Quarterly National Accounts (QNA)
Source details
- Canonical URL
- Quarterly National Accounts (QNA)
Other formats
Bibliographic details
- Published: July 29, 2025
Overview
- 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 and qualifications
- Target Audience:
- Officials worldwide responsible for compiling national accounts.
- Qualifications:
- Participants are expected to have a degree in economics or statistics; or equivalent experience.
Course content and techniques taught
- 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.
Data quality, revisions, and real-time databases
- 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.
Course objectives (learning outcomes)
Upon completion of the 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 databases can be used to assess the reliability of the QNA estimates.