Using the flow of conjectural information for short term forecasting of economic activity in Argentina

Autores
D'Amato, Laura; Garegnani, María Lorena; Blanco, Emilio
Año de publicación
2009
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We exploit the richness of a large data set of daily and monthly business cycle indicators by combining them to produce nowcast of contemporaneous real GDP growth as well as forecast. Nowcast outperforms two benchmark models: the one-quarter ahead forecast of an AR(1) in the previous quarter and previous quarter actual value of GDP growth used as current value predictor. When we combine indicators to produce forecasts, the RMSE forecast pooling outperforms the AR(1) benchmark model predictions at the 3, 6 and 12 month horizons. The methodology offers a valuable approach for providing timely information for policy decision making.
Explotamos la riqueza de un gran conjunto de indicadores del ciclo de frecuencia diaria y mensual para producir predicciones en tiempo real y pronósticos del crecimiento del producto real en Argentina. Las predicciones en tiempo real superan en capacidad predictiva a dos predictores usados como benchmark: el pronóstico de un AR(1) en el trimestre previo y el propio valor observado en ese trimestre. El pronóstico fuera de la muestra utilizando ponderaciones basadas en RMSE supera al modelo AR(1) en capacidad predictiva. La metodología ofrece una alternativa valiosa para proveer información en tiempo para la toma de decisiones de política económica.
Facultad de Ciencias Económicas
Materia
Ciencias Económicas
indicators
real GDP growth
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:10915/170447

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network_name_str SEDICI (UNLP)
spelling Using the flow of conjectural information for short term forecasting of economic activity in ArgentinaD'Amato, LauraGaregnani, María LorenaBlanco, EmilioCiencias Económicasindicatorsreal GDP growthWe exploit the richness of a large data set of daily and monthly business cycle indicators by combining them to produce nowcast of contemporaneous real GDP growth as well as forecast. Nowcast outperforms two benchmark models: the one-quarter ahead forecast of an AR(1) in the previous quarter and previous quarter actual value of GDP growth used as current value predictor. When we combine indicators to produce forecasts, the RMSE forecast pooling outperforms the AR(1) benchmark model predictions at the 3, 6 and 12 month horizons. The methodology offers a valuable approach for providing timely information for policy decision making.Explotamos la riqueza de un gran conjunto de indicadores del ciclo de frecuencia diaria y mensual para producir predicciones en tiempo real y pronósticos del crecimiento del producto real en Argentina. Las predicciones en tiempo real superan en capacidad predictiva a dos predictores usados como benchmark: el pronóstico de un AR(1) en el trimestre previo y el propio valor observado en ese trimestre. El pronóstico fuera de la muestra utilizando ponderaciones basadas en RMSE supera al modelo AR(1) en capacidad predictiva. La metodología ofrece una alternativa valiosa para proveer información en tiempo para la toma de decisiones de política económica.Facultad de Ciencias Económicas2009-11info: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/10915/170447enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-99570-7-3info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2009/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:UNLP2025-09-29T11:43:20Zoai:sedici.unlp.edu.ar:10915/170447Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:43:21.272SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Using the flow of conjectural information for short term forecasting of economic activity in Argentina
title Using the flow of conjectural information for short term forecasting of economic activity in Argentina
spellingShingle Using the flow of conjectural information for short term forecasting of economic activity in Argentina
D'Amato, Laura
Ciencias Económicas
indicators
real GDP growth
title_short Using the flow of conjectural information for short term forecasting of economic activity in Argentina
title_full Using the flow of conjectural information for short term forecasting of economic activity in Argentina
title_fullStr Using the flow of conjectural information for short term forecasting of economic activity in Argentina
title_full_unstemmed Using the flow of conjectural information for short term forecasting of economic activity in Argentina
title_sort Using the flow of conjectural information for short term forecasting of economic activity in Argentina
dc.creator.none.fl_str_mv D'Amato, Laura
Garegnani, María Lorena
Blanco, Emilio
author D'Amato, Laura
author_facet D'Amato, Laura
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
indicators
real GDP growth
topic Ciencias Económicas
indicators
real GDP growth
dc.description.none.fl_txt_mv We exploit the richness of a large data set of daily and monthly business cycle indicators by combining them to produce nowcast of contemporaneous real GDP growth as well as forecast. Nowcast outperforms two benchmark models: the one-quarter ahead forecast of an AR(1) in the previous quarter and previous quarter actual value of GDP growth used as current value predictor. When we combine indicators to produce forecasts, the RMSE forecast pooling outperforms the AR(1) benchmark model predictions at the 3, 6 and 12 month horizons. The methodology offers a valuable approach for providing timely information for policy decision making.
Explotamos la riqueza de un gran conjunto de indicadores del ciclo de frecuencia diaria y mensual para producir predicciones en tiempo real y pronósticos del crecimiento del producto real en Argentina. Las predicciones en tiempo real superan en capacidad predictiva a dos predictores usados como benchmark: el pronóstico de un AR(1) en el trimestre previo y el propio valor observado en ese trimestre. El pronóstico fuera de la muestra utilizando ponderaciones basadas en RMSE supera al modelo AR(1) en capacidad predictiva. La metodología ofrece una alternativa valiosa para proveer información en tiempo para la toma de decisiones de política económica.
Facultad de Ciencias Económicas
description We exploit the richness of a large data set of daily and monthly business cycle indicators by combining them to produce nowcast of contemporaneous real GDP growth as well as forecast. Nowcast outperforms two benchmark models: the one-quarter ahead forecast of an AR(1) in the previous quarter and previous quarter actual value of GDP growth used as current value predictor. When we combine indicators to produce forecasts, the RMSE forecast pooling outperforms the AR(1) benchmark model predictions at the 3, 6 and 12 month horizons. The methodology offers a valuable approach for providing timely information for policy decision making.
publishDate 2009
dc.date.none.fl_str_mv 2009-11
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_5794
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url http://sedici.unlp.edu.ar/handle/10915/170447
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2009/damato.pdf
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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