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The marginal likelihood of Structural Time Series Models, with application to the euroareaa nd US NAIRU

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Author Info

  • Christophe Planas

    ()
    (Joint Research Centre of the European Commission)

  • Alessandro Rossi

    ()
    (Joint Research Centre of the European Commission)

  • Gabriele Fiorentini

    ()
    (University of Florence, Italy and The Rimini Centre for Economic Analysis, Italy)

Abstract

We propose a simple procedure for evaluating the marginal likelihood in univariate Structural Time Series (STS) models. For this we exploit the statistical properties of STS models and the results in Dickey (1968) to obtain the likelihood function marginally to the variance parameters. This strategy applies under normal-inverted gamma-2 prior distributions for the structural shocks and associated variances. For trend plus noise models such as the local level and the local linear trend, it yields the marginal likelihood by simple or double integration over the (0,1)-support. For trend plus cycle models, we show that marginalizing out the variance parameters greatly improves the accuracy of the Laplace method. We apply this ethodology to the analysis of US and euro area NAIRU.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 21-08.

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Date of creation: Jan 2008
Date of revision: Jan 2008
Handle: RePEc:rim:rimwps:21-08

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Keywords: Marginal likelihood; Markov Chain Monte Carlo; unobserved components; bridge sampling; Laplace method; NAIRU;

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References

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Cited by:
  1. Philippe Moës, 2012. "Multivariate models with dual cycles: implications for output gap and potential growth measurement," Empirical Economics, Springer, vol. 42(3), pages 791-818, June.

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