This paper considers model selection and forecasting issues in two closely related models for nonstationary periodic autoregressive time series [PAR]. Periodically integrated seasonal time series [PIAR] need a periodic differencing filter to remove the stochastic trend. On the other hand, when the nonperiodic first order differencing filter can be applied, one can have a periodic model with a nonseasonal unit root [PARI]. In this paper, we discuss and evaluate two testing strategies to select between these two models. Furthermore, we compare the relative forecasting performance of each model using Monte Carlo simulations and some U.K. macroeconomic seasonal time series. One result is that forecasting with PARI models while the data generating process is a PIAR process seems to be worse thanvice versa.

Additional Metadata
Keywords Monte Carlo simulations, autoregressive time series, forecasting models
Persistent URL dx.doi.org/10.1007/BF02926158, hdl.handle.net/1765/2046
Journal Statistical Papers
Citation
Franses, Ph.H.B.F, & Paap, R. (1996). Periodic Integration: Further Results on Model Selection and Forecasting. Statistical Papers, 33–52. doi:10.1007/BF02926158