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dc.contributor.authorStrid, Ingvaren
dc.contributor.authorGiordani, Paoloen
dc.contributor.authorKohn, Roberten
dc.date.accessioned2012-02-14-
dc.date.accessioned2012-03-28T13:10:27Z-
dc.date.available2012-03-28T13:10:27Z-
dc.date.issued2010-
dc.identifier.urihttp://hdl.handle.net/10419/56330-
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
dc.language.isoengen
dc.publisher|aStockholm School of Economics, The Economic Research Institute (EFI) |cStockholmen
dc.relation.ispartofseries|aSSE/EFI Working Paper Series in Economics and Finance |x724en
dc.subject.jelC11en
dc.subject.jelC63en
dc.subject.ddc330en
dc.subject.keywordMarkov Chain Monte Carlo (MCMC)en
dc.subject.keywordAdaptive Metropolis-Hastingsen
dc.subject.keywordParallel algorithmen
dc.subject.keywordDSGE modelen
dc.subject.keywordCopulaen
dc.subject.stwDynamisches Gleichgewichten
dc.subject.stwMarkovscher Prozessen
dc.subject.stwMonte-Carlo-Methodeen
dc.subject.stwAlgorithmusen
dc.subject.stwKopula (Mathematik)en
dc.subject.stwTheorieen
dc.titleAdaptive hybrid Metropolis-Hastings samplers for DSGE models-
dc.typeWorking Paperen
dc.identifier.ppn618803564en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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