ESTIMATORS OF BINARY SPATIAL AUTOREGRESSIVE MODELS: A MONTE CARLO STUDY
系统比较了空间二元选择模型的主要估计量，通过模拟发现吉布斯估计量在低空间自相关时最优，递归重要性抽样在高自相关时最优，线性化广义矩估计在低自相关大样本下最快且准确。
ABSTRACT 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.