Predicting Elections from Biographical Information about Candidates

J. Armstrong and Andreas Graefe ()

MPRA Paper from University Library of Munich, Germany

Abstract: Using the index method, we developed the PollyBio model to predict election outcomes. The model, based on 49 cues about candidates’ biographies, was used to predict the outcome of the 28 U.S. presidential elections from 1900 to 2008. In using a simple heuristic, it correctly predicted the winner for 25 of the 28 elections and was wrong three times. In predicting the two-party vote shares for the last four elections from 1996 to 2008, the model’s out-of-sample forecasts yielded a lower forecasting error than 12 benchmark models. By relying on different information and including more variables than traditional models, PollyBio improves on the accuracy of election forecasting. It is particularly helpful for forecasting open-seat elections. In addition, it can help parties to select the candidates running for office.

Keywords: forecasting; unit weighting; Dawes rule; differential weighting (search for similar items in EconPapers)
JEL-codes: C53 D72 (search for similar items in EconPapers)
Date: 2009-06-23
New Economics Papers: this item is included in nep-cdm, nep-ecm, nep-for and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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https://mpra.ub.uni-muenchen.de/16461/1/MPRA_paper_16461.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/16702/2/MPRA_paper_16702.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/17709/1/MPRA_paper_17709.pdf revised version (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:16461

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