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Consistent two-stage estimation in heterogeneous network autoregressive model

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  • Zhao, Jiayang
  • Liu, Jie
  • Su, Yuting

Abstract

Obtaining consistent estimates of network effects in the heterogeneous network autoregressive model presents significant challenges. These challenges arise from the large number of target parameters, potential endogeneity, and non-identifiability issues. To overcome these challenges, we reformulate the model into a higher-order version. Our proposed two-stage estimation procedure first reduces parameter complexity by screening out nodes with negligible network effects. Then, we employ the ordinary least squares method and the instrumental variables technique for effective post-screening estimation. We further investigate the consistency and asymptotic normality of the estimators under appropriate assumptions and explore the cases of heteroscedasticity. The finite sample performance of the two-stage method is evaluated by simulation studies and an empirical analysis.

Suggested Citation

  • Zhao, Jiayang & Liu, Jie & Su, Yuting, 2024. "Consistent two-stage estimation in heterogeneous network autoregressive model," Statistics & Probability Letters, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:stapro:v:212:y:2024:i:c:s0167715224001160
    DOI: 10.1016/j.spl.2024.110147
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    References listed on IDEAS

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