Bayesian Time Series Modelling with Long-Range Dependence

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.

Some key words: Bayesian time series analysis; Non-parametric models; Long memory; Spectral analysis

Here is the full postscript text for this technical report. It is 471617 bytes long.