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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 article DOI: 10.1002/for.2242

Abstract/Summary

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
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical and Physical Sciences > Department of Mathematics and Statistics
ID Code:25669
Publisher:Wiley

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