PROBIT WITH SPATIAL AUTOCORRELATION

1992/08/01 by Daniel P. McMillen · 295 citations
Economics, Econometrics and Finance · Mathematics · #Autocorrelation #Econometrics #Economic and Environmental Valuation #Estimator #Heteroscedasticity #Housing Market and Economics #Mathematics #Multinomial probit #Multivariate probit model #Ordinary least squares #Probit #Probit model #Spatial analysis #Spatial and Panel Data Analysis #Statistics

paper · doi:10.1111/j.1467-9787.1992.tb00190.x

published in Journal of Regional Science 32(3), 335-348 (Wiley)

openalex publication_date 1992/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25

Abstract

ABSTRACT. 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.

Citations

• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity

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• ESTIMATORS OF BINARY SPATIAL AUTOREGRESSIVE MODELS: A MONTE CARLO STUDY
• The Determinants of Brownfields Redevelopment in England. [europepmc]

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