Abstract
The objective of this chapter is to provide an overview of estimation methods of spatial regression models including endogenous variables in addition to the spatial lag variable. We first provide evidence that spatial autocorrelation matters when dealing with endogenous variables. In particular, in terms of estimation, omitting a spatial lag and using spatially autocorrelated instruments induces bias in instrumental variables estimates. In terms of testing, wrongly omitted spatial autocorrelation under the form of a spatial lag or a spatial error significantly decreases the power of Hausman and Sargan tests, which are widely used in applied microeconometrics. We then describe instrumental variables, generalized method of moments and maximum likelihood procedures for cross-sectional and panel spatial models including endogenous variables, and suggest how standard diagnostics might be adapted given the presence of spatial error dependence. We finish by presenting other identification strategies, drawing from the impact evaluation econometrics literature, and discuss how they can be adapted in a spatial context.
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Le Gallo, J., Fingleton, B. (2019). Endogeneity in Spatial Models. In: Fischer, M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36203-3_122-1
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DOI: https://doi.org/10.1007/978-3-642-36203-3_122-1
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