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 Login for the JSON version of this page.
- Title
- Improving the Short Term Forecast of World Trade During the Covid 19 Pandemic Using Swift Data on Letters of Credit
- ISBN-10
- 1-5135-6288-6
- ISBN-13
- 978-1-5135-6288-9
- Author(s)
- Aneta Radzikowski, Kei Moriya, Joannes Mongardini, Benjamin Carton, Nan Hu
- Publisher
- International Monetary Fund
- Published
- 2020
- Format
- Subtitle
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- Series
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- Imprint
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- Pages
- 798
- Language
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- Subjects
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- Genre
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Description
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Metadata
- EAN
- 9781513562889
- ASIN
- 1513562886
- Prefix
- 978
- Group
- 1
- Group Name
- English language
- Group Identifier
- 978-1
- Registrant
- 5135
- Publication
- 6288
- Check Digit
- 9
- Formatted
- 978-1-5135-6288-9