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

50 Pages Posted: 16 Jun 2010 Last revised: 2 Nov 2010

See all articles by Takahisa Yokoi

Takahisa Yokoi

Tohoku University - Graduate School of Information Sciences

Date Written: November 2, 2010

Abstract

The likelihood functions for spatial autoregressive models with normal but heteroskedastic disturbances have been derived [Anselin (1988, ch.6)], but there is no implementation of maximum likelihood estimation for these likelihood functions in general cases with heteroskedastic disturbances. Therefore, less efficient IV-based methods must be applied if disturbance terms might be heteroskedastic. In the present paper, we develop a new computer program for maximum likelihood estimation of heteroskedastic models that also utilizes multiple spatial weight matrices and confirm the efficiency of the obtained estimator in cases with heteroskedastic disturbances through Monte Carlo simulations.

Keywords: Spatial autoregressive model, Heteroskedasticity, Maximum likelihood estimation, Multiple spatial weight matrices

JEL Classification: C13, C21

Suggested Citation

Yokoi, Takahisa, Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal But Heteroskedastic Disturbances (November 2, 2010). Available at SSRN: https://ssrn.com/abstract=1625588 or http://dx.doi.org/10.2139/ssrn.1625588

Takahisa Yokoi (Contact Author)

Tohoku University - Graduate School of Information Sciences ( email )

Aoba 6-3-09
Aramaki, Aoba-ku
Sendai, Miyagi 980-8579
Japan

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