Long-memory models for count time series

In this paper we analyze two different approaches for modeling dependent count data with long-memory. The first model we consider explicitly takes into account the integer nature of data and the long-range correlation, while the second model is a count-data long-memory model where the distribution of the current observation is specified conditionally upon past observations. We compare these two different models by looking at their estimation and forecasting performances.