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Endogeneity in Spatial Models

  • Julie Le Gallo
  • Bernard FingletonEmail author
Living reference work entry

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.

Keywords

Spatial autocorrelation Endogenous variables Instrumental variables Hausman test Sargan test Stable unit treatment value assumption 

JEL Classification

C21 C23 C26 C31 C33 C36 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CESAER UMR 1041AgroSup Dijon, INRA, Université de Bourgogne Franche-ComtéDijonFrance
  2. 2.Department of Land EconomyUniversity of CambridgeCambridgeUK

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