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

Improving the Short-term Forecast of World Trade During the Covid-19 Pandemic Using Swift Data on Letters of Credit
READ MORE...
Volume/Issue: Volume 2020 Issue 247
Publication date: November 2020
ISBN: 9781513561196
$18.00
Add to Cart by clicking price of the language and format you'd like to purchase
Available Languages and Formats
paperback else
pdf else
epub else
English
Prices in red indicate formats that are not yet available but are forthcoming.
Topics covered in this book

This title contains information about the following subjects. Click on a subject if you would like to see other titles with the same subjects.

Exports and Imports , Economics- Macroeconomics , 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

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.