Efficient Estimation of Spatial Autoregressive Models
AbstractThis 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.
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Bibliographic InfoPaper provided by Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg in its series Working Papers of BETA with number 2001-05.
Date of creation: 2001
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Instrumental variables; Spatial dependence; Nonparametric estimation;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-07-30 (All new papers)
- NEP-ECM-2001-07-30 (Econometrics)
- NEP-ETS-2001-07-30 (Econometric Time Series)
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