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 |