Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners

Autores
Alejo, Javier; Favata, Federico; Montes Rojas, Gabriel Victorio; Trombetta, Martin
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper analyzes two econometric tools that are used to evaluate distributional effects, condi-tional quantile regression (CQR) and unconditional quantile regression (UQR). Our main objectiveis to shed light on the similarities and differences between these methodologies. An interestingtheoretical derivation to connect CQR and UQR is that, for the effect of a continuous covariate,the UQR is a weighted average of the CQR. This imposes clear bounds on the values that UQRcoefficients can take and provides a way to detect misspecification. The key here is a match be-tween CQR whose predicted values are the closest to the unconditional quantile. For a binarycovariate, however, this relationship is not valid. We illustrate these models using age returns andgender gap in Argentina for 2019 and 2020.
Fil: Alejo, Javier. Universidad de la República; Uruguay
Fil: Favata, Federico. Universidad Nacional de San Martín; Argentina
Fil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina
Fil: Trombetta, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de General Sarmiento; Argentina
Materia
QUANTILE REGRESSION
UNCONDITIONAL QUANTILE REGRESSION
INFLUENCE FUNCTIONS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/157743
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Conditional vs Unconditional Quantile Regression Models: A Guide to PractitionersAlejo, JavierFavata, FedericoMontes Rojas, Gabriel VictorioTrombetta, MartinQUANTILE REGRESSIONUNCONDITIONAL QUANTILE REGRESSIONINFLUENCE FUNCTIONShttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5This paper analyzes two econometric tools that are used to evaluate distributional effects, condi-tional quantile regression (CQR) and unconditional quantile regression (UQR). Our main objectiveis to shed light on the similarities and differences between these methodologies. An interestingtheoretical derivation to connect CQR and UQR is that, for the effect of a continuous covariate,the UQR is a weighted average of the CQR. This imposes clear bounds on the values that UQRcoefficients can take and provides a way to detect misspecification. The key here is a match be-tween CQR whose predicted values are the closest to the unconditional quantile. For a binarycovariate, however, this relationship is not valid. We illustrate these models using age returns andgender gap in Argentina for 2019 and 2020.Fil: Alejo, Javier. Universidad de la República; UruguayFil: Favata, Federico. Universidad Nacional de San Martín; ArgentinaFil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; ArgentinaFil: Trombetta, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de General Sarmiento; ArgentinaPontificia Universidad Católica of Peru. Departamento de Economía2021-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/157743Alejo, Javier; Favata, Federico; Montes Rojas, Gabriel Victorio; Trombetta, Martin; Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners; Pontificia Universidad Católica of Peru. Departamento de Economía; Economía; 44; 88; 10-2021; 1-182304-4306CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://revistas.pucp.edu.pe/index.php/economia/article/view/24201info:eu-repo/semantics/altIdentifier/doi/10.18800/economia.202102.004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:45Zoai:ri.conicet.gov.ar:11336/157743instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:47:45.696CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
title Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
spellingShingle Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
Alejo, Javier
QUANTILE REGRESSION
UNCONDITIONAL QUANTILE REGRESSION
INFLUENCE FUNCTIONS
title_short Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
title_full Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
title_fullStr Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
title_full_unstemmed Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
title_sort Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
dc.creator.none.fl_str_mv Alejo, Javier
Favata, Federico
Montes Rojas, Gabriel Victorio
Trombetta, Martin
author Alejo, Javier
author_facet Alejo, Javier
Favata, Federico
Montes Rojas, Gabriel Victorio
Trombetta, Martin
author_role author
author2 Favata, Federico
Montes Rojas, Gabriel Victorio
Trombetta, Martin
author2_role author
author
author
dc.subject.none.fl_str_mv QUANTILE REGRESSION
UNCONDITIONAL QUANTILE REGRESSION
INFLUENCE FUNCTIONS
topic QUANTILE REGRESSION
UNCONDITIONAL QUANTILE REGRESSION
INFLUENCE FUNCTIONS
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv This paper analyzes two econometric tools that are used to evaluate distributional effects, condi-tional quantile regression (CQR) and unconditional quantile regression (UQR). Our main objectiveis to shed light on the similarities and differences between these methodologies. An interestingtheoretical derivation to connect CQR and UQR is that, for the effect of a continuous covariate,the UQR is a weighted average of the CQR. This imposes clear bounds on the values that UQRcoefficients can take and provides a way to detect misspecification. The key here is a match be-tween CQR whose predicted values are the closest to the unconditional quantile. For a binarycovariate, however, this relationship is not valid. We illustrate these models using age returns andgender gap in Argentina for 2019 and 2020.
Fil: Alejo, Javier. Universidad de la República; Uruguay
Fil: Favata, Federico. Universidad Nacional de San Martín; Argentina
Fil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina
Fil: Trombetta, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de General Sarmiento; Argentina
description This paper analyzes two econometric tools that are used to evaluate distributional effects, condi-tional quantile regression (CQR) and unconditional quantile regression (UQR). Our main objectiveis to shed light on the similarities and differences between these methodologies. An interestingtheoretical derivation to connect CQR and UQR is that, for the effect of a continuous covariate,the UQR is a weighted average of the CQR. This imposes clear bounds on the values that UQRcoefficients can take and provides a way to detect misspecification. The key here is a match be-tween CQR whose predicted values are the closest to the unconditional quantile. For a binarycovariate, however, this relationship is not valid. We illustrate these models using age returns andgender gap in Argentina for 2019 and 2020.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/157743
Alejo, Javier; Favata, Federico; Montes Rojas, Gabriel Victorio; Trombetta, Martin; Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners; Pontificia Universidad Católica of Peru. Departamento de Economía; Economía; 44; 88; 10-2021; 1-18
2304-4306
CONICET Digital
CONICET
url http://hdl.handle.net/11336/157743
identifier_str_mv Alejo, Javier; Favata, Federico; Montes Rojas, Gabriel Victorio; Trombetta, Martin; Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners; Pontificia Universidad Católica of Peru. Departamento de Economía; Economía; 44; 88; 10-2021; 1-18
2304-4306
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://revistas.pucp.edu.pe/index.php/economia/article/view/24201
info:eu-repo/semantics/altIdentifier/doi/10.18800/economia.202102.004
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pontificia Universidad Católica of Peru. Departamento de Economía
publisher.none.fl_str_mv Pontificia Universidad Católica of Peru. Departamento de Economía
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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