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Integer autoregressive models with structural breaks

Author

Listed:
  • Akanksha S. Kashikar
  • Neelabh Rohan
  • T.V. Ramanathan

Abstract

Even though integer-valued time series are common in practice, the methods for their analysis have been developed only in recent past. Several models for stationary processes with discrete marginal distributions have been proposed in the literature. Such processes assume the parameters of the model to remain constant throughout the time period. However, this need not be true in practice. In this paper, we introduce non-stationary integer-valued autoregressive (INAR) models with structural breaks to model a situation, where the parameters of the INAR process do not remain constant over time. Such models are useful while modelling count data time series with structural breaks. The Bayesian and Markov Chain Monte Carlo (MCMC) procedures for the estimation of the parameters and break points of such models are discussed. We illustrate the model and estimation procedure with the help of a simulation study. The proposed model is applied to the two real biometrical data sets.

Suggested Citation

  • Akanksha S. Kashikar & Neelabh Rohan & T.V. Ramanathan, 2013. "Integer autoregressive models with structural breaks," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2653-2669, December.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2653-2669
    DOI: 10.1080/02664763.2013.823920
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    Cited by:

    1. Yang, Kai & Yu, Xinyang & Zhang, Qingqing & Dong, Xiaogang, 2022. "On MCMC sampling in self-exciting integer-valued threshold time series models," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    2. Manik Awale & N. Balakrishna & T. V. Ramanathan, 2019. "Testing the constancy of the thinning parameter in a random coefficient integer autoregressive model," Statistical Papers, Springer, vol. 60(5), pages 1515-1539, October.

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