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Monthly recession predictions in real time: A density forecast approach for German industrial production

  • Katja Rietzler
  • Sabine Stephan

In this paper we present a methodology which can help to improve the assessment of the current economic situation. We propose an approach which combines multivariate single equations to forecast the monthly growth rate of industrial production with a density forecast. This allows to estimate the current recession probability. In the analysis the focus is on the real-time problem, i.e. the fact that the reference series (industrial production) as well as important indicators are not available on a timely basis and are often revised substantially over an extended period. For this reason the whole analysis is carried out under real-time conditions. Indeed the forecast of the recession probabilities allows to identify the recession well before it can be seen in the official data. This result is encouraging. But there is still a substantial need for further research.

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Paper provided by IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute in its series IMK Working Paper with number 94-2012.

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Length: 26 pages
Date of creation: 2012
Date of revision:
Handle: RePEc:imk:wpaper:94-2012
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  1. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
  2. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, October.
  3. Tom Stark and Dean Croushore, 2001. "Forecasting with a Real-Time Data Set for Macroeconomists," Computing in Economics and Finance 2001 258, Society for Computational Economics.
  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. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
  6. 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.
  7. Katja Drechsel & Rolf Scheufele, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10, Halle Institute for Economic Research.
  8. Carstensen, Kai & Wohlrabe, Klaus & Ziegler, Christina, 2011. "Predictive ability of business cycle indicators under test: A case study for the Euro area industrial production," Munich Reprints in Economics 19953, University of Munich, Department of Economics.
  9. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo, vol. 56(2), pages 192-220, June.
  10. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
  11. Hinze, Jörg, 2003. "Prognoseleistung von Frühindikatoren : Die Bedeutung von Frühindikatoren für Konjunkturprognosen - Eine Analyse für Deutschland," HWWA Discussion Papers 236, Hamburg Institute of International Economics (HWWA).
  12. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1 National Bureau of Economic Research, Inc.
  13. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
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