Paul Labonne

Labonne, Paul (2024)

Asymmetric uncertainty: Nowcasting using skewness in real-time data

International Journal of Forecasting, s. 1- 22. Doi: 10.1016/j.ijforecast.2024.05.003

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.

Cross, Jamie; Hoogerheide, Lennart, Labonne, Paul & van Dijk, Herman K. (2024)

Bayesian mode inference for discrete distributions in economics and finance

Economics Letters, 235, s. 1- 4. Doi: 10.1016/j.econlet.2024.111579

We propose a straightforward technique for mode inference in discrete data distributions which involves fitting a mixture of novel shifted-Poisson distributions. The credibility and utility of our approach is demonstrated through applications pertaining to loan default risk and inflation expectations.

Labonne, Paul & Weale, Martin (2020)

Temporal disaggregation of overlapping noisy quarterly data: estimation of monthly output from UK value-added tax data

Journal of the Royal Statistical Society: Series A (Statistics in Society), 183(3), s. 1211- 1230. Doi: 10.1111/rssa.12568