First-order spatial random coefficient non-negative integer-valued autoregressive (SRCINAR(1,1)) model

In this paper we introduce first-order spatial random coefficient non-negative integer-valued autoregressive (SRCINAR(1,1)) model to fit the spatial count data on a two-dimensional regular lattice. We use a thinning operator in order to define the processes. The mean, variance and autocorrelation function of the model are derived. Three methods (Yule-Walker, Conditional least squares and Conditional maximum likelihood) for the estimation of the parameters of the model are discussed and are compared with respect to the root mean squared error (RMSE), in a simulation study. Finally, SRCINAR(1,1) model is applied to a real data set of striga counts at the ICRISAT Sahelian Center near Niamey (Niger).