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Forecasting Elections in Multiparty Systems: AÂ Bayesian Approach Combining Polls and Fundamentals

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  • Stoetzer, Lukas F.
  • Neunhoeffer, Marcel
  • Gschwend, Thomas
  • Munzert, Simon
  • Sternberg, Sebastian

Abstract

We offer a dynamic Bayesian forecasting model for multiparty elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multiparty nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multiparty setting.

Suggested Citation

  • Stoetzer, Lukas F. & Neunhoeffer, Marcel & Gschwend, Thomas & Munzert, Simon & Sternberg, Sebastian, 2019. "Forecasting Elections in Multiparty Systems: AÂ Bayesian Approach Combining Polls and Fundamentals," Political Analysis, Cambridge University Press, vol. 27(2), pages 255-262, April.
    • Handle: RePEc:cup:polals:v:27:y:2019:i:02:p:255-262_00
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    Cited by:

    1. Quinlan, Stephen & Lewis-Beck, Michael S., 2021. "Forecasting government support in Irish general elections: Opinion polls and structural models," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1654-1665.
    2. Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
    3. Hanretty, Chris, 2021. "Forecasting multiparty by-elections using Dirichlet regression," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1666-1676.

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