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MCMC for Integer-Valued ARMA processes


  • Peter Neal
  • T. Subba Rao


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. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.

Suggested Citation

  • Peter Neal & T. Subba Rao, 2007. "MCMC for Integer-Valued ARMA processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 92-110, January.
  • Handle: RePEc:bla:jtsera:v:28:y:2007:i:1:p:92-110

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    Cited by:

    1. Jung, Robert C. & Liesenfeld, Roman & Richard, Jean-François, 2011. "Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 73-85.
    2. Víctor Enciso-Mora & Peter Neal & T. Subba Rao, 2009. "Efficient order selection algorithms for integer-valued ARMA processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 1-18, January.
    3. repec:eee:csdana:v:122:y:2018:i:c:p:33-44 is not listed on IDEAS
    4. David T. Frazier & Worapree Maneesoonthorn & Gael M. Martin & Brendan P.M. McCabe, 2018. "Approximate Bayesian forecasting," Monash Econometrics and Business Statistics Working Papers 2/18, Monash University, Department of Econometrics and Business Statistics.
    5. Mohammadipour, Maryam & Boylan, John E., 2012. "Forecast horizon aggregation in integer autoregressive moving average (INARMA) models," Omega, Elsevier, vol. 40(6), pages 703-712.

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