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An alternative bootstrap procedure for factor-augmented regression models

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  • Peiyun Jiang
  • Takashi Yamagata

Abstract

In this paper, we propose a novel bootstrap algorithm that is more efficient than existing methods for approximating the distribution of the factor-augmented regression estimator for a rotated parameter vector. The regression is augmented by $r$ factors extracted from a large panel of $N$ variables observed over $T$ time periods. We consider general weak factor (WF) models with $r$ signal eigenvalues that may diverge at different rates, $N^{\alpha _{k}}$, where $0

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  • Peiyun Jiang & Takashi Yamagata, 2025. "An alternative bootstrap procedure for factor-augmented regression models," Papers 2510.00947, arXiv.org.
    • Handle: RePEc:arx:papers:2510.00947
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    References listed on IDEAS

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