Nowcasting aggregate services trade

Alexander Jaax, Frédéric Gonzales and Annabelle Mourougane

No 253, OECD Trade Policy Papers from OECD Publishing

Abstract: The increasing importance of services trade in the global economy contrasts with the lack of timely data to monitor recent developments. The nowcasting models developed in this paper are aimed at providing insights into current changes in total services trade, as recorded in monthly statistics of the G7 countries. Combining machine-learning techniques and dynamic factor models, the methodology exploits traditional data and Google Trends search data. No single model outperforms the others, but 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 G7 countries and trade flows. Nowcasting models are estimated to have captured about 67% of the fall in services exports due to the COVID-19 shock and 60% of the fall in imports on average across G7 economies.

Keywords: Dynamic factor models; G7 economies; Machine learning (search for similar items in EconPapers)
JEL-codes: C22 C4 F17 (search for similar items in EconPapers)
Date: 2021-09-23
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-int, nep-isf and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:oec:traaab:253-en

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