Ensemble MCMC Sampling for DSGE Models

Gregor Boehl

CRC TR 224 Discussion Paper Series from University of Bonn and University of Mannheim, Germany

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.

Keywords: Bayesian Estimation; Monte Carlo Methods; DSGE Models; Heterogeneous Agents (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 E10 (search for similar items in EconPapers)
Pages: 28
Date: 2022-06
New Economics Papers: this item is included in nep-dge, nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:bon:boncrc:crctr224_2022_355

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