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Accuracy of German federal election forecasts, 2013 & 2017

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  • Graefe, Andreas

Abstract

The present study reviews the accuracy of four methods (polls, prediction markets, expert judgment, and quantitative models) for forecasting the two German federal elections in 2013 and 2017. On average across both elections, polls and prediction markets were most accurate, while experts and quantitative models were least accurate. However, the accuracy of individual forecasts did not correlate across elections. That is, the methods that were most accurate in 2013 did not perform particularly well in 2017. A combined forecast, calculated by averaging forecasts within and across methods, was more accurate than three of the four component forecasts. The results conform to prior research on US presidential elections in showing that combining is effective in generating accurate forecasts and avoiding large errors.

Suggested Citation

  • Graefe, Andreas, 2019. "Accuracy of German federal election forecasts, 2013 & 2017," International Journal of Forecasting, Elsevier, vol. 35(3), pages 868-877.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:3:p:868-877
    DOI: 10.1016/j.ijforecast.2019.01.004
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    References listed on IDEAS

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    1. Andreas Graefe, 2017. "Prediction Market Performance in the 2016 U.S. Presidential Election," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 45, pages 38-42, Spring.
    2. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    3. Drew A. Linzer, 2013. "Dynamic Bayesian Forecasting of Presidential Elections in the States," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 124-134, March.
    4. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    5. Alfred Cuzan & J. Scott Armstrong & Randall J. Jones, Jr., 2005. "How We Computed the Pollyvote," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 1, pages 51-52, June.
    6. repec:cup:judgdm:v:13:y:2018:i:4:p:334-344 is not listed on IDEAS
    7. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
    8. Graefe, Andreas & Küchenhoff, Helmut & Stierle, Veronika & Riedl, Bernhard, 2015. "Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems," International Journal of Forecasting, Elsevier, vol. 31(3), pages 943-951.
    9. Paul W. Rhode & Koleman S. Strumpf, 2004. "Historical Presidential Betting Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 127-141, Spring.
    10. Andreas Graefe & J. Scott Armstrong & Alfred G. Cuzán & Randall J. Jones, Jr., 2009. "Combined Forecasts of the 2008 Election: The Pollyvote," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 12, pages 41-42, Winter.
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    Cited by:

    1. UMEDA, Michio, 2023. "Aggregating qualitative district-level campaign assessments to forecast election results: Evidence from Japan," International Journal of Forecasting, Elsevier, vol. 39(2), pages 956-966.
    2. Graefe, Andreas, 2023. "Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)," International Journal of Forecasting, Elsevier, vol. 39(1), pages 170-177.

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