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Econometrics 2018, 6(2), 24; https://doi.org/10.3390/econometrics6020024

A Hybrid MCMC Sampler for Unconditional Quantile Based on Influence Function

Laboratoire d’Économie d’Orléans (LEO), Faculté de Droit, D’économie et de Gestion, University of Orleans, LEO (FRE CNRS 2014), Rue de Blois, F-45067 Orleans, France
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Received: 29 December 2017 / Revised: 25 April 2018 / Accepted: 26 April 2018 / Published: 4 May 2018
(This article belongs to the Special Issue Econometrics and Income Inequality)
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Abstract

In this study, we provide a Bayesian estimation method for the unconditional quantile regression model based on the Re-centered Influence Function (RIF). The method makes use of the dichotomous structure of the RIF and estimates a non-linear probability model by a logistic regression using a Gibbs within a Metropolis-Hastings sampler. This approach performs better in the presence of heavy-tailed distributions. Applied to a nationally-representative household survey, the Senegal Poverty Monitoring Report (2005), the results show that the change in the rate of returns to education across quantiles is substantially lower at the primary level. View Full-Text
Keywords: hybrid MCMC sampler; quantile regression; influence function; return to education hybrid MCMC sampler; quantile regression; influence function; return to education
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Laghlal, E.M.; Ndoye, A.A.J. A Hybrid MCMC Sampler for Unconditional Quantile Based on Influence Function. Econometrics 2018, 6, 24.

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