GMM Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances
Osman Dogan and
No 1, Working Papers from City University of New York Graduate Center, Ph.D. Program in Economics
We consider a spatial econometric model containing a spatial lag in the dependent variable and the disturbance term with an unknown form of heteroskedasticity in innovations. We first prove that the maximum likelihood (ML) estimator for spatial autoregressive models is generally inconsistent when heteroskedasticity is not taken into account in the estimation. We show that the necessary condition for the consistency of the ML estimator of spatial autoregressive parameters depends on the structure of the spatial weight matrices. Then, we extend the robust generalized method of moment (GMM) estimation approach in Lin and Lee (2010) for the spatial model allowing for a spatial lag not only in the dependent variable but also in the disturbance term. We show the consistency of the robust GMM estimator and determine its asymptotic distribution. Finally, through a comprehensive Monte Carlo simulation, we compare finite sample properties of the robust GMM estimator with other estimators proposed in the literature.
Keywords: spatial autoregressive models; unknown heteroskedasticity; robustness; GMM; asymptotics; MLE (search for similar items in EconPapers)
JEL-codes: C13 C21 C31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-geo, nep-ore and nep-ure
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