Nowcasting the Portuguese GDP with monthly data
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | |
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. |
id |
RCAP_fabc9428295767530e07d19149e68764 |
---|---|
oai_identifier_str |
oai:repositorio.ucp.pt:10400.14/38574 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
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 |
instacron_str |
RCAAP |
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 |
repository.mail.fl_str_mv |
|
_version_ |
1799132038422331392 |