Peiyun Jiang, Yoshimasa Uematsu, Takashi Yamagata
arXiv 2 Sep 2025 · Statistics — Methodology
arXiv:2509.02066 · PDF · Extracted main text
In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the $r$ factors extracted from a large number of $N$ variables with $T$ observations. In particular, we consider general weak latent factor models with $r$ signal eigenvalues that may diverge at different rates, $N^{\alpha _{k}}$, $0<\alpha _{k}\leq 1$, $k=1,\dots,r$. In the existing literature, the bias has been derived using an approximation for the estimated factors with a specific data-dependent rotation matrix $\hat{H}$ for the model with $\alpha_{k}=1$ for all $k$, whereas we derive the bias for weak factor models. In addition, we derive the bias using the approximation with a different rotation matrix $\hat{H}_q$, which generally has a smaller bias than with $\hat{H}$. We also derive the bias using our preferred approximation with a purely signal-dependent rotation $H$, which is unique and can be regarded as the population version of $\hat{H}$ and $\hat{H}_q$. Since this bias is parametrically inestimable, we propose a split-panel jackknife bias correction, and theory shows that it successfully reduces the bias. The extensive finite-sample experiments suggest that the proposed bias correction works very well, and the empirical application illustrates its usefulness in practice.
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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 | 13 | 4 | 100% |
| 2 | Goncalves, S. and B. Perron (2020) Bootstrapping factor models with cross sectional dependence | 1.000 | 8 | 4 | 100% |
| 3 | Bai, J. and S. Ng (2006) Confidence intervals for diffusion index forecasts and inference with factor-augmented regressions | 1.000 | 7 | 5 | 100% |
| 4 | Bai, J. and S. Ng (2023) Approximate factor models with weaker loadings | 0.950 | 7 | 4 | 86% |
| 5 | Jiang, P., Y. Uematsu, and T. Yamagata (2023) Revisiting asymptotic theory for principal component estimators of approximate factor models self | 0.863 | 14 | 4 | 64% |
| 6 | Cochrane, J. H. and M. Piazzesi (2005) Bond risk premia | 0.737 | 3 | 2 | 100% |
| 7 | Stock, J. H. and M. W. Watson (2002) Forecasting using principal components from a large number of predictors | 0.737 | 3 | 2 | 100% |
| 8 | Bickel, P. J. and E. Levina (2008) Covariance regularization by thresholding | 0.644 | 2 | 2 | 100% |
| 9 | Fan, J., Y. Liao, and M. Mincheva (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.644 | 2 | 2 | 100% |
| 10 | Freyaldenhoven (2022) Factor models with local factors - determining the number of relevant factors | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 30 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | An alternative bootstrap procedure for factor-augmented regression models | 0.865 | 17 | 4 |
| 2 | Bootstrap Inference in Nonlinear Panel Data Models with Interactive Fixed Effects | 0.644 | 2 | 2 |