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Generalized canonical correlation variables improved estimation in high dimensional seemingly unrelated regression models

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  • Zhao, Li
  • Xu, Xingzhong

Abstract

After defining generalized canonical correlation variable pairs, this study proposes a new estimator of regression coefficients in seemingly unrelated regression models. The properties of the estimator are also discussed. The results of simulations show that the proposed estimator outperforms the ordinary least squares estimator.

Suggested Citation

  • Zhao, Li & Xu, Xingzhong, 2017. "Generalized canonical correlation variables improved estimation in high dimensional seemingly unrelated regression models," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 119-126.
    • Handle: RePEc:eee:stapro:v:126:y:2017:i:c:p:119-126
    DOI: 10.1016/j.spl.2017.02.037
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    References listed on IDEAS

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    Cited by:

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    3. Shun Matsuura & Hiroshi Kurata, 2020. "Covariance matrix estimation in a seemingly unrelated regression model under Stein’s loss," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 79-99, March.
    4. 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).
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    6. Junqian Xu & Yong Liu & Liling Yang, 2018. "A Comparative Study of the Role of China and India in Sustainable Textile Competition in the U.S. Market under Green Trade Barriers," Sustainability, MDPI, vol. 10(5), pages 1-21, April.

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