On incentive-compatible estimators

Kfir Eliaz, Ran Spiegler*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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 languageEnglish
Pages (from-to)204-220
Number of pages17
JournalGames and Economic Behavior
StatePublished - Mar 2022


FundersFunder number
Sapir Center
Israel Science Foundation


    • Incentive-compatible estimators
    • Lasso
    • Online platforms
    • Penalized regression


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