Best Spatial Two‐Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances

By Lung-fei Lee. 2003

422 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/64f9a9f7e547f20d6dcc4e5f1ff61a1ab8b23a0b

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In Economics and Econometrics

Citations in the corpus

Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances (2010, background): “The above assumptions are similar to those maintained in Kelejian and Prucha (1998, 2004), and Lee (2003), and so a discussion which is quite similar to those given in these papers also applies here.”
Spatial weights matrix selection and model averaging for spatial autoregressive models (2017, methodology): “Lee (2003) chooses some specific IVs and obtains the best two-stage least square (B2SLS) estimators. Lee (2004) studies the asymptotic properties of the maximum likelihood (ML) and quasi-maximum likelihood (QML) estimators of the SARmodel. For the SARmodel, the spatial correlation can provide nonlin”
Instrumental Variable Estimation of a Spatial Autoregressive Panel Model with Random Effects (2011, positive): “…argued that in the cross-section spatial model, the optimal instruments for estimating in Equation (2) is E (Z) = E [X;Wy] = X;WA 1X : Therefore, a Lee (2003) type optimal instruments for estimating in the 1=2 transformed panel autoregressive spatial model in Equation (6) is E (Z ) = E 1=2Z = E h…”
Efficient estimation of the semiparametric spatial autoregressive model (2006, methodology): “39) with the corresponding bounds in Robinson (2005, p.1823) indicates that again the latter dominate, so that the rest of the proof for A41 in the latter reference implies that a4 = op(n 1 2 ) (indeed the remaining bounds needed are slightly better than those in Robinson (2005), because of the long”
An efficient GMM estimator of spatial autoregressive models (2010, methodology): “The two-stage least squares (2SLS) method has been noted for the estimation of the MRSAR model in Anselin (1988, 1990), Kelejian and Robinson (1993), Kelejian and Prucha (1997, 1998) and Lee (2003), among others.”
On model specification and parameter space definitions in higher order spatial econometric models (2012, methodology): “4 See also Kelejian and Prucha (2002), Kelejian et al. (2004), Lee (2002, 2003) among others.”
Some recent developments in spatial panel data models (2010, methodology): “Please cite this article as: Lee, L., Yu, J., So (2009), doi:10.”
Estimation of Sample Selection Models with Spatial Dependence (2012, methodology): “Alternatively, Lee (2003) proposes to use the estimated optimal instruments, which, in the context of the selection equation, are given by (I − bδC)x. Kelejian et al. (2004) present simulation evidence about the finite sample performance of these two sets of IVs in the context of the SAL model,…”
Inference on Higher-Order Spatial Autoregressive Models with Increasingly Many Parameters (2015, neutral): “…it is computationally simpler than procedures which may have better statistical properties, such as generalized method of moments (Kelejian and Prucha (1999) and Lee and Liu (2010)), optimal IV (Lee (2003)), Gaussian pseudo maximum likelihood (Lee (2004)), and adaptive estimation (Robinson (2010)).”