Some properties of multivariate INAR(1) processes

Xanthi Pedeli and Dimitris Karlis

Computational Statistics & Data Analysis, 2013, vol. 67, issue C, 213-225

Abstract: INteger-valued AutoRegressive (INAR) processes are common choices for modeling non-negative discrete valued time series. In this framework and motivated by the frequent occurrence of multivariate count time series data in several different disciplines, a generalized specification of the bivariate INAR(1) (BINAR(1)) model is considered. In this new, full BINAR(1) process, dependence between the two series stems from two sources simultaneously. The main focus is on the specific parametric case that arises under the assumption of a bivariate Poisson distribution for the innovations of the process. As it is shown, such an assumption gives rise to a Hermite BINAR(1) process. The method of conditional maximum likelihood is suggested for the estimation of its unknown parameters. A short application on financial count data illustrates the model.

Keywords: Autocorrelation; Bivariate Hermite; Bivariate Poisson; Full BINAR(1) (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:67:y:2013:i:c:p:213-225

DOI: 10.1016/j.csda.2013.05.019

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