Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, methods based on the quasi-likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference (II) approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the II procedure to take account of the parameterization of the variance–covariance matrix of the disturbances. Finite-sample performance of the new estimator is assessed in a Monte Carlo study. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under robustification to heterogeneity in the equation errors.

空间单位通常会因许多特征而异，从而引入潜在的未观察到的异质性，从而使常用的同方差条件无效。在存在未观察到的异方差性的情况下，基于准似然函数的方法通常会对空间参数和外生回归变量的系数产生不一致的估计。文献中已经提出了一种鲁棒的广义矩估计方法和一种改进的似然法来解决这个问题。本文构建了一种替代间接推理 (II) 方法，该方法依赖于简单的普通最小二乘法程序作为起点。通过利用在 II 过程中应用的新版本的连续更新来考虑干扰的方差-协方差矩阵的参数化，从而适应异方差性。新估计器的有限样本性能在蒙特卡罗研究中进行了评估。该方法在波士顿地区房价数据的实证应用中实施，发现在对方程误差异质性进行鲁棒化的情况下，房价确定的空间效应更为显着。