Smoothing Parameter Selection in Nonparametric Spectral Density Estimation

  • Kang, Kee-Hoon (Department of Computer Science and Statistics, Seoul National University) ;
  • Park, Byeong-U (Department of Computer Science and Statistics, Seoul National University) ;
  • Cho, Sin-Sup (Department of Computer Science and Statistics, Seoul National University) ;
  • Kim, Woo-Chul (Department of Computer Science and Statistics, Seoul National University)
  • Published : 1995.12.01

Abstract

In this paper we consider kernel type estimator of the spectral density at a point in the analysis of stationary time series data. The kernel entails choice of smoothing parameter called bandwidth. A data-based bandwidth choice is proposed, and it is obtained by solving an equation similar to Sheather(1986) which relates to the probability density estimation. A Monte Carlo study is done. It reveals that the spectral density estimates using the data-based bandwidths show comparatively good performance.

Keywords

References

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