Generic Latent Variable Sampling
21 Pages Posted: 10 Feb 2013
Date Written: February 10, 2013
Abstract
We propose a generic latent variable approximation (GLVS) scheme for posterior inference in models with possibly high-dimensional spaces of latent variables. Direct sampling from the approximate posterior is possible. The new technique is applied to univariate and multivariate stochastic volatility models. The method is illustrated in both articial and real data.
Keywords: Bayesian analysis, Markov Chain Monte Carlo
JEL Classification: C11, C13
Suggested Citation: Suggested Citation
Tsionas, Efthymios (Efthymios) G., Generic Latent Variable Sampling (February 10, 2013). Available at SSRN: https://ssrn.com/abstract=2214579 or http://dx.doi.org/10.2139/ssrn.2214579
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