A simulation study of biased estimators against the ordinary least squares estimator

1993 ◽  
Vol 22 (2) ◽  
pp. 569-589
Author(s):  
C Thiart ◽  
T.T Dunne ◽  
C.G Troskie ◽  
D.O Chalton
Keyword(s):  
Least Squares ◽  
Simulation Study ◽  
Ordinary Least Squares ◽  
Least Squares Estimator ◽  
Ordinary Least Squares Estimator ◽  
Biased Estimators

scholarly journals STUDY ON MEASUREMENT RELIABILITY BASED ON LIU ESTIMATOR

2018 ◽  
Vol 48 (3) ◽  
pp. 187-192
Author(s):  
J. W. HUANG ◽  
L. MA ◽  
R. LI
Keyword(s):  
Least Squares ◽  
Simulation Study ◽  
Ordinary Least Squares ◽  
Least Squares Estimator ◽  
Measurement Reliability ◽  
Liu Estimator ◽  
Process Measurement ◽  
Study Results ◽  
Ordinary Least Squares Estimator ◽  
Reasonable Range

In this paper, we introduce the Liu estimator in the measurement process as an alternative method to the ordinary least squares estimator. To compare the Liu estimator and the ordinary least squares estimator under the reliability criterion, a simulation study is conducted. Simulation study results show that Liu estimator is an effective method to replace OLS estimator in process measurement. When the Liu parameter choose in a reasonable range, Liu estimator superior to ordinary least squares estimator in terms of reliability.

ASYMPTOTIC EFFICIENCY OF THE ORDINARY LEAST SQUARES ESTIMATOR FOR REGRESSIONS WITH UNSTABLE REGRESSORS

2002 ◽  
Vol 18 (5) ◽  
pp. 1121-1138 ◽  
Author(s):  
DONG WAN SHIN ◽  
MAN SUK OH
Keyword(s):  
Least Squares ◽  
Asymptotic Efficiency ◽  
Characteristic Root ◽  
Sufficient Conditions ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Least Squares Estimator ◽  
Characteristic Roots ◽  
Generalized Least Squares Estimator ◽  
Ordinary Least Squares Estimator

For regression models with general unstable regressors having characteristic roots on the unit circle and general stationary errors independent of the regressors, sufficient conditions are investigated under which the ordinary least squares estimator (OLSE) is asymptotically efficient in that it has the same limiting distribution as the generalized least squares estimator (GLSE) under the same normalization. A key condition for the asymptotic efficiency of the OLSE is that one multiplicity of a characteristic root of the regressor process is strictly greater than the multiplicities of the other roots. Under this condition, the covariance matrix Γ of the errors and the regressor matrix X are shown to satisfy a relationship (ΓX = XC + V for some matrix C) for V asymptotically dominated by X, which is analogous to the condition (ΓX = XC for some matrix C) for numerical equivalence of the OLSE and the GLSE.

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