fbpx Choosing among Regularized Estimators in Empirical Economics: The Risk of Machine Learning | Harvard Kennedy School

Additional Authors:

  • Maximilian Kasy

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." Link