Data Visualization

An Approach to Predicting the District Vote Shares in German Federal Elections

Almost half of the total seats in the German Bundestag are awarded through first-past-the post elections at the electoral-district level. However, many election forecasting models do not consider this. In this paper we present an approach to predicting the candidate-vote shares at the district level for the German Federal Elections. To that end, we combine the national-level election prediction model from with two district-level prediction models, a linear regression and an artificial neural network, that both use the same candidate and district characteristics for their predictions. All data in our approach are publicly available prior to the respective election; thus, our model yields real forecasts. The model is therefore able to provide valuable information to running candidates and the interested public in future elections. Moreover, our prediction results are also relevant for substantive research; with the aid of the resulting odds of winning, better measures can be created to characterize the competitiveness of an electoral district and the expected closeness of electoral-district elections, which can influence political behaviour. Furthermore, the prediction allows empirical statements to be made about the expected size of the Bundestag as well as the composition of its personnel. A structural-dynamic forecasting model for German federal elections

We present results of an ex-ante forecast of party-specific vote shares at the German Federal Election 2017. To that end, we combine data from published trial heat polls with structural information. 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. The forecasts of our model are continuously being updated on the platform The value of our approach goes beyond the realms of academia - We equip journalists, political pundits, and ordinary citizens with information that can help make sense of the parties’ latent support and ultimately make voting decisions better informed.