@article{StoetzerNeunhoefferGschwendetal., author = {St{\"o}tzer, Lukas F. and Neunhoeffer, Marcel and Gschwend, Thomas and Munzert, Simon and Sternberg, Sebastian}, title = {Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals}, series = {Political Analysis}, volume = {27}, journal = {Political Analysis}, number = {2}, doi = {10.1017/pan.2018.49}, pages = {255 -- 262}, abstract = {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.}, language = {en} } @article{GschwendMuellerMunzertetal., author = {Gschwend, Thomas and M{\"u}ller, Klara and Munzert, Simon and Neunhoeffer, Marcel and Stoetzer, Lukas}, title = {The Zweitstimme Model: A Dynamic Forecast of the 2021 German Federal Election}, series = {PS: Political Science \& Politics}, volume = {55}, journal = {PS: Political Science \& Politics}, number = {1}, doi = {10.1017/S1049096521000913}, pages = {85 -- 90}, language = {en} } @article{ErfortStoetzerGschwendetal., author = {Erfort, Cornelius and Stoetzer, Lukas F. and Gschwend, Thomas and Koch, Elias and Munzert, Simon and Rajski, Hannah}, title = {The Zweitstimme Forecast for the German Federal Election 2025: Coalition Majorities and Vacant Districts}, series = {PS: Political Science \& Politics}, journal = {PS: Political Science \& Politics}, publisher = {Cambridge University Press (CUP)}, issn = {1049-0965}, doi = {10.1017/S1049096525000150}, pages = {1 -- 12}, language = {en} } @article{StoetzerErfortRajskietal., author = {Stoetzer, Lukas F. and Erfort, Cornelius and Rajski, Hannah and Gschwend, Thomas and Munzert, Simon and Koch, Elias}, title = {An election forecasting model for subnational elections}, series = {Electoral Studies}, volume = {95}, journal = {Electoral Studies}, publisher = {Elsevier BV}, doi = {10.1016/j.electstud.2025.102939}, abstract = {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.}, language = {en} }