{
  "title": "Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa",
  "publication": "IMF Working Papers, May 6, 2022",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2022/05/07/sub-saharan-africa-economic-activity-gdp-machine-learning-nowcasting-covid-19-517646",
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  "summary": "The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies.",
  "sections": [
    {
      "heading": "Overview and Purpose",
      "content": "- Objective: Outline a machine-learning framework to track economic activity in real time for economies with delayed publication of official GDP statistics.\n- Application: Framework applied as illustrative examples to selected sub-Saharan African economies.\n- Main claim: The framework is able to provide timely information on economic activity more swiftly than official statistics."
    },
    {
      "heading": "Key Findings",
      "content": "- The COVID-19 crisis has had a tremendous economic impact for all countries.\n- Delayed publication of official GDP statistics in several emerging market and developing economies hampers assessment of the full impact of the crisis.\n- A machine-learning nowcasting framework can overcome data sparsity to track economic activity in real time.\n- As illustrated for selected sub-Saharan African economies, the framework provides timelier information than official statistics."
    },
    {
      "heading": "Methodological Themes",
      "content": "- Approach: Machine learning applied to nowcasting economic activity.\n- Focus areas and related topics: COVID-19, Economic forecasting, Foreign exchange, Health, Machine learning, Oil prices, Prices, Real effective exchange rates, Technology."
    },
    {
      "heading": "Policy-Relevant Implications and Recommendations",
      "content": "- Use of machine-learning nowcasts can supplement official statistics to inform timely policy responses during crises characterized by delayed data release.\n- Policymakers in sub-Saharan Africa and other data-sparse environments can leverage real-time machine-learning estimates to better assess economic conditions amid rapid shocks such as the COVID-19 pandemic."
    },
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      "content": "- Authors: Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew J Tiffin, Jiaxiong Yao\n- Date: May 6, 2022\n- Series: Working Paper No. 2022/088\n- Issue: 088\n- Volume: 2022\n- Pages: 23\n- DOI: https://doi.org/10.5089/9798400210136.001\n- Stock No: WPIEA2022088\n- ISBN: 9798400210136\n- ISSN: 1018-5941\n- Subjects and Keywords: Africa; COVID-19; crisis in Sub-Saharan Africa; data sparsity; Economic Activity; GDP; GDP statistics; Global; learning framework; Machine Learning; machine learning approach; Nowcasting; Oil prices; Real effective exchange rates; Sub-Saharan Africa\n\nIMF Working Paper — Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa (Working Paper No. 2022/088, May 6, 2022).\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/2022/05/07/sub-saharan-africa-economic-activity-gdp-machine-learning-nowcasting-covid-19-517646"
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    "Authors: Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew J Tiffin, Jiaxiong Yao",
    "Published: May 6, 2022",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798400210136.001",
    "Objective: Outline a machine-learning framework to track economic activity in real time for economies with delayed publication of official GDP statistics.",
    "Application: Framework applied as illustrative examples to selected sub-Saharan African economies.",
    "Main claim: The framework is able to provide timely information on economic activity more swiftly than official statistics.",
    "The COVID-19 crisis has had a tremendous economic impact for all countries.",
    "Delayed publication of official GDP statistics in several emerging market and developing economies hampers assessment of the full impact of the crisis.",
    "A machine-learning nowcasting framework can overcome data sparsity to track economic activity in real time.",
    "As illustrated for selected sub-Saharan African economies, the framework provides timelier information than official statistics.",
    "Approach: Machine learning applied to nowcasting economic activity.",
    "Focus areas and related topics: COVID-19, Economic forecasting, Foreign exchange, Health, Machine learning, Oil prices, Prices, Real effective exchange rates, Technology.",
    "Use of machine-learning nowcasts can supplement official statistics to inform timely policy responses during crises characterized by delayed data release.",
    "Policymakers in sub-Saharan Africa and other data-sparse environments can leverage real-time machine-learning estimates to better assess economic conditions amid rapid shocks such as the COVID-19 pandemic.",
    "Authors: Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew J Tiffin, Jiaxiong Yao",
    "Date: May 6, 2022",
    "Series: Working Paper No. 2022/088",
    "Issue: 088",
    "Volume: 2022",
    "Pages: 23",
    "DOI: https://doi.org/10.5089/9798400210136.001",
    "Stock No: WPIEA2022088",
    "ISBN: 9798400210136",
    "ISSN: 1018-5941",
    "Subjects and Keywords: Africa; COVID-19; crisis in Sub-Saharan Africa; data sparsity; Economic Activity; GDP; GDP statistics; Global; learning framework; Machine Learning; machine learning approach; Nowcasting; Oil prices; Real effective exchange rates; Sub-Saharan Africa",
    "**Working Paper**"
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