  Estimating discrete games of complete information is often computationally
difficult due to partial identification and the absence of closed-form moment
characterizations. This paper proposes computationally tractable approaches to
estimation and inference that remove the computational burden associated with
equilibria enumeration, numerical simulation, and grid search. Separately for
unordered and ordered-actions games, I construct an identified set
characterized by a finite set of generalized likelihood-based conditional
moment inequalities that are convex in (a subvector of) structural model
parameters under the standard logit assumption on unobservables. I use
simulation and empirical examples to show that the proposed approaches generate
informative identified sets and can be several orders of magnitude faster than
existing estimation methods.
