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Journal article
Predicting presidential elections with equally weighted regressors in Fair's equation and the Fiscal Model
Political analysis, Vol.17, pp.333-340
17
2009
Web of Science ID: WOS:000267889400007
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.
Details
- Title
- Predicting presidential elections with equally weighted regressors in Fair's equation and the Fiscal Model
- Publication Details
- Political analysis, Vol.17, pp.333-340
- Resource Type
- Journal article
- Publisher
- Oxford University Press on behalf of the Society for Political Methodology; United Kingdom
- Series
- 17
- Format
- pdf
- Copyright
- © The Author 2009.
- Identifiers
- WOS:000267889400007; 99380090330006600
- Academic Unit
- College of Arts, Social Sciences, and Humanities; Reubin O'D. Askew Department of Government
- Language
- English