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Exact Tests of Equal Forecast Accuracy with an Application to the Term Structure of Interest Rates

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  • Richard Luger

Abstract

The author proposes a class of exact tests of the null hypothesis of exchangeable forecast errors and, hence, of the hypothesis of no difference in the unconditional accuracy of two competing forecasts. The class includes analogues of the well-known Diebold and Mariano (1995) parametric and non-parametric test statistics. The forecast errors can be non-normal and contemporaneously correlated, and general forms of the loss function are admitted. The nonparametric distribution-free property of these new tests makes them robust to the presence of conditional heteroscedasticity, heavy tails, and outliers in the loss-differential series. These tests are used with a randomization or “Monte Carlo” resampling technique, which yields an exact and computationally inexpensive inference procedure. Simulations confirm the reliability of the new test procedure, and its power is found to be comparable with that of the size-corrected parametric Diebold-Mariano test. The test procedure is illustrated with an application to the term structure of interest rates. The application shows that exchangeable forecast errors can be found empirically even when comparing forecasts from estimated models.

Suggested Citation

  • Richard Luger, 2004. "Exact Tests of Equal Forecast Accuracy with an Application to the Term Structure of Interest Rates," Staff Working Papers 04-2, Bank of Canada.
  • Handle: RePEc:bca:bocawp:04-2
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    References listed on IDEAS

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    1. Jean-Marie Dufour, 2003. "Identification, weak instruments, and statistical inference in econometrics," Canadian Journal of Economics, Canadian Economics Association, vol. 36(4), pages 767-808, November.
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    Cited by:

    1. Pär Österholm, 2008. "Can forecasting performance be improved by considering the steady state? An application to Swedish inflation and interest rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 41-51.
    2. Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
    3. Ryan Ratcliff, 2010. "Predicting nominal exchange rate movements using skewness information from options prices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(1), pages 75-92.
    4. Alessandra Amendola & Vincenzo Candila & Antonio Scognamillo, 2017. "On the influence of US monetary policy on crude oil price volatility," Empirical Economics, Springer, vol. 52(1), pages 155-178, February.
    5. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.

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    More about this item

    Keywords

    Econometric and statistical methods;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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