In recent years, there has been great interest in using network structure to improve classic statistical models in cases where individuals are dependent. The network vector autoregressive (NAR) model assumes that each node’s response can be affected by the average of its connected neighbors. This article focuses on the problem of individual effects in NAR models, as different nodes have different effects on others. We propose a penalty method to estimate the NAR model with different individual effects and investigate some theoretical properties. Two simulation experiments are performed to verify effectiveness and tolerance compared with the original NAR model. The proposed model is also applied to an international trade data set.

中文翻译：

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具有个体效应的网络向量自回归

近年来，人们对使用网络结构在个体依赖的情况下改进经典统计模型产生了极大的兴趣。网络向量自回归 (NAR) 模型假设每个节点的响应都会受到其连接邻居的平均值的影响。本文重点讨论 NAR 模型中个体效应的问题，因为不同的节点对其他节点有不同的影响。我们提出了一种惩罚方法来估计具有不同个体效应的 NAR 模型并研究一些理论特性。进行了两次仿真实验以验证与原始 NAR 模型相比的有效性和耐受性。所提出的模型也适用于国际贸易数据集。