Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-05T16:26:05.808Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected]
*
e-mail: [email protected] (corresponding author)
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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.

References

Alesina, Alberto, and Rosenthal, Howard. 1995. Partisan politics, divided government, and the economy. Cambridge: Cambridge University Press.Google Scholar
Armstrong, J. Scott. 1985. Long range forecasting. 2nd ed. http://www.forecastingprinciples.com/.Google Scholar
Campbell, James E., and Lewis-Beck, Michael. 2008. US presidential election forecasting: An introduction. International Journal of Forecasting 24: 189–92.Google Scholar
Claudy, John G. 1972. A comparison of five variable weighting procedures. Educational and Psychological Measurement 32: 311–22.Google Scholar
Cuzán, Alfred G., and Bundrick, Charles M. 2005. Fair's presidential vote equation and the fiscal model compared. Paper presented at the International Symposium on Forecasting, San Antonio, TX. http://uwf.edu/govt/CuzanPublications.htmCuzán.Google Scholar
Cuzán, Alfred G., and Bundrick, Charles M. 2008. Forecasting the 2008 presidential election. A challenge for the fiscal model. PS: Political Science and Politics 41: 717–22.Google Scholar
Cuzán, Alfred G., and Heggen, Richard J. 1984. A fiscal model of presidential elections in the United States, 1880–1980. Presidential Studies Quarterly XIV: 98–108.Google Scholar
Cuzán, Alfred G., Heggen, Richard J., and Bundrick, Charles M. 2009. Fiscal policy in American presidential elections: A simulation. Simulation 85: 17–32.CrossRefGoogle Scholar
Dana, Jason, and Dawes, Robyn M. 2004. The superiority of simple alternatives for social science predictions. Journal of Educational and Behavioral Statistics 29: 317–31.CrossRefGoogle Scholar
Dawes, Robyn M. 1979. The robust beauty of improper linear models in decision making. American Psychologist 34: 571–82.Google Scholar
Dawes, Robyn M., and Corrigan, Bernard. 1974. Linear models in decision making. Psychological Bulletin 81: 95–106.Google Scholar
Einhorn, Hillel J., and Hogarth, Robin M. 1975. Unit weighting schemes for decision making. Organizational Behavior and Human Performance 13: 171–92.Google Scholar
Fair, Ray C. 1978. The effect of economic events on votes for president. The Review of Economics and Statistics 60: 159–73.CrossRefGoogle Scholar
Fair, Ray C. 2006. The effect of economic events on votes for president: 2004 update. Fair, Ray C., Presidential Vote Equation Page: http://fairmodel.econ.yale.edu/RAYFAIR/PDF/2006CHTM.HTM.Google Scholar
Hogarth, Robin M. 2006. On ignoring scientific evidence: The bumpy road to enlightenment. Universita Pompeu Fabra: Working Paper 973. Forthcoming under the title “When simple is hard to accept.” In Todd, P. M., Gigerenzer, G., and The ABC Research Group, ed. Ecological rationality: Intelligence in the world. Oxford: Oxford University Press.Google Scholar
Jones, Randall J. Jr. 2008. The state of presidential election forecasting: The 2004 experience. International Journal of Forecasting 24: 308–19.Google Scholar
Nordhaus, William. 2006. Electoral victory and statistical defeat? Economics, politics, and the 2004 presidential election. Quarterly Journal of Political Science 1: 313–22.Google Scholar
Schmidt, Frank L. 1971. The relative efficiency of regression and simple unit predictor weights in applied differential psychology. Educational and Psychological Measurement 31: 699–714.CrossRefGoogle Scholar
Weiner, Howard. 1976. Estimating coefficients in linear models: It don't make no nevermind. Psychological Bulletin 83: 213–7.Google Scholar