Arbeitspapier
Adaptive hybrid Metropolis-Hastings samplers for DSGE models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: SSE/EFI Working Paper Series in Economics and Finance ; No. 724
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Computational Techniques; Simulation Modeling
- Thema
-
Markov Chain Monte Carlo (MCMC)
Adaptive Metropolis-Hastings
Parallel algorithm
DSGE model
Copula
Dynamisches Gleichgewicht
Markovscher Prozess
Monte-Carlo-Methode
Algorithmus
Kopula (Mathematik)
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Strid, Ingvar
Giordani, Paolo
Kohn, Robert
- Ereignis
-
Veröffentlichung
- (wer)
-
Stockholm School of Economics, The Economic Research Institute (EFI)
- (wo)
-
Stockholm
- (wann)
-
2010
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:46 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Strid, Ingvar
- Giordani, Paolo
- Kohn, Robert
- Stockholm School of Economics, The Economic Research Institute (EFI)
Entstanden
- 2010