Journal article

Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form.

Abstract:

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 sampling the unobserved variance series directly, we sample in the space of the disturbances, which proves to lower correlation in the sampler and thus increases the quality of the Markov chain. Using ou...

Authors

Journal:
Econometric Reviews
Volume:
25
Publication date:
2006-01-01
ISSN:
0747-4938
Language:
English
UUID:
Local pid:
oai:economics.ouls.ox.ac.uk:10404
Deposit date:
2011-08-16