Combining Forecasts: Evidence on the Relative Accuracy of the Simple Average and Bayesian Model Averaging for Predicting Social Science Problems

A reanalysis and extension of a published study, which had the purpose to demonstrate the usefulness of Ensemble Bayesian Model Averaging (EBMA), provides evidence on the relative accuracy of EBMA and the simple average for US presidential election forecasting. For this task, the error of the EBMA forecasts was 31% higher than the corresponding error the simple average. Simple averages produce accurate forecasts, are easy to describe, easy to understand, and easy to use. Researchers who develop new methods for combining forecasts need to compare the accuracy of their method to this widely established benchmark method. Forecasting practitioners should favor simple averages over more complex methods unless there is strong evidence in support of differential weights.