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Efficient order selection algorithms for integer‐valued ARMA processes
Author(s) -
EncisoMora Víctor,
Neal Peter,
Subba Rao T.
Publication year - 2009
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2008.00592.x
Subject(s) - autoregressive model , mathematics , integer (computer science) , selection (genetic algorithm) , algorithm , star model , jump , reversible jump markov chain monte carlo , markov chain , order (exchange) , mathematical optimization , monte carlo method , markov chain monte carlo , autoregressive integrated moving average , computer science , statistics , time series , artificial intelligence , finance , physics , quantum mechanics , economics , programming language
. 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.