Abstract
Spatial–temporal linear models and the corresponding likelihood-based statistical inference are important tools for the analysis of spatial–temporal lattice data. In this paper, we study the asymptotic properties of maximum likelihood estimates under a general asymptotic framework for spatial–temporal linear models. We propose mild regularity conditions on the spatial–temporal weight matrices and derive the asymptotic properties (consistency and asymptotic normality) of maximum likelihood estimates. A simulation study is conducted to examine the finite-sample properties of the maximum likelihood estimates.
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