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

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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 languageEnglish
Pages (from-to)165-171
Number of pages7
JournalEconomics Letters
Volume76
Issue number2
DOIs
StatePublished - 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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