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QNAx

Quarterly National Accounts/High Frequency Indicators of Economic Activity (QNAx)

Deadline passed

Session No.: OL 24.101

Location: Course conducted online

Date: March 28, 2024 - May 16, 2024 (10 weeks) New dates

Delivery Method: Online Training

Primary Language: French

    Target Audience

    This online course is designed for officials worldwide who are responsible for compiling quarterly national accounts (QNA) and/or high frequency indicators of economic activity (HFIEA).

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    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.

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    Course Description

    This course, presented by the Statistics Department, prepares participants to compile QNA and/or HFIEA by providing them with 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. It introduces participants to benchmarking, seasonal adjustment techniques, as well as volume estimates; and explains the application of these techniques to time series data. Participants will learn how to identify and assess available data sources for compiling QNA and HFIEAs; use related real-time series databases to assess the quality of QNA and HFIEAs; and implement a suitable revisions policy.

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    Course Objectives

    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.
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