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Seasonal Adjustment in the Ifo Business Survey – Conversion to the X-13ARIMA-SEATS Procedure

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  • Stefan Sauer
  • Klaus Wohlrabe

Abstract

The seasonal adjustment is a fundamental component of the calculation of the results series of the Ifo Business Survey. The method used in this calculation will be converted as of January 2015 from the ASA-II procedure used to date to the X-13ARIMA-SEATS procedure widely used internationally. This article describes the basic concept, the partly changed characteristics of time series thanks to the conversion, and the advantages of the new process.

Suggested Citation

  • Stefan Sauer & Klaus Wohlrabe, 2015. "Seasonal Adjustment in the Ifo Business Survey – Conversion to the X-13ARIMA-SEATS Procedure," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(01), pages 32-42, January.
    • Handle: RePEc:ces:ifosdt:v:68:y:2015:i:01:p:32-42
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    References listed on IDEAS

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    1. Klaus Abberger & Wolfgang Nierhaus, 2009. "Months for cyclical dominance and the Ifo Business Climate," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(07), pages 11-19, April.
    2. Timo Wollmershäuser & Wolfgang Nierhaus & Tim Oliver Berg & Christian Breuer & Teresa Buchen & Christian Grimme & Steffen Henzel & Atanas Hristov & Nikolay Hristov & Michael Kleemann & Wolfgang Meiste, 2014. "Ifo Economic Forecast 2014/2015: German Economy Gradually Regains Impetus," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 67(24), pages 37-81, December.
    3. Klaus Abberger & Klaus Wohlrabe, 2006. "Forecasting qualities of the Ifo Business Climate Index - a look at recent studies," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    4. 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.
    5. Christian Seiler & Klaus Wohlrabe, 2013. "The Ifo Business Climate and the German Economy," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
    6. Hylleberg, S. (ed.), 1992. "Modelling Seasonality," OUP Catalogue, Oxford University Press, number 9780198773184.
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    Citations

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    Cited by:

    1. Robert Lehmann & Felix Leiss & Simon Litsche & Stefan Sauer & Michael Weber & Annette Weichselberger & Klaus Wohlrabe, 2019. "Mit den ifo-Umfragen regionale Konjunktur verstehen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(09), pages 45-49, May.
    2. Klaus Borger & Stefan Sauer, 2019. "KfW-ifo-Mittelstandsbarometer: Unterkühlte Stimmung im Mittelstand," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(18), pages 49-51, September.
    3. 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.
    4. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    5. Wolfgang Nierhaus & Klaus Abberger, 2015. "ifo Konjunkturampel Revisited," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(05), pages 27-32, March.
    6. Przemyslaw Brandt & Christian Grotemeier & Klaus Wohlrabe, 2017. "BVL Logistics Indicator – Background, Developments and Results," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(20), pages 36-42, October.

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    More about this item

    JEL classification:

    • 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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