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Profile quasi-maximum likelihood estimation for semiparametric varying-coefficient spatial autoregressive panel models with fixed effects

Author

Listed:
  • Ruiqin Tian

    (Hangzhou Normal University)

  • Miaojie Xia

    (Hangzhou Normal University)

  • Dengke Xu

    (Hangzhou Dianzi University)

Abstract

This paper aims to propose a profile quasi-maximum likelihood estimation method for semiparametric varying-coefficient spatial autoregressive(SVCSAR) panel models with fixed effects. The proposed estimation approach can directly estimate the desired parameters on the basis of B-spline approximations of nonparametric components, and skip the estimation of individual effects. Under some mild assumptions, the consistency for the parametric part and the nonparametric part are given respectively and the asymptotic normality for the parametric part is established. The finite sample performance of the proposed method is investigated through Monte Carlo simulation studies. Finally, a real data analysis of the carbon emission dataset is carried out to illustrate the usefulness of the proposed estimation method.

Suggested Citation

  • Ruiqin Tian & Miaojie Xia & Dengke Xu, 2024. "Profile quasi-maximum likelihood estimation for semiparametric varying-coefficient spatial autoregressive panel models with fixed effects," Statistical Papers, Springer, vol. 65(8), pages 5109-5143, October.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:8:d:10.1007_s00362-024-01586-6
    DOI: 10.1007/s00362-024-01586-6
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