Excerpt
Excerpt
Choosing among Regularized Estimators in Empirical Economics: The Risk of Machine Learning. Maximilian Kasy, 2018, Paper, "Many settings in empirical economics involve estimation of a large number of parameters. In such settings methods that combine regularized estimation and data-driven choices of regularization parameters are useful. We provide guidance to applied researchers on (i) the choice between regularized estimators and (ii) data-driven selection of regularization parameters. We characterize the risk and relative performance of regularized estimators as a function of the data generating process, and show that data-driven choices of regularization parameters yield estimators with risk uniformly close to the risk attained under the optimal (unfeasible) choice of regularization parameters. We illustrate using examples from empirical economics."