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

  • 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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Metadaten
Document Type:Article
Language:English
Author(s):Lukas F. Stötzer, Marcel Neunhoeffer, Thomas Gschwend, Simon MunzertORCiD, Sebastian Sternberg
Parent Title (English):Political Analysis
Publication year:2019
Publishing Institution:Hertie School
First Page:255
Last Page:262
DOI:https://doi.org/10.1017/pan.2018.49
Release Date:2020/01/27
Volume:27
Issue:2
Licence of document (German):Metadaten / metadata
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