Non-Gaussian Season Adjustment: X-12 ARIMA Versus Robust Structural Models

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-ll-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 sea* sonal 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 X12-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. This work was started while Dr. Bruce was a visiting research statistician at the U.S. Bureau of the Census. It was completed through the support of a Joint Statistical Agreement between the U.S. Bureau of the Census and Victoria University of Wellington. The authors are indebted to David Findley (U.S. Bureau of the Census), who inspired, supported and guided this project. Peter Thomson (Victoria University) also deserves special thanks for his very substantive contributions. William Bell, Brian Monsell, and Mark Otto (U.S. Bureau of the Census) gave invaluable support, providing the data, X-1ZARIMA software, REGCMPNT software, and the choice of ARIMA models for X-12-ARIMA. In addition, Genshiro Kitagawa provided software upon which the MING program is based. Finally, Alistair Gray (New Zealand Department of Statistics), Magdalena Cordera, and Jim Durbin contributed many insightful comments.