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Markov-Switching and the Ifo Business Climate: The Ifo Business Cycle Traffic Lights

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  • Klaus Abberger
  • Wolfgang Nierhaus

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Abstract

Business cycle indicators are used to assess the economic situation of countries or regions. They are closely watched by the public, but are not easy to interpret. Does a current movement of the indicator signal a turning point or not? With the help of Markov Switching Models movements of indicators can be transformed in probability statements. In this article, the most important leading indicator of the German business cycle, the Ifo Business Climate, is described by a Markov Switching Model. Real-time probabilities for the current business-cycle regime are derived and presented in an innovative way: as the Ifo traffic lights.

Suggested Citation

  • Klaus Abberger & Wolfgang Nierhaus, 2010. "Markov-Switching and the Ifo Business Climate: The Ifo Business Cycle Traffic Lights," CESifo Working Paper Series 2936, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_2936
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    References listed on IDEAS

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    1. Klaus Abberger & Wolfgang Nierhaus, 2009. "Months for Cyclical Dominance und ifo Geschäftsklima," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(07), pages 11-19, April.
    2. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    3. Potter, Simon M, 1999. " Nonlinear Time Series Modelling: An Introduction," Journal of Economic Surveys, Wiley Blackwell, vol. 13(5), pages 505-528, December.
    4. Klaus Abberger & Wolfgang Nierhaus, 2007. "Das ifo Geschäftsklima und Wendepunkte der deutschen Konjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 60(03), pages 26-31, February.
    5. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    6. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-391, July.
    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.
    8. Daniel R. Smith & Allan Layton, 2007. "Comparing Probability Forecasts in Markov Regime Switching Business Cycle Models," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(1), pages 79-98.
    9. Benoit Bellone, 2005. "Classical Estimation of Multivariate Markov-Switching Models using MSVARlib," Econometrics 0508017, EconWPA.
    10. repec:ces:ifosdt:v::y:2009:i::p:11-19 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Micha³ Bernardelli, 2015. "The Economic Situation In Poland Through The Prism Of The Situation In The Enterprises On The Basis Of The Business Tendency Survey," GUT FME Conference Publications,in: Blazej Prusak (ed.), ENTERPRISES IN UNSTABLE ECONOMY, chapter 10, pages 109-136 Faculty of Management and Economics, Gdansk University of Technology.
    2. Klaus Abberger & Wolfgang Nierhaus, 2010. "The Ifo Business Cycle Clock: Circular Correlation with the Real GDP," CESifo Working Paper Series 3179, CESifo Group Munich.
    3. 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, October.
    4. Kai Carstensen & Markus Heinrich & Magnus Reif & Maik H. Wolters, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," CESifo Working Paper Series 6457, CESifo Group Munich.
    5. Klaus Abberger & Wolfgang Nierhaus, 2010. "Drei Monitorsysteme zur Analyse der sächsischen Industriekonjunktur," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 17(06), pages 33-39, December.
    6. Gerdesmeier, Dieter & Reimers, Hans-Eggert & Roffia, Barbara, 2015. "Consumer and asset prices: Some recent evidence," Wismar Discussion Papers 01/2015, Hochschule Wismar, Wismar Business School.
    7. 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.
    8. Silvia Palasca & Elisabeta Jaba, 2014. "Leading and Lagging Indicators Of the Economic Crisis," Romanian Statistical Review, Romanian Statistical Review, vol. 62(3), pages 31-47, September.
    9. repec:ces:ifosdt:v:70:y:2017:i:24:p:28-81 is not listed on IDEAS
    10. Robert Lehmann & Andreas Sharik & Michael Weber, 2014. "Der Erklärungsgehalt der regionalen ifo-Indikatoren am Beispiel der Industrie- und Bauumsätze," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 21(04), pages 18-24, August.
    11. Klaus Abberger & Wolfgang Nierhaus, 2011. "Die aktuelle Wirtschaftsentwicklung im Lichte der ifo Konjunkturampel," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 36-38, November.
    12. Klaus Abberger & Wolfgang Nierhaus, 2011. "Die ifo Konjunkturuhr: Zirkulare Korrelation mit dem realen Bruttoinlandsprodukt," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 5(3), pages 179-201, December.
    13. Wolfgang Nierhaus, 2017. "Wirtschaftskonjunktur 2016: Prognose und Wirklichkeit," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(02), pages 72-78, January.
    14. Timo Wollmershäuser & Wolfgang Nierhaus & Tim Oliver Berg & Christian Breuer & Johanna Garnitz & Christian Grimme & Atanas Hristov & Nikolay Hristov & Wolfgang Meister & Magnus Reif & Felix Schröter &, 2015. "ifo Konjunkturprognose 2015/2017: Verhaltener Aufschwung setzt sich fort," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(24), pages 23-66, December.
    15. Christian Seiler & Klaus Wohlrabe, 2013. "Das ifo Geschäftsklima und die deutsche Konjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.

    More about this item

    Keywords

    Ifo business climate; growth cycle; turning points; Markov-switching;

    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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