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Lorenz Regression: an implementation of the Lorenz and penalized Lorenz regressions in R

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  • Jacquemain, Alexandre

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Heuchenne, Cédric

    (Université de Liège)

Abstract

Lorenz regressions are statistical tools for measuring the extent of inequality in a response variable that is attributable to a set of covariates. These regression techniques estimate the explained Gini coefficient, a measure of inequality in the conditional expectation of the response given the covariates, assuming a single-index model. In this paper, we describe the LorenzRegression package, which implements the non-penalized and penalized Lorenz regressions. The non-penalized procedure is implemented via a genetic algorithm, making use of the GA package. For the penalized case, the user can choose between using a lasso or SCAD penalty. In the former, the estimation procedure is performed with the FABS algorithm, while the latter is implemented with the SCAD-FABS algorithm. Computationally intensive parts of the code are written in C++. A mathematical description of the procedure is provided. The main function is carefully described and its use is illustrated on income data.

Suggested Citation

  • Jacquemain, Alexandre & Heuchenne, Cédric, 2023. "Lorenz Regression: an implementation of the Lorenz and penalized Lorenz regressions in R," LIDAM Discussion Papers ISBA 2023027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2023027
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