pairwise likelihood inference for spatial regressions estimated on very large datasets

This paper proposes a pairwise likelihood specification of a spatial regression model that simplifies the derivation of the log-likelihood and leads to a closed form expression for the estimation of the parameters. With respect to the more traditional specifications of spatial autoregressive models, our method avoids the arbitrariness of the specification of a weight matrix, presents analytical and computational advantages and provides interesting interpretative insights. We establish small sample and asymptotic properties of the estimators and we derive the associated Fisher information matrix needed in confidence interval estimation and hypothesis testing. We also present an illustrative example of application based on simulated data.