A Heuristic for Combining Correlated Experts When There Are Few Data

David Soule (), Yael Grushka-Cockayne () and Jason Merrick ()
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David Soule: University of Richmond, Richmond, Virginia 23273
Yael Grushka-Cockayne: Darden School of Business, University of Virginia, Charlottesville, Virginia 22906
Jason Merrick: Virginia Commonwealth University, Richmond, Virginia 23284

Management Science, 2024, vol. 70, issue 10, 6637-6668

Abstract: 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.

Keywords: linear opinion pool; variance weights; covariance weights; clustering; forecast combination puzzle; small crowd; wisdom of the crowd (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:70:y:2024:i:10:p:6637-6668

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