TY - JOUR
T1 - Model averaging estimation for high-dimensional covariance matrices with a network structure
AU - Zhu, Rong
AU - Zhang, Xinyu
AU - Ma, Yanyuan
AU - Zou, Guohua
N1 - Funding Information:
We thank the referee, the associate editor, the co-editor Victor Chernozhukov and Prof. Hansheng Wang for many constructive comments and suggestions. We thank Prof. Wei Lan for providing his codes. Zhang, the corresponding author, was supported by the National Key R&D Program of China (2020AAA0105200), the National Natural Science Foundation of China (grant nos. 71925007, 11688101 and 71631008), the Beijing Academy of Artificial Intelligence, and the Youth Innovation Promotion Association of the Chinese Academy of Sciences. Mawas supported by grants from the National Science Foundation and the National Institutes of Health. Zou was supported by the National Natural Science Foundation of China (grant nos. 11971323 and 12031016).
Publisher Copyright:
© 2020 Royal Economic Society.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
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U2 - 10.1093/ectj/utaa030
DO - 10.1093/ectj/utaa030
M3 - Article
C2 - 33746562
AN - SCOPUS:85118763588
SN - 1368-4221
VL - 24
SP - 177
EP - 197
JO - Econometrics Journal
JF - Econometrics Journal
IS - 1
ER -