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Author: Heuchenne, Cedric
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Heuchenne, Cedric Jacquemain, Alexandre |
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Inference for Monotone Single-index Conditional Means: A Lorenz Regression Approach Computational Statistics and Data Analysis published online (9 September 2021): DOI: 10.1016/j.csda.2021.107347. Also: https://www.sciencedirect.com/science/article/pii/S016794732100181X Cohort(s): Young Men Publisher: Elsevier Keyword(s): Educational Attainment; Monte Carlo; Regions; Statistical Analysis; Wages The Lorenz regression procedure quantifies the inequality of a response explained by a set of covariates. Formally, it gives a weight to each covariate to maximize the concentration index between the response and a weighted average of the covariates. The obtained index is called the explained Gini coefficient. Unlike methods based on decompositions of inequality measures, the procedure does not assume a linear relationship between the response and the covariates. Inference can be performed by noticing a similarity with the monotone rank estimator, introduced in the context of the single-index model. A continuity correction is presented in the presence of discrete covariates. The Lorenz-R2 is a goodness-of-fit measure evaluating the proportion of explained inequality and is used to build a test of joint significance of several covariates. Monte-Carlo simulations and a real-data example are presented. |
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Bibliography Citation
Heuchenne, Cedric and Alexandre Jacquemain. "Inference for Monotone Single-index Conditional Means: A Lorenz Regression Approach." Computational Statistics and Data Analysis published online (9 September 2021): DOI: 10.1016/j.csda.2021.107347.
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