OECD Trade Policy Papers

This series is designed to make available to a wider readership selected trade policy studies prepared for use within the OECD.

NB. No. 1 to No. 139 were released under the previous series title OECD Trade Policy Working Papers.


Nowcasting aggregate services trade

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: Machine learning, Dynamic factor models, G7 economies
JEL: C22: Mathematical and Quantitative Methods / Single Equation Models; Single Variables / Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; C4: Mathematical and Quantitative Methods / Econometric and Statistical Methods: Special Topics; F17: International Economics / Trade / Trade: Forecasting and Simulation
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