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Construction of Composite Business Cycle Indicators in a Sparse Data Environment

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  • Klaus Abberger
  • Wolfgang Nierhaus

Abstract

Business cycle indicators are important instruments for monitoring economic development. When employing indicators one usually relies on a sound statistical database. This paper deals with indicator development in a sparse data situation. Indicator building is merged with temporal disaggregation, which is often used by statistical offices. The discussed tools are applied in a case study for Abu Dhabi. Because the economy of Abu Dhabi is very dependent on oil, real income reflects the economic situation better than real gross domestic product (GDP). For this reason a measure of real gross domestic income (GDI) was chosen as reference series.

Suggested Citation

  • Klaus Abberger & Wolfgang Nierhaus, 2011. "Construction of Composite Business Cycle Indicators in a Sparse Data Environment," CESifo Working Paper Series 3557, CESifo.
    • Handle: RePEc:ces:ceswps:_3557
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    File URL: https://www.cesifo.org/DocDL/cesifo1_wp3557.pdf
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    References listed on IDEAS

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    Cited by:

    1. Klaus Abberger & Biswa Nath Bhattacharyay & Chang Woon Nam & Gernot Nerb & Siegfried Schönherr, 2014. "How Can the Crisis Vulnerability of Emerging Economies Be Reduced?," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.

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    More about this item

    Keywords

    business cycle indicators; temporal disaggregation; terms of trade; oil-producing countries;
    All these keywords.

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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