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Estimation of spectral density for seasonal time series models

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  • Shin, Dong Wan

Abstract

For estimating spectral densities of stationary seasonal time series processes, a new kernel is proposed. The proposed kernel is of the shape which is in harmony with oscillating patterns of the autocorrelation functions of typical seasonal time series process. Basic properties such as consistency and nonnegativity of the spectral density estimator are discussed. A Monte-Carlo simulation is conducted for multiplicative monthly autoregressive process and moving average process, which reveal that the proposed kernel provides more efficient spectral density estimator than the classical kernels of Bartlett, Parzen, and Tukey-Hanning.

Suggested Citation

  • Shin, Dong Wan, 2004. "Estimation of spectral density for seasonal time series models," Statistics & Probability Letters, Elsevier, vol. 67(2), pages 149-159, April.
    • Handle: RePEc:eee:stapro:v:67:y:2004:i:2:p:149-159
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    References listed on IDEAS

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    1. Li, Yuan & Xie, Zhongjie, 1997. "The wavelet detection of hidden periodicities in time series," Statistics & Probability Letters, Elsevier, vol. 35(1), pages 9-23, August.
    2. Shin, Dong Wan & Oh, Man-Suk, 2000. "Semiparametric tests for seasonal unit roots based on a semiparametric feasible GLSE," Statistics & Probability Letters, Elsevier, vol. 50(3), pages 207-218, November.
    3. Breitung, Jörg & Franses, Philip Hans, 1998. "On Phillips–Perron-Type Tests For Seasonal Unit Roots," Econometric Theory, Cambridge University Press, vol. 14(2), pages 200-221, April.
    4. Xiao, Zhijie & Phillips, Peter C. B., 1998. "Higher-order approximations for frequency domain time series regression," Journal of Econometrics, Elsevier, vol. 86(2), pages 297-336, June.
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    Keywords

    Efficiency Kernel Spectral density;

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