Heteroskedasticity and non-normality robust LM tests for spatial dependence
AbstractThe 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Regional Science and Urban Economics.
Volume (Year): 43 (2013)
Issue (Month): 5 ()
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Web page: http://www.elsevier.com/locate/regec
Centering; Heteroskedasticity; Non-normality; LM test; Panel model; Spatial dependence;
Other versions of this item:
- Badi H. Baltagi & Zhenlin Yang, 2013. "Heteroskedasticity and Non-normality Robust LM Tests for Spatial Dependence," Center for Policy Research Working Papers 156, Center for Policy Research, Maxwell School, Syracuse University.
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
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