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La Demanda de Especies Monetarias en Colombia: Estructura y Pronóstico

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  • Carlos A. Arango A.

    ()

  • Martha Misas A.

    ()

  • Juan Nicolás Hernández

    ()

Abstract

Las tesorerías de los Bancos Centrales enfrentan el problema de pronosticar las necesidades de especies monetarias requeridas por los agentes económicos para finalizar sus transacciones. Dichos pronósticos son utilizados para hacer sus planes a mediano plazo (2 a 3 años en el caso colombiano) de producción, e inventarios de materia prima y unidades terminadas por denominación. El objetivo de este trabajo es evaluar distintas técnicas de pronóstico que sean lo suficientemente flexibles como para incorporar las innovaciones recientes en los determinantes de la demanda y la estructura denominacional de las especies monetarias, y reconocer las posibles no-linealidades en la relación de aquellos con el uso del efectivo. La estrategia seguida se basa en la utilización de redes neuronales artificiales (ANN) y mínimos cuadrados flexibles (FLS), dos técnicas econométricas bastante robustas frente a cambios estructurales y que permiten incorporar elementos no-lineales en la modelación del efectivo.

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Paper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 309.

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Handle: RePEc:bdr:borrec:309

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  1. Cheung, Yin-Wong & Lai, Kon S, 1993. "Finite-Sample Sizes of Johansen's Likelihood Ration Tests for Conintegration," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 55(3), pages 313-28, August.
  2. 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.
  3. Franses, Philip Hans & Kippers, Jeanine, 2007. "An empirical analysis of euro cash payments," European Economic Review, Elsevier, vol. 51(8), pages 1985-1997, November.
  4. Martha Misas Arango & Enrique López Enciso & Pablo Querubín, 2002. "La Inflación En Colombia: Una Aproximación Desde Las Redes Neuronales," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE.
  5. Martha Alicia Misasarango & Enrique Antonio Lopezenciso & Carlos Arango & Juan Nicolashernandez, 2004. "No-Linealidades En Lademanada De Efectivo En Colombia: Las Redes Neuronales Como Herramientadepronostico," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE.
  6. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  7. Jerry Mushin, 1998. "Modeling the currency issue," Atlantic Economic Journal, International Atlantic Economic Society, vol. 26(3), pages 252-258, September.
  8. Kalaba, Robert E. & Tesfatsion, Leigh S., 1989. "Time-Varying Linear Regression Via Flexible Least Squares," Staff General Research Papers 11196, Iowa State University, Department of Economics.
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Cited by:
  1. Jorge Eduardo Galán Camacho & Miguel Sarmiento Paipilla, 2007. "Banknote Printing At Modern Central Banking:Trends, Costs, And Efficiency," BORRADORES DE ECONOMIA 004393, BANCO DE LA REPÚBLICA.

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