NETWORK VECTOR AUTOREGRESSION
NETWORK VECTOR AUTOREGRESSION
复制标题
网络向量自回归
DOI：
10.1214/16-aos1476
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发表时间：
2017-06-01
影响因子：
4.5
通讯作者：
Wang, Hansheng
中科院分区：
文献类型：
--
作者：
Zhu, Xuening;Pan, Rui;Wang, Hansheng
我们在这里考虑一个大规模的社会网络，在等间隔的时间点观察到每个节点的连续响应。来自不同节点的响应构成一个超高维向量，其时间序列动态性有待研究。此外，网络结构也被考虑，我们提出了一个网络向量自回归（NAR）模型。NAR模型假设每个节点在给定时间点的响应为以下各项的线性组合：（a）其先前值，（B）其连接邻居的平均值，（c）一组特定于节点的协变量，以及（d）独立噪声。相应的系数分别称为动量效应、网络效应和节点效应。得到了NAR模型严格平稳的条件。为了估计NAR模型，提出了一种普通的最小二乘型估计，并研究了它的渐近性质。我们通过一些有趣的潜在应用进一步说明了NAR模型的有用性。仿真研究和实证例子。
We consider here a large-scale social network with a continuous response observed for each node at equally spaced time points. The responses from different nodes constitute an ultra-high dimensional vector, whose time series dynamic is to be investigated. In addition, the network structure is also taken into consideration, for which we propose a network vector autoregressive (NAR) model. The NAR model assumes each node's response at a given time point as a linear combination of (a) its previous value,(b) the average of its connected neighbors, (c) a set of node-specific covariates and (d) an independent noise. The corresponding coefficients are referred to as the momentum effect, the network effect and the nodal effect, respectively. Conditions for strict stationarity of the NAR models are obtained. In order to estimate the NAR model, an ordinary least squares type estimator is developed, and its asymptotic properties are investigated. We further illustrate the usefulness of the NAR model through a number of interesting potential applications. Simulation studies and an empirical example are presented.