Corpus ID: 88515107

Flexible Density Tempering Approaches for State Space Models with an Application to Factor Stochastic Volatility Models

@article{Gunawan2018FlexibleDT,
  title={Flexible Density Tempering Approaches for State Space Models with an Application to Factor Stochastic Volatility Models},
  author={David Gunawan and Robert Kohn and Chris Carter and Minh-Ngoc Tran},
  journal={arXiv: Methodology},
  year={2018}
}
Duan (2015) propose a tempering or annealing approach to Bayesian inference for time series state space models. In such models the likelihood is often analytically and computationally intractable. Their approach generalizes the annealed importance sampling (AIS) approach of Neal (2001) and DelMoral (2006) when the likelihood can be computed analytically. Annealing is a sequential Monte Carlo approach that moves a collection of parameters and latent state variables through a number of levels… Expand

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References

SHOWING 1-10 OF 32 REFERENCES
Flexible Particle Markov chain Monte Carlo methods with an application to a factor stochastic volatility model
TLDR
This paper shows how the three ways of generating variables mentioned above can be combined in a flexible manner to give sampling schemes that converge to a desired target distribution and investigates the properties of this flexible sampling scheme. Expand
Density-Tempered Marginalized Sequential Monte Carlo Samplers
We propose a density-tempered marginalized sequential Monte Carlo (SMC) sampler, a new class of samplers for full Bayesian inference of general state-space models. The dynamic states areExpand
A flexible particle Markov chain Monte Carlo method
TLDR
This paper derives some convergence properties of the sampling scheme and investigates its performance empirically by applying it to univariate and multivariate stochastic volatility models and comparing it to other PMCMC methods proposed in the literature. Expand
Analysis of high dimensional multivariate stochastic volatility models
This paper is concerned with the fitting and comparison of high dimensional multivariate time series models with time varying correlations. The models considered here combine features of theExpand
Annealed importance sampling
  • R. Neal
  • Mathematics, Physics
  • Stat. Comput.
  • 2001
TLDR
It is shown how one can use the Markov chain transitions for such an annealing sequence to define an importance sampler, which can be seen as a generalization of a recently-proposed variant of sequential importance sampling. Expand
On general sampling schemes for Particle Markov chain Monte Carlo methods
TLDR
A general approach to constructing sampling schemes that converge to the target distributions given in Andrieu et al. Expand
Rao-Blackwellization of Particle Markov Chain Monte Carlo Methods Using Forward Filtering Backward Sampling
  • J. Olsson, T. Rydén
  • Mathematics, Computer Science
  • IEEE Transactions on Signal Processing
  • 2011
TLDR
It is shown that by simulating multiple realizations of the particle filter and adding a Metropolis-Hastings step, one obtains a Markov chain Monte Carlo scheme whose stationary distribution is the exact smoothing distribution. Expand
Stochastic Volatility: Likelihood Inference And Comparison With Arch Models
In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effectiveExpand
Particle gibbs with ancestor sampling
TLDR
PGAS provides the data analyst with an off-the-shelf class of Markov kernels that can be used to simulate, for instance, the typically high-dimensional and highly autocorrelated state trajectory in a state-space model. Expand
Marginal Likelihood From the Metropolis–Hastings Output
This article provides a framework for estimating the marginal likelihood for the purpose of Bayesian model comparisons. The approach extends and completes the method presented in Chib (1995) byExpand
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