This study proposes an alternative, fully probabilistic approach to combining model-based forecasts with surveys or other judgmental forecasts. This approach allows data to determine how informative survey forecasts are regarding the data-generating process by probabilistically exploiting...

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and...