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Best equivariant estimator of regression coefficients in a seemingly unrelated regression model with known correlation matrix

Listed author(s):
  • Hiroshi Kurata


    (The University of Tokyo)

  • Shun Matsuura

    (Keio University)

Registered author(s):

    Abstract This paper derives the best equivariant estimator (BEE) of the regression coefficients of a seemingly unrelated regression model with an elliptically symmetric error. Equivariance with respect to the group of location and scale transformations is considered. We assume that the correlation matrix of the error term is known. Since the correlation matrix is a maximal invariant parameter under the group action, the model treated in this paper is generated as exactly one orbit on the parameter space. It is also shown that the BEE can be viewed as a generalized least squares estimator.

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    Article provided by Springer & The Institute of Statistical Mathematics in its journal Annals of the Institute of Statistical Mathematics.

    Volume (Year): 68 (2016)
    Issue (Month): 4 (August)
    Pages: 705-723

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    Handle: RePEc:spr:aistmt:v:68:y:2016:i:4:d:10.1007_s10463-015-0512-2
    DOI: 10.1007/s10463-015-0512-2
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    1. Liu, Aiyi, 2002. "Efficient Estimation of Two Seemingly Unrelated Regression Equations," Journal of Multivariate Analysis, Elsevier, vol. 82(2), pages 445-456, August.
    2. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
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