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Robust forecasting with scaled independent component analysis

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  • Shu, Lei
  • Lu, Feiyang
  • Chen, Yu

Abstract

Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance.

Suggested Citation

  • Shu, Lei & Lu, Feiyang & Chen, Yu, 2023. "Robust forecasting with scaled independent component analysis," Finance Research Letters, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322005761
    DOI: 10.1016/j.frl.2022.103399
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    References listed on IDEAS

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