Modelling income distribution using the log Student’s t distribution: New evidence for European Union countries

Barroso, Francisco Javier Callealta; Pérez, Carmelo García; Alaiz, Mercedes Prieto

doi:10.1016/j.econmod.2019.11.021

Cited by 7 publications

(2 citation statements)

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“…Barroso et al. (2020) also proposed a log Student's t distribution as a generalization of the lognormal with a better‐fitting tail.…”

Section: Discussionmentioning

confidence: 99%

“…Other special cases of the Champernowne distribution include Fisk's sech-squared distribution (aka, sech 2 , square-of-hyperbolic-sequant, log-logistic, Fisk, Champernowne-Fisk, Burr distribution with d = 1), and the Yule distribution (aka Yule-Simon distribution; Yule, 1925;Xekalaki, 1983;Kleiber & Kotz, 2003:242−247;Duan, 2016), which shares properties with the lognormal distribution (Aitchison & Brown, 1957;Simon, 1955;Simon & Bonini, 1958). Barroso et al (2020) also proposed a log Student's t distribution as a generalization of the lognormal with a better-fitting tail.…”

Section: (Generalized) Pareto Familymentioning

confidence: 99%

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Parametric representation of the top of income distributions: Options, historical evidence, and model selection

Hlásny

2021

Journal of Economic Surveys

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Empirical distributions of top incomes suffer from statistical problems affecting the measurement of inequality and its trend. Researchers and practitioners have been increasingly noting parametric regularities across income distributions and turning to parametric functions to approximate or supplement the observed distributions, both for descriptive purposes and for correcting distributional statistics derived from data. The proliferation of distinct branches of modeling literature has highlighted the need to compare the alternative modeling options, and develop systematic tools to discriminate between them. This paper reviews the state of methodological and empirical knowledge regarding the adoptable parametric functions, and lists references and statistical programs allowing practitioners to apply these models to microdata in household surveys and administrative registers, or grouped‐records data from national accounts statistics. Implications for modeling the distributions of other economic outcomes including consumption and wealth, and incomes below the topmost tail, are drawn. For incomes, a handful of distribution functions hold promise for modeling the top tails based on theoretical and empirical properties—namely the extreme values distributions, the generalized Pareto, the Singh–Maddala and the generalized beta type 2. Understanding these functions in relation to other commonly invoked alternatives is a contribution of this review.

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“…Barroso et al. (2020) also proposed a log Student's t distribution as a generalization of the lognormal with a better‐fitting tail.…”

Section: Discussionmentioning

confidence: 99%