Bayesian Forecasting of Electoral Outcomes with new Parties' Competition
Omiros Papaspiliopoulos and
No 1065, Working Papers from Barcelona Graduate School of Economics
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.
Keywords: multilevel model; Bayesian machine learning; inverse regression; evidence synthesis; elections (search for similar items in EconPapers)
JEL-codes: C11 C53 C63 D72 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cdm, nep-cmp, nep-for and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bge:wpaper:1065
Access Statistics for this paper
More papers in Working Papers from Barcelona Graduate School of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Bruno Guallar ().