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Early Warning with Calibrated and Sharper Probabilistic Forecasts

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  • Reason Lesego Machete
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    Abstract

    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.

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    File URL: http://arxiv.org/pdf/1112.6390
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1112.6390.

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    Date of creation: Dec 2011
    Date of revision: Jan 2012
    Handle: RePEc:arx:papers:1112.6390

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    11. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 197-207.
    12. Clements, Michael P. & Smith, Jeremy, 2001. "Evaluating forecasts from SETAR models of exchange rates," Journal of International Money and Finance, Elsevier, Elsevier, vol. 20(1), pages 133-148, February.
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