Estimation of simultaneous systems of spatially interrelated cross sectional equations

By Harry H. Kelejian, Ingmar R. Prucha. 2004

417 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/13b3026baf06b4ea84821fe056cef55fa70cdfdb

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In Economics and Econometrics

Citations in the corpus

Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models (2017, neutral): “Then, the nonsingularity condition (3.11) is satisfied if |ρ(z)| 1 (Kelejian & Prucha, 2004, 2010).”
Spatial Spillovers in the Development of Institutions (2013, positive): “Under reasonable conditions, these estimates are consistent and asymptotically normal (Kelejian and Prucha, 2004).”
EFFICIENT GMM ESTIMATION OF HIGH ORDER SPATIAL AUTOREGRESSIVE MODELS WITH AUTOREGRESSIVE DISTURBANCES (2009, background): “For instance, Kelejian and Prucha (2004) proposed the G2SLS estimation for the spatial simultaneous equation model, where a structural equation may have spatial lags of several endogenous variables on the right-hand HIGH ORDER SPATIAL AUTOREGRESSIVE MODELS 189 side.”
A Command for Estimating Spatial-Autoregressive Models with Spatial-Autoregressive Disturbances and Additional Endogenous Variables (2013, methodology): “Kelejian and Prucha (1998, 1999, 2004, 2010) and the references cited therein derive the main results used by the estimator implemented in spivreg, with Drukker, Egger, and Prucha (2013) and Arraiz et al. (2010) producing some important extensions that are used in the code.”
A Race to the Bottom in Labour Standards? An Empirical Investigation (2011, positive): “Kelejian and Prucha (2004), however, do present one for purely cross-sectional data.”
Urban Sprawl and Farmland Prices (2006, positive): “To estimate the system we adapt the stepwise generalized spatial 3SLS estimator (GS3SLS) developed by Kelejian and Prucha (2004).”
Thirty years of spatial econometrics (2010, background): “Papers in Regional Science, Volume 89 Number 1 March 2010. simultaneous equations and endogeneity combined with spatial correlation are considered in Kelejian and Prucha (2004); Rey and Boarnet (2004); Anselin and Lozano-Gracia (2008) and Fingleton and Le Gallo (2008).”
A non-parametric spatial independence test using symbolic entropy (2010, background): “…in connection with spatial models, which leads to a new battery of tests of spatial dependence based, directly or indirectly, on the GMM principle (Anselin and Kelejian, 1997, Kelejian and Prucha, 1999, Conley, 1999, Saavedra, 2003, Kelejian and Prucha, 2004 and 2007, and Fingleton, 2008).”
Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances (2010, methodology): “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.”
Panel data models with spatially correlated error components (2007, neutral): “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…”
HAC estimation in a spatial framework (2007, background): “Under reasonable conditions, if (9) represents one equation of a linear system of equations, the discussion in Kelejian and Prucha (2004) implies that EY n = P ∞ s=0 W s n X n Π s , where X n denotes the matrix of all exogenous variables in the system and Π s are (reduced form) parameter matrices.”
Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes (2014, background): “…of geographical proximity 1Among others, studies based on an exogenous weighting matrix are Baltagi et al. (2007), Kelejian and Prucha (1998), Kelejian and Prucha (1999), Kelejian and Prucha (2001), Kelejian and Prucha (2004), Kapoor et al. (2007), Lee (2004), Piras (2011), Rey and Boarnet (2004).”
splm: Spatial Panel Data Models in R (2012, methodology): “Kelejian and Prucha (2004) generalize results given by Kelejian and Prucha (1998) in a single equation framework to a simultaneous system of spatially interrelated cross sectional equations.”
On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors (2013, methodology): “In the following we verify the claim, made in the text after this assumption, that those properties are automatically implied if y  and Y  are generated by a simultaneous system of  equations with Cliff-Ord type spatial interactions as considered in Kelejian and Prucha (2004).”
Estimation of the Spatial Weights Matrix under Structural Constraints (2013, positive): “Likewise, Pinkse et al. (2002) and Kelejian and Prucha (2004) developed methods accommodating uncertainty regarding distance measures and allowing for spatial nonstationarity.”