## Quarterly National Accounts (QNA)

## Source details

**Canonical URL:** [Quarterly National Accounts (QNA)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/jv/2025/qnajv25-24)

## Other formats

- [Markdown version](/en/capacity-development/training/icdtc/schedule/jv/2025/qnajv25-24/index.md)
- [Structured JSON version](/en/capacity-development/training/icdtc/schedule/jv/2025/qnajv25-24/index.json)
- [Bundle manifest](/en/capacity-development/training/icdtc/schedule/jv/2025/qnajv25-24/bundle-manifest.json)

## Bibliographic details
- Session: JV 25.24
- Location: Vienna, Austria
- Dates: August 18-29, 2025 (2 weeks)
- Delivery method: In-person Training
- Primary language: English
- Interpretation language: Russian
- Status: Deadline passed

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### Training program and target audience
- Training Program: Quarterly National Accounts
- Target Audience: Officials worldwide responsible for compiling quarterly national accounts (QNA).
- Qualifications: Participants are expected to have a degree in economics or statistics; or equivalent experience.
- Deadline passed

### Course description
- The course is presented by the Statistics Department.
- "This one-week course" prepares participants to compile QNA by providing 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.
- Introduces benchmarking, seasonal adjustment techniques, as well as volume estimates; and explains the application of these techniques to time series data.
- Broadly based on the IMF Quarterly National Accounts Manual (2017 Edition), and expands on techniques required to dealing with alternative data sources, and mixed frequencies.
- 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.

### 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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## References

- [In-person Training](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods)
- [Core Elements of Banking Supervision (CBS)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/at/2027/cbsat27-10)
- [Advanced Government Finance Statistics (GFS-A)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/ce/2026/gfs-ace26-45)
- [Fiscal Sustainability (FS)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/ce/2026/fsce26-38)
- [Safeguards Assessments of Central Banks (SAC)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/at/2026/sacat26-30)

_Source: https://www.imf.org/en/capacity-development/training/icdtc/schedule/jv/2025/qnajv25-24_