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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
Article

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.

Keywords

Cluster-based forecast macroeconomics sliced inverse regression 

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References

  1. [1]
    Boivin J and Ng S, Understanding and comparing factor-based forecasts, International Journal of Central Banking, 2005.Google Scholar
  2. [2]
    Eickmeier S and Ziegler C, How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach, Journal of Forecasting, 2008, 27(3): 237–265.MathSciNetGoogle Scholar
  3. [3]
    Stock J H and Watson M W, Generalized shrinkage methods for forecasting using many predictors, Journal of Business & Economic Statistics, 2012, 30(4): 481–493.CrossRefMathSciNetGoogle Scholar
  4. [4]
    Duan N and Li K C, Slicing regression: A link-free regression method, The Annals of Statistics, 1991, 19(2): 505–530.CrossRefzbMATHMathSciNetGoogle Scholar
  5. [5]
    Li K C, Sliced inverse regression for dimension reduction, Journal of the American Statistical Association, 1991, 86(414): 316–327.CrossRefzbMATHMathSciNetGoogle Scholar
  6. [6]
    Zhong W, Zeng P, Ma P, Liu J S, and Zhu Y, Rsir: Regularized sliced inverse regression for motif discovery, Bioinformatics, 2005, 21(22): 4169–4175.CrossRefGoogle Scholar
  7. [7]
    Li L and Yin X, Sliced inverse regression with regularizations, Biometrics, 2008, 64(1): 124–131.CrossRefzbMATHMathSciNetGoogle Scholar
  8. [8]
    Li L, Cook R D, and Tsai C L, Partial inverse regression, Biometrika, 2007, 94(3): 615–625.CrossRefzbMATHMathSciNetGoogle Scholar
  9. [9]
    Li K C, High dimensional data analysis via the sir/phd approach, 2000.Google Scholar
  10. [10]
    Ward J H, Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association, 1963, 58(301): 236–244.CrossRefMathSciNetGoogle Scholar
  11. [11]
    Eaton M L, A characterization of spherical distributions, Journal of Multivariate Analysis, 1986, 20(2): 272–276.CrossRefzbMATHMathSciNetGoogle Scholar
  12. [12]
    Hall P and Li K C, On almost linearity of low dimensional projections from high dimensional data, The Annals of Statistics, 1993, 21(2): 867–889.CrossRefzbMATHMathSciNetGoogle Scholar
  13. [13]
    Friedman J H, Regularized discriminant analysis, Journal of the American Statistical Association, 1989, 84(405): 165–175.CrossRefMathSciNetGoogle Scholar
  14. [14]
    Cook R D and Weisberg S, Discussion of Li (1991), Journal of the American Statistical Association, 1991, 86: 328–332.Google Scholar
  15. [15]
    Li K C, Sliced inverse regression for dimension reduction: Rejoinder, Journal of the American Statistical Association, 1991, 86(414): 337–342.Google Scholar
  16. [16]
    Li B and Wang S, On directional regression for dimension reduction, Journal of the American Statistical Association, 2007, 102(479): 997–1008.CrossRefzbMATHMathSciNetGoogle Scholar
  17. [17]
    Ye Z and Yang J, Sliced inverse moment regression using weighted chi-squared tests for dimension reduction, Journal of Statistical Planning and Inference, 2010, 140(11): 3121–3131.CrossRefzbMATHMathSciNetGoogle Scholar

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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