SELECTION BIAS IN SPATIAL ECONOMETRIC MODELS
研究了空间数据中因忽略空间自相关导致的选择偏误问题，提出最大似然估计方法，并用1920年代芝加哥的土地利用与价值数据验证了异方差和选择偏误的存在。
ABSTRACT. The problem of spatial autocorrelation has been ignored in selection‐bias models estimated with spatial data. Spatial autocorrelation is a serious problem in these models because the heteroskedasticity with which it commonly is associated causes inconsistent parameter estimates in models with discrete dependent variables. This paper proposes estimators for commonly‐employed spatial models with selection bias. A maximum‐likelihood estimator is applied to data on land use and values in 1920s Chicago. Evidence of significant heteroskedasticity and selection bias is found.