The Modeling and Seasonal Adjustment of Weekly Observations
针对每周特定日记录的周度经济时间序列，因周数变化和节日影响导致季节调整困难，本文通过结构时间序列模型和状态空间滤波平滑算法，实现趋势提取和季节调整，并以英国央行货币供应数据为例。
Several important economic time series are recorded on a particular day every week. Seasonal adjustment of such series is difficult because the number of weeks varies between 52 and 53 and the position of the recording day changes from year to year. In addition certain festivals, most notably Easter, take place at different times according to the year. This article presents a solution to problems of this kind by setting up a structural time series model that allows the seasonal pattern to evolve over time and enables trend extraction and seasonal adjustment to be carried out by means of state-space filtering and smoothing algorithms. The method is illustrated with a Bank of England series on the money supply.