Sihan Tu, Zhaoxing Gao
arXiv 21 Feb 2025 · Econometrics
arXiv:2502.15275 · PDF · Extracted main text
Factor-based forecasting using Principal Component Analysis (PCA) is an effective machine learning tool for dimension reduction with many applications in statistics, economics, and finance. This paper introduces a Supervised Screening and Regularized Factor-based (SSRF) framework that systematically addresses high-dimensional predictor sets through a structured four-step procedure integrating both static and dynamic forecasting mechanisms. The static approach selects predictors via marginal correlation screening and scales them using univariate predictive slopes, while the dynamic method screens and scales predictors based on time series regression incorporating lagged predictors. PCA then extracts latent factors from the scaled predictors, followed by LASSO regularization to refine predictive accuracy. In the simulation study, we validate the effectiveness of SSRF and identify its parameter adjustment strategies in high-dimensional data settings. An empirical analysis of macroeconomic indices in China demonstrates that the SSRF method generally outperforms several commonly used forecasting techniques in out-of-sample predictions.
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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 | Huang, D., F. Jiang, K. Li, G. Tong, and G. Zhou (2022) Scaled PCA: A New Approach to Dimension Reduction | 1.000 | 6 | 3 | 100% |
| 2 | Gao, Z. and R. S. Tsay (2024) Supervised dynamic pca: Linear dynamic forecasting with many predictors self | 0.928 | 4 | 3 | 100% |
| 3 | Fan, J. and R. Li (2001) Variable selection via nonconcave penalized likelihood and its oracle properties | 0.737 | 3 | 2 | 100% |
| 4 | McCracken, M. W. and S. Ng (2016) FRED-MD: A monthly database for macroeconomic research | 0.737 | 3 | 2 | 100% |
| 5 | Zou, H. and T. Hastie (2005) Regularization and variable selection via the elastic net | 0.737 | 3 | 2 | 100% |
| 6 | Bai, J. and S. Ng (2002) Determining the number of factors in approximate factor models | 0.644 | 2 | 2 | 100% |
| 7 | Bai, J. and S. Ng (2008) Forecasting economic time series using targeted predictors | 0.644 | 2 | 2 | 100% |
| 8 | Bai, J. and S. Ng (2023) Approximate factor models with weaker loadings | 0.644 | 2 | 2 | 100% |
| 9 | Ling, B., C. Ma, Y. Tu, and X. Xie (2023) Mutation analysis of China 's macroeconomic structure based on high-dimensional factor model (in Chinese) | 0.644 | 2 | 2 | 100% |
| 10 | Fan, J. and J. Lv (2008) Sure independence screening for ultrahigh dimensional feature space | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 22 scored citations.