{"title":"Profile quasi-maximum likelihood estimation for semiparametric varying-coefficient spatial autoregressive panel models with fixed effects","authors":"Ruiqin Tian, Miaojie Xia, Dengke Xu","doi":"10.1007/s00362-024-01586-6","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"409 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Papers","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s00362-024-01586-6","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
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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.
期刊介绍：
The journal Statistical Papers addresses itself to all persons and organizations that have to deal with statistical methods in their own field of work. It attempts to provide a forum for the presentation and critical assessment of statistical methods, in particular for the discussion of their methodological foundations as well as their potential applications. Methods that have broad applications will be preferred. However, special attention is given to those statistical methods which are relevant to the economic and social sciences. In addition to original research papers, readers will find survey articles, short notes, reports on statistical software, problem section, and book reviews.
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