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Combination forecasting for directional accuracy: An application to survey interest rate forecasts

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  • Mark Greer

Abstract

Using published interest rates forecasts issued by professional economists, two combination forecasts designed to improve the directional accuracy of interest rate forecasting are constructed. The first combination forecast takes a weighted average of the individual forecasters' predictions. The more successful the forecaster was in past forecasts at predicting the direction of change in interest rates, the greater is the weight given to his/her current forecast. The second combination forecast is simply the forecast issued by the forecaster who had the greatest success rate at predicting the direction of change in interest rates in previous forecasts. In cases where two or more forecasters tie for best historic directional accuracy track record, the arithmetic mean of these forecasters is used. The study finds that neither combination forecasting method performs better than coin-flipping at predicting the direction of change in interest rates. Nor does either method beat the simple arithmetic mean of the predictions of all the forecasters surveyed at predicting the direction of change in interest rates.

Suggested Citation

  • Mark Greer, 2005. "Combination forecasting for directional accuracy: An application to survey interest rate forecasts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(6), pages 607-615.
    • Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:607-615
    DOI: 10.1080/02664760500079027
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    Cited by:

    1. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    2. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    3. Blaskowitz, Oliver & Herwartz, Helmut, 2014. "Testing the value of directional forecasts in the presence of serial correlation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 30-42.
    4. Esteban Fernández-Vázquez & Blanca Moreno, 2017. "Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator," Journal of Geographical Systems, Springer, vol. 19(4), pages 349-370, October.
    5. López Menéndez, Ana Jesús & Pérez Suárez, Rigoberto, 2017. "Forecasting Performance and Information Measures. Revisiting the M-Competition /Evaluación de Predicciones y Medidas de Información. Reexamen de la M-Competición," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 299-314, Mayo.
    6. Blaskowitz, Oliver J. & Herwartz, Helmut, 2008. "Testing directional forecast value in the presence of serial correlation," SFB 649 Discussion Papers 2008-073, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Baghestani, Hamid & Kherfi, Samer, 2008. "How well do U.S. consumers predict the direction of change in interest rates?," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 725-732, November.
    8. repec:hum:wpaper:sfb649dp2008-073 is not listed on IDEAS

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