GDP Nowcasting: assessing business cycle conditions in Argentina

Autores
D'Amato, Laura Inés; Garegnani, María Lorena; Ruiz y Blanco, Emilio R.
Año de publicación
2014
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. Thus we develop a GDP growth nowcasting exercise using two approaches: bridge equations and a dynamic factor model. Both outperform a typical AR(1) benchmark in terms of forecasting accuracy. Moreover, the factor model outperforms the nowcast using bridge equations. Following Giacomini and White (2004) we confirm that these differences are statistically significant.
Tener una correcta evaluación de las condiciones actuales del ciclo económico es uno de los mayores retos para la conducción de la política monetaria. Teniendo en cuenta que las cifras del PIB están disponibles con un retraso significativo, el uso de Nowcasting para tener una percepción inmediata de las condiciones cíclicas de la economía ha sido crecientemente adoptado por los bancos centrales. Desarrollamos un ejercicio de Nowcast del crecimiento del PIB utilizando dos enfoques: bridge equations y factor models. Ambos métodos superan en capacidad predictiva a un benchmark AR(1). Adicionalmente, el Nowcast basado en un factor model supera al de bridge equations. Finalmente, Siguiendo a Giacomini y White (2004) confirmamos que estas diferencias son estadísticamente significativas.
Facultad de Ciencias Económicas
Materia
Ciencias Económicas
nowcasting
bridge equations
dynamic factor models
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:10915/173779

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spelling GDP Nowcasting: assessing business cycle conditions in ArgentinaD'Amato, Laura InésGaregnani, María LorenaRuiz y Blanco, Emilio R.Ciencias Económicasnowcastingbridge equationsdynamic factor modelsHaving 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. Thus we develop a GDP growth nowcasting exercise using two approaches: bridge equations and a dynamic factor model. Both outperform a typical AR(1) benchmark in terms of forecasting accuracy. Moreover, the factor model outperforms the nowcast using bridge equations. Following Giacomini and White (2004) we confirm that these differences are statistically significant.Tener una correcta evaluación de las condiciones actuales del ciclo económico es uno de los mayores retos para la conducción de la política monetaria. Teniendo en cuenta que las cifras del PIB están disponibles con un retraso significativo, el uso de Nowcasting para tener una percepción inmediata de las condiciones cíclicas de la economía ha sido crecientemente adoptado por los bancos centrales. Desarrollamos un ejercicio de Nowcast del crecimiento del PIB utilizando dos enfoques: bridge equations y factor models. Ambos métodos superan en capacidad predictiva a un benchmark AR(1). Adicionalmente, el Nowcast basado en un factor model supera al de bridge equations. Finalmente, Siguiendo a Giacomini y White (2004) confirmamos que estas diferencias son estadísticamente significativas.Facultad de Ciencias Económicas2014-11info: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/10915/173779enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-28590-2-2info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2014/damato.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:UNLP2025-09-29T11:43:21Zoai:sedici.unlp.edu.ar:10915/173779Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:43:22.064SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv GDP Nowcasting: assessing business cycle conditions in Argentina
title GDP Nowcasting: assessing business cycle conditions in Argentina
spellingShingle GDP Nowcasting: assessing business cycle conditions in Argentina
D'Amato, Laura Inés
Ciencias Económicas
nowcasting
bridge equations
dynamic factor models
title_short GDP Nowcasting: assessing business cycle conditions in Argentina
title_full GDP Nowcasting: assessing business cycle conditions in Argentina
title_fullStr GDP Nowcasting: assessing business cycle conditions in Argentina
title_full_unstemmed GDP Nowcasting: assessing business cycle conditions in Argentina
title_sort GDP Nowcasting: assessing business cycle conditions in Argentina
dc.creator.none.fl_str_mv D'Amato, Laura Inés
Garegnani, María Lorena
Ruiz y Blanco, Emilio R.
author D'Amato, Laura Inés
author_facet D'Amato, Laura Inés
Garegnani, María Lorena
Ruiz y Blanco, Emilio R.
author_role author
author2 Garegnani, María Lorena
Ruiz y Blanco, Emilio R.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Económicas
nowcasting
bridge equations
dynamic factor models
topic Ciencias Económicas
nowcasting
bridge equations
dynamic factor models
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. Thus we develop a GDP growth nowcasting exercise using two approaches: bridge equations and a dynamic factor model. Both outperform a typical AR(1) benchmark in terms of forecasting accuracy. Moreover, the factor model outperforms the nowcast using bridge equations. Following Giacomini and White (2004) we confirm that these differences are statistically significant.
Tener una correcta evaluación de las condiciones actuales del ciclo económico es uno de los mayores retos para la conducción de la política monetaria. Teniendo en cuenta que las cifras del PIB están disponibles con un retraso significativo, el uso de Nowcasting para tener una percepción inmediata de las condiciones cíclicas de la economía ha sido crecientemente adoptado por los bancos centrales. Desarrollamos un ejercicio de Nowcast del crecimiento del PIB utilizando dos enfoques: bridge equations y factor models. Ambos métodos superan en capacidad predictiva a un benchmark AR(1). Adicionalmente, el Nowcast basado en un factor model supera al de bridge equations. Finalmente, Siguiendo a Giacomini y White (2004) confirmamos que estas diferencias son estadísticamente significativas.
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. Thus we develop a GDP growth nowcasting exercise using two approaches: bridge equations and a dynamic factor model. Both outperform a typical AR(1) benchmark in terms of forecasting accuracy. Moreover, the factor model outperforms the nowcast using bridge equations. Following Giacomini and White (2004) we confirm that these differences are statistically significant.
publishDate 2014
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