Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2015
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
Discipline
Econometrics | Economics
Research Areas
Econometrics
Publication
Econometrics
Volume
3
Issue
2
First Page
376
Last Page
411
ISSN
2225-1146
Identifier
10.3390/econometrics3020376
Publisher
MDPI
Citation
LIU, Shew Fan and YANG, Zhenlin.
Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Dependence Model. (2015). Econometrics. 3, (2), 376-411.
Available at: https://ink.library.smu.edu.sg/soe_research/1752
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.3390/econometrics3020376