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Confidence Sets for the Coefficients Vector of a Linear Regression Model with Nonspherical Disturbances

In this present paper, considering a linear regression model with nonspherical disturbances, improved confidence sets for the regression coefficients vector are developed using the Stein rule estimators. We derive the large-sample approximations for the coverage probabilities and the expected volumes of the confidence sets based on the feasible generalized least-squares estimator and the Stein rule estimator and discuss their ranking.
Year of publication:
1997
Authors: Chaturvedi, Anoop ; Hasegawa, Hikaru ; Chaturvedi, Ajit ; Shukla, Govind
Published in:
Econometric Theory. - Cambridge University Press. - Vol. 13.1997, 03, p. 406-429
Publisher: Cambridge University Press
Description of contents: Abstract [journals.cambridge.org]
Type of publication: Article
Source:
Persistent link: https://ebvufind01.dmz1.zbw.eu/10005411957
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