Assessing the 2016 U.S. Presidential Election Popular Vote Forecasts

Andreas Graefe, J. Armstrong, Randall J. Jones and Alfred G. Cuzan

MPRA Paper from University Library of Munich, Germany

Abstract: The PollyVote uses evidence-based techniques for forecasting the popular vote in presidential elections. The forecasts are derived by averaging existing forecasts generated by six different forecasting methods. In 2016, the PollyVote correctly predicted that Hillary Clinton would win the popular vote. The 1.9 percentage-point error across the last 100 days before the election was lower than the average error for the six component forecasts from which it was calculated (2.3 percentage points). The gains in forecast accuracy from combining are best demonstrated by comparing the error of PollyVote forecasts with the average error of the component methods across the seven elections from 1992 to 2012. The average errors for last 100 days prior to the election were: public opinion polls (2.6 percentage points), econometric models (2.4), betting markets (1.8), and citizens’ expectations (1.2); for expert opinions (1.6) and index models (1.8), data were only available since 2004 and 2008, respectively. The average error for PollyVote forecasts was 1.1, lower than the error for even the most accurate component method.

Keywords: election; forecasting; voting (search for similar items in EconPapers)
JEL-codes: C53 D72 (search for similar items in EconPapers)
Date: 2017-02-07
New Economics Papers: this item is included in nep-cdm, nep-for and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/83282/1/MPRA_paper_83282.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:83282

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().