We study inference in complete information games with discrete strategy spaces. Unlike binary games, we allow for rich strategy spaces and we only assume that they are ordinal in nature. We derive observable implications of equilibrium play under mild shape restrictions on payoff functions, and we characterize sharp identified sets for model parameters. We propose a novel inference method based on a test statistic that embeds conditional moment inequalities implied by equilibrium behavior. Our statistic has asymptotically pivotal properties that depend on the measure of contact sets, to which our statistic adapts automatically. In the case of two players and strategic substitutes we show that certain payoff parameters are point identified under mild conditions. We embed conventional point estimates for these parameters in our conditional moment inequality test statistic in order to perform inference on the remaining (partially identified) parameters. We apply our method to model the number of stores operated by Lowe’s and Home Depot in geographic markets and perform inference on several quantities of economic interest.
Centre for microdata methods and practice