Quotation Frühwirth-Schnatter, Sylvia. 2007. Stochastic Model Specification Search for Gaussian and Non-Gaussian State Space Models. BISP5 - Fifth Workshop on Bayesian Inference in Stochastic Processes, Valencia, Spanien, 14.06.-16.06.


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Abstract

State space models are a widely used tool in time series analysis to deal with processes which gradually change over time. Whereas estimation of these models is studied by many authors, model selection is somewhat neglected, the main reason being that this issue leads in general to a non-regular statistical testing problem. For practical application, however, it seems important to test if the components in a state space model are actually dynamic or not. The main strategy is usually to compute the marginal likelihood for each model under investigation and to choose the model with the largest likelihood. In this talk, the application of model space MCMC methods will be suggested to deal with state space models under model uncertainty. This model uncertainty may concern the issue whether a certain component, like a dynamic trend, should be added to the model, and whether this component is static or dynamic. It will be shown, how a Bayesian variable selection approach can be implemented which simultaneously allows adding and deleting components and choosing between static and dynamic components. This approach will be applied both to Gaussian linear state space models as well as to non-Gaussian state space models based on the Poisson distribution and to binary and multinomial state space models.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title Stochastic Model Specification Search for Gaussian and Non-Gaussian State Space Models
Event BISP5 - Fifth Workshop on Bayesian Inference in Stochastic Processes
Year 2007
Date 14.06.-16.06
Country Spain
Location Valencia
URL http://www.uv.es/bisp5/abstracts-posters.html

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Frühwirth-Schnatter, Sylvia (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
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