Constrained estimation for the binomial AR(1) model: on Bayesian approach

In this paper, we consider the constrained estimation problem for the binomial AR(1) model using Bayesian approach. We show that by using Gibbs sampling algorithm, the constrains for the parameters and latent variables can be routinely implemented. While obtaining all the full conditional distributions under the unconstrained environment, we only need to generate samples from them and make restrictions to easily described cross-sections, respectively, which avoids complex numerical integrations under the overall constraint set. In the simulation study, the performance of our algorithm is checked. Finally, the method is applied to two real data examples.