Peiyun Jiang, Takashi Yamagata
arXiv 1 Oct 2025 · Statistics — Methodology
arXiv:2510.00947 · PDF · DOI · OpenAlex · Extracted main text
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 $0<\alpha _{k}\leq 1$ for $k=1,2,...,r$. We establish the asymptotic validity of our bootstrap method using not only the conventional data-dependent rotation matrix $\hat{\bH}$, but also an alternative data-dependent rotation matrix, $\hat{\bH}_q$, which typically exhibits smaller asymptotic bias and achieves a faster convergence rate. Furthermore, we demonstrate the asymptotic validity of the bootstrap under a purely signal-dependent rotation matrix ${\bH}$, which is unique and can be regarded as the population analogue of both $\hat{\bH}$ and $\hat{\bH}_q$. Experimental results provide compelling evidence that the proposed bootstrap procedure achieves superior performance relative to the existing procedure.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Goncalves, S. and B. Perron (2014) Bootstrapping factor-augmented regression models | 1.000 | 17 | 3 | 100% |
| 2 | Goncalves, S. and B. Perron (2020) Bootstrapping factor models with cross sectional dependence | 1.000 | 12 | 3 | 100% |
| 3 | Jiang, P., Y. Uematsu, and T. Yamagata (2024) Bias correction in factor-augmented regression models with weak factors self | 0.865 | 17 | 4 | 65% |
| 4 | Bai, J. and S. Ng (2023) Approximate factor models with weaker loadings | 0.811 | 4 | 2 | 100% |
| 5 | Jiang, P., Y. Uematsu, and T. Yamagata (2023) Revisiting asymptotic theory for principal component estimators of approximate factor models self | 0.751 | 11 | 2 | 64% |
| 6 | Bai, J. and S. Ng (2006) Confidence intervals for diffusion index forecasts and inference with factor-augmented regressions | 0.644 | 4 | 1 | 100% |
| 7 | Stock, J. H. and M. W. Watson (2002) Forecasting using principal components from a large number of predictors | 0.585 | 3 | 1 | 100% |
| 8 | Freyaldenhoven (2022) Factor models with local factors - determining the number of relevant factors | 0.511 | 2 | 1 | 100% |
| 9 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.405 | 1 | 1 | 100% |
| 10 | Bai, J. and S. Ng (2002) Determining the number of factors in approximate factor models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bias Correction in Factor-Augmented Regression Models with Weak Factors | 0.405 | 1 | 1 |