网络自回归模型的不可识别性
Non-Identifiability in Network Autoregressions
摘要：
研究了网络上自回归模型参数的可辨识性。这些模型的大多数识别条件要么依赖于重复观察的网络，要么仅仅是充分的，要么需要强分布假设。本文推导出的条件，即使组成网络的个人只观察一次，是必要的和充分的识别，并需要弱分布的假设。我们发现，一般的模型参数，在测量理论的意义上，即使没有重复的观察，识别，并分析的相互作用矩阵和回归矩阵的组合导致识别失败。这在原始模型中和样本空间中的某些变换之后都可以完成，后一种情况是相关的，例如，在某些固定效应规格中。
英文摘要：
We study identifiability of the parameters in autoregressions defined on a network. Most identification conditions that are available for these models either rely on the network being observed repeatedly, are only sufficient, or require strong distributional assumptions. This paper derives conditions that apply even when the individuals composing the network are observed only once, are necessary and sufficient for identification, and require weak distributional assumptions. We find that the model parameters are generically, in the measure theoretic sense, identified even without repeated observations, and analyze the combinations of the interaction matrix and the regressor matrix causing identification failures. This is done both in the original model and after certain transformations in the sample space, the latter case being relevant, for example, in some fixed effects specifications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
