Cancelled :Blended HFI and QNA introductory course (QNA)
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- Cancelled :Blended HFI and QNA introductory course (QNA)
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Bibliographic details
- Session: AT 23.16V
- Location: Ebene, Mauritius
- Dates: March 6-17, 2023 (2 weeks)
- Delivery method: Virtual Training
- Primary language: English
- Interpretation language: French
Target audience and qualifications
- Target audience:
- Officials worldwide who are 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.
- Access to a computer with a reliable Internet connection and a Google Chrome web browser are essential.
Course description
- Presented by the Statistics Department.
- Purpose:
- 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.
- Seasonal adjustment techniques.
- Volume estimates and application to time series data.
- Practical skills and tools:
- Identifying and assessing available data sources for compiling QNA and HFIEAs.
- Using related real-time series databases to assess the quality of QNA and HFIEAs.
- Implementing a suitable revisions policy.
Course objectives (learning outcomes)
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.
- 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/HFIEA estimates.