TY - JOUR A1 - Stötzer, Lukas F. A1 - Neunhoeffer, Marcel A1 - Gschwend, Thomas A1 - Munzert, Simon A1 - Sternberg, Sebastian T1 - Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals JF - Political Analysis N2 - 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. Y1 - 2019 U6 - https://doi.org/10.1017/pan.2018.49 VL - 27 IS - 2 SP - 255 EP - 262 ER - TY - JOUR A1 - Gschwend, Thomas A1 - Müller, Klara A1 - Munzert, Simon A1 - Neunhoeffer, Marcel A1 - Stoetzer, Lukas T1 - The Zweitstimme Model: A Dynamic Forecast of the 2021 German Federal Election JF - PS: Political Science & Politics Y1 - 2022 U6 - https://doi.org/10.1017/S1049096521000913 VL - 55 IS - 1 SP - 85 EP - 90 ER - TY - JOUR A1 - Erfort, Cornelius A1 - Stoetzer, Lukas F. A1 - Gschwend, Thomas A1 - Koch, Elias A1 - Munzert, Simon A1 - Rajski, Hannah T1 - The Zweitstimme Forecast for the German Federal Election 2025: Coalition Majorities and Vacant Districts JF - PS: Political Science & Politics Y1 - 2025 U6 - https://doi.org/10.1017/S1049096525000150 SN - 1049-0965 SP - 1 EP - 12 PB - Cambridge University Press (CUP) ER - TY - JOUR A1 - Stoetzer, Lukas F. A1 - Erfort, Cornelius A1 - Rajski, Hannah A1 - Gschwend, Thomas A1 - Munzert, Simon A1 - Koch, Elias T1 - An election forecasting model for subnational elections JF - Electoral Studies N2 - While election forecasts predominantly focus on national contests, many democratic elections take place at the subnational level. Subnational elections pose unique challenges for traditional fundamentals forecasting models due to less available polling data and idiosyncratic subnational politics. In this article, we present and evaluate the performance of Bayesian forecasting models for German state elections from 1990 to 2024. Our forecasts demonstrate high accuracy at lead times of two days, two weeks, and two months, and offer valuable ex-ante predictions for three state elections held in September 2024. These findings underscore the potential for applying election forecasting models effectively to subnational elections. Y1 - 2025 U6 - https://doi.org/10.1016/j.electstud.2025.102939 VL - 95 PB - Elsevier BV ER -