PROBIT WITH SPATIAL AUTOCORRELATION

1992/08/01 by Daniel P. McMillen · 4 citations
Economics, Econometrics and Finance · #Spatial and Panel Data Analysis #Economic and Environmental Valuation #Housing Market and Economics

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

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

Cited by

• Spatial Autoregressive Models for Geographically Hierarchical Data Structures
• Bayesian Estimation of Limited Dependent Variable Spatial Autoregressive Models
• Partially Linear Spatial Probit Models
• Are There Spatial Spillovers in the Adoption of Clean Technology? The Case of Organic Dairy Farming

Related

• Partially Linear Spatial Probit Models
• A General Method for Resampling Autocorrelated Spatial Data
• A General Method for Resampling Autocorrelated Spatial Data
• Bayesian Estimation of Limited Dependent Variable Spatial Autoregressive Models
• Heavy tailed spatial autocorrelation models