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http://hdl.handle.net/10419/56330
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| Title: | | Adaptive hybrid Metropolis-Hastings samplers for DSGE models  |
| Authors: | | Strid, Ingvar Giordani, Paolo Kohn, Robert |
| Issue Date: | | 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 |
| 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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