An alternative bootstrap procedure for factor-augmented regression models

Peiyun Jiang and Takashi Yamagata

Papers from arXiv.org

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

Date: 2025-10
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