Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87411
Authors: 
Barra, Istvan
Hoogerheide, Lennart
Koopman, Siem Jan
Lucas, Andre
Year of Publication: 
2012
Series/Report no.: 
Tinbergen Institute Discussion Paper 13-050/III
Abstract: 
We propose a new methodology for the Bayesian analysis of nonlinear non-Gaussian state space models with a Gaussian time-varying signal, where the signal is a function of a possibly high-dimensional state vector. The novelty of our approach is the development of proposal densities for the joint posterior density of parameter and state vectors: a mixture of Student's t-densities as the marginal proposal density for the parameter vector, and a Gaussian density as the conditional proposal density for the signal given the parameter vector. We argue that a highly efficient procedure emerges when these proposal densities are used in an independent Metropolis-Hastings algorithm. A particular feature of our approach is that smoothed estimates of the states and an estimate of the marginal likelihood are obtained directly as an output of the algorithm. Our methods are computationally efficient and produce more accurate estimates when compared to recently proposed alternativ es. We present extensive simulation evidence for stochastic volatility and stochastic intensity models. For our empirical study, we analyse the performance of our method for stock return data and corporate default panel data.
Subjects: 
nonlinear non-Gaussian state space model
Bayesian inference
Monte Carlo estimation
Metropolis-Hastings algorithm
mixture of Student's t-distributions
JEL: 
C11
C15
C22
C32
C58
Document Type: 
Working Paper

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