Publication Type
Working Paper
Version
publishedVersion
Publication Date
1-2013
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
Citation
Baltagi, Badi H. and YANG, Zhenlin.
Non-normality and heteroscedasticity robust LM tests of spatial dependence. (2013).
Available at: https://ink.library.smu.edu.sg/soe_research/1505
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.