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

Yan-Yong Zhao, Ling-Ling Ge and Yuan Liu ()
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Yan-Yong Zhao: Southeast University
Ling-Ling Ge: Nanjing Audit University
Yuan Liu: Southeast University

Statistical Papers, 2025, vol. 66, issue 1, No 9, 37 pages

Abstract: 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.

Keywords: Semiparametric estimation; Spatial autoregressive errors; Time-varying coefficients model; 62G08; 62H12; 62J10 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00362-024-01620-7

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