Prior Information on the Coefficients when the Disturbance Covariance Matrix is Unknown

In a linear regression model with arbitrary disturbance covariance structure, least squares estimators subject to correct linear restrictions dominate unrestricted least squares for all estimable functions of the parameters if and only if the covariance matrix obeys conditions closely related to tho...

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Bibliographic Details
Published inEconometrica Vol. 44; no. 4; pp. 725 - 739
Main Author Taylor, William E.
Format Journal Article
LanguageEnglish
Published Menasha, Wis The Econometric Society 01.07.1976
George Banta Pub. Co. for the Econometric Society
Blackwell Publishing Ltd
Subjects
Consistent estimators
Covariance
Covariance matrices
Econometrics
Eigenvectors
Estimates
Estimators
Euclidean space
Least squares
Mathematical vectors
Matrices
Regression analysis
Sufficient conditions
Unbiased estimators
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ISSN0012-9682
1468-0262
DOI10.2307/1913439

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Summary:In a linear regression model with arbitrary disturbance covariance structure, least squares estimators subject to correct linear restrictions dominate unrestricted least squares for all estimable functions of the parameters if and only if the covariance matrix obeys conditions closely related to those of the Gauss-Markov theorem.
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ObjectType-Article-1
ISSN:0012-9682
1468-0262
DOI:10.2307/1913439