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Given a nonlinear model, a probabilistic forecast may be obtained by MonteCarlo simulations. At a given forecast horizon, Monte Carlo simulations yieldsets of discrete forecasts, which can be converted to density forecasts. Theresulting density forecasts will inevitably be downgraded by modelmis-specification. In order to enhance the quality of the density forecasts,one can mix them with the unconditional density. This paper examines the valueof combining conditional density forecasts with the unconditional density. Thefindings have positive implications for issuing early warnings in differentdisciplines including economics and meteorology, but UK inflation forecasts areconsidered as an example.

Early Warning with Calibrated and Sharper Probabilistic Forecasts