Bayesian estimation of agent-based models

Jakob Grazzini, Matteo Richiardi and Mike Tsionas

Journal of Economic Dynamics and Control, 2017, vol. 77, issue C, 26-47

Abstract: We consider Bayesian inference techniques for agent-based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters.

Keywords: Agent-based; Estimation; Bayes; Approximate Bayesian computation; Likelihood (search for similar items in EconPapers)
JEL-codes: C11 C15 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (87)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165188917300222
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Bayesian Estimation of Agent-Based Models (2015)
Working Paper: Bayesian Estimation of Agent-Based Models (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:77:y:2017:i:c:p:26-47

DOI: 10.1016/j.jedc.2017.01.014

Access Statistics for this article

Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok

More articles in Journal of Economic Dynamics and Control from Elsevier
Bibliographic data for series maintained by Catherine Liu ().