GMM estimation of spatial autoregressive models with unknown heteroskedasticity

By Xu Lin, Lung-fei Lee. 2010

277 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a725113844b8d18c1867c97b8cbb6e0e991d703e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models (2010, background): “The books by Cliff and Ord (1973), Paelinck and Klaassen (1979), Anselin (1988), Cressie (1993), Anselin and Florax (1995) contribute significantly to the development of the field.”
An efficient GMM estimator of spatial autoregressive models (2010, background): “A subclass P2n of P1n consisting of Pjn’s with a zero diagonal is also interesting, as the corresponding GMME is robust against unknown heteroskedasticity (Lin and Lee, 2010) and distributional assumptions.”
Monetary Policy Through Production Networks: Evidence from the Stock Market (2017, unknown): “Heteroskedasticity-based Identification Lin and Lee (2010) propose a generalized method of moments (GMM) estimator to ensure the consistency of the estimates under heteroskedasticity. In Table 2, we directly allow for heteroskedastic error terms and find our results do not change when we allow diffe”
GMM estimation of social interaction models with centrality (2010, methodology): “For the quadratic moments in the GMM estimation, as the number of quadratic moments is nite, based on a similar idea as in Lin and Lee (2006), it may be possible to modify the proposed GMM procedure so that the estimator is robust to unknown heteroscedasticity.”
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).”
Errors in variables and spatial effects in hedonic house price models of ambient air quality (2008, background): “15 For a recent discussion of technical aspects associated with heteroskedastic robust estimation in spatial models, see Kelejian and Prucha (2006b) and Lin and Lee (2005) .”
Large sample properties of the matrix exponential spatial specification with an application to FDI (2015, methodology): “7For the SAR model under unknown heteroskedasticity, Lin and Lee (2010) have not discussed the possible best GMME.”