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The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models

Author

Listed:
  • Daniel F. Waggoner
  • Hongwei Wu
  • Tao Zha

Abstract

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.

Suggested Citation

  • Daniel F. Waggoner & Hongwei Wu & Tao Zha, 2014. "The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models," FRB Atlanta Working Paper 2014-21, Federal Reserve Bank of Atlanta.
    • Handle: RePEc:fip:fedawp:2014-21
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    References listed on IDEAS

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    Cited by:

    1. Andrew Binning & Junior Maih, 2015. "Applying Flexible Parameter Restrictions in Markov-Switching Vector Autoregression Models," Working Paper 2015/17, Norges Bank.

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    More about this item

    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;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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