Choosing among regularized estimators in empirical economics: the risk of machine learning
Name
rest_a_00812.pdf
Description
Accepted version
Size
3.03 MB
Format
Adobe PDF
Checksum (MD5)
59b3d43742fbaf3de5bd2538e4d22ec9
Author(s)
Abadie, Alberto
Kasy, Maximilian
Date Issued
December 2019
Journal
Review of economics and statistics
Publisher
MIT Press - Journals
Citation
Abadie, Alberto and Maximilian Kasy, "Choosing among regularized estimators in empirical economics: the risk of machine learning." Review of economics and statistics 101, 5 (December 2019): p. 743-62 doi
10.1162/rest_a_00812 ©2019 Authors
Version
Final published version
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.©2019
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1162/REST_A_00812
