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

Yafen Ye, Renyong Chi, Yuan-Hai Shao (), Chun-Na Li and Xiangyu Hua
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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

Computational Economics, 2022, vol. 60, issue 3, No 8, 990 pages

Abstract: 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.

Keywords: Variable selection; Lasso; Robustness; Regression (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10614-021-10175-w

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