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Leading Indicators of German Business Cycles: An Assessment of Properties

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  • Ulrich Fritsche
  • Sabine Stephan

Abstract

A reliable leading indicator should possess the following properties: (1) The movements in the indicator series should resemble those in the business cycle reference series. (2) The relation between the reference series and the indicator should be statistically significant and stable over time. (3) The inclusion of the indicator in out-of-sample forecasting procedures should improve the predictive power. Our analysis deals with tests for these requirements applied to German data. We used frequency domain analysis, different Granger-causality tests and out-of sample forecasts. Only few indicators passed all tests. Their inclusion into VAR-based forecasts improves the forecast in the very short run. Brauchbare Frühindikatoren sollten folgende Eigenschaften besitzen: (1) Die konjunkturellen Bewegungen des Frühindikators sollten denen der Referenzreihe folgen. (2) Die Beziehung zwischen den Reihen sollte stabil und signifikant sein. (3) Die Einbeziehung des Indikators sollte die Out-of-sample-Prognose verbessern. Unsere Untersuchung testet diese Anforderungen für deutsche Daten. Dazu werden Methoden der Spektralanalyse, verschiedene Granger-Tests und Out-of-sample-Prognosen verwendet. Nur wenige Indikatoren bestehen die Tests auf die geforderten Eigenschaften. Ihre Einbeziehung in VAR-basierte Prognosen verbessert die Prognoseleistung in der sehr kurzen Frist.

Suggested Citation

  • Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Discussion Papers of DIW Berlin 207, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp207
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    1. Norbert Funke, 1997. "Predicting recessions: Some evidence for Germany," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 133(1), pages 90-102, March.
    2. Canova, Fabio, 1998. "Detrending and business cycle facts," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 475-512, May.
    3. Bernanke, Ben S & Blinder, Alan S, 1992. "The Federal Funds Rate and the Channels of Monetary Transmission," American Economic Review, American Economic Association, vol. 82(4), pages 901-921, September.
    4. Gebhardt Kirschgässner & Marcel Savioz, 2001. "Monetary Policy and Forecasts for Real GDP Growth: An Empirical Investigation for the Federal Republic of Germany," German Economic Review, Verein für Socialpolitik, vol. 2(4), pages 339-365, November.
    5. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, July.
    6. Döpke, Jörg, 1998. "Leading indicators for Euroland's business cycle," Kiel Working Papers 886, Kiel Institute for the World Economy (IfW Kiel).
    7. Erich Langmantel, 1999. "Das ifo Geschäftsklima als Indikator für die Prognose des Bruttoinlandsprodukts," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 52(16-17), pages 16-21, October.
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    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • L70 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - General

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