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A Time‐Domain Semi‐parametric Estimate for Strongly Dependent Continuous‐Time Stationary Processes

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  • Takeshi Kato
  • Elias Masry

Abstract

. A covariance‐based estimator of the memory parameter of strongly dependent continuous‐time stationary processes is proposed. The consistency and asymptotic normality of the estimator are established. All assumptions, the form of the estimator, and the proofs are made in time‐domain only.

Suggested Citation

  • Takeshi Kato & Elias Masry, 2003. "A Time‐Domain Semi‐parametric Estimate for Strongly Dependent Continuous‐Time Stationary Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(6), pages 679-703, November.
    • Handle: RePEc:bla:jtsera:v:24:y:2003:i:6:p:679-703
    DOI: 10.1111/j.1467-9892.2003.00329.x
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    References listed on IDEAS

    as
    1. Lobato, I. & Robinson, P. M., 1996. "Averaged periodogram estimation of long memory," Journal of Econometrics, Elsevier, vol. 73(1), pages 303-324, July.
    2. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
    3. Liudas Giraitis & Peter M. Robinson & Alexander Samarov, 1997. "Rate Optimal Semiparametric Estimation Of The Memory Parameter Of The Gaussian Time Series With Long‐Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 18(1), pages 49-60, January.
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