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Likelihood-Based Inference in Nonlinear Error-Correction Models

  • Dennis Kristensen
  • Anders Rahbek


    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

We consider a class of vector nonlinear error correction models where the transfer function (or loadings) of the stationary relation- ships is nonlinear. This includes in particular the smooth transition models. A general representation theorem is given which establishes the dynamic properties of the process in terms of stochastic and deter- ministic trends as well as stationary components. In particular, the behaviour of the cointegrating relations is described in terms of geo- metric ergodicity. Despite the fact that no deterministic terms are included, the process will have both stochastic trends and a linear trend in general. Gaussian likelihood-based estimators are considered for the long- run cointegration parameters, and the short-run parameters. Asymp- totic theory is provided for these and it is discussed to what extend asymptotic normality and mixed normaity can be found. A simulation study reveals that cointegration vectors and the shape of the adjust- ment are quite accurately estimated by maximum likelihood, while at the same time there is very little information about some of the individual parameters entering the adjustment function.

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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2007-38.

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Length: 44
Date of creation: 19 Nov 2007
Date of revision:
Handle: RePEc:aah:create:2007-38
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