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Bayesian stochastic model specification search for seasonal and calendar effects

Author

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  • Stefano Grassi

    (Aarhus University and CREATES)

  • Tommaso Proietti

    (Università di Roma “Tor Vergata”)

Abstract

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.

Suggested Citation

  • Stefano Grassi & Tommaso Proietti, 2011. "Bayesian stochastic model specification search for seasonal and calendar effects," CREATES Research Papers 2011-08, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2011-08
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    References listed on IDEAS

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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.
    2. Grassi, S. & Proietti, T., 2014. "Characterising economic trends by Bayesian stochastic model specification search," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 359-374.
    3. Tommaso Proietti & Stefano Grassi, 2015. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," Empirical Economics, Springer, vol. 48(3), pages 983-1011, May.
    4. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.

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    More about this item

    Keywords

    Seasonality; Structural time series models; Variable selection; Bayesian Estimation.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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