Bayesian stochastic model specification search for seasonal and calendar effects
AbstractWe 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 InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 27305.
Date of creation: 2010
Date of revision:
Seasonality; Structural time series models; Variable selection.;
Other versions of this item:
- Stefano Grassi & Tommaso Proietti, 2011. "Bayesian stochastic model specification search for seasonal and calendar effects," CREATES Research Papers 2011-08, School of Economics and Management, University of Aarhus.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-12-18 (All new papers)
- NEP-ECM-2010-12-18 (Econometrics)
- NEP-ORE-2010-12-18 (Operations Research)
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