论文标题
具有未知信息结构的动态游戏的识别和估计
Identification and Estimation of Dynamic Games with Unknown Information Structure
论文作者
论文摘要
本文研究了研究人员未知的基础信息结构时，研究了动态游戏的识别和估计。为了漫步地表征马尔可夫完美的平衡预测，同时保持对玩家信息的弱假设，我们引入了\ textit {马尔可夫相关平衡}，这是贝叶斯相关平衡的动态类似物。马尔可夫一组相关的平衡预测与马尔可夫的完美平衡预测相吻合，当玩家观察到比分析师假设的更多信号时，可能会出现的。使用Markov将平衡相关联作为解决方案概念，我们提出了可牵引的计算策略，以进行信息强大的估计，推理和反事实分析，以涉及在动态环境中产生的非偶像性。我们使用我们的方法来分析美国星巴克与邓肯'之间的动态竞争以及信息假设的作用。
This paper studies the identification and estimation of dynamic games when the underlying information structure is unknown to the researcher. To tractably characterize the set of Markov perfect equilibrium predictions while maintaining weak assumptions on players' information, we introduce \textit{Markov correlated equilibrium}, a dynamic analog of Bayes correlated equilibrium. The set of Markov correlated equilibrium predictions coincides with the set of Markov perfect equilibrium predictions that can arise when the players can observe more signals than assumed by the analyst. Using Markov correlated equilibrium as the solution concept, we propose tractable computational strategies for informationally robust estimation, inference, and counterfactual analysis that deal with the non-convexities arising in dynamic environments. We use our method to analyze the dynamic competition between Starbucks and Dunkin' in the US and the role of informational assumptions.
