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Verfahren der konjunkturellen Wendepunktbestimmung unter Berücksichtigung der Echtzeit-Problematik

Author

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

    () (IMK at the Hans-Boeckler-Foundation)

  • Thomas Theobald

    () (IMK at the Hans-Boeckler-Foundation)

  • Sabine Stephan

    () (IMK at the Hans-Boeckler-Foundation)

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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    1. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    2. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, Elsevier.
    3. 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.
    4. 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.
    5. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters,in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
    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. Theobald, Thomas, 2013. "Markov Switching with Endogenous Number of Regimes and Leading Indicators in a Real-Time Business Cycle Forecast," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79911, Verein für Socialpolitik / German Economic Association.
    2. 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.
    3. Schreiber, Sven, 2013. "Forecasting business-cycle turning points with (relatively large) linear systems in real time," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79709, Verein für Socialpolitik / German Economic Association.
    4. Anna Billharz & Steffen Elstner & Marcus Jüppner, 2012. "Methoden der ifo Kurzfristprognose am Beispiel der Ausrüstungsinvestitionen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(21), pages 24-33, November.
    5. 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, Research Program on Forecasting.
    6. 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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