Estimating Supermodular Games Using Rationalizable Strategies

Abstract We propose a set-estimation approach to supermodular games using the restrictons of rationalizable strategies, which is a weaker solution concept than Nash equilibrium. The set of rationalizable strategies of a supermodular game forms a complete lattice, and are bounded above and below by two extremal Nash equilibria. We use a well-known alogrithm to compute the two extremal equilibria, and then construct moment inequalities for set estimation of the supermodular game. Finally, we conduct Monte Carlo experiments to illustrate how the estimated confidence sets vary in response to changes in the data generating process.