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Predicting elections from biographical information about candidates: A test of the index method

Listed author(s):
  • Armstrong, J. Scott
  • Graefe, Andreas

This study uses 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 each candidate rates favorably, 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 is useful 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 tests 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 favorably 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 help to improve on the accuracy of election forecasting, especially for longer-term forecasts.

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File URL: http://www.sciencedirect.com/science/article/pii/S0148-2963(10)00156-6
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Article provided by Elsevier in its journal Journal of Business Research.

Volume (Year): 64 (2011)
Issue (Month): 7 (July)
Pages: 699-706

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Handle: RePEc:eee:jbrese:v:64:y:2011:i:7:p:699-706
Contact details of provider: Web page: http://www.elsevier.com/locate/jbusres

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  1. Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2007. "Partisan Impacts on the Economy: Evidence from Prediction Markets and Close Elections," The Quarterly Journal of Economics, Oxford University Press, vol. 122(2), pages 807-829.
  2. Cuzán, Alfred G. & Bundrick, Charles M., 2009. "Predicting Presidential Elections with Equally Weighted Regressors in Fair's Equation and the Fiscal Model," Political Analysis, Cambridge University Press, vol. 17(03), pages 333-340, June.
  3. Fair, Ray C, 1978. "The Effect of Economic Events on Votes for President," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 159-173, May.
  4. Amy King & Andrew Leigh, 2009. "Beautiful Politicians," Kyklos, Wiley Blackwell, vol. 62(4), pages 579-593, November.
  5. Berggren, Niclas & Jordahl, Henrik & Poutvaara, Panu, 2010. "The looks of a winner: Beauty and electoral success," Journal of Public Economics, Elsevier, vol. 94(1-2), pages 8-15, February.
  6. Nelson, James, 2005. "Corporate governance practices, CEO characteristics and firm performance," Journal of Corporate Finance, Elsevier, vol. 11(1-2), pages 197-228, March.
  7. Jason Dana & Robyn M. Dawes, 2004. "The Superiority of Simple Alternatives to Regression for Social Science Predictions," Journal of Educational and Behavioral Statistics, , vol. 29(3), pages 317-331, September.
  8. Berg, Joyce E. & Nelson, Forrest D. & Rietz, Thomas A., 2008. "Prediction market accuracy in the long run," International Journal of Forecasting, Elsevier, vol. 24(2), pages 285-300.
  9. Allan Lichtman, 2006. "Keys to the White House: Forecast for 2008," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 3, pages 5-9, February.
  10. Paul W. Rhode & Koleman S. Strumpf, 2004. "Historical Presidential Betting Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 127-141, Spring.
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