A model selection approach to detect seasonal unit roots

The popular "airline" model for a seasonal time series assumes that a variable needsdouble differencing, i.e. first and seasonal (or annual) differencing.The resultant time series can usually be described by a low order movingaverage model with estimated roots close to the unit circle. This latterfeature complicates the standard autoregression-based tests for (seasonal)unit roots which are often used in practice.In this paper we propose an alternative route to detect seasonal unitroots by analyzing (versions of) the basic structural model [BSM].This BSM can generate data which are (approximately) observationallyequivalent to data generated from an airline model.Using Monte Carlo simulations, we show that our method works very well.We illustrate our approach for a large set of macroeconomic time series variables.