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

Author/Editor:

Benjamin Carton ; Nan Hu ; Joannes Mongardini ; Kei Moriya ; Aneta Radzikowski

Publication Date:

November 13, 2020

Electronic Access:

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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.

Series:

Working Paper No. 2020/247

Subject:

Frequency:

regular

English

Publication Date:

November 13, 2020

ISBN/ISSN:

9781513561196/1018-5941

Stock No:

WPIEA2020247

Format:

Paper

Pages:

71

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