BeschreibungState space models are widely used in time series analysis to deal with processes which gradually change over time. Model specification, however, is a difficult task as one has to decide first, which components to include into the model, and second, whether these components are fixed or stochastic.
Using a Bayesian approach, one could determine the posterior probabilities of each model separately, which requires estimation of the marginal likelihood for each model by some numerical method. A modern approach to Bayesian model selection is to apply some model space MCMC methods by sampling jointly model indicators and parameters as is done, e.g., in the stochastic variable selection approach for regression models.
In this talk we discuss model space MCMC for state space models. To this aim, we rewrite the state space model in a non-centered version and extend the stochastic variable selection approach to state space models. This allows to choose appropriate components and to decide, if these components are deterministic or stochastic. Details will be provided for time-varying parameter models and unobserved component time series models. The method is extended to non-Gaussian state space models for binary, multinomial or count data, where we make use of auxiliary mixture sampling.
|18 Juni 2009 → 20 Juni 2009
|BISP6, Sixth Workshop on Bayesian Inference in Stochastic Processes