Paper #1624

Title:

Bayesian forecasting of electoral outcomes with new parties' competition

Authors:

José Garcia Montalvo, Omiros Papaspiliopoulos and Timothée Stumpf-Fétizon

Date:

December 2018

Abstract:

We propose a new methodology for predicting electoral results that com- bines 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 car- ried out in open-source software. The methodology is largely motivated by the specic 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 al- location 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 specications, 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.

Keywords:

Multilevel models, Bayesian machine learning, inverse regression, evidence synthesis, elections

Area of Research:

Political Economy

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