This study compares X-12-ARIMA and MING, two new seasonal adjustment methods designed to handle outliers and structural changes in a time series. X-12-ARIMA is a successor to the X-11-ARIMA seasonal adjustment method, and is being developed at the U.S. Bureau of the Census [Findley et al. (1988)]. MING is a "Mixture based Non-Gaussian" method for seasonal adjustment using time series structural models. It 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 the 29 series, the X-12-ARIMA decomposition consistently leads to smoother seasonal factors which are as or more "flexible" than the MING seasonal component. On the other hand, MING is more stable, particularly in the way it handles outliers and level shifts.

This study relied heavily on graphical tools for comparing seasonal adjustment methods. Use of graphics is critical in forming the conclusions of this paper.