Asymmetric uncertainty : Nowcasting using skewness in real-time data

TL;DR

This paper introduces a novel approach to GDP nowcasting that incorporates skewness and dispersion in real-time macroeconomic data, enhancing uncertainty measurement and forecast accuracy.

Contribution

It models location, scale, and shape factors in real-time data to better capture downside and upside risks in GDP nowcasting, a novel integration of skewness into macroeconomic forecasting.

Findings

Inclusion of skewness improves nowcast precision.

Scale and shape factors enhance uncertainty measurement.

Method yields more reliable GDP growth estimates during high uncertainty periods.

Abstract

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.