Estimation of spectral density for seasonal time series models

Dong Wan Shin

doi:10.1016/j.spl.2003.09.012

Estimation of spectral density for seasonal time series models

Dong Wan Shin

Department of Statistics

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

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.

Original language	English
Pages (from-to)	149-159
Number of pages	11
Journal	Statistics and Probability Letters
Volume	67
Issue number	2
DOIs	https://doi.org/10.1016/j.spl.2003.09.012
State	Published - 1 Apr 2004

Bibliographical note

Funding Information:
The author is very grateful to the referee for the helpful comments which lead to an improvement of the paper. This work was supported by Korea Research Foundation Grant (KRF-2002-070-C0001).

Keywords

Efficiency
Kernel
Spectral density

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10.1016/j.spl.2003.09.012

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title = "Estimation of spectral density for seasonal time series models",

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.",

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author = "Shin, {Dong Wan}",

note = "Funding Information: The author is very grateful to the referee for the helpful comments which lead to an improvement of the paper. This work was supported by Korea Research Foundation Grant (KRF-2002-070-C0001). ",

year = "2004",

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doi = "10.1016/j.spl.2003.09.012",

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T1 - Estimation of spectral density for seasonal time series models

AU - Shin, Dong Wan

N1 - Funding Information: The author is very grateful to the referee for the helpful comments which lead to an improvement of the paper. This work was supported by Korea Research Foundation Grant (KRF-2002-070-C0001).

PY - 2004/4/1

Y1 - 2004/4/1

N2 - 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.

AB - 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.

KW - Efficiency

KW - Kernel

KW - Spectral density

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U2 - 10.1016/j.spl.2003.09.012

DO - 10.1016/j.spl.2003.09.012

M3 - Article

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SP - 149

EP - 159

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 2

ER -

Estimation of spectral density for seasonal time series models

Abstract

Bibliographical note

Keywords

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