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SELECTION BIAS IN SPATIAL ECONOMETRIC-MODELS
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DOI：10.1111/j.1467-9787.1995.tb01412.x.png)
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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.
Keyword:
LAND-VALUE FUNCTIONS
UNITED-STATES
AUTOCORRELATION
PROBIT
DEMAND
MARKET
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