Stationary autoregressive models via a Bayesian nonparametric approach

An approach to constructing strictly stationary AR(1)-type models with arbitrary stationary distributions and a flexible dependence structure is introduced. Bayesian nonparametric predictive density functions, based on single observations, are used to construct the one-step ahead predictive density. This is a natural and highly flexible way to model a one-step predictive/transition density