Bayesian forecasting of electoral outcomes with new parties' competition

TL;DR

This paper introduces a Bayesian hierarchical model for electoral outcome forecasting that effectively combines fundamental data and poll information, especially useful when new parties emerge, demonstrated through the 2015 Spanish election.

Contribution

The paper presents a novel Bayesian hierarchical approach that integrates fundamental and poll data for electoral predictions, adaptable to new parties and frequent updates.

Findings

Model outperforms alternative methods in predictive accuracy.

Particularly accurate in seat allocation predictions.

Effective in elections with new parties entering the race.

Abstract

This paper proposed a methodology to forecast electoral outcomes using the result of the combination of a fundamental model and a model-based aggregation of polls. We propose a Bayesian hierarchical structure for the fundamental model that synthesises data at the provincial, regional and national level. We use a Bayesian strategy to combine the fundamental model with the information coming for recent polls. This model can naturally be updated every time new information, for instance a new poll, becomes available. This methodology is well suited to deal with increasingly frequent situations in which new political parties enter an electoral competition, although our approach is general enough to accommodate any other electoral situation. We illustrate the advantages of our method using the 2015 Spanish Congressional Election in which two new parties ended up receiving 30\% of the votes. We compare the predictive performance of our model versus alternative models. In general the predictions of our model outperform the alternative specifications, including hybrid models that combine fundamental and polls models. Our predictions are, in relative terms, particularly accurate in predicting the seats obtained by each political party.