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Comparing the accuracy of density forecasts from competing models

  • Giorgio Valente

    (University of Warwick, UK)

  • Lucio Sarno

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

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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File URL: http://hdl.handle.net/10.1002/for.930
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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
DOI: 10.1002/for.930
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. Giorgio Valente & Lucio Sarno, 2004. "Empirical Exchange Rate Models and Currency Risk: Some Evidence from Density Forecasts," Working Papers wp04-10, Warwick Business School, Finance Group.
  2. Lucio Sarno & Mark P. Taylor, 2002. "Purchasing Power Parity and the Real Exchange Rate," IMF Staff Papers, Palgrave Macmillan, vol. 49(1), pages 5.
  3. Jose A. Lopez, 1995. "Evaluating the predictive accuracy of volatility models," Research Paper 9524, Federal Reserve Bank of New York.
  4. 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.
  5. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  6. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-74, October.
  7. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521586115, December.
  8. Wallis, Kenneth F., 2003. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," International Journal of Forecasting, Elsevier, vol. 19(2), pages 165-175.
  9. Richard Clarida & Lucio Sarno & Mark Taylor & Giorgio Valente, 2001. "The Out-of-Sample Success of Term Structure Models as Exchange Rate Predictors: A Step Beyond," NBER Working Papers 8601, National Bureau of Economic Research, Inc.
  10. Corker, R. J. & Holly, S. & Ellis, R. G., 1986. "Uncertainty and forecast precision," International Journal of Forecasting, Elsevier, vol. 2(1), pages 53-53.
  11. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
  12. Anderson, N. H. & Hall, P. & Titterington, D. M., 1994. "Two-Sample Test Statistics for Measuring Discrepancies Between Two Multivariate Probability Density Functions Using Kernel-Based Density Estimates," Journal of Multivariate Analysis, Elsevier, vol. 50(1), pages 41-54, July.
  13. 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.
  14. Kenneth Rogoff, 1996. "The Purchasing Power Parity Puzzle," Journal of Economic Literature, American Economic Association, vol. 34(2), pages 647-668, June.
  15. Richard H. Clarida & Mark P. Taylor, 1997. "The Term Structure Of Forward Exchange Premiums And The Forecastability Of Spot Exchange Rates: Correcting The Errors," The Review of Economics and Statistics, MIT Press, vol. 79(3), pages 353-361, August.
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