Modelling count data time series with Markov processes based on binomial thinning

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  1. Department of Statistics
  2. Modelling count data time series with Markov processes based on binomial thinning

Modelling count data time series with Markov processes based on binomial thinning

TitleModelling count data time series with Markov processes based on binomial thinning
Publication TypeJournal Article
Year of Publication2006
AuthorsZhu, R, Joe, H
JournalJournal of Time Series Analysis
Volume27
Pagination725-738
Date PublishedSEP
Type of ArticleArticle
ISSN0143-9782
KeywordsBinomial thinning, count data time series, discrete self-decomposability, higher order Markov dependence, overdispersion, time-varying mean
AbstractWe obtain new models and results for count data time series based on binomial thinning. Count data time series may have non-stationarity from trends or covariates, so we propose an extension of stationary time series based on binomial thinning such that the univariate marginal distributions are always in the same parametric family, such as negative binomial. We propose a recursive algorithm to calculate the probability mass functions for the innovation random variable associated with binomial thinning. This simplifies numerical calculations and estimation for the classes of time series models that we consider. An application with real data is used to illustrate the models.
DOI10.1111/j.1467-9892.2006.00485.x