Strong separability in Circulant SSA
Authors
Senra Díaz, EvaIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/60534DOI: 10.1007/978-3-319-96941-1_20
ISBN: 9783319969404
Publisher
Springer
Date
2018-07-01Affiliation
Universidad de Alcalá. Departamento de EconomíaBibliographic citation
Nonparametric Statistics: 3rd ISNPS, Avignon, France, June 2016 3, 2018, Springer, 2018, p. 15-29
Keywords
Business cycle
Circulant matrices
GDP
Seasonal adjust-ment
Singular spectrum analysis
Separability
Document type
info:eu-repo/semantics/bookPart
Version
info:eu-repo/semantics/acceptedVersion
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Access rights
info:eu-repo/semantics/openAccess
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 paper 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
Files in this item
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Strong_Serna_Nonparametric_2018.pdf | 1.044Mb |
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Strong_Serna_Nonparametric_2018.pdf | 1.044Mb |
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