论文标题
不对称不确定性：在实时数据中使用偏斜的态度
Asymmetric uncertainty : Nowcasting using skewness in real-time data
论文作者
论文摘要
本文在产生GDP增长的密度时提出了一种解决下行和上行风险的新方法。该方法依赖于实时宏观经济数据中的建模位置，规模和形状的常见因素。虽然位置中的运动会在预测密度的中央部分产生变化，但量表控制其分散（类似于一般的不确定性），并形成其不对称性或偏度（类似于下行和上空风险）。经验应用以美国GDP的增长为中心，实时数据来自FRED-MD。结果表明，实时数据不仅仅是它们的水平或手段：它们的分散和不对称性为现实的经济活动提供了宝贵的信息。尺度和形状常见因素（i）产生更可靠的不确定性度量，并且（ii）当宏观经济不确定性达到峰值时，提高精度。
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.
