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Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal but Heteroskedastic Disturbances

Takahisa Yokoi ()

ERSA conference papers from European Regional Science Association

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.

Date: 2011-09
New Economics Papers: this item is included in nep-ecm and nep-ore
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