Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stochastic volatility processes. We show that conventional MCMC algorithms for this class of models are ineffective, but that the problem can be alleviated by reparameterizing the model. Instead of sampli...
| Autors principals: | , |
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| Format: | Journal article |
| Idioma: | English |
| Publicat: |
Taylor and Francis
2006
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| Matèries: |
Search Result 1
Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form.
Publicat 2006
Journal article
Search Result 2
Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form.
Publicat 2004
Working paper