Forecasting Presidential Elections: A Comparative Study of Two Multivariate Models
The primary aim of this paper is to study the developing science of election forecasting and determine the two best models created by scholars in this field and update them with current data. After choosing models by Alan Abramowitz and Ray Fair, the equations were updated using time-series data ranging from the early 20111 century until the present. Both Abramowitz's and Fair's models ex post facto predicted elections on average more accurately than the Gallup poll conducted closest to the election time and are still considered valuable tools-although Fair's model performed more consistently against the test of time. Using these updated regressions, I created conditional forecasts for each model based on an array of economic and popularity rating indicators. Finally, I created my own prediction-based on the results of the two models-that forecasts a crushing defeat for the incumbent Republicans.
U.S. copyright laws protect this material. Commercial use or distribution of this material is not permitted without prior written permission of the copyright holder.