BANDWIDTH SELECTION IN KERNEL SMOOTHING OF TIME SERIES

Tae Yoon Kim and Dennis D. Cox

Journal of Time Series Analysis, 1996, vol. 17, issue 1, 49-63

Abstract: Abstract. The kernel smoothing method has been considered as a useful tool for identification and prediction in time series models. In practice this method is to be tuned by a smoothing parameter. For selection of the smoothing parameter, Härdle and Vieu (Kernel regression smoothing of time series. J. Time Ser. Anal. 13(1992), 209–32) considered a cross‐validation rule and proved its asymptotic optimality. In this paper we strengthen their result for a wider use of the kernel smoothing of time series.

Date: 1996
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