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The Inventory Cycle of the German Economy

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  • Knetsch, Thomas A.

Abstract

Using aggregate data, the paper analyzes the importance of inventory investment for German business cycles since 1960. In contrast to U.S. experience, the traditional productionsmoothing/ buffer-stock model is not rejected by empirical evidence. Preliminary national accounts data of inventory investment have particularly poor quality. In order to be able to analyze recent stockbuilding trends in Germany, we propose a composite index aggregating information drawn from monthly production and sales statistics as well as from the Ifo business survey.

Suggested Citation

  • Knetsch, Thomas A., 2004. "The Inventory Cycle of the German Economy," Discussion Paper Series 1: Economic Studies 2004,09, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdp1:2018
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    Cited by:

    1. Thomas A. Knetsch, 2005. "Evaluating the German Inventory Cycle Using Data from the Ifo Business Survey," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 61-92, Springer.
    2. Katsuyuki Shibayama, 2008. "On the Periodicity of Inventories," Studies in Economics 0806, School of Economics, University of Kent.
    3. Warmedinger, Thomas & Vetlov, Igor, 2006. "The German block of the ESCB multi-country model," Working Paper Series 654, European Central Bank.
    4. Obermaier, Robert, 2012. "German inventory to sales ratios 1971–2005—An empirical analysis of business practice," International Journal of Production Economics, Elsevier, vol. 135(2), pages 964-976.
    5. Döpke, Jörg, 2004. "Real-time data and business cycle analysis in Germany," Discussion Paper Series 1: Economic Studies 2004,11, Deutsche Bundesbank.

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    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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