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

  • Lukas F. Stoetzer (a1), Marcel Neunhoeffer (a2), Thomas Gschwend (a2), Simon Munzert (a3) and Sebastian Sternberg (a2)...

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.

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Footnotes

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Authors’ note: Replication materials are available online as a dataverse repository (Neunhoeffer et al.2018) https://doi.org/10.7910/DVN/MLYNX0. We thank Marc Debus, Helmut Norpoth, Kai-Uwe Schnapp, and Steffen Zittlau for their helpful comments. Furthermore, we thank Jochen Groß, Marcel Noack, Gertrud Petrig (Institut für Demoskopie Allensbach), and Rainer Schnell for making available historic polling data for Germany. We also thank Peter Ellis for making available data for New Zealand through the R package nzelect.

Contributing Editor: Jeff Gill

Footnotes

References

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