Title: Robust SUR estimation under an independent contamination model
Authors: Fatemah Alqallaf - Kuwait University (Kuwait) [presenting]
Abstract: 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 estimate 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 contamination in the error terms, while their performance becomes very poor in the presence of cell-wise contamination and as the number of equations is large. A new robust approach is proposed which is able to perform well under both contamination types as well as is fast to compute. Illustrations based on Monte Carlo simulations and real examples are provided.