Quarterly National Accounts/High Frequency Indicators of Economic Activity

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Canonical URL: Quarterly National Accounts/High Frequency Indicators of Economic Activity

Training Program title: Quarterly National Accounts/High Frequency Indicators of Economic Activity (QNAx).
Session No.: OL 26.192.
Location: Course conducted online.
Date: May 1, 2026 - April 15, 2027 (50 weeks).
Delivery Method: Online Training.
Primary Language: Spanish.
Target Audience: Officials worldwide responsible for compiling quarterly national accounts (QNA) and/or high frequency indicators of economic activity (HFIEA).
Qualifications: Participants are expected to have a degree in economics or statistics; or equivalent experience.

The course prepares participants to compile QNA and/or HFIEA by providing a thorough understanding of the concepts, source data, and compilation techniques used for producing these datasets.
Coverage:
- Theoretical and practical compilation issues.
- Benchmarking techniques.
- Seasonal adjustment techniques.
- Volume estimates and their application to time series data.
- Identification and assessment of available data sources for compiling QNA and HFIEAs.
- Use of related real-time series databases to assess the quality of QNA and HFIEAs.
- Formulation and implementation of a suitable revisions policy.

Compilation frameworks and methods:
- Role, scope, and uses of QNA and HFIEAs.
- Compilation framework for the QNA.
- Different compilation methods for HFIEAs, including composite leading indicators.
Data approaches:
- Review of available data sources for compiling QNA by the income, expenditure and production approaches, and HFIEAs.
Volume and price measures:
- Use of volume measures and the basic relation between value, quantity, and price — including how to detect and address issues such as the need for updated weights and recognizing the loss of additivity for chain-linked volume estimates.
Practical compilation techniques:
- Compile benchmarked series using the recommended techniques.
- Apply basic techniques for filling data gaps.

Formulate a balanced revisions policy taking account of how related real-time databases can be used to assess the reliability of the QNA/HFIEA estimates.
Use of real-time series databases to evaluate and improve estimate quality.

Upon completion of the course, participants should be able to:

Recognize the role, scope, and uses of QNA and HFIEAs.
Describe the compilation framework for the QNA and the different compilation methods for HFIEAs (including composite leading indicators).
Review available data sources for compiling QNA by the income, expenditure and production approaches, and HFIEAs.
Explain the use of volume measures and the basic relation between value, quantity, and price — expanding on how to detect and address issues such as the need for updated weights; and recognizing the loss of additivity for chain-linked volume estimates.
Compile benchmarked series using the recommended techniques.
Apply basic techniques for filling data gaps.
Formulate a balanced revisions policy taking account of how related real-time databases can be used to assess the reliability of the QNA/HFIEA estimates.