Time series of count data: modeling, estimation and diagnostics
Computational Statistics & Data Analysis
A model for integer-valued time series with conditional overdispersion
Computational Statistics & Data Analysis
Statistical analysis of discrete-valued time series using categorical ARMA models
Computational Statistics & Data Analysis
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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.