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

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

November 13, 2020

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Format: Chicago

Benjamin Carton, Nan Hu, Joannes Mongardini, Kei Moriya, and Aneta Radzikowski. Improving the Short-term Forecast of World Trade During the Covid-19 Pandemic Using Swift Data on Letters of Credit, (USA: International Monetary Fund, 2020) accessed September 20, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Exports, Imports, International trade, Oil prices, Prices, Trade balance, Trade finance

Keywords: Africa, Asia and Pacific, Baltics, Brent crude oil price, Exports, Global, Imports, Letter of credit, Linear regression forecast, Machine learning, Merchandise trade, Oil prices, SWIFT, Trade advance, Trade balance, Trade finance, Trade forecast, Trade message, World trade, World trade sample, WP

Publication Details

Pages:

71
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2020/247
Stock No:

WPIEA2020247
ISBN:

9781513561196
ISSN:

1018-5941

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