Alternative GMM estimators for spatial regression models

Jörg Breitung () and Christoph Wigger

No 89, Working Paper Series in Economics from University of Cologne, Department of Economics

Abstract: Using approximations of the score of the log-likelihood function we derive optimal moment conditions for estimating spatial regression models. Our approach results in computationally simple and robust estimators. The moment conditions resemble those proposed by Kelejian & Prucha (1999), hence we provide an intuitive interpretation of their estimator as a second order approximation to the log-likelihood function. Furthermore we propose simplified and efficient GMM estimators based on a convenient modification of the moment conditions. Heteroskedasticity robust versions of our estimators are also provided. Finally, a first order approximation for the spatial lag model is also considered. Monte Carlo results suggest that a simple just-identified estimator based on a quadratic moment derived from a first order approximation of the score of the log-likelihood function performs similar to the GMM estimator proposed by Kelejian & Prucha (2010).

Keywords: Spatial Econometrics; Spatial error correlation; GMM-estimation (search for similar items in EconPapers)
JEL-codes: C01 C13 C31 (search for similar items in EconPapers)
Date: 2017-01-12
New Economics Papers: this item is included in nep-ecm and nep-ore
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Journal Article: Alternative GMM estimators for spatial regression models (2018)
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