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La Inflación en Colombia: Una Aproximación desde las Redes Neuronales

  • Martha Misas Arango

    ()

  • Enrique López Enciso

    ()

  • Pablo Querubín Borrero

Las redes neuronales (ANN) 1 son modelos computacionales diseñados para simular el funcionamiento del cerebro y, en particular, la forma como éste procesa información. En el contexto de análisis de series de tiempo, se clasifican como modelos no lineales entrenados para (i) realizar conexiones entre los valores pasados y presentes de una serie de tiempo y (ii) extraer estructuras y relaciones escondidas que gobiernan el sistema de información. El atractivo de este enfoque, inspirado en la neurología, es su habilidad para aprender, es decir, para identificar dependencias con base en una muestra finita, de manera que el conocimiento adquirido pueda ser generalizado a muestras no observadas (Herbrich et.al, 1999). Si bien, como señalan Kuan y White (1994), las redes neuronales y sus algoritmos de aprendizaje asociados están todavía lejos de ofrecer una descripción acertada de cómo funciona el cerebro, éstas se han constituido en un marco de modelación muy poderoso e interesante cuyo potencial ha sido comprobado en diversas aplicaciones en todas las ciencias2. Para Moshiri y Cameron (1998), los investigadores son atraídos hacia ese enfoque porque las redes neuronales no están sujetas a supuestos restrictivos como la linealidad, que suele ser necesaria para la aplicación de los modelos matemáticos tradicionales.

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Paper provided by BANCO DE LA REPÚBLICA in its series BORRADORES DE ECONOMIA with number 003029.

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Length: 51
Date of creation: 28 Feb 2002
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Handle: RePEc:col:000094:003029
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  1. Martha Misas A. & Enrique López & Luis Fernando Melo, 1999. "La Inflación Desde Una Perspectiva Monetaria: Un Modelo P* Para Colombia," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE, issue 35, pages 5-53, May.
  2. Jean-François Fillion & André Léonard, 1997. "La courbe de Phillips au Canada : un examen de quelques hypothèses," Staff Working Papers 97-3, Bank of Canada.
  3. Lütkepohl, Helmut & Teräsvirta, Timo & Wolters, Jürgen, 1995. "Investigating Stability and Linearity of a German M1 Money Demand Function," SSE/EFI Working Paper Series in Economics and Finance 64, Stockholm School of Economics.
  4. S. Baranzoni & P. Bianchi & L. Lambertini, 2000. "Multiproduct Firms, Product Differentiation, and Market Structure," Working Papers 368, Dipartimento Scienze Economiche, Universita' di Bologna.
  5. Svensson, Lars, 1999. "Monetary Policy Issues for the Eurosystem," Seminar Papers 667, Stockholm University, Institute for International Economic Studies.
  6. Enrique López E & Martha Misas A, 1998. "Un Examen Empírico De La Curva De Phillips En Colombia," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE, vol. 17(34), pages 39-87, December.
  7. A. M. Gazely & J. M. Binner, 2000. "The application of neural networks to the Divisia index debate: evidence from three countries," Applied Economics, Taylor & Francis Journals, vol. 32(12), pages 1607-1615.
  8. Luis Eduardo Arango & Andrés González, 1998. "Some Evidence Of Smooth Transition Nonlinearity In Colombian Inflation," BORRADORES DE ECONOMIA 003515, BANCO DE LA REPÚBLICA.
  9. Gerlach, Stefan & Svensson, Lars E O, 2002. "Money and Inflation in the Euro-Area: A Case for Monetary Indicators?," CEPR Discussion Papers 3392, C.E.P.R. Discussion Papers.
  10. Nikola Gradojevic & Jing Yang, 2000. "The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables," Staff Working Papers 00-23, Bank of Canada.
  11. Laurence Ball & N. Gregory Mankiw, 1992. "Asymmetric Price Adjustment and Economic Fluctuations," NBER Working Papers 4089, National Bureau of Economic Research, Inc.
  12. James Peery Cover, 1992. "Asymmetric Effects of Positive and Negative Money-Supply Shocks," The Quarterly Journal of Economics, Oxford University Press, vol. 107(4), pages 1261-1282.
  13. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-75, July.
  14. Jeffrey J. Hallman & Richard D. Porter & David H. Small, 1989. "M2 per unit of potential GNP as an anchor for the price level," Staff Studies 157, Board of Governors of the Federal Reserve System (U.S.).
  15. Tkacz, Greg & Hu, Sarah, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Staff Working Papers 99-3, Bank of Canada.
  16. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  17. Raimundo Soto, . "Nonlinearities in the Demand for money: A Neural Network Approach," ILADES-Georgetown University Working Papers inv107, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines.
  18. Nicoletti-Altimari, Sergio, 2001. "Does money lead inflation in the euro area?," Working Paper Series 0063, European Central Bank.
  19. Donald P. Morgan, 1993. "Asymmetric effects of monetary policy," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 21-33.
  20. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415, june. pag.
  21. Tkacz, Greg, 2000. "Non-Parametric and Neural Network Models of Inflation Changes," Staff Working Papers 00-7, Bank of Canada.
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