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Homogeneous analysis on network effects in network autoregressive model

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  • Zhao, Jiayang
  • Liu, Jie

Abstract

Network effects are pivotal for understanding the mutual influence of nodes within a network. The homogeneous group structure holds significant importance across networks in various fields. This paper introduces the Classifier-L2 regularized approach for homogeneous analysis of network effects under the network autoregressive model. This approach offers high flexibility with data constraints and provides a completely data-driven procedure. Analysis of international trade data offers meaningful perspectives for refining trade policies. Empirical results derived from Chinese mutual fund data, both before and after the outbreak of the COVID-19 pandemic, provide valuable insights for mitigating potential risks.

Suggested Citation

  • Zhao, Jiayang & Liu, Jie, 2023. "Homogeneous analysis on network effects in network autoregressive model," Finance Research Letters, Elsevier, vol. 58(PD).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pd:s1544612323010437
    DOI: 10.1016/j.frl.2023.104671
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    References listed on IDEAS

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