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

Author

Listed:
  • Benjamin Carton
  • Nan Hu
  • Mr. Joannes Mongardini
  • Kei Moriya
  • Aneta Radzikowski

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.

Suggested Citation

  • Benjamin Carton & Nan Hu & Mr. Joannes Mongardini & Kei Moriya & Aneta Radzikowski, 2020. "Improving the Short-term Forecast of World Trade During the Covid-19 Pandemic Using Swift Data on Letters of Credit," IMF Working Papers 2020/247, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2020/247
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