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Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates
Barde, Sylvain - 2022
Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their … parameters from empirical data. Most simulation models do not possess closed form expressions for their likelihood function …, requiring the use of simulation-based inference, such as simulated method of moments, indirect inference or approximate Bayesian …
Persistent link: https://www.econbiz.de/10013439970
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Simulation-Based Likelihood Inference for Limited Dependent Processes
Manrique, Aurora; Shephard, Neil G. - 1999
simulation to carry out either classical inference through a simulated score method (simulated EM algorithm) or Bayesian analysis …. The central tools we use to deal with the time series dimension of the models are the scan sampler and the simulation …
Persistent link: https://www.econbiz.de/10014197180
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Stochastic household forecasts by coherent random shares predictions
Keilman, Nico; Duin, Coen van - 2014 - This version 14 April 2014
We compute a stochastic household forecast for the Netherlands by the random share method. Time series of shares of persons in nine household positions, broken down by sex and five-year age group for the years 1996-2010 are modelled by means of the Hyndman-Booth-Yasmeen product-ratio variant of...
Persistent link: https://www.econbiz.de/10010354141
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Bayesian Estimation of the Multi-Factor Heston Stochastic Volatility Model
Fruhwirth-Schnatter, Sylvia; Sögner, Leopold - 2014
The goal of this article is an exact Bayesian analysis of the Heston (1993) stochastic volatility model. We carefully study the effect different parameterizations of the latent volatility process and the parameters of the volatility process have on the convergence and the mixing behavior of the...
Persistent link: https://www.econbiz.de/10014221761
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Novel techniques for Bayesian inference in univariate and multivariate stochastic volatility models
Tsionas, Mike G. - 2022
In this paper we exploit properties of the likelihood function of the stochastic volatility model to show that it can be approximated accurately and efficiently using a response surface methodology. The approximation is across the plausible range of parameter values and all possible data and is...
Persistent link: https://www.econbiz.de/10014084542
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The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models
Grothe, Oliver - 2017
We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian state-space models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the Gibbs procedure to update the latent and potentially...
Persistent link: https://www.econbiz.de/10012970355
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Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods
Fičura, Milan - 2015
We are comparing two approaches for stochastic volatility and jumps estimation in the EUR/USD time series - the non-parametric power-variation approach using high-frequency returns, and the parametric Bayesian approach (MCMC estimation of SVJD models) using daily returns. We find that both of...
Persistent link: https://www.econbiz.de/10013030080
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Bayesian Estimation of an Extended Local Scale Stochastic Volatility Model
Deschamps, Philippe J. - 2011
Bayesian posterior simulator and a new simulation smoother are presented. The model is applied to publicly available daily …
Persistent link: https://www.econbiz.de/10013120871
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Bayesian Student-T Stochastic Volatility Models Via Scale Mixtures
Choy, Boris - 2009
The normal error distribution for the observations and log-volatilities in a stochastic volatility (SV) model is replaced by the Student-t distribution for robustness consideration. The model is then called the t-t SV model throughout this paper. The objectives of the paper are two-fold....
Persistent link: https://www.econbiz.de/10013156986
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Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion
Chan, Joshua - 2014
The deviance information criterion (DIC) has been widely used for Bayesian model comparison. In particular, a popular metric for comparing stochastic volatility models is the DIC based on the conditional likelihood — obtained by conditioning on the latent variables. However, some recent...
Persistent link: https://www.econbiz.de/10013051070