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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. Jean-François Fillion & André Léonard, 1997. "La courbe de Phillips au Canada : un examen de quelques hypothèses," Working Papers 97-3, Bank of Canada.
  2. 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.
  3. repec:cup:cbooks:9780521770415 is not listed on IDEAS
  4. 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.).
  5. H. Lütkepohl & T. Teräsvirta & J. Wolters, 1995. "Investigating Stability and Linearity of a German M1 Money Demand Function," SFB 373 Discussion Papers 1995,57, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  6. Svensson, Lars E. O., 1999. "Monetary policy issues for the Eurosystem," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 51(1), pages 79-136, December.
  7. S. Baranzoni & P. Bianchi & L. Lambertini, 2000. "Market Structure," Working Papers 368, Dipartimento Scienze Economiche, Universita' di Bologna.
  8. Laurence Ball & N. Gregory Mankiw, 1992. "Asymmetric Price Adjustment and Economic Fluctuations," NBER Working Papers 4089, National Bureau of Economic Research, Inc.
  9. Gerlach, Stefan & Svensson, Lars E. O., 2003. "Money and inflation in the euro area: A case for monetary indicators?," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1649-1672, November.
  10. Enrique López & Martha Misas, . "Un Exámen Empírico de la Curva de Phillips en Colombia," Borradores de Economia 117, Banco de la Republica de Colombia.
  11. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  12. 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.
  13. 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.
  14. Tkacz, Greg, 2000. "Non-Parametric and Neural Network Models of Inflation Changes," Working Papers 00-7, Bank of Canada.
  15. Martha Misas Arango & Enrique López Enciso & Luis Fernando Melo velandia, 1999. "La Inflación Desde Una Perspectiva Monetaria : Un Modelo P* Para Colombia," BORRADORES DE ECONOMIA 003028, BANCO DE LA REPÚBLICA.
  16. 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.
  17. 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.
  18. Tkacz, Greg & Hu, Sarah, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Working Papers 99-3, Bank of Canada.
  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. Nicoletti-Altimari, Sergio, 2001. "Does money lead inflation in the euro area?," Working Paper Series 0063, European Central Bank.
  21. 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.
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