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On Using Predictive-ability Tests in the Selection of Time-series Prediction Models: A Monte Carlo Evaluation

Author

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  • Costantini, Mauro

    (Department of Economics and Finance, Brunel University, London)

  • Kunst, Robert M.

    (Institute for Advanced Studies, Vienna, and University of Vienna)

Abstract

Comparative ex-ante prediction experiments over expanding subsamples are a popular tool for the task of selecting the best forecasting model class in finite samples of practical relevance. Flanking such a horse race by predictive-accuracy tests,such as the test by Diebold and Mariano (1995), tends to increase support for the simpler structure. We are concerned with the question whether such simplicity boosting actually benefits predictive accuracy in finite samples. We consider two variants of the DM test, one with naive normal critical values and one with bootstrapped critical values, the predictive-ability test by Giacomini and White (2006), which continues to be valid in nested problems, the F test by Clark and McCracken (2001), and also model selection via the AIC as a benchmark strategy. Our Monte Carlo simulations focus on basic univariate time-series specifications, such as linear (ARMA) and nonlinear (SETAR) generating processes.

Suggested Citation

  • Costantini, Mauro & Kunst, Robert M., 2018. "On Using Predictive-ability Tests in the Selection of Time-series Prediction Models: A Monte Carlo Evaluation," Economics Series 341, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:341
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    File URL: https://irihs.ihs.ac.at/id/eprint/4712
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    References listed on IDEAS

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    More about this item

    Keywords

    Forecasting; 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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