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Cointegration

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Introduction to Modern Time Series Analysis

Part of the book series: Springer Texts in Business and Economics ((STBE))

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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, St. Gallen, Switzerland

    Gebhard Kirchgässner

  2. Institute for Statistics and Econometrics, FU Berlin, Berlin, Germany

    Jürgen Wolters

  3. Applied Econometrics and International Economic Policy, Goethe University Frankfurt, Frankfurt, Germany

    Uwe Hassler

Authors
  1. Gebhard Kirchgässner
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  2. Jürgen Wolters
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  3. Uwe Hassler
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Kirchgässner, G., Wolters, J., Hassler, U. (2013). Cointegration. In: Introduction to Modern Time Series Analysis. Springer Texts in Business and Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33436-8_6

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  • DOI: https://doi.org/10.1007/978-3-642-33436-8_6

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