Generalizing the OLS and Grid Estimators
展示了如何推广普通最小二乘法和网格调整估计量，以结合两者的优点，并发现推广后的方法定义了一个空间自回归模型，在实证中表现优于两者。
The vast majority of market valuations employ either some formal estimator such as ordinary least squares (OLS) or rely upon an informal set of rules defining the grid adjustment estimator. The success of the grid adjustment estimator suggests the data do not obey the ideal assumptions underlying OLS. However, the grid adjustment estimator's lack of a formal statistical foundation makes it difficult to use for inference and other purposes. This article demonstrates how to generalize the grid estimator and OLS to potentially obtain the best features of both. Interestingly, the generalization defines a spatial autoregression. On an empirical example the spatial autoregression outperforms the grid estimator which in turn outperforms OLS.