A New Bivariate INAR(1) Model with Time-Dependent Innovation Vectors

Huaping Chen (), Fukang Zhu and Xiufang Liu
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Huaping Chen: School of Mathematics and Statistics, Henan University, Kaifeng 475004, China
Fukang Zhu: School of Mathematics, Jilin University, Changchun 130012, China
Xiufang Liu: College of Mathematics, Taiyuan University of Technology, Taiyuan 030024, China

Stats, 2022, vol. 5, issue 3, 1-22

Abstract: Recently, there has been a growing interest in integer-valued time series models, especially in multivariate models. Motivated by the diversity of the infinite-patch metapopulation models, we propose an extension to the popular bivariate INAR(1) model, whose innovation vector is assumed to be time-dependent in the sense that the mean of the innovation vector is linearly increased by the previous population size. We discuss the stationarity and ergodicity of the observed process and its subprocesses. We consider the conditional maximum likelihood estimate of the parameters of interest, and establish their large-sample properties. The finite sample performance of the estimator is assessed via simulations. Applications on crime data illustrate the model.

Keywords: bivariate INAR model; bivariate poisson distribution; time-dependent innovation; time series of counts; stability; parameters estimation (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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