Markov Chain Monte Carlo

Michael Johannes () and Nicholas Polson ()
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Michael Johannes: Columbia University, Graduate School of Business
Nicholas Polson: University of Chicago, Graduate School of Business,

Chapter 43 in Handbook of Financial Time Series, 2009, pp 1001-1013 from Springer

Abstract: Abstract This chapter provides an overview of Markov Chain Monte Carlo (MCMC) methods. MCMC methods provide samples from high-dimensional distributions that commonly arise in Bayesian inference problems. We review the theoretical underpinnings used to construct the algorithms, the Metropolis-Hastings algorithm, the Gibbs sampler, Markov Chain convergence, and provide a number of examples in financial econometrics.

Keywords: Posterior Distribution; Markov Chain Monte Carlo; Gibbs Sampler; Hybrid Algorithm; Stochastic Volatility (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-71297-8_43

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DOI: 10.1007/978-3-540-71297-8_43

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