Mostrar el registro sencillo

Fast algorithms for Quantile Regression with Selection

dc.contributor.authorPereda Fernández, Santiago 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2025-09-02T09:31:00Z
dc.date.issued2025-07-09
dc.identifier.issn2156-6674
dc.identifier.otherTED2021-131763A-I00es_ES
dc.identifier.urihttps://hdl.handle.net/10902/36978
dc.description.abstractThe estimation of Quantile Regression with Selection (QRS) requires the estimation of the entire quantile process several times to estimate the parameters that model self-selection. Moreover, closed-form expressions of the asymptotic variance are too cumbersome, making the bootstrap more convenient to perform inference. I propose streamlined algorithms for the QRS estimator that significantly reduce computation time through preprocessing techniques and quantile grid reduction for the estimation of the parameters. I show the optimization enhancements and how they can improve the precision of the estimates without sacrificing computational efficiency with some simulations.es_ES
dc.description.sponsorshipThis work is part of the I + D + i project Ref. TED2021-131763A-I00 financed by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. I gratefully acknowledge financial support from the Spanish Ministry of Universities and the European Union-NextGenerationEU (RMZ-18).es_ES
dc.format.extent23 p.es_ES
dc.language.isoenges_ES
dc.publisherWalter de Gruyteres_ES
dc.rightsThis is an accepted manuscript of an article published by De Gruyter in Journal of Econometric Methods on 09/07/2025, available at http://wwww.degruyter.com/10.1515/jem-2024-0022. It is deposited under the terms of the CreativeCommons Attribution-NonCommercial-NoDerivatives 4.0 Internationales_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceJournal of Econometric Methods, 2025, 14(1), 35-47es_ES
dc.subject.otherCopulaes_ES
dc.subject.otherEstimation algorithmes_ES
dc.subject.otherLinear programminges_ES
dc.subject.otherQuantile Regression with Selectiones_ES
dc.subject.otherRotated quantile regressiones_ES
dc.titleFast algorithms for Quantile Regression with Selectiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1515/jem-2024-0022es_ES
dc.rights.accessRightsembargoedAccesses_ES
dc.identifier.DOI10.1515/jem-2024-0022
dc.type.versionacceptedVersiones_ES
dc.embargo.lift2026-07-09
dc.date.embargoEndDate2026-07-09


Ficheros en el ítem

Thumbnail
Nombre:
FastAlgorithmsforQRS.pdf
Tamaño:
485.3Kb
Formato:
PDF
Descripción:
FastAlgorithmsforQRS

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo

This is an accepted manuscript of an article published by De Gruyter in Journal of Econometric Methods on 09/07/2025, available at http://wwww.degruyter.com/10.1515/jem-2024-0022. It is deposited under the terms of the CreativeCommons Attribution-NonCommercial-NoDerivatives 4.0 InternationalExcepto si se señala otra cosa, la licencia del ítem se describe como This is an accepted manuscript of an article published by De Gruyter in Journal of Econometric Methods on 09/07/2025, available at http://wwww.degruyter.com/10.1515/jem-2024-0022. It is deposited under the terms of the CreativeCommons Attribution-NonCommercial-NoDerivatives 4.0 International