Asymptotics of improved generalized moment estimators for spatial autoregressive error models

This article considers linear models with a spatial autoregressive error structure. Extending Arnold and Wied (2010) , who develop an improved generalized method of moment (GMM) estimator for the parameters of the disturbance process to reduce the bias of existing estimation approaches, we establish...

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 45; no. 7; pp. 1939 - 1952
Main Authors Drinkuth, Carsten, Arnold, Matthias
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.04.2016
Taylor & Francis Ltd
Subjects
62E20, 62J05
Asymptotic normality
Asymptotic properties
Autoregressive processes
C 13
C 21
Computer simulation
Disturbances
Econometrics
Economic models
Errors
Estimating techniques
Estimators
Generalized method of moments
GMM estimation
Regression
Regression residuals
Spatial autoregression
Statistics
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Summary:This article considers linear models with a spatial autoregressive error structure. Extending Arnold and Wied (2010) , who develop an improved generalized method of moment (GMM) estimator for the parameters of the disturbance process to reduce the bias of existing estimation approaches, we establish the asymptotic normality of a new weighted version of this improved estimator and derive the efficient weighting matrix. We also show that this efficiently weighted GMM estimator is feasible as long as the regression matrix of the underlying linear model is non stochastic and illustrate the performance of the new estimator by a Monte Carlo simulation and an application to real data.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2013.870203