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Construction of Composite Business Cycle Indicators in a Sparse Data Environment

Author

Listed:
  • 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 Group Munich.
  • Handle: RePEc:ces:ceswps:_3557
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    File URL: http://www.cesifo-group.de/DocDL/cesifo1_wp3557.pdf
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    References listed on IDEAS

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    1. Fabio Canova & Filippo Ferroni, 2011. "Multiple filtering devices for the estimation of cyclical DSGE models," Quantitative Economics, Econometric Society, vol. 2(1), pages 73-98, March.
    2. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    3. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    4. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    5. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    6. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    7. Kohli, Ulrich, 2004. "Real GDP, real domestic income, and terms-of-trade changes," Journal of International Economics, Elsevier, vol. 62(1), pages 83-106, January.
    8. Wolfgang Nierhaus, 2000. "Realeinkommen im neuen Europäischen System Volkswirtschaftlicher Gesamtrechnungen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 53(04), pages 07-13, February.
    9. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    10. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, January.
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    More about this item

    Keywords

    business cycle indicators; temporal disaggregation; terms of trade; oil-producing countries;

    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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