{
  "title": "Nowcasting Growth Using the Bayesian Structural Time Series Model: Application to Tanzania",
  "publication": "IMF Working Papers, March 20, 2026",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2026/03/20/nowcasting-growth-using-the-bayesian-structural-time-series-model-574851",
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  "summary": "In light of recent global shocks and rising external volatility, there is a growing need to effectively monitor short-term economic fluctuations, especially in countries with limited access to high-frequency growth data.",
  "sections": [
    {
      "heading": "Overview",
      "content": "- Title: Nowcasting Growth Using the Bayesian Structural Time Series Model: Application to Tanzania\n- Author: Sunwoo Lee\n- Date: March 20, 2026\n- Series: Working Paper No. 2026/049\n- Issue: 049\n- Volume: 2026\n- Pages: 27\n- DOI: https://doi.org/10.5089/9798229042116.001\n- Stock No: WPIEA2026049\n- ISBN: 9798229042116\n- ISSN: 1018-5941\n- Format availability: PDF (Download PDF), More Formats on IMF eLibrary, Order a Print Copy, Create Citation"
    },
    {
      "heading": "Research objective and context",
      "content": "- Purpose: Examine application of the Bayesian Structural Time Series (BSTS) model to nowcasting quarterly economic growth in Tanzania using high-frequency economic indicators.\n- Motivation: Rising external volatility and recent global shocks increase need to effectively monitor short-term economic fluctuations, especially in countries with limited access to high-frequency growth data."
    },
    {
      "heading": "Methodology: Bayesian Structural Time Series (BSTS) framework",
      "content": "- Model components:\n  - Incorporates trends\n  - Incorporates seasonal variations\n  - Incorporates regression effects\n- Variable selection:\n  - Uses spike-and-slab variable selection to identify relevant indicators\n- Flexibility and extensions:\n  - Capacity to adapt to longer forecast horizons\n  - Supports dynamic regressors"
    },
    {
      "heading": "Model selection, evaluation, and robustness",
      "content": "- Framework components:\n  - Outlines a framework for model selection and evaluation\n  - Includes robustness checks\n  - Includes sensitivity analysis\n- Performance assessment:\n  - Demonstrates the model’s relative performance (comparative performance details are presented in the paper)"
    },
    {
      "heading": "Findings and utility",
      "content": "- Nowcasting application:\n  - BSTS applied to nowcast quarterly economic growth for Tanzania leveraging a range of high-frequency indicators\n- Benefits highlighted:\n  - Flexible framework for trend and seasonality decomposition\n  - Spike-and-slab aids in isolating informative predictors\n  - Adaptability to changing economic environments through dynamic regressors and longer horizons\n- Intended use:\n  - Enhances capacity to monitor short-term growth trends in low-frequency-data environments"
    },
    {
      "heading": "Subjects and keywords",
      "content": "- Subject: Agricultural sector, Econometric analysis, Economic and financial statistics, Economic forecasting, Economic sectors, Sensitivity analysis, Time series analysis\n- Keywords: Africa, Agricultural sector, application of the Bayesian Structural Time Series, Bayesian models, Bayesian Structural Time Series model, economic activity, GDP, Global, IMF working papers, inclusion probability, low-income countries, Nowcasting, prediction error, Sensitivity analysis, Sub-Saharan Africa, Time series analysis\n\nIMF Working Papers — Nowcasting Growth Using the Bayesian Structural Time Series Model: Application to Tanzania, Sunwoo Lee, March 20, 2026.\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/2026/03/20/nowcasting-growth-using-the-bayesian-structural-time-series-model-574851"
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    "Authors: Sunwoo Lee",
    "Published: March 20, 2026",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798229042116.001",
    "Title: Nowcasting Growth Using the Bayesian Structural Time Series Model: Application to Tanzania",
    "Author: Sunwoo Lee",
    "Date: March 20, 2026",
    "Series: Working Paper No. 2026/049",
    "Issue: 049",
    "Volume: 2026",
    "Pages: 27",
    "DOI: https://doi.org/10.5089/9798229042116.001",
    "Stock No: WPIEA2026049",
    "ISBN: 9798229042116",
    "ISSN: 1018-5941",
    "Format availability: PDF (Download PDF), More Formats on IMF eLibrary, Order a Print Copy, Create Citation",
    "Purpose: Examine application of the Bayesian Structural Time Series (BSTS) model to nowcasting quarterly economic growth in Tanzania using high-frequency economic indicators.",
    "Motivation: Rising external volatility and recent global shocks increase need to effectively monitor short-term economic fluctuations, especially in countries with limited access to high-frequency growth data.",
    "Model components:",
    "Variable selection:",
    "Flexibility and extensions:",
    "Framework components:",
    "Performance assessment:",
    "Nowcasting application:",
    "Benefits highlighted:",
    "Intended use:",
    "Subject: Agricultural sector, Econometric analysis, Economic and financial statistics, Economic forecasting, Economic sectors, Sensitivity analysis, Time series analysis",
    "Keywords: Africa, Agricultural sector, application of the Bayesian Structural Time Series, Bayesian models, Bayesian Structural Time Series model, economic activity, GDP, Global, IMF working papers, inclusion probability, low-income countries, Nowcasting, prediction error, Sensitivity analysis, Sub-Saharan Africa, Time series analysis",
    "**Working Paper**"
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