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Bayes Estimators of the Cointegration Space

Author

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  • Villani, Mattias

    (Research Department, Central Bank of Sweden)

Abstract

A neglected aspect of the otherwise fairly well developed Bayesian analysis of cointegration is the point estimation of the cointegration space. It is pointed out here that, due to the well known non-identification of the cointegration vectors, the parameter space is not an inner product space and conventional Bayes estimators therefore stand without their usual decision theoretic foundation. We present a Bayes estimator of the cointegration space which takes the curved geometry of the parameter space into account. Contrary to many of the Bayes estimators used in the literature, this estimator is invariant to the ordering of the time series. A dimension invariant overall measure of cointegration space uncertainty is also proposed. A small simulation study shows that the Bayes estimator compares favorably to the maximum likelihood estimator.

Suggested Citation

  • Villani, Mattias, 2003. "Bayes Estimators of the Cointegration Space," Working Paper Series 150, Sveriges Riksbank (Central Bank of Sweden).
  • Handle: RePEc:hhs:rbnkwp:0150
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    References listed on IDEAS

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    1. Kleibergen, Frank & van Dijk, Herman K., 1994. "On the Shape of the Likelihood/Posterior in Cointegration Models," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 514-551, August.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bayesian inference; Cointegration analysis; Estimation; Grassman manifold; Subspaces.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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