Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

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

Alfred G. Cuzán and Charles M. Bundrick
Political Analysis
Vol. 17, No. 3 (Summer 2009), pp. 333-340
Stable URL: http://www.jstor.org/stable/25791978
Page Count: 8
  • Download ($42.00)
  • Cite this Item
Predicting Presidential Elections with Equally Weighted Regressors in Fair's Equation and the Fiscal Model
Preview not available

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.

Page Thumbnails

  • Thumbnail: Page 
333
    333
  • Thumbnail: Page 
334
    334
  • Thumbnail: Page 
335
    335
  • Thumbnail: Page 
336
    336
  • Thumbnail: Page 
337
    337
  • Thumbnail: Page 
338
    338
  • Thumbnail: Page 
339
    339
  • Thumbnail: Page 
340
    340