This paper provides sufficient conditions for nonparametrically identifying dynamic games with incomplete information, allowing for multiple equilibria and payoff-relevant unobservables. Our identification involves two steps. We first identify the equilibrium conditional choice probabilities and state transitions using the Markov property and four-period data. The first step of our identification relies on eigenvalue-eigenvector decomposition, and thus incurs the same issue of identification up-to-label-swapping as the existing literature. This makes it difficult to identify payoff primitives in the second step, which requires consistent matching of unobserved types across different values of the observed variables. Instead of imposing assumptions such as monotonicity, we address this type-matching problem by exploiting the Markov property and longitudinal variations of observables in the intermediate periods to link different decompositions.

本文为非参数识别具有不完全信息的动态博弈提供了充分条件，允许多个均衡和与收益相关的不可观察量。我们的识别包括两个步骤。我们首先使用马尔可夫性质和四周期数据确定均衡条件选择概率和状态转换。我们识别的第一步依赖于特征值-特征向量分解，因此会产生与现有文献相同的识别到标签交换的问题。这使得在第二步中识别收益原语变得困难，这需要在观察变量的不同值之间一致匹配未观察到的类型。而不是强加诸如单调性之类的假设，