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Combining probability forecasts

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  • Clements, Michael P.
  • Harvey, David I.

Abstract

We consider different methods for combining probability forecasts. In empirical exercises, the data generating process of the forecasts and the event being forecast is not known, and therefore the optimal form of combination will also be unknown. We consider the properties of various combination schemes for a number of plausible data generating processes, and indicate which types of combinations are likely to be useful. We also show that whether forecast encompassing is found to hold between two rival sets of forecasts or not may depend on the type of combination adopted. The relative performances of the different combination methods are illustrated, with an application to predicting recession probabilities using leading indicators.

Suggested Citation

  • Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223.
  • Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:208-223
    DOI: 10.1016/j.ijforecast.2009.12.016
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    Cited by:

    1. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters,in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
    2. Pauwels, Laurent & Vasnev, Andrey, 2014. "Forecast combination for U.S. recessions with real-time data," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 138-148.
    3. Lyon, Aidan & Wintle, Bonnie C. & Burgman, Mark, 2015. "Collective wisdom: Methods of confidence interval aggregation," Journal of Business Research, Elsevier, vol. 68(8), pages 1759-1767.
    4. Pirschel, Inske, 2015. "Forecasting Euro Area Recessions in real-time with a mixed-frequency Bayesian VAR," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113031, Verein für Socialpolitik / German Economic Association.
    5. Bentes, Sonia R. & Menezes, Rui, 2013. "On the predictability of realized volatility using feasible GLS," Journal of Asian Economics, Elsevier, vol. 28(C), pages 58-66.
    6. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.
    7. Bermejo, Miguel Ángel & Peña, Daniel & Sánchez, Ismael, 2011. "Densidad de predicción basada en momentos condicionados y máxima entropía : aplicación a la predicción de potencia eólica," DES - Working Papers. Statistics and Econometrics. WS ws111813, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
    9. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    10. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    11. Lee, Seohyun, 2017. "Three essays on uncertainty: real and financial effects of uncertainty shocks," MPRA Paper 83617, University Library of Munich, Germany.
    12. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    13. Fabian Krüger & Ingmar Nolte, 2011. "Disagreement, Uncertainty and the True Predictive Density," Working Paper Series of the Department of Economics, University of Konstanz 2011-43, Department of Economics, University of Konstanz.
    14. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW).
    15. Rodrigues, Bruno Dore & Stevenson, Maxwell J., 2013. "Takeover prediction using forecast combinations," International Journal of Forecasting, Elsevier, vol. 29(4), pages 628-641.
    16. Giovanni De Luca & Alfonso Carfora, 2014. "Predicting U.S. recessions through a combination of probability forecasts," Empirical Economics, Springer, vol. 46(1), pages 127-144, February.

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