Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56330
Authors: 
Strid, Ingvar
Giordani, Paolo
Kohn, Robert
Year of Publication: 
2010
Series/Report no.: 
SSE/EFI Working Paper Series in Economics and Finance 724
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.
Subjects: 
Markov Chain Monte Carlo (MCMC)
Adaptive Metropolis-Hastings
Parallel algorithm
DSGE model
Copula
JEL: 
C11
C63
Document Type: 
Working Paper

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