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On random coefficient INAR processes with long memory

Author

Listed:
  • Jan Beran

    (University of Konstanz)

  • Frieder Droullier

    (University of Konstanz)

Abstract

We consider random coefficient INAR(1) processes with a strongly dependent latent random coefficient process. It is shown that, in spite of its conditional Markovian structure, the unconditional process exhibits long-range dependence. Short-term prediction and estimation of parameters involved in the prediction are considered. Asymptotic rates of convergence are derived.

Suggested Citation

  • Jan Beran & Frieder Droullier, 2025. "On random coefficient INAR processes with long memory," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 109(2), pages 281-311, June.
    • Handle: RePEc:spr:alstar:v:109:y:2025:i:2:d:10.1007_s10182-025-00523-8
    DOI: 10.1007/s10182-025-00523-8
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    References listed on IDEAS

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    1. Mawuli Segnon & Manuel Stapper, 2019. "Long Memory Conditional Heteroscedasticity in Count Data," CQE Working Papers 8219, Center for Quantitative Economics (CQE), University of Muenster.
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    5. Schweer, Sebastian & Weiß, Christian H., 2014. "Compound Poisson INAR(1) processes: Stochastic properties and testing for overdispersion," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 267-284.
    6. A. M. M. Shahiduzzaman Quoreshi, 2014. "A long-memory integer-valued time series model, INARFIMA, for financial application," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2225-2235, December.
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