{
  "title": "Quarterly National Accounts",
  "sourceUrl": "https://www.imf.org/en/capacity-development/training/icdtc/schedule/sa/2025/qnasa25-26",
  "canonical": "https://www.imf.org/en/capacity-development/training/icdtc/schedule/sa/2025/qnasa25-26",
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  "summary": "Quarterly National Accounts Training Program Target Audience Officials responsible for compiling quarterly national accounts (QNA) from SARTTAC member Countries.",
  "publishDate": "2025-07-01",
  "sections": [
    {
      "heading": "Program overview",
      "content": "- Training Program focused on compiling quarterly national accounts (QNA).\n- Target Audience: Officials responsible for compiling quarterly national accounts (QNA) from SARTTAC member Countries.\n- Qualifications: Participants are expected to have a degree in economics or statistics, or equivalent experience.\n- Session No.: SA 25.26\n- Deadline: Deadline passed\n- Location: New Delhi, India\n- Date: June 23-27, 2025 (1 week)\n- Delivery Method: In-person Training\n- Primary Language: English\n- Presented by: Statistics Department"
    },
    {
      "heading": "Course description and scope",
      "content": "- One-week course preparing participants to compile QNA by providing a thorough understanding of the concepts, source data, and compilation techniques used for producing these datasets.\n- Covers both theoretical and practical compilation issues.\n- Introduces benchmarking, seasonal adjustment techniques, and volume estimates; explains the application of these techniques to time series data.\n- Broadly based on the IMF Quarterly National Accounts Manual (2017 Edition), with expanded techniques for dealing with alternative data sources and mixed frequencies.\n- Participants will learn to:\n  - Identify and assess available data sources for compiling QNA.\n  - Use related real-time series databases to assess the quality of QNA.\n  - Implement a suitable revisions policy."
    },
    {
      "heading": "Course objectives (learning outcomes)",
      "content": "Upon completion of this 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 database can be used to assess the reliability of the QNA estimates."
    }
  ],
  "bullets": [
    "Training Program focused on compiling quarterly national accounts (QNA).",
    "Target Audience: Officials responsible for compiling quarterly national accounts (QNA) from SARTTAC member Countries.",
    "Qualifications: Participants are expected to have a degree in economics or statistics, or equivalent experience.",
    "Session No.: SA 25.26",
    "Deadline: Deadline passed",
    "Location: New Delhi, India",
    "Date: June 23-27, 2025 (1 week)",
    "Delivery Method: In-person Training",
    "Primary Language: English",
    "Presented by: Statistics Department",
    "One-week course preparing participants to compile QNA by providing a thorough understanding of the concepts, source data, and compilation techniques used for producing these datasets.",
    "Covers both theoretical and practical compilation issues.",
    "Introduces benchmarking, seasonal adjustment techniques, and volume estimates; explains the application of these techniques to time series data.",
    "Broadly based on the IMF Quarterly National Accounts Manual (2017 Edition), with expanded techniques for dealing with alternative data sources and mixed frequencies.",
    "Participants will learn to:",
    "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 database can be used to assess the reliability of the QNA estimates."
  ],
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  },
  "generatedAtUtc": "2026-09-29T20:09:03.659Z"
}