Improving Causal Models for Election Forecasting: Further Evidence on the Golden Rule of Forecasting

Abstract

The Golden Rule of Forecasting counsels forecasters to be conservative when making forecasts. We tested the value of three of the four Golden Rule guidelines that apply to causal models: modify effect estimates to reflect uncerainty; use all important variables; and combine forecasts from diverse models. These guidelines were tested using out-of-sample forecasts from eight US presidential election forecasting models across the 15 elections from 1956 to 2012. Moderating effect sizes via equalizing regression coefficients reduced the error relative to the original model forecasts by 5%. Including all 25 variables from the eight models in a single equal-weights index model reduced error by 46%, and combining forecasts from the eight models reduced error by 36%.

Keywords: Econometrics, election forecasting, equalizing, equal weights, index method, political economy models, regression