Abstract
Abstract
The accuracy of human forecasters is often reduced because of incomplete
information and cognitive biases that affect the judges. One approach to
improve the accuracy of the forecasts is to recalibrate them by means of
non-linear transformations that are sensitive to the direction and the
magnitude of the biases. Previous work on recalibration has focused on
binary forecasts. We propose an extension of this approach by developing an
algorithm that uses a single free parameter to recalibrate complete
subjective probability distributions. We illustrate the approach with data
from the quarterly Survey of Professional Forecasters (SPF) conducted by the
European Central Bank (ECB), document the potential benefits of this
approach, and show how it can be used in practical applications.
Publisher
Cambridge University Press (CUP)
Subject
Economics and Econometrics,Applied Psychology,General Decision Sciences
Cited by
1 articles.
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