Cluster-based regularized sliced inverse regression for forecasting macroeconomic variables |
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Authors: | Yue Yu Zhihong Chen Jie Yang |
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Institution: | 1. TradeLink L.L.C., 71 S. Wacker Drive Suite 1900, Chicago, Illinois, USA 2. School of International Trade and Economics, University of International Business and Economics, Beijing, 100029, China 3. Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, Illinois, USA
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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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Keywords: | Cluster-based forecast macroeconomics sliced inverse regression |
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