Circulant Singular Spectrum Analysis to monitor the state of the economy in real time.
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60256DOI: 10.3390/math9111169
ISSN: 2227-7390
Date
2021-05-22Affiliation
Universidad de Alcalá. Departamento de EconomíaFunders
Ministerio de Ciencia e Innovación
Bibliographic citation
Mathematics, 2021, v. 9, n. 11
Keywords
ARIMA
Business cycle
CiSSA
Revision
Project
info:eu-repo/grantAgreement/MICINN//PID2019-107161GB-C32
info:eu-repo/grantAgreement/MICINN//PID2019-108079GB-C22
info:eu-repo/grantAgreement/MICINN/AIE/10.13039/501100011033
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Access rights
info:eu-repo/semantics/openAccess
Abstract
Real-time monitoring of the economy is based on activity indicators that show regular patterns such as trends, seasonality and business cycles. However, parametric and non-parametric methods for signal extraction produce revisions at the end of the sample, and the arrival of new data makes it difficult to assess the state of the economy. In this paper, we compare two signal extraction procedures: Circulant Singular Spectral Analysis, CiSSA, a non-parametric technique in which we can extract components associated with desired frequencies, and a parametric method based on ARIMA modelling. Through a set of simulations, we show that the magnitude of the revisions produced by CiSSA converges to zero quicker, and it is smaller than that of the alternative procedure.
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