Bayesian time series analysis

Abstract

This article describes the use of Bayesian methods in the statistical analysis of time series. The use of Markov chain Monte Carlo methods has made even the more complex time series models amenable to Bayesian analysis. Models discussed in some detail are ARIMA models and their fractionally integrated counterparts, state space models, Markov switching and mixture models, and models allowing for time-varying volatility. A final section reviews some recent approaches to nonparametric Bayesian modelling of time series.