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Cluster-based regularized sliced inverse regression for forecasting macroeconomic variables

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Abstract

This paper concerns the dimension reduction in regression for large data set. The authors introduce a new method based on the sliced inverse regression approach, called cluster-based regularized sliced inverse regression. The proposed method not only keeps the merit of considering both response and predictors’ information, but also enhances the capability of handling highly correlated variables. It is justified under certain linearity conditions. An empirical application on a macroeconomic data set shows that the proposed method has outperformed the dynamic factor model and other shrinkage methods.

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Correspondence to Yue Yu.

Additional information

This paper was supported by the National Science Foundation of China under Grant No. 71101030 and the Program for Innovative Research Team in UIBE under Grant No. CXTD4-01.

This paper was recommended for publication by Editor WANG Shouyang.

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Yu, Y., Chen, Z. & Yang, J. Cluster-based regularized sliced inverse regression for forecasting macroeconomic variables. J Syst Sci Complex 27, 75–91 (2014). https://doi.org/10.1007/s11424-014-3281-8

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  • DOI: https://doi.org/10.1007/s11424-014-3281-8

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