Abstract
An estimator is incentive-compatible (for a given prior belief regarding the model's true parameters) if it does not give an agent an incentive to misreport the value of his covariates. Eliaz and Spiegler (2019) studied incentive-compatibility of estimators in a setting with a single binary explanatory variable. We extend this analysis to penalized-regression estimation in a simple multi-variable setting. Our results highlight the incentive problems that are created by the element of variable selection/shrinkage in the estimation procedure.
Original language | English |
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Pages (from-to) | 204-220 |
Number of pages | 17 |
Journal | Games and Economic Behavior |
Volume | 132 |
DOIs | |
State | Published - Mar 2022 |
Keywords
- Incentive-compatible estimators
- Lasso
- Online platforms
- Penalized regression
All Science Journal Classification (ASJC) codes
- Finance
- Economics and Econometrics