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On incentive-compatible estimators

Eliaz, Kfir; Spiegler, Ran; (2022) On incentive-compatible estimators. Games and Economic Behavior , 132 pp. 204-220. 10.1016/j.geb.2022.01.002.

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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.

Type: Article
Title: On incentive-compatible estimators
DOI: 10.1016/j.geb.2022.01.002
Publisher version: https://doi.org/10.1016/j.geb.2022.01.002
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Incentive-compatible estimators, Penalized regression, Lasso, Online platforms
UCL classification: UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
UCL > Provost and Vice Provost Offices > UCL SLASH
URI: https://discovery.ucl.ac.uk/id/eprint/10144954
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