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Bayesian stochastic model specification search for seasonal and calendar effects

  • Stefano Grassi

    ()

    (Aarhus University and CREATES)

  • Tommaso Proietti

    ()

    (Università di Roma “Tor Vergata”)

We extend a recent methodology, Bayesian stochastic model specification search (SMSS), for the selection of the unobserved components (level, slope, seasonal cycles, trading days effects) that are stochastically evolving over time. SMSS hinges on two basic ingredients: the non-centered representation of the unobserved components and the reparameterization of the hyperparameters representing standard deviations as regression parameters with unrestricted support. The choice of the prior and the conditional independence structure of the model enable the definition of a very efficient MCMC estimation strategy based on Gibbs sampling. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and deterministic trends, fixed and evolutive seasonal and trading day effects.

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File URL: ftp://ftp.econ.au.dk/creates/rp/11/rp11_08.pdf
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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-08.

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Length: 21
Date of creation: 21 Feb 2011
Date of revision:
Handle: RePEc:aah:create:2011-08
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  1. Strickland, Chris M. & Martin, Gael M. & Forbes, Catherine S., 2008. "Parameterisation and efficient MCMC estimation of non-Gaussian state space models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2911-2930, February.
  2. Busetti, Fabio & Harvey, Andrew, 2003. "Seasonality Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 420-36, July.
  3. Hannan, E J & Terrell, R D & Tuckwell, N E, 1970. "The Seasonal Adjustment of Economic Time Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 11(1), pages 24-52, February.
  4. Hylleberg, S. & Pagan, A.R., 1995. "Seasonal Integration and the Evolving Seasonals Model," Papers 281, Australian National University - Department of Economics.
  5. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2010. "Stochastic model specification search for Gaussian and partial non-Gaussian state space models," Journal of Econometrics, Elsevier, vol. 154(1), pages 85-100, January.
  6. Dagum, Estela Bee & Quenneville, Benoit, 1993. "Dynamic linear models for time series components," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 333-351.
  7. repec:cup:cbooks:9780521565882 is not listed on IDEAS
  8. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
  9. Koop, Gary & Dijk, Herman K. Van, 2000. "Testing for integration using evolving trend and seasonals models: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 97(2), pages 261-291, August.
  10. Arnold Zellner, 1978. "Seasonal Analysis of Economic Time Series," NBER Books, National Bureau of Economic Research, Inc, number zell78-1, August.
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