Using a Nowcast approach to assess business cycle conditions in Argentina
- Autores
- D'Amato, Laura Inés; Garegnani, María Lorena; Blanco, Emilio
- Año de publicación
- 2013
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Real-time judgment of current and future business cycle conditions is one of the mayor challenges monetary policy conduct faces. GDP Nowcasting has been increasingly taken into account by central banks as a measure of immediate perception of economic conditions. We conduct a pseudo-real-time one quarter ahead forecasting exercise of GDP growth using bridge equations. Comparing forecasting performance our results to the typical AR benchmark, we conclude that the Nowcasts is superior in almost 74% of the cases. Using the Giacomoni and White (2004) test, we also concude this dfferences are statistically significant.
Evaluar en tiempo real las condiciones económicas actuales y futuras es uno de los mayores retos a los que se enfrenta la política monetaria. El Nowcasting de actividad representa una potencial medida con la cual los bancos centrales pueden estudiar percepciones inmediatas de las condiciones económicas. Realizamos un pronóstico de crecimiento del PIB de un trimestre adelante en pseudo-tiempo real mediante bridge equations. Comparando la capacidad predictiva de nuestro ejercicio con un benchmark AR(1), concluimos que el Nowcast es superior en el 74% de los casos. Usando el test de Giacomini y Whte (2004), comprobamos que estas diferencias son significativas.
Facultad de Ciencias Económicas - Materia
-
Ciencias Económicas
Nowcasting
bridge equations
mixed-frequency data - 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:10915/173854
Ver los metadatos del registro completo
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Using a Nowcast approach to assess business cycle conditions in ArgentinaD'Amato, Laura InésGaregnani, María LorenaBlanco, EmilioCiencias EconómicasNowcastingbridge equationsmixed-frequency dataReal-time judgment of current and future business cycle conditions is one of the mayor challenges monetary policy conduct faces. GDP Nowcasting has been increasingly taken into account by central banks as a measure of immediate perception of economic conditions. We conduct a pseudo-real-time one quarter ahead forecasting exercise of GDP growth using bridge equations. Comparing forecasting performance our results to the typical AR benchmark, we conclude that the Nowcasts is superior in almost 74% of the cases. Using the Giacomoni and White (2004) test, we also concude this dfferences are statistically significant.Evaluar en tiempo real las condiciones económicas actuales y futuras es uno de los mayores retos a los que se enfrenta la política monetaria. El Nowcasting de actividad representa una potencial medida con la cual los bancos centrales pueden estudiar percepciones inmediatas de las condiciones económicas. Realizamos un pronóstico de crecimiento del PIB de un trimestre adelante en pseudo-tiempo real mediante bridge equations. Comparando la capacidad predictiva de nuestro ejercicio con un benchmark AR(1), concluimos que el Nowcast es superior en el 74% de los casos. Usando el test de Giacomini y Whte (2004), comprobamos que estas diferencias son significativas.Facultad de Ciencias Económicas2013-11info: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/10915/173854enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-28590-1-5info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2013/damato_garegnani.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:UNLP2025-09-29T11:43:20Zoai:sedici.unlp.edu.ar:10915/173854Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:43:21.267SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Using a Nowcast approach to assess business cycle conditions in Argentina |
title |
Using a Nowcast approach to assess business cycle conditions in Argentina |
spellingShingle |
Using a Nowcast approach to assess business cycle conditions in Argentina D'Amato, Laura Inés Ciencias Económicas Nowcasting bridge equations mixed-frequency data |
title_short |
Using a Nowcast approach to assess business cycle conditions in Argentina |
title_full |
Using a Nowcast approach to assess business cycle conditions in Argentina |
title_fullStr |
Using a Nowcast approach to assess business cycle conditions in Argentina |
title_full_unstemmed |
Using a Nowcast approach to assess business cycle conditions in Argentina |
title_sort |
Using a Nowcast approach to assess business cycle conditions in Argentina |
dc.creator.none.fl_str_mv |
D'Amato, Laura Inés Garegnani, María Lorena Blanco, Emilio |
author |
D'Amato, Laura Inés |
author_facet |
D'Amato, Laura Inés Garegnani, María Lorena Blanco, Emilio |
author_role |
author |
author2 |
Garegnani, María Lorena Blanco, Emilio |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Económicas Nowcasting bridge equations mixed-frequency data |
topic |
Ciencias Económicas Nowcasting bridge equations mixed-frequency data |
dc.description.none.fl_txt_mv |
Real-time judgment of current and future business cycle conditions is one of the mayor challenges monetary policy conduct faces. GDP Nowcasting has been increasingly taken into account by central banks as a measure of immediate perception of economic conditions. We conduct a pseudo-real-time one quarter ahead forecasting exercise of GDP growth using bridge equations. Comparing forecasting performance our results to the typical AR benchmark, we conclude that the Nowcasts is superior in almost 74% of the cases. Using the Giacomoni and White (2004) test, we also concude this dfferences are statistically significant. Evaluar en tiempo real las condiciones económicas actuales y futuras es uno de los mayores retos a los que se enfrenta la política monetaria. El Nowcasting de actividad representa una potencial medida con la cual los bancos centrales pueden estudiar percepciones inmediatas de las condiciones económicas. Realizamos un pronóstico de crecimiento del PIB de un trimestre adelante en pseudo-tiempo real mediante bridge equations. Comparando la capacidad predictiva de nuestro ejercicio con un benchmark AR(1), concluimos que el Nowcast es superior en el 74% de los casos. Usando el test de Giacomini y Whte (2004), comprobamos que estas diferencias son significativas. Facultad de Ciencias Económicas |
description |
Real-time judgment of current and future business cycle conditions is one of the mayor challenges monetary policy conduct faces. GDP Nowcasting has been increasingly taken into account by central banks as a measure of immediate perception of economic conditions. We conduct a pseudo-real-time one quarter ahead forecasting exercise of GDP growth using bridge equations. Comparing forecasting performance our results to the typical AR benchmark, we conclude that the Nowcasts is superior in almost 74% of the cases. Using the Giacomoni and White (2004) test, we also concude this dfferences are statistically significant. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-11 |
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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http://sedici.unlp.edu.ar/handle/10915/173854 |
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eng |
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eng |
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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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