Forecasting Elections in Multi-Party Systems: A Bayesian Approach Combining Polls and Fundamentals

Forecast of the 2017 election 2 days prior to the election. Point estimates along with 56 (≈ 83%) (dark grey) credible intervals and 95% (light grey) credible intervals, the light grey histogram bars represent the election results.


We offer a dynamic Bayesian forecasting model for multi-party elections. It com- bines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multi-party 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 multi-party setting.

Political Analysis, 2019, 27(2): 255-262
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Marcel Neunhoeffer
Ph.D. Candidate

I’m a quantitative social scientist with an interest in how new methods form computer sciences can be of use for social scientists.