Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86469
Authors: 
Bos, Charles S.
Shephard, Neil
Year of Publication: 
2004
Series/Report no.: 
Tinbergen Institute Discussion Paper 04-015/4
Abstract: 
In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by reparameterising the model. We illustrate our results on an example from financial economics and one from the nonparametric regression model. We also develop an effective particle filter for this model which is useful to assess the fit of the model.
Subjects: 
Markov chain Monte Carlo
particle filter
cubic spline
state space form
stochastic volatility
JEL: 
C15
C32
C51
F31
Document Type: 
Working Paper

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