Detection of additive outliers in Poisson INteger-valued AutoRegressive time series

Outlying observations are commonly encountered in the analysis of time series. In this paper the problem of detecting additive outliers in integer-valued time series is considered. We show how Gibbs sampling can be used to detect outlying observations in INAR(1) processes. The methodology proposed is illustrated using examples as well as an observed data set.