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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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