Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

Comparison of Maximum Likelihood and Generalized Method of Moments in Spatial Autoregressive Model with Heteroskedasticity

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290489,
        author={Rohimatul  Anwar and Anik  Djuraidah and Aji Hamim  Wigena},
        title={Comparison of Maximum Likelihood and Generalized Method of Moments in Spatial Autoregressive Model with Heteroskedasticity},
        proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia},
        publisher={EAI},
        proceedings_a={ICSA},
        year={2020},
        month={1},
        keywords={heteroskedasticity spatial autoregressive maximum likelihood generalized moment method},
        doi={10.4108/eai.2-8-2019.2290489}
    }
    
  • Rohimatul Anwar
    Anik Djuraidah
    Aji Hamim Wigena
    Year: 2020
    Comparison of Maximum Likelihood and Generalized Method of Moments in Spatial Autoregressive Model with Heteroskedasticity
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290489
Rohimatul Anwar1,*, Anik Djuraidah1, Aji Hamim Wigena1
  • 1: DepartementStatistics, IPB University, Bogor, 16680, Indonesia
*Contact email: rohimatul_anwar@apps.ipb.ac.id

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.