We propose an easy-to-implement framework for combining quantile forecasts, applied to forecasting GDP growth. Using quantile regressions, our combination scheme assigns weights to individual forecasts from different indicators based on quantile scores. Previous studies suggest distributional...

We consider a situation in which the forecaster has available M individual forecasts of a univariate target variable. We propose a 3-step procedure designed to exploit the interrelationships among the M forecast-error series (estimated from a large time-varying parameter VAR model of the errors,...

A flexible forecast density combination approach is introduced that can deal with large data sets. It extends the mixture of experts approach by allowing for model set incompleteness and dynamic learning of combination weights. A dimension reduction step is introduced using a sequential...