GMM quantile regression

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dc.contributor.authorSERGIO PINHEIRO FIRPO
dc.contributor.authorGalvao, Antonio F.
dc.contributor.authorCRISTINE CAMPOS DE XAVIER PINTO
dc.contributor.authorPoirier, Alexandre
dc.contributor.authorSanroman, Graciela
dc.coverage.paisNão Informadopt_BR
dc.creatorGalvao, Antonio F.
dc.creatorPoirier, Alexandre
dc.creatorSanroman, Graciela
dc.date.accessioned2022-10-14T14:29:12Z
dc.date.available2022-10-14T14:29:12Z
dc.date.issued2022
dc.description.notesTexto Completopt_BR
dc.description.otherThis paper develops generalized method of moments (GMM) estimation and inference procedures for quantile regression models. We propose a GMM estimator for simultaneous estimation across multiple quantiles. This estimator allows us to model quantile regression coefficients using flexible parametric restrictions across quantiles. The restrictions and simultaneous estimation lead to efficiency gains compared to standard methods. We establish the asymptotic properties of the GMM estimators when the number of quantiles used is fixed and when it diverges to infinity jointly with the sample size. As an alternative to GMM, we also propose a minimum distance estimator over a given subset of quantiles. Moreover, we provide specification tests for the imposed restrictions. The estimators and tests we propose are simple to implement in practice. Monte Carlo simulations provide numerical evidence of the finite sample properties of the methods. Finally, we apply the proposed methods to estimate the effects of smoking on birthweight of live infants at the extreme bottom of the conditional distribution.pt_BR
dc.format.extentp. 432-452pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doihttps://doi.org/10.1016/j.jeconom.2020.11.014pt_BR
dc.identifier.issn0304-4076pt_BR
dc.identifier.issue2pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4286
dc.identifier.volume230pt_BR
dc.language.isoInglêspt_BR
dc.publisherElsevierpt_BR
dc.relation.ispartofJournal of Econometricspt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DO USUÁRIO VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.pt_BR
dc.subject.keywordsQuantile regressionpt_BR
dc.subject.keywordsGeneralized method of momentspt_BR
dc.titleGMM quantile regressionpt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://www.sciencedirect.com/science/article/pii/S0304407621001299?via%3Dihub
local.subject.cnpqCiências Exatas e da Terrapt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication1063735c-3cb1-4177-82ac-0d9814544887
relation.isAuthorOfPublicationfb10226e-e3f9-41af-9eab-3fd279ec531f
relation.isAuthorOfPublication.latestForDiscovery1063735c-3cb1-4177-82ac-0d9814544887
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