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Business Cycle Measurement with Semantic Filtering: A Micro Data Approach

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Author Info

  • Christian Müller

    ()
    (Zurich University of Applied Sciences, School of Management and ETH Zurich, KOF Swiss Economic Institute)

  • Eva Köberl

    ()
    (KOF Swiss Economic Institute, ETH Zurich, Switzerland)

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. Futhermore, 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.

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Bibliographic Info

Paper provided by KOF Swiss Economic Institute, ETH Zurich in its series KOF Working papers with number 08-212.

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Length: 17 pages
Date of creation: Nov 2008
Date of revision:
Handle: RePEc:kof:wpskof:08-212

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Keywords: business cycle measurement; semantic cross validation; shock identification;

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  1. Ivaldi, Marc, 1992. "Survey Evidence on the Rationality of Expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(3), pages 225-41, July-Sept.
  2. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Economics Papers 2004-W17, Economics Group, Nuffield College, University of Oxford.
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