Nowcasting Activity in Argentina using Dynamic Factor Models

Autores
Blanco, Emilio Fernando; D'amato, Laura; Dogliolo, Fiorella; Garegnani, María Lorena
Año de publicación
2018
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 major 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 each individual model with alternative combinations in a pseuro-real time out-ofsample exercise and find an improvement in predictive performance using the Giacomini and White (2004) test. Finally we introduce a DFM state-space approach, being able to measure the impact of data releases or news on sequential forecast.
Facultad de Ciencias Económicas
Materia
Ciencias Económicas
Nowcasting
state-space
DFM
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:10915/169113
Acceder
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spelling Nowcasting Activity in Argentina using Dynamic Factor ModelsBlanco, Emilio FernandoD'amato, LauraDogliolo, FiorellaGaregnani, María LorenaCiencias EconómicasNowcastingstate-spaceDFMHaving a correct assessment of current business cycle conditions is one of the major 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 each individual model with alternative combinations in a pseuro-real time out-ofsample exercise and find an improvement in predictive performance using the Giacomini and White (2004) test. Finally we introduce a DFM state-space approach, being able to measure the impact of data releases or news on sequential forecast.Facultad de Ciencias Económicas2018-11info: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/10915/169113enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-28590-6-0info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2018/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:UNLP2025-10-22T17:24:20Zoai:sedici.unlp.edu.ar:10915/169113Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:24:20.654SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Nowcasting Activity in Argentina using Dynamic Factor Models
title Nowcasting Activity in Argentina using Dynamic Factor Models
spellingShingle Nowcasting Activity in Argentina using Dynamic Factor Models
Blanco, Emilio Fernando
Ciencias Económicas
Nowcasting
state-space
DFM
title_short Nowcasting Activity in Argentina using Dynamic Factor Models
title_full Nowcasting Activity in Argentina using Dynamic Factor Models
title_fullStr Nowcasting Activity in Argentina using Dynamic Factor Models
title_full_unstemmed Nowcasting Activity in Argentina using Dynamic Factor Models
title_sort Nowcasting Activity in Argentina using Dynamic Factor Models
dc.creator.none.fl_str_mv Blanco, Emilio Fernando
D'amato, Laura
Dogliolo, Fiorella
Garegnani, María Lorena
author Blanco, Emilio Fernando
author_facet Blanco, Emilio Fernando
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
state-space
DFM
topic Ciencias Económicas
Nowcasting
state-space
DFM
dc.description.none.fl_txt_mv Having a correct assessment of current business cycle conditions is one of the major 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 each individual model with alternative combinations in a pseuro-real time out-ofsample exercise and find an improvement in predictive performance using the Giacomini and White (2004) test. Finally we introduce a DFM state-space approach, being able to measure the impact of data releases or news on sequential forecast.
Facultad de Ciencias Económicas
description Having a correct assessment of current business cycle conditions is one of the major 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 each individual model with alternative combinations in a pseuro-real time out-ofsample exercise and find an improvement in predictive performance using the Giacomini and White (2004) test. Finally we introduce a DFM state-space approach, being able to measure the impact of data releases or news on sequential forecast.
publishDate 2018
dc.date.none.fl_str_mv 2018-11
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/169113
url http://sedici.unlp.edu.ar/handle/10915/169113
dc.language.none.fl_str_mv eng
language eng
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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)
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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
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