Improving the Short-term Forecast of World Trade During the Covid-19 Pandemic Using Swift Data on Letters of Credit /
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 interna...
| 主要作者: | |
|---|---|
| 其他作者: | , , |
| 格式: | 雜誌 |
| 語言: | English |
| 出版: |
Washington, D.C. :
International Monetary Fund,
2020.
|
| 叢編: | IMF Working Papers; Working Paper ;
No. 2020/247 |
| 在線閱讀: | Full text available on IMF |
| 總結: | 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. |
|---|---|
| Item Description: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| 實物描述: | 1 online resource (71 pages) |
| 格式: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| 訪問: | Electronic access restricted to authorized BRAC University faculty, staff and students |