In some situations, it is well-known that the linear credibility approximation is too restrictive and does not model correctly predictive premiums. Recently, Boucher, Denuit and Guillén (2006a, 2006b) proposed various generalisations of the zero-inflated Poisson and the hurdle distributions. For these models, the authors showed that the predictive premium do not depend exclusively on the number of past claims, but also on the number of insured periods with at least one claim. We use the bivariate linear credibility theory to estimate the future premiums based on these two statistics, and compare the approximation with the exact result using Bayesian theory. We show that the approximations for the zero-inflated models are satisfying. However, we also show that the approximations of the hurdle distributions cause major errors. We explain these results by showing that the linear credibility premiums do not suppose dependence between random effects of the models.