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A Markov Chain Quasi-Monte Carlo Method for Bayesian Estimation of Stochastic Volatility Model
Fung, Eric - 2012
In this paper, we propose a Markov Chain Quasi-Monte Carlo (MCQMC) approach for Bayesian estimation of a discrete-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under the conventional Bayesian estimation method for SV...
Persistent link: https://www.econbiz.de/10013116422
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Improving MCMC Using Efficient Importance Sampling
Liesenfeld, Roman; Richard, Jean-Francois - 2006
This paper develops a systematic Markov Chain Monte Carlo (MCMC) framework based upon Efficient Importance Sampling … solution. EIS is a simple, generic and yet accurate Monte-Carlo integration procedure based on sampling densities which are … MCMC components such as auxiliary sampling densities, normalizing constants and starting values. The potential of this …
Persistent link: https://www.econbiz.de/10014058202
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Generalized exogenous processes in DSGE : a Bayesian approach
Meyer-Gohde, Alexander; Neuhoff, Daniel - 2015
The Reversible Jump Markov Chain Monte Carlo (RJMCMC) method can enhance Bayesian DSGE estimation by sampling from a …
Persistent link: https://www.econbiz.de/10010503919
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Generalized exogenous processes in DSGE : a Bayesian approach
Meyer-Gohde, Alexander; Neuhoff, Daniel - 2018 - This version: September 24, 2018
models. Sampling from noninvertibleMA representations, a negative response of hours to a positive technology shock is …
Persistent link: https://www.econbiz.de/10011901706
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Discrete-time stochastic volatility models and MCMC-based statistical inference
Hautsch, Nikolaus (contributor); Ou, Yangguoyi (contributor) - 2008
In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical...
Persistent link: https://www.econbiz.de/10003770817
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Markov Chain Monte Carlo Models, Gibbs Sampling, & Metropolis Algorithm for High-Dimensionality Complex Stochastic Problems
Malhotra, Yogesh - 2015
Algorithms, Gibbs Sampling and Metropolis-Hastings Algorithm. Network and security risk management application focus is on how …
Persistent link: https://www.econbiz.de/10013029835
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The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models
Grothe, Oliver - 2017
Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the … generic and easily implementable SMC approach known as Particle Efficient Importance Sampling (PEIS). By using SMC importance … sampling densities which are approximately fully globally adapted to the targeted density of the states, PEIS can substantially …
Persistent link: https://www.econbiz.de/10012970355
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Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models
Barra, Istvan - 2016
these proposal densities are used in an independent Metropolis-Hastings algorithm or in importance sampling. Our method …
Persistent link: https://www.econbiz.de/10013005987
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Joint independent metropolis-hastings methods for nonlinear non-Gaussian state space models
Barra, Istvan; Hoogerheide, Lennart; Koopman, Siem Jan; … - 2013
Persistent link: https://www.econbiz.de/10010191411
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Bayesian Analysis of a Threshold Stochastic Volatility Model
Wirjanto, Tony S. - 2013
This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution...
Persistent link: https://www.econbiz.de/10013084224