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Zero variance in Markov chain Monte Carlo with an application to credit risk estimation

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Author Info
Tenconi Paolo () (Department of Economics, University of Insubria, Italy)
Abstract

We propose a general purpose variance reduction technique for Markov Chain Monte Carlo estimators based on the Zero-Variance principle introduced in the physics lit- erature by Assaraf and Ca arel ( 1999). The potential of the new idea is illustrated with some toy examples and a real application to Bayesian inference for credit risk estimation.

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File URL: http://eco.uninsubria.it/dipeco/Quaderni/files/QF2008_4.pdf
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Publisher Info
Paper provided by Department of Economics, University of Insubria in its series Economics and Quantitative Methods with number qf0803.

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Length: 23 pages
Date of creation: Apr 2008
Date of revision:
Handle: RePEc:ins:quaeco:qf0803

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Related research
Keywords: Markov chain Monte Carlo; Metropolis-Hastings algorithm; Variance re- duction; Zero-Variance principle.;

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