Alternative GMM estimators for spatial regression models
Jörg Breitung () and
No 89, Working Paper Series in Economics from University of Cologne, Department of Economics
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)
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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Persistent link: https://EconPapers.repec.org/RePEc:kls:series:0089
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