Bayesian forecasting of electoral outcomes with new parties’ competition

Abstract

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 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 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 in predicting the seats obtained by each political party.

Multi-aspect multilingual and cross-lingual parliamentary speech analysis

“…Han [25] explored the performance of spatial voting models to the roll calls of the EU parliament. Montalvo et al [43] proposed a new methodology for predicting electoral results that combines a fundamental model and national polls within an evidence synthesis framework.…”

Forecasting two-horse races in new democracies: Accuracy, precision and error

“…It is built in the tradition of Jackman, who was among the first to use Bayesian methods to simulate the "true" state of an election using just polls (Jackman, 2005). But it also takes into account the body of literature that has since been developed in North America (Linzer, 2013;Lock & Gelman, 2010;Pickup & Johnston, 2007;Rigdon et al, 2009), the United Kingdom (Fisher & Lewis-Beck, 2015;Hanretty et al, 2016;Whiteley et al, 2016), and continental Europe (Bodell, 2016;Montalvo et al, 2019;Stoetzer et al, 2019;Stoltenberg, 2013;Walther, 2015).…”

Scalable computation for Bayesian hierarchical models

“…Hence, it is interesting to synthesize them with national polls carried out by media outlets and institutions. Montalvo et al (2019) propose such a Bayesian evidence synthesis framework, a component of which is the electoral survey crossed effect model, which we focus upon here. For the analysis in this paper we focus on a simplified data structure with L = 3 parties, K = 7 input variables, and p = 140 regression parameters overall (due to the identifiability constraint there are 2 per level, and I = 70 levels overall).…”