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...

We introduce a new class of ARFIMA models, which removes the restrictions that the roots of AR and MA polynomials are outside the unit circle. We establish consistency and asymptotic normality of the least absolute deviation estimator under non-Gaussian setting.

Classical estimation techniques for linear models either are inconsistent, or perform rather poorly, under α-stable error densities; most of them are not even rate-optimal. In this paper, we propose an original one-step R-estimation method and investigate its asymptotic performances under...