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Forecasting Elections from Voters’ Perceptions of Candidates’ Ability to Handle Issues

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Author Info
Graefe, Andreas
Armstrong, J. Scott

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Abstract

Ideally, presidential elections should be decided based on how the candidates would handle issues facing the country. If so, knowledge about the voters’ perception of the candidates should help to forecast election outcomes. Our model, named PollyIssues, provides a forecast of the winner of the popular vote in U.S. Presidential Elections. It is based on the voters’ overall perception of which candidate will do the best job in handling the issues facing the country. PollyIssues correctly picked the winner for nine of the last ten elections from 1972 to 2008, with one tie. In addition, it provided an idea of the margin of victory. In predicting the two-party vote percentages for the last three elections from 2000 to 2008, its out-of-sample forecasts outperformed those derived from well-established econometric models.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 9829.

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Date of creation: 04 Aug 2008
Date of revision: 27 Jan 2009
Handle: RePEc:pra:mprapa:9829

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Related research
Keywords: forecasting methods; regression models; index method; experience tables; accuracy; in-sample; out-of-sample;

Other versions of this item:

Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C5 - Mathematical and Quantitative Methods - - Econometric Modeling
D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Models of Political Processes: Rent-seeking, Elections, Legislatures, and Voting Behavior

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Lichtman, Allan J., 2008. "The keys to the white house: An index forecast for 2008," International Journal of Forecasting, Elsevier, vol. 24(2), pages 301-309. [Downloadable!] (restricted)
  2. S. Wilks, 1938. "Weighting systems for linear functions of correlated variables when there is no dependent variable," Psychometrika, Springer, vol. 3(1), pages 23-40, March. [Downloadable!] (restricted)
  3. Armstrong, J. Scott & Green, Kesten C. & Jones, Randall J. & Wright, Malcolm, 2008. "Predicting elections from politicians’ faces," MPRA Paper 9150, University Library of Munich, Germany. [Downloadable!]
  4. 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 159-73, May. [Downloadable!] (restricted)
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  5. Randall J. Jones, Jr. & Alfred G. Cuzán, 2008. "Forecasting U.S. Presidential Elections: A Brief Review," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 10, pages 29-34, Summer. [Downloadable!]
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Armstrong, J. Scott & Graefe, Andreas, 2009. "Predicting Elections from Biographical Information about Candidates," MPRA Paper 16461, University Library of Munich, Germany, revised 07 Oct 2009. [Downloadable!]
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