Yuya Sasaki is an econometrician producing empirical methods for cross section and panel data. His research focuses on semi- and non-parametric identification of economic models with unobserved heterogeneity, measurement errors, and endogenous selection.
Please see my personal website for a list of research papers.
180.356 – Big Data 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 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 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.