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Business Cycle Measurement with Semantic Filtering: A Micro Data Approach

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  • Christian Mueller
  • Eva M. Koeberl

Abstract

In this paper we develop a business cycle measure that can be shown to have excellent ex-ante forecasting properties for GDP growth. For identifying business cycle movements, we use a semantic approach. We infer nine different states of the economy directly from firms' responses in business tendency surveys. Hence, we can identify the current state of the economy. We therewith measure business cycle fluctuations. One of the main advantages of our methodology is that it is a structural concept based on shock identification and therefore does not need any - often rather arbitrary - statistical filtering. Furthermore, it is not subject to revisions, it is available in real-time and has a publication lead to official GDP data of at least one quarter. It can therefore be used for one quarter ahead forecasting real GDP growth.

Suggested Citation

  • Christian Mueller & Eva M. Koeberl, 2008. "Business Cycle Measurement with Semantic Filtering: A Micro Data Approach," KOF Working papers 08-212, KOF Swiss Economic Institute, ETH Zurich.
  • Handle: RePEc:kof:wpskof:08-212
    DOI: 10.3929/ethz-a-005717922
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    File URL: http://dx.doi.org/10.3929/ethz-a-005717922
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    References listed on IDEAS

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    2. David F. Hendry & Hans‐Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
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    Keywords

    Business cycle measurement; Semantic cross validation; Shock identification;
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