Stepwise local influence in generalized autoregressive conditional heteroskedasticity models
Stepwise local influence in generalized autoregressive conditional heteroskedasticity models
复制标题
广义自回归条件异方差模型中的逐步局部影响
DOI：
10.1080/02664763.2014.957661
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发表时间：
2015-02
影响因子：
1.5
通讯作者：
石磊
中科院分区：
文献类型：
--
作者：
石磊
Detection of outliers or influential observations is an important work in statistical modeling, especially for the correlated time series data. In this paper we propose a new procedure to detect patch of influential observations in the generalized autoregressive conditional heteroskedasticity (GARCH) model. Firstly we compare the performance of innovative perturbation scheme, additive perturbation scheme and data perturbation scheme in local influence analysis. We find that the innovative perturbation scheme give better result than other two schemes although this perturbation scheme may suffer from masking effects. Then we use the stepwise local influence method under innovative perturbation scheme to detect patch of influential observations and uncover the masking effects. The simulated studies show that the new technique can successfully detect a patch of influential observations or outliers under innovative perturbation scheme. The analysis based on simulation studies and two real data sets show that the stepwise local influence method under innovative perturbation scheme is efficient for detecting multiple influential observations and dealing with masking effects in the GARCH model.
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影响因子：
3.4
作者：
TSAY, RS
通讯作者：
TSAY, RS
DOI：
--
发表时间：
2005-09
期刊：
--
影响因子：
--
作者：
J. Doornik;Marius Ooms
通讯作者：
J. Doornik;Marius Ooms
DOI：
10.1111/j.2517-6161.1989.tb01435.x
发表时间：
1989-07
期刊：
Journal of the royal statistical society series b-methodological
影响因子：
--
作者：
Andrew G Bruce;R. Douglas;Martin
通讯作者：
Andrew G Bruce;R. Douglas;Martin
影响因子：
6.1
作者：
ENGLE, RF
通讯作者：
ENGLE, RF
DOI：
10.1201/b11823-20
发表时间：
2012-03
期刊：
--
影响因子：
--
作者：
L. K. Hotta;R. Tsay
通讯作者：
L. K. Hotta;R. Tsay