Applied Microeconometrics I

This course provides an introduction to the analysis of microdata - data in which the unit of analysis in an individual decision-maker, such as a consumer, worker, or firm. The course familiarizes students with the various types of microdata structures and draws links between theoretical economic models and statistical models. It devotes considerable attention to analytical techniques for describing univariate and bivariate distributions, including the use of kernel density estimators and scatterplots. The course addresses statistical inference using multivariate regressions, covering a series of techniques and issues of particular significance in the analysis of microdata. Examples include Monte Carlo methods; bootstrapping; the use of sampling weight; omitted variable bias; measurement error; and instrumental variables. Completion of an empirical project is a course requirement.