Stochastic model specification search for Gaussian and partial non-Gaussian state space models
AbstractModel specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time-varying. To this aim a new model space MCMC method is developed in this paper. It is based on extending the Bayesian variable selection approach which is usually applied to variable selection in regression models to state space models. For non-Gaussian state space models stochastic model search MCMC makes use of auxiliary mixture sampling. We focus on structural time series models including seasonal components, trend or intervention. The method is applied to various well-known time series.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 154 (2010)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/locate/jeconom
Auxiliary mixture sampling Bayesian econometrics Non-centered parameterization Markov chain Monte Carlo Variable selection;
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