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Introducing Non-Linearity Into Cointegration

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  • Granger, Clive W. J.

Abstract

A wide class of processes is considered in which initially a persistent series and a transient series are generated separately, as unobserved components, and from them a pair of observable series are generated as a weighted combination of the components. It follows that the observable series will be persistent but have a linear combination that is transient, aud so can be considered cointegrated. From the construction, the series need not be I(1) and other linear aspects of standard theory is removable, but often error-correction models are available. Some theoretical examples are provided.

Suggested Citation

  • Granger, Clive W. J., 1996. "Introducing Non-Linearity Into Cointegration," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 16(2), November.
    • Handle: RePEc:sbe:breart:v:16:y:1996:i:2:a:2874
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    1. Corradi, Valentina & Swanson, Norman R. & White, Halbert, 2000. "Testing for stationarity-ergodicity and for comovements between nonlinear discrete time Markov processes," Journal of Econometrics, Elsevier, vol. 96(1), pages 39-73, May.
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    5. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, Decembrie.
    6. Escribano, A. & , ., 1987. "Co-integration, time co-trends and error-correction systems: an alternative approach," LIDAM Discussion Papers CORE 1987015, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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