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Ensemble MCMC Sampling for DSGE Models

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  • Gregor Boehl

Abstract

This paper develops an adaptive differential evolution Markov chain Monte Carlo (ADEMC) sampler. The sampler satisfies five requirements that make it suitable especially for the estimation of models with high-dimensional posterior distributions and which are computationally expensive to evaluate: (i) A large number of chains (the "ensemble") where the number of chains scales inversely (nearly one-to-one) with the number of necessary ensemble iterations until convergence, (ii) fast burn-in and convergence (thereby superseding the need for numerical optimization), (iii) good performance for bimodal distributions, (iv) an endogenous proposal density generated from the state of the full ensemble, which (v) respects the bounds of prior distribution. Consequently, ADEMC is straightforward to parallelize. I use the sampler to estimate a heterogeneous agent New Keynesian (HANK) model including the micro parameters linked to the stationary distribution of the model.

Suggested Citation

  • Gregor Boehl, 2022. "Ensemble MCMC Sampling for DSGE Models," CRC TR 224 Discussion Paper Series crctr224_2022_355, University of Bonn and University of Mannheim, Germany.
  • Handle: RePEc:bon:boncrc:crctr224_2022_355
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    File URL: https://www.crctr224.de/research/discussion-papers/archive/dp355
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    Keywords

    Bayesian Estimation; Monte Carlo Methods; DSGE Models; Heterogeneous Agents;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

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