Zusammenfassung:
Building on the sequential identification result of Aguirregabiria and Mira (2019), this paper develops estimation and inference procedures for static games of incomplete information with payoff-relevant unobserved heterogeneity and multiple equilibria. With payoff-relevant unobserved heterogeneity, sequential estimation and inference face two main challenges: the matching-types problem and a large number of matchings. We tackle the matching-types problem by constructing a new minimum-distance criterion for the correct matching and the payoff function with both correct and incorrect "moments." To handle large numbers of matchings, we propose a novel and computationally fast multistep moment selection procedure. We show that asymptotically, it achieves a time complexity that is linear in the number of "moments" when the occurrence of multiple equilibria does not depend on the number of "moments." Based on this procedure, we construct a consistent estimator of the payoff function, an asymptotically uniformly valid and easy-to-implement test for linear hypotheses on the payoff function, and a consistent method to group payoff functions according to the unobserved heterogeneity. Extensive simulations demonstrate the finite sample efficacy of our procedures.