Assumption-light and computationally cheap inference on inequality measures by sample splitting : the Student t approach
Midões, Catarina (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
de Crombrugghe, Denis (Maastricht University)

Fecha:	2023
Resumen:	Inference on inequality indices remains challenging, even in large samples. Heavy right tails in income and wealth distributions hinder the quality and threaten the validity of asymptotic approximations to finite sample distributions. Attempts to improve on asymptotic approximations by bootstrap techniques or permutation tests are only partial successes. We evaluate a different approach to robust inference, relying on Student t statistics obtained from split samples. This relatively simple 't-based' approach requires no consistent variance estimators, no random sampling of populations, and only mild distributional assumptions. We compare its performance with that of refined bootstrap and permutation techniques. We find that the more complex bootstrap methods still have the edge in one-sample tests, where the t-approach suffers from a negative skew. In two-sample comparisons though, the t-approach offers advantages: it is undersized while bootstrap tests and permutation tests are often oversized. In certain circumstances it is less powerful than permutation tests and bootstrap tests, but for large samples, this difference dissipates. It is also more generally applicable than permutation tests and easily generates confidence intervals. These differences are illustrated with an empirical application using two different sources of household data from the Russian Federation.
Ayudas:	European Commission 956107
Nota:	Unidad de excelencia María de Maeztu CEX2019-000940-M
Nota:	Altres ajuts: acords transformatius de la UAB
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Inference on inequality measures ; Difference-in-inequality testing ; Bootstrap inference ; Permutation tests ; Sample splitting
Publicado en:	Journal of Economic Inequality, (July 2023) , ISSN 1573-8701

DOI: 10.1007/s10888-023-09574-w

26 p, 1.2 MB

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