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Introduction to Modern Time Series Analysis pp 199–239Cite as

Cointegration

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Abstract

In the preceding chapter, we used stochastic trends to model nonstationary behaviour of time series, i.e. the variance of the data generating process increases over time, the series exhibits persistent behaviour and its first difference is stationary. For many economic time series, such a data generating process is a sufficient approximation, so that in the following we only consider processes which are integrated of order one (I(1)).

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Authors and Affiliations

  1. SIAW-HSG, University of St. Gallen, Bodanstrasse 8, CH-9000, St. Gallen, Switzerland

    Professor Dr. Gebhard Kirchgässner

  2. Institute for Statistics and Econometrics, Freie Universität Berlin, Boltzmannstraße 20, 14195, Berlin, Germany

    Professor Dr. Jürgen Wolters

Authors
  1. Professor Dr. Gebhard Kirchgässner
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  2. Professor Dr. Jürgen Wolters
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Kirchgässner, G., Wolters, J. (2007). Cointegration. In: Introduction to Modern Time Series Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73291-4_6

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