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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/169113
Ver los metadatos del registro completo
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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:UNLP2025-09-29T11:43:25Zoai: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:13292025-09-29 11:43:26.028SEDICI (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 Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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conferenceObject |
status_str |
publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/169113 |
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http://sedici.unlp.edu.ar/handle/10915/169113 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf |
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