Publication Type
Journal Article
Version
acceptedVersion
Publication Date
9-2013
Abstract
The standard LM tests for spatial dependence in linear and panel regressions are derived under the normality and homoskedasticity assumptions of the regression disturbances. Hence, they may not be robust against non-normality or heteroskedasticity of the disturbances. Following Born and Breitung (2011), we introduce general methods to modify the standard LM tests so that they become robust against heteroskedasticity and non-normality. The idea behind the robustification is to decompose the concentrated score function into a sum of uncorrelated terms so that the outer product of gradient (OPG) can be used to estimate its variance. We also provide methods for improving the finite sample performance of the proposed tests. These methods are then applied to several popular spatial models. Monte Carlo results show that they work well in finite sample.
Keywords
Centering, Heteroskedasticity, Non-normality, LM test, Panel model, Spatial dependence
Discipline
Econometrics
Research Areas
Econometrics
Publication
Regional Science and Urban Economics
Volume
43
Issue
5
First Page
725
Last Page
739
ISSN
0166-0462
Identifier
10.1016/j.regsciurbeco.2013.05.001
Publisher
Elsevier
Citation
BALTAGI, Badi H. and YANG, Zhenlin.
Heteroskedasticity and Non-normality Robust LM Tests of Spatial Dependence. (2013). Regional Science and Urban Economics. 43, (5), 725-739.
Available at: https://ink.library.smu.edu.sg/soe_research/1548
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.2013.05.001