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

Apply online by April 1, 2024

Session No.: OL 23.232

Location: Course conducted online

Date: September 1, 2023 - April 15, 2024 (32 weeks)

Delivery Method: Online Training

Primary Language: English

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    Target Audience

    This introductory online course is designed for anyone interested in learning how to compile and disseminate Quarterly National Accounts and High-Frequency Indicators of Economic Activity, according to the recommendations of the System of National Accounts, and using them to better understand the role of high-frequency data in analyzing current economic developments.

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    Knowledge of economics and statistics or equivalent experience is helpful. 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, teaches you how to compile timely and high-quality Quarterly National Accounts (QNA) and High-Frequency Indicators of Economic Activity (HFIEAs). The course presents the concepts, data sources, and compilation techniques needed for producing QNA and HFIEAs, covering 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. The course also discussed how to identify and assess available data sources, use real-time series databases to assess the quality, and implement a suitable revisions policy for compiling QNA and HFIEAs.

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    Course Objectives

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

    • Recognize the role, scope, uses, and compilation frameworks for QNA and HFIEAs. 
    • Review available data sources for compiling QNA 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.
    • Apply basic techniques for compiling benchmarked series and filling data gaps. 
    • Apply basic seasonal adjustment techniques to time series, based on good seasonal adjustment practices.
    • Describe a balanced revisions policy taking into consideration how a related real-time database can be used to assess the reliability of the QNA and HFIEA estimates.
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