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Comparing the accuracy of density forecasts from competing models

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  • Giorgio Valente

    (University of Warwick, UK)

  • Lucio Sarno

    (University of Warwick, UK, and Centre for Economic Policy Research, UK)

Abstract

A rapidly growing literature emphasizes the importance of evaluating the forecast accuracy of empirical models on the basis of density (as opposed to point) forecasting performance. We propose a test statistic for the null hypothesis that two competing models have equal density forecast accuracy. Monte Carlo simulations suggest that the test, which has a known limiting distribution, displays satisfactory size and power properties. The use of the test is illustrated with an application to exchange rate forecasting. Copyright © 2004 John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 23 (2004)
Issue (Month): 8 ()
Pages: 541-557

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Handle: RePEc:jof:jforec:v:23:y:2004:i:8:p:541-557

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Lucio Sarno, 2003. "Nonlinear Exchange Rate Models: A Selective Overview," Rivista di Politica Economica, SIPI Spa, vol. 93(4), pages 3-46, July-Augu.
  2. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Tinbergen Institute Discussion Papers 08-050/4, Tinbergen Institute.
  3. Andreas Lindemann & Christian Dunis & Paulo Lisboa, 2005. "Probability distributions and leveraged trading strategies: an application of Gaussian mixture models to the Morgan Stanley Technology Index Tracking Fund," Quantitative Finance, Taylor and Francis Journals, vol. 5(5), pages 459-474.
  4. John Stachurski, 2006. "Computing the Distributions of Economic Models Via Simulation," KIER Working Papers 615, Kyoto University, Institute of Economic Research.
  5. Clarida, Richard & Sarno, Lucio & Taylor, Mark P & Valente, Giorgio, 2002. "The Out-of-Sample Success of Term Structure Models as Exchange Rate Predictors: A Step Beyond," CEPR Discussion Papers 3281, C.E.P.R. Discussion Papers.
  6. Mouratidis, Kostas, 2008. "Evaluating currency crises: A Bayesian Markov switching approach," Journal of Macroeconomics, Elsevier, vol. 30(4), pages 1688-1711, December.
  7. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.
  8. Giorgio Valente & Lucio Sarno, 2005. "Modelling and forecasting stock returns: exploiting the futures market, regime shifts and international spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 345-376.
  9. Giorgio Valente & Lucio Sarno, 2004. "Empirical Exchange Rate Models and Currency Risk: Some Evidence from Density Forecasts," Working Papers wp04-10, Warwick Business School, Financial Econometrics Research Centre.
  10. Shamiri, Ahmed & Shaari, Abu Hassan & Isa, Zaidi, 2008. "Comparing the accuracy of density forecasts from competing GARCH models," MPRA Paper 13662, University Library of Munich, Germany.
  11. John W. Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Working Papers 07-1, Bank of Canada.
  12. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
  13. Anthony Garratt & Kevin Lee, 2006. "Investing Under Model Uncertainty: Decision Based Evaluation of Exchange Rate and Interest Rate Forecasts in the US, UK and Japan," Birkbeck Working Papers in Economics and Finance 0616, Birkbeck, Department of Economics, Mathematics & Statistics.

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