NON-GAUSSIAN SEASONAL ADJUSTMENT - X-12-ARIMA VERSUS ROBUST STRUCTURAL MODELS

This study compares X-12-ARIMA and MING, two new seasonal adjustment m ethods designed to handle outliers and structural changes in a time se ries. X-12-ARIMA is a successor to the X-11-ARIMA seasonal adjustment method, and is being developed at the US Bureau of the Census. MING is a 'Mixture based Non-Gaussian' method for seasonal adjustment using t ime series structural models and is implemented as a function in the S -Plus language. The procedures are compared using 29 macroeconomic tim e series from the US Bureau of the Census. These series have both outl iers and structural changes, providing a good testbed for comparing no n-Gaussian methods. For the 29 series, the X-12-ARIMA decomposition co nsistently leads to smoother seasonal factors which are as or more 'fl exible' than the MING seasonal component. On the other hand, MING is m ore stable, particularly in the way it handles outliers and level shif ts. This study relies heavily on graphical tools for comparing seasona l adjustment methods.