Network vector autoregressive moving average model

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)593-615
Journal / PublicationStatistics and its Interface
Volume16
Issue number4
Online published14 Apr 2023
Publication statusPublished - 2023

Abstract

Modeling a continuous response of a large-scale network is an important task and it has become prevailing in practice at present. This paper proposes a novel network vector autoregressive moving average (NARMA) model which considers the responses from both an ultra-high dimension vector and the network structure effects. Compared with the network vector autoregressive (NAR, [26]) model, we take into account the lagged innovations and corresponding network effect in our proposed model. With more parameters considered and a moving average term incorporated, the proposed NARMA model can fit the data more closely and accurately, thus has a better performance than the NAR model. A modified least square estimation for the NARMA model is introduced, and the consistency properties are fully investigated. Finally, we demonstrate the superiority of the proposed NARMA model by investigating the financial contagions of S&P500 index constituents. © 2023, Statistics and its Interface. All Rights Reserved.

Research Area(s)

  • High dimensional time series, Modified least square estimator, Network data, Vector autoregressive moving average

Citation Format(s)

Network vector autoregressive moving average model. / Chen, Xiao; Chen, Yu; Hu, Xixu.
In: Statistics and its Interface, Vol. 16, No. 4, 2023, p. 593-615.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review