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

Ruiqin Tian, Miaojie Xia and Dengke Xu ()
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Ruiqin Tian: Hangzhou Normal University
Miaojie Xia: Hangzhou Normal University
Dengke Xu: Hangzhou Dianzi University

Statistical Papers, 2024, vol. 65, issue 8, No 12, 5109-5143

Abstract: 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.

Keywords: Spatial autoregressive panel models; Fixed effects; Profile quasi-maximum likelihood; Varying-coefficient; B-spline (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-024-01586-6

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