moments. Prerequisite: ECON 8300.


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.