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Hierarchical Bayes Prediction for the 2008 US Presidential Election

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  • Pankaj Sinha
  • Ashok K. Bansal

Abstract

In this paper a procedure is developed to derive the predictive density function of a future observation for prediction in a multiple regression model under hierarchical priors for the vector parameter. The derived predictive density function is applied for prediction in a multiple regression model given in Fair (2002) to study the effect of fluctuations in economic variables on voting behavior in U.S. presidential election. Numerical illustrations suggest that the predictive performance of Fair's model is good under hierarchical Bayes setup, except for the 1992 election. Fair's model under hierarchical Bayes setup indicates that the forthcoming 2008 US presidential election is likely to be a very close election slightly tilted towards Republicans. It is likely that republicans will get 50.90% vote with probability for win 0.550 in the 2008 US presidential election.

Suggested Citation

  • Pankaj Sinha & Ashok K. Bansal, 2008. "Hierarchical Bayes Prediction for the 2008 US Presidential Election," Journal of Prediction Markets, University of Buckingham Press, vol. 2(3), pages 47-59, December.
    • Handle: RePEc:buc:jpredm:v:2:y:2008:i:3:p:47-59
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    References listed on IDEAS

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    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. Douglas Hibbs, 2000. "Bread and Peace Voting in U.S. Presidential Elections," Public Choice, Springer, vol. 104(1), pages 149-180, July.
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    Cited by:

    1. 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.
    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. 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.
    4. 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.
    5. 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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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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