We consider network autoregressive models for count data with a non-random time-varying neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference. We consider both cases of fixed and increasing network dimension and we show that quasi-likelihood inference provides consistent and asymptotically normally distributed estimators. The work is complemented by simulation results and a data example.

翻译：我们认为,计算数据的网络自动递减模式与非随机时间变化的邻里结构不相容,主要的方法贡献是发展能够保证稳定性和有效统计推断的条件,我们考虑了固定和不断增长的网络层面两个案例,我们表明,准相似的推论提供了一致和无症状的正常分布估计数据,这项工作得到了模拟结果和数据实例的补充。