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Choosing among regularized estimators in empirical economics: the risk of machine learning

Author(s)
Abadie, Alberto; Kasy, Maximilian
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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
Date issued
2019-12
URI
https://hdl.handle.net/1721.1/124406
Department
Massachusetts Institute of Technology. Department of Economics
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
ISSN
1530-9142
0034-6535

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