Conditional maximum likelihood estimation for a class of observation-driven time series models for count data

被引：17

作者：

Cui, Yunwei ^{[1
]}

Zheng, Qi ^{[2
]}

机构：

[1] Towson Univ, Dept Math, Towson, MD 21252 USA

[2] Univ Louisville, Dept Bioinformat & Biostat, Louisville, KY 40202 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2017年 / 123卷

关键词：

Observation-driven models; One-parameter exponential family; INGARCH(p; q); models; Time series of counts; INFERENCE;

D O I：

10.1016/j.spl.2016.11.002

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 investigates the statistical inference for a class of observation-driven time series models of count data based on the conditional maximum likelihood estimator (CMLE), where the conditional distribution of the observed count given a state process is from the one-parameter exponential family. Under certain regularity conditions, the strong consistency and asymptotic normality of the CMLE of the misspecified likelihood function are established. (C) 2016 Elsevier B.V. All rights reserved.

引用

收藏

页码：193 / 201

页数：9

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