Journal of Systems Science and Complexity

, Volume 27, Issue 1, pp 75–91 | Cite as

Cluster-based regularized sliced inverse regression for forecasting macroeconomic variables

  • Yue Yu
  • Zhihong Chen
  • Jie Yang


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.


Cluster-based forecast macroeconomics sliced inverse regression 


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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.TradeLink L.L.C.ChicagoUSA
  2. 2.School of International Trade and EconomicsUniversity of International Business and EconomicsBeijingChina
  3. 3.Department of Mathematics, Statistics, and Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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