Conditional likelihood estimators for hidden Markov models and stochastic volatility models

被引：4

作者：

Genon-Catalot, V ^{[1
]}

Jeantheau, T ^{[1
]}

Laredo, C ^{[1
]}

机构：

[1] INRA, Lab Biometrie, F-78350 Jouy En Josas, France

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2003年 / 30卷 / 02期

关键词：

conditional likelihood; diffusion processes; discrete time observations; hidden Markov models; parametric inference; stochastic volatility; ASYMPTOTIC NORMALITY; CONSISTENCY; PARAMETER;

D O I：

10.1111/1467-9469.00332

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：

This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimators) and to the. discretely observed stochastic volatility models.

引用

页码：297 / 316

页数：20

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