论文标题
一般整数值值时间序列的一致模型选择程序
Consistent model selection procedure for general integer-valued time series
论文作者
论文摘要
本文介绍了整个Integer价值时间序列的通用类别的模型选择问题。 我们提出了基于模型的泊松类似样的惩罚标准。 在某些规律性条件下，建立了该过程的一致性以及一致性和渐近正态性，泊松准式估计值是所选模型的。 对某些经典模型进行了仿真实验，例如具有非线性动力学的泊松，二进制INGARCH和负二项式模型。另外，还提供了对真实数据集的应用程序。
This paper deals with the problem of model selection for a general class of integer-valued time series. We propose a penalized criterion based on the Poisson quasi-likelihood of the model. Under certain regularity conditions, the consistency of the procedure as well as the consistency and the asymptotic normality of the Poisson quasi-likelihood estimator of the selected model are established. Simulation experiments are conducted for some classical models such as Poisson, binary INGARCH and negative binomial model with nonlinear dynamic. Also, an application to a real dataset is provided.
