Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time

Juan Bógalo, Pilar Poncela and Eva Senra
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Juan Bógalo: Departamento de Análisis Económico, Economía Cuantitativa, Facultad de Económicas, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Eva Senra: Departamento de Economía, Facultad C.C. Economía, Empresariales y Turismo, Universidad de Alcalá, 28802 Alcalá de Henares, Spain

Mathematics, 2021, vol. 9, issue 11, 1-17

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.

Keywords: ARIMA; business cycle; CiSSA; revision (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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