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Inflation uncertainty revisited: a proposal for robust measurement

Author

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  • Christian Grimme
  • Steffen Henzel
  • Elisabeth Wieland

Abstract

Any measure of unobserved inflation uncertainty relies on specific assumptions which are most likely not fulfilled completely. This calls into question whether an individual measure delivers a reliable signal. To reduce idiosyncratic measurement error, we propose using common information contained in different measures derived from survey data, a variety of forecast models, and volatility models. We show that all measures are driven by a common component, which constitutes an indicator for inflation uncertainty. Moreover, our results suggest that using only one individual disagreement measure may be misleading, particularly during turbulent times. Finally, we study the Friedman–Ball hypothesis. Using the indicator, we show that higher inflation is followed by higher uncertainty. By contrast, we obtain contradictory results for the individual measures. We also document that, after an inflationary shock, uncertainty decreases in the first two months, which is traceable to the energy component in CPI inflation. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Christian Grimme & Steffen Henzel & Elisabeth Wieland, 2014. "Inflation uncertainty revisited: a proposal for robust measurement," Empirical Economics, Springer, vol. 47(4), pages 1497-1523, December.
  • Handle: RePEc:spr:empeco:v:47:y:2014:i:4:p:1497-1523
    DOI: 10.1007/s00181-013-0789-z
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    Cited by:

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    2. Steffen Henzel & Elisabeth Wieland, 2013. "Synchronization and Changes in International Inflation Uncertainty," CESifo Working Paper Series 4194, CESifo.
    3. Christian Grimme & Marc Stöckli, 2017. "Macoeconomic Uncertainty in Germany," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(06), pages 41-50, March.
    4. Claudiu Tiberiu Albulescu & Aviral Kumar Tiwari & Stephen M. Miller & Rangan Gupta, 2019. "Time–frequency relationship between US inflation and inflation uncertainty: evidence from historical data," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(5), pages 673-702, November.
    5. Ingrid Groessl & Artur Tarassow, 2015. "A Microfounded Model of Money Demand Under Uncertainty, and some Empirical Evidence," Macroeconomics and Finance Series 201504, University of Hamburg, Department of Socioeconomics, revised Jan 2018.
    6. Oscar Claveria, 2020. "Business and consumer uncertainty in the face of the pandemic: A sector analysis in European countries," Papers 2012.02091, arXiv.org.
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    8. Gabriel Caldas Montes & Caio Ferrari Ferreira, 2019. "Does monetary policy credibility mitigate the effects of uncertainty about exchange rate on uncertainties about both inflation and interest rate?," International Economics and Economic Policy, Springer, vol. 16(4), pages 649-678, October.
    9. Binder Carola Conces, 2017. "Economic policy uncertainty and household inflation uncertainty," The B.E. Journal of Macroeconomics, De Gruyter, vol. 17(2), pages 1-20, June.
    10. Tino Berger & Sibylle Grabert & Bernd Kempa, 2016. "Global and Country-Specific Output Growth Uncertainty and Macroeconomic Performance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(5), pages 694-716, October.
    11. Steffen R. Henzel & Malte Rengel, 2017. "Dimensions Of Macroeconomic Uncertainty: A Common Factor Analysis," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 843-877, April.
    12. Knüppel, Malte & Vladu, Andreea L., 2016. "Approximating fixed-horizon forecasts using fixed-event forecasts," Discussion Papers 28/2016, Deutsche Bundesbank.
    13. Glas, Alexander, 2020. "Five dimensions of the uncertainty–disagreement linkage," International Journal of Forecasting, Elsevier, vol. 36(2), pages 607-627.
    14. Herwartz, Helmut & Rohloff, Hannes, 2018. "Less bang for the buck? Assessing the role of inflation uncertainty for U.S. monetary policy transmission in a data rich environment," University of Göttingen Working Papers in Economics 358, University of Goettingen, Department of Economics.
    15. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.

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    More about this item

    Keywords

    Inflation uncertainty; Inflation; Survey data; Stochastic volatility; GARCH; Principal component analysis; C53; E31; E37;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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