Bayesian Mixtures of Autoregressive Models

Sally A. Wood, Melbourne Business School, University of Melbourne
Ori Rosen, University of Texas at El Paso
Robert Kohn, University of New South Wales

Abstract

In this paper we propose a class of time-domain models for analyzing possibly nonstationary time series. This class of models is formed as a mixture of time series models, whose mixing weights are a function of time. We consider specifically mixtures of autoregressive models with a common but unknown lag. The model parameters, including the number of mixture components, are estimated via Markov chain Monte Carlo methods. The methodology is illustrated with simulated and real data.