Open Access
June 2017 Network vector autoregression
Xuening Zhu, Rui Pan, Guodong Li, Yuewen Liu, Hansheng Wang
Ann. Statist. 45(3): 1096-1123 (June 2017). DOI: 10.1214/16-AOS1476


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.


Download Citation

Xuening Zhu. Rui Pan. Guodong Li. Yuewen Liu. Hansheng Wang. "Network vector autoregression." Ann. Statist. 45 (3) 1096 - 1123, June 2017.


Received: 1 August 2015; Revised: 1 April 2016; Published: June 2017
First available in Project Euclid: 13 June 2017

zbMATH: 1381.62256
MathSciNet: MR3662449
Digital Object Identifier: 10.1214/16-AOS1476

Primary: 62M10
Secondary: 62J05

Keywords: multivariate time series , ordinary least squares , Social network , vector autoregression

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.45 • No. 3 • June 2017
Back to Top