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Title:Efficient high-dimensional importance sampling in mixture frameworks PDF Logo
Authors:Kleppe, Tore Selland
Liesenfeld, Roman
Issue Date:2011
Series/Report no.:Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2011,11
Abstract:This paper provides high-dimensional and flexible importance sampling procedures for the likelihood evaluation of dynamic latent variable models involving finite or infinite mixtures leading to possibly heavy tailed and/or multi-modal target densities. Our approach is based upon the efficient importance sampling (EIS) approach of Richard and Zhang (2007) and exploits the mixture structure of the model when constructing importance sampling distributions as mixture of distributions. The proposed mixture EIS procedures are illustrated with ML estimation of a student-t state space model for realized volatilities and a stochastic volatility model with leverage effects and jumps for asset returns.
Subjects:dynamic latent variable model
importance sampling
marginalized likelihood
Monte Carlo
realized volatility
stochastic volatility
Document Type:Working Paper
Appears in Collections:Economics Working Papers, Department of Economics, CAU Kiel

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