An Observation-Driven Random Parameter INAR(1) Model Based on the Poisson Thinning Operator

This paper presents a first-order integer-valued autoregressive time series model featuring observation-driven parameters that may adhere to a particular random distribution. We derive the ergodicity of the model as well as the theoretical properties of point estimation, interval estimation, and parameter testing. The properties are verified through numerical simulations. Lastly, we demonstrate the application of this model using real-world datasets.

Automated summary

This paper introduces a first-order integer-valued autoregressive time series model with observation-driven parameters that may follow a specific random distribution. The ergodicity of the model and the theoretical properties of point estimation, interval estimation, and parameter testing are derived. These properties are verified through numerical simulations. Finally, the application of this model is demonstrated using real-world datasets.