On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study

Autores
Galvao, Antonio; Montes Rojas, Gabriel Victorio
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temporal resampling is performed from the time series. Finally, a more general resampling scheme, which considers sampling from both the cross-sectional and temporal dimensions, is introduced. The bootstrap algorithms are computationally attractive and easy to use in practice. We evaluate the performance of the bootstrap confidence interval by means of Monte Carlo simulations. The results show that the bootstrap methods have good finite sample performance for both location and location-scale models.
Fil: Galvao, Antonio. University of Iowa; Estados Unidos
Fil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. City University of London; Reino Unido
Materia
QUANTILE REGRESSION
PANEL DATA
FIXED EFFECTS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/69996
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/69996
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo StudyGalvao, AntonioMontes Rojas, Gabriel VictorioQUANTILE REGRESSIONPANEL DATAFIXED EFFECTShttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temporal resampling is performed from the time series. Finally, a more general resampling scheme, which considers sampling from both the cross-sectional and temporal dimensions, is introduced. The bootstrap algorithms are computationally attractive and easy to use in practice. We evaluate the performance of the bootstrap confidence interval by means of Monte Carlo simulations. The results show that the bootstrap methods have good finite sample performance for both location and location-scale models.Fil: Galvao, Antonio. University of Iowa; Estados UnidosFil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. City University of London; Reino UnidoMDPI2015-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/69996Galvao, Antonio; Montes Rojas, Gabriel Victorio; On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study; MDPI; Econometrics; 3; 3; 10-2015; 654-6662225-1146CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/2225-1146/3/3/654info:eu-repo/semantics/altIdentifier/doi/10.3390/econometrics3030654info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:05:15Zoai:ri.conicet.gov.ar:11336/69996instacron: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 10:05:15.613CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
title On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
spellingShingle On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
Galvao, Antonio
QUANTILE REGRESSION
PANEL DATA
FIXED EFFECTS
title_short On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
title_full On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
title_fullStr On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
title_full_unstemmed On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
title_sort On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
dc.creator.none.fl_str_mv Galvao, Antonio
Montes Rojas, Gabriel Victorio
author Galvao, Antonio
author_facet Galvao, Antonio
Montes Rojas, Gabriel Victorio
author_role author
author2 Montes Rojas, Gabriel Victorio
author2_role author
dc.subject.none.fl_str_mv QUANTILE REGRESSION
PANEL DATA
FIXED EFFECTS
topic QUANTILE REGRESSION
PANEL DATA
FIXED EFFECTS
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 evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temporal resampling is performed from the time series. Finally, a more general resampling scheme, which considers sampling from both the cross-sectional and temporal dimensions, is introduced. The bootstrap algorithms are computationally attractive and easy to use in practice. We evaluate the performance of the bootstrap confidence interval by means of Monte Carlo simulations. The results show that the bootstrap methods have good finite sample performance for both location and location-scale models.
Fil: Galvao, Antonio. University of Iowa; Estados Unidos
Fil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. City University of London; Reino Unido
description This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temporal resampling is performed from the time series. Finally, a more general resampling scheme, which considers sampling from both the cross-sectional and temporal dimensions, is introduced. The bootstrap algorithms are computationally attractive and easy to use in practice. We evaluate the performance of the bootstrap confidence interval by means of Monte Carlo simulations. The results show that the bootstrap methods have good finite sample performance for both location and location-scale models.
publishDate 2015
dc.date.none.fl_str_mv 2015-10
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/11336/69996
Galvao, Antonio; Montes Rojas, Gabriel Victorio; On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study; MDPI; Econometrics; 3; 3; 10-2015; 654-666
2225-1146
CONICET Digital
CONICET
url http://hdl.handle.net/11336/69996
identifier_str_mv Galvao, Antonio; Montes Rojas, Gabriel Victorio; On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study; MDPI; Econometrics; 3; 3; 10-2015; 654-666
2225-1146
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/2225-1146/3/3/654
info:eu-repo/semantics/altIdentifier/doi/10.3390/econometrics3030654
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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
_version_ 1842269901499138048
score 13.13397

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