Comparison of BINAR(1) models with bivariate negative binomial innovations and explanatory variables
Comparison of BINAR(1) models with bivariate negative binomial innovations and explanatory variables
复制标题
具有双变量负二项创新点和解释变量的 BINAR(1) 模型的比较
DOI：
10.1080/00949655.2020.1863965
复制
发表时间：
2021
影响因子：
1.2
通讯作者：
Fukang Zhu
中科院分区：
文献类型：
--
作者：
Bing Su;Fukang Zhu
二元1阶整数值自回归模型（BINAR（1））是拟合二元计数时间序列的常用模型，其新息分布可以选择二元负二项分布（BNB），比传统的二元泊松分布更灵活。众所周知，BNB分布可以用不同的方法构造，本文将对这些分布进行综述。比较了基于这些BNB分布的BINAR（1）模型的性能，其中解释变量包含在生存概率中。为了估计未知参数，条件最大似然法被认为是和评估的Monte Carlo模拟。用两个销售量比较了上述模型的性能，并给出了一些有趣的结论。
The bivariate integer-valued autoregressive model of order 1 (BINAR(1)) is popular in fitting bivariate time series of counts, and the bivariate negative binomial (BNB) distribution can be chosen as its innovation's distribution, which is more flexible than the traditional bivariate Poisson distribution. It is well known that BNB distributions can be constructed in different ways, and these distributions will be reviewed in this paper. Performances of BINAR(1) models based on these BNB distributions with explanatory variables being included in the survival probability are compared. To estimate unknown parameters, the conditional maximum likelihood method is considered and evaluated by Monte Carlo simulations. Two sales counts are used to compare performances of the above models, and some interesting conclusions are also given.