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Predicting Presidential Elections with Equally Weighted Regressors in Fair's Equation and the Fiscal Model

Published online by Cambridge University Press:  04 January 2017

Alfred G. Cuzán  and
Charles M. Bundrick
Alfred G. Cuzán*
Affiliation:
Department of Government, The University of West Florida, Pensacola, FL 32514
Charles M. Bundrick
Affiliation:
Department of Mathematics and Statistics, The University of West Florida, Pensacola, FL 32514. e-mail: mbundric@uwf.edu
*
e-mail: acuzan@uwf.edu (corresponding author)
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Abstract

Three-decade-old research suggests that although regression coefficients obtained with ordinary least squares (OLS) are optimal for fitting a model to a sample, unless the N over which the model was estimated is large, they are generally not very much superior and frequently inferior to equal weights or unit weights for making predictions in a validating sample. Yet, that research has yet to make an impact on presidential elections forecasting, where models are estimated with fewer than 25 elections, and often no more than 15. In this research note, we apply equal weights to generate out-of-sample and one-step-ahead predictions in two sets of related presidential elections models, Fair's presidential equation and the fiscal model. We find that most of the time, using equal weights coefficients does improve the forecasting performance of both.

Type
Research Article
Information
Political Analysis , Volume 17 , Issue 3 , Summer 2009 , pp. 333 - 340
Copyright
Copyright © The Author 2009. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: Many thanks to J. Scott Armstrong, Ray Fair, Robin Hogarth, Robyn Dawes, Randall J. Jones, Jr., Geoffrey Allen, and James E. Campbell for their comments, suggestions, or encouragement. Thanks, also, to two anonymous reviewers for their criticisms and suggestions. An on-line data appendix is available on the Political Analysis Web site.

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