Inference in Games Without Equilibrium Restriction: An Application to Restaurant Competition in Opening Hours
放松了贝叶斯纳什均衡假设，将玩家信念视为未知函数，研究离散选择博弈中信念与收益函数的联合识别，并应用于麦当劳与肯德基在中国的营业时间竞争，发现均衡信念假设被拒绝，错误施加均衡假设会导致互动效应估计偏低超过50%。
This article relaxes the Bayesian Nash equilibrium assumption in the estimation of discrete choice games with incomplete information. Instead of assuming unbiased/correct expectations, the model specifies a player’s belief about the behaviors of other players as an unrestricted unknown function. I then study the joint identification of belief and payoff functions in a game where players have different numbers of actions (e.g., 3 × 2 game). This asymmetry in action sets partially identifies the payoff function of the player with more actions. Moreover, if usual exclusion restrictions are satisfied, the payoff and belief functions are point identified up to a scale, and the restriction of equilibrium beliefs is testable. Finally, under a multiplicative separability condition on payoffs, the above identification results are extended to the player with fewer actions and to games with symmetric action sets. I apply this model and its identification results to study the store hours competition between McDonald’s and Kentucky Fried Chicken in China. The null hypothesis of unbiased beliefs is rejected. If researchers incorrectly impose the equilibrium assumption, then the estimated interactive effect would be biased downward by more than 50%.