Journal of the Korean Statistical Society

The Korean Statistical Society (한국통계학회)
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PRELIMINARY DETECTION FOR ARCH-TYPE HETEROSCEDASTICITY IN A NONPARAMETRIC TIME SERIES REGRESSION MODEL

  • HWANG S. Y. (Department of Statistics, Sookmyung Women's University) ;
  • PARK CHEOLYONG (Department of Statistics, Keimyung University) ;
  • KIM TAE YOON (Department of Statistics, Keimyung University) ;
  • PARK BYEONG U. (Department of Statistics, Seoul National University) ;
  • LEE Y. K. (Department of Statistics, Seoul National University)
  • Published : 2005.06.01
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Abstract

In this paper a nonparametric method is proposed for detecting conditionally heteroscedastic errors in a nonparametric time series regression model where the observation points are equally spaced on [0,1]. It turns out that the first-order sample autocorrelation of the squared residuals from the kernel regression estimates provides essential information. Illustrative simulation study is presented for diverse errors such as ARCH(1), GARCH(1,1) and threshold-ARCH(1) models.

Keywords

References

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