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Estimation of spectral density for seasonal time series models

Title
Estimation of spectral density for seasonal time series models
Authors
Shin D.W.
Ewha Authors
신동완
SCOPUS Author ID
신동완scopus
Issue Date
2004
Journal Title
Statistics and Probability Letters
ISSN
0167-7152JCR Link
Citation
vol. 67, no. 2, pp. 149 - 159
Indexed
SCIE; SCOPUS WOS scopus
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. © 2003 Elsevier B.V. All rights reserved.
DOI
10.1016/j.spl.2003.09.012
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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