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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.

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Bibliographic Info

Article provided by University of Buckingham Press in its journal Journal of Prediction Markets.

Volume (Year): 2 (2008)
Issue (Month): 3 (December)
Pages: 47-59

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Handle: RePEc:buc:jpredm:v:2:y:2008:i:3:p:47-59

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  1. Hibbs Jr., Douglas A., 2000. "Bread and Peace Voting in U.S. Presidential Elections," Working Papers in Economics 20, University of Gothenburg, Department of Economics.
  2. Ray C. Fair, 1976. "The Effects of Economic Events on Votes for President," Cowles Foundation Discussion Papers 418, Cowles Foundation for Research in Economics, Yale University.
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Cited by:
  1. 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.
  2. Sinha, Pankaj & Sharma, Aastha & Singh, Harsh Vardhan, 2012. "Prediction for the 2012 United States Presidential Election using Multiple Regression Model," MPRA Paper 41486, University Library of Munich, Germany.

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