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Modeling integer-valued time series based on COM-Poisson INGARCH models
ZHU Fukang *
School of Mathematics, Jilin University, ChangChun 130012
*Correspondence author
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Funding: National Natural Science Foundation of China （No.11001105）, Specialized Research Fund for the DoctoralProgram of Higher Education （No.20090061120037）
Opened online:18 January 2012
Accepted by: none
Citation: ZHU Fukang.Modeling integer-valued time series based on COM-Poisson INGARCH models[OL]. [18 January 2012] http://en.paper.edu.cn/en_releasepaper/content/4458613
 
 
Frequently count time series exhibits overdispersion, but the opposite phenomenon of underdispersion is well documentedin some situations thus may be encountered in real applications. The INGARCH model is a popular tool for modeling time series of counts.The Poisson and negative binomial models can only deal with overdispersion, and the double Poisson model can treat both of them, but thelatter model has some shortcomings or limitations. The revived COM-Poisson distribution is flexible in modeling a wide range ofoverdispersion and underdispersion with only two parameters, while possessing properties that make it methodologically appealing anduseful in practice. Thus we introduce a new INGARCH model based on this distribution. We give approximate condition for stationarity andexpressions for mean, variance and autocorrelation function. We discuss the maximum likelihood estimation procedure for the parameters of interest.
Keywords:COM-Poisson distribution; Integer-valued GARCH models; Maximum likelihood estimation; Overdispersion; Underdispersion
 
 
 

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