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Australian Real-Time Database: An Overview and an Illustration of its Use in Business Cycle Analysis

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  • KEVIN LEE
  • NILSS OLEKALNS
  • KALVINDER SHIELDS
  • ZHENG WANG

Abstract

This paper describes a newly constructed macroeconomic database for Australia including measures of GDP, its components, prices, and key monetary and labour market statistics over the last fifty years as published and revised in real time. Data vintages are collated from various sources and accommodate multiple definitional changes, providing a comprehensive description of the macroeconomic environment actually experienced by Australian policy- and decision-makers. The database exposes the difficulties in drawing inferences and decision-making based on macroeconomic data that is subsequently revised. Methods are described that can exploit the real-time dataset and they are illustrated through an analysis of the Australian output gap.
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  • Kevin Lee & Nilss Olekalns & Kalvinder Shields & Zheng Wang, 2012. "Australian Real-Time Database: An Overview and an Illustration of its Use in Business Cycle Analysis," The Economic Record, The Economic Society of Australia, vol. 88(283), pages 495-516, December.
    • Handle: RePEc:bla:ecorec:v:88:y:2012:i:283:p:495-516
    DOI: 10.1111/ecor.2012.88.issue-283
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    Cited by:

    1. Andreas Dibiasi & Samad Sarferaz, 2020. "Measuring Macroeconomic Uncertainty: The Labor Channel of Uncertainty from a Cross-Country Perspective," Papers 2006.09007, arXiv.org, revised Dec 2020.
    2. Luke Hartigan & Tom Rosewall, 2024. "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," Working Papers 2024-15, University of Sydney, School of Economics.
    3. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    4. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    5. Dmitry Gornostaev & Alexey Ponomarenko & Sergei Seleznev & Alexandra Sterkhova, 2022. "A Real-Time Historical Database of Macroeconomic Indicators for Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 88-103, March.
    6. Dibiasi, Andreas & Sarferaz, Samad, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," European Economic Review, Elsevier, vol. 153(C).
    7. Anthony Garratt & Kevin Lee & Kalvinder Shields, 2014. "Forecasting Global Recessions in a GVAR Model of Actual and Expected Output in the G7," Discussion Papers 2014/06, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    8. Phan, Tuan, 2016. "Has Monetary Policy Become More Aggressive, But Less Effective Over Time?," MPRA Paper 107200, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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