IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/575.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://repositorio.banrep.gov.co/bitstream/handle/20.500.12134/5592/be_575.pdf?sequence=1&isAllowed=y
    Download Restriction: no

    References listed on IDEAS

    as
    1. Fabiani, S. & Druant, M. & Hernando, I. & Kwapil, C. & Landau, B. & Loupias, C. & Martins, F. & Mathä, T. & Sabbatini, R. & Stahl, H. & Stockman, A., 2005. "The Pricing Behaviour of Firms in the Euro Area: New Survey Evidence," Working papers 135, Banque de France.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, April.
    3. Martha Misas Arango & Enrique López Enciso & Pablo Querubín, 2002. "La Inflación En Colombia: Una Aproximación Desde Las Redes Neuronales," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE, vol. 20(41-42), pages 143-214, June.
    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," 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. 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.
    10. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Red neuronal artificial; no linealidad; PIB; Rolling de pronóstico; evaluación de pronóstico. Classification JEL: C45; C53; E17; E23.;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdr:borrec:575. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Clorith Angélica Bahos Olivera). General contact details of provider: http://edirc.repec.org/data/brcgvco.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.