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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| Published in | Econometrica Vol. 44; no. 4; pp. 725 - 739 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Menasha, Wis
The Econometric Society
01.07.1976
George Banta Pub. Co. for the Econometric Society Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0012-9682 1468-0262 |
| DOI | 10.2307/1913439 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Taylor, William E. |
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| Copyright | Copyright 1976 The Econometric Society Copyright Econometric Society Jul 1976 |
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| DOI | 10.2307/1913439 |
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| SubjectTerms | Consistent estimators Covariance Covariance matrices Econometrics Eigenvectors Estimates Estimators Euclidean space Least squares Mathematical vectors Matrices Regression analysis Sufficient conditions Unbiased estimators |
| Title | Prior Information on the Coefficients when the Disturbance Covariance Matrix is Unknown |
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