IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v24y2008i2p301-309.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169-2070(08)00025-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    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. 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. 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. 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.
    10. Armstrong, J. Scott & Graefe, Andreas, 2009. "Predicting Elections from Biographical Information about Candidates," MPRA Paper 16461, University Library of Munich, Germany.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.

    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. 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. 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.

    More about this item

    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:eee:intfor:v:24:y:2008:i:2:p:301-309. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.