Finite sample properties of estimators of spatial autoregressive models with autoregressive disturbances

By D. Das, Harry H. Kelejian, Ingmar R. Prucha. 2003

106 citations recorded by the source corpus.

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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): “, Das et al. (2003). Given instruments Hn, one-step GMM estimators for all parameters, i.”
Best Spatial Two‐Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances (2003, methodology): “The inconsistency of the nonlinear 2SLS method has been clearly pointed out in Kelejian and Prucha (1997) for the estimation of a regression model with spatial autoregressive disturbances.”
Spatial Spillovers in the Development of Institutions (2013, unknown): “Das et al. (2003) give results which suggest that instruments based on such a linear truncation of the mean of Wy t are almost as efficient as the maximum likelihood estimator, which is based on the normality assumption.”
On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors (2013, unknown): “…contributions include Baltagi and Li (2001a, 2001b, 2004) Baltagi et al. (2003); Blonigen et al. (2007), Bao and Ullah (2007), Conley (1999), Das et al. (2003), Kelejian and Prucha (1998, 1999, 2001, 2004, 2007, 2010), Kelejian et al. (2004), Kapoor et al. (2007), Lee (2002, 2003, 2004),…”
Finite sample properties of maximum likelihood estimator in spatial models (2007, methodology): “To our knowledge, the existing finite sample analysis in this subject has been done through Monte Carlo simulations, for example, Anselin (1980, 1982) presented the small sample bias, mean squared error (MSE), and mean absolute percentage error of the ML estimator as well as several other estimators”
Does `Smart Growth' Matter to Public Finance? (2008, background): “Like the alternative, maximum likelihood (ML) estimation, this strategy yields efficient, unbiased parameter estimates, even in the presence of spatial error dependence (Das et al. 2003).”
EC3SLS Estimator for a Simultaneous System of Spatial Autoregressive Equations with Random Effects (2015, result): “This is not surprising considering the inconsistency of the OLS estimator, see Das et al. (2003) and Kelejian et al.”
Estimation of simultaneous systems of spatially interrelated cross sectional equations (2004, methodology): “Das et al. (2003) also found minor di3erences in the small sample eIciencies of the maximum likelihood and generalized moments estimators of the spatial autoregressive coeIcient in the disturbance process.”
Comparing Implementations of Estimation Methods for Spatial Econometrics (2015, background): “Given the reduced form of the model it follows that the best instruments can be expressed in terms of the E ( Wy ) = W ( I − ρ Lag W ) − 1 X β (Lee 2003, 2007; Kelejian, Prucha, and Yuzefovich 2004; Das, Kelejian, and Prucha 2003).”
Panel data models with spatially correlated error components (2007, background): “2Recent theoretical contributions include Baltagi and Li (2001a, b, 2004), Baltagi et al. (2003), Conley (1999), Das et al. (2003), Kelejian and Prucha (1997, 1998, 1999, 2001, 2004), Lee (2001a, b, 2002, 2003, 2004), LeSage (1997, 2000), Pace and Barry (1997), Pinkse and Slade (1998), Pinkse et…”
Tax Mimicking and Yardstick Competition Among Local Governments in the Netherlands (2005, background): “Although consistent, one of the objections to IV-estimators of the spatial lag model is that they ignore the Jacobian term (Anselin, 1988, pp. 81-88) and therefore are less accurate than their ML counterparts (Das et al., 2003).”
Thirty years of spatial econometrics (2010, background): “…(ML) and generalized method of moments (GMM) estimators.6 Among others, this includes a formal proof of the asymptotic distribution of ML and quasi-ML estimators in Lee (2004), and the derivation of optimal GMM estimators in Lee (2003, 2007) (see also Das et al. 2003; Kelejian et al. 2004).”
Estimation Problems in Models with Spatial Weighting Matrices Which Have Blocks of Equal Elements (2006, unknown): “3Concerning 2SLS estimation of spatial models see, for example, Das, Kelejian, and Prucha (2003), Kelejian and Prucha (1998), Lee (2003), and Rey and Boarnet (2004).”
INSTRUMENTAL VARIABLE ESTIMATION OF A SPATIAL AUTOREGRESSIVE MODEL WITH AUTOREGRESSIVE DISTURBANCES: LARGE AND SMALL SAMPLE RESULTS (2004, methodology): “First, we consider more experiments involving “extreme” values of the spatial autoregressive parameters λ and ρ, namely the values of 0.9 and -0.9, and, second, we considered estimators which were not considered in Das, Kelejian, and Prucha (2003).”