{
  "title": "Nowcasting GCC GDP: A Machine Learning Solution for Enhanced Non-Oil GDP Prediction",
  "publication": "IMF Working Papers, December 19, 2025",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2025/12/20/nowcasting-gcc-gdp-a-machine-learning-solution-for-enhanced-non-oil-gdp-prediction-571785",
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  "summary": "This paper presents a machine learning–based nowcasting framework for estimating quarterly non-oil GDP growth in the Gulf Cooperation Council (GCC) countries.",
  "sections": [
    {
      "heading": "Summary / Objective",
      "content": "- Presents a machine learning–based nowcasting framework for estimating quarterly non-oil GDP growth in the Gulf Cooperation Council (GCC) countries.\n- Leverages machine learning models tailored to each country to produce timely, sector-specific estimates.\n- Aims to enhance granularity, responsiveness, and transparency of short-term forecasts to support faster, data-driven policy decisions across the GCC."
    },
    {
      "heading": "Data and Inputs",
      "content": "- Integrates a broad range of high-frequency indicators—including real activity, financial conditions, trade, and oil-related variables.\n- Incorporates high-frequency, cross-border indicators to move beyond single-model methodologies."
    },
    {
      "heading": "Methodology and Innovations",
      "content": "- Tailored data integration strategy that broadens and automates the use of high-frequency indicators.\n- Novel application of Shapley value decompositions to:\n  - Enhance model interpretability.\n  - Guide iterative selection of predictive indicators.\n- Framework flexibility designed to account for:\n  - The region’s unique economic structures.\n  - Ongoing reform agendas.\n  - Spillover effects of oil market volatility on non-oil sectors."
    },
    {
      "heading": "Findings and Contributions",
      "content": "- Advances the nowcasting literature for the MENA region by combining richer high-frequency datasets with country-specific machine learning models.\n- Improves sector-specific and short-term estimates of non-oil GDP growth in GCC countries.\n- Enhances the transparency and interpretability of model outputs through Shapley value decompositions."
    },
    {
      "heading": "Policy Relevance and Applications",
      "content": "- Enables faster, data-driven policy decisions strengthening economic surveillance and enhancing policy agility across the GCC.\n- Suitable for use amid a rapidly evolving global environment where oil market volatility affects non-oil sectors."
    },
    {
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      "content": "- Authors: Greta Polo, Yuan Gao Rollinson, Yevgeniya Korniyenko, Tongfang Yuan\n- Date: December 19, 2025\n- Series: Working Paper No. 2025/268\n- Issue: 268\n- Volume: 2025\n- Pages: 36\n- DOI: https://doi.org/10.5089/9798229031851.001\n- Stock No: WPIEA2025268\n- ISBN: 9798229031851\n- ISSN: 1018-5941\n- Subjects: Commodities, Economic forecasting, Oil, Oil prices, Prices\n- Keywords: Caribbean, Central America, Central Asia, GCC, Global, growth in the Gulf Cooperation Council, IMF working papers, Machine Learning, machine learning model, Middle East, Non-oil Growth, Nowcasting, nowcasting framework, Nowcasting Gcc, Oil, Oil prices, South America, Southeast Asia\n\nIMF Working Papers — Nowcasting GCC GDP: A Machine Learning Solution for Enhanced Non-Oil GDP Prediction\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/2025/12/20/nowcasting-gcc-gdp-a-machine-learning-solution-for-enhanced-non-oil-gdp-prediction-571785"
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    "Authors: Greta Polo, Yuan Gao Rollinson, Yevgeniya Korniyenko, Tongfang Yuan",
    "Published: December 19, 2025",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798229031851.001",
    "Presents a machine learning–based nowcasting framework for estimating quarterly non-oil GDP growth in the Gulf Cooperation Council (GCC) countries.",
    "Leverages machine learning models tailored to each country to produce timely, sector-specific estimates.",
    "Aims to enhance granularity, responsiveness, and transparency of short-term forecasts to support faster, data-driven policy decisions across the GCC.",
    "Integrates a broad range of high-frequency indicators—including real activity, financial conditions, trade, and oil-related variables.",
    "Incorporates high-frequency, cross-border indicators to move beyond single-model methodologies.",
    "Tailored data integration strategy that broadens and automates the use of high-frequency indicators.",
    "Novel application of Shapley value decompositions to:",
    "Framework flexibility designed to account for:",
    "Advances the nowcasting literature for the MENA region by combining richer high-frequency datasets with country-specific machine learning models.",
    "Improves sector-specific and short-term estimates of non-oil GDP growth in GCC countries.",
    "Enhances the transparency and interpretability of model outputs through Shapley value decompositions.",
    "Enables faster, data-driven policy decisions strengthening economic surveillance and enhancing policy agility across the GCC.",
    "Suitable for use amid a rapidly evolving global environment where oil market volatility affects non-oil sectors.",
    "Authors: Greta Polo, Yuan Gao Rollinson, Yevgeniya Korniyenko, Tongfang Yuan",
    "Date: December 19, 2025",
    "Series: Working Paper No. 2025/268",
    "Issue: 268",
    "Volume: 2025",
    "Pages: 36",
    "DOI: https://doi.org/10.5089/9798229031851.001",
    "Stock No: WPIEA2025268",
    "ISBN: 9798229031851",
    "ISSN: 1018-5941",
    "Subjects: Commodities, Economic forecasting, Oil, Oil prices, Prices",
    "Keywords: Caribbean, Central America, Central Asia, GCC, Global, growth in the Gulf Cooperation Council, IMF working papers, Machine Learning, machine learning model, Middle East, Non-oil Growth, Nowcasting, nowcasting framework, Nowcasting Gcc, Oil, Oil prices, South America, Southeast Asia",
    "**Working Paper**"
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