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March 2017 Bayesian Endogenous Tobit Quantile Regression
Genya Kobayashi
Bayesian Anal. 12(1): 161-191 (March 2017). DOI: 10.1214/16-BA996

Abstract

This study proposes p-th Tobit quantile regression models with endogenous variables. In the first stage regression of the endogenous variable on the exogenous variables, the assumption that the α-th quantile of the error term is zero is introduced. Then, the residual of this regression model is included in the p-th quantile regression model in such a way that the p-th conditional quantile of the new error term is zero. The error distribution of the first stage regression is modelled around the zero α-th quantile assumption by using parametric and semiparametric approaches. Since the value of α is a priori unknown, it is treated as an additional parameter and is estimated from the data. The proposed models are then demonstrated by using simulated data and real data on the labour supply of married women.

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Genya Kobayashi. "Bayesian Endogenous Tobit Quantile Regression." Bayesian Anal. 12 (1) 161 - 191, March 2017. https://doi.org/10.1214/16-BA996

Information

Published: March 2017
First available in Project Euclid: 15 February 2016

zbMATH: 1384.62275
MathSciNet: MR3597571
Digital Object Identifier: 10.1214/16-BA996

Keywords: asymmetric Laplace distribution , Bayesian Tobit quantile regression , Dirichlet process mixture , endogenous variable , Markov chain Monte Carlo , skew normal distribution

Rights: Copyright © 2017 International Society for Bayesian Analysis

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Vol.12 • No. 1 • March 2017
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