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Macroeconomic Statistics

Quarterly National Accounts (QNA)

Apply online by May 11, 2025 Deadline extended

Session No.: JV 25.24

Location: Vienna, Austria

Date: August 18-29, 2025 (2 weeks)

Delivery Method: In-person Training

Primary Language: English

Interpretation Language: Russian

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    Target Audience

    Officials worldwide responsible for compiling quarterly national accounts (QNA).

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    Qualifications

    Participants are expected to have a degree in economics or statistics; or equivalent experience.

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    Course Description

    This one-week course, presented by the Statistics Department, prepares participants to compile QNA by providing them with a thorough understanding of the concepts, source data, and compilation techniques used for producing these datasets. The course covers both theoretical and practical compilation issues. It introduces participants to benchmarking, seasonal adjustment techniques, as well as volume estimates; and explains the application of these techniques to time series data.While the course is broadly based on the IMF Quarterly National Accounts Manual (2017 Edition), it expands on techniques required to dealing with alternative data sources, and mixed frequencies. The course covers both theoretical and practical compilation issues.Participants will learn how to identify and assess available data sources for compiling QNA; use related real-time series databases to assess the quality of QNA; and implement a suitable revisions policy. 

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    Course Objectives

    Upon completion of this course, participants should be able 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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