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SELECTION BIAS IN SPATIAL ECONOMETRIC MODELS
Author(s) -
McMillen Daniel P.
Publication year - 1995
Publication title -
journal of regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.171
H-Index - 79
eISSN - 1467-9787
pISSN - 0022-4146
DOI - 10.1111/j.1467-9787.1995.tb01412.x
Subject(s) - heteroscedasticity , estimator , econometrics , autocorrelation , spatial analysis , selection (genetic algorithm) , selection bias , statistics , model selection , spatial econometrics , mathematics , computer science , artificial intelligence
. 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.