Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/26401
Authors: 
Badinger, Harald
Egger, Peter
Year of Publication: 
2008
Series/Report no.: 
CESifo working paper 2356
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.
Subjects: 
Higher-order spatial dependence
eteroskedasticity
two-stages least squares
generalized moments estimation
asymptotics
JEL: 
C21
C31
Document Type: 
Working Paper

Files in This Item:
File
Size
553.32 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.