1 citations · 1 across the 3 of their papers we have counts for
3 papers
stat.ME2025
Bootstrap inference in factor-augmented regressions with weak factors
Peiyun Jiang, Takashi Yamagata
In this paper, we develop bootstrap methods for factor-augmented regressions with potentially weak common components, allowing signal eigenvalues to diverge at heterogeneous rates…
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★ 1 cited
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…