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...

The robustness of the LM tests for spatial error dependence of Burridge (1980) and Born and Breitung (2011) for the linear regression model, and Anselin (1988) and Debarsy and Ertur (2010) for the panel regression model with random or fixed effects are examined. While all tests are...

The robustness of the LM tests for spatial error dependence of Burridge (1980) for the linear regression model and Anselin (1988) for the panel regression model are examined. While both tests are asymptotically robust against distributional misspecification, their finite sample behavior can be...

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...

We examine the pricing trends in the online toy markets based on a unique set of panel data collected across three years’ span. The analysis was made through panel data regression models with error components and serial correlation, allowing comparisons of prices and price dispersions between...

Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with...