The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models
Daniel Waggoner (),
Hongwei Wu () and
Tao Zha ()
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Hongwei Wu: Federal Reserve Bank of Atlanta, Postal: Federal Reserve Bank of Atlanta, Research Department, 1000 Peachtree Street NE, Atlanta, GA
No 2014-21, FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta
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 equi-energy 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.
Keywords: 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 (search for similar items in EconPapers)
JEL-codes: C32 C63 E17 (search for similar items in EconPapers)
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