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La Inflación en Colombia: Una Aproximación desde las Redes Neuronales

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  • Martha Misas Arango

    ()

  • Enrique López Enciso

    ()

  • Pablo Querubín Borrero

Abstract

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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Bibliographic Info

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. Lars E.O. Svensson, 1999. "Monetary policy issues for the Eurosystem," Proceedings, Federal Reserve Bank of San Francisco.
  2. 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.
  3. 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.
  4. 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.
  5. Lütkepohl, Helmut & Teräsvirta, Timo & Wolters, Jürgen, 1995. "Investigating Stability and Linearity of a German M1 Money Demand Function," Working Paper Series in Economics and Finance 64, Stockholm School of Economics.
  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.
  7. Ball, Laurence & Mankiw, N Gregory, 1994. "Asymmetric Price Adjustment and Economic Fluctuations," Economic Journal, Royal Economic Society, vol. 104(423), pages 247-61, March.
  8. Luis Eduardo Arango & Andrés González, . "Some Evidence of Smooth Transition Nonlinearity in Colombian Inflation," Borradores de Economia 105, Banco de la Republica de Colombia.
  9. Nicoletti-Altimari, Sergio, 2001. "Does money lead inflation in the euro area?," Working Paper Series 0063, European Central Bank.
  10. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, April.
  11. 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.
  12. 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.
  13. Tkacz, Greg & Hu, Sarah, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Working Papers 99-3, Bank of Canada.
  14. S. Baranzoni & P. Bianchi & L. Lambertini, 2000. "Market Structure," Working Papers 368, Dipartimento Scienze Economiche, Universita' di Bologna.
  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. 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.
  17. Jean-François Fillion & André Léonard, 1997. "La courbe de Phillips au Canada : un examen de quelques hypothèses," Working Papers 97-3, Bank of Canada.
  18. Donald P. Morgan, 1993. "Asymmetric effects of monetary policy," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 21-33.
  19. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
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Cited by:
  1. Luis Fernando Melo & Rubén Albeiro Loaiza Maya, 2012. "Bayesian Forecast Combination for Inflation Using Rolling Windows: An Emerging Country Case," Borradores de Economia 705, Banco de la Republica de Colombia.
  2. Carlos A. Arango A., 2004. "La Demanda De Especies Monetarias En Colombia: Estructura Y Pronóstico," BORRADORES DE ECONOMIA 002964, BANCO DE LA REPÚBLICA.
  3. Ignacio Lozano, . "Budget Deficit, Money Growth and Inflation: Evidence from the Colombian Case," Borradores de Economia 537, Banco de la Republica de Colombia.
  4. Eliana González Molano & Luis Fernando Melo Velnadia & Anderson Grajales Olarte, 2007. "Pronósticos directos de la inflación colombiana," BORRADORES DE ECONOMIA 004247, BANCO DE LA REPÚBLICA.
  5. Norberto Rodríguez & Patricia Siado, 2003. "Un Pronóstico No Paramétrico De La Inflación Colombiana," BORRADORES DE ECONOMIA 003691, BANCO DE LA REPÚBLICA.
  6. María Clara Aristizábal Restrepo, . "Evaluación asimétrica de una red neuronal artificial:Aplicación al caso de la inflación en Colombia," Borradores de Economia 377, Banco de la Republica de Colombia.
  7. José Mauricio Salazar Sáenz, . "Evaluación de pronóstico de una red neuronal sobre el PIB en Colombia," Borradores de Economia 575, Banco de la Republica de Colombia.
  8. Luis Fernando Melo Velandia & Martha Alicia Misas Arango, 2004. "Modelos Estructurales de Inflación en Colombia: Estimación a través de Mínimos Cuadrados Flexibles," BORRADORES DE ECONOMIA 003244, BANCO DE LA REPÚBLICA.
  9. Héctor Mauricio Nuñez Amortegui, 2005. "Una evaluación de los pronósticos de inflación en Colombia bajo el esquema de inflación objetivo," REVISTA DE ECONOMÍA DEL ROSARIO, UNIVERSIDAD DEL ROSARIO.

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