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Autor:innen: 
Erscheinungsjahr: 
2010
Schriftenreihe/Nr.: 
50th Congress of the European Regional Science Association: "Sustainable Regional Growth and Development in the Creative Knowledge Economy", 19-23 August 2010, Jönköping, Sweden
Verlag: 
European Regional Science Association (ERSA), Louvain-la-Neuve
Zusammenfassung: 
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.
Schlagwörter: 
Spatial autoregressive model
Heteroskedasticity
JEL: 
C13
C21
Dokumentart: 
Conference Paper

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