Premium
PROBIT WITH SPATIAL AUTOCORRELATION
Author(s) -
McMillen Daniel P.
Publication year - 1992
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.1467-9787.1992.tb00190.x
Subject(s) - heteroscedasticity , autocorrelation , estimator , probit model , econometrics , statistics , probit , spatial analysis , multivariate probit model , mathematics , multinomial probit , ordinary least squares
. Commonly‐employed spatial autocorrelation models imply heteroskedastic errors, but heteroskedasticity causes probit to be inconsistent. This paper proposes and illustrates the use of two categories of estimators for probit models with spatial autocorrelation. One category is based on the EM algorithm, and requires repeated application of a maximum‐likelihood estimator. The other category, which can be applied to models derived using the spatial expansion method, only requires weighted least squares.