GM Estimation of Higher-Order Spatial Autoregressive Processes in Cross-Section Models with Heteroskedastic Disturbances
By Harald Badinger, Peter H. Egger. Social Science Research Network, 2008
65 citations recorded by the source corpus.
This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the case of spatial autoregressive models with spatial autoregressive disturbances of arbitrary (finite) order (SARAR(R,S)). We derive the moment conditions and the optimal weighting matrix for a generalized moments (GM) estimation procedure of the spatial regressive parameters of the disturbance process and define a generalized two-stages least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their (joint) asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/f1920efd168f0cc380382190c7b5260ed9dc6578
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In Economics and Econometrics
- Peter H. Egger
- Harald Badinger
- J. Fidrmuc
- Céline Carrère
- Hidemi Kimura
- Yasuyuki Todo
- J. Bergstrand
- Inmaculada Martínez-Zarzoso
Citations in the corpus
- Estimation of Higher-Order Spatial Autoregressive Panel Data Error Component Models (2009, methodology): “4 Compare Lemma C.1 in Kelejian and Prucha (2008) for the case of a cross-sectional SARAR(1,1) model and Lemma C.1 in Badinger and Egger (2008b) for the case of a crosssectional SARAR(R,S) model.”