Adaptive hybrid Metropolis-Hastings samplers for DSGE models
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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 724.
Length: 33 pages
Date of creation: 14 Feb 2010
Date of revision:
Contact details of provider:
Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden
Phone: +46-(0)8-736 90 00
Fax: +46-(0)8-31 01 57
Web page: http://www.hhs.se/
More information through EDIRC
Markov Chain Monte Carlo (MCMC); Adaptive Metropolis-Hastings; Parallel algorithm; DSGE model; Copula;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-02-27 (All new papers)
- NEP-CBA-2010-02-27 (Central Banking)
- NEP-CMP-2010-02-27 (Computational Economics)
- NEP-DGE-2010-02-27 (Dynamic General Equilibrium)
- NEP-ECM-2010-02-27 (Econometrics)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Marco Del Negro & Frank Schorfheide, 2012. "DSGE model-based forecasting," Staff Reports 554, Federal Reserve Bank of New York.
- Edward P. Herbst & Frank Schorfheide, 2013.
"Sequential Monte Carlo Sampling for DSGE Models,"
NBER Working Papers
19152, National Bureau of Economic Research, Inc.
- Edward Herbst & Frank Schorfheide, 2012. "Sequential Monte Carlo sampling for DSGE models," Working Papers 12-27, Federal Reserve Bank of Philadelphia.
- Edward P. Herbst & Frank Schorfheide, 2013. "Sequential Monte Carlo sampling for DSGE models," Finance and Economics Discussion Series 2013-43, Board of Governors of the Federal Reserve System (U.S.).
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Helena Lundin).
If references are entirely missing, you can add them using this form.