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Early warning with calibrated and sharper probabilistic forecasts

Machete, R. L. (2013) Early warning with calibrated and sharper probabilistic forecasts. Journal of Forecasting, 32 (5). pp. 452-468. ISSN 1099-131X

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To link to this item DOI: 10.1002/for.2242


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:25669

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