On incentive-compatible estimators

被引：3
|
作者：
Eliaz, Kfir [1 ,2 ]
Spiegler, Ran [1 ,3 ,4 ]
机构：
[1] Tel Aviv Univ, Sch Econ, Tel Aviv, Israel
[2] Univ Utah, David Eccles Sch Business, Salt Lake City, UT 84112 USA
[3] UCL, Dept Econ, London, England
[4] CfM, Dunblane, Scotland
来源：
GAMES AND ECONOMIC BEHAVIOR | 2022年 / 132卷
关键词：
Incentive-compatible estimators; Penalized regression; Lasso; Online platforms; SELECTION; MODEL;
D O I：
10.1016/j.geb.2022.01.002
中图分类号：
F [经济];
学科分类号：
02 ;
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.
引用
收藏
页码：204 / 220
页数：17
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