There is an increasing interest in models for discrete valued time series. Among them, the integer autoregressive conditional heteroscedastic (INGARCH) is a model that has found several applications. In the present article, we study the problem of model selection for this family of models. Namely we consider that an observation conditional on the past follows a Poisson distribution where its mean depends on its past mean values and on past observations. We consider both linear and log-linear models. Our purpose is to select the most appropriate order of such models, using a trans-dimensional Bayesian approach that allows jumps between competing models. A small simulation experiment supports the usage of the method. We apply the methodology to real datasets to illustrate the potential of the approach.

人们对离散值时间序列的模型越来越感兴趣。其中，整数自回归条件异方差（INGARCH）是一个已经发现了多种应用的模型。在本文中，我们研究了该系列模型的模型选择问题。也就是说，我们认为以过去为条件的观察遵循泊松分布，其中其均值取决于其过去的平均值和过去的观察。我们考虑线性和对数线性模型。我们的目的是使用允许在竞争模型之间跳转的跨维贝叶斯方法来选择此类模型的最合适的顺序。一个小型的模拟实验支持了该方法的使用。我们将该方法应用于真实数据集以说明该方法的潜力。