A Structural Equation Approach to Models with Spatial Dependence
Year: 2008
Type: article
Abstract: We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it possible to obtain a closer correspondence between theory and empirics, to explicitly account for measurement errors, and to reduce multicollinearity. We extend the standard SEM maximum likelihood estimator to allow for spatial dependence and propose easily accessible SEM software like LISREL 8 and Mx. We present an illustration based on Anselin's Columbus, OH, crime data set. Furthermore, we combine the spatial lag model with the latent multiple‐indicators–multiple‐causes model and discuss estimation of this latent spatial lag model. We present an illustration based on the Anselin crime data set again.
Source: Geographical Analysis
Authors: Johan H. L. Oud, Henk Folmer
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2166020104