Smoothing quantile regressions

Detalhes bibliográficos
Autor(a) principal: Fernandes, Marcelo
Data de Publicação: 2017
Outros Autores: Guerre, Emmanuel, Horta, Eduardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: http://hdl.handle.net/10438/18390
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, MarceloGuerre, EmmanuelHorta, EduardoEscolas::EESP2017-06-28T20:05:14Z2017-06-28T20:05:14Z2017TD 457http://hdl.handle.net/10438/18390We 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.engEESP - Textos para Discussão;TD 457Asymptotic expansionBahadur-Kiefer representationConditional quantileConvolution-based smoothingData-driven bandwidthEconomiaAnálise de regressãoSuavização (Estatística)Smoothing quantile regressionsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlereponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessTEXTTD 457_CEQEF 39_1.pdf.txtTD 457_CEQEF 39_1.pdf.txtExtracted texttext/plain81569https://repositorio.fgv.br/bitstreams/62b9ea05-3962-4113-a591-1ebaed75b98b/downloadcd1d6a1a613895fd8a0d4ffe5cf4f0c9MD55ORIGINALTD 457_CEQEF 39_1.pdfTD 457_CEQEF 39_1.pdfapplication/pdf988330https://repositorio.fgv.br/bitstreams/9a938022-bb96-43d9-9305-f9044205ddfd/downloadc444c25fa852c704d2ef05ff525aaa86MD51LICENSElicense.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
Economia
Análise de regressão
Suavização (Estatística)
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
Guerre, Emmanuel
Horta, Eduardo
author_role author
author2 Guerre, Emmanuel
Horta, Eduardo
author2_role author
author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EESP
dc.contributor.author.fl_str_mv Fernandes, Marcelo
Guerre, Emmanuel
Horta, Eduardo
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
Economia
Análise de regressão
Suavização (Estatística)
dc.subject.area.por.fl_str_mv Economia
dc.subject.bibliodata.por.fl_str_mv Análise de regressão
Suavização (Estatística)
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 2017
dc.date.accessioned.fl_str_mv 2017-06-28T20:05:14Z
dc.date.available.fl_str_mv 2017-06-28T20:05:14Z
dc.date.issued.fl_str_mv 2017
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10438/18390
dc.identifier.sici.none.fl_str_mv TD 457
identifier_str_mv TD 457
url http://hdl.handle.net/10438/18390
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartofseries.por.fl_str_mv EESP - Textos para Discussão;TD 457
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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