Ensemble MCMC sampling for robust Bayesian inference

  • The author proposes a Differential-Independence Mixture Ensemble (DIME) sampler for the Bayesian estimation of macroeconomic models.It allows sampling from particularly challenging, high-dimensional black-box posterior distributions which may also be computationally expensive to evaluate. DIME is a “Swiss Army knife”, combining the advantages of a broad class of gradient-free global multi-start optimizers with the properties of a Monte Carlo Markov chain (MCMC). This includes fast burn-in and convergence absent any prior numerical optimization or initial guesses, good performance for multimodal distributions, a large number of chains (the “ensemble”) running in parallel, an endogenous proposal density generated from the state of the full ensemble, which respects the bounds of the prior distribution. The author shows that the number of parallel chains scales well with the number of necessary ensemble iterations. DIME is used to estimate the medium-scale heterogeneous agent New Keynesian (“HANK”) model with liquid and illiquid assets, thereby for the first time allowing to also include the households’ preference parameters. The results mildly point towards a less accentuated role of household heterogeneity for the empirical macroeconomic dynamics.

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Author:Gregor BöhlORCiDGND
URN:urn:nbn:de:hebis:30:3-691753
URL:https://www.imfs-frankfurt.de/de/forschung/imfs-working-papers/details/mm_publication/detail/publication/ensemble-mcmc-sampling-for-robust-bayesian-inference.html
Parent Title (German):Working paper series / Institute for Monetary and Financial Stability ; 177
Series (Serial Number):Working paper series / Institute for Monetary and Financial Stability (177)
Publisher:Johann Wolfgang Goethe-Univ., Inst. for Monetary and Financial Stability
Place of publication:Frankfurt am Main
Document Type:Working Paper
Language:English
Year of Completion:2022
Year of first Publication:2022
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2023/01/13
Tag:Bayesian Estimation; Global Optimization; Heterogeneous Agents; Monte Carlo Methods; Swiss Army Knife
Edition:November 23, 2022
Page Number:43
Note:
Part of the research leading to the results in this paper has received financial support from the Alfred P. Sloan Foundation under the grant agreement G-2016-7176 for the Macroeconomic Model Comparison Initiative (MMCI) at the Institute for Monetary and Financial Stability. I also gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) under CRC-TR 224 (projects C01 and C05) and under project number 441540692.
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / Institute for Monetary and Financial Stability (IMFS)
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Wissenschaftliche Zentren und koordinierte Programme / Sustainable Architecture for Finance in Europe (SAFE)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C11 Bayesian Analysis
C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C13 Estimation
C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C15 Simulation Methods
E Macroeconomics and Monetary Economics / E1 General Aggregative Models / E10 General
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht