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不完全信息博弈的成对差分估计AI 解读

This paper contributes to the literature on econometric estimation of incomplete information games with Nash equilibrium behavior by introducing a two-step estimation procedure that makes no parametric assumptions about the distribution of unobservable payoffs shocks. Instead, its asymptotic properties rely on assuming only that these distributions satisfy an invertibility condition, and that the underlying equilibrium selection mechanism is degenerate. Our methodology…

同时离散博弈中纳什均衡行动的非参数概率界AI 解读

We study a simultaneous, complete-information game played by p = 1 P agents.Each p has an ordinal decision variable Y p ∈ A p = {0 1 M p }, where M p can be unbounded, A p is p's action space, and each element in A p is an action, that is, a potential value for Y p .The collective action space is the Cartesian product A = P p=1 A p .A profile of actions y ∈ A is a Nash equilibrium (NE) profile if y is played with positive probability in some existing NE.Assuming that we…

不完全信息同时行动博弈的半参数估计AI 解读

This paper studies the problem of estimating the normal-form payoff parameters of a simultaneous, discrete game where the realization of such payoffs is not common knowledge. The paper contributes to the existing literature in two ways. First, by making a comparison with the complete information case it formally describes a set of conditions under which allowing for private information in payoffs facilitates the identification of various features of the game. Second,…

简单博弈中均衡假设的识别能力AI 解读

We examine the identification power that (Nash) equilibrium assumptions play in conducting inference about parameters in some simple games. We focus on three static games in which we drop the Nash equilibrium assumption and instead use rationalizability as the basis for strategic play. The first example examines a bivariate discrete game with complete information of the kind studied in entry models. The second example considers the incomplete-information version of the…