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...
| 主要な著者: | , |
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| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
Taylor and Francis
2006
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| 主題: |
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