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Proyecciones desagregadas de la variación del índice de precios al consumidor (IPC), del índice de precios al por Mayor (IPM) y del Crecimiento del Producto Real (PBI)

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Author Info
Carlos Barrera-Chaupis () (Central Bank of Peru)
Abstract

El presente trabajo hace una evaluación ex post de la precisión de las proyecciones de un conjunto de modelos de corto plazo para el Índice de Precios al Consumidor (IPC), el Índice de Precios al por Mayor (IPM) y el crecimiento del Producto Real (PBI) utilizando una muestra reciente de datos de Perú. Se busca determinar si la incorporación de información desagregada a nivel de rubros componentes mejora la precisión de estos modelos. Las proyecciones de corto plazo forman parte integral de un sistema de proyección puesto que suelen servir como “punto de partida” en las proyecciones que se realizan utilizando modelos estructurales. En ese sentido contar con predicciones de corto plazo más precisas ayuda a minimizar los errores de predicción de los modelos de mediano plazo. Se encuentra que utilizando información desagregada la precisión de las proyecciones del IPC mejora en el muy corto plazo pero no la de las proyecciones del IPM y del PBI para el mismo horizonte temporal, aún cuando se consideran modelos con parámetros cambiantes en el tiempo para el caso del IPM. Finalmente, para horizontes de proyección mayores a 12 meses no es posible mejorar la precisión de las proyecciones de los tres agregados utilizando información desagregada. Estos resultados están condicionados al valor informativo del agregado y a los niveles de desagregación utilizados.

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Paper provided by Banco Central de Reserva del Perú in its series Working Papers with number 2005-006.

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Date of creation: Apr 2005
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Handle: RePEc:rbp:wpaper:2005-006

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Related research
Keywords: Forecasting; Sparse VAR; Monetary Policy;

Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Forni, Mario & Reichlin, Lucrezia, 1995. "Let's Get Real: A Dynamic Factor Analytical Approach to Disaggregated Business Cycle," CEPR Discussion Papers 1244, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  2. Francis X. Diebold & Lutz Kilian, 1997. "Measuring predictability: theory and macroeconomic applications," Working Papers 97-23, Federal Reserve Bank of Philadelphia. [Downloadable!]
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  3. John C. Robertson & Ellis W. Tallman, 1998. "Data vintages and measuring forecast model performance," Economic Review, Federal Reserve Bank of Atlanta, issue Q 4, pages 4-20. [Downloadable!]
  4. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November. [Downloadable!] (restricted)
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  5. Forni, Mario & Reichlin, Lucrezia, 1995. "Dynamic Common Factors in Large Cross-Sections," CEPR Discussion Papers 1285, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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