Publication Type

Journal Article

Version

Postprint

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

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1016/j.regsciurbeco.2013.05.001

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Econometrics Commons

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