MCMC for Integer‐Valued ARMA processes

Peter Neal and T. Subba Rao

Journal of Time Series Analysis, 2007, vol. 28, issue 1, 92-110

Abstract: Abstract. The classical statistical inference for integer‐valued time‐series has primarily been restricted to the integer‐valued autoregressive (INAR) process. Markov chain Monte Carlo (MCMC) methods have been shown to be a useful tool in many branches of statistics and is particularly well suited to integer‐valued time‐series where statistical inference is greatly assisted by data augmentation. Thus in this article, we outline an efficient MCMC algorithm for a wide class of integer‐valued autoregressive moving‐average (INARMA) processes. Furthermore, we consider noise corrupted integer‐valued processes and also models with change points. Finally, in order to assess the MCMC algorithms inferential and predictive capabilities we use a range of simulated and real data sets.

Date: 2007
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:28:y:2007:i:1:p:92-110

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