## Quarterly National Accounts

## Source details

**Canonical URL:** [Quarterly National Accounts](https://www.imf.org/en/capacity-development/training/icdtc/schedule/sa/2025/qnasa25-26)

## Other formats

- [Markdown version](/en/capacity-development/training/icdtc/schedule/sa/2025/qnasa25-26/index.md)
- [Structured JSON version](/en/capacity-development/training/icdtc/schedule/sa/2025/qnasa25-26/index.json)
- [Bundle manifest](/en/capacity-development/training/icdtc/schedule/sa/2025/qnasa25-26/bundle-manifest.json)

## Bibliographic details
- Session: SA 25.26
- Location: New Delhi, India
- Dates: June 23-27, 2025 (1 week)
- Delivery method: In-person Training
- Primary language: English
- Status: Deadline passed

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### Program overview
- 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

### Course description and scope
- 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:
  - Identify and assess available data sources for compiling QNA.
  - Use related real-time series databases to assess the quality of QNA.
  - Implement a suitable revisions policy.

### Course objectives (learning outcomes)
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)
- [Executive Training 2 on Fiscal Policy Considerations (FPA)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/ce/2026/fpcce26-16)
- [Tax Regional Workshop on Strategic Management](https://www.imf.org/en/capacity-development/training/icdtc/schedule/ce/2026/taxce26-43)
- [Institutional Sector Accounts-Advanced - Blended (ISA-A)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/hq/2027/isa-ahq27-02)

_Source: https://www.imf.org/en/capacity-development/training/icdtc/schedule/sa/2025/qnasa25-26_