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

ABSTRACT: In 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.

SUBMITTER: Zhu R

PROVIDER: S-EPMC7946866 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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

Zhu Rong R Zhang Xinyu X Ma Yanyuan Y Zou Guohua G

The econometrics journal 20200929 1

In 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 num ...[more]

PMID: 33746562

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