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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 dise�ados para simular el funcionamiento del cerebro y, en particular, la forma como �ste procesa informaci�n. En el contexto de an�lisis 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 informaci�n. El atractivo de este enfoque, inspirado en la neurolog�a, 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 se�alan Kuan y White (1994), las redes neuronales y sus algoritmos de aprendizaje asociados est�n todav�a lejos de ofrecer una descripci�n acertada de c�mo funciona el cerebro, �stas se han constituido en un marco de modelaci�n muy poderoso e interesante cuyo potencial ha sido comprobado en diversas aplicaciones en todas las ciencias2. Para Moshiri y Cameron (1998), los investigadores son atra�dos hacia ese enfoque porque las redes neuronales no est�n sujetas a supuestos restrictivos como la linealidad, que suele ser necesaria para la aplicaci�n de los modelos matem�ticos 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. Luis Eduardo Arango & Andrés González, 1998. "Some Evidence Of Smooth Transition Nonlinearity In Colombian Inflation," BORRADORES DE ECONOMIA 003515, BANCO DE LA REPÚBLICA.
  2. Enrique López E. & Martha Misas A., 1999. "Un Examen Empirico De La Curva De Phillips En Colombia," BORRADORES DE ECONOMIA 003676, BANCO DE LA REPÚBLICA.
  3. S. Baranzoni & P. Bianchi & L. Lambertini, 2000. "Market Structure," Working Papers 368, Dipartimento Scienze Economiche, Universita' di Bologna.
  4. Jean-François Fillion & André Léonard, 1997. "La courbe de Phillips au Canada : un examen de quelques hypothèses," Working Papers 97-3, Bank of Canada.
  5. 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.
  6. Nicoletti-Altimari, Sergio, 2001. "Does money lead inflation in the euro area?," Working Paper Series 0063, European Central Bank.
  7. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  8. Martha Misas & Enrique López & Luis Fernando Melo, . "La Inflación desde una Perspectiva Monetaria: Un Modelo P* para Colombia," Borradores de Economia 133, Banco de la Republica de Colombia.
  9. 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.
  10. Nikola Gradojevic & Jing Yang, 2000. "The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables," Working Papers 00-23, Bank of Canada.
  11. Lars E.O. Svensson, 1999. "Monetary Policy Issues for the Eurosystem," NBER Working Papers 7177, National Bureau of Economic Research, Inc.
  12. 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.
  13. 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.
  14. 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.
  15. 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.).
  16. Donald P. Morgan, 1993. "Asymmetric effects of monetary policy," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 21-33.
  17. Ball, Laurence & Mankiw, N Gregory, 1994. "Asymmetric Price Adjustment and Economic Fluctuations," Economic Journal, Royal Economic Society, vol. 104(423), pages 247-61, March.
  18. Tkacz, Greg, 2000. "Non-Parametric and Neural Network Models of Inflation Changes," Working Papers 00-7, Bank of Canada.
  19. repec:cup:cbooks:9780521770415 is not listed on IDEAS
  20. Tkacz, Greg & Hu, Sarah, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Working Papers 99-3, Bank of Canada.
  21. Cover, James Peery, 1992. "Asymmetric Effects of Positive and Negative Money-Supply Shocks," The Quarterly Journal of Economics, MIT Press, vol. 107(4), pages 1261-82, November.
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