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...

The issue of modelling observations generated in matrix form over time is key in economics, finance and many domains of application. While it is common to model vectors of observations through standard vector time series analysis, original matrix-valued data often reflect different types of...

BAYSTAR provides Bayesian MCMC methods for iteratively sampling to provide parameter estimates and inference for the two-regime SETAR model. A convenient user interface for importing data from a file or specifying true values for simulated data is easy to apply for analysis. Parameter inferences...