论文标题
适当的评分规则，用于评估密度预测的不对称性
Proper scoring rules for evaluating asymmetry in density forecasting
论文作者
论文摘要
本文提出了一种新型的不对称连续概率评分（ACP），用于评估和比较密度预测。它扩展了提议的分数并定义了加权版本，该版本强调了感兴趣的区域，例如尾巴或变量范围的中心。还引入了测试以比较不同预测的预测能力。在决策者在评估预测中具有不对称偏好的任何情况下，ACP都是一般使用的。在人工实验中，说明了改变ACP中不对称水平的含义。然后，提出的分数和测试将用于评估和比较宏观经济相关数据集（美国就业增长）和商品价格（石油和电价）的密度预测，并特别关注最近的Covid-19-19危机期。
This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable's range. A test is also introduced to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (US employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.
