期刊
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
卷 42, 期 4, 页码 723-740出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610926.2012.754466
关键词
BINAR; Count data; Frank copula; Negative correlation; 62M10; 62H12
资金
- Athens University of Economics and Business
Multivariate count time series data occur in many different disciplines. The class of INteger-valued AutoRegressive (INAR) processes has the great advantage to consider explicitly both the discreteness and autocorrelation characterizing this type of data. Moreover, extensions of the simple INAR(1) model to the multi-dimensional space make it possible to model more than one series simultaneously. However, existing models do not offer great flexibility for dependence modelling, allowing only for positive correlation. In this work, we consider a bivariate INAR(1) (BINAR(1)) process where cross-correlation is introduced through the use of copulas for the specification of the joint distribution of the innovations. We mainly emphasize on the parametric case that arises under the assumption of Poisson marginals. Other marginal distributions are also considered. A short application on a bivariate financial count series illustrates the model.
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