  This paper proposes a bivariate marginal likelihood specification of spatial
econometrics models 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.
