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Lookup NU author(s): Professor Kevin Wilson
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
Expert judgement plays an important role in forecasting and elsewhere as it can be used to quantify models when no data are available and to improve predictions from models when combined with data. In order to provide defensible estimates of unknowns in an analysis the judgements of multiple experts can be elicited. Mathematical aggregation methods can be used to combine these individual judgements into a single judgement for the decision maker. However, most mathematical aggregation methods assume judgements coming from experts that are independent. This is unlikely to be the case in practice. This paper investigates dependence in expert judgement studies, both within and between experts. It gives the most comprehensive analysis to date by considering all studies in the TU Delft database. It then assesses the practical significance of the dependencies identified in the studies by comparing the performance of several mathematical aggregation methods with varying dependence assumptions. Between expert correlations were more prevalent than within expert correlations. For studies which contained between expert correlations, models which include these improved forecasts. The implications for the use of expert judgement in forecasting are discussed.
Author(s): Wilson KJ
Publication type: Article
Publication status: Published
Journal: International Journal of Forecasting
Year: 2017
Volume: 33
Issue: 1
Pages: 325–336
Print publication date: 01/01/2017
Online publication date: 22/03/2016
Acceptance date: 30/11/2015
Date deposited: 30/11/2015
ISSN (print): 0169-2070
ISSN (electronic): 1872-8200
Publisher: Elsevier
URL: http://dx.doi.org/10.1016/j.ijforecast.2015.11.014
DOI: 10.1016/j.ijforecast.2015.11.014
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