Comparison of BINAR(1) models with bivariate negative binomial innovations and explanatory variables. Issue 8 (24th May 2021)

• Record Type: Journal Article
• Authors: Su, Bing; Zhu, Fukang
• Is Part Of: Journal of statistical computation and simulation. Volume 91:Issue 8(2021)
• Page Start: 1616
• Page End: 1634
• Publication Date: 2021-05-24
• Subjects: Bivariate integer-valued autoregressive model -- bivariate negative binomial distribution -- explanatory variables -- INAR -- time series of counts; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1863965
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : 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.