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Model averaging estimation for high-dimensional covariance matrices with a network structure

Author

Listed:
  • Rong Zhu
  • Xinyu Zhang
  • Yanyuan Ma
  • Guohua Zou

Abstract

SummaryIn this paper, we develop a model averaging method to estimate a high-dimensional covariance matrix, where the candidate models are constructed by different orders of polynomial functions. We propose a Mallows-type model averaging criterion and select the weights by minimizing this criterion, which is an unbiased estimator of the expected in-sample squared error plus a constant. Then, we prove the asymptotic optimality of the resulting model average covariance estimators. Finally, we conduct numerical simulations and a case study on Chinese airport network structure data to demonstrate the usefulness of the proposed approaches.

Suggested Citation

  • Rong Zhu & Xinyu Zhang & Yanyuan Ma & Guohua Zou, 2021. "Model averaging estimation for high-dimensional covariance matrices with a network structure," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 177-197.
    • Handle: RePEc:oup:emjrnl:v:24:y:2021:i:1:p:177-197.
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    File URL: http://hdl.handle.net/10.1093/ectj/utaa030
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    Cited by:

    1. Jin Yuan & Xianghui Yuan, 2023. "A Best Linear Empirical Bayes Method for High-Dimensional Covariance Matrix Estimation," SAGE Open, , vol. 13(2), pages 21582440231, June.

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