ESTIMATORS OF BINARY SPATIAL AUTOREGRESSIVE MODELS: A MONTE CARLO STUDY

Anping Chen, Marlon Boarnet, Mark Partridge, Raffaella Calabrese () and Johan A. Elkink

Journal of Regional Science, 2014, vol. 54, issue 4, 664-687

Abstract: type="main">

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

Date: 2014
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