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Estimation of panel data partially linear time-varying coefficient models with cross-sectional spatial autoregressive errors

Author

Listed:
  • Yan-Yong Zhao

    (Southeast University
    Nanjing Audit University)

  • Ling-Ling Ge

    (Nanjing Audit University)

  • Yuan Liu

    (Southeast University
    Nanjing Audit University)

Abstract

A more efficient estimation procedure on a panel data partially linear time-varying coefficient model (PDPLTVCM) with both fixed effects and spatial autoregressive errors is discussed in this paper. Without taking the first-order difference, we develop a new procedure for estimating the autoregressive parameter by taking a dummy variate-based semiparametric least-squares estimation (SLSE) approach and a new generalized method of moments (GMM) method. Asymptotic properties of the resultant estimators are established under some mild assumptions. Further, we derive the weighted semiparametric estimators for both the parameters and coefficient functions, and the main results show that they have the optimal convergence rate and are more efficient than the unweighted versions. Some Monte Carlo experiments are conducted to evaluate the finite sample performance of the proposed methods, and an authentic data example is investigated for illustration.

Suggested Citation

  • Yan-Yong Zhao & Ling-Ling Ge & Yuan Liu, 2025. "Estimation of panel data partially linear time-varying coefficient models with cross-sectional spatial autoregressive errors," Statistical Papers, Springer, vol. 66(1), pages 1-37, February.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:1:d:10.1007_s00362-024-01620-7
    DOI: 10.1007/s00362-024-01620-7
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