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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/173779
Ver los metadatos del registro completo
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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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2014-11 |
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eng |
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