Asymptotic inference for periodic ARCH processes

被引：3
|
作者：
Lescheb, Ines [1 ]
机构：
[1] Univ Mentouri, Dept Math, Constantine, Algeria
来源：
RANDOM OPERATORS AND STOCHASTIC EQUATIONS | 2011年 / 19卷 / 03期
关键词：
Periodic ARCH processes; conditional least squares estimator; asymptotic efficiency; LAN;
D O I：
10.1515/ROSE.2011.015
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper, we consider the conditional least squares (CLS) estimators for periodic ARCH models (P-ARCH). The CLS estimators applied to the square-transformed P-ARCH model have an explicit form which does not depend on the distribution of the innovation. Since the CLS are not asymptotically efficient in general, we give a necessary and sufficient condition ensuring the asymptotic efficiency of the CLS based on the local asymptotic normality (LAN) approach.
引用
收藏
页码：283 / 294
页数：12
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