GMM estimation of SAR models with endogenous regressors

Xiaodong Liu and Paulo Saraiva

Regional Science and Urban Economics, 2015, vol. 55, issue C, 68-79

Abstract: In this paper, we extend the GMM estimator in Lee (2007) to estimate SAR models with endogenous regressors. We propose a new set of quadratic moment conditions exploiting the correlation of the spatially lagged dependent variable with the disturbance term of the main regression equation and with the endogenous regressor. The proposed GMM estimator is more efficient than IV-based linear estimators in the literature, and computationally simpler than the ML estimator. With carefully constructed quadratic moment equations, the GMM estimator can be asymptotically as efficient as the ML estimator under normality. Monte Carlo experiments show that the proposed GMM estimator performs well in finite samples.

Keywords: Spatial models; Endogeneity; Simultaneous equations; Moment conditions; Efficiency (search for similar items in EconPapers)
JEL-codes: C31 C36 R15 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:55:y:2015:i:c:p:68-79

DOI: 10.1016/j.regsciurbeco.2015.09.002

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