Topics in Econometrics

Catalog description

This course will begin with a survey of the literature on identification using instrumental variables, including identification bounds, conditional moment restrictions, and control function approaches. The next part of class will cover some of the theoretical foundations of machine learning, including regularization and data-driven choice of tuning parameters. We will discuss in some detail the canonical normal means model, Gaussian process priors, (empirical) Bayes estimation, and reproducing kernel Hilbert space norms. We will finally cover some selected additional topics in machine learning, including (deep) neural nets, text as data (topics models), multi-armed bandits, and data visualization.