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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)
Publication Information
Journal of the Korean Statistical Society / v.34, no.2, 2005 , pp. 161-172 More about this Journal
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
ARCH; conditionally heteroscedastic errors; kernel regression; squared residual;
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