Pseudo maximum likelihood estimation of the univariate GARCH (2,2) and asymptotic normality under dependent innovations

被引：0

作者：

Kouassi, Eugene ^{[1
]}

Takam, Patrice Soh ^{[2
]}

Brou, Jean Marcelin Bosson ^{[3
]}

Ndoumbe, Emile Herve ^{[2
]}

机构：

[1] West Virginia Univ, Resource Econ, Morgantown, WV 26506 USA

[2] Univ Yaounde I, Dept Math, Yaounde, Cameroon

[3] Univ Felix Houphouet Boigny, Dept Econ, Abidjan, Cote Ivoire

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2017年 / 46卷 / 23期

关键词：

Asymptotic normality; Pseudo-maximum likelihood estimator; Quadratic exponential family; Univariate GARCH (2; 2) model; 60G; MODELS;

D O I：

10.1080/03610926.2016.1275694

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 first consider the pseudo maximum likelihood estimation of the univariate GARCH (2,2) model and derive the underlying estimator. Then, we make use of the technique of martingales to establish the asymptotic normality of the pseudo-maximum likelihood estimator (PMLE) of the univariate GARCH (2,2) model. Contrary to previous approaches encountered in the statistical literature, the pseudo-likelihood function uses the general form of the density laws of the quadratic exponential family.

引用

页码：11558 / 11574

页数：17

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