Bayesian Forecasting of Electoral Outcomes with new Parties’ Competition

• Authors: Timothée Stumpf-Fétizon, Omiros Papaspiliopoulos and José García-Montalvo.
• BSE Working Paper: 110537 | December 18
• Keywords: elections , multilevel model , Bayesian machine learning , inverse regression , evidence synthesis
• JEL codes: C11, C53, C63, D72
• elections
• multilevel model
• Bayesian machine learning
• inverse regression
• evidence synthesis

We propose a new methodology for predicting electoral results that combines a fundamental model and national polls within an evidence synthesis framework. Although novel, the methodology builds upon basic statistical structures, largely modern analysis of variance type models, and it is carried out in open-source software. The methodology is largely motivated by the specific challenges of forecasting elections with the participation of new political parties, which is becoming increasingly common in the post-2008 European panorama. Our methodology is also particularly useful for the allocation of parliamentary seats, since the vast majority of available opinion polls predict at the national level whereas seats are allocated at local level. We illustrate the advantages of our approach relative to recent competing approaches using the 2015 Spanish Congressional Election. In general, the predictions of our model outperform the alternative specifications, including hybrid models that combine fundamental and polls’ models. Our forecasts are, in relative terms, particularly accurate to predict the seats obtained by each political party.