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摘要翻译：
给出一个非线性模型，通过蒙特卡罗模拟可以得到概率预报。在给定的预测范围内，Monte Carlo模拟产生离散预测集，这些离散预测集可以转换为密度预测。由此产生的密度预测将不可避免地因模型错误而降级。为了提高密度预测的质量，可以将它们与无条件密度混合。本文考察了条件密度预测与无条件密度预测相结合的价值。这些发现对经济学和气象学等不同学科发布预警具有积极意义，但英国通胀预测被认为是一个例子。
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英文标题：
《Early Warning with Calibrated and Sharper Probabilistic Forecasts》
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作者：
Reason Lesego Machete
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最新提交年份：
2012
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分类信息：
一级分类：Physics 物理学
二级分类：Chaotic Dynamics 混沌动力学
分类描述：Dynamical systems, chaos, quantum chaos, topological dynamics, cycle expansions, turbulence, propagation
动力系统，混沌，量子混沌，拓扑动力学，循环展开，湍流，传播
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一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
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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PDF链接：
https://arxiv.org/pdf/1112.6390
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