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Heteroskedasticity And Non-Normality Robust Lm Tests Of Spatial Dependence, Badi H. Baltagi, Zhenlin Yang
Heteroskedasticity And Non-Normality Robust Lm Tests Of Spatial Dependence, Badi H. Baltagi, Zhenlin Yang
Research Collection School Of Economics
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 …
Non-Normality And Heteroscedasticity Robust Lm Tests Of Spatial Dependence, Badi H. Baltagi, Zhenlin Yang
Non-Normality And Heteroscedasticity Robust Lm Tests Of Spatial Dependence, Badi H. Baltagi, Zhenlin Yang
Research Collection School Of Economics
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 …
A Simple And Robust Method Of Inference For Spatial Lag Dependence, Zhenlin Yang, Yan Shen
A Simple And Robust Method Of Inference For Spatial Lag Dependence, Zhenlin Yang, Yan Shen
Research Collection School Of Economics
A simple and reliable method of inference for the spatial parameter in spatial autoregressive models is introduced, based on a statistic obtained by centering and rescaling the numerator of the concentrated Gaussian score function. The resulted tests and confidence intervals are robust against the distributional misspecifications and are insensitive to the spatial layouts and the error standard deviation. In contrast, the standard methods based on Gaussian score and information matrix may lead to inconsistent inference when errors are non normal, and can be quite sensitive to the spatial layouts and the error standard deviation even when errors are normally distributed. …