  Given a nonlinear model, a probabilistic forecast may be obtained by Monte
Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield
sets of discrete forecasts, which can be converted to density forecasts. The
resulting density forecasts will inevitably be downgraded by model
mis-specification. In order to enhance the quality of the density forecasts,
one can mix them with the unconditional density. This paper examines the value
of combining conditional density forecasts with the unconditional density. The
findings have positive implications for issuing early warnings in different
disciplines including economics and meteorology, but UK inflation forecasts are
considered as an example.
