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Asymptotic Distribution and Finite Sample Bias Correction of QML Estimators for Spatial Error Dependence Model

Shew Fan Liu () and Zhenlin Yang ()
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Shew Fan Liu: School of Economics, Singapore Management University, 90 Stamford Road, Singapore 178903, Singapore

Econometrics, 2015, vol. 3, issue 2, 1-36

Abstract: In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model, as it leads potentially to much improved inferences for the regression coefficients. Contrary to the common perceptions, both the large and small sample behaviors of the QML estimators for the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the bias can be severe, and the proposed bias correction procedure is very effective.

Keywords: asymptotics; bias correction; bootstrap; concentrated estimating equation; Monte Carlo; spatial layout; stochastic expansion (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2015
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Related works:
Working Paper: Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Error Dependence Model (2014) Downloads
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