Incentive-Compatible Estimators∗

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. ∗We thank Yoav Binyamini, Assaf Cohen, Rami Atar, Lorens Imhof, Benny Moldovanu, Ron Peretz and especially Martin Cripps for helpful conversations. We are also grateful to seminar audiences at BRIQ, DICE and the Warwick Economic Theory conference, for their useful comments. †School of Economics, Tel-Aviv University and Economics Dept., Aarhus University. E-mail: [email protected]. ‡School of Economics, Tel Aviv University; Department of Economics, University College London; and CfM. E-mail: [email protected].