{
  "title": "Quarterly National Accounts (QNA)",
  "sourceUrl": "https://www.imf.org/en/capacity-development/training/icdtc/courses/qna",
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  "summary": "The course covers both theoretical and practical compilation issues.",
  "publishDate": "2025-07-29",
  "sections": [
    {
      "heading": "Overview",
      "content": "- This Training Program Course prepares participants to compile quarterly national accounts (QNA) by providing a thorough understanding of the concepts, source data, and compilation techniques used for producing QNA.\n- The course covers both theoretical and practical compilation issues.\n- The course is broadly based on the IMF Quarterly National Accounts Manual (2017 Edition) and expands on techniques required for dealing with alternative data sources and mixed frequencies.\n- Introduces benchmarking, seasonal adjustment techniques, and volume estimates, and explains the application of these techniques to time series data.\n- Participants learn to identify and assess available data sources for compiling QNA, use related real-time series databases to assess QNA quality, and implement a suitable revisions policy.\n- Topic: Macroeconomic Statistics"
    },
    {
      "heading": "Target audience and qualifications",
      "content": "- Target Audience:\n  - Officials worldwide responsible for compiling national accounts.\n- Qualifications:\n  - Participants are expected to have a degree in economics or statistics; or equivalent experience."
    },
    {
      "heading": "Course content and techniques taught",
      "content": "- Theoretical and practical compilation issues for QNA.\n- Benchmarking techniques for reconciling quarterly series with annual or benchmark data.\n- Seasonal adjustment techniques, including identification of good practices and application of basic seasonal adjustment methods to time series.\n- Volume estimation methods for producing real (constant-price) series.\n- Techniques for dealing with alternative data sources and mixed-frequency data.\n- Methods for filling data gaps and backcasting."
    },
    {
      "heading": "Data quality, revisions, and real-time databases",
      "content": "- Guidance on identifying and assessing available data sources for QNA compilation.\n- Use of related real-time series databases to assess the reliability and quality of QNA estimates.\n- Formulation of a balanced revisions policy that takes account of how real-time databases can inform revision practices."
    },
    {
      "heading": "Course objectives (learning outcomes)",
      "content": "Upon completion of the course, participants should be able to:\n- Recognize the role, scope, and uses of QNA.\n- Describe the compilation framework for the QNA and early estimates.\n- Compile benchmarked series using the recommended techniques.\n- Apply basic techniques for filling data gaps and backcasting.\n- Identify good seasonal adjustment practices and apply basic seasonal adjustment techniques to time series.\n- Formulate a balanced revisions policy taking account of how related real-time databases can be used to assess the reliability of the QNA estimates."
    }
  ],
  "bullets": [
    "This Training Program Course prepares participants to compile quarterly national accounts (QNA) by providing a thorough understanding of the concepts, source data, and compilation techniques used for producing QNA.",
    "The course covers both theoretical and practical compilation issues.",
    "The course is broadly based on the IMF Quarterly National Accounts Manual (2017 Edition) and expands on techniques required for dealing with alternative data sources and mixed frequencies.",
    "Introduces benchmarking, seasonal adjustment techniques, and volume estimates, and explains the application of these techniques to time series data.",
    "Participants learn to identify and assess available data sources for compiling QNA, use related real-time series databases to assess QNA quality, and implement a suitable revisions policy.",
    "Topic: Macroeconomic Statistics",
    "Target Audience:",
    "Qualifications:",
    "Theoretical and practical compilation issues for QNA.",
    "Benchmarking techniques for reconciling quarterly series with annual or benchmark data.",
    "Seasonal adjustment techniques, including identification of good practices and application of basic seasonal adjustment methods to time series.",
    "Volume estimation methods for producing real (constant-price) series.",
    "Techniques for dealing with alternative data sources and mixed-frequency data.",
    "Methods for filling data gaps and backcasting.",
    "Guidance on identifying and assessing available data sources for QNA compilation.",
    "Use of related real-time series databases to assess the reliability and quality of QNA estimates.",
    "Formulation of a balanced revisions policy that takes account of how real-time databases can inform revision practices.",
    "Recognize the role, scope, and uses of QNA.",
    "Describe the compilation framework for the QNA and early estimates.",
    "Compile benchmarked series using the recommended techniques.",
    "Apply basic techniques for filling data gaps and backcasting.",
    "Identify good seasonal adjustment practices and apply basic seasonal adjustment techniques to time series.",
    "Formulate a balanced revisions policy taking account of how related real-time databases can be used to assess the reliability of the QNA estimates."
  ],
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  "generatedAtUtc": "2026-09-29T16:40:42.401Z"
}