Scaled PCA: A New Approach to Dimension Reduction

Dashan Huang (), Fuwei Jiang (), Kunpeng Li (), Guoshi Tong () and Guofu Zhou ()
Additional contact information
Dashan Huang: Lee Kong Chian School of Business, Singapore Management University, 178899, Singapore
Fuwei Jiang: School of Finance, Central University of Finance and Economics, 102206 China
Kunpeng Li: International School of Economics and Management, Capital University of Economics and Business, 100070 China
Guoshi Tong: Fanhai International School of Finance, Fudan University, 200001 China
Guofu Zhou: Olin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130

Management Science, 2022, vol. 68, issue 3, 1678-1695

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 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2021.4020 (application/pdf)

Related works:
Working Paper: Scaled PCA: A New Approach to Dimension Reduction (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:3:p:1678-1695

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().