MCMC for Integer-Valued ARMA processes
AbstractThe 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. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Journal of Time Series Analysis.
Volume (Year): 28 (2007)
Issue (Month): 1 (01)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782
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- Jung, Robert & Liesenfeld, Roman & Richard, Jean-François, 2008.
"Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity,"
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- Drost, F.C. & Akker, R. van den & Werker, B.J.M., 2007. "Efficient Estimation of Autoregression Parameters and Innovation Distributions for Semiparametric Integer-Valued AR(p) Models (Subsequently replaced by DP 2008-53)," Discussion Paper 2007-23, Tilburg University, Center for Economic Research.
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