Heteroskedasticity and Non-Normality Robust LM Tests for Spatial Dependence

Author(s)/Creator(s)

Badi H. Baltagi, Syracuse UniversityFollow
Zhenlin Yang, Singapore Management UniversityFollow

Description/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. JEL Classification:

Document Type

Working Paper

Date

5-2013

Keywords

Centering; Heteroskedasticity; Non-normality; LM test; Panel model; Spatial dependence.

Language

English

Series

Working Papers Series

Disciplines

Economic Policy | Economics | Public Affairs, Public Policy and Public Administration

ISSN

1525-3066

Additional Information

Working paper no.156

Recommended Citation

Baltagi, Badi H. and Yang, Zhenlin, "Heteroskedasticity and Non-Normality Robust LM Tests for Spatial Dependence" (2013). Center for Policy Research. 387.
https://surface.syr.edu/cpr/387

Source

Local input

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.