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Prognose konjunktureller Wendepunkte unter Echtzeit-Bedingungen

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  • Schreiber, Sven
  • Stephan, Sabine

Abstract

Sven Schreiber, Sabine Stephan 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 later subject to substantial revisions. 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 methods employed 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

  • Schreiber, Sven & Stephan, Sabine, 2012. "Prognose konjunktureller Wendepunkte unter Echtzeit-Bedingungen," WSI-Mitteilungen, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 65(4), pages 289-296.
    • Handle: RePEc:nms:wsimit:10.5771/0342-300x-2012-4-289
    DOI: 10.5771/0342-300X-2012-4-289
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