The Seemingly Unrelated Regressions (SUR) model is a wide used estimation procedure in econometrics, insurance and finance, where very often, the regression model contains more than one equation. Unknown parameters, regression coefficients and covariances among the errors terms, are estimated using algorithms based on Generalized Least Squares or Maximum Likelihood, and the method, as a whole, is very sensitive to outliers. To overcome this problem M-estimators and S-estimators are proposed in the literature together with fast algorithms. However, these procedures are only able to cope with row-wise outliers in the error terms, while their performance becomes very poor in the presence of cell-wise outliers and as the number of equations increases. A new robust approach is proposed which is able to perform well under both contamination types as well as it is fast to compute. Illustrations based on Monte Carlo simulations and a real data example are provided.

翻译：似乎没有关联的回归模型(SUR)是计量经济学、保险和金融方面广泛使用的估计程序,在这种模型中,回归模型通常包含不止一个方程式。未知参数、回归系数和误差术语中的共差值都是使用基于通用最低方位或最大可能性的算法估计的,而整个方法对外部线非常敏感。为了克服这一问题,文献中提出了M-估计器和S-估计器以及快速算法。然而,这些程序只能用错误术语处理行偏差,而当细胞偏差和方程数量增加时,其性能则非常差。提出了一种新的稳健方法,既能在污染类型下很好地运行,又能快速计算。提供了基于蒙特卡洛模拟和真实数据实例的说明。