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Structural Change in (Economic) Time Series

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Abstract

Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. Several recent methods for dating structural changes are compared using a time series of oil prices spanning more than 60 years. The methods broadly agree for the first part of the series up to the mid-1980s, for which changes are associated with major historical events, but provide somewhat different solutions thereafter, reflecting a gradual increase in oil prices that is not well described by a step function. As a further illustration, 1990s data on the volatility of the Hang Seng stock market index are reanalyzed.

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Authors and Affiliations

  1. Faculty of Business and Economics, University Basel, Peter Merian-Weg 6, 4002, Basel, Switzerland

    Christian Kleiber

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  1. Christian Kleiber
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Correspondence to Christian Kleiber .

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Editors and Affiliations

  1. Institute of Experimental Physics, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

    Stefan C. Müller

  2. Department of Physical Chemistry, Fritz-Haber-Institute of the MPG, Berlin, Germany

    Peter J. Plath

  3. Institute of Physics, Complex Systems, Technical University Chemnitz, Chemnitz, Germany

    Günter Radons

  4. Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida, USA

    Armin Fuchs

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Kleiber, C. (2018). Structural Change in (Economic) Time Series. In: Müller, S., Plath, P., Radons, G., Fuchs, A. (eds) Complexity and Synergetics. Springer, Cham. https://doi.org/10.1007/978-3-319-64334-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-64334-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64333-5

  • Online ISBN: 978-3-319-64334-2

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