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ifo Konjunkturampel Revisited

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  • Wolfgang Nierhaus
  • Klaus Abberger

Abstract

Monthly changes in the Ifo Business Climate can be converted into probabilities for both the “expansion” and/or “contraction” phase of the economic cycle using a Markov switching model. These probabilities – presented in the ifo Konjunkturampel – offer key information for the early detection of economic turning points. The changeover of the seasonal adjustment process used in the Ifo Business Climate to the census X-13ARIMA-­SEATS procedure called for a recalculation of the ifo Konjunkturampel. This article presents the methods used and their results.

Suggested Citation

  • 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.
    • Handle: RePEc:ces:ifosdt:v:68:y:2015:i:05:p:27-32
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    References listed on IDEAS

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    1. Klaus Abberger & Wolfgang Nierhaus, 2010. "Markov-Switching and the Ifo Business Climate: the Ifo Business Cycle Traffic Lights," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-13.
    2. Klaus Abberger & Wolfgang Nierhaus, 2008. "Markov Switching and the Ifo Business Climate," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(10), pages 25-30, May.
    3. Klaus Abberger & Wolfgang Nierhaus, 2011. "Current Economic Developments in View of the Ifo Economic Traffic Light," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 36-38, November.
    4. Wolfgang Nierhaus & Klaus Abberger, 2014. "Forecasting business-cycle turning points: the three-times-in-succession rule vs. Markov switching," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 67(16), pages 21-25, August.
    5. 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.
    6. 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.
    7. 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, March.
    8. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1, National Bureau of Economic Research, Inc.
    9. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    10. Kai Carstensen & Wolfgang Nierhaus & Tim Oliver Berg & Christian Breuer & Christian Grimme & Steffen Henzel & Atanas Hristov & Nikolay Hristov & Michael Kleemann & Wolfgang Meister & Johanna Garnitz &, 2013. "Ifo Economic Forecast 2013/2014: Favourable Perspectives for the German Economy," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(13), pages 17-64, July.
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    Cited by:

    1. Timo Wollmershäuser & Wolfgang Nierhaus & Nikolay Hristov & Dorine Boumans & Johanna Garnitz & Marcell Göttert & Christian Grimme & Stefan Lauterbacher & Robert Lehmann & Wolfgang Meister & Magnus Rei, 2016. "Ifo Economic Forecast 2016–2018: Germany’s Robust Economy Faces a Year of Uncertain International Economic Policy," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(24), pages 28-73, December.
    2. Christian Glocker & Philipp Wegmueller, 2020. "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, vol. 58(1), pages 73-105, January.
    3. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    4. Wolfgang Nierhaus, 2017. "Economic Activity in 2016: Forecast and Reality," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(02), pages 72-78, January.
    5. 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.

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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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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