Nowcasting the Portuguese GDP with monthly data

Detalhes bibliográficos
Autor(a) principal: Assunção, João B.
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/10400.14/38574
Resumo: In this article, we present a method to forecast the Portuguese gross domestic product (GDP) in each current quarter (nowcasting). It combines bridge equations of the real GDP on readily available monthly data like the Economic Sentiment Indicator (ESI), industrial production index, cement sales or exports and imports, with forecasts for the jagged missing values computed with the well-known Hodrick and Prescott (HP) filter. As shown, this simple multivariate approach can perform as well as a Targeted Diffusion Index (TDI) model and slightly better thanthe univariate Theta method in terms of out-of-sample mean errors.
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spelling Nowcasting the Portuguese GDP with monthly dataTime seriesMacroeconomic forecastingNowcastingError correction modelsCombining forecastsIn this article, we present a method to forecast the Portuguese gross domestic product (GDP) in each current quarter (nowcasting). It combines bridge equations of the real GDP on readily available monthly data like the Economic Sentiment Indicator (ESI), industrial production index, cement sales or exports and imports, with forecasts for the jagged missing values computed with the well-known Hodrick and Prescott (HP) filter. As shown, this simple multivariate approach can perform as well as a Targeted Diffusion Index (TDI) model and slightly better thanthe univariate Theta method in terms of out-of-sample mean errors.Veritati - Repositório Institucional da Universidade Católica PortuguesaAssunção, João B.Fernandes, Pedro Afonso2022-08-10T10:30:44Z2022-06-142022-06-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/38574eng10.48550/arXiv.2206.06823info: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:RCAAP2023-07-12T17:44:04Zoai:repositorio.ucp.pt:10400.14/38574Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:31:30.339438Repositório 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 Nowcasting the Portuguese GDP with monthly data
title Nowcasting the Portuguese GDP with monthly data
spellingShingle Nowcasting the Portuguese GDP with monthly data
Assunção, João B.
Time series
Macroeconomic forecasting
Nowcasting
Error correction models
Combining forecasts
title_short Nowcasting the Portuguese GDP with monthly data
title_full Nowcasting the Portuguese GDP with monthly data
title_fullStr Nowcasting the Portuguese GDP with monthly data
title_full_unstemmed Nowcasting the Portuguese GDP with monthly data
title_sort Nowcasting the Portuguese GDP with monthly data
author Assunção, João B.
author_facet Assunção, João B.
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 B.
Fernandes, Pedro Afonso
dc.subject.por.fl_str_mv Time series
Macroeconomic forecasting
Nowcasting
Error correction models
Combining forecasts
topic Time series
Macroeconomic forecasting
Nowcasting
Error correction models
Combining forecasts
description In this article, we present a method to forecast the Portuguese gross domestic product (GDP) in each current quarter (nowcasting). It combines bridge equations of the real GDP on readily available monthly data like the Economic Sentiment Indicator (ESI), industrial production index, cement sales or exports and imports, with forecasts for the jagged missing values computed with the well-known Hodrick and Prescott (HP) filter. As shown, this simple multivariate approach can perform as well as a Targeted Diffusion Index (TDI) model and slightly better thanthe univariate Theta method in terms of out-of-sample mean errors.
publishDate 2022
dc.date.none.fl_str_mv 2022-08-10T10:30:44Z
2022-06-14
2022-06-14T00:00:00Z
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/10400.14/38574
url http://hdl.handle.net/10400.14/38574
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.48550/arXiv.2206.06823
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
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institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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
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