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On the Usefulness of the Diebold-Mariano Test in the Selection of Prediction Models

Author

Listed:
  • Costantini, Mauro

    (BWZ, University of Vienna, Vienna, Austria)

  • Kunst, Robert M.

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and Department of Economics, University of Vienna, Austria)

Abstract

In evaluating prediction models, many researchers flank comparative ex-ante prediction experiments by significance tests on accuracy improvement, such as the Diebold-Mariano test. We argue that basing the choice of prediction models on such significance tests is problematic, as this practice may favor the null model, usually a simple benchmark. We explore the validity of this argument by extensive Monte Carlo simulations with linear (ARMA) and nonlinear (SETAR) generating processes. For many parameter constellations, we find that utilization of additional significance tests in selecting the forecasting model fails to improve predictive accuracy.

Suggested Citation

  • Costantini, Mauro & Kunst, Robert M., 2011. "On the Usefulness of the Diebold-Mariano Test in the Selection of Prediction Models," Economics Series 276, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:276
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    File URL: https://irihs.ihs.ac.at/id/eprint/2097
    File Function: First version, 2011
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    References listed on IDEAS

    as
    1. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, September.
    2. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    3. 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.
    4. Mauro Costantini & Robert M. Kunst, 2011. "Combining forecasts based on multiple encompassing tests in a macroeconomic core system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 579-596, September.
    5. Bénédicte Vidaillet & V. d'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    6. Inoue, Atsushi & Kilian, Lutz, 2006. "On the selection of forecasting models," Journal of Econometrics, Elsevier, vol. 130(2), pages 273-306, February.
    7. Julia Campos & David F. Hendry & Hans‐Martin Krolzig, 2003. "Consistent Model Selection by an Automatic Gets Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 803-819, December.
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    Cited by:

    1. Guglielmo Maria Caporale & Juncal Cuñado & Luis A. Gil-Alana, 2013. "Modelling long-run trends and cycles in financial time series data," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 405-421, May.
    2. Bec Frédérique & Salem Melika Ben, 2013. "Inventory investment and the business cycle: the usual suspect," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 335-343, May.
    3. Guglielmo Caporale & Luis Gil-Alana, 2016. "Persistence and cyclical dependence in the monthly euribor rate," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(1), pages 157-171, January.
    4. Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
    5. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2017. "Persistence and cycles in the us federal funds rate," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 1-8.

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    More about this item

    Keywords

    Forecsting; time series; predictive accuracy; model selection!;
    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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