The Group Calibration Index: a group‐based approach for assessing forecasters’ expertise when external outcome data are missing

The Group Calibration Index: a group‐based approach for assessing forecasters’ expertise when external outcome data are missing

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Article ID: iaor20124901
Volume: 73
Issue: 4
Start Page Number: 671
End Page Number: 685
Publication Date: Oct 2012
Journal: Theory and Decision
Authors: ,
Keywords: decision theory
Abstract:

The Group Calibration Index (GCI) provides a means of assessing the quality of forecasters’ predictions in situations that lack external feedback or outcome data. The GCI replaces the missing outcome data with aggregated ratings of a well‐defined reference group. A simulation study and two experiments show how the GCI classifies forecaster performance and distinguishes between forecasters with restricted information and those with complete information. The results also show that under certain circumstances, where members of the reference group have high‐quality information, the new GCI will outperform expert classification that is based on traditional calibration indices.

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