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Chapitre D'ouvrage Année : 2021

Bayesian Inference for Parametric Growth Incidence Curves

Résumé

The growth incidence curve of Ravallion and Chen (2003) is based on the quantile function. Its distribution-free estimator behaves erratically with usual sample sizes leading to problems in the tails. The authors propose a series of parametric models in a Bayesian framework. A first solution consists in modeling the underlying income distribution using simple densities for which the quantile function has a closed analytical form. This solution is extended by considering a mixture model for the underlying income distribution. However, in this case, the quantile function is semi-explicit and has to be evaluated numerically. The last solution consists in adjusting directly a functional form for the Lorenz curve and deriving its first-order derivative to find the corresponding quantile function. The authors compare these models by Monte Carlo simulations and using UK data from the Family Expenditure Survey. The authors devote a particular attention to the analysis of subgroups.

Dates et versions

hal-03541743 , version 1 (24-01-2022)

Identifiants

Citer

Edwin Fourrier-Nicolaï, Michel Lubrano. Bayesian Inference for Parametric Growth Incidence Curves. Sanghamitra Bandyopadhyay. Research on Economic Inequality: Poverty, Inequality and Shocks, 29, Emerald Publishing Limited, pp.31-55, 2021, Research on Economic Inequality, 978-1-80071-558-5 / 978-1-80071-557-8. ⟨10.1108/S1049-258520210000029003⟩. ⟨hal-03541743⟩
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