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Cross-Validation Model Averaging for Generalized Functional Linear Model

Author

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  • Haili Zhang

    (University of Chinese Academy of Sciences, Beijing 100049, China
    Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

  • Guohua Zou

    (School of Mathematical Sciences, Capital Normal University, Beijing 100048, China)

Abstract

Functional data is a common and important type in econometrics and has been easier and easier to collect in the big data era. To improve estimation accuracy and reduce forecast risks with functional data, in this paper, we propose a novel cross-validation model averaging method for generalized functional linear model where the scalar response variable is related to a random function predictor by a link function. We establish asymptotic theoretical result on the optimality of the weights selected by our method when the true model is not in the candidate model set. Our simulations show that the proposed method often performs better than the commonly used model selection and averaging methods. We also apply the proposed method to Beijing second-hand house price data.

Suggested Citation

  • Haili Zhang & Guohua Zou, 2020. "Cross-Validation Model Averaging for Generalized Functional Linear Model," Econometrics, MDPI, vol. 8(1), pages 1-35, February.
  • Handle: RePEc:gam:jecnmx:v:8:y:2020:i:1:p:7-:d:324497
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    References listed on IDEAS

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