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dc.contributor.authorStrid, Ingvaren_US
dc.contributor.authorGiordani, Paoloen_US
dc.contributor.authorKohn, Roberten_US
dc.description.abstractBayesian 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.en_US
dc.publisherEkonomiska Forskningsinst. Stockholmen_US
dc.relation.ispartofseriesSSE/EFI Working Paper Series in Economics and Finance 724en_US
dc.subject.keywordMarkov Chain Monte Carlo (MCMC)en_US
dc.subject.keywordAdaptive Metropolis-Hastingsen_US
dc.subject.keywordParallel algorithmen_US
dc.subject.keywordDSGE modelen_US
dc.subject.stwDynamisches Gleichgewichten_US
dc.subject.stwMarkovscher Prozessen_US
dc.subject.stwKopula (Mathematik)en_US
dc.titleAdaptive hybrid Metropolis-Hastings samplers for DSGE modelsen_US
dc.typeWorking Paperen_US
Appears in Collections:SSE/EFI Working Paper Series in Economics and Finance, EFI - The Economic Research Institute, Stockholm School of Economics

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