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Some overall properties of seemingly unrelated regression models

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  • Yuqin Sun
  • Rong Ke
  • Yongge Tian

Abstract

Seemingly unrelated regression models are extensions of linear regression models which allow correlated errors between equations. Estimations and inferences of singular seemingly unrelated regression models involve some complicated operations of the given matrices in the models and their generalized inverses. In this study, we characterize the consistency, natural restrictions, estimability of parametric functions under a singular seemingly unrelated regression model using the matrix rank method. We also derive necessary and sufficient conditions for the ordinary least squares estimators and the best linear unbiased estimators of parametric functions to be equal under seemingly unrelated regression models. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Yuqin Sun & Rong Ke & Yongge Tian, 2014. "Some overall properties of seemingly unrelated regression models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(2), pages 103-120, April.
    • Handle: RePEc:spr:alstar:v:98:y:2014:i:2:p:103-120
    DOI: 10.1007/s10182-013-0212-2
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    2. Haithem Awijen & Younes Ben Zaied & Ahmed Imran Hunjra, 2023. "Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 561-587, August.
    3. Jiang, Hong & Qian, Jianwei & Sun, Yuqin, 2020. "Best linear unbiased predictors and estimators under a pair of constrained seemingly unrelated regression models," Statistics & Probability Letters, Elsevier, vol. 158(C).
    4. Nesrin Güler & Melek Eriş Büyükkaya & Melike Yiğit, 2022. "Comparison of Covariance Matrices of Predictors in Seemingly Unrelated Regression Models," Indian Journal of Pure and Applied Mathematics, Springer, vol. 53(3), pages 801-809, September.

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