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Usando información adicional en la estimación de la brecha producto en el Perú: una aproximación multivariada de componentes no observados

Author

Listed:
  • Gonzalo Llosa

    () (Central Bank of Peru)

  • Shirley Miller

    () (Central Bank of Peru)

Abstract

Uno de los elementos claves para el régimen de metas de inflación es la correcta identificación de las presiones inflacionarias y deflacionarias a través de la brecha producto. En este trabajo brindamos una estimación de la brecha producto para la economía peruana utilizando un modelo multivariado de componentes no observados (MUC), el cual se basa en una relación explícita de corto plazo entre la inflación y la brecha producto (Curva de Phillips) y restricciones estructurales sobre la dinámica del producto. Los resultados muestran que el estimado MUC de la brecha producto es menos sensible al problema de fin de muestra y exhibe una dinámica más cercana al proceso de inflación que las brechas estimadas a partir de metodologías estándares.

Suggested Citation

  • Gonzalo Llosa & Shirley Miller, 2005. "Usando información adicional en la estimación de la brecha producto en el Perú: una aproximación multivariada de componentes no observados," Working Papers 2005-0041, Banco Central de Reserva del Perú.
  • Handle: RePEc:rbp:wpaper:2005-0041
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    File URL: http://www.bcrp.gob.pe/docs/Publicaciones/Documentos-de-Trabajo/2005/Documento-Trabajo-04-2005.pdf
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    More about this item

    Keywords

    Brecha producto; Inflación; Modelo de componentes no observados;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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