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Confidence ellipsoids based on a general family of shrinkage estimators for a linear model with non-spherical disturbances

Anoop Chaturvedi (), Suchita Gupta and Muhammad Bhatti

Journal of Multivariate Analysis, 2012, vol. 104, issue 1, 140-158

Abstract: This paper considers a general family of Stein rule estimators for the coefficient vector of a linear regression model with nonspherical disturbances, and derives estimators for the Mean Squared Error (MSE) matrix, and risk under quadratic loss for this family of estimators. The confidence ellipsoids for the coefficient vector based on this family of estimators are proposed, and the performance of the confidence ellipsoids under the criterion of coverage probability and expected volumes is investigated. The results of a numerical simulation are presented to illustrate the theoretical findings, which could be applicable in the area of economic growth modeling.

Keywords: Asymptotic; distribution; Linear; models; Non-spherical; disturbances; Shrinkage; estimator; Confidence; ellipsoid; Concentration; probability; Expected; volume (search for similar items in EconPapers)
Date: 2012
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