A Heuristic for Combining Correlated Experts When There Are Few Data

• 成果类型: Article
• 署名作者: Soule, David; Grushka-Cockayne, Yael; Merrick, Jason
• 署名单位: University of Richmond; University of Virginia; Virginia Commonwealth University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.02009
• 发表日期: 2024
• 页码: 6637-6668
• 关键词: linear opinion pool; variance weights; covariance weights; clustering; forecast combination puzzle; small crowd; wisdom of the crowd

摘要

It is intuitive and theoretically sound to combine experts' forecasts based on their proven skills, while accounting for correlation among their forecast submissions. Simpler combination methods, however, which assume independence of forecasts or equal skill, have been found to be empirically robust, in particular, in settings in which there are few historical data available for assessing experts' skill. One explanation for the robust performance by simple methods is that empirical estimation of skill and of correlations introduces error, leading to worse aggregated forecasts than simpler alternatives. We offer a heuristic that accounts for skill and reduces estimation error by utilizing a common correlation factor. Our theoretical results present an optimal form for this common correlation, and we offer Bayesian estimators that can be used in practice. The common correlation heuristic is shown to outperform alternative combination methods on macroeconomic and experimental forecasting where there are limited historical data.