Comparison of Covariance Matrices of Predictors in Seemingly Unrelated Regression Models

被引:5
作者
Guler, Nesrin [1 ]
Buyukkaya, Melek Eris [2 ]
Yigit, Melike [3 ]
机构
[1] Sakarya Univ, Dept Econometr, TR-54187 Sakarya, Turkey
[2] Karadeniz Tech Univ, Dept Stat & Comp Sci, TR-61080 Trabzon, Turkey
[3] Sakarya Univ, Dept Math, TR-54187 Sakarya, Turkey
关键词
BLUP; Covariance matrix; Inertia; OLSP; Rank; Seemingly unrelated regression model; LINEAR UNBIASED PREDICTION; EQUALITIES; ESTIMATORS;
D O I
10.1007/s13226-021-00174-w
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This paper considers comparison problems of predictor and estimator in the context of seemingly unrelated regression models (SURMs). SURMs are a class of multiple regression equations with correlated errors among the equations from linear regression models. Our aim is to establish a variety of equalities and inequalities for comparing covariance matrices of the best linear unbiased predictors (BLUPs) and the ordinary least squares predictors (OLSPs) of unknown vectors under SURMs by using various rank and inertia formulas of block matrices. The results for comparisons of the best linear unbiased estimators (BLUEs) and the ordinary least squares estimators (OLSEs) in the models are also considered.
引用
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页码:801 / 809
页数:9
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