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

Juan Bógalo,Eva Senra,Pilar Poncela

doi:10.3390/math9111169

Juan Bógalo, Eva Senra + Show 1 more

Open Access

https://doi.org/10.3390/math9111169

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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.

Highlights

Real-time monitoring of the economy is key to assess the state of the business cycle [1].business cycle signals are subject to revisions, and this might condition the real-time decisions taken by economic policy authorities [2]
Business cycle signals are subject to revisions, and this might condition the real-time decisions taken by economic policy authorities [2]
We study the evolution of the revisions, period by period, until reaching the final estimate, with the objective of analyzing the impact of any economic event on the posterior estimation of the business cycle

Summary

Introduction

Business cycle signals are subject to revisions, and this might condition the real-time decisions taken by economic policy authorities [2]. This has motivated the central banks’ interest in revisions, for instance, as shown by the Bank of England [3], the European Central Bank [4] or the Federal Reserve [5]. As [8] points out, institutions are reluctant to enact large data revisions, and analysts value signal extraction methods that necessitate few revisions. There are two causes for revising the estimates of the extracted signals: to adjust to the new information incorporated in “old”, already published data (of periods s < t), or to update the estimates of the unobserved signal due to the appearance of new data in the following periods, s > t

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Results

Conclusion