Abstract
Many decision-theorists and forecasters have advocated the use of a linear combination of forecasts for decision-making purposes. However, there have been two separate themes. One has looked at providing linear weights which minimise the forecast error variance. The other has utilised the posterior probabilities derived from the conventional Bayesian model discrimination procedure. This paper has attempted to identify some practical circumstances in which one of these two approaches becomes the more appropriate.
Similar content being viewed by others
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Bunn, D. Two Methodologies for the Linear Combination of Forecasts. J Oper Res Soc 32, 213–222 (1981). https://doi.org/10.1057/jors.1981.44
Published:
Issue Date:
DOI: https://doi.org/10.1057/jors.1981.44