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The keys to the white house: An index forecast for 2008

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  • Lichtman, Allan J.

Abstract

The Keys to the White House are an index-based prediction system that retrospectively accounts for the popular-vote winners of every American presidential election from 1860 to 1980, and prospectively forecast the winners of every presidential election from 1984 through 2004 well ahead of time. The Keys give specificity to the theory that presidential election results turn primarily on the performance of the party controlling the White House. The Keys include no polling data and consider a much wider range of performance indicators than economic concerns. Already, the Keys are lining up for 2008, showing how changes in the structure of politics will produce a Democratic victory, in a dramatic reversal from 2004. The Keys also suggest that candidates need not follow the empty scripted campaigns of the recent past, but should instead be liberated to offer forthright discussions of the issues and ideas that will shape America's future.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:intfor:v:24:y:2008:i:2:p:301-309
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    1. 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.
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    Cited by:

    1. Cote, Joseph A., 2011. "Predicting elections from biographical information about candidates: A commentary essay," Journal of Business Research, Elsevier, vol. 64(7), pages 696-698, July.
    2. 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.
    3. 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.
    4. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
    5. repec:cup:judgdm:v:6:y:2011:i:1:p:73-88 is not listed on IDEAS
    6. Graefe, Andreas, 2023. "Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)," International Journal of Forecasting, Elsevier, vol. 39(1), pages 170-177.
    7. Graefe, Andreas, 2015. "Improving forecasts using equally weighted predictors," Journal of Business Research, Elsevier, vol. 68(8), pages 1792-1799.
    8. 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.
    9. Armstrong, J. Scott & Graefe, Andreas, 2009. "Predicting Elections from Biographical Information about Candidates," MPRA Paper 16461, University Library of Munich, Germany.
    10. 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.
    11. Wolfgang Gaissmaier & Julian N. Marewski, 2011. "Forecasting elections with mere recognition from small, lousy samples: A comparison of collective recognition, wisdom of crowds, and representative polls," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(1), pages 73-88, February.
    12. 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.
    13. 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.
    14. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzan, Alfred G., 2017. "Assessing the 2016 U.S. Presidential Election Popular Vote Forecasts," MPRA Paper 83282, University Library of Munich, Germany.
    15. 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.
    16. 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.

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    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. Cote, Joseph A., 2011. "Predicting elections from biographical information about candidates: A commentary essay," Journal of Business Research, Elsevier, vol. 64(7), pages 696-698, July.

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