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Joint Bayesian analysis of parameters and states in nonlinear, non-Gaussian state space models

Barra, István; Hoogerheide, Lennart; Koopman, Siem Jan; … - 2014

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent...

Persistent link: https://www.econbiz.de/10010399681

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

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

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

Bayesian modelling of VAR precision matrices using stochastic block networks

Huber, Florian; Koop, Gary; Marcellino, Massimiliano; … - 2024

Commonly used priors for Vector Autoregressions (VARs) induce shrinkage on the autoregressive coefficients. Introducing shrinkage on the error covariance matrix is sometimes done but, in the vast majority of cases, without considering the network structure of the shocks and by placing the prior...

Persistent link: https://www.econbiz.de/10015395756

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

Bayesian Inference of Asymmetric Stochastic Conditional Duration Models

Men, Zhongxian - 2015

This paper extends a stochastic conditional duration (SCD) model for financial transaction data to allow for correlation between error processes or innovations of observed duration process and latent log duration process with the aim of improving the statistical fit of the model. Suitable...

Persistent link: https://www.econbiz.de/10013035789

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

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

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