DP12804 | Incentive Compatible Estimators

Publication Date

03/22/2018

JEL Code(s)

Keyword(s)

Programme Area(s)

Abstract

We study a model in which a "statistician" takes an action on behalf of an agent, based on a random sample involving other people. The statistician follows a penalized regression procedure: the action that he takes is the dependent variable's estimated value given the agent's disclosed personal characteristics. We ask the following question: Is truth-telling an optimal disclosure strategy for the agent, given the statistician's procedure? We discuss possible implications of our exercise for the growing reliance on "machine learning" methods that involve explicit variable selection.