Recalibrating probabilistic forecasts to improve their accuracy

Autor: Ying Han, David V. Budescu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Judgment and Decision Making, Vol 17, Iss 1, Pp 91-123 (2022)
Druh dokumentu: article
ISSN: 1930-2975
Popis: 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.
Databáze: Directory of Open Access Journals