Comparison of Maximum Likelihood and Generalized Method of Moments in Spatial Autoregressive Model with Heteroskedasticity Rohimatul Anwar 1 , Anik Djuraidah 2 , Aji Hamim Wigena 3 {rohimatul_anwar@apps.ipb.ac.id 1 , anikdjuraidah@apps.ipb.ac.id 2, aji_hw@apps.ipb.ac.id 3 } Student of DepartementStatistics, IPB University, Bogor, 16680, Indonesia 1 Lecturer of DepartementStatistics, IPB University, Bogor, 16680, Indonesia 2,3 Abstract.Spatial dependence and spatial heteroskedasticity are problems in spatial regression. Spatial autoregressive regression (SAR) concerns only to the dependence on lag. The estimation of SAR parameters containingheteroskedasticityusing the maximum likelihood estimation (MLE) method provides biased and inconsistent. The alternative method is the generalized method of moments (GMM). GMM uses a combination of linear and quadratic moment functions simultaneously so that the computation is easier than MLE. The bias is used to evaluate the GMM in estimating parameters of SAR model with heteroskedasticity disturbances in simulation data. The results show that GMM provides the bias of parameter estimates relatively consistent and smaller compared to the MLE method. Keywords: Heteroskedasticity, spatial autoregressive, maximum likelihood, generalized moment method. 1 Introduction Spatial dependence and spatial heteroskedasticity are problems inspatial data [1]. Lesage [2] stated that spatial dependence can be described in regression models, such as autoregressive response, error, or both. Models with dependencies in response are called spatial autoregressive models (SAR).Fotheringham[3] stated that spatial heteroskedasticity can be described using geographically weighted regression (GWR). Ord [4] considered the maximum likelihood (ML) for the estimation of the regression model. Kelejian and Prucha [5]extended that the MLE estimator is inconsistent in heteroskedasticity disturbances. Anselin[1] introduced the two-stage least squares (S2SLS) method. Kelejian and Prucha [6] introduced the generalized method of moments (GMM). GMM does not require a distribution assumption of the disturbance andcomputationally easier than the ML methods [7]. The results of Kelejian and Prucha’s research [5] showed that the estimation method is valid if the assumption of errors is stochastic and identical normal. However, heteroscedasticitycan occur in aggregation data. In this case,heteroskedasticity originates from a data averaging process with many different observations at the time of aggregation [6]. Kelejian and Prucha [7]developed the GMM method into a robust form that has been proven to be consistent if there is heteroskedasticity. Combination of linear and quadratic in moment functionsare simultaneously assumed by GMM. ICSA 2019, August 02-03, Bogor, Indonesia Copyright © 2020 EAI DOI 10.4108/eai.2-8-2019.2290489