GM Estimation of Higher-Order Spatial Autoregressive Processes in Cross-Section Models with Heteroskedastic Disturbances

No 2356, CESifo Working Paper Series from CESifo

Abstract: This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the case of spatial autoregressive models with spatial autoregressive disturbances of arbitrary (finite) order (SARAR(R,S)). We derive the moment conditions and the optimal weighting matrix for a generalized moments (GM) estimation procedure of the spatial regressive parameters of the disturbance process and define a generalized two-stages least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their (joint) asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.

Keywords: higher-order spatial dependence; heteroskedasticity; two-stages least squares; generalized moments estimation; asymptotics (search for similar items in EconPapers)
JEL-codes: C21 C31 (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (4)

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