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The Persistence of Inflation in OECD Countries:a Fractionally Integrated Approach

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  • Laura Mayoral
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    Abstract

    The statistical properties of inflation and, in particular, its degree of persistence and stability over time is a subject of intense debate and no consensus has been achieved yet. The goal of this paper is to analyze this controversy using a general approach, with the aim of providing a plausible explanation for the existing contradictory results. We consider the inflation rates of 21 OECD countries which are modelled as fractionally integrated (FI) processes. First, we show analytically that FI can appear in inflation rates after aggregating individual prices from firms that face different costs of adjusting their prices. Then, we provide robust empirical evidence supporting the FI hypothesis using both classical and Bayesian techniques. Next, we estimate impulse response functions and other scalar measures of persistence, achieving an accurate picture of this property and its variation across countries. It is shown that the application of some popular tools for measuring persistence, such as the sum of the AR coefficients, could lead to erroneous conclusions if fractional integration is present. Finally, we explore the existence of changes in inflation inertia using a novel approach. We conclude that the persistence of inflation is very high (although non-permanent) in most post-industrial countries and that it has remained basically unchanged over the last four decades.

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    Bibliographic Info

    Paper provided by Barcelona Graduate School of Economics in its series Working Papers with number 259.

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    Date of creation: Feb 2005
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    Handle: RePEc:bge:wpaper:259

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    References

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    1. Kim, Jae-Young, 2000. "Detection of change in persistence of a linear time series," Journal of Econometrics, Elsevier, vol. 95(1), pages 97-116, March.
    2. Juan J. Dolado & Jesus Gonzalo & Laura Mayoral, 2002. "A Fractional Dickey-Fuller Test for Unit Roots," Econometrica, Econometric Society, vol. 70(5), pages 1963-2006, September.
    3. Oscar Jorda, 2007. "Inference for Impulse Responses," Working Papers 77, University of California, Davis, Department of Economics.
    4. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    5. Gary Koop, 1995. "Bayesian Analysis of Long Memory and Persistence using ARFIMA Models," Working Papers gkoop-95-01, University of Toronto, Department of Economics.
    6. Ignazio Angeloni & Gunter Coenen & Frank Smets, 2003. "Persistence, The Transmission Mechanism And Robust Monetary Policy," Scottish Journal of Political Economy, Scottish Economic Society, vol. 50(5), pages 527-549, November.
    7. Kwiatkowski, D. & Phillips, P.C.B. & Schmidt, P., 1990. "Testing the Null Hypothesis of Stationarity Against the Alternative of Unit Root : How Sure are we that Economic Time Series have a Unit Root?," Papers 8905, Michigan State - Econometrics and Economic Theory.
    8. Lee, D. & Schmidt, P., 1993. "On the Power of the KPSS Test of Stationarity Against Fractionally-Integrated Alternatives," Papers 9111, Michigan State - Econometrics and Economic Theory.
    9. Karim Abadir & Gabriel Talmain, . "Aggregation, Persistence and Volatility in a Macromodel," Discussion Papers 01/03, Department of Economics, University of York.
    10. Joseph G. Haubrich & Andrew W. Lo, 2001. "The sources and nature of long-term memory in aggregate output," Economic Review, Federal Reserve Bank of Cleveland, issue Q II, pages 15-30.
    11. George Hondroyiannis & Sophia Lazaretou, 2004. "Inflation Persistence during Periods of Structural Change: An Assessment Using Greek Data," Working Papers 13, Bank of Greece.
    12. Coenen, Günter, 2003. "Inflation persistence and robust monetary policy design," Working Paper Series 0290, European Central Bank.
    13. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
    14. Perron, Pierre & Zhu, Xiaokang, 2005. "Structural breaks with deterministic and stochastic trends," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 65-119.
    15. Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, EconWPA.
    16. David Byers & James Davidson & David Peel, 1997. "Modelling Political Popularity: an Analysis of Long-range Dependence in Opinion Poll Series," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 471-490.
    17. Marc Henry & Paolo Zaffaroni, 2002. "The long range dependence paradigm for macroeconomics and finance," Discussion Papers 0102-19, Columbia University, Department of Economics.
    18. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    19. Walter Kramer & Philipp Sibbertsen, 2002. "Testing for Structural Changes in the Presence of Long Memory," International Journal of Business and Economics, College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 1(3), pages 235-242, December.
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