  This paper considers the estimation of discrete games of complete information
without assumptions on the equilibrium selection rule, which is often viewed as
computationally difficult. We propose computationally attractive approaches
that avoid simulation and grid search. We show that the moment inequalities
proposed by Andrews, Berry, and Jia (2004) can be expressed in terms of
multinomial logit probabilities, and the corresponding identified set is
convex. When actions are binary, we can characterize the sharp identified set
using closed-form inequalities. We also propose a simple approach to inference.
Two real-data experiments illustrate that our methodology can be several orders
of magnitude faster than the existing approaches.

Econometrics