Estimation of Covariance Matrices in Fixed and Mixed Effects Linear Models (Subsequently published in "Journal of Multivariate Analysis", 97, 2242-2261, 2006. )

Tatsuya Kubokawa and Ming-Tien Tsai
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Tatsuya Kubokawa: Faculty of Economics, University of Tokyo
Ming-Tien Tsai: Institute of Statistical Science, Academia Sinica

No CARF-F-020, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo

Abstract: The estimation of the covariance matrix or the multivariate components of variance is considered in the multivariate linear regression models with effects being fixed or random. In this paper, we propose a new method to show that usual unbiased estimators are improved on by the truncated estimators. The method is based on the Stein-Haff identity, namely the integration by parts in the Wishart distribution, and it allows us to handle the general types of scale-equivariant estimators as well as the general fixed or mixed effects linear models.

Pages: 32 pages
Date: 2005-01
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Persistent link: https://EconPapers.repec.org/RePEc:cfi:fseres:cf020

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