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Estimation of large covariance matrices with mixed factor structures

Author

Listed:
  • Runyu DaiGraduate
  • Yoshimasa Uematsu
  • Yasumasa MatsudaGraduate

Abstract

SummaryWe extend the principal orthogonal complement thresholding (POET) framework by J. Fan, Y. Liao, and M. Mincheva (2013) to estimate large covariance matrices with a ‘mixed’ structure of observable and unobservable strong/weak factors, and we call this method the extended POET (ePOET). Especially, the weak factor structure allows the existence of slowly divergent eigenvalues of the covariance matrix that are frequently observed in real data. Under some mild conditions, we derive the uniform consistency of the proposed estimator for the cases with or without observable factors. Furthermore, several simulation studies show that the ePOET achieves good finite-sample performance regardless of data with strong, weak, or mixed factors structure. Finally, we conduct empirical studies to present the practical usefulness of the ePOET.

Suggested Citation

  • Runyu DaiGraduate & Yoshimasa Uematsu & Yasumasa MatsudaGraduate, 2024. "Estimation of large covariance matrices with mixed factor structures," The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 62-83.
    • Handle: RePEc:oup:emjrnl:v:27:y:2024:i:1:p:62-83.
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    File URL: http://hdl.handle.net/10.1093/ectj/utad018
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