Smoothing quantile regressions

Detalhes bibliográficos
Autor(a) principal: Fernandes, Marcelo
Data de Publicação: 2018
Outros Autores: Emmanuel, Guerre
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: https://hdl.handle.net/10438/27664
Resumo: We propose to smooth the entire objective function rather than only the check function in a linear quantile regression context. We derive a uniform Bahadur-Kiefer representation for the resulting convolution-type kernel estimator that demonstrates it improves on the extant quantile regression estimators in the literature. In addition, we also show that it is straightforward to compute asymptotic standard errors for the quantile regression coefficient estimates as well as to implement Wald-type tests. Simulations confirm that our smoothed quantile regression estimator performs very well in finite samples.
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spelling Fernandes, MarceloEmmanuel, GuerreDemais unidades::RPCA2019-07-03T14:57:45Z2019-07-03T14:57:45Z2018-04-08https://hdl.handle.net/10438/27664We propose to smooth the entire objective function rather than only the check function in a linear quantile regression context. We derive a uniform Bahadur-Kiefer representation for the resulting convolution-type kernel estimator that demonstrates it improves on the extant quantile regression estimators in the literature. In addition, we also show that it is straightforward to compute asymptotic standard errors for the quantile regression coefficient estimates as well as to implement Wald-type tests. Simulations confirm that our smoothed quantile regression estimator performs very well in finite samples.engAsymptotic expansionBahadur-Kiefer representationConditional quantileConvolution-based smoothingData-driven bandwidthRegressão quantílica linearEconomiaExpansões assintóticasAnálise de regressãoSmoothing quantile regressionsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVRede de Pesquisa e Conhecimento AplicadoORIGINAL042_2018_Smoothing quantile regressions_MARCELO FERNANDES.PDF042_2018_Smoothing quantile regressions_MARCELO FERNANDES.PDFapplication/pdf501094https://repositorio.fgv.br/bitstreams/e8d7116f-a262-4cf2-9a71-abb5a11c72fe/download1bf60520cc6110ff3dddcefe8d918577MD51LICENSElicense.txtlicense.txttext/plain; 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dc.title.eng.fl_str_mv Smoothing quantile regressions
title Smoothing quantile regressions
spellingShingle Smoothing quantile regressions
Fernandes, Marcelo
Asymptotic expansion
Bahadur-Kiefer representation
Conditional quantile
Convolution-based smoothing
Data-driven bandwidth
Regressão quantílica linear
Economia
Expansões assintóticas
Análise de regressão
title_short Smoothing quantile regressions
title_full Smoothing quantile regressions
title_fullStr Smoothing quantile regressions
title_full_unstemmed Smoothing quantile regressions
title_sort Smoothing quantile regressions
author Fernandes, Marcelo
author_facet Fernandes, Marcelo
Emmanuel, Guerre
author_role author
author2 Emmanuel, Guerre
author2_role author
dc.contributor.unidadefgv.por.fl_str_mv Demais unidades::RPCA
dc.contributor.author.fl_str_mv Fernandes, Marcelo
Emmanuel, Guerre
dc.subject.eng.fl_str_mv Asymptotic expansion
Bahadur-Kiefer representation
Conditional quantile
Convolution-based smoothing
Data-driven bandwidth
topic Asymptotic expansion
Bahadur-Kiefer representation
Conditional quantile
Convolution-based smoothing
Data-driven bandwidth
Regressão quantílica linear
Economia
Expansões assintóticas
Análise de regressão
dc.subject.por.fl_str_mv Regressão quantílica linear
dc.subject.area.por.fl_str_mv Economia
dc.subject.bibliodata.por.fl_str_mv Expansões assintóticas
Análise de regressão
description We propose to smooth the entire objective function rather than only the check function in a linear quantile regression context. We derive a uniform Bahadur-Kiefer representation for the resulting convolution-type kernel estimator that demonstrates it improves on the extant quantile regression estimators in the literature. In addition, we also show that it is straightforward to compute asymptotic standard errors for the quantile regression coefficient estimates as well as to implement Wald-type tests. Simulations confirm that our smoothed quantile regression estimator performs very well in finite samples.
publishDate 2018
dc.date.issued.fl_str_mv 2018-04-08
dc.date.accessioned.fl_str_mv 2019-07-03T14:57:45Z
dc.date.available.fl_str_mv 2019-07-03T14:57:45Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
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dc.identifier.uri.fl_str_mv https://hdl.handle.net/10438/27664
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dc.language.iso.fl_str_mv eng
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