Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/118922 
Authors: 
Year of Publication: 
2010
Series/Report no.: 
50th Congress of the European Regional Science Association: "Sustainable Regional Growth and Development in the Creative Knowledge Economy", 19-23 August 2010, Jönköping, Sweden
Publisher: 
European Regional Science Association (ERSA), Louvain-la-Neuve
Abstract: 
Likelihood functions of spatial autoregressive models with normal but heteroskedastic disturbances have been already derived [Anselin (1988, ch.6)]. But there is no implementation for maximum likelihood estimation of these likelihood functions in general (heteroskedastic disturbances) cases. This is the reason why less efficient IV-based methods, 'robust 2-SLS' estimation for example, must be applied when disturbance terms may be heteroskedastic. In this paper, we develop a new computer program for maximum likelihood estimation and confirm the efficiency of our estimator in heteroskedastic disturbance cases using Monte Carlo simulations.
Subjects: 
Spatial autoregressive model
Heteroskedasticity
JEL: 
C13
C21
Document Type: 
Conference Paper

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