一种基于泊松稀疏算子的观测驱动随机参数INAR(1)模型
An Observation-Driven Random Parameter INAR(1) Model Based on the Poisson Thinning Operator.
作者信息
Yu Kaizhi, Tao Tielai
机构信息
School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China.
出版信息
Entropy (Basel). 2023 May 27;25(6):859. doi: 10.3390/e25060859.
Abstract
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.
摘要
本文提出了一种一阶整数值自回归时间序列模型，其特征在于观测驱动参数可能遵循特定的随机分布。我们推导了该模型的遍历性以及点估计、区间估计和参数检验的理论性质。通过数值模拟验证了这些性质。最后，我们使用实际数据集展示了该模型的应用。
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