Identification and QML estimation of multivariate and simultaneous equations spatial autoregressive models

This paper investigates a simultaneous equations spatial autoregressive model which incorporates simultaneity effects, own-variable spatial lags and cross-variable spatial lags as explanatory variables, and allows for correlation between disturbances across equations. In exposition, we also discuss a multivariate spatial autoregressive model that can be treated as a reduced form of the simultaneous equations model. We study parameter spaces, parameter identification, asymptotic properties of the quasi-maximum likelihood estimation, and computational issues. Monte Carlo experiments illustrate the advantages of the QML, broader applicability and efficiency, compared to instrumental variables based estimation methods in the existing literature. (C) 2016 Elsevier B.V. All rights reserved.