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Long memory and changing persistence

Author

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  • Robinson Kruse

    () (School of Economics and Management, Aarhus University and CREATES)

  • Philipp Sibbertsen

    () (Leibniz University Hannover, School of Economics and Management, Institute of Statistics)

Abstract

We study the empirical behaviour of semi-parametric log-periodogram estimation for long memory models when the true process exhibits a change in persistence. Simulation results confirm theoretical arguments which suggest that evidence for long memory is likely to be found. A recently proposed test by Sibbertsen and Kruse (2009) is shown to exhibit noticeable power to discriminate between long memory and a structural change in autoregressive parameters.

Suggested Citation

  • Robinson Kruse & Philipp Sibbertsen, 2010. "Long memory and changing persistence," CREATES Research Papers 2010-42, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2010-42
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    File URL: ftp://ftp.econ.au.dk/creates/rp/10/rp10_42.pdf
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    2. Noriega, Antonio E. & Ramos-Francia, Manuel, 2009. "The dynamics of persistence in US inflation," Economics Letters, Elsevier, vol. 105(2), pages 168-172, November.
    3. Halunga, Andreea G. & Osborn, Denise R. & Sensier, Marianne, 2009. "Changes in the order of integration of US and UK inflation," Economics Letters, Elsevier, vol. 102(1), pages 30-32, January.
    4. Luisa Bisaglia & Margherita Gerolimetto, 2009. "Testing structural breaks versus long memory with the Box–Pierce statistics: a Monte Carlo study," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(4), pages 543-553, November.
    5. Granger, Clive W. J. & Terasvirta, Timo, 1999. "A simple nonlinear time series model with misleading linear properties," Economics Letters, Elsevier, vol. 62(2), pages 161-165, February.
    6. Philipp Sibbertsen & Robinson Kruse, 2009. "Testing for a break in persistence under long-range dependencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(3), pages 263-285, May.
    7. Philipp Sibbertsen & Juliane Willert, 2012. "Testing for a break in persistence under long-range dependencies and mean shifts," Statistical Papers, Springer, vol. 53(2), pages 357-370, May.
    8. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
    9. Davidson, James & Sibbertsen, Philipp, 2009. "Tests of bias in log-periodogram regression," Economics Letters, Elsevier, vol. 102(2), pages 83-86, February.
    10. Lee, Jin, 2005. "Estimating memory parameter in the US inflation rate," Economics Letters, Elsevier, vol. 87(2), pages 207-210, May.
    11. Stephen Leybourne & Robert Taylor & Tae-Hwan Kim, 2007. "CUSUM of Squares-Based Tests for a Change in Persistence," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(3), pages 408-433, May.
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    Citations

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    Cited by:

    1. Kruse Robinson & Ventosa-Santaulària Daniel & Noriega Antonio E., 2017. "Changes in persistence, spurious regressions and the Fisher hypothesis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(3), pages 1-28, June.
    2. repec:eee:jebusi:v:92:y:2017:i:c:p:45-62 is not listed on IDEAS
    3. Chen, Zhanshou & Xing, Yuhong & Li, Fuxiao, 2016. "Sieve bootstrap monitoring for change from short to long memory," Economics Letters, Elsevier, vol. 140(C), pages 53-56.
    4. Till Strohsal & Lars Winkelmann, 2012. "Assessing the Anchoring of Inflation Expectations," SFB 649 Discussion Papers SFB649DP2012-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

    More about this item

    Keywords

    Long memory; changing persistence; structural break; semi-parametric estimation;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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