Department of Statistics Unitmark
Dietrich College of Humanities and Social Sciences

Bayesian Time Series Modelling with Long-Range Dependence

Publication Date

November, 1998

Publication Type

Tech Report


Giovanni Petris and Mike West


We present a class of models for trend plus stationary component time series, in which the spectral densities of stationary components are represented via non-parametric smoothness priors combined with long-range dependence components. We discuss model fitting and computational issues underlying Bayesian inference under such models, and provide illustration in studies of a climatological time series. These models are of interest to address the questions of existence and extent of apparent long-range effects in time series arising in specific scientific applications.