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Approximate least squares estimation for spatial autoregressive models with covariates

Author

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  • Ma, Yingying
  • Lan, Wei
  • Zhou, Fanying
  • Wang, Hansheng

Abstract

Due to the rapid development of social network sites, the spatial autoregressive model with covariates has been popularly applied in real practice. However, traditional estimation methods such as the quasi-maximum likelihood estimator are computationally infeasible if the network size n is huge. To circumvent this infeasibility, a novel method named approximate least square estimator (ALSE) is proposed by optimizing an approximate least squares objective function. It can reduce the computational complexity from O(n3) to O(n). Under certain appropriate conditions, the ALSE is consistent and asymptotically normal. In addition, a novel test statistic is proposed to test the identifiability of the parameters in covariates. Extensive simulation studies and a Sina Weibo dataset are analyzed to assess the finite-sample performance of the ALSE.

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  • Ma, Yingying & Lan, Wei & Zhou, Fanying & Wang, Hansheng, 2020. "Approximate least squares estimation for spatial autoregressive models with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:csdana:v:143:y:2020:i:c:s0167947319301884
    DOI: 10.1016/j.csda.2019.106833
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    2. Gao, Min & Yang, Wenzhi & Wu, Shipeng & Yu, Wei, 2022. "Asymptotic normality of residual density estimator in stationary and explosive autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).

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