Choosing Among Regularized Estimators in Empirical Economics: The Risk of Machine Learning

Alberto Abadie and Maximilian Kasy

The Review of Economics and Statistics, 2019, vol. 101, issue 5, 743-762

Abstract: 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 the choice between regularized estimators and 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.

Date: 2019
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