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Seasonal and Cyclical Properties of Ifo Business Test Variables

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  • Flaig, Gebhard

Abstract

In this paper, an Unobserved Components Model is used to decompose the balances of Ifo Business survey data into the cyclical, the seasonal and the irregular components, as well as the working day effect. The empirical results show that the total cycle consists of three subcycles with about 3,5 and 11 years. Each subcycle of the assessment variable is \"similar\" to the corresponding subcycle of the expectations variable. The seasonal pattern is changing over time and the working day effect is significant for the assessment of the current business situation and for the Ifo Business Climate, but not for the expectation series.

Suggested Citation

  • Flaig, Gebhard, 2003. "Seasonal and Cyclical Properties of Ifo Business Test Variables," Munich Reprints in Economics 20379, University of Munich, Department of Economics.
    • Handle: RePEc:lmu:muenar:20379
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    Cited by:

    1. Steffen Henzel, 2015. "Forecasting Accuracy of the Ifo Business Survey – Influence of New Seasonal Adjustment with X-13ARIMA-SEATS," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(01), pages 59-63, January.
    2. Goldrian Georg, 2004. "On the Efficiency of Unobserved Components Models: A Comment," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(6), pages 751-755, December.
    3. Sascha O. Becker & Klaus Wohlrabe, 2008. "European Data Watch: Micro Data at the Ifo Institute for Economic Research – The “Ifo Business Survey”, Usage and Access," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 128(2), pages 307-319.
    4. Klaus Abberger & Sascha Becker & Barbara Hofmann & Klaus Wohlrabe, 2007. "Mikrodaten im ifo Institut für Wirtschaftsforschung – Bestand, Verwendung und Zugang," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 1(1), pages 27-42, June.
    5. Stefan Mittnik & Peter Zadrozny, 2005. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly Ifo Business Conditions Data," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 19-48, Springer.

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