  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.
