DP12804 Incentive Compatible Estimators

Author(s): Kfir Eliaz, Ran Spiegler
Publication Date: March 2018
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Programme Areas: Industrial Organization
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=12804

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.