Publication Type
Journal Article
Version
submittedVersion
Publication Date
5-2015
Abstract
In the presence of heteroskedasticity, Lin and Lee (2010) show that the quasi-maximum likelihood (QML) estimator of the spatial autoregressive (SAR) model can be inconsistent as a ‘necessary’ condition for consistency can be violated, and thus propose robust GMM estimators for the model. In this paper, we first show that this condition may hold in certain situations and when it does the regular QML estimator can still be consistent. In cases where this condition is violated, we propose a simple modified QML estimation method robust against unknown heteroskedasticity. In both cases, asymptotic distributions of the estimators are derived, and methods for estimating robust variances are given, leading to robust inferences for the model. Extensive Monte Carlo results show that the modified QML estimator outperforms the GMM and QML estimators even when the latter is consistent. The proposed methods are then extended to the more general SARAR models.
Keywords
Spatial dependence, Unknown heteroskedasticity, Nonnormality, Modified QML estimator, Robust standard error, SARAR models
Discipline
Econometrics | Economics
Research Areas
Econometrics
Publication
Regional Science and Urban Economics
Volume
52
First Page
50
Last Page
70
ISSN
0166-0462
Identifier
10.1016/j.regsciurbeco.2015.02.003
Publisher
Elsevier
Citation
LIU, Shew Fan and YANG, Zhenlin.
Modified QML Estimation of Spatial Autoregressive Models with Unknown Heteroskedasticity and Nonnormality. (2015). Regional Science and Urban Economics. 52, 50-70.
Available at: https://ink.library.smu.edu.sg/soe_research/1647
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.regsciurbeco.2015.02.003