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Bandwidth Selection In Kernel Smoothing Of Time Series

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  • Tae Yoon Kim
  • Dennis D. Cox

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.

Suggested Citation

  • Tae Yoon Kim & Dennis D. Cox, 1996. "Bandwidth Selection In Kernel Smoothing Of Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(1), pages 49-63, January.
    • Handle: RePEc:bla:jtsera:v:17:y:1996:i:1:p:49-63
    DOI: 10.1111/j.1467-9892.1996.tb00264.x
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    Cited by:

    1. Xia, Yingcun & Li, W. K., 2002. "Asymptotic Behavior of Bandwidth Selected by the Cross-Validation Method for Local Polynomial Fitting," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 265-287, November.
    2. Federico M Bandi & Valentina Corradi & Daniel Wilhelm, 2016. "Possibly Nonstationary Cross-Validation," CeMMAP working papers CWP11/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Federico M Bandi & Valentina Corradi & Daniel Wilhelm, 2016. "Possibly Nonstationary Cross-Validation," CeMMAP working papers 11/16, Institute for Fiscal Studies.
    4. Jiang, George J., 1997. "A generalized one-factor term structure model and pricing of interest rate derivative securities," Research Report 97A34, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    5. repec:dgr:rugsom:97a34 is not listed on IDEAS

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