具有异质信念的大型贝叶斯博弈的计量经济学推断
Econometric Inference on a Large Bayesian Game with Heterogeneous Beliefs
AI总结：
本文聚焦具有异质信念的大型贝叶斯博弈，通过引入事后遗憾概念及其无信念界，开发了不依赖邻域规模且一致有效的自助法推断程序，用于对结构参数进行实证检验。
摘要：
关于个人或企业之间策略互动的计量经济学模型在文献中受到了广泛关注。但对于关于智能体如何形成对其他智能体信念的潜在假设的作用，受到的关注较少。我们聚焦于一个具有特异性策略邻域的大型贝叶斯博弈，并开发了一种实证建模方法，该方法放松了理性预期假设，允许参与者以不同方式形成信念。借鉴 Kalai (2004) 的主要直觉，我们引入了事后遗憾的概念，用于衡量每个参与者对其他参与者类型信息的事后价值，并获得了事后遗憾的无信念界。利用该界限，我们推导出了可检验的含义，并为结构参数开发了一种自助法推断程序。无论策略邻域的规模如何，我们的推断方法都是一致有效的，并且当邻域较大时往往表现出高功效。我们通过 Monte Carlo 模拟展示了该方法的有限样本性能。
英文摘要：
Econometric models of strategic interactions among people or firms have received a great deal of attention in the literature. Less attention has been paid to the role of the underlying assumptions about the way agents form beliefs about other agents. We focus on a single large Bayesian game with idiosyncratic strategic neighborhoods and develop an approach of empirical modeling that relaxes the assumption of rational expectations and allows the players to form beliefs differently. By drawing on the main intuition of Kalai (2004), we introduce the notion of hindsight regret, which measures each player's ex-post value of other players' type information, and obtain the belief-free bound for the hindsight regret. Using this bound, we derive testable implications and develop a bootstrap inference procedure for the structural parameters. Our inference method is uniformly valid regardless of the size of strategic neighborhoods and tends to exhibit high power when the neighborhoods are large. We demonstrate the finite sample performance of the method through Monte Carlo simulations.
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