We analyze the behavior of experts who quote forecasts for monthlySKU-level sales data where we compare data before and after the momentthat experts received different kinds of feedback on their behavior. Wehave data for 21 experts located in as many countries who make SKUlevelforecasts for a...

Experts can rely on statistical model forecasts when creating their own forecasts.Usually it is not known what experts actually do. In this paper we focus on threequestions, which we try to answer given the availability of expert forecasts andmodel forecasts. First, is the expert forecast...

Forecasts from various experts are often used in macroeconomic forecasting models. Usually the focus is on the mean or median of the survey data. In the present study we adopt a different perspective on the survey data as we examine the predictive power of disagreement amongst forecasters. The...

Experts frequently adjust statistical model-based forecasts. Sometimes this leads to higher forecast accuracy, but expert forecasts can also be dramatically worse. We explore the potential drivers of the relevance and quality of experts' added knowledge. For that purpose, we examine a very large...

We perform a large-scale empirical analysis of the question whether model-based forecasts can be improved by adding expert knowledge. We consider a huge database of a pharmaceutical company where the head office uses a statistical model to generate monthly sales forecasts at various horizons for...

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...