IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/9829.html
   My bibliography  Save this paper

Forecasting Elections from Voters’ Perceptions of Candidates’ Positions on Issues and Policies

Author

Listed:
  • Graefe, Andreas
  • Armstrong, J. Scott

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. We make two forecasts of the winner of the popular vote in the U.S. Presidential Election. One is based on voters’ perceptions of how the candidates would deal with issues (problems facing the country) if elected. We show that this approach would have correctly picked the winner for the three elections from 1996 to 2004. The other is based on voters’ preference for policies and their perceptions of which policies the candidates are likely to pursue. Both approaches lead to a forecast that Democrat candidate Barack Obama will win the popular vote.

Suggested Citation

  • Graefe, Andreas & Armstrong, J. Scott, 2008. "Forecasting Elections from Voters’ Perceptions of Candidates’ Positions on Issues and Policies," MPRA Paper 9829, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:9829
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/9829/1/MPRA_paper_9829.pdf
    File Function: original version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/10133/1/MPRA_paper_10133.pdf
    File Function: revised version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/11729/1/MPRA_paper_11729.pdf
    File Function: revised version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/11730/1/MPRA_paper_11730.pdf
    File Function: revised version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/13079/3/MPRA_paper_13079.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. S. Wilks, 1938. "Weighting systems for linear functions of correlated variables when there is no dependent variable," Psychometrika, Springer;The Psychometric Society, vol. 3(1), pages 23-40, March.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Graefe, Andreas, 2015. "Improving forecasts using equally weighted predictors," Journal of Business Research, Elsevier, vol. 68(8), pages 1792-1799.
    3. Armstrong, J. Scott & Graefe, Andreas, 2009. "Predicting Elections from Biographical Information about Candidates," MPRA Paper 16461, University Library of Munich, Germany.
    4. Graefe, Andreas & Armstrong, J. Scott, 2011. "Conditions under which index models are useful: Reply to bio-index commentaries," Journal of Business Research, Elsevier, vol. 64(7), pages 693-695, July.
    5. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Armstrong, J. Scott & Graefe, Andreas, 2011. "Predicting elections from biographical information about candidates: A test of the index method," Journal of Business Research, Elsevier, vol. 64(7), pages 699-706, July.
    2. Sinha, Pankaj & Srinivas, Sandeep & Paul, Anik & Chaudhari, Gunjan, 2016. "Forecasting 2016 US Presidential Elections Using Factor Analysis and Regression Model," MPRA Paper 74618, University Library of Munich, Germany, revised 17 Oct 2016.
    3. Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020. "Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression," MPRA Paper 103889, University Library of Munich, Germany, revised 31 Oct 2020.
    4. Pankaj Sinha & Aastha Sharma & Harsh Vardhan Singh, 2012. "Prediction For The 2012 United States Presidential Election Using Multiple Regression Model," Journal of Prediction Markets, University of Buckingham Press, vol. 6(2), pages 77-97.
    5. Armstrong, J. Scott & Graefe, Andreas, 2009. "Predicting Elections from Biographical Information about Candidates," MPRA Paper 16461, University Library of Munich, Germany.
    6. Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020. "Prediction for the 2020 United States Presidential Election using Linear Regression Model," MPRA Paper 103890, University Library of Munich, Germany, revised 20 Oct 2020.
    7. Sinha, Pankaj & Thomas, Ashley Rose & Ranjan, Varun, 2012. "Forecasting 2012 United States Presidential election using Factor Analysis, Logit and Probit Models," MPRA Paper 42062, University Library of Munich, Germany.
    8. Sinha, Pankaj & verma, Kaushal & Biswas, Sumana & Tyagi, Shashank & Gogia, Shaily & Singh, Aakhyat & Kumar, Amit, 2024. "Modeling and forecasting US presidential election 2024," MPRA Paper 122319, University Library of Munich, Germany, revised 08 Oct 2024.
    9. Hibbs, Douglas A, Jr, 2000. "Bread and Peace Voting in U.S. Presidential Elections," Public Choice, Springer, vol. 104(1-2), pages 149-180, July.
    10. Jeroen Klomp, 2020. "Election or Disaster Support?," Journal of Development Studies, Taylor & Francis Journals, vol. 56(1), pages 205-220, January.
    11. Janet Pack, 1987. "The political policy cycle: Presidential effort vs. presidential control," Public Choice, Springer, vol. 54(3), pages 231-259, August.
    12. Boryana Dimitrova, 2000. "An Empirical Model of Voting Behavior in the Bulgarian Parliamentary Elections of 1994," The American Economist, Sage Publications, vol. 44(2), pages 71-77, October.
    13. Paola Assael & Felipe Larraín, 1994. "El Ciclo Político-económico: Teoría, Evidencia y Extensión para una Economía Abierta," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 31(92), pages 87-114.
    14. Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2007. "Partisan Impacts on the Economy: Evidence from Prediction Markets and Close Elections," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(2), pages 807-829.
    15. Cáceres, Neila & Malone, Samuel W., 2015. "Optimal Weather Conditions, Economic Growth, and Political Transitions," World Development, Elsevier, vol. 66(C), pages 16-30.
    16. Candel-Sanchez, Francisco, 2007. "Incentives for budget discipline in the presence of elections," European Journal of Political Economy, Elsevier, vol. 23(4), pages 863-884, December.
    17. Brown, Craig O., 2020. "Economic leadership and growth," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 298-333.
    18. Alberto Alesina & Roberto Perotti & José Tavares, 1998. "The Political Economy of Fiscal Adjustments," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 29(1), pages 197-266.
    19. Larcinese, Valentino & Puglisi, Riccardo & Snyder, James M., 2011. "Partisan bias in economic news: Evidence on the agenda-setting behavior of U.S. newspapers," Journal of Public Economics, Elsevier, vol. 95(9), pages 1178-1189.
    20. Ray Fair, 2007. "Presidential and Congressional Vote-Share Equations," Yale School of Management Working Papers amz2389, Yale School of Management, revised 18 Mar 2007.

    More about this item

    Keywords

    forecasting methods; regression models; index method; experience tables; accuracy;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:9829. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.