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Do Ifo Indicators Help Explain Revisions in German Industrial Production?

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  • Jan Jacobs
  • Jan-Egbert Sturm

Abstract

This paper studies the information content of some Ifo indicators. In particular, we investigate whether two Ifo indicators, one on the current business situation, the other on current production development, provide information on revisions of German industrial production. A new feature of our analysis is the construction and use of a real-time dataset. We conclude that the Ifo indicators play a role in explaining revisions, but counterintuitively the business situation indicator performs better than the production indicator.

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File URL: http://www.cesifo-group.de/portal/page/portal/DocBase_Content/WP/WP-CESifo_Working_Papers/wp-cesifo-2004/wp-cesifo-2004-05/cesifo1_wp1205.pdf
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Bibliographic Info

Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 1205.

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Date of creation: 2004
Date of revision:
Handle: RePEc:ces:ceswps:_1205

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Keywords: Ifo Business Survey indicators; German industrial production; real-time analysis; data revision;

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References

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  1. Wolfgang Nierhaus & Jan-Egbert Sturm, 2003. "Methoden der Konjunkturprognose," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 56(04), pages 7-23, 02.
  2. Shaun P. Vahey & Andreas Pick & Don M. Egginton, 2001. ""Keep it real!": A real-time UK macro data set," Economics Bulletin, AccessEcon, vol. 28(18), pages A0.
  3. 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.
  4. Christopher Bajada, 2002. "The Effects of Inflation and the Business Cycle on Revisions of Macroeconomic Data," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 35(3), pages 276-286.
  5. Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia.
  6. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer, vol. 21(1), pages 243-247, December.
  7. Schröder, Michael & Hüfner, Felix P., 2002. "Forecasting economic activity in Germany: how useful are sentiment indicators?," ZEW Discussion Papers 02-56, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  8. Erich Langmantel, 1999. "Das ifo Geschäftsklima als Indikator für die Prognose des Bruttoinlandsprodukts," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 52(16-17), pages 16-21, October.
  9. Zarnowitz, Victor, 1992. "Business Cycles," National Bureau of Economic Research Books, University of Chicago Press, number 9780226978901.
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Citations

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Cited by:
  1. Jan Jacobs & Jan-Egbert Sturm, 2008. "The information content of KOF indicators on Swiss current account data revisions," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2008(2), pages 161-181.
  2. Gerberding, Christina & Worms, Andreas & Seitz, Franz, 2004. "How the Bundesbank really conducted monetary policy: An analysis based on real-time data," Discussion Paper Series 1: Economic Studies 2004,25, Deutsche Bundesbank, Research Centre.
  3. Gerit Vogt, 2007. "Analyse der Prognoseeigenschaften von ifo-Konjunkturindikatoren unter Echtzeitbedingungen," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 227(1), pages 87-101, February.
  4. Konstantin A. Kholodilin & Boriss Siliverstovs, 2009. "Do Forecasters Inform or Reassure?: Evaluation of the German Real-Time Data," Discussion Papers of DIW Berlin 858, DIW Berlin, German Institute for Economic Research.
  5. Döpke, Jörg & Hartmann, Daniel & Pierdzioch, Christian, 2005. "Forecasting stock market volatility with macroeconomic variables in real time," Discussion Paper Series 2: Banking and Financial Studies 2006,01, Deutsche Bundesbank, Research Centre.
  6. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
  7. Jens Boysen-Hogrefe & Stefan Neuwirth, 2012. "The Impact of Seasonal and Price Adjustments on the Predictability of German GDP Revisions," Kiel Working Papers 1753, Kiel Institute for the World Economy.
  8. Jan Jacobs & Jan-Egbert Sturm, 2007. "A real-time analysis of the Swiss trade account," Money Macro and Finance (MMF) Research Group Conference 2006 167, Money Macro and Finance Research Group.
  9. Gerit Vogt, 2009. "Konjunkturprognose in Deutschland. Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene," ifo Beiträge zur Wirtschaftsforschung, Ifo Institute for Economic Research at the University of Munich, number 36, July.
  10. Anna Scharschmidt & Klaus Wohlrabe, 2011. "Sektorale Prognosen im Verarbeitenden Gewerbe," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
  11. Klaus Abberger & Gebhard Flaig & Wolfgang Nierhaus, 2007. "ifo Konjunkturumfragen und Konjunkturanalyse : ausgewählte methodische Aufsätze aus dem ifo Schnelldienst," ifo Forschungsberichte, Ifo Institute for Economic Research at the University of Munich, number 33, 8.
  12. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.

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