Selection Bias in Spatial Econometric Models

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.