Two Methodologies for the Linear Combination of Forecasts

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.

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