This paper considers quantile regression models using an asymmetric Laplace distribution from a Bayesian point of view. We develop a simple and efficient Gibbs sampling algorithm for fitting the quantile regression model based on a location-scale mixture representation of the asymmetric Laplace distribution. It is shown that the resulting Gibbs sampler can be accomplished by sampling from either normal or generalized inverse Gaussian distribution. We also discuss some possible extensions of our approach, including the incorporation of a scale parameter, the use of double exponential prior, and a Bayesian analysis of Tobit quantile regression. The proposed methods are illustrated by both simulated and real data.

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This study is based on a survey of 324 financial analysts in Japan. The survey concerns analysts’ perceptions of intellectual capital (IC) information and its links to the evaluation of companies. The value relevance of and the access to IC‐related information reveals a large gap on many items. The analysis further shows that the lack of access to information hampers analysts’ use of IC in their evaluation of companies, particularly in their use of human capital measures. Attitudes towards more disclosure and standardisation are mainly driven by perceptions of what generates value in companies.

Yardstick regulation, Incentive regulation, Efficiency, Cost frontier function, L51, L53, L90, K23,

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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