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On a Class of Estimation and Test for Long Memory

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  • Fu, Hui

Abstract

This paper advances a new analysis technology path of estimation and test for long memory time series. I propose the definitions of time scale series, strong variance scale exponent and weak variance scale exponent, and prove the strict mathematical equations that strong and weak variance scale exponent can accurately identify the time series of white noise, short memory and long memory, especially derive the equation relationships between weak variance scale exponent and long memory parameters. I also construct two statistics which SLmemory statistic tests for long memory properties. The paper further displays Monte Carlo performance for MSE of weak variance scale exponent estimator and the empirical size and power of SLmemory statistic, giving practical recommendations of finite-sample, and also provides brief empirical examples of logarithmic return rate series data for Sino-US stock markets.

Suggested Citation

  • Fu, Hui, 2012. "On a Class of Estimation and Test for Long Memory," MPRA Paper 47978, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:47978
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    References listed on IDEAS

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    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    2. Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2009. "Two estimators of the long-run variance: Beyond short memory," Journal of Econometrics, Elsevier, vol. 150(1), pages 56-70, May.
    3. GIRAITIS, Liudas & KOKOSZKA, Piotr & LEIPUS, Remigijus & TEYSSIÈRE, Gilles, 2003. "Rescaled variance and related tests for long memory in volatility and levels," LIDAM Reprints CORE 1594, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    5. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    6. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus & Teyssiere, Gilles, 2005. "Corrigendum to "Rescaled variance and related tests for long memory in volatility and levels": [J. Econom. 112 (2003) 265-294]," Journal of Econometrics, Elsevier, vol. 126(2), pages 571-572, June.
    7. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus & Teyssiere, Gilles, 2003. "Rescaled variance and related tests for long memory in volatility and levels," Journal of Econometrics, Elsevier, vol. 112(2), pages 265-294, February.
    8. Mohamed Boutahar & Velayoudom Marimoutou & Leila Nouira, 2007. "Estimation Methods of the Long Memory Parameter: Monte Carlo Analysis and Application," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(3), pages 261-301.
    9. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    10. Robinson, P.M., 2005. "Robust Covariance Matrix Estimation: Hac Estimates With Long Memory/Antipersistence Correction," Econometric Theory, Cambridge University Press, vol. 21(1), pages 171-180, February.
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    More about this item

    Keywords

    Long Memory; Weak Variance Scale Exponent; SLmemory Statistic; Time Scale Series.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    Statistics

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