Circulant singular spectrum analysis to monitor the state of the economy in real time
Entidad
UAM. Departamento de Análisis Económico: Economía CuantitativaEditor
MDPI, Basel, SwitzerlandFecha de edición
2021-05-22Cita
10.3390/math9111169
Mathematics 9.11 (2021): 1169
ISSN
2227-7390DOI
10.3390/math9111169Financiado por
This research was funded by the Spanish Ministerio de Ciencia e Innovación, grant numbers PID2019-107161GB-C32 and PID2019-108079GB-C22/AIE/10.13039/501100011033.Proyecto
Gobierno de España. PI18/01366PID2019-107161GB-C32; Gobierno de España. PID2019-108079GB-C22/AIE/10.13039/501100011033.Versión del editor
https://doi.org/10.3390/math9111169Materias
ARIMA; Business cycle; CiSSA; Revision; EconomíaDerechos
© 2021 The authorsResumen
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.
Lista de ficheros
Google Scholar:Bógalo, Juan
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Poncela Blanco, Pilar
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Senra, Eva
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