Variance reduction in MCMC

Mira Antonietta (), Tenconi Paolo () and Bressanini Dario ()
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Mira Antonietta: Department of Economics, University of Insubria, Italy
Tenconi Paolo: University of Switzerland
Bressanini Dario: University of Insubria, Italy

Economics and Quantitative Methods from Department of Economics, University of Insubria

Abstract: We propose a general purpose variance reduction technique for MCMC estimators. The idea is obtained by combining standard variance reduction principles known for regular Monte Carlo simulations (Ripley, 1987) and the Zero-Variance principle introduced in the physics literature (Assaraf and Caffarel, 1999). The potential of the new idea is illustrated with some toy examples and an application to Bayesian estimation

Keywords: Markov chain Monte carlo; Metropolis-Hastings algorithm; Variance reduction; Zero-Variance principle (search for similar items in EconPapers)
Pages: 18 pages
Date: 2003-09
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:ins:quaeco:qf0310

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