In this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in the autoregressive representation or in a separate state equation. Tests based on the former are analogous to Dickey-Fullertests of unit roots, while the latter are analogous to KPSS tests of trend-stationarity. We use Bayesian methods to survey the properties of the likelihood function in such models and to calculate posterior odds ratios comparing models with and without stochastic trends. In addition, we extend these ideas to the problem of testing for integration at seasonal frequencies and show how techniques can be used to carry out Bayesian variants of HEGY test or the Canova-Hansen test.
State space models Bayes Factor Gibbs sampler unit root seasonality