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Evaluación de pronóstico de una red neuronal sobre el PIB en Colombia

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  • José Mauricio Salazar Sáenz

Abstract

Las redes neuronales artificiales han mostrado ser modelos robustos para dar cuenta del comportamiento de diferentes variables. En el presente trabajo se emplean para modelar la relación no lineal del crecimiento del PIB. Tres modelos son considerados: dos autoregresivos (especificación lineal y no lineal) y una red neuronal que usa la tasa de interés. Evaluando el desempeño de los modelos dentro y fuera de muestra, los pronósticos realizados por las redes neuronales artificiales superan ampliamente a los modelos lineales, siendo esta evidencia de relaciones asimétricas en el comportamiento del PIB en Colombia.

Suggested Citation

  • José Mauricio Salazar Sáenz, 2009. "Evaluación de pronóstico de una red neuronal sobre el PIB en Colombia," Borradores de Economia 575, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:575
    DOI: 10.32468/be.575
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    1. Silvia Fabiani & Martine Druant & Ignacio Hernando & Claudia Kwapil & Bettina Landau & Claire Loupias & Fernando Martins & Thomas Mathä & Roberto Sabbatini & Harald Stahl & Ad Stokman, 2006. "What Firms' Surveys Tell Us about Price-Setting Behavior in the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 2(3), September.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, May.
    3. Martha Misas & Enrique López & Pablo Querubín, 2002. "La Inflación en Colombia: Una Aproximación desde las Redes Neuronales," Borradores de Economia 199, Banco de la Republica de Colombia.
    4. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    5. Martha Misas A. & Enrique López E. & Carlos A. Arango A. & Juan Nicolás Hernández A., 2003. "La Demanda de Efectivo en Colombia: Una Caja Nagra a la Luz de las Redes Neuronales," BORRADORES DE ECONOMIA 002963, BANCO DE LA REPÚBLICA.
    6. Sims, Christopher A, 1980. "Comparison of Interwar and Postwar Business Cycles: Monetarism Reconsidered," American Economic Review, American Economic Association, vol. 70(2), pages 250-257, May.
    7. Carmen M. Reinhart & Vincent Raymond Reinhart, 1991. "Fluctuaciones del Producto y Choques Monetarios: Evidencia Colombiana," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 0(20), pages 53-85, December.
    8. Munir A. Jalil. B & Martha Misas, 2006. "Evaluación de pronósticos del tipo de cambio utilizando," BORRADORES DE ECONOMIA 002636, BANCO DE LA REPÚBLICA.
    9. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
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    More about this item

    Keywords

    Red neuronal artificial; no linealidad; PIB; Rolling de pronóstico; evaluación de pronóstico.;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production

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