Thomas Gschwend, Klara Müller, Simon Munzert, Marcel Neunhoeffer, Lukas Stoetzer
The Zweitstimme Model: A Dynamic Forecast of the 2021 German Federal Election

PS: Political Science and Politics, 2022: 55, Heft 1, S. 85-90
ISSN: 1049-0965 (print), 1537-5935 (online)

When German citizens head to the polling booths on Sunday, September 26, 2021, no party is expected to gain an outright majority in the Bundestag. Given that several parties are likely to gain representation, only a coalition government will be able to secure a majority of seats. Which parties will gain enough seats and are likely to be in the position to sign a coalition agreement? This election also will determine who will follow Angela Merkel as the new chancellor. Our forecasting project developed the “Zweitstimme” model (i.e., the term for the party vote that Germans cast on Election Day), which performed decently in the 2017 election (Munzert et al. Reference Munzert, Stoetzer, Gschwend, Neunhoeffer and Sternberg2017; Stoetzer et al. Reference Stoetzer, Neunhoeffer, Gschwend, Munzert and Sternberg2019; see also The model allows us to predict party-vote shares, coalition shares, the likelihood of a majority for certain coalitions, and many other relevant quantities of interest.
Our point of departure is a Bayesian forecasting approach that combines polls and fundamentals. This follows the tradition of synthetic forecasting models (Graefe Reference Graefe2017; Lewis-Beck and Dassonneville Reference Lewis-Beck and Dassonneville2015), which combine the merits of fundamentals-based with poll-based models. Although dynamic versions of these models—which are updated by incorporating published polling data over time—have been applied especially in the US context (Erikson and Wlezien Reference Erikson and Wlezien2013; Heidemanns, Gelman, and Morris Reference Heidemanns, Gelman and Morris2020; Linzer Reference Linzer2013; Silver Reference Silver2020), their methodology does not easily transfer to multiparty settings. Forecasting the outcomes of multiparty elections poses particular challenges. We must predict simultaneously the support of multiple parties and, therefore, must account for the compositional nature of the data when modeling them.
This article applies our dynamic Bayesian forecasting model to predict the outcome of the 2021 German federal election. It systematically combines published pre-election public-opinion poll results with information from fundamentals-based forecasting models while also accounting for the dynamic evolution of party support in multiparty systems.
This article applies our dynamic Bayesian forecasting model to predict the outcome of the 2021 German federal election.
This article presents an early forecast of our model, calibrates it on the basis of historical data, and reports various quantities of interest, including the probabilities of a plurality of votes for a party, a majority of seats for certain coalitions in parliament, and the expected overall size of parliament due to the distribution of overhang (i.e., surplus) and compensatory seats. In its current form, the model generates forecasts for various points of interest beginning as early as 200 days before Election Day. Updated forecasts based on incoming polling information will be disseminated in the online edition of Süddeutsche Zeitung, a major German quality newspaper.