带有非平稳回归元的网络向量自回归模型的稳健估计
Robust Estimation in Network Vector Autoregression with Nonstationary Regressors
AI总结：
本文研究了带有非平稳回归元的网络向量自回归模型的识别与估计，通过非随机邻接矩阵刻画网络依赖性，利用 local-to-unity 参数化捕捉持续性，并采用 IVX 型估计量在双重渐近机制下实现稳健估计。
摘要：
本文研究了带有非平稳回归元的网络向量自回归模型的识别与估计。具体而言，网络依赖性由非随机邻接矩阵刻画。信息集包含一个平稳的回归被解释变量和一个节点特定的非平稳回归元向量，两者均在相同的等间距时间频率上观测。我们提出的计量经济学设定对应于时间序列非平稳性下的 NVAR 模型，该模型依赖 local-to-unity 参数化来捕捉这些节点特定回归元的未知持续性形式。稳健的计量经济学估计通过使用 IVX 型估计量实现，并且基于网络规模和时间维度均趋于无穷大的双重渐近机制，研究了增强回归元向量的渐近理论分析。
英文摘要：
This article studies identification and estimation for the network vector autoregressive model with nonstationary regressors. In particular, network dependence is characterized by a nonstochastic adjacency matrix. The information set includes a stationary regressand and a node-specific vector of nonstationary regressors, both observed at the same equally spaced time frequencies. Our proposed econometric specification correponds to the NVAR model under time series nonstationarity which relies on the local-to-unity parametrization for capturing the unknown form of persistence of these node-specific regressors. Robust econometric estimation is achieved using an IVX-type estimator and the asymptotic theory analysis for the augmented vector of regressors is studied based on a double asymptotic regime where both the network size and the time dimension tend to infinity.
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