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(2008)
An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
- Publisher
- Springer Journals
- Copyright
- Copyright © 2015 by Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg
- Subject
- Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
- ISSN
- 0168-9673
- eISSN
- 1618-3932
- DOI
- 10.1007/s10255-015-0502-4
- Publisher site
- See Article on Publisher Site
Abstract
In the system of m (m ≥ 2) seemingly unrelated regressions, we show that the Gauss-Markov estimator (GME) of any regression coefficients has unique simplified form, which exactly equals to the one-step covariance-adjusted estimator of the regression coefficients, and hence we conclude that for any finite k ≥ 2 the k-step covariance-adjusted estimator degenerates to the one-step covariance-adjusted estimator and the corresponding two-stage Aitken estimator has exactly one simplified form. Also, the unique simplified expression of the GME is just the estimator presented in the Theorem 1 of Wang’ work [1988]. A new estimate of regression coefficients in seemingly unrelated regression system, Science in China, Series A 10, 1033-1040].
Journal
Acta Mathematicae Applicatae Sinica – Springer Journals
Published: Nov 3, 2015