{
  "title": "Another Piece of the Puzzle: Adding Swift Data on Documentary Collections to the Short-Term Forecast of World Trade",
  "publication": "IMF Working Papers, December 17, 2021",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2021/12/17/another-piece-of-the-puzzle-adding-swift-data-on-documentary-collections-to-the-short-term-511091",
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  "summary": "This paper extends earlier research by adding SWIFT data on documentary collections to the short-term forecast of international trade.",
  "sections": [
    {
      "heading": "Overview",
      "content": "- This paper extends earlier research by adding SWIFT data on documentary collections to the short-term forecast of international trade.\n- While SWIFT documentary collections accounted for just over one percent of world trade financing in 2020, they have strong explanatory power to forecast world trade and national trade in selected economies.\n- The informational content from documentary collections helps improve the forecast of world trade.\n- A horse race with machine learning algorithms shows significant non-linearities between trade and its determinants during the Covid-19 pandemic."
    },
    {
      "heading": "Key findings and statistics",
      "content": "- SWIFT documentary collections accounted for just over one percent of world trade financing in 2020.\n- Documentary collections have strong explanatory power to forecast:\n  - world trade\n  - national trade in selected economies\n- Inclusion of documentary-collections information improves short-term forecasts of world trade.\n- During the Covid-19 pandemic, relationships between trade and its determinants display significant non-linearities, as revealed in comparisons with machine learning algorithms."
    },
    {
      "heading": "Methodological notes and analysis",
      "content": "- The paper adds SWIFT data on documentary collections to existing short-term trade forecasting frameworks.\n- The authors conduct a \"horse race\" between traditional forecasting approaches and machine learning algorithms to assess non-linearities in trade determinants during the Covid-19 pandemic.\n- The analysis emphasizes the informational content of SWIFT documentary-collections messages for enhancing trade forecasts."
    },
    {
      "heading": "Subjects and keywords",
      "content": "- Subjects: Credit, Exports, Imports, International trade, Money, Trade balance, Trade finance\n- Keywords: Asia and Pacific, Australia and New Zealand, Credit, DFM forecast, Exports, Global, IMF working paper No. 21/293, IMF working papers, Imports, linear regression forecast, machine learning, Merchandise export, SWIFT, Trade balance, Trade finance, trade forecast, trade message"
    },
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      "heading": "Publication metadata",
      "content": "- Authors: Narek Ghazaryan, Alexei Goumilevski, Joannes Mongardini, Aneta Radzikowski\n- Date: December 17, 2021\n- Series: Working Paper No. 2021/293\n- Issue: 293\n- Volume: 2021\n- Pages: 63\n- DOI: https://doi.org/10.5089/9781616357634.001\n- ISBN: 9781616357634\n- ISSN: 1018-5941\n- Stock No: WPIEA2021293\n\nIMF Working Paper No. 2021/293 (December 17, 2021) — Narek Ghazaryan, Alexei Goumilevski, Joannes Mongardini, and Aneta Radzikowski.\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2021/12/17/another-piece-of-the-puzzle-adding-swift-data-on-documentary-collections-to-the-short-term-511091"
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    "Authors: Narek Ghazaryan, Alexei Goumilevski, Joannes Mongardini, Aneta Radzikowski",
    "Published: December 17, 2021",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9781616357634.001",
    "This paper extends earlier research by adding SWIFT data on documentary collections to the short-term forecast of international trade.",
    "While SWIFT documentary collections accounted for just over one percent of world trade financing in 2020, they have strong explanatory power to forecast world trade and national trade in selected economies.",
    "The informational content from documentary collections helps improve the forecast of world trade.",
    "A horse race with machine learning algorithms shows significant non-linearities between trade and its determinants during the Covid-19 pandemic.",
    "SWIFT documentary collections accounted for just over one percent of world trade financing in 2020.",
    "Documentary collections have strong explanatory power to forecast:",
    "Inclusion of documentary-collections information improves short-term forecasts of world trade.",
    "During the Covid-19 pandemic, relationships between trade and its determinants display significant non-linearities, as revealed in comparisons with machine learning algorithms.",
    "The paper adds SWIFT data on documentary collections to existing short-term trade forecasting frameworks.",
    "The authors conduct a \"horse race\" between traditional forecasting approaches and machine learning algorithms to assess non-linearities in trade determinants during the Covid-19 pandemic.",
    "The analysis emphasizes the informational content of SWIFT documentary-collections messages for enhancing trade forecasts.",
    "Subjects: Credit, Exports, Imports, International trade, Money, Trade balance, Trade finance",
    "Keywords: Asia and Pacific, Australia and New Zealand, Credit, DFM forecast, Exports, Global, IMF working paper No. 21/293, IMF working papers, Imports, linear regression forecast, machine learning, Merchandise export, SWIFT, Trade balance, Trade finance, trade forecast, trade message",
    "Authors: Narek Ghazaryan, Alexei Goumilevski, Joannes Mongardini, Aneta Radzikowski",
    "Date: December 17, 2021",
    "Series: Working Paper No. 2021/293",
    "Issue: 293",
    "Volume: 2021",
    "Pages: 63",
    "DOI: https://doi.org/10.5089/9781616357634.001",
    "ISBN: 9781616357634",
    "ISSN: 1018-5941",
    "Stock No: WPIEA2021293",
    "**Working Paper**"
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