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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/169227
Ver los metadatos del registro completo
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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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2015-11 |
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eng |
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