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Forecasting Chilean Industrial Production and Sales with Automated Procedures

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  • ROMULO A. CHUMACERO

Abstract

This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate linear models perform about as well for almost every forecasting horizon

Suggested Citation

  • Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:112
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    File URL: http://repec.org/sce2004/up.14438.1077669344.pdf
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    References listed on IDEAS

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    1. Timmermann, Allan & Patton, Andrew, 2003. "Properties of Optimal Forecasts," CEPR Discussion Papers 4037, C.E.P.R. Discussion Papers.
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    3. Claudio Soto G., 2004. "Unemployment and Consumption in Chile," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(1), pages 31-50, April.
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    7. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    8. Inoue, Atsushi & Kilian, Lutz, 2006. "On the selection of forecasting models," Journal of Econometrics, Elsevier, vol. 130(2), pages 273-306, February.
    9. Fang, Yue, 2003. "Forecasting combination and encompassing tests," International Journal of Forecasting, Elsevier, vol. 19(1), pages 87-94.
    10. 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.
    11. Hansen Bruce E., 1997. "Inference in TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-16, April.
    12. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    13. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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    Cited by:

    1. Norman Loayza & Pablo Fajnzylber & César Calderón, 2005. "Economic Growth in Latin America and the Caribbean : Stylized Facts, Explanations, and Forecasts," World Bank Publications - Books, The World Bank Group, number 7315.
    2. Roque Montero, 2012. "Does Linearity in the Dynamics of Inflation Gap and Unemployment Rate Matter?," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 27(1), pages 3-26, April.

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    More about this item

    Keywords

    Forecasting; Threshold; Artificial Neural Networks; Reality Check; Bootstrap.;
    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
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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