Decomposing bias in expert forecast

Forecasts in the airline industry are often based in part on statistical models but mostly on expert judgment. It is frequently documented in the forecasting literature that expert forecasts are biased but that their accuracy is higher than model forecasts. If an expert forecast can be approximated by the weighted sum of a part that can be replicated by an analyst and a non-replicable part containing managerial intuition, the question arises which of two causes the bias. This paper advocates a simple regression-based strategy to decompose bias in expert forecasts. An illustration of the method to a unique database on airline revenues shows how it can be used to improve their experts’ forecasts.