ECON 769 / Advanced Microeconometrics

3 unit(s)

In this course, we will explore theoretical and practical aspects of advanced methods in microeconometrics. We start by reviewing asymptotic theory that emphasizes maximum likelihood estimation, the generalized method of moments, and semiparametric methods estimation. Then, we will look at identification and estimation in specific models for cross-sectional and panel data. For example, we will study models for binary choice data and methods for linear fixed effects models. In the last part of the course, we will discuss topics selected according to our mutual interest. Possible topics include, but are not limited to, advanced methods in program evaluation methods; methods for the analysis of missing data; modern methods for inference; partial identification; and the use of machine learning methods in econometrics. Time permitting, we will implement some of the methods in the programming language R, and study some recent applications of these methods in various subfields of economics.