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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.

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Type
Articles
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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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