Bayesian Analysis of Random-Intercept Models with the Skew-Laplace Distribution

In fitting random-intercept models, it is commonly assumed that the random effects and the error terms follow the normal distribution. In many empirical applications, the true distribution of data obeys non-normality and thus the main concern of most recent studies is the use of alternative distributions. In this paper, we propose a new class of random-intercept models using the Skew-Laplace distribution. The new regression model is flexible in the analysis of correlated data and simple in the implementation of Markov Chain Monte Carlo methods, such as the Gibbs sampling approach. Using the stochastic representation of the Skew-Laplace distribution we derive the full conditional posteriors distributions in order to present the Bayesian inference of model parameters. A real data analysis is illustrated from the economic contexts to show the usefulness of the proposed model.