This study compares two new seasonal adjustment methods designed to handle outliers and structural changes: X-IZARIMA and GAUSUM-STM. X-12-ARIMA is a successor to the X-ll-ARIMA seasonal adjustment method, and is being developed at the U.S. Bureau of the Census (Findley et al. (1988)). GAUSUM-STM is a non-Gaussian method using time series structural models, and was developed for this study based on methodology proposed by Kitagawa (1990).

The procedures are compared using 29 macroeconomic time series from the U.S. Bureau of the Census. These series have both outliers and structural changes, providing a good testbed for comparing non-Gaussian methods. For these series, the X-12-ARIMA decomposition consistently leads to smoother seasonal factors which are as or more “flexible” than the GAUSUM-STM seasonal component. On the other hand, with some significant exceptions, GAUSUM-STM generally handles outliers and level shifts better than X-12-ARIMA. The differences between GAUSUM-STM and X-12-ARIMA in handling outliers and structural changes are swamped by the fundamental differences in the nature of the seasonal decompositions.

Recognizing that seasonal adjustment is a subjective enterprise, we feel the X-12-ARIMA procedure yields more appealing seasonal adjustments for most of the series examined. However, GAUSUM-STM potentially offers some important advantages. This study gives guidance on what problems need to be tackled to improve STM-based seasonal adjustment.