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Bayesian stochastic model specification search for seasonal and calendar effects

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  • Tommaso, Proietti
  • Stefano, Grassi

Abstract

We apply 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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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 27305.

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Date of creation: 2010
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Handle: RePEc:pra:mprapa:27305

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Keywords: Seasonality; Structural time series models; Variable selection.;

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  1. Gary Koop & Herman K. van Dijk, 1999. "Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach," Tinbergen Institute Discussion Papers 99-072/4, Tinbergen Institute.
  2. 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.
  3. Hylleberg, S. & Pagan, A.R., 1995. "Seasonal Integration and the Evolving Seasonals Model," Papers 281, Australian National University - Department of Economics.
  4. Chris M Strickland & Gael Martin & Catherine S Forbes, 2006. "Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models," Monash Econometrics and Business Statistics Working Papers 22/06, Monash University, Department of Econometrics and Business Statistics.
  5. Dagum, Estela Bee & Quenneville, Benoit, 1993. "Dynamic linear models for time series components," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 333-351.
  6. Arnold Zellner, 1979. "Seasonal Analysis of Economic Time Series," NBER Books, National Bureau of Economic Research, Inc, number zell79-1, October.
  7. 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.
  8. Busetti, Fabio & Harvey, Andrew, 2003. "Seasonality Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 420-36, July.
  9. 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.
  10. Ghysels,Eric & Osborn,Denise R., 2001. "The Econometric Analysis of Seasonal Time Series," Cambridge Books, Cambridge University Press, number 9780521562607, November.
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Cited by:
  1. Rolando Gonzales Martinez, 2012. "Baysian seasonal analysis with robust priors," Investigación & Desarrollo 0312, Universidad Privada Boliviana, revised Jan 2012.

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