Nowcasting GDP in Argentina: comparing the predictive ability of different models

Autores
Ruiz y Blanco, Emilio R.; D'Amato, Laura; Dogliolo, Fiorella; Garegnani, María Lorena
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Having a correct assessment of current business cycle conditions is one of the mayor challenges for monetary policy conduct. Given that GDP figures are available with a significant delay central banks are increasingly using Nowcasting as a useful tool for having an immediate perception of economic conditions. We develop a GDP growth Nowcasting exercise using a broad and restricted set of indicators to construct different models including dynamic factor models as well as a FAVAR. We compare their relative forecasting ability using the Giacomini and White (2004) and find no significant difference in predictive ability among them. Nevertheless a combination of them proves to significantly improve predictive performance.
Facultad de Ciencias Económicas
Materia
Ciencias Económicas
nowcasting
dynamic factor models
forecast pooling
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/169606
Acceder
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spelling Nowcasting GDP in Argentina: comparing the predictive ability of different modelsRuiz y Blanco, Emilio R.D'Amato, LauraDogliolo, FiorellaGaregnani, María LorenaCiencias Económicasnowcastingdynamic factor modelsforecast poolingHaving a correct assessment of current business cycle conditions is one of the mayor challenges for monetary policy conduct. Given that GDP figures are available with a significant delay central banks are increasingly using Nowcasting as a useful tool for having an immediate perception of economic conditions. We develop a GDP growth Nowcasting exercise using a broad and restricted set of indicators to construct different models including dynamic factor models as well as a FAVAR. We compare their relative forecasting ability using the Giacomini and White (2004) and find no significant difference in predictive ability among them. Nevertheless a combination of them proves to significantly improve predictive performance.Facultad de Ciencias Económicas2017-11info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/169606enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-28590-5-3info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2017/blanco.pdfinfo:eu-repo/semantics/altIdentifier/issn/1852-0022info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:24:11Zoai:sedici.unlp.edu.ar:10915/169606Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:24:11.563SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Nowcasting GDP in Argentina: comparing the predictive ability of different models
title Nowcasting GDP in Argentina: comparing the predictive ability of different models
spellingShingle Nowcasting GDP in Argentina: comparing the predictive ability of different models
Ruiz y Blanco, Emilio R.
Ciencias Económicas
nowcasting
dynamic factor models
forecast pooling
title_short Nowcasting GDP in Argentina: comparing the predictive ability of different models
title_full Nowcasting GDP in Argentina: comparing the predictive ability of different models
title_fullStr Nowcasting GDP in Argentina: comparing the predictive ability of different models
title_full_unstemmed Nowcasting GDP in Argentina: comparing the predictive ability of different models
title_sort Nowcasting GDP in Argentina: comparing the predictive ability of different models
dc.creator.none.fl_str_mv Ruiz y Blanco, Emilio R.
D'Amato, Laura
Dogliolo, Fiorella
Garegnani, María Lorena
author Ruiz y Blanco, Emilio R.
author_facet Ruiz y Blanco, Emilio R.
D'Amato, Laura
Dogliolo, Fiorella
Garegnani, María Lorena
author_role author
author2 D'Amato, Laura
Dogliolo, Fiorella
Garegnani, María Lorena
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Económicas
nowcasting
dynamic factor models
forecast pooling
topic Ciencias Económicas
nowcasting
dynamic factor models
forecast pooling
dc.description.none.fl_txt_mv Having a correct assessment of current business cycle conditions is one of the mayor challenges for monetary policy conduct. Given that GDP figures are available with a significant delay central banks are increasingly using Nowcasting as a useful tool for having an immediate perception of economic conditions. We develop a GDP growth Nowcasting exercise using a broad and restricted set of indicators to construct different models including dynamic factor models as well as a FAVAR. We compare their relative forecasting ability using the Giacomini and White (2004) and find no significant difference in predictive ability among them. Nevertheless a combination of them proves to significantly improve predictive performance.
Facultad de Ciencias Económicas
description Having a correct assessment of current business cycle conditions is one of the mayor challenges for monetary policy conduct. Given that GDP figures are available with a significant delay central banks are increasingly using Nowcasting as a useful tool for having an immediate perception of economic conditions. We develop a GDP growth Nowcasting exercise using a broad and restricted set of indicators to construct different models including dynamic factor models as well as a FAVAR. We compare their relative forecasting ability using the Giacomini and White (2004) and find no significant difference in predictive ability among them. Nevertheless a combination of them proves to significantly improve predictive performance.
publishDate 2017
dc.date.none.fl_str_mv 2017-11
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/169606
url http://sedici.unlp.edu.ar/handle/10915/169606
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2017/blanco.pdf
info:eu-repo/semantics/altIdentifier/issn/1852-0022
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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