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

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• 作者：Yunwei Cui^a ; ^{ycui@towson.edu} ; Qi Zheng^b ; ^{qi.zheng@louisville.edu}
• 关键词：Observation-driven models ; One-parameter exponential family ; INGARCH(p ; qp ; q) models ; Time series of counts
• 刊名：Statistics & Probability Letters
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：123
• 期：Complete
• 页码：193-201
• 全文大小：469 K
• 卷排序：123

文摘

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.