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Inflation Volatility and Forecast Accuracy

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  • Jamie Hall
  • Jarkko P. Jääskelä

Abstract

This paper examines the statistical properties of inflation in a sample of inflation-targeting and non-inflation-targeting countries. First, it analyses the time-varying volatility of a measure of the persistent component of inflation. Based on this measure, inflation-targeting countries (Australia, Canada, New Zealand, Sweden and the United Kingdom) have experienced a relatively more pronounced fall in the volatility of inflation than non-inflation-targeting countries (Austria, France, Germany, Japan and the United States). But it is hard to say whether inflation is more volatile in inflation-targeting or non-inflation-targeting countries. Second, it analyses whether inflation became easier to forecast after the introduction of inflation targeting. It finds that inflation became easier to forecast in both inflation-targeting and non-inflation-targeting countries; the improvement was greater for the former group but forecast errors remain smaller for the latter group.
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Suggested Citation

  • Jamie Hall & Jarkko P. Jääskelä, 2011. "Inflation Volatility and Forecast Accuracy," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 44(4), pages 404-417, December.
  • Handle: RePEc:bla:ausecr:v:44:y:2011:i:4:p:404-417
    DOI: j.1467-8462.2011.00656.x
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    File URL: http://hdl.handle.net/10.1111/j.1467-8462.2011.00656.x
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    References listed on IDEAS

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    1. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, vol. 53.
    2. Stephen G. Cecchetti & Alfonso Flores-Lagunes & Stefan Krause, 2006. "Has Monetary Policy become more Efficient? a Cross-Country Analysis," Economic Journal, Royal Economic Society, vol. 116(511), pages 408-433, April.
    3. Ivan Roberts, 2005. "Underlying Inflation: Concepts, Measurement and Performance," RBA Research Discussion Papers rdp2005-05, Reserve Bank of Australia.
    4. Kuttner, Kenneth N & Posen, Adam S, 2001. "Beyond Bipolar: A Three-Dimensional Assessment of Monetary Frameworks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(4), pages 369-387, October.
    5. Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2010. "Inflation-Gap Persistence in the US," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 43-69, January.
    6. Georgios Chortareas & David Stasavage & Gabriel Sterne, 2002. "Does it pay to be transparent? international evidence form central bank forecasts," Review, Federal Reserve Bank of St. Louis, issue Jul, pages 99-118.
    7. Franck Sédillot & Hervé Le Bihan, 2002. "Implementing and interpreting indicators of core inflation: the case of France," Empirical Economics, Springer, vol. 27(3), pages 473-497.
    8. James H. Stock & Mark W. Watson, 2005. "Understanding Changes In International Business Cycle Dynamics," Journal of the European Economic Association, MIT Press, vol. 3(5), pages 968-1006, September.
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    Cited by:

    1. Ivan Kitov & Oleg Kitov, 2013. "Does Banque de France control inflation and unemployment?," Papers 1311.1097, arXiv.org.
    2. Ivan Kitov & Oleg Kitov, 2011. "The Australian Phillips curve and more," Papers 1102.1851, arXiv.org.

    More about this item

    JEL classification:

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

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