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Verfahren der konjunkturellen Wendepunktbestimmung unter Berücksichtigung der Echtzeit-Problematik

Author

Listed:
  • Daniel Detzer
  • Christian R. Proaño
  • Katja Rietzler
  • Sven Schreiber

    (Macroeconomic Policy Institute (IMK))

  • Thomas Theobald

    (Macroeconomic Policy Institute (IMK))

  • Sabine Stephan

    (Macroeconomic Policy Institute (IMK))

Abstract

Forecasting business-cycle turning points under real-time conditions One of the greatest challenges in business cycle research is the timely and reliable identification of cyclical turning points.The data availability in real time constitutes a fundamental problem:First there is a publication lag of several months for some of the indicators concerning the real economy, and secondly those indicators are subject to substantial revisions even afterwards. The IMK undertook a systematic analysis of the business-cycle turning point detection problem in real time for Germany, applying and comparing four different econometric model classes. The employed methods recognize turning points two to four months ahead of official statistics in real time, for the evaluation sample of 2007 through 2010. A (nonlinear) dynamic probit model and a (linear) so-called subset VAR model seem to be especially well suited for this task. Based on our research results we conclude that it is advisable for the detection of turning points to combine many indicators.

Suggested Citation

  • Daniel Detzer & Christian R. Proaño & Katja Rietzler & Sven Schreiber & Thomas Theobald & Sabine Stephan, 2012. "Verfahren der konjunkturellen Wendepunktbestimmung unter Berücksichtigung der Echtzeit-Problematik," IMK Studies 27-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    • Handle: RePEc:imk:studie:27-2012
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    References listed on IDEAS

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    2. Stark, Tom & Croushore, Dean, 2002. "Reply to the comments on 'Forecasting with a real-time data set for macroeconomists'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 563-567, December.
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    5. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    6. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 95-156, National Bureau of Economic Research, Inc.
    7. Jonathan H. Wright, 2006. "The yield curve and predicting recessions," Finance and Economics Discussion Series 2006-07, Board of Governors of the Federal Reserve System (U.S.).
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    Cited by:

    1. Bofinger, Peter & Feld, Lars P. & Schmidt, Christoph M. & Schnabel, Isabel & Wieland, Volker, 2018. "Vor wichtigen wirtschaftspolitischen Weichenstellungen. Jahresgutachten 2018/19 [Setting the Right Course for Economic Policy. Annual Report 2018/19]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201819.
    2. Theobald, Thomas, 2013. "Markov Switching with Endogenous Number of Regimes and Leading Indicators in a Real-Time Business Cycle Forecast," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79911, Verein für Socialpolitik / German Economic Association.
    3. 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.
    4. Schreiber, Sven, 2013. "Forecasting business-cycle turning points with (relatively large) linear systems in real time," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79709, Verein für Socialpolitik / German Economic Association.
    5. Anna Billharz & Steffen Elstner & Marcus Jüppner, 2012. "Ifo Short-Term Forecasting Methods Illustrated Using Investment in Equipment," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(21), pages 24-33, November.
    6. Heilemann Ullrich & Schnorr-Bäcker Susanne, 2017. "Could the start of the German recession 2008–2009 have been foreseen? Evidence from Real-Time Data," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(1), pages 29-62, February.
    7. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    8. Ulrich Heilemann & Susanne Schnorr-Bäcker, 2016. "Could The Start Of The German Recession 2008-2009 Have Been Foreseen? Evidence From Real-Time Data," Working Papers 2016-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Katja Rietzler & Sabine Stephan, 2012. "Monthly recession predictions in real time: A density forecast approach for German industrial production," IMK Working Paper 94-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.

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