Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals

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.

The Zweitstimme Model: A Dynamic Forecast of the 2021 German Federal Election

“…As a synthetic forecasting model, the Zweitstimme model has two components (for a more detailed description, see Munzert et al 2017 andStoetzer et al 2019). The mathematical details of this dynamic Bayesian measurement model are described in Stoetzer et al (2019). For this symposium, we summarize the model's components.…”

“…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 2017;Stoetzer et al 2019; see also http://zweitstimme.org). 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.…”

Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach

“…The problem of understanding and predicting election outcomes has long been part of political science research. However, lack of pre-election poll data especially in developing countries is one of the main challenges associated with forecasting election outcomes [1][2][3][4][5][6][7][8]. Nevertheless, in developed countries opinion polls are now easily accessible through online polling.…”

A Case Study of Forecasting Elections Results: Beyond Prediction based on Business Intelligence

“…The application of AI and statistical methods to electoral forecasting has been widely studied by the scientific community. Stoetzer [1] offer a dynamic Bayesian forecasting model for multiparty elections. It combines data from published pre-election public opinion polls with information from fundamentalsbased forecasting models.…”