Bias Correction in Factor-Augmented Regression Models with Weak Factors

Peiyun Jiang, Yoshimasa Uematsu and Takashi Yamagata

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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

Date: 2025-09
New Economics Papers: this item is included in nep-ets and nep-inv
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