Nowcasting GDP: an application to Portugal

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/10400.14/38631
Resumo: Forecasting the state of an economy is important for policy makers and business leaders. When this is conducted in real-time, it is called nowcasting. In this paper, we present a method that shows how forecasting errors decline as additional contemporaneous information unfolds and becomes available. When the economic environment changes fast, as has happened often in the last decades across most developed economies, it is important to use forecasting methods that are both flexible and robust. This can be achieved with bridge equations and non-parametric estimates of the trend growth using only publicly available information. The method presented in this paper achieves, by the end of a quarter, an accuracy that is equivalent to the methods used by official entities.
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spelling Nowcasting GDP: an application to PortugalTime seriesMacroeconomic forecastingNowcastingError correction modelsCombining forecastsForecasting the state of an economy is important for policy makers and business leaders. When this is conducted in real-time, it is called nowcasting. In this paper, we present a method that shows how forecasting errors decline as additional contemporaneous information unfolds and becomes available. When the economic environment changes fast, as has happened often in the last decades across most developed economies, it is important to use forecasting methods that are both flexible and robust. This can be achieved with bridge equations and non-parametric estimates of the trend growth using only publicly available information. The method presented in this paper achieves, by the end of a quarter, an accuracy that is equivalent to the methods used by official entities.Veritati - Repositório Institucional da Universidade Católica PortuguesaAssunção, João B.Fernandes, Pedro Afonso2022-08-31T10:24:31Z2022-08-152022-08-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/38631eng2571-939410.3390/forecast403003985164476407000856419300001info: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:RCAAP2024-01-16T01:44:27Zoai:repositorio.ucp.pt:10400.14/38631Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:31:33.065212Repositório 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 GDP: an application to Portugal
title Nowcasting GDP: an application to Portugal
spellingShingle Nowcasting GDP: an application to Portugal
Assunção, João B.
Time series
Macroeconomic forecasting
Nowcasting
Error correction models
Combining forecasts
title_short Nowcasting GDP: an application to Portugal
title_full Nowcasting GDP: an application to Portugal
title_fullStr Nowcasting GDP: an application to Portugal
title_full_unstemmed Nowcasting GDP: an application to Portugal
title_sort Nowcasting GDP: an application to Portugal
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 Forecasting the state of an economy is important for policy makers and business leaders. When this is conducted in real-time, it is called nowcasting. In this paper, we present a method that shows how forecasting errors decline as additional contemporaneous information unfolds and becomes available. When the economic environment changes fast, as has happened often in the last decades across most developed economies, it is important to use forecasting methods that are both flexible and robust. This can be achieved with bridge equations and non-parametric estimates of the trend growth using only publicly available information. The method presented in this paper achieves, by the end of a quarter, an accuracy that is equivalent to the methods used by official entities.
publishDate 2022
dc.date.none.fl_str_mv 2022-08-31T10:24:31Z
2022-08-15
2022-08-15T00:00:00Z
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url http://hdl.handle.net/10400.14/38631
dc.language.iso.fl_str_mv eng
language eng
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10.3390/forecast4030039
85164476407
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