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Comparación de los modelos SETAR y STAR para el índice de empleo industrial colombiano

Author

Listed:
  • Milena Hoyos
  • Mario Galindo

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 desempeno 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 desempeno de pronósticos, sino también presenta ventajas frente a su rival en términos de facilidad de interpretación.

Suggested Citation

  • Milena Hoyos & Mario Galindo, 2011. "Comparación de los modelos SETAR y STAR para el índice de empleo industrial colombiano," Documentos de Trabajo, Escuela de Economía 8347, Universidad Nacional de Colombia, FCE, CID.
    • Handle: RePEc:col:000178:008347
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    File URL: http://www.fce.unal.edu.co/centro-editorial/docs/escuela-de-economia/25-comparacion-de-los-modelos-setar-y-star-para-el-indice-de-empleo-industrial-colombiano
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    References listed on IDEAS

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    1. 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.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, January.
    3. 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 119-136, Suppl. De.
    4. Brown, Bryan W. & Mariano, Roberto S., 1989. "Predictors in Dynamic Nonlinear Models: Large-Sample Behavior," Econometric Theory, Cambridge University Press, vol. 5(3), pages 430-452, December.
    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. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    8. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
    9. Olivier Jean Blanchard & Stanley Fischer, 1989. "Lectures on Macroeconomics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262022834, December.
    10. Stephen Leybourne & Paul Newbold & Dimitrios Vougas, 1998. "Unit roots and smooth transitions," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 83-97, January.
    11. 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.
    12. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, February.
    13. Teräsvirta, Timo, 1996. "Smooth Transition Models," SSE/EFI Working Paper Series in Economics and Finance 132, Stockholm School of Economics.
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    More about this item

    Keywords

    Ciclo económico; no linealidad; modelo SETAR; modelo STAR; índice de empleo industrial.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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