Unobserved-Components Models for Seasonal Adjustment Filters
提出一类时间序列模型，其线性最小二乘信号提取得到的季节成分估计与广泛使用的Census X-11程序标准选项的结果高度接近。研究扩展了早期工作，考虑了更广泛的模型类别，并检验了非对称滤波器。讨论了指导不可观测成分模型设定的多种准则，提出了新的优选模型。
Time series models are presented for which the seasonal component estimates delivered by linear least squares signal extraction closely approximate those of the standard option of the widely-used Cencus X-11 program. Earlier work is extended by consideration of a broader class of models and by examination of asymmetric filters in addition to the symmetric filter implicit in the adjustment of historical data. Various criteria that guide the specification of unobserved-component models are discussed, and a new preferred model is presented. Other models generate filters that approximate X-11 rather well, explaining the wide acceptance of the X-11 method.