On the Usefulness of the Diebold-Mariano Test in the Selection of Prediction Models
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.
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- Inoue, Atsushi & Kilian, Lutz, 2006.
"On the selection of forecasting models,"
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- Todd E. Clark & Michael W. McCracken, 2000.
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- Todd E. Clark & Michael W. McCracken, 1999. "Tests of equal forecast accuracy and encompassing for nested models," Research Working Paper 99-11, Federal Reserve Bank of Kansas City.
- Todd E. Clark & Michael McCracken, 1999. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Computing in Economics and Finance 1999 1241, Society for Computational Economics.
- 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.
- Costantini, Mauro & Kunst, Robert M., 2009.
"Combining Forecasts Based on Multiple Encompassing Tests in a Macroeconomic Core System,"
243, Institute for Advanced Studies.
- 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.
- repec:hal:journl:hal-00287137 is not listed on IDEAS
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