Network vector autoregressive moving average model

摘要

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.

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