 Penalized quadratic inference functions estimation of fixed effects partially linear varying coefficient spatial error modelJianbao Chen and Fen Li Economic Modelling, 2025, vol. 146, issue C Abstract: This study introduces a novel fixed effects partially linear varying coefficient spatial error model featuring a correlation structure within individuals. A penalized quadratic inference functions estimation method for unknowns is proposed by employing B-spline to approximate the varying coefficient functions. Under certain regular conditions, the consistency and asymptotic normality of parametric estimators and the optimal convergence rate of nonparametric estimators are derived. Monte Carlo simulation indicates that the estimates perform strongly in finite sample scenarios. Empirical data analysis demonstrates that the model effectively captures the spatial error correlation of CO2 emissions and diverse factors’ linear and nonlinear influences on CO2 emissions. The proposed model and estimation method can be useful for researchers in related disciplines. Keywords: Partially linear varying coefficient spatial error model; Penalized quadratic inference functions estimation; Correlation within individuals; Asymptotic property; Monte Carlo simulation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:146:y:2025:i:c:s0264999325000173 DOI: 10.1016/j.econmod.2025.107022 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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