Network vector autoregression with individual effects

Yiming Tang (), Yang Bai () and Tao Huang ()
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Yiming Tang: Shanghai Lixin University of Accounting and Finance
Yang Bai: Shanghai University of Finance and Economics
Tao Huang: Shanghai University of Finance and Economics

Metrika: International Journal for Theoretical and Applied Statistics, 2021, vol. 84, issue 6, No 4, 875-893

Abstract: Abstract 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.

Keywords: Network vector autoregression; Individual effects; Cohesion penalty (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00184-020-00805-y

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