Improving the Short-term Forecast of World Trade During the Covid-19 Pandemic Using Swift Data on Letters of Credit

Benjamin Carton, Nan Hu, Joannes Mongardini, Kei Moriya and Aneta Radzikowski

No 2020/247, IMF Working Papers from International Monetary Fund

Abstract: An essential element of the work of the Fund is to monitor and forecast international trade. This paper uses SWIFT messages on letters of credit, together with crude oil prices and new export orders of manufacturing Purchasing Managers’ Index (PMI), to improve the short-term forecast of international trade. A horse race between linear regressions and machine-learning algorithms for the world and 40 large economies shows that forecasts based on linear regressions often outperform those based on machine-learning algorithms, confirming the linear relationship between trade and its financing through letters of credit.

Keywords: SWIFT; trade forecast; machine learning; WP; world trade; trade message; Brent crude oil price; trade advance; letter of credit; linear regression forecast; Merchandise trade; World trade sample; Oil prices; Exports; Imports; Trade finance; Trade balance; Global; Africa; Asia and Pacific; Baltics (search for similar items in EconPapers)
Pages: 71
Date: 2020-11-13
New Economics Papers: this item is included in nep-big, nep-for and nep-int
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