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The marginal likelihood of Structural Time Series Models, with application to the euro area and US NAIRU

Author

Listed:
  • 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 methodology to the analysis of US and euro area NAIRU.

Suggested Citation

  • Christophe Planas & Alessandro Rossi & Gabriele Fiorentini, 2008. "The marginal likelihood of Structural Time Series Models, with application to the euro area and US NAIRU," Working Paper series 21_08, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:21_08
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    References listed on IDEAS

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    Cited by:

    1. Lennart Hoogerheide & Richard Kleijn & Francesco Ravazzolo & Herman K. Van Dijk & Marno Verbeek, 2010. "Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 251-269.
    2. 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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