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Measuring Uncertainty with Survey Data

Author

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  • Klaus Abberger
  • Wolfgang Nierhaus

Abstract

Economic uncertainty cannot be directly observed. One operationalisation is to measure uncertainty as dispersion in the ifo Institute's company surveys. Dispersion measures drawing on the business expectations of the companies surveyed in the sectors of manufacturing, construction, wholesaling and retailing and services is presented in ifo Schnelldienst 15/2017. In the article dispersion measures are constructed based on all of the sector-specific expectation questions featured in the ifo Business Surveys. It shows that the measures calculated in this manner correlate with dispersion measures that are only based on companies' business expectations. This result highlights that the dispersion of business expectations can be used as a basis for sectoral dispersion measures, as well as cross-sector measures. The ifo Institute began publishing such measures in August 2017 as part of the monthly results of the ifo Business Surveys for Germany. They serve as constructs both for estimating uncertainty in selected economic sectors - manufacturing, construction, wholesaling and retailing and services - and uncertainty in the entire private sector. The fact that the dispersion of business expectations with individual sectors correlates strongly with certain individual questions or with the average results offers an incentive to conduct further research into the sector-specific idiosyncrasies of uncertainty.

Suggested Citation

  • Klaus Abberger & Wolfgang Nierhaus, 2017. "Measuring Uncertainty with Survey Data," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(16), pages 25-29, August.
    • Handle: RePEc:ces:ifosdt:v:70:y:2017:i:16:p:25-29
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    References listed on IDEAS

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    1. Carlos Bowles & Roberta Friz & Veronique Genre & Geoff Kenny & Aidan Meyler & Tuomas Rautanen, 2007. "The ECB survey of professional forecasters (SPF) – A review after eight years’ experience," Occasional Paper Series 59, European Central Bank.
    2. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    3. Kenny, Geoff & Genre, Véronique & Bowles, Carlos & Friz, Roberta & Meyler, Aidan & Rautanen, Tuomas, 2007. "The ECB survey of professional forecasters (SPF) - A review after eight years' experience," Occasional Paper Series 59, European Central Bank.
    4. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
    5. Christian Grimme, 2017. "Measurement of Corporate Uncertainty in Germany – the ifo Dispersion Measure," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(15), pages 19-25, August.
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    Cited by:

    1. Bozena Gajdzik, 2022. "How Steel Mills Transform into Smart Mills: Digital Changes and Development Determinants in the Polish Steel Industry," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 27-42.

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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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