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Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests

  • Francis X. Diebold

    ()

    (Department of Economics, University of Pennsylvania)

The Diebold-Mariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Unfortunately, however, much of the large subsequent literature uses DM-type tests for comparing models, in (pseudo-) out-of-sample environments. In that case, much simpler yet more compelling full-sample model comparison procedures exist; they have been, and should continue to be, widely used. The hunch that (pseudo-) out-of-sample analysis is somehow the “only," or “best," or even a “good" way to provide insurance against in-sample over fitting in model comparisons proves largely false. On the other hand, (pseudo-) out-of-sample analysis may be useful for learning about comparative historical predictive performance.

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File URL: http://economics.sas.upenn.edu/system/files/12-035.pdf
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Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 12-035.

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Length: 16 pages
Date of creation: 07 Sep 2012
Date of revision:
Handle: RePEc:pen:papers:12-035
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  1. 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.
  2. 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.
  3. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
  4. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
  5. Inoue, Atsushi & Kilian, Lutz, 2003. "On the Selection of Forecasting Models," CEPR Discussion Papers 3809, C.E.P.R. Discussion Papers.
  6. Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City.
  7. Douglas Rivers & Quang Vuong, 2002. "Model selection tests for nonlinear dynamic models," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 1-39, June.
  8. Kilian, Lutz & Taylor, Mark P., 2003. "Why is it so difficult to beat the random walk forecast of exchange rates?," Journal of International Economics, Elsevier, vol. 60(1), pages 85-107, May.
  9. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
  10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  11. Li, Tong, 2009. "Simulation based selection of competing structural econometric models," Journal of Econometrics, Elsevier, vol. 148(2), pages 114-123, February.
  12. Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers 2011-025, Federal Reserve Bank of St. Louis.
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