Supervised kernel principal component analysis for forecasting

Puyi Fang, Zhaoxing Gao and Ruey S. Tsay

Finance Research Letters, 2023, vol. 58, issue PA

Abstract: Principal component analysis (PCA) is a versatile tool for dimension reduction with many applications in finance, economics, and machine learning. This paper proposes a new approach to improving the forecasting ability of the Principal Components (PCs) of observed high-dimensional predictors in a factor-augmented forecasting framework. The approach is carried out in a three-step procedure, where we first perform a nonparametric kernel regression of the target variable on each predictor to obtain a new high-dimensional predictor vector formed by stacking all estimated kernel regression functions, we then extract PCs from these new predictors using PCA, and finally, we employ the extracted PCs as predictors to forecast the target variable. A real example on macroeconomic forecasting is analyzed and numerical results show that the proposed kernel PCA can outperform some commonly used forecasting approaches in out-of-sample prediction.

Keywords: Forecasting; Nonparametric regression; Principal component analysis; Dimension reduction (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612323006645
Full text for ScienceDirect subscribers only

Related works:
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:eee:finlet:v:58:y:2023:i:pa:s1544612323006645

DOI: 10.1016/j.frl.2023.104292

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().