Econometric Methods

ECON 2140 Subject & Catalog Number

Description

This course continues the first year sequence in econometrics and covers a variety of topics and ideas that are important for pursuing and interpreting empirical research in economics. The first half of the course covers core econometric approaches that are important for a wide range of applications, including identification analysis, asymptotic approximations, large sample theory for estimation and hypothesis testing, and the bootstrap.  The second part of the course examines a range of complementary topics and new developments, including reasons why canonical econometric methods may be unreliable (such as model misspecification, identification failure, and the incidental parameters problem) and extensions of and alternatives to the traditional econometric paradigm (such as partial identification, Bayesian inference, nonparametrics, and machine learning).  Economic applications will be discussed throughout. Enrollment limited.