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Testing for Relative Predictive Accuracy: A Critical Viewpoint


  • Kunst, Robert M.

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna)


Tests for relative predictive accuracy have become a widespread addendum to forecast comparisons. Many empirical research reports conclude that the difference between the entertained forecasting models is 'insignificant'. This paper collects arguments that cast doubt on the usefulness of relative predictive accuracy tests. The main point is not that test power is too low but that their application is conceptually mistaken. The features are highlighted by means of some Monte Carlo experiments for simple time-series decision problems.

Suggested Citation

  • Kunst, Robert M., 2003. "Testing for Relative Predictive Accuracy: A Critical Viewpoint," Economics Series 130, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihsesp:130

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    References listed on IDEAS

    1. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    4. Robert Kunst, 1997. "Augmented ARCH models for financial time series: stability conditions and empirical evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 7(6), pages 575-586.
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    Cited by:

    1. Marcelo Moura, 2010. "Testing the Taylor Model Predictability for Exchange Rates in Latin America," Open Economies Review, Springer, vol. 21(4), pages 547-564, September.
    2. Andreas Brunhart, 2014. "Stock Market's Reactions to Revelation of Tax Evasion: An Empirical Assessment," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 150(III), pages 161-190, September.
    3. Moura, Marcelo L. , & Lima, Adauto R. S. & Mendonça, Rodrigo M., 2008. "Exchange Rate and Fundamentals: The Case of Brazil," Insper Working Papers wpe_114, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    4. Jesús Crespo Cuaresma & Ernest Gnan & Doris Ritzberger-Grünwald, 2005. "The term structure as a predictor of real activity and inflation in the euro area: a reassessment," BIS Papers chapters,in: Bank for International Settlements (ed.), Investigating the relationship between the financial and real economy, volume 22, pages 177-92 Bank for International Settlements.
    5. Maria Antoinette Silgoner, 2005. "An Overview of European Economic Indicators: Great Variety of Data on the Euro Area, Need for More Extensive Coverage of the New EU Member States," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 66-89.
    6. Jean-Stéphane MESONNIER, 2007. "The predictive content of the real interest rate gap for macroeconomic variables in the euro area," Money Macro and Finance (MMF) Research Group Conference 2006 102, Money Macro and Finance Research Group.
    7. Andreas Breitenfellner & Jesus Crespo Cuaresma, 2008. "Crude Oil Prices and the USD/EUR Exchange Rate," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 4.
    8. Mariola Pilatowska, 2011. "Information and Prediction Criteria in Selecting the Forecasting Model," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 21-40.
    9. Mésonnier, J-S., 2006. "The Reliability of Macroeconomic Forecasts based on Real Interest Rate Gap Estimates in Real Time: an Assessment for the Euro Area," Working papers 157, Banque de France.
    10. Moser, Gabriel & Rumler, Fabio & Scharler, Johann, 2007. "Forecasting Austrian inflation," Economic Modelling, Elsevier, vol. 24(3), pages 470-480, May.

    More about this item


    Information criteria; Forecasting; Hypothesis testing;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


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