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SSE/EFI Working Paper Series in Economics and Finance, EFI - The Economic Research Institute, Stockholm School of Economics >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/56330
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Full metadata record
| DC Field | | Value | | Language |
| dc.contributor.author | | Strid, Ingvar | | en_US |
| dc.contributor.author | | Giordani, Paolo | | en_US |
| dc.contributor.author | | Kohn, Robert | | en_US |
| dc.date.accessioned | | 2012-02-14 | | en_US |
| dc.date.accessioned | | 2012-03-28T13:10:27Z | | - |
| dc.date.available | | 2012-03-28T13:10:27Z | | - |
| dc.date.issued | | 2010 | | en_US |
| 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_US |
| dc.language.iso | | eng | | en_US |
| dc.publisher | | Ekonomiska Forskningsinst. Stockholm | | en_US |
| dc.relation.ispartofseries | | SSE/EFI Working Paper Series in Economics and Finance 724 | | en_US |
| dc.subject.jel | | C11 | | en_US |
| dc.subject.jel | | C63 | | en_US |
| dc.subject.ddc | | 330 | | en_US |
| dc.subject.keyword | | Markov Chain Monte Carlo (MCMC) | | en_US |
| dc.subject.keyword | | Adaptive Metropolis-Hastings | | en_US |
| dc.subject.keyword | | Parallel algorithm | | en_US |
| dc.subject.keyword | | DSGE model | | en_US |
| dc.subject.keyword | | Copula | | en_US |
| dc.subject.stw | | Dynamisches Gleichgewicht | | en_US |
| dc.subject.stw | | Markovscher Prozess | | en_US |
| dc.subject.stw | | Monte-Carlo-Methode | | en_US |
| dc.subject.stw | | Algorithmus | | en_US |
| dc.subject.stw | | Kopula (Mathematik) | | en_US |
| dc.subject.stw | | Theorie | | en_US |
| dc.title | | Adaptive hybrid Metropolis-Hastings samplers for DSGE models | | en_US |
| dc.type | | Working Paper | | en_US |
| dc.identifier.ppn | | 618803564 | | en_US |
| dc.rights | | http://www.econstor.eu/dspace/Nutzungsbedingungen | | en_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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