Performance of Seasonal Adjustment Procedures: Simulation and Empirical Results

In this chapter we use a simulation experiment to examine whether the seasonal adjustment methods Census X12-ARIMA and TRAMO/SEATS effectively remove seasonality properties from time series data, while preserving other features like the stochastic trend. As data generating processes we use a variety of processes that are actually found in practice. These processes include constant seasonality, changing seasonal patterns due to seasonal unit roots and processes with periodically varying parameters. To check for seasonality, we consider tests for seasonal unit roots, for deterministic seasonality, for seasonality in the variance, and for periodicity in the parameters. Our simulation results show that both adjustment methods are able to remove stochastic seasonal patterns from the data with the exception of changing seasonal patterns due to periodicity in the parameters. On average, the two methods perform equally well.