Adaptive hybrid Metropolis-Hastings samplers for DSGE models

Ingvar Strid (), Paolo Giordani () and Robert Kohn ()

No 724, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

Abstract: Bayesian inference for DSGE models is typically carried out by single block random walk Metropolis, involving very high computing costs. This paper combines two features, adaptive independent Metropolis-Hastings and parallelisation, to achieve large computational gains in DSGE model estimation. The history of the draws is used to continuously improve a t-copula proposal distribution, and an adaptive random walk step is inserted at predetermined intervals to escape difficult points. In linear estimation applications to a medium scale (23 parameters) and a large scale (51 parameters) DSGE model, the computing time per independent draw is reduced by 85% and 65-75% respectively. In a stylised nonlinear estimation example (13 parameters) the reduction is 80%. The sampler is also better suited to parallelisation than random walk Metropolis or blocking strategies, so that the effective computational gains, i.e. the reduction in wall-clock time per independent equivalent draw, can potentially be much larger.

Keywords: Markov Chain Monte Carlo (MCMC); Adaptive Metropolis-Hastings; Parallel algorithm; DSGE model; Copula (search for similar items in EconPapers)
JEL-codes: C11 C63 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2010-02-14
New Economics Papers: this item is included in nep-cba, nep-cmp, nep-dge and nep-ecm
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:hastef:0724

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