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

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.
Year of publication:
1991
Authors: Keener, Robert W. ; Kmenta, Jan ; Weber, Neville C.
Published in:
Econometric Theory. - Cambridge University Press. - Vol. 7.1991, 01, p. 22-45
Publisher: Cambridge University Press
Description of contents: Abstract [journals.cambridge.org]
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Type of publication: Article
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Persistent link: https://www.econbiz.de/10005411843
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