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2 décembre 2021
- handle: 10670/1.cw5iw5
- isbn: 978-1-80071-558-5 / 978-1-80071-557-8
- hal: hal-03541743
- doi: 10.1108/S1049-258520210000029003
- WOS: WOS:001053331300003
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info:eu-repo/semantics/altIdentifier/doi/10.1108/S1049-258520210000029003
Mots-clés
Bayesian Inference Growth incidence curve Distributional changes Inequality Mixtures of log-normals Lorenz curves C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C11 - Bayesian Analysis: General D - Microeconomics/D.D3 - Distribution/D.D3.D31 - Personal Income, Wealth, and Their Distributions I - Health, Education, and Welfare/I.I3 - Welfare, Well-Being, and Poverty/I.I3.I31 - General Welfare, Well-BeingSujets proches
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Edwin Fourrier-Nicolaï et al., « Bayesian Inference for Parametric Growth Incidence Curves », HAL-SHS : économie et finance, ID : 10.1108/S1049-258520210000029003
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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.