An iterated parametric approach to nonstationary signal extraction

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• 作者：Tucker McElroy and Andrew Sutcliffe
• 关键词：ARIMA component model ; Nonstationary time series ; Seasonal adjustment ; Signal extraction ; Wiener– ; Kolmogorov filtering ; X-11
• 刊名：Computational Statistics and Data Analysis
• 出版年：2006
• 出版时间：1 May 2006
• 年：2006
• 卷：50
• 期：9
• 页码：2206-2231
• 全文大小：250 K

文摘

Consider the three-component time series model that decomposes observed data (Y) into the sum of seasonal (S), trend (T), and irregular (I) portions. Assuming that S and T are nonstationary and that I is stationary, it is demonstrated that widely used Wiener–Kolmogorov signal extraction estimates of S and T can be obtained through an iteration scheme applied to optimal estimates derived from reduced two-component models for S plus I and T plus I. This “bootstrapping” signal extraction methodology is reminiscent of the iterated nonparametric approach of the US Census Bureau's X-11 program. The analysis of the iteration scheme provides insight into the algebraic relationship between full model and reduced model signal extraction estimates.