Estimation of Discrete Games with Weak Assumptions on Information

Lorenzo Magnolfi, University of Wisconsin-Madison and Camilla Roncoroni, University of Warwick

We propose a method to estimate static discrete games with weak assumptions on the information available to players. We do not fully specify the information structure of the game, but allow instead for all information structures consistent with players knowing their own payoffs. To make this approach tractable we adopt as a solution concept Bayes Correlated Equilibrium (BCE) (Bergemann and Morris, 2016). We characterize the sharp identified set under BCE and unrestricted equilibrium selection, and find that in simple games with limited variation in covariates identified sets are informative. In an application, we estimate a model of entry in the Italian supermarket industry and quantify the effect of large malls on local supermarkets. Estimates and predictions differ from those obtained under more restrictive assumptions.