The Group Calibration Index: A group-Based approach for assessing forecasters' expertise when external outcome data are missing

Ilan Fischer, Ravid Bogaire

Research output: Contribution to journalArticlepeer-review

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 welldefined 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.

Original languageEnglish
Pages (from-to)671-685
Number of pages15
JournalTheory and Decision
Volume73
Issue number4
DOIs
StatePublished - Oct 2012

Bibliographical note

Funding Information:
Acknowledgments This research was supported by Grant no. 1065/05 from the Israel Science Foundation to the first author. The authors thank Peter Wakker, and two anonymous referees for their comments on earlier drafts of the manuscript.

Keywords

  • Calibration
  • Experts
  • Group decisions
  • Uncertainty

ASJC Scopus subject areas

  • General Decision Sciences
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Applied Psychology
  • General Social Sciences
  • Economics, Econometrics and Finance (all)
  • Computer Science Applications

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