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Comparing the accuracy of density forecasts from competing models

Author

Listed:
  • 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.

Suggested Citation

  • Giorgio Valente & Lucio Sarno, 2004. "Comparing the accuracy of density forecasts from competing models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(8), pages 541-557.
    • Handle: RePEc:jof:jforec:v:23:y:2004:i:8:p:541-557
    DOI: 10.1002/for.930
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    1. Clarida, Richard H. & Sarno, Lucio & Taylor, Mark P. & Valente, Giorgio, 2003. "The out-of-sample success of term structure models as exchange rate predictors: a step beyond," Journal of International Economics, Elsevier, vol. 60(1), pages 61-83, May.
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    14. Fuchun Li & Greg Tkacz, 2001. "A Consistent Bootstrap Test for Conditional Density Functions with Time-Dependent Data," Staff Working Papers 01-21, Bank of Canada.
    15. Hendry, David F., 1984. "Monte carlo experimentation in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 16, pages 937-976, Elsevier.
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    Cited by:

    1. Hua, Zhongsheng & Zhang, Bin, 2008. "Improving density forecast by modeling asymmetric features: An application to S&P500 returns," European Journal of Operational Research, Elsevier, vol. 185(2), pages 716-725, March.
    2. Erik Kole & Dick Dijk, 2017. "How to Identify and Forecast Bull and Bear Markets?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 120-139, January.
    3. Mouratidis, Kostas, 2008. "Evaluating currency crises: A Bayesian Markov switching approach," Journal of Macroeconomics, Elsevier, vol. 30(4), pages 1688-1711, December.
    4. 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.
    5. John Stachurski & Vance Martin, 2008. "Computing the Distributions of Economic Models via Simulation," Econometrica, Econometric Society, vol. 76(2), pages 443-450, March.
    6. Clarida, Richard H. & Sarno, Lucio & Taylor, Mark P. & Valente, Giorgio, 2003. "The out-of-sample success of term structure models as exchange rate predictors: a step beyond," Journal of International Economics, Elsevier, vol. 60(1), pages 61-83, May.
    7. 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.
    8. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003;," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.
    9. 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.
    10. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    11. 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.
    12. 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 & Francis Journals, vol. 5(5), pages 459-474.
    13. Lucio Sarno & Giorgio Valente, 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, March.
    14. Sarno, Lucio & Valente, Giorgio, 2005. "Empirical exchange rate models and currency risk: some evidence from density forecasts," Journal of International Money and Finance, Elsevier, vol. 24(2), pages 363-385, March.
    15. Diks, Cees & Fang, Hao, 2020. "Comparing density forecasts in a risk management context," International Journal of Forecasting, Elsevier, vol. 36(2), pages 531-551.
    16. Lucio Sarno, 2003. "Nonlinear Exchange Rate Models: A Selective Overview," Rivista di Politica Economica, SIPI Spa, vol. 93(4), pages 3-46, July-Augu.
    17. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    18. repec:hal:journl:peer-00834423 is not listed on IDEAS
    19. Andrea Cipollini & Bassam Fattouh & Kostas Mouratidis, 2009. "Fiscal Readjustments In The United States: A Nonlinear Time‐Series Analysis," Economic Inquiry, Western Economic Association International, vol. 47(1), pages 34-54, January.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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