非对称不确定性：利用实时数据中的偏度进行即时预测
Asymmetric uncertainty : Nowcasting using skewness in real-time data
AI总结：
本文提出利用实时数据中的偏度建模GDP增长密度即时预测，通过位置、规模与形状共同因子区分一般不确定性与下行/上行风险，实证表明其能提供更可靠的不确定性度量并提升峰值不确定性时的精度。
摘要：
本文提出了一种在生成GDP增长的密度即时预测时，考虑下行风险和上行风险的新方法。该方法依赖于对实时宏观经济数据中的位置、规模和形状共同因子进行建模。位置的变化会引起预测密度中心部分的移动，而规模控制其离散程度（类似于一般不确定性），形状则控制其非对称性或偏度（类似于下行和上行风险）。实证应用聚焦于美国GDP增长，实时数据来自Fred-MD。结果表明，实时数据的价值不仅在于其水平或均值：其离散程度和非对称性为即时预测经济活动提供了宝贵信息。规模和形状共同因子（i）能产生更可靠的不确定性度量，（ii）在宏观经济不确定性达到顶峰时能提高预测精度。
英文摘要：
This paper presents a new way to account for downside and upside risks when producing density nowcasts of GDP growth. The approach relies on modelling location, scale and shape common factors in real-time macroeconomic data. While movements in the location generate shifts in the central part of the predictive density, the scale controls its dispersion (akin to general uncertainty) and the shape its asymmetry, or skewness (akin to downside and upside risks). The empirical application is centred on US GDP growth and the real-time data come from Fred-MD. The results show that there is more to real-time data than their levels or means: their dispersion and asymmetry provide valuable information for nowcasting economic activity. Scale and shape common factors (i) yield more reliable measures of uncertainty and (ii) improve precision when macroeconomic uncertainty is at its peak.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
