Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/114478 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
Working Paper No. 2014-21
Verlag: 
Federal Reserve Bank of Atlanta, Atlanta, GA
Zusammenfassung: 
Having efficient and accurate samplers for simulating the posterior distribution is crucial for Bayesian analysis. We develop a generic posterior simulator called the "dynamic striated Metropolis-Hastings (DSMH)" sampler. Grounded in the Metropolis-Hastings algorithm, it draws its strengths from both the equienergy sampler and the sequential Monte Carlo sampler by avoiding the weaknesses of the straight Metropolis-Hastings algorithm as well as those of importance sampling. In particular, the DSMH sampler possesses the capacity to cope with incredibly irregular distributions that are full of winding ridges and multiple peaks and has the flexibility to take full advantage of parallelism on either desktop computers or clusters. The high-dimensional application studied in this paper provides a natural platform to put to the test generic samplers such as the DSMH sampler.
Schlagwörter: 
dynamic striation adjustments
simultaneous equations
Phillips curve
winding ridges
multiple peaks
independent striated draws
irregular posterior distribution
importance weights
tempered posterior density
effective sample size
JEL: 
C32
C63
E17
Dokumentart: 
Working Paper

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