Estimators of binary spatial autoregressive models: A Monte Carlo study

The goal of this paper is to provide a cohesive description and a critical comparison ofthe main estimators proposed in the literature for spatial binary choice models. The properties of suchestimators are investigated using a theoretical and simulation study, followed by an empirical appli-cation. To the authors’ knowledge, this is the first paper that provides a comprehensive Monte Carlostudy of the estimators’ properties. This simulation study shows that the Gibbs estimator performs bestfor low spatial autocorrelation, while the recursive importance sampler performs best for high spatialautocorrelation. The same results are obtained by increasing the sample size. Finally, the linearizedgeneral method of moments estimator is the fastest algorithm that provides accurate estimates for lowspatial autocorrelation and large sample size.