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Qualitative Business Surveys in Manufacturing and Industrial Production - What can be Learned from Industry Branch Results?


  • Klaus Abberger


Business tendency surveys are a popular tool for the timely assessment of the business cycle, used by economists and by the public. This article considers survey results in the manufacturing sector in more detail and looks into the question of, whether the analysis of branch results leads to an information gain. The business cycle turning points are identified in the filtered series and average leads to the turning point of industrial production are calculated. In addition to these leads the ratios of the signal variances to the noise variances are calculated to assess the clarity of the signal contained in the indicator series. Apart from assessing the general business cycle course the survey results in manufacturing are often used to forecast moment-to-moment changes of industrial production. Analyses based on wavelets show that the survey balances are useful to forecast the larger scale movements only. Nevertheless, the comparison of out-of-sample forecast errors show that the inclusion of survey results as independent variables in an autoregressive model improves the forecasts.

Suggested Citation

  • Klaus Abberger, 2006. "Qualitative Business Surveys in Manufacturing and Industrial Production - What can be Learned from Industry Branch Results?," ifo Working Paper Series 31, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  • Handle: RePEc:ces:ifowps:_31

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    References listed on IDEAS

    1. repec:crs:wpaper:9313 is not listed on IDEAS
    2. Entorf, Horst, 1993. "Constructing leading indicators from non-balanced sectoral business survey series," International Journal of Forecasting, Elsevier, vol. 9(2), pages 211-225, August.
    3. Michael ARTIS & Massimiliano MARCELLINO & Tommaso PROIETTI, 2002. "Dating the Euro Area Business Cycle," Economics Working Papers ECO2002/24, European University Institute.
    4. Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1681-1690, July.
    5. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    6. 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, January.
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    Cited by:

    1. Klaus Abberger & Sascha Becker & Barbara Hofmann & Klaus Wohlrabe, 2007. "Mikrodaten im ifo Institut für Wirtschaftsforschung – Bestand, Verwendung und Zugang," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 1(1), pages 27-42, June.
    2. Sascha O. Becker & Klaus Wohlrabe, 2008. "European Data Watch: Micro Data at the Ifo Institute for Economic Research – The “Ifo Business Survey”, Usage and Access," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 128(2), pages 307-319.
    3. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    4. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sektorale Prognosen im Verarbeitenden Gewerbe," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    5. Gerit Vogt, 2009. "Konjunkturprognose in Deutschland. Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 36, April.
    6. Christian Seiler & Klaus Wohlrabe, 2013. "Das ifo Geschäftsklima und die deutsche Konjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.

    More about this item


    Business tendency surveys; business cycle analysis; turning points; Grnager causality; wavelet cross-correlations.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles


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