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Some statistical aspects of methods for detection of turning points in business cycles

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  • E. Andersson
  • D. Bock
  • M. Frisen

Abstract

Methods for online turning point detection in business cycles are discussed. The statistical properties of three likelihood-based methods are compared. One is based on a Hidden Markov Model, another includes a non-parametric estimation procedure and the third combines features of the other two. The methods are illustrated by monitoring a period of the Swedish industrial production. Evaluation measures that reflect timeliness are used. The effects of smoothing, seasonal variation, autoregression and multivariate issues on methods for timely detection are discussed.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500445517
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

Volume (Year): 33 (2006)
Issue (Month): 3 ()
Pages: 257-278

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Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:257-278

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Related research

Keywords: Monitoring; surveillance; early warning system; regime switching;

References

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