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• W2784112492 abstract "Seemingly unrelated regression models generalize linear regression models by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Robust inference for seemingly unrelated regression models is considered. MM-estimators are introduced to obtain estimators that have both a high breakdown point and a high normal efficiency. A fast and robust bootstrap procedure is developed to obtain robust inference for these estimators. Confidence intervals for the model parameters as well as hypothesis tests for linear restrictions of the regression coefficients in seemingly unrelated regression models are constructed. Moreover, in order to evaluate the need for a seemingly unrelated regression model, a robust procedure is proposed to test for the presence of correlation among the disturbances. The performance of the fast and robust bootstrap inference is evaluated empirically in simulation studies and illustrated on real data." @default.
• W2784112492 created "2018-01-26" @default.
• W2784112492 creator A5051795351 @default.
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• W2784112492 date "2018-09-01" @default.
• W2784112492 modified "2023-09-27" @default.
• W2784112492 title "Robust inference for seemingly unrelated regression models" @default.
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• W2784112492 doi "https://doi.org/10.1016/j.jmva.2018.05.002" @default.
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