Replication data for: Limitations of Ensemble Bayesian Model Averaging for forecasting social science problemsdoi:10.7910/DVN/EBMAHarvard Dataverse2014-07-211Graefe, Andreas, 2014, "Replication data for: Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems", https://doi.org/10.7910/DVN/EBMA, Harvard Dataverse, V1Replication data for: Limitations of Ensemble Bayesian Model Averaging for forecasting social science problemsdoi:10.7910/DVN/EBMAGraefe, Andreas2014Harvard DataverseHarvard Dataverse Network2014-07-212014Bayesian Model Averaging, election forecastingWe compare the accuracy of simple unweighted averages and Ensemble Bayesian Model Averaging (EBMA) to combining forecasts in the social sciences. A review of prior studies from the domain of economic forecasting finds that the simple average was more accurate than EBMA in four out of five studies. On average, the error of EBMA was 5% higher than the error of the simple average. A reanalysis and extension of a published study provides further evidence for US presidential election forecasting. The error of EBMA was 33% higher than the corresponding error of the simple average. Simple averages are easy to describe, easy to understand and thus easy to use. In addition, simple averages provide accurate forecasts in many settings. Researchers who develop new approaches to combining forecasts need to compare the accuracy of their method to this widely established benchmark. Forecasting practitioners should favor simple averages over more complex methods unless there is strong evidence in support of differential weights.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Limitations of Ensemble Bayesian Model Averaging for forecasting social science problemsLimitations of Ensemble Bayesian Model Averaging for forecasting social science problemsEBMA.xlsxapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheet