Trade in value added (TiVA) indicators are increasingly used to monitor countries’
integration into global supply chains. However, they are published with a significant
lag - often two or three years - which reduces their relevance for monitoring recent
economic developments. This paper aims to provide more timely insights into the international
fragmentation of production by exploring new ways of nowcasting five TiVA indicators
for the years 2021 and 2022 covering a panel of 41 economies at the economy-wide level
and for 24 industry sectors. The analysis relies on a range of models, including Gradient
boosted trees (GBM), and other machine-learning techniques, in a panel setting, uses
a wide range of explanatory variables capturing domestic business cycles and global
economic developments and corrects for publication lags to produce nowcasts in quasi-real
time conditions. Resulting nowcasting algorithms significantly improve compared to
the benchmark model and exhibit relatively low prediction errors at a one- and two-year
horizon, although model performance varies across countries and sectors.