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Estimation of Sparsity-Induced Weak Factor Models

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  • Yoshimasa Uematsu
  • Takashi Yamagata

Abstract

This article investigates estimation of sparsity-induced weak factor (sWF) models, with large cross-sectional and time-series dimensions (N and T, respectively). It assumes that the kth largest eigenvalue of a data covariance matrix grows proportionally to Nαk with unknown exponents 0

Suggested Citation

  • Yoshimasa Uematsu & Takashi Yamagata, 2022. "Estimation of Sparsity-Induced Weak Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 213-227, December.
    • Handle: RePEc:taf:jnlbes:v:41:y:2022:i:1:p:213-227
    DOI: 10.1080/07350015.2021.2008405
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    Cited by:

    1. Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
    2. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    3. He, Yong & Kong, Xinbing & Trapani, Lorenzo & Yu, Long, 2023. "One-way or two-way factor model for matrix sequences?," Journal of Econometrics, Elsevier, vol. 235(2), pages 1981-2004.

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