  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.
