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Network vector autoregression with individual effects

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yiming Tang & Yang Bai & Tao Huang, 2021. "Network vector autoregression with individual effects," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(6), pages 875-893, August.
    • Handle: RePEc:spr:metrik:v:84:y:2021:i:6:d:10.1007_s00184-020-00805-y
    DOI: 10.1007/s00184-020-00805-y
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    References listed on IDEAS

    as
    1. Yuan Zhang & Elizaveta Levina & Ji Zhu, 2017. "Estimating network edge probabilities by neighbourhood smoothing," Biometrika, Biometrika Trust, vol. 104(4), pages 771-783.
    2. Zhu, Xuening & Wang, Weining & Wang, Hansheng & Härdle, Wolfgang Karl, 2019. "Network quantile autoregression," Journal of Econometrics, Elsevier, vol. 212(1), pages 345-358.
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