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Bias Correction in Factor-Augmented Regression Models with Weak Factors

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  • Peiyun Jiang
  • Yoshimasa Uematsu
  • Takashi Yamagata

Abstract

In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the $r$ factors extracted from a large number of $N$ variables with $T$ observations. In particular, we consider general weak latent factor models with $r$ signal eigenvalues that may diverge at different rates, $N^{\alpha _{k}}$, $0

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  • Peiyun Jiang & Yoshimasa Uematsu & Takashi Yamagata, 2025. "Bias Correction in Factor-Augmented Regression Models with Weak Factors," Papers 2509.02066, arXiv.org, revised Oct 2025.
    • Handle: RePEc:arx:papers:2509.02066
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    References listed on IDEAS

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