This paper examines the application of the Least Absolute Deviations (LAD) method for ridge-type parameter estimation of Seemingly Unrelated Regression Equations (SURE) models. The methodology is aimed to deal with the SURE models with non-Gaussian error terms and highly collinear predictors in...

In this paper we introduce an interesting feature of the Generalized Least Absolute Deviations (GLAD) method for Seemingly Unrelated Regression Equations (SURE) models. Contrary to the collapse of Generalized Least Squares (GLS) parameter estimations of SURE models to the Ordinary Least Squares...

In this paper, we modify a number of new biased estimators of seemingly unrelated regression (SUR) parameters which are developed by Alkhamisi and Shukur (2008), AS, when the explanatory variables are affected by multicollinearity. Nine ridge parameters have been modified and compared in terms...

In this paper we generalize the median regression method in order to make it applicable to systems of regression equations. Given the existence of proper systemwise medians of the errors from different equations, we apply the weighted median regression with the weights obtained from the...

In this paper we introduce an interesting feature of the generalized least absolute deviations method for seemingly unrelated regression equations (SURE) models. Contrary to the collapse of generalized leasts-quares parameter estimations of SURE models to the ordinary least-squares estimations...

In this paper, the ridge estimation method is generalized to the median regression. Though the least absolute deviation (LAD) estimation method is robust in the presence of non-Gaussian or asymmetric error terms, it can still deteriorate into a severe multicollinearity problem when...

In this paper we generalize the median regression method to be applicable to system of regression equations, in particular SURE models. Giving the existence of proper system wise medians of the residuals from different equations, we apply the weighted median regression with the weights obtained...