Permutation Tests for Comparing Inequality Measures - Aix-Marseille Université Accéder directement au contenu
Article Dans Une Revue Journal of Business and Economic Statistics Année : 2019

Permutation Tests for Comparing Inequality Measures

Résumé

Asymptotic and bootstrap tests for inequality measures are known to perform poorly in finite samples when the underlying distribution is heavy-tailed. We propose Monte Carlo permutation and bootstrap methods for the problem of testing the equality of inequality measures between two samples. Results cover the Generalized Entropy class, which includes Theil’s index, the Atkinson class of indices, and the Gini index. We analyze finite-sample and asymptotic conditions for the validity of the proposed methods, and we introduce a convenient rescaling to improve finite-sample performance. Simulation results show that size correct inference can be obtained with our proposed methods despite heavy tails if the underlying distributions are sufficiently close in the upper tails. Substantial reduction in size distortion is achieved more generally. Studentized rescaled Monte Carlo permutation tests outperform the competing methods we consider in terms of power.
Fichier principal
Vignette du fichier
Dufour_Flachaire_Khalaf_PermutionTestsComparingInequalityMeasuresHeavyTailed_2017.pdf (278.25 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02172793 , version 1 (29-04-2020)

Identifiants

Citer

Jean-Marie Dufour, Emmanuel Flachaire, Lynda Khalaf. Permutation Tests for Comparing Inequality Measures. Journal of Business and Economic Statistics, 2019, 37 (3), pp.457-470. ⟨10.1080/07350015.2017.1371027⟩. ⟨hal-02172793⟩
61 Consultations
368 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More