KERNEL DISCRIMINATION OF TIME SERIES DATA

Rahim Chiniparadaz
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Rahim Chiniparadaz: Shahid Chamran University, Ahwaz, Iran.

No 167, Computing in Economics and Finance 2000 from Society for Computational Economics

Abstract: A normality assumption is usually made for the discrimination between two stationary time series processes. A kernel approach is desirable whenever there is doubt concerning the validity of this normality assumption. In this paper a nonparametric approach is suggested based on kernel Density estimation firstly on secondly on ( ple autocorrelations and kernel density discrimination for AR and MA processes with and without Gaussian noise. The methods are applied to some seismological data.}

Date: 2000-07-05
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf0:167

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