Bayesian Analysis

Bayesian Endogenous Tobit Quantile Regression

Genya Kobayashi

Full-text: Open access

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.

Article information

Source
Bayesian Anal., Volume 12, Number 1 (2017), 161-191.

Dates
First available in Project Euclid: 15 February 2016

Permanent link to this document
https://projecteuclid.org/euclid.ba/1455559718

Digital Object Identifier
doi:10.1214/16-BA996

Mathematical Reviews number (MathSciNet)
MR3597571

Zentralblatt MATH identifier
1384.62275

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

Citation

Kobayashi, Genya. Bayesian Endogenous Tobit Quantile Regression. Bayesian Anal. 12 (2017), no. 1, 161--191. doi:10.1214/16-BA996. https://projecteuclid.org/euclid.ba/1455559718


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