Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Cluster-based regularized sliced inverse regression for forecasting macroeconomic variables

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Boivin J and Ng S, Understanding and comparing factor-based forecasts, International Journal of Central Banking, 2005.

    Google Scholar 

  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.

    MathSciNet  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  4. Duan N and Li K C, Slicing regression: A link-free regression method, The Annals of Statistics, 1991, 19(2): 505–530.

    Article  MATH  MathSciNet  Google Scholar 

  5. Li K C, Sliced inverse regression for dimension reduction, Journal of the American Statistical Association, 1991, 86(414): 316–327.

    Article  MATH  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  7. Li L and Yin X, Sliced inverse regression with regularizations, Biometrics, 2008, 64(1): 124–131.

    Article  MATH  MathSciNet  Google Scholar 

  8. Li L, Cook R D, and Tsai C L, Partial inverse regression, Biometrika, 2007, 94(3): 615–625.

    Article  MATH  MathSciNet  Google Scholar 

  9. Li K C, High dimensional data analysis via the sir/phd approach, 2000.

    Google Scholar 

  10. Ward J H, Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association, 1963, 58(301): 236–244.

    Article  MathSciNet  Google Scholar 

  11. Eaton M L, A characterization of spherical distributions, Journal of Multivariate Analysis, 1986, 20(2): 272–276.

    Article  MATH  MathSciNet  Google Scholar 

  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.

    Article  MATH  MathSciNet  Google Scholar 

  13. Friedman J H, Regularized discriminant analysis, Journal of the American Statistical Association, 1989, 84(405): 165–175.

    Article  MathSciNet  Google Scholar 

  14. Cook R D and Weisberg S, Discussion of Li (1991), Journal of the American Statistical Association, 1991, 86: 328–332.

    Google Scholar 

  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. Li B and Wang S, On directional regression for dimension reduction, Journal of the American Statistical Association, 2007, 102(479): 997–1008.

    Article  MATH  MathSciNet  Google Scholar 

  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.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TradeLink L.L.C., 71 S. Wacker Drive Suite 1900, Chicago, Illinois, USA

    Yue Yu

  2. School of International Trade and Economics, University of International Business and Economics, Beijing, 100029, China

    Zhihong Chen

  3. Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, Illinois, USA

    Jie Yang

Authors
  1. Yue Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhihong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

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.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation