摘要翻译：
使用适当的评分规则来评估概率预测的样本外精度，不同的评分规则奖励不同的预测性能方面。在此，我们重新研究了使用适当的评分规则来产生根据给定分数“最优”的概率预测的做法，并根据该分数评估其样本外精度何时优于替代预测。特别注意在预测模型不规范的情况下的相对预测性能。通过数字说明，我们记录了这个范式中的几个新发现，这些发现突出了真实数据生成过程、假设的预测模型和评分规则之间的重要相互作用。值得注意的是，我们表明，只有当一个预测模型与真实过程充分兼容，允许一个特定的评分标准奖励它设计奖励的东西时，这种预测方法才会获得好处。但在这种兼容性下，最优预报的优越性就越大，错配的程度就越大。我们在一系列不同的场景下探索这些问题，并使用人工模拟和经验数据。
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英文标题：
《Optimal probabilistic forecasts: When do they work?》
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作者：
Gael M. Martin, Rub\'en Loaiza-Maya, David T. Frazier, Worapree
  Maneesoonthorn, Andr\'es Ram\'irez Hassan
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Herein, we re-investigate the practice of using proper scoring rules to produce probabilistic forecasts that are `optimal' according to a given score, and assess when their out-of-sample accuracy is superior to alternative forecasts, according to that score. Particular attention is paid to relative predictive performance under misspecification of the predictive model. Using numerical illustrations, we document several novel findings within this paradigm that highlight the important interplay between the true data generating process, the assumed predictive model and the scoring rule. Notably, we show that only when a predictive model is sufficiently compatible with the true process to allow a particular score criterion to reward what it is designed to reward, will this approach to forecasting reap benefits. Subject to this compatibility however, the superiority of the optimal forecast will be greater, the greater is the degree of misspecification. We explore these issues under a range of different scenarios, and using both artificially simulated and empirical data.
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PDF链接：
https://arxiv.org/pdf/2009.09592