Quarterly National Accounts/High Frequency Indicators of Economic Activity

Source details

Canonical URL

Quarterly National Accounts/High Frequency Indicators of Economic Activity

Other formats

• Markdown version
• Structured JSON version
• Bundle manifest

Bibliographic details

• Session: OL 23.186
• Location: Course conducted online
• Dates: May 1, 2023 - August 31, 2023 (18 weeks)
• Delivery method: Online Training
• Primary language: English
• Status: Deadline passed

Program logistics and qualifications

• Session No.: OL 23.186
• Location: Course conducted online
• Date: May 1, 2023 - August 31, 2023 (18 weeks)
• Delivery Method: Online Training
• Primary Language: English
• Qualifications: 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.

Target audience and purpose

• Target Audience:
  • Intended for anyone interested in learning how to compile and disseminate Quarterly National Accounts (QNA) and High-Frequency Indicators of Economic Activity (HFIEAs).
  • Focus on applying recommendations of the System of National Accounts and using high-frequency data to analyze current economic developments.
• Purpose:
  • Introductory course to teach compilation and dissemination practices for QNA and HFIEAs and the role of high-frequency data in economic analysis.

Course content and methods

• Core topics covered:
  • Concepts, data sources, and compilation techniques for producing timely and high-quality QNA and HFIEAs.
  • Theoretical and practical compilation issues.
  • Benchmarking and seasonal adjustment techniques.
  • Volume estimates and the application of these techniques to time series data.
  • Identification and assessment of available data sources.
  • Use of real-time series databases to assess data quality.
  • Design and implementation of a suitable revisions policy for QNA and HFIEAs.
• Practical skills emphasized:
  • Compiling benchmarked series and filling data gaps.
  • Applying basic seasonal adjustment techniques to time series based on good seasonal adjustment practices.
  • Applying volume measures and understanding the basic relation between value, quantity, and price.
  • Detecting and addressing issues such as the need for updated weights and recognizing the loss of additivity for chain-linked volume estimates.
  • Describing a balanced revisions policy and using a related real-time database to assess reliability of estimates.

Course objectives (learning outcomes)

• Upon completion, 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 relation between value, quantity, and price, including addressing updated weights and 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 practices.
  • Describe a balanced revisions policy and how a real-time database can be used to assess reliability of QNA and HFIEA estimates.

References

• Online Training
• Core Elements of Banking Supervision (CBS)
• Advanced Government Finance Statistics (GFS-A)
• Fiscal Sustainability (FS)
• Safeguards Assessments of Central Banks (SAC)
• Macroeconomic Management in Resource Rich Countries (MRC)