Asymmetric uncertainty: Nowcasting using skewness in real-time data
提出一种新方法，通过建模实时宏观经济数据的位置、尺度和形状共同因子，来考虑GDP增长密度即时预测中的下行和上行风险，发现数据的离散度和偏度能提供有价值的信息。
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.