SSE/EFI Working Paper Series in Economics and Finance
No 724:
Adaptive hybrid Metropolis-Hastings samplers for DSGE models
Ingvar Strid ()
, Paolo Giordani ()
and Robert Kohn ()
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; (follow links to similar papers)
JEL-Codes: C11; C63; (follow links to similar papers)
33 pages, February 14, 2010
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