Hierarchical Bayes Prediction for the 2008 US Presidential Election
AbstractIn 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 InfoArticle provided by University of Buckingham Press in its journal Journal of Prediction Markets.
Volume (Year): 2 (2008)
Issue (Month): 3 (December)
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Web page: http://www.ubpl.co.uk/
Other versions of this item:
- Sinha, Pankaj & Bansal, Ashok, 2008. "Hierarchical Bayes prediction for the 2008 US Presidential election," MPRA Paper 10470, University Library of Munich, Germany.
- 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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- 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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