 Nowcasting services trade for the G7 economiesAlexander Jaax, Annabelle Mourougane and Frederic Gonzales The World Economy, 2024, vol. 47, issue 4, 1336-1386 Abstract: The growing importance of services trade in the global economy contrasts with the scarcity of timely data on this part of international trade. This paper develops models to nowcast aggregate services imports and exports, as measured by monthly services trade data, for the G7 countries. The methodology relies on machine‐learning techniques and dynamic factor models and combines traditional high‐frequency data with the use of Google Trends search data. The estimated services trade nowcasting models display a higher out‐of‐sample predictive power than a simple benchmark model. However, there does not seem to be one approach that outperforms other model specifications. Rather, a weighted average of the best models, combining machine‐learning with dynamic factor models, seems to be a promising avenue. The best models improve one step ahead predictive performance relative to a simple benchmark by 30%–35% on average across the G7 countries and across trade flows. Models capture approximately 67% of the fall in services exports following the COVID‐19 shock and 60% of the fall in imports on average across G7 economies. Date: 2024 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:worlde:v:47:y:2024:i:4:p:1336-1386 Ordering information: This journal article can be ordered from Access Statistics for this article The World Economy is currently edited by David Greenaway More articles in The World Economy from Wiley Blackwell |
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