W-based versus latent variables spatial autoregressive models: Evidence from Monte Carlo simulations
SourceAnnals of Regional Science, 47, 3, (2011), pp. 619-639
Article / Letter to editor
Display more detailsDisplay less details
SW OZ BSI OGG
Annals of Regional Science
In this paper, we compare by means of Monte Carlo simulations two approaches to take spatial autocorrelation into account: the classical spatial autoregressive model and the structural equations model with latent variables. The former accounts for spatial dependence and spillover effects in georeferenced data by means of a spatial weights matrix W. The latter represents spatial dependence and spillover effects by means of a latent variable in the structural (regression) model while the observed spatially lagged variables are related to the latent spatial dependence variable in the measurement model. The simulation results based on Anselin's Columbus, Ohio, crime data set show that the misspecified latent variables approach slightly trails the correctly specified classical approach in terms of bias and root mean squared error of the coefficient estimators.
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.