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Institutions which publish macroeconomic forecasts usually do not rely on a single econometric model to generate their forecasts. The combination of judgments with information from different models complicates the problem of characterizing the predictive density. This paper proposes a parametric approach to construct the joint and marginal densities of macroeconomic forecasting errors, combining judgments with sample and model information. The author assumes that the relevant variables are linear combinations of latent independent two-piece normal variables.
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