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

Charles Bos and Neil Shephard ()

No 04-015/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This discussion paper led to a publication in 'Econometric Reviews' , 2006, 25(2-3), 219-244.

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.

Keywords: Markov chain Monte Carlo; particle filter; cubic spline; state space form; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C15 C32 C51 F31 (search for similar items in EconPapers)
Date: 2004-01-27
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form (2006)
Working Paper: Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20040015

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