Bayesian forecasting of electoral outcomes with new parties' competition

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• dc.contributor.author Garcia Montalvo, José
• dc.contributor.author Papaspiliopoulos, Omiros
• dc.contributor.author Stumpf-Fétizon, Timothée
• dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
• dc.date.accessioned 2020-05-25T09:26:45Z
• dc.date.available 2020-05-25T09:26:45Z
• dc.date.issued 2018-12-01
• dc.date.modified 2020-05-25T09:25:43Z
• dc.description.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 specic 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 specications, 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.
• dc.format.mimetype application/pdf*
• dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=1624
• dc.identifier.citation
• dc.identifier.uri http://hdl.handle.net/10230/44684
• dc.language.iso eng
• dc.relation.ispartofseries Economics and Business Working Papers Series; 1624
• dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
• dc.subject.keyword multilevel models
• dc.subject.keyword bayesian machine learning
• dc.subject.keyword inverse regression
• dc.subject.keyword evidence synthesis
• dc.subject.keyword elections
• dc.title Bayesian forecasting of electoral outcomes with new parties' competition
• dc.title.alternative
• dc.type info:eu-repo/semantics/workingPaper