Seasonal Adjustment and Other Data Transformations
指出，当季节调整不是唯一的数据变换时，最优信号提取滤波器的优势并不明显；若变换顺序不当，均匀滤波器可能更优，从而支持了实践中广泛使用的均匀滤波方法。
In this article, it is shown that the case for using optimal signal-extraction filters is not all that convincing once it is recognized that seasonal adjustment is typically not the only transformation applied to data. Seasonal adjustment is viewed as any general linear filter. All other data transformations are also assumed to be linear. Although optimal filters always dominate uniform filters, their dominance critically depends on performing seasonal adjustment and the other data transformations in the right sequence. The conclusions of our article make a strong case in favor of the wide practice of uniform filtering.