Moment matrices in conditional heteroskedastic models under elliptical distributions with applications in AR-ARCH models

被引：0
|
作者：
Shuangzhe Liu
Chris C. Heyde
Wing-Keung Wong
机构：
[1] University of Canberra,Faculty of Information Sciences and Engineering
[2] Columbia University,Department of Statistics
[3] Australian National University,Mathematical Sciences Institute
[4] Hong Kong Baptist University,Department of Economics and Institute for Computational Mathematics
来源：
Statistical Papers | 2011年 / 52卷
关键词：
Heteroskedasticity; Likelihood; BHHH method; Newton–Raphson method; Scoring method; AR-ARCH model;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
It is well known that moment matrices play a very important rôle in econometrics and statistics. Liu and Heyde (Stat Pap 49:455–469, 2008) give exact expressions for two-moment matrices, including the Hessian for ARCH models under elliptical distributions. In this paper, we extend the theory by establishing two additional moment matrices for conditional heteroskedastic models under elliptical distributions. The moment matrices established in this paper implement the maximum likelihood estimation by some estimation algorithms like the scoring method. We illustrate the applicability of the additional moment matrices established in this paper by applying them to establish an AR-ARCH model under an elliptical distribution.
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页码：621 / 632
页数：11
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