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Statistical inference on seemingly unrelated single-index regression models

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Abstract

In this article, we consider a class of seemingly unrelated single-index regression models. By taking the contemporaneous correlation among equations into account we construct the weighted estimators (WEs) for unknown parameters of the coefficients and the improved local polynomial estimators for the unknown functions, respectively. We establish the asymptotic normalities of these estimators, and show both of them are more asymptotically efficient than those ignoring the contemporaneous correlation. The performances of the proposed procedures are evaluated through simulation studies.

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Author information

Authors and Affiliations

  1. School of Mathematics, Jilin University, Changchun, 130012, China

    Bing He

  2. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Jin-hong You

  3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Min Chen

Authors
  1. Bing He
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  2. Jin-hong You
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  3. Min Chen
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Additional information

Supported by the National Natural Science Foundation of China (No. 11471140).

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He, B., You, Jh. & Chen, M. Statistical inference on seemingly unrelated single-index regression models. Acta Math. Appl. Sin. Engl. Ser. 32, 945–956 (2016). https://doi.org/10.1007/s10255-016-0615-4

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  • DOI: https://doi.org/10.1007/s10255-016-0615-4

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