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Do Leading Indicators Help to Predict Business Cycle Turning Points in Germany?

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  • Ulrich Fritsche
  • Vladimir Kuzin

Abstract

Using a binary reference series based on the dating procedure of Artis, Kontolemis and Osborn (1997) different procedures for predicting turning points of the German business cycles were tested. Specifically, a probit model as proposed by Estrella and Mishkin (1997) as well as Markov-switching models were taken into consideration. The overall results indicate that the interest rate spread, the longterm interest rate as well as some monetary indicators and some survey indicators can help predicting turning points of the business cycle.

Suggested Citation

  • Ulrich Fritsche & Vladimir Kuzin, 2002. "Do Leading Indicators Help to Predict Business Cycle Turning Points in Germany?," Discussion Papers of DIW Berlin 314, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp314
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    References listed on IDEAS

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    2. Canova, Fabio, 1998. "Detrending and business cycle facts: A user's guide," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 533-540, May.
    3. Canova, Fabio, 1998. "Detrending and business cycle facts," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 475-512, May.
    4. Estrella, Arturo & Mishkin, Frederic S., 1997. "The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank," European Economic Review, Elsevier, vol. 41(7), pages 1375-1401, July.
    5. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Macroeconomics 0004005, University Library of Munich, Germany.
    6. Michael J. Dueker, 1997. "Strengthening the case for the yield curve as a predictor of U.S. recessions," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 41-51.
    7. Döpke, Jörg, 1999. "Predicting Germany's recessions with leading indicators: Evidence from probit models," Kiel Working Papers 944, Kiel Institute for the World Economy (IfW Kiel).
    8. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
    9. 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.
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    Cited by:

    1. 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.
    2. Michael J. Lamla & Sarah M. Lein & Jan-Egbert Sturm, 2020. "Media reporting and business cycles: empirical evidence based on news data," Empirical Economics, Springer, vol. 59(3), pages 1085-1105, September.
    3. Claus Brand & Hans-Eggert Reimers & Franz Seitz, 2003. "Narrow Money and the Business Cycle: Theoretical aspects and euro area evdence," Macroeconomics 0303012, University Library of Munich, Germany.
    4. Brand, Claus & Reimers, Hans-Eggert & Seitz, Franz, 2003. "Forecasting real GDP: what role for narrow money?," Working Paper Series 254, European Central Bank.
    5. Deimante Teresiene & Greta Keliuotyte-Staniuleniene & Yiyi Liao & Rasa Kanapickiene & Ruihui Pu & Siyan Hu & Xiao-Guang Yue, 2021. "The Impact of the COVID-19 Pandemic on Consumer and Business Confidence Indicators," JRFM, MDPI, vol. 14(4), pages 1-23, April.
    6. Cruz-Rodriguez, Alexis, 2014. "¿Puede un índice de sostenibilidad fiscal predecir la ocurrencia de crisis cambiarias? Evidencias para algunos países seleccionados [Can a fiscal sustainability indicator predict the occurrence of ," MPRA Paper 54103, University Library of Munich, Germany.
    7. Cruz-Rodríguez, Alexis, 2015. "Sostenibilidad fiscal y crisis cambiarias: Un análisis empírico [Fiscal sustainability and currency crises: An empirical analysis]," MPRA Paper 67741, University Library of Munich, Germany.

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    More about this item

    Keywords

    Business cycle; leading indicators; probit model; McFadden's R2; Markov switching models;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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