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Article: Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models

TitleNonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models
Authors
KeywordsHomogeneity pursuit
Nonparametric approach
Oracle property
Panel data model
Quantile regression
Issue Date10-Oct-2022
PublisherTaylor and Francis Group
Citation
Journal of Business & Economic Statistics, 2023, v. 41, n. 4, p. 1238-1250 How to Cite?
Abstract

Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved significantly by pooling the information of individuals within each group. However, the currently assumed parametric and semiparametric relationship between the response and predictors may be misspecified, which leads to a wrong grouping result, and the nonparametric approach hence can be considered to avoid such mistakes. Moreover, the response may depend on predictors in different ways at various quantile levels, and the corresponding grouping structure may also vary. To tackle these problems, this article proposes a nonparametric quantile regression method for homogeneity pursuit in panel data models with individual effects, and a pairwise fused penalty is used to automatically select the number of groups. The asymptotic properties are established, and an ADMM algorithm is also developed. The finite sample performance is evaluated by simulation experiments, and the usefulness of the proposed methodology is further illustrated by an empirical example.


Persistent Identifierhttp://hdl.handle.net/10722/338337
ISSN
2023 Impact Factor: 2.9
2023 SCImago Journal Rankings: 3.385
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Xiaoyu-
dc.contributor.authorWang, Di-
dc.contributor.authorLian, Heng-
dc.contributor.authorLi, Guodong-
dc.date.accessioned2024-03-11T10:28:07Z-
dc.date.available2024-03-11T10:28:07Z-
dc.date.issued2022-10-10-
dc.identifier.citationJournal of Business & Economic Statistics, 2023, v. 41, n. 4, p. 1238-1250-
dc.identifier.issn0735-0015-
dc.identifier.urihttp://hdl.handle.net/10722/338337-
dc.description.abstract<p> Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved significantly by pooling the information of individuals within each group. However, the currently assumed parametric and semiparametric relationship between the response and predictors may be misspecified, which leads to a wrong grouping result, and the nonparametric approach hence can be considered to avoid such mistakes. Moreover, the response may depend on predictors in different ways at various quantile levels, and the corresponding grouping structure may also vary. To tackle these problems, this article proposes a nonparametric quantile regression method for homogeneity pursuit in panel data models with individual effects, and a pairwise fused penalty is used to automatically select the number of groups. The asymptotic properties are established, and an ADMM algorithm is also developed. The finite sample performance is evaluated by simulation experiments, and the usefulness of the proposed methodology is further illustrated by an empirical example. <br></p>-
dc.languageeng-
dc.publisherTaylor and Francis Group-
dc.relation.ispartofJournal of Business & Economic Statistics-
dc.subjectHomogeneity pursuit-
dc.subjectNonparametric approach-
dc.subjectOracle property-
dc.subjectPanel data model-
dc.subjectQuantile regression-
dc.titleNonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models-
dc.typeArticle-
dc.identifier.doi10.1080/07350015.2022.2118125-
dc.identifier.scopuseid_2-s2.0-85139840518-
dc.identifier.volume41-
dc.identifier.issue4-
dc.identifier.spage1238-
dc.identifier.epage1250-
dc.identifier.eissn1537-2707-
dc.identifier.isiWOS:000865632700001-
dc.identifier.issnl0735-0015-

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