Stationary Autoregressive Models via a Bayesian Nonparametric Approach

Abstract

Abstract. 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.