Semiparametric identification of binary decision games of incomplete information with correlated private signals

This paper studies the identification and estimation of a static binary decision game of incomplete information. We make no parametric assumptions on the joint distribution of private signals and allow them to be correlated. We show that the parameters of interest can be point-identified subject to a scale normalization under mild support requirements for the regressors (publicly observed state variables) and errors (private signals). Following Manski and Tamer (2002), we propose a maximum score type estimator for the structural parameters and establish the asymptotic properties of the estimator.