Predicting Elections from Biographical Information About Candidates: A Test of the Index Method

43 Pages Posted: 9 Aug 2010  

J. Scott Armstrong

University of Pennsylvania - Marketing Department

Andreas Graefe

Ludwig Maximilians University of Munich - Department of Communication Science and Media Research

Date Written: March 1, 2010

Abstract

We used 59 biographical variables to create a “bio-index” for forecasting U.S. presidential elections. The bio-index method counts the number of variables for which a candidate rates favourably, and the forecast is that the candidate with the highest score would win the popular vote. The bio-index relies on different information and includes more variables than traditional econometric election forecasting models. The method can be used in combination with simple linear regression to estimate a relationship between the index score of the candidate of the incumbent party and his share of the popular vote. The study tested the model for the 29 U.S. presidential elections from 1896 to 2008. The model’s forecasts, calculated by cross-validation, correctly predicted the popular vote winner for 27 of the 29 elections; this performance compares favourably to forecasts from polls (15 out of 19), prediction markets (22 out of 26), and three econometric models (12 to 13 out of 15 to 16). Out-of-sample forecasts of the two-party popular vote for the four elections from 1996 to 2008 yielded a forecast error almost as low as the best of seven econometric models. The model can help parties to select the candidates running for office, and it can help to improve on the accuracy of election forecasting, especially for longer-term forecasts.

Keywords: econometric model, election forecasts, forecast accuracy, index model, political forecasting political marketing, unit-weighting

Suggested Citation

Armstrong, J. Scott and Graefe, Andreas, Predicting Elections from Biographical Information About Candidates: A Test of the Index Method (March 1, 2010). Journal of Business Research, Forthcoming. Available at SSRN: https://ssrn.com/abstract=1656060

J. Scott Armstrong (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-5087 (Phone)
215-898-2534 (Fax)

HOME PAGE: http://marketing.wharton.upenn.edu/people/faculty/armstrong.cfm

Andreas Graefe

Ludwig Maximilians University of Munich - Department of Communication Science and Media Research ( email )

Geschwister-Scholl-Platz 1
Munich, Bavaria 80539
Germany

HOME PAGE: http://www.andreas-graefe.org

Paper statistics

Downloads
61
Rank
292,995
Abstract Views
975