PROBIT WITH SPATIAL AUTOCORRELATION
针对空间自相关模型中异方差导致Probit估计不一致的问题，提出了基于EM算法和加权最小二乘法的两类估计方法，适用于空间计量经济学研究者。
ABSTRACT. Commonly‐employed spatial autocorrelation models imply heteroskedastic errors, but heteroskedasticity causes probit to be inconsistent. This paper proposes and illustrates the use of two categories of estimators for probit models with spatial autocorrelation. One category is based on the EM algorithm, and requires repeated application of a maximum‐likelihood estimator. The other category, which can be applied to models derived using the spatial expansion method, only requires weighted least squares.