Disentangling the source of non-stationarity in a panel of seasonal data

In dealing with a panel of seasonal data with cross-section dependence, this paper establishes a common factor model to investigate whether the seasonal and non-seasonal non-stationarity in a series is pervasive, or specific, or both. Without knowing a priori whether the data are seasonal stationary or not, we propose a procedure for consistently estimating the model; thus, the seasonal non-stationarity of common factors and idiosyncratic errors can be separately detected accordingly. We evaluate the methodology in a series of Monte Carlo simulations and apply it to test for non-stationarity and to disentangle their sources in panels of worldwide real exchange rates and of consumer price indexes for 37 advanced economies.