Forecasting Elections in Multi-party Systems: A Backwards Random-walk Approach

Abstract

This paper presents a dynamic Bayesian forecasting model for multi-party elections. Our modeling approach combines data from published pre-election pub- lic 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 certain events, such as the plurality of votes for a party or the majority for coalition options in parliament. We apply this model to forecast the German Federal election 2017. The forecasts of our model are continuously being updated on the platform zweitstimme.org. The value of our approach goes beyond the realms of academia: We equip journalists, political pundits, and ordinary citizens with information that can help to make sense of the parties’ latent support and ultimately making better informed voting decisions.

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