Robust filtering with quantile regression

Detalhes bibliográficos
Autor(a) principal: Assunção, João Borges
Data de Publicação: 2022
Outros Autores: Fernandes, Pedro Afonso
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.14/38332
Resumo: 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.Veritati - Repositório Institucional da Universidade Católica PortuguesaAssunção, 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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-12T17:43:48Zoai:repositorio.ucp.pt:10400.14/38332Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:31:17.781030Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
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 - Repositório Institucional da Universidade Católica Portuguesa
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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