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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Strong Separability in Circulant SSA
verfasst von : J. Bógalo, P. Poncela, E. Senra
Erschienen in: Nonparametric Statistics
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Abstract
Circulant singular spectrum analysis (CSSA) is an automated variant of singular spectrum analysis (SSA) developed for signal extraction. CSSA allows to identify the association between the extracted component and the frequencies they represent without the intervention of the analyst. Another relevant characteristic is that CSSA produces strongly separable components, meaning that the resulting estimated signals are uncorrelated. In this contribution we deepen in the strong separability of CSSA and compare it to SSA by means of a detailed example. Finally, we apply CSSA to UK and US quarterly GDP to check that it produces reliable cycle estimators and strong separable components. We also test the absence of any seasonality in the seasonally adjusted time series estimated by CSSA.