01848cas a2200277 a 4500 AALejournalIMF021343 230101c9999 xx r poo 0 0eng d 5.00 USD 9781513561196 1018-5941 BD-DhAAL BD-DhAAL Carton, Benjamin. 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. Washington, D.C. : International Monetary Fund, 2020. 1 online resource (71 pages) IMF Working Papers <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. Mode of access: Internet Hu, Nan. Mongardini, Joannes. Moriya, Kei. IMF Working Papers; Working Paper ; No. 2020/247 Full text available on IMF http://elibrary.imf.org/view/journals/001/2020/247/001.2020.issue-247-en.xml IMF e-Library