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

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  • Takahisa Yokoi

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.

Suggested Citation

  • Takahisa Yokoi, 2011. "Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal but Heteroskedastic Disturbances," ERSA conference papers ersa10p536, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa10p536
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