The accuracy of human forecasters is often reduced because of incompleteinformation and cognitive biases that affect the judges. One approach to improvethe accuracy of the forecasts is to recalibrate them by means of non-lineartransformations that are sensitive to the direction and the magnitude of thebiases. Previous work on recalibration has focused on binary forecasts. Wepropose an extension of this approach by developing an algorithm that uses asingle free parameter to recalibrate complete subjective probabilitydistributions. We illustrate the approach with data from the quarterly Survey ofProfessional Forecasters (SPF) conducted by the European Central Bank (ECB),document the potential benefits of this approach, and show how it can be used inpractical applications.
