Limit Theorems for Weakly Dependent Non-stationary Random Field Arrays and Asymptotic Inference of Dynamic Spatio-temporal Models

We obtain the law of large numbers (LLN) and the central limit theorem (CLT) for weakly dependent non-stationary arrays of random fields with asymptotically unbounded moments. The weak dependence condition for arrays of random fields is proved to be inherited through transformation and infinite shift. This paves a way to prove the consistency and asymptotic normality of maximum likelihood estimation for dynamic spatio-temporal models (i.e. so-called ultra high-dimensional time series models) when the sample size and/or dimension go to infinity. Especially the asymptotic properties of estimation for network autoregression are obtained under reasonable regularity conditions.