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Comparación De Los Modelos Setar Y Star Para El Índice De Empleo Industrial Colombiano

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  • Milena Hoyos

    ()

  • Mario Galindo

    ()

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    Abstract

    Este trabajo pretende mostrar evidencia de no linealidad en el índice de empleo industrial colombiano. Para esto, se estiman los modelos SETAR y STAR, usando la serie mensual del índice para el periodo 1990-2010. El artículo presenta además una comparación del desempeño de pronósticos de los modelos para diferentes horizontes de predicción. Los principales resultados muestran evidencia de no linealidad, explicada por un SETAR de cuatro regímenes y un LSTAR de dos regímenes, así como la superioridad del segundo modelo en capacidad predictiva. El LSTAR no solamente ofrece ganancias importantes en desempeño de pronósticos, sino también presenta ventajas frente a su rival en términos de facilidad de interpretación.

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    File URL: http://www.fce.unal.edu.co/media/files/documentos/Comunicaciones/dochoyos_fce_ee_25.pdf
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    Bibliographic Info

    Paper provided by UN - RCE - CID in its series DOCUMENTOS DE TRABAJO - ESCUELA DE ECONOMÍA with number 008347.

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    Length: 20
    Date of creation: 27 Apr 2011
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    Handle: RePEc:col:000178:008347

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    Related research

    Keywords: Ciclo económico; no linealidad; modelo SETAR; modelo STAR; índice de empleo industrial.;

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    1. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    2. Clements, Michael P. & Smith, Jeremy, 1997. "The performance of alternative forecasting methods for SETAR models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 463-475, December.
    3. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415, October.
    4. Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
    5. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    6. Francis X. Diebold & Glenn D. Rudebusch, 1994. "Measuring Business Cycles: A Modern Perspective," NBER Working Papers 4643, National Bureau of Economic Research, Inc.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
    8. Brown, Bryan W. & Mariano, Roberto S., 1989. "Predictors in Dynamic Nonlinear Models: Large-Sample Behavior," Econometric Theory, Cambridge University Press, vol. 5(03), pages 430-452, December.
    9. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De.
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