Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances

By Harry H. Kelejian, Ingmar R. Prucha. Journal of Econometrics, 2010

1,234 citations recorded by the source corpus.

This study develops a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the GMM estimator suggested in Kelejian and Prucha (1998,1999) for the spatial autoregressive parameter in the disturbance process. We also define IV estimators for the regression parameters of the model and give results concerning the joint asymptotic distribution of those estimators and the GMM estimator. Much of the theory is kept general to cover a wide range of settings.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/4f0d5d7e46064005ac66d87efa2c0cb7f972883a

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Citations in the corpus

A semiparametric spatial dynamic model (2014, background): “[6]; Kelejian and Prucha [8]; Ord [12]; Su and Jin [13]) and varying coefficient models (Cheng et al.”
Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models (2017, background): “:3 max 1≤i≤n |λi {ρ(Z)W}| < 1, (2.3) where λi{A} is the ith eigenvalue of an n× n matrix A. 3See Kelejian & Prucha (2010) for an excellent discussion of the assumptions concerning the parameter space of a spatial autoregressive parameter.”
Large Panels with Common Factors and Spatial Correlation (2011, methodology): “Estimation of panels with spatially correlated errors can be based on maximum likelihood (ML) techniques (Lee (2004)), or on the generalized method of moments (GMM) (Kelejian and Prucha (1999); Lee (2007); Kelejian and Prucha (2009)).”
The Incidence of EU Per-Hectare Payments on Farmland Rental Rates: A Spatial Econometric Analysis of German Farm-Level Data (2011, methodology): “In contrast, Kelejian and Prucha (2010) propose a method for estimating the general spatial model, which is robust against unknown heteroscedasticity.”
The rural growth trifecta: outdoor amenities, creative class and entrepreneurial context (2011, methodology): “We relax the parametric and structural assumptions about the error process, and use a non-parametric approach to estimate a spatial heteroskedastic-spatial autocorrelation robust (spatial HAC) covariance matrices (Conley, 1999; Kelejian and Prucha, 2008).”
Strategic interaction in local governments’ industrial land supply: Evidence from China (2017, methodology): “To tackle such endogeneity problems, we employ a generalised spatial two-stage least squares method, which was developed by Kelejian and Prucha (1998, 2010).”
A Command for Estimating Spatial-Autoregressive Models with Spatial-Autoregressive Disturbances and Additional Endogenous Variables (2013, methodology): “…space is not restricted to geographic space, and many recent applications employ these techniques in other situations of cross-unit dependence, such as socialinteraction models and network models; see, for example, Kelejian and Prucha (2010) and Drukker, Egger, and Prucha (2013) for references.”
Comparing Implementations of Estimation Methods for Spatial Econometrics (2015, methodology): “The present discussion is almost entirely based on Kelejian and Prucha (2010), Drukker, Egger, and Prucha (2013), Arraiz, Drukker, Kelejian, and Prucha (2010) and Drukker et al. (2011b) that provide some important extensions to Kelejian and Prucha (1998, 1999).”