Efficient Estimation of Spatial Autoregressive Models
This paper considers estimating general spatial autoregressive models using the generalized method of moments (GMM). I propose nonparametric estimates of the optimal instruments based on conditional second moment restrictions. I show that these instruments are optimal over all possible instruments, especially over those usually suggested in a spatial context. I provide a nonparametric estimator of sample autocovariances function for irregularly spaced spatial processes, and I show that this estimator converges in probability. I then derive the consistency in norm L_2 of the resulting asymptotic variance matrix estimator. Finally, the asymptotic distribution of the GMM estimator is stated.
|Date of creation:||2001|
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- Kelejian, Harry H & Prucha, Ingmar R, 1999.
"A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
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- Théophile AZOMAHOU & Agénor LAHATTE, 2000. "On the Inconsistency of the Ordinary Least Squares Estimator for Spatial Autoregressive Processes," Working Papers of BETA 2000-12, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
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