Bayesian analysis of quantile regression for censored dynamic panel data |
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Authors: | Genya Kobayashi Hideo Kozumi |
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Institution: | 1. Graduate School of Business Administration, Kobe University, 2-1 Rokko, Kobe, 657-8501, Japan
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Abstract: | This paper develops a Bayesian approach to analyzing quantile regression models for censored dynamic panel data. We employ
a likelihood-based approach using the asymmetric Laplace error distribution and introduce lagged observed responses into the
conditional quantile function. We also deal with the initial conditions problem in dynamic panel data models by introducing
correlated random effects into the model. For posterior inference, we propose a Gibbs sampling algorithm based on a location-scale
mixture representation of the asymmetric Laplace distribution. It is shown that the mixture representation provides fully
tractable conditional posterior densities and considerably simplifies existing estimation procedures for quantile regression
models. In addition, we explain how the proposed Gibbs sampler can be utilized for the calculation of marginal likelihood
and the modal estimation. Our approach is illustrated with real data on medical expenditures. |
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