Seasonal Adjustment of Time Series Using One-Sided Filters
提出一种基于模型的信号提取季节调整方法，利用单边滤波器从观测时间序列中提取不可观测的季节和非季节成分，无需预测且无估计修正问题，并与双边滤波器方法进行比较。
Abstract This article presents a model-based signal extraction seasonal adjustment procedure to extract estimates of the independent unobserved seasonal and nonseasonal components from an observed time series. The decomposition yields a one-sided filter that is optimal for adjusting the most recent observation under the assumption of using only the past observed series. Some advantages of this procedure are that no forecasts are required for implementation and there are no problems of revision of estimates or questions of concurrent adjustment. Comparisons are made with existing procedures using two-sided filters. KEY WORDS: ARIMA modelsSignal extractionTime series decompositionTwo-sided filters