Choosing among regularized estimators in empirical economics: the risk of machine learning
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 estimat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MIT Press - Journals
2020
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| Online Access: | https://hdl.handle.net/1721.1/124406 |
| Summary: | 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 |
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