On the construction of stationary AR(1) models via random distributions
Alberto Contreras‐Cristán, Ramsés H. Mena, Stephen Graham Walker · Statistics · 2008
We explore a method for constructing first-order stationary autoregressive-type models with given marginal distributions. We impose the underlying dependence structure in the model using Bayesian non-parametric predictive distributions. This approach allows for nonlinear dependency and at the same time works for any choice of marginal distribution. In particular, we look at the case of discrete-valued models; that is the marginal distributions are supported on the non-negative integers.