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Heteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-varying coefficients

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  • Hongjie Wei
  • Yan Sun

Abstract

Heteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-varying coefficients. Spatial Economic Analysis. The spatial model with space-varying coefficients proposed by Sun et al. in 2014 has proved to be useful in detecting the location effects of the impacts of covariates as well as spatial interaction in empirical analysis. However, Sun et al.’s estimator is inconsistent when heteroskedasticity is present – a circumstance that is more realistic in certain applications. In this study, we propose a kind of semi-parametric generalized method of moments (GMM) estimator that is not only heteroskedasticity robust but also takes a closed form written explicitly in terms of observed data. We derive the asymptotic distributions of our estimators. Moreover, the results of Monte Carlo experiments show that the proposed estimators perform well in finite samples.

Suggested Citation

  • Hongjie Wei & Yan Sun, 2017. "Heteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-varying coefficients," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(1), pages 113-128, January.
    • Handle: RePEc:taf:specan:v:12:y:2017:i:1:p:113-128
    DOI: 10.1080/17421772.2017.1250940
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    Cited by:

    1. Jianbao Chen & Suli Cheng, 2021. "GMM Estimation of a Partially Linear Additive Spatial Error Model," Mathematics, MDPI, vol. 9(6), pages 1-28, March.
    2. Li, Deng-Kui & Mei, Chang-Lin & Wang, Ning, 2019. "Tests for spatial dependence and heterogeneity in spatially autoregressive varying coefficient models with application to Boston house price analysis," Regional Science and Urban Economics, Elsevier, vol. 79(C).

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