Template-Type: ReDIF-Paper 1.0
Title: Ensemble MCMC Sampling for DSGE Models
Author-Name: Gregor Boehl
Author-Email: gboehl@uni-bonn.de
Classification-JEL:  C11, C13, C15, E10
Keywords: Bayesian Estimation, Monte Carlo Methods, DSGE Models, Heterogeneous Agents



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.


Note: 
Length: 28
Creation-Date: 2022-06
Revision-Date: 
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp355
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2022_355