@techreport{Strid2010Adaptive,
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.},
address = {Stockholm},
author = {Ingvar Strid and Paolo Giordani and Robert Kohn},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {C11; C63; 330; Markov Chain Monte Carlo (MCMC); Adaptive Metropolis-Hastings; Parallel algorithm; DSGE model; Copula; Dynamisches Gleichgewicht; Markovscher Prozess; Monte-Carlo-Methode; Algorithmus; Kopula (Mathematik); Theorie},
language = {eng},
number = {724},
publisher = {Ekonomiska Forskningsinst.},
title = {Adaptive hybrid Metropolis-Hastings samplers for DSGE models},
type = {SSE/EFI Working Paper Series in Economics and Finance},
url = {http://hdl.handle.net/10419/56330},
year = {2010}
}