A new kernel for long-run variance estimates in seasonal time series models

Dong Wan Shin; Man Suk Oh

doi:10.1016/S0165-1765(02)00048-4

A new kernel for long-run variance estimates in seasonal time series models

Dong Wan Shin, Man Suk Oh

Department of Statistics

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

1 Scopus citations

Abstract

A new kernel for estimating long-run variances of stationary seasonal time series is proposed. The proposed kernel has an oscillating pattern which is in harmony with that of the autocovariance functions of seasonal time series. A Monte-Carlo experiment shows that the estimator based on the proposed kernel outperforms estimators based on existing kernels such as the Bartlett kernel, Parzen kernel, and Turkey-Hanning kernel for two typical monthly time series processes with moderate autocorrelations.

Original language	English
Pages (from-to)	165-171
Number of pages	7
Journal	Economics Letters
Volume	76
Issue number	2
DOIs	https://doi.org/10.1016/S0165-1765(02)00048-4
State	Published - Jul 2002

Bibliographical note

Funding Information:
This work was supported by the MOST through national R&D program for women’s university (grant # 00-B-WB-06-A-03).

Keywords

Autocovariance function
Efficiency
Seasonality

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10.1016/S0165-1765(02)00048-4

Cite this

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title = "A new kernel for long-run variance estimates in seasonal time series models",

abstract = "A new kernel for estimating long-run variances of stationary seasonal time series is proposed. The proposed kernel has an oscillating pattern which is in harmony with that of the autocovariance functions of seasonal time series. A Monte-Carlo experiment shows that the estimator based on the proposed kernel outperforms estimators based on existing kernels such as the Bartlett kernel, Parzen kernel, and Turkey-Hanning kernel for two typical monthly time series processes with moderate autocorrelations.",

keywords = "Autocovariance function, Efficiency, Seasonality",

author = "Shin, \{Dong Wan\} and Oh, \{Man Suk\}",

note = "Funding Information: This work was supported by the MOST through national R\&D program for women{\textquoteright}s university (grant \# 00-B-WB-06-A-03).",

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AU - Oh, Man Suk

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N2 - A new kernel for estimating long-run variances of stationary seasonal time series is proposed. The proposed kernel has an oscillating pattern which is in harmony with that of the autocovariance functions of seasonal time series. A Monte-Carlo experiment shows that the estimator based on the proposed kernel outperforms estimators based on existing kernels such as the Bartlett kernel, Parzen kernel, and Turkey-Hanning kernel for two typical monthly time series processes with moderate autocorrelations.

AB - A new kernel for estimating long-run variances of stationary seasonal time series is proposed. The proposed kernel has an oscillating pattern which is in harmony with that of the autocovariance functions of seasonal time series. A Monte-Carlo experiment shows that the estimator based on the proposed kernel outperforms estimators based on existing kernels such as the Bartlett kernel, Parzen kernel, and Turkey-Hanning kernel for two typical monthly time series processes with moderate autocorrelations.

KW - Autocovariance function

KW - Efficiency

KW - Seasonality

UR - https://www.scopus.com/pages/publications/0035997443

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DO - 10.1016/S0165-1765(02)00048-4

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SP - 165

EP - 171

JO - Economics Letters

JF - Economics Letters

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ER -

A new kernel for long-run variance estimates in seasonal time series models

Abstract

Bibliographical note

Keywords

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