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Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients

Liang Xuan, Jiti Gao and Gong Xiaodong

MPRA Paper from University Library of Munich, Germany

Abstract: This paper develops a time--varying coefficient spatial autoregressive panel data model with individual fixed effects to capture the nonlinear effects of the regressors, which vary over the time. To effectively estimate the model, we propose a method that incorporates local linear estimation and concentrated quasi-maximum likelihood estimation to obtain consistent estimators for the spatial autoregressive coefficient, variance of error term and nonparametric time-varying coefficient function. The asymptotic properties of these estimators are derived as well, showing regular the standard rate of convergence for the parametric parameters and common standard rate of convergence for the time-varying component, respectively. Monte Carlo simulations are conducted to illustrate the finite sample performance of our proposed method. Meanwhile, we apply our method to study the Chinese labor productivity to identify the spatial influences and the time--varying spillover effects among 185 Chinese cities with comparison to the results on a subregion--East China.

Keywords: Concentrated quasi-maximum likelihood estimation; local linear estimation; time-varying coefficient (search for similar items in EconPapers)
JEL-codes: C1 C14 C3 C33 (search for similar items in EconPapers)
Date: 2021-01-07, Revised 2021-05-30
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-ure
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Related works:
Journal Article: Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients (2022) Downloads
Working Paper: Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients (2021) Downloads
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