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Indicator Selection of Index Construction by Adaptive Lasso with a Generic $$\varepsilon $$ ε -Insensitive Loss

Author

Listed:
  • Yafen Ye

    (Zhejiang University of Technology)

  • Renyong Chi

    (Zhejiang University of Technology)

  • Yuan-Hai Shao

    (Hainan University)

  • Chun-Na Li

    (Hainan University)

  • Xiangyu Hua

    (Zhejiang University
    Economic Monitoring and Forecasting Office, Zhejiang Economic Information Center (Zhejiang Price Research Institute))

Abstract

A successful index helps policy decision makers identify benchmark performances and trends and set policy priorities. Selecting representative variables from a large number of potential candidates in the system is crucial to the success of index construction. A robust and sparse method reflects an urgent need to effectively select useful variables for index construction. Although the least absolute shrinkage and selection operator (Lasso) is a popular technique for variable selection, it suffers from the influence of outlier or noise. In this paper, we propose a robust Lasso with a generic insensitive and adaptive loss function (GIA-Lasso) for variable selection. The generic loss function can achieve great robustness against outliers by adjusting an insensitive parameter, an elastic interval parameter, and an adaptive robustification parameter. The $$L_1$$ L 1 -norm regularization term and the supervised selection process in GIA-Lasso ensure that the most representative variables are selected. The variable selection results of the Financial Conditions Index and Innovation and Entrepreneurship Index confirm that GIA-Lasso is not only robust against outliers, but also selects representative variables. The Granger causality test further proves the reasonability of the selected variables.

Suggested Citation

  • Yafen Ye & Renyong Chi & Yuan-Hai Shao & Chun-Na Li & Xiangyu Hua, 2022. "Indicator Selection of Index Construction by Adaptive Lasso with a Generic $$\varepsilon $$ ε -Insensitive Loss," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 971-990, October.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:3:d:10.1007_s10614-021-10175-w
    DOI: 10.1007/s10614-021-10175-w
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    References listed on IDEAS

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    1. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1533-1547.
    2. Yang Li & Jun S. Liu, 2019. "Robust Variable and Interaction Selection for Logistic Regression and General Index Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 271-286, January.
    3. Brian Gendreau & Yong Jin & Mahendrarajah Nimalendran & Xiaolong Zhong, 2019. "CVaR-LASSO Enhanced Index Replication (CLEIR): outperforming by minimizing downside risk," Applied Economics, Taylor & Francis Journals, vol. 51(52), pages 5637-5651, November.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Johannes Tang Kristensen, 2017. "Diffusion Indexes With Sparse Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 434-451, July.
    6. Rogge, Nicky, 2012. "Undesirable specialization in the construction of composite policy indicators: The Environmental Performance Index," Working Papers 2012/08, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
    7. Wang, Hansheng & Li, Guodong & Jiang, Guohua, 2007. "Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 347-355, July.
    8. L Cherchye & W Moesen & N Rogge & T Van Puyenbroeck & M Saisana & A Saltelli & R Liska & S Tarantola, 2008. "Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 239-251, February.
    9. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    10. Yunlu Jiang & Yan Wang & Jiantao Zhang & Baojian Xie & Jibiao Liao & Wenhui Liao, 2021. "Outlier detection and robust variable selection via the penalized weighted LAD-LASSO method," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(2), pages 234-246, January.
    11. Abberger, Klaus & Graff, Michael & Siliverstovs, Boriss & Sturm, Jan-Egbert, 2018. "Using rule-based updating procedures to improve the performance of composite indicators," Economic Modelling, Elsevier, vol. 68(C), pages 127-144.
    12. Arslan, Olcay, 2012. "Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1952-1965.
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