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We develop a forecasts combination scheme that assigns weights to the individual predictive density forecasts based on quantile scores. Compared to standard combination schemes, our approach has the advantage of assigning different set of combination weights to the various quantiles of the predictive distribution. We apply our approach to US GDP growth forecasts based on quantile regressions using a broad set of common leading indicators.

This paper applies a Bayesian beta mixture model to derive a combined and calibrated density function using random calibration functionals and random combination weights.