3 papers
stat.ME2025
An alternative bootstrap procedure for factor-augmented regression models
Peiyun Jiang, Takashi Yamagata
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 f…
stat.ME2025
Bias Correction in Factor-Augmented Regression Models with Weak Factors
Peiyun Jiang, Yoshimasa Uematsu, Takashi Yamagata
In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the factors extracted from a large number of $…
math.ST2023
Revisiting Asymptotic Theory for Principal Component Estimators of Approximate Factor Models
Peiyun Jiang, Yoshimasa Uematsu, Takashi Yamagata
It is well known that approximate factor models exhibit rotation indeterminacy. Principal component (PC) estimators are typically analyzed relative to a rotated factor-loading repr…