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Long memory and changing persistence

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  • Kruse, Robinson
  • Sibbertsen, Philipp

Abstract

We study the empirical behaviour of semi-parametric estimation for long-memory models when the true data generating process exhibits a change in persistence. Evidence for long memory is likely to be found. Procedures for discrimination between different models are proposed.

Suggested Citation

  • Kruse, Robinson & Sibbertsen, Philipp, 2012. "Long memory and changing persistence," Economics Letters, Elsevier, vol. 114(3), pages 268-272.
    • Handle: RePEc:eee:ecolet:v:114:y:2012:i:3:p:268-272
    DOI: 10.1016/j.econlet.2011.10.026
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    References listed on IDEAS

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    1. Noriega, Antonio E. & Ramos-Francia, Manuel, 2009. "The dynamics of persistence in US inflation," Economics Letters, Elsevier, vol. 105(2), pages 168-172, November.
    2. 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.
    3. 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.
    4. 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.
    5. Lee, Jin, 2005. "Estimating memory parameter in the US inflation rate," Economics Letters, Elsevier, vol. 87(2), pages 207-210, May.
    6. 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.
    7. 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.
    8. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    9. 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.
    10. 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.
    11. Clifford M. Hurvich & Rohit Deo & Julia Brodsky, 1998. "The mean squared error of Geweke and Porter‐Hudak's estimator of the memory parameter of a long‐memory time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 19-46, January.
    12. Davidson, James & Sibbertsen, Philipp, 2009. "Tests of bias in log-periodogram regression," Economics Letters, Elsevier, vol. 102(2), pages 83-86, February.
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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:hum:wpaper:sfb649dp2012-022 is not listed on IDEAS
    3. Strohsal, Till & Winkelmann, Lars, 2012. "Assessing the anchoring of inflation expectations," SFB 649 Discussion Papers 2012-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. 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.
    5. Canarella, Giorgio & Miller, Stephen M., 2017. "Inflation targeting and inflation persistence: New evidence from fractional integration and cointegration," Journal of Economics and Business, Elsevier, vol. 92(C), pages 45-62.

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    More about this item

    Keywords

    Long memory; Changing persistence; Structural break; Semi-parametric estimation;
    All these keywords.

    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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