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On incentive-compatible estimators

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  • Eliaz, Kfir
  • Spiegler, Ran

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.

Suggested Citation

  • Eliaz, Kfir & Spiegler, Ran, 2022. "On incentive-compatible estimators," Games and Economic Behavior, Elsevier, vol. 132(C), pages 204-220.
    • Handle: RePEc:eee:gamebe:v:132:y:2022:i:c:p:204-220
    DOI: 10.1016/j.geb.2022.01.002
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    References listed on IDEAS

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    1. Abhijit Banerjee & Sylvain Chassang & Sergio Montero & Erik Snowberg, 2017. "A Theory of Experimenters," NBER Working Papers 23867, National Bureau of Economic Research, Inc.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Mehmet Caner & Kfir Eliaz, 2021. "Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso," Papers 2101.01144, arXiv.org, revised Sep 2021.
    4. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    5. Xavier Gabaix, 2014. "A Sparsity-Based Model of Bounded Rationality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1661-1710.
    6. Ran Spiegler, 2006. "The Market for Quacks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 1113-1131.
    7. Kfir Eliaz & Ran Spiegler, 2019. "The Model Selection Curse," American Economic Review: Insights, American Economic Association, vol. 1(2), pages 127-140, September.
    8. Perote, Javier & Perote-Pena, Juan, 2004. "Strategy-proof estimators for simple regression," Mathematical Social Sciences, Elsevier, vol. 47(2), pages 153-176, March.
    9. Sylvain Chassang & Gerard Padro I Miquel & Erik Snowberg, 2012. "Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments," American Economic Review, American Economic Association, vol. 102(4), pages 1279-1309, June.
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    Cited by:

    1. Annie Liang & Erik Madsen, 2020. "Data and Incentives," Papers 2006.06543, arXiv.org, revised Sep 2022.
    2. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
    3. Ian Ball, 2019. "Scoring Strategic Agents," Papers 1909.01888, arXiv.org, revised May 2024.

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