On incentive-compatible estimators

• 成果类型: Article
• 署名作者: Eliaz, Kfir; Spiegler, Ran
• 署名单位: Tel Aviv University; Utah System of Higher Education; University of Utah; University of London; University College London
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/j.geb.2022.01.002
• 发表日期: 2022
• 页码: 204-220
• 关键词: Incentive-compatible estimators; Penalized regression; Lasso; Online platforms

摘要

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 penalizedregression 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.