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Efficient estimation of varying coefficient seemly unrelated regression model

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Abstract

In this paper, we propose a class of varying coefficient seemingly unrelated regression models, in which the errors are correlated across the equations. By applying the series approximation and taking the contemporaneous correlations into account, we propose an efficient generalized least squares series estimation for the unknown coefficient functions. The consistency and asymptotic normality of the resulting estimators are established. In comparison with the ordinary least squares ones, the proposed estimators are more efficient with smaller asymptotical variances. Some simulation studies and a real application are presented to demonstrate the finite sample performance of the proposed methods. In addition, based on a B-spline approximation, we deduce the asymptotic bias and variance of the proposed estimators.

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Author information

Authors and Affiliations

  1. School of Sciences, Zhejiang Agriculture and Forestry University, Lin’an, 311300, China

    Qun-fang Xu

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

    Qun-fang Xu & Yang Bai

  3. Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, Shanghai, 200433, China

    Yang Bai

Authors
  1. Qun-fang Xu
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  2. Yang Bai
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Corresponding author

Correspondence to Yang Bai.

Additional information

Xu’s research was supported by Key Academic Project from Bureau of Statistics of Zhejiang Province (201325) and Research Project of the National Statistics (2013LY119). Bai’s work was partially supported by National Natural Science Funds for Young Scholar (No.11001162) and Shanghai University of Finance and Economics through Project 211 Phase IV and Shanghai Leading Academic Discipline Project (No. B804).

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Xu, Qf., Bai, Y. Efficient estimation of varying coefficient seemly unrelated regression model. Acta Math. Appl. Sin. Engl. Ser. 30, 119–144 (2014). https://doi.org/10.1007/s10255-014-0301-3

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  • DOI: https://doi.org/10.1007/s10255-014-0301-3

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