A Heuristic for Combining Correlated Experts When There Are Few Data
提出一种考虑专家技能和共同相关性的启发式方法，在历史数据有限时比传统组合方法更准确，适用于宏观经济和实验预测。
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. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Supplemental Material: The data file is available at https://doi.org/10.1287/mnsc.2021.02009 .