Robust filtering with quantile regression

Bibliographic Details
Main Author: Assunção, João Borges
Publication Date: 2022
Other Authors: Fernandes, Pedro Afonso
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.14/38332
Summary: This working paper proposes a new, practical method to compute the non-linear Mosheiov-Raveh (MR) filter using least absolute deviations (LAD) instead of the linear programming approach proposed by these two authors. This paper is embodied with an implementation in the R programming language of the proposed method which facilitates the computation of the MR filter in current applications to produce a robust estimate, namely, of the GDP trend growth. This technique may be appropriate to deal with non linear time series or structural changes.
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spelling Robust filtering with quantile regressionBusiness cyclesNon linear time seriesRobust filteringSoftwareThis working paper proposes a new, practical method to compute the non-linear Mosheiov-Raveh (MR) filter using least absolute deviations (LAD) instead of the linear programming approach proposed by these two authors. This paper is embodied with an implementation in the R programming language of the proposed method which facilitates the computation of the MR filter in current applications to produce a robust estimate, namely, of the GDP trend growth. This technique may be appropriate to deal with non linear time series or structural changes.VeritatiAssunção, João BorgesFernandes, Pedro Afonso2022-07-21T14:18:36Z2022-02-212022-02-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/38332enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-13T14:28:19Zoai:repositorio.ucp.pt:10400.14/38332Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T02:05:19.472717Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Robust filtering with quantile regression
title Robust filtering with quantile regression
spellingShingle Robust filtering with quantile regression
Assunção, João Borges
Business cycles
Non linear time series
Robust filtering
Software
title_short Robust filtering with quantile regression
title_full Robust filtering with quantile regression
title_fullStr Robust filtering with quantile regression
title_full_unstemmed Robust filtering with quantile regression
title_sort Robust filtering with quantile regression
author Assunção, João Borges
author_facet Assunção, João Borges
Fernandes, Pedro Afonso
author_role author
author2 Fernandes, Pedro Afonso
author2_role author
dc.contributor.none.fl_str_mv Veritati
dc.contributor.author.fl_str_mv Assunção, João Borges
Fernandes, Pedro Afonso
dc.subject.por.fl_str_mv Business cycles
Non linear time series
Robust filtering
Software
topic Business cycles
Non linear time series
Robust filtering
Software
description This working paper proposes a new, practical method to compute the non-linear Mosheiov-Raveh (MR) filter using least absolute deviations (LAD) instead of the linear programming approach proposed by these two authors. This paper is embodied with an implementation in the R programming language of the proposed method which facilitates the computation of the MR filter in current applications to produce a robust estimate, namely, of the GDP trend growth. This technique may be appropriate to deal with non linear time series or structural changes.
publishDate 2022
dc.date.none.fl_str_mv 2022-07-21T14:18:36Z
2022-02-21
2022-02-21T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.14/38332
url http://hdl.handle.net/10400.14/38332
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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