Estimation of multiequation cross-section models in the presence of spatial autocorrelation

We describe econometric techniques to treat spatial autocorrelation in multiequation cross-section models. The cross-section approaches discussed here are heavily based on the spatial GMM procedure, proposed by Conley (1999). An extension for fullinformation instrumental variable models is presented. Monte Carlo simulations are employed in order to verify some asymptotic properties of the Spatial GMM approach. The simulations suggest that, even in the presence of spatial nonstationarity, the spatial GMM still delivers valid standard errors. Besides, usual t-statistics appear to have a standard normal distribution. An application for estimating labor and wage equations to study regional growth and development of the Brazilian municipalities, between 1991 and 2000, is presented.