An efficient GMM estimator of spatial autoregressive models

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Estimating a spatial autoregressive model with an endogenous spatial weight matrix (2015, methodology): “…literature: the maximum likelihood estimation (MLE) of Ord (1975) and Lee (2004); the instrumental variable (IV) methods of Anselin (1980) and Kelejian and Prucha (1998, 1999), and the generalized method of moments (GMM) of Lee (2007), Lee and Liu (2010), Lin and Lee (2010), and Liu et al. (2010).”
Large sample properties of the matrix exponential spatial specification with an application to FDI (2015, methodology): “This ratio is defined as the variance of β1Xn1+β2Xn2 over the sum of variances of β1Xn1+β2Xn2 and ǫn (see Liu et al., 2010).”
Semiparametric GMM estimation of spatial autoregressive models (2012, methodology): “Third, one can extend the analysis in Section 4.3 by considering the joint GMM estimation of ρ0n and γ 0 n based on both linear and quadratic mo- ment conditions for εn = εn,1, . . . , εn,n ′ as in Liu et al. (2010).”
Spatial weights matrix selection and model averaging for spatial autoregressive models (2017, background): “Liu et al. (2010) show that carefully designed linear and quadratic moment functions can generate a GMM estimator that is more efficient relative to the QML estimate when the disturbances are not normal. https://doi.org/10.1016/j.jeconom.2017.05.021 0304-4076/© 2017 Elsevier B.V.”