In this paper, we introduce a new first-order generalized Poisson integer-valued autoregressive process, for modeling integer-valued time series exhibiting a piecewise structure and overdispersion. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares and conditional maximum likelihood estimators are derived. The asymptotic properties of the estimators are established. Moreover, two special cases of the process are discussed. Finally, some numerical results of the estimates and a real data example are presented.

中文翻译：

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具有结构变化的广义泊松整数值自回归过程

在本文中，我们介绍了一种新的一阶广义泊松整数值自回归过程，用于对表现出分段结构和过度离散的整数值时间序列进行建模。讨论了该模型的基本概率和统计特性。导出条件最小二乘和条件最大似然估计量。建立了估计量的渐近性质。此外，还讨论了该过程的两种特殊情况。最后，给出了一些估计的数值结果和一个真实的数据示例。