Assumption-light and computationally cheap inference on inequality measures by sample splitting
The Student t approach
Bibliographic Data
| ID | 12836755 |
|---|---|
| Authors | Catarina Midões (0000-0003-4058-9182, Ca' Foscari University of Venice, corresponding author), Denis de Crombrugghe (Nazarbayev University) |
| Year | 2023 |
| Volume | 21 |
| Issue | 4 |
| Pages | 899-924 |
| Publication date | 2023-07-24 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Journal of Economic Inequality (JOURNAL) |
| Journal identifiers | ISSN: 1569-1721 • E-ISSN: 1573-8701 |
| Publisher | Springer Science+Business Media (PUBLISHER • DE) |
| DOI | 10.1007/s10888-023-09574-w |
| OpenAlex | W4385156838 |
| Language | EN |
| References cited | 17 |
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
Asymptotic analysis · Confidence interval · Delta method · Econometrics · Economics · Estimator · Inference · Permutation (music · Resampling · Sample (material · Skew · Statistical hypothesis testing · Statistical inference · Statistics · Variance (accounting · Computer Science · Economics of Agriculture and Food Markets · Income, Poverty, and Inequality · Mathematics · Monetary Policy and Economic Impact · Artificial Intelligence
| Highly cited | No |
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