Different Approaches to Inflation Forecasting in Argentina

Autores
Basco, Emiliano; Blanco, Emilio; D'Amato, Laura; Garegnani, María Lorena
Año de publicación
2015
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We apply some recently developed and more traditional methods to forecast in ation in Argentina and compare their predictive ability at di¤erent horizons. Our variety of models includes: (i) Traditional time series models -AR(1) and a monetary VAR-, (ii) a factor model combining a large number of business cycle indicators and (iii) micro-funded models including a conventional New Keynesian Phillips Curve and one that incorporates money to evaluate its information content as a predictor of in ation. We compare the predictive performance of the di¤erent methods using the Giacomini-White test over the relevant horizons for monetary policy decisions. We nd that the monetary VAR outperforms the rest of the models.
Aplicamos metodologías recientes y tradicionales para pronosticar la in ación en la Argentina, comparando su capacidad predictiva para diferentes horizontes. Nuestra variedad de modelos incluye: (i) Modelos tradicionales de series de tiempo -AR (1) y un VAR monetario-, (ii) modelos de factores que combinan un gran número de indicadores del ciclo económico y (iii) modelos microfundados incluyendo una Curva de Phillips nuevo keynesiana convencional y una que incorpora dinero para evaluar su contenido informativo como predictor de la in- ación. Comparamos la capacidad predictiva de los diferentes métodos empleando el test de Giacomini-White para horizontes relevantes en la toma de decisiones de política monetaria. Encontramos que el VAR monetario supera al resto de los modelos.
Facultad de Ciencias Económicas
Materia
Ciencias Económicas
Inflation Forecasting
Time Series Models
Phillips Curve
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:10915/169227

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spelling Different Approaches to Inflation Forecasting in ArgentinaBasco, EmilianoBlanco, EmilioD'Amato, LauraGaregnani, María LorenaCiencias EconómicasInflation ForecastingTime Series ModelsPhillips CurveFactor ModelsWe apply some recently developed and more traditional methods to forecast in ation in Argentina and compare their predictive ability at di¤erent horizons. Our variety of models includes: (i) Traditional time series models -AR(1) and a monetary VAR-, (ii) a factor model combining a large number of business cycle indicators and (iii) micro-funded models including a conventional New Keynesian Phillips Curve and one that incorporates money to evaluate its information content as a predictor of in ation. We compare the predictive performance of the di¤erent methods using the Giacomini-White test over the relevant horizons for monetary policy decisions. We nd that the monetary VAR outperforms the rest of the models.Aplicamos metodologías recientes y tradicionales para pronosticar la in ación en la Argentina, comparando su capacidad predictiva para diferentes horizontes. Nuestra variedad de modelos incluye: (i) Modelos tradicionales de series de tiempo -AR (1) y un VAR monetario-, (ii) modelos de factores que combinan un gran número de indicadores del ciclo económico y (iii) modelos microfundados incluyendo una Curva de Phillips nuevo keynesiana convencional y una que incorpora dinero para evaluar su contenido informativo como predictor de la in- ación. Comparamos la capacidad predictiva de los diferentes métodos empleando el test de Giacomini-White para horizontes relevantes en la toma de decisiones de política monetaria. Encontramos que el VAR monetario supera al resto de los modelos.Facultad de Ciencias Económicas2015-11info: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/10915/169227enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-28590-3-9info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2015/Basco_AAEP2015.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:UNLP2025-09-03T11:15:10Zoai:sedici.unlp.edu.ar:10915/169227Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:15:10.646SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Different Approaches to Inflation Forecasting in Argentina
title Different Approaches to Inflation Forecasting in Argentina
spellingShingle Different Approaches to Inflation Forecasting in Argentina
Basco, Emiliano
Ciencias Económicas
Inflation Forecasting
Time Series Models
Phillips Curve
Factor Models
title_short Different Approaches to Inflation Forecasting in Argentina
title_full Different Approaches to Inflation Forecasting in Argentina
title_fullStr Different Approaches to Inflation Forecasting in Argentina
title_full_unstemmed Different Approaches to Inflation Forecasting in Argentina
title_sort Different Approaches to Inflation Forecasting in Argentina
dc.creator.none.fl_str_mv Basco, Emiliano
Blanco, Emilio
D'Amato, Laura
Garegnani, María Lorena
author Basco, Emiliano
author_facet Basco, Emiliano
Blanco, Emilio
D'Amato, Laura
Garegnani, María Lorena
author_role author
author2 Blanco, Emilio
D'Amato, Laura
Garegnani, María Lorena
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Económicas
Inflation Forecasting
Time Series Models
Phillips Curve
Factor Models
topic Ciencias Económicas
Inflation Forecasting
Time Series Models
Phillips Curve
Factor Models
dc.description.none.fl_txt_mv We apply some recently developed and more traditional methods to forecast in ation in Argentina and compare their predictive ability at di¤erent horizons. Our variety of models includes: (i) Traditional time series models -AR(1) and a monetary VAR-, (ii) a factor model combining a large number of business cycle indicators and (iii) micro-funded models including a conventional New Keynesian Phillips Curve and one that incorporates money to evaluate its information content as a predictor of in ation. We compare the predictive performance of the di¤erent methods using the Giacomini-White test over the relevant horizons for monetary policy decisions. We nd that the monetary VAR outperforms the rest of the models.
Aplicamos metodologías recientes y tradicionales para pronosticar la in ación en la Argentina, comparando su capacidad predictiva para diferentes horizontes. Nuestra variedad de modelos incluye: (i) Modelos tradicionales de series de tiempo -AR (1) y un VAR monetario-, (ii) modelos de factores que combinan un gran número de indicadores del ciclo económico y (iii) modelos microfundados incluyendo una Curva de Phillips nuevo keynesiana convencional y una que incorpora dinero para evaluar su contenido informativo como predictor de la in- ación. Comparamos la capacidad predictiva de los diferentes métodos empleando el test de Giacomini-White para horizontes relevantes en la toma de decisiones de política monetaria. Encontramos que el VAR monetario supera al resto de los modelos.
Facultad de Ciencias Económicas
description We apply some recently developed and more traditional methods to forecast in ation in Argentina and compare their predictive ability at di¤erent horizons. Our variety of models includes: (i) Traditional time series models -AR(1) and a monetary VAR-, (ii) a factor model combining a large number of business cycle indicators and (iii) micro-funded models including a conventional New Keynesian Phillips Curve and one that incorporates money to evaluate its information content as a predictor of in ation. We compare the predictive performance of the di¤erent methods using the Giacomini-White test over the relevant horizons for monetary policy decisions. We nd that the monetary VAR outperforms the rest of the models.
publishDate 2015
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info:eu-repo/semantics/altIdentifier/issn/1852-0022
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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