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초록
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.
키워드
ARCH; conditionally heteroscedastic errors; kernel regression; squared residual.
- 제목
- Preliminary Detection for ARCH-Type Heteroscedasticity in a Nonparametric Time Series Regression Model
- 저자
- 황선영; 박철용; 김태윤; 박병욱; Y. K 이
- 발행일
- 2005-06
- 권
- 34
- 호
- 2
- 페이지
- 161 ~ 172