arXiv 15 Jul 2023 · Econometrics · publishedJournal of the American Statistical Association (2024) · 6 citations (OpenAlex)
arXiv:2307.07689 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between predictors and the target variable of interest by scaling and combining the predictors and their lagged values, resulting in an effective dynamic forecasting. Unlike the traditional diffusion-index approach, which does not learn the relationships between the predictors and the target variable before conducting PCA, we first re-scale each predictor according to their significance in forecasting the targeted variable in a dynamic fashion, and a PCA is then applied to a re-scaled and additive panel, which establishes a connection between the predictability of the PCA factors and the target variable. Furthermore, we also propose to use penalized methods such as the LASSO approach to select the significant factors that have superior predictive power over the others. Theoretically, we show that our estimators are consistent and outperform the traditional methods in prediction under some mild conditions. We conduct extensive simulations to verify that the proposed method produces satisfactory forecasting results and outperforms most of the existing methods using the traditional PCA. A real example of predicting U.S. macroeconomic variables using a large number of predictors showcases that our method fares better than most of the existing ones in applications. The proposed method thus provides a comprehensive and effective approach for dynamic forecasting in high-dimensional data analysis.
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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, Jiang, Li, Tong, and Zhou (2022) Scaled PCA: A new approach to dimension reduction | 1.000 | 36 | 5 | 100% |
| 2 | Stock and Watson (2002) Forecasting Using Principal Components From a Large Number of Predictors | 1.000 | 23 | 5 | 100% |
| 3 | Stock and Watson (2002) Macroeconomic Forecasting Using Diffusion Indexes | 1.000 | 11 | 3 | 100% |
| 4 | Bai and Ng (2023) Approximate Factor Models with Weaker Loadings | 1.000 | 10 | 3 | 100% |
| 5 | McCracken and Ng (2016) FRED-MD: A monthly database for macroeconomic research | 0.874 | 11 | 2 | 100% |
| 6 | Bai and Ng (2006) Confidence intervals for diffusion index forecasts and inference with factor-augmented regressions | 0.737 | 3 | 2 | 100% |
| 7 | Fan, Liao, and Mincheva (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.737 | 3 | 2 | 100% |
| 8 | Bai and Ng (2002) Determining the number of factors in approximate factor models | 0.644 | 4 | 1 | 100% |
| 9 | Bai and Ng (2013) Principal components estimation and identification of static factors | 0.644 | 2 | 2 | 100% |
| 10 | Bühlmann and Van De Geer (2011) Statistics for High-Dimensional Data: Methods, Theory and Applications | 0.644 | 2 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.