Rustam Ibragimov, Paul Kattuman, Anton Skrobotov
arXiv 11 May 2021 · Econometrics · publishedEconometric Reviews (2025) · 3 citations (OpenAlex)
arXiv:2105.05335 · PDF · DOI · OpenAlex · Extracted main text
Empirical analyses on income and wealth inequality and those in other fields in economics and finance often face the difficulty that the data is heterogeneous, heavy-tailed or correlated in some unknown fashion. The paper focuses on applications of the recently developed t-statistic based robust inference approaches in the analysis of inequality measures and their comparisons under the above problems. Following the approaches, in particular, a robust large sample test on equality of two parameters of interest (e.g., a test of equality of inequality measures in two regions or countries considered) is conducted as follows: The data in the two samples dealt with is partitioned into fixed numbers $q_1, q_2\ge 2$ (e.g., $q_1=q_2=2, 4, 8$) of groups, the parameters (inequality measures dealt with) are estimated for each group, and inference is based on a standard two-sample $t-$test with the resulting $q_1, q_2$ group estimators. Robust $t-$statistic approaches result in valid inference under general conditions that group estimators of parameters (e.g., inequality measures) considered are asymptotically independent, unbiased and Gaussian of possibly different variances, or weakly converge, at an arbitrary rate, to independent scale mixtures of normal random variables. These conditions are typically satisfied in empirical applications even under pronounced heavy-tailedness and heterogeneity and possible dependence in observations. The methods dealt with in the paper complement and compare favorably with other inference approaches available in the literature. The use of robust inference approaches is illustrated by an empirical analysis of income inequality measures and their comparisons across different regions in Russia.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dufour, Flachaire \ Khalaf (2019) `Permutation tests for comparing inequality measures', Journal of Business & Economic Statistics 37, 457––470 | 1.000 | 24 | 3 | 100% |
| 2 | Ibragimov \ Müller (2010) `$t-$statistic based correlation and heterogeneity robust inference', Journal of Business and Economic Statistics 28, 453–468 | 1.000 | 17 | 3 | 100% |
| 3 | Davidson \ Flachaire (2007) `Asymptotic and bootstrap inference for inequality and poverty measures', Journal of Econometrics 141, 141–166 | 1.000 | 16 | 4 | 100% |
| 4 | Cowell \ Flachaire (2007) `Income distribution and inequality measurement: The problem of extreme values', Journal of Econometrics 141, 1044–1072 | 1.000 | 14 | 4 | 100% |
| 5 | Ibragimov, Ibragimov \ Walden (2015) Heavy-Tailed Distributions and Robustness in Economics and Finance, Vol | 1.000 | 11 | 3 | 100% |
| 6 | Ibragimov \ Müller (2016) `Inference with few heterogeneous clusters', Review of Economics and Statistics 98, 83–96 | 0.874 | 12 | 2 | 100% |
| 7 | Dufour, Flachaire, Khalaf \ Zalghout (2020) `Identification-robust inequality analysis', Working paper Cahier 03-2020, CIREQ | 0.874 | 6 | 2 | 100% |
| 8 | Ibragimov \ Ibragimov (2018) `Heavy tails and upper-tail inequality: The case of Russia', Empirical Economics 54, 823–837 | 0.874 | 5 | 2 | 100% |
| 9 | Fontanari, Taleb \ Cirillo (2018) `Gini estimation under infinite variance', Physica A: Statistical Mechanics and its Applications 502, 256–269 | 0.843 | 3 | 3 | 100% |
| 10 | Toda (2012) `The double power law in income distribution: Explanations and evidence', Journal of Economic Behavior and Organization 84, 364–… | 0.811 | 4 | 2 | 100% |
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