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https://hdl.handle.net/10419/56330
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Strid, Ingvar | en |
dc.contributor.author | Giordani, Paolo | en |
dc.contributor.author | Kohn, Robert | en |
dc.date.accessioned | 2012-02-14 | - |
dc.date.accessioned | 2012-03-28T13:10:27Z | - |
dc.date.available | 2012-03-28T13:10:27Z | - |
dc.date.issued | 2010 | - |
dc.identifier.uri | http://hdl.handle.net/10419/56330 | - |
dc.description.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. | en |
dc.language.iso | eng | en |
dc.publisher | |aStockholm School of Economics, The Economic Research Institute (EFI) |cStockholm | en |
dc.relation.ispartofseries | |aSSE/EFI Working Paper Series in Economics and Finance |x724 | en |
dc.subject.jel | C11 | en |
dc.subject.jel | C63 | en |
dc.subject.ddc | 330 | en |
dc.subject.keyword | Markov Chain Monte Carlo (MCMC) | en |
dc.subject.keyword | Adaptive Metropolis-Hastings | en |
dc.subject.keyword | Parallel algorithm | en |
dc.subject.keyword | DSGE model | en |
dc.subject.keyword | Copula | en |
dc.subject.stw | Dynamisches Gleichgewicht | en |
dc.subject.stw | Markovscher Prozess | en |
dc.subject.stw | Monte-Carlo-Methode | en |
dc.subject.stw | Algorithmus | en |
dc.subject.stw | Kopula (Mathematik) | en |
dc.subject.stw | Theorie | en |
dc.title | Adaptive hybrid Metropolis-Hastings samplers for DSGE models | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 618803564 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
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