Two-stage generalized moment method approach for bidimensional random coefficient autoregressive models

A two-dimensionally indexed random coefficients autoregressive models (2D - RCAR) and the corresponding statistical inference are important tools for the analysis of spatial lattice data. The study of such models is motivated by their second-order properties that are similar to those of 2D - (G)ARCH which play an important role in spatial econometrics. In this article, we study the asymptotic properties of two-stage generalized moment method (2S - GMM) under general asymptotic framework for 2D - RCA models. So, the efficiency, strong consistency, the asymptotic normality, and hypothesis tests of 2S - GMM estimation are derived. A simulation experiment is presented to highlight the theoretical results.