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Estimation of the Covariance Matrix of the Least-Squares Regression Coefficients When the Disturbance Covariance Matrix Is of Unknown Form

Published online by Cambridge University Press:  11 February 2009

Robert W. Keener ,
Jan Kmenta  and
Neville C. Weber
Robert W. Keener
Affiliation:
University of Michigan, Ann Arbor
Jan Kmenta
Affiliation:
University of Michigan, Ann Arbor
Neville C. Weber
Affiliation:
University of Sydney
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Abstract

This paper deals with the problem of estimating the covariance matrix of the least-squares regression coefficients under heteroskedasticity and/or autocorrelation of unknown form. We consider an estimator proposed by White [17] and give a relatively simple proof of its consistency. Our proof is based on more easily verifiable conditions than those of White. An alternative estimator with improved small sample properties is also presented.

Type
Articles
Information
Econometric Theory , Volume 7 , Issue 1 , March 1991 , pp. 22 - 45
Copyright
Copyright © Cambridge University Press 1991

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References

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