Publication Type
Journal Article
Version
submittedVersion
Publication Date
3-2022
Abstract
This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to outperform the PCA. A real data example on macroeconomic forecasting shows that the sPCA has better performance in general.
Keywords
Forecasting, PCA, Big Data, Dimension Reduction, Machine Learning
Discipline
Management Sciences and Quantitative Methods
Research Areas
Finance
Publication
Management Science
Volume
68
Issue
3
First Page
1678
Last Page
1695
ISSN
0025-1909
Identifier
10.1287/mnsc.2021.4020
Publisher
Institute for Operations Research and Management Sciences
Citation
HUANG, Dashan; JIANG, Fuwei; LI, Kunpeng; TONG, Guoshi; and ZHOU, Guofu.
Scaled PCA: A new approach to dimension reduction. (2022). Management Science. 68, (3), 1678-1695.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/6924
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1287/mnsc.2021.4020