Journal article
Choosing among regularized estimators in empirical economics: the risk of machine learning
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.8MB, Terms of use)
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- Publisher copy:
- 10.1162/rest_a_00812
Authors
- Publisher:
- Massachusetts Institute of Technology Press
- Journal:
- Review of Economics and Statistics More from this journal
- Volume:
- 101
- Issue:
- 5
- Pages:
- 743-762
- Publication date:
- 2019-12-11
- Acceptance date:
- 2018-09-17
- DOI:
- EISSN:
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1530-9142
- ISSN:
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0034-6535
- Language:
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English
- Keywords:
- Pubs id:
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1108752
- Local pid:
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pubs:1108752
- Deposit date:
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2020-06-25
- ARK identifier:
Terms of use
- Copyright holder:
- President and Fellows of Harvard College and the Massachusetts Institute of Technology
- Copyright date:
- 2019
- Rights statement:
- © 2018 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology
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