Sinha, Pankaj and Bansal, Ashok (2008): Hierarchical Bayes prediction for the 2008 US Presidential election.

PDF
MPRA_paper_10470.pdf Download (205kB)  Preview 
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.
Item Type:  MPRA Paper 

Original Title:  Hierarchical Bayes prediction for the 2008 US Presidential election 
Language:  English 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics 
Item ID:  10470 
Depositing User:  Pankaj Sinha 
Date Deposited:  15. Sep 2008 05:55 
Last Modified:  12. Feb 2013 20:36 
References:  Aitchison, J. and Dunsmore, I. R. (1975) Statistical Prediction Analysis, Cambridge, Cambridge University Press. Berger, J. O. (1985) Statistical Decision Theory and Bayesian Analysis Springer, New York. Berger, J. O. & Berliner, M. (1986): Robust Bayes and empirical Bayes analysis with Îcontaminated priors, The Annals of Statistics, 14 (2), pp. 461486. Berry, B., Elliot, E., and Harpham, E. J. (1996) The yield curve as an electoral bellwether, Technical forecasting and social change, 51, pp. 281294. Erikson, R. S., and Wlezien, C. (1996) Of time and presidential election forecasts PS: Political Science and politics, 31, pp. 3739. Fair, R. C. (1978) The effect of economic events on votes for president, Review of Economics and Statistics, 60, pp. 159173. Fair, R. C. (1996) The effect of economic events on votes for president: 1992 update, Political Behavior, 18, pp. 119139 Fair, R. C. (2002) Predicting Presidential Elections and Other Things, Stanford: Stanford University Press. Fair, R. C. (2004) A vote equation and the2004 election, Website: http:// fairmodel.econ.yale.edu/vote2004 Gleisner, R. F. (1992) Economic developments of presidential election: The Fair model, Political Behavior, 14, pp. 383394. Gleisner, R. F. (2005) Comments for presentation at the roundtable on Fair’s presidential vote equation in International Symposium on forecasting, San Antonio, June 14, 2005 Hastings, C. (1955) Approximation for Digital Computers, Princeton, NJ, Princeton Uiversity Press. Hibbs, D. A. (2000) Bread and peace voting in U.S. presidential election, Public Choice, 104, pp. 149180. Lindley, D. V. and Smith, A. F. M.(1972) Bayes estimates for the linear model, (with discussion). Journal of Royal Statistical Society, B 34, pp. 141. Polasek, W. (1984) Multivariate regression systems: Estimation and sensitivity analysis for two dimensional data. Robustness in Bayesian Statistics, (J. Kadane, ed.) Amsterdam: NorthHolland, pp. 141. Polasek, W. and Potzelberger, K. (1988) Robust Bayesian Analysis in Hierarchical Models, Bayesian Statistics 3, Oxford University Press, pp. 377394 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/10470 