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Optimal Bandwidth Selection in Non-Parametric Spectral Density Estimation


  • Fortin, Ines

    (Abteilung Finanzwirtschaft, Institut fuer Hoehere Studien)

  • Kuzmics, Christoph

    (Abteilung Finanzwirtschaft, Institut fuer Hoehere Studien)


This paper deals with optimal window width choice in non-parametric lag- or spectral window estimation of the spectral density of a stationary zero-mean process. Several approaches are reviewed: the cross-validation based methods described by Hurvich (1985), Beltrao & Bloomfield (1987) and Hurvich & Beltrao (1990), an iterative procedure due to Buehlmann (1996), and a bootstrap approach followed by Franke & Haerdle (1992). These methods are compared in terms of the mean square error, the mean square percentage error, and a third measure of distance between the true spectral density and its estimate. The comparison is based on a small simulation study. The processes that are simulated are in the class of ARMA (5,5) processes. Based on the simulation evidence, we suggest to use a slightly modified version of Buehlmann's (1996) iterative method.

Suggested Citation

  • Fortin, Ines & Kuzmics, Christoph, 1999. "Optimal Bandwidth Selection in Non-Parametric Spectral Density Estimation," Economics Series 62, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihsesp:62

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    References listed on IDEAS

    1. Franke,J. & Haerdle,W., 1987. "On bootstrapping Kernel spectral estimates," Discussion Paper Serie A 121, University of Bonn, Germany.
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    More about this item


    Window Width; Bandwidth; Non-Parametric Spectral Estimation; Simulation;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes


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