Testing for integration using evolving trend and seasonal models: A Bayesian Approach
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–Fuller tests 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. We extend these ideas to the problem of testing for integration at seasonal frequencies and show how our techniques can be used to carry out Bayesian variants of either the HEGY or Canova–Hansen test. Stochastic integration rules, based on Markov Chain Monte Carlo, as well as deterministic integration rules are used. Strengths and weaknesses of each approach are indicated.
|Keywords||Bayes factor, Gibbs sampler, seasonality, state space models, unit root|
|Persistent URL||dx.doi.org/10.1016/S0304-4076(99)00071-8, hdl.handle.net/1765/11332|
Koop, G., & van Dijk, H.K.. (2000). Testing for integration using evolving trend and seasonal models: A Bayesian Approach. Journal of Econometrics. doi:10.1016/S0304-4076(99)00071-8