IDEAS home Printed from https://ideas.repec.org/a/bla/ausecr/v44y2011i4p404-417.html
   My bibliography  Save this article

Inflation Volatility and Forecast Accuracy

Author

Listed:
  • 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.
(This abstract was borrowed from another version of this item.)

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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1467-8462.2011.00656.x
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/j.1467-8462.2011.00656.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    2. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    3. Canova, Fabio, 2007. "G-7 Inflation Forecasts: Random Walk, Phillips Curve Or What Else?," Macroeconomic Dynamics, Cambridge University Press, vol. 11(1), pages 1-30, February.
    4. 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.
    5. Ivan Roberts, 2005. "Underlying Inflation: Concepts, Measurement and Performance," RBA Research Discussion Papers rdp2005-05, Reserve Bank of Australia.
    6. 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.
    7. 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.
    8. 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, vol. 84(Jul), pages 99-118.
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ivan Kitov & Oleg Kitov, 2013. "Does Banque de France control inflation and unemployment?," Papers 1311.1097, arXiv.org.
    2. Bruno Ferreira Frascaroli & Wellington Charles Lacerda Nobrega, 2019. "Inflation Targeting and Inflation Risk in Latin America," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(11), pages 2389-2408, September.
    3. Koirala, Niraj P. & Nyiwul, Linus, 2023. "Inflation volatility: A Bayesian approach," Research in Economics, Elsevier, vol. 77(1), pages 185-201.
    4. Ivan Kitov & Oleg Kitov, 2011. "The Australian Phillips curve and more," Papers 1102.1851, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    2. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, January.
    3. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2020. "The Effectiveness Of Monetary Policy In South Africa Under Inflation Targeting: Evidence from a Time-Varying Factor-Augmented Vector Autoregressive Model," Journal of Developing Areas, Tennessee State University, College of Business, vol. 54(4), pages 55-73, October-D.
    4. Joshua C. C. Chan, 2017. "The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 17-28, January.
    5. Benoit Mojon, 2007. "Monetary policy, output composition and the Great Moderation," Working Paper Series WP-07-07, Federal Reserve Bank of Chicago.
    6. Ahrens, Steffen & Hartmann, Matthias, 2014. "State-dependence vs. timedependence: An empirical multi-country investigation of price sluggishness," Kiel Working Papers 1907, Kiel Institute for the World Economy (IfW Kiel).
    7. Zsolt Darvas, 2013. "Monetary transmission in three central European economies: evidence from time-varying coefficient vector autoregressions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 40(2), pages 363-390, May.
    8. Ayse Kabukcuoglu & Enrique Martínez-García, 2016. "What Helps Forecast U.S. Inflation?—Mind the Gap!," Koç University-TUSIAD Economic Research Forum Working Papers 1615, Koc University-TUSIAD Economic Research Forum.
    9. Gary Koop & Dimitris Korobilis, 2019. "Forecasting with High‐Dimensional Panel VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 937-959, October.
    10. Kenneth N Kuttner, 2004. "A Snapshot of Inflation Targeting in its Adolescence," RBA Annual Conference Volume (Discontinued), in: Christopher Kent & Simon Guttmann (ed.),The Future of Inflation Targeting, Reserve Bank of Australia.
    11. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    12. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
    13. Ivan Kitov & Oleg Kitov, 2013. "Does Banque de France control inflation and unemployment?," Papers 1311.1097, arXiv.org.
    14. Chin, Kuo-Hsuan & Li, Xue, 2019. "Bayesian forecast combination in VAR-DSGE models," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 278-298.
    15. Johanna Posch & Fabio Rumler, 2015. "Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 145-162, March.
    16. Pagliari, Maria Sole, 2024. "Does one (unconventional) size fit all? Effects of the ECB’s unconventional monetary policies on the euro area economies," European Economic Review, Elsevier, vol. 168(C).
    17. Giuseppe Ciccarone & Enrico Marchetti & Giovanni Di Bartolomeo, 2007. "Unions, Fiscal Policy And Central Bank Transparency," Manchester School, University of Manchester, vol. 75(5), pages 617-633, September.
    18. Okimoto, Tatsuyoshi & Shimotsu, Katsumi, 2010. "Decline in the persistence of real exchange rates, but not sufficient for purchasing power parity," Journal of the Japanese and International Economies, Elsevier, vol. 24(3), pages 395-411, September.
    19. Christian Bauer & Sebastian Weber, 2016. "The Efficiency of Monetary Policy when Guiding Inflation Expectations," Research Papers in Economics 2016-14, University of Trier, Department of Economics.
    20. Kapetanios, George & Millard, Stephen & Price, Simon & Petrova, Katerina, 2018. "Time varying cointegration and the UK Great Ratios," Essex Finance Centre Working Papers 23320, University of Essex, Essex Business School.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:ausecr:v:44:y:2011:i:4:p:404-417. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/mimelau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.