The Accuracy of Combining Judgemental and Statistical Forecasts

Abstract

Judgement based forecasts are widely used in practice either alone or in conjunction with computer prepared forecasts. This study empirically examines the improvement in accuracy which can be gained from combining judgemental forecasts, either with other judgemental or with quantitatively derived forecasts. Two judgemental forecasting approaches are used by each of two different groups in a laboratory setting to give four sets of judgemental forecasts for the 68 monthly time series of the M-competition. These are combined either with each other or with forecasts from deseasonalised single exponential smoothing. Combined forecasts are found to be more accurate than single forecasts with the greatest benefit realised at short forecast horizons and for easier (as opposed to harder) forecast series. Averaging was observed to be a far better way of combining judgemental forecasts than a judgemental, nonsystematic combination.