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Scaled PCA: A New Approach to Dimension Reduction

Author

Listed:
  • 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)

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.

Suggested Citation

  • Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:3:p:1678-1695
    DOI: 10.1287/mnsc.2021.4020
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    References listed on IDEAS

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