论文标题
网络自动加入的非可识别性
Non-Identifiability in Network Autoregressions
论文作者
论文摘要
我们研究了网络上定义的自动锻炼中参数的可识别性。这些模型可用的大多数识别条件要么依赖于反复观察到的网络，仅仅是足够的，要么需要强烈的分布假设。本文得出了即使仅观察到组成网络的个体，就可以得出适用的条件，这是必要的，足以识别，并且需要弱分布假设。我们发现，在衡量理论意义上，模型参数也是一般性的，即使没有重复观察，也可以识别，并分析相互作用矩阵的组合和导致识别失败的回归矩阵的组合。这既可以在原始模型中进行，又在样本空间中的某些转换之后进行，例如，在某些固定效果规范中相关。
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.
