Bayesian stochastic model specification search for seasonal and calendar effects

Stefano Grassi () and Tommaso Proietti

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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.

Keywords: Seasonality; Structural time series models; Variable selection; Bayesian Estimation. (search for similar items in EconPapers)
JEL-codes: C01 C11 C22 (search for similar items in EconPapers)
Pages: 21
Date: 2011-02-21
New Economics Papers: this item is included in nep-ets and nep-ore
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https://repec.econ.au.dk/repec/creates/rp/11/rp11_08.pdf (application/pdf)

Related works:
Working Paper: Bayesian stochastic model specification search for seasonal and calendar effects (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2011-08

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