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Robust estimation for semiparametric spatial autoregressive models via weighted composite quantile regression

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  • Xinrong Tang
  • Peixin Zhao
  • Xiaoshuang Zhou
  • Weijia Zhang

Abstract

In this article, the robust estimation for a class of semiparametric spatial autoregressive models has been investigated. By combining the QR decomposition technique for matrix and the weighted composite quantile regression method, we propose a robust estimation procedure for the parametric and non parametric components. Under certain regularity conditions, asymptotic properties of the resulting estimators are proved. Several simulation analyses have been conducted for further illustrating the performance of the proposed method, and the simulation results demonstrate that the proposed method improve the robustness of the models.

Suggested Citation

  • Xinrong Tang & Peixin Zhao & Xiaoshuang Zhou & Weijia Zhang, 2024. "Robust estimation for semiparametric spatial autoregressive models via weighted composite quantile regression," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(12), pages 3494-3511, September.
    • Handle: RePEc:taf:lstaxx:v:54:y:2024:i:12:p:3494-3511
    DOI: 10.1080/03610926.2024.2395881
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