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Efficient order selection algorithms for integer valued ARMA processes

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>01/2009
<mark>Journal</mark>Journal of Time Series Analysis
Issue number1
Number of pages18
Pages (from-to)1-18
<mark>Original language</mark>English


We consider the problem of model (order) selection for integer-valued autoregressive moving-average (INARMA) processes. A very efficient reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is constructed for moving between INARMA processes of different orders. An alternative in the form of the EM algorithm is given for determining the order of an integer-valued autoregressive (INAR) process. Both algorithms are successfully applied to both simulated and real data sets.