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Evaluación asimétrica de una red neuronal artificial:Aplicación al caso de la inflación en Colombia

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  • María Clara Aristizábal Restrepo
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    Abstract

    El objetivo de este trabajo es explorar la relación no lineal entre el dinero y la inflación en Colombia a través de una red neuronal artificial (RNA), utilizando información mensual de la variación del IPC y del agregado monetario M3, desde enero de 1982 hasta febrero de 2005. La Constitución de 1991 le otorgo al Banco de la República la responsabilidad de velar por la estabilidad de precios. Este hecho, sumado al rezago con el que las políticas monetarias afectan a su variable objetivo, en este caso la inflación, hace indispensable para las autoridades monetarias, contar con los mejores modelos para pronosticarla y guiar sus decisiones de política. Las RNA aparecen como una excelente alternativa para lograr este propósito, dado el comportamiento intrínsecamente no lineal exhibido por la relación entre estas variables. El presente trabajo incorpora algunas innovaciones en la modelación de dinero e inflación, que permiten generar pronósticos más confiables, debido a que el modelo se aproxima con mayor exactitud a la realidad. Tales innovaciones se refieren a una selección mas sofisticada de los rezagos significativos que deben ser incorporados en el modelo, una construcción de pronósticos que actualiza su base de datos y una función de costos asimétricos para su evaluación.

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    Bibliographic Info

    Paper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 377.

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    Handle: RePEc:bdr:borrec:377

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    Keywords: Red Neuronal Artificial; No linealidad; Unidad Escondida; Función de Activación; Rolling de Pronósticos; Función de Perdida Asimétrica.;

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    1. Christoffersen & Diebold, . "Further Results on Forecasting and Model Selection Under Asymmetric Loss," Home Pages _059, University of Pennsylvania.
    2. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    3. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
    4. Tkacz, Greg & Hu, Sarah, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Working Papers 99-3, Bank of Canada.
    5. Luis Fernando Melo & Martha Misas, . "Análisis del Comportamiento de la Inflación Trimestral en Colombia Bajo Cambios de Régimen: Una Evidencia a Través del Modelo: "Switching" de Hamilton," Borradores de Economia 086, Banco de la Republica de Colombia.
    6. Arango, Luis E. & Melo, Luis F., 2006. "Expansions and contractions in Brazil, Colombia and Mexico: A view through nonlinear models," Journal of Development Economics, Elsevier, vol. 80(2), pages 501-517, August.
    7. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-64, Oct.-Dec..
    8. Munir A. Jalil & Luis Fernando Melo, . "Una Relación no Líneal entre Inflación y los Medios de Pago," Borradores de Economia 145, Banco de la Republica de Colombia.
    9. 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.
    10. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De.
    11. Luis Eduardo Arango & Andrés González & Carlos Esteban Posada, 2001. "Returns And Interest Rate: A Nonlinear Relationship In The Bogota Stock Market," BORRADORES DE ECONOMIA 003468, BANCO DE LA REPÚBLICA.
    12. Jean Imbs & Haroon Mumtaz & Morten O. Ravn & Hélène Rey, 2003. "Nonlinearities and Real Exchange Rate Dynamics," Journal of the European Economic Association, MIT Press, vol. 1(2-3), pages 639-649, 04/05.
    13. Luis Arango & Andres Gonzalez, 2001. "Some evidence of smooth transition nonlinearity in Colombian inflation," Applied Economics, Taylor & Francis Journals, vol. 33(2), pages 155-162.
    14. Steven Gonzalez, . "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada.
    15. Martha Misas & Enrique López & Pablo Querubín, . "La Inflación en Colombia: Una Aproximación desde las Redes Neuronales," Borradores de Economia 199, Banco de la Republica de Colombia.
    16. Christoffersen & Diebold, . "Optimal Prediction Under Asymmetric Loss," Home Pages 167, 1996., University of Pennsylvania.
    17. Yochanan Shachmurove & Doris Witkowska, . "Utilizing Artificial Neural Network Model to Predict Stock Markets," Penn CARESS Working Papers cae679cdc2e020f74d692ae73, Penn Economics Department.
    18. Munir A. Jalil. B & Martha Misas, 2006. "Evaluación de pronósticos del tipo de cambio utilizando," BORRADORES DE ECONOMIA 002636, BANCO DE LA REPÚBLICA.
    19. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    20. Yochanan Shachmurove, 2002. "Applying Artificial Neural Networks to Business, Economics and Finance," Penn CARESS Working Papers 5ecbb5c20d3d547f357aa1306, Penn Economics Department.
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